The first wave of the COVID‐19 pandemic and its impact on socioeconomic inequality in psychological distress in the UK

Abstract

We use data from the UK Household Longitudinal Study (UKHLS) to compare measures of socioeconomic inequality in psychological distress, measured by the General Health Questionnaire (GHQ), before (Waves 9 and the Interim 2019 Wave) and during the first wave of the COVID‐19 pandemic (April to July 2020). Based on a caseness measure, the prevalence of psychological distress increased from 18.5% to 27.7% between the 2019 Wave and April 2020 with some reversion to earlier levels in subsequent months. Also, there was a systematic increase in total inequality in the Likert GHQ‐12 score. However, measures of relative socioeconomic inequality have not increased. A Shapley‐Shorrocks decomposition analysis shows that during the peak of the first wave of the pandemic (April 2020) other socioeconomic factors declined in their share of socioeconomic inequality, while age and gender account for a larger share. The most notable increase is evident for younger women. The contribution of working in an industry related to the COVID‐19 response played a small role at Wave 9 and the Interim 2019 Wave, but more than tripled its share in April 2020. As the first wave of COVID‐19 progressed, the contribution of demographics declined from their peak level in April and chronic health conditions, housing conditions, and neighbourhood characteristics increased their contributions to socioeconomic inequality.

Economic expectations and anxiety during the COVID-19 pandemic: a one-year longitudinal evaluation on Italian university students

“…Direct socio-economic effects of lockdowns usually consist of job losses and business closures (Miles et al, 2020 ). The COVID-19-related non-pharmaceutical interventions have impacted on the already existing socio-economic inequalities (Davillas and Jones, 2021 ; Perugini and Vladisavljevic, 2021 ). Indeed, some jobs are more flexible than others as regards the possibility to be performed remotely: those requiring lower skills or education are usually the most affected by the closures and, thus, vulnerable economic groups such as females (Alon et al, 2020 ) and young people (Brunori et al, 2021 ) will be ones suffering the most by the economic risks brought about by COVID-19 and, in particular, the most exposed to the prospect of unemployment.…”

Impact of COVID‐19 lockdowns on mental health: Evidence from a quasi‐natural experiment in England and Scotland

“…We carried out several robustness checks to our analysis. First, we employed alternative measures of mental health following earlier studies (Banks & Xu, 2020 ; Davillas & Jones, 2021 ; Jackson, 2007 ; Thomson et al., 2018 ): (i) a Likert scale from 0 to 36 (“GHQ‐likert”), (ii) a binary indicator of whether any item has the maximum score of 4 (“GHQ‐binary”), and (iii) a binary indicator of whether the individual reports the two most symptomatic answers (out of the 4 possible) in at least 4 of the 12 GHQ dimensions (“GHQ‐binary2”). 9 Our main indicator, GHQ‐caseness, and GHQ‐likert may be thought of measures of the level of mental well‐being of the population, whereas both GHQ‐binary and GHQ‐binary2 are more extreme measures of mental health and can be thought of a proxy for suffering mental health disorders (Jackson, 2007 ; Thomson et al., 2018 ).…”

Gender Inequality and Mental Health During the COVID-19 Pandemic

“…Correlated with economic concerns and food insecurity, mental health deteriorated significantly during the early months of the COVID-19 pandemic [5][6][7]. Specifically, high overall levels of depression, anxiety, and distress in countries such as United States, Canada, and United Kingdom have been reported [5,[8][9][10][11][12]. In light of the fact that women are more likely to present depressive symptoms in general [11], as well as to assume more housework and childcare activities than men [13,14], a link between gender and mental health and well-being deterioration in the context of the pandemic is plausible.…”