Industrial Organization

1
Industrial Organization

• Product Differentiation (II)

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Differentiation Some remarks

• Why is there differentiation?
• Increases market power PgtMC
• When is there differentiation?
• Horizontal differentiation increases with income
  (more developed countries have wider product
  variety)
• Differentiation increases with product
  fungibility, e.g.
• Numerous cell phones
• Energy, telecommunication services (internet,
  phone calls), commodities, are difficult to
  heterogenize
• Demand for quality increases with income
• Income distribution of consumers varies by
  region, country.

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Differentiation Some remarks

• Hotelling and vertical models
• Few (usually), symmetric firms
• Single unit demands
• Single dimension of differentiation
• In reality
• Heterogeneity in preferences along several
  dimensions
• Many firms, possibly with multiple brands
• Consumers may demand multiple units

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Differentiation Empirical Work

• Again, focus on market power plus
• Antitrust applications
• Definition of markets
• Mergers
• Managerial/policy applications
• Welfare analysis (e.g. product introductions)
• But
• Environment is more complex
• Demand has to capture consumers prefernces over
  many products
• Supply has to capture strategic interactions with
  respect to many brands and firms

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Differentiation Market Power

• Extension of conjectural variations approach to a
  complex environment
• Each firm f maximizes profit over its portfolio
  of brands
• Each firm has Ff FOC, where Ff is the number of
  brands in

Bertrand-Nash Competition
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Estimation Issues

• To study market power, one needs to estimate
  demand
• But, J products implies the computation of J2
  own- and cross-price coefficients
• Assume a simple linear demand equation
• With 50 brands (for example)
• 50 equations and 25,000 cross- and own- price
  coefficients.
• How to reduce dimensionality?

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Estimation Issues Solutions

• Solutions
• Nest products into mutually exclusive categories
  and estimate coefficients in every nest
• Impose restrictions in estimation (e.g. symmetry)

Cereal (25 brands)
Kids (8)
Healthy (10)
Healthy (7)
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Estimation Issues Solutions

• Solutions
• Assume a discrete choice model to project J onto
  a lower dimensional space (namely characteristics)

Each consumer has an idiosyncratic
shock Assumption IID, distributed extreme value
Logit Model (McFadden, 1978)
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Logit Discrete Choice (DC) Demand

• Parsimonious many substitution patterns
  recovered via few parameters
• In theory one needs each consumers purchase
  decision
• Search for parameters that make the logit
  probability as close as possible to the actual
  0/1 choice
• In practice this model is widely used with
  aggregate data
• Daily data 100 lbs of Cereal A
  (price/lb1.2) 150 pounds of Cereal B
  (price/lb1.5)
• Assumptions
• There is serving size (e.g. 0.1 lb/day) -
  consumers purchase one serving size of the brand
  that gives them the highest utility
• There is a fraction of consumers that consider
  buying but do not (outside good) this number is
  assumed

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Logit Discrete Choice (DC) Demand

• Logit model with aggregate data
• Example
• 3,000 consumers consider buying cereal
  (potential 300 lbs) Note this is unobserved
  and hence assumed by researcher
• Observed data 100 lbs of Cereal A (1,000
  servings) 150 pounds of Cereal B (1,500
  servings)
• Transform aggregate quantities to market shares
  (sj)
• Cereal A 1/3 of market potential 0.333
  market share
• Cereal B ½ of market potential 0.500
• Outside good 0.1667
• Now, data looks discrete choice friendly

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Logit Discrete Choice (DC) Demand

• Very parsimonious many substitution patterns
  recovered via few parameters
• Logit Simplest DC model
• Independence of Irrelevant Alternatives property
  off-diagonal entries in a column of elasticity
  matrix are equal.
• Substitution patterns are driven solely by market
  shares (sj).

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Logit Discrete Choice (DC) Demand

• Nested Logit
• Products grouped into mutually exclusive sets.
• Cross-elasticities across different groups are
  not restricted.
• IIA property remains within groups.
• Example IIA remains for cereals within kids
  category

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Logit Discrete Choice (DC) Demand

• Random Coefficients Logit
• Berry, Levinsohn and Pakes (BLP)
• Also known as mixed logit (McFadden and Train)
• Most general of DC models.
• Allows taste parameters to have a distribution
• Implication flexible substitution patterns

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Other Models Continuous Choice (CC) Demand

• Also called representative consumer models
• Not parsimonious in nature. Suitable to model
  broad categories of goods. Solutions
• Multistage Budgeting (e.g. Hausman, Leonard and
  Zona)
• Demand estimated in stages
• Bottom stage has mutually exclusive sets of
  products.
• Problems separability structure is difficult to
  test as the number of products increases, the
  problem of having to estimate too many parameters
  arises again.

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Other Alternatives Continuous Choice (CC) Models

• Distance Metric (DM) Method (Pinkse, Slade,
  Brett)
• Based on brands location in product space (need
  product characteristics data)
• Intuition cross-price effects are a function of
  closeness in product space to reduce
  dimensionality
• It does not restrict the choice of CC demand
  model

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Other Alternatives Continuous Choice (CC) Models

• Distance Metric (DM)
• Example

One parameter instead of n-1
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Nearest neighbors Common Boundaries
logP
logP
A?
A?

• Location and Common Boundaries of Beers in
  Chicago, 4Q-1992

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Approaches to Estimation

• Disadvantages of Continuous Choice
• Dimensionality is usually larger (J2 parameters)
• Certain analyses are difficult (e.g. evaluating
  the introduction of a new brand)
• Disadvantages of Characteristics Space approach
• Data on characteristics may be hard to get
• Dealing with non-discrete choice goods and
  complements is difficult
• Computational burden

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Estimation Remarks

• Continuous Choice Models
• Several functional forms
• Convenient Linear, log-log
• Theory based Almost Ideal Demand System
  trans-log. Parameters estimated have a
  theoretical meaning (e.g. you can impose
  homogeneity of degree 1)
• Regardless of functional form, the ultimate goal
  is to obtain a measure of to conduct
  empirical analyses

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Application

• Competition in the US Beer Industry (Journal of
  Industrial Economics, 2008)

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Differentiation Application

• Beer in the US
• gt 200 brands
• Large degree of subjective differentiation hard
  to distinguish brands in blind testing
• High concentration (2 brands gt40 market)
• (P-CM)/P is high gt 30
• Question Why is there a 30 mark-up?
• Product Differentiation?
• Collusion?
• Leadership models?

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Accelerated Concentration
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Data

• Three-dimensional data on prices and quantities
  58 cities, 64 brands (13 brewers), 20 quarters
  (1988-1992) Source IRI
• Data representative of supermarket sales in
  broadly defined metropolitan areas
• Product characteristics
• Continuous alcohol content, container size,
  city-coverage
• Discrete product type (e.g. import/domestic
  light/regular budget/premium/superpremium)
  brewer type national coverage (regional/national)

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Market Definitions
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Intuition

• Mark-up depends on prices, marginal cost and
  demand elasticity

Assumed
Observed
Estimated from data
Unknown
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What this paper does
Estimate demand for differentiated brands
Pre-increase period Use demand estimates in FOC
of various games to recover corresponding MCs
Add 100 tax increase to pre-increase MCs to
simulate post-increase prices for each game
Compare with actual price increases
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Demand Estimation (Results)

• SAMPLE OF PRICE ELASTICITIES (MEDIAN)

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Supply

• Firms profit function
• Using the demand parameters, marginal costs (ck)
  are solved from FOCs from different games
• Games - Bertrand-Nash
• - Stackelberg Leadership (Budweiser /
  Anheuser-Busch)
• - Collusive Leadership (All brands match
  Buds increase)
• - 2 Collusive scenarios

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Recovered PCMs
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Predicted Actual Price Increases

• Using ck, predicted prices are solved in FOC
  above under each game when Excise tax E is
  introduced

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Actual Price Increases Predicted Price
Increases Bertrand-Nash
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Actual Price Increases Predicted Price
Increases Budweiser Leadership