2014
DOI: 10.1007/s11135-014-0012-0
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Colouring the socio-economic development into green: I-distance framework for countries’ welfare evaluation

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Cited by 24 publications
(16 citation statements)
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“…Measuring the level of development thus requires choosing appropriate descriptive variables. This study used a set of 15 variables of national socioeconomic development to cover the areas of economy, environment and society (Borys 2010; Išljamović et al 2015). Specifically, we employed the following indicators:

X 1 : GDP per capita (USD),

X 2 : rate of inflation (%),

X 3 : rate of unemployment (%),

X 4 : changes in industrial production (%, 2012–2011),

X 5 : energy intensity of the economy (kg oil equivalent/1000 EUR),

X 6 : employment rate (%),

X 7 : resource efficiency [GDP/domestic material consumption (DMC)],

X 8 : percentage of people at risk of poverty/exclusion (%),

X 9 : export/import indicator (%),

X 10 : participation of hi-tech exports in exports (%),

X 11 : rate of poverty risk/exclusion for people at the age 25–29 (%),

X 12 : lifelong learning participation by educational attainment (% of people aged 25–64 benefiting from education and trainings),

X 13 : percentage of population who has never used Internet (% of population aged 16–74),

X 14 : proportion of expenditures on R&D in GDP (%),

X 15 : inflow of FDI (% of GDP).

…”
Section: Methodsmentioning
confidence: 99%
“…Measuring the level of development thus requires choosing appropriate descriptive variables. This study used a set of 15 variables of national socioeconomic development to cover the areas of economy, environment and society (Borys 2010; Išljamović et al 2015). Specifically, we employed the following indicators:

X 1 : GDP per capita (USD),

X 2 : rate of inflation (%),

X 3 : rate of unemployment (%),

X 4 : changes in industrial production (%, 2012–2011),

X 5 : energy intensity of the economy (kg oil equivalent/1000 EUR),

X 6 : employment rate (%),

X 7 : resource efficiency [GDP/domestic material consumption (DMC)],

X 8 : percentage of people at risk of poverty/exclusion (%),

X 9 : export/import indicator (%),

X 10 : participation of hi-tech exports in exports (%),

X 11 : rate of poverty risk/exclusion for people at the age 25–29 (%),

X 12 : lifelong learning participation by educational attainment (% of people aged 25–64 benefiting from education and trainings),

X 13 : percentage of population who has never used Internet (% of population aged 16–74),

X 14 : proportion of expenditures on R&D in GDP (%),

X 15 : inflow of FDI (% of GDP).

…”
Section: Methodsmentioning
confidence: 99%
“…As previous research proved, the I-distance method can be used as a way to enhance an index (Maricic et al 2014) or create a new one (Isljamovic et al 2014;Seke et al 2013;Radojicic et al 2012;Al-Lagilli et al 2011). By using the I-distance method, a synthesized indicator that incorporates many indicators of social responsibility and competitiveness can be refined.…”
Section: Discussionmentioning
confidence: 99%
“…Different analytical approaches can be used to explore whether the dimensions of the phenomenon are statistically well-balanced in a composite index (OECD 2005). In order to analyze whether the RCI is statistically sound, we employed a statistical I-distance method which can easily overcome the problem of subjectively chosen weighting factors (Isljamovic et al 2014). Besides analyzing the weights assigned to RCI domains, we were able to provide the ranking of countries without the limitation of subjectively assigned weights, as the methodology applied in this paper does not place any weighting factor on its indicators or domains ).…”
Section: Introductionmentioning
confidence: 99%
“…Nowadays there is a large and growing body of literature on the construction of composite indicators of well-being: the quality of life indices (Cox et al 1992;Fayers and Hand 2002;Hagerty and Land 2007). In general, composite indicators are very familiar in country performance comparison in globalization, competitiveness, education, health, human rights, ecological footprint, corruption, quality of work, technology achievement, social cohesion and trust in public institutions (OECD 2008;Marozzi 2012;Isljamovic et al 2014). Other fields where composite indicators have been successfully used are: quality assessment of industrial products (Lago and Pesarin 2000), customer satisfaction (Arboretti et al 2007;Marozzi 2009) and analysis of industrial development policies (Barbieri et al 2012).…”
Section: The Methodsmentioning
confidence: 99%