Is the discounted line really where pricing goes wrong?
Partly — and the research is more specific than the obvious version of
this argument, in a way worth reporting honestly because some of it cuts the
other way. The literature on checkout price accuracy does not find that
discounts in general are error-prone. It finds that advertised
specials are, while other kinds of price reduction are not.
The reference point is the FTC’s 1998 Price Check II — described by
NIST as a larger, more comprehensive review than the 1996 survey that preceded
it — run using the price-verification procedure NIST and the National
Conference on Weights and Measures developed jointly, which 42 states had
adopted. It compared scanned prices with the lowest posted or
advertised price across 107,096 items. Overall accuracy was decent — one
in 30 items was mispriced, and errors split roughly evenly between overcharges
and undercharges. Sale items were the exception on both counts.
1 in 28 sale items mispriced — versus 1 in 32 non-sale items (FTC/NIST Price Check II, 1998)
~two-thirds of sale-item errors were overcharges; for non-sale items, only slightly more than one-third were
98% of prices must match for a store to pass inspection under the NIST Examination Procedure for Price Verification
The FTC’s own consumer-protection director drew the practical conclusion
at the time: consumers can protect themselves by paying close attention to the
prices they are charged, particularly when buying sale items, because
almost two-thirds of the scanner errors in that category were overcharges. The
inspectors also catalogued the causes, and they are mundane and familiar —
out-of-date signs, incorrect prices in the computer, shelf prices that never got
updated. Several of those are failures to propagate a price change, which is a
plausible reason advertised prices would be the fragile ones — though the
inspectors catalogued causes rather than testing that explanation, so read it as
a hypothesis, not a finding.
Sixteen years later, a team of pricing researchers went back to that same FTC
dataset and re-ran it in the Journal of Retailing, this time measuring
the size of each error rather than only its direction. The asymmetry
held: undercharges ran about 22% for both sale and non-sale items, while
average overcharges were 19% on non-sale items and 38% on sale items. The same
2014 paper sets those figures against the work that opened this line of
research: it reports that Goodstein’s 1994 study in the Journal of
Marketing found average overcharges of 6.9% against
average undercharges of 23.7%. Worth flagging, in the same
spirit as the caveats elsewhere on this page: Goodstein’s original 1994
article sits behind a publisher paywall we could not open directly, so those
two figures reach this page as quoted inside the 2014 re-analysis, not
independently re-verified against the 1994 text itself. On that basis, the
2014 authors’ reading is that overcharges have become more severe even
as the overall rate of mistakes has fallen.
38% vs 19%Average size of an overcharge on sale items versus
non-sale items, in the FTC’s 1998 Price Check II data as re-analysed in
the Journal of Retailing, measured as a share of the price charged.
This is how big an error is once one has occurred — not how likely one is.
Now the part that argues against the easy version of this page’s claim,
and which the same 2014 paper is explicit about. When its authors modelled
promotional activity, only advertised sale items came out worse:
they were associated with significantly greater error overall. Items on a
temporary price reduction went the other way — errors and overcharges both
declined significantly, which the authors state as a formal generalisation that
errors are significantly less likely on temporarily reduced items.
Permanently discounted items had no significant effect on error rates, and
neither did items tied to store coupons.
So the honest version is narrower: “discounts are
fragile” is not what this research shows. Where errors did cluster was on
items whose price had been advertised — an association the authors
report, not a mechanism they test. In that study the advertising was a supermarket flyer, which is not a
happy-hour board, and the researchers made no claim about restaurants. The
resemblance is the reason the parallel is worth drawing at all — and the
difference is the reason it stays a parallel rather than evidence.
The overcharge asymmetry on sale items does replicate outside the FTC data. The
same 2014 paper obtained a separate set from Washington State — the last
quarter of 2005, 100 items checked in each of 559 stores — and found the
same direction there: controlling for the price of the product, relative errors
were greater for sale overcharges (36.39%) than for sale
undercharges (26.46%). Worth knowing how that is measured, because the wording
invites a different reading: these are error sizes expressed as a percentage of
the price charged, not the share of items priced wrongly.
Second, and more important: this literature examines retail and grocery checkout
scanning, not restaurant point-of-sale check splitting. Nobody has run a Price
Check study on split restaurant checks, and none of these findings measure what a
POS does to a discount when a server moves an item. They are context for why an
advertised price is a plausible weak point, not evidence about splitting. The
claim that a split check strips a discount rests entirely on the Toast and Square
documentation above — which is a much stronger kind of evidence for that
specific claim anyway, because it is the vendors describing their own systems.
Sources: Price Check II, NIST/FTC, 1998; Price Verification FAQs, NIST; The Accuracy of Scanned Prices, Journal of Retailing 90(2), 2014; UPC Scanner Pricing Systems: Are They Accurate?, Journal of Marketing 58(2), 1994