Predicting the Development Levels of Countries Using the Decision Tree Method with the Help of World Governance Indicators

Year 2024, , 245 - 269, 30.07.2024

Batuhan Özkan , Hakan Kaya

https://doi.org/10.54733/smar.1448083

Abstract

Understanding and evaluating the development levels of countries is a complex and multidimensional process. In an environment where economic, social and political factors are integrated, international development indicators play an important role in measuring and comparing the performance of countries. The aim of this study is to determine the development levels of countries through world governance indicators. In this context, the decision tree method is used in the analysis. The sample of the study consists of 187 countries. The data set consists of World Development Indicators (WDI) and Worldwide Governance Indicators (WGI) for 2020. Gross Domestic Product (GDP) values of the countries are used as a development indicator. GDP values are divided into four different classes according to the ATLAS method: high, upper-middle, lower-middle and low-income countries. As a result of the analysis, it is found that the accuracy value of the decision tree prediction model was 80.85%. It has been observed that the Regulatory Quality (RQ) dimension, which allows and encourages the development of the private sector, is effective in the classification of countries belonging to the high-income group. It has been observed that the Right to Voice and Accountability (VA) variable is effective in the classification of countries with high and upper-middle income levels, and the Political Stability (PS) and Rule of Law (RL) variables are effective for countries belonging to the lower-middle and low-income groups.

Keywords

level of development, quality of governance, decision tree, machine learning

Dünya Yönetişim Göstergeleri Yardımıyla Ülkelerin Gelişmişlik Seviyelerinin Karar Ağacı Yöntemiyle Tahmin Edilmesi

Abstract

Ülkelerin gelişmişlik düzeylerini anlamak ve değerlendirmek, karmaşık ve çok boyutlu bir süreçtir. Ekonomik, sosyal ve politik faktörlerin birbirine entegre olduğu bir ortamda uluslararası kalkınma göstergeleri bu ülkelerin performansını ölçmede ve karşılaştırmada önemli bir rol oynamaktadır. Bu çalışmanın amacı, dünya yönetişim göstergeleri aracılığıyla ülkelerin gelişmişlik düzeylerinin belirlenmesidir. Bu bağlamda analizlerde makine öğrenmesi yöntemlerinden biri olan karar ağacı yöntemi kullanılmıştır. Çalışmanın örneklemini 187 ülke oluşturmaktadır. Veri setini, 2020 yılına ait Dünya Gelişmişlik Göstergeleri (DGG) ve Dünya Çapında Yönetişim Göstergeleri (DÇYG) oluşturmaktadır. Gelişmişlik göstergesi olarak ülkelerin Gayrisafi Yurt İçi Hasıla (GSYH) değerleri kullanılmıştır. GSYH değerleri ATLAS yöntemine göre yüksek, üst-orta, alt-orta ve düşük gelir seviyesine sahip ülkeler olmak üzere dört farklı sınıfa ayrılmıştır. Yapılan analizler neticesinde karar ağacı tahmin modeline ait doğruluk değerinin %80,85 olduğu görülmüştür. Bir başka ifadeyle yönetişim göstergeleri kullanılarak ülkelere ait gelişmişlik düzeyleri hakkındaki tahminler yüksek doğrulukla tahmin edilmiştir. Model tahminin bu denli yüksek olması neticesinde yönetişim göstergeleri ile ülkelerin gelişmişlik seviyeleri arasında bir ilişki olduğu çalışmanın sonuçları arasında ifade edilmiştir. Yüksek gelir grubuna ait ülkelerin sınıflandırılmasında özel sektörün gelişmesine izin veren ve teşvik eden Düzenleyici Kalite (DK) boyutunun etkin olduğu görülmüştür. Yüksek ve üst-orta gelire seviyesine sahip ülkelerin sınıflandırılmasında İfade Özgürlüğü ve Hesap Verebilirlik (İÖ) değişkeni, alt-orta ve düşük gelir grubuna ait ülkeler için ise Siyasi Istikrar (Sİ) ve Hukukun Üstünlüğü (HÜ) değişkenlerinin etkili olduğu görülmüştür.

Keywords

gelişmişlik seviyesi, yönetişim kalitesi, karar ağacı, makine öğrenmesi

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