As life becomes more expensive, energy prices and supply disruptions tend to dominate the headlines. But fourth-year economics PhD student Qingyuan Fang studies a stealthier culprit: fiscal inflation. Fang, who last year won the Department of Economics’ Clarence M. Guggenheimer Award for outstanding performance for work toward his PhD, examines what happens when governments spend big without a clear plan to foot the bill.

What exactly is fiscal inflation?

Many people think high inflation comes from supply issues. But there’s another driver, too. When governments increase their spending without raising taxes, people expect higher future inflation to erode the debt. This creates what we call “fiscal inflation,” the component of inflation that the central bank allows to stabilize the part of government debt that is not backed by tax revenues.

You studied fiscal inflation in the U.K. What did you discover?

My coauthors and I found that fiscal inflation was high in the 1960s and ’70s, then remained low between the ’80s and early 2000s. The 2016 Brexit referendum was a turning point. After that, agents in our model increasingly saw a portion of government spending as “unfunded,” which pushed fiscal inflation positive. And although supply shocks drove the initial COVID inflation spike, it was government spending packages during the pandemic which made inflation persistently high.

Why should policymakers care about fiscal inflation?

In deep downturns, coordination between monetary and fiscal policy can prevent deflation and speed recovery. But policymakers should be aware that even if headline inflation declines, fiscal inflation, which tends to be persistent, could still be elevated. Along with targeting inflation, keeping prices stable also requires fiscal credibility.

What’s your other research about?

I’m comparing how households and professional forecasters predict economic uncertainty and risk versus machine-learning algorithms’ predictions. And it turns out that even experts overreact to uncertainty-increasing events in the news, and then become overconfident when uncertainty decreases. I aim to show that AI can correct the systematic mistakes of human forecasters and lead to better policymaking.

What drives your research?

Expectations about the future shape every major decision we make. Understanding how people form these expectations, and the biases they may have, can help policy-makers design better policies for our increasingly uncertain world.