Data for Good

After the Storm: How Data-Driven Insights Can Help Build Resilience

Researchers study consumer spending after a hurricane to shed light on how to help people and businesses better prepare for the next one.

September 18, 2018

There has been a big debate around the world about the changing weather patterns. Ever since experts have been keeping track of the data, the latest decade compared to the previous one has witnessed more extreme weather days, hurricanes, cyclones and storms.

As the events become more frequent, economists are starting to look at their economic costs.  There are numerous anecdotes of businesses (gas stations, motels) charging consumers excessively high prices in the wake of a disaster. It’s important for policymakers to understand how people respond to disasters so they can make informed, evidence-based decisions to help communities in real time.

A new program from the Mastercard Center for Inclusive Growth is offering some of the top minds in research an opportunity to become Data Fellows and use Mastercard’s anonymous and aggregated transaction data to better understand how to help build more resilient communities and address other economic development issues. One of the first projects will analyze the impact of Hurricane Harvey in 2017. Harvey inflicted $125 billion in damage, primarily from catastrophic flooding in the Houston metropolitan area. It ended a 12-year record span in which no hurricanes made landfall at such an intensity in the United States.

Among the questions we’ll be exploring:

·      Did the survivors of Hurricane Harvey and similar natural disasters become more reliant on credit after the storm, and did they use a card to manage their finances and better plan for and cope with the devastation?

·      Were they able to afford food and emergency medical expenses?

·      Were there differences in spending patterns between suburban and urban areas?

·      Was there an increased reliance on digital payments? 

·      How did spending patterns change over time? Was there more or less spending at certain types of stores or businesses? 

·      As demand changes, how are businesses responding to the increased need for inventory ahead of a disaster? 

·      With the changing demand, do merchants price-gouge or can they adjust their inventory ahead of the disaster?

A big advantage of a data-driven analysis is the ability to compare spending and consumption patterns across different regions, communities and cities affected by the hurricane. Researchers can derive insights and identify trends in consumer segments’ consumption patterns—are certain merchant categories and products types, for example, in more demand during a disaster or are there certain lessons for first responders about the needs of local communities? We are also able to compare spending patterns in neighborhoods that were affected by a natural disaster with those that were unaffected. In addition, we are able to do a before-and-after comparison. Therefore, we can directly measure the impact of the natural disaster on the population living in the disaster-affected neighborhood by comparing them to a control group that was unaffected, and also by comparing the disaster-affected group before and after the disaster. We hope that any changes in consumption patterns in disaster areas, such as greater spending in a category such as medical services, for example, will offer a picture of what communities need in the aftermath of severe weather.

More specifically, the above framework will allow us to learn how spending patterns change after a hurricane among people in its path. Specifically, we can better understand shifts in the types of products purchased. Do people buy more goods and services to protect them from hurricanes? Do they spend more on medical expenses?  Natural disasters may also create a permanent shift in payment forms used. For instance, consumers overall might shift to cards and digital payments as opposed to using cash. Although we do not have data on cash, by comparing consumption patterns of the affected and control groups, and more specifically of the affected groups before and after a hurricane, we can see trends toward the greater use of credit/debit payments in the long run after a hurricane. This would signify a shift to digitization. It is possible that a shift does not occur across all spending categories, but rather only in certain subcategories. This is useful for better understanding which categories have experienced a shift to digitization.

Anonymized and aggregated consumer transaction data has limitless applications for development research. By allowing researchers to use data-driven insights, the Mastercard Center for Inclusive Growth is showing how these very insights can advise policymakers and researchers and foster positive social impact.         

Authors:

Sumit Agarwal is professor of finance at the National University of Singapore. Leora Klapper is a Mastercard data fellow and lead economist in the finance and private sector research team of the Development Research Group at the World Bank. Manu Bhardwaj, our late director of research and insights, assisted with the article.