The Upward Pricing Pressure Test and Sensitivity of the Diversion Ratio Lydia Cheung Auckland University of Technology Presented at the 2nd ATE Symposium 16 December, 2014 Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 1 / 21
Introduction Contents Introduction 1 Sensitivity of Elasticities and Diversion Ratios 2 Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 2 / 21
Introduction Merger Policy “in a Hurry” Antitrust agencies receive many merger proposals (e.g. FTC & DoJ in the U.S. receive 1000+ merger proposals each year) Need to block mergers with large anti-competitive harm, e.g. big price increases (“ unilateral effects ”) 2 opposing price effects in even the simplest, static merger: loss in competition (P ↑ ) vs. cost savings (P ↓ ) Ideal: Specify model of industry / market, estimate it on computer, then simulate the merger Reality: Data and time constrained (30 days); may need shortcuts Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 3 / 21
Introduction Old Shortcut: Market Share Analysis Define market boundary 1 Find all firms that are “in”, and their market shares 2 Calculate increase in market concentration due to merger 3 Problem: Market boundary definition is subjective , since most products are differentiated E.g. Merger of Whole Foods & Wild Oats (2007) Is the market boundary “premium organic supermarkets” or “all supermarkets”? Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 4 / 21
Introduction Debate in court (2008): Are these substitutable? Should I include Walmart in the relevant merger market? vs. The UPP doesn’t require market definition anymore! Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 5 / 21
Introduction New Shortcut: Upward Pricing Pressure (UPP) Test UPP finds how much a firm’s pricing incentive changes with merger by approximating (via first order condition): Price increase with profits diverted to merging partner Price decrease with cost reduction Why is this new shortcut better? No need to define market boundary Allows product differentiation to affect merger outcome Big influence: U.S. and U.K. revised Horizontal Merger Guidelines in 2010, both incorporating UPP; Australia and E.U. are following suit Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 6 / 21
Introduction What is the UPP? Single product firms 1, 2 are merging. UPP for firm 1: UPP 1 = D 12 ( p 2 − c 2 ) − ec 1 Demand-side input: diversion ratio D 12 Cost-side input: ec 1 = cost saving Analogous for firm 2: UPP 2 = D 21 ( p 1 − c 1 ) − ec 2 Natural extension to multi-product firms Appeals: Small data requirement; easy to calculate Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 7 / 21
Introduction Where to Get Diversion Ratio D 12 ? Farrell & Shapiro suggested “company internal documents” or “customer surveys” Some practitioners say this is picking a number out of the blue More data-driven way to get D 12 : simple demand system estimation � � �� � � − 1 ∂ q 2 � = ∂ q 2 ∂ q 1 = ε 21 | ε 1 | · q 2 � � � � D 12 = · 1 � � � � ∂ q 1 ∂ p 1 ∂ p 1 � � � q 1 � ∂ q 2 � � � �� � � − 1 ∂ q 2 � · q 1 p 1 ∂ q 1 p 1 = ε 21 � � � � D 12 = = 2 � � � � ∂ q 1 q 2 ∂ p 1 q 2 ∂ p 1 q 1 | ε 1 | � � � But demand estimation requires market definition! I show that this “catch-22” is not a big deal empirically Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 8 / 21
Sensitivity of Elasticities and Diversion Ratios Contents Introduction 1 Sensitivity of Elasticities and Diversion Ratios 2 Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 9 / 21
Sensitivity of Elasticities and Diversion Ratios Experimenting with Market Definitions Supermarket consumer goods are ideal for experiments: product proliferation blurs market boundaries Smallest product category has 25+ items; big categories have 150+ Today I use a smaller category: sugar substitutes, in 2006 data Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 10 / 21
Sensitivity of Elasticities and Diversion Ratios Summary of Sugar Substitutes Category 1433 supermarkets with 10+ unique products (UPC’s) 150 unique products Average store carries 20 products (min 4; max 37) Mean price per product: US$3.27 Mean price per oz.: US$1.01 Ingredient Obs. Saccharin & Dextrose 20% Nutra sweet 18% Aspartame 18% Saccharin 16% Sucralose 15% Fructose 6% Glucose 3% Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 11 / 21
Sensitivity of Elasticities and Diversion Ratios Demand Model: Simple Nested Logit Random utility of consumer i from product j : u ij = β 0 − α p j + ξ j + [ ζ iN + ( 1 − τ ) ǫ ij ] � �� � combined error term ξ j : product j fixed effect (Mimicking practitioner’s situation, I do not use rich product characteristic variables) Equation to estimate: ln ( s j ) − ln ( s 0 ) = β 0 − α p j + ξ j + τ ln ( s j | N ) “BLP instruments” for endogenous prices Demand system gives explicit functional forms for elasticities and diversion ratios Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 12 / 21
Sensitivity of Elasticities and Diversion Ratios Changing Market Definition I experiment on market definitions by: Dropping an entire sugar type, in all markets Dropping 1-8 largest competing items, one by one, in each market When set of products changes (while fixing M ), red terms below will also change in the data: ln ( s j ) − ln ( s 0 ) = β 0 − α p j + ξ j + τ ln ( s j | N ) The set of observations in the dataset also changes with J Thus, the “BLP” instruments will change too So estimated elasticities and diversion ratios will change Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 13 / 21
Sensitivity of Elasticities and Diversion Ratios Diversion Ratios Unconstrained by Model Simple nested logit model gives explicit functional forms for elasticities, thus the diversion ratios have explicit forms too ( 1 − τ ) s 1 | G + τ s 1 D 12 = ε 21 x | ε 1 | = 1 − [( 1 − τ ) s 1 | G + τ s 1 ] , which takes the form 1 − x : So the simple model does not inherently constrain values of the diversion ratio Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 14 / 21
Sensitivity of Elasticities and Diversion Ratios Keeping track of 2 biggest goods Two most frequently occurring items: Sweet ‘n’ Low 4.5oz; packets in box (3.86% obs.) Equal 3.5oz; packets in box (3.75% obs.) Item Variable Mean Min Max elasticity − 1.27 − 1.89 − 0.91 Sweet ‘n’ Low 4.5oz. diversion ratio 0.017 0.00028 0.058 elasticity − 2.96 − 4.14 − 1.48 Equal 3.5oz. diversion ratio 0.0087 0.000039 0.035 I keep track of their elasticities and diversion ratios as market definition changes Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 15 / 21
Sensitivity of Elasticities and Diversion Ratios Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 16 / 21
Sensitivity of Elasticities and Diversion Ratios Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 17 / 21
Sensitivity of Elasticities and Diversion Ratios Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 18 / 21
Sensitivity of Elasticities and Diversion Ratios Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 19 / 21
Sensitivity of Elasticities and Diversion Ratios Takeaway from Experiments As market definition changes, elasticities of the two goods often move in same direction Thus the diversion ratio does not change much in magnitude As more items are dropped from the market, diversion ratios have a tendency to increase (though by very small magnitude) Although my experiments see a ∼ 50% increase in diversion ratio, it is still a small magnitude ( 10 − 2 ) relative to price ( ∼ $5 ) Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 20 / 21
Sensitivity of Elasticities and Diversion Ratios Alternative Approach Some economists have estimated the diversion ratio using exogenous changes in product set Conlon & Mortimer (2013), “An Experimental Approach to Merger Evaluation”: Exogenously removing snack foods from vending machines and see how customers switch Another example: when a hospital exogenously shuts down, researcher can track where patients switch to in the data Lydia Cheung (AUT) Sensitivity of Diversion Ratio in UPP 21 / 21
Recommend
More recommend
