A Whanganui family sitting down to dinner in 2014. According to Stats NZ, they spent $7,798 on food that year. The year before? $15,641. Same family. Same house. Half the spending.

This isn't a story about thrift. It's a story about how official data can suddenly flatline, and what that means when you're trying to understand whether your grocery bill is actually going up or whether it just feels that way.

The numbers tell a clear pattern: Whanganui food spending climbed steadily from $14,652 in 2010 to $15,641 in 2012 and 2013. Then in 2014, it dropped to a level not seen since 1993. (Source: Stats NZ, food-price-index-detailed)

Here's why this matters beyond Whanganui. When regional food price data breaks, it creates blind spots. Policy makers rely on these figures to track cost-of-living pressures. Community groups use them to justify food bank funding. Researchers need them to understand which regions are getting squeezed hardest.

A 50% drop in a single year doesn't happen in the real world. Whanganui didn't suddenly discover meal prep hacks or bulk-buying secrets that halved household food costs. Either the methodology changed, the sample size collapsed, or something broke in how the data was collected.

The problem is wider than one city. Regional food price data in New Zealand is patchy. Small centres get lumped together, sample sizes shrink, and sometimes collection methods shift without clear documentation. The result: numbers that look precise but hide enormous uncertainties.

For Whanganui specifically, this creates a historical record with a crater in it. Anyone trying to track long-term trends in the region now has to either ignore 2014 entirely or explain why that year looks like households stopped eating.

The food price index is supposed to be one of our most reliable economic indicators. It feeds into inflation calculations, poverty measurements, and household budget planning. When a regional figure suddenly halves without explanation, it undermines confidence in the whole system.

Stats NZ typically documents methodology changes. But absent a clear note explaining the 2014 Whanganui figure, users are left guessing. Was it a change in how the region was defined? A shift in which stores were surveyed? A smaller sample that skewed results?

This is the unsexy side of data journalism: sometimes the story isn't what the numbers show, but what they don't show. A missing explanation. A sudden break in trend. A figure that raises more questions than it answers.

Somewhere in Whanganui, families were buying food in 2014. The official record suggests they spent half what they did the year before. The real story is that we don't know what actually happened, and that gap in knowledge matters every time someone tries to use this data to understand how New Zealanders are coping with rising costs.