Originally published in Business Insider
By Randy Koch
Emerging from COVID-19 lockdowns is a difficult balancing act, to be sure, but this is no time for guesswork.
Over the past month, too many of our local and federal policymakers have made important decisions about the reopening of our economy without sufficient consumer spending data. This haphazard approach has led to polarization and recriminations as we are already seeing a resurgence of cases in more than 30 states.
If the country is going to successfully tackle the virus and reopen our economy, policymakers should make decisions based on data — not politics, not bias — that balances both the economic benefits and health risks.
Re-opening is not a one-size-fits-all process — each state and local economy has its own density and risk factors to consider. So states are greenlighting their economies in a patchwork fashion, often on a county-by-county basis. On the surface, that can allow for innovation and experimentation.
But we’re trying to get ourselves back to economic and physical health as quickly as possible, and trial-and-error is not the smartest way to accomplish that task.
What are people actually spending their money on?
Foot traffic, cell phone location data and anecdotes are often used to gauge current economic activity. But the mall can be crowded without the shoppers spending very much.
The trends within debit and credit card data make it possible to see huge swaths of real-time consumer spending. If America’s states and counties were to pair this economic data with even the simplest health risk assessments for activities (how many people gather together, how close do they get, how much touching or interaction between people is usually involved), we could have a more thoughtful approach for reopening.
The goal is to see where people want to spend money and compare what activities pose the most risk. That way reopening states could get the most bang for their reopening buck.
We need a common structure — a framework — to aid in decisions on reopening. This would provide a real-time data lens on what economic activities are simultaneously least COVID-19-risky and most economically powerful, rather than those that risk new outbreaks but don’t add much fuel to the recovery engine.
The roadmap, as seen in the chart below, contains the two most important data vectors for policymakers: The vertical axis represents consumer spending impact from 1,000+ banks. The horizontal axis is “health risk,” combining 26 health analyses sources that sought to capture top epidemiologists’ views, as well as those of other healthcare experts.
We placed 19 consumer spending categories into the analysis, based on their respective economic benefits and health risks. But again, it’s the methodology I want to focus on. Officials can adjust the health risks in their geographies as conditions dictate, so long as they’re accurately measuring economic impact.
In the analysis, retail stores rank higher in terms of this combined safety and economic impact score, while sit-down restaurants are a little riskier – and just a little less powerful in terms of consumer spending.
Based on this data, we can start to build a rough procedure that can be adapted as needed:
- We should reopen those sectors that are in the High Value, Low Risk quadrant. This is non-controversial, and should be done by all states.
- Based on population density and COVID-19 incidents, governments should look into selectively re-opening activities in the High Value, Higher Risk areas to help boost economic activity but with a keen eye on doing everything possible to keep exposure to the virus at a minimum.
- Leaders should proceed cautiously before opening businesses in the Low Value, Higher Risk quadrant, and monitor them closely once opened.
A policymaker might use this rubric to decide that economic activity in the green High Value, Low Risk zone should be phase one when it comes to activities brought back on line. The yellow categories should be phases two and three and businesses in the red quadrant really need careful analysis before the public is allowed to re-engage with them.
Using this assessment, state policymakers might start to think about whether they’ve taken full advantage and opened up the High Value, Low Risk business categories from lodging to retail stores.
Likewise, policymakers would be wise to think hard about reopening business categories in the Low Value, Higher Risk quadrant because the ability to social distance and take other precautions are more difficult and the pay off in economic benefit is low as well.
Consumers are able to use the roadmap as well. As a guide it may help them understand it is far safer to patronize a restaurant’s outdoor patio than it is to go to a crowded indoor movie theater. And they would also be better able to understand the decisions, which can otherwise seem arbitrary.
Keeping people safe using data, not guesswork
In an opaque and arbitrary environment, pandemic decisions become politicized — to the point where public health officials are abandoning their posts due to angry public reactions to their efforts.
Is the roadmap flawless? Of course not. But the approach is. This analysis can be helpful in another important respect: providing an ongoing scorecard. Nobody knows how different states will emerge from the COVID pandemic. There will likely be ups and downs, flare ups and second waves. And not all states are alike. For this reason, we do need an ongoing data-based means to measure and adjust, based upon the weekly updates from the health and economic sectors.
After all, in polarized times we need three things: current data, comparative data for context and trends, and simplicity for users.
Too many data sets and analyses during this pandemic are sketchy and time-lagged. Too many models are overly detailed and opaque.
I’ve heard from municipal policymakers about their frustrations when their discussions devolve into a technical debate over data rather than how to move the economic ball forward safely. This roadmap is an attempt to avoid those pitfalls.
In a final illustrative example, below are two charts that showcase consumer spending trends within the department stores and restaurant industries in four states compared to the rest of the country.
As you can see in the first chart, South Carolina reopened certain industries earlier than California and many other states and experienced significant economic gains from activities that had pent-up consumer demand. Given that South Carolina is now one of the states experiencing increased infection rates, the roadmap might have been helpful in providing a data-driven framework for governments and other entities to understand how to safely get the economic engine going again.
The same is true for Georgia in the second chart. New York has been much more cautious and the economic hardships have been more pronounced. A roadmap could have been helpful to both states’ policy makers.
Data, context and simple frameworks aren’t a magic prescription — but they are a means by which our traumatized and divided country might be able to forge some agreements and get some insights to create the lowest-risk “wins” possible.
The nation needs objective, data-driven analysis to safely reopen the economy and get our employees working again. We have the tools. Let’s use them.
Randy Koch is the CEO of Facteus.