Exploring the Relationship Between Higher Education and Knowledge Economy Indicators

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1 Exploring the Relationship Between Higher Education and Knowledge Economy Indicators ALENA BUŠÍKOVÁ Vysoká škola manažmentu v Trenčíne Abstract The knowledge economy is determined by two most crucial factors: the massive development and use of information and communication technology, and the process of globalization. Human capital plays an enormous role as it stands behind both of these factors. Our main task is to determine the role of universities in the knowledge economy and for this reason, we search for correlations between two groups of indicators: the knowledge economy indicators (R&D expenditure, Patents applications, High-tech exports, etc.) and the indicators reflecting the tertiary education for particular country (Labor force with tertiary education, Expenditure per student, Tertiary educational attainment, etc.). If there is a positive strong relationship between the two set of indicators, it would suggest that the tertiary education plays an important role in forming of human capital in the knowledge economy. Key words: higher education, knowledge economy, correlation. 1 Introduction The emergence of the knowledge economy has brought challenges for universities especially due to the fact that the two driving forces globalization and the increased use of information and communication technologies have changed the world and have had a high impact on education and human capital investment as well. Globalization pushes on improving the quality of education because the competition among schools is getting fiercer. Globalization also impacts the mobility of both faculty and students (which is generally viewed as a positive thing) but also allows human capital flight (commonly referred to as brain drain ) which is more of a disadvantage for smaller, less economically developed countries and more of an advantage to countries such as the United States, where many foreign nationals are part of the top tier of skilled workers. The use of information and communication technology has grown at an unprecedented rate and provides a revolutionary way of learning because specific information is easy to find on the Internet. Nowadays, university teachers need to upgrade their knowledge and skills much more frequently during their lives and universities are obliged to constantly change their curriculums (Malhotra, 2003). ICT also offers new learning methods, such as e-learning, which serves as a substitute for the traditional classroom setting and offers education with fewer space or time limitations, education in which discrimination against age and race is almost non-existent, the record keeping much easier and discipline problems kept to a minimum (Njenga-Fourie, 2010). E- learning might be a more efficient way of learning for some students because it offers flexibility compatible with busy schedules, reduces the time and cost of travelling and often is at a more affordable price. In the U.S., e-learning has become very popular in recent years as indicated by a survey based on responses from more than 2,500 colleges and universities carried out by Sloan Consortium: in 2008 the student enrollment growth rate in online courses was 17%, compared to an overall enrollment growth rate of 1.2% in public higher education (Allen-Seaman, 2010). We try to determine whether countries that report satisfactory data in terms of tertiary education are also in good standing as far as the data reporting the knowledge economy. 2 Correlations among tertiary education indicators and indicators of knowledge economy P age 256

2 In our paper, we search for correlations between two groups of indicators: the knowledge economy indicators (R&D expenditure, Patents applications, High-tech exports, etc.) and the indicators reflecting the tertiary education for particular country (Labor force with tertiary education, Expenditure per student, Tertiary educational attainment, etc.). Some of the indicators may be considered as input indicators to the human capital development (R&D expenditure, Expenditure per student, ICT expenditure, etc.), the others can be viewed as output indicators (Patents application, High tech exports, ICT goods exports, etc.). The correlation among these indicators is presented in Table 1. Data is extracted from several international databases (Eurostat, OECD Education at a Glance database, World Development Indicators of the World Bank) and serve as a base for constructing the panel data for the period of the years , 2005, 2007 and for EU27 countries in addition to Switzerland, Norway, the United States and Japan. We decided to use the panel data analysis in order to obtain more reliable results by analyzing observations on multiple phenomena observed over multiple times in multiple countries. In this work, only correlation (R) of above 0.5 is determined to be an indicator of a strong correlation between the indicators: if a correlation of above 0.5 is found (whether positive or negative), it indicates that there is a strong relationship between selected knowledge economy indicators and tertiary educational indicators. The advantage of the correlation analysis is that, unlike the regression analysis, it shows how those variables affect each other regardless of the direction. On the other hand, it does not suffice in determining whether there is a cause-and-effect link between the variables. At this stage, the correlation analysis suffices as we are interested in testing whether there is a correlation of any kind in between selected two variables and if so, how strong. P age 257

3 Table 1: Correlation between various indicators (panel data) expressed by correlation coefficient (R) RES RES R&D LFT ICTEXPO ICTEX TEREX GDPTOT GDPCAP TERAT COMPET ARWU THE POP UNIV PATAP PATGR HTEXPO R&D LFT ICTEXPO ICTEX TEREX GDPTOT N/A GDPCAP TERAT COMPET ARWU THE POP UNIV N/A N/A N/A N/A PATAP PATGR N/A HTEXPO N/A N/A N/A ECOM Source: own computation based on Eurostat, OECD Education at a Glance, World Bank, ARWU, THE

4 Table 2: Description of variables used in the correlation analysis based on the panel data 1 INDICATORS OF KNOWLEDGE ECONOMY RES R&D Number of Researchers in R&D (per million people) R&D expenditure (both public and private expressed as % of GDP) ICTEXPO ICT goods exports (% of total goods exports) ICTEX ICT expenditure (% of GDP) GDPTOT Percentage of World GDP GDPCAP GDP per capita COMPET The Global Competitiveness Index Ranking PATAP Total number of patent applications to the European Patent Office (EPO) PATGR HTEXPO ECOM Total number of patents granted by the United States Patent and Trademark Office (USPTO) High-tech exports - Exports of high technology products as a share of total exports Percentage of enterprises' total turnover from E-commerce via Internet INDICATORS OF TERTIARY EDUCATION LFT TEREX TERAT ARWU THE UNIV Labor force with tertiary education (% of total) Public expenditure per student, tertiary (% of GDP per capita) Tertiary educational attainment, age group as a share on total population age group Percentage of universities ranked in Academic Ranking of World Universities by country Percentage of universities ranked in Times Higher Education by country Total number of universities and other HEIs OTHER INDICATORS POP Population (head count) COUNTRIES INCLUDED IN THE ANALYSIS Austria Finland Italy Norway Spain Belgium France Latvia Poland Sweden Bulgaria Germany Lithuania Portugal Switzerland Cyprus Greece Luxembourg Romania United Kingdom Czech Republic Hungary Malta Slovak Republic United States Denmark Ireland Netherlands Slovenia Japan Estonia 1 Variables are chosen based on their relatedness to either knowledge economy or tertiary education as well as their availability in the international databases such as Eurostat, OECD Education at a Glance and the World Bank. We expect that these indicators will be highly and positively correlated between themselves within the group but also in between the groups. P age 259

5 3 The correlation based on the panel data provides these useful findings: 1. The results reveal a strong correlation (correlation coefficient of 0.5 or above) between the R&D expenditure expressed as a % of GDP (R&D) and the indicators of knowledge economy such as GDP per capita (GDPCAP), the Global Competitiveness Index (COMPET), Patent applications to the EPO (PATAP) and Patents granted by the USPTO (PATGR). Similar to the R&D expenditure, the number of researchers in R&D (per million people) is highly correlated with all above mentioned indicators. These findings lead to an assumption that if one indicator is improved, it may positively affect the others (even though at this stage we cannot determine which indicator is the dependent variable and which indicators are the independent variables). However, we haven t found any correlation between the indicators of ICT goods exports (ICTEXPO), ICT expenditure (ICTEX) and Exports of high technology products (HTEXPO) and knowledge economy indicators, or tertiary education indicators. These results are rather in contrast with the conventional wisdom that more educated workforce and more researchers would increase the engagement in ICT and high-tech production (or vice versa). In this regard, two things need to be taken into account which can explain the unexpected findings: 1. It does not mean that technology and human capital do not affect each other but they may affect each other with a certain time lag. 2. In context of globalization, the outsourcing and relocation of production exists, the results might not be that unusual. The following charts outline the correlations between the domestic spending on R&D (both private and public) and the Patent applications to the European Patent Office, and the domestic spending on R&D (both private and public) and the ICT goods export in In contrast to Table 1, these charts present the relationship between these indicators for one year only 2 and depict the coefficient of determination R 2. The charts are in line with the afore-made statements that there is a correlation between the R&D spending and the knowledge economy indicators (in this case Patent applications to the European Patent Office) with the exception of ICT goods exports, ICT expenditure and Exports of high technology products (in this case ICT goods exports). Chart 1: Correlation between the domestic expenditure on R&D and selected indicators Source: own computation based on data from various databases 2 The reason for change in the methodology from several years to one year is to provide a clearer graphical design of the relationships and to avoid the outliners. By decreasing the number of years, the number of observations dropped but the coefficient of correlation did not change significantly. P age 260

6 2. There seems to be a strong correlation between some of the knowledge economy indicators and tertiary education indicators (correlation coefficient of 0.5 or above), as outlined below. This finding is one of the most essential results with regard to our goal, as it confirms that there is a positive strong relationship between the two set of indicators, which in turn suggests that tertiary education plays an important role in forming human capital in the knowledge economy. We confirm that the indicators of knowledge-economy (e.g. R&D expenditure, Patents applications, High-tech exports) are highly correlated with the indicators of tertiary education for particular country (e.g. Labor force with tertiary education, Expenditure per student, Tertiary educational attainment). Chart 2: Correlation between selected indicators of knowledge economy and tertiary education Source: own computation based on Eurostat, ARWU and THE More importantly, countries which universities placed highly in the Academic Ranking of World Universities and in the Times Higher Education Ranking are correlated with both the country s portion on the world GDP and the GDP per capita which may mean two things: 1.) the richer countries can afford to spend more on education and R&D and subsequently succeed more in the academic P age 261

7 ranking or 2.) the countries with high quality universities have high quality workforce which through their increased productivity, increase country s GDP. Additionally, the correlation between the placement in ARWU and THE and the competitiveness index is also evident. Our findings correspond with the Salmi s statement (2009) that world-class universities make an important contribution to global competitiveness and economic growth. 3. Unexpectedly, the correlations between some of the tertiary education indicators have not been confirmed. For instance, the expenditure per university student (TEREXPE) and Percentage of universities ranked in Academic Ranking of World Universities by country (ARWU) or Percentage of universities ranked in Times Higher Education by country (THE) is faint 3 suggesting that the countries with higher funding of tertiary education per student do not necessarily have higher representation of their universities in the international rankings. This finding is in line with Coleman (1966) and Hanushek (1995) study that there is little, if any, association between the university funding and the educational outcomes. Chart 3: Correlation between the Percentage of universities ranked in Academic Ranking of World Universities/Times Higher Education by country and Expenditure per student Source: own computation based on Eurostat, ARWU and THE 3 Conclusion In conclusion, we confirm that there is a strong correlation between numerous indicators of the knowledge economy and tertiary education with the exception of a few indicators; e.g. the ICT goods exports, ICT expenditure and Exports of high technology products and Population. It may signal that countries with better tertiary education systems are better prepared for the knowledge economy, even though the cause-and-effect relationship would need to be confirmed by the regression analysis. Literature ALLEN, E. SEAMAN, J Learning on Demand. Online Education in the United States, [online]. Sloan Consortium. Babson Survey Research Group Retrieved at < ISBN COLEMAN, J Equality of Educational Opportunity. Washington: Government 3 R = in case of ARWU and R = in case of THE P age 262

8 Printing Office, EDUCATION AT A GLANCE, OECD indicators 2010, [online] Retrieved multiple times in 2011 and 2012 at < HANUSHEK, E Measuring Investment in Education. In Journal of Economic Perspectives, [online]. 1996, Vol. 10, No. 4, pages Retrieved September 9, 2011 at < 3A4%3C9%3AMIIE %3E 2.0.CO%3B2-1> MALHOTRA, Y Measuring Knowledge Assets of a Nation: Knowledge Systems for Development. In Knowledge Management Measurement: State of Research. [online]. Retrieved February 29, 2011 at < measuremetn research.pdf> NJENGA, J., FOURIE, L The myths about e-learning in higher education. In British Journal of Educational Technology, 2010, Vol. 41, No. 2., pages SALMI, J The Challenge of Establishing World Class Universities (Directions in Development). Washinghton, DC, USA : The World Bank, pages. ISBN SJTU (Shanghai Jiao Tong University) Academic Ranking of World Universities [online]. Retrieved numerous times in 2010 and at < Statistical database Eurostat, [online]. Retrieved numerous times in 2010 and 2011 at < ec.europa.eu/eurostat> Times Higher Education The Times Higher Education World University Rankings Retrieved numerous times in 2010 and 2011 at < WORLD BANK (2002b), Constructing Knowledge Societies: New Challenges for Tertiary Education, Washington, D.C.: World Bank. Retrieved September 16, 2010 at < Societies.pdf> Contact: Alena Bušíková, M.B.A. Vysoká škola manažmentu v Trenčíne Panónska cesta 17 Bratislava abusikova@vsm.sk P age 263