Türkiye’de Girişimcilik ve Sürdürülebilir Bölgesel Kalkınma: Makine Öğrenmesi Yaklaşımlarından Elde Edilen Bulgular

Year 2021, Volume: 6 Issue: 3, 882 - 911, 30.12.2021

Ebru Tomris Aydoğan , Esra Karadeniz , Mehmet Güney Celbiş

https://doi.org/10.30784/epfad.961838

Abstract

Birleşmiş Milletler Kalkınma Programı (UNDP), Sürdürülebilir Kalkınma’nın (SK) sağlanmasında girişimciliği önemli bir faktör olarak tanımlamaktadır. Bu çalışmanın amacı, sosyal bilimlerde nispeten yeni bir yöntem olan makine öğrenmesini uygulayarak ve Küresel Girişimcilik Monitörü (GEM) verilerini kullanarak Türkiye'deki girişimcilik faaliyetlerini SK açısından etkileyen faktörleri analiz etmektir. Rassal Orman model bulgularına göre çalışma durumu, başka bir girişimci tanımak, kişinin kendi bilgisi ve becerisi hakkındaki görüşü, ihtiyaç/mecburiyet ve yaş, girişimci olmak için önemli faktörlerdir. Ayrıca, hanehalkı geliri ve nüfusu, kişinin çalışma durumu, cinsiyeti, eğitim düzeyi ve anket zamanı itibariyle şirket içi girişimcilik aktivitesinde bulunması da önemli faktörler olarak tespit edilmişlerdir. Stokastik Gradyan Artırma sonuçlarına göre Türkiye’de bölgesel kalkınmanın ve koşulların bireylerin girişimci olma olasılıklarını etkilediği gözlenmiştir. Bu araştırma, Özdemir ve Karadeniz (2011) ile Karadeniz ve Özçam'ın (2018) Türkiye bağlamında bireysel girişimci kazanımlarından buldukları önceki sonuçları ilk kez makine öğrenmesi algoritmaları uygulaması aracılığıyla analiz etmekte ve genişletmektedir. Girişimcilik faaliyetleri sürdürülebilir kalkınmayı destekleyen politikalar çerçevesinde planlanmalıdır.

Keywords

Ekonomik Kalkınma, Girişimcilik, Bölgesel Analiz, Makine Öğrenmesi

Entrepreneurship and Sustainable Regional Development in Turkey: Findings Obtained from Machine Learning Approaches

Abstract

The United Nations Development Programme (UNDP) defines entrepreneurship as an important factor in achieving Sustainable Development (SD). The aim of this study is to analyze the factors affecting entrepreneurship activities in Turkey in terms of SD by applying machine learning, which is a relatively new method in social sciences, and using Global Entrepreneurship Monitor (GEM) data. According to the results of the Random Forest model, working status, knowing another entrepreneur, one’s opinion about one’s own knowledge and skills, need/obligation and age are important factors for being an entrepreneur. Also, household income and population, the employment status, gender, education level, and the engagement in intra-company entrepreneurial activity of the person at the time of the survey were also determined as important factors. According to the results of the Stochastic Gradient Boosting, it is observed that regional development and conditions in Turkey affect the probability of individuals to become entrepreneurs. This research analyzes and extends the previous results of Özdemir and Karadeniz (2011) and Karadeniz and Özçam (2018) from individual entrepreneur gains in the Turkish context for the first time through the application of machine learning algorithms. Entrepreneurial activities should be planned within the framework of policies that support sustainable development.

Keywords

Economic Development, Entrepreneurship, Regional Analysis, Machine Learning

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