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.