Human capital heterogeneity of the unemployed and jobless recoveries

The jobless recovery enigma remains largely unsolved. As a special case of broader unemployment, the term "jobless recovery” describes an economic recovery where output recovers—and even expands—yet employment growth remains anemic. While the effects of these prolonged recoveries are significant—from increased crime to a lifetime reduction in wages—they are not well understood. Building on the insights of labor market matching models that incorporate heterogeneity among workers, this paper sheds light on jobless recoveries, developing a first-of-its-kind index of human capital heterogeneity for the unemployed, and testing that index using of a Structural Vector Autoregression. I demonstrate that the extent to which unemployed human capital is heterogeneous and specific, rather than homogeneous and general, plays a key and under-appreciated role in the labor market;increases in human capital heterogeneity can account for between one-quarter to three-quarters of the joblessness of the past three recoveries in the pre-COVID era.

Impact of the COVID-19 pandemic and generational heterogeneity on ecommerce shopping styles – A case study of Sacramento, California

The COVID pandemic has accelerated the growth of ecommerce and reshaped shopping patterns, which in turn impacts trip-making and vehicle miles traveled. The objectives of this study are to define shopping styles and quantify their prevalence in the population, investigate the impact of the pandemic on shopping style transition, understand the generational heterogeneity and other factors that influence shopping styles, and comment on the potential impact of the pandemic on long-term shopping behavior. Two months after the initial shutdown (May/June 2021), we collected ecommerce behavioral data from 313 Sacramento Region households using an online survey. A K-means clustering analysis of shopping behavior across eight commodity types identified five shopping styles, including ecommerce independent, ecommerce dependent, and three mixed modes in-between. We found that the share of ecommerce independent style shifted from 55% pre-pandemic to 27% during the pandemic. Overall, 30% kept the same style as pre-pandemic, 54% became more ecommerce dependent, and 16% became less ecommerce dependent, with the latter group more likely to view shopping an excuse to get out. Heterogeneity was found across generations. Pre-pandemic, Millennials and Gen Z were the most ecommerce dependent, but during the pandemic they made relatively small shifts toward increased ecommerce dependency. Baby Boomers and the Silent Generation were bimodal, either sticking to in-person shopping or shifting to ecommerce-dependency during the pandemic. Post-pandemic intentions varied across styles, with households who primarily adopt non-food ecommerce intending to reverse back to in-person shopping, while the highly ecommerce dependent intend to limit future in-store activities. © 2023 The Authors

[The situation of people with intellectual and developmental disabilities in the COVID-19 pandemic-risk factors, problem areas, and measures]. / Die Situation von Menschen mit geistiger Behinderung in der COVID-19-Pandemie ­ Risikofaktoren, Problembereiche, Maßnahmen.

Many people with intellectual and developmental disabilities (IDD) were much more affected by COVID-19 than the average population. The morbidity in this population group was significantly higher due to the form of their housing and care as well as disability-associated health factors (such as Down's syndrome). This led, among other things, to a higher incidence of inpatient settings, a higher mortality rate among hospitalized patients, and a higher case fatality rate among certain subgroups. Risk factors were co-diseases such as dysphagia, epilepsy, or mental disorders. In addition, there were health consequences from restrictive exposure prophylaxis measures at the beginning of the COVID-19 pandemic.This overview article describes the main developments in the care of people with IDD since the beginning of the pandemic. New mental problems or an increase in existing mental problems occurred more frequently in people with IDD who were cared for on an outpatient basis or by relatives. People with IDD in inpatient residential facilities were less frequently affected. The main reasons for psychological problems were lack of social contacts, lost work and employment opportunities, the frightening pandemic situation, and the major structural and personnel changes in the facilities at the beginning of the pandemic. During the pandemic, there were also problems with the implementation of therapeutic and preventive measures. On the other hand, especially in the inpatient area, the "slowing down of life" associated with the restrictions at the beginning of the pandemic also had a stress-reducing effect on some of the people with IDD and was perceived by them as something positive. Caregivers had more time for the genuine (i.e., pedagogical) work. Overall, it was shown that health-promoting measures must also take into account the great heterogeneity of the people with IDD and their living conditions during the pandemic.

From cause and effect to causes and effects.

It is now-at least loosely-acknowledged that most health and clinical outcomes are influenced by different interacting causes. Surprisingly, medical research studies are nearly universally designed to study-usually in a binary way-the effect of a single cause. Recent experiences during the coronavirus disease 2019 pandemic brought to the forefront that most of our challenges in medicine and healthcare deal with systemic, that is, interdependent and interconnected problems. Understanding these problems defy simplistic dichotomous research methodologies. These insights demand a shift in our thinking from 'cause and effect' to 'causes and effects' since this transcends the classical way of Cartesian reductionist thinking. We require a shift to a 'causes and effects' frame so we can choose the research methodology that reflects the relationships between variables of interest-one-to-one, one-to-many, many-to-one or many-to-many. One-to-one (or cause and effect) relationships are amenable to the traditional randomized control trial design, while all others require systemic designs to understand 'causes and effects'. Researchers urgently need to re-evaluate their science models and embrace research designs that allow an exploration of the clinically obvious multiple 'causes and effects' on health and disease. Clinical examples highlight the application of various systemic research methodologies and demonstrate how 'causes and effects' explain the heterogeneity of clinical outcomes. This shift in scientific thinking will allow us to find the necessary personalized or precise clinical interventions that address the underlying reasons for the variability of clinical outcomes and will contribute to greater health equity.

Travel on the road: does China's high-speed rail promote local tourism?

Following a Chinese saying: To be rich, roads first, high-speed rail (HSR) opening and station construction are indispensable for economic developing. Probing the nexus between HSR, as a vital part of modern transportation system, and local tourism development provides a scan for reviving tourism and gaining low-carbon transition after COVID-19 pandemic. Drawing on prefecture-level panel data, this study takes difference-in-difference and instrument variable methods to detect the overall and heterogeneous effects of HSR connection on cities' tourism development. The results showed that HSR connection had an overall positive effect on cities' domestic tourist arrivals. The heterogeneity of the effect from HSR to tourism development appears to be that central and western cities, non-resource-based cities, and small cities benefited more from the opening of HSR. From a dynamic perspective, HSR connection promoted local tourism development in the 0 and 1 year of HSR opening but failed to show a positive effect in the long term. Hence, the study proposed some adjustments for evaluating the efficiency of HSR with consideration for the tourism effect, redesigning the system of HSR with consideration for local heterogeneity, and optimizing the HSR environment. These measures can optimize China's HSR management and the design of HSR systems.

When might host heterogeneity drive the evolution of asymptomatic, pandemic coronaviruses?

Controlling many infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), requires surveillance followed by isolation, contact-tracing and quarantining. These interventions often begin by identifying symptomatic individuals. However, actively removing pathogen strains causing symptomatic infections may inadvertently select for strains less likely to cause symptomatic infections. Moreover, a pathogen's fitness landscape is structured around a heterogeneous host pool; uneven surveillance efforts and distinct transmission risks across host classes can meaningfully alter selection pressures. Here, we explore this interplay between evolution caused by disease control efforts and the evolutionary consequences of host heterogeneity. Using an evolutionary epidemiology model parameterized for coronaviruses, we show that intense symptoms-driven disease control selects for asymptomatic strains, particularly when these efforts are applied unevenly across host groups. Under these conditions, increasing quarantine efforts have diverging effects. If isolation alone cannot eradicate, intensive quarantine efforts combined with uneven detections of asymptomatic infections (e.g., via neglect of some host classes) can favor the evolution of asymptomatic strains. We further show how, when intervention intensity depends on the prevalence of symptomatic infections, higher removal efforts (and isolating symptomatic cases in particular) more readily select for asymptomatic strains than when these efforts do not depend on prevalence. The selection pressures on pathogens caused by isolation and quarantining likely lie between the extremes of no intervention and thoroughly successful eradication. Thus, analyzing how different public health responses can select for asymptomatic pathogen strains is critical for identifying disease suppression efforts that can effectively manage emerging infectious diseases. Supplementary Information: The online version contains supplementary material available at 10.1007/s11071-022-07548-7.

Towards green recovery: Platform economy and its impact on carbon emissions in China

China's economy and environment urgently require a green recovery as COVID-19's consequences expand over time, and the platform economy is a practical means of pursuing this goal. By employing the Generalized Divisia Index Method (GDIM), this paper aims to analyze the impact of platform economy on carbon emissions in China during the period 2013–2020. Overall, the platform economy has increased carbon emissions, but there was a decrease in carbon emissions in the platform economy between 2014 and 2016. The scale factors of platform economy are the primary contributors to the increase in China's overall emissions and most provincial carbon emissions, while the carbon intensity of platform economy factors contributes most to the decrease in carbon emissions. In particular, the carbon intensity of platform economy factors promoted the most cumulative carbon emissions in Jiangsu, Heilongjiang, Yunnan, Qinghai, and Ningxia between 2013 and 2020, and the energy intensity of platform economy factors reduced most of the cumulative carbon emissions of Heilongjiang, Yunnan, Qinghai, and Xinjiang during the same period. From the perspective of the heterogeneity of platform economic development, the main contributors of carbon mitigation from high-platform economic provinces are intensity effects. However, the platform economic structure of low-platform economic provinces significantly reduces carbon emissions. In addition, we also found significant differences in the factors influencing emissions in different groups under the influence of the pandemic. Finally, we provide some valuable references for China's platform economic development to achieve "carbon neutrality” targets. © 2023 Economic Society of Australia, Queensland

The Measurement of Precarious Work and Market Conditions: Insights from the COVID-19 Disruption on Sample Selection

The precarious work construct combines employment instability and employment-contingent outcomes. Yet, I argue that confining the scope of the investigation to employed individuals creates a sample selection that disguises the heterogeneous nature of employment instability. The COVID-19 skyrocketing unemployment rate provides both a compelling motivation and a unique opportunity to revisit the construct of precarious work. Using pre-COVID and COVID-19 era data of the working-age population in Israel, the results demonstrate that by pushing less stable individuals out of employment, the COVID-19 recession strengthened the negative relationship between volatility and employment opportunities and accentuated sample selection. Because the selection into employment was not random, this introduces a bias into the measurement of precarious work, one that is more severe during a recession than in a full-employment market. The discussion highlights the broader significance of this lacuna and suggests a way to hone the conceptualization and operationalization of the precarious work construct.

Heterogeneity of inbound tourism driven by exchange rate fluctuations: implications for tourism business recovery and resilience in Australia

Quantifying risk spillovers from exchange rates to inbound tourist arrivals by purpose of visit is essential for Australia to take proactive measures to achieve tourism business recovery and resilience after such critical events like the recent bushfires and the COVID-19 pandemic. Using a monthly dataset over the period January 1998–March 2020, this paper calculates the conditional value-at-risk (CoVaR) to evaluate how different types of inbound tourists to Australia respond to exchange rate fluctuations. The empirical results identify inbound tourist arrivals with the highest sensitivity to exchange rate fluctuations, confirming the role of exchange rates in determining inbound tourist arrivals by purpose of visit. Furthermore, these results shed light on provisions of tourism products, services, and infrastructural facilities to satisfy different requirements of Australia's inbound tourists by purpose of visit, aiming to promote tourism business recovery and resilience in Australia.

Rise in income inequality before and after the Covid-19 crisis in Argentina

This article examines inequality in income distribution in Argentina between 2014 and 2020 in a context of stagnation and economic crisis, which coincided with the outbreak of the Covid-19 pandemic. The determining factors of income distribution were analyzed based on a household survey, and a breakdown of the Gini coefficient was implemented to determine the factors that explained the increase in inequality. From a structuralist point of view, the retraction of formal employment, the expansion of the informal sector, and greater coverage of social protection policies were the central factors that explained the increased level of inequality at that time. Social transfers helped to mitigate inequality in the face of the Covid-19 pandemic. © 2023 Universidad Nacional Autonoma de Mexico. All rights reserved.

Racial Health Disparities and Black Heterogeneity in COVID-19: A Case Study of Miami Gardens

The COVID-19 pandemic is a critical public health concern that has disproportionately affected the Black community in the United States. The purpose of this study was to examine the risk and protective factors faced by residents in the City of Miami Gardens during the COVID-19 pandemic, with emphases placed on racial health disparities and Black heterogeneity. Using convenience and snowball sampling, quantitative and qualitative data for this study were collected via an anonymous online questionnaire using QuestionPro. Survey links were distributed by e-mail invitations with assistance from city officials to the residents of this predominantly Black city in Florida (n = 83). Descriptive statistics and relevant qualitative responses are presented. Furthermore, a machine learning (ML) approach was used to select the most critical variables that characterized the two racial groups (Black versus non-Black participants) based on four ML feature selectors. Study findings offered important and interesting insights. Specifically, despite the greater prevalence of adopting measures to protect themselves and others from COVID-19, Black participants were more susceptible to activities that increased their COVID-19 risk levels. In addition, their rate of infection, particularly among the Afro-Caribbean ethnic group, was reported to be higher, indicating the need to further investigate the underlying conditions and root causes (including vaccine hesitancy and refusal) that contribute to their greater health disparities. © The Author(s) 2023.

Toward Trustworthy AI: Blockchain-Based Architecture Design for Accountability and Fairness of Federated Learning Systems

Federated learning is an emerging privacy-preserving AI technique where clients (i.e., organizations or devices) train models locally and formulate a global model based on the local model updates without transferring local data externally. However, federated learning systems struggle to achieve trustworthiness and embody responsible AI principles. In particular, federated learning systems face accountability and fairness challenges due to multistakeholder involvement and heterogeneity in client data distribution. To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture. We first design a smart contract-based data-model provenance registry to enable accountability. Additionally, we propose a weighted fair data sampler algorithm to enhance fairness in training data. We evaluate the proposed approach using a COVID-19 X-ray detection use case. The evaluation results show that the approach is feasible to enable accountability and improve fairness. The proposed algorithm can achieve better performance than the default federated learning setting in terms of the model's generalization and accuracy.

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

This research aimed to investigate the spatial autocorrelation and heterogeneity throughout Bangladesh's 64 districts. Moran I and Geary C are used to measure spatial autocorrelation. Different conventional models, such as Poisson-Gamma and Poisson-Lognormal, and spatial models, such as Conditional Autoregressive (CAR) Model, Convolution Model, and modified CAR Model, have been employed to detect the spatial heterogeneity. Bayesian hierarchical methods via Gibbs sampling are used to implement these models. The best model is selected using the Deviance Information Criterion. Results revealed Dhaka has the highest relative risk due to the city's high population density and growth rate. This study identifies which district has the highest relative risk and which districts adjacent to that district also have a high risk, which allows for the appropriate actions to be taken by the government agencies and communities to mitigate the risk effect. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Evidence of sample selectivity in highway injury-severity models: The case of risky driving during COVID-19

Research in highway safety continues to struggle to address two potentially important issues;the role that unobserved factors may play on resulting crash and injury-severity likelihoods, and the issue of identification in safety modeling caused by the self-selective sampling inherent in commonly used safety data (the fact that drivers in observed crashes are not a random sample of the driving population, with riskier drivers being over-represented in crash data bases). This paper addresses unobserved heterogeneity using mixing distributions and attempts to provide insight into the potential sample-selection problem by considering data before and during the COVID-19 pandemic. Based on a survey of vehicle usage (vehicle miles traveled) and subsequent statistical modeling, there is evi-dence that riskier drivers likely made up a larger proportion of vehicle miles traveled dur-ing the pandemic than before, suggesting that the increase in injury severities observed during COVID-19 could potentially be due to the over-representation of riskier drivers in observed crash data. However, by exploring Florida crash data before and during the pan-demic (and focusing on crashes where risky behaviors were observed), the empirical anal-ysis of observed crash data suggests (using random parameters multinomial logit models of driver-injury severities with heterogeneity in means and variances) that the observed increase in injury severity during the COVID-19 pandemic (calendar year 2020) was likely due largely to fundamental changes in driver behavior and less to changes in the sample selectivity of observed crash data. The findings of this paper provide some initial guidance to future work that can begin to more rigorously explore and assess the role of selectivity and resulting identification issues that may be present when using observed crash data.(c) 2022 Elsevier Ltd. All rights reserved.

Analyzing diversity amongst visitors of Portuguese wine routes based on their wine involvement

Purpose>This paper focuses on the wine tourist market in the central region of Portugal, and it aims to analyze the wine tourist's demographic and travel behavior and preferences profile, based on their level of wine involvement.Design/methodology/approach>This paper presents results from an exploratory study with a quantitative design, including a total of 1,029 survey responses from visitors of three wine routes. A K-mean cluster analysis was carried out, and the emerging groups of wine tourists were statistically compared (ANOVA or Chi-squared test).Findings>Participants present a demographic profile of the wine route visitor similar to that found in other studies, with an average involvement with wine. There were three clusters of wine tourists, with different levels of involvement with wine: less wine-involved;medium wine-involved;and highly wine-involved. Significant differences between the three mentioned categories are visible for gender, age and attractions visited and expenses, suggesting the possibility of a differentiated market approach. Additionally, most respondents report high interest in a variety of attractions that are not exclusively wine-related. This finding supports the conceptualization of (particularly rural) wine tourism as "terroir tourism.”Research limitations/implications>The pandemic context in which data collection was undertaken led to a smaller sample than expected, which was also more domestic than would have been in "non-COVID” times.Practical implications>This study provides relevant insights about visitors of wine routes in Central Portugal, which may resonate in other wine tourism destinations. Implications for both theory and practice are also discussed.Originality/value>This paper fulfills an identified need to study the wine tourism market in the Central Region of Portugal and expands our understanding about wine tourists' profiles, behavior and interests, adding with empirical findings to the debate on heterogeneity in the wine tourist market, the role of wine involvement and of terroir.

Optimal Capacity-Constrained COVID-19 Vaccination for Heterogeneous Populations

COVID-19 and the ensuing vaccine capacity constraints have emphasized the importance of proper prioritization during vaccine rollout. This problem is complicated by heterogeneity in risk levels, contact rates, and network topology which can dramatically and unintuitively change the efficacy of vaccination and must be taken into account when allocating resources. This paper proposes a general model to capture a wide array of network heterogeneity while maintaining computational tractability and formulates vaccine prioritization as an optimal control problem. Pontryagin's Maximum Principle is used to derive properties of optimal, potentially highly dynamic, allocation policies, providing significant reductions in the set of candidate policies. Extensive numerical simulations of COVID-19 vaccination are used to corroborate these findings and further illicit optimal policy characteristics and the effects of various system, disease, and population parameters. © 2022 IEEE.

Interactive impacts of walkability, social vulnerability, & travel behavior on COVID-19 mortality: A hierarchical Bayesian spatial random parameter approach.

While existing research highlights the built and social environment impacts on COVID-19 mortality, no empirical evidence exists on how the built and social environments may interact to influence COVID-19 mortality. This study presents a rigorous empirical assessment of the interactive impacts of social vulnerability and walkability on neighborhood-level COVID-19 mortality rates. Based in King County, WA, a unique data infrastructure is created by spatially integrating diverse census tract-level data on COVID-19 mortalities, walkability characteristics, social vulnerability, and travel behavior measures. Advanced Markov Chain Monte Carlo (MCMC) based Full Bayes hierarchical spatial random parameter models are developed to simultaneously capture spatial and unobserved random heterogeneity. Around 46% of the neighborhoods had opposite levels of walkability and social vulnerability. Compared to low walkability and high social vulnerability, neighborhoods with high walkability and low social vulnerability (i.e., best case scenario) had on average 20.2% (95% Bayesian CI: -37.2% to -3.3%) lower COVID-19 mortality rates. Analysis of the interactive impacts when only one of the social and built environment metrics was in a healthful direction revealed significant offsetting effects - suggesting that the underlying structural social vulnerability issues faced by our communities should be addressed first for the infectious disease-related health impacts of walkable urban design to be observed. Concerning travel behavior, the findings indicate that COVID-19 mortality rates may be reduced by discouraging auto use and encouraging active transportation. The study methodologically contributes by simultaneously capturing spatial and unobserved heterogeneity in a holistic Full Bayesian framework.

Individual Genetic Heterogeneity.

Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.

Heterogeneity of neutrophils and inflammatory responses in patients with COVID-19 and healthy controls.

Severe respiratory viral infections, including SARS-CoV-2, have resulted in high mortality rates despite corticosteroids and other immunomodulatory therapies. Despite recognition of the pathogenic role of neutrophils, in-depth analyses of this cell population have been limited, due to technical challenges of working with neutrophils. We undertook an unbiased, detailed analysis of neutrophil responses in adult patients with COVID-19 and healthy controls, to determine whether distinct neutrophil phenotypes could be identified during infections compared to the healthy state. Single-cell RNA sequencing analysis of peripheral blood neutrophils from hospitalized patients with mild or severe COVID-19 disease and healthy controls revealed distinct mature neutrophil subpopulations, with relative proportions linked to disease severity. Disruption of predicted cell-cell interactions, activated oxidative phosphorylation genes, and downregulated antiviral and host defense pathway genes were observed in neutrophils obtained during severe compared to mild infections. Our findings suggest that during severe infections, there is a loss of normal regulatory neutrophil phenotypes seen in healthy subjects, coupled with the dropout of appropriate cellular interactions. Given that neutrophils are the most abundant circulating leukocytes with highly pathogenic potential, current immunotherapies for severe infections may be optimized by determining whether they aid in restoring an appropriate balance of neutrophil subpopulations.

Risk Factors Associated with the Spatiotemporal Spread of the SARS-CoV-2 Omicron BA.2 Variant — Shanghai Municipality, China, 2022

< -type="Summary"> <sec> What is already known about this topic? Previous studies have explored the spatial transmission patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and have assessed the associated risk factors. However, none of these studies have quantitatively described the spatiotemporal transmission patterns and risk factors for Omicron BA.2 at the micro (within-city) scale.</sec><sec> What is added by this report? This study highlights the heterogeneous spread of the 2022 Omicron BA.2 epidemic in Shanghai, and identifies associations between different metrics of spatial spread at the subdistrict level and demographic and socioeconomic characteristics of the population, human mobility patterns, and adopted interventions.</sec><sec> What are the implications for public health practice? Disentangling different risk factors might contribute to a deeper understanding of the transmission dynamics and ecology of coronavirus disease 2019 and an effective design of monitoring and management strategies.</sec>