MUNI ECON Towards inclusive prosperity and development in European countries using the transitioning performance index Rashidatu Bassabi Department of Regional Economics Faculty of Economics and Administration Masaryk University AIM OF THE STUDY - The aim of the paper is to analyse the various indicators for the transitioning performance published by the European Commission for the year 2020 using multivariate analysis. - The main reason is to allow for a break-down of the original four dimensions (economic, social, governance, environment) into more finer components and give a better understanding of the relationships among the indicators. - Secondly, to first allow for examining and reducing the original 37 indicators whiles keeping the original variation to allow for better understanding of the efforts that go into the performance method 2 MUNI ECON Introducing the TPI data and methodology - The Transitions Performance Index shortened as (TPI) is a composite indicator that measures the performance of countries along four main transitions, namely, economic, social, environmental and governance. - Most of the indicators for this index are outcome-oriented to present a combined impact of the policy mix implemented in each country. - Moreover, the TPI does not present geographical predetermination and hence there is no clear-cut North-South, East-West divide in the final assessment. - The TPI index indicators for the year 2020 lists 37 indicator scores between 0 to 100. - All 27 EU countries were used except for Greece, Malta and Luxembourg due to missing data from the set in order to reduce bias from the multivariate analysis. 3 MUNI ECON Analysis and Results The following results are presented: - Relationship (correlation) matrix for original 37 indicators. - Relevant new principal components deduced from the variables. -Original variables that are well represented by relevant new components via correlations. - Graphical outlook of countries expressed in terms of first component accounting for about 39% total variation and second component accounting for about 16% total variation. 4 MUNI ECON Correlation among indicators Figure: Correlation matrix color plot for indicators Education Pfoduclwty Health Free time Emission reduction Resource productivity Fundamental righrts Transparency i(%ofGDP per capita) 11 CT skills (composite) jreonR&D (% of GDP) (per billion PPPS GDP) it 10 gender gap 25+ (%) js and transfers (0-100) s (KBAs) protected (%) area of cropland (kg/ha) xint (tonnes per capita) e of law Index (z-sc ore) laundering index (0-10) o - cn o ^5 .a m C 0 1 5 r. "c V c o > ft 'S - _ 2 c $ Q. S I 3 £ 5- E .2 > Ll 111 LU CO o E n i-' TJ (D c - =1 U L. D c « W t- C as (A c i s. s o 9 5 = ft L. c * D ft o 2 ft e — O =1 CL O. S I 0. o 4> 5 * 3 "a ■e tj a. _ I ° m w o o CD 0 1 § « E 0. LU I i ft q o = cl „ !| £ a tu o I* — « o> > 111 >. 09 BS c o n — ■—■ ^— ~ I- ü. 0) o « -3 ft to u _ 3 ^ o 1 - Finally, is the proportion of explained and cumulative variance. - The first 8 components account for approximately 89% of the variation while the first 10 components accounted for approximately 93% of the cumulative variation to explain the changes in the sustainability performance according to the TPI methodology. - The new components are in descending order according to their weight in influencing the variation, and there is no correlation between the new components, therefore they can be used to fix the issue of multicollinearity in regression studies. 6 MUNI ECON Proportion of explained variances new PCs Table: Summary Results of Principal Component Analysis PCA Standard deviation Proportion of Variance Cumulative Proportion PCA 1 3.8401 0.3985 0.3985 PCA 2 2.4790 0.1661 0.5646 PCA 3 1.9744 0.1054 0.6700 PCA 4 1.57875 0.06736 0.73736 PCA 5 1.34998 0.04926 0.78662 PCA 6 1.21874 0.04014 0.82676 PCA 7 1.14906 0.03568 0.86245 PCA 8 1.04703 0.02963 0.89207 PCA 9 0.89866 0.02183 0.91390 PCA 10 0.80879 0.01768 0.93158 PCA 11 0.78021 0.01645 0.94803 PCA 12 0.7067 0.0135 0.9615 PCA 13 0.62759 0.01065 0.97218 PCA 14 0.54453 0.00801 0.98019 PCA 15 0.49358 0.00658 0.98677 PCA 16 0.39843 0.00429 0.99106 PCA 17 0.33466 0.00303 0.99409 PCA 18 0.3042 0.0025 0.9966 PCA 19 0.21781 0.00128 0.99788 PCA 20 0.19007 0.00098 0.99885 PCA 21 0.16350 0.00072 0.99957 PCA 22 0.11236 0.00034 0.99992 PCA 23 0.05595 0.00008 1.00000 PCA 24 1.358e-15 0.000e+00 1.000e+00 7 MUNI ECON Scree-plot Figure: Scree-plot for variance of Principal Component Analysis Source: author's own processing Correlation between original variables and new PCs - The following components have particularly higher correlations with original variables and hence represent these variables well. - PCA 1: has negative correlations with following variables (>-0.7). Educ, wealth, productivity, fund, rights, people with ICT, GERD, patent filed, voice and accountability index, rue of law index, corruption perc. Index, output/worker, internet users, transparency, free time, work and inclusion. This variables are under dimensions of economic and social. g MUNI ECON Correlation between original variables and new PCs - PCA 2: has positive correlations of > 0.6 with variables resource productivity and health. It also has negative correlations > -0.6 with variables sound public finance, pesticides use and biodiversity. - PCA 3: has positive correlations >0.6 with variables industrial base, protected freshwater, gross value added to manufacturing. - These 2 PCs highly express variables mainly under environmental. - PCA 4: has negative correlations > -0.6 with variables equality, income share of poorest quintile, coefficient of disposable income. 10 MUNI ECON Expressing countries in terms of first 2 PCs Figure: Countries expressed in terms of PCA 1 ( Dim1 with 39.9% variation) and PCA 2 (Dim2 with 16.6% variation) Individuals - PCA 5.0 2.5 " 0.0 Q -2.5- -5.0 Source: author's own processing 11 Dim1 (39.9%) ; i i Ireland • i Slovenia i-iungary • Czechic * i Latvia * Belgium ** Finland * Poland * Cyprus " * Portugal * Slo i/akia Jenmark * Spain * Sweden • Croatia Netherlands * Romania * Germa * Bulgaria * Lithuania ltalY * * Estonia • 8 I ECON Conclusions - The results for the paper show that individual countries can use the indicators to determine areas of strengths and weakness by observing the relationships that exist among the original variables. - Moreover, since there are no correlations between the new components, decisions on which areas (indicators) are better expressed by the new dimensions and hence are more relevant can be determined and examined further by exploring the PC As. - These can also provide areas of opportunities to ensure that actions are targeted to what is particularly needed for each individual country, rather bulk geographical expectations by exploring further the components for each country. lilUN ECO Conclusions - Finally, the results also show that countries are not bound by geographical demarcations in terms of performance. Although majority of countries in similar geographical locations share common socio-economic and cultural characteristics that might influence decisions and performance orientation, ultimately, performance enhancement focus should be based on the individual needs and challenges for the countries. - This will mean that countries in clear geographical locations will still perform differently as observed for countries falling into dispersed groups, a trend that is observed in most geographical south, north, east, west countries as well. 13 lilU EC NI ON References - BILBAO-UBILLOS, J., (2013). The Limits of Human Development Index: The Complementary Role of Economic and Social Cohesion, Development Strategies and Sustainability. Sustainable Development, 21(6), 400-412. doi:10.1002/sd.525 - CARIUS, N, SPECK, M., & LAUB, K., (2018). Regional Impact Assessment: A Methodology to Measure the Regional Value Added of Trans-Sectoral Urban Planning. In Smart and Sustainable Planning for Cities and Regions, Cham. - COELHO, P., MASCARENHAS, A., VAZ, P., DORES, A., & RAMOS, T. B., (2010). A framework for regional sustainability assessment: developing indicators for a Portuguese region. Sustainable Development, 18(4), 211-219. doi:10.1002/sd.488 - EU. (2021). EU regional and urban development, [online] [cit. 2023.03.30] Accessible: https://ec.europa.eu/regional policy/home en - EU. (2020). Towards fair and prosperous sustainability: transitions performance index 2020. - EU. (2022). Transitions performance index 2021: key findings and rankings. - FALCIOLA, I, JANSEN, M., & ROLLO, V, (2020). Defining firm competitiveness: A multidimensional framework. World Development, 129. doi: 10.1016/j.worlddev.2019.104857 - FREIDENFELDE, I., (2011). Highly Qualified Workforce Attraction from Abroad - Issues in the European Union. In 12th International Scientific Conference on Economic Science for Rural Development. Latvia Univ Agr, Fac Econ, Jelgava, LATVIA. - GALGOCZI, B., (2009). Boom and Bust in Central and Eastern Europe: Lessons on the Sustainability of an Externally Financed Growth Model. Journal of Contemporary European Research, 5(4), 614-625. - HORVATH, B., IVANOV, A., & PELEAH, M., (2012). The Global Crisis and Human Development: A Study on Central and Eastern Europe and the CIS Region. Journal of Human Development and Capabilities, 13(2), 197-225. doi:10.1080/19452829.2011.645531 - JIANG, Y. Q., (2014). Knowledge economy and knowledge-based development: a tentative discussion. In China: Trade, Foreign Direct Investment, and Development Strategies (pp. 205-210). - JONES, E., (2006). Europe's market liberalization is a bad model for a global trade agenda. Journal of European Public Policy, 13(6), 943-957. doi: 10.1080/13501760600838714 14 MUNI ECON References - MADALENO, I. M., (2008). How the resource curse affects urban development in East Timor. In 5th International Conference on Urban Regeneration and Sustainability (The Sustainable City). Skiathos Isl, Greece. ODUGBESAN, J. A., IKE, G., OLOWU, G, & ADELEYE, B. N., (2022). Investigating the causality between financial inclusion, financial development and sustainable development in Sub-Saharan Africa economies: The mediating role of foreign direct investment. Journal of Public Affairs, 22(3). doi: 10.1002/pa.2569 - OZGUR, G., ELGIN, C, & ELVEREN, A. Y, (2021). Is informality abarrierto sustainable development? Sustainable Development, 29(1), 45-65. doi:10.1002/sd.2130 - PAWLONKA, C, (2019). Flexicurity In The Central Eastern Europe, Decade After Economic Crisis. In 39th International Scientific Conference on Economic and Social Development -Sustainability from an Economic and Social Perspective. Lisbon, Portugal. - RODRIGUES-FILHO, S., LINDOSO, D. P., BURSZTYN, M., BROUWER, F., DEBORTOLI, N, & DE CASTRO, V. M., (2013). Regional sustainability contrasts in Brazil as indicated by the Compass of Sustainability - CompasSus. Environmental Science & Policy, 32(58-67. doi: 10.1016/j.envsci.2013.01.014 - RUIZ, V. R. L., NAVARRO, J. L. A., & PENA, D. N, (2013). Index Knowledge Cities Or Growth Capacity From Intellectual Capital Outlook. In 6th Knowledge Cities World Summit (KCWS). Istanbul, Turkey. - SATROVIC, E., & MUSLIJA, A., (2019). Fresh Evidence On The Investment-Economic Freedom-Growth Nexus In Oecd Member States. In 39th International Scientific Conference on Economic and Social Development - Sustainability from an Economic and Social Perspective. Lisbon, Portugal. - SLOKA, B., JEKABSONE, I., CIPANE, K., & VASINA, S. A., (2019). Income Differences in Regions of Latvia - Problems and Challenges. European Integration Studies, (13), 52-60. doi: 10.5755/j01.eis.0.13.23562 - VILCINA, A., & BORONENKO, V, (2011). Actual Clustering Paths of Economic Sectors in Latvia by the Labour Flows. In 12th International Scientific Conference on Economic Science for Rural Development. Latvia Univ Agr, Fac Econ, Jelgava, Latvia. - VONDROVA, A., & FIFEKOVA, E., (2015). The Impact of the Transformation Process to the Institutional Complementarity of Transition Countries. In 25th International-Business-Information-Management-Association Conference. Amsterdam, Netherlands. - ZADOROZNAJA, O., (2010). Methodological Aspects for Estimation of the Nation's Competitiveness. In International Scientific Conference on Economic Science for Rural Development, Latvia Univ Agr, Fac Econom, Jelgava, Latvia. 15 MUNI ECON