The Covid-19 pandemic as an accelerator of economic worries and labor-related mental health polarization in Germany? A longitudinal interacted mediation analysis with a difference-in-difference comparison

OBJECTIVES: Labor-related mental health polarization refers to exposure to low-paid employment and unemployment decreasing mental health. Previous research identified economic worries as a key mediator. Against this background, the Covid-19 pandemic is often assumed to have accelerated already existing processes and affected vulnerable populations the most. Our study sought to investigate whether the Covid-19 pandemic accelerated the mediation by economic worries between employment type and mental health. METHOD: Using the German Socioeconomic Panel (GSOEP) from 2016 onwards, we created a pre-Covid-19 sample (N = 8266) and a per-Covid-19 sample (N = 7294), with each having a t(0) wave (2016/2018) and a t(1) wave (2018/2020). We applied the mediational g-formula for longitudinal mediation with exposure-mediator (XM) interaction between employment type (X) and economic worries (M). We decomposed the total effect into a direct, indirect, and interacted effect of employment on mental health and provided a difference-in-difference comparison of the effects. RESULTS: During the Covid-19 pandemic, economic worries increased, and mental health decreased. However, the mediation by economic worries reduced by approx. 18.0% (e.g., from 35.0% to 28.9%). A decreased indirect effect caused the reduction in mediation, while the direct and interacted effect remained rather stable. We also found stark gender differences towards males having a higher total effect but a lower mediated effect during the Covid-19 pandemic. CONCLUSION: Our results highlight that mediators competing to economic worries must have emerged during the Covid-19 pandemic. Such mediators could be the risk of infection at the workplace, the possibility of remote work, and gender-specific mediators. Our study is also the first to extend the mediational g-formula with the difference-in-difference approach. It can be used as a blueprint for researchers interested in evaluating the impact of events, such as the Covid-19 pandemic, on preexisting processes.