Purpose: Since competitiveness is crucial in international trade, this paper contributes to the literature by interrogating the information and communication technology (ICT)-trade nexus on competitiveness in Eastern and Western European countries. Does ICT usage promote or hinder the impact of trade openness on competitiveness? This study attempts to answer two questions: (1) is the interaction of trade and ICT significant in promoting competitiveness? (2) Is the effect significantly different by European classification?Design/methodology/approach: With data on 17 European countries from 2007 to 2020 and using mobile phones and fixed telephone usage as ICT indicators, the study engages the bootstrapped ordinary least squares (BOLS) and method of moments quantile regression (MM-QR) techniques to probe the discourse.Findings: The empirical findings reveal that (1) the interaction of trade and ICT boost competitiveness; (2) the effect of mobile phone is consistent across the full, East, and West European samples; (3) the interaction effect is also significant across the conditional distribution of competitiveness and (4) mobile phones and fixed broadband usage reveal “leapfrog” effect across the quantiles. Overall, the study submits that ICT usage will enhance the impact of trade, and thus, ICT is a critical enabler of competitiveness in Europe; policy recommendations were discussed.Originality/value: To the best of the authors' knowledge, this is the first study examining the interaction effect of trade openness and ICT usage on competitiveness in Europe. In other words, the authors attempt to analyze how ICT usage influences trade-competitiveness dynamics. To fill the gap in the literature, the authors' use a sample of 17 European countries from 2007 to 2020. The variables of interest are the competitiveness index, trade openness, and four ICT indicators (mobile phone, fixed telephone subscriptions, fixed telephone subscriptions, and Internet users).

This paper examines the role of tourism in foreign direct investment-growth relation in upper-middle income countries. We deploy static and dynamic panel analysis to evaluate how tourism indicators influence the impact of FDI net inflows on growth using an unbalanced panel data on 29 upper middle-income countries from 2010-2019. The tourism indicators are receipts, arrivals and expenditures. The results from static and dynamic analyses indicate that for the most part (1) FDI and tourism exert asymmetric effects on growth, (2) tourism indicators reduce the negative effect of FDI on growth, (4) trade openness is a positive and significant predictor of growth, and (5) domestic credit negatively contributes to growth. Deductively, results evidence that tourism indicators are critical drivers of economic growth in upper middle-income countries. Overall, tourism receipts show the largest influence on FDI to spur the most appreciable impact on growth. Despite this, the fact that tourism indicators cannot completely eliminate the destructive impact of FDI on economic growth shows that tourism development policies should be based on a greener and sustainable ground, taking into account the effects of the coronavirus.

Purpose: This study examines how the rapid adoption of artificial intelligence (AI) and automation affects employment dynamics across G7 economies. While previous research has often focused on either AI or robotics in isolation, their combined and long-term effects on employment remain poorly understood. Addressing this gap is crucial for policymakers seeking to balance technological progress with labor market stability. Design/methodology/approach: Using a balanced panel dataset covering 2010–2024 for the G7 countries, the study investigates the relationships between AI investment (proxied by information and communication technology (ICT) investment), robot density (ROBOT), wages, productivity (PRD) and education spending (EDU), and their impact on employment. The analysis employs panel unit root and cross-sectional dependence tests, a panel autoregressive distributed lag (ARDL) framework estimated via the pooled mean group (PMG) estimator, and robustness checks using Driscoll–Kraay fixed effects, common correlated effects (CCE) estimators, country-specific regressions and Dumitrescu–Hurlin panel causality tests. Findings: The results reveal that AI investment has a significant negative effect on employment in the long run, whereas ROBOT shows a positive but context-dependent relationship. Wage levels are negatively associated with employment, while PRD shows only a modest positive influence. Education expenditure exhibits mixed behavior – positive in the short run but negative in the long run – suggesting potential misalignment with evolving labor market needs. Causality tests confirm a unidirectional link from AI investment to employment, underscoring its structural role in labor market change. Research limitations/implications: This study is limited by data availability, particularly the lack of detailed sectoral or occupational breakdowns across countries. As a result, it cannot fully capture the distributional effects of AI and automation across different worker groups. The use of proxies, such as ICT investment for AI, may not reflect the full scope of AI deployment. Despite these limitations, the findings highlight important macro-level dynamics and suggest that technological investments significantly shape employment trends. Future research should utilize micro-level data to explore sector-specific impacts, wage effects and labor force transitions in response to digital transformation. Practical implications: The findings suggest that without targeted policy interventions, increased AI investment may displace workers in the long run. Policymakers should prioritize reskilling, adapt education systems to evolving technological needs, and differentiate strategies across sectors and worker skill levels. Social implications: This study highlights the potential for AI and automation to reshape labor markets, with implications for income distribution, job security and social cohesion. The displacement of routine jobs may disproportionately affect low-skilled and vulnerable workers, increasing the risk of inequality and social exclusion. To prevent deepening divides, social policies must focus on equitable access to education, digital literacy and lifelong learning. Supporting workforce adaptability through inclusive training programs and social safety nets is essential. The results underscore the urgent need for collaborative efforts between governments, educational institutions and industries to ensure a socially sustainable digital transformation. Originality/value: This study is among the first to jointly analyze AI and robotics within a dynamic panel framework, offering new cross-country evidence on their heterogeneous employment effects in advanced economies. By integrating multiple estimation strategies and country-specific perspectives, the paper contributes to a more nuanced understanding of how technological transformation reshapes labor markets and highlights the institutional conditions that mediate these effects.

The primary objective of any nation's economy is to achieve economic stability and a high standard of living. This can be attained through sustainable economic growth. However, global economic competition often brings about challenges, one of which is the increasing demand for energy products driven by population growth. Historically, this demand has been predominantly met by fossil fuels, but due to resource depletion and environmental concerns, there has been a growing interest in alternative energy sources. This shift towards renewable energy has become a global trend. The energy sector serves as a key indicator of a nation's prosperity and development, with renewable energy production, transportation, and costs being vital metrics of development. For Turkey, like many developing nations, investing in renewable energy is crucial for sustainable growth. This study explores the relationship between renewable energy, economic growth, and inflation in Turkey from 1975 to 2022. It examines the process of economic growth in the first part, followed by an analysis of energy dynamics and inflation in subsequent sections. The study employs unit root tests such as ADF and PP, along with the Toda-Yamamoto causality test, using available data. The final results are then interpreted.

The impact of shadow banking on financial stability remains a controversial issue today due to the size and complexity of these activities and the inadequate regulatory frameworks for systemic risks. In this context, shadow banking has become the focus of financial regulators due to its potential effects on financial stability. Therefore, this study was conducted to understand the impact of shadow banking on financial stability. In this study, the relationship between shadow banking and financial instability is examined using the VAR method for the example of the United States, covering the period 2000-2020. In order to create an accurate model, firstly unit root tests were performed, followed by autocorrelation and heteroscedasticity tests. The findings were found to be significant and Johansen cointegration test was applied. In the cointegration results, it was seen that the series were cointegrated, that is, they moved together in the long run. Finally, a Granger causality test was conducted between shadow banking and financial instability, and according to the empirical findings, it was concluded that there was a causality from shadow banking to financial instability for the period in question.

Bu çalışma, Türkiye’de 1994–2024 dönemini kapsayan zaman serisi verileri aracılığıyla döviz kuru, enflasyon ve faiz oranlarının Borsa İstanbul (BIST) üzerindeki etkilerini incelemektedir. Uygulanan Vektör Hata Düzeltme Modeli (VECM) aracılığıyla, değişkenler arasındaki uzun ve kısa dönemli ilişkiler analiz edilmiş; kriz ve pandemi gibi olağanüstü dönemlere özgü yapısal kırılmalar da dikkate alınmıştır. Johansen eşbütünleşme testi sonuçları, enflasyon ve faiz oranı ile BIST arasında uzun dönemli bir denge ilişkisinin varlığını göstermektedir. Bulgular, enflasyonun BIST üzerinde pozitif; faiz oranlarının ise negatif etkili olduğunu ortaya koymuştur. Granger nedensellik testleri, döviz kuru ve faiz oranlarının BIST üzerinde öngörü gücüne sahip olduğunu göstermektedir. Ayrıca, kriz ve pandemi dönemlerinde bu ilişkilerin yön değiştirdiği ve istatistiksel olarak anlamlı yapısal kırılmalar yaşandığı tespit edilmiştir. Etki-tepki ve varyans ayrıştırma analizleri, BIST’in faiz oranı şoklarına karşı daha kalıcı tepkiler verdiğini, döviz kuru şoklarına ise daha kısa vadeli yanıtlar ürettiğini göstermektedir. Çalışma, Türkiye’deki sermaye piyasalarının makroekonomik değişkenlere duyarlılığını ortaya koyarken, politika yapıcılar ve yatırımcılara yönelik stratejik çıkarımlar da sunmaktadır.