FOCUS – The Challenges of Sustainability
In their study Gergely Baksay and Ágnes Nagy examine the possibilities of further strengthening competitiveness in order for Hungary to grow sustainably and catch up. According to the Magyar Nemzeti Bank’s approach, an economy is competitive if it utilises its available resources optimally to attain the highest possible, but at the same time sustainable, level of welfare. Relying on the MNB’s research the authors find that In the past decade, the macroeconomic conditions necessary for a turn in competitiveness have developed in Hungary, which also provided a stable basis for managing the economic impact of the coronavirus pandemic. Hungary was ranked 18th in the MNB’s competitiveness ranking among the 27 countries of the European Union in 2021. The result shows that further strengthening competitiveness and the effective release of growth reserves, in particular in the areas of high-quality human capital and the digital and green transition of the economy, are essential to achieve sustainable catching-up and avoid the middle-income trap.
Anita Boros, Csaba Lentner and Vitéz Nagy examine the characteristics of ESG reports, whether they reflect the sustainability performance of individual market players, as well as what the most relevant problems are regarding this issue, not underestimating the fact that the most serious problem of corporate sustainability in 2022 was energy supply difficulties and price problems. Companies must report compliance with environmental (E), social (S) and governmental (G) criteria in accordance with the disclosure rules (framework) for non-financial information. In the course of their research, the authors come to the conclusion that there are a number of parallel mandatory and optional disclosure requirements that require the publication of different data, so they are only partially suitable for comparing the sustainability activities of companies. Some of the corporate reports deal with ESG issues only in principle and only a small proportion reports on actions and results. They also make suggestions regarding the comparability support of companies based on ESG indicators.
András Póra and Valéria Széplaki present the case of China, as the sovereign creditor of emerging markets and developing economies. The study's three main questions are: 1. What trends can be observed in Chinese sovereign lending? 2. How does the contractual setup differ from the Western one? 3. What proposals have been made to mitigate the related risks, and which ones seem feasible? The research relied on recently established databases and regulatory materials. China is the world's largest sovereign creditor at the moment. Its credit expansion began as early as 2008, well before the official announcement of the intention. Several conditions in its contracts differ from those of the West, which pose a risk relevant to an international debt settlement. Their purpose is twofold: to make a profit secured by strong collaterals and, if necessary, "soft power" influence. On the other hand, China does not use "debt-trap diplomacy". Any global reform in sovereign debt management needs the involvement of China, but in the longer term, Chinese lending conditions should also ease.
Sabri Alipanah, Mercédesz Mészáros and Gábor Dávid Kiss in their study present the re-emergence of sovereign spread in emerging countries in the post-Covid-19 economy. The Covid-19 crisis and its economic consequences for emerging countries have highlighted the role of robust, inclusive, and equitable elements of multiple contingency lines to keep these economies away from falling into a devastating cycle of rising sovereign spread. This study first summarizes the crisis-fighting performance of the IMF and eight major RFAs since the outbreak of Covid-19. Then the theoretical model focuses on the deterioration of market expectations (namely about future global economic growth, funding conditions in key currencies and public default) influence on the sovereign spread, by employing a structural panel Vector Autoregression. The results show that sovereign spread depended not only on the global and local growth or the external funding environment but on the market sentiment as well. Also, the results point out the importance of financial supports by international actors like the IMF and partially the RFAs in managing the sovereign spread.
Vivien Czeczeli and Martin Vilonya present the exchange rate developments of cryptocurrencies based on event study analysis. As the cryptocurrency market dynamically evolves, important financial and economic issues arise. Following the exploration of the literature base, special emphasis is put on the comparison between the crypto market and markets for different asset classes (gold, stocks, foreign currency) and on the identification of connection points. The article focuses on the period after 2020, and applies the event study methodology in order to establish, how the two cryptocurrencies with the highest market capitalization (bitcoin and ethereum) reacted to selected events. These events mainly encompassed hacker attacks aimed at the systems that form the basis of the operation of cryptocurrencies, and also certain steps regarding their regulation and application. The authors find that hacker attacks did not have a significant effect on the exchange rates of the two examined cryptocurrencies. Effects of regulatory action on prices are mixed, however even significant effects can be regarded as short-lived.
Máté Csíki examines the relationship between asset purchases, monetary aggregates, and inflation between 2007 and 2022 through the example of the Federal Reserve. The large asset purchase programs following the 2008 crisis led to a significant expansion of money aggregates, which led to an appreciation of the relationship between inflation and money supply. In this study, The author analyses the changes in monetary aggregates caused by quantitative easing, the framework for the implementation of monetary policy with ample reserves and their impact on price levels using a vector autoregressive (VAR) model between 2007 and 2022 based on data for the United States. The study includes the pandemic after 2020, however, due to the limited length of time available and the uncertainty of the effects, the focus of the study is on pre-pandemic processes. Inflation fears caused by the significant expansion of the money supply during the period were not substantiated due to the increase of excess reserves, the changing monetary policy operational framework and negative output gap. According to the model monetary aggregate shocks are built into inflation expectations, changes in the money aggregates caused by asset purchases help the central bank to reach its medium-term inflation target.
M. Mustafa Erdoğdu and Sevda Akar in their study overview the theoretical and empirical research on tax amnesties and weighs their advantages and disadvantages. The paper questions if tax amnesties have any tax compliance impacts in the medium to long term as some papers have claimed or if they are more of a hindrance to tax compliance because of their unjust and degenerating effects. The authors examine the available data for the effects of tax amnesty programs on the ratio of tax revenues to GDP and the Gini coefficient for 12 countries focusing specifically on Turkey. The main aim of the paper is to identify better alternatives to tax amnesties in terms of both tax revenue and tax justice. The results of the study show that while the short-term revenue effect of tax amnesties is uncertain, their medium and long-term negative effects on tax justice and income distribution are almost certain. In addition, the study reveals that improving tax revenue is hardly the main reason behind most tax amnesties.
As a gap-filling analysis, Alexandra Prisznyák’s study examines supervised (classification, regression), unsupervised (clustering, anomaly detection), and hybrid machine learning models and algorithms operating based on highly unbalanced dataset of anti-money laundering and terrorism financing prevention of banking risk management. The author emphasizes that there is no one ideal algorithm. The choice between machine learning algorithm is highly determined based on the underlying theoretical logic and additional comparative. Model building requires a hybrid perspective of the give business unit, IT and visionary management.
The studies published in this issue can be viewed and downloaded at the following link