Kantil regresyon karma model ile sosyal bağlılık endeksinin heterojenliğinin incelenmesi

Year 2022, Volume: 28 Issue: 4, 625 - 631, 31.08.2022

Tolga Kurtuluş Serpil Kılıç Depren

Abstract

Çalışmanın amacı, Ekonomik Kalkınma ve İşbirliği Örgütü (OECD)’ne üye ülkelerin arasındaki sosyal bağlılık yapısının görselleştirilmesi ve ülkelerdeki sosyo-demografik, ekonomik, din ve resmi dil faktörlerinin Sosyal Bağlılık Endeksine (SCI) etkisinin parametrik olmayan bir yöntemle incelenmesidir. Çalışmadaki veriseti 3 farklı veri kaynağından alınmıştır: Dünya Bankası, OECD ve Facebook. Facebook firmasından alınan veriler OECD üye ülkeleri arasındaki sosyal ağ yapısının görselleştirilmesi ve özelliklerinin anlaşılması için kullanılmıştır. Ek olarak veri kaynaklarından alınan tüm verilerin birleştirilmesiyle elde edilen kümüle veri seti, SCI’ya etki eden faktörlerin Kantil Regresyon Karma Modeller (QRMIX) ile belirlenmesi amacıyla kullanılmıştır. QRMIX sonucunda, SCI’ya etkisi farklılaşan 4 küme belirlenmiştir. Önem derecesi analizine göre, SCI’nın düşük seviyelerinde ülkenin resmi dini önemi en düşük faktör iken SCI’nın yüksek seviyelerinde en önemli ikinci faktördür. Benzer yaş, eğitim seviyesi, dil ve dine mensup ülkeler arasında güçlü bir sosyal ağ yapısı olduğu gösterilmiştir. Ayrıca, literatürdeki çalışmalarda benzer sosyal ağ gücüne sahip ülkelerarasında ekonomik olarak da güçlü bir bağ olduğu gösterilmiştir. Bu çalışmada da, SCI değerinin farklı kantillerine göre bağımsız faktörlerin öneminin değiştiği, bu sebeple farklı sosyal bağlantılara sahip ülkelerin ithalat-ihracat ve finansal işlemler gibi göstergeleri için farklı aksiyon planları oluşturulabileceği önerilmiştir. Sonuç olarak, bu çalışma farklı bağlantı gücüne sahip OECD ülkeleri için politika yapıcılarına yardımcı olabilir.

Keywords

Kantil regresyon karma modeller, Sosyal bağlılık endeksi, Parametrik olmayan yöntemler, Sosyal ağ analizi

Assessing the heterogeneity of social connectedness index via quantile regression mixture model

Abstract

This study aims to visualize a network of Social Connectedness Index (SCI) in Organization for Economic Co-operation and Development (OECD) countries and then explores the importance of sociodemographic, economic, religion, and distance metrics between countries on SCI using a non-parametric test. The final dataset is aggregated from 3 different data sources: Worldbank, OECD, and Facebook. Drawing upon a data set from Facebook Inc. is used to visualize and understand the network structure among OECD countries. Furthermore, the aggregated dataset used in this study is the first usage of Quantile Regression Mixture Models (QRMIX) to determine factors affecting SCI. As a result of the QRMIX model, 4 clusters are identified in different quantiles where the impact of independent factors are differentiated. Based on the variable importance analysis, almost the least important variable at the lower level of SCI value is religion while it is the second most important factor at the highest level of SCI value. SCI mostly shows up as strong relationships between countries with residents of similar ages and education levels where using common language and having same religions, as well. Also, based on the literature review, it is shown that countries with a higher proportion of similar connections to other countries have more positive economic connections among OECD countries. Thus, given the variable importance of SCI for different subgroups of based on SCI quantiles, this study suggests that different action plans about improving importexport and other financial transactions for the country pairs might be created. To sum up, according to different social connection power of OECD countries, this study can help policymakers.

Keywords

Quantile regression mixture models, Social connectedness index, Non-Parametric method, Social network analysis

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