Determinants of Sustainable Cross-Border Cooperation: A Structural Model for the Hungarian Context Using the PLS-SEM Methodology

The importance of this research stems from the need to ensure the sustainability of cross-border cooperation through a better understanding of its determinants and causal relationships. While having common features and patterns, cross-border cooperation is always expressed through the relations of specific countries and peoples. Therefore, based upon the PLS-SEM methodology, the authors consider the fundamental factors influencing the external cooperation of Hungary’s transboundary regions. The advantage of the PLS-SEM method is that it enables researchers to simultaneously identify and approximate hidden connections between input data and to construct a regression model describing the relationship between input data. Despite widespread application in economic studies, the authors have not found the use of PLS-SEM for studying cross-border cooperation issues in the current scientific literature. The authors have built a model to assess the hidden factors of cross-border cooperation and to identify the indirect influence of certain factors. The novelty of the research is to identify the determinants of sustainable cross-border cooperation and the relationship between them in a multi-level system of cross-border interaction between businesses, people, and the State. In the Hungarian context, transport infrastructure and business travel are shown to have a direct positive impact on cross-border cooperation. For the first time, tourism and socio-economic conditions have been shown to have powerful but indirect impacts. This work could be the beginning of gathering new evidence on the determinants and causation of cross-border cooperation in the context of other countries. An important finding of the study is the growing importance of indicators of the new, post-industrial economy. As for recommendations, the authors focus on state, regional, and municipal support measures, awareness of the possibilities of cross-border cooperation, the need to develop e-commerce, and alternative energy as a modern basis for converting Hungary’s cross-border position into a competitive advantage.

COVID-19 policies in practice and their direct and indirect impacts in Northern California jails

Context: Although the increased risk of COVID-19 in carceral facilities is well documented, little is known about the practical barriers to infection control and indirect impacts of pandemic policies in these settings. Evidence in jails is especially scarce. Methods: Between July 8, 2020 and April 30, 2021 we performed SARS-CoV-2 serology testing and administered a questionnaire among residents and staff in four Northern California jails. We analyzed seroprevalence in conjunction with demographic factors and survey responses of self-perceived COVID-19 risk, recent illness, COVID-19 test results, and symptom reporting behaviors. We additionally assessed COVID-19 policies in practice and evaluated their impacts on court dates, mental health, and routine health care. We engaged stakeholder representatives, including incarcerated individuals and their advocates, to guide study design, conduct, and interpretation. Findings: We enrolled 788 incarcerated individuals and 380 staff across four county jails. Most seropositive individuals had not previously tested positive for COVID-19, despite many suspecting prior infection. Among incarcerated participants, we identified deficient access to face masks and prevalent symptom underreporting associated with fears of isolation and perceptions of medical neglect in jail. Incarcerated participants also reported substantial hindrances to court cases and reductions in routine health care due to COVID-19. Incarcerated individuals and staff both cited worsened mental health due to COVID-19, which for incarcerated individuals was largely attributable to further isolation from loved ones and other pandemic restrictions on recreation and programming. Conclusions: Perceptions of inadequate protection from COVID-19 were pervasive among incarcerated individuals. Simultaneously, restrictive measures compounded poor mental health and fostered fears of isolation that undermined effective infection control. Custody officials should work to systematically improve provision of masks, understand and mitigate fears and mistrust, and take proactive steps to minimize the detrimental impacts of restrictive policies on residents' mental health and well-being.

Socioeconomic Effects of COVID-19 in Mexico: A Multisectoral Approach and Policy Options

The health crisis caused by the spread of COVID-19 has caused a profound social and economic disruption in Mexico. Our purpose in this paper is to contribute to the knowledge about the economic impact of the pandemic in Mexico and to evaluate social policy options to mitigate its effects. We do so based on a multisectoral-multiplier model and the most recent Social Accounting Matrix for Mexico, with which we estimate the direct and indirect impacts of COVID-19 as well the likely effects of two alternatives for mitigating them: an unconditional cash transfer scheme for households living in poverty, and the establishment of an unemployment insurance program for workers who have lost their jobs during the pandemic. We find that the first alternative alleviates more value added and loss of income, and thus has a greater effect in reducing inequality and the incidence of poverty.

Strategic Supplier Selection's Effects on Buyer Competitive Performance in Matching Domains: Is Supplier Integration Effective at Mediating Relationships?

The purpose of this research is to determine whether strategic supplier selection based on supplier capability in new product development, supplier quality, and supplier cost directly or indirectly improves the buyer's competitive performance capabilities in the matched domains of buyer product innovation, buyer quality, and buyer competitive pricing. The resource-based view of the firm is used to frame the direct effects of strategic supplier selection, arguing that a buyer's ability to select a supplier with resources and expertise in a specified domain should improve the buyer's performance capability in the "matched" domain (but not necessarily in "unmatched" domains). Two supplier integration techniques are considered as potential mediators, assuming indirect pathways. The research hypotheses examine both direct and indirect impacts for each of the matched domains, but do not assume cross-domain interactions. For instance, supplier selection for new product development capability should have an effect on buyer product innovation (in matched domains), but not always on buyer quality capability (unmatched domains). While the direct impacts of strategic supplier selection on buyer performance are supported in each matched domain, the indirect effects via supplier integration are not substantial for the matched domains. Strategic supplier selection is identified as a viable source of competitive advantage in the resource-based view. By contrast, supplier development and supplier partnership do not provide additional performance benefits in a particular domain over and beyond those obtained from strategic supplier selection in that domain; rather, it is the type of the resources selected that determines competitive advantage.

Impact of Transit Quality on Anchor-Mediated Knowledge Economies Across Large U.S. Cities

Universities, medical centers, and headquarters are the driving forces behind cities’ innovation productivity and anchor-based urban revitalization efforts, such as innovation districts. As a result, there is increasing competition for corporate anchors, as well as emerging partnerships and conflicts. In each case, transit has a major role to play. Yet we know little about precisely how transit fits into the dynamics of anchor-based revitalization. To address this empirical gap, this study employed structural equation modeling to grasp both the direct and indirect impacts of transit on the knowledge-intensive firm location in 500 large U.S. cities. We conceptualized the indirect impact of transit as mediated by anchors with a composite value that we developed to quantitatively represent the presence and size of major innovation anchors in each city. According to our findings, the positive impact of transit on a city’s overall knowledge economy occurred through its role in supporting anchor institutions, a role that, in turn, significantly increased the likelihood of knowledge-based firms locating in the city. In short, transit quality was positively associated with larger and more established anchors, as well as the availability of more employees, which expands the city’s talent pool, a critical driver of knowledge-intensive employers’ location decisions. These findings call for greater attention to be paid to transit in cities’ anchor-based urban revitalization plans and to partnerships between cities, metropolitan organizations, and anchors in planning future transit systems.

Self-organizing Maps and Bayesian Regularized Neural Network for Analyzing Gasoline and Diesel Price Drifts

Any nation’s growth depends on the trend of the price of fuel. The fuel price drifts have both direct and indirect impacts on a nation’s economy. Nation’s growth will be hampered due to the higher level of inflation prevailing in the oil industry. This paper proposed a method of analyzing Gasoline and Diesel Price Drifts based on Self-organizing Maps and Bayesian regularized neural networks. The US gasoline and diesel price timeline dataset is used to validate the proposed approach. In the dataset, all grades, regular, medium, and premium with conventional, reformulated, all formulation of gasoline combinations, and diesel pricing per gallon weekly from 1995 to January 2021, are considered. For the data visualization purpose, we have used self-organizing maps and analyzed them with a neural network algorithm. The nonlinear autoregressive neural network is adopted because of the time series dataset. Three training algorithms are adopted to train the neural networks: Levenberg-Marquard, scaled conjugate gradient, and Bayesian regularization. The results are hopeful and reveal the robustness of the proposed model. In the proposed approach, we have found Levenberg-Marquard error falls from − 0.1074 to 0.1424, scaled conjugate gradient error falls from − 0.1476 to 0.1618, and similarly, Bayesian regularization error falls in − 0.09854 to 0.09871, which showed that out of the three approaches considered, the Bayesian regularization gives better results.

Model for Determination of Musculoskeletal Disorder Cost/Benefit

It is generally considered that the costs related to musculoskeletal disorders (MSDs) stem from absenteeism, medical expenses, reduced productivity, and quality of life. To estimate the costs associated with MSDs in organizations and thus determine the benefits for a company of implementing an effective and sustainable prevention program, it is necessary to consider the need to know the current and future prevalence of MSDs as well as to understand whether these data vary according to the economic sector, geographic location, and demographic aspects. It is also necessary to understand whether there are comorbidities associated with MSDs as well as the direct and indirect impacts in terms of the productive and work capacity of organizations and individuals. In the development of this study, the authors wanted to generate an analysis tool to estimate the incidence of MSDs and their economic costs, which also serves as a baseline to evaluate the possible benefits derived from prevention programs for this type of work-related disease.

New Nosocomephobia? Changes in Hospitalizations during the COVID-19 Pandemic

While the coronavirus disease-2019 (COVID-19) pandemic directly caused millions of hospitalizations and deaths, its indirect impacts on people with other illnesses can be of equal importance. Using discharge records in a major Chinese megacity where there was a limited number of COVID-19 cases, we find significant declines in the number of hospital admissions for a whole spectrum of disease categories during the pandemic. The declines were larger in COVID-19 designated hospitals and top-grade hospitals. In-hospital mortality and length of stay (LOS) were higher for stroke, ischaemic heart diseases, and malignant neoplasms, while women delivering in hospitals had fewer C-sections and shorter LOS. Our results suggest that people avoided necessary hospitalization out of fear of being infected by COVID-19. To prevent the adverse impacts of delaying health care, policymakers should establish clear guidelines encouraging people to seek necessary care, especially during the reopening period.

Does Industrial Transfer Change the Spatial Structure of CO2 Emissions?—Evidence from Beijing-Tianjin-Hebei Region in China

As an important cause of global warming, CO2 emissions have become a research hotspot in recent years. Industrial transfer impacts regional CO2 emissions and is related to the low-carbon development of regional industries. Taking the Beijing-Tianjin-Hebei region (BTH region) as an example, this study analysed industrial transfer’s direct and indirect impacts on CO2 emissions based on a mediating model and two-way fixed effect panel regression. The results obtained indicate that industrial transfer-in has promoted CO2 emissions to a small extent, and the positive impact of industrial transfer-in on CO2 emissions wanes over time. Industrial transfer affects CO2 emissions by acting on the economic level, on population size, and on urbanisation level, but the indirect effect is weaker than the direct effect. Industrial transfer does not lead to technological upgrading, but the latter is an effective means of carbon emission reduction. Industrial transfer-in has shown a positive effect on CO2 emissions for most cities, but there are exceptions, such as Cangzhou. In the future, the BTH region should maintain coordinated development among cities and improve the cooperative innovation mechanism for energy conservation and emission reduction.

Understanding the impact of the COVID-19 pandemic on a socially deprived UK coastal town: a preliminary exploratory analysis of health and socioeconomic data

Introduction In addition to the direct impact of COVID-19 infections on health and mortality, a growing body of literature indicates there are wide-ranging indirect impacts of the COVID-19 pandemic and associated public health measures on population health and wellbeing. Exploring these indirect impacts in the context of a socially deprived UK coastal town will help identify priority areas to focus COVID-19 recovery efforts on. Methods Data on primary care diagnosis, hospital admissions, and several socioeconomic outcomes between 2016 and Spring 2021 in the UK town of Fleetwood were collected and analysed in an exploratory analysis looking at pre- and post- COVID-19 patterns in health and social outcomes. Weekly and monthly trends were plotted by time and differences between periods examined using Chi-squared and t-tests. Results Initial falls in hospital admissions and diagnoses of conditions in primary care in March 2020 were followed by sustained changes to health service activity for specific diagnostic and demographic groups, including for chronic kidney disease and young people. Increases in the number of people receiving Universal Credit and children eligible for free school meals appear to be greater for those in the least deprived areas of the town. Discussion These exploratory findings provide initial evidence of the sustained impact of the pandemic across several health and social outcomes. Examining these trends in multivariate analyses will further test these associations and establish the strength of the medium term impact of the pandemic on the population of this coastal town. Advanced modelling of this data is ongoing and will be published shortly.