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Dergiler / Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi / 2026 / Sayı: 80

Yükseköğretim okullaşmasıyla ilişkili ülkeler arası ve ülke içi faktörler: 41 ülkelik sabit etkiler panel analizi

Between- and within-country correlates of tertiary enrollment: A 41-country fixed-effects panel

Bediha Şahin(HACETTEPE ÜNİVERSİTESİ, EĞİTİM FAKÜLTESİ)0000-0003-0645-6431
Sayfa
59–91
Yayın Tarihi
2026-10-26
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bir yıl gecikmeli açıklayıcı değişkenlerle kurulan model, ortaöğretim okullaşması, genç işgücü ve internet bulgularını doğrulamakta, kentleşme katsayısı ise kesinliğini yitirmektedir.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Kamu eğitim harcaması daha yüksek ve internet erişimi daha yaygın olan ülkeler, genellikle daha yüksek yükseköğretim okullaşma oranları bildirmektedir.
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Temel Bulgular & Veri:Bu çalışmada, Dünya Bankası göstergeleri kullanılarak 41 ülkeyi 2013-2022 döneminde kapsayan bir panel (N = 410 ülke-yıl) üzerinde, ülkeler arası tüm farklılıkların soğurulduğu ve katsayıların yalnızca ülke içi değişimden tanımlandığı iki yönlü sabit etkiler modeli tahmin edilmiştir.
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Sonuç & Katkı:Bir yıl gecikmeli açıklayıcı değişkenlerle kurulan model, ortaöğretim okullaşması, genç işgücü ve internet bulgularını doğrulamakta, kentleşme katsayısı ise kesinliğini yitirmektedir.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2013-2022 Verileri
⚙️Değişkenler:Yükseköğretim okullaşması · yükseköğretim politikası · sabit etkiler panel verisi

Özet

Kamu eğitim harcaması daha yüksek ve internet erişimi daha yaygın olan ülkeler, genellikle daha yüksek yükseköğretim okullaşma oranları bildirmektedir. Ancak bu durum, bir ülkenin kendi eğitim bütçesini artırdığında ya da bağlantı altyapısını genişlettiğinde okullaşmanın da artacağı anlamına gelmez; politika açısından asıl önemli olan bu ikinci, ülke içi sorudur. Bu çalışmada, Dünya Bankası göstergeleri kullanılarak 41 ülkeyi 2013-2022 döneminde kapsayan bir panel (N = 410 ülke-yıl) üzerinde, ülkeler arası tüm farklılıkların soğurulduğu ve katsayıların yalnızca ülke içi değişimden tanımlandığı iki yönlü sabit etkiler modeli tahmin edilmiştir. Sonuçlar, kesit verilerinden elde edilen tablodan belirgin biçimde farklıdır. Toplam kamu eğitim harcamasının GSYH içindeki payı anlamlı değildir (β = -0,604; p = 0,494) ve internet yaygınlığı küçük bir negatif katsayı taşımaktadır (β = -0,136; p = 0,019). Geçerliliğini koruyan ilişkiler yapısal niteliktedir: ortaöğretim okullaşma oranı (β = 0,231; p < 0,01), kentsel nüfus payı (β = 1,529; p = 0,019) ve negatif işaretle genç işgücüne katılım oranı (β = -0,790; p < 0,001). Bir yıl gecikmeli açıklayıcı değişkenlerle kurulan model, ortaöğretim okullaşması, genç işgücü ve internet bulgularını doğrulamakta, kentleşme katsayısı ise kesinliğini yitirmektedir. Bu tahminler nedensel etkiler olarak değil, koşullu ülke içi ilişkiler olarak yorumlanmakta ve yükseköğretim politikası ile SKA 4 erişim gündemi açısından çıkarımları tartışılmaktadır.

Abstract

Countries that spend more on public education and have wider internet access tend to report higher tertiary enrollment. It does not follow that a country will see enrollment rise when it raises its own budget or expands connectivity, and it is this second, within-country question that matters for policy. Using World Bank indicators for 41 countries over 2013-2022 (N = 410 country-years), we estimate a two-way fixed-effects panel model in which all between-country variation is absorbed and the coefficients are identified from change within countries. The results differ markedly from the cross-sectional picture. Total public education expenditure (all levels) as a share of GDP is not significant (β = -0.604, p = 0.494), and internet penetration carries a small negative coefficient (β = -0.136, p = 0.019). The associations that hold up are structural: secondary-school enrollment (β = 0.231, p < 0.01), the urban population share (β = 1.529, p = 0.019) and, with a negative sign, youth labor-force participation (β = -0.790, p < 0.001). A specification with one-year lagged regressors confirms the secondary-enrollment, youth labor-force and internet results, while the urban coefficient loses precision. We read these estimates as conditional within-country associations and not as causal effects, and we discuss what they imply for higher-education policy and the SDG 4 access agenda.

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Bu Makaleye Atıf Yap

Şahin, B. (2026). Yükseköğretim okullaşmasıyla ilişkili ülkeler arası ve ülke içi faktörler: 41 ülkelik sabit etkiler panel analizi. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, Sayı: 80, 59–91. https://doi.org/10.21764/maeuefd.1965068

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