The effect of increasing health disaster risk and public spending on economy conditions: a DSGE perspective

PurposeThe main motivation of the present study is to understand the severity of the effect of health shock on Iran's oil economy and analyze the role of government under these conditions.Design/methodology/approachDynamic stochastic general equilibrium (DSGE) models can show the precise interactions between market decision-makers in the context of general equilibrium. Since the duration of the virus outbreak and its effect on the economy is not known, it is more appropriate to use these models.FindingsThe results of the survey of hands-on policies scenarios compared to the state of hands-off policy indicate that the effect of government expending shocks on the economy under pandemic disease conditions has much less feedback on macroeconomic variables.Originality/valueAs a proposed policy, it is recommended that the government play a stabilizing role under pandemic disease conditions.Key messages There is no study regarding health shock and its economic effects in Iran using DSGE models. Also, in foreign studies, the health shock in an oil economy has not been modeled.The general idea in the present study is how the prevalence of a pandemic infectious disease affects the dynamics of macroeconomic variables.In three different scenarios, according to the persistence of health disaster risk and the deterioration rate of health capital due to this shock, the model is simulated.In modeling pandemic diseases, quarantine hours are considered as part of the total time of individuals.According to the research findings, it is recommended that the government, as a policy-maker, play a stabilizing role under pandemic crises conditions.

Uncertain hypothesis test for uncertain differential equations

Uncertain hypothesis test is a statistical tool that uses uncertainty theory to determine whether some hypotheses are correct or not based on observed data. As an application of uncertain hypothesis test, this paper proposes a method to test whether an uncertain differential equation fits the observed data or not. In order to demonstrate the test method, some numerical examples are provided. Finally, both uncertain currency model and stochastic currency model are used to model US Dollar to Chinese Yuan (USD–CNY) exchange rates. As a result, it is shown that the uncertain currency model fits the exchange rates well, but the stochastic currency model does not.

On the role of innovation and market structure on trade performance: is Schumpeter right?

PurposeThe goal of the paper is to examine the dynamics between innovation, market structure and trade performance. Firstly, the author first investigates the effects of innovation on trade performance. Secondly, the author then examines how market structure affect trade by classifying industries based on their innovation intensity.Design/methodology/approachThe author uses a detailed level data set of eight OECD countries in a panel of 17 industries from the STAN and ANBERD Database. The author employs both a pooled regression and a two-stage quantile regression analysis. The author first investigates the effects of innovation at the aggregate level, and then the author assesses the effects at the disaggregated or firm level.FindingsThe author finds that at the aggregate level, innovation and market size have a positive and significant effect on competitivity in most of the specifications. However, innovation is negatively associated with trade performance in the case of bilateral trade between Spain and the Netherlands. Also, the sectoral analysis provides evidence that the innovation-trade nexus depends on technological classification. The author shows that: (1) the effect of innovation activity on trade performance economic performance is lower for the high technology and high concentration (HTHC) market compared to the low technology (LT) market;(2) the impact of innovation on economic performance is ambiguous for firms in the high technology and low concentration (HTLC) market.Research limitations/implicationsAlthough the database provides a rich data set on industrial data, it fails to provide innovation output such as patent data which may underestimate the innovation activities of firms that do not have a separate R&D records. In the current context of subdue economic growth these research results have important policy implications. Firstly, the positive impact of innovation on trade performance strengthens its role for sustainable development. The negative coefficient on innovation is an indication that research intensity in some cases has not been able to create a new demand capable to boost economic performance.Practical implicationsThe market classification analysis provides new evidence that innovation in the LT market has the potential to enhance competition. Secondly, market size supports industries that are competing in the international market. Policy makers must therefore put in place incentives to encourage firms to grow in size if they want to remain globally competitive.Social implicationsSustainable development can be supported through investment in research and development in the low technology sector.Originality/valueThe study is the first as far as the author knows, to examine the impact of innovation on bilateral trade performance using industry level data from OECD countries. Secondly, the author complements the existing literature by examining how innovation activities (classified as high technological intensive or low technological intensive) affect trade performance.

The Pandemic Waves' Impact on the Crude Oil Price and the Rise of Consumer Price Index: Case Study for Six European Countries

This study examines the response of the Consumer Price Index (CPI) in local currency to the COVID-19 pandemic using monthly data (March 2020–February 2022), comparatively for six European countries. We have introduced a model of multivariate adaptive regression that considers the quasi-periodic effects of pandemic waves in combination with the global effect of the economic shock to model the variation in the price of crude oil at international levels and to compare the induced effect of the pandemic restriction as well and the oil price variation on each country's CPI. The model was tested for the case of six emergent countries and developed European countries. The findings show that: (i) pandemic restrictions are driving a sharp rise in the CPI, and consequently inflation, in most European countries except Greece and Spain, and (ii) the emergent economies are more affected by the oil price and pandemic restriction than the developed ones.

Social spending and chronic unemployment: evidence from OECD countries

PurposeSocial spending is at the forefront of the tools used to repair the damage caused by the global epidemic. However, one of the most critical questions in recent days is as follows: what are the effects of social expenditures in eliminating unemployment? The primary purpose of this article is to provide empirical evidence on the impact of social spending on chronic unemployment in the selected organization for economic co-operation and development (OECD) countries.Design/methodology/approachIn this study, the data of 30 selected OECD countries between 1991 and 2018 have been compiled. First, countries have been divided into four categories according to their spending intensity to determine the effects of social spending on the long-term unemployment rate. Then, the auto-regressive distributed lag (ARDL) approach and the error correction models (ECM) examine the variables' short- and long-term interactions.FindingsThe author found that the change in the share of social expenditures in GDP affects chronic unemployment similarly. This finding is consistent with the results of studies in the literature dealing with the relationship between public sector size and unemployment. However, the research findings are specifically about the effects of social expenditures on chronic unemployment. In this respect, the results reflect that expenditures with passive characteristics have an expansionary effect on long-term unemployment. In addition, the progressive effect of social expenditures on chronic unemployment is increasing in countries with high expenditure intensity. In countries with relatively low spending intensity, the impact of social spending is limited to the short run and is lower.Originality/valueMultiple studies have reported that public policies developed in line with the incentives of active employment and public or private sector investments reduce the unemployment rate by positively affecting the output/employment level. This study, unlike other studies, focuses on the effects of social expenditures on chronic unemployment. It also compares the effects of social spending on the long-term unemployment rate for countries with varying spending intensities. Therefore, this article tests the impact of social expenditures used against a concrete socioeconomic problem in the OECD sample. In this respect, the findings contribute to the literature by addressing the relationship between social spending and chronic unemployment.

Energy Sufficiency in the Passenger Transport of Lithuania

This paper aims to understand the significance of energy sufficiency (ES) in passenger transport for the long-term resolution of energy, climate, and sustainable development issues in Lithuania. It computes related indicators, by fixing the passenger-kilometres (pkm) travelled by various modes of transportation and applying a scenario analysis with the MESSAGE model. The findings indicated that the country's final energy consumption (FEC) in transportation could be reduced by 21.8% by 2050 due to slowing growth rate of distances travelled by passenger car but increasing use of public transport and bicycles. This would result in a decrease in the growth rate of primary energy consumption (PEC) by half (to 0.3% a year), an increase in the use of renewable energy sources (RES) to 67.2% in the PEC structure, savings of oil products by 6.4 TWh, and savings of new electricity generation capacity by 550 MW. Furthermore, 20 MtCO2eq. in greenhouse gas (GHG) emission reductions could be realised between 2021 and 2050. To take advantage of the potential of ES, the policy measures of passenger car demand containment and a shift to non-motorised and less polluting modes of transportation should be implemented. Furthermore, priority should be given to policy measures that encourage use of public transportation.

Czochralski (CZ) process modification with cooling tube in the response to market Global silicon shortage

Recently, the silicon wafer producers, affected by Covid-19 and USA-China competition, looks for new production processes to increase the production. On the other hand, the common parts of CZ puller such as heater, crucible and thermal shield are optimized over time and now the common CZ process is reached to limitation for further improvement. Here, we propose a modified CZ method by adding a cooling tube into the growth zone. The new proposed Cz method is applied to the 8″ crystal growth process. A fully 3D transition model including energy equation, Navier–Stokes equation, surface-to-surface radiation heat transfer, moving mesh and thermal stress equations is implemented. The simulation is performed for both original and new CZ method. It was proved that the new CZ method increases the pulling speed up to 25 %. To ensure about the crystal quality, the thermal stress is compared between original and new proposed CZ method. Although it was found that the thermal stress increases about twice but still the maximum von Mises stress never exceeds the critical value 25 MPa. Additionally, the power consumption is also found to enhance maximum 2 kW under new conditions. To evaluate the model the interface and heater power for the original CZ puller is compared with industrial CZ process and it shows acceptable accuracy. © 2023 Elsevier B.V.

Digital Economy, Industrial Structure, and Environmental Quality: Assessing the Roles of Educational Investment, Green Innovation, and Economic Globalization

This study constructs a digital economy (DE) index and explores its impact on environmental quality by utilizing data from China's 287 prefecture-level cities from 2013 to 2019. Unlike past studies, this research examines the indirect effect of DE on environmental pollution through the channels of industrial structure and educational investment. Further, it also analyzes the moderating role of economic globalization and green technology innovation in the nexus between DE and environmental quality. The empirical results indicate that DE significantly and positively enhances environmental quality by mitigating environmental pollution. This outcome remained stable after a series of empirical analyses and stability checks. Secondly, DE positively affects ecological and environmental quality by improving education levels and upgrading industrial structures. Thirdly, green technological innovation and economic globalization positively and significantly moderate the effect of DE development on ecological and environmental quality. Fourthly, associations between the development of DE and environmental quality are heterogeneous in terms of regions and markets, among which the most significant impact exists in the eastern area and the area with higher marketization. Based on the empirical findings, this paper provides comprehensive recommendations for promoting the DE and advancing China's environmental quality. Based on the results, important policy implications are suggested.

The role of coworking spaces in the changing landscape of future workplace agility: linking boundary organization theory and organizational development models

PurposeThis study aims to illustrate the potential of coworking spaces as one way to achieve optimal workplace arrangements and corporate real estate (CRE) agility, especially for large organizations. The authors suggest understanding coworking spaces from the boundary organization theory and organizational growth model.Design/methodology/approachThis study takes a threefold theoretical approach: conducting a literature review and identifying the gaps in coworking studies for large organizations, applying the organizational boundary theory in tandem with organizational growth models in the context of coworking spaces as a part of the workplace ecology and identifying future research agendas for coworking studies.FindingsThis study proposes a conceptual framework of how coworking spaces can be viewed and used as a boundary object throughout the organizational growth phases. Besides, four major future research areas are proposed: case studies and/or empirical evidence of coworking spaces as CRE buffer zones and boundary objects for organizations, coworking space design and different formats of boundary object-infused collaboration, coworking space design and management for its own agility and flexibility and how coworking affects employees' performance, health and well-being and professional training/mentoring.Practical implicationsFor large organizations, there is a clear pressure to rethink CRE to increase workplace agility, flexibility and resilience, much accelerated with the recent COVID-19 pandemic. Understanding the effective use of coworking spaces as a part of CRE portfolios will help enhance corporates' state and ability to reassess, realign and replan their CRE portfolios.Originality/valueMany existing studies about coworking spaces are based on observations and self-reported justification at an individual level. Whether and how coworking can benefit companies at an organizational level is largely unstudied and worth more attention. This study illustrates a new theoretical understanding of how coworking spaces can be a part of CRE portfolios and bring potential benefits of inter and intraorganizational collaboration throughout the phases of organizational growth.

ModInterv COVID-19: An online platform to monitor the evolution of epidemic curves.

We present the software ModInterv as an informatics tool to monitor, in an automated and user-friendly manner, the evolution and trend of COVID-19 epidemic curves, both for cases and deaths. The ModInterv software uses parametric generalized growth models, together with LOWESS regression analysis, to fit epidemic curves with multiple waves of infections for countries around the world as well as for states and cities in Brazil and the USA. The software automatically accesses publicly available COVID-19 databases maintained by the Johns Hopkins University (for countries as well as states and cities in the USA) and the Federal University of Viçosa (for states and cities in Brazil). The richness of the implemented models lies in the possibility of quantitatively and reliably detecting the distinct acceleration regimes of the disease. We describe the backend structure of software as well as its practical use. The software helps the user not only to understand the current stage of the epidemic in a chosen location but also to make short term predictions as to how the curves may evolve. The app is freely available on the internet (http://fisica.ufpr.br/modinterv), thus making a sophisticated mathematical analysis of epidemic data readily accessible to any interested user.

How the COVID-19 Pandemic and its Consequences Affect the Presence of and Search for Meaning of Life: A Longitudinal Study.

The presence of meaning in life (PML) and the search for meaning in life (SML) are crucial when facing difficult times. Although several theoretical frameworks have tried to explain the dynamics of meaning in life during adversity, empirical evidence about interactions among both constructs using longitudinal designs is scarce. This study examined the trajectories of both PML and SML during the COVID-19 lockdown period in Spain. In total, 220 adults fulfilled an online survey during two periods: a strict and a relaxed lockdown period. Latent growth models showed that both PML and SML declined slightly during the strict lockdown, but they reached a plateau during the relaxed lockdown. Results also showed that age and having a partner predicted higher PML and lower SML at baseline, whereas being male predicted higher scores on PML. PML and SML were negatively associated at baseline, higher SML at baseline was related to a steeper decreasing PML slope during the strict lockdown, and the PML and SML slopes in the relaxed lockdown period were negatively related. This study contributes to better understanding longitudinal fluctuations of meaning in life in situations of adversity.

Mapping Linguistic Shifts During Psychological Coping With the COVID-19 Pandemic

How does language change reveal the psychological trajectories of people coping with a COVID-19 infection? This study examined writings on social media over 12 weeks from people who self-reported having tested positive for COVID-19. People used fewer words reflecting anxiety and distancing but more words indicating reinterpretation over time. The language patterns for describing the experience of COVID-19 infections differed from those for describing other unrelated topics. The findings reveal the temporal dynamics of psychological adjustment to an unfolding crisis.

Daily affect intensity and variability of adolescents and their parents before and during a COVID-19 lockdown.

INTRODUCTION: The corona virus (COVID-19) pandemic may have a prolonged impact on people's lives, with multiple waves of infections and lockdowns, but how a lockdown may alter emotional functioning is still hardly understood. METHODS: In this 100-daily diaries study, we examined how to affect intensity and variability of adolescents (N = 159, Mage = 13.3, 61.6% female) and parents (N = 159, Mage = 45.3, 79.9% female) changed after the onset and during (>50 days) the second COVID-19 lockdown in the Netherlands, using preregistered piecewise growth models. RESULTS: We found only an unexpected increase in parents' positive affect intensity after the lockdown onset, but no immediate changes in negative affect intensity or variability. However, both adolescents and parents reported gradual increases in negative affect intensity and variability as the lockdown prolonged. Lockdown effects did not differ between adolescents and parents. However, within groups, individuals differed. The individual differences in the effects were partly explained by life satisfaction, depressive symptoms, and self-reported lockdown impact. CONCLUSIONS: Overall, these findings suggests that a lockdown triggers changes in daily affective well-being especially as the lockdown prolongs. Individual differences in the effects indicate heterogeneity in the impact of the lockdown on daily affect that was partly explained by baseline life satisfaction and depressive symptoms. However, more knowledge on the causes of this heterogeneity is needed to be able to increase resilience to lockdown effects in the population.

Modelling the link between Covid-19 cases, hospital admissions and deaths in England

Analysing the mass of time series data accumulating daily and weekly from the coronavirus pandemic has become ever more important as the pandemic has progressed through its numerous phases. Econometric techniques are particularly suited to analysing this data and research using these techniques is now appearing. Much of this research has focused on short-term forecasting of infections, hospital admissions and deaths, and on generalising to stochastic settings compartmental epidemiological models, such as the well-known "susceptible (S), infected (I) and recovered or deceased (R)", or SIR, model. The focus of the present paper is rather different, however, in that it investigates the changing dynamic relationship between infections, hospital admissions and deaths using daily data from England. It does this using two approaches, balanced growth models and autoregressive distributed lag/error correction models. It is found that there has been a substantial decrease over time in the number of deaths and hospital admissions associated with an increase in infections, with patients being kept alive longer, as clinical practice has improved and the vaccination program rolled out. These responses may be tracked and monitored through time to ascertain whether such improvements have been maintained.

Smart economic development patterns in Europe: interaction with competitiveness

Purpose>This paper aims to investigate the smart economic development (SED) patterns in Europe in relation to competitiveness. Motivational focus corresponds to global events: the fourth industrial revolution, transition to a low-carbon economy, economic shocks (such as the 2008 financial crisis, Brexit or the coronavirus pandemic), which requires rethinking development policies, targeting competitiveness increase and reducing imbalances in economic development.Design/methodology/approach>The analysis includes self-organising neural networks cluster analysis and correlations, comparative analysis of SED indicators structure and cumulative index estimation with World Economic Forum (WEF) global competitiveness index. The panel data set of 19 years from 2000 to 2018 for 30 European countries.Findings>Overall, cross-country examination suggests that European countries of higher competitiveness illustrate higher estimates in SED. The key determinants are juridical fairness, social responsibility, competence building, intelligence and welfare employment to develop smart patterns for reaching higher competitiveness.Research limitations/implications>The limitations relate to the particular sample of European countries and gathering statistical data and a methodology of the SED index calculation. In addition, the paper contains a macroeconomic environment focus on competitiveness estimation. Further research may be improved with micro and mezzo environment incorporation at a cross-country analysis level.Practical implications>By linking well-known terms of competitiveness and economic development with a concept of smartness, new approaches to policymaking emerged. The methodology presented in this paper has implications for territorial cohesion policies, competitiveness and branching strategies. The combination of SED sub-indexes and WEF GCI might aid a more accurate ex ante measurement.Social implications>The findings are essential for fostering a smart approach in economic development for long-term competitiveness.Originality/value>This paper provides original empirical evidence about the relationship between SED and competitiveness and adds new knowledge that smartness becomes a way for building countries' competitiveness by identified two profiles of SED patterns by development stages, namely, integrated to economic development and institutional-based which is divided to focus and balanced.

ISLAMIC BANKING SECTOR DEVELOPMENT AND ECONOMIC GROWTH: EMPIRICAL EVIDENCE FROM MALAYSIA

This paper examines the linear association between Malaysian Islamic banking sector and short- and long-run economic growth for the quarterly period of 2007Q1 - 2019Q4 by employing the Pesaran et al. (2001) Autoregressive Distributed Lag (ARDL) technique of cointegration, The findings show that in the short run, the link between Islamic bank deposits and economic growth is positively insignificant. Meanwhile, in the long run, the findings suggest that Islamic bank deposits affect growth in a positively significant way, confirming the supply-leading hypothesis. Islamic bank financing is found to have a negative significant link with short-run economic growth. Meanwhile, in the long run, Islamic bank financing has positive and significant effect on growth, hence, supporting the supply-leading hypothesis. Furthermore, there is a significant positive correlation between Islamic bank assets and short- and long-run economic development, again supporting the supply-leading hypothesis. Generally, it can be concluded that Islamic banking development is affecting economic growth positively both in the short and long run, which supports the supply-leading hypothesis, as the majority of the Islamic bank indicators are found to affect both short- and long-run economic growth positively.

Multiplicity Matters: The Development of Interest Profiles During Dynamic Institutional Transitions

Students pursue multiple interests within their daily lives. The current study aims to describe the development of interest profiles (the total composition of all interests one pursues) in terms of the amount of interests, their domain divergence and developmental stages. Using Latent Growth Models on interest data of 309 students over the course of three years (10 timepoints), we will specifically explore interest profile development, also in the context of institutional transitions and COVID-19 measures. © ISLS.

Impact of Education and Income Inequality on Per Capita Income Amid COVID-19 pandemic

The present research study is an attempt to analyse the effects of various levels of education and income inequality on real GDP per capita, in the COVID-19 pandemic era, including 70 countries. We found empirical evidence that the relationship of various levels of education with growth of real income per capita remained the same in 2020 as it was in 2010, except for the slight difference in the magnitudes of the coefficients. The primary school enrolment of the two decades earlier has revealed as the increasing factor to the income per person. However, when we took a decade early or so values the relationship became converse indicating primary school enrolment as the decreasing factor to the income per capita. The empirics indicated that the secondary school enrolment remained contributing factor to the growth of income per capita, and it is the most effective level of education to increase the real GDP per capita. Moreover, there is no empirical evidence in the favour of relationship between tertiary education and the income per person. The income inequality has also provided insignificant results in our analysis.

Using Hydrological Concepts and an Artificial Neural Network to Model the Rate for COVID-19 Infections versus Deaths

Models were run to reproduce COVID-19 infections versus deaths in Mexico City. The first model was made using rain runoff concept, emulating rain as number of infections reproducing runoff as number of deaths given as of March 2020. The second consisted of using an artificial neural network (ANN) proposed as an initial condition function to be implemented in the model with delay. These models were applied to fit accumulated confirmed case data, obtaining fit corroborated by coefficient of determination, R2. The R2 value produced by model was 0.0528 in case of infections comparison vs. official deaths reported by the Ministry of Health, 0.0571 for t case of infections vs. modelling using the HEC-HMS tool, and 0.0937 for case of contagion vs. modelling using ANN.

Circular economy, circular regenerative processes, agrowth and placemaking for tourism future

Purpose>This conceptual paper explores the possibility to envision circular regenerative processes embracing agrowth and placemaking within tourism;an industry remarkably connected to the Anthropocene and its detrimental impacts on the planet. Drawing on theorisations of circular economy, on the concept of agrowth, and on theories of placemaking, this viewpoint offers a novel conceptual framework to imagine a regenerative future for tourism.Design/methodology/approach>The authors connect the ancient archetype of “circularity”, largely used to make sense of life on Earth, with the Greek concept of oikonomia. The resulting notion of a circular oikonomia is then intersected with theories of placemaking. In doing so, the authors are driven by the idea of de-growth, as an “a-growthism” urging the abandonment of the faith towards growth for an enduring stable regenerative agrowth.Findings>The authors offer a novel conceptual framework to counteract the negative impacts of Anthropocene and envision future scenarios in which tourism can make a difference by enacting enduring regenerative processes for places and human and non-human entities.Originality/value>The originality of this study lies in the conceptual framework proposed to imagine the future of tourism, hospitality and mobilities in circular regenerative terms. This study envisions stable and enduring regenerative processes of natural assets, materials, products, services and resources as well as a tourism space made up of lively, multiple, transformative relationships and interactions among people and the environments people live in and travel to.