Measuring sources of economic growth in OECD countries

Škare, Marinko; Rabar, Danijela

doi:10.5755/j01.ee.28.4.18502

Cited by 9 publications

(12 citation statements)

References 36 publications

(36 reference statements)

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“…To identify the factors associated with energy and environmental efficiencies, Parker and Liddle (2016) assessed the impact of OECD manufacturing prices on energy efficiencies from 1980 to 2009 and concluded that energy efficiency is a major driver for energy intensity reductions and that price increases improve efficiency. Skare and Rabar (2017) used DEA to compare the time series of 30 OECD countries from 2002 to 2011 and employed four different hypothetical models to compare economic, social, and environmental goals, noting that the most frequent inefficiency scores result from gross domestic product (GDP) and that inflation has the least effect on inefficiency.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries

Liu

Chiu

Wang

et al. 2019

Tropical Conservation Science

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Greater and greater attention is being paid to air pollution problems, because of their negative impact on the environment and human health. This article measures energy efficiency, carbon dioxide emissions efficiency, and particulate matter (PM 2.5) concentration efficiency to compare the energy efficiency differences between Organisation for Economic Cooperation and Development (OECD) member countries and non-OECD member countries from 2010 to 2014 using a metafrontier dynamic Data Envelopment Analysis model. We calculate technology gap ratio and input and output efficiency values to measure the energy efficiencies of each economy, finding that (a) OECD countries have a technology gap ratio of 1 or very close to 1; and except for the United Arab Emirates and Singapore, both of which exhibit annual improvements, the non-OECD countries have a significant need for efficiency improvements; (b) the average technology gap ratio of OECD is higher than that of non-OECD countries; that is, while OECD countries' technology gap ratio (TGR) changes are relatively stable, non-OECD countries' TGRs are gradually increasing; (c) non-OECD countries have large PM 2.5 concentration efficiency gaps, with the annual efficiencies in China, India, and Nepal being less than 0.2; (d) Switzerland, Denmark, France, the United Kingdom, Iceland, Luxembourg, Norway, the United States, and the United Arab Emirates all have new and traditional energy efficiency values of 1; and (e) Botswana, Algeria, and Cambodia have poor traditional energy efficiencies, but better new energy efficiencies, whereas Hungary, South Korea, Slovakia, and Slovenia have poor new energy efficiencies and better traditional energy efficiencies.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries

Liu

Chiu

Wang

et al. 2019

Tropical Conservation Science

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“…DEA was derived from the classical microeconomic theory of production. The focus of analysis is on decision making units (DMU), subjects that use multiple inputs to generate multiple outputs (Škare & Rabar, 2016). DEA has been accepted as a useful tool for performance assessment and ranking of DMUs O N L I N E F I R S T (Rahmani et al 2020).…”

Section: Data Envelopment Analysismentioning

confidence: 99%

“…Such formulation allows the inclusion of inputs and outputs expressed in diverse and mutually incomparable units of measure, which is one of DEA's biggest advantages (Škare & Rabar, 2016). Some other advantages have already been mentioned, first and foremost the possibility of multiple inputs and outputs inclusion at the same time.…”

Section: Data Envelopment Analysismentioning

confidence: 99%

“…The main disadvantages of DEA are sensitivity to the choice of input and output variables and the inability to predict. DEA presents an ex-post analysis based on already known data (Škare & Rabar, 2016). 'Rule of thumb' states that, to apply DEA successfully, the number of selected DMUs has to be at least two to three times higher than the number of variables used as inputs and outputs combined, so that efficiency results would be adequately dispersed (Sarkis, 2007).…”

Section: Data Envelopment Analysismentioning

confidence: 99%

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Assessing sustainable economic development efficiency: A DEA approach

Sotiroski¹,

Kempler²,

Horvat³

et al. 2023

Strategic Management

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Background: Widely used in efficiency analysis, data envelopment analysis (DEA) found its use in country efficiency measurement concerning the achievement of desired values of macroeconomic indicators, most often the goals from the category of economic growth. Purpose: The objective of the paper is to examine the possibility of DEA application in sustainable development research. Methodology: The analysis was conducted using a non-oriented DEA model with variable return-to-scale in a group of 26 EU countries and Serbia, as a membership candidate. Four variables were used as input variables: inflation rate, unemployment rate, poverty rate and ecological footprint per capita. Three variables were used on the outputs side: inequality-adjusted human development index, GDP per capita and ecological deficit or reserve per capita. The annual data was collected for the time period of eight years, form 2010 until 2017. Findings: Results show that Finland is the only country efficient throughout the entire period. Average efficiency close to maximum was achieved by the Netherlands. Significant efficiency was achieved by Luxembourg, Germany and Sweden among countries that were EU members before 1995. Among other EU countries, Slovenia and Hungary achieved efficiency on a nearly maximum level. Also, efficient in more than half of the observed years were Cyprus and Romania. The most inefficient countries were the three Baltic countries: Lithuania, Latvia, and Estonia. Among the EU member countries before 1995, Italy and Portugal were the most inefficient. Concerning EU candidate Serbia, the efficiency achieved was generally close to average. Limitations: The performed analysis can answer the question of which country is the most efficient on the way to sustainability. However, the DEA method cannot show whether a country is developing absolutely sustainably or unsustainably, because DEA is a relative method and can only measure efficiency compared to the other units.

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“…Previous research focussed on determinants of public debt in single countries as well in various groups of countries. One group of authors deals with the analysis of predictors of public debt in members of OECD (Ogawa et al, 2016;Reinhart & Rogoff, 2010;Skare & Rabar, 2017). Another group of authors analyzes public debt in individual countries only (Spilioti & Vamvoukas, 2015;Neaime et al, 2018;Pirtea et al, 2013;Galinski, 2015;Karafolas & Alexandrakis, 2015).…”

mentioning

confidence: 99%

Public debt’s predictors in EU: evidence from members and non-members of European Monetary Union

Pjanić

Milenković

Andrašić

et al. 2020

Economic Research-Ekonomska Istraživanja

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The global economic crisis destabilised the public debt of many countries. The purpose of this paper is to investigate the predictors of public debt in European Union countries divided into nonmember countries and members of the European Monetary Union in the period from 2001 to 2018. The aim is to discover their relationship with public debt and make recommendations for economic policy-makers. The empirical analysis was based on comparative research design, quantitative methodology and secondary data collection. It included 13 variables, with public debt being the dependent variable and the selected 12 economic indicators were treated as predictors. The analysis was based on a procedure for linear mixed models in the IBM SPSS. The basic finding was that only unemployment was statistically significant predictor of public debt in both groups of countries. Other predictors differed, and there were statistically significant differences in the magnitude of their impacts. Obtained results indicate that unemployment is one of the most important problems of all European Union countries. In addition, a major challenge for monetary and fiscal policy-makers will be profiling adequate tax, credit, and interest rate policies to reduce debt and accelerate economic growth.

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Measuring sources of economic growth in OECD countries

Cited by 9 publications

References 36 publications

A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries

A Comparative Study of Different Energy Efficiency of OECD and Non-OECD Countries

Assessing sustainable economic development efficiency: A DEA approach

Public debt’s predictors in EU: evidence from members and non-members of European Monetary Union

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