Price Elasticity of Demand by Product Category

Near 0 (flour, rice, pasta, biscuits); -1.10 (citrus)

Staple grains and prepared flour products sit at the inelastic extreme of this dataset because they meet the two conditions economists treat as the strongest predictors of low elasticity: no meaningful substitute, and a habitual rather than deliberated purchase. A shopper doesn't compare five brands of flour before an occasional bake. They buy what they always buy, and a moderate price change rarely disrupts that. Citrus breaks the pattern for a structural reason: a shopper can switch to a different fruit, or buy less, without losing anything from their routine, and citrus prices also swing with growing-season supply shocks in a way flour doesn't. Both push measured elasticity higher. For a grocery or specialty-food seller, the category-level average hides a wide internal range. How replaceable a specific SKU feels to a specific customer predicts its elasticity, not which broad food category it sits in.

Beer -0.17 to -0.30, wine -0.23 to -0.66, spirits -0.39 to -0.65

Alcohol is one of the most heavily studied categories in applied demand economics, because governments use these coefficients to model the revenue and consumption effects of excise tax changes. That's why the sourcing here is unusually strong: three separate meta-analyses, each aggregating dozens of underlying studies, arrive at the same ranking even though their corrected point estimates differ. Beer being the least price-sensitive of the three and spirits the most tracks with habitual, low-consideration purchasing for beer against a wider field of close substitutes for hard liquor. One thing to flag for anyone citing this category: elasticity estimates for alcohol ran higher in uncorrected literature reviews than in analyses that adjust for publication bias and outlier studies. The corrected figures here are 28 to 29 percent less elastic than the older consensus averages still circulating in some secondary sources.

-1.06 to -1.37

Soft drinks sit firmly on the elastic side. The two country-level studies behind this figure, Mexico and Chile, were both conducted around proposed or enacted sugar taxes, so the underlying data captures real consumer behavior under an actual price shock, not a hypothetical one. Soft drinks compete against a wide field of close substitutes, including plain water: one of the underlying studies measured that a 10 percent increase in soft drink prices pushed water consumption up by 6.3 percent. That cross-price relationship matters for a beverage seller beyond the headline number. A price increase on a sugar-sweetened product shrinks total volume and pushes customers toward a specific, identifiable alternative, a more actionable signal than an aggregate elasticity coefficient alone.

-1.17 (t-shirts) to -2.86 (casual/athletic wear)

Apparel is the widest-ranging category in this dataset, and the range itself is the finding. Casual and athletic wear, at a coefficient near -2.86, sits among the most elastic goods measured anywhere in this reference. Basics like t-shirts, at -1.17, are elastic but far less so. Substitutability at the point of purchase likely drives the gap: an athletic-wear SKU competes against dozens of near-identical alternatives across brands the customer already trusts, while a basic t-shirt purchase is closer to routine replenishment than a considered comparison. One dataset also found a gender gap: a matched price change moved men's clothing demand more than women's (2.83 percent versus 2.13 percent), which the underlying study attributes to differences in brand loyalty between the two segments, not any difference in the garments themselves. Averaging elasticity across an entire apparel catalog will mislead. A hero athletic-wear SKU and a basics staple in the same store are different pricing problems.

Refrigerators -0.40, clothes washers -0.31, dishwashers -0.32 (avg -0.35)

Major appliances behave inelastically for a reason that has nothing to do with brand loyalty or habit and everything to do with timing: these are need-triggered purchases. A refrigerator gets bought because the old one failed or a household moved, not because a discount happened to be running, which caps how much a price change can pull demand forward or push it back. The two sources behind this figure, a US Department of Energy study and a Lawrence Berkeley National Laboratory analysis, work from real market-level sales data rather than surveys, a stronger evidentiary basis than most consumer-goods elasticity research relies on. One caveat from the underlying research: brand-level elasticity for the same appliance categories runs higher, often -2.0 or more. A customer who has already decided to buy a refrigerator this month will switch between brands over price, even though the decision to buy a refrigerator at all barely responds to price. That distinction, category-level versus brand-level elasticity, matters beyond appliances too.

-1.4 aggregate; e-books -0.20 to -0.27 (trending less elastic over time)

Physical book demand is elastic overall, but the e-book figure inside this same research is one of the more interesting findings in this dataset. E-book elasticity measured -0.270 in January 2011 and drifted to -0.201 by December 2012, a decline of about 0.003 per month: digital book buyers grew less price-sensitive over the study period, not more. The likely explanation is platform lock-in. As a reader accumulates a library inside one ecosystem (annotations, purchase history, a specific device), the switching cost of comparison-shopping a single new title rises even if the sticker price doesn't. The same research found that bestseller titles draw a more price-sensitive customer than the broader catalog, which is why retailers discount bestsellers specifically rather than applying a flat markdown across the whole category.

-0.42 (moderate price increases) to -0.56 (larger increases); -1.3 is the commonly-cited textbook figure

Furniture is a contested category in the literature, and we're presenting that disagreement directly rather than resolving it artificially. The -1.3 figure in most introductory economics material traces back to older, broader estimates and gets repeated because it's easy to cite, not necessarily because it's the most reliable current number. A more recent industry-cycle analysis, built from real furniture sales data tracked against actual price movements since 1965, found a less elastic response: -0.42 in years with moderate price increases, widening to -0.56 in years with sharper increases. That same analysis found something that matters for a seller thinking about macro timing as much as individual pricing decisions. In years when existing home sales rose by a median of 9 percent, furniture demand increased alongside furniture prices, a positive elasticity reading, because a strengthening housing market pulls new-home furniture purchases forward hard enough to overwhelm the normal price effect. Elasticity read from a single time window can mislead without accounting for what else was happening in the broader market.

-0.7 general market; premium/branded footwear much more elastic

The general footwear market reads as inelastic at the category level, the same logic that applies to apparel basics: most footwear purchases replace a worn-out pair rather than respond to a price signal. That aggregate figure hides a sharp split once brand enters the picture. Sources discussing premium and heavily-branded footwear describe demand as more elastic than the category average, the same category-versus-brand distinction seen in household appliances above. A customer who has decided to buy shoes this month isn't very price-sensitive about whether to buy shoes at all, but grows more price-sensitive once comparing a specific premium brand against its close substitutes. We're marking this Tier 2 because the numeric estimates we found trace to a single frequently-cited figure, not a corroborating meta-analysis like food, alcohol, or appliances above.