An Empirical Model of Television Advertising and Viewing Markets

Transcription

1 An Empirical Model of Televiion Advertiing and Viewing Market Kenneth C. Wilbur Aitant Profeor of Marketing Marhall School of Buine Univerity of Southern California 3660 Troudale Parkway, ACC 301L Lo Angele, California Phone: Fax: uc.edu

2 An Empirical Model of Televiion Advertiing and Viewing Market Abtract Televiion network compete for both viewer and advertier. Recent theoretical work ha modeled the impact of viewer and advertier network uage on the other group. Empirical work ha not kept pace. I etimate viewer demand for televiion program a a function of program characteritic, audience flow, day and time effect, and time given to advertiing. I find audience are highly reponive to advertiing quantitie. When a highly-rated network increae it advertiing level 10%, the model predict it loe about 30% of it audience; a low-rated network loe 70-90% of it viewer. I eparately etimate advertier demand for program audience a a function of audience ize, quantity of advertiing old, and program characteritic. I find the price elaticity of advertiing demand i -2.9, and the audience elaticity of advertiing demand i The reult indicate that advertier preference are at leat a important a viewer preference when network chooe program. Viewer two mot preferred program genre, Action/Drama and New, account for jut 16% of network program-hour. Advertier two mot preferred genre, Reality and Scripted Comedy, account for 47% of network program-hour. I ue viewer and advertier demand etimate in conjunction with a game-theoretic model of televiion network competition to peculate how advertiement-avoidance technology will impact market equilibria. The model ugget that ad-avoidance tend to increae equilibrium advertiing level and decreae network revenue. Key Word: Advertiing; Broadcating; Demand Etimation; Empirical Indutrial Organization; Endogeneity; Entertainment Marketing; Televiion; Two-Sided Market

3 1 1. Introduction Televiion continue to be advertier mot important advertiing medium. It account for about a quarter of all advertiing volume in the United State. Houehold televiion viewing increaed every year between 1996 and 2004, to more than eight hour per day. 1 The televiion indutry i an example of a two-ided market. Network compete to attract viewer attention, and then ell that attention to advertier. A network ha to get both ide of the market on board to be ucceful. Advertier and marketer have long undertood the twoided nature of the indutry, and recent theoretical treatment have tarted modeling the interplay of advertier and viewer preference. Yet there ha not been any empirical tudy of the indutry that conider cro-group externalitie: the effect the number of advertier ha on audience ize, and the effect that audience ize ha on advertier demand. 2 Thi paper propoe an equilibrium model of the televiion indutry. Network et advertiing level to coordinate viewer and advertier. I etimate the model uing data from four ource: US broadcat televiion network audience hare in 50 geographic televiion market, advertiing quantitie and price per half-hour, program characteritic, and audience demographic collected by the US Cenu. There i clear evidence of cro-group externalitie. A 10% increae in advertiing level typically reduce audience ize 30%-93%, with more popular network experiencing maller audience loe. In turn, a 10% increae in audience ize typically raie advertiement price 8.3%. Viewer and advertier preference for program characteritic are compared to network programming choice. Viewer mot prefer to watch Action Drama and New program. Advertier mot prefer to buy time during Scripted Comedy and Reality program. Advertier preference eem to be at leat a important to network programming choice a viewer preference. Comedie and Realitie contitute 48% of network programming, while Action and New program account for jut 16%. Analyzing either ide of the market in iolation would ugget network were failing to atify their cutomer tate. 1 Source: Univeral McCann and Nielen Media Reearch data reported at 2 Controlling for advertiing level i neceary to obtain unbiaed viewer demand etimate. For example, a popular program will contain a lot of ad, o the etimate of it popularity would be biaed downward when advertiing i unoberved. Thi iue i imilar to a ituation in which demand for a conumer product i etimated in the abence of price data. Similarly, controlling for audience ize and ad quantity i neceary to obtain unbiaed advertier demand etimate.

4 2 A current topic in the televiion indutry i how digital video recorder will impact network advertiing revenue (ee, e.g., Garfield 2005). I olve a counterfactual to provide educated peculation about thee effect. I ue the viewer and advertier demand etimate to calibrate a model of network conduct and infer unoberved tune-in level. 3 I then olve for new market equilibria given a hypothetical advertiement-avoidance technology. I find that adavoidance technology lead to increaing advertiing level and falling network revenue. The next ection review the academic literature on televiion viewing and advertiing market. I then decribe the model of viewer utility, advertier demand, and network upply of advertiement. Section four decribe the data, endogeneity, and etimation. Section five dicue empirical reult. In ection ix I decribe the counterfactual and preent it reult. The paper conclude with a ummary and direction for future reearch. 2. Relevant Literature Thi paper contribute to the literature on televiion and advertiing market. Much of the recent work ha been baed on theoretical advance in our undertanding of two-ided market. Twoided market are generally characterized a indutrie in which platform enable interaction between ditinct group of agent, and try to get the variou ide on board (Rochet and Tirole, 2004). 4 The pioneering treatment in the two-ided market literature are Armtrong (forthcoming), Caillaud and Jullien (2001, 2003), and Rochet and Tirole (2003, forthcoming). For advertiement-upported media, the mot important inight from the two-ided market literature i that ad price reflect both the value of reaching a given audience, and the marginal effect of the advertiement ale on total audience ize. A key aumption in thee model concern agent platform ue. Televiion viewer are uually aumed to inglehome that i, to only watch one televiion network at a time. Advertier are often aumed to multihome that i, to ue multiple network and program to reach viewer. Advertier and marketer have long undertood the two-ided nature of the televiion indutry, but academic have only recently ued model of two-ided market to decribe 3 Tune-in are advertiement for future network program. They are known in the indutry a promo. 4 Example of two-ided market include computer oftware (operating-ytem manufacturer coordinate oftware uer and developer) and credit card (credit-card companie coordinate merchant and conumer). Two-ided market are alo called multi-ided platform indutrie.

5 3 advertiing media. Anderon and Coate (2006) invetigate the nature of market failure and entry in the broadcating indutry. They find that the market can provide uboptimally low or high quantitie of advertiing. Overadvertiing can occur becaue the broadcater diincentive to ell ad i it marginal audience lo. Thi audience lo doe not account for nonwitching viewer ad diutility. Underadvertiing can occur when program are cloe ubtitute and viewer are very ad-avere. Thi lead broadcater to compete for viewer by lowering ad level to uch an extent that the advertiing market i undererved. Duke and Gal-Or (2003) model interaction between viewer, broadcater, and advertier, and alo conider the effect of informative advertiing on competition in product market. They how that when increaed advertiing lead to better-informed conumer, product-market competition intenifie. Network and advertier can then benefit from excluive-advertiing contract. Liu, Putler, and Weinberg (2004) conider the effect of competition between televiion network on network incentive to invet in program quality. They how that increaed network competition can lead to diminihed program invetment and lower viewer welfare. There i alo ome empirical evidence on two-ided media market. Kaier and Wright (2006) apply Armtrong (forthcoming) model to competition in the women magazine indutry. They find evidence that reader value magazine advertiement, and that magazine advertier value reader expoure till more. Their etimate how that an increae in magazine reader demand raie advertiing rate, while a boot in magazine advertier demand lower magazine cover price. Ryman (2004) etimate a model of competition between yellow-page directorie. He find that competition both reduce directory ad price and alter directorie nonlinear pricing function, diproportionately benefiting large advertier. Thi work alo relate to the long tradition of examining each ide of the televiion market in iolation. The firt academic paper to etimate viewer demand for televiion program wa Lehmann (1971). Rut and Alpert (1984) pioneered the ue of dicrete choice model to etimate viewer preference. In the recent literature, Shachar and Emeron (2000) added interaction between viewer and program characteritic to the model. They find that the match between viewer and the demographic characteritic of the character on the program they watch i a good predictor of viewing choice. Danaher and Mawhinney (2001) and Goettler and Shachar (2001) each model viewer preference the firt et of author with a latent cla logit

6 4 model, and the econd with a multidimenional ideal-point framework with the purpoe of calibrating model of optimal program cheduling. Gode and Mayzlin (2004) how that the amount of online buzz and it diperion acro online communitie are aociated with higher rating for new televiion program. Several paper have ued Bayeian framework to model viewer uncertainty about televiion program content. Anand and Shachar (2004) etimate the effect of previou program conumption on agent belief about future program. They find thi uncertainty-reduction mechanim ha a greater effect on viewing choice than unoberved heterogeneity. Anand and Shachar (2005) etimate the effect of tune-in expoure on viewer information et and program choice. They find that tune-in expoure have both peruaive and informative effect. Tune-in informative effect increae the efficiency of viewer/program matche. Yang, Narayan, and Aael (2006) etimate a random-effect Tobit model to invetigate preference interdependencie between married viewer. They find that huband effect on wive viewing intention exceed wive impact on huband viewing intention. The advertiing ide of the televiion indutry ha received le attention than the viewing ide. Crandall (1972) and Bowman (1976) etimated advertier demand for televiion audience, but neither paper ued data on advertiing level. Two recent working paper, Goettler (1999) and Wildman, McCullough, and Kiechnick (2004), alo etimate the relationhip between audience ize and advertiement price. But neither paper ued data on advertiing quantitie. Another related literature i the erie of tudie meauring viewer reponivene to advertiing. Siddarth and Chattopadhyay (1998) found that viewer propenity to change channel during a commercial i J-haped in the number of time they are expoed to the commercial, with a minimum at fourteen expoure. Thi paper contribute to the literature by etimating demand on both ide of the televiion indutry. The importance of the two-ided approach i undercored when viewer and advertier preference are compared to network programming choice. The preent analyi include one of the firt effort to meaure the enitivity of televiion audience ize to the time devoted to national advertiing within a program. It alo contain the firt etimate of the price elaticity of advertiing demand. The model etimate are applied in a counterfactual to gain ome inight into how ad-avoidance technology may affect market equilibria.

7 5 3. A Model of Televiion Advertier, Network, and Viewer Thi ection decribe the model of viewer utility, advertier demand, and network upply of televiion commercial. The viewer and advertier model preented in ection 3.1 and 3.2 are ued in model etimation. The model of network behavior in ection 3.3 i ued in conjunction with parameter etimate in two way. Firt, it implication are ued to infer unoberved tune-in level. Second, it underpin the counterfactual analyi in ection Viewer Televiion viewer typically watch one program at a time. Thu reearcher typically ue dicrete choice model to etimate viewer demand for televiion program. I ue a random-coefficient logit to model televiion viewer. I chooe thi model becaue it bet uit the available data. It primary benefit are extenive control for unoberved viewer heterogeneity, and reliance on conumer characteritic to identify ubtitution pattern. Each viewer i in city m i aumed to either watch one of J-1 broadcat televiion network (which are indexed by j), to watch ome other televiion channel (denoted option J), or to engage in ome non-televiion puruit at each half-hour t. Let viewer i utility from watching network j in city m at time t be u im = q α + x β + ξ + η + ε * im m * im m im, (1) where q i the number of econd of advertiing on network j during half-hour t; x i a vector that include the obervable characteritic of the how on network j at time t (e.g., genre), audience flow effect, 5 * * and market, day and time dummie; and are viewer i tate α im β im m parameter; ξ capture mean tate for the unoberved characteritic of the program airing on network j during time t; η m meaure a deviation from mean tate for unoberved how 5 Mohkin and Shachar (2004) preent evidence that tate dependence play an important role in televiion viewing choice. I do not oberve viewer tate dependence. Therefore I ue network j audience ize in market m at time t-1 and t+1 to control for audience flow. The indutry term for thee audience are the lead-in and lead-out.

8 characteritic common to viewer in city m at time t; 6 and ε im i viewer i idioyncratic tate for network j time-t program. To define viewer i tate parameter, let * α im α = + ΠDim + Σv * β im β im, D im * * ~ P ( D), v P ( v) where α and β are mean tate for ad quantity and program characteritic, Dm im ~, (2) v D im i a vector of 6 viewer demographic characteritic (income, age, and age 2 ), * P Dm ( D) i the market-pecific joint ditribution of viewer demographic, and v im i a vector of unoberved preference heterogeneity. I make the tandard aumption that the component of v im are independently ditributed normal. Π i a parameter matrix that meaure how tate for program characteritic and advertiing vary with oberved viewer demographic, and Σ i a diagonal matrix that meaure the relative importance of unoberved viewer preference heterogeneity. where If viewer i watche any non-broadcat network, her utility i given by xmjt u imjt = x β + ξ + η + π D + σ v + ε, mjt i Jt mjt J contain audience flow, market, day, and time effect, ξ Jt i the mean value of the bet available non-broadcat network at time t, and η mjt i a deviation from mean preference for non-broadcat TV network hared by viewer in city m at time t. The utility of the non-televiion option (option 0) i uim0t = ξ 0t + ηm0t + π 0Dim + σ 0vim + ε im0t where ξ 0t and η m0t are normalized to zero (and the ξ im J im imjt and η m are identified relative to thi 0Dim σ 0 im normalization), and π + v i interpreted a a fixed effect that meaure the timeinvariant component of viewer i value of the non-televiion option. I aume viewer act to maximize utility. Thu, the et of demographic and preference hock that lead viewer i in city m to watch network j at time t i A {( D v, ) u > u, k j} m = im im im t, ε, im imkt 6 The effect of unoberved regional difference in peech and culture on viewer preference for unoberved how characteritic i aumed to be the primary determinant of η m.

9 7 where ε im t i a vector of the ε im. If the idioyncratic error term are ditributed identically and independently, the viewing hare of network j in market m at time t i given by m = A m dp * * ( ) dp ( v) dp ( D) * ε ε v Dm. I aume the idioyncratic error term ε im are ditributed i.i.d. Type I Extreme Value and integrate out over them in the tandard fahion. Thu the predicted hare can be re-written a where * dp ( v, D ε ) m = A m dp * * ( v, D) dp ( v) dp ( D) * ε v Dm, (3) i the tandard multinomial logit hare function. Equation (3) will be ued to etimate viewer demand for televiion program. The integral on the right-hand ide doe not have a cloed-form olution, o imulation will be ued to approximate it Advertier I model advertier through their aggregate demand for advertiing on a given program. Derivation of advertier demand from firt principle would be preferable. But it i complicated by an aignment problem in the matching of advertier to their preferred audience. I conider here a imple example to illutrate the aignment problem. Conider a ingle advertier with utility function S V, where V i the number of viewer watching how and S i the et of how purchaed by the advertier. There are two audience available: audience A conit of 9 viewer, and audience B of 16 viewer. Viewer are homogeneou and audience do not overlap. If the advertier only purchae audience A, it willingne to pay for A i 3 ( = 9 ). If the advertier only purchae audience B, it willingne to pay for B i 4 ( = 16 ). If the advertier purchae both audience, it willingne to pay for B i B marginal contribution to total advertier utility: 2 ( = ). The aignment problem i the dependence of the advertier willingne to pay for audience B on whether it purchae audience A.

10 8 The aignment problem in the advertiing marketplace could potentially be addreed uing a two-ided, many-to-many matching routine. 7 But many-to-many matching model have unreolved technical challenge regarding the tability and uniquene of the equilibrium aumption. I am not aware of any paper that etimate many-to-many matching model. I therefore ue a reduced-form aumption in the place of a tructural model of advertier behavior. I aume there exit an aggregate demand curve for advertiing during each program and repreent that curve with a linear pecification. 8 Advertier demand for a particular televiion audience i influenced by many factor, including audience ize, viewer demographic, and program characteritic that influence the efficacy of the program advertiing meage delivery. I aume that aggregate invere demand for advertiing i given by p = q λ + V λ + d λ + x λ + φ, (4) q V where p i the price of an ad during how, q i the how ad level, V i the number of viewer watching how, d i a vector of viewer demographic, x repreent program characteritic that affect advertiing effectivene, the λ are advertier preference parameter, and φ i an error term. Poible ource of error include unoberved audience demographic or meaurement error in ad price. The drawback of auming equation (4) i linear i the lack of clarity in the underlying aumption about advertier preference and behavior, and the attendant rik of pecification error. It advantage i that it obviate the large theoretical and computational burden of uing a many-to-many matching routine to etimate advertier demand parameter. I conclude that the linear form of (4) i a jutifiable implification. A hown in ection 5.3, the aumed functional form i found to explain 87% of the variation in advertiement price. d x 3.3. Network The complexity of network trategic interaction require trong aumption about their behavior. Due to the trength of thee aumption and the limitation of the data, the model of 7 Two-ided matching ha been tudied ince Gale and Shapley (1962). Thee model are ued mot often to analyze labor market, marriage market, and chool choice. Roth and Sotomayor (1990) provide a detailed introduction. 8 I teted everal alternate pecification, including Cobb-Dougla. Model fit and qualitative reult were imilar acro all model teted.

11 9 network competition i not ued to etimate demand parameter. It i only ued to infer miing data and in the counterfactual decribed in ection ix. I aume network compete in three tage. In the firt tage, network chooe their program; in the econd tage, network chedule their program; and in the third tage, network et ad quantitie. 9,10 I focu here on competition in the final tage, in which program cot are unk, and program chedule have been finalized. Tune-in are not oberved in the data. But they account for about 25% of non-program material 11 o it i important to account for them in the counterfactual imulation. I aume network et advertiement and tune-in level to maximize current-period advertiing revenue. A higher number of tune-in in the current period will inform viewer of upcoming programming, but tune-in may reduce audience ize ince ome viewer flip channel during tune-in. Thoe audience loe reduce current-period advertiing revenue. I aume that a network benefit from a viewer tune-in expoure i contant at τ during each how. τ i the marginal effect of expoure to one tune-in on the probability any given viewer will watch the advertied how, time the network profit from the increae in the advertied how audience. 12 If we denote the number of tune-in aired during how a r, the network tage-three profit can be written a the um of it advertiing revenue and tune-in benefit during the how: π = max [q p + r V τ ], (5) j { q, r } S j S j 9 The timing of the game i conitent with reality. The firt tage take place before the upfront market, when network renew returning how, and buy new one. The econd tage take place at the tart of the upfront, when network announce their program chedule. The third tage occur during the remainder of the upfront and catter market. Yet it hould be noted that network replace and rechedule how during the eaon and typically ell about 20% of their primetime advertiing inventory after the upfront (Reynold 2006). 10 The ad-quantity-etting aumption i tandard in the literature. Network can control the number of minute of advertiing in a program by editing it appropriately. The upfront contain coniderable uncertainty about advertiing demand and protracted price negotiation with advertier in which advertier are known to pay nonuniform price. Thi information eem inconitent with an aumption that network et a ingle price per how. Anderon and Coate (2006) how that ad price and ad quantity game yield identical reult when viewer pay no ubcription fee. But the incluion of tune-in in the model make thi framework more imilar to Crampe, Haritchabalet, and Jullien (2005). Thee author find that an ad price etting aumption yield equilibrium advertiing level and price equivalent to the ad quantity game, but higher media profit and ubcription price. If network et ad price, the empirical implication i that the inferred tune-in level preented in ection 5.5 will be too mall. 11 Source: 2001 Televiion Commercial Monitoring Report (TCMR). 12 Thi tune-in benefit could vary acro how for the ame reaon that advertiing demand varie acro how: ome how deliver advertiing more effectively than other, and ome viewer are more valuable than other.

12 where S j i network j catalogue of how. 10 yield Subtituting ad demand (4) into equation (5) and differentiating with repect to q and r p V dv qλ q + qλv + r τ = 0, and (6) q dq + V qλv r + V τ + r V τ r The firt two term in equation (6) are imilar to marginal revenue in any monopoly or oligopoly firt-order condition. The third term capture the two-ided nature of the market, the decreae in V advertiement price through the audience lo ( q = ) engendered by commercial ale. The fourth term i the marginal effect of the network advertiing ale on it gro tune-in benefit. The firt-order condition taken with repect to r contain imilar logic. The firt term i the marginal effect of a tune-in on advertiing revenue, and the econd two term are the network marginal benefit of airing a tune-in. I make the additional aumption that viewer are equally avere to advertiement and tune-in. Tune-in are more likely to be relevant to the mean viewer than advertiement. But they are alo repeated more frequently in hort period of time, o they are accordingly more likely to wear out. Thi aumption i very trong, but it i only ued to infer unoberved tune-in level. It i not ued in the etimation. It implie that the marginal effect of ad ale on audience ize i equal to that of airing tune-in: relationhip: p q V q p V =. Equation (6) and (7) then imply a third r + = Vτ. (8) q (7) 13 A computer imulation wa conducted to invetigate the exitence, tability, and uniquene of the equilibria of V thi oligopoly game. Auming i trictly negative, two equilibria exit: one i an interior, table equilibrium in q which all network play nonzero advertiing level. In the other equilibrium, network advertiing quantitie go to infinity and audience ize go to zero.

13 11 Equation (8) ay that, holding audience ize contant, the network will chooe advertiing and tune-in level to equate it marginal benefit from each activity. Equation (7) and (8) will be ued in conjunction with advertier and viewer demand parameter etimate to make inference about unoberved upply parameter and tune-in level. 4. Data, Endogeneity, and Etimation I tart here with the overview of the empirical trategy depicted in Figure 1. The model ha three et of primitive: aumption about viewer, advertier, and network. Thee primitive are ued to generate four et of reult. Firt, the viewer demand model i etimated. Second, viewer demand parameter provide intrument to etimate advertier demand for audience. 14 Third, the two et of parameter etimate are ued in conjunction with the model of network competition to make inference about miing data. Fourth, all aumption, empirical reult, and data inference are ued to derive the counterfactual reult. In thi ection, I decribe in turn the data, viewer demand-ide endogeneity and etimation, advertier demand-ide endogeneity and etimation, and the inference procedure. The counterfactual i dicued in ection 6 and decribed in detail in the technical appendix Data The model i etimated uing data on televiion audience, viewer demographic, advertiement, and program characteritic from four ource. The unit of obervation i a network-day-time period. For each obervation, the data report econd of national advertiing, the average cot of a 30-econd commercial, the characteritic of the program, and houehold audience hare in each of 50 televiion market. The ample include the program and ad aired by the ix mot-watched US broadcat televiion network (ABC, CBS, FOX, NBC, UPN, and WB) between 8:00 p.m. and 10:00 p.m., 14 The two ide of the model are etimated eparately for two reaon. The computation time required for the viewer demand model wa extenive, and increaed exponentially with the dimenionality of the parameter pace. Second, the program quality etimate mut be averaged over program half-hour to account for network trategic conideration before they can be ued a intrument to identify advertier demand. Berry and Waldfogel (1999) found in a imilar context that imultaneou etimation of the two ide of the market yield reult that are nearly identical to eparate etimation.

14 12 Monday to Friday, April 24 to May 21, I decribe each component of the data in turn, report decriptive tatitic, and conclude by decribing ome limitation of the dataet. Audience hare data come from Nielen Media Reearch (NMR) Viewer in Profile report covering the 50 larget US Deignated Market Area (DMA). More than 90% of US televiion houehold are repreented in the ample. NMR collected houehold audience data in each market with a ample of audimeter, et-top boxe that record viewing choice and tranmit data to Nielen via telephone line. The data report audience by half-hour, day, and network affiliate. Two important characteritic of the audience data merit dicuion. Firt: if a houehold watched a program for five not-necearily-conecutive minute during a 15-minute period, Nielen include that houehold in the program audience. (The data are reported at the halfhour level, a the average of the two fifteen-minute block in each half-hour.) It i therefore poible for a houehold in Nielen ample to watch a program, avoid commercial perfectly with a remote control, and till be counted among the program audience. Thi would ugget the houehold i extremely ad-avere, but it ad-averion would not be detectable in the data. To the extent thi occur, I will underetimate viewer true ad diutility. However the main purpoe here i to meaure how Nielen audience meaurement change with advertiing level, ince thoe meaurement were the currency of the advertiing marketplace in Second, Nielen excluded DVR houehold from it ample in The Yankee Group etimated DVR penetration at 2% of US houehold in mid Therefore, viewer demand parameter can be interpreted a decribing the tate of the remaining 98% of US TV houehold. The viewing data ued in thi paper hould be contrated with the dataet ued in prior literature to aid interpretation of the etimation reult. Many previou tudie have ued 1980 or era individual-level viewing data collected by Nielen Peoplemeter for thouand of viewer. Thi dataet i relatively coare in comparion: it conit of aggregated choice of houehold in 50 geographic market. It i alo relevant to conider that the houehold that 15 Saturday and Sunday were excluded becaue UPN doe not broadcat on either day, and WB doe not broadcat on Saturday :00 p.m. wa excluded becaue FOX, UPN, and WB do not broadcat at that time. I alo exclude two night in which program with unpredictable ad level aired: a preidential addre, on Thurday, May 1; and a NBA baketball game, on Thurday, May 15.

15 13 accept peoplemeter may differ from the houehold that accept audimeter and diarie. Diarie require viewer to manually record the program they view each week, while Peoplemeter require viewer to log in once or twice per hour. The relative diadvantage of the current dataet i that it i not a panel of individual viewer, o viewer peritence i not directly oberved. The relative advantage of thi dataet i it ize. The ample on which it i baed i large (NMR ampled 1,000-1,500 houehold in each of the 50 DMA) and it four-week time period and ix broadcat network are unuually large. Audience demographic data were collected from US Cenu 1% IPUMS file for each Conolidated Metropolitan Statitical Area correponding to a DMA. The demographic I ue are viewer age and houehold income. Data on ad quantitie and price come from TNS Media Intelligence/CMR. For each program in the ample, I oberve the time given to national advertiing and the etimated price of a 30-econd commercial during the how. The etimated price were recorded from network report of the etimated cot of a 30-econd pot after the program aired. Thee data are commonly ued by advertier and agencie to budget for future advertiing campaign. 16 Program characteritic were recorded by the author from videotape of network programming made during the ample period. The videotape were upplemented with data from a webite, TVTome. Obervable program characteritic include genre, thematic element, main and upporting character demographic 17 (including gender, race, age, and family tructure), program age, etting, and current and pat Emmy nomination. Advertiement quantitie exhibit ubtantial intratemporal variation in the ample. The mean ad level per half-hour i 5:15 minute, and the average difference between the maximal and minimal network ad level within a half-hour i 2:49 minute. Program that are more attractive to viewer, relative to within-time-period competition, typically contain more ad. Broadcater 16 There i evidence that ad tranaction price are not uniform acro advertier. Advertier can ecure lower price by procuring quantity dicount (Auletta 1992) and trong media negotiator (Bloom 2005). Network keep tranaction price trictly confidential to avoid weakening their negotiating poition. The aumption that ad price are contant within a program will only be a problem if advertiement price are ytematically correlated with the preference of advertier receiving dicount. Fournier and Martin (1983) i the only paper I know of that analyze a ample of actual advertiement tranaction price but it doe not preent any evidence on thi quetion. 17 A main character i defined a a character on which major plotline are baed.

16 14 had limited themelve to ix ad minute per hour until an antitrut uit in 1982 but non-program time ha more than doubled ince then. Table 1 report the mean and tandard deviation of ad price, ad quantity, cot per thouand houehold (CPM), and audience ize, by network and day. Some intereting reult emerge. ABC aired the mot national advertiing during the ample, averaging 6:07 minute of national ad per half-hour, while FOX aired the leat, 4:44 minute per half-hour. FOX and NBC hared the highet average CPM ($28), while UPN charged the leat ($19). FOX attracted the larget average audience, followed by CBS, NBC, ABC, WB, and UPN. Figure 2 how average nightly audience by network and day. The three highet-rated network nightly audience varied coniderably over day of the week. FOX dominated Tueday and Wedneday with it American Idol franchie, but wa near average otherwie. CBS and NBC both attracted large audience on Thurday night. The bottom three network audience howed much le variation, with tandard deviation le than half a large a CBS, FOX, and NBC. Figure 3 how how network price per viewer moved over the coure of the average week. NBC wa the only network to conitently charge in the upper echelon of per-viewer price. CBS take per viewer dipped precipitouly on Tueday, while FOX CPM fell dramatically on Thurday. The three lowet-rated network all charged per-viewer price with lower mean and le variation. Table 2 lit the program characteritic, their definition, and decriptive tatitic, weighted by total half-hour of programming in the ample. The table how that the two mot commonly programmed genre, by far, were Scripted Comedy and Pychological Drama. African-American appeared in prominent role in nearly half of all how, while other minoritie were cat in leading role le frequently (9%). The average program in the ample wa 3.3 year old, wa nominated for 1.4 Emmy in 2003, and had been nominated for 3.6 Emmy in previou year. Table 3 how the correlation between program audience ize, advertiing time, ad price, and genre. Ad price and audience ize are highly collinear, with a correlation of Advertiing time i lightly negatively correlated with audience ize (-0.06). Ad time i alo negatively correlated with ad price (-0.10). Some of the genre correlation are intereting.

17 Scripted Comedy i the only genre poitively correlated with advertiing time but i not correlated with audience ize or ad price. The Reality genre, by contrat, wa the mot poitively correlated with ad price (0.26) and wa poitively correlated with audience ize (0.14), but it wa negatively correlated with ad time (-0.1). It i important to note that local advertiing and national tune-in remain unoberved. The 2001 Televiion Commercial Monitoring Report (TCMR) report detailed information for all non-program material baed on one week of data from November, It how that the average half-hour of programming contained 8:22 minute of non-program material, of which 4:52 minute were national advertiement, 2:03 minute were national tune-in, and 1:24 minute were local advertiing time. 18 Local ad time i determined by long-term contract between network and affiliate. It i nearly uncorrelated with national advertiing time. In the TCMR ample, thi correlation wa Non-program time doe not vary acro market. It effect on viewer utility hould be captured by ξ. Network do not et local advertiing time baed on contemporaneou program quality, o it omiion i unlikely to bia etimate of viewer reponivene to advertiing. The TCMR data ugget the abence of tune-in data may be more important. The program dummie, ξ, will capture the mean effect of tune-in level acro a program epiode. Thi ugget a poible bia in program etimated attractivene to viewer. I am left without a way to tet the magnitude of the bia, but I upect that program characteritic will be tronger driver of etimated program quality than tune-in level Viewer Demand Endogeneity and Etimation In the etimation of the viewer demand model preented in ection 3.1, mean tate for unoberved program characteritic ξ and market-pecific deviation from mean tate for unoberved program characteritic η m are unoberved by the econometrician. Yet thee data are likely to be known by the televiion network and taken into account when it et it 18 Local advertiing time within a national broadcat i contant acro affiliated tation. Affiliate vary in how much time they ell to local advertier, ue to promote their own program, or air public ervice announcement. 19 I thank an anonymou reviewer for pointing thi out.

18 advertiing level q. To avoid bia in the advertiing repone parameter, I ue program 16 dummie to etimate ξ. Thu the correlation between q and ξ exit between obervable variable, rather than between an oberved variable and an error. 20 There i a further concern that the network ha knowledge of the market-pecific tate for unoberved program characteritic η m and ue that knowledge in etting it advertiing level. There are two reaon thi could be the cae. Firt, larger market tate matter more becaue large market contain more viewer. Second, even after controlling for audience ize and demographic (like age and income), the network might have preference over the geographic ditribution of the viewer in an audience. To correct for the firt concern, I premultiply η m in equation (3) with ~ ωm ω m, M 1 ω M n= 1 where ω m i the number of houehold in city m and M i the number of citie in the ample. Thu, by contruction, the program-pecific fixed effect i etimated to be the mean effect of unoberved program characteritic acro all houehold in the ample rather than the mean effect acro DMA in the ample. The data i defined at the market level, rather than the houehold level, o thi premultiplication i neceary to enure the effect captured by ξ and η m are conitent with their definition in ection 3.1. There i not a imilarly parimoniou trategy to control for the econd concern lited above. I acknowledge thi a a poible objection, but I think it i not a firt-order iue. While individual advertier are certain to have preference over geographic ditribution of audience member, if thoe preference are very trong, they are likely to buy audience in the pot televiion market (advertiing on local tation) in place of national advertiing. Additionally, geographic preference are likely to vary acro advertier, and network incentive are influenced by the cumulative preference of all advertier in the market. n 20 Thi approach i imilar to that ued by Nevo (2000, 2001). The program dummie are identified: every program air in 50 DMA in each time period, and air in multiple time period in the ample. ξ

19 17 I now decribe the algorithm and the moment ued to etimate the tructural parameter of the viewer demand model preented in ection 3.1. The etimation routine i imilar to that introduced by Berry, Levinohn, and Pake (1995; hereafter, BLP ) and decribed in detail by Nevo (2000, 2001). The main idea of thi etimation routine i to numerically olve the market hare function defined by equation (3) for program mean utility level, and to ue thee imputed mean utilitie in a moment condition. Ue of thi algorithm confer two primary benefit. It define the objective function a a mooth function of the parameter, which reduce imulation error. And it ignificantly reduce the number of parameter to be etimated nonlinearly. Thi peed computation greatly. 21 To facilitate explanation of the etimation routine, I define ome new notation. Let N be the number of city/day/time/network market hare oberved in the ample, and let P be the number of program in the ample. Let H be a NxP matrix of program dummie and ψ be the Px1 vector of mean program utilitie that are contant acro viewer and time period in which the program air. Let two partition of x be labeled x, which include program dummie, and m x~ m, which contain the data that varie acro market and/or time period within a program. 22 Label two partition of β with β 1, which interact with x, and β 2, which interact with x~ m. Denote the et of parameter to be etimated uing GMM with θ = { ψ, α, β 2, Π, Σ }. 23 Define the mean utility that viewer in city m derive from watching network j at time t a δ m = ψ p + q α + ~ xmβ ~ 2 + ωmη m, where ψ p i the non-ad, mean utility (NAMU) of the program on network j at time t. The integral that define the predicted market hare function (equation 3) ha no cloed form olution. I ue imulation to approximate it. I draw imulated viewer demographic D im from the city-pecific nonparametric marginal ditribution defined by the IPUMS data, and I draw ν i from a tandard multivariate normal ditribution. The predicted audience hare of network j at time t in market m i then the fraction of the imulated viewer in the market for 21 The method traditionally ued to etimate random coefficient logit model i to define the objective function a the difference between the predicted market hare and the oberved market hare. The advantage of the BLP etimation algorithm lited here are relative to the traditional etimation method. 22 x~ m contain the advertiing level (which varied over network, day and half-hour, but not DMA), audience flow effect, the lat-half-hour dummy, and day-, time-, and market-pecific fixed effect. 23 will be etimated uing the minimum-ditance procedure explained below. β 1

20 18 whom network j time-t program maximize utility, conditional on the time-t program aired by all network and a gue of the parameter et θ. I ue the contraction mapping uggeted by BLP to olve for the J-vector of mean utilitie ~ m ( θ ) that, for a given value of θ, equate predicted market hare to oberved market hare in δ t market m at time t, ~ ( m t ( θ )) Sm t δ. m t = I then contruct the error term, the market-pecific deviation from mean tate for network j time-t broadcat, a ~ 1 ~ η ( ) ( ( ) ~ m θ = ~ δ m θ ψ p q α xmβ 2 ). ω m Next, I contruct the moment condition, EX 'η ~ ( θ ) = 0, where X i a matrix of intrument defined below, and ~ η (θ ) i a N-vector of the ~ (θ ). The GMM etimate of θ i η m ˆ θ = argmin ~ η ~, where A i a poitive-definite weighting matrix. 24 θ ( θ )' XA 1 X ' η ( θ ) I ue the minimum-ditance procedure uggeted by Nevo (2000) to decompoe the program-level mean utilitie ψˆ into the tate parameter aociated with oberved program characteritic ( β 1) and unoberved program characteritic (ξ ). Let X p be a PxK matrix of program characteritic and let ψˆ be the Px1 vector of etimated mean program utilitie. Then, ince ψ X β + ξ, the etimate of 1 = p 1 pβ 1 ˆ 1 = X ' ˆ X X ' ˆ ˆ, and 1 1 β i computed a β ( Ω ) Ω ψ ˆ ξ = ˆ ψ X ˆ, where Ω ˆ i the etimated covariance matrix of ψˆ. Thi minimum-ditance procedure i analogou to a GLS regreion wherein the dependent variable i the et of etimated program mean utilitie, the independent variable are the program oberved characteritic, and the number of obervation i equal to the number of program in the ample. The column of the X matrix mut all be orthogonal to the vector of market deviation from mean program utility, η. The obviou candidate for incluion in X are the program dummie. Thee are valid intrument becaue the incluion of ~ ω m enure that the mean program utilitie are mean-independent of η. X alo include the market, day, and time 1 p p p 24 The mot efficient choice of A i the covariance matrix of the moment. I follow the tandard method of etting A = X ' X to obtain a conitent etimate of θ. I then ue thi etimate to contruct a conitent etimate of the aymptotically efficient weighting matrix, E X ' ~ η ˆ θ ~ η ˆ θ ', which i ued to obtain the final etimate of θ. ( ) ( ) X

21 dummie, and interaction between ome element of x m 19 and moment of the market-pecific ditribution of viewer demographic. For each variable k whoe effect on utility varie with k conumer demographic, and for each obervable viewer demographic d, I include, a N- vector whoe n th k element i x Ed, where Ed i the expected value of viewer demographic d im im in market m. Thee interaction are equal in number to the number of parameter to be etimated in Π and Σ. Note that the audience flow effect cannot be ued, a they are highly likely to be correlated with market-pecific tate for unoberved program characteritic. To meet neceary identification condition, I alo include a NxJ matrix Q, whoe n th row contain the time-t ad level of the aociated network and it J-1 competitor. Incluion of advertiing level in X i jutified by the dicuion at the beginning of thi ubection. I impoe ome zero-retriction on Π and Σ to limit the number of parameter that enter the GMM objective function nonlinearly. 25 Three regreor are aumed to interact with viewer demographic: the viewer tate for the outide option, her tate for non-broadcat-network televiion, and her diutility of advertiement. The firt two variable were choen to improve the reliability of predicted ubtitution pattern among variou option, and the third variable improve the reliability of predicted audience change reulting from varying level of advertiement. X d 4.3. Advertier Demand Endogeneity and Etimation Advertier demand parameter are etimated uing intrumental variable. A limited-information approach i ued, in place of a full-information approach baed on the network upply model preented in ection 3.3, for two reaon. Firt, the aumption underlying the model in ection 3.3 are trong and therefore potentially unreliable. Second, there are not good intrument available for the unoberved element in the network firt order condition. I proceed here by decribing the endogeneity concern in advertiement demand etimation, the intrument ued, a data manipulation, and the etimation trategy. 25 If there are d element in D i, each obervable program characteritic whoe effect on utility varie with viewer demographic add d + 1 nonlinear parameter. Thi i a problem becaue computation time increae at an increaing rate in the number of parameter that enter the objective function nonlinearly.

22 The error in the advertiement demand equation, φ, i aumed to primarily reflect 20 unoberved program and audience characteritic that influence advertier demand for ad. 26 For example, φ might reflect viewer level of engagement with the program, or it might repreent the fraction of audience member who drink cola. The network i likely to have partial knowledge of φ and take it into account when etting it advertiing level. Thu could be correlated with φ ; and, becaue audience ize depend on q, V could alo be correlated with φ. (Note, thi econd correlation i due olely to the direct dependence of V on q. There i no obviou reaon to think that audience ize i ytematically related to advertier preference for unoberved program and audience demographic.) Intrument are required to obtain unbiaed etimate of the advertier demand parameter in equation (4). Intrument mut meet two requirement for validity: they hould be correlated with V and q, and uncorrelated with advertier preference for unoberved audience demographic φ. I follow the typical trategy of uing program characteritic a intrument. I ue two et of program characteritic a intrument: the oberved program characteritic thought to influence advertier demand,, and the non-ad, mean utility (NAMU) of the program and it within-time-period competition etimated by the viewer demand model. (In the notation of ection 4.2, the NAMU i ψˆ p or x ˆ ξ.) Thee intrument meet the firt requirement for validity: they enter directly into program hare function in equation (3), o they are correlated with V. They are correlated with q becaue the network optimal advertiing level depend X p ˆ1 β + partly on it audience ize, a illutrated by equation (6) and (7). They can alo be aumed to meet the econd requirement of valid intrument, that they are uncorrelated with advertier preference for unoberved program and audience characteritic. NAMU i the mean utility of program conumption acro all viewer, which only influence advertier preference through the ize of the audience the program garner. And the program characteritic x q are included in 26 φ could alo be aumed to reflect meaurement error in advertiement price. Thi i not a troubling a advertier preference for unoberved program and audience characteritic. The ad price data i reported by network after the program air, and i ued by media buyer to plan future purchae; if it were conitently unreliable, we would probably oberve regular complaint from media buyer.

23 ad demand (4), o φ by definition conit of advertier preference for thoe program and 21 audience characteritic that are not included in x. Before etimating advertier demand parameter I modify the data to account for how the market operate. Audience are old and price are reported at the program level, but network ditribute ad within a program baed on trategic conideration. Longer program tend to contain more ad in their latter tage, a viewer are relatively more captive at that point and the program i le likely to attract new viewer from competing network. Thi phenomenon i likely to be an important ource of variation in the advertiement price/quantity relationhip. To account for it, I average each program audience characteritic over the half-hour in which the program aired, o the unit of obervation i a network/day/program, rather than a network/day/half-hour. 27 To etimate advertiement demand parameter, I interact the intrument decribed above with the ad demand function reidual and ue GMM to olve the moment condition. Let Z be a vector that include the et of intrument for how decribed above; and let Z be a matrix contructed by tacking the Z. Then the moment are E Z' φ = 0, where φ i a vector of the φ. The GMM etimate of advertiement demand and upply parameter are ˆ λ = argminφ' ZB λ where B i the covariance matrix of the moment. Following tandard practice, I et 1 Z ' φ, B = Z' Z to obtain a conitent etimate of the aymptotically efficient weighting matrix, which I then ue to re-etimate the model and obtain the final reult Network Supply Inference I ue demand parameter etimate in conjunction with network firt-order condition to infer unoberved tune-in level and benefit. I tart with equation (8), the condition that ay the profit-maximizing network will air tune-in and ad to equate it marginal benefit from each activity. Solving equation (8) for τ give 27 For example, a two-hour movie i treated a one obervation, a i a half-hour program. Breaking the movie up into four half-hour increment would fail to account for network trategic ditribution of advertiement acro half-hour within the movie.

24 ˆ τ p + q ˆ λq =. (10) V 22 We can ue τˆ to make inference about tune-in level. From either of the network firt-order condition (equation 6 and 7) it can be hown that V qλv + V ˆ τ q rˆ =. (11) V ˆ τ q In the next ection, I check whether inferred per-viewer tune-in benefit and tune-in level eem reaonable by comparing inferred tune-in level with data reported in the TCMR. I find that they are largely conitent with prior expectation, and ue them in the counterfactual exercie decribed in ection Empirical Finding In thi ection, I report and dicu etimation reult. I begin with etimation of a multinomial Logit model (MNL) of viewer demand for televiion program. I then preent the reult from the full random coefficient Logit (RCL). In ection 5.3 I report advertier demand parameter etimate. Section 5.4 compare advertier and viewer preference for program characteritic to network oberved programming choice. In ection 5.5, I report inference about unoberved network tune-in level Viewer Demand: Multinomial Logit Reult In thi ection, I preent empirical reult baed on OLS etimation of the multinomial Logit model. The MNL i a pecial cae of the full Random Coefficient Logit model wherein parameter matrice Π and Σ are retricted to zero. While the MNL ha unrealitic ubtitution pattern, it eae of computation make it a good tool for comparing reult from variou pecification. In the pecification reported below advertiing quantity (q ) enter viewer utility linearly. I etimated everal alternative model but found no evidence that ad level have nonlinear effect on utility. Nor wa there any evidence that the number of commercial break

25 23 affect program audience ize (controlling for ad quantity). I therefore maintain the aumption that viewer marginal utility of advertiing i linear in advertiing econd. 28 Table 4 report etimation reult from eight MNL pecification. Parameter etimate are computed from OLS regreion of tranformed log hare, log( ) log( ), on alternate m m 0t mean utility pecification. The pecification are permutation of including program-pecific fixed effect, market dummie, and market-ize-indice ~ ω m. Program dummie control for correlation between ad level and unoberved program quality, market dummie approximate the way the full model control for unoberved viewer heterogeneity (ince imulated viewer demographic are drawn from market-pecific ditribution), and market-ize-indice control for the effect of market ize on network ad quantity deciion. The MNL model fit the data very well: the Adjuted R 2 range from 0.79 to Thee high fit ugget there i not much unoberved viewer heterogeneity for the random coefficient to explain. Table 4 how that the point etimate of advertiing time on viewer utility i negative acro all pecification. Incluion of program dummie ha the effect of making thi point etimate ignificant and quadrupling it. Thi i conitent with tandard model of conumer demand. Failure to control for unoberved product characteritic biae the price coefficient toward zero. Thee finding, and the improved fit of the model, validate the endogeneity control decribed in ection 4.2. They how that network know their program qualitie and take them into account when etting advertiing level, o a model that ignore thi trategic behavior will yield biaed parameter etimate. The finding that higher aggregate advertiing level are aociated with maller program audience i intuitive. But it contrat with Kaier and Wright (2006) finding that women magazine readerhip grow with the number of advertiing page the magazine contain. The difference in the ign of the effect might be attributed to the medium varying level of conumer control over their advertiing expoure. Magazine reader are free to chooe how much time they pend with each ad baed on how much they value it. Televiion viewer have le perfect 28 It hould be recalled that the data are relatively inenitive to viewer zapping of commercial break. Thi i not a tet of whether the number of ad break affect viewer zapping. See Danaher (1995) or Zufryden, Pedrick, and Sankaralingam (1993) for tudie of viewer zapping.

26 24 control over their advertiing expoure. It hould alo be noted that ome egment of viewer may like televiion advertiing; Roja-Mendez and Davie (2005) find that preent- and futureoriented people are relatively more likely to be receptive to advertiing than thoe who are patoriented. Audience flow effect and the lat-half-hour dummy are included to control for viewing peritence. Numerou tudie find evidence of thi phenomenon. Among thee, Shachar and Emeron (2000) find that viewer witching cot grow a they become more experienced with a program, and are higher when they have fewer new program to ample on competing network. Mohkin and Shachar (2002) find that uncertainty reduction through program conumption play a larger role in explaining viewing peritence than tate dependence. Zhou (2004) how that network tend to air more and longer commercial break toward the end of a popular program. Table 4 how that the lead-in and lead-out effect are poitive and ignificant. The lat-half-hour dummy i poitive but not ignificant. The inignificance of thi effect i likely due to mall audience loe at the end of a program being offet by maller number of viewer tuning in (Shachar and Emeron 2000). 29 The level of aggregation of viewer in the current dataet make thi model a le reliable judge of the preence of tate dependence than other tudie that oberve individual viewer behavior. The intereted reader i referred to Mohkin and Shachar (2002) Viewer Demand: Random Coefficient Logit Reult I preent here etimation reult of the full random coefficient Logit model of viewer demand. The model fit the data quite well. A 2 χ goodne-of-fit tatitic 30 reject the null hypothei that all parameter etimate are zero at a very high confidence level. The model peudo R 2 i The Root Mean Square Error i 1.12, and the average relative error i Table 5a how the parameter etimate of the random utility parameter. The mean effect of advertiing econd i negative and ignificant. The heterogeneity parameter aociated 29 Thee reult are not a formal tet of viewer peritence. A proper tet would require data on the number of viewer in each market who tune in and tune away from each network in each time period. I only oberve the net change in audience ize. I thank an anonymou reviewer for pointing thi out. 30 Thi tatitic i defined a ˆ θ ˆ 1 ' ϑ ˆ θ, where ϑˆ i the etimated covariance matrix of θˆ. Thi tatitic ha a value of 8.41e+9 and i ditributed chi-quare with 141 degree of freedom.

27 25 with tate for advertiing are not ignificant, indicating that market-level difference in age and income do not affect audience advertiing reponivene. The mean tate for non-broadcat-network programming i negative. Thi indicate that the TV-Off option i generally preferred to non-broadcat option like cable network. There are two reaon for thi. Firt, the TV-off market hare exceeded the non-broadcat-tv market hare in 77% of the DMA/time-period in the ample. Second, advertiing quantitie on cable network are not oberved, o their effect i captured by the Non dummy. The random utility parameter aociated with option J were not ignificant. 31 Table 5a indicate that unoberved heterogeneity play a large role in tate for the nonteleviion option. The other eight random utility parameter etimate are not ignificant. I conidered removing the random effect from the demand model. I teted the null hypothee that Π = 0, Σ = 0, and { Π = 0} { Σ = 0}. All hypothee were rejected at the 1% confidence level by Newey-Wet D tet and Wald tet. Yet ome parameter etimate are impreciely etimated. I therefore do not ue the random utility parameter etimate to contruct unoberved audience demographic d. Table 5b report the effect of audience flow, program, and time characteritic. Many previou tudie have found that audience flow effect are very trong predictor of audience rating. Conitent with thi reearch, the lead-out effect i very large, poitive, and ignificant. The lead-in effect i not ignificant, but thi i probably due to it high correlation (0.88) with the lead-out effect. The day effect indicate that viewer exhibit a tatitically ignificant preference for watching televiion on Thurday and Friday night. Thi correpond to intuition oberving that mot televiion viewer are more likely to engage in leiure puruit like televiion on Friday night. The large Friday effect contrat with the broadcat network tendency to air low-quality program on Friday night (thi i explored further in ection 5.4). The Friday reult contrat with Goettler and Shachar (2001), who found that the only ignificant day effect wa a poitive tendency of children to watch televiion on Friday night. 31 Goettler and Shachar (2001) find that tate for non-broadcat-network programming correlate poitively with viewer income level below $20,000 and negatively with viewer age in the range of 2-11, 18-24, and 65+. They ue individual level data, a better etting for teting thee hypothee than the DMA-level data ued here. I thank an anonymou reviewer for pointing thi out.

28 26 The half-hour parameter etimate indicate that the 8:00-8:30 p.m. (EST) time block i preferred to the 8:30-9:00 block. The 9:00-9:30 block i preferred to both of the preceding halfhour, while the 9:30-10:00 block i not tatitically different from the 8:00-8:30 utility. Thee reult agree with Goettler and Shachar (2001), who find that televiion utility peak in the firt quarter of each hour, and decline teadily after 9:30. Program genre i a powerful predictor of audience ize. Viewer preference order i Action Drama, New, Pychological Drama, Reality, Movie, and Scripted Comedy. New i the only genre whoe effect i not ignificantly different from Pychological Drama. Thee genre reult differ from Shachar and Emeron (2000) reult that viewer preference order i Sport, followed by Sitcom, Movie, New, Pychological Drama, and Action Drama. They alo contrat with Yang, Narayan, and Aael (2006), who find that male viewer preference order i Drama, New, Comedy, and female viewer preference order i Drama, Comedy, New. I peculate that thee difference in reult are caued by three factor. Firt, there wa nearly complete turnover in network program between the ample. 32 Second, the current data ample include two maller network (WB and UPN), no Sport program, many Reality program, and a different et of Movie. Third, conumer tate and program production technology may have changed between the year the ample were recorded (1996/1997 and 2003). I find that main character demographic influence audience ize. Viewer prefer program that include African American and 18- to 34-year-old main character. Main character in other age group and of other minority race have no ignificant effect on audience ize. Incluion of married or ingle-parent main character had no ignificant effect on audience ize. Program without female main character fared lightly better, while excluion of male main character had no ignificant effect. Thee lat two reult are reminicent of Yang, Narayan, and Aael (2006) finding that huband exert tronger influence on wive viewing than vice vera. Cat demographic alo influence viewer program choice. Program featuring allwhite cat drew lightly maller audience. Higher level of minority cat repreentation and 32 The only program to appear in all three ample were 20/20, 48 Hour, America Mot Wanted, Dateline, Law and Order, Primetime Live, and the Simpon.

29 27 female cat repreentation do not affect audience ize. The intereted reader i referred to Shachar and Emeron (2000) for evidence that viewer trongly prefer to watch program whoe cat are demographically imilar to themelve. Program etting and thematic element influence viewer choice. Viewer mot prefer program with Houe etting, followed by Apartment, TV Studio, Outdoor, and Workplace. Workplace cene may be unpopular becaue they involve tenion that interfere with viewer recreational ue of televiion, while Houe and Apartment cene may be popular for the oppoite reaon. Three of four etting effect are ignificant, but they are relatively mall in magnitude. Of the thematic effect, Sci-Fi ha a large and poitive impact on viewerhip, but Cop and Supernatural are not ignificant. Pat Emmy award nomination increae a program attractivene to viewer Emmy nomination were not announced until after the ample concluded, and actually correlate negatively with program viewerhip. Thi ugget that Emmy nomination play an important role in ignaling program quality to both viewer and network. The negative effect of currentyear Emmy nomination more likely reflect unfavorable timelot and tune-in upport given to unproven new how. I am not aware of any other tudie that meaure the impact of program etting, thematic element, or award nomination on audience ize. Two of five network effect are ignificant. Viewer prefer ABC to the excluded network (CBS), and prefer CBS to UPN. The preference of ABC over CBS i conitent with Shachar and Emeron (2000), who find the network preference order to be NBC, ABC, FOX, CBS. But I do not find evidence that NBC i preferred to any other network beide UPN. Network effect likely repreent two factor: network abilitie to cro-promote their program, and affiliate ignal trength. The mot likely explanation for NBC fall i it inability to replace it mot popular program (e.g., Seinfeld) during the time between the two ample. Table 5c report market-pecific fixed effect. The excluded DMA i Louiville, Kentucky. Televiion utilitie vary coniderably acro market, and are ignificantly different from the excluded DMA in 24 of 49 market. The highet point etimate are found in uch divere market a San Francico-Oakland-San Joe, Greenville-Spartanburg-Aheville, Sacramento-Stockton-Modeto, Wet Palm Beach-Fort Pierce, and Atlanta. There are no clear correlation between televiion utility and DMA ize or location.

30 28 Table 5d how median own- and cro-advertiing elaticitie by network. Audience ize are very enitive to advertiing level. For example, if CBS were to increae it advertiing level 10%, it median audience lo would be 30%, while ABC audience would increae 8.3%. The three lowet-rated network have the mot elatic audience demand, but audience elaticity i not uniformly related to network audience hare. ABC had a higher average program rating than the WB, but a ubtantially more elatic viewer demand (-9.05 to -7.12). Thi i probably becaue the WB tended to provide narrower niche programming than ABC. Higher-rated network audience were generally le reponive to advertiing quantity. Thi i becaue program are differentiated vertically a well a horizontally. Program with high level of vertical differentiation garner larger, more divere audience and have fewer good ubtitute. Thi logic alo underlie why all ix broadcat network are more likely to loe audience hare to the non-televiion option than to the non-broadcat-network televiion option. Cable program tend to exhibit high level of horizontal differentiation and low level of vertical differentiation. Therefore cable program are unlikely to be better ubtitute for broadcat programming than other broadcat network program Advertier Demand Parameter Etimate Advertiement demand parameter were etimated uing the intrument and the procedure decribed in ection 4.4. In the advertiement demand regreion I include program characteritic that can be preumed to influence audience receptivity to advertiement or proxy for unoberved demographic valued by advertier. Thee included Network dummie: to account for network varying abilitie to bundle deirable program audience with maller audience. Genre: viewer mood at the time of expoure to advertiing affect ad meage proceing. (Goldberg and Gorn 1987) Main Character and Cat demographic: Shachar and Emeron (2000) found that people like to watch program about character demographically imilar to themelve, o cat demographic hould correlate with audience demographic.

31 29 A pecial dummy: irregularly-cheduled program typically preempt network weaket program. 33 Thematic element: thee may correlate with viewer pychographic, which advertier ue to target ad meage. Program Age: network typically renew their bet program, o program age may reflect viewer and advertier pat appraial of a how. Award nomination and pat award nomination: thee eem likely to indicate the degree to which viewer are emotionally involved in a how, and to correlate with deirable viewer demographic. Weekday: many conumer hop on the weekend and have limited memorie, o many advertier prefer to air their commercial later in the week (Auletta 1992). Table 6 report advertier demand parameter etimate. The model fit the data very well, with a peudo R 2 of 0.87, an average relative error of 0.248, and a root mean quare error of 39,509 (the dependent variable mean i 142,813). Mot of the reult conform to the hypothee above. The direct effect of advertiing quantity on ad price wa negative and ignificant and implied a mean marketwide price elaticity (holding audience ize contant) of Thi i ubtantially more elatic than other author finding; Crandall (1972) etimated a marketwide price elaticity of -0.45, and Bowman (1976) found elaticitie (uing two different pecification) of and Thee difference in reult are likely due to increaed competition: the number of broadcat network ha increaed from three to ix, cable network and other media have entered into competition for advertiing dollar, and broadcater are no longer allowed to collude by etting a cap on advertiing minute. The effect of audience ize on ad price i poitive and ignificant, with a mean elaticity of Thi figure i ubtantial, epecially given the large variation in audience ize. It alo punctuate the importance of conidering both ide of the televiion indutry in thi paper, howing that ad revenue are dependent on audience ize, jut a audience ize i highly reponive to advertiing level. 33 An example of a pecial in the data i Mi Dog Beauty Pageant, which aired Thurday, May 22, on FOX. 34 The bound of the 95% confidence interval for thi tatitic are 2.40 and 3.53.

32 30 The genre parameter etimate how that advertier value Reality program mot, followed by Scripted Comedy, Pychological Drama, New, Movie, and Action Drama. Reality program receive, ceteri paribu, $146,000 more per pot than how in the Action genre, a difference that i larger than the mean ad price. Thi help to explain the rapid proliferation of Reality program after their introduction in the mid Why do Reality program earn o much more than other how? Their ditinguihing characteritic are uncripted action, nonprofeional cat member, competitive theme, and increaed ability to accommodate product placement. It might be that advertier are eeking to take advantage of companion advertiing to complement their product placement, or to jam rival advertier product placement. It alo could be that viewer feeling of identifying with the nonprofeional program cat enhance their receptivity to ad meage. Thi quetion certainly deerve further tudy. Comedie earn more than average becaue they generate poitive feeling among viewer, which reduce reitance to peruaion and increae liking, a feeling that can be tranferred to advertiing (Goldberg and Gorn 1987). Cat demographic alo affected ad price. Program featuring African American main character earned lightly more than average, while program featuring other minoritie earned far more than average. Thi latter finding might be due to the relative carcity of non-white and non-african-american actor working on televiion. Show with married or ingle-parent main character earned more than average. Advertier pay ignificantly le for program featuring 50% or greater minority cat repreentation, but program with 25%-50% minority cat charge a premium. The firt reult likely reflect the audience demographic of 50+% minority cat program, ince nearly all of thee how feature African-American cat, and African-American viewer tend to earn le than average. Program thematic element influence advertier demand. Cop how earn lightly more than average, a do program with upernatural element. But cience fiction program earn $60,000 le per pot than do other how. Thi i perhap due to incongruitie between programming and advertiing content. Irregularly-cheduled program earn le per ad but there doe not appear to be any advertiing premium aociated with program age. The effect of current-year Emmy nomination (which were not announced until after the ample wa complete) wa quite large, uggeting that

33 31 advertier are able to predict program quality well. Pat Emmy nomination increaed advertier demand but their effect wa much maller. Thee reult give evidence of the halo of quality effect conferred by highly-involving program on their advertiing. Thurday program command a $41,766 premium over Monday programming. Next came Friday ($12,303), followed by Wedneday ($7,340), and Tueday ($3,152). Conumer ave ome hopping trip (e.g., auto or movie) for the weekend, o advertier eek to end meage on Thurday. The relative preference for Friday over Wedneday, while much maller, doe not conform to conventional widom, and i dicued further in the next ubection Comparing Advertier and Viewer Demand Parameter Etimate It i intereting to compare advertier and viewer preference for program characteritic to network programming deciion. Viewer preference reflect program characteritic entertainment value, while advertier preference are more likely to meaure the effect of program characteritic on advertiing delivery. Network revenue count on getting both ide of the market on board, o they mut take both et of preference into account when they make program and cheduling deciion. 35 I now compare viewer and advertier preference for program genre. Viewer mot preferred genre wa Action Drama, with a 95% confidence interval of [.04,.18]; followed by New ([-.06,.15]), Pychological Drama (retricted to zero), Reality ([-.18,-.03]), Movie ([-.30,-.11]), and Scripted Comedy ([-.27,-.16]). It i triking that viewer three mot preferred genre account for only 42% of network chedule. The reaon why become clear when we ee that advertier preference over genre i nearly oppoite viewer preference lit. Advertier mot prefer to buy time during Reality program ([72049,74479]), followed by Scripted Comedy ([38053,39880]), Pychological Drama (retricted to zero), New ([-19454,-16061]), Movie ([-23246, ]), and Action Drama ([-74212,-71159]). Now it become more clear why Reality and Scripted Comedy program account for 47% of network programming: they 35 I focu here on program genre and day-of-the-week effect. Further inight about main character and cat demographic, thematic element, and award nomination can be drawn by comparing table 2, 5b, and 6. For example, viewer exhibit a ignificant preference for Science Fiction programming, while advertier trongly prefer to avoid it. Accordingly Science-Fiction element are preent in jut 6% of network program-hour.

34 32 command large premium over other genre. Analyzing either ide of the market in iolation might ugget that network were failing to atify their cutomer tate. It i alo intereting to conider the interplay between conumer and advertier preference over day of the week. Viewer preferred evening for televiion i Friday ([.21,1.33]), followed by Thurday ([.06,.59]). Advertier mot preferred night i Thurday ([40642,42892]), followed by Friday ([11196,13410]), Wedneday ([6159,8523]), Tueday ([2021,4285]), and Monday (retricted to zero). The etimation reult ugget that network chedule tronger program a the week progree, peaking on Thurday before falling harply on Friday. The average program NAMU on Monday wa -.20, -.15 on Tueday, -.14 on Wedneday, -.07 on Thurday, and -.28 on Friday. It i eay to ee why network competition for viewer ha hitorically been fiercet on Thurday night. Advertier are willing to pay a ignificant premium on Thurday night, while viewer are more likely than average to watch televiion. What i le intuitive i Friday night, when network aired lower-quality program. Yet thi i viewer mot preferred night to watch televiion, a freedom from going to work or chool the next day allow many viewer extra leiure time. And advertier are even willing to pay lightly more for Friday night lot than for night earlier in the week. But there are everal trategic factor that may explain broadcat network lack of trong how on Friday night. Firt, viewer a a group are more likely to watch on Friday night, but any individual viewer may be more likely to have occaional ocial opportunitie that would preempt involvement with a regular televiion erie. 36 Second, the value of tune-in i lower on Friday night. With the network tronget program on Thurday night, tune-in are more likely to be remembered on Monday, Tueday, and Wedneday. Third, broadcat network face tronger-than-average competition from cable network on Friday night. For example, the USA and Sci-Fi cable network uually debut original programming on Friday. Cumulative broadcat audience fall from 45.8% to 29.3% from Thurday night to Friday night, but non-broadcat network hare rie from 22.4% to 28.9%. Thi final reaon i perhap the mot compelling. Flint (2006) note that network cheduled trong program like Dalla, Miami Vice, and Duke of Hazzard on 36 Thi i upported by the obervation that nonerial how (e.g., movie) are often programmed on Friday night.

35 33 Friday night in the 1980, before cable network offered trong competition. He alo note that broadcat network trengthened their Friday-night chedule in the televiion eaon Network Tune-In Inference I ue advertier demand parameter etimate to infer network per-viewer tune-in benefit and tune-in level in the manner decribed in ection 4.4. Figure 4 contain a hitogram of τˆ, the imputation of network tune-in benefit (per thouand houehold) acro how. τˆ wa unretricted, but the imputation were trictly poitive, with a mean of $ The minimum tune-in benefit wa $3.16, for the WB Reba on Friday night at 9:00; thi how aired on a lowrated network on a night before that network goe dark. The maximum tune-in benefit wa on NBC Wedneday-night Law & Order; thi how aired at 9 p.m. the night before the network Mut See TV Thurday night line-up. Other program have imilarly intuitive place in the ditribution of tune-in benefit, lending credence to the finding, with program on higher-rated network having much higher per-viewer tune-in benefit. I alo checked the reaonablene of the imputed tune-in benefit by meauring their correlation with the mean program utilitie etimated by the viewer demand model. Thi potential correlation wa the reaon why I did not ue the tructural model of network competition to etimate advertier demand parameter. The correlation ha the hypotheized ign and turn out to be quite large: Thee check ugget the imputed network tune-in benefit are reaonable, o I proceed by imputing network tune-in level. Figure 5 depict the hitogram of imputed tune-in level. The ditribution appear to be approximately lognormal. Like imputed tune-in benefit, imputed tune-in level are trictly poitive, depite the abence of non-negativity retriction. The program with the highet inferred tune-in level are the eaon finale of American Idol (FOX) and Everybody Love Raymond (CBS). The lowet tune-in inference correponded to econdhour, Friday-night program on low-rated network: Reba and Grounded for Life on the WB, and movie on UPN. 37 Thi correlation i ignificant at the 1% confidence level.

36 34 A ueful check on inference reaonablene i the TCMR data, which report tune-in level and ad level for one week in November The mean imputed tune-in level in thi analyi i 2.63 minute, 0.58 minute more than the mean of 2.05 in the TCMR data, while the average national ad level in my data (5.4 minute) exceed the TCMR mean level by 0.55 minute. The imputed tune-in level are lightly larger than thoe reported in the TCMR, but the difference i mall and i matched by a imilar difference in oberved advertiing level. The difference eem reaonable becaue of the eaonal difference in the ample: the TCMR data were collected in November, in the midt of the fall eaon when program lineup are motly table. The data ued in thi tudy were collected in May, hortly before network ignificantly alter program chedule for the ummer month. Greater chedule variability ugget higher return to tune-in, and conequently more tune-in in equilibrium. I proceed with the aumption that the imputed tune-in level are reaonable, and ue them in the counterfactual exercie decribed in the next ection. 6. Gaining Inight into the Effect of Ad-Avoidance Technology Proliferation Advertiement-avoidance technology (AAT) proliferation could have two poible effect on equilibrium advertiing time. Firt, viewer channel witching i network primary incentive to keep advertiing level low. It might be that AAT primary effect i to reduce network audience loe from advertiing, becaue AAT uer fat-forward pat ad meage rather than witching channel. Falling diincentive to advertie would lead network to increae their advertiing time. Rie in ad time would make AAT more valuable to ad-avere viewer. AAT penetration and riing ad time could poitively reinforce each other. The other poibility i that AAT penetration primary effect i to lower advertier willingne to pay for viewer uing AAT. Thi would make non-aat uer more carce, and thi increaed carcity will lead to higher per-viewer advertiing price. Network might repond by competing more intenely for non-ad-avoiding viewer. Thi competition would take the form of lower advertiing level. Falling ad level could dampen the advertiement-avoidance benefit of AAT ownerhip and therefore low it rate of growth.

37 35 I ue a counterfactual to gain inight about the quetion of how AAT affect the indutry. I olve the etimated model of network competition to tet the enitivity of ad level to a hypothetical AAT. I report predicted equilibrium ad quantitie, ad revenue, and audience ize, given plauible aumption about the effect of ad-avoidance technology on viewer and advertier behavior. I model an AAT that allow each viewer to view or record one program per half-hour. I conider here a pure ad-avoidance technology that give all ad-avere televiion viewer an identical, proportional reduction in ad diutility. I do not model non-ad-avoidance apect of the digital video recorder, uch a enhanced program information or ability to record multiple network at once. I alo do not conider the effect of AAT ue on network program cheduling or quality invetment. The exercie here i a counterfactual: how would market equilibria in May 2003 have been different if x% of viewer had acce to the aumed ad-avoidance technology? The reult hould be interpreted a educated peculation, rather than a prediction. The effect of the hypothetical AAT are governed by three parameter, and which viewer have the technology (the technology ditribution rule ). The parameter are Ad-avoider proportional reduction in ad nuiance γ 1 γ 2 The proportion of ad-avoider in the viewing population γ Advertier valuation of an ad-avoider expoure to a commercial, 3 relative to a non-ad-avoider expoure I decribe in detail how the model primitive are repecified to account for AAT and how how to olve for the reulting equilibrium in the Technical Appendix. There i no publihed reearch to guide the election of value for the unoberved parameter, and it i not poible to etimate them from the available data. I therefore ue range of value that eem reaonable. I report the enitivity of the counterfactual reult to the aumption ued. I aume that the proportional reduction of advertiing diutility reulting from AAT ue i 0.67, that γ 3 i mall, 38 and that AAT i firt adopted by the mot ad-avere 38 Wilbur (2007) review reearch on advertiing expoure and find three reaon that advertier might attach ome value to ad-kipping viewer. Firt, viewer learning ha been hown to increae with advertiing expoure peed. Second, there i evidence that advertiing can have latent effect on conumer conideration et, even when

38 36 viewer in the population. The counterfactual reult are mot enitive to aumption about γ 2 and γ 3, o I report prediction for variou combination of thoe parameter. 39 Figure 6 how mean predicted advertiing time (advertiing econd plu tune-in econd) for variou aumption about γ 2 and γ An increae in AAT penetration from 5% to 35% increae equilibrium advertiing time by about 14%. Thi indicate the attenuation in audience enitivity to advertiing outweigh the viewer-carcity effect decribed above. Ad time alo increae with advertier valuation of AAT-uing viewer. When ad-kipper are more valuable, network have an incentive to ell more ad, to take advantage of ad-kipper le elatic viewing demand. Figure 7 how how mean audience ize and effective audience ize change with AAT penetration. Cumulative audience ize rie a AAT proliferate, a AAT uer watch more televiion. Thoe rie are negatively impacted by AAT uer value to advertier, becaue network increae ad level when ad-avoider are relatively more valuable. However, network effective audience ize fall with AAT penetration. The bottom et of line in Figure 7 how the value of the expanded audience ize when tranlated to non-ad-avoider equivalent. 41 Adavoider fat-forwarded expoure to commercial repreent an unavoidable lo of audience value. Figure 8 how that network revenue fall with AAT penetration. The ize of the fall depend on advertier valuation of ad-kipper. A conervative etimate (1 non-aat uer i worth 100 AAT uer) indicate network revenue fall 38% when AAT ue climb to 35%. A liberal etimate (1 non-aat uer i worth 5 AAT uer) indicate network revenue fall 22%. The main implication of thi reult for marketer i a likely decreae in network incentive to invet in program quality. But it i important to note that I have not accounted for program conumer do not remember the advertiing. Third, the heightened attention required to fat-forward pat ad ha been linked to increaed conumer awarene and recall. 39 The model prediction change very little with aumption about γ 1 and the technology ditribution rule becaue thee two aumption primarily affect viewer with AAT. Thee viewer are not highly valued by advertier o their action have little effect on network trategie. 40 Prediction are baed on the firt week of data due to large computational cot. 41 A an example, aume an audience contain 10 viewer with AAT, and ten viewer without, and that γ = The cumulative audience ize i 20, but the effective audience ize i 11, ince each of the viewer with AAT i worth one-tenth a much a a viewer without AAT.

39 37 cheduling; network might repond to AAT by decreaing intertemporal competition among high-quality program. (For example they might move ome good how to Monday or Friday night.) There i alo a quetion about the extent to which falling ad revenue will be ditributed among the network and their content provider, and other poible trategic change in advertiing content and delivery. 7. Concluion and Dicuion Televiion network operate in a multi-ided platform environment, chooing program to match both advertier and viewer preference. Thi paper etimate viewer demand for televiion program a a function of program characteritic, audience flow, advertiing quantity, and day and time effect. The analyi find that a 10% increae in advertiing time tend to decreae audience ize by about 30% on a highly-rated broadcat network, and 70-90% on a low-rated broadcat network. It alo etimate a model of advertier demand for televiion program, a a function of audience ize, ad quantity, and program characteritic. Advertiement price are highly reponive to advertiing quantity (elaticity of -2.9) and audience ize (elaticity of 0.8). Thee reult indicate the advertiing market ha become ubtantially more competitive than in the Etimated advertier and viewer preference parameter were compared to network programming choice. It wa found that viewer two mot preferred genre (Action Drama and New) account for jut 16% of network program chedule. Advertier two mot preferred genre (Reality and Scripted Comedy) occupy 47% of network timelot. It i clear that advertier preference are at leat a important a viewer preference to network programmer. I invetigated a counterfactual to iolate the effect of an aumed advertiementavoidance technology on viewer and advertiing market equilibria. Thi exercie offer educated peculation about the effect of digital video recorder proliferation on market equilibria. The model ugget that proliferation of a hypothetical ad-avoidance technology increae equilibrium advertiing level, and decreae network advertiing revenue. A limitation of the preent analyi i that the market-level audience data ued in thi paper mak viewer zapping behavior. (But it hould be noted that the program audience rating ued a currency in the advertiing market alo mak viewer zapping behavior.) It will be

40 38 intereting to ee how the reult would vary uing individual level data or ad break rating in place of program rating. Thi paper can be extended in everal intereting way. Network program cheduling deciion could be endogenized. Advertier could be modeled from firt principle to olve the aignment problem decribed in ection 3.2. It would be intereting to model the third ide of the televiion indutry: network program acquiition and expenditure. And it would be intereting to conider how AAT penetration impact the value of advertiing expoure, a oppoed to the number of ad that AAT uer avoid.

41 Technical Appendix: Model Repecification In thi technical appendix, I lay out the detail of the repecification of the etimated model of viewer demand, advertier demand, and network upply of program and televiion commercial. The repecification hown here aume that AAT i firt adopted by the mot ad-avere viewer in the population. Any alternate technology ditribution rule i a traightforward extenion to what i preented here. I denote viewer with AAT with a upercript a, and other viewer with a upercript n. I rewrite ad-avoider i utility of watching network j at time t in city m a ˆim ˆim u a im * im * [ γ ( α im < )] + x ˆ * 1 ˆ m β im + ˆ 11 0 ξ + ηm ε im = [ q + r rˆ ] ˆ α ˆ + m, (1') * * where α, β, ξˆ, and ˆη are viewer demand parameter etimate, i the network tunein level, rˆ ( ˆ 0) i the network inferred tune-in level (a defined by equation 11 in ection 4), and 1 α * < i an indicator function that equal one if the etimated effect of advertiing on viewer im i utility i negative. The difference r rˆ i added to ad quantity to allow for the poibility that the network may change it tune-in level in repone to AAT proliferation. Type-n viewer utility can be rewritten a u n im = [ q + r rˆ ] ˆ α + x ˆ β + ˆ ξ + ˆ η + ε * im AAT hould alo affect the utility of the non-broadcat-network option, a type-a viewer who chooe thi option are more likely to watch advertiement-upported cable network than ad-free alternative like PBS. I aume that an ad-avoider utility from option J at time t increae by the ame amount a the average network option at the ame time: u a imjt = ˆ ξ Jt + ˆ η mjt + ˆ π D J im + ˆ σ ν + ε J i imjt m * im * ˆ α (1 γ )1 i 1 m r im J * 1 ( ˆ ) αi < 0 J 1 =. 1 j 1 [ q + r rˆ ]. The utility from the non-televiion option remain unchanged. I contruct predicted market hare ŝ m a n function for each type of viewer, and, in the manner decribed in ection 3.1. ŝ m Network j total audience at time t depend on it hare of each type of viewer, and the proportion of ad-avoider in it audience ( γ 2 ). Thu where N m i the number of viewer in city m. V n a = [( 1 γ 2 ) ŝ + γ ŝ m 2 m ] N m, (8) m γ 3 capture advertier value of an ad-avoiding viewer, relative to a non-ad-avoider. If the expoure of an ad-avoider to the advertier meage i worthle, γ 3 will be zero; or, if 39

42 40 uch an expoure i worth half a much a the expoure of a non-ad-avoider to the advertier meage, then γ 3 =. 5. I ue γ 3 to contruct the effective audience ize, which i the number of type-n viewer in the audience, plu the number of type-a viewer, weighted by advertier relative valuation of thi latter group. Effective audience ize i n a V ~ = [( 1 γ 2 ) ŝ + γ ŝ m 2γ 3 m ] N m. m I then rewrite ad price in term of effective audience ize, ~ ~ p = q ˆ λ + V ˆ λ + x ˆ λ + ˆ φ (9') q V where the λˆ are advertier demand etimate and ˆφ i interpreted a unoberved audience demographic. Next, I ue the repecified viewer and advertier demand function to rewrite network j advertiing/tune-in equilibrium condition for how (originally given by equation 8) a ~ ~ p q ˆ λ V ˆ τ = 0. (8') + q I olve the J-1 equilibrium condition defined by equation (8') for new equilibrium ad level. I then ue thee ad level and network firt-order condition to predict new tune-in level, ~ q ˆ λv V r = ~. (11') ˆ τ V q Equilibrium prediction about ad price, audience ize, and tune-in level are calculated a function of predicted ad level. For a gue of the q and r, the tep to find the new equilibrium in a given time period are: n a 1) Calculate predicted audience hare, and, for all network; 2) Calculate the effective audience ize, V ~, and it derivative, 3) Calculate new ad price ; p~ 4) Ue equation (8') to find new predicted advertiing level; 5) Calculate new tune-in prediction uing equation (11'); and ŝ m x ŝ m V ~ q, for all network; 6) Numerically optimize over the q, repeating tep (1)-(5) for each gue of the equilibrium ad level, to olve the ytem of network advertiing/tune-in equilibrium condition.

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48 46 Cot Per Thouand Viewer ($) 1 Audience Size (000) 2 Table 1. Decriptive Statitic: Ad Price, Ad Quantity, CPM, and Audience Size, by Network and Day All Net ABC CBS FOX NBC UPN WB Adv Price (per 30 econd; $000) 147 (125) 125 (40) 179 (114) 241 (186) 212 (125) 55 (26) 71 (25) Adv Second (74) (83) (55) (66) (81) (68) (58) Monday (6) (4) (7) (7) (7) (2) (2) Tueday (9) (5) (4) (6) (6) (3) (6) Wedneday (9) (6) (13) (7) (8) (6) (7) Thurday (11) (13) (13) (7) (7) (3) (4) Friday (7) (3) (10) (8) (3) (1) (3) All Night (9) (7) (11) (9) (8) (6) (5) Monday 5,846 5,779 8,862 6,119 6,928 2,699 4,690 (2386) (1275) (2369) (1723) (1569) (250) (549) Tueday 6,376 5,299 8,291 11,303 5,703 2,768 4,895 (3087) (546) (551) (2932) (1509) (819) (670) Wedneday 6,613 6,678 5,409 12,574 8,551 3,260 3,210 (4036) (1134) (589) (4984) (2552) (725) (1362) Thurday 3 6,723 4,281 13,098 4,269 12,737 3,807 2,144 (4659) (1336) (2547) (1150) (828) (334) (411) Friday 4,206 5,722 5,610 4,131 5,574 1,940 2,260 (1734) (549) (932) (916) (901) (290) (248) All Night 5,868 5,693 7,716 8,058 7,361 2,793 3,584 (3294) (1182) (2825) (4573) (2752) (791) (1379) Number in paranthee are tandard deviation 1 averaged over half-hour, 8:00-10:00 p.m., and week 2 meaured in houehold; averaged over half-hour, 8:00-10:00 p.m., and week 3 Thurday, May 1, and Thurday, May 15, were removed from the ample Source: Nielen Media Reearch; TNS Media Intelligence/CMR

49 47 variable name decription mean t. dev. Adv Second Second of national advertiement aired during the program 308 (75) ScriptedComedy =1 if the how i a cripted comedy.31 (.46) ActionDrama =1 if the how i a cripted drama that contain action cene.10 (.30) PychDrama =1 if the how i a cripted drama that doe not contain action cene.26 (.44) Reality =1 if the how i uncripted.16 (.36) New =1 if the how i a new program or a newmagazine.06 (.24) Movie =1 if the how i a movie.11 (.31) African-American =1 if at leat one African-American main character.44 (.50) Other Nonwhite =1 if at leat one non-white, non-african American main character.09 (.29) MC<18 =1 if at leat one main character i under (.40) MC18-34 =1 if at leat one main character i between 18 and 34 year old.67 (.47) MC35-49 =1 if at leat one main character i between 35 and 49 year old.62 (.49) MC50+ =1 if at leat one main character i over 50 year old.17 (.38) Married =1 if at leat one main character i married to another character.19 (.40) Single Parent =1 if at leat one main character i ingle and ha children.10 (.30) Female Only =1 if none of the main character are male.09 (.29) Male Only =1 if none of the main character are female.23 (.42) 50+% Nonwhite =1 if 50% or more of the how cat i non-white.20 (.40) 25+% Nonwhite =1 if 25-49% of the how cat i non-white.39 (.49) 10+% Nonwhite =1 if 10-24% of the how cat i non-white.55 (.50) 50+%Female =1 if 50% or more of the how cat i female.46 (.50) 25+%Female =1 if 25-49% of the how cat i female.74 (.44) Houe =1 if the how contain cene et in a character houe.23 (.42) Apartment =1 if the how contain cene et in a character apartment.06 (.23) Workplace =1 if the how contain cene et in a buine or workplace.34 (.48) Outdoor =1 if the how contain outdoor cene.49 (.50) Studio =1 if the how contain cene et in a TV tudio.20 (.40) Cop =1 if the how ha ome law enforcement element.10 (.31) Sci-Fi =1 if the how contain element of cience fiction (i.e. Star Trek).06 (.24) Supernatural =1 if the how contain upernatural element (i.e. angel, witchcraft).07 (.26) Age # of year ince the how' debut 3.28 (3.5) 2003EmmyNom 2004 Emmy Nomination 1.4 (3.1) Pat Emmy Nom All pre-2004 Emmy Nomination 3.6 (11.9) ABC, CBS, FOX, Network-pecific dummy variable NBC, UPN, WB Mon, Tue, Wed, Thu, Fri Day-pecific dummy variable 1tHH, 2ndHH, 3rdHH, 4thHH Table 2. Variable Definition and Decriptive Statitic Dummy variable for 1t half-hour of prime time, 2nd half-hour of prime time, 3rd, 4th, etc.

50 48 Table 3. Correlation between Audience Size, Ad Price, Ad Second, and Program Genre Audience Size Adv Price Adv Second Audience Size 1.00 Adv Price Adv Second Comedy Action Drama Reality New Movie

51 49 Table 4. Multinomial Logit Viewer Demand Parameter Etimate (i) (ii) (iii) (iv) (v) (vi) (vi i) (viii) Advertiing Second -5.9E-5-2.2E-4 * -5.7E-5-2.2E-4 * -5.4E-5-2.2E-4 * -4.9E E-4 (4.7E-5) (4.6E-5) (4.3E-5) (4.2E-5) (4.7E-5) (4.6E-5) (4.3E-5 ) (4.1E-5) Lead-In Audience * * * * * * * (0.083) (0.081) (0.077) (0.075) (0.085) (0.084) (0.078) (0.075) Lead-Out Audience * * * * * * * (0.083) (0.082) (0.078) (0.075) (0.084) (0.082) (0.077) (0.074) Non-Broadcat-Network * * * * * * * (3.378) (0.027) (3.125) (0.030) (3.406) (0.026) (3.093) (0.025) Lat Half-Hour a -4.6E-3 3.9E-3-8.1E-3 2.0E-3 2.0E E E-3 (0.010) (0.011) (9.3E-3) (9.8E-3) (0.010) (0.011) (9.1E- 3) (9.7E-3) Include Program Dummie? --- ye --- ye --- ye --- ye Include Market Dummie? ye ye y e ye Weighted by Market Size? ye ye y e ye Adjuted R * = Significant at the 1% level a Indicate whether a program wa in it lat half-hour only for thoe program that lated 60 minute or longer Note : Reported here are OLS regreion wherein the dependent variable i log( m )-log( m0t ) Number of Obervation: 23,588 All Specification included Day (Tue, Wed, Thur, Fri) and Half-hour (8:30, 9:00, 9:30) Dummie When Program Dummie are not included, obervable program characteritic (genre, etting, main character and cat demographic, age, and current and pat award nomination) and a contant are included

52 π 50 Mean Standard Deviation raction with Demographic Variable ( π ') ( β ') ( ') Income Age Age2 Advertiing Second -1.5E-3 ** E E-4 (2.1E-4) (9.269) (4.9E-5) (1.799) (0.019) Non-Broadcat-Network ** E E-3 (1.387) (0.935) (1.1E-4) (0.389) (4.6E-3) TV-Off 0 a ** 0 b 0 b 0 b (0.479) GMM Objective χ 2 Goodne-of-Fit (degree of freedom) 1.00E+10 (163) Peudo R Number of Obervation: 23,588 ** Significant at the 1% confidence level a The mean utility of "TV-Off" i normalized to zero. It i not eparately identified from the mean utilitie of the "inide" option. Table 5a. Random Utility Parameter Etimate b Interaction between "TV-Off" and interaction with conumer demographic are, in practice, very difficult to etimate eparately from the market-pecific fixed effect. They are et to zero. σ

53 51 Variable Etimate Variable Etimate (td. error) (td. error) Lead-in Main Character: Male Only b ** (2.152) (0.037) Lead-out ** Cat: 50+% NonWhite b (6.055) (0.044) Lat Half-Hour c Cat: 25+% NonWhite b (0.038) (0.040) Tueday Cat: 10+% NonWhite b ** (0.507) (0.047) Wedneday Cat: 50+%Female b (0.099) (0.038) Thurday ** Cat: 25+%Female b (0.136) (0.047) Friday ** Setting: Houe b ** (0.287) (0.040) 2nd Prime-Time Half-Hour ** Setting: Apartment b (0.092) (0.057) 3rd Prime-Time Half-Hour ** Setting: Workplace b ** (0.042) (0.027) 4th Prime-Time Half-Hour Setting: On Location b ** (0.070) (0.026) Contant b Special b (6.644) (0.041) Genre: Scripted Comedy b ** Cop b 5.8E-3 (0.028) (0.039) Genre: Action Drama b ** Sci-Fi b ** (0.037) (0.065) Genre: Reality b ** Supernatural b (0.037) (0.040) Genre: New b SeaonFirtAired b -2.9E-3 (0.053) (3.3E-3) Genre: Movie b ** 2003 Emmy Nomination b ** (0.047) (5.5E-3) Main Character: African- American b ** Pat Emmy Nomination b ** (0.039) (1.1E-3) Main Character: Other Nonwhite b ABC b ** (0.040) (0.042) Main Character: <18 year old b FOX b (0.045) (0.038) Main Character: year old b ** NBC b (0.031) (0.036) Main Character: year old b UPN b ** (0.026) (0.049) Main Character: Married b WB b (0.038) (0.050) Main Character: Single Parent b Minimum-Ditance χ E+07 (0.031) Main Character: Female Only b Minimum-Ditance Peudo R (0.035) ** Significant at the 1% confidence level Table 5b. Non-Random Utility Parameter Etimate a a Parameter are GMM etimate, except where noted. b Minimum-ditance etimate c Defined only for thoe program whoe duration exceed 30 minute

54 Market Size (000 Etimate Market Size (000 Etimate Houehold) (td. error) Houehold) (td. error) New York, NY ** San Diego, CA (0.80) (0.38) Lo Angele, CA Hartford & New Haven, CT ** (0.58) (0.32) Chicago, IL ** Charlotte, NC (0.54) (0.26) Philadelphia, PA Raleigh-Durham ** (0.63) (Fayetteville), NC (0.28) San Francico-Oakland-San ** Nahville, TN Joe, CA (0.48) (0.38) Boton, MA (Mancheter, NH) Milwaukee, WI ** (0.45) (0.50) Dalla-Ft. Worth, TX ** Cincinnati, OH (0.38) (0.40) Wahington, DC (Hagertown, MD) Kana City, MO ** (0.68) (0.49) Atlanta, GA ** Columbu, OH (0.41) (0.34) Detroit, MI Greenville-Spartanburg, SC ** (0.33) Aheville, NC-Anderon, SC (0.41) Houton, TX ** Salt Lake City, UT (0.42) (0.58) Seattle-Tacoma, WA San Antonio, TX ** (0.31) (0.77) Tampa-St. Peterburg Grand Rapid-Kalamazoo (0.59) Battle Creek, MI (0.77) Minneapoli-St. Paul, MN Wet Palm Beach-Ft. Pierce, ** (0.27) FL (0.46) Cleveland-Akron (Canton), ** Birmingham (Anniton and OH (0.48) Tucalooa), AL (0.60) Phoenix (Precott), AZ Norfolk-Portmouth ** (0.51) Newport New, VA (0.52) Miami-Ft. Lauderdale, FL ** New Orlean, LA (0.35) (0.35) Denver, CO Memphi, TN ** (0.21) (0.58) Sacramento-Stockton ** Buffalo, NY (Including ** Modeto, CA (0.36) Canadian Audience) (0.55) Orlando-Daytona Beach- Melbourne, FL Oklahoma City, OK ** (0.60) (0.37) Greenboro-High Point- (0.58) Winton Salem, NC (0.54) Harriburg-Lancater- (0.39) Lebanon-York, PA (0.50) Providence, RI-New (0.53) Bedford, MA (0.45) Albuquerque-anta Fe, NM (0.28) (0.73) Pittburgh, PA ** St. Loui, MO ** Portland, OR ** Baltimore, MD ** Indianapoli, IN ** (0.30) ** Significant at the 1% confidence level Table 5c. Market Effect Etimate 52

55 53 Table 5d. Median Own- and Cro-Advertiing-Level Audience Elaticitie a Network ABC CBS FOX NBC UPN WB ABC CBS FOX NBC UPN WB Non-Broadcat-Network TV Outide Option (TV-Off) a Table entry i,j report the etimated elaticity of option i ' national audience, given a 30- econd increae in network j ' oberved advertiing level. Reported elaticitie are the median of the ditribution of national audience elaticitie over day and half-hour.

56 54 Table 6. Advertiement Demand Parameter Etimate Etimate Etimate Variable (td. error) Variable (td. error) Advertiing Second -1,302.4 ** Main Char: Single Parent 7,144.7 ** (127.1) (500.8) Audience Size 19.4 ** Cat: 50+% NonWhite -88,517.6 ** (6.3) (691.7) Network: ABC -14,307.7 ** Cat: 25+% NonWhite 36,147.8 ** (1679.5) (631.1) Network: FOX -15,960.4 ** Special -36,644.9 ** (662.2) (842.9) Network: NBC -16,261.2 ** Theme: Cop 6,678.1 ** (681.3) (791.2) Network: UPN -7,228.7 ** Theme: Sci-Fi -60,251.8 ** (1094.7) (1132.4) Network: WB -57,648.9 ** Theme: Supernatural 4,119.5 ** (667.5) (685.9) Genre: Scripted Comedy 38,966.3 ** SeaonFirtAired (466.0) (71.3) Genre: Action Drama -72,658.6 ** 2003 Emmy Nomination 12,949.7 ** (778.8) (99.0) Genre: Reality 73,264.1 ** Pat Emmy Nomination ** (619.8) (18.3) Genre: New -17,757.6 ** Day: Tue 3,152.7 ** (865.7) (577.5) Genre: Movie -20,272.5 ** Day: Wed 7,340.9 ** (1517.0) (603.0) Main Char: African American 1,058.8 ** Day: Thur 41,766.7 ** (519.1) (573.9) Main Char: Other Nonwhite 52,127.7 ** Day: Fri 12,303.1 ** (711.4) (564.7) Main Char: Married 16,958.8 ** Contant 83,812.4 ** (532.7) ( ) Peudo R Average Relative Error Note: Number of obervation = 262. Dependent variable i advertiement price.

57 55 Figure 1. Layout of Empirical Strategy Viewer Demand Model Advertier Demand Function (equation 4) 1. Viewer Demand Parameter Etimate Intrument Model of Network Competition 2. Advertier Demand Parameter Etimate 3. Tune-in Inference 4. Counterfactual Reult

58 56 Figure 2. Average Nightly Audience Size, by Network 14 12,000) Houehold ( ABC CBS FOX NBC UPN WB 2 0 Mon Tue Wed Thu Fri Weekday Figure 3. Average Nightly Cot Per Thouand Houehold, by Network $40 $35 $30 $25 $20 $15 ABC CBS FOX NBC UPN WB $10 $5 $0 Mon Tue Wed Thu Fri Weekday

59 57 Figure 4: Hitogram of Imputed Tune-In Benefit (per 1,000 houehold) Imputed per-viewer tune-in benefit ($) Figure 5: Hitogram of Imputed Tune-In Level Imputed tune-in level (econd)

60 58 Figure 6. Mean Equilibrium Advertiing Second per Network Half-hour, by AAT Penetration (auming γ 1 =.667 and mot ad-avere viewer have AAT ) Second of National Advertiing γ 3 γ 3 γ 3 =.01 =.10 =.20 Note 1: If advertier' relative ad-avoider valuation (γ 3 ) i.10, advertier are indifferent between reaching ten viewer with AAT and reaching one viewer without AAT Note 2: Thee reult aume that AAT ue reduce viewer' advertiing diutility by 2/ % 20% 35% AAT Penetration Figure 7. Mean Equilibrium Audience and "Effective" Audience per Network Half-hour, by AAT Penetration (auming γ 1 =.667 and mot ad-avere viewer have AAT ) γ 3 γ 3 =.01 =.10 Total Program Audience γ 3 = ,000 Houeholdld Audience' Equivalent Value to Advertier Note 1: If advertier' relative ad-avoider valuation (γ 3 ) i.10, advertier are indifferent between reaching ten viewer with AAT and reaching one viewer without AAT Note 2: Thee reult aume that AAT ue reduce viewer' advertiing diutility by 2/ % 20% 35% AAT Penetration

61 59 Figure 8. Mean Equilibrium Advertiing Revenue per Network Half-hour, by AAT Penetration (auming γ 1 =.667 and mot ad-avere viewer have AAT ) $1,800,000 $1,600,000 $1,400,000 γ 3 γ 3 γ 3 =.01 =.10 =.20 $1,200,000 $1,000,000 $800,000 $600,000 Note 1: If advertier' relative ad-avoider valuation (γ 3 ) i.10, advertier are indifferent between reaching ten viewer with AAT and reaching one viewer without AAT $400,000 $200,000 Note 2: Thee reult aume that AAT ue reduce viewer' advertiing diutility by 2/3 $0 5% 20% 35% AAT Penetration