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RAPAPORT... Many U.S. consumers have reduced their spending due to the government shutdown, according to a special report conducted by the International Council of Shopping Centers (ICSC) and Goldman Sachs. While lower-income households, or those earning less than $50,000 annually, were more affected by the shutdown than those earning above $50,000, overall, two out of five consumers reported having scaled back spending.
Forty-seven percent of consumers with annual income of $35,000 or less were more likely to be scaling back spending, compared with 32 percent of those with incomes of $100,000 or more. Of those who have reduced spending, the majority said that the amount was “a little,” while the remainder indicate the degree of reduction was “considerable.”
Michael P. Niemira, ICSC's vice president of research, said, “As congressional leaders optimistically predict a budget deal may soon be reached, it is clear that the fallout of the past two-week impasse in Congress has affected consumers’ willingness and maybe their ability to spend. Hopefully, if the end of the government shutdown truly is in sight, this is likely to restore consumer confidence quickly and well ahead of the holiday season.”
Meanwhile, U.S. chain-store sales rose 1 percent year on year for the week that ended on October 12, according to ICSC and Goldman Sachs. However, on a weekly basis, comparable-store sales contracted 0.7 percent.
“Sales weakened across the board by store segment — partially a result of the drag from the government shutdown,” Niemira said, adding it is difficult to predict how quickly and strongly consumer spending will respond when the deadlock ends.
ICSC Research anticipates that comparable-store sales will increase between 3 percent and 4 percent in October. The weekly chain-store sales snapshot is produced by ICSC and Goldman Sachs to measure U.S. nominal same-store, or comparable-store, sales while excluding restaurant and vehicle demand. The weekly sales index is presented on an adjusted basis to account for normal seasonal and other data anomalies.
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