FARKLI GELİR GRUPLARINDAKİ ÜLKELERİN KÜRESEL REKABET VE İNSANİ KALKINMA BAKIMINDAN VERİ ZARFLAMA ANALİZİ İLE DEĞERLENDİRİLMESİ

Küreselleşme ve küresel rekabetin ön plana çıktığı çağımızda, küresel rekabet gücünün insani kalkınma seviyesine yeterince yansıtılıp yansıtılamadığı sorusu sorgulanması gereken önemli konulardan biridir. Bu çalışmanın amacı, ülkelerin üç ayrı gelir grubuna (yüksek gelirli, üst-orta gelirli ve alt-orta gelirli) göre küresel rekabet güçlerini, insani kalkınma düzeyine yansıtma konusundaki göreli etkinliklerini Veri Zarflama Analizi (VZA) ile belirlemektir. Bu çerçevede, yüksek gelirli ülkeler grubunda 50 ülkenin, üst-orta gelirli ülkeler grubunda 33 ülkenin ve alt-orta gelirli ülkeler grubunda 29 ülkenin verileri kullanılmıştır. Söz konusu ülkelerin 2016 yıllına ait temel gereklilikler (Girdi 1 – G1), verimlilik artırıcı faktörler (Girdi 2 – G2), inovasyon ve çeşitlilik faktörleri (Girdi 3 – G3) girdi olarak; kişi başına düşen milli gelir (GNI) (Çıktı 1 – Ç1), doğuşta beklenen yaşam süresi (Çıktı 2 – Ç2), beklenen okullaşma süresi (Çıktı 3 – Ç3) ve ortalama okullaşma süresi (Çıktı 4 – Ç4) çıktı olarak kullanılmıştır. Sonuçta ortalama etkinlik değerlerine bakıldığında yüksek gelir grubunda bulunan ülkelerin diğer gelir gruplarında yer alan ülkelere göre daha yüksek ortalamaya sahip olduğu ve etkin çıkan ülkelerin küresel rekabet gücünü insani kalkınma seviyesine yansıtabilme anlamında performanslarının her etkin ülkenin kendi gelir grubu içerisinde iyi olduğu saptanmıştır.

THE EVALUATION OF THE COUNTRIES IN DIFFERENT INCOME GROUPS IN TERMS OF GLOBAL COMPETITION AND HUMAN DEVELOPMENT VIA DATA ENVELOPMENT ANALYSIS

In our era of globalization and global competition, the question of whether global competition is adequately reflected in human welfare is one of the important issues to be questioned. The purpose of this study is to determine the global competitive powers of countries in terms of three separate income groups (high-income, upper-middle income, and low-middle income) and their relative efficiency regarding their reflection to human development via Data Envelopment Analysis (DEA). To achieve this, the data from the three groups of 29 high-income countries, 33 upper-middle-income countries, and 29 lower-middle-income countries were used. The Basic Requirements, Productivity-Increasing Factors, Innovation and Diversity Factors of the relevant countries in 2016 were used as input; National Income per Capita (GNI), Life Expectancy since Birth, Expected Schooling Time, and Average School Time were used as output as well as CCR and BCC models. Human development data were obtained from United Nations Development Program’s (UNDP) website and global competitiveness data was obtained from The Global Competitiveness Report 2016-2017. DEA Solver program was used for this evaluation. When the average efficiency values are taken into consideration, it is determined that the countries in the high-income group had a higher average. High-income countries had efficiency values ranging from 1.000 to 0.838 in the CCR Model, and in the study, 13 countries were found to be efficient. In addition, the average efficiency value for the efficient countries was 0.933, and the standard deviation was 0.052. High-income countries had efficiency values ranging from 1.000 to 0.929 according to the BCC Model, and 27 countries were found to be efficient. Moreover, for the efficient high-income countries, the average efficiency value was 0.989 and the standard deviation was 0.017. The upper-middle incomes countries had efficiency values ranging from 1.000 to 0.760 in the CCR Model and 10 countries were found to be efficient. The average efficiency value for efficient countries was 0.921 and the standard deviation was 0.075. The upper-middle-income countries had efficiency values ranging from 1.000 to 0.836 according to the BCC Model, but 18 countries were found to be efficient. In addition, the average efficiency value for the efficient countries was 0.973, and the standard deviation was calculated as 0.041. The lower-middle-income countries had efficiency values ranging from 1,000 to 0.790 and 10 countries were found to be efficient. In addition, the average efficiency value for efficient countries was 0.933 and the standard deviation was 0.062. According to the BCC Model, upper-middle incomes countries had efficiency values ranging from 1.000 to 0.888, but 12 countries were efficient. In addition, the average efficiency value for the efficient countries wa s0.967 and the standard deviation was 0.037. According to the results of the study done via the DEA, it was seen that the countries that were inefficient in their performance had a good performance in terms of reflecting the global competitiveness to the human development. It is the main objective of every policy applied in economics to be reflected in human life in a good way and to increase the living standards of individuals. In this context, it would be correct to point out that the policies implemented in countries with good performance in reflecting global competitiveness to human life (education, health, and income) are successful. Countries whose performance is low and inefficient in reflecting global competition power to human development should increase their performance. Although it is important for countries to increase their global competitiveness in the global world, it is very important to reflect the global competitiveness to human well-being. Therefore, one of the most important implications of this study is to increase the global competitiveness of the countries as well as their global competitiveness, the living standards, economic and social well-being of individuals; it is also important to positively reflect the global competitiveness on human life. The point to be taken into account is that the results show the relative efficiency values. The fact that a country is 100% efficient indicates that it is only 100% efficient in relation to the determined inputs and outputs. The inputs and outputs used in this study were available until 2016. Different assessments can be done by determining new inputs and outputs in time or by using different methods.

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Kaynak Göster