Variability in Aggregate Personal Income Across Industrial Sectors During COVID-19 Shock: A Time-Series Exploration

Abstract

This study explored the variability in Aggregate Personal Income (PI) across 13 major industrial sectors in the US during the COVID-19 pandemic. Utilizing time-series data from 2010 Q1 to 2019 Q4, we employed Autoregressive Integrated Moving Average (ARIMA) models to establish baseline trends in Personal Income (PI) before the pandemic. We then extended these models to forecast PI values for the subsequent 14 quarters, from 2020 Q1 to 2023 Q2, as if the pandemic had never happened. This forecasted data was compared with the actual PI data collected during the pandemic to quantify its impacts. This approach allowed for the assessment of both immediate and extended effects of COVID-19 on sector-specific PI. Our study highlighted the resilience of PI in sectors like Utilities, Retail, Finance, Real Estate, and Healthcare, with Farming showing an early recovery in PI, despite significant initial setbacks. In contrast, PI in Accommodation and Food Services experienced delayed recovery, contributing significantly to the overall impact variance alongside Farming (53.26\% and 33.26\% respectively). Finance and Utilities demonstrated positive deviations, suggesting a lesser impact or potential benefit in early pandemic stages. Meanwhile, sectoral PI in Manufacturing, Wholesale and Education showed moderate recovery, whereas Construction and Government lagged in resilience. The aggregate economic impact, initially negative at -0.027 in 2020 Q1, drastically worsened to -1.42 in Q2, but improved by Q4, reflecting a broader trend of adaptation and resilience across all the sectors during the pandemic.