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Research purpose. This study identifies analyses four key higher education policy models on the basis of OECD and EU data base with background discussion. The study provides information and knowledge how higher education policies and some key economic indicators can be combined? Study provides comparative trend analyses, which are policy-relevant and give insights to decision-makers. Design / Methodology / Approach. Since the well-known Mincer-Earnings-Equation in the early 1970s, there has emerged an extensive literature concerning the monetary returns on higher education. Tuition fees in higher education can be considered as private investment in higher personal incomes. Free educa-tion has been opposed on the basis of its unfairness: At the same time as the proportion of public expendi-ture on tertiary educational institutions is an average of near 70 percent of total expenditure in all OECD countries, less educated tax-payers support higher futures incomes of privileged students. In this paper we focus on key trends of economies with their higher education systems. Findings. At present, only few countries, in addition to the Nordic Countries, have adopted tuition-free higher education system. There are growing financial and political pressures to adopt tuition also in these countries. Thus, it is of the greatest importance to acquire research knowledge in this matter. First, we briefly review the relevant theoretical and empirical economic literature. Next, we discuss the potential economic benefits of tuition-free higher education system in terms of dynamic macro efficiency. We suggest an eclectic approach, where aspects of endogenous growth theory and dynamic public economics are em-phasized. Criterion for national success is the balanced growth path with social welfare maximization. We argue that there are plausible links between national success indicators, like competitiveness and welfare, and free higher education. In this paper, we present some empirical comparative analyses which are rele-vant for the assessment of higher education systems in the global OECD study context. The study contrib-utes to (1) the global analysis of higher education systems, (2) to the policy of higher education finance, ,(3) the education and inequality discussion, (4) to the discussion about returns of higher education and (5) to the discussion about education and development. Originality / Value / Practical implications. This study helps decision-makers in the field of higher edu-cation policy to create a big picture of on-going trends of higher education systems. The applies OECD´s analyses as a analytical framework. OECD classifies in its "Education at a Glance" report (2014, 2020) four alternative models of tuition fees and student support systems. Model 1: Countries with no or low tuition fees and generous student support system (Denmark, Finland, Iceland, Norway and Sweden). Model 2: Countries with high tuition fees and well-developed student support systems (Australia, Canada, New Zea-land, the United Kingdom and US). Model 3: Countries with high tuition fees and less-developed student support systems (Chile, Japan and South-Korea); and Model 4: Countries with low tuition fees and less-developed student support systems (Austria, Belgium, the Czech Republic, France, Ireland, Italy, Poland, Portugal, Switzerland and Spain). The study executes an empirical analysis of the higher education systems in these countries. A data pool covers higher education data, economic growth data and key welfare indicators. The study executes an empirical analysis of the higher education systems in these countries. A data pool covers higher education data, economic growth data and key welfare indicators.

Research purpose. This study identifies analyses four key higher education policy models on the basis of OECD and EU data base with background discussion. The study provides information and knowledge how higher education policies and some key economic indicators can be combined? Study provides comparative trend analyses, which are policy-relevant and give insights to decision-makers. Design / Methodology / Approach. Since the well-known Mincer-Earnings-Equation in the early 1970s, there has emerged an extensive literature concerning the monetary returns on higher education. Tuition fees in higher education can be considered as private investment in higher personal incomes. Free educa-tion has been opposed on the basis of its unfairness: At the same time as the proportion of public expendi-ture on tertiary educational institutions is an average of near 70 percent of total expenditure in all OECD countries, less educated tax-payers support higher futures incomes of privileged students. In this paper we focus on key trends of economies with their higher education systems. Findings. At present, only few countries, in addition to the Nordic Countries, have adopted tuition-free higher education system. There are growing financial and political pressures to adopt tuition also in these countries. Thus, it is of the greatest importance to acquire research knowledge in this matter. First, we briefly review the relevant theoretical and empirical economic literature. Next, we discuss the potential economic benefits of tuition-free higher education system in terms of dynamic macro efficiency. We suggest an eclectic approach, where aspects of endogenous growth theory and dynamic public economics are em-phasized. Criterion for national success is the balanced growth path with social welfare maximization. We argue that there are plausible links between national success indicators, like competitiveness and welfare, and free higher education. In this paper, we present some empirical comparative analyses which are rele-vant for the assessment of higher education systems in the global OECD study context. The study contrib-utes to (1) the global analysis of higher education systems, (2) to the policy of higher education finance, ,(3) the education and inequality discussion, (4) to the discussion about returns of higher education and (5) to the discussion about education and development. Originality / Value / Practical implications. This study helps decision-makers in the field of higher edu-cation policy to create a big picture of on-going trends of higher education systems. The applies OECD´s analyses as a analytical framework. OECD classifies in its "Education at a Glance" report (2014, 2020) four alternative models of tuition fees and student support systems. Model 1: Countries with no or low tuition fees and generous student support system (Denmark, Finland, Iceland, Norway and Sweden). Model 2: Countries with high tuition fees and well-developed student support systems (Australia, Canada, New Zea-land, the United Kingdom and US). Model 3: Countries with high tuition fees and less-developed student support systems (Chile, Japan and South-Korea); and Model 4: Countries with low tuition fees and less-developed student support systems (Austria, Belgium, the Czech Republic, France, Ireland, Italy, Poland, Portugal, Switzerland and Spain). The study executes an empirical analysis of the higher education systems in these countries. A data pool covers higher education data, economic growth data and key welfare indicators. The study executes an empirical analysis of the higher education systems in these countries. A data pool covers higher education data, economic growth data and key welfare indicators.

Since the creation of the EU, its focal economic objective has been to achieve economic growth and improved employment. The European Union's present 'Growth, Jobs and Investments' –strategy (GJI) is a recent attempt to promote these goals. Since the global economic and financial crisis the EU has been suffering from low level of investments. The purpose of this study is to assess the development of growth, employment, and investments in the Member States from 1995 to 2015. For this purpose, a relatively simple 'GEI-index' is developed. This aggregate index is a composition of indicators in GJI, which in general evolve in the same direction. The study provides: (1) a comparative evaluation of the haves and losers among the EU countries and (2) an empirical summary for the main objectives in the GJI-strategy. The primary methods used are based on the GEI indicator and data-analyses. The key findings of the study are: First, there seems to be some catching up concerning new member states that joined in the EU in the 2000s. Second, during the whole period from 1995 to the beginning of the financial crisis all the EU-28 countries – including the late members – show a positive development in the GEI-index. However, from 2009 to 2015 seven countries – all of which belong into the euro area –had declining GEI-index. These same countries had low level of investments and long lasting economic recession. Third, all the other EU-28 countries, but except Greece, had positive development in the GEI-index in 2015 as compared to the previous year. Obviously, our GEI-analyses cannot give a straight answer about the success of the EU's GJI-strategy as such. However, we see that our GEI-index provides a simple but effective tool for the practical assessments of the EU's growth policies. It is easy to interpret and visualize. Based on our illustration of the GEI-index, we recommend that there is a serious need to re-evaluate the EU's growth strategies and economic policies concerning the employment and investments.

In the European Union, smart specialization is an important concept in regional policy. Its primary aim is to achieve inclusive and sustainable economic growth. There is a lack of convenient region specific measures to operationalize smart specialization startegies (S3). The purpose of the paper is to find "indices of smart specialization" on a regional level. We propose indices that are based on (1) the rate of industrial diversification, (2) revealed comparative advantage and (3) regions' overall relative specialization. In the empirical part, we analyze smart specialization in Finland using structural data provided by Statistics Finland for seventy sub-regions (LAU1) and 24 sub-industries in manufacturing. These industries are the most important for exports, productivity, and regional economic performance for a small country. The following indices are used in empirical evaluations: Herfindahl-Hirschman Index (HHI) for regional diversity, Balassa-Hoover Index (BHI) for revealed comparative advantage, and Region's Relative Specialization Index (RRSI) for aggregate regional specialization differences. The concept of smart specialization is related to these measures. Index analyses reveal that many growing sub-regions have similar comparative advantages. This means inter-regional synergy, and it enables opportunities for strategic cooperation between regions. To develop smart specialization strategies for Europe's regions, we need these kinds of empirical knowledge-based management tools and planning approaches.

In the European Union, smart specialization is an important concept in regional policy. Its primary aim is to achieve inclusive and sustainable economic growth. There is a lack of convenient region specific measures to operationalize smart specialization startegies (S3). The purpose of the paper is to find "indices of smart specialization" on a regional level. We propose indices that are based on (1) the rate of industrial diversification, (2) revealed comparative advantage and (3) regions' overall relative specialization. In the empirical part, we analyze smart specialization in Finland using structural data provided by Statistics Finland for seventy sub-regions (LAU1) and 24 sub-industries in manufacturing. These industries are the most important for exports, productivity, and regional economic performance for a small country. The following indices are used in empirical evaluations: Herfindahl-Hirschman Index (HHI) for regional diversity, Balassa-Hoover Index (BHI) for revealed comparative advantage, and Region's Relative Specialization Index (RRSI) for aggregate regional specialization differences. The concept of smart specialization is related to these measures. Index analyses reveal that many growing sub-regions have similar comparative advantages. This means inter-regional synergy, and it enables opportunities for strategic cooperation between regions. To develop smart specialization strategies for Europe's regions, we need these kinds of empirical knowledge-based management tools and planning approaches.

In the European Union, smart specialization is an important concept in regional policy. Its primary aim is to achieve inclusive and sustainable economic growth. There is a lack of convenient region specifc measures to operationalize smart specialization startegies (S3). The purpose of the paper is to fnd "indices of smart specialization" on a regional level. We propose indices that are based on (1) the rate of industrial diversifcation, (2) revealed comparative advantage and (3) regions' overall relative specialization. In the empirical part, we analyze smart specialization in Finland using structural data provided by Statistics Finland for seventy sub-regions (LAU1) and 24 sub-industries in manufacturing. These industries are the most important for exports, productivity, and regional economic performance for a small country. The following indices are used in empirical evaluations: Herfndahl-Hirschman Index (HHI) for regional diversity, BalassaHoover Index (BHI) for revealed comparative advantage, and Region's Relative Specialization Index (RRSI) for aggregate regional specialization differences. The concept of smart specialization is related to these measures. Index analyses reveal that many growing sub-regions have similar comparative advantages. This means inter-regional synergy, and it enables opportunities for strategic cooperation between regions. To develop smart specialization strategies for Europe's regions, we need these kinds of empirical knowledge-based management tools and planning approaches.

In the European Union, smart specialization is an important concept in regional policy. Its primary aim is to achieve inclusive and sustainable economic growth. There is a lack of convenient region specifc measures to operationalize smart specialization startegies (S3). The purpose of the paper is to fnd "indices of smart specialization" on a regional level. We propose indices that are based on (1) the rate of industrial diversifcation, (2) revealed comparative advantage and (3) regions' overall relative specialization. In the empirical part, we analyze smart specialization in Finland using structural data provided by Statistics Finland for seventy sub-regions (LAU1) and 24 sub-industries in manufacturing. These industries are the most important for exports, productivity, and regional economic performance for a small country. The following indices are used in empirical evaluations: Herfndahl-Hirschman Index (HHI) for regional diversity, BalassaHoover Index (BHI) for revealed comparative advantage, and Region's Relative Specialization Index (RRSI) for aggregate regional specialization differences. The concept of smart specialization is related to these measures. Index analyses reveal that many growing sub-regions have similar comparative advantages. This means inter-regional synergy, and it enables opportunities for strategic cooperation between regions. To develop smart specialization strategies for Europe's regions, we need these kinds of empirical knowledge-based management tools and planning approaches.

The profound changes in Earth systems dynamics are affecting the health of the entire planet and the realization of a broad range of human rights. In this paper, we propose that the grand narrative of human rights including the legal right to a clean, healthy and sustainable environment recognized by the United Nations in 2022 requires the acknowledgment of the interconnected challenges posed by planetary crises. We discuss how planetary boundaries (PB) research can provide evidence-based arguments and clarify State duties concerning their international human rights law commitments. The economic, social and cultural rights are deeply connected with the right to a healthy environment. Human rights to water, food, or health, for example, can all be understood in the context of Earth systems change. Civil and political rights go beyond individuals to include also collective action and participation to tackle planetary social-ecological challenges. Gaps remain in human rights law concerning some of the PBs, which risks overlooking the interconnected drivers of ecosystem degradation. Clearer legal standing and justification for legal demands, for example concerning the impacts of water use, land use and deforestation, are needed to tackle PB overshoot. States must act at various spaces including the global economic systems and the global supply chains of goods and services for humanity to reach planetary safe and just spaces. Weaving international human rights law and advances at various geographical scales on the right to a healthy environment with PB provides a powerful tool for defending the prerequisites of good life for everyone, everywhere.