Economic Trends Research Articles

Creation of a Collaborative “School–University” Environment to Support Research Activities in Schools

This study identifies the issue of insufficient scientific and methodological support for school research projects. To address this, the study proposes the creation of a collaborative “school–university” environment, enabling schoolteachers and students to receive necessary guidance and methodological assistance in preparing and implementing school research projects in the humanities. The study suggests leveraging the Digital Humanities field to incorporate modern digital tools for creating competitive research projects that meet not only the requirements of the Ministry of Education, but also global scientific and economic trends. The assessment of the current situation in the preparation and implementation of school projects involved a survey of more than 600 teachers from the southern region of Kazakhstan. The survey results reveal that over 80% of teachers require scientific and methodological support in preparing school research projects. Based on the survey findings and the expressed demand for methodological assistance, the development of a pilot digital platform is planned. This platform will facilitate collaborative work among schoolteachers, students, university professors, and gifted students to create research projects in Digital Humanities. Such collaborative efforts aim to address the lack of adequate preparation for school research projects.

Time-Varying Autoregressive Models for Economic Forecasting

This research introduces a time-varying autoregressive (TVAR) model, developed to improve the precision of forecasting in economic time series data. The model advances the conventional TVAR framework by incorporating potential mean adjustments and utilizing the Kalman Filter within a Maximum Likelihood Estimation (MLE) framework, with further optimization through the NelderMead method. Applied to the real gross national product (GNP) data of the United States (U.S.), the model effectively captures dynamic patterns and structural changes that traditional models often overlook. The model’s performance is rigorously compared with the widely used Markov switching autoregressive (MSAR) model, demonstrating superior results in both training and testing forecasts. The TVAR model consistently achieves lower error metrics, underscoring its robustness and flexibility in capturing dynamic economic trends and providing reliable forecasts. This research emphasizes the TVAR models potential for broader applications in economic policy analysis, strategic planning, and decision-making processes, particularly in understanding and predicting economic growth. The models adaptability and precision make it a valuable tool for economists and policymakers aiming to navigate complex economic fluctuations with greater confidence as well as accuracy.

Is Winemaking Central to Georgia’s Economy?

Based on the primary data and review of secondary sources, this paper argues that wine is more than a symbol of Georgia; it is a foundation of the country’s economy and social life. The article also looks beyond the direct current statistics of the formal economy and considers the indirect economic impact, trends, and potential, an assessment of winemaking and wine-related tourism points to these sectors as a business priority for Georgia and an important economic and social development vehicle. The study also highlights the potential risks associated with climate change, including increased heat stress, drought, altered growing seasons, and other relatively minor obstacles. The paper identifies specific grapevine cultivars that may be particularly vulnerable to these challenges and explores adaptation strategies to mitigate their negative effects. The results of this study offer valuable insights for Georgian winemakers and policymakers to ensure the sustainability of the country’s wine industry in the face of a changing climate.

Accelerating BRICS Economic Growth: AI-Driven Data Analytics for Informed Policy and Decision Making

This paper analyzes how the Artificial Intelligence (AI) and Machine Learning (ML) are bridging the gap between economic growth in the BRICS countries. BRICS countries are emerging economies that are challenged by increasing income inequality, industrial transformation and the need for infrastructure development. Driven by AI, this study applies data analytics to macroeconomic datasets, tracking down patterns and functional takeaways regarding policy formulation and strategic decision making. The research employs techniques, including predictive modeling, clustering, and natural language processing (NLP), in areas such as trade optimization, resource allocation and labour market analysis. Case examples document successful introduction of AI systems to solve critical economic problems, from increasing healthcare access to raising productivity in agriculture. The findings illustrate the role of AI and ML in helping BRICS policymakers to an informed, data driven development. The research puts AI as core to the process of economic advancement, a solution to developmental gaps and a driver for growth. This research contributes both to its practical outcomes and by providing insights into how AI and ML can solve the complex economic problems of emerging markets. The paper introduces predictive modeling, which anticipates economic trends based on past data and clustering which groups similar economic behaviors to find patterns as tools that are important in economic analysis. Further, Natural Language Processing (NLP) is covered as a highly effective approach to understand policy documents, news, and unstructured data to improve the ability to make decisions. By helping students, researchers, and policymakers understand these AI powered techniques that optimize trade, resource management and labor, these scalable solutions to sustainable development are available. This study touts data driven innovation as a critical means to solve global challenges, well-equipped readers with the skills and knowledge to leverage AI for economic progress in a geography of the dynamic and connected.

APPLICATION OF DATA ANALYSIS TOOLS IN PYTHON TO ASSESS THE STANDARD OF LIVING OF THE POPULATION: THE CASE OF KAZAKHSTAN

The article discusses the application of Python data analysis tools to assess the standard of living of the Kazakhstani population based on statistical data for the period 1991–2021. The main focus is using modern methods and technologies, such as linear regression, correlation analysis, and time series processing. The study uses Python libraries (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn) and the PostgreSQL relational database to store and process large amounts of information. The analysis revealed key trends, including a steady increase in per capita income and a decrease in unemployment. The constructed regression models showed that population incomes are closely related to the subsistence minimum and minimum wage. Forecasting for the coming years indicates a continued increase in living standards while maintaining current economic trends. The results confirm that information technology can improve calculation accuracy, optimize data analysis, and present the results in an easy-to-interpret form. This work can be helpful for government agencies, analysts, and researchers involved in assessing socio-economic indicators. This article highlights the importance of information technologies in data analysis and forecasting and demonstrates their potential to solve urgent social policy problems. Keywords: standard of living, data analysis, Python, regression analysis, forecasting, correlation analysis, time series.

Forecasting House Prices in Shanghai Using Common Factor Analysis and Multiple Linear Regression Analysis

Predicting housing prices is critical for understanding economic trends and making decisions in real estate markets. This is accomplished by identifying important influencers and utilizing prediction models. Predicting these prices is essential for understanding broader economic trends and guiding decisions in real estate markets. The model was trained and evaluated using economic and financial data from the CEInet Statistics Database, with key variables representing major financial, demographic, and economic characteristics included. To estimate housing prices, a combination method combining common factor analysis and multiple linear regression was used. The KMO test result of 0.845 and the scores of Bartlett's Test of Sphericity (2=4296.29, p=0.000) confirmed the data's suitability for factor analysis. Factor analysis reduced the dimension of the dataset, revealing two important components responsible for 80.01% of the volatility in home costs. The multiple linear regression model based on these characteristics was tuned to attain good prediction accuracy. The model predicted housing prices in Shanghai with an accuracy of 94.54%.

Nasdaq and Shanghai Composite Index Forecast Based on ARIMA and ETS Models

Accurately predicting asset prices is crucial for investors, as it not only helps them avoid risks and seize market opportunities, but also influences economic trends, optimizes resource allocation, and ensures the scientific and effective nature of investment decisions. In this paper, ARIMA model and ETS model are used to predict the time series of NASDAQ index and Shanghai Composite Index respectively. By comparing the prediction performance indexes of the two models, such as mean square error (MSE) and root mean square error (RMSE), it is found that ARIMA model has higher prediction accuracy in most cases. Besides, the results show that the performance of different models in different markets is different, which provides a basis for investors to choose the right forecasting model. This study provides valuable empirical analysis for the field of asset price forecasting, especially in the context of global economic uncertainty, and has important practical application value.

Economic development reloaded: the AI revolution in developing nations

This paper explores how Artificial Intelligence (AI) is poised to reshape economic development in developing countries. It examines AI's potential to enhance productivity, optimize resource allocation, and improve access to critical services like healthcare and education. Despite these opportunities, challenges such as technological infrastructure gaps and job displacement are significant. The paper emphasizes the need for tailored policy frameworks to harness AI's benefits while mitigating risks of widening inequalities or AI-poverty traps. By analyzing current economic trends and projections, the paper aims to provide insights into navigating AI adoption in developing economies, promoting sustainable growth and inclusive development in a rapidly evolving global landscape.

Financial Worries of Americans Before the Presidential Elections in the U.S.

The article addresses the concerns of Americans about their financial state, despite the stabilizing economic conditions post-pandemic. The overall positive economic trends don’t correspond to a wild range of concerns about personal finances, including difficulties with paying bills, cutting on nonessentials (like entertainment, holiday trips etc.), and other household worries. With the uncertainty that prices may rise again, the adults are afraid they won’t be able to finance their families’ needs. At the same time more Americans are experiencing anxiety not only about their own financial position, but also about the economic situation in the country, the direction the economy is headed; worry about the deficit of federal budget. Being in the process of transformation, the opinions of Americans reflect growing challenges to their financial stability, both currently and in the long term.

FINANCIAL AND ECONOMIC RELATIONS BETWEEN KYRGYZSTAN AND CHINA, AS FACTORS AND CONDITIONS FOR THE FORMATION OF NEW RELATIONSHIPS IN THE MODERN WORLD

This study examines foreign trade relations between the Kyrgyz Republic and PRC as an important factor in forming new economic ties. Using 2021-2023 statistical data, it examines trade turnover dynamics, reaching US$1,734.9 million in 2023. The methodology is based on a comparative analysis of official sources and current expert assessments. The findings reveal that PRC is a strategic partner of Kyrgyzstan, with exports comprising agricultural raw materials and minerals and imports consisting of construction materials and industrial goods. Potential cooperation in environment-friendly agricultural products was identified. This research enhances the understanding of the current Kyrgyz-Chinese economic trends.

Business cycle in residential construction in Poland 2007–2022

This article explores the dynamic interplay between the business cycle and the housing market in Poland from 2007 to 2022. It examines the complex relationship between economic cycles and the residential property sector, focusing on the impact of government intervention and market fluctuations. The research shows that the housing market is highly sensitive to external factors. It highlights the central role of the National Bank of Poland (NBP) in shaping the housing market through changes in interest rates and credit availability. The COVID-19 pandemic temporarily disrupted developer activity and housing demand, highlighting the vulnerability of the market to unexpected shocks. In addition to observations, this study calls for a critical analysis of housing policies, identifying potential shortcomings and offering recommendations for improvement. It underlines that economic crises are an inherent part of housing market cycles, promoting market cleansing and healthy competition. Finally, this research emphasises that the housing market is intertwined with broader economic trends. It highlights the need for central banks to strike a balance between managing inflation and supporting economic growth. This study contributes to a deeper understanding of the relationship between the housing market and the business cycle, providing valuable insights for policymaking and investment decisions.

Quantitative Easing in Japan: A Longitudinal Analysis of Monetary Policy Effects and Challenges

This study presents a comprehensive analysis of the effects and challenges of quantitative easing as a monetary policy tool in Japan. Over a period of more than two decades, Japan has implemented quantitative easing to stimulate its economy, resulting in a unique economic landscape characterized by both short-term benefits and long-term uncertainties. By examining historical developments, recent economic trends, and potential risks, this research sheds light on the complex interplay between monetary policy and economic outcomes in Japan. This academic study will provide a comprehensive analysis of the effects and challenges of quantitative easing in Japan, focusing on both short-term benefits and long-term consequences. It will also examine recent economic trends and potential risks to the Japanese economy, offering insights and recommendations for policymakers while acknowledging the uncertainties surrounding the long-term effects of this monetary policy.

Analisis Tren Ekonomi Digital terhadap Kebijakan Moneter di Indonesia

This study aims to determine the effect of fintech, the amount of money in circulation, interest rates and economic growth on the analysis of digital economic trends on monetary policy in Indonesia. There are four variables in this study, namely fintech, the amount of money in circulation, interest rates and economic growth. The analysis method used is Vector Autoregression with the Impluse Response Function test or abbreviated as IRF and the Forecast Error Variance Decomposition test commonly abbreviated as FEVD, stationarity test, cointegration test, lag structure stability test and optimal lag length test. There is a contribution from each variable to the variable itself and other variables, according to the results of the Vector Autoregression study with a lag basis of 2. In addition, the results of the Vector Autoregression analysis show that the past variable (t-1) contributes to the current variable both to the variable itself and to other variables. The results of the analysis show that there is a reciprocal relationship between the variables. By using response function analysis, we can see if there is a response from other variables to changes in one variable in the short, medium, or long term. In addition, we know that the stability of all variables is formed in the short, medium, and long term. According to the Variance Decomposition Analysis, factors such as Fintech and Money Supply contribute the most to the variable itself. However, other variables that have the greatest influence on the variable itself and are supported by other variables in the short, medium, and long term are economic growth and interest rates are most influenced by Fintech.

Dynamic Modeling of Money Supply and GDP: A State-Space Approach

Using data from 2008 to 2023, this study investigates the dynamic relationship between money supply (M2) and GDP through a state-space modeling approach. Addressing the question of how effectively money supply impacts GDP, the study is grounded in the theoretical framework that monetary policy influences economic output, a concept drawn from classical and Keynesian economic theories. The data, sourced from official economic reports, highlights significant variability in both M2 and GDP, with GDP exhibiting slightly lower volatility. The distributional characteristics of the variables show a slight positive skew and a flatter-than-normal distribution, suggesting potential deviations from typical economic trends. The state-space model captures the relationship between M2 and GDP, indicating that money supply is an important explanatory factor for GDP. While the model’s observation equation shows a low and statistically significant standard deviation of the error term—suggesting that it explains variations in GDP effectively—the state transition equation reveals a mostly deterministic evolution of unobserved state variables, though this result lacks full statistical significance and warrants cautious interpretation. The log-likelihood value supports a fair model fit, yet additional macroeconomic variables or alternate model structures may enhance the analysis. The findings affirm the critical role of money supply in economic growth, consistent with economic theory. However, future research could explore more complex dynamics, including non-linear relationships and the effects of external shocks, to refine the understanding of the M2-GDP nexus. This study contributes empirical insights using a state-space framework, offering valuable implications for monetary policy and macroeconomic forecasting.

Projecting future migration with Bayesian hierarchical gravity models of migration: an application to Africa

In this paper, I present and discuss a novel approach to parameterize a gravity model of migration using Bayesian hierarchical models with random intercepts that are free to vary by country of origin, destination, and directed origin-destination country pairs. I then utilize this model to project transboundary migration flows between African countries to the horizon 2050. To do so, I use data on projected future crop yields and water availability from the ISIMIP2b scenarios in combination with projections on future economic and demographic trends from the Shared Socio-Economic Pathways (SSPs). The results indicate that over the period 2010–2050 between 8 to 17 millions people are projected to migrate internationally on the African continent. Yet, only a small portion of these migrants will be induced to move because of climate change. To the contrary, comparisons between SSPs scenarios suggests that economic development will have a far larger impact on projected level of international migration on the continent than climate change.

Деінституціоналізація корпоратизму в радянській економіці та її наслідки для економіки сучасної України

The article is devoted to the study of the Soviet period of Ukraine’s economy, in particular the transformation of institutions in the absence of Ukrainian statehood. The subject of the study is corporatism as a mechanism for coordinating and implementing the interests of the state, society and business in different economic systems: the command-administrative and market ones. The aim of the article is to identify the main factors of the deinstitutionalization of corporatism in the Soviet economy and to clarify their impact on the modern economy of Ukraine. General scientific methods of interdisciplinary research, analysis and synthesis, induction and deduction and a systemic approach are used in generalizing economic trends and determining cause-and-effect relationships. It is argued that the ideology of Bolshevism was the basis for changing the economic system, on whose basis the transformation of economic institutions in the command-administrative economy was carried out. The author reveals the content and mechanisms of the nationalization of the economy, the degeneration of formal institutions of corporatism and their use to restrict economic freedom and suppress civil society, which contradicts the very idea of corporatism as a union of equals. The importance of excluding national interests from economic consciousness and practice is emphasized, as evidenced in particular by the inefficiency of the allocation and use of Ukraine's productive resources. The article established the consequences of the deinstitutionalization of corporatism in the Soviet period for the economy of modern Ukraine, namely: high concentration of property, monopolism, the inability of large industry to self-sustainable development, the lack of concentration of capital in high-tech industries, and the lack of social partnership. The conclusion is made about the replacement of corporatism by bureaucratic corporatism in the Soviet period of Ukrainian history and its further transformation into oligarchic corporatism in the 90s of the 20th century.

Research Paper on Economic Survey of Infrastructure Sector

This paper provides an overview of the current state of the infrastructure industry, focusing on economic trends, challenges, and opportunities. It analyzes key sectors, investment flows, and policy implications, drawing on data from various sources, including government reports, industry analyses, and international organizations. Economic Survey 2023-24 has been released on 22nd July 2024. Every year, the Ministry of Finance publishes a report called the Economic Survey of India. This report usually comes out a day before the Union Budget is presented in the Parliament. KEYWORDS: Budget, Infrastructure

Кількісні аспекти взаємозв’язку між теоріями економічного зростання та економічних циклів

This paper discovers some problems that take place in the economic growth theories and in their relationship with the economic cycle models. The author shows that, if the long-term economic trend formation is considered regardless of the economic activity fluctuations, whose impact is eliminated by numerous assumptions that distort economic reality (those about perfect competition, full employment, etc.), it would lead to some difficulties in identification of real economic situation and in the accuracy of macroeconomic forecasting. Demonstrated the nature of this kind of difficulties considering schematically the process of economic trends formation. Since the economic growth theories only deal with the long-term trend, abstracting through various assumptions from short-term fluctuations in economic activities, a stereotype of independence from fluctuations in economic activity can be created. An example of this is the identification by some of economists of the problem of the economic growth slowing down for the world economy (and the US economy, in particular) in the last decades of the 20th century and attempts to explain this slowdown. These explanations are mostly aimed at justifying the reason for the change in the inclination of the long-term trend of economic growth, which is assumed straightforward for at least 25 years. However, the inclination of this 25-year trend depends significantly on the choice of the period of time for which it is calculated. The work shows that another choice of time period for calculating a long-term trend for the US economy can prove that there is no such a problem as the slowdown in economic growth over time. Calculations, presented in the paper, show, in particular, that it is impossible to ensure sustainable economic growth in the long run, if you allow deep recessions in the short term. When choosing a period of time, which determines the long-term trend of economic growth, it is necessary to take into account the configuration of economic cycles, since it is the depth of lowest and highest points of the real fluctuation curve that determines the inclination of the trend of economic development. In other words, the future long-term trend is shaped today based on short-term fluctuations in real time. And all the drawbacks of regulating the cycles that have arisen today will somehow affect the slope of the long-term trend.

STUDI TATANAN TERITORIAL PADA RUANG HUNIAN PARA PENGRAJIN PERAK DI CELUK, SUKAWATI

This research explores the territorial transformation in the residential spaces of silver artisans in Celuk Village, Sukawati, Gianyar, Bali, an area undergoing changes amidst a decline in the number of traditional silver craftsmen. The study aims to understand how socio-economic dynamics and urbanization affect residential spaces and silver crafting practices. This research employs a territorial analysis approach to evaluate changes in demographics, economics, and migration patterns of artisans and to map how these factors interact with the traditional values and cultural identity of the artisan community. The study's findings are expected to provide strategic insights for the preservation efforts of silver craftsmanship in Celuk, highlighting the need for more inclusive and sustainable urban planning. This research also investigates the shift from traditional workshop spaces to commercial spaces, offering insights into how adaptations to market demands and economic trends impact the lives and work of the artisan community. These overall findings are crucial to supporting local government strategies and stakeholders in promoting the sustainability of the silver craft industry while preserving the rich cultural heritage of Celuk Village. The results reveal that most houses originally used as silver workshops have been repurposed into other businesses, such as general commercial enterprises and banks. This indicates significant economic diversification, with around 46.67% of the houses that were previously silver shops now converted to other uses. Economic factors, such as the declining demand for silver products, are the primary reasons for this change.

The Current Situation and Trend of Sino-US Trade Development in the Context of Economic Globalization

The evolution of trade and commercial relations between China and the US is particularly noteworthy, given the significant impact of economic globalization and its role as a fundamental pillar of global economic stability and progress. The trade volume between China and the US is steadily increasing, and this is happening at the same time as the trade structure is changing significantly. The process of commerce between China and the US is not without its difficulties, though, as seen by the US's gradual growth in trade protectionism, the frequency of trade disputes, and the imbalance in trade relations. This paper provides an in-depth analysis of the dynamic evolution trend of China-US trade in the context of economic globalization and discusses the current status and challenges of China-US trade relations, as well as the future trend based on global economic, political, and technological development trends. In addition, the paper forecasts how China-US trade relations will develop in the future in light of new technological advancements, policy adjustments, and shifts in the global economic landscape. It also offers policy recommendations and strategic adjustment guidelines to encourage collaboration between the two sides and produce win-win outcomes.