1,720,979 research outputs found
Global power shifting tendencies influenced by the conflict’s outcome: regional and global implications /
No full textThe initiation of military operations by Russia against Ukraine in February 2022 sparked a full-scale war in Europe, impacting the global economy and geopolitical dynamics. This chapter delves into the underlying factors that ignited the conflict, with a particular focus on Russia’s historical inclination towards expansion through warfare, its reliance on resource exports, and military capabilities, and its response to technological changes. The modern world’s decreasing reliance on resources in favour of technological development poses a challenge to resource-rich countries like Russia, leading to efforts to maintain dominance through military aggression. Russia’s economic dependence on resource exports, particularly energy, exacerbates its vulnerability to global market shifts. Understanding the multifaceted drivers of conflict is essential for addressing geopolitical tensions and fostering global stability. Mitigating the risk of future conflicts requires strategic diplomacy, economic diversification, and technological innovation to navigate evolving geopolitical landscapes
Investments in Agricultural Machinery and its Efficiency in Ukraine
Full text linkOne of the major conditions of effective agriculture production is sufficient farm mechanization. However, the unstable economic situation in Ukraine, combined with bureaucratic problems, an unstable currency exchange rate, and sharply changed trade routes (which has caused major losses to a number of farms and traders working with the Custom Union) created significant obstacles for investing in machinery in Ukraine. It is especially topical for small and medium farms that usually function in poor economic conditions without any adequate access to the credit market. Consequently, Ukrainian agriculture producers often have an inadequate mechanization rate. As a result, the productivity of Ukrainian farms is significantly lower as compared to other countries that have similar natural conditions in terms of temperatures, precipitation and quality of agricultural lands.A no less important problem is the lack of awareness of small and medium farms, which may not realize the effect that investment has in agriculture machinery. Thus, in order to provide specific numbers for potential investors and prove the efficiency of this fund placement, an expected direct economic effect from machinery investment (as an increased profit from higher yield) was estimated. The first step was to define those types of agricultural machinery that have significant impact on the yield and productivity levels for each of the most important crop types: grain, oil crops, vegetables, fruits, etc. Then, an impact of additional investment in various machinery means on crops yield was estimated. Finally, based on fixed prices and a discount rate, an expected additional profit generated by newly purchased machinery on an average farm was estimated. The model proved especially high profitability of investment in such machinery as ploughs, fertilizers spreaders, harvesters, tractors, and machines for irrigation – most of them are paid off (on a land parcel with area around 2000 ha) in three years or less
Predicting Mortgage Loan Defaults Using Machine Learning Techniques
Full text linkMortgage default prediction is always on the table for financial institutions. Banks are interested in provision planning, while regulators monitor systemic risk, which this sector may possess. This research is focused on predicting defaults on a one-year horizon using data from the Ukrainian credit registry applying machine-learning methods. This research is useful for not only academia but also policymakers since it helps to assess the need for implementation of macroprudential instruments. We tested two data balancing techniques: weighting the original sample and synthetic minority oversampling technique and compared the results. It was found that random forest and extreme gradient-boosting decision trees are better classifiers regarding both accuracy and precision. These models provided an essential balance between actual default precision and minimizing false defaults. We also tested neural networks, linear discriminant analysis, support vector machines with linear kernels, and decision trees, but they showed similar results to logistic regression. The result suggested that real gross domestic product (GDP) growth and debt-service-to-income ratio (DSTI) were good predictors of default. This means that a realistic GDP forecast as well as a proper assessment of the borrower’s DSTI through the loan history can predict default on a one-year horizon. Adding other variables such as the borrower’s age and loan interest rate can also be beneficial. However, the residual maturity of mortgage loans does not contribute to default probability, which means that banks should treat both new borrowers equally and those who nearly repaid the loan
SDGs realization for the renovation of Ukraine /
No full textThe chapter explores the post-war reconstruction of Ukraine and its integration into the European and global economy. It emphasizes the need for strategic planning to achieve peace and outlines steps for sustainable development until 2030. The paper discusses the necessity of rapid reforms to modernize the economy, prioritize clean and resource-efficient practices, and address environmental risks. Special attention is given to the management of post-war waste and the adoption of new principles for resource processing. The importance of harmonizing Ukrainian legislation with EU standards is highlighted, with a focus on reforms necessary for EU accession. The paper outlines a roadmap for achieving Sustainable Development Goals post-war. Key challenges include geopolitical shifts, the duration of the conflict, and the need for significant financial assistance. The chapter concludes with the potential for Ukraine to become a regional economic centre in case of SDG implementation with EU support and substantial financial aid for post-war recovery and integration
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Financial Dollarization: Trojan Horse for Ukraine?
Full text linkThe paper revisits the causes and consequences of financial dollarization in Ukraine during the past decade (monthly data). Dollarization in emerging markets plays a dual role: positive and negative. This study of financial dollarization is in the context of resident household holdings of foreign currency-denominated bank deposits and loans. If exchange rates are stable, deposit dollarization allows the withdrawal of money from the shadow economy, and loan dollarization allows the lending of long-term money, which is not possible with domestic currency due to inflation expectations. At the same time, the instability and lack of supply of foreign currencies in the market result in the collapse of household and bank finances, leading to currency risk, credit risk, and liquidity risk. Therefore, the study uses estimate indicators, the deposit dollarization index (DDI), household foreign currency deposits and loans, loan to deposit ratio (LTD), and inflation to find out the tendencies in the context of a changing domestic currency exchange rate. We present three models to reveal the influence of financial dollarization on banking stability. The first one explains the real value of domestic currency deposits through indicators such as M2 (positive), exchange rate (negative), domestic currency deposits (positive), and panic effects (negative). The second one describes the influence of the exchange rate (negative) and panic effects (negative) on foreign currency deposits. The third one explains the DDI through such the exchange rate, M2, and interest rates. The combined models provide an insight about the time necessary to stabilize the Ukrainian banking system
THE ECONOMETRIC MODELING OF UKRAINIAN MACROECONOMIC TENDENCIES
Full text linkEconometric models are widely used in economic policies of many states. They help to build a great variety of econometric systems for every country and take into account the specifics of each economy.In this article, the structural macroeconomic models that describe the main aspects of the economic policy were applied. The interdependence between the level of inflation, the value of investment, savings, consumption, export and import transactions, taxes on the foreign trade were defined based on the analysis of the key macroeconomic parameters of Ukraine. After investigating all economic indicators, they were transformed into stationary time series for a correct use in the model. In addition, heteroscedasticity and autocorrelation of residuals were excluded in all econometric equations.As a result, the research shows that a large share of black economy leads to a rather high level of inflation in the state, because its value is primarily determined by expectations of the population under such circumstances. The paper indicates that the further export growth leads to a lower consumption growth and also to a lower growth of savings. Such a situation indicates an insufficient development of the domestic market. Investment growth has been fund not to be directly linked to consumption increase and economic development in general. Unfortunately, the main sources of investment in Ukraine are the funds of enterprises and foreign sources. The analysis shows a need to encourage public involvement into investment processes. For example, the creation of public–private partnerships is especially useful while implementing infrastructural projects
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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