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The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on a scale that significantly alters the betweenness centrality distributions in existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of similar prize-winning properties that may have been overlooked among other regular publications. [ABSTRACT FROM AUTHOR]

The organizational forms of infrastructure in China are divided into two categories, the traditional Public Procurement Model (PUB) model and Public–Private Partnership(PPP) model. The main difference is the separation or binding of the construction and operation phases. A systematic understanding is needed of how Chinese local governments choose between these two models. In this paper, we take public capital congestion and local government objectives as the entry point to study the effects of both on PPP choice. Firstly, by constructing an endogenous economic growth model under the PPP model, and comparing it with the model under the PUB model, this paper initially explains how the rise in public capital congestion affects the choice of the PPP by growth-oriented local governments. Then the data from prefecture-level cities from 2009–2018 are utilized to conduct empirical tests. We find that urban economic growth pressures have a positive effect on the choice of PPP when the congestion of public capital increases. Furthermore, the implementation of PPP is indeed conducive to economic performance, and its core mechanism is to provide more infrastructure (like roads) rather than tax competition. The PPP model is more sustainable. We are the first to employ both modeling approach and the empirical research to address the implementation of Public–Private Partnership in China. And we have systematically analyzed the conditions and results of PPP selection by local governments. It formulates the Chinese PPP theory. [ABSTRACT FROM AUTHOR]

This paper investigates the complex causal relationships between various types of environmental regulatory instruments (ERI) and agri-firms' technological innovation employing fuzzy set qualitative comparative analysis (fsQCA). The study finds a well-designed set of ERI can promote technological innovation in agribusiness; control-command ERI cannot promote technological innovation in agribusiness solely, market-incentivized ERI is indispensable in promoting firms' innovation performance, implicit ERI plays an important role in promoting firms' innovation and voluntary ERI does not play a significant role in promoting firms' technological innovation. The government should coordinate among various types of ERI and improve the design of ERI to achieve a win-win situation for both economic and environmental performance in the agriculture sector. [ABSTRACT FROM AUTHOR]

Nowadays, the international situation is severe and complex, and the structural issues within the Chinese economy are prominent. Consequently, the financial risks faced by Chinese listed companies continue to escalate. Hence, it is of great practical significance to furnish effective early warnings for financial crises in listed companies. In this paper, we first employ web crawler technology and natural language processing technology to assess four text-linguistic features in the Management Discussion and Analysis (MD&A) section of the annual financial reports of listed companies in China from 2011 to 2020. These features are text tone, forward-looking, readability and similarity. Subsequently, we combine these features with traditional financial indicators and explore thirteen mainstream machine learning models to comparatively analyze their effectiveness in predicting financial crises in listed companies. The empirical findings of this research reveal that MD&A text readability and similarity indicators contribute valuable incremental information to prediction models, whereas text tone and forward-looking indicators exhibit the opposite effect. The latter two indicators can be manipulated more effortlessly by management, as this study's empirical findings indicate no evidence of their contributions in incremental informational value. In fact, the forward-looking indicator even introduces additional noise to the prediction. Finally, by comparing the early warning effects of thirteen machine learning models, it is found that RF, Bagging, CatBoost, GBDT, XGBoost and LightGBM models maintain stable and accurate sample recognition ability. In general, this paper constructs a more effective financial crisis early warning model by exploring the MD&A text-linguistic feature indicators, thereby offering a fresh research perspective for further investigations in this field. [ABSTRACT FROM AUTHOR]

Improving the efficiency of converting natural resources into social benefits is an important issue for sustainable development in today's world. Based on this background this paper applies the super-efficient SBM model with non-expected output to measure the ecological welfare performance (EWP) of Chinese provinces from 2005–2019, and explores the relationship between government competition and EWP under different assessment systems. The research results show that government competition under economic performance assessment is self-interested and short-sighted, which can negatively affect ecological welfare performance in the current period as well as in the next four years. In contrast, government competition under the environmental assessment system promotes EWP in both the current and long term, balancing short-term and long-term benefits. The results of the spatial GMM found that government competition under economic performance appraisal can deteriorate EWP in local as well as surrounding areas, but government competition under the environmental assessment system can achieve an increase in local ecological welfare performance and the spillover effect is not significant. To alleviate the limitations of a single appraisal system, this paper incorporates both economic and ecological appraisals into the multidimensional appraisal system. When the weights of both are between 1:9 and 3:7, the government competition under multidimensional performance appraisal can promote both current and longer-term EWP, and achieve its own ecological welfare performance without affecting the surrounding areas. [ABSTRACT FROM AUTHOR]

Carbon price prediction is of great importance to regulators and participants in the carbon trading market. It is the basis for developing policies related to the carbon trading market and stabilizing that market. Considering the numerous factors that influence carbon prices in China, dimensionality reduction is needed to improve the prediction accuracy and efficiency. However, the traditional dimensionality reduction methods fail to fully consider the role of influencing factors, which has certain limitations. In this paper, a new dimensionality reduction method, namely scaled principal component analysis (s-PCA), is employed to improve the prediction accuracy of carbon prices. Firstly, a factor library that influence carbon prices is constructed from three perspectives: technical indicators, financial indicators and commodities indicators. Then, the s-PCA method is used to reduce the dimensionality of factors influencing carbon price. Next, two different methods are used to predict carbon prices, including traditional regression method and Long Short-Term Memory (LSTM) method. Finally, the economic value of the s-PCA method is examined by constructing investment portfolios. The empirical results of the Hubei Emissions Exchange show that the s-PCA model outperforms other competing models both in- and out-of-sample. In addition, the LSTM model could improve the performance of the s-PCA model in carbon price prediction. From a market timing perspective, investors can achieve a greater return and a larger Sharpe ratio using the s-PCA method than using other comparative methods and buy-and-hold strategy. Therefore, the s-PCA method is effective and robust in predicting carbon price. [ABSTRACT FROM AUTHOR]

In the current knowledge-based economy, the abilities of the national research system are a key driver of the country's competitiveness and socio-economic development. This paper compares the scientific standing of the OECD countries and eight other relevant economies. We use a bibliometric indicator of research performance, applied first at the individual level. This approach avoids the distortions of the aggregate-level analyses extant in literature and practice, which overlook the different publication intensities across research fields. We find a strong correlation between research performance and the economic competitiveness of nations and a moderate but significant correlation between research performance and the propensity to spend on research. [ABSTRACT FROM AUTHOR]

Noncommunicable diseases and mental health conditions (referred to collectively as NMHs) are the greatest cause of preventable death, illness, and disability in South America and negatively affect countries' economic performance through their detrimental impacts on labor supply and capital investments. Sound, evidence-based policy-making requires a deep understanding of the macroeconomic costs of NMHs and of their distribution across countries and diseases. The paper estimates and projects the macroeconomic burden of NMHs over the period 2020–2050 in 10 South American countries. We estimate the impact of NMHs on gross domestic product (GDP) through a human capital-augmented production function approach, accounting for mortality and morbidity effects of NMHs on labor supply, for the impact of treatment costs on physical capital accumulation, and for variations in human capital by age. Our central estimates suggest that the overall burden of NMHs in these countries amounts to $7.3 trillion (2022 international $, 3% discount rate, 95% confidence interval: $6.8–$7.8 trillion). Overall, the macroeconomic burden of NMHs is around 4% of total GDP over 2020–2050, with little variation across countries (from 3.2% in Peru to 4.5% in Brazil). In other words, without NMHs, annual GDP over 2020–2050 would be about 4% larger. In most countries, the largest macroeconomic burden is associated with cancers. Results from the paper point to a significant macroeconomic burden of NMHs in South America and provide a strong justification for investment in NMH prevention, early detection, treatment, and formal and informal care. [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 research delves into the underexplored area of how production network structures influence the severity of economic downturns, particularly during the last financial crisis. Utilizing the RSTAN database from the OECD, we meticulously derived critical measures from the input-output matrices for 61 economies. Our methodology entailed a panel analysis spanning from 2008 to 2010, which is a period marked by significant recessionary pressures. This analysis aimed to correlate economic performance with various production network metrics, taking into account control factors such as interest rates and the prevalence of service sectors. The findings reveal a noteworthy positive correlation between the density of production networks and economic resilience during the crisis, which remained consistent across multiple model specifications. Conversely, as anticipated, higher interest rates were linked to poorer economic performance, highlighting the critical interplay between monetary policy and economic outcomes during periods of financial instability. Given these insights, we propose a policy recommendation emphasizing the strategic enhancement of production network density as a potential buffer against economic downturns. This approach suggests that policymakers should consider the structural aspects of production networks in designing economic stability and growth strategies, thus potentially mitigating the impacts of future financial crises. [ABSTRACT FROM AUTHOR]

Recently, the economy in Guangdong province has ranked first in the country, maintaining a good growth momentum. The prediction of Gross Domestic Product (GDP) for Guangdong province is an important issue. Through predicting the GDP, it is possible to analyze whether the economy in Guangdong province can maintain high-quality growth. Hence, to accurately forecast the economy in Guangdong, this paper proposed an Elman neural network combining with wavelet function. The wavelet function not only stimulates the forecast ability of Elman neural network, but also improves the convergence speed of Elman neural network. Experimental results indicate that our model has good forecast ability of regional economy, and the forecast accuracy reach 0.971. In terms of forecast precision and errors, our model defeats the competitors. Moreover, our model gains advanced forecast results to both individual economic indicator and multiple economic indicators. This means that our model is independently of specific scenarios in regional economic forecast. We also find that the investment in education has a major positive impact on regional economic development in Guangdong province, and the both surges positive correlation. Experimental results also show that our model does not exhibit exponential training time with the augmenting of data volume. Consequently, we propose that our model is suitable for the prediction of large-scale datasets. Additionally, we demonstrate that using wavelet function gains more profits than using complex network architectures in forecast accuracy and training cost. Moreover, using wavelet function can simplify the designs of complexity network architectures, reducing the training parameter of neural networks. [ABSTRACT FROM AUTHOR]

The values that people hold are linked to their economic performance. These links can be either direct or indirect, operating through moderating variables such as social network participation, interpersonal trust, trust in institutions, human capital, managerial skills and hours worked. In this paper these effects are studied using structural equation modelling (SEM) methodology applied to European Social Survey data from 28 European countries in 2018. Schwartz classification of values is used, distinguishing between Self-Enhancement (Power, Achievement), Openness to Change (Self-Direction), Conservation (Tradition, Security, Conformity) and Self-Transcendence (Universalism, Benevolence) values. It is found that Power has the strongest positive direct effect on economic performance, further strengthened by a positive indirect structural effect through hours worked. Self-Direction is indirectly positively linked to economic performance through higher managerial skills and hours worked. Tradition has a strong negative direct effect on economic performance. Security is indirectly negatively linked with economic performance, owing to its negative effects on interpersonal trust, management skills and hours worked. Some of the identified effects are context-dependent and vary across European welfare state regimes. For example, Power is statistically significantly linked to economic performance only in the liberal and conservative regime. Values promoted by respective welfare state regimes are not necessarily associated with higher incomes within those regimes, e.g., Tradition and Security values promoted in the conservative and Mediterranean regime are associated with lower incomes. These findings may lead to a range of policy implications, particularly in relation to the policies on immigration, demographics, the labor market, and work-life balance. Unfortunately, due to the cross-sectional character of the dataset, causal relations among the variables of interest could not be identified. [ABSTRACT FROM AUTHOR]

In order to improve the laws and regulations of the financial system, in the construction of laws and regulations, the traditional financial risk Early Warning (EW) model is optimized. The financial prevention and control measures with legal protection are implemented to warn the financial risks, which plays an important role in the construction of the rule of law in the Financial Market (FM) and the establishment of financial risk prevention and control laws and regulations. This paper combines the deep learning model and the Markov regime Switching Vector Auto Regression (MS-VAR) model and constructs a regional financial risk EW model from the following aspects: macroeconomic operation EW indicators, regional economic risk EW indicators, regional financial institution risk EW indicators. The model is empirically researched and analyzed. The results show that the fluctuation trend of the macroeconomic pressure index in the time series is relatively large, and the overall fluctuation of the regional economic pressure index is small, and fluctuates around 0 in most periods. After the financial crisis, local governments stepped up their supervision of non-performing corporate and household loans. From 2011 to 2018, the non-performing loan ratio began to decline, and the overall fluctuation of the regional financial comprehensive stress index was small, fluctuating around 0. Due to the lack of legal regulation, from the perspective of the regional economy, the risk level is more likely to change from low risk to moderate risk, while the risk status is less likely to change from high risk to moderate risk. From the perspective of regional financial institutions, the probabilities of maintaining low risk and moderate risk are 0.98 and 0.97, respectively, which is stronger than maintaining the stability of high risk. From the perspective of the state transition of the regional financial risk composite index, the probability of maintaining low risk and high risk is 0.97 and 0.93, which is higher than maintaining the stability of medium risk. The Deep Learning (DL) regional financial risk EW MS-VAR model has strong risk prediction ability. The model can better analyze the conversion probability of regional financial risk EW index and has better risk EW ability. This paper enhances the role of legal systems in financial risk prevention and control. The regional financial risk EW model incorporating financial legal indicators can better describe the regional financial risk level, and the EW results are basically consistent with the actual situation. In order to effectively prevent financial risks and ensure the safety of the financial system, it is recommended that the government improve local debt management, improve financial regulations and systems, and improve the legislative level of financial legal supervision. [ABSTRACT FROM AUTHOR]

China's economy has shifted from the stage of high-speed growth to the stage of high-quality development. This paper selects panel data of 30 provinces, municipalities directly under the Central Government and autonomous regions (except Tibet) in mainland China with the time dimension of 2006–2016, selects indicators from five dimensions of innovation, coordination, greenness, openness and sharing, uses principal component analysis to construct a system of indicators of high-quality economic development, and uses the spatial Durbin model to empirically the impact of government investment behavior in the three industries on the regional economic high-quality development was studied. The results show that: first, there is a significant positive spatial correlation between China's economic quality development. Second, government investment in different industries has different effects on the quality development of the economy. Investment in the tertiary industry can boost employment, raise income level, promote economic quality development, and enhance sharing, coordination and innovation; investment in the secondary industry can enhance innovation, but because of the "crowding-out effect", it will reduce openness and sharing, and is not conducive to promoting economic quality development; investment in the primary industry can Investment in the primary industry can significantly improve coordination, but it will reduce innovation, greenness, openness and sharing, which will also have a negative impact on the overall high-quality economic development. Third, there is a significant spatial spillover effect of government investment behavior on regional economic quality development, and the economic quality development of this region will have an impact on neighboring regions. Finally, based on the research results, this paper puts forward countermeasure suggestions for promoting high-quality economic development. [ABSTRACT FROM AUTHOR]

Many studies have shown that scaling laws widely exist in various complex systems, such as living organisms, cities, and online communities. In this research, we found that scaling laws also hold for companies. The macroscopic variables of companies, such as incomes, expenses, or total liability, all have power-law relationships with respect to the sizes of companies, which can be measured by sales, total assets, or the total number of employees. What is more, we also found the power law exponents always deviate from 1. That means large companies naturally have certain advantages, but the widely used financial indicators based on total volume or ratio may not reflect the company's status well because they are also size-dependent. To tackle this problem, this paper proposes a new set of evaluation indices based on the deviations of the macroscopic variables from the scaling law to eliminate the size-dependent effect. We found that the indicators based on deviations can give more reasonable evaluations for companies and can outperform other conventional indicators to predict the financial distress of companies. [ABSTRACT FROM AUTHOR]

Achieving universal health coverage (UHC) is a priority of most low- and middle-income countries, reflecting governments' commitments to improved population health. However, high levels of informal employment in many countries create challenges to progress toward UHC, with governments struggling to extend access and financial protection to informal workers. One region characterized by a high prevalence of informal employment is Southeast Asia. Focusing on this region, we systematically reviewed and synthesized published evidence of health financing schemes implemented to extend UHC to informal workers. Following PRISMA guidelines, we systematically searched for both peer-reviewed articles and reports in the grey literature. We appraised study quality using the Joanna Briggs Institute checklists for systematic reviews. We synthesized extracted data using thematic analysis based on a common conceptual framework for analyzing health financing schemes, and we categorized the effect of these schemes on progress towards UHC along the dimensions of financial protection, population coverage, and service access. Findings suggest that countries have taken a variety of approaches to extend UHC to informal workers and implemented schemes with different revenue raising, pooling, and purchasing provisions. Population coverage rates differed across health financing schemes; those with explicit political commitments toward UHC that adopted universalist approaches reached the highest coverage of informal workers. Results for financial protection indicators were mixed, though indicated overall downward trends in out-of-pocket expenditures, catastrophic health expenditure, and impoverishment. Publications generally reported increased utilization rates through the introduced health financing schemes. Overall, this review supports the existing evidence base that predominant reliance on general revenues with full subsidies for and mandatory coverage of informal workers are promising directions for reform. Importantly, the paper extends existing research by offering countries committed to progressively realizing UHC around the world a relevant updated resource, mapping evidence-informed approaches toward accelerated progress on the UHC goals. [ABSTRACT FROM AUTHOR]

Social equity is a critical component of social justice and is measured in multiple ways. Conventionally, researchers use literacy levels, workforce participation, political participation and representation, corporate presence, and demographic parity as indicators of social and economic equity. We add law enforcement outcomes to this literature by examining the demographic profiles of convicts in prisons in India for each state and compare it with that of the population of the respective state. To test whether entrenched social inequities have permeated into the law enforcement system, we use three indicators of social identity–religion, caste, and domicile–to create a social equity index (SEI). This is a composite index combining caste, religion and domicile similar in method to the Human Development Index which combines income, education and health. Our indicators are not considered in other popular development indices and is a conceptual innovation. Our paper innovates by combining prison data and census data at the state level over the two latest census rounds (2001 and 2011). We use a spatial panel analysis as well as a distributional dynamics approach to test for bias and transitions over time at the state level. We find that entrenched social hierarchies are mirrored in conviction outcomes and that social identities influence law enforcement. In contrast to earlier studies, we find that states that are conventionally considered to perform poorly in terms of economic and human development have done better in terms of social equity than economically advanced states. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) is China's substantial energy consumption base. The issue of carbon emission efficiency directly affects the ecological protection and high-quality development of the YRB. It is the key to achieving carbon peak in 2030 and carbon neutralization in 2060 ("30.60") double carbon emission reduction targets. Therefore, taking YRB as the research object, this paper first calculates the carbon emission and the decoupling state in the YRB. Secondly, the super-efficiency slacks-based measurement (SE-SBM) model is combined with the Malmquist index to analyze the temporal and spatial evolution characteristics of YRB's carbon emission efficiency from static and dynamic perspectives. Thirdly, the dynamic evolution characteristics of carbon emission efficiency are analyzed with the help of the Kernel density function. Finally, the Tobit model analyzes the influencing factors of YRB's and China's carbon emission efficiency. The results show that: (1) Among the nine provinces of YRB, the decoupling state between carbon emissions and economic growth in most provinces changes from weak decoupling to strong decoupling, and the decoupling elasticity index shows a fluctuating downward trend. (2) There are significant differences in carbon emission efficiency among provinces, but on the whole, it shows a stable growth trend. The high-value area of carbon emission efficiency is increasing, and the phenomenon of two-level differentiation is improving. The decline of the technological progress index causes the Malmquist index in Qinghai and Ningxia. On the contrary, the rise of the Malmquist index in the other seven provinces is caused by improving the technical efficiency index. (3) Industrial structure, economic development, and industrialization are the main positive factors affecting YRB's carbon emission efficiency. Urbanization level, green development level, and energy consumption level are the leading negative indicators hindering YRB's improvement of carbon emission efficiency. Therefore, targeted emission reduction suggestions should be formulated according to YRB's resource endowment and development stage characteristics. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the paper is to identify management practices that are characteristic for SMEs that achieve market success measured by their business performance in last their years of their operation analyze the relationships between management practices applied in small and medium-sized enterprises and their success measured by their business performance drawing on the data from 2710 SMEs operating on the Polish market. Approach/Methodology/Design: A cluster analysis was used to distinguish homogenous SME groups in view of their management practices. We examined differences between groups in terms of their business performance. The HINoV algorithm allowed six variables to be selected out of 32 management practices chosen initially for testing, with these variables providing the basis for grouping. Modal values and medians were calculated for 17 business performance measures in the three clusters produced. The subsequent analysis of those findings was focused on capturing significant differences. Findings: In the group of 2710 Polish SMEs, it was possible to verify that there existed an association between management practices in the field of modern HRM, computer systems supporting management and the company's economic performance, as measured by an increase in net revenue and number of customers over the last three years. In clusters where the above mentioned practices were appreciated, modal and median values of the increase reported in net revenue and number of customers were significantly higher. Practical implications: The research has shown that at a time marked by a shortage of highly skilled personnel one should pay particular attention to building an integrated and committed team of workers and to employee empowerment. The research has also shown that SME managers monitor only a fraction of basic business performance measures, which may prove to be a major risk to SMEs. Originality/Value: Previous studies have been largely conducted in a fragmentary manner, i.e. they were concerned with the relationships between the application of some practices (strategic management, BPR, entrepreneurial orientation, monitoring, etc.) and selected business effectiveness measures. In this paper, the research covered SME management practices from a variety of areas which were then compared with the entrepreneurs' assessment as to whether the company's economic condition changed over the last three years. It is also the first attempt in post-socialist economies to identify those SME management practices that are related to better economic results. [ABSTRACT FROM AUTHOR]

Since 2016, fall armyworm (FAW) has threatened sub-Saharan 'Africa's fragile food systems and economic performance. Yet, there is limited evidence on this transboundary pest's economic and food security impacts in the region. Additionally, the health and environmental consequences of the insecticides being used to control FAW have not been studied. This paper presents evidence on the impacts of FAW on maize production, food security, and human and environmental health. We use a combination of an agroecology-based community survey and nationally representative data from an agricultural household survey to achieve our objectives. The results indicate that the pest causes an average annual loss of 36% in maize production, reducing 0.67 million tonnes of maize (0.225 million tonnes per year) between 2017 and 2019. The total economic loss is US$ 200 million, or 0.08% of the gross domestic product. The lost production could have met the per capita maize consumption of 4 million people. We also find that insecticides to control FAW have more significant toxic effects on the environment than on humans. This paper highlights governments and development partners need to invest in sustainable FAW control strategies to reduce maize production loss, improve food security, and protect human and environmental health. [ABSTRACT FROM AUTHOR]

Differences in institutions have garnered much attention in recent years as one of the primary reasons for long-term economic performance, and a vast corpus of theoretical and empirical research demonstrates that financial development can have a substantial impact on economic and financial performance. In fast-growing emerging markets, the demand for financial services has considerably increased, which amplified the need for an efficient regulatory framework to deal with the sensitive nature of financial activities. Surprisingly, the existing literature on the impact of institutions and finance is focused on developed economies, while emerging markets are rarely focused on. Since emerging markets are becoming increasingly important as a result of the swift evolution of institutional and financial transformation, it is crucial to examine how the growth of financial markets reacts to institutional quality. This study investigates the role of institutions in promoting finance in emerging economies using the balanced panel dataset of 21 emerging market. Given the endogenous relationship between institutions and finance, the paper applies a two-stage least squares estimator to test the settler mortality hypothesis in this context. Aligned with theoretical intuition, the empirical result shows that settler mortality is a relevant instrument to institutions. Furthermore, the study performs robustness, using framework that deals with heterogeneity, and cross-section dependence. This empirical effort validates La Porta's Law and Finance Hypothesis in the context of emerging markets. The results of the study are found robust across the variety of alternative measures of institutions and finance, and standard error correction specifications. Therefore, the study offers useful insight into policy implications. [ABSTRACT FROM AUTHOR]

In China, official promotion evaluation based on economic performance motivates local governments to develop high economic growth targets, which has played an active role in boosting China's economic growth in the past decades, whereas its environmental consequences have not been fully exploited. This paper finds that the economic growth target overweight has a stronger positive impact on the output of high-polluting industries than on the output of low-polluting industries, thus inducing more polluting activities. To deal with the issues of reverse causality and omitted variables bias, we take an instrumental variable approach. Examining mechanisms, we show that economic growth target overweight promotes polluting activities through the deregulation of the polluting activities in high-polluting industries. We also find an increase in the impact of the economic growth target overweight after the 2008 global economic crisis. Our study provides new evidence for explaining the dual presence of rapid economic growth and heavy environmental pollution in China. [ABSTRACT FROM AUTHOR]

The implementation of tax reduction policies in China represents a significant and effective strategy. Accordingly, this strategy has been designed to facilitate the development of a green economy by establishing a market-oriented allocation system for environmental and resource elements, while simultaneously invigorating microeconomic entities. As the nation navigates towards the adoption of green, low-carbon production, and lifestyles, the role of clean and green energy emerges as a vital necessity. Therefore, to explain the impact of tax reduction policies on the green energy industry, this study collected and compiled financial indicator data from 100 listed companies in the green energy sector, utilizing the China Stock Market Accounting Research database (CSMAR) as a source for research samples. A Panel Vector Auto Regression (PVAR) model was employed to observe the effects of tax reduction policies on the energy industry, while the dosage effects Difference in Difference (DID) model was utilized to verify and supplement the findings. In summary, the findings of this study can be summarized as follows: firstly, tax reduction policies exert a positive impact on the green energy industry by effectively mitigating the financial cost burden on green energy enterprises, thereby reducing production expenses and amplifying their profitability. Secondly, such policies bolster the capital turnover rate of enterprises in the short term, thereby enabling augmented research and development investments, refining production efficiency, and enhancing competitiveness. Through rigorous analysis and demonstration, the research findings accentuate the stimulative and propulsive impacts of tax reduction policies on the flourishing development of the green energy industry. Furthermore, this study provides relevant fiscal and tax policy recommendations, thoughtfully derived from the research findings. [ABSTRACT FROM AUTHOR]

Following the approval of sustainable development goals at the global level, the link between fiscal policy, institutional quality, and economic growth has attracted special attention in economic literature. This study scrutinizes the effect of government revenue-institutional quality interaction on the economic growth of 43 Sub-Saharan Africa countries for the period of 2012–2022. Methodology-wise, the study employed the System Generalized Method of Moment (SGMM) to analyze the panel data gained from dependable data sources; the World Development Indicator and the Heritage Economic Freedom Index. The novelty of this study emanates from the estimation technique designated and the introduction of revenue-institutional quality into the economic growth model of SSA. The result of the study reveals that government revenue adversely affects economic growth while institutional quality positively enhances economic growth before interacting with each other. However, the interactive coefficient of government revenue and economic growth positively affected the real GDP growth rate of SSA countries over the study periods. Precisely, before interacting with institutional quality, a percentage change in government revenue, keeping all other things constant, leads to a 0.0866 percent decline in economic growth while it marks a 0.2329 percent upsurge in economic growth in the presence of institutional quality. The result of the study further shows that government revenue promotes the economic growth of the region when combined with institutional quality. On the other hand, foreign direct investment and openness to trade were the key sources of economic growth whereas the population growth rate adversely impacted economic growth in SSA countries. The policy implication of the study is that SSA needs to strengthen government revenue management. Further, the finding of the study implies that SSA countries need to improve institutional quality through promoting efficiency of the regulatory quality and the size of the SSA governments. In addition to this, the fast real GDP growth rate of SSA countries demands improved institutional quality indicators such as the rule of law and extended access to the open market. [ABSTRACT FROM AUTHOR]

This paper analyses the interaction between the novel coronavirus pandemic (COVID-19), unemployment rate, stock market, consumer confidence index (CCI), and economic policy uncertainty (EPU) index in China within a time-frequency framework. We compare the changes in economic indicators during the global financial crisis (GFC) and study the different impacts of the two events on China's economy. An unprecedented impact of COVID-19 shocks on the unemployment rate, CCI, EPU index, and stock market volatility over the low frequency bands is uncovered by applying the coherence wavelet method to China monthly data. The COVID-19 effect on the stock market volatility and the EPU index is substantially higher than on the unemployment rate and the CCI. On the contrary, the GFC's impact on the unemployment rate is much greater than that on the EPU index and CCI. Additionally, the impact of the GFC on the economy is more cyclical in the long-term, while the COVID-19 pandemic is a short-term shock with a relatively short oscillation cycle. This study concludes that the economic impact of COVID-19 will not spread into a financial crisis for China and believe that the COVID-19 pandemic is more of a health event than an economic crisis for Chinese economy. [ABSTRACT FROM AUTHOR]

In this paper, a novel early warning system that has usually been applied to predict the financial stress events is established to predict the likelihood of housing price crises in China. To achieve this goal, a signal extraction approach is used to monitor the evolution of a number of economic indicators that tend to exhibit the abnormal behaviors. 13 economic variables were selected as the individual indicators, and constructed as the four comprehensive indicators. Our empirical work shows that the early warning system for urban housing price crises is suitable for China's four province-level municipalities. The in-sample forecasting results indicate the reliability of the early warning system for urban housing price crises. By studying the out-of-sample forecasting results, the likelihood of housing price crises for the four cities can be effectively predicted. We construct a novel weighted average comprehensive indicator, which performs better than the three others in terms of overall performance across all of the criteria considered in. It is shown that the extended system is more flexible in decision making than the traditional early warning system. [ABSTRACT FROM AUTHOR]

In this paper we assess the impact of the recent European recession on stillbirth indices over the course of the 2000s and 2010s; the analysis focuses on four Southern European countries (Greece, Italy, Spain, Portugal), which were seriously affected by the sovereign debt crisis from around 2008 to 2017. We use national vital statistics and established economic indicators for the period 2000–2017; stillbirth ratios (stillbirths per 1000 livebirths) are the chosen response variable. For the purpose of the study, we employ correlation analysis and fit regression models. The overall impact of economic indicators on the stillbirth indices is sizeable and statistically robust. We find that a healthy economy is associated with low and declining levels of stillbirth measures. In contrast, economic recession appears to have an adverse effect (Greece, Italy and Spain), or an unclear impact (Portugal), on the stillbirth outcome. This study provides evidence of the adverse effect of the European sovereign debt crisis and ensuing period of austerity on a scarcely explored aspect of health. [ABSTRACT FROM AUTHOR]

Rice is a crucial crop in Sri Lanka, influencing both its agricultural and economic landscapes. This study delves into the complex interplay between economic indicators and rice production, aiming to uncover correlations and build prediction models using machine learning techniques. The dataset, spanning from 1960 to 2020, includes key economic variables such as GDP, inflation rate, manufacturing output, population, population growth rate, imports, arable land area, military expenditure, and rice production. The study's findings reveal the significant influence of economic factors on rice production in Sri Lanka. Machine learning models, including Linear Regression, Support Vector Machines, Ensemble methods, and Gaussian Process Regression, demonstrate strong predictive accuracy in forecasting rice production based on economic indicators. These results underscore the importance of economic indicators in shaping rice production outcomes and highlight the potential of machine learning in predicting agricultural trends. The study suggests avenues for future research, such as exploring regional variations and refining models based on ongoing data collection. [ABSTRACT FROM AUTHOR]

Economic resilience provides a new perspective for megacities to achieve sustainable development when facing multiple shocks, and its accurate evaluation is an essential prerequisite for optimizing urban governance. There are currently no generally accepted methods for empirical evaluation or measuring economic resilience, and the present study aims to contribute to in both the research field and methodology. The present study sets dimensions and indicators based on economic resilience's theoretical and empirical research and used Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interactive Structural Modeling (ISM) methods to exclude the effect indicators and divide the indicator hierarchy, respectively. Subsequently, the present study conducts model validation using Chinese megacities as a case study. The game theory weighting method, which combines the Analytic Hierarchy Process (AHP) and Entropy methods, is used to calculate indicator weights, and the VIKOR (VIseKriterijumska Optimizacija i KOmpromisno Resenje) method is used to evaluate and compare economic resilience of megacities. The research findings indicate that the evaluation model constructed in the present study included 15 indicators (after excluding three effect indicators) divided into four levels. After merging the levels, they correspond to three dimensions: resistance, recoverability, and adaptability. In addition, using Chinese megacities as a case study, the evaluation results found that Beijing, Shanghai, and Shenzhen have high economic resilience, Tianjin and Guangzhou have moderate economic resilience, Chengdu has low economic resilience, and Chongqing has the lowest economic resilience. This result is consistent with previous studies and verifies the model's effectiveness. The present study also found that megacities with lower levels of economic resilience exhibit a more significant upward trend, as well as the highest and higher proportion of economic resilience in Chinese megacities depending on time passes, indicating that megacities' economic resilience is weakening. The evaluation result obtained in the present study is more specific, precise, and focused on depicting the distribution differences and development trends of economic resilience at the urban level. [ABSTRACT FROM AUTHOR]

Non-governmental organizations (NGOs) are becoming increasingly significant stakeholders in global governance and business operations. However, measuring their efficiency is a challenging task due to their mission-driven nature. While previous research has proposed financial and non-financial indicators to measure NGO efficiency, none of them has compared the differences between environmental and social NGOs. This study aims to investigate the factors influencing the program efficiency of NGOs in China and compare the differences between environmental and social NGOs. 12 indicators are employed and tested using data collected from the Chinese Research Data Services (CNRDS) platform. The study employs multiple regression analysis to examine the influential factors identified in the dataset. The findings demonstrated different influential factors of program efficiency among environmental and social NGOs. The results of the analysis provide valuable insights for NGO operators, policymakers, and researchers in the field of NGOs. [ABSTRACT FROM AUTHOR]

To estimate the economic and financial viability of a pig farm in central sub-tropical Mexico within a 5-year planning horizon, a Monte Carlo simulation model was utilized. Net returns were projected using simulated values for the distribution of input and product processes, establishing 2021 as base scenario. A stochastic modelling approach was employed to determine the economic and financial outlook. The findings reveal a panorama of economic and financial viability. Net income increased by 555%, return on assets rose from 3.36% in 2022 to 11.34% in 2026, and the probability of decapitalization dropped from 58% to 13%, respectively in the aforesaid periods. Similarly, the probability of obtaining negative net income decreased from 40% in 2022 to 18% in 2026. The technological, productive, and economic management of the production unit allowed for a favorable scenario within the planning horizon. There is a growing interest in predicting the economic sectors worth investing in and supporting, considering their economic and development performance. This research offers both methodological and scientific evidence to demonstrate the feasibility of establishing a planning schedule and validating the suitability of the pork sector for public investment and support. [ABSTRACT FROM AUTHOR]

The Economic Fitness Index describes industrial completeness and comprehensively reflects product diversification with competitiveness and product complexity in production globalization. The Fitness-Complexity Algorithm offers a scientific approach to predicting GDP and obtains fruitful results. As a recursion algorithm, the non-linear iteration processes give novel insights into product complexity and country fitness without noise data. However, the Country-Product Matrix and Revealed Comparative Advantage data have abnormal noises which contradict the relative stability of product diversity and the transformation of global production. The data noise entering the iteration algorithm, combined with positively related Fitness and Complexity, will be amplified in each recursion step. We introduce the Shortest Duration Constrained Hidden Markov Model (SDC-HMM) to denoise the Country-Product Matrix for the first time. After the country-product matrix test, the country case test, the noise estimation test and the panel regression test of national economic fitness indicators to predict GDP growth, we show that the SDC-HMM could reduce abnormal noise by about 25% and identify change points. This article provides intra-sample predictions that theoretically confirm that the SDC-HMM can improve the effectiveness of economic fitness indicators in interpreting economic growth. [ABSTRACT FROM AUTHOR]

This study investigated the linkage between fiscal policy-governance indicators interaction and economic growth in 36 Sub-Saharan Africa (SSA) countries from the periods of 2011–2021 inclusive. The study employed two-step system Generalized Method of Moment (GMM) estimation technique due to its practical relevance in panel data analysis. The data obtained from World Bank and World governance indicator was checked for unit root through the help of Im Pesaran Shin and Levin-Lin-Chu unit-root tests, and the result revealed that data was stationary and safe for further analysis. The result of the study also presented that direct economic effect of fiscal policy is negative and significant in SSA countries. However, the interaction of fiscal policy with governance indicators has positive and significant effect on economic growth. Accordingly, before interacting with governance indicators, a percentage change in fiscal policy leads to a 0.20 percent decline in economic growth of SSA countries. Contrary to this, the interactive coefficient of fiscal policy and government effectiveness (0.019) and interactive coefficient of fiscal policy and corruption control (0.0046) are found to be positive and significant. Further, the finding of the study revealed that fiscal policy-voice and accountability interaction coefficient (0.011) and interactive coefficient of fiscal policy-regulatory qualities (0.014) are positively and significantly affecting economic growth of SSA countries. The policy implication is that policy makers in SSA countries should encourage economic policies that improve government effectiveness, strong corruption control, clean public services and better regulatory qualities. [ABSTRACT FROM AUTHOR]

The implementation of statistical techniques in on-line surveillance of financial markets has been frequently studied more recently. As a novel approach, statistical control charts which are famous tools for monitoring industrial processes, have been applied in various financial applications in the last three decades. The aim of this study is to propose a novel application of control charts called profile monitoring in the surveillance of the cryptocurrency markets. In this way, a new control chart is proposed to monitor the price variation of a pair of two most famous cryptocurrencies i.e., Bitcoin (BTC) and Ethereum (ETH). Parameter estimation, tuning and sensitivity analysis are conducted assuming that the random explanatory variable follows a symmetric normal distribution. The triggered signals from the proposed method are interpreted to convert the BTC and ETH at proper times to increase their total value. Hence, the proposed method could be considered a financial indicator so that its signal can lead to a tangible increase of the pair of assets. The performance of the proposed method is investigated through different parameter adjustments and compared with some common technical indicators under a real data set. The results show the acceptable and superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) emissions have become a critical aspect of the economic and sustainable development indicators of every country. In Pakistan, where there is a substantial increase in the population, industrialization, and demand for electricity production from different resources, the fear of an increase in CO2 emissions cannot be ignored. This study explores the link that betwixt CO2 emissions with different significant economic indicators in Pakistan from 1960 to 2018 using the autoregressive distributed lag (ARDL) modelling technique. We implemented the covariance proportion, coefficient of determination, the Durbin Watson D statistics, analysis of variance (ANOVA), variance inflating factor (VIF), the Breusch-Pagan test, the Theil's inequality, the root mean quare error (RMSE), the mean absolute percentage error (MAPE), and the mean absolute error (MAE) for the diagnostics, efficiency, and validity of our model. Our results showed a significant association between increased CO2 emissions and increased electricity production from oil, gas, and other sources. An increase in electricity production from coal resources was seen to have resulted in a decrease in CO2 emissions. We observed that an increase in the gross domestic product (GDP) and population growth significantly contributed to the increased CO2 emissions. The increment in CO2 emissions resulting from industrial growth was not significant. The increment in CO2 emissions in the contemporary year is significantly associated with the preceding year's increase. The rate of increase was very alarming, a sign that no serious efforts have been channelled in this regard to reduce this phenomenon. We call for policy dialogue to devise energy-saving and CO2 emission reduction strategies to minimize the impact of climate change on industrialization, population growth, and GDP growth without deterring economic and human growth. Electricity production from different sources with no or minimal CO2 emissions should be adopted. We also recommend rigorous tree planting nationwide to help reduce the concentration of CO2 in the atmosphere as well as environmental pollution. [ABSTRACT FROM AUTHOR]

Background: The reserve of a country is a reflection of the strength of fulfilling its financial liabilities. However, during the past several years, a regular variation of the total reserve has been observed on a global scale. The reserve of Bangladesh is also influenced by several economic and financial indicators such as total debt, net foreign assets, net domestic credit, inflation GDP deflator, net exports (% of GDP), and imports of goods and services (% of GDP), as well as foreign direct investment, GNI growth, official exchange rate, personal remittances, and so on. Therefore, the authors aimed to identify the nature of the relationship and influence of economic indicators on the total reserve of Bangladesh using a suitable statistical model. Methods and materials: To meet the objective of this study, the secondary data set was extracted from the World Bank's website which is openly accessible over the period 1976 to 2020. Moreover, the model used the appropriate splines to describe the non-linearity. The performance of the model was evaluated by the Akaike information criterion (AIC), Bayesian information criterion (BIC), and adjusted R-square. Results: The total reserve of Bangladesh gradually increased since 2001, and it reached its peak in 2020 which was 43172 billion US dollars. The data were first utilized to build a multiple linear regression model as a base model, but it was later found that the model has severe multicollinearity problems, with a maximum value of VIF for GNI of 499.63. Findings revealed that total debt, inflation, import, and export are showing a non-linear relationship with the total reserve in Bangladesh. Therefore, the authors applied the Generalized Additive Model (GAM) model to take advantage of the nonlinear relationship between the reserve and the selected covariates. The overall response, which is linearly tied to the net foreign asset in the GAM model, will change by 14.43 USD for every unit change in the net foreign asset. It is observed that the GAM model performs better than the multiple linear regression. Conclusion: A non-linear relationship is observed between the total reserve and different economic indicators of Bangladesh. The authors believed that this study will be beneficial to the government, monetary authorities also to the people of the country to better understand the economy. [ABSTRACT FROM AUTHOR]

Introduction: Although there is a growing number of scientific publications on financial monitoring, combating money laundering, the shadow economy, and the impact of corruption on economic development, further research needs to determine the stability of the national financial system in dynamics. The dynamic stability of the national financial monitoring system subjects will allow to adequately assess the effectiveness of the existing national financial monitoring system in each country and determine the influential factors. Materials and methods: The article investigates an approach to identifying the dynamic stability of the national financial monitoring system subjects based on the calculation of the integrated indicator of the country's financial system propensity to ALM, vector autoregression (VAR) model taking into account time lag. The proposed integrated indicator allowed to adequately assess the existing financial monitoring systems of the countries (15 countries of the European Union for 2000–2020: Austria, Belgium, Cyprus, Estonia, Finland, France, Greece, Ireland, Italy, Latvia, Malta, Netherlands, Portugal, Slovak Republic, Spain). In addition, vector autoregression models (VAR) of the dependence of the country's financial system propensity to ALM on the regressors Government Integrity, Index of economic freedom, Monetary Sector credit to the private sector (% GDP), were built, taking into account time lags in general and for each studied country. Results: According to the modeling results, the national financial monitoring systems in Austria, Belgium, Estonia, Finland, France, Ireland, Netherlands, Slovak Republic, Spain were resistant to money laundering. It is vice versa in Malta, Greece, Cyprus, Portugal, Italy, Latvia. These conclusions are also confirmed based on a binary approach. Such exogenous variables as Government Integrity (with a lag of 2 years) and the Index of economic freedom (taking into account the time delays of the regression reflection under the influence of this regressor for 1 and 2 years) have a statistically significant effect on the country's financial system. Conclusion: The general vector autoregression (VAR) model shows that the current value of the country's financial system propensity to ALM by 92.78% is determined by its previous value. With an increase of Government Integrity by 1%, the country's financial system's propensity to ALM will decrease by 0.000616 units with a lag of two years. The nature of the impact made by the Index of economic freedom on the performance feature was specific—when this indicator increases by 1% for a lag delay in one year, the PFSALM value will decrease by 0.001997 units, and for a lag delay of two years it will change the trend and increase by 0.003076 units per unit, respectively. [ABSTRACT FROM AUTHOR]

This study investigates the association between objective and subjective indicators of economic uncertainty, generated by the COVID-19 health and economic crisis, and young Italians' fertility plans during the 2020. We use unique repeated cross-sectional data, collected at different time points during the pandemic (March and October/November 2020) together with pre-COVID data (2016). The data offer a standard fertility intention question pre- and during-COVID, and also a direct question on whether pre-COVID fertility plans have been confirmed, postponed or abandoned. In March 2020, individuals with more vulnerable occupations show a lower probability of intending to have a(nother) child in the short-term and a higher probability of abandoning their pre-COVID fertility plan; in October 2020 changes in fertility plans do not vary by employment condition. Instead, both in March and October, those who suffered from a negative income shock and those with negative expectations on their future income and occupation are more likely to abandon their pre-pandemic fertility plan compared to their better off counterparts. Overall, economic uncertainty seems to have similarly affected men and women's fertility intentions. Our findings point to the fact that the unequal economic consequences of the pandemic also produced and will produce heterogeneous effects on fertility intentions. [ABSTRACT FROM AUTHOR]

Background: Worldwide, an estimated 400,000 children develop cancer each year. The bulk of the mortalities from these cases occur in low-and-middle-income countries (LMICs). In Sub-Saharan Africa, there is a tremendous need to strengthen the capacity of health systems to provide high-quality cancer care for children. However, a lack of data on the economic impact of cancer treatment in low-resource settings hinders its consideration as a healthcare priority. To address this gap, this study models the clinical and financial impact of pediatric cancer care in Tanzania, a lower-middle income country in East Africa. Methods: We conducted a retrospective review of patients with cancer under the age of 19 years treated at Bugando Medical Centre from January 2010 to August 2014. Information was collected from a total of 161 children, including demographics, type of cancer, care received, and five-year survival outcomes. This data was used to calculate the number of averted disability-adjusted life-years (DALYs) with treatment. Charges for all direct medical costs, fixed provider costs, and variable provider costs were used to calculate total cost of care. The societal economic impact of cancer treatment was modeled using the value of statistical life (VSL) and human capital methods. Findings: The total health impact for these 161 children was 819 averted DALYs at a total cost of $846,743. The median cost per patient was $5,064 ($4,746–5,501 interquartile range). The societal economic impact of cancer treatment ranged from $590,534 to $3,647,158 using VSL method and $1,776,296 using a human capital approach. Interpretation: Despite the limitations of existing treatment capacity, economic modeling demonstrates a positive economic impact from providing pediatric cancer care in Tanzania. As many countries like Tanzania progress towards achieving Universal Health Coverage, these key economic indicators may encourage future investment in comprehensive pediatric cancer care programs in low-resource settings to achieve clinically and economically beneficial results not only for the individual patients, but for the country as a whole. [ABSTRACT FROM AUTHOR]

Roughly one-fifth of the global population is affected by poor visual acuity. Despite the fact that inhabitants of rural areas in low-income countries are most distressed by this, no prior research has studied the impact of poor visual acuity on the economic performance of farms. We conduct a standardized eye test with 288 farm managers in rural Cambodia and find that around 30 percent of our sample suffers from poor visual acuity in terms of nearsightedness (myopia). Our analyses indicate a statistically significant and economically meaningful association of poor visual acuity with economic farm performance. Our results show that gross margins for cropping activities per year could be, on average, around 630 USD higher if farm managers were able to correct for poor vision. Our results suggest that poor visual acuity impairs farm managers from tapping the full potential of their business, which in turn decreases their chance to break the vicious cycle of poverty. [ABSTRACT FROM AUTHOR]

The research presents an analysis of the food security policy effectiveness on the component of food availability and access in two developing countries, Colombia and Kyrgyzstan, during the period from 2000 to 2018. Determining the state of their food balance trade and the regression analysis for the Food Production Index of the countries, considering four economic indicators. Thus the study attempts to show that policies and strategies have not reached the expected results in terms of reduction of food imports dependency and strengthening of national production and export industry. Furthermore was found that among the economic indicators considered, food inflation, food imports, food exports, and extreme monetary poverty; the last one was the indicator that presented influence on the Food Production Index of both countries, during the period analyzed, showing that access was the main component that defines the food production. The results highlighted the need of integrating food security with the monetary and trade policies of these countries. [ABSTRACT FROM AUTHOR]

Using comprehensive flood data from China, we find a significant, negative impact of flooding on firm performance, which is mainly driven by unexpected flooding. We use multiple identification strategies to address endogeneity concerns and find that the documented impact of flooding on firm performance is likely causal. The impact is more pronounced for firms with more tangible asset investment, firms located in cities with low government quality, firms facing tight financial constraints, firms controlled by non-government entities and firms with low geographic diversification. Flood-exposed firms react to the threat by altering their investment, financial, cash, payout and executive compensation policies. Finally, flooding also exerts a significant impact on local economic and employment growth. [ABSTRACT FROM AUTHOR]

The novel coronavirus pandemic of 2019 (COVID-19) has significantly harmed numerous social and economic activities worldwide, drawing researchers and governments' attention to the problem and tackling it via persistent healthcare measures. The study's primary purpose is to examine the effects of total coronavirus cases, total fatalities, total recovered cases, unemployment, and trade openness on stock prices and economic growth in the world's top 39 affected nations. An analysis of the coronavirus outbreaks found that wealthier countries had a well-established healthcare infrastructure, but they were disproportionately affected by the virus. Conversely, the less wealthy nations had inadequate healthcare infrastructures, but they were not as affected as the wealthier countries. Is it possible to buy health with money? That was the question at the heart of the study's money-and-health curve. The robust least square regression results indicate that an increase in coronavirus cases influences economic growth and stock market performance due to massive healthcare funding distributed globally, sustaining economic and financial activities for a shorter period. However, a continuous increase in coronavirus fatalities depresses the stock market, resulting in financial depression worldwide. Additionally, a rise in overall coronavirus recovered cases has a negative effect on the country's economic development and stock market performance because of greater uncertainty in economic and financial activities. Case fatality ratios influence economic growth, whereas case recovery ratios decrease economic and financial performance due to greater healthcare concerns across countries. Finally, trade openness is critical in sustaining the country's economic development and stock market performance in the wake of the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Economic security can be considered as an economic aspect of the life of an individual or household that provides the opportunity to achieve financial stability and improve living standards. In this context, understanding the impact of economic affordability of food on the economic security of the country is of great importance. The authors propose to use the indicator of economic affordability of food for in-depth analysis of the economic security of the country. Indicators that determine the level of economic affordability of food products in Ukraine for 2000–2018 are calculated. The regression analysis made it possible to establish a close relationship between the indicators of food expenditures and the level of GDP per capita, between the level of income and expenditure of food of decile groups. Correlation coefficients between the consumption of six basic foodstuffs and the level of per capita income in Ukraine are determined. The obtained indicators, which determine the economic affordability of food, allow us to conclude that there is a lack of economic protection at the level of individual low-income households. Overcoming economic instability and minimizing the possible consequences of the low level of economic affordability of food should become one of the main tasks of Ukraine's economic security. [ABSTRACT FROM AUTHOR]