UNIVERSITAS U21 RANKING OF NATIONAL HIGHER EDUCATION SYSTEMS Universitas 21, an international group of 25 world universities, devised in 2012 a composite index for ranking national higher education systems and named it U21. The basic idea of the Universitas 21 group was to draw attention from the ranking of universities and individual institutions on the ranking of the national higher education systems. To achieve its goal, the U21 composite index includes four categories (Resources, Environment, Connectivity, and Outpun and 25 indicators. The index is calculated for 50 world countries, but due to the lack of data, the recreated database, which we used to perform the analysis, contains data for 48 countries; Brazil and Saudi Arabia had to be excluded from further research. The data for 25 indicators, which are used for the computation of the ranking according to the latest methodology, is collected from reputable sources such as OECD, UNESCO, and the World Economic Forum (WEF) (Universitas 21, 2015). Table 1 provides the categories, indicators, effective weights, and codes of indicators, assigned by the authors for easier presentation in the rest the manuscript The Resources category provides information on the material and immaterial pre-conditions for the core transformation processes in a higher education system. This dimension aims at measuring the government’s readiness to invest in higher education and at investigating what are the consequences of their investment. Although resources are vital for a system to operate, they are not enough to ensure its efficient and proper functioning Williams et al., 2013). To ensure the effective use of inputs, a regulatory Environment is needed. Lately, the idea of internalization became fimly embedded in the institutional mission statements, strategies, as well as national policy frameworks. Category Connectivity aims at measuring the relationship between the national higher education system and the industry and government. Finally, the last category (Output) includes the measurement of higher education systems output through the scientific output (research papers), the number of scientific workers, enrolment rate in higher education institutions, as well as the percentage of the population that has a college degree. Although being a comprehensive ranking methodology obtained using mostly objective data, U21 ranking has its limitations. Namely, U21 methodology has been criticized for several reasons (Rauhvargers, 2014). One of the drawbacks is that the model is, in fact, a production model (Cheng, 2012). Another is that the index does not include measurements of teaching while it gives high significance to output (Millot, 2014). For this research, the most important critiques are the ones regarding the weighting scheme and the aggregation method. As many composite indexes, U21 faces the issue of expert-driven weights. The lack of information and justification of the assigned weights are one of the major drawbacks of this newcomer to the university ranking scene (Soh, 2014). The aim of this research is the alteration of the U21 weighting scheme, thus making it data-driven. We propose the twofold Composite i-distance Indicator (CIDI) methodology to obtain objective and unbiased weights. 3. METHODOLOGY 3.1 Composite I-distance Indicator (CIDI) Methodology A need for an unbiased ranking of countries was emphasized in the 1960s. Many ranking methodologies emerged, but one that stood out was the l-distance method devised by Ivanovic (1977). Namely, his method ranked countries according to their level of socio-economic development by using several indicators. What sets the I-distance apart from the others is its ability to bypass the process of weighting individual indicators as it aggregates indicators without assigning weights. Also, recent research shows that the I-distance can be applied with success not only in various fields but for both creation (Zornic et al., 2015) and evaluation of composite indexes (Dobrota et al., 2015). 5.0% Table 1 U21 categories, indicators, devised indicator codes, and their effective weights (Universitas 21, 2015) Effective Category Indicator Code weight Government expenditure on tertiary education as a % of GDP, 2011 R1 5.0% Total expenditure on tertiary education as a % of GDP, 2011 R2 Annual expenditure per student (full-time equivalent) by tertiary education R3 5.0% Resources institutions at USD purchasing power prices, 2011 (20%) Expenditure in tertiary institutions for research and development as a R4 2.5% percent of GDP, 2012 Expenditure in tertiary institutions for research and development per head R5 2.5% of population at USD purchasing power prices, 2012 Proportion of female students in tertiary education, 2012 E1 1.0% Proportion of academic staff in tertiary institutions who are female, 2012 E2 2.0% Environment Data quality E3 2.0% (20%) Qualitative measure of the policy environment E4 10% Responses to WEF survey question: “How well does the educational E5 5.0% system in your country meet the needs of a competitive economy?” Proportion of international students in tertiary education, 2012 C1 4.0% Proportion of articles with international collaborators C2 4.0% Number of open access full text files on the Web, per head of population, C3 2.0% Connectivity 2009-2013 External links that university web domains receive from third parties, per (20%) C4 2.0% head of population, 2009-2013 Rating of knowledge transfer between university and companies, 2013 C5 4.0% Percentage of university research publications co-authored with industry C6 4.0% researchers, 2009-2012 Total number of journal articles produced by higher education institutions, 01 10.0% 2012 Total articles produced by higher education institutions per head of 02 3.0% population, 2012 Average impact of articles measured by citations in 2012 to articles published in previous years using Karolinska Institute normalized impact 03 5.0% factor Output Weighted Shanghai ranking scores for universities per head of population, 04 3.0% (40%) 2014 Average of nation’s best three universities on the 2014 Shanghai Jiao 05 7.0% Tong ranking Tertiary enrolment rates as a percentage of the eligible population, 2012 06 3.0% Percentage of population aged 24-64 with a tertiary qualification, 2012 07 3.0% Number of researchers in the nation per head of population, 2012 08 3.0% Unemployment rate of the tertiary educated compared with unemployment rates of those with only upper or post-secondary non-tertiary education, 09 3.0% 2012

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