Primary Submission Category: Machine Learning and Causal Inference
From Paychecks to Plates: Tracing the Impact of Layoffs on Food Access in the United States
Authors: Kiet Le, Thanh Nguyen, Nina Rutledge,
Presenting Author: Kiet Le*
The COVID-19 pandemic resulted in the numerous closures of businesses, caused widespread job losses, and negatively impacted the food security of US families. This study measures the extent to which food expenditure, food consumption, and the quality of food consumed by individuals were affected by layoffs. We estimated the treatment effect of involuntary job losses on these food security items using difference-in-difference regression, logistic regression, and augmented inverse propensity weighted estimation, and found that involuntary job losses caused Americans to be more vulnerable to food insecurity. Particularly, we found statistically significant evidence suggesting that layoffs caused people to eat less and switch to lower-quality food and unbalanced meals. The main purpose of this study is to shed light on the immediate economic implications of job loss on food security, which is having a ripple effect across public health, community development, and employment policies. We emphasize the importance of the social safety net and welfare programs that can provide immediate assistance to those who are directly impacted by layoffs.