Coronavirus (COVID-19) has wreaked havoc across the American economy. The virus has also demonstrated the importance of real-time data to accurately understanding the situation. Without it, leaders are flying blind. We’ve been closely watching our own real-time data here at Homebase and have noticed three important trends.
Businesses are finding ways to operate, even in states that haven’t done full-scale reopenings
Let’s take Georgia (one of the first states to reopen) and California (one of the last states to ease lockdown restrictions) as examples.
On April 6, 45% of Georgia businesses were closed, similar to the 47% of California businesses that were closed. Georgia began reopening restaurants on April 27 (a few days after reopening gyms and beauty / personal care businesses). At that point, only 36% of Georgia businesses were closed, compared to 45% for California.
Most interestingly, while the closure rate in Georgia has fallen to 26% (decrease of 19% compared to April 5, the closure rate in California has also fallen to 34% (a decrease of 13%). So while Georgia is certainly accelerating business openings faster than California, even states that remain under stricter lockdown orders are finding ways to operate.
Some industries were impacted more than others
Among industries that Homebase tracks, beauty and personal care businesses (think barbershops, nail salons, etc) felt the impacts the hardest. At the industry’s low point on April 12, 95% of the beauty and personal care businesses in Homebase’s dataset were not open. While that number has improved to a 57% closure rate, the closure rate for all Homebase businesses is 29%.
While large corporations have largely been able to avoid mass layoffs, the same can’t be said for small businesses. Most hourly workers cannot work from home. As a result, they felt the impacts of lockdown orders at a disproportionate level.
Our data is publicly available so that policy makers and academics can understand the impact of coronavirus on small businesses. If you’re interested in more granular data, or have questions about the dataset, email us at email@example.com.