Relative deprivation, inequality and the Covid-19 pandemic.

There is a growing concern that inequalities are hindering health outcomes. This paper's primary objective is to investigate the role of relative deprivation and inequality in explaining the daily spread of the Covid-19 pandemic. For this purpose, we use secondary cross-sectional data across 119 (developed and developing) countries from January 2020 - to April 2021. For the empirical analysis, we use a recent dynamic panel data modelling approach that allows us to identify the role of time-invariant variables such as degree of globalisation, political freedom and income inequality on the dynamics of the pandemic and fatality rates across countries. We find that new cases per million and fatality rates are highly persistent processes. After controlling for time-varying mobility statistics from the Google mobility database and region-specific dummy variables, the two significant factors that explain the severity of Covid-19 spread in a country are per-capita Gross Domestic Product (GDP) and Yitzhaki's relative income deprivation index. Lagged value of new cases per million significantly explains cross-country variations in the daily case fatality rates. A higher proportion of the older population and pollution increased fatality rates while better medical infrastructure reduced it.

Measuring COVID-19’s impact on international HE students and intervention satisfaction: implications for marketing theory and practice

Research is urgently required to understand COVID-19’s impact on international students and satisfaction with support interventions. This study investigated these dimensions for international business students studying in Australia to inform international HE policy and marketing. Qualitative research identified the main impacts: financial hardship and income loss, social and lifestyle changes, cessation of travel, and mental health concerns. Six COVID-19 intervention dimensions were identified and a survey then captured ratings of importance, as well as the performance of CDU and the government across these dimensions. Intervention improvement priorities varied according to the analytical approach used. For example, importance-performance analysis revealed financial support as the priority for improvement. In contrast, regression analysis revealed social distancing as the key driver of intervention satisfaction. Closing the loop research then revealed ways to improve COVID-19 interventions and provided input for future marketing for attracting and maintaining international students. Implications for measuring student satisfaction are also discussed. [ABSTRACT FROM AUTHOR] Copyright of Journal of Marketing for Higher Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)