the news hour: estimating the value of local television news · 9/15/2014 · lnlloo 21 3.4 75.6...

Introduction and Motivation Model Estimation Estimation Results Conclusions

The News Hour: Estimating the Value of LocalTelevision News

Matthew J. Bakerand

Lisa M. GeorgeHunter College and the Graduate Center, CUNY

September 29, 2014

Matthew J. Baker and Lisa M. George Hunter College and the Graduate Center, CUNY

The News Hour: Estimating the Value of Local Television News


Overview

Background on Local TV News

A regulatory interest in “localism” in media and whether localinterests are being served by media.

Like other sources of local information, local television news isin decline. Declining, aging viewership.

What if it disappeared?

Basic question: is there too little local news?




Overview

More pointedly

How does actual provision of local news compare with optimal

provision of local news?

Many-faceted question: Perspectives of viewers, advertisers,and stations are all important.

Value of variety to consumers versus duplication of effort alsoimportant.

Interesting aspects of TV market:

Two-sidedFixed broadcasting/ad time - if a station doesn’t provide localnews, what does it provide instead?




Overview

Analysis preview

For a welfare analysis, we need:

Model of viewership.Model of advertising demand.Model of station competition for viewership and advertisingrevenue.Illustrate how:

Viewership, broadcast type, and ad revenues change together.Decisions relate to viewers & advertisers’ surplus.Counterfactual profits to stations.




Analytical Ideas and Methods

Local TV Markets

Essential features:

Competition among long-established rivals.

Through broadcasting decisions, stations compete for viewers,and resulting advertising revenues (90% of station revenues).

Advertising minutes don’t vary much.

Point of departure: Stations make programming lineup choicesaccording to a complete-information noncooperative game.Stations choose programming lineups in competition with rivals.




Analytical Ideas and Methods

Literature

Literature on media, advertising, and variety:

Berry and Waldfogel (1999), Wilbur (2008), Crawford (2003),Mooney (2009).

No complete welfare analysis from perspective of mediaproviders, consumers, and advertisers.

Econometrics of strategic interaction:

Bajari, Hong and Ryan (2010), Ciliberto and Tamer (2009) -game-theory built into estimation techniques.

Extensions: inclusion of about payoffs, not just observedactions. Product diversity decision, not just modeling an entrygame.




Data Description and Overview

Television ratings and advertising revenues:

Weekdays for 4 weeks in Feb. 2010: “all” viewership shares inhalf hour blocks, 5:00 to 8:00, via Nielsen Media Research.

Station characteristics from Warren Communication Television

and Cable Factbook

Program characteristics: Nielsen, Kantar, IMDB.

Most stations more or less follow a fixed daily schedule through thetime period.We therefore focus on average viewership and advertising revenuein a given time slot as a function of programming type.





Viewership

A simple approach that gives a notion of differentiation andsubstitutability across programming types:

Viewers chooses not to watch, or type of programming:1 Local news2 Other local/network programming3 National news4 Other cable programming

Then choose a broadcast of the given type.

Nested Multinomial Logit (NML).

LOTS of other alternatives, but MNL captures nature ofcompetition nicely.





Competitive aspects of station choices

Viewership decision tree reduces stations’ options to two typesof programming - “local news” or “other local.”

National news - a “fixed” decision - local stations arecontractually obligated to carry it at specified times.

Cable stations don’t make programming decisions withcapturing viewers in a particular market in mind, so theirdecisions are assumed exogenous to a given market.

Really, local stations are choosing an early evening sequenceof programs.





Picturing the game(s)

c c c c c c

n n n n n n

n o l o l o

l n o l o o

l o o n l o

5:00-5:30

5:30-6:00

6:00-6:30

6:30-7:00

7:00-7:30

7:30-8:00

Channel 5

Channel 4

Channel 3

Channel 2

Channel 1

Figure: A depiction of the scheduling game. Local stations 1,2, and 3 choosesequences of l ’s and o’s (Gray nodes denote fixed programming decisions)




Summary statistics

Station type and market size

Small Markets < 2.5 mil. (N=39)Station Type

Local Stations (n=39) 7.4 1.4 5.0 12.0Cable Stations (n=39) 64.9 9.8 51.0 102.0Total Stations (n=39) 72.3 10.0 59.0 109.0

Mid-size Markets 2.5 to 5 mil. (N=31)Station Type

Local Stations 9.5 1.7 6.0 13.0Cable Stations 89.5 16.9 55.0 110.0Total Stations 99.0 17.6 63.0 118.0

Large Markets > 5 mil. (N=31)Station Type

Local Stations 13.6 4.0 7.0 22.0Cable Stations 95.9 8.0 81.0 114.0Total Stations 109.5 8.8 92.0 125.0




Summary statistics

Market-level statistics (by Station)

cases Mean N Mean NShare Price-per-Second Price-per-Second

5:00 - Local news 0.0019 340 $9.80 317Natl. news 0.0001 133 $4.58 3Other Local 0.0003 636 $5.21 303Other cable 0.0001 8,174 0





7:30 - Local news 0.0002 19 $14.09 5Natl. news 0.0001 97 0Other Local 0.0011 937 $22.37 618Other cable 0.0001 8,230 0




Summary statistics

Aggregate programming shares by type and timing

Time Slot MeanLocal news Nat’l news Other local Other cable Total

5:00-5:30 0.0064 0.0001 0.0018 0.0077 0.01615:30-6:00 0.0032 0.0037 0.0020 0.0081 0.01706:00-6:30 0.0081 0.0003 0.0019 0.0085 0.01886:30-7:00 0.0006 0.0044 0.0052 0.0091 0.01937:00-7:30 0.0005 0.0003 0.0099 0.0104 0.02107:30-8:00 0.0000 0.0001 0.0105 0.0109 0.0215Total 0.0031 0.0015 0.0052 0.0091 0.0190




Summary statistics

Broadcasts by programming type

Time Slot MeanLocal news Freq Nat’l news Freq Other local Freq

5:00-5:30 3.4 34.9% 1.3 14.6% 6.3 62.7%5:30-6:00 1.9 19.0% 3.2 34.9% 5.9 58.2%6:00-6:30 3.5 37.1% 2.0 22.0% 5.3 51.0%6:30-7:00 0.8 7.1% 3.8 40.8% 6.3 62.2%7:00-7:30 0.5 5.6% 1.2 12.2% 8.7 87.5%7:30-8:00 0.2 1.9% 1.0 9.5% 9.3 93.9%Total 1.7 17.6% 2.1 22.4% 7.0 69.2%




Summary statistics

Most common lineups

Programming lineups No. Col % Cum %

oooooo 213 34.2 34.2lnlooo 106 17.0 51.2lllnoo 96 15.4 66.6oolnoo 35 5.6 72.2lnlloo 21 3.4 75.6lnoooo 21 3.4 79.0lllnlo 17 2.7 81.7lloooo 16 2.6 84.3lllooo 15 2.4 86.7oooloo 10 1.6 88.3other 73 11.7 100.0Total 623 100.0




Summary statistics

0

200

400

600

0 2 4 6

Local newsNational NewsOther Local




A preview

A first question: how and why might the interests of stations,viewers, and advertisers diverge?

Viewers that prefer one type of programming are not asvaluable to advertisers.

“Business stealing” - fixed number of viewers split betweenmultiple stations - less product diversity.

“Dynamic business stealing” - desire to broadcast news first inthe evening.




A preview

What could go wrong in pictures

Station b → local news other

Station a ↓

local news 2 2.72 6

other 6 52.7 5

local news other

local news 0.04 0.050.04 0.09

other 0.09 0.060.05 0.06

local news other

local news 1 1.351 3

other 3 2.51.35 2.5

Station Payoffs ($ sec.)

Advertisers’ Surplus ($ sec.)

Viewership shares (%)

Hypothetical two-station game.

Dominant strategy Nash eq - both stations broadcast other.Advertisers’ interests are aligned with broadcasters, more or less.Viewers would prefer one station to broadcast local news.




Setup

Model definitions

Some definitions:

Geographic identifier: m.

Time identifier t (time slot).

Market description:

Vm set of all stations (cable and local) in a market: viewershipshares simt and covariates Ximt , i ∈ Vm.Ad. revenue rjmt for j ∈ Lm ⊆ Vm local stations.Broadcast decisions for bjt ∈ Dmt ⊆ Lm, and broadcastsbimt ∈ Vm \ Dmt for all stations.

Also, let bjm = bjmtTt=1 denote a station’s broadcast menu -

sequence of broadcast decisions over the course of the night.




Setup

Some additional notation

Let the set of broadcast menus available firm j in m be Bjm. As wespecify, this set will typically have between 25 = 32 and 26 = 64members, depending on whether or not a broadcast is fixed.Also, let Bm = B1m × B2m × . . .BLm denote the set of possiblebroadcast menus across the local channels for the night. A verylarge set!




Setup

Overview

Components of the model:

Viewership: vjt = vjt(bj , b−j , εj , ε−j ,Xj ,X−j , θv ) or for short:vjt = vjt(bj , b−j). Viewership of j at t depends upon chosenmenus of all stations.

Early evening advertising revenues:Rj(bj , b−j , θ) =

∑6t=1 rjt(vjt(bj , b−j , θv ), bj , ε

pj ,X

pj , θp)

bj ∈ Bj chosen to maximize revenues Rj given actions b−j ofother stations.

Competition flows through viewership.




Viewership

Viewership model

Following Berry(1994), suppose that viewer n gets utility:

unjmtb = αbj + δvjmt + ζjmt(µbj ) + (1− µbj )ǫnjmt , bj ∈ Sb

ζ and ǫ are related as described in Cardell (1987). We specify that:

δvjmt = Xjtβ + ωvj + ωv

mt + ǫvjmt

Berry (1994) uses the error structure to get a share form MNLwhich depends upon observed and unobserved components.




Viewership

The MNL Viewership Model

Integrating out extreme-value RVs results in (dropping m, t for thetime being):

sj =e

αbj+δv

j

1−µbj

∑

k∈Sbje

αbk+δv

k1−µbk

∑

k∈Sbje

αbk+δv

k

1−µbk

∑

b∈B

∑

l∈Sbe

αbl+δv

l1−µbl

Non-viewership share utility is normalized to zero so:

s0 =1

∑

b∈B

∑

l∈Sbe

αbl+δv

l1−µbl




Viewership

Linear share form

Forming ln[sjmt

s0mt

]

and doing a little algebra gives:

ln sjmt − ln s0mt = µbj ln sGjmt + Xjtβ

v + ωvj + ωv

mt + ǫvjmt

where sGjmt =sjmt∑

k∈Sbjskmt

is j ’s within-group share. A linear

estimating equation (with endogeneity). Alternatively:

ln sjmt − ln s0mt = µbj ln sGjmt + Xjtβ

v + evjmt

where evm ∼ N(0,Ωv ). See Baltagi(2008).




Viewership

Practical aspects of viewership

Explanatory variables:

Time-Market random effect, station random effect.

Dummies and nests for programming type (local news, otherlocal, national news, other cable).

Dummies for sequenced news types (i.e., broadcasting localnews after previous local news broadcast).

Lagged shares and interactions (“lead-in” effects).

Cumulative news totals in market up until the time slot(“breaking news” effect).




Viewership

Role of explanatory variables

Collectively, the viewership model makes the scheduling problemquite rich:

Stations have to think about lead-in effects, in addition towhat other stations are broadcasting simultaneously.

Stations have to think about cumulative news throughout thebroadcast time.




Viewership

Why NMNL? Part 1

Expected utility:

EU = ln

∑

b∈B

∑

i∈Sb

eui

1−µb

1−µb

Suppose we just had two types of goods, which each gave equalutility. Then:

EU = ln[

N1−µ11 eu1 + N

1−µ22 eu2

]

Suppose we have N = N1 + N2 channels and want to maximizeexpected utility. We require:

(1− µ1)N−µ11 eu1 − (1− µ2)N

−µ22 eu2 = 0




Viewership

Why MNL? Part 1 cont.

Using the above, we can compute:

∂N∗

1

∂u1=

N∗

1N∗

2

µ1N2 + µ2N1

and also get elasticity:

E1u1 = u1N − N∗

1

Nµ1 + N∗

1 (µ2 − µ1)

This is the elasticity of optimal variety wrt “quality” of a good.Note role of µ1 and µ1 relative to µ2. At symmetric solution:

u

µ1 + µ2




Viewership

Why NMNL? Part 2

Using observed shares, solve for mean utility:

e

ui1−µbi =

si

s0

(sg

s0

)µbi

1−µbi

Plug this back into expected utility and simplify:

EU = ln

∑

b∈B

∑

i∈Sb

si

s0

(sg

s0

)µbi

1−µbi

= − ln s0

Max utility from viewing = min share of null activity, or

maximal viewing = maximal utility.




Advertising Revenue

Advertising Revenue model (Berry and Waldfogel, 1999)

Advertisers’ demand for viewer as a function of the number ofviewers:

p = Kvη−1

If that is the price-per-viewer per unit time, we have:

pv = r = Kvη

as price per unit time. Integrate to get advertisers’ surplus:

ATS =K

ηvη

Advertisers pay r , so net surplus as ATS − r :

AS =K

ηvη − rp =

(1

η− 1

)

Kvη




Advertising Revenue

Estimating revenue equation

We use r = pv , with allowance for market, time, and broadcasttype idiosyncracies:

rjmtb = Kjmtbvηb

jmtb

We specify Kjmtb as:

lnKjmtb = αrb + ωr

j + ωrmt + εrjmt

where εpjmt is an idiosyncratic error term. Estimating equation:

ln rjmtb = ηb ln vjmtb + αrb + ωr

j + ωrmt + εrjmt

︸︷︷︸

erjmt

Use erm ∼ N(0,Ωp), as specified in Baltagi (2008).




Advertising Revenue

Practical aspects of estimation

Included variables:

Station and Market-time random effects.

programming type dummy

Interaction between viewers and programming type.




The problem(s) with estimation

Seemingly a straightforward basis for a simultaneous equationsmodel. For each observation:

Viewership equation,

Revenue equation.

Likelihood (market-level):

L(b,X , θ) = fv (ǫv ; b,X , θ)|Jv (ǫ)| ×

fp(ǫp ; b,X , θ)




Can’t just be estimated and used for counterfactuals.

Selection - we observe profit-maximizing lineups.

Cohesiveness/incompleteness - Parameters, data, and errorsdo not uniquely determine outcome.




Selection problem

Recall that:

Rj(bj , b−j , εpj , θ) =

6∑

t=1

rjt(vjt(bj , b−j , θv ), bj , εpj ,X

pj , θp)

Add a term:

Prob

(

Rj(bj , b−j , εpj , θ) ≥ Rj(b

′

j , b−j , εp′

j , θ) ∀b′j ∈ Bj

)

to the likelihood. Some of εpj′ can be calculated from model.




Sketch of cohesiveness problem

Example: (based on Ciliberto and Tamer (2010))

Two-station, one time slot market where b1 = o, b2 = l isobserved.

Mean revenues are α if stations have different broadcasts,α− δ if same broadcast.

Profit maximization requires: α ≥ α− δ + ǫi , or δ > ǫi .

Is probability of the market outcome F (δ)2? No!.




Expanding on the example

The previously described model implies:

S1↓,S2→ l o

l α− δ + ǫ1, α− δ + ǫ2 α,α

o α,α α− δ + ǫ1, α− δ + ǫ2




Expanding on the example

The previously described model implies:

S1↓,S2→ l o

l α− δ + ǫ1, α− δ + ǫ2 α,α

o α,α α− δ + ǫ1, α− δ + ǫ2

While ǫ1, ǫ2 < δ, observed outcome is not completely identified bythe model. We need to include an equilibrium selection mechanism

as well. Simplest possibility: mechanism is P(o, l) = 12 ,P(l , o) =

12

and the likelihood selection 12F (δ)

2. See Bajari, Hong, and Ryan(2011).




Tackling cohesiveness

Tack on a term to the likelihood:

Prob

(

b|Rj(bj , b−j , εpj , θ) ≥ Rj(b

′

j , b−j , εp′

j , θ) ∀b′j ∈ Bj , j ∈ Lj

)

The probability b is the outcome, given that it is a Nashequilibrium.




Complete likelihood

Contribution of a market to the likelihood L(b,X , θ) =

fv (ǫv ; b,X , θ)|Jv (ǫ)| ×

fp(ǫp ; b,X , θ) ×

Prob

(

Rj(bj , b−j , εpj , θ) ≥ Rj(b

′

j , b−j , εp′

j , θ) ∀b′j ∈ Bj

)

×

Prob

(

b|Rj(bj , b−j , εpj , θ) ≥ Rj(b

′

j , b−j , εp′

j , θ) ∀b′j ∈ Bj , j ∈ Lj

)




Actual estimation

Computational problems

Difficulties in actual estimation.

Simulation deals with difficult integrations.

Calculating Nash equilibria for games with large amounts ofplayers with a large dimensional strategy set istime-consuming.

Counterfactual shares themselves are time-consuming tocalculate.

Simulation-based estimator is good, but introducesdiscontinuities, doubts about standard errors.




Actual estimation

Estimation overview

Use techniques developed in Ackerberg (2010).

A way of using importance sampling to reduce burden.

Idea: separate model into hard (nonlinear, computationallyintensive parts) and easy parts.

Get a reasonable approximate model, simulate the hard partsand solve once or a small number of times.

Re-weight the solutions in estimation using the easy parts.

Techniques in Chernozhukov and Hong (2003).

QBE/LTE estimator. Essentially, avoid bootstrap andmaximizing likelihood directly.

If interested, Baker (2014) describes the method and aStata/Mata package for implementation, and some wrappers.




Actual estimation

Estimation in a nutshell

1 Estimate the simple linear model with no selection/cohesioneffects.

2 Draw error terms subject to selection constraint.

3 For each market and draw, solve for additional Nashequilibrium.

4 Treat draws as an Ackerberg-Keane-Wolpin importancesample, estimate via MCMC simulation (Baker, 2014).

Each step has a few nuances. Errors are hard to draw. Games canonly be approximately solved (MCMC method).




Actual estimation

In equations, roughly

Log-likelihood contribution of a market: ln L =

ln fp(ǫp ; b,X , θp) + ln fv(ǫv ; b,X , θv )|+ ln |Jv (ǫ)|+

ln

1

S

S∑

s=1

1

NE(s)Prob

(

R(s)j ≥ R

′(s)j

) h(R(s)j , R

′(s)j , θ)

g(R(s)j , R

′(s)j , θ0)

Idea (from AKW) is to estimate a preliminary model, and thendraw an importance sample using this model. Estimation reweightsthe sample solutions.




Model estimates

Results

Equationby-equation Maximum-Likelihood Complete Model

ViewershipMu: local news 0.345 (0.294) 0.312 (0.00415)Mu: other local 0.313 (0.280) 0.259 (0.00282)Mu: national news 0.169 (0.205) 0.157 (0.00161)Mu: other cable 0.881 (0.469) 1.000 .Local news? -1.675 (0.647) -3.098 (0.0164)Local other? -2.145 (0.732) -3.464 (0.0158)National news? -3.727 (0.965) -4.673 (0.00796)loc. news(t),loc. news(t-1) -0.171 (0.207) -0.164 (0.00742)loc. news(t),nat. news(t-1) -0.0258 (0.0803) -0.142 (0.0113)nat. news(t),loc. news(t-1) 1.719 (0.656) 1.311 (0.0130)nat. news(t),nat. news(t-1) 0.0235 (0.0767) 0.0944 (0.00786)share(t-1) 14.54 (1.906) 10.58 (0.324)shares: loc. news(t),loc. news(t-1) 34.90 (2.954) 27.60 (1.021)shares: loc. news(t),nat. news(t-1) 10.87 (1.649) 12.33 (0.753)shares: nat. news(t),loc. news(t-1) 1.772 (0.666) 3.579 (0.447)shares: nat. news(t),nat. news(t-1) 111.4 (5.278) 86.01 (4.297)Cum. share local news -14.06 (1.875) -8.448 (0.269)Cum. share nat. news -2.282 (0.755) -2.392 (0.259)constant -4.368 (1.045) -3.081 (0.0168)

RE vViewership: log(sd) station RE -1.130 (0.532) -0.807 (0.00900)Viewership: log(sd) market RE 0.190 (0.218) 0.334 (0.0218)Viewership: log(sd) model -1.791 (0.669) -1.876 (0.00362)

PriceLocal news viewer elast. 0.575 (0.379) 0.521 (0.0207)Other viewer elast. 0.607 (0.389) 0.509 (0.00580)Nat. news viewer elast. 0.215 (0.232) 0.153 (0.00352)Local news? -3.503 (0.936) -3.628 (0.167)Nat. news? -3.616 (0.951) -3.332 (0.0318)constant 0.331 (0.288) 0.0868 (0.0265)

Re pRevenue: log(sd) station RE -0.785 (0.443) 0.00543 (0.0416)Revenue: log(sd) market RE -0.651 (0.403) -0.932 (0.0417)Revenue: log(sd) model -1.241 (0.557) -1.350 (0.00852)

N

Total obs. 102720 102720MarketsGames 55698 55698PlayersLocal news 102720 102720Other local 102720 102720Nat’l news 102720 102720

Standard errors in parentheses




Model estimates

Some highlights

Other local and local news don’t seem profoundly differentlydifferentiated.

Time, day, affiliate dummies (not shown) are about what onewould expect.

Other points easier to understand via simulation.

Inclusion of selection effects seems to make a big difference inparameters.

Have not yet fully implemented estimation procedure.




Welfare simulations

Questions:

How closely do broadcasting decisions coincide with whatviewers want?

How closely do broadcasting decisions coincide with whatadvertisers want?

The interesting aspects of two-sided markets are really all aboutthe potential for divergence of these interests.




Simulation Results

Calculate “observed” error terms using model estimates.

Draw values for “unobserved” errors consistent with modelestimates, selection, and incompleteness.

For each simulation run:1 Action profiles maximizing stations’ profits.2 Actions profiles maximizing advertisers’ surplus.3 Action profiles maximizing viewership.




Simulation Results

Market-level statistics (by Station)











Simulation Results

Shares (viewer optimum)











Simulation Results

Shares (station optimum)











Simulation Results

Total Shares


5:00-5:30 0.0064 0.0001 0.0018 0.0077 0.01615:30-6:00 0.0032 0.0037 0.0020 0.0081 0.01706:00-6:30 0.0081 0.0003 0.0019 0.0085 0.01886:30-7:00 0.0006 0.0044 0.0052 0.0091 0.01937:00-7:30 0.0005 0.0003 0.0099 0.0104 0.02107:30-8:00 0.0000 0.0001 0.0105 0.0109 0.0215Total 0.0031 0.0015 0.0052 0.0091 0.0190




Simulation Results

Total Shares (viewer optimum)


5:00-5:30 0.0057 0.0001 0.0028 0.0077 0.01625:30-6:00 0.0029 0.0037 0.0024 0.0082 0.01746:00-6:30 0.0076 0.0003 0.0023 0.0086 0.01906:30-7:00 0.0032 0.0046 0.0040 0.0092 0.02097:00-7:30 0.0055 0.0003 0.0074 0.0105 0.02387:30-8:00 0.0062 0.0001 0.0074 0.0110 0.0251Total 0.0052 0.0015 0.0044 0.0092 0.0204




Simulation Results


5:00-5:30 0.0027 0.0001 0.0050 0.0077 0.01555:30-6:00 0.0020 0.0022 0.0035 0.0082 0.01606:00-6:30 0.0055 0.0003 0.0041 0.0086 0.01856:30-7:00 0.0022 0.0043 0.0049 0.0093 0.02087:00-7:30 0.0035 0.0003 0.0096 0.0107 0.02407:30-8:00 0.0001 0.0001 0.0109 0.0112 0.0223Total 0.0027 0.0012 0.0063 0.0093 0.0195




Simulation Results



5:00-5:30 3.4 34.9% 1.3 14.6% 6.3 62.7%5:30-6:00 1.9 19.0% 3.2 34.9% 5.9 58.2%6:00-6:30 3.5 37.1% 2.0 22.0% 5.3 51.0%6:30-7:00 0.8 7.1% 3.8 40.8% 6.3 62.2%7:00-7:30 0.5 5.6% 1.2 12.2% 8.7 87.5%7:30-8:00 0.2 1.9% 1.0 9.5% 9.3 93.9%Total 1.7 17.6% 2.1 22.4% 7.0 69.2%




Simulation Results



5:00-5:30 4.5 45.4% 1.3 14.6% 5.1 52.1%5:30-6:00 3.0 29.7% 3.2 34.9% 4.6 45.6%6:00-6:30 4.1 42.9% 2.0 22.0% 4.7 45.1%6:30-7:00 2.2 21.1% 3.8 40.8% 4.9 48.7%7:00-7:30 2.6 26.3% 1.2 12.2% 6.8 68.2%7:30-8:00 2.7 27.8% 1.0 9.5% 6.6 66.3%Total 3.2 32.2% 2.1 22.4% 5.4 54.3%




Simulation Results



5:00-5:30 3.3 31.8% 1.3 14.6% 6.4 65.8%5:30-6:00 2.4 23.5% 3.2 34.9% 5.4 53.7%6:00-6:30 3.2 33.0% 2.0 22.0% 5.6 55.2%6:30-7:00 1.6 15.6% 3.8 40.8% 5.5 53.8%7:00-7:30 1.9 18.6% 1.2 12.2% 7.3 74.5%7:30-8:00 0.2 2.6% 1.0 9.5% 9.2 93.1%Total 2.1 20.8% 2.1 22.4% 6.6 66.0%




Simulation Results

0

200

400

600

0 2 4 6

0

200

400

600

0 2 4 6

0

200

400

600

0 2 4 6

0

200

400

600

0 2 4 6

Local newsNational NewsOther Local




Summary of welfare results

Viewers would seem to like more “other” stuff earlier in theday, and more local news later in the day.

Stations and advertisers would also like to see the world workthis way, but to a lesser extent.

It looks like there is some inefficient business stealing androom for pareto-improving policies.




Conclusions-Extensions-To be done

Conclusions

Application of Game-Theoretic ideas in estimation: new andexciting!

Some improvements over the state-of-the-art both inevaluation of media markets and in game-theory basedestimation.

Some other applications that I’m thinking about...



the news hour: estimating the value of local television news · 9/15/2014 · lnlloo 21 3.4 75.6...

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