When wind speeds mislead: What hurricanes really do to local economies—and how real-time data can shape policy

Hurricane categories make headlines, but for local businesses and workers, the economic impacts don’t always follow. While much research has focused on household preparedness and physical damage, far less is known about how local business activity responds to hurricanes across industries.

In our study, we use anonymized Mastercard transaction data aggregated at the ZIP-code level to track merchant sales before, during, and after 21 Atlantic hurricanes between 2017 and 2024, covering nearly 8,000 affected ZIP codes. Although our analysis centers on the U.S., the mechanisms and policy lessons apply broadly, especially in developing economies that face frequent extreme weather shocks yet lack timely data.
 

How Hurricanes Hit Local Economies

On average, storms reduce local merchant sales by 12.4%, equivalent to about $1.38 billion in lost revenue per storm. The timeline is the following:

• Preparation (five to two days before local impact): Sales rise ~3.3%, driven by stockpiling and travel adjustments.
• Impact (from one day before through two days after): This is the economic cliff, with average daily sales drop by ~39.3%.
• Recovery (three to ten days after): Sales remain ~7.6% below baseline, with activity returning to normal around day ten. By the third week, both sales and the number of active merchants are back to pre-storm levels.
   

Figure 1: Event Study for the Impact of Hurricanes on Local Merchants

Is Spending Simply Shifting Elsewhere?

No. We find no evidence of substitution across channels or regions. Sales at major online retailers and in nearby areas also decline, and ATM withdrawals fall by more than 10%, indicating a real reduction in consumption rather than a shift in where or how people spend.  
 

Does Storm Intensity Matter?

Not as much as expected. While stronger hurricanes (Categories 3–5) trigger steeper immediate drops in sales, the total economic losses over the full event window are statistically similar to those from lower-intensity storms. Even so-called “moderate” storms can cause substantial net economic losses, challenging the idea that only major hurricanes deserve policy attention.
 

Who Bears the Brunt?

Impacts differ sharply across industries and business types:

• Most affected: businesses that depend on in-person interactions, such as restaurants, retail stores, and home improvement merchants, experience the most severe and prolonged sales declines, driven by anticipatory closures, power outages, and supply chain disruptions.
• Mixed effects: gas stations see pre-storm sales spikes, while hotels experience a mix of canceled leisure bookings and increased demand from displaced residents and emergency workers.
• More resilient: businesses with a larger share of online sales consistently experience more minor disruptions, underscoring the importance of digital readiness as a tool for economic resilience.

Local vs non-local demand: consumer spending declines less than merchant sales, suggesting losses are partly due to reduced demand from tourists and other non-local customers.
 

Implications for policymakers

Current relief frameworks often allocate aid based on storm category. Our findings suggest shifting toward impact-based, data-driven responses:

1. Move beyond storm categories. Because total economic losses do not differ significantly across storm intensities, thresholding support solely on wind categories risks missing substantial needs after “moderate” storms. Rapid, data-driven assessments can better target relief to actual local disruptions.
2. Target by sector and business type. Restaurants, retail stores, and home improvement merchants face the sharpest and longest disruptions. Independent stores often recover more slowly than chains. Relief programs should prioritize these vulnerable sectors and businesses, aligning with expected recovery timelines.
3. Invest in digital resilience. Firms with strong online capacity are less exposed. This points to the need for policies strengthening digital commerce. Complementary investments in reliable power and connectivity can further reduce vulnerability.
4. Don’t count on substitution.  Local downturns are not offset by spending elsewhere or online. Demand collapses are real and require timely and targeted support.
    

A practical path forward

Although our study focuses on U.S. hurricanes, the mechanisms are widely relevant. In many developing economies facing frequent storms, floods, and other weather-related shocks, our findings highlight the importance of targeting recovery aid based on actual economic impacts rather than meteorological metrics alone. Support should prioritize service industries and small, independent businesses, which are most vulnerable to sudden revenue losses and operational disruptions. At the same time, investing in digital readiness is essential to help businesses withstand and recover faster from future hurricane shocks.