Carl Bialik
Data Science Editor
April 2020
Q1 2020
For the Q1 2020 Yelp Economic Average report, we’re using a new methodology that tracks several indicators on a daily basis, and extends into Q2. For more on the methodology for this report, click here.
The economic changes from the first quarter of 2020 were unlike anything we’ve ever seen. In a period of about 15 days as the nation reacted to the threat of the coronavirus pandemic, the economy transformed as much as it had in Yelp’s prior 15 years of operation, combined. So rather than share one indicator of economic strength for the whole quarter, as we normally report in our Yelp Economic Average, we are sharing several indicators that track what happened throughout the quarter, from its typical first 10 weeks to its final three weeks of upheaval. We’re also tracking several indicators through the first 19 days of the second quarter, as the pandemic’s effects continued past March 31.
The effects of the pandemic were felt everywhere, abruptly and intensely: Businesses closed their doors nationwide, with closure rates abruptly increasing by 200% or more in metros and states around the country; consumer interest in all local businesses plummeted, by 50% or more in many categories, in a matter of a week or two; businesses overhauled their operational models and workers and consumers changed lifelong habits overnight to protect themselves and their neighbors – trends demonstrated in our series of periodic reports about the coronavirus’s economic impact. The overall impact on major local economies amounted to four or more times the magnitude of the biggest prior economic shocks they’d experienced in the last decade, such as major hurricanes.
Within the broader transformation, businesses of all kinds were affected differently as the threat of the virus evolved. In January, interest in Chinese restaurants declined from stigma and fear of the virus, then increased (up 63% in share of seasonally adjusted consumer actions among similar businesses since March 1) when delivery and takeout reigned. As people started staying home, consumer interest surged for gun stores (up 191%) and pet breeders (up 130%), while cocktail bars (down 32%) and yoga studios (down 47%) suffered.
This Yelp Economic Average report is also about public health and how local businesses and consumers pivoted. In healthy times, local businesses bring people together. In times when a deadly virus looms and people must respect social distancing to save lives, those same local businesses suffer and in many cases are forced to close (even permanently) for the greater good.
People sacrificed by pausing their businesses, jobs, and, in turn, their local economies to slow the spread of the pandemic, turning what was a steady, low rate of business closures at the beginning of March into a much higher rate throughout the country.
The rate of closures nationwide, and so many other indicators of local economic strength, sharply turned during the second week of March, as warnings gave way to shelter-in-place orders and other governmental measures to curb the pandemic. While infection rates and risk factors such as public transit and population density differ markedly in metros across the country, the pattern was largely the same everywhere: On or around March 16, closure rates increased by two to four times, and have remained at that new, elevated rate ever since.
In some cases, business owners decided to close their doors for good. Far more often, they’re closing indefinitely and informing their customers via Yelp, which allows us to detect and count the closures.
As of April 19, more than 175,000 businesses have shut down – temporarily or permanently – with the Los Angeles metro area hit the hardest with the largest number of closed businesses since March 1, followed by New York and Chicago. Seattle and San Francisco have the highest rate of business closures, as a share of all businesses, among major metros, while Philadelphia and Miami have the lowest rate of business closures among major metros.
Every type of business was affected. The businesses marked as closed include more than 48,000 shopping establishments, 30,000 restaurants, and 24,000 spas and other beauty businesses.
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Businesses Closed on --
How does this pandemic compare to the economic impact of other devastating events? From a purely economic perspective, based on consumer interest in local businesses, New Yorkers experienced the equivalent of eight Hurricane Sandys: a bigger magnitude of decline, lasting for far longer. Houston’s metro area was hit by the economic equivalent of four Hurricane Harveys, Miami’s by four Irmas, and New Orleans’s by four Isaacs. We found that hurricanes were the closest precedents in economic impact, with the largest immediate effect among other economic shocks.
While some polls have shown Americans’ attitude toward the virus differed by political affiliation, and protests calling for reopening the economy seem largely partisan, our data suggests that nothing seems to align local economies like a virus that recognizes no political boundaries. By state, metro, or county, businesses and consumers responded in much the same way, whatever the differences in local conditions, or which political party holds sway over local offices.
Comparing how consumers reacted to the pandemic in counties that voted for Donald Trump in 2016 to those that voted for Hillary Clinton, his main opponent, shows little significant difference. And the share of the vote won by Trump in each county did little to help predict how and when consumer interest fell for nearly all significant types of businesses.
Consumer activity turned steeply downward across the board just as businesses began closing nationwide. The exact date and rate of steep decline in searches varied by state, depending in part on what people were instructed by elected officials. In Washington state, site of the first confirmed coronavirus cases nationwide, the transformation began on March 6; in remote Hawaii, it began 10 days later. Everywhere, though, it happened fast—moving from the old level to a new level in a matter of days. And, notably, consumers often were out in front of their leaders: New Yorkers’ search behavior started to reflect the new reality on March 11, two days before Californians’ and 11 days before Gov. Andrew Cuomo ordered his state’s residents to stay home. News reports of the pandemic’s impact appeared to spur action even before many local policy changes did: On March 11, the NBA suspended its season after a player tested positive for the virus, actors and married couple Tom Hanks and Rita Wilson announced that they had tested positive, and the World Health Organization declared the outbreak a pandemic.
Important Dates
Education Facilities Closed
Non-Essential Services Closed
Stay-at-Home Order Issued
Yelp Search Activity
The data shows how some businesses were unusually well-suited to meet the needs of customers stuck at home, and how others adapted with virtual services, delivery, and even shifts to their business model: for example restaurants operating grocery delivery services. Businesses that enabled people to carry on with their lives from the confines of their homes were in great demand, from cosmetics sellers for people unable to attend their regular beauty and wellness appointments, to community-supported agriculture services sending boxes of produce straight from farmers to consumers’ front doors. Pizzerias, fast-food restaurants, and chicken-wing joints were able to shift faster to a world where dining at home via delivery and takeout dominated. As people stopped dining out at restaurants, the ratio of searches for dining in on restaurant food to dining out increased by 300 times in just a couple of weeks. At the same time, many high-end restaurants and cocktail bars quickly pivoted to preparing sturdy, portable versions of their offerings as well. The trend didn’t stop with restaurants – photographers taking portraits of clients from a safe distance, teachers of arts and fitness using live video to conduct classes, and party planners going virtual all have found ways to keep customers and find new ones.
Among broad business categories, the ones hit hardest include bars and other nightlife businesses (down 81% in page views, reviews, and photos since March 10), salons and other beauty businesses (down 77%), and hotels and other travel businesses (down 75%). Restaurants are down 52%.
Consumers changed as rapidly as businesses. Within days they’d moved their consumption of restaurant food from the corner booth to their corner nooks and found ways to support each other and businesses by ordering cake deliveries for birthdays, and flowers at near-Valentine’s Day rates—with particularly elevated interest in the Northeast and Great Lakes regions. Through Yelp searches, people are showing how they’re interested not just in getting necessities delivered, but also in taking care of friends and family emotionally. While some of the increase in flower delivery appears from business reviews to be a result of the tragic human toll of the virus, far more of the recent reviews about flower delivery mention celebrations, indicating people want to offer some tangible presence for each other in times of joy even when they can’t be there in person.
People have also shown their support for local businesses and each other through Yelp reviews, by noting the health measures taken by establishments and by their fellow consumers. Mentions of sanitizer, gloves, and phrases related to keeping physical distance from each other began to rise right when the other economic shifts took hold, in the second week of March. Mentions of masks rose steeply after the federal government recommended their usage for all Americans. But their rate of appearance in reviews had been rising steadily beforehand, showing that reviewers were already keeping this protective measure in mind, and noting its use by workers, even before the White House made it official. Reviewers also increased their usage of several phrases touching on support of businesses: supporting restaurants, supporting their favorite businesses, and supporting locally. The effect of recommendations to keep physical distance from each other surfaced in one disquieting shift in reviews: the steep decline in mention of service staff, dropping off to mentions by under 2% of reviewers from a typical rate of over 10% – indicating a decline in consumers interacting with service staff and instead adopting other ways of transacting, such as curbside pick-up or contact-less delivery.
Hanging over all of this is the pervasive presence of the pandemic. By the end of March, roughly one in six reviewers each day mentioned it by one of its names, a staggeringly high rate seen for few other types of phrases in our reviews.
Since the inception of our quarterly report, there’s always been something new to share: Where are the nation’s boomtowns, what’s up with auto businesses, and why California’s struggling. These shifts are often in the range of 1% to 2%, similar to many regularly scheduled government reports. While that might sound boring, it’s not. It’s the sign of a well-functioning ecosystem.
As business owners and consumers build toward restoring a strong local economy, they’ll likely need to continue to adapt. As our data shows, they’ve already proven themselves adaptable. On the front lines or holding down the home front, they’re doing what it takes to protect themselves, their neighbors, and their colleagues.
—Daniel Gole, Danyang Kong, Shichao Ma, and Amy Shapiro contributed to this report
Business Closures
On each date, starting with March 1, we count U.S. businesses that were open on March 1 and were closed on that day. Closure can be permanent or temporary, and is signaled by a business owner marking the business as closed, including by changing its hours or through a Covid-19 banner on its Yelp page. Closure counts are likely an estimate of the businesses most impacted, with many others not counted because they remain open with curtailed hours and staffing, or because they have not yet updated their Yelp business pages to reflect closures. Closures are counted by state, metro area, and category; some businesses are in more than one category. One-day closures that appear to be unrelated to the pandemic, such as for Easter, are not counted. The counts of closures shown in the map on each day are averages of the prior seven days’ closure counts.
Local Economic Impact
We measure daily consumer interest, in terms of daily U.S. counts of a few of the many actions people take to connect with businesses on Yelp: viewing business pages or posting photos or reviews. By metro area (core-based statistical areas), we compare daily consumer interest to the level expected based on a forecast model accounting for seasonal, day-of-week, holiday, and other underlying trends.
We then use this model to identify anomalous events that cause the actual activity to deviate significantly from the expected level. The beginning of each event is defined to be one day before the day on which user activity first deviates more than 10% from the normal level. We say the event’s impact has ended when activity has returned to roughly the expected level for several days. We start tracking the effect of the pandemic on March 9 for each location.
The total impact of the event is the total difference between expected and actual activity for the duration of the event’s impact. For example, if during a hurricane we see a daily average of 25% less activity than expected over 10 days and during the pandemic we see a daily average of 50% less activity than expected over 40 days, we would say that the pandemic had eight times the economic impact of the hurricane: twice the daily impact, for four times as many days.
We are estimating the economic impact based solely on the short-term decline in consumer activity. The structural damage often caused by extreme weather events can have major additional, long-term effects on local economies that aren’t measured here.
Consumer Interest Changes By Political Party
We measure consumer interest by page views, and political affiliation by 2016 presidential election results, using data from MIT Elections Data and Science Lab. We aggregate consumer interest and political affiliation at the county level and then by business category. We compute the relative changes of page views with respect to March 10. Then within each category, we compare these relative changes along the temporal and the political dimensions.
When Consumers Changed Their Behavior
The turning points for consumer behavior by state is measured using seasonally adjusted daily search volume. The overall period of change is the period in which daily search volume was continuously falling. The period of rapid change shown in the chart is the period between when search volume fell to 25% below its initial level, and when it reached within 25% of the new level.
Consumer Interest By Business Category
We measure daily consumer interest, in terms of seasonally adjusted daily U.S. counts of a few of the many actions people take to connect with businesses on Yelp: viewing business pages or posting photos or reviews. We start with the biggest U.S. categories by consumer actions. Among those, we select the biggest gainers and biggest decliners in terms of their seasonally adjusted share of all root category consumer actions since March 1. Then we choose representative ones to show the trend, which we’re charting from March 1 through April 19.
Searches and Reviews
We look for phrases whose frequency in daily and weekly mentions in search queries or users’ reviews changed significantly, grouping related terms. For reviews, we evaluate the increase or decrease in frequency of users who use a specific word or phrase since February 2020. For search queries, we compare frequencies of search terms to all queries over the same weeks this year and last year to identify the largest changes. We aggregate searches by state to identify trends in specific localities, and compare the frequency of mentions of celebrations to the frequency of mentions of illness or death in recent florist reviews, to understand the reasons for increased search frequency in flower delivery.