Journal of Sustainable Development Issues (JSDI) - An interdisciplinary, double-blind peer-reviewed journal focusing on Environment, Economics, and Society. Explore the latest research and developments in theoretical and practical aspects of sustainable development.

This study investigated the impact of economic growth and unemployment on obesity within the Obesity Kuznets Curve (OKC) framework across 15 post-Soviet countries using annual data from 1991 to 2016. Employing the Augmented ARDL (A-ARDL) cointegration test for the first time in this context, the study reveals cointegration in nine countries, with the OKC hypothesis validated for Armenia, Azerbaijan, Belarus, Kazakhstan, Russia, and Turkmenistan. Initial economic growth increases obesity rates, but beyond specific GDP thresholds, healthier lifestyles emerge. The FADF-SB unit root test confirms variable stationarity at varying levels. Findings also highlight unemployment’s diverse regional effects: a negative impact in Kazakhstan and Uzbekistan but a positive one elsewhere. The Fourier Toda-Yamamoto test identifies bidirectional causality between GDP and obesity in Kyrgyzstan and varying causal relationships in other countries. These results emphasize the complex interplay between economic and health factors, informing policies to address obesity trends in transitioning economies.

Askeri harcamalar, bir ülkenin savunma politikalarının temelini oluşturarak uluslararası arenada hayati bir rol oynamaktadır. Askeri harcamalar ile ekonomik büyüme arasındaki ilişki ilk kez Benoit (1973, 1978) tarafından incelenmiş ve bu ilişki literatüre "Benoit hipotezi" olarak geçmiştir. Bu çalışmada askeri harcamalar ve finansal gelişmenin ekonomik büyüme üzerindeki etkisi Benoit hipotezi kapsamında BRICS+T (Brezilya, Rusya, Hindistan, Çin, Güney Afrika ve Türkiye) ülkeleri için 1992-2021 yıllık veri kullanılarak araştırılmıştır. Değişkenlerin durağanlıkları, ADF ve Esnek Fourier ADF birim kök testleri kullanılarak incelenmiş ve ardından Fourier Bootstrap ARDL ve Bayer-Hanck yöntemleriyle değişkenler arasında eş-bütünleşme ilişkisi araştırılmıştır. FB-ARDL yöntemi, eş-bütünleşme ilişkisini sadece Türkiye'de bulurken, Bayer-Hanck eş-bütünleşme testi tüm ülkelerde eş-bütünleşme ilişkisi tespit etmiştir. FB-ARDL uzun dönem tahmin sonucuna göre Türkiye`de finansal gelişme ekonomik büyümeyi pozitif, askeri harcamalar ise negatif etkilemektedir. Dolayısıyla Türkiye`de Benoit hipotezi geçersizdir. BRICS ülkeleri için FMOLS uzun dönem tahmin sonuçları şu şekildedir; A) Brezilya ve Güney Afrika hariç tüm ülkelerde finansal gelişmenin ekonomik büyüme üzerindeki etkisi pozitif ve anlamlıdır. B) Rusya ve Çin'de askeri harcamaların ekonomik büyüme üzerinde istatistiksel olarak anlamlı bir etkisi bulunmamaktadır. C) Brezilya, Hindistan ve Güney Afrika`da askeri harcamaların ekonomik büyümeyi negatif etkilediği görülmüştür (Benoit hipotezi geçersizdir). Son olarak, çalışmada tüm ülkeler için değişkenler arasındaki nedensellik ilişkisi Fourier Toda-Yamamoto yöntemiyle araştırılmış ve sonuçlar çalışmanın son bölümünde raporlanmıştır. Çıkan ekonometrik sonuçlara dayanarak, farklı politika önerileriyle çalışma sonlandırılmıştır. Anahtar Kelimeler: Benoit Hipotezi, Fourier Bootstrap ARDL, Finansal Gelişme, Ekonomik Büyüme, Fourier ADF, Fourier Toda-Yamamoto JEL Sınıflandırılması: P24, O16, H56, C32, C51

This study examines the dynamic linkages among Bitcoin (BTC) prices, U.S. interest rates, and economic growth using quarterly data from 2012Q3 to 2025Q1 within a nonlinear time series framework that incorporates structural breaks. Stationarity is assessed using the Kruse and Fourier unit root tests, while long-run relationships are evaluated with the Augmented-ARDL cointegration method. Long-run coefficients are estimated with CCR, DOLS, and FMOLS. Causal relationships are explored through the Fourier-Toda-Yamamoto test. Results show a significant and positive long-run effect of interest rates on BTC, implying its role as a speculative hedge during monetary tightening. No cointegration is found between BTC and growth, indicating weak macro-financial integration. Additionally, growth Granger-causes interest rate decisions, consistent with Taylor Rule expectations, while no causality is observed from monetary policy(interest rate) to BTC. These findings highlight the decoupling of cryptocurrency markets from traditional financial tools, underscoring the need for adaptable regulatory strategies.

Background/Objectives: Referral-based and hospital-based disease registries may contain high-quality data on registered patients while incompletely and differentially capturing the eligible case population. We developed an identification framework that separates post-capture problems from genuine ascertainment problems and links population correction to the information available about uncaptured cases. Methods: The framework was informed by a targeted methodological synthesis and evaluated using Monte Carlo simulations of 5,000 eligible cases over 1,000 replications. We examined non-differential capture, differential capture depending on measured covariates, residual capture depending on an unmeasured determinant, and a separate two-source capture-recapture experiment under independent and positively dependent capture. Results: Under measured-covariate differential capture, the registry prevalence had mean bias of −0.0277, whereas valid external standardization and inverse-probability selection weighting reduced mean bias to 0.0001. When capture also depended on an unmeasured determinant, X-only correction remained biased (0.0184). Under independent two-source capture, the Chapman estimator was approximately unbiased for N=5,000 (mean 4,998.9), whereas positive source dependence produced substantial downward bias (mean 3,936.4). Conclusions: Direct population identification may arise from eligible-case target data, valid capture probabilities, or overlapping sources, whereas general-population covariate margins and indirect completeness indicators are generally insufficient on their own. Incomplete case ascertainment should therefore be addressed by matching the inferential target to the information that identifies it.