Download Lecture 11 - Econometrics Laboratory, UC Berkeley

Lecture 11
Applications of BLP
Bronwyn H. Hall
Economics 220C, UC Berkeley
Spring 2005
Outline
• Autos
• Breakfast cereal
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Autos
• Petrin, A. (2002), “Quantifying the Benefits of
New Products: The Case of the Minivan,”
Journal of Political Economy 110 (4): 705-729.
• Berry, S., J. Levinsohn and A. Pakes (2004),
“Differentiated Product Demand Systems From a
Combination of Micro and Macro Data: The New
Car Market,” Journal of Political Economy 112(1,
Part 1): 68-105.
• BLP (1999), Effects of VERs, AER
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Effects of VERs
• “Voluntary Export Restraints” in Japan between
1981 and 1990 – what was welfare effect in the
US?
• BLP assume the VER acted as a unit-specfic tax
and changed the profit function:
πf =
(p j
∑
j J
∈
− mc j − λVer j )s j ( p , x , ξ ;θ )
f
• Allow λ to vary by country and by year
– US should not have significant λ
– variation by year tells us if they bind in practice
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Predicted impact of VER
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Results
• VERs only bind for Japan
• Japanese prices go up by 10-20%
– US prices go up a small amount; European
prices actually go down
• Loss in profits to Japan small; gain to US
large
• US consumers lost more in welfare than
US firms gained
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Demand for differentiated products
• Aviv Nevo (2001) on breakfast cereal
– “Measuring market power in the RTE cereal industry,”
Econometrica 69(2): 307-342.
• Ron Anand and Bharat Shachar (1998) on TV viewing
– “The Effectiveness and Targeting of Television Advertising,”
Journal of Economics & Management Strategy 7(3): 363-396.
• Peter Davis (2001) on movie theaters
– “Spatial Competition in Retail Markets: Movie Theaters,” LSE
Working Paper No.
• Phillip Leslie (2003) on Broadway plays and price
discrimination
– “Price discrimination in Broadway Theatre,” Rand Journal of
Economics
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Nevo 2001
• Measuring market power in the RTE
(ready-to-eat) cereal industry
• estimates price-cost margins for a number
of brands; asks how they are determined:
– markups due to differentiated product
competition
– portfolio effect if a firm holds two similar
brands (will price higher)
– price collusion among manufacturers
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Nevo 2001
• Strategy:
– PCM determined from demand without using
cost data (as in BLP)
– Then use estimated demand to predict
markups under the 3 scenarios
• Findings:
– markups match Nash-Bertrand pricing game
between firms (result 2)
– even though typical markup is 45-50%
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Nevo - details
•
Data on sales ($9 billion in 1992) of RTE cereals:
–
–
–
–
•
25 brands of cereal (out of 200, covering 50-60% of
total market), largest share is corn flakes, with 5%
6 major firms (C6>90%): Kellog, General Mills, Post,
Quaker Oats, Ralston, Nabisco
65 US cities
20 quarters 1988-1992
Data for estimation:
–
–
–
market shares and prices in each city-quarter
brand characteristics, advertising
distribution of demographic characteristics
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Nevo - econometrics
1. correlation between price and brand-city-quarter
specific demand shocks (error term)
1. use panel data, assume city-specific demand shocks
uncorrelated conditional on brand and demographics
2. observed variation in city-specific marginal costs
2. large number of own and cross-price elasticities
1. use BLP-type methods, add brand fixed effect, but.
2. do not assume brand characteristics are exogenous
3. model heterogeneity across consumers using
demographic data
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Cereal Market Shares
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Production cost and profit
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Sample information
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Instruments for price
Disturbance = city-quarter specific deviation from
brand fixed effect
– want instruments correlated with price but not with
city-specific demand shocks
– previous work used characteristics of other products
(not really exogenous)
– Nevo uses different instruments
• average price of the brand in other cities in the region
(problem if advertising is regional)
• marginal costs vary across cities due to transportation costs,
shelf space, and labor – use region dummies, city density,
aveage supermarket earnings
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