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This study examines the importance of financial literacy in explaining financial resilience in four continental European countries during the coronavirus disease 2019 (COVID-19) crisis while controlling for a wide set of additional determinants.

Variable importance may vary with the technique applied. Therefore, different classification techniques, such as logistic regression, partial proportional odds regression, and conditional random forest, have been employed. The analysis relies on the Survey of Health, Ageing and Retirement in Europe in the context of COVID-19, collecting 4,781 observations from France, Germany, Italy, and Spain.

In line with previous studies, financial resilience is found to increase with financial literacy that consistently ranks in the midfield in terms of variable importance among all explanatory variables.

The findings reveal the most important features that improve financial resilience. Financial literacy is one of the few determinants of financial resilience that can be actively shaped. To increase preparedness for future crises, a policy mix of financial education, regulation, and nudging may help increase financial literacy and, subsequently, financial resilience.

The better the financial literacy, the more protected individuals are from macroeconomic shocks. However, most previous studies do not rely on data samples that cover such crises. Moreover, most of the previous studies rely on single classification techniques, while this study applied traditional and data-mining techniques to assess feature importance.

This study aims to assess the satisfaction level of middle-income group (MIG) residents of government group housings of Kolkata, India. Further, a Composite Satisfaction Index (CSI) was also devised for assessing the satisfaction level.

The satisfaction level was assessed in three domains – “within premises”, “at neighbourhood” and “with cost” – incurred and questionnaires were designed to conduct primary survey. Question on overall level of satisfaction “with location” was also included. All satisfaction responses were recorded on a five-point Likert scale. Subsequently, a CSI – a weighted average of satisfaction for attribute “with location” and “with cost” – was devised. The weights were assigned through expert opinion survey using Delphi technique. Data being ordinal in nature, a two-step approach was adopted – initially through exploratory factor analysis, contributing attributes were identified and later a Generalised Ordered Logit Model was fitted in STATA. “Monetary benefits” were calculated as a difference of actual expenditure incurred from recommended expenditure towards regular transportation and housing. Mean satisfaction scores for attributes “with cost” were validated with “monetary benefits”.

Attribute “with cost” contributed significantly towards the overall level of satisfaction “with location”. The computed CSI values also comply with the findings, indicating the reliability of the index in similar contexts. The government group housing model was successful in its outreach towards the intended beneficiaries.

The CSI devised will help the MIG in identifying appropriate residential housing locations and enable policymakers in reviewing group housings.

Residential satisfaction studies were not specific to MIG residents considering attribute “with cost”. Hence, this study contributed to the existing knowledge in this specific context.

The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway.

A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption.

The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe.

The research results are very useful for mitigating the consequences of high-speed rail accidents.

The present study examines a range of police force on the continuum (firearms, TASER/chemical spray and physical force) to see whether they are associated with individual (subject and officer), situational and/or neighborhood factors.

A partial proportional odds model is used to analyze police use of force data from 2003 to 2016 in Dallas. Independent variables are allowed for varying effects across the different cumulative dichotomizations of the dependent variable (firearms vs TASER/chemical spray and physical force and firearms and TASER/chemical spray vs physical force).

Most officer demographic and situational factors are consistently significant across the cumulative dichotomizations of police force. In addition, suspect race/ethnicity (Hispanic) and violent crime rates play significant roles when officers make decisions to use firearms, as opposed to TASER/chemical spray and physical force. Overall, situational variables (subject gun possession and contact types) play greater roles than other variables in affecting police use of force.

Despite the large body of police use of force research, little to no research has used the partial proportional odds model to examine the ordinal nature of police force from physical to intermediate to deadly force. The current findings can provide important implications for policy and research.

The purpose of this paper is to evaluate the effectiveness of a national alcohol-prevention social marketing campaign in Thailand, investigating specifically the competing forces of the campaign vis-à-vis alcohol advertising.

Based on repeated cross-sectional, nationally representative data from the 2011-2014 Annual Survey of Buddhist Lent Campaign Evaluation (N = 10,133), a generalized ordered logit (partial proportional odds) model is used. The outcome of interest is self-reported alcohol consumption during the campaign period, compared to before. The main explanatory variables include exposure to the campaign and exposure to alcohol advertising.

Results show that exposure to the campaign and exposure to alcohol advertising positively and negatively influence alcohol consumption, respectively. Compared to those with one type of exposure and those without any exposure to alcohol-related messages, drinkers with exposure to both the campaign and alcohol advertising are estimated to have the highest probability of drinking reduction during the campaign period.

The paper highlights the importance of accounting for a countervailing force (in this case, alcohol advertising) in campaign evaluation studies. It also suggests that alcohol-control social marketing be continued and that the government should disseminate alcohol-prevention messages in a balanced manner, ensuring that both costs and benefits of alcohol consumption are well-understood by the intended audience.

Purpose – In this chapter, we examine individual- and country-level differences in perceived job insecurity in the 27 European Union countries (EU27) within a multilevel framework.

Design/methodology/approach – We primarily focus on cross-national differences in perceived job insecurity in the EU27 and consider several possible explanations of it, including flexible employment practices, economic conditions, labor market structure, and political institutions. We examine both individual- and country-level determinants using multilevel partial proportional odds models based on individual-level data from the 2006 Eurobarometer 65.3 and country-level data from a variety of sources.

Findings – We find that European workers feel most insecure in countries with high unemployment, low union density, low levels of part-time and temporary employment, relatively little social spending on unemployment benefits, and in post-socialist countries.

Research limitations/implications – The findings from this study suggest that flexible employment practices do not necessarily cause workers to feel insecure in their jobs. This is likely due to the different nature of part-time and temporary employment in different institutional contexts.

Originality/value – This study is one of the most comprehensive accounts of perceived job insecurity in Europe given the focus on a larger number of countries and macro-level explanations for perceived job insecurity.

The purpose of this study is to identify what skills gained during PhD study are linked with preparation and differences between industry and academic positions.

Using student-level data from nearly 2,400 early career PhD alumni from 50 US PhD granting institutions, this study uses a generalized ordered logit model with partial proportional odds to test the main and conditional effects of 14 areas of skill development on perceptions of career preparation.

This study contributes empirical evidence to show that research skills and communicating with non-technical audiences are positively linked with job preparation.

The combination of sophisticated research and noncognitive skills promotes the idea of a well-rounded PhD – a highly skilled and versatile researcher that can interact in both academic and industry settings – as a goal of doctoral education. The knowledge gained from this study will assist scholars and graduate educators, including faculty advisors, program directors, graduate deans, graduate education professionals and career development professionals to reconceptualize professional preparation for work in industry.

For-profit businesses, particularly start-up companies, are vital for economic development and many require PhD-level employees across research and management roles. Developing PhDs with the appropriate skills for industry will better align higher education and economic interests.

The wide scope of doctoral institutions allows for a quantitative approach not appropriate for single-institution case studies of doctoral career pathways research.

Ecolabels will undoubtedly play a central role in promoting more sustainable production methods and consumption behaviors. Although numerous recent studies have explored consumer awareness, interpretation and preferences toward ecolabels and certifications, little is yet known about how they perceive the regulatory schemes that underpin them.

Data were collected using a survey answered by a representative random sample of 1,032 Canadian consumers. Unconstrained partial-proportional odds models were used to perform statistical analyses.

The results suggest that consumers generally do not differentiate between regulatory schemes for organic, local and non-GMO products. The level of perceived control and strictness appears to be influenced by multiple variables involving risk perception, trust and motivations, although this influence varies across labels.

In addition to geographical specificities, this survey includes self-reported variables that might be subject to desirability biases and intention variables which do not necessarily predict behavior. Finally, this study does not consider interaction effects, since claims and ecolabels have not been studied in relation to specific products.

These misperceptions about ecolabels' regulatory schemes could be addressed through better communication about schemes and certifications, although the authors agree that information alone would not be enough to deal with the trust issue suggested by the results. Deliberative and behavioral approaches might be more efficient to embed consumers' values, perceptions and concerns related to food labeling and certifications into the policymaking process.

This work explores the role played by risk perceptions, trust, egoistic and altruistic motives, and the importance of third-party certifications in the consumer's understanding of ecolabels' regulatory schemes.

Purpose – This chapter gives an overview of methods for defining and analysing crash severity.

Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling.

Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling.

Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling.

Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.