Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Optimal Product Variety in Radio Markets
Steven Berry (Yale University)
Alon Eizenberg (Hebrew University)
Joel Waldfogel (University of Minnesota)
2014 Media and Communications Conference
The Becker Friedman Institute, University of Chicago
May 2014
Berry, Eizenberg, Waldfogel (2014)
Optimal Product Variety
May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Optimal Variety in Local Radio Markets
How do firms (Radio stations) differentiate their products, both
horizontally and vertically?
Interesting for practitioners and policy makers
In oligopoly equilibrium, set of offered products may be inefficient
(Spence 1976, Mankiw & Whinston 1986)
Under / Over provision of varieties? an empirical question
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
“Inefficient Duplication” / “Excessive Entry” in Local
Radio Markets?
Likely if (i) fixed costs are large (ii) stations are close substitutes
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
“Inefficient Duplication” / “Excessive Entry” in Local
Radio Markets?
Likely if (i) fixed costs are large (ii) stations are close substitutes
Stations enter as long as private returns (ad revenue) exceed
fixed operating costs; but social returns differ from private ones
1
If differentiation is minimal, entry mostly produces a negative
externality on rivals (output reduction a-la Mankiw & Whinston,
“business stealing”)
2
Under strong differentiation, entry more likely to produce positive
externalities (e.g., on advertisers)
Our empirical framework estimates (i) the extent of station
differentiation (ii) the magnitude of fixed costs
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Previous work: Berry & Waldfogel 1999 (BW99)
Find: A Social Planner maximizing the joint surplus of
broadcasters & advertisers only would like to reduce number of
stations by 74%!
But: positive externality to listeners not taken into account. If
listeners valued an hour of listening at three times its market price,
free-entry equilibrium would be efficient
They assume symmetric stations, no systematic horizontal or
vertical differentiation (unique benefits to listeners only via taste
shocks at listener-station level)
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Contribution of Current Paper:
Relax the symmetric station assumption by allowing:
1
2
Horizontal (format) differentiation
Unobserved vertical differentiation
Berry, Eizenberg, Waldfogel (2014)
Optimal Product Variety
May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Contribution of Current Paper:
Relax the symmetric station assumption by allowing:
1
2
Horizontal (format) differentiation
Unobserved vertical differentiation
Introduce a novel methodology for dealing with unobserved quality
differentiation in estimation of listeners’ demand for programming
Estimate bounds on fixed costs without (i) relying on a parametric
distribution assumption (ii) imposing a unique equilibrium
Compute the socially-optimal market structure, compare to
observed equilibrium
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Preview of findings
1
Vertical & horizontal differentiation are key determinants of
listeners’ utility; Advertiser’s elasticity of demand for listeners is −2
2
The social planner eliminates about 50%-60% of stations;
elimination uniform across formats and qualities
Robust to whether we allow vertical differentiation
Suggestive evidence that listeners’ benefits not large enough to
offset social benefits from station elimination
3
Misallocation of quality in equilibrium
Welfare can be unambiguously improved by “downgrading” a high-q
station in 80%-94% of the cases where such a station exists;
upgrades are not welfare-enhancing
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Why was the Symmetry Assumption Useful?
In BW99, market structure defined by number of stations N
Use data on listenership & ad prices to estimate the per-station
revenue as a function of total number of stations in market, v (N)
Market has N stations in equilibrium ⇔ per-station fixed cost F
satisfies v (N + 1) ≤ F ≤ v (N)
Assume fixed costs normally-distributed → ordered-probit
estimation strategy for that distribution
Key: value for F → unique equilibrium prediction for N
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Challenge with Format Differentiation
Now market structure is a vector
~ = (N
N
“Rock” , N“Spanish” , N“News/Talk” , ...)
Consider a vector of fixed costs
~ = (F
F
“Rock” , F“Spanish” , F“News/Talk” , ...)
~ given F
~
Issue: No unique equilibrium prediction of N
E.g. both (N“Rock” = 1, N“News/Talk” = 2) and
(N“Rock” = 2, N“News/Talk” = 1) could be equilibria
~ to N,
~ so cannot write down likelihood
No unique map from F
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Intuition for Relaxing Symmetry Assumption
Consider observed equilibrium - which may not be unique
For each format g, a necessary equilibrium condition:
~ + eg ) ≤ Fg ≤ v g (N)
~
v g (N
(where eg a vector with zeros and an entry of 1 at the g th location)
These easy-to-compute bounds are sufficient for us to perform the
welfare analysis
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
(Some) Related Literature
Bresnahan and Reiss (1990,1991): symmetric entry; Reiss and
Spiller (1989), Berry and Waldfogel (1999): estimate variable
profits outside the entry model
Manski (2003) incomplete models that yield bounds; Haile-Tamer
(2003), Ishii (2006), Pakes, Porter, Ho & Ishii (2011),
Ciliberto-Tamer (2009), Eizenberg (2014): incomplete IO models
Impact of mergers on variety in Radio industry (reduced-form):
Berry & Waldfogel (2001), Sweeting (2010)
Dynamic modeling of stations’ format switching / mergers’ impact
on variety: Jeziorski (2012, 2013a, 2013b) Sweeting (2013)
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Data
A cross-section of metropolitan radio markets in 2001
Market definitions follow Arbitron, a media marketing research firm;
some coincide with Census MSA definitions, others do not
Station-level data (From the Spring 2001 edition of American
Radio, by Duncan’s American Radio):
Station’s “AQH-listeners” (number of 12+ listeners who during the
average quarter-hour); figures based on Arbitron’s diaries
submitted by surveyed individuals in each market
Broadcasting format
“In-metro” status
Berry, Eizenberg, Waldfogel (2014)
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Data (cont)
Market-level data (“Duncan’s Radio Market Guide” 2001-02
editions)
Estimates of total market revenue in 2001
Demographic information (for 2000): market’s percentage of Black
and Hispanic population, average income, percentage of
college-educated
As in BW99, we compute the market’s ad price
The average price paid by advertisers for an AQH-listener
Obtained by dividing total market revenue by the total number of
listeners to in-metro stations
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Variable
Share in-metro
Share Out-metro
N1 (in-metro)
N2 (out-metro)
Population
Ad Price
Income
College
Model & Estimation
Welfare Analysis
Table 1: Description of Market-Level Data
Units
Mean
Std. Deviation Minimum
%
0.111
0.026
0.030
%
0.015
0.023
0.000
integer
19.644
7.565
4.000
integer
7.209
8.320
0.000
millions
1.016
1.687
0.075
$
570.480
237.653
258.222
10,000$
4.584
0.860
2.482
%
21.200
5.370
10.200
Conclusion
Maximum
0.151
0.104
45.000
37.000
14.481
2691.177
8.010
37.100
Computed using 163 markets with full data. Table parallels Table 1 in BW99.
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Format Group
”Mainstream”
CHR
Country
Rock
Oldies
Religious
Urban
Spanish
News/Talk
Other
Data
Model & Estimation
Welfare Analysis
Table 2: Classification of Formats into Ten Categories
Formats Included
Adult Cont.
Hot AC
Modern AC
Soft AC
Classic Hits
80s Hits
CHR
Country
Classic Cntry. Trad. Country
Rock
Active Rock
Modern Rock
Classic Rock
Oldies
Religious
Cont. Christ.
Black Gospel
Gospel
Urban
Urban AC
Urban Oldies
Rhythmic Old.
Spanish
Span.-Oldies
Span.-Adult Alt Span.-C. Christ
Span.-Cl. Hits Span.-EZ
Span.-Hits
Span.-NT
Span.-Talk
Tejano
Tropical
Reg’l Mex.
Ranchero
Romantica
News/Talk
News
Talk
Hot Talk
Sports
Farm
Variety
Bluegrass
Blues
cp-new
Pre-teen
Ethnic
Silent
A22
A30
N/A
Jazz
Smooth Jazz
Classical
Adult Stand.
Easy List.
Berry, Eizenberg, Waldfogel (2014)
Optimal Product Variety
Conclusion
Adult Altern.
S. Gospel
Span.-CHR
Span.-Relig.
Span.-Stand.
Bus. News
Americana
A26
Dance
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Table 3: Format Category Performance
Format Group
”Mainstream”
Rock
Country
News/Talk
Urban
CHR
Other
Oldies
Spanish
Religious
Frequency*
Mean stations**
Max stations**
Mean share**
100.00%
100.00%
99.39%
100.00%
73.62%
93.25%
94.48%
98.16%
40.49%
79.75%
4.48
3.42
2.99
4.31
2.10
1.66
2.80
1.48
1.63
1.88
11
9
9
13
6
6
9
5
15
6
2.31%
1.88%
1.85%
1.55%
1.24%
1.16%
1.09%
0.79%
0.40%
0.37%
* Frequency with which a metro has at least one station (in- or out-metro) in format
** Statistics computed over the 163 markets, both in- and out-metro taken into account
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Model
1
Listening model (listeners’ demand for programming)
As in BW99, nested-logit, but here with format differentiation
We then extend to format-quality differentiation, show how to
address endogenous quality
2
Advertisers’ demand for listeners
As in BW99, ad price downward-sloping in total listening share,
constant-elasticity specification
3
Model of station entry
Choose whether to operate & in which format (format-quality) cell
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model: Horizontal Differentiation Only
Discrete-choice model: listen to one of the “inside” stations, or
choose outside option (not listening to commercial radio)
Nested-logit, 11 nests (ten format categories + “not listening”)
Listener i’s utility from listening to station j, which belongs to
format category g, in market t, is given by:
uijgt = xgt β + ξgt +νigt (σ) + (1 − σ)ijgt
| {z }
δgt
x includes: market average income, share college educated,
share Black & Hispanic, regional dummies, format dummies,
interactions (“country× South”); ξgt taste shock
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model: Horizontal Differentiation Only
uijgt = xgt β + ξgt +νigt (σ) + (1 − σ)ijgt
| {z }
δgt
Comments:
1
Implies within-format symmetric mean-utility & market share
2
Complication: account for in-metro vs. out-metro (“home dummy”)
3
0 ≤ σ < 1 a business-stealing parameter (highest as σ → 1)
νigt has the unique distribution derived by Cardell (1997) which
depends on the parameter σ
νigt → 0 as σ → 0
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model: Horizontal Differentiation Only
Estimation equation for the typical station in format g, market t
(follow Berry 1994):
ln(sjt ) − ln(s0t ) = xgt β + σln(sj/g,t ) + ξgt
1
One observation for each format-market pair; Within-format
symmetry imposed: sjt = Sgt /Ngt , sj/g,t = 1/Ngt
2
The above adjusted to allow for home vs. nonhome stations (so
really two observations for each format-market pair)
3
Estimation using 2SLS accounting for the endogeneity of sj/g,t with
instruments (i) market population (ii) number of out-metro stations
(taken to be exogenous) (iii) number of out-metro stations in same
format
4
Selection challenge for “Urban,” “Spanish,” “Religious”
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model: Horizontal Differentiation Only
The Estimated Listening Equation (2SLS)
Home dummy
home
Format Dummies
mainstream
chr
country
rock
oldies
religious
urban
spanish
nt
Constant
Observations
R-squared
0.639***
(0.082)
0.595***
(0.058)
0.431***
(0.056)
0.389***
(0.053)
0.561***
(0.049)
0.0447
(0.061)
-1.264***
(0.072)
-0.406***
(0.098)
-1.165***
(0.096)
0.214***
(0.053)
-5.325***
(0.15)
1919
0.72
Interactions
hispXspan
blackXurban
southXreligious
southXcountry
Correlation
σ
0.352***
(0.036)
0.506***
(0.050)
0.809***
(0.095)
0.316***
(0.072)
0.519***
(0.063)
Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1
Regional & Demographic effects included
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model with unobserved quality
Allowing for Both Horizontal & Vertical Differentiation
Two important new challenges:
1
How to define / measure quality?
Quality as an unobserved station characteristic, discrete parameter to
be estimated
2
Deal with endogeneity of quality (more likely to enter as “high
quality Country” if market’s unobserved taste for Country is high?)
Incorporate market-format fixed effects
Caveats: quality differentiation in Mainstream and News/Talk only;
out-metro stations set to low quality
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model with unobserved quality
Listening model with unobserved station quality
ui,j∈g,t = γ q · qjt + γ h · hjt + ψgt +νigt (σ) + (1 − σ)ijt
{z
}
|
δjt
qjt “high-quality” dummies - treated as parameters to be estimated
hjt in-metro dummy
ψgt : a market-format taste fixed effect
ψgt = dgt λ + ξgt ,
E[ξgt |Z ] = 0
Model predicts identical station shares within
market-format-quality cell; data violations rationalized by Arbitron’s
data sampling error
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model with unobserved quality
Identification
Assume: expected market shares perfectly observed, i.e., #
diaries → ∞)
Then if two in-metro stations have different observed shares, the
one with the higher (lower) share is IDed as high (low) quality
Difficult problem when in-metro stations have identical shares how do we separately ID their quality from ψgt ?
Idea: Use within-format shares - ψ “differences out” of those; exploit
presence of out-metro stations
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model with unobserved quality
Identification (cont)
Define κ1 ≡ γ q /(1 − σ), κ2 ≡ γ h /(1 − σ)
exp(κ1 · qjt + κ2 · hjt )
`∈g exp(κ1 · q`t + κ2 · h`t )
pj/gt (κ, q) = P
ID κ1 using a market where j is known to be high-q and k is known
to be low-q:
κ1 = ln(sjt ) − ln(skt )
ID κ2 using a market where ` is out-metro, j is known to be
in-metro low-quality:
κ2 = ln(sjt ) − ln(s`t )
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model with unobserved quality
Identification (cont2)
Given κ1 , κ2 , ID quality of any in-metro station j in any
market-format that has some out-metro station `:
qjt =
κ2
1
ln sjt /s`t −
κ1
κ1
Markets with equal shares and no out-metro stations: partial ID all stations are high-q or low-q; note that set of markets with IDed
quality is exogenously determined
Finally, having IDed qjt , remaining ID problem looks like the
horizontal case: we identify the remaining parameters (including σ
by imposing that ξ be uncorrelated with instruments
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model with unobserved quality
Estimation in a nutshell
A two-step procedure - analogous to ID argument
1
Estimate κ1 , κ2 , q from likelihood of within-format shares (q a long
vector of discrete parameters, problem breaks down to
computationally feasible)
2
A closed form obtains for ψ(σ, κ̂, q̂) - obtain remaining parameters
from GMM:
0
J(σ, λ; κ̂, q̂) = ψ(σ, κ̂, q̂) − dλ Z ΦZ 0 ψ(σ, κ̂, q̂) − dλ .
Analog of ID problem: qualities unassigned in markets with
“similar shares” and no out-metro stations; so second stage
performed on an exogenously-selected subset of markets with
out-metro stations in Mainstream, News/Talk
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Listening Model with unobserved quality
Table 5: The listening equation - horizontal & vertical differentiation
A. ”First step” estimates for κ1 , κ2
Parameter
Estimate
SE
κ1
1.472
0.0063
κ2
1.134
0.0077
B. ”Second step” estimates for the remaining parameters
Estimate
SE
σ
0.589
0.017
Estimate
SE
religious
-0.954
0.004
constant
-5.143
northeast
0.097
0.007
urban
-0.473
0.006
0.008
spanish
-1.235
midwest
0.067
0.007
0.010
nt
0.189
south
0.088
0.004
0.011
income
-0.092
0.003
mainstream
chr
0.450
0.007
college
-0.656
0.001
0.438
0.007
black
-0.712
0.002
country
0.617
0.007
hisp
-0.370
0.003
rock
0.642
0.011
blackXurban
5.555
0.001
oldies
0.067
0.006
hispXspan
3.962
0.002
γh
0.466
C. Quality and Home effects*
γq
0.604
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Advertisers’ Demand Model
Advertisers’ Demand Model
Follows BW99 exactly
Market t’s “ad price” pt paid for each listener stations “produce”;
determined from advertisers’ inverse-demand curve:
pt = αt × (St1 )−η
Demand downward-sloping in listenership to in-metro station (St1 );
1/η elasticity of demand
Let ln(αt ) ≡ kt γ + ωt → ln(pt ) = kt γ − ηln(St1 ) + ωt
Estimate with 2SLS (instruments: population, # out-metro
stations)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Advertisers’ Demand Model
Table 5: Demand For Listeners
northeast
midwest
south
income
college
black
hisp
−η
Constant
Observations
R-squared
OLS
IV
-0.0746
(0.064)
0.0835
(0.061)
0.0148
(0.060)
0.0567*
(0.030)
0.167***
(0.043)
-0.0231
(0.021)
-0.0120
(0.014)
-0.541***
(0.062)
4.492***
(0.17)
163
0.52
-0.0739
(0.063)
0.0799
(0.059)
0.0132
(0.059)
0.0606**
(0.029)
0.164***
(0.042)
-0.0242
(0.020)
-0.0124
(0.013)
-0.510***
(0.072)
4.554***
(0.18)
163
0.52
Standard errors in parentheses. Instruments:
population, number of out-metro stations
*** p < 0.01, ** p < 0.05, * p < 0.1
Elasticity of demand approx. (-2), very similar as in BW99
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Entry Model
Entry Model
Large number of identical (ex-ante) potential entrants into market t
Each potential entrant chooses (i) whether to enter the market and
(ii) if so, in which format/quality to operate; simultaneous decisions
Entrants into format g incur fixed cost fgt ; they produce listeners as
described by the listening equation, and sell them to advertisers at
a price determined from the demand for listeners model
Static, complete information Nash Equilibrium; appropriate for
cross-sectional setups where powerful instruments, such as the
size of the market population, dictate the long-run market potential
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Entry Model
Necessary Equilibrium Conditions on Fixed Costs
Fix market t
~ t , obtain bounds
Given an observed equilibrium market structure N
on fgt for every format g = 1, ..., 10:
~ t + eg ; θ̂) ≤ fgt ≤ v gt (N
~ )
v gt (N
{z
}
|
| {z t }
f gt
f gt
(β 0 , σ, γ, η)0
Where θ =
are the parameters of the listening &
advertisers’ demand models
The estimated interval [f gt , f gt ] is what we use in the policy
analysis
If Ngt = 0, we set f gt = 0, f gt = ∞
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Entry Model
Bounds on the true CDF of fixed costs
Can obtain upper and lower bounds on the EDF of fgt across
markets; denote number of markets by Nm , then for every
constant c > 0:
Nm
Nm
Nm
1 X
1 X
1 X
I{f gt ≤ c} ≤
I{fgt ≤ c} ≤
I{f gt ≤ c}
Nm
Nm
Nm
t=1
t=1
t=1
That is, the EDF of the lower (upper) bounds on fixed costs in
format g is an upper (lower) bound on the EDF of these costs
If one assumes that fgt are iid across markets t, then these
converge to bounds on the true CDF of format-g fixed costs (recall
we do not actually need this for policy evaluations)
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
gures
Data
Model & Estimation
Welfare Analysis
Conclusion
Entry Model
CDF for fixed costs − "Mainstream" format
1
0.8
Pr(F<=c)
Pr(F<=c)
0.8
0.6
0.4
Lower bound
Upper bound
0.2
0
0
0.4
Lower bound
Upper bound
0
0
20
40
60
c (in M$)
CDF for fixed costs − "Rock" format
1
0.8
Pr(F<=c)
0.8
Pr(F<=c)
0.6
0.2
10
20
30
c (in M$)
CDF for fixed costs − "Country" format
1
0.6
0.4
Lower bound
Upper bound
0.2
0
CDF for fixed costs − "CHR" format
1
0
10
20
c (in M$)
0.6
0.4
Lower bound
Upper bound
0.2
30
0
0
10
20
c (in M$)
30
40
Figure 1: Estimated bounds on the CDF of fixed costs
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Entry Model
CDF for fixed costs − "Oldies" format
1
CDF for fixed costs − "Religious" format
1
0.8
Pr(F<=c)
Pr(F<=c)
0.8
0.6
0.4
Lower bound
Upper bound
0.2
0
0
0
Lower bound
Upper bound
4
6
8
c (in M$)
CDF for fixed costs − "Spanish" format
1
0
2
0.8
Pr(F<=c)
0.8
Pr(F<=c)
0.4
0.2
10
20
30
c (in M$)
CDF for fixed costs − "Urban" format
1
0.6
0.4
Lower bound
Upper bound
0.2
0
0.6
0
10
20
c (in M$)
30
0.6
0.4
Lower bound
Upper bound
0.2
40
0
0
5
10
c (in M$)
15
20
Figure 2: Estimated bounds on the CDF of fixed costs
Berry, Eizenberg, Waldfogel (2014)
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c Data
(in M$)
Introduction
Model & Estimation
c (in M$)
Welfare Analysis
Conclusion
Figure 2: Estimated bounds on the CDF of fixed costs
Entry Model
CDF for fixed costs − "News/Talk" format
1
Pr(F<=c)
0.8
0.6
0.4
Lower bound
Upper bound
0.2
0
0
5
10
15
c (in M$)
CDF for fixed costs − "Other" format
20
1
Pr(F<=c)
0.8
0.6
0.4
Lower bound
Upper bound
0.2
0
0
2
4
6
8
c (in M$)
10
12
14
16
Figure 3: Estimated bounds on the CDF of fixed costs
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Horizontal Case
Optimal Market Structures
Once we have θ and bounds on F, we can place bounds on the
optimal number of stations
With 10 formats, hard computational problem
Caveats:
1
As in BW99, we can only look at the welfare of market participants:
producers (stations) and consumers (advertisers)
2
“Empty” market-format cells remain empty in this exercise
3
We first hold F at mid-point of [f gt , f gt ], get point estimate of optimal
N vector; then use the bounds on F to create bounds on N vector
Berry, Eizenberg, Waldfogel (2014)
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Horizontal Case
Table 6: Observed vs. Optimal Mean
Number of In-metro Stations
Format
Mainstream
CHR
Country
Rock
Oldies
Religious
Urban
Spanish
News/Talk
Other
Total In-metro
Observed
Optimal
% Difference
3.35
1.06
2.10
2.33
1.02
1.66
1.50
1.34
3.08
2.12
1.38
0.85
1.05
1.09
0.88
0.81
0.73
0.60
1.35
1.07
0.59
0.20
0.50
0.53
0.14
0.51
0.51
0.56
0.56
0.50
19.58
9.79
0.50
Table 7: Observed vs. Optimal Welfare,
Listening Shares and Prices
Welfare ( million)
Mean In-Metro Listening Share (%)
Mean Ad Price ()
Berry, Eizenberg, Waldfogel (2014)
Observed
Optimal
11,977
11.10%
570.48
13,779
8.15%
662.54
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Horizontal Case
Going beyond averages
Only two markets (out of 163) display efficiency of the free-entry
equilibrium: Bloomington, IL, and Lancaster, PA (small markets,
scope for excess entry a-priori limited)
Insufficient entry is hardly ever detected: in the entire sample, only
six market-format pairs in which the optimal number of stations
exceeds the observed number! Three of these cases involve the
News/Talk format
In the Radio market of Portsmouth-Dover-Rochester (New
Hampshire), the social planner would like to eliminate one Rock
station and one Country station—and add one Mainstream station
and one News/Talk station instead. Such re-allocation is very
a-typical
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Horizontal Case
What about listeners’ welfare?
Station elimination prescribed by planner reduces total number of
AQH-listeners by 6.02 million; to offset gains, each would need to
be willing to pay $299; A monthly subscription to XM Sirius’ basic
satellite radio service cost $14.49 in August 2013; Annually:
$173.8
Another strategy: station elimination results in loss of 6.56 million
expected “utility units”; to offset gains, each would need to be
worth $274
Removing a single Rock station from NYC market results in
listener welfare losses of $5.3 million using this conversion rate;
but gains to other market participants are $6.4 million - so some
station elimination would still be optimal
Berry, Eizenberg, Waldfogel (2014)
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Horizontal & Vertical Differentiation
Table 10: Optimal and observed market structures, horizontal and vertical differentiation
A. Formats with a single quality level:
Mean number observed
Mean number optimal
Mean optimal reduction
1.06
2.10
2.33
1.02
1.66
1.50
1.34
2.12
0.85
1.02
1.04
0.86
0.77
0.71
0.50
1.01
20.2%
51.3%
55.3%
16.2%
53.5%
52.6%
63.0%
52.3%
CHR
Country
Rock
Oldies
Religious
Urban
Spanish
Other
B. Formats with quality differentiation:
Format:
Mainstream
News/Talk
Low quality
High quality
Low quality
High quality
1.18-1.95
0.37-0.66
44.1%-81%
1.40-2.17
0.56-0.86
38.6%-74.2%
1.83-2.02
0.79-0.89
51.3%-61.2%
1.06-1.25
0.40-0.51
51.7%-67.6%
Mean number observed
Mean number optimal
Mean optimal reduction
Berry, Eizenberg, Waldfogel (2014)
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Local Analysis: Quality Misallocation
How often is quality misallocated in equilibrium?
Beginning with the market structure observed in equilibrium, how
often can welfare be improved by converting an observed
low-quality station into a high-quality one? And vice versa?
Only “local” changes to the observed market structure, holding the
total number of stations fixed
Does converting one low-q station into high-q operation increase
social benefits by more than f gth − f gt` ?
Does converting one high-q station into low-q decrease benefits
by less than f gth − f gt` ?
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Local Analysis: Quality Misallocation
Findings:
Out of 90 markets with observed high-quality mainstream stations,
in 72 cases welfare can be unambiguously improved by converting
one of those stations to low quality operation. News/Talk: 74 out of
78 markets
There are no cases where a market has observed low-quality
stations—in either format—and converting one of them to high
quality would unambiguously improve welfare.
Conclusion: over-provision of quality appears to be widespread,
whereas under-provision is not encountered
Berry, Eizenberg, Waldfogel (2014)
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May 2014
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Introduction
Data
Model & Estimation
Welfare Analysis
Conclusion
Conclusion
Dealing with interesting horizontal and vertical variety is now
feasible
In radio, adding variety means that “optimal” reduction in the
number of stations goes from 75% to about 50%
Extension to multi-product firms: do they have lower costs?
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May 2014
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