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Aim/purpose - This paper investigates the accuracy of leading indicators in the case of the 2001 sovereign default crisis and the 2018 currency turmoil in Argentina. Design/methodology/approach - In this paper, we conducted early warning signals analysis based on a-priori selected variables. For each of the macroeconomic variables, we computed yearly changes and selected the threshold to minimise the noise-to-signal ratio, i.e. the ratio of percentage of false signals in 'normal' times to percentage of good signals in a two-year period preceding each of the crises. Findings - The predictive power of indicators differs significantly in various crisis episodes. For the 2001 crisis, the decline in value of bank deposits was the best leading indicator based on the noise-to-signal ratio. For the 2018 currency crisis, the lowest noise-to-signal ratio was observed for the lending-deposit rate ratio. Research implications/limitations - The survey is limited mostly by the data availability and their quality. Originality/value/contribution - This paper gives a complex review of the major early warning indicators in the context of the most recent history of Argentina's economy. It applies a set of classical leading indicators to two modern cases of financial crises. The paper proposes an original 'knocking the window' approach to the presentation of traditional warning concepts in the context of current economic events. [ABSTRACT FROM AUTHOR]

The article determines the advancements surrounding the privatization process in the MEDA region using the new database monitoring system, PRIVMEDA. It has been noted that the process of privatization in MEDA countries contributes several similarities with other regions. In addition, private sector activity was concentrated in a small number of large firms that benefited from protective policies together with a number of micro-enterprises with little access to formal finance, markets or government support programs.

The paper reviews some of the basic economic indicators that reflect the three essential macroeconomic problems (economic growth, inflation and unemployment) and correlates them with economic well-being expressed through the happiness index. Having in mind the basic premise of the economy as a science to manage limited resources in order to meet unlimited human needs, the question that arises is when people feel well, have a feeling that they have "quality" life, and when they feel happy. Although the question of well-being has been of interest to economic science since its inception, the answer to what makes people happy is more of a philosophical than an economic question and difficult to measure and answer. Well-being depends on a huge number of economic, but also on a number of non-economic variables. In addition2 to the economic indicators that show the level of economic development, the standard of living, employment and unemployment, the poverty rate and the inflation rate, an indicator is needed that will show the personal perception of quality of life. The level of well-being and happiness have a strong expression of a subjective experience and are determined in addition to economic, also by other non-economic factors (psychological, sociological, philosophical, cultural, etc.). Taking into account the complexity of the subject and the spatial limitation of a paper of this kind, a regression analysis was done in it, using data on GDP per capita, PPP (current international $), Inflation, consumer prices (annual %), Poverty gap at $1.90 a day (2011 PPP) (%), and Happiness Index for 67 countries in the world. The analysis is based on data from 2019 as the last year for which data is available, and in which there were no restrictions of any kind. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between entrepreneurship and unemployment. Using Eurostat business demography data, a strongly balanced panel model pertaining to 148 European regions from 2008 to 2017 is assembled and a fixed effects regression technique is used to analyse the relationship between net business population growth and lagged unemployment. Results from the analysis show evidence of a negative relationship. However, when we stratify regions based on their economic performance, we find that this relationship is positive in higher performing regions and negative in lower performing regions, suggesting that push-factor or necessity-based entrepreneurship may be more prevalent in leading rather than lagging regions. Implications for policy are discussed. Results for the influence of industrial concentration and variety on entrepreneurship are also obtained. [ABSTRACT FROM AUTHOR]

One of the stages of crisis management is planning and initial preparation to deal with the crisis. During natural disasters, one of the main activities is the logistics of relief groups and the activities of relief teams to save the lives of the victims of the accident. A review of past events shows that the chances of rescuing the injured decrease and that a quick and correct decision is important in this situation. This paper presents a two-phase hybrid approach to decision-making and prioritization of affected regions to send relief teams. In this approach, multi-criteria decision-making methods in two phases are used to consider different indicators in achieving the optimal solution. In the first phase, with the help of the primary decision matrix, the AHP, TOPSIS and AHP-TOPSIS methods are used. And in the second phase, according to the results obtained from the first phase, the secondary decision matrix is created. With the CoCoSo method's help, one of the newest methods in this field, areas are prioritized for relief. In order to implement the proposed approach, the city of Amol has been studied. [ABSTRACT FROM AUTHOR]

This paper will compare and contrast Visegrád and Southeast European mobility towards (and away from) the European Union (EU) ideal of cohesion, in the process investigating conflicting "core" and "peripheral" assertions of "Europeanness." Though the Visegrád states have exceeded economic expectations, they have to varying degrees stood in opposition to the values of the EU's self-professed "European identity," with Hungary and Poland in particular demonstrating increasing illiberalism. Meanwhile, in Southeast Europe, lacklustre economic performance has tended to contrast with increasingly liberal democratic rule and strong popular support for the EU "project." The EU's cohesion strategy has prioritized economic convergence and, ultimately, this has meant that budgetary considerations, and political rhetoric and scrutiny, have often favoured the rebellious but economically resurgent Visegrád states over the weaker economies of a more compliant Southeast Europe. The EU's integration strategy of constructing "identity hegemony" depends upon both economic and socio-political convergence. This paper questions the congruence of these focuses, given the discriminatory application of integration incentives and the persistence of Orientalism/Balkanism in West European rhetoric. [ABSTRACT FROM AUTHOR]

Purpose: By 2040, 14.5% of Malaysia's population will consist of those aged 65 years and above. The purpose of this paper is to examine the time-varying impact of an ageing population on the economy of Malaysia. Design/methodology/approach: The relationship between the ageing population and economic performance was examined from the period 1971–2019. The time-varying rolling convergence estimation of 40 + k observations sample frame of the trace statistics was analysed. Findings: The ageing population had affected the economic activities in Malaysia over all sampling time frame from 1971 to 2010, 1972–2011, ...to 1980–2019. The growth in total population and economic activities also caused a significant increase in the ageing population in the long run. An improved economic performance signifies the affordability for better healthcare services and improvement in medical science technology to treat diseases. Originality/value: Its ageing population has gradually slow down economic activities in Malaysia. Now is the time to be prepared and address an ageing workforce issues such as productivity, retirement policy, savings behaviour and life-long learning capabilities. Peer review: The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-04-2021-0234. [ABSTRACT FROM AUTHOR]

Economic risks faced by manufacturing enterprises are gradually increasing and risk reduction whilst maintaining high financial performance has become key to their survival and development of enterprises. Enterprise performance affects not only enterprise development but also does the interests of investors and creditors. Therefore, a well‐performing model for financial performance prediction is particularly important. In this paper, we combine unsupervised and supervised learning, fusing self‐organizing mapping neural networks and convolutional neural networks, and apply deep learning to financial analysis to construct a new financial performance prediction model, called SNN‐CNN. This paper uses crawler technology to obtain financial data of listed manufacturing enterprises and classifies their financial performance into five levels. It finds that enterprises with high financial performance tend to have balanced financial indicators, strong corporate vitality and stable development of various capabilities, while enterprises with low financial performance have poor repayment and profitability, significant risks in corporate operation and limited growth and development. Compared with traditional risk prediction models, the SOM‐CNN model has a higher accuracy rate, up to 95.69%. [ABSTRACT FROM AUTHOR]

Fuzzy neural networks have seen an increasing presence in state-owned enterprise performance evaluation. Fuzzy-based financial and non-financial indicators are combined to evaluate state-owned enterprise performance. In this paper, a combination model is proposed for enterprise performance evaluation. This model is built using fuzzy neural network and uses the fuzzy method along with the neural network's self-learning ability to reduce the impact of subjective factors on the evaluation results while realizing intelligent enterprise performance evaluation. First, this paper proposes construction ideas and principles for the state-owned enterprise' performance evaluation index system and then examines its efficiency. There are two types of indicators in the system: financial and non-financial. The Takagi-Sugeno fuzzy neural network is used to build a financial indicator model. The BP neural network is used to construct a non-financial indicator model for a state-owned enterprise performance evaluation portfolio model based on fuzzy neural networks. Both these networks use a decision support system during the process of building the model. Finally, a comparison is made between the results from Takagi-Sugeno fuzzy neural network and those from the BP neural network performance evaluation model after sample data have been selected, standardization processing has been applied, and a simulation of the network model has been trained. The experiments show that the combined model can be used to evaluate the performance of state-owned enterprises which opens a new direction for evaluating enterprise performance. [ABSTRACT FROM AUTHOR]

Macroeconomic situation is the overall performance of the economic situation of a country and region. Making accurate forecasts of macroeconomic trends is of great significance for analyzing the success or failure of macroeconomic control policies, evaluating the quality of economic system operation, and correctly formulating future development planning strategies. The macroeconomic system is a nonlinear system, the environment is constantly changing, and additional disturbing factors directly affect the operation of the macroeconomic system, which has a great impact on the forecast results. The historical information required for macroeconomic modeling is unstable, unclear, and incomplete, which makes it very difficult to solve such problems with traditional forecasting methods. In response to the multivariate and nonlinear characteristics of macroeconomic forecasting, this paper proposes the application of artificial neural networks for forecasting. This paper introduces the recurrent neural network into the field of economic forecasting to solve the problems of the traditional BP (back propagation) neural network method. The experimental data are verified and the experimental results prove that the studied scheme based PSO-GRU improve the performance of economic forecasting. [ABSTRACT FROM AUTHOR]

The paper deals with the production and economic indicators of farms in the Republic of Serbia based on data collected from the FADN sample for 2019. The aim of the research is to analyze and evaluate the influence of important factors on profitability of farms of different economic size. According to their economic size, farms are classified into four groups: very small, small-sized, mid-sized and large-sized farms. Factors influencing profitability are grouped into: production management, financial management, human resources management and subsidies and natural factors. The statistical technique used in the paper is a multiple regression model applied to determine statistically significant influence of certain factors on profitability. The results of the research show that equity turnover is the factor with the greatest positive impact on profitability of farms regardless of their economic size. Paid labour has the greatest negative impact on very small, small-sized and mid-sized farms. Further research should focus on analysis of the financial performance of small and medium farms which, based on the available capacities and income, are the main drivers of development of the entire agricultural sector. [ABSTRACT FROM AUTHOR]

The uneven recovery in the world economy since the start of Covid-19 wreaking havoc in 2020 has given us a new shape in economic development: the K-shape. As an alternative to the K-shape, we found the possibility of an even worse development: the G-shape. Indonesia, which experienced the deceleration of its economic growth in the prepandemic period, can struggle with a G-shaped recovery. This paper evaluates this possibility by comparing the country to other emerging economies. This study uses 10 economic performance indicators to determine the possibility of a G-shaped recovery in an economy, which represent the demand side, supply side, monetary policy, fiscal policy, health and institutional quality (credit, labor, Total Factor of Productivity, Human Development Index, regulatory quality, CA balance to GDP, current health expenditure, internet bandwidth, debt to GDP, and economic openness). The research sample covers 33 countries. Among them, 7 countries with the highest possibility of G-Shaped economic development were identified: Pakistan, Mexico, Egypt, South Africa, Nigeria, Indonesia, and Brazil. Several counties in samples, including Pakistan, Mexico, Egypt, South Africa, Nigeria, Indonesia, and Brazil, are trapped in long stagnation. The regression result shows a positive relationship between each dependent variable and the GDP, except for economic openness. From the results, the Human Development Index has the highest coefficient among the other independent variables. This paper also employed the Computable General Equilibrium simulation analysis model to project Indonesian economic growth, which shows Indonesia's possibility of being trapped in the G-shaped recovery. [ABSTRACT FROM AUTHOR]

As the "sixth largest city group in the world", the Yangtze River Delta region is an important economic growth point and core economic area in China. While achieving rapid economic growth, the amount of waste generated is increasing day by day, and the investment in environmental protection is constantly increasing. Among them, MSW collection and disposal funds accounted for 57.40% of the fixed assets investment in environmental sanitation in 2017. To improve the efficiency of existing environmental protection investment, this paper is based on the existing academic research at China and abroad, with the help of the Data Envelopment Analysis (DEA) model. This paper analyzed the economic indicators of 27 cities in the Yangtze River Delta for MSW collection, transportation and disposal to measure economic efficiency, identify and improve inefficiencies. The research results show that the average efficiency of the 27 cities in the Yangtze River Delta is at a relatively high level. Six cities have the most effective comprehensive technical efficiency. These six cities constitute the effective frontier of the municipal solid waste transfer system. The overall efficiency has a comparative advantage; 9 cities have the best pure technical efficiency, accounting for 33 of all cities. %, the pure technical efficiency of the remaining 18 cities is low, indicating that these cities have not fully utilized their resource input under the current scale, and the efficiency of resource utilization needs to be improved. Finally, this paper proposes suggestions for improvement from the perspective of environmental sustainability. To improve MSW collection and disposal efficiency, differentiated management should be implemented in cities in the Yangtze River Delta. Implications: This paper conducts an empirical analysis on the efficiency of MSW collection and disposal in the Yangtze River Delta in China in 2017. Based on the calculation of the DEA model, our conclusion is that although the collection and disposal system of MSW in the Yangtze River Delta has been significantly improved, the level of collection and disposal of some MSW has not reached the optimal level: (1) Comprehensive technical efficiency is the most effective Cities accounted for 22% of the total number of cities studied. (2) 33% of the cities in the Yangtze River Delta achieve the best pure technical efficiency; (3) Among the 27 cities, 6 cities have the best scale efficiency, and the remaining 21 cities have not reached the best scale efficiency. [ABSTRACT FROM AUTHOR]

Provides information on Japan's "White Paper on International Trade 2004." Focus and arguments of the government report; Description of economic globalization and changes in the associated macroeconomic mechanisms; Analysis of intra-regional trade and the division of labor in East Asia.

This paper investigates Lieberman's claim that Chow's money demand data supports the transactions over the asset motive for holding money. The results indicate that permanent income and the long-term interest rate perform best in the long-run specification, whereas current income and the commercial paper rate perform better in the short-run specification. These results appear to conflict, however, if lagged money captures the effects on money demand up to the last period so that other variables measure their effect on money at the margin, then it is not necessarily the case. For example, the results are consistent with the butter stock analysis of Cart and Darby and with a general expectations interpretation of the term structure of interest rates. Thus, the results presented here suggested that Chow's original conclusion emerges unrefuted. [ABSTRACT FROM AUTHOR]

Cooperatives are key players in the dairy sector both globally and in Poland. Increasing pressure on the economic performance of dairy cooperatives poses questions about the drivers that shape their competitiveness. The aim of the paper is twofold. Firstly, the paper assesses the productivity and efficiency of dairy cooperatives and how these features evolved over time. Secondly, it examines whether and to what extent productivity and efficiency determine the competitiveness of entities under the study. As an empirical illustration, the authors use a dataset on dairy cooperatives operating in Poland, which is an important producer of dairy products in the European Union (EU). The results (obtained using partial productivity and data envelopment analysis) confirm that productivity and efficiency affect the competitiveness of dairy cooperatives. However, a primary contribution is to demonstrate that the relationship between productivity and efficiency versus competitiveness is more complex. Taking into account factors like the qualitative features of market offerings and their influence on consumer behaviur and satisfaction is essential for a thorough assessment of the competitiveness of dairy cooperatives. [ABSTRACT FROM AUTHOR]

Innovation research is motivated by the understanding that new technologies contribute to address societal challenges and foster welfare. Extant research in the economics of innovation has, however, adopted a narrow definition of social welfare, which focuses on economic performance and material well‐being, and that mostly disregards distributional impacts of innovation. This paper critically reviews the concept of social welfare in the economics of innovation literature, and it outlines a new research agenda that will investigate the impacts of innovation on individuals' well‐being and aggregate social welfare. The new research program has two major pillars. First, it adopts a broader notion of agents' well‐being that comprises also non‐economic factors and capabilities alongside income and material wealth. Second, it argues that welfare analyses of innovation must explicitly take into account equity and social justice in addition to efficiency and economic performance. This new research agenda opens up several novel questions for future innovation research and policy‐making, pointing to the existence of trade‐offs and dilemmas between efficiency and equity, and between short‐ and long‐run impacts of innovation on social welfare. [ABSTRACT FROM AUTHOR]

Copyright of Field & Vegetable Crops Research / Ratarstvo i povrtarstvo is the property of Institute of Field & Vegetable Crops and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The purpose of this study is to determine the extent to which institutional pressures affect supplier and customer collaboration and how collaboration explains green supply chain performance using institutional and stakeholder theories. Design/methodology/approach: The paper builds on empirical evidence gathered from responses of Iranian industrial managers based on a self-administered survey. The hypotheses in the conceptual model are tested using Smart-PLS (partial least square). Findings: The results provide evidence that regulatory requirements and internal motivators, as institutional pressure, enable customer and supplier collaboration. Furthermore, customer collaboration has a positive effect on supplier collaboration and, consequently, positively affects environmental and economic performance. But supplier collaboration is just positively affecting environmental performances and is not effective on economic performance. Research limitations/implications: The subject is that the study is a questionnaire-based survey, and the validity and reliability of the results are influenced by the respondents' idiosyncrasies. This study provides a comprehensive model of drivers, supply chain collaboration and performance in the context of the industry sector. The study contributes by providing empirical data with a focus on customer and supplier collaboration and their interrelationship in GSCM modelling. Originality/value: The current paper is one of the new field of research which demonstrate the significance of customer and supplier collaboration with each other to attain green performance. It also contributes to show the effect of institutional pressure on the customer-supplier collaboration that fosters green performance in the supply chain. [ABSTRACT FROM AUTHOR]

Existing studies offer conflicting analyses of the effect of the economy on voter turnout; some studies suggest that a poor economy leads to lower turnout while other studies find the opposite, or no significant effects. One reason for this ambiguity might be rooted in a limited understanding of how voters form attitudes about economic performance. Previous research implicitly presumes that voters' economic assessments are based solely on information about the retrospective domestic economy. In contrast, this study suggests that voters compare their nation's economy with the economies of other nations. If the economy has an effect on turnout, the relative economy will affect voters' evaluations, and consequently their decision to turn out. Employing 'relative economy' variables measured by media-identified spatial reference points across elections and countries from 29 democracies since the 1980s, this paper finds that a poor relative economy leads to lower turnout, while a good relative economy appears to have no effect. The finding has significant implications for the electoral effect of turnout, and thus on democratic accountability. [ABSTRACT FROM AUTHOR]

Addressing risks and pandemics at a country level is a complex task that requires transdisciplinary approaches. The paper aims to identify groups of the European Union countries characterized by a similar COVID-19 Resilience Index (CRI). Developed in the paper CRI index reflects the countries' COVID-19 risk and their readiness for a crisis situation, including a pandemic. Moreover, the study detects the factors that significantly differentiate the distinguished groups. According to our research, Bulgaria, Hungary, Malta, and Poland have the lowest COVID-19 Resilience Index score, with Croatia, Greece, Czechia, and Slovakia following close. At the same time, Ireland and Scandinavian countries occupy the top of the leader board, followed by Luxemburg. The Kruskal-Wallis test results indicate four COVID-19 risk indicators that significantly differentiate the countries in the first year of the COVID-19 pandemic. Among the significant factors are not only COVID-19-related factors, i.e., the changes in residential human mobility, the stringency of anti-COVID-19 policy, but also strictly environmental factors, namely pollution and material footprint. It indicates that the most critical global environmental issues might be crucial in the phase of a future pandemic. Moreover, we detect eight readiness factors that significantly differentiate the analysed country groups. Among the significant factors are the economic indicators such as GDP per capita and labour markets, the governance indicators such as Rule of Law, Access to Information, Implementation and Adaptability measures, and social indicators such as Tertiary Attainment and Research, Innovation, and Infrastructure. [ABSTRACT FROM AUTHOR]

The optimal configuration of the rated capacity, rated power and daily output power is an important prerequisite for energy storage systems to participate in peak regulation on the grid side. Economic benefits are the main reason driving investment in energy storage systems. In this paper, the relationship between the economic indicators of an energy storage system and its configuration is first analyzed, and the optimization objective function is formulated. Then, according to the objective limitations of the energy storage system configuration and operation, the constraints are formulated. A set of typical parameters is selected, and the CPLEX (IBM ILOG CPLEX Optimization Studio) solver is used in MATLAB to solve the optimal configuration results. Several sets of optimization results are obtained by taking different subjective coefficient β values, and the economic and social benefits of the optimization results are analyzed. When the economic benefits of the energy storage system are more important, the value of β needs to be smaller, such as a value of 1000. Conversely, when the peak-regulation effect is more important, the value of β should be larger. Finally, configuration results of the control groups are given, and the effect of optimizing the calculation for improving economic benefits is verified. [ABSTRACT FROM AUTHOR]

This paper reiterates the importance of corporate governance in banks. Failure prediction studies have mainly relied on using financial ratios as predictors. The most suitable financial predictors for banks are financial ratios following the CAMEL rating system. Also, corporate governance has been proven to be an important aspect of banks, especially after the financial crisis. Given its importance, the novelty of this paper is to test the ability of corporate governance to increase the accuracy and extend the time-horizon of bank failure prediction in the US market. Using discriminant analysis, we predict the failure of banks insured by the Federal Deposit Insurance Corporation from 2010 to 2018. Using financial and non-financial predictors, we find that combining CAMEL ratios with corporate governance variables not only increases the accuracy of prediction but also extends the time horizon to three years before failure. We also show that bank earnings is a more significant predictor than capital structure and asset quality. The results further reveal that CEO compensation, voting rights and institutional ownership are significant predictors. These results are robust when using logit regression and out-of-sample examination. This study shows that corporate governance plays a key role in the success or failure of banks. The regulatory implication of this paper is that more attention needs to be directed to corporate governance and earnings aspects of banks rather than focusing on capital structure. [ABSTRACT FROM AUTHOR]

Copyright of Turkish Journal of Social Research / Turkiye Sosyal Arastirmalar Dergisi is the property of Turkish Journal of Social Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Under the tremendous challenges of frequent disasters, disaster resilience is imperative for risk reduction and sustainable management in poverty and disaster-prone areas. Ganzi Prefecture has a complicated topography and vulnerable ecosystems. Geological disasters have historically been the most serious risks in the region. To fully understand the potential risks and strengthen resilience, the study investigates the resilience level of 18 counties in Ganzi. Firstly, the paper develops a multidimensional index system based on the Baseline Resilience Indicators for Communities (BRIC) framework. And the entropy weighting method is used to calculate Ganzi's disaster resilience level from the aspects of "society-economy-infrastructure-environment." Then, the study uses exploratory spatial data (ESDA) to analyze the spatial–temporal evolution of disaster resilience. Finally, Geodetector is used to investigate the main driving factors of disaster resilience and their interactions. The results indicated that Ganzi's disaster resilience had maintained an upward trend from 2011 to 2019, with significant spatial divergence, which shows high resilience in the southeast and low resilience in the northwest. The economic indicator is the driving factor in the spatial difference of disaster resilience, and the interaction factor has a significantly stronger explanatory power for resilience. Therefore, the government should strengthen ecotourism development to help alleviate poverty in special industries and promote synergistic regional development. [ABSTRACT FROM AUTHOR]

In the conditions of the modern global economy, tax policy is a direct tool of management that influences the economic development of the country. This paper dwells on the assessment of the interconnection between tax policy and economic development of the Kyrgyz Republic. The paper considers the issue of adapting the country’s tax policy to the conditions of the world economy, as well as to domestic needs and possibilities. This research focuses on the specific features of Kyrgyzstan's tax system, including the structure and recent changes in the legislation and their influence on fiscal policy and economic indicators. The authors emphasize that to achieve sustainable economic growth and raise the population’s well-being, it is important to create a tax system that would adequately react to the changes in the world economy. This paper contains a detailed analysis of macroeconomic factors and key economic indicators that is aimed at determining the current trends and potential opportunities for the optimisation of the tax burden. Special attention is paid to the development of tax tools that facilitate the formation of a favourable environment for sustainable economic development and prosperity of the national economy. This research is a complete overview of the tax system of the Kyrgyz Republic, with an analysis of the recent changes in the context of their impact on fiscal policy and economic indicators. Summing up, this paper distinguishes the strategic significance of effective tax policy for the stimulation of sustainable economic growth. [ABSTRACT FROM AUTHOR]

Based on empirical data, this paper attempts modelling, in general terms, the relationship between money supply and inflation, i.e., the relationship between the inflation rate and monetary aggregates. Our basic idea is to estimate time-shift parameters and, subsequently, a leading indicator that would provide information on whether and with what delay changes in the money supply will be reflected in the price level evolution. The aim of the paper is to formulate and, on the basis of the data, to confirm or refute the hypothesis that changes in the value of monetary aggregates imply changes in the inflation rate and, therefore, whether or not monetary aggregates are certain indicators signalling further evolution of the inflation rate. Monthly data for the Czech Republic from the years 2002-2022 have been used to model and test our hypotheses. The analysis has failed to show a statistically significant relationship between the individual monetary aggregates and the inflation rate. [ABSTRACT FROM AUTHOR]

The right to a decent living implies the human possibility to benefit from the goods and achievements of civilization, access to education, medicine, culture, technical and scientific progress, insurance with food, housing, social protection, but also a qualitative environment, aspects that are identified among the most important. Without them, the free development of the human personality is impossible as the full realization of the right to a decent living is directly proportional to the economic resources available within the state, the way they are allocated, but also the way the state sets priorities for budget expenditures and achieves the efficiency of public expenditures. In this paper, the author proposes to carry out an analysis of the public expenditures carried out by the authorities of the Republic of Moldova in order to ensure the right to a decent living for its citizens. Since the basis of a decent living is the functional economy, the state being responsible for the performances in economic development and the corresponding social security measures, the paper also presents an analysis of the economic and social performance of the country. Method: In order to achieve the objective of this paper, various research methods were used, such as: quantitative and qualitative analysis of relevant documents and statistical data, synthesis method, comparative method, induction and deduction method, etc. Originality: this study presents the analysis of the efficiency of public expenditures related to the standard of living in the Republic of Moldova in order to evaluate the respect for the right to a decent living. [ABSTRACT FROM AUTHOR]

The paper analyses the economic situation of the districts located along the Southern border of Slovakia, which have sizeable Hungarian-speaking populations. Unemployment ratios, industrial pay levels and corporate profitability figures make up the backbone of the analysis. Our research reveals that, as a whole, the Southern districts fall short of the national average in terms of all economic and demographic indicators. In our research focusing on Slovakia, we have demonstrated the disadvantaged economic situation of the Southern part of the country, where its Hungarian population is concentrated, noting that this may further weaken its ability to retain their population. We used the same methodology for our analysis of the Northern Hungarian counties bordering on Slovakia. In both countries, there are discrepancies between Western and Eastern regions. [ABSTRACT FROM AUTHOR]

While there is a correlation between economic growth and the increase in living standards, economic theory is evident in the purpose of the Gross Domestic Product (GDP) measurement. GDP measures only the production capacity and economic growth, not the nation's development, as often claimed by policymakers. Furthermore, as several studies show, by focusing on economic growth and neglecting the other two crucial systems, the ecosystem, and the social system, we will not be able to achieve economic growth in the future. Since the GDP was developed in 1937, it has faced criticism not only for its limitation related to its construction. Also, the concept of economic growth is currently being challenged and being replaced by the pursuit of sustainable development. Therefore, the role of GDP, as an economic growth indicator, in our economy needs to be clarified and re-assessed. The main aim of this paper is to investigate the usefulness of the GDP in the era of sustainable development. To achieve this, we investigated the historical development of various measures of economic growth, as well as the historical development of the GDP as an economic growth metric. Furthermore, we argue that the future of GDP should be in the multi-dimensional indicators, which are used to measure the sustainable development of a country, as opposed to a self-standing single-dimensional measure of economic growth. As a case study, we also construct two measures, based on Analytical Hierarchical Process (AHP) using the free online software 'SuperDecisions: our Simple three-dimensional index of the nation's growth and the enhanced holistic three-dimensional index - Holistic Nation's Growth Index. Using the AHP method, we showcase the difference between the countries' rankings if we consider only economic growth and if we focus on the country's development more holistically. Our comparative study reveals that it is not enough to look only at three main systems, the economic, social, and ecosystem while assessing the countries' development. It is necessary to also investigate and try to capture the relationships between those three main systems. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including world's economy; business relations; and innovations.

These distinctly human capabilities are increasingly being recognized as core across the accounting profession, as global accounting bodies redesign their educational frameworks to future proof the industry. It is with great foresight and some irony that I Issues in Accounting Education i launched a Call for Papers entitled "Educating the Future Accounting Professional: Actively Shaping Professional Identities for a Rapidly Changing World" at the end of the last decade. [Extracted from the article]

Changes in regional policy could affect the development and sustainability of small businesses, which react to external conditions. The main goal of this paper is to propose a regional system of indicators of an assessment of the business environment. The proposed system of indicators reflects the specifics of regions that have undergone sectoral change. The structure of the paper follows a top-down approach, first describing the national and then the regional context of the chosen, somewhat similar, regions within the selected countries. The paper first describes current analytical results from an overall national evaluation of two different countries (Czechia and China) with selected structurally changed regions. Lastly, it proposes a system of indicators. The systematic part of the paper contains a practical case study from one Czech region to illustrate the examined problems and how to connect business needs with regional policy issues. According to the findings, when an entrepreneur is in daily contact with the local government, the quality of the business environment is very important for each of them to exploit a comparative advantage from the market. Proposals are based on a field survey study of 215 entrepreneurs, conducted in the selected Moravian-Silesian region that has undergone sectoral change similar to Shanxi Province today. The results could be applicable as an example of good practice by means of which to evaluate other regions in the same situation. [ABSTRACT FROM AUTHOR]

Social media has been used as both a communication tool and a policy evaluation tool in the public sector. Moreover, it has gradually become an important source for big data analytics in policymaking. However, little academic research has focused on the socio-technical problems that stem from the use of social media as a source for big data analytics in the public sector. This paper sheds light on such problems by focusing on several economic development indicators from social media analytics in the Japanese government. The author analyses three types of challenges identified in previous literature on big data analytics: (1) governance and privacy, (2) organisational settings, and (3) quality and bias of the data. The analysis reveals that the Ministry of Economy, Trade and Industry (METI) has tackled the problems by (1) collecting data from a study group, (2) involving academically and/or industrially highly skilled professionals across the public and the private sectors, and (3) giving explanations for the developed indicator. This paper concludes with some recommendations for other governments. [ABSTRACT FROM AUTHOR]

One of the main purposes of the countries' economic expenditure in research is to achieve higher levels of scientific results which could impact in better living standards for society. Besides, research efficiency could be considered as a way of getting the largest number of scientific results with the minimum amount of financial investment. To assess the repercussion of research investment in scientific production and measure scientific efficiency we selected a sample of 19 countries representing each region of the world. We used 17 years of social and economic indicators from the UNESCO database, and scientific data from Scopus and SciVal. We introduce two new notions of economic efficiency for national research systems: one based on the capability for assimilating R&D investment, and another based on overall productivity and impact. Through a causal model based on multiple linear regression on panel data, we model assimilation efficiency and confirm that the scientific production of a country can be explained in 98% through the GERD expressed as a percentage of GDP, and the number of Academic and Research Institutions that concentrate at least 50% of the national scientific production. And to measure countries efficiency on productivity and impact we introduce four indicators that quantify the relation between economic inputs and scientific outputs (dollars per paper (DPP), dollars per citation (DPC), citations received per 1,000 dollars invested (CIREDI) and papers produced per 1,000 dollars invested (PAPPDI)). [ABSTRACT FROM AUTHOR]

The topic of the dividend policies and their impact on company value is a hot topic in economics, being one of the few unanswered questions in the field of finance. Lately, many researchers are adding their contributions to the body of research on this topic, using new, modern methods of research and going beyond the classic financial influence factors on dividend policy, studying the influence of socio-political factors such as corporate governance and new composite financial indicators. The aim of this paper is to summarize the recent trends in research on the topic of dividend policies and emphasize the development of new research areas by employing a bibliometric analysis on the body of research from the recent years. The results of the analysis show that many classical dividend policy theories still stand, and that new influence factors on the reason why firms pay dividends have been discovered. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including tourism and growth.

This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods. [ABSTRACT FROM AUTHOR]

This paper empirically examines the aggregate effects of global uncertainty using monthly South African data. The empirical analysis is implemented in the context of vector autoregressions (VAR), augmented with various proxies for economic and financial indicators. The evidence shows that global uncertainty shocks are a significant source of economic fluctuations—they are estimated to significantly explain the cyclical downturn and exert negative impact on the financial and stock markets. These results are consistent with the view that uncertainty shocks are an important exogenous source of cyclical fluctuations and the reactions in the financial market play an important role in the impact of uncertainty shocks on the real economy. The evidence shows that uncertainty is important to the inflation dynamics while inflation and output co‐move negatively conditional on uncertainty shocks. [ABSTRACT FROM AUTHOR]

Macroeconomic indicators include gross domestic product (GDP), consumer price index (CPI), and retail price index (RPI). These indicators are important for understanding the macroeconomic situation and controlling the macroeconomic trend, as they provide a macroscopic view of a country or region's economic performance. If macroeconomic indicators can be predicted accurately in advance, the government and relevant macroeconomic control departments can propose more forward-looking and targeted macroeconomic control policies and deploy the necessary control measures. In addition, individuals can make more reasonable decisions on their investments and savings if they know the macroeconomic indexes in advance. From these two aspects, prediction of macroeconomic indexes is of great research significance. In this paper, we propose a prediction model based on chaotic vector autoregression and neural networks for macroeconomic forecasting, and we model and test the prediction with GDP and inflation as the main concerns, and we find that the improvement of GDP forecasting shows an increase of expected inflation rate, indicating the usefulness of using expected GDP data. [ABSTRACT FROM AUTHOR]

This paper estimates the impact of access to mobile money services on local economic activity. The analysis combines data from the early expansion of the mobile agent network in Kenya with a local-level measure of economic performance proxied by the intensity of nighttime lights. Leveraging the variation in areas that gained access to mobile money services at different times and the high resolution of the data, the paper shows that access to mobile money services increased local economic activity and that these effects increased over time. The positive effects are more pronounced for areas that were initially more affluent, urban, and better connected to infrastructure. These results suggest that mobile money can complement, rather than just substitute for, other alternatives that enable people to connect, trade, and allocate investments within their networks. [ABSTRACT FROM AUTHOR]

In the big data environment, the factors affecting the operation of small- and medium-sized enterprises are becoming more and more complex. In order to more accurately measure the financial crisis of small- and medium-sized enterprises, from the perspective of multisource information fusion, based on the traditional financial data reflecting the private information of enterprises, the public information data for measuring the macroeconomic and market environment are integrated and analyzed. Considering the multidimensional heterogeneity of multisource information data, BPNN, SVM, KNN, LOG, and MDA are introduced to build models, and ensemble learning is used to integrate the results of different early warning models to reduce the risk of inconsistent results from different models. The empirical results show that the early warning model integrating multisource information can improve the early warning accuracy. The research results show that the financial crisis early warning of small- and medium-sized enterprises should not only pay attention to financial indicators but also pay attention to the impact of corporate governance, industry, and the overall economic environment of the country; the accuracy of the model in this paper is significantly higher than that of the other models in terms of the accuracy of financial crisis early warning. It shows that the model in this paper can better predict the real financial status of small- and medium-sized enterprises. [ABSTRACT FROM AUTHOR]

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