Price and Distribution Effects

Summary

Empirical generalizations on price own-elasticities, cross-price elasticities, asymmetric price effects, and distribution effects. Key findings: own-price elasticity ≈ −2.5; cross-price elasticity ≈ 0.5; cross-effects are asymmetric; brands closer in price have larger cross-effects; distribution elasticity exceeds advertising elasticity.

Own-Price Elasticity

Price Elasticity Generalization

The elasticity of price on own brand sales is negative and elastic.

Meta-analytic mean: approximately −2.5

• Tellis (1988) meta-analysis: −2.5 (correcting for method biases)
• Bolton (1989): −2.5 (frozen waffles −1.74, liquid bleach −2.41, bathroom tissue −3.12, ketchup −2.55)
• Ehrenberg & England (1990): −2.6 (weighted mean across cereal, confectionery, soup, tea, biscuits)
• Hamilton, East & Kalafatis (1997) UK 100 markets: −2.5

Brand-specific variation (tuna fish in Chicago, Table 8-4): Star Kist −3.30, Chicken of the Sea −3.62, Bumble Bee −4.19. Within-brand geographic variation (Star Kist, Table 8-5): Boston −2.53, Chicago −3.30, Houston −1.51, Los Angeles −3.19.

Key contrast with advertising: $|{\eta }_{\text{price}}|\gg |{\eta }_{\text{advertising}}|$, approximately 25:1. This has led to debates about whether price cutting is more cost-effective than advertising (Broadbent 1989; Tellis 1989).

Upside vs. Downside Price Asymmetry

A brand’s upside and downside own-price elasticity can differ.

Consumers notice price cuts more readily than price increases when they are not well-informed about the prevailing price. This asymmetry implies the brand may face a kinked demand curve (Sweezy model — see Reaction Functions and Competitive Dynamics).

Cross-Price Elasticity

Cross-Price Elasticity

The cross-elasticity of price on rival brand sales is nonnegative.

Mean cross-price elasticity: approximately 0.52 (Sethuraman, Srinivasan & Kim 1999, 1,060 elasticities across 280 brands, 19 grocery categories).

~70% between 0 and 1; ~15% between 1 and 2. Mean cross-elasticity for liquid dishwasher detergent: 0.6 (Kopalle, Mela & Marsh 1999).

Cross-Price Asymmetry

Price cross-effects are asymmetric.

A national brand’s price promotion draws heavily from store brands, but store brand price promotions have much weaker effects on national brand sales (Kadiyali, Chintagunta & Vilcassim 2000, Table 8-6: refrigerated juice in Chicago).

This phenomenon is captured by competitive clout and vulnerability measures (Cooper 1988):

$${\text{competitive clout}}_i=\sum _{j\ne i}{\eta }_{ji}^2\text{\hspace{1em}(Eq 8.14)}$$ $${\text{vulnerability}}_i=\sum _{j\ne i}{\eta }_{ij}^2\text{\hspace{1em}(Eq 8.15)}$$

where ${\eta }_{ji}$ = cross-price elasticity of brand $j$ with respect to brand $i$‘s price.

Theorem

Brands that are closer to each other in price have larger cross-price effects than brands that are priced further apart.

A brand is affected the most by discounts of its closest higher-priced brand, followed closely by discounts of its closest lower-priced brand. [SSK]

The category-adjusted cross-effects response sensitivity (Eq 8.16):

$${\gamma }_{ij}=\frac{\partial M{S}_i}{\partial P_j}\times (0.01P_C)={\eta }_{MS,ij}\times \frac{M{S}_i}{P_j}\times (0.01P_C)$$

where $P_C$ is the category-weighted average price. Using this measure eliminates the “scaling bias” that makes national brand cross-effects appear larger.

Life Cycle and Price Elasticity

Life Cycle Price Dynamics

Brand-level and category-level price elasticities first decrease in absolute value then ultimately increase in absolute value as the product life cycle enters the decline phase.

Two patterns (Parker 1992, 17 durable categories):

1. For necessities or categories with penetration >90%: elasticity constant or declining across life cycle
2. For non-necessities facing decline or non-necessities with stable penetration: elasticity increasing in absolute value

Distribution Effects

Distribution is consistently found to be a strong driver of long-run market performance:

Distribution and Market Share

From the long-run time series literature (Bronnenberg et al. 2000, 5-year weekly panel):

Distribution coverage drives long-run market shares, especially the coverage evolution early in the life cycle.

Distribution elasticity typically exceeds advertising elasticity for new products — gaining distribution in new stores provides incremental reach that advertising alone cannot achieve.

In the VAR framework, distribution gains have non-zero multivariate persistence (they are “sticky” — brands hold distribution once gained), while promotional gains have near-zero persistence.

Price Competition Structure (Russell-Kamakura LSES Model)

The Latent Symmetric Elasticity Structure (LSES) model decomposes cross-price elasticity as:

$${\eta }_{ij}={\text{clout}}_j\times {\text{substitution index}}_{ij}$$

Powdered detergents example (Table 8-7, Russell & Kamakura 1994):

• Tide: highest momentum (0.294), highest vulnerability
• Private label: lowest momentum (0.029), extreme vulnerability
• Cross-elasticities: Tide vs. Surf (0.396), Tide vs. Oxydol (0.13), reflecting price-tier proximity

See Also

• Advertising effects: Advertising and Promotion Effects
• Market share model foundations: Market Share Models
• Reaction functions and competitive structure: Reaction Functions and Competitive Dynamics
• Long-run distribution effects in VAR: Multivariate Persistence and Cointegration
• Optimal pricing decisions: Optimal Marketing Decisions and Forecasting
• Discrete choice econometrics for cross-price modeling: Discrete Choice Models — random-coefficients logit (BLP) is the structural model underlying cross-price elasticity estimation
• Estimation methods for price response models: Parameter Estimation in Market Response