Abstract
The COVID-19 pandemic has profoundly impacted the economy and human lives worldwide, particularly the vulnerable low-income population. We employ a large panel data of 5.6 million daily transactions from 2.6 million debit cards owned by the low-income population in the U.S. to quantify the joint impacts of the state lockdowns and stimulus payments on this population’s spending along the inter-temporal, geo-spatial, and cross-categorical dimensions. Leveraging the difference-in-differences analyses at the per card and zip code levels, we uncover three key findings. (1) Inter-temporally, the state lockdowns diminished the daily average spending relative to the same period in 2019 by $3.9 per card and $2,214 per zip code, whereas the stimulus payments elevated the daily average spending by $15.7 per card and $3,307 per zip code. (2) Spatial heterogeneity prevailed: Democratic zip codes displayed much more volatile dynamics, with an initial decline three times that of Republican zip codes, followed by a higher rebound and a net gain after the stimulus payments; also, Southwest exhibited the highest initial decline whereas Southeast had the largest net gain after the stimulus payments. (3) Across 26 categories, the stimulus payments promoted spending in those categories that enhanced public health and charitable donations, reduced food insecurity and digital divide, while having also stimulated non-essential and even undesirable categories, such as liquor and cigar. In addition, spatial association analysis was employed to identify spatial dependency and local hot spots of spending changes at the county level. Overall, these analyses reveal the imperative need for more geo- and category-targeted stimulus programs, as well as more effective and strategic policy communications, to protect and promote the well-being of the low-income population during public health and economic crises.
Highlights
Spatial association analysis was employed to identify spatial dependency and local hot spots of spending changes at the county level. These analyses reveal the imperative need for more geo- and category-targeted stimulus programs, as well as more effective and strategic policy communications, to protect and promote the well-being of the low-income population during public health and economic crises
While the latest research has focused on the impact of human mobility restrictions on the virus spread, disparities in COVID-19 transmission, and the environmental and macroeconomic consequences under the COVID-19 lockdowns [4,5,6,7,8,9,10,11,12,13], our research focuses on the dynamics of micro-level consumer spending during the pandemic that is of vital importance to the economic recovery
Revolving around the above key research questions, we find that (RQ1) inter-temporally, the lockdowns diminished the daily average spending relative to the same period in 2019 by $3.9 per card and $2,214 per zip code, whereas the stimulus payments elevated the daily average spending by $15.7 per card and $3,307 per zip code; (RQ2) spatial heterogeneity prevailed, for instance, Democratic zip codes displayed much more volatile dynamics than Republican ones; (RQ3) across 26 categories, the stimulus payments promoted spending in those essential to the population’s well-being, yet increased spending in undesirable categories such as liquor and cigar; (RQ4) the discovered geo- and category-heterogeneities call for more geo- and category-targeted stimulus programs to protect the low-income population during the public health and economic crises
Summary
We implement a difference-in-differences (DID) model to estimate the effects of the lockdowns and stimulus payments on spending. The dependent variable is either the dollar change or percentage change. The model captures the shift of the dependent variable before versus after the initial lockdown on March 19, 2020 (first treatment), and before versus after the distribution of the stimulus payments on April 11, 2020 (second treatment). The empirical specification for the dollar change goes as follows. DYzd 1⁄4 b D1 þ g D2 þ yzm þ zd; ð1Þ. Where ΔYzd is the dollar change for zip code z on a day d, that is, ΔYzd = Yzd,2020 − Yzd,2019. We have estimated the same model for the percentage change, in which case
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