The article explores the factors influencing the satisfaction with the domestic tourism at the Black Sea national resorts in Bulgaria, focusing on the usage of the structural equation modelling (SEM) to assess both observable and latent variables affecting the tourists’ satisfaction. The domestic tourism is crucial for Bulgaria’s economy, contributing significantly to the regional development, employment, and local culture. The research focuses on the tourists’ experiences at the Black Sea resorts, given their popularity over other forms of tourism such as the mountain, spa, and wine tourism. The study employs SEM to analyse how various factors, including the accommodation, tourist resources, general resort conditions, and the specific features like pollution and overbuilding, impact the tourists’ overall satisfaction. Data from a nationally representative survey conducted in 2023 provides the basis for this analysis. The results show that the general conditions and the restaurants and entertainment establishments have the strongest positive influence on tourist satisfaction, while specific issues like water pollution and poor infrastructure negatively impact the experience. The research underscores the importance of improving the resort facilities, security, and service quality to enhance the tourist satisfaction.

The study is motivated by the (global) trend of excess savings and the restrictive response to investment in productive capital despite abundant financing. While there is heterogeneity among countries, in Bulgaria we can observe both declining market interest rates (rm) and long-standing investment stagnation in terms of gross capital formation as a share of GDP. This stylized fact can be described and explained through the NeoWicksellian concept of the natural interest rate (r*), with potentially secular low values of r* posing serious challenges to further stimulating interest-sensitive aggregate expenditures. In contrast to other studies, a trend towards a decrease in the natural interest rate is observed for Bulgaria, which provides a more accurate explanation for the structural factors, including underutilized fiscal space (with r*<g) In addition, the nominal rate gap – defined as the spread between the rm and the estimated nominal r* was used as robustness check. The measured nominal rate gap covers a risk premium shift and potentially a large range of other financial (or institutional) factors, but it is still an informative indicator for financial conditions and reveals a strong explanation of private fixed capital expenditures variance in the last two decades.

Oceans and seas have an important impact on economic growth, environmental sustainability, and human wellbeing. Unfortunately, marine ecosystem services are exploited due to marine degradation, such as plastic pollution, ocean acidification, and dumping of industrial chemical but it is most and severely affected by oil spills. The current study attempts to check the validation of Environmental Kuznets Curve Hypothesis for global marine degradation by using time series econometrics techniques with structural breaks. The world level data time series data on oil spillage, oil consumption and GDP per capita is used, covering the time period of 50 years from 1970 to 2019. The results concluded that there is an existence of inverted U-shaped between marine degradation and global economic activities. The turning point has already appeared in the dataset, most probably, due to the shifting trend from non-renewable energy to renewable energy as well as various conventions and laws being introduced on the prevention of marine oil spillage.

This study analyses whether the Global Political Uncertainty Index has an impact on volatility in some developed and emerging markets. This study, which analyses the period between January 2003 and October 2022, uses the GARCH MIDAS approach, which allows low-frequency and high-frequency data to be analyzed together. The results show that the Global Political Uncertainty Index has a significant impact on the volatility of all markets included in the analysis. According to the results of the analysis, while an increase in global political uncertainty increases volatility in all markets, emerging markets are more affected by such uncertainty than developed markets. The markets most affected by global political uncertainty are Greece, India and China, while the least affected markets are Turkey and Canada.

In the context of Fourth Industrial Revolution, digitalization has been recognized as a key driver of long-term development in developing countries. This study aims to explore the impact of digitalization on the global value chain (GVC) participation of Vietnamese enterprises between 2005 and 2023. Utilizing a probit model, the findings show that digitalization positively affects firms’ GVC participation. The results are robust across various definitions of GVC participation. The study also finds that labor productivity, costs at custom gates, and import licenses play important roles in promoting firms engaged in GVCs. Meanwhile, firm age does not have significant effect on GVC engagement. Regarding heterogeneous effects, digitalization has a more profound effect on medium-sized firms than on large ones. Based on these findings that we propose several policy implications to encourage Vietnamese firms to adopt digitalization and deepen their involvement in global value chains.

This study investigates the dynamic connectedness among conventional cryptocurrencies (Bitcoin), gold-backed currencies (PAXG and DGX), gold, and G7 banking sector indices (USA, Germany, Canada, France, UK, Italy, and Japan). Employing Quantile Vector Autoregression and Temporal-Frequency Connectivity methodologies, our analysis reveals nuanced relationships among these market blocks. France and Germany’s banking indices exhibit the highest connectedness, emphasizing their central roles. Peaks in the Total Connectedness Index coincide with global events, underscoring the market’s sensitivity to external shocks. G7 banking sectors emerge as stable information transmitters, while Bitcoin, PAXG, DGX, and gold act as net receivers of shocks, reflecting their effectiveness as hedges during economic uncertainties. Our time-quantile space approach unveils a symmetrical pattern in dynamic connectivity, emphasizing robust interconnections between positively and negatively shifted assets. The time-frequency connectedness analysis highlights the market’s short-term sensitivity, emphasizing the need for adaptive risk management. Decomposing net directional connectivity into short and long-term dynamics provides valuable insights for investors and risk managers. Ultimately, our findings contribute to a deeper understanding of dynamic connectedness in the cryptocurrency market, offering insights for effective risk management and decisionmaking in this evolving financial landscape.

In economic research, the trade deficit in developing countries has consistently been a critical and chronic issue. The examination of imports, which is the primary cause of the trade deficit, is of great importance in this regard. This study has a goal to identify the determinants of imports. Türkiye, as a country that has been grappling with the challenges of being classified as a developing nation for years, has consistently experienced imports surpassing exports. For developing countries like Türkiye, debts and economic growth are as important as the concepts of trade deficit and imports. This study aims to examine the effects of industrial production, the private sector’s external debt trend, and exports as proxies for growth on Türkiye’s imports. Time series data covering from January 2005 to December 2023 was used in the study. The Autoregressive Distributed Lag (ARDL) method is utilized to identify cointegration among variables, followed by causality analyses employing the Toda-Yamamoto method to enhance the research findings. The results revealed that independent variables are cointegrated with imports. Additionally, production, exports, and debts are identified as Granger causes of imports. These results are of critical importance in demonstrating that Türkiye’s exports and production are also dependent on imports.

This article examines the key macroeconomic trends and early warning signals for the Bulgarian economy in 2023– 2024, in the context of an unstable external environment. The study aims to identify the main drivers of growth, assess inflationary dynamics, and evaluate the applicability of an early warning indicator. The analysis is quantitative and empirical, combining descriptive and comparative examination of official data with econometric modeling using a Markov-switching dynamic factor model. The findings show that GDP growth slowed from 4.0% in 2022 to 1.9% in 2023, before rebounding to 2.8% in 2024, driven primarily by domestic demand, while external demand weakened. Inflation declined from 13.0% in 2022 to 3.1% by the end of 2024; however, price pressures in the services sector remained elevated due to rising wages and labor costs. The proposed early warning indicator, constructed from five high-frequency variables, successfully signaled a 70% probability of low growth in mid-2023, which decreased to 55% by the end of 2024. These results highlight the value of high-frequency data and advanced modeling techniques for timely forecasting of cyclical fluctuations.

This study examines the relationship between fiscal deficit and trade deficit, known as ‘twin deficits hypotheses, in the Indian economy between 1977-2022. This study’s empirical results are derived using the Asymmetric cointegration technique (Nonlinear Autoregressive Distributed Lag model - NARDL) to estimate the long-run and short-run relationship. Zivot and Andrew’s (ZA) unit root test determines structural breaks in the series of the twin deficit variables. The asymmetric NARDL results for the short-run and long-run confirm that the trade deficit hypothesis can decide India’s fiscal deficit. Their relationship is healthier in the long-run.