Determinants of carbon emissions in Kenya and policy implications.

Carbon dioxide emissions are the primary greenhouse gasses behind global climate change and its associated effects. Climate change adversely impacts the environment, biodiversity, and human health. The ability to tame carbon dioxide emissions will help in the fight against climate change. However, the recent pace of rising carbon dioxide emissions raises concerns about winning the war against climate change. World leaders are determined to take actions that will lower emissions. Thus, there is a worldwide effort to achieve net-zero emissions. Consequently, it is crucial to comprehend the specific factors that contribute to carbon emissions of specific economies so that countries can develop and enforce effective strategies for reducing carbon output, mitigating the effects of climate change, and improving recovery programs. Despite many empirical studies conducted in Africa on the determinants of carbon emissions, the results remain inconclusive and inadequate, leaving a void for further research. This study seeks to fill this knowledge gap by modeling the relationship between Kenya's carbon emissions (which have been increasing since the early 2000s), and mobile technology adoption, renewable energy mix, tourism development, development assistance, income, and global financial crises, utilizing the most current data from 1995 to 2021 from the World Bank's database. The ARDL, FMOLS, CCR, and DOLS analysis results reveal that mobile technology, tourism, development aid, and global financial crises increase Kenya's carbon emissions in the long run. At the same time, income and renewable energy reduce short- and long-term impacts. The significant insight from these results is that enhancing renewable energy development, mobile technology, and development assistance can promote a sustainable environment. From these findings, the study proposes many policy recommendations to help decision-makers, communities, companies, and the government in Kenya allocate resources and implement resilience and mitigation policies and programs in the areas most susceptible to the effects of climate change.

Changes in maximum parental leave payment in Iceland and total fertility rates.

BACKGROUND: Following the 2008 financial crisis, the Icelandic Government reduced the maximum parental payment until 2016, when it was increased again. The aim of this study was to investigate the effect of the changes in the maximum parental leave payment in Iceland during 2009 and 2016 on total fertility rates and birth rates during 2002-2019. METHODS: Publicly available aggregated data on yearly total fertility rates, birth rates, unemployment rates, gross domestic product (GDP) and maximum parental leave payments were obtained for 2002-2019. Segmented regression analyses were used to measure the impact of changes in parental leave payment on term births for the two periods in which changes were implemented (2008-2010 and 2016-2017). RESULTS: The decrease in maximum parental leave payment during 2008-2010 was associated with a 15% decrease in the estimated total fertility rate compared with the expected rate (-15.7%; 95% CI -22.7 to -8.7), whereas the increased payments during 2016-2017 indicated a possible 3% increase in the estimated total fertility rate (3.2%; 95% CI -29.1 to 35.5). Neither adjustment for the unemployment rate nor the GDP appeared to affect these results. The overall birth rate followed a similar trend and was most pronounced for women aged 25-34 years. CONCLUSIONS: These results suggest that total fertility rates in Iceland may have been affected by changes in the maximum parental leave payment that occurred in 2009 and 2016, although the effect of the 2008 financial crisis cannot be excluded despite adjustment for the unemployment rate and GDP.

The impact of national and international financial crises on mental health and well-being: a systematic review.

BACKGROUND: Evidence suggests that financial crises and poor mental health are reciprocally related, but no systematic review has been conducted to synthesise the existing literature on the impact of national and international financial crises on population-level mental health and well-being. AIMS: The aim of this study was to systematically review the available literature on the global impact of financial crises on mental health and well-being outcomes. METHODS: After registration on PROSPERO, a systematic search was conducted in PsycINFO, MEDLINE, Wiley, and Web of Science for papers published until 21 November 2022. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, 98 papers were identified as meeting eligibility criteria. Included studies were assessed using the Mixed Methods Appraisal Tool (MMAT) and results were presented in a formal narrative synthesis. RESULTS: Our findings show that financial crises are significantly associated with well-being and occurrence of psychological conditions. Several socio-demographic, cultural, and country-specific characteristics played a crucial role in the prevention of population mental health decline in periods of financial crises. CONCLUSIONS: Based on the findings of this review, evidence-based recommendations were developed to guide the design of policy actions that protect population mental health during and after financial crises.

Income inequality and entrepreneurship: Lessons from the 2020 COVID-19 recession.

We study entry into entrepreneurship during the COVID-19 recession of 2020 using new data from an extensive survey of more than 24,000 Spanish households, conducted between June and November 2020. We find that while the overall decline in the startup rate in 2020 was large, and of a similar magnitude as that during the Great Recession, the differential impact depending on ex ante income was starkly different. During 2020, the drop in firm entry was entirely concentrated among low- and medium-income households. We show that the entrepreneurship gap between these households and their high-income counterparts is not directly explained by social distancing, since it is mostly driven by the sectors not directly affected by lockdown measures, and it is larger among households that did not suffer a negative income shock during the pandemic. Our results instead indicate that high-income households performed relatively better during the COVID-19 recession because they had the means to exploit new business opportunities, thanks to their larger wealth and better access to external finance.

Reprint of: Do retail traders destabilize financial markets? An investigation surrounding the COVID-19 pandemic.

Existing research suggests that retail trading is associated with volatility in financial markets. To extend the literature, we study the dynamic effects of retail trading on volatility during the COVID-19 pandemic. Using marketable retail trades identified from the Boehmer et al. (2021) algorithm and novel empirical methods discussed in Jordá (2005), we document a negative, persistent impact of retail trading on the stability of stock prices that is particularly stronger during the pandemic than during the pre-pandemic period. These results highlight how periods of crises - like the pandemic - affect the destabilizing influence of retail trading. To provide additional evidence, we replicate our empirical exercise during the 2008-09 financial crisis. Consistent with the COVID-19 period, we again find that retail trading leads to more volatility during the financial crisis vis-á-vis the pre-crisis period. These results again support the idea that periods of crises strengthen the link between retail trading and volatility.

Detecting Nonlinear Interactions in Complex Systems: Application in Financial Markets.

Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point detection may not be sensitive to it. In this article, we present a novel scheme for detecting structural breaks through the occurrence or vanishing of nonlinear causal relationships in a complex system. A significance resampling test was developed for the null hypothesis (H0) of no nonlinear causal relationships using (a) an appropriate Gaussian instantaneous transform and vector autoregressive (VAR) process to generate the resampled multivariate time series consistent with H0; (b) the modelfree Granger causality measure of partial mutual information from mixed embedding (PMIME) to estimate all causal relationships; and (c) a characteristic of the network formed by PMIME as test statistic. The significance test was applied to sliding windows on the observed multivariate time series, and the change from rejection to no-rejection of H0, or the opposite, signaled a non-trivial change of the underlying dynamics of the observed complex system. Different network indices that capture different characteristics of the PMIME networks were used as test statistics. The test was evaluated on multiple synthetic complex and chaotic systems, as well as on linear and nonlinear stochastic systems, demonstrating that the proposed methodology is capable of detecting nonlinear causality. Furthermore, the scheme was applied to different records of financial indices regarding the global financial crisis of 2008, the two commodity crises of 2014 and 2020, the Brexit referendum of 2016, and the outbreak of COVID-19, accurately identifying the structural breaks at the identified times.

Investigating Dynamical Complexity and Fractal Characteristics of Bitcoin/US Dollar and Euro/US Dollar Exchange Rates around the COVID-19 Outbreak.

This article investigates the dynamical complexity and fractal characteristics changes of the Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns in the period before and after the outbreak of the COVID-19 pandemic. More specifically, we applied the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method to investigate the temporal evolution of the asymmetric multifractal spectrum parameters. In addition, we examined the temporal evolution of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. Our research was motivated to contribute to the comprehension of the pandemic's impact and the possible changes it caused in two currencies that play a key role in the modern financial system. Our results revealed that for the overall trend both before and after the outbreak of the pandemic, the BTC/USD returns exhibited persistent behavior while the EUR/USD returns exhibited anti-persistent behavior. Additionally, after the outbreak of COVID-19, there was an increase in the degree of multifractality, a dominance of large fluctuations, as well as a sharp decrease of the complexity (i.e., increase of the order and information content and decrease of randomness) of both BTC/USD and EUR/USD returns. The World Health Organization (WHO) announcement, in which COVID-19 was declared a global pandemic, appears to have had a significant impact on the sudden change in complexity. Our findings can help both investors and risk managers, as well as policymakers, to formulate a comprehensive response to the occurrence of such external events.

Establishing a Multidisciplinary Team-Based Pleural Service in the Era of Financial Austerity: The Role of the Thoracic Surgeon.

Medical thoracoscopy/pleuroscopy has become, after bronchoscopy, the second most commonly utilized endoscopic procedure in interventional pulmonology. Due to their common origin, medical thoracoscopy/pleuroscopy and video-assisted thoracic surgery (VATS) are quite similar procedures technically. In contrast to the prevailing attitude that it should predominantly be performed by interventional pulmonologists, we believe that, like all hybrid-in-nature techniques, it should be implemented as part of a combined specialist care service/team. Herewith, we describe our attempt to establish a multidisciplinary pleural disease program during a difficult economic period for our country, comprising thoracic surgeons, pulmonologists and anesthesiologists, all of whom brought in their experience, expertise and resources to establish and develop the service resulting in a hybridization of the technique, with, as reported, quite favorable results.

Measuring economic crises impact transitioning to a circular economy.

Increasing global concern about climate change and the circular economy have successfully established itselves in international and national policies over the last decade, with the aim of reshaping the production and consumer behavior. The circular economy is one of the core pillars of European Union policy and its success depends on the energy efficiency, reducing production costs, and maintaining employment levels by ensuring continuous strong economic independency of the region. While crises are unavoidable and continue to appear, this paper aims to project the impact of any crisis on sustainability transitions using data analysis of the Global Financial crisis from 2008 to 2009 and discuss how the success of the circular economy implementation and environmental policies could be affected. The paper notes that the global financial crisis of 2008-2009 had a short-term positive impact on environmental degradation and that economic interests overshadowed environmental goals. Due to the recent events of the ongoing Russia and Ukraine war, COVID-19 societal and industrial behavior has shifted from sustainable to linear and has taken a step backward in reducing environmental pollution and achieving Sustainable Development Goals. Analysis of already present data and the context of the 2008-2009 global financial crisis, reviewing of COVID-19 impact on the global economy, health sector, and environmental policies allows us to predict the consequences, as it relates to the future of circular economy policy. Supplementary information: The online version contains supplementary material available at (10.1007/s10668-023-03367-x).

This time truly is different: The cyclical behaviour of fiscal policy during the Covid-19 crisis.

Fiscal policy was more countercyclical during the Covid-19 crisis than in previous (crisis) episodes. This paper presents empirical evidence in favour of a "this time truly is different" moment based on analysing the cyclical behaviour of fiscal policy for 28 advanced economies over 1995-2021. Discretionary fiscal policy during the Covid-19 crisis (2020-2021) did more to counteract the downturn - especially in the Eurozone -, as we do not find comparable evidence for countercyclicality during the financial crisis or Euro crisis. Automatic fiscal stabilisers, the non-discretionary domain of fiscal policy, significantly contributed to countercyclical stabilisation during the pandemic.

Financial and economic uncertainties and their effects on the economy.

We estimate new indices measuring financial and economic uncertainty in the euro area, Germany, France, the United Kingdom and Austria, following the approach of Jurado et al. (Am Econ Rev 105:1177-1216, 2015), which measures uncertainty by the degree of predictability. We perform an impulse response analysis in a vector error correction framework, where we focus on the impact of both local and global uncertainty shocks on industrial production, employment and the stock market. We find that global financial and economic uncertainties have significant negative effects on local industrial production, employment, and the stock market, while we find hardly any influence of local uncertainty on these variables. In addition we perform a forecasting analysis, where we assess the merits of uncertainty indicators for forecasting industrial production, employment and the stock market, using different performance measures. The results suggest that financial uncertainty significantly improves the forecasts of the stock market in terms of profit-based measures, while economic uncertainty gives, in general, more insight when forecasting macroeconomic variables.

Economic crisis in Greece: The invisible enemy of blood donation or not?

INTRODUCTION: Volunteering presupposes having free time and refers to the provision of services without the motivation of material reward, for the benefit of society. In this study, we aimed to provide insight into the impact of economic crisis on blood donors and their motivation to donate blood during that period. STUDY DESIGN AND METHODS: We asked blood donors about their blood donation activity and motivation to donate using a standardized, anonymous questionnaire (n = 3000). Descriptive analysis was performed for the consideration of donor turnout during this economic period. The results were analyzed using the χ2 test and Spearman's correlation coefficient. RESULTS: Regarding gender, 68.2% were males, while 31.8% were females. Most blood donors donated voluntarily (75.8%) and only 24.2% were replacement or family blood donors. The economic crisis has affected the inhabitants of Athens more than the inhabitants of the province (χ2 = 9.910,p = 0.007). The influence of economic crisis on the regular blood donors' quality of life was greater than the non-regular donors (χ2 = 16.227,p < 0.001). According to our results, the economic crisis reduced the quality of life, but it did not affect the frequency of blood donations in a percentage of 87,3%. Not any significant difference was found between employment status, economic crisis and blood donation. CONCLUSION: Although the economic crisis has affected the lives of blood donors, it does not seem to affect the frequency of blood donation. We suggest that blood collection services should consider specialist campaigns that focus on the altruistic motivation of donors during an economic crisis.

Implications for health system reform, workforce recovery and rebuilding in the context of the Great Recession and COVID-19: a case study of workforce trends in Ireland 2008-2021.

BACKGROUND: Workforce is a fundamental health systems building block, with unprecedented measures taken to meet extra demand and facilitate surge capacity during the COVID-19 pandemic, following a prolonged period of austerity. This case study examines trends in Ireland's publicly funded health service workforce, from the global financial crisis, through the Recovery period and into the COVID-19 pandemic, to understand resource allocation across community and acute settings. Specifically, this paper aims to uncover whether skill-mix and staff capacity are aligned with policy intent and the broader reform agenda to achieve universal access to integrated healthcare, in part, by shifting free care into primary and community settings. METHODS: Secondary analysis of anonymised aggregated national human resources data was conducted over a period of almost 14 years, from December 31st 2008 to August 31st 2021. Comparative analysis was conducted, by professional cadre, across three keys periods: 'Recession period' December 31st 2008-December 31st 2014; 'Recovery period' December 31st 2014-December 31st 2019; and the 'COVID-19 period' December 31st 2019-August 31st 2021. RESULTS: During the Recession period there was an overall decrease of 8.1% (n = 9333) between December 31st 2008 and December 31st 2014, while the Recovery period saw the overall staff levels rebound and increase by 15.2% (n = 16,789) between December 31st 2014 and December 31st 2019. These figures continued to grow, at an accelerated rate during the most recent COVID-19 period, increasing by a further 8.9% (n = 10,716) in under 2 years. However, a notable shift occurred in 2013, when the number of staff in acute services surpassed those employed in community services (n = 50,038 and 49,857, respectively). This gap accelerated during the Recovery and COVID-19 phase. By August 2021, there were 13,645 more whole-time equivalents in acute settings compared to community, a complete reverse of the 2008 situation. This was consistent across all cadres. Workforce absence trends indicate short-term spikes resulting from shocks while COVID-19 redeployment disproportionately impacted negatively on primary care and community services. CONCLUSIONS: This paper clearly demonstrates the prioritisation of staff recruitment within acute services-increasing needed capacity, without the same commitment to support government policy to shift care into primary and community settings. Concerted action including the permanent redistribution of personnel is required to ensure progressive and sustainable responses are learned from recent shocks.

A Proposal for Integrating Theories of Complexity for Better Understanding Global Systemic Risks.

The global financial crisis of 2008 has shown that the present financial system involves global systemic risks. The dimension of these risks is hard to grasp with the conceptual tools that have been developed to tackle conventional risks like fire or car accidents. While modern societies know quite well how to deal with conventional risks, we have not yet been equally successful at dealing with global systemic risks. For managing this kind of risks, one needs to understand critical features of specific global systems where many human agents interact in ever changing complex networks. Here we apply two specific dimensions of complexity theory for dealing with global systemic risk in an integrated fashion: normal accidents and extended evolution. Both of them have successfully been applied to the analysis of systemic risks. As a paradigmatic example of global systemic risks, we focus on the global financial crisis that began in 2008, and suggest that the future evolution of the financial system could either see a further increase in complexity, or a reversal to a less complex system. We explore and contrast the implications of normal accident theory and extended evolution perspectives and suggest a four-point research strategy informed by complexity theory for better understanding global systemic risks in financial systems.

COVID-19 and credit risk: A long memory perspective.

The COVID-19 pandemic shows significant impacts on credit risk, which is the key concern of corporate bond holders such as insurance companies. Credit risk, quantified by agency credit ratings and credit default swaps (CDS), usually exhibits long-range dependence (LRD) due to potential credit rating persistence. With rescaled range analysis and a novel affine forward intensity model embracing a flexible range of Hurst parameters, our studies on Moody's rating data and CDS prices reveal that default intensities have shifted from the long-range to the short-range dependence regime during the COVID-19 period, implying that the historical credit performance becomes much less relevant for credit prediction during the pandemic. This phenomenon contrasts sharply with previous financial-related crises. Specifically, both the 2008 subprime mortgage and the Eurozone crises did not experience such a great decline in the level of LRD in sovereign CDS. Our work also sheds light on the use of historical series in credit risk prediction for insurers' investment.

Does oil impact gold during COVID-19 and three other recent crises?

The ongoing COVID-19 pandemic has inspired an examination of the oil-gold prices nexus during four recent crises: the COVID-19 pandemic, the gold market crash, the European sovereign debt crisis, and the global financial crisis. Using daily data from May 2007-August 2021, we employ the nonlinear autoregressive distributed lag method to reveal five novel findings. First, this study contrasts with much of the literature, which infers that the relationship between oil and gold prices is strongly positive. Second, we find no oil and gold price relationship in the long term during all the crisis periods. Third, oil prices have substantially lost their power to predict gold prices in recent times and the oil-gold price linkage is not functional across all crisis periods. Fourth, in the short term, only negative Brent and negative West Texas Intermediate price changes cause positive gold price changes during the pandemic and gold market crash, respectively. Fifth, Brent prices have shown no link to gold prices before COVID-19. We argue that gold prices are less sensitive to oil prices than ever, and the uncertainty resulting from the COVID-19 crisis has attracted investors to gold. Our main findings hold under robustness analyses using fractional cointegration/integration models, lag length, and heteroskedasticity-consistent standard errors.

Do retail traders destabilize financial markets? An investigation surrounding the COVID-19 pandemic.

Existing research suggests that retail trading is associated with volatility in financial markets. To extend the literature, we study the dynamic effects of retail trading on volatility during the COVID-19 pandemic. Using marketable retail trades identified from the Boehmer et al. (2021) algorithm and novel empirical methods discussed in Jordá (2005), we document a negative, persistent impact of retail trading on the stability of stock prices that is particularly stronger during the pandemic than during the pre-pandemic period. These results highlight how periods of crises - like the pandemic - affect the destabilizing influence of retail trading. To provide additional evidence, we replicate our empirical exercise during the 2008-09 financial crisis. Consistent with the COVID-19 period, we again find that retail trading leads to more volatility during the financial crisis vis-á-vis the pre-crisis period. These results again support the idea that periods of crises strengthen the link between retail trading and volatility.

Regularity in Stock Market Indices within Turbulence Periods: The Sample Entropy Approach.

The aim of this study is to assess and compare changes in regularity in the 36 European and the U.S. stock market indices within major turbulence periods. Two periods are investigated: the Global Financial Crisis in 2007-2009 and the COVID-19 pandemic outbreak in 2020-2021. The proposed research hypothesis states that entropy of an equity market index decreases during turbulence periods, which implies that regularity and predictability of a stock market index returns increase in such cases. To capture sequential regularity in daily time series of stock market indices, the Sample Entropy algorithm (SampEn) is used. Changes in the SampEn values before and during the particular turbulence period are estimated. The empirical findings are unambiguous and confirm no reason to reject the research hypothesis. Moreover, additional formal statistical analyses indicate that the SampEn results are similar both for developed and emerging European economies. Furthermore, the rolling-window procedure is utilized to assess the evolution of SampEn over time.

How new Fed corporate bond programs cushioned the Covid-19 recession.

In the financial crisis and recession induced by the Covid-19 pandemic, many investment-grade firms became unable to borrow from securities markets. In response, the Fed not only reopened its commercial paper funding facility but also announced it would purchase newly issued and seasoned corporate bonds rated as investment grade before the Covid pandemic. We assess the effectiveness of this program using long sample periods, spanning the Great Depression through the Great and Covid Recessions. Findings indicate that the announcement of corporate bond backstop facilities helped stop risk premia from rising further than they had by late-March 2020. In doing so, these backstop facilities limited the role of external finance premia in amplifying the macroeconomic impact of the Covid pandemic. Nevertheless, the corporate bond programs blend the roles of the Federal Reserve in conducting monetary policy via its balance sheet, acting as a lender of last resort, and pursuing credit policies.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.