Understanding pathways form socioeconomic indicators to effect of COVID-19 Lockdown via Structural Equation Modeling (SEM)

Abstract

Covid-19 pandemic has created unusual and uncertain conditions with massive economic and social changes. The negative association between Covid-19 and Global economic indicators is obvious and started to be documented at macro-level, however, on individual level the mechanism of action between socioeconomic indicators and impact of Covid-19 has not been explored yet. We have hypothesized that due to Covid-19 lockdowns generally people are suffering or will suffer economically, bearing the brunt of global economic crisis originating currently. COVID-19 is marking its impression on mental health, sleep patterns, social capital and financial status of the people. We did pathway analysis using Structural equation modeling (SEM) to understand the direct and indirect impacts on Covid-19 due to socioeconomic indicator (SEI) change. Over the course of 3 months, through purposive sampling technique, a data from 2532 respondents is collected out of which 62% are female and 38% are males. Our results show that the probability of overall impact of Corona (IES-R score) increases 16% of SD (standard deviation) for every one SD decrease in socioeconomic indicators. For the identification of likely interventions we use SEM via four latent variables including, financial stress, sleep quality, mental health and social capital. Covid-19 is a pandemic whose end is unforeseen hitherto. Just like any other natural calamity, there are going to be far reaching social, economic and psychological impacts on a common man. World needs to better understand the mechanism of socioeconomic actions that influence overall negative impact of Covid-19 on the people in order to reduce the burden of these impacts.