Pandemic or Not, Worker Subjective Wellbeing Pivots About the Living Wage Point: A Replication, Extension, and Policy Challenge in Aotearoa New Zealand.

Recent pre-pandemic research suggests that living wages can be pivotal for enhancing employee attitudes and subjective wellbeing. This article explores whether or not the present COVID-19 pandemic is impacting pivotal links between living wages and employee attitudes and subjective wellbeing, with replication indicating robustness. Twin cohorts each of 1,000 low-waged workers across New Zealand (NZ), one pre- (2018), and one present-pandemic (2020) were sample surveyed on hourly wage, job attitudes, and subjective wellbeing as linked to changes in the world of work associated with the pandemic (e.g., job security, stress, anxiety, depression, and holistic wellbeing). Using locally estimated scatter-point smoothing, job attitudes and subjective wellbeing scores tended to pivot upward at the living wage level in NZ. These findings replicate earlier findings and extend these into considering subjective wellbeing in the context of a crisis for employee livelihoods and lives more generally. Convergence across multiple measures, constructs, and contexts, suggests the positive impacts of living wages are durable. We draw inspiration from systems dynamics to argue that the present government policy of raising legal minimum wages (as NZ has done) may not protect subjective wellbeing until wages cross the living wage Rubicon. Future research should address this challenge.

Deliberating Upon the Living Wage to Alleviate In-Work Poverty: A Rhetorical Inquiry Into Key Stakeholder Accounts.

Most developed nations have a statutory minimum wage set at levels insufficient to alleviate poverty. Increased calls for a living wage have generated considerable public controversy. This article draws on 25 interviews and four focus groups with employers, low-pay industry representatives, representatives of chambers of commerce, pay consultants, and unions. The core focus is on how participants use prominent narrative tropes for the living wage and against the living wage to argue their respective perspectives. We also document how both affirmative and negative tropes are often combined by participants to craft their own rhetorical positions on the issue.

Monitoring COVID-19 pandemic through the lens of social media using natural language processing and machine learning.

PURPOSE: It has been over a year since the first known case of coronavirus disease (COVID-19) emerged, yet the pandemic is far from over. To date, the coronavirus pandemic has infected over eighty million people and has killed more than 1.78 million worldwide. This study aims to explore "how useful is Reddit social media platform to surveil COVID-19 pandemic?" and "how do people's concerns/behaviors change over the course of COVID-19 pandemic in North Carolina?". The purpose of this study was to compare people's thoughts, behavior changes, discussion topics, and the number of confirmed cases and deaths by applying natural language processing (NLP) to COVID-19 related data. METHODS: In this study, we collected COVID-19 related data from 18 subreddits of North Carolina from March to August 2020. Next, we applied methods from natural language processing and machine learning to analyze collected Reddit posts using feature engineering, topic modeling, custom named-entity recognition (NER), and BERT-based (Bidirectional Encoder Representations from Transformers) sentence clustering. Using these methods, we were able to glean people's responses and their concerns about COVID-19 pandemic in North Carolina. RESULTS: We observed a positive change in attitudes towards masks for residents in North Carolina. The high-frequency words in all subreddit corpora for each of the COVID-19 mitigation strategy categories are: Distancing (DIST)-"social distance/distancing", "lockdown", and "work from home"; Disinfection (DIT)-"(hand) sanitizer/soap", "hygiene", and "wipe"; Personal Protective Equipment (PPE)-"mask/facemask(s)/face shield", "n95(s)/kn95", and "cloth/gown"; Symptoms (SYM)-"death", "flu/influenza", and "cough/coughed"; Testing (TEST)-"cases", "(antibody) test", and "test results (positive/negative)". CONCLUSION: The findings in our study show that the use of Reddit data to monitor COVID-19 pandemic in North Carolina (NC) was effective. The study shows the utility of NLP methods (e.g. cosine similarity, Latent Dirichlet Allocation (LDA) topic modeling, custom NER and BERT-based sentence clustering) in discovering the change of the public's concerns/behaviors over the course of COVID-19 pandemic in NC using Reddit data. Moreover, the results show that social media data can be utilized to surveil the epidemic situation in a specific community.