Social Networks, Female Unemployment, and the Urban-Rural Divide in Turkey: Evidence from Tree-Based Machine Learning Algorithms

Yıl 2021, Cilt: 29 Sayı: 50, 73 - 93, 31.10.2021

Mehmet Güney Celbiş

https://doi.org/10.17233/sosyoekonomi.2021.04.04

Cited By: 1

Öz

This study takes a novel, algorithmic approach for understanding the underlying mechanisms related to the employment status of individuals. Using the data from the most recent survey of the International Social Survey Programme (ISSP) on Turkey, the present study examines how social connectivity and location play a role in the prediction of employment status through the use of two tree-based modern machine learning techniques, namely random forest, and extreme gradient boosting. We obtain a wide array of observations, with gender being the most prominent finding when periphery and rural locations are considered.

Anahtar Kelimeler

Machine Learning, Unemployment, Turkey, Rural, Urban

Sosyal Ağlar, Kadın İşsizliği ve Türkiye’de Kentsel-Kırsal Farklılaşmaları: Ağaç Bazlı Makine Öğrenmesi Algoritmalarından Bulgular

Öz

Bu çalışma kişi bazında işsizlik durumlarının nedenlerini anlamak amacıyla yeni ve algoritmik bir yaklaşım getirmektedir. Uluslararası Sosyal Anket Programı’nın (International Social Survey Programme, ISSP) Türkiye üzerine olan en güncel verilerini kullanarak kişilerin sosyal bağlantılarının ve bulundukları lokasyonların işsizlik statülerini tahmin etmede oynadıkları roller iki farklı modern makine öğrenmesi tekniği ile irdelenmektedir. Bu teknikler rassal orman ve ekstrem gradyan artırma modelleridir. Çalışmanın bulgularından yola çıkarak kırsal ve çevre bölgeler özelinde cinsiyet faktörünün rolünün en önde gözüktüğü bir dizi gözlem yapılmaktadır.

Anahtar Kelimeler

Makine Öğrenmesi, İşsizlik, Türkiye, Kırsal, Kentsel

Kaynakça

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