Vanderbilt University1 Quantitative Benefit-cost Analysis of Mergers Luke Froeb Oct. 26, 2001 Federal Trade Commission

Vanderbilt University2 References mba.vanderbilt.edu/luke.froeb/papers/ Coauthors, Tschantz & Werden Simulating Merger Effects Among Capacity- constrained Firms Pass Through rates and the Price Effects of Mergers Merger Effects When Firms Compete by Choosing Both Price and Advertising Does retail sector matter for manufacturing mergers? [very preliminary]

Vanderbilt University3 Quantitative benefit-cost analysis Goal: quantitative estimate of merger effect. Necessary to weigh efficiencies against loss of competition Two methodologies Empirical comparisons, e.g. Staples/Office Depot Model-based simulations

Vanderbilt University4 Empirical Comparisons e.g., Staples-Office Depot Good natural experiments or comparisons Benefit-cost analysis still requires structural estimate of pass through Depends on demand curvature big pass-through iff big anticompetitive effect

Vanderbilt University5 Model-based simulation Model current competition Estimate model parameters Simulate loss of competition from merger

Vanderbilt University6 e.g. Parking Merger Key parameters cost of walking Sensitivity of choice to price location of merging lots location of non- merging lots capacity of lots location of office buildings

Vanderbilt University7 Simple approach: Bertrand Price-setting game Static game What about dynamic strategies? Price-setting competition What about product, promotion, placement? Unilateral Effects What about coordinated effects? Does retail sector matter? Kroger-Winn Dixie vs. Quaker-Pepsi

Vanderbilt University8 Simple approach: Modeling Critique How well does model capture loss of competition from merger? Coke strategy is “share of throat” More about placement and product than price MCI-Sprint Tele-market new plans to rivals’ customers More about promotion than price Is Bertrand a good metaphor for loss of competition?

Vanderbilt University9 Simple approach: Does retail sector matter? When is retail sector transparent? Constant or constant percentage markup two-part tariffs, and retail sector must carry profitable products Retail sector earns no profit When does it matter? Double marginalization  price effect Two-part tariffs, and option of exclusivity  no price effect

Vanderbilt University10 Simple approach: What about advertising? FOC’s if q=q(a,p) {0=q+(p-mc)dq/dp, 0=-1+(p-mc)dq/da} FOC if q=q(a(p),p) 0=q+(p-mc’)dq/dp; mc’=mc+(da/dp)/(dq/dp) Pre-merger: Price-only model with mc’ ≈ price+advertising model Does advertising increase with quantity?

Vanderbilt University11 Simple approach: Implementation Estimate AIDS demand Scanner data Instruments None needed for weekly data LR vs. SR elasticities (Nevo & Hendel) Prices in other cities Correlated through costs Results High variance Inelastic demand? Goods are complements?

Vanderbilt University12 Implementation Critique: too many parameters AIDS has too many parameters Confidence intervals include both pro- and anti- scenarios. Elasticity matrix for merging products is most important. Alternatives: Logit, nested logit, PD GEV (Bres.&Stern), mixed logit (BLP) + census data (Nevo) But all goods are substitutes Only fool would admit post-merger price rise to FTC Agencies discount efficiencies as not merger-specific So parties are reluctant to admit even small price increase. Proposal: assume 5% MC reduction Then simulate post-merger prices

Vanderbilt University13 PD GEV Bresnahan & Stern (multiple) dimensions of differentiation Implies substitution patterns

Vanderbilt University14 Implementation Critique: Higher derivatives of demand f(x),f’(x), and f’’(x) influence predicted price rise. Need location, velocity, and acceleration but observe only location If we cannot estimate f’(x) Product margins Hall vs. Hausman in MCI-Sprint If we cannot estimate f’’(x) Sensitivity analysis; or Use linear or logit for extrapolation to be conservative; or compensating cost differentials don’t depend on acceleration

Vanderbilt University15 Implementation Critique: Average revenue instead of price Average revenue is quantity share-weighted price index. Price changes cause weights to change. Leads to inelasticity bias Use fixed weight index when possible. Or use disaggregated data store-level data exist but we don’t use them Individual choice data exist but we don’t use them