Price elasticity of demand, three real cases and four worked examples
Most elasticity examples are either academic (a national tax study you can't apply to your own business) or vague marketing claims with no real numbers behind them. This one keeps the two kinds separate: three real, documented cases, clearly labeled as real, and four verified small-business worked examples, clearly labeled as constructed.
Two kinds of examples
The three cases below are real, documented, and sourced, a national tax study, an agricultural economics estimate, and a widely misunderstood streaming pricing story. They show what elasticity looks like at national scale, and one of them shows how easy it is to misread a real event as a clean elasticity case when it isn't. The four examples after that are constructed for this piece specifically to be realistic and verifiable, not claims about actual named businesses.
Case 1: Mexico's sugar tax, a clean natural experiment
In 2014, Mexico introduced a one-peso-per-liter excise tax on sugar-sweetened beverages, roughly a 10% price increase. Purchases of taxed drinks fell relative to the pre-tax trend, with the decline growing larger over the following months as the price change settled in. Researchers studying the rollout estimated the implied elasticity at roughly -0.6 in the first year, and steeper in the years after, moderately elastic, and more elastic the longer the higher price stayed in place. This is one of the cleaner real-world elasticity studies available specifically because it was a deliberate policy change with a defined before-and-after period, not a market with several forces moving at once.
Case 2: Eggs in 2025, a textbook inelastic staple
Avian flu outbreaks reduced the U.S. egg-laying flock sharply in late 2024 and early 2025, and retail egg prices roughly doubled in some markets, reaching over $6 a dozen. Agricultural economists at Purdue's Center for Food Demand Analysis had already estimated egg demand elasticity at around -0.15, meaning a 1% price increase reduces quantity demanded by only about 0.15%. The 2025 price spike is broadly consistent with that estimate: purchases barely moved even as prices climbed dramatically, because eggs are a dietary staple with few workable substitutes in everyday cooking and baking.
Case 3: Netflix, the case study that isn't one
Netflix is frequently cited as proof that streaming demand is inelastic: the company raised prices multiple times across 2023 to 2025 while continuing to add subscribers, including its largest-ever quarterly gain (18.9 million subscribers) in the fourth quarter of 2024. Looking closer breaks the story: that record quarter happened before the January 2025 price increases, not during or after them. The subscriber growth through 2023 and 2024 was driven substantially by a separate initiative, a crackdown on password sharing that pushed people who were already watching for free onto paid accounts, running at the same time as, and largely independent of, the pricing decisions.
With two major forces moving simultaneously, growth from converting free riders and a series of price increases, there's no way to isolate how much of the subscriber trend was actually a response to price from public data alone. Citing Netflix as clean proof of inelastic demand is a real, common mistake, and a useful one to recognize before repeating it about any company running several changes at once.
Two things happening in the same quarter isn't evidence that one caused the other. Netflix's subscriber growth and its price increases are the clearest example of why that distinction matters.
Why these don't tell you your number
All three cases above are national or company-wide figures, averaged across millions of transactions, many customer segments, and (in Netflix's case) a confound that makes the number unusable at all. None of that transfers cleanly to one specific business deciding whether to raise its own price by a specific amount. A national -0.6 for sugared drinks says nothing about how price-sensitive the specific customers of one specific corner store actually are. The four examples below are built at the scale an actual small business would recognize.
Four worked small-business examples
Constructed for this piece to be realistic and independently verifiable, not claims about real, named businesses.
The spread is the actual lesson. The hair salon and the SaaS subscription behave close to the general "service with real switching cost" pattern: inelastic, because a customer's existing relationship, habit, or data locked into the product makes switching feel costlier than the price difference. The restaurant sits exactly at unit elasticity by construction here, a genuinely useful middle case to recognize, where the percentage revenue effect of the price change and the volume effect offset almost exactly. The e-commerce item is sharply elastic, consistent with how easy it is to comparison shop the identical product across other sellers with one more browser tab open.
Testing your own elasticity
A past price increase, a supplier-driven cost pass-through, or a discount period all count. Pull the sales volume for a comparable window before and after, and run it through the elasticity formula directly, no need to run a fresh experiment if the data already exists.
Compare the same length of time, ideally the same season, before and after the change. A summer-to-winter comparison for a seasonal product will show a swing that has nothing to do with price.
A blended, store-wide elasticity number can hide the fact that one specific product or one specific sales channel is driving all the movement. Check individual items before trusting a single company-wide figure.
A single price change gives one data point on what is, in reality, a curve that shifts with season, competition, and the broader economy. Useful for the next decision, not a permanent constant.
Run the actual calculation, including the break-even volume change a price move requires, on the price change calculator, which includes the full elasticity formula and typical benchmark ranges by category alongside the break-even math.
Frequently asked questions
Mexico's 2014 sugar-sweetened beverage tax is a well-documented case: a roughly 10% price increase produced an estimated elasticity near -0.6 in the first year, growing more elastic over time as substitutes became habitual. See the case studies above for this and two others.
It's commonly cited that way, but it isn't a clean example. Netflix's record subscriber growth in late 2024 happened before its January 2025 price increases, driven by a separate password-sharing crackdown running at the same time. The two effects can't be cleanly separated from public data, which makes Netflix a lesson in confounding factors more than a usable elasticity case.
Eggs are a dietary staple with few practical substitutes, especially in baking and cooking. Agricultural economists estimate the elasticity at around -0.15, meaning a 1% price increase reduces quantity demanded by only about 0.15%, which is consistent with prices roughly doubling in early 2025 without a proportional drop in purchases.
Only as a rough starting point. National and academic estimates average across every business in a category; your specific price point, customer base, and local competition can push your actual elasticity well away from that average. See the section on testing your own elasticity for a more reliable approach.
Run an actual price change (or use a natural one, like a supplier-driven cost increase you passed through), measure the resulting change in units sold over a comparable period, and apply the elasticity formula, price and volume changes, using the midpoint method for accuracy. See the price change calculator for the full formula.
Run your own price elasticity calculation on the price change calculator.