Impact of Income and Different Generation Cohorts on Nursery Products and Landscaping Project Spending

Socioeconomic factors influencing consumer demand for nursery products and landscape projects were investigated using consumer survey data collected from North Carolina in 2008. Tobit models were estimated for censored dependent variables, budget expenditure shares on nursery products, and landscape spending. The most significant factors influencing the share of income spent on nursery products were age and household income. The elderly and baby boomers tend to spend less on bedding plants, perennials, and outdoor hardscapes than Generations X and Y. The income elasticities suggest that the amount spent on outdoor living projects is sensitive to changes in household income, whereas spending in vegetable plants and chemicals is less responsive to income.

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France

10.1093/oso/9780198807032.003.0006 ◽

2018 ◽

Author(s):

Philippe Askenazy ◽

Bruno Palier

Keyword(s):

Income Inequality ◽

Labour Market ◽

Rapid Growth ◽

Household Income ◽

General Trend ◽

Age Groups ◽

Wage Growth ◽

The Elderly ◽

Wage Setting ◽

Rich Countries

This chapter describes France as apparently one of the few rich countries to have avoided a significant increase in income inequality in recent decades. However, stable average inequalities mask an asymmetric trend of income between age groups, the elderly improving their situation while the young see theirs worsening. Furthermore, it shows that behind this relatively still surface, a general trend of precarization of more and more ordinary workers is occurring. The importance of wage-setting processes and of regulation of the labour market is brought out, together with the way the tax and transfer systems have operated, in restraining the forces driving inequality upwards. Wage growth, while limited, has thus been reasonably uniform across the distribution and together with the redistributive system have kept household income inequality within bounds. However, in response to high unemployment both regulatory and tax–transfer systems have served to underpin the very rapid growth in precarious working over the last decade, representing a very serious challenge for policy.

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Trust in Artificial Intelligence: Meta-Analytic Findings

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/00187208211013988 ◽

2021 ◽

pp. 001872082110139

Author(s):

Alexandra D. Kaplan ◽

Theresa T. Kessler ◽

J. Christopher Brill ◽

P. A. Hancock

Keyword(s):

Artificial Intelligence ◽

Empirical Research ◽

Meta Analysis ◽

Automated Vehicles ◽

Factors Influencing ◽

Moderating Factors ◽

Shared Context ◽

Build Systems ◽

Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

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Household consumption allocation and the collective household model: Children share of household resources in The Gambia

Statistical Journal of the IAOS ◽

10.3233/sji-200676 ◽

2021 ◽

pp. 1-13

Author(s):

Madi Mangan

Keyword(s):

Household Income ◽

Nonlinear Least Squares ◽

The Gambia ◽

Factor Price ◽

Sharing Rule ◽

Income Elasticities ◽

Household Resources ◽

Price And Income Elasticities ◽

Household Model ◽

Household Members

This paper applies the collective household model to allocate household resources among household members. With a Collective Quadratic Almost Ideal Demand System (CQUAIDS) estimated by a Feasible Generalized Nonlinear Least Squares (FGNLS) method, it studies the household demand for six categories of household goods using household income and expenditure survey data from The Gambia, directed to studying the allocation of resources among young and adult members of households in The Gambia. It establishes the sharing rule for children and adult members of the household and shows the effect of demographic, distributive factor, price and income elasticities on the shares of household resources. The results establish that a higher share of resources goes for children while the sharing rule varies for different household types. Also, the findings show significant effects of demographic, distributive factor, price and income on the allocation of the household resources of consumption goods by the household.

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Factors Affecting Secondary Traumatic Stress of Nurses Caring for COVID-19 Patients in South Korea

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18136843 ◽

2021 ◽

Vol 18 (13) ◽

pp. 6843

Author(s):

Mee Sun Lee ◽

Sujin Shin ◽

Eunmin Hong

Keyword(s):

Social Support ◽

Regression Analysis ◽

Traumatic Stress ◽

Secondary Traumatic Stress ◽

Direct Care ◽

Hierarchical Regression ◽

Factors Affecting ◽

Factors Influencing ◽

Affect Patient Care ◽

Significant Factors

The secondary traumatic stress (STS) of nurses caring for COVID-19 patients is expected to be high, and it can adversely affect patient care. The purpose of this study was to examine the degree of STS of nurses caring for COVID-19 patients, and we identified various factors that influence STS. This study followed a descriptive design. The data of 136 nurses who had provided direct care to COVID-19 patients from 5 September to 26 September 2020 were collected online. Hierarchical regression analysis was conducted to identify the factors influencing STS. Participants experienced moderate levels of STS. The regression model of Model 1 was statistically significant (F = 6.21, p < 0.001), and the significant factors influencing STS were the duration of care for patients with COVID-19 for more than 30 days (β = 0.28, p < 0.001) and working in an undesignated COVID-19 hospital (β = 0.21, p = 0.038). In Model 2, the factor influencing STS was the support of a friend in the category of social support (β = −0.21, p = 0.039). The nurses caring for COVID-19 patients are experiencing a persistent and moderate level of STS. This study can be used as basic data to treat and prevent STS.

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Individual Condensation Trails in Aircraft Trajectory Optimization

Sustainability ◽

10.3390/su11216082 ◽

2019 ◽

Vol 11 (21) ◽

pp. 6082 ◽

Cited By ~ 3

Author(s):

Judith Rosenow ◽

Hartmut Fricke

Keyword(s):

Global Warming ◽

Trajectory Optimization ◽

Wind Shear ◽

Impact Factors ◽

Factors Influencing ◽

Order Of Magnitude ◽

Aircraft Trajectory ◽

The Impact ◽

Arbitrary Trajectory ◽

Significant Factors

Contrails are one of the driving contributors to global warming, induced by aviation. The quantification of the impact of contrails on global warming is nontrivial and requires further in-depth investigation. In detail, condensation trails might even change the algebraic sign between a cooling and a warming effect in an order of magnitude, which is comparable to the impact of aviation-emitted carbon dioxides and nitrogen oxides. This implies the necessity to granularly consider the environmental impact of condensation trails in single-trajectory optimization tools. The intent of this study is the elaboration of all significant factors influencing on the net effect of single condensation trails. Possible simplifications will be proposed for a consideration in single-trajectory optimization tools. Finally, the effects of the most important impact factors, such as latitude, time of the year, and time of the day, wind shear, and atmospheric turbulence as well as their consideration in a multi-criteria trajectory optimization tool are exemplified. The results can be used for an arbitrary trajectory optimization tool with environmental optimization intents.

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