It’s a common assumption in economics that people are long-term thinkers about spending. They consume at rates pegged to their expected earnings over a lifetime—their permanent income, as Milton Friedman put it in the late 1950s. A banker’s dining habits are thus influenced as much by her accumulated wealth and year-end bonuses as by her salary. Conversely, a senior manager in a growing industry may be unlikely to cancel a vacation after losing his job if he sees it as a temporary setback.

As influential as the permanent income hypothesis, or PIH, has been for more than half a century, it doesn’t always fit the data. Hyun Yeol Kim, a research fellow at the Korea Institute of Finance, has identified one group of Americans who tighten their belts even though they have every reason to expect that long-term income gains are around the corner. The findings may complicate economists’ understanding of consumers’ spending behavior.

Using the NielsenIQ Consumer Panel Data housed at Chicago Booth’s Kilts Center for Marketing, Kim analyzed the incomes and expenditures of Americans who moved across counties (or made similar moves in terms of distance, as measured by zip code). She finds that people lowered their spending in the year ahead of a move by an average of 6 percent compared with nonmovers. Then they jacked up purchases by 9 percent more than nonmovers in the year after relocating. Meanwhile, the relative earnings of those who relocated rose by 3 percent over the entire period.

Friedman’s permanent income hypothesis does not foretell that kind of swing. “Migration usually produces an increase in future earnings,” Kim says. “The standard PIH models predict people should raise their spending ahead of their move if they already know their income is set to rise. Instead, the evidence shows them only starting to spend more in the year after the move.”

The NielsenIQ consumption data come from 40,000–60,000 US households that scan receipts after every shopping trip. Collection periods last three years on average, and because people share their locations and other personal information such as income, Kim was able to identify about 6,000 migration cases where spending and income were observable at least 12 months before and after a move. All the relocations took place between 2004 and 2019. She supplemented income information with data from the US Census Bureau’s Survey of Income and Program Participation.

Given the findings, Kim suggests that states hoping to entice people might make price data as well as information about local financial benefits more readily available. 

Kim argues that the data most likely understate movers’ spending swings because they heavily reflect grocery purchases, which fluctuate less in response to income changes than services, for example. Nor do the changes represent people simply clearing out the pantry ahead of moving day and restocking once in a new location. She finds that spending falls and then rises for both perishable and nonperishable items. In addition, in the year before a move, people choose less expensive items than nonmovers and buy more expensive goods afterward. That pattern suggests precautionary saving rather than inventory clearing.

Given the mismatch with Friedman’s theory, Kim developed a model that acknowledges individuals might reduce spending and restrict borrowing as a hedge against risk—in this case, income risk. Kim observes that in her sample, household income fluctuates considerably around the time of a move—falling by 4 percent year over year for movers compared with nonmovers in the four months ahead of a relocation, often because they take time off for the move. Income then rises after the move, driven by higher wages in the new location.

She notes, however, that income risk does not entirely explain the spending swings. Movers might also be saving up to protect themselves against relocation costs they cannot predict, or the risk of not finding a new job or of a new job falling through.

“The model demonstrates that hedging against income risk explains about 40 percent of the postmove precautionary spending,” Kim says. “But people might also be reacting to consumption uncertainty. For example, different maintenance costs for cars or homes in a new climate, or a different mix of local amenities, such as public swimming pools or childcare, that might change their private expenditures.”

Given the findings, Kim suggests that states hoping to entice people might make price data as well as information about local financial benefits more readily available. “This approach would acknowledge that there’s a cost to moving that goes beyond hiring movers,” Kim says.

The Internal Revenue Service estimates that 5.5 percent of US residents relocate every year; the US Census Bureau and Kim’s analysis put the figure closer to 3–3.5 percent. Her work supports the conclusions of studies that consider relocation as an investment, as it tends to create short-term expenses and loss of income that higher long-term income later more than makes up for.

Because Kim was not able to access wealth or credit data about the people who moved, she could not test another line of investigation—whether migrants save ahead of a relocation because they need the cash to fund the move itself. “People might be living more hand to mouth than we realize,” Kim says.

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