BEEPS’ten Yolsuzluk Modellerini Tahmin Etmek Üzere MLT Kullanımı
Özet
Makale, kümeleme yoluyla yolsuzluğu tahmin etmek için makine öğrenimi teknolojilerini (MLT) kullanma fırsatlarını anlatmaktadır. Makalede, Avrupa İmar ve Kalkınma Bankasının 2014 yılına ait Gelişmiş İş Ortamı ve İşletme Performansı Araştırması (BEEPS)verileri kullanıldı. Avrupa İmar ve Kalkınma Bankası için Nielsen gibi saygın kurumlar tarafından üretilen 1672 değişken ve 59619 gözlemden faydalanıldı. MLT ile farklı göstergelerin analizi, ülkeleri potansiyel yolsuzluk modellerine göre kümelememize olanak tanımaktadır. Bu yöntemin klasik alan araştırması yaklaşımının gözlem eksikliklerinin üstesinden gelebileceğini ifade ettik çünkü bu yöntemle verilerinde bazı çarpıklıklar veya yetersizlikler bulunan ülkeleri de tahmin edebilmemiz mümkün hale geldi. (örneğin, firmaların bazı nedenlerden dolayı yolsuzluk hakkında yalan söylemek isteyebileceği zamanlarda). Bu yöntem bize gerçek yolsuzluk alanını analiz etmek için kullanılabilecek ek bir ölçüm sağlamaktadır. Ulaştığımız sonuçlar, farklı ülkelerdeki yolsuzluk modellerini analiz etmek için firmalar, bilim adamları ve politika yapıcılar için yararlı olabilir.
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