Electronically Filed Docket: 14-CRB-0011-SD (2010-2013) Filing Date: 03/22/2019 06:42:07 PM EDT
Before the COPYRIGHT ROYALTY JUDGES Washington, D.C.
______) In the Matter of ) ) Distribution of the ) Docket No. 14-CRB-0011-SD (2010-13) ) 2010, 2011, 2012, and 2013 ) Satellite Royalty Funds ) ______)
PROGRAM SUPPLIERS’ ALLOCATION PHASE WRITTEN DIRECT STATEMENT
Gregory O. Olaniran D.C. Bar No. 455784 Lucy Holmes Plovnick D.C. Bar No. 488752 Alesha M. Dominique D.C. Bar No. 990311 Leo M. Lichtman D.C. Bar No. 1026600 Mitchell Silberberg & Knupp LLP 1818 N Street NW, 7th Floor Washington, DC 20036 (202) 355-7917 (Telephone) (202) 355-7887 (Facsimile) [email protected] [email protected] [email protected]
Attorneys for Program Suppliers
March 22, 2019
Before the COPYRIGHT ROYALTY JUDGES Washington, D.C.
______) In the Matter of ) ) Distribution of the ) Docket No. 14-CRB-0011-SD (2010-13) ) 2010, 2011, 2012, and 2013 ) Satellite Royalty Funds ) ______)
PROGRAM SUPPLIERS’ ALLOCATION PHASE WRITTEN DIRECT STATEMENT
The Motion Picture Association of America, Inc. (“MPAA”), on behalf of producers and/or distributors of syndicated series, movies, specials, and non-team sports broadcast by television stations who have agreed to representation by MPAA (“Program
Suppliers”),1 in accordance with the procedural schedule set forth in the November 2,
2018 Order Adopting Satellite Allocation Phase Procedural Schedule issued by the
Copyright Royalty Judges (“Judges”), hereby submits its Allocation Phase Written Direct
Statement (“WDS-A”) for the 2010-2013 satellite royalty years in consolidated Docket
No. 14-CRB-0011-SD (2010-13). MPAA submits this introductory memorandum in order to summarize the evidence it will present in the Allocation Phase of this proceeding, and to state the royalty shares that Program Suppliers seek for the 2010,
2011, 2012, and 2013 satellite royalty funds (“2010-2013 Funds”).
1 Lists of MPAA-represented Program Suppliers for each of the satellite royalty years at issue in this consolidated proceeding were included in MPAA’s 2010-2012 satellite Petitions to Participate filed on January 21, 2015, and 2013 satellite Petition to Participate filed on July 6, 2015.
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 1
I. PROCEDURAL HISTORY
On September 9, 2015, the Judges issued an order formally consolidating royalty distribution proceedings regarding the 2010-2013 Funds and sought written submissions from the parties in an effort to clarify the definitions of claims categories for purposes of this proceeding.2 Thereafter, on December 1, 2015, the Judges adopted five program categories for the allocation phase (“Agreed Categories”) to which the Judges may approve an allocation of royalties for the 2010-2013 Funds.3 The Agreed Categories are as follows:
“Commercial Television Claimants.” Programs produced by or for a U.S. commercial television station and broadcast only by that station during the calendar year in question, except those listed in subpart (3) of the Program Suppliers category.
“Devotional Claimants.” Syndicated programs of a primarily religious theme, but not limited to programs produced by or for religious institutions.
“Joint Sports Claimants.” Live telecasts of professional and college team sports broadcast by U.S. and Canadian television stations, except programs in the Canadian Claimants category.
“Music Claimants.” Musical works performed during programs that are in the following categories: Program Suppliers, Joint Sports Claimants, Commercial Television Claimants, Public Television Claimants, Devotional Claimants, Canadian Claimants.
2 See Notice Of Participants, Notice Of Consolidation, And Order For Preliminary Action To Address Claims Categories at 2 (September 9, 2015).
3 See Amended Notice Of Participant Groups, Commencement Of Voluntary Negotiation Period (Allocation), And Scheduling Order at Exhibit A (December 1, 2015) (“Amended Notice”).
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“Program Suppliers.” Syndicated series, specials, and movies, except those included in the Devotional Claimants category. Syndicated series and specials are defined as including (1) programs licensed to and broadcast by at least one U.S. commercial television station during the calendar year in question, (2) programs produced by or for a broadcast station that are broadcast by two or more U.S. television stations during the calendar year in question, and (3) programs produced by or for a U.S. commercial television station that are comprised predominantly of syndicated elements, such as music videos, cartoons, “PM Magazine,” and locally-hosted movies.4
The Judges further recognized that the Agreed Categories are intended to be “mutually exclusive,” such that each program eligible to receive satellite statutory license royalties in this proceeding would fall within only one of the Agreed Categories.5
On December 14, 2016, the parties informed the Judges that they reached a settlement concerning the royalty share to be awarded to the Music Claimants for each of the 2010-2013 Funds.6 In light of this Allocation Phase settlement, the Judges need only determine royalty shares for the remaining four Agreed Categories in this proceeding.
The four Agreed Categories for which allocation remains in controversy are the
Commercial Television Claimants (“CTV”), Devotional Claimants (“Devotionals”), Joint
Sports Claimants (“JSC”), and Program Suppliers. In this WDS-A, MPAA sets forth its testimony and evidence in support of royalty allocation awards for the Program Suppliers category.
4 See id. at Exhibit A.
5 See id.
6 See Amended Order Granting Renewed Joint Motion for Final Distribution of 2010-2013 Satellite Royalty Funds to Music Claimants (September 12, 2017).
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 3
II. SUMMARY OF PROGRAM SUPPLIERS’ CASE
The Judges must distribute royalties that satellite carriers paid to the Copyright
Office for the privilege of retransmitting broadcast stations to distant subscribers pursuant to the statutory license set forth in Section 119 of the Copyright Act for the years 2010,
2011, 2012, and 2013. Although there has never been a prior litigated satellite Allocation
Phase proceeding, the standard that the Judges have used for allocating satellite royalties in past Distribution Phase proceedings is “the relative marketplace value” of the distant broadcast signal programming retransmitted by satellite carriers during the royalty years at issue.7 MPAA believes that the Judges should apply the relative marketplace value standard to allocation of the 2010-2013 Funds.
MPAA has long maintained that the relative marketplace value of distantly retransmitted programming is most clearly expressed through measurements of distant viewing because viewing reflects consumption, and consumption reflects value attributed to the viewed programming by the subscriber. It is axiomatic that consumers subscribe to a satellite system to watch the programming they wish to view that is made available via their subscriptions. Subscriptions to satellite services are driven by the desire of subscribers to consume programming, and viewership of programming types by those subscribers thus reflects, and is a reasonable measure of, marketplace value.
7 See Final Determination Of Royalty Distribution, Consolidated Docket Nos. 2012-6 CRB CD 2004-2009 (Phase II) and 2012-7 CRB SD 1999-2009 (Phase II) at 5-6 (February 13, 2019).
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 4
Because each of the Agreed Categories is simply an aggregation of distantly retransmitted individual programs,8 an aggregation of the distant viewing for all of the programs falling into each of the Agreed Categories provides a reasonable and reliable measure of that category’s relative marketplace value. Accordingly, Program Suppliers are presenting evidence of aggregated distant viewing for each Agreed Category that remains in controversy, measured by Nielsen, and estimated through a multiple regression analysis performed by our economist witness, Dr. Jeffrey S. Gray.
As Dr. Gray explains in his testimony, his regression methodology does not consider Nielsen’s raw estimates of distant viewing data from 2010 to 2013 alone. It also reflects the mathematical relationship each year between the raw distant viewing estimates for each program and (1) the total number of distant subscribers of that station,
(2) the time of day the program aired by quarter hour, and (3) the type of program aired.
Dr. Gray’s regression analysis shows that during 2010 through 2013, Program Suppliers programming had overwhelmingly the highest relative marketplace value based on their share of the total compensable programming volume (79.65% average during 2010-
2013), their share of total compensable volume weighted by subscribers (62.57% average during 2010-2013), and their share of the total enhanced viewing of all compensable programming (69.46% average during 2010-2013).
8 See Amended Notice at Exhibit A (describing the mutually exclusive Agreed Categories as “non-exhaustive descriptions of the types of programs or other creative works that fall within each of the agreed categories of claimants (Agreed Categories) to which categories the Judges may approve an allocation of cable retransmission royalties.”).
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 5
To corroborate Dr. Gray’s analysis showing that Program Suppliers’ programming generates the highest marketplace value in comparison to other programming at issue in this proceeding, Program Suppliers also present an analysis of an analogous market performed by industry expert, Howard Homonoff. Mr. Homonoff has decades of experience in the media and entertainment industry, including in the cable and satellite industries. Using the cable network marketplace as the analogous market during 2010-
2013, Mr. Homonoff concludes that Program Suppliers content clearly demonstrated the highest marketplace value to satellite operators in attracting and retaining subscribers, both in terms of the breadth of distribution of their content and license fees paid by satellite operators to acquire programming. He found that 80% of the top 50 most widely-distributed cable networks (“Top 50 Networks”) would be categorized as carrying
Program Suppliers programming; 91.6% of the actual programming on the Top 50
Networks would be categorized as Program Suppliers’ content; and 58.5% of the total license fees paid by multichannel video programming distributors (“MVPDs”) to the Top
50 Networks during 2010-2013 was paid for Program Suppliers’ content.
Both Dr. Gray’s and Mr. Homonoff’s analyses are consistent, as they show that
Program Suppliers’ programming has the highest marketplace value. The following table summarizes Dr. Gray and Mr. Homonoff’s findings as to each of the Agreed Categories, averaged for the 2010-2013 time frame.
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 6
Program Suppliers' Evidence Of Relative Marketplace Value, 2010-2013 Satellite Homonoff Percentage Homonoff Gray Average Gray Average Volume Gray Average Homonoff Of Total Per- Percentage Of Agreed Category Volume Calculation Calculation (Weighted Enhanced Viewing Percentage Of Subscriber Licensing Programming On (Unweighted) By Subscribers) Regression (Model 1) Top 50 Networks Fees Paid For Content Top 50 Networks On Top 50 Networks
Commercial Television Claimants 16.83% 26.57% 18.00% 14.00% 7.10% 10.00%
Devotional Claimants 0.96% 1.34% 0.39% 0.00% 0.30% 0.00%
Joint Sports Claimants 2.57% 9.52% 12.15% 6.00% 1.00% 31.50%
Program Suppliers 79.65% 62.57% 69.46% 80.00% 91.60% 58.50%
In their recent 2010-2013 Cable Determination, the Judges found that “relative
levels of viewership do not adequately explain the premium that certain types of
programming can demand in the marketplace” and concluded that “viewership, without
any additional evidence to account for the premium that certain categories of
programming fetch in an open market, is not an adequate basis for apportioning relative
value among disparate program categories.” See 84 Fed. Reg. 3552, 3600 (February 12,
2019). Mr. Homonoff’s analysis clearly provides the Judges with additional evidence of
the marketplace value of the different programming categories to MVPDs during the
2010-2013 time frame.
Consequently, Program Suppliers submit that Dr. Gray’s regression estimates, as
corroborated by Mr. Homonoff’s analysis, are reliable evidence of relative marketplace
value for the Agreed Categories, and should be the basis for the Judges’ royalty
allocation awards to Program Suppliers in this proceeding. Taken together, the evidence
presented in Program Suppliers’ WDS-A supports royalty allocation awards for Program
Suppliers for the 2010-2013 Funds averaging approximately 69.46%. The specific
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 7
royalty shares that Program Suppliers are seeking in this proceeding for each of the 2010-
2013 Funds are set forth infra on page 10.
III. DIRECT TESTIMONY SUBMITTED BY MPAA
MPAA will present the following witnesses, each of whom will sponsor his or her testimony and accompanying exhibits or appendices (copies of which are contained in
MPAA’s WDS-A):
Jane V. Saunders, serves as Managing Director & Senior Vice-President, Rights
Management Policy and Relations (“RMPR”) at MPAA. Ms. Saunders oversees
MPAA’s RMPR department, which is responsible for copyright royalty policy as it relates to collective and statutory rights management both in the United States and worldwide on behalf of MPAA’s members and other producers and distributors of protected works. Ms. Saunders will provide information regarding Program Suppliers’ claims in this proceeding. Ms. Saunders will also describe some of her international experience in negotiating and collecting retransmission royalties on behalf of MPAA members and other producers and distributors, and the important role that viewing information plays in international royalty allocations and distributions.
Paul Lindstrom, was a Senior Vice President with Nielsen until his retirement on
June 2, 2017. While at Nielsen, Mr. Lindstrom was responsible for research design and analysis as part of the Nielsen Media Analytics group. Mr. Lindstrom will provide information about the Nielsen viewing data on which Program Suppliers rely in this proceeding, including his role in designing the custom analyses of viewing to distant
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 8
satellite households that Program Suppliers commissioned from Nielsen for 2010, 2011,
2012, and 2013.
Jeffrey S. Gray, Ph.D., is the founder and President of Analytics Research Group,
LLC. Relying on certain basic economic principles, Dr. Gray employs Nielsen data, multiple other data sources, and regression analyses to estimate the level of distant viewing to all stations carrying compensable works for each of the Agreed Categories for each of the royalty years at issue in this proceeding. Dr. Gray’s economic analyses produce the relative marketplace value of the Program Suppliers category for each of the
2010-2013 Funds based on multiple factors, including volume and distant viewership.
Dr. Gray also performs fees-based regression analyses of the 2010-2013 Funds and explains why he does not recommend these analyses as a basis for royalty allocation in this proceeding.
Howard B. Homonoff, is currently the Managing Director of Homonoff Media
Group, LLC. Prior to that position, he held executive positions in both media companies and MVPDs. Mr. Homonoff, an industry expert, will explain the factors that influence satellite operators’ programming carriage decisions and provide an analysis of actual programming marketplace decisions made by MVPDs during 2010-2013. Mr.
Homonoff’s analysis will demonstrate that in an analogous cable network marketplace, relative to the other program categories, Program Suppliers’ programming is the most widely-distributed and represents the largest percentage of monthly per-subscriber fees paid by cable and satellite MVPDs.
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 9
Jonda K. Martin, is the President and Owner of Cable Data Corporation
(“CDC”), which collects, digitizes, and electronically stores the data contained in the satellite carrier statements of account (“SOAs”) on file with the Copyright Office. Ms.
Martin will provide an overview of CDC’s operations and data collection methodologies.
She will also describe data reports that CDC generated and provided to Program
Suppliers and its witnesses in connection with this proceeding, and the county analyses for each of the 2010-2013 satellite royalty years that she performed and provided for
Nielsen to use in connection with distant viewing analyses they provided for this proceeding.
IV. PROGRAM SUPPLIERS’ SATELLITE ALLOCATION PHASE CLAIMS
Based on the evidence submitted to the Judges in this proceeding as a part of this
WDS-A, Program Suppliers are seeking the following percentage shares of the 2010-
2013 Funds:
Satellite Royalty Year Program Suppliers
2010 72.49%
2011 66.79%
2012 68.20%
2013 70.37%
Program Suppliers reserve the right to change their allocation claims in light of the evidence presented by other claimants in this proceeding.
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 10
Respectfully submitted,
/s/ Gregory O. Olaniran ______Gregory O. Olaniran D.C. Bar No. 455784 Lucy Holmes Plovnick D.C. Bar No. 488752 Alesha M. Dominique D.C. Bar No. 990311 Leo M. Lichtman D.C. Bar No. 1026600 Mitchell Silberberg & Knupp LLP 1818 N Street NW, 7th Floor Washington, DC 20036 (202) 355-7917 (Telephone) (202) 355-7887 (Facsimile) [email protected] [email protected] [email protected]
Attorneys for Dated: March 22, 2019 Program Suppliers
Introductory Memorandum, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 11
Before the COPYRIGHT ROYALTY JUDGES Washington, D.C.
______) In the Matter of ) ) Distribution of the ) Docket No. 14-CRB-0011-SD (2010-13) ) 2010, 2011, 2012, and 2013 ) Satellite Royalty Funds ) ______)
DIRECT TESTIMONY OF
JANE V. SAUNDERS
MARCH 22, 2019
TABLE OF CONTENTS
I. BIOGRAPHICAL INFORMATION ...... 1
II. PURPOSE OF TESTIMONY ...... 3
III. OVERVIEW OF PROGRAM SUPPLIERS’ CLAIM ...... 3
IV. VIEWING AND MARKET VALUE ...... 6
Jane Saunders Written Direct Testimony, 2010-13 Satellite Allocation | i
DIRECT TESTIMONY OF JANE V. SAUNDERS
I. BIOGRAPHICAL INFORMATION
My name is Jane V. Saunders. I serve as Managing Director & Senior Vice
President, Rights Management Policy and Relations (“RMPR”), at the Motion Picture
Association of America (“MPAA”). Prior to joining MPAA in 1995, I was an attorney engaged in private practice for seven years in Atlanta, Georgia and the District of
Columbia. Immediately prior to joining MPAA, I worked for three years as an associate attorney handling compulsory license matters, including participating in royalty distribution proceedings before the Copyright Royalty Tribunal. I received my baccalaureate degree from Dartmouth College and my law degree from Emory
University. I am a member, with inactive status, of the Georgia State and District of
Columbia Bar Associations. I am fluent in Spanish and French and have a working knowledge of German and Italian.
I represent MPAA and its member companies on the boards of various collective management organizations, including AGICOA,1 where I sit on the Executive Board; the
German collection society, GWFF USA; Danish collection society Producent Rettigheder
Danmark (“PRD”); and the Copyright Collective of Canada. I head MPAA’s RMPR department, which is responsible for copyright royalty policy as it relates to collective and statutory rights management both domestically and worldwide on behalf of MPAA’s members and, in some cases, other producers and distributors of compensable works.
1 AGICOA, the Association For The International Collective Management Of Audiovisual Works, is headquartered in Geneva, Switzerland.
Jane Saunders Written Direct Testimony, 2010-13 Satellite Allocation | 1
On the international front, my responsibilities include assisting in the development of policies aimed at protecting the rights of U.S. producers and copyright holders in the area of collective and statutory rights management and promoting best operational practices within rights management societies; advising MPAA member companies on aspects of U.S. and foreign copyright policy in the area of collective and statutory rights management; helping to formulate strategies to support individual management of rights; and managing relationships between MPAA-represented companies and collective rights management organizations (“CMOs”), the foreign collection societies, which are responsible for the collection and administration of retransmission and other types of royalties.
In Europe, for example, I work to ensure that MPAA-represented U.S. producers receive their fair share of collectively managed retransmission and other royalties throughout Europe. To do so, I actively engage the CMOs through which the producers
MPAA represents claim royalties and participate in the development of best practices for distribution processes. Further, I represent the interests of MPAA’s represented companies in negotiation of agreements whereby they claim royalty shares and, in particular, support the international retransmission royalty CMO, AGICOA, as necessary as it negotiates agreements for collection of retransmission royalties. I also participate as a Board member in the development of distribution rules and policies that apply to
AGICOA activities throughout Europe.
In Canada, I am responsible for all of the operations of MPAA’s retransmission royalty program via the Copyright Collective of Canada (“CCC”), including supervision of CCC staff, negotiations with retransmitters (i.e., cable and satellite companies)
Jane Saunders Written Direct Testimony, 2010-13 Satellite Allocation | 2
regarding setting rates and negotiating with the other collection society groups (i.e.,
claimants) regarding allocation of collected royalties.
In the U.S., I supervise all of MPAA’s activities in connection with the
administration of U.S. cable and satellite retransmission royalties (Sections 111 and 119
of the Copyright Act, respectively) for its represented claimants, including supervision of
the royalty administration and the enforcement of the compulsory licenses; supervision of
MPAA employees; engagement of outside vendors; procurement of data; and supervision
of outside counsel. In that regard, I manage a team who works closely with information
technology contractors and with financial, legal and statistical professionals to provide
fair and efficient distribution of royalties among our represented claimants.
II. PURPOSE OF TESTIMONY
First, I will describe the nature and extent of Program Suppliers’ claims in this
proceeding, including the different types of programs that comprise our claim. Second, I
will discuss my international experience regarding the role of viewership as a measure of
value for purposes of the distribution of retransmission royalties.
III. OVERVIEW OF PROGRAM SUPPLIERS’ CLAIM
Beginning with the first royalty distribution proceeding addressing the allocation
of 1978 cable royalties, MPAA has been the de facto category representative of all
Program Suppliers claimants in Phase I (now Allocation) proceedings.2
2 For purposes of this proceeding, the Copyright Royalty Judges (“Judges”) have defined the Program Suppliers category as syndicated series, specials and movies, except those included in the Devotional Claimants category. See Amended Notice of Participants Groups, Commencement of Voluntary Negotiation Period (Allocation), and Scheduling Order, Docket No. 14-CRB-0011-SD (2010-13) at Exhibit A (Dec. 1, 2015). The Judges have further defined syndicated series and specials as including (1) programs licensed to and broadcast by at least one U.S. commercial television station during the calendar year in question; (2) programs produced by or for a broadcast station that are broadcast by two or more U.S. television stations during the calendar year in question; and (3) programs produced by or for a U.S. commercial television station that are comprised predominately of syndicated elements, such as music videos, cartoons, “PM Magazine,” and locally-hosted movies. See id.
Jane Saunders Written Direct Testimony, 2010-13 Satellite Allocation | 3
Program Suppliers include dozens of copyright claimants, including smaller
producers and syndicators, as well as major studios from both the U.S. and many parts of
the world – all of whom have filed claims seeking a share of the pool for some or all of
the 2010-13 satellite royalty years. By way of example, the owners of network and non- network series, movies, specials, and non-team sports who have agreed to representation by MPAA (“MPAA-represented Program Suppliers”) asserting claims to Section 119 royalties in this proceeding are set forth in MPAA’s 2010-2012 satellite Petitions to
Participate filed on January 21, 2015, and 2013 satellite Petition to Participate filed on
July 6, 2015.
Although Program Suppliers’ programs fit generally under the umbrella of series, movies, specials, and non-team sports, these programs also include game shows, sitcoms, news magazines, non-team sporting events, interview shows, sports shows and sporting events, awards shows, health and fitness shows, and animal shows, as well as similar works in Spanish language. The following are examples of our programs:
• Animated series and sitcoms, such as: FRIENDS (Warner Bros. Entertainment,
Inc.), MODERN FAMILY (Fox Entertainment Group, Inc.), and THE SIMPSONS
(Fox Entertainment Group, Inc.).
• Dramatic Series, such as: THE GOOD WIFE (CBS Studios, Inc.), LOST (Disney
ABC Domestic Television), and GRIMM (NBC Universal, Inc.).
• Movies, such as: X-MEN: THE LAST STAND (Fox Entertainment Group, Inc.),
NO COUNTRY FOR OLD MEN (Miramax Film NY, LLC), and RUNAWAY
BRIDE (Paramount Pictures Corporation).
Jane Saunders Written Direct Testimony, 2010-13 Satellite Allocation | 4
• Game shows, such as: FAMILY FEUD (Fremantlemedia North America, Inc.),
WHEEL OF FORTUNE (Califon Productions, Inc.), and JEOPARDY! (Jeopardy
Productions, Inc.).
• Non-Team Sports Events, Sports shows and sports-related programs, such as:
2011 WIMBLEDON CHAMPIONSHIPS (IMG, Inc.), 2011 MASTERS
TOURNAMENT (Augusta National Golf Club), PISTONS WEEKLY (National
Basketball Association), THIS WEEK IN BASEBALL (Major League Baseball
Properties, Inc.), NASCAR RACING (NASCAR Media Group), 2010 AMERICAN
SKI CLASSIC (Jalbert Productions, Inc.), and WWE FRIDAY NIGHT
SMACKDOWN! (World Wrestling Entertainment, Inc.).
• Awards shows and pageants, such as: AMERICAN LATINO AWARDS
(Latination, LLC), 68TH ANNUAL GOLDEN GLOBE AWARDS (dick clark
productions, inc.), and THE 65TH ANNUAL TONY AWARDS (The Fremantle
Corporation).
• News shows, such as: MCLAUGHLIN GROUP (Oliver Productions, Inc.) and
WALL STREET JOURNAL REPORT WITH MARIA BARTIROMO (NBC
Universal, Inc.).
• Reality shows, such as: AMERICAN IDOL (Fremantlemedia North America, Inc.),
SURVIVOR: HEROES VS. VILLAINS (CBS Broadcasting, Inc.), and THE
BACHELORETTE (Warner Bros. Entertainment, Inc.).
• Animal shows, such as: WILD ABOUT ANIMALS (Steve Rotfeld Productions,
Inc.), ANIMAL RESCUE (Telco Productions, Inc.), and INCREDIBLE DOG
CHALLENGE (Eclipse Television, LLC).
Jane Saunders Written Direct Testimony, 2010-13 Satellite Allocation | 5
• Interview and talk shows, such as: THE OPRAH WINFREY SHOW (CBS Studios,
Inc.), THE ELLEN DEGENERES SHOW (Warner Bros. Entertainment, Inc.), and
LATE SHOW WITH DAVID LETTERMAN (CBS Broadcasting, Inc.).
IV. VIEWING AND MARKET VALUE
I understand that the standard for allocation of royalties in this proceeding is the
relative marketplace value of program categories in a hypothetical market, absent compulsory licensing. Program Suppliers have consistently considered viewing of programs an important measure of the relative marketplace value of those programs, and categories of programs. I think the Judges might find it useful that my international experience working with retransmission systems, including in Europe and in Canada, confirms that viewing of programs is the predominant metric used to distribute retransmission royalties paid by cable and satellite operators to producers, such as the
MPAA’s represented companies.
Thank you for the opportunity to present the information in this testimony. I hope it will be helpful in the Judges’ deliberations.
Jane Saunders Written Direct Testimony, 2010-13 Satellite Allocation | 6 DECLARATION OF JANE V. SAUNDERS
I declare under penalty of perjury that the foregoing testimony is true and correct, and of my personal knowledge.
Executed on March 2. "L, 2019
Jane V. Saunders
Jane Saunders Written Direct Testimony, 2010-13 Satellite Allocation I 7
Before the COPYRIGHT ROYALTY JUDGES Washington, D.C.
______) In the Matter of ) ) Distribution of the ) Docket No. 14-CRB-0011-SD (2010-13)
) 2010, 2011, 2012, and 2013 ) Satellite Royalty Funds ) ______)
DIRECT TESTIMONY OF PAUL LINDSTROM
MARCH 22, 2019
TESTIMONY OF PAUL B. LINDSTROM
My name is Paul Lindstrom. Until my retirement on June 2, 2017, I worked for Nielsen, most recently serving as a Senior Vice President. During my time at
Nielsen I was responsible for research design and analysis as a part of the Nielsen
Media Analytics group. Nielsen is a global leader in information services for the media and entertainment industries. Nielsen serves the information and marketing needs of television and radio broadcasters, cable networks, advertisers, agencies, media planners, music companies, publishers, motion-picture studios, distributors and exhibitors, and the Internet industry.
The Nielsen name is synonymous with television ratings. Ratings are the percent of the universe of U.S. households tuned to a TV program during the average minute or quarter-hour. Nielsen ratings provide an estimate of the U.S. television audience size and are a barometer for viewing choices and preferences. Viewing information is important to broadcast networks, local and national syndicated programs, local cable system operators, multi-system cable operators (MSOs), and satellite carriers.
As more local cable ad sellers sell local advertising time on cable channels, they need an agreed “currency” in order to maximize the value of their advertising time. Nielsen ratings offer that currency. Nielsen’s charter as an independent measurement service is to provide both the buyer and seller of time with unbiased estimates of viewing behavior.
Paul Lindstrom Written Direct Testimony, 2010-13 Satellite Allocation | 1
I. BACKGROUND AND EXPERIENCE
Prior to my retirement, I worked for Nielsen for thirty-eight years. I spent the majority of that time designing custom research with a particular focus on new television viewing sources and audience measurement of new services that might compete with television. These have included cable television, pay-TV, satellite services, over-the-air subscription television, VCRs, PCs, on-line services, the
Internet, DVDs, cinema, and most recently, place-based and location-based digital networks. I was responsible for national custom research and custom research for local cable. I worked with clients to determine the best methodologies to answer their audience research questions. In the television area, these methods can involve either the analysis of existing databases of previously collected meter data, local television diary samples, or the development of new databases through the use of new single- client sponsored data collections.
Through the years I have worked on projects as varied as the pre-launch concept tests for ESPN, The Weather Channel and DirecTV, the design of Nielsen’s
Syndicated Pay Cable, VCR Usage, Syndicated Satellite and Home Technology
Reports,1 the CommerceNet Study of Internet Usage, the Nielsen Cinema Audience
Report, and Nielsen On Location Media. I have been involved in all of the studies that the Motion Picture Association of America (“MPAA”) and MPAA-represented
Program Suppliers (“Program Suppliers”) have directed Nielsen to conduct for proceedings before the Copyright Royalty Tribunal, the Copyright Arbitration Royalty
1 Nielsen’s Syndicated Satellite Report was the first study to utilize diary data to examine satellite viewing. The Home Technology Report is a trending study which has now produced estimates of the growth of new technologies over the last 20 years.
Paul Lindstrom Written Direct Testimony, 2010-13 Satellite Allocation | 2
Panel, and the Copyright Royalty Judges since 1980. Also, I have testified before those bodies.
II. PURPOSE OF TESTIMONY
The purpose of my testimony is to provide an overview of Nielsen’s sampling process and method for generating television ratings, and to explain the type of
Nielsen data on which MPAA is relying in this proceeding.
III. SAMPLING AND TV RATINGS
The Nielsen rating you may see reported in newspapers or magazines is simply a statistical estimate of the number of homes tuned to a program. For example, a rating of 5 for a network television program means that 5% of the estimated U.S. television homes at the time of measurement are estimated to have been tuned in to that program at any point in time.
Above, I describe a rating as a “statistical estimate.” Ratings are based, not on a count of all television households, but on the count within a sample of television households selected from all television households. The sample results are then projected to national totals. We also sometimes use the phrase “share” to quantify audience viewing levels. “Share” is an estimate of the households tuned to a particular channel or program. In other words, a rating measures what percentage of the universe of television households tuned in to a program, while a share measures what percentage of the number of television households in use are tuned in to any particular channel or program.
Paul Lindstrom Written Direct Testimony, 2010-13 Satellite Allocation | 3
IV. NIELSEN DATA USED IN THIS PROCEEDING
During 2010-2013, Nielsen utilized meters as its basic data collection instrument. A meter is an electronic device attached to a television set in a particular household that detects the channel to which the television is being tuned. The data from these meters are then converted into household ratings. Household meter data was collected year-round in Nielsen’s metered markets during 2010-2013.
Program Suppliers contacted Nielsen and sought to obtain custom analyses of national household metered viewing data to satellite distant households for the 2010-
2013 royalty years. Accordingly, Nielsen designed, for MPAA, custom analyses of national household metered viewing data for each of the 2010-2013 years. I understand that Dr. Jeffrey Gray makes use of Nielsen’s custom analyses in his economic analysis.
Our team of professionals designed custom analyses of Nielsen national household metered viewing data for 2010-2013 which estimate actual distant viewing by satellite households. The methodology for our custom analyses, in brief, is as follows:
1) Dr. Gray supplied Nielsen with a list of stations for each of the 2010-2013 satellite royalty years. I understand that Dr. Gray relied on data from Cable Data
Corporation (“CDC”) in order to select these satellite stations for each year.
2) Based on county analyses it performed, CDC provided Nielsen with the identity of the counties considered local to each station selected by Dr. Gray.
3) For the 2010-2013 satellite custom analyses, Nielsen eliminated all non- satellite viewing of programs for Dr. Gray’s stations. Further, it separated all viewing
Paul Lindstrom Written Direct Testimony, 2010-13 Satellite Allocation | 4
to each station into two categories—viewing that occurred within the station’s local area (as determined by CDC’s county analyses) and viewing that occurred outside the station’s local area. Nielsen then provided a report to Dr. Gray separately identifying both local viewing and distant viewing among satellite households for Dr. Gray’s
2010-2013 satellite stations. This was reported in the form of quarter hours of viewing by households.
V. “ZERO VIEWING” INSTANCES
One concern raised in past Phase II proceedings, and which may be raised in the allocation phase of this proceeding, is the so-called “zero viewing” instances that appear in Nielsen’s custom analysis of national household metered viewing data. The appearance of these “zero viewing” instances, or instances where no distant viewing is reported by Nielsen, is consistent with what I would expect to find in a custom analysis of viewing to distant signals by satellite subscribers. This is because the amount of actual viewing minutes to certain distant signals is very small. Where the viewing minutes to particular distant signal programs were so small as to be statistically insignificant, Nielsen’s custom analysis would not report a value for that viewing in its custom analyses.
Thank you for the opportunity to testify in this proceeding.
Paul Lindstrom Written Direct Testimony, 2010-13 Satellite Allocation | 5 DECLARATION OF PAUL B. LINDSTROM
I declare under penalty of perjmy that the foregoing testimony is true and conect, and of my personal knowledge.
Executed on March )..\ , 2019
Before the COPYRIGHT ROYALTY JUDGES Washington, D.C. In re CONSOLIDATED PROCEEDING DISTRIBUTION OF SATELLITE NO. 14-CRB-0011-SD ROYALTY FUNDS (2010-13)
TESTIMONY OF JEFFREY S. GRAY, PH.D.
March 22, 2019
Table of Contents
I. Qualifications ...... 1 II. Executive Summary...... 2 III. The Allocation and Distribution of Satellite Royalties ...... 4 IV. Changed Circumstances: Subscriber Demand Measures the Relative Market Value of Programming ...... 8 V. Data Relied Upon to Measure The Relative Market Value of Programming ...... 15 VI. Empirical Evidence Measuring the Relative Market Values of Programming: Volume And Enhanced Viewing ...... 18 VII. Applying Fees-Based Regression Methodology to Determining Satellite Royalty Allocations ...... 32 VIII. Conclusion: Royalty Share Allocations ...... 42 APPENDIX A: CURRICULUM VITAE ...... 43 APPENDIX B: SATELLITE CARRIER CHANNEL LINEUP CARD ...... 46 APPENDIX C: DISTANTLY RETRANSMITTED STATIONS ...... 48 APPENDIX D: ENHANCED VIEWING REGRESSION MODELS ...... 51 APPENDIX E: SHARES BASED ON TOTAL VIEWING REGRESSIONS ...... 99 APPENDIX F: FEES-BASED REGRESSION MODELS ...... 101
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | i
I. QUALIFICATIONS
1. I, Jeffrey Gray, am an economist and President of Analytics Research Group, LLC
(“ARG”). ARG provides expert analysis concerning economic, statistical, and data issues.
2. I received training in economics and statistics at the University of Pennsylvania, where I earned a Ph.D. in economics. In 1991, I was appointed to a one-year position on the staff of the President’s Council of Economic Advisers, where I concentrated on the economic impact of government policies and regulation. From 1993 to 1997, I served on the faculty of the University of Illinois, where I taught graduate and undergraduate courses covering survey techniques, demand analysis, labor economics, and statistics.
My research has been published in some of the top peer-reviewed journals in the economics profession, including The American Economic Review. I have received grants to pursue my research from the U.S. Department of Labor, the U.S. Department of
Agriculture, and the Research Board of the University of Illinois. I have presented my research findings before a variety of seminars at universities, and at meetings of professional societies and conferences on specialized topics in the United States and abroad. Throughout my professional career, I have been asked to serve as a referee for leading economics journals, such as The American Economic Review and the Review of
Economics and Statistics, concerning the appropriate application of economics and statistics.
3. I have served as a consultant for companies, law firms, and government agencies on a variety of economic and statistical issues related to antitrust, copyright and patent
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 1 infringement, and complex commercial disputes. My consulting work has included analyzing economic markets, as well as valuing copyrighted material and assessing efficient price and advertising levels. I have been engaged by cable system operators
(“CSOs”) to analyze the content and viewership of certain channels and by music performance rights owners to determine the economic value of the right to perform copyrighted music. I have provided expert testimony before the Copyright Royalty
Judges (“Judges”), as well as in state, federal, and international courts, and have presented my research methodology and analytical findings before the Securities and
Exchange Commission, the Texas Commissioner of Insurance, and the New York and
Massachusetts State Offices of the Attorney General.
4. My curriculum vitae, which includes a list of my publications in the last ten years, and a list of cases in which I have testified in the last four years, is attached as Appendix
A. This report is based upon information currently available to me; I reserve the right to supplement this report should additional information be made available.
II. EXECUTIVE SUMMARY
5. I have been asked by Program Suppliers claimants to propose a methodology to allocate the 2010, 2011, 2012, and 2013 satellite royalty funds (“2010-2013 Satellite
Royalties”) paid by satellite carriers under Section 119 of the Copyright Act (“Section
119”) among four broad categories of self-organized claimants.1 The immediately
1 Originally, there were five broad claimant categories applicable to this proceeding: (1) Program Suppliers; (2) Joint Sports Claimants (JSC); (3) Commercial Television Claimants (Commercial Television); (4) Devotional Claimants (Devotionals); and (5) Music Claimants. See Amended Notice Of Participant Groups, Commencement Of Voluntary Negotiation Period (Allocation), And Scheduling
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 2 following paragraphs set forth the background, market, data, and the calculations underlying my proposed allocation methodology.
6. Section III, provides a brief background on the satellite royalty allocation and distribution process.
7. Section IV describes the broader market in which satellite carriers compete to attract and maintain subscribers by offering bundles of television services at varying prices. The section also presents statistics demonstrating a significant increase in the average number of channels received by U.S. households over the past twenty-plus years.
This trend underscores the changed television subscription market, as niche channels and programming have been made increasingly available to consumers. The section concludes that given the changed circumstances in the television services market, together with the prevalence of program and channel bundling, subscriber viewing of programming carried on distantly retransmitted stations is the best available measure of the relative market value of programming.
8. Section V describes the data I relied upon to estimate the relative market value of individual programs and categories of programs.
9. Section VI provides empirical evidence for two types of measures of the relative market value of programming by claimant category: (i) volume measures based on the relative number of minutes programs were carried and retransmitted by category type;
Order at Exhibit A (December 1, 2015). The Music Claimants have settled with the remaining four parties and are no longer a part of this proceeding.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 3 and (ii) enhanced viewing measures based on the relative number of minutes retransmitted programming was viewed by subscribers.
10. Section VII describes an approach to apply regulated-fees regressions to satellite system royalties paid, and presents claimant group royalty shares based on this methodology. The section also highlights major differences in the regulatory framework faced by satellite carriers compared to cable system operators that need to be considered when applying a fees-based regression. Due to these regulatory differences, I believe a simple rescaling of cable royalty shares to account for a different regulatory market is not a reliable methodology to allocate the 2010-2013 Satellite Royalties.
11. Concluding remarks are contained in Section VIII. It is my opinion that the most reliable estimates of the relative market value of programming carried on distantly retransmitted signals are based on my enhanced distant viewing shares. On average across the 2010-2013 royalty years, these viewing shares imply a 69.46% share for
Program Suppliers, 18.00% share for Commercial Television, 0.39% share for the
Devotionals, and a 12.15% share for JSC. Table 4, contained in Section VI, presents estimates of each claimant category’s share of royalties for each annual royalty pool from
2010 to 2013.
III. THE ALLOCATION AND DISTRIBUTION OF SATELLITE ROYALTIES
12. The purpose of this proceeding is to allocate the 2010-2013 Satellite Royalties paid by satellite carriers under Section 119 among four broad categories of self-organized claimants. Satellite carriers, like cable systems, are multichannel video programming
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 4 distributors (MVPDs).2 As MVPDs, satellite carriers bundle basic and premium cable channels, pay-per-view channels, and broadcast stations3 into different packages and add- on packages to existing and potential subscribers at varying prices.
13. As do cable system operators, satellite carriers negotiate and pay fees to cable (i.e., non-broadcast) networks for the right to retransmit the signals to their subscribers.4 They provide their subscribers access to content carried on these networks for a periodic subscription fee. Households choose from sets of bundled packages of channels offered by competing satellite carriers and cable systems and become subscribing customers.5
14. Cable and satellite systems also carry broadcast signals. While cable systems and satellite carriers negotiate license agreements with cable networks, statutory
(compulsory) licenses allow these MVPDs to retransmit broadcast television signals out- of-market without the need to negotiate individual private license agreements with the station owners and/or with the multitude of copyright owners whose programs air on those signals.
2 The Telecommunications Act of 1996 defines a MVPD as “a person such as, but not limited to, a cable operator, a multichannel multipoint distribution service, a direct broadcast satellite service, or a television receive-only satellite program distributor, who makes available for purchase, by subscribers or customers, multiple channels of video programming.” 47 U.S.C. § 522(13).
3 Satellite carriers carry broadcast station on in-market (i.e., local) and out-of-market (i.e., distant) bases.
4 See, generally, www.fcc.gov and Media Programming: Strategies and Practices, 8th ed., S.T. Eastman and D.A. Ferguson, 2009.
5 For the bundling and pricing options currently available to consumers from one satellite carrier, see, for example, www.dish.com. Appendix B provides an example of a list of packages, with add-on packages, offered by one satellite carrier with prices as of February 2014.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 5
15. Section 111 of the Copyright Act (“Section 111”) established the compulsory license for cable systems’ carriage of broadcast signals and Section 119 of the Copyright
Act (“Section 119”) did so for satellite carriers. The allocation of funds paid by cable system operators under Section 111 was the focus of a separate proceeding.6
16. The “allocation” of the 2010-2013 Satellite Royalties is one phase of a non- sequential, two-phase process intended to compensate copyright owners of compensable programming carried on broadcast signals that were distantly retransmitted by satellite carriers. The copyright owners are organized, by agreement, into program categories as claimants.
17. The other phase of this two-phase compensation process is the “distribution” of each category’s allocated shares to copyright owners, or their representatives, within each broad program category (i.e., claimant category). I understand that distribution phase shares have been resolved for all parties for both the 2010-2013 satellite and the 2010-
2013 cable royalty funds.7 In the case of the Program Suppliers category, the Judges ordered that the royalty shares proposed by MPAA, which were based on the relative viewing of compensable programming within the Program Suppliers category, be adopted.
18. The royalty shares allocated to each claimant in this proceeding will in turn be distributed within its claimant group.
6 See In re Distribution of Cable Royalty Funds, Consolidated Proceeding No. 14-CRB-0010-CD (2010- 13).
7 See, e.g., 83 Fed. Reg. 38326, 38326-27 (August 6, 2018) (Devotional category); 83 Fed. Reg. 61683, 61683-84 (November 30, 2018) (Program Suppliers category).
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 6
19. I understand that, like Section 111, Section 119 does not specify how compulsory license fees paid by satellite operators should be allocated among the claimants. The standard for allocating and distributing cable royalties is the “relative marketplace value” of programming carried on retransmitted stations.8 I assume the same standard applies with respect to the satellite allocation.
20. The relative market value of a program corresponds to the price at which the right to retransmit the program, carried on a distant broadcast signal, would change hands between a willing buyer (a satellite carrier) and a willing seller (a copyright owner), neither being under any compulsion to buy or to sell and both having reasonable knowledge of relevant facts.9
21. Consumers subscribe to a satellite system to watch the programming made available via their subscriptions. Therefore, a measure of the happiness, or “utility,” an individual subscriber gets from a specific program is the number of minutes that subscriber spends viewing the program offered to him or her by the satellite system. A measure of the utility all subscribers get, in total, from a specific program is the total level of subscriber viewing of the program. Thus, even though satellite carriers may be the buyers of the programming bundles at issue in this proceeding (at a regulated price), a reasonable measure of the relative marketplace value of a retransmitted program is the relative level of subscriber viewing of that program. The higher the subscriber viewing,
8 See 84 Fed. Reg. 3555 (February 12, 2019) (“Cable Determination”) for discussion.
9 The Judges concluded that whether the willing seller is the program’s copyright owner or the broadcast station that has local rights to transmit the program is not of economic consequence. Cable Determination, 84 Fed. Reg. at 3555.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 7 all else equal, the greater the subscribers’ utility and the greater the satellite carriers’ retention of subscribers.
IV. CHANGED CIRCUMSTANCES: SUBSCRIBER DEMAND MEASURES THE RELATIVE MARKET VALUE OF PROGRAMMING
22. The overall market where satellite carriers attempt to attract and retain subscribers who pay subscription fees has changed dramatically since the Copyright Act was enacted.
As shown in Figure 1 below, on average, U.S. households received fewer than 19 channels in 1985; by 1995, this average more than doubled to over 41 channels; by 2010,
U.S. households received over 151 channels; and by 2013, U.S. households received approximately 189 channels, on average.10
10 Source for statistics in Figure 1: Nielsen. See “Advertising & Audiences – State of the Media,” Nielsen Holdings (May 2014); and, https://www.zdnet.com/article/average-number-of-tv-channels-receivable-by- us-household-drops-in-2004 (last visited March 22, 2019).
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 8
23. Among the dramatic increase in new channels made available to, and received by,
U.S. households were cable channels emphasizing, sometimes exclusively, certain types of programming including movie channels, sports channels, religious channels, and news channels.
24. Over time, satellite carriers selected more and more channels, bundling them into various tiers of service to attract subscribers and maximize profits. Figure 1 above reflects that U.S. households, on average, have increasingly selected tiers with a greater number of channels since 1985.
25. While households had access to an increasing number of channels, the number of channels they actually tuned to remained fairly constant. Figure 2 below illustrates that while the average number of channels received by U.S. households increased each year
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 9 between 2008 and 2013, the number of channels they tuned to remained at approximately
17-18 channels per year.11 This implies that, each year, households had access to a greater number of channels that they did not watch or value. Therefore, to simply ascribe value to a program as proportional to the number of subscriber households receiving the retransmitted channel is demonstrably wrong. As I discuss later in this testimony, that is a fundamental flaw in a fees regression analysis.
Source:Source: Nielsen Nielsen
11 Source for statistics in Figure 2: Nielsen. See, “Advertising & Audiences – State of the Media,” Nielsen Holdings (May 2014).
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 10
26. Figure 2 illustrates that the average U.S. household received dozens of channels during the 2010 to 2013 royalty years that it had no interest in, as evidenced by the percentage of channels received and not tuned to.
27. Bundling of channels explains why households receive channels they do not watch. Appendix B provides an example of a channel lineup card of packages made available to consumers by one satellite carrier, DISH Network Corporation (DISH) with pricing as of February 2014. Three channel packages featured prominently by DISH were its (1) “America’s Top 120”, (2) “America’s Top 200”, and (3) “America’s Top
250” packages.
28. Each of DISH’s “America’s Top” packages had incrementally more channels bundled than its lower numbered package. That is, DISH’s America’s Top 200 package included all channels included in its America’s Top 120 package, plus an additional
(approximately) 80 channels; America’s Top 250 package included all channels included in America’s Top 120 and America’s Top 200 packages, plus an additional
(approximately) 50 channels.
29. The bundled nature of the satellite carrier’s package forced subscribers to receive channels they did not value. For example, DISH designated some channels in each of its offered tiers as “most popular channels.”12 These so-called popular channels were bundled with other channels not deemed as popular. DISH’s America’s Top 200 package included most popular channels Animal Planet, BET, Bravo, the Hallmark Channel and
12 Channels in bold typeface in Appendix B are considered “most popular channels.”
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 11
NFL Network bundled together with channels not designated as popular including RFD-
TV, The Hub, UP, and WGN America (WGNA).
30. As a result of DISH’s bundling practices, if its customers valued programming on
Bravo or the NFL Network, for example, they had to subscribe to a tier containing
WGNA and RFP-TV, even if they placed no value at all on WGNA or RFP-TV programming.
31. WGNA was the most distantly retransmitted broadcast signal during 2010-2013, and it carried programming compensable under Section 119.13 Any reliable methodology to assess the relative marketplace value of compensable programming carried on such a highly-retransmitted signal like WGNA must take into account the fact that satellite carriers bundled it with other channels.
32. The goal of this proceeding is to allocate the 2010-2013 Satellite Royalties among the broad program categories based on their relative marketplace values. However, programs, as well as program categories, are bundled together on stations that are selected by satellite carriers to retransmit. Satellite carriers choose which station to retransmit, not which programs. While a selected station might have programming highly valued by the carrier’s subscribers, such programming would also be bundled with programming with lesser value.
33. Moreover, as demonstrated by the WGNA example above, satellite carriers bundle the retransmitted station with other channels in packages offered to their subscribers.
13 Under Section 119, programming on WGN’s local feed not simultaneously broadcast on WGNA is not compensable.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 12
Due to the channel bundling, simply relying on the number of subscribers who receive a signal does not allow for a reliable measure of the relative marketplace value of individual programs carried on the signal.
34. The available data does not disentangle the relative value of individual retransmitted programming or groups of programming. Absent data which reveals the value of each bundled program, the most reasonable approach to determining the relative value of programs is to analyze how frequently it is consumed by subscribers – namely, subscriber viewing.
35. As the Judges wrote in their Cable Determination, “[v]iewing is the engine that drives the entire industry.”14 The Judges went on to conclude, however, that “without any additional evidence to account for the premium that certain categories of programming fetch in the open market,” viewership alone is insufficient for apportioning relative value.15 The conclusion does not specify what additional evidence would satisfy the premium requirement.
36. My professional opinion is that viewing levels alone do account for differing premiums, both within program categories and across program categories. Category premiums are evidenced by higher viewing shares relative to volume shares for a category. If no program category enjoyed a valuation premium over other categories, then each category’s share of total minutes available to subscribers would equal its share
14 Cable Determination, 84 Fed. Reg. at 3600.
15 Ibid.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 13 of total minutes viewed by subscribers. A category with ten percent of volume would garner ten percent of viewing, if program valuations were equal across categories. As demonstrated in more detail later in this testimony, valuations are not equal across categories in the current context.
37. My valuation methodology relies on far more than just raw estimated viewing data. It relies on additional non-viewing factors, which further enhance the regression analysis itself and the resulting estimates. I refer to the resulting program valuation estimates as “enhanced viewing” measures of the relative market value of programing in order to distinguish my valuation methodology from raw estimated viewing data.
38. The enhanced viewing approach reflects relative programming premium. At one extreme, Devotional programming comprised 1.3% of compensable programming minutes carried on retransmitted stations (weighted by the number of subscribers reached), yet only 0.4% of viewing of compensable programming. At the other extreme,
JSC programming comprised 9.5% of all retransmitted programming, yet it represented
12.2% of viewing of compensable programming.16 JSC programming’s ratio of viewing to volume of 1.3 (.122/.095) is over four times greater than Devotional programming’s viewing to volume ratio of 0.3 (.004/.013). The difference in the relative share of viewing of JSC and Devotional programming reflects the different premium the categories of programming would fetch in the open market.
16 Details concerning the calculation of these statistics are provided in the next section.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 14
39. Viewing levels themselves are the best available measure of value, and themselves account for different average relative market valuations within and across program categories.
40. The relative market value of an individual program, or categories of programming, ultimately depends upon the subscriber demand for the programming. As described later in this testimony, I calculate subscriber demand for programs in each category based on a multiple regression methodology.17 This methodology quantifies subscriber demand for every program included on retransmitted signals, including instances when household viewing information is unavailable or limited.
41. The following section describes the data I rely upon to measure the relative market value of programming. While enhanced viewing is the best measure, I present alternative relative valuations approaches based on volume measures and fees-based regressions and analyze why those alternatives are not reliable.
V. DATA RELIED UPON TO MEASURE THE RELATIVE MARKET VALUE OF PROGRAMMING
42. I relied upon raw Nielsen’s raw estimated viewing data, Gracenote programming data, and Cable Data Corporation (CDC) carriage data to calculate the following: (1) the volume in minutes by type of programming category, weighted by the number of subscribers reached by the programming; (2) an enhanced viewing measure of
17 See Daniel L. Rubinfeld, “Reference Guide on Multiple Regression Analysis” in Federal Judicial Center Reference Manual on Scientific Evidence, 2nd Ed., 2000, pages 179-227 for a discussion of multiple regression analysis including its uses and potential misuses.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 15 compensable programs by category; and, (3) the relationship between fees paid and program minutes by category carried on a retransmitted station. These measures of relative market value are calculated and reported for each royalty year from 2010 through
2013.
Nielsen Data
43. Nielsen is a well-regarded and highly-used source of audience measurement information in the television industry. Prior decisions by the Judges and their predecessors have concluded that Nielsen data provides “relevant” and “reliable” estimates of viewing to programs retransmitted on distant signals.18 I rely on the raw
Nielsen Distant Viewing Household Data for 2010-2013 (“Nielsen Viewing Data”) provided by Nielsen following its custom analysis of viewing to programming on retransmitted broadcast stations.
44. The Nielsen Viewing Data are based on a random sample of households in the
United States. The data are collected by electronic meters attached to television sets together with individual meters held by individual household members. Based upon the national data they collected, Nielsen performed custom analyses to estimate the level of viewing by satellite-subscribing households to all television stations, respectively, for each fifteen-minute interval (quarter hour) of the day, each day for 2010 through 2013.19
18 See, e.g., 78 Fed. Reg. at 64986 and 64996 (Oct. 30, 2013); 55 Fed. Reg. 5647 (Feb. 16, 1990); 1998-99 Cable Phase I CARP Report (Oct. 21, 2003), at 44; 1990-92 Cable Phase I CARP Report (May 31, 1996), at 84.
19 See Written Direct Testimony of Paul Lindstrom, Consolidated Proceeding No. 14-CRB-0011-SD
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 16
From its estimates of total metered viewing, Nielsen extracts both the local and distant viewing to stations retransmitted by satellite carriers for 2010 through 2013.
Gracenote Data
45. The Gracenote data are a compilation of information about each program airing on each station throughout each day. The compiled information includes when the program aired; the station on which the program aired; whether the program was station-produced, was local, network, or syndicated; the program title; the episode title, if applicable; and the type of program (movie, game show, etc.); and so on. These data allow me to calculate the number of programming minutes by program category for the entire population of programs carried on signals distantly retransmitted by satellite carriers.20
CDC Data
46. The CDC data are information catalogued by CDC from statements of accounts
(SOAs) that satellite carriers filed with the Licensing Division of the Copyright Office each month and totaled semi-annually. These data include information regarding the distant signals carried, the number of subscribers receiving each distant signal, and the
(2010-13).
20 I developed a computer algorithm to assign individual programs to one of the four program categories at issue in this proceeding. Given the quantity of programs airing each year, assigning programs on a title-by-title basis is not reasonably feasible. I reserve the right to amend my algorithm should additional information be made available.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 17 estimate of fees generated by each distantly retransmitted signal during years covered by this proceeding.21
47. Based on the CDC data, there were a total of between 82 and 143 stations that were distantly retransmitted by satellite carriers each year from 2010 to 2013.22 This is the universe of all stations carried on a distant basis by satellite carriers over the 2010-
2013 royalty years. Lists of these distantly retransmitted stations were given to Nielsen to provide viewing data for each quarter hour, and to Gracenote to provide programming information.
48. The following section uses these data to quantify satellite carriers’ choices of broadcast signals to distantly retransmit and presents reasonable and reliable measures of relative marketplace value of programs.
VI. EMPIRICAL EVIDENCE MEASURING THE RELATIVE MARKET VALUES OF PROGRAMMING: VOLUME AND ENHANCED VIEWING
Relative Market Value Measure 1: Volume
49. Satellite carriers select stations with programming to help retain their current
subscribers and to help attract new subscribers. The more programs of a certain type
aired on the stations selected for retransmission, the more valuable that type of
programming is to the satellite carrier and its subscribers, all else equal. The volume of
21 See, Direct Testimony of Jonda Martin, Consolidated Proceeding No. 14-CRB-0011-SD (2010-13). Note that there is no regulatorily-prescribed way to allocate royalties paid by satellite carriers to copyright owners of individual programs or broad program categories.
22 The list of all stations retransmitted by satellite carries each royalty year 2010-2013 is contained in Appendix C.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 18
programming by category type, therefore, provides a rough measure of the relative
market value of that category’s programming. Volume measures should be deemed only
as approximate measures of value due to the bundling issues described above.
50. Table 1 below presents statistics concerning the number of programs and the number of compensable programs in the four claimant categories that aired on stations that were distantly retransmitted by satellite systems, as well as the annual share of programs those retransmissions represent. From 2010 to 2013, between approximately
865,000 and 977,000 compensable programs aired on the stations retransmitted by satellite systems.
Table 1: Number and Share of Retransmitted Broadcasts, by Claimant Category Share of All Share of All Compensable Compensable Year Claimant Category Retransmissions Retransmissions Retransmissions Retransmissions Commercial Television 152,650 17.14% 152,531 17.34% Devotionals 8,863 1.00% 8,291 0.94% Program Suppliers 723,033 81.20% 712,812 81.04% 2010 JSC 5,911 0.66% 5,911 0.67% Total 890,457 100.00% 879,545 100.00%
Commercial Television 173,108 17.53% 173,101 17.73% Devotionals 10,594 1.07% 10,122 1.04% Program Suppliers 797,317 80.74% 786,433 80.56% 2011 JSC 6,549 0.66% 6,549 0.67% Total 987,568 100.00% 976,205 100.00%
Commercial Television 167,402 16.96% 167,342 17.15% Devotionals 12,339 1.25% 11,639 1.19% Program Suppliers 800,615 81.10% 790,162 80.97% 2012 JSC 6,789 0.69% 6,786 0.70% Total 987,145 100.00% 975,929 100.00%
Commercial Television 148,264 16.91% 148,120 17.11% Devotionals 9,653 1.10% 8,904 1.03% Program Suppliers 712,881 81.31% 702,575 81.17% 2013 JSC 5,958 0.68% 5,956 0.69% Total 876,756 100.00% 865,555 100.00%
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 19
51. Broadcasts can vary in duration. Syndicated shows, including situation-comedies or dramas, are typically half an hour in duration. This is also true of local news and religious programming. Live team sports and special programming, such as awards shows, can be 2-hours, 3-hours, or longer in duration.
52. Table 2 shows the total number of hours of programming aired and retransmitted by satellite carries over the 2010-2013 years by category, as well as the volume share these numbers represent. Insofar as a longer show has greater relative value, all else equal, these shares would present a better measure of value than shares of retransmissions.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 20
Table 2: Hours of Broadcasts and Shares, by Claimant Category Compensable Share of All Hours Hours Compensable Year Claimant Category Retransmitted Share of Hours Retransmitted Hours Commercial Television 114,335 16.57% 114,267 16.74% Devotionals 6,456 0.94% 6,155 0.90% Program Suppliers 551,535 79.95% 544,535 79.79% 2010 JSC 17,543 2.54% 17,541 2.57% Total 689,869 100% 682,498 100%
Commercial Television 129,839 17.07% 129,833 17.24% Devotionals 7,180 0.94% 6,944 0.92% Program Suppliers 604,591 79.48% 597,233 79.31% 2011 JSC 19,040 2.50% 19,037 2.53% Total 760,649 100% 753,047 100%
Commercial Television 126,190 16.46% 126,162 16.62% Devotionals 8,457 1.10% 8,107 1.07% Program Suppliers 612,408 79.87% 605,126 79.71% 2012 JSC 19,744 2.57% 19,731 2.60% Total 766,798 100% 759,126 100%
Commercial Television 112,318 16.53% 112,221 16.71% Devotionals 6,673 0.98% 6,298 0.94% Program Suppliers 543,046 79.93% 535,815 79.77% 2013 JSC 17,375 2.56% 17,355 2.58% Total 679,412 100% 671,690 100%
Note: Numbers may not add up exactly to 100.00% due to rounding.
53. Comparing each category’s shares of retransmission and shares of hours retransmitted shows the most dramatic difference is for the JSC category. Whereas JSC represented approximately 0.7% of retransmissions over 2010-2013, their share of volume was approximately 2.6% each year. This is a result of JSC-represented programming tending to be longer in duration than programming in the other categories.
Whereas non-live team sports programming averaged 45 minutes in duration, the average
JSC program was almost 3 hours long.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 21
54. The retransmissions and program duration statistics presented in Tables 1 and 2 measure satellite systems’ choices of programming purchased and the frequency of retransmission of those programs to attract and retain subscribers. However, they do not consider the number of subscribers reached by each retransmitted broadcast signal. The statistics presented in Table 3 below present volume measures weighted by the number of subscribers who receive each retransmitted signal.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 22
Table 3: Shares of Hours Weighted by Subscribers, by Claimant Category Share of Compensable Year Claimant Category Share of All Weighted Hours Weighted Hours Commercial Television 10.41% 23.53% Devotionals 3.33% 2.29% Program Suppliers 82.91% 66.22% 2010 JSC 3.35% 7.95% Total 100% 100%
Commercial Television 10.00% 25.65% Devotionals 2.53% 1.61% Program Suppliers 84.22% 64.40% 2011 JSC 3.25% 8.34% Total 100% 100%
Commercial Television 9.89% 29.55% Devotionals 3.37% 0.93% Program Suppliers 83.10% 58.71% 2012 JSC 3.64% 10.81% Total 100% 100%
Commercial Television 9.49% 27.53% Devotionals 3.49% 0.52% Program Suppliers 83.40% 60.96% 2013 JSC 3.62% 10.99% Total 100% 100%
Commercial Television 9.95% 26.57% Devotionals 3.18% 1.34% Program Suppliers 83.41% 62.57% JSC 3.47% 9.52%
Average: 2010 Average: 2013 through Total 100% 100%
55. Table 3 shows that each category’s share of retransmitted program minutes, weighted by subscriber reach, is very different depending on whether all minutes or only compensable minutes are considered. For example, while Program Suppliers programming represented 83.41% of all program minutes on retransmitted stations on
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 23 average during 2010-2013, their programming represented 62.57% of compensable program hours.
56. For the current proceeding, all non-compensable minutes are program hours broadcasted on WGNA, and retransmitted, when the identical program was not simultaneously broadcasted on WGN’s local feed. Approximately 95% of all non- compensable programming over the 2010-2013 royalty years belonged to the Program
Suppliers category.
57. While some WGNA retransmitted broadcasts are not compensable in this proceeding, they may have been highly valued by satellite carriers and their subscribers who viewed them. Valued non-compensable WGNA programming can bias the valuation of compensable WGNA programming in the current proceeding.
58. For example, if subscribers placed no value on compensable programming carried on WGNA, yet valued non-compensable programming carried on WGNA, satellite carriers would still be incentivized to include WGNA in its channel lineups to subscribers. Satellite carriers would retransmit WGNA to attract and retain subscribers who wished to view the non-compensable programming. In such a scenario, the relative market value of compensable programming carried on WGNA is zero. Any reliable methodology should be able to distinguish between the varying levels of value of bundled programming on the same signal. My enhanced viewing methodology accomplishes this task.
59. Tables 1 through 3 quantify how much programming, by category type, was carried on distantly retransmitted stations and made available to satellite system
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 24 subscribers over 2010-2013. These volume-based statistics are useful measures of program category values as they reflect satellite systems’ choices in their efforts to attract and retain customers. However, they are imperfect measures of relative market values.
These volume-based measures do not consider what time of day a program aired. A program aired during primetime is likely to be more valuable to a satellite subscriber and a greater influencer of subscription decisions than a program that aired on the same station in the middle of the night. More generally, volume-based measures fail to account for whether any satellite-subscribing household valued a program enough to watch the show. Therefore, measures of subscriber viewing are superior measures of relative market value.
60. As I state above, viewing alone may be used to measure value as the programming premium manifests itself in the relative viewing levels. Nonetheless, to allay the concerns expressed by the Judges’ in the Cable Determination regarding the insufficiency of relying on viewing data alone for allocation purposes, my estimate of royalty allocation relies on an enhanced estimate of viewing that provides a measure of subscriber demand for each program carried on distantly retransmitted stations. The enhanced viewing measures of program value consider the following non-viewing factors: the number of households who can view the program, the time of day the program was aired and retransmitted (e.g., prime-time 8:00-8:15 PM versus. graveyard slot 3:00-3:15 AM), and the detailed type of programming (e.g., public affairs programming versus a first-run syndication).
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 25
61. I present statistics demonstrating that these enhanced viewing measures subsume households’ demand for different types of programming (e.g., live team sports programs versus Program Suppliers programs). The following subsections describe viewing-based methods to assess the relative market value of compensable programming.
Relative Market Value Measure 2: Enhanced Viewing
62. Nielsen Household Meter Data are estimates of the number of U.S. households viewing a channel each quarter hour of each day. Given the large number of channels available to any household over the 2010-2013 years, and the small fraction of those channels that households tuned to, as demonstrated above in Figure 2, it is not surprising to find many quarter hours for many channels where no one in the Nielsen sample viewed programming carried on the channel on a distant basis. Former Nielsen representatives familiar with their data and sampling methodologies have testified that they are not surprised or concerned by these instances of non-recorded viewing on distantly retransmitted stations.23
63. In response to the parties’ prior expressed concerns regarding instances of non- recorded viewing, I present three sets of viewing statistics by program category. Each viewing regression methodology calculates the mathematical relationship each year from
2010 to 2013 between estimated raw distant viewing for a program and (1) the total number of distant subscribers of that station, (2) the time of day the program aired by
23 See Testimony of Paul Lindstrom.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 26 quarter hour, and (3) the type of program aired. The methodologies differ in the econometric treatment of non-recorded viewing instances and the treatment of predicted versus Nielsen viewing estimates.24 For each of the econometric models, I rely upon
Nielsen weighted viewing.25
Enhanced Viewing Model 1
64. Model 1 is my preferred approach, and is one which I have presented in the past cable allocation proceeding, and which has been accepted by the Judges in prior distribution proceedings. This approach treats non-recorded viewing instances as appropriate and generally informative instances of low distant viewing. I treat, as recorded, estimated raw distant viewing of programs in each instance where a program on a distantly retransmitted channel is watched by a Nielsen household, on a distant basis, during any quarter hour over the year. I treat, as non-recorded distant viewing, those same quarter hours where there is no positive viewing of a program. If the channel is never viewed on a distant basis throughout the year, I treat each quarter hour as having no information on distant viewing levels.
24 In addition to enhanced viewing estimates based on these three models, Appendix E presents viewing estimates relying on total viewing. That is, regressions analyzed local and distant viewing combined. This provides an alternative measure of viewing that captures when households tend to be watching television based on the quarter hour of the day.
25 Several experts in the cable proceeding argued Nielsen weighted viewing is more reliable than Nielsen unweighted viewing. Because Nielsen weights are intended for estimating national viewing levels, each of my regression models includes a variable to control for the number of distant subscribers of each retransmitted signal.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 27
65. I use this information of non-recorded viewing, and positive Nielsen viewing distant estimates, together with other available information described above to estimate a mathematical relationship, then calculate an enhanced viewing measure for each program on the quarter hour.
Enhanced Viewing Model 2
66. In Model 2, I treat all non-recorded viewing instances in the Nielsen data as uninformative. I calculate the relationship between only the positive Nielsen estimated raw distant viewing and the number of subscribers with access to the program, the detailed type of program, and the time of day the program aired. With this mathematical relationship between positive Nielsen estimated raw distant viewing and program characteristics, I estimate enhanced viewing metrics for each program carried on a retransmitted signal for each quarter hour. Differences between the enhanced viewing estimates of Model 1 and Model 2 demonstrate the importance of instances of non- recorded viewing in the Nielsen data.
Enhanced Viewing Model 3
67. Viewing Models 1 and 2 produce enhanced estimates of distant viewing levels for every program carried on retransmitted stations for every quarter hour throughout the year, for each royalty year. Several parties in the 2010-2013 Cable Proceeding argued that I should have relied upon Nielsen estimates of distant viewing, when Nielsen’s estimates were positive, and estimated distant viewing levels only for those quarter hours
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 28 where Nielsen’s data indicated no recorded viewing. I disagree with this suggestion as it fails to account for the number of households who receive the signal on a distant basis.
Nonetheless, I present a third methodological viewing approach where I follow the regression methodology described by Viewing Model 1 above, but replace my enhanced estimates of distant viewing with Nielsen’s estimates of distant viewing when they are positive.
68. Table 4 below reports distant viewing shares for each category and royalty year based on the three different methodological approaches described above. Details of each model’s specifications and full regression results are presented in Appendix Tables D.26
26 As shown in Appendix D, the models are the same in terms of the sets of explanatory variables used to calculated enhanced viewing metrics; the models vary by how Nielsen distant viewing estimates are used in the calculations. Because the signal WGNA is distinct in the number of subscribers reached on a distant basis, separate regressions are performed for WGNA and non-WGNA stations for each model.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 29
Table 4: Distant Viewing Shares of Compensable Programming, by Claimant Category
Model 1: Estimated Model 2: Estimated Based Model 3: Model 1 with Non-Recorded on Positive Nielsen Estimated, but Use Year Claimant Category Viewing Utilized Viewing Only Actual Positive Nielsen Commercial Television 14.29% 15.32% 13.72% Devotionals 0.66% 0.82% 0.35% Program Suppliers 72.49% 78.86% 73.20% 2010 JSC 12.56% 4.99% 12.72% Total 100% 100% 100%
Commercial Television 19.35% 16.19% 17.97% Devotionals 0.57% 0.68% 0.19% Program Suppliers 66.79% 78.71% 68.44% 2011 JSC 13.30% 4.42% 13.40% Total 100% 100% 100%
Commercial Television 19.62% 15.32% 17.01% Devotionals 0.20% 0.82% 0.08% Program Suppliers 68.20% 79.41% 70.85% 2012 JSC 11.98% 4.44% 12.06% Total 100% 100% 100%
Commercial Television 18.73% 16.86% 17.43% Devotionals 0.13% 0.78% 0.04% Program Suppliers 70.37% 78.53% 71.65% 2013 JSC 10.76% 3.83% 10.88% Total 100% 100% 100%
Commercial Television 18.00% 15.92% 16.53% Devotionals 0.39% 0.78% 0.17% Program Suppliers 69.46% 78.88% 71.03% JSC 12.15% 4.42% 12.27%
Average: 2010 Average: 2013 through Total 100% 100% 100%
69. The results presented in Table 4 show that shares based on viewing metrics are similar using Models 1 and 3. This demonstrates that replacing the enhanced viewing estimates in Model 1 with Nielsen estimated viewing, when Nielsen estimates are positive, does not have a dramatic impact on calculated shares. It is my opinion that the latter approach of supplanting with Nielsen actual positive viewing is an inferior
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 30 approach as it ignores the number of subscribers who have access to programming carried on distantly retransmitted signals.
70. Enhanced viewing shares are lower overall for the Commercial Television and
JSC claimant categories (and actually favor Program Suppliers) when only estimating relationships based upon positive Nielsen estimated viewing instances, as was done in
Model 2. These results indicate that ignoring the information when Nielsen does not record instances of viewing on a distant basis has an impact on viewing shares. I do not know of any reason to ignore this valuable Nielsen information.
71. Table 5 below reports again the average distant viewing over the years 2010-2013, using Model 1, side-by-side with average weighted compensable hours carried on signals that were distantly retransmitted. JSC’s 12.15% share of viewing compared to their
9.52% share of programming subsumes the category’s valuation premium compared to other categories.
Table 5: Summary of Volume Shares and Compensable Distant Viewing Shares, 2010-2013
Weighted Compensable Model 1 Viewing Shares of Ratio: Viewing to Year Claimant Category Hours Shares Compensable Programming Volume Commercial Television 26.57% 18.00% 0.68 Devotionals 1.34% 0.39% 0.29 Program Suppliers 62.57% 69.46% 1.11 JSC 9.52% 12.15% 1.28
Average: 2010 Average: 2013 through Total 100% 100% 1.00
72. The final column reports the ratio of each of the categories’ viewing share to their total retransmitted volume share. The ratios greater than one for Program Suppliers and
JSC reflect higher valuation premiums for these two categories of programming.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 31
VII. APPLYING FEES-BASED REGRESSION METHODOLOGY TO DETERMINING SATELLITE ROYALTY ALLOCATIONS
73. In the Cable Determination, the Judges applied the standard of “relative marketplace value” in allocating royalties among the broad program categories at issue in the cable proceeding described in Section III above.27 In arriving at the allocation determination, the Judges considered five methodologies the parties presented to quantify the relative market value of programming types: (1) regulated-fees regressions, (2) CSO surveys, (3) subscriber viewership analysis, (4) cable content analysis, and (5) changed circumstances analysis.
74. Ultimately, the Judges placed primary reliance on a regulated-fees regression analysis submitted by Commercial Television.28 The fees-based regression analysis focused on the correlation between CSO royalties paid (largely determined by the number of cable system subscribers) and the minutes of programming in each program category on each signal distantly retransmitted.
75. I respectfully disagree with the Judges’ decision to place primary reliance on the fees-based regression analysis. As I testified in the Cable Proceeding, cable system royalty payments are a statutory construct of Section 111 and can bear no relationship to marketplace value generally, or specifically, to the value cable systems or their subscribers place on programming carried on stations retransmitted or not retransmitted
27 Cable Determination, 84 Fed. Reg. at 3555-56.
28 Ibid, 84 Fed. Reg. at 3610.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 32
by cable systems.29 Also, analysis of regulated fees paid did not meaningfully address
the minimum fee requirement under Section 111.30
76. However, because the Judges considered and gave weight to a regulated fees analysis in the context of cable royalty allocations, I provide an analogous regulated fees analysis for consideration in the current proceeding. However, as described above, I maintain that an enhanced measure of viewing, that takes into account the detailed program type, the time of day the program aired, and the number of subscribers the retransmitted program reached, provides a far superior measure of the relative marketplace value of an individual program, and therefore of program categories carried on distantly retransmitted signals.
77. The premise underlying the regulated fees framework is that distant broadcast stations that are more highly valued by households are more likely to be carried by systems, and therefore more responsible for the royalties paid into the royalty pool.31
29 See Written Direct Testimony of Jeffrey S. Gray, Ph.D., filed December 22, 2016, and amended March 9, 2017, and corrected April 3, 2017, Docket No. 14-CRB-0010-CD (2010-13), Exhibit 6036 (“Gray CWRT”) at ¶¶ 14-15. 30 Dr. Crawford’s hearing testimony that his indicator, or “dummy” variable adjusted for cable system operators’ minimum fee requirement, see 84 Fed. Reg. at 3586, is not correct. See, e.g., William H. Greene, Econometric Analysis, 8th Ed, (2018). The Commercial Television analysis magnified the issue of mandated minimum fees by examining the relationship between royalty fees calculated and each category’s minutes of programming within subscriber-groups. This modeling choice ignored that cable system operators at or below the minimum fees requirement could arbitrarily redistribute stations across subscriber-groups at no additional cost yet with varying fees to which the Commercial Television model inappropriately attached significance. See Gray CWRT, ¶¶ 20-22, Table 2. It remains my opinion that the Commercial Television regulated fees analysis submitted in the Cable Proceeding is unreliable. 31 See Written Direct Testimony of Gregory S. Crawford, Ph.D., filed December 22, 2016, and corrected April 11, 2017, Docket No. 14-CRB-0010-CD (2010-13), Exhibit 2004 (“Crawford CWDT”), ¶ 104.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 33
78. A fundamental problem of the fees-based approach to value programs is that carriers do not offer individual programs, or even individual stations, to their subscribers.
Instead, they offer an array of different bundles of stations, or “tiers,” as well as promotional packages, to attract and retain their subscribers.
79. Data are not available to reasonably model and analyze consumers’ decision to subscribe to a particular cable or satellite system and therefore of systems’ choices of stations to distantly retransmit.32 Systems’ distant retransmissions choices are a trivial component of their attempts to attract subscribers. Fees regressions ignore these structural choices and instead attribute value to minutes of programming on retransmitted stations, whether or not households value the programming or subscribe to view said programs.
That is, one-half of the premise of the fees-based regression has no justification.
80. Nonetheless, the econometric models described later in this section mimic the estimated relationship performed by parties in the cable proceeding: they relate the natural logarithm of the royalties paid to the minutes of programming of the respective categories carried on distant broadcast signals within a satellite system provider.
81. An econometric model for a satellite fee-based regression analysis cannot be identical to the models estimated in the Cable Proceeding because cable system operators and satellite carriers face materially different compulsory license schemes. These major regulatory differences, which impact the structure of any regulated fees regression model include the following:
32 Gray CWDT, par. 12
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 34
Minimum Fees are Relevant to Cable Analysis, not to Satellite Analysis.
82. Cable systems are subject to a minimum fee requirement. Cable systems that make carriage choices, which cause their royalty obligation to exceed the statutorily-set minimum fee requirement, pay royalties based upon the number of distant signal equivalents (DSEs) and their gross revenues. Aggregated, these fees comprise the “Basic
Fund” of the cable royalties funds; some cable systems are required to pay royalties into the “Syndex Fund” and/or the “3.75% Fund” maintained by the Copyright Office.
83. Satellite carriers face no minimum fee requirement. Fees paid by satellite carriers follow a different formula that is based on the number of systems the carrier retransmits and the number and type of satellite system subscribers receiving the signal(s) in bundled packages chosen.
84. Moreover, satellite carries are not required to pay into the 3.75% Fund or Syndex
Fund.
Subscriber Groups are Potentially Relevant to Cable Analysis, not to Satellite Analysis.
85. The Satellite Television Extension and Localism Act of 2010 (STELA) allowed cable operators to calculate royalties due under Section 111 on a community-by- community or subscriber-group basis. However, before and after the passage of STELA, cable operators continued to make channel carriage decisions and pay royalty fees on a system-wide basis. In my opinion as an economist, this subscriber-group accounting provision included in STELA did not yield reliable information concerning cable
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 35 operators’ (or their subscribers’) relative valuation of programming.33 Nonetheless, one participant to the Cable Proceeding chose to analyze the relative value of programming at the subscriber-group level.34
86. STELA’s subscriber-group accounting provision is not available to satellite carriers’ calculations of royalties due under Section 119. Therefore, subscriber groups are irrelevant to any regulated-fees valuation methodology in the satellite context.
Relevant Programming Categories Different in Cable and Satellite Proceedings
87. The purpose of the Cable Proceeding was to determine the allocation of shares of the 2010-2013 cable royalty funds among six claimant groups: Commercial Television,
Canadian Claimants Group, Joint Sports Claimants, Settling Devotional Claimants,
Program Suppliers, and Public Television Claimants.
88. The purpose of the current Satellite Proceeding is to determine the allocation of shares of the 2010-2013 satellite royalty funds among four claimant groups: Commercial
Television, Joint Sports Claimants, Settling Devotional Claimants, and Program
Suppliers. That is, programming belonging to the Canadian Claimants Group and Public
Television Claimants categories are not eligible for royalties in this satellite proceeding.
33 See Gray CWRT, par 20-22.
34 See Crawford CWDT, par. 66.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 36
ABC, CBS, NBC Network Programming Relevant to Satellite Analysis, not to Cable
Analysis
89. Network programming broadcasted on ABC, CBS, and NBC is eligible for
Section 119 satellite license royalties, but is not eligible for Section 111 cable license royalties. Any appropriate relative value of programming analysis presented in the Cable
Proceeding therefore excluded all such network programming as non-compensable programming.
90. The distribution of programming volume by category is distinctly different for network programming and non-network compensable programming for the 2010-2013 satellite years. This difference renders the analyses performed, and the associated recommended royalty shares submitted by parties in the Cable Proceeding, inapplicable to the current proceeding.
91. Table 6 below shows that Program Suppliers programming constituted approximately 58% of compensable non-network programming carried on stations retransmitted by satellite carriers over the 2010-2013 royalty years. In contrast, Program
Suppliers programming represent almost 94% of ABC, CBS, and NBC network programming that was carried and retransmitted by satellite carriers – programming that would not be eligible for cable royalties. This higher share of network programming belonging to the Program Suppliers category came at the expense of lower Commercial
Television and Devotionals category weighted-volume share. Whereas Commercial
Television and Devotionals programming represented approximately one-third of non-
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 37 network compensable minutes, those programming categories had close to a zero share of network programming.
Table 6: Volume Shares and Compensable Distant Viewing Shares by Network Programming, 2010-2013
Panel A: Data Includes Compensable Non-Network Programming Only Weighted Compensable Model 1 Viewing Shares of Year Claimant Category Hours Shares Compensable Programming Commercial Television 31.16% 25.78% Devotionals 1.56% 0.55% Program Suppliers 56.99% 61.89% JSC 10.28% 11.78%
Average: 2010 Average: 2013 through Total 100% 100%
Panel B: Data Includes Compensable Network Programming Only Weighted Compensable Model 1 Viewing Shares of Year Claimant Category Hours Shares Compensable Programming Commercial Television 0.00% 0.00% Devotionals 0.01% 0.00% Program Suppliers 94.82% 86.93% JSC 5.17% 13.07%
Average: 2010 Average: 2013 through Total 100.00% 100.00%
92. The difference in the distribution of the type of programming comprising compensable network programming compared with compensable non-network programming retransmitted by satellite carriers between 2010 and 2013 underscores that methodologies based on cable royalty shares are not meaningful in the current proceeding. However, if one chooses to apply a regulated-fees regression in the satellite context, such application must include compensable network programming.
Fees-Based Regression Model Specifications
93. Cable operators file statements of accounts (“SOAs”) with the Licensing Division of the Copyright Office semi-annually; satellite operators also file SOAs semi-annually,
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 38 but in those filings subscriber counts are summed on a monthly basis, and satellite carriers pay associated fees on a semi-annual basis. I therefore present two econometric models: one estimating the relationship between programming minutes and royalties on a semi-annual basis, as was done in the Cable Proceeding; and the other estimating the relationship between programming minutes and accrued royalties on a monthly basis.
The latter provides more observations and therefore allows more precise estimates of the relationship. Both models employ a “fixed effect” estimation procedure.
94. The econometric models relate the natural log of the royalties paid by a particular satellite system to the minutes of programming by category type carried on the distant broadcast signals the satellite system carried – Model 1 on an accounting period, semi- annual basis, and Model 2 on a monthly basis. Each model also employs other control variables.
95. The key explanatory variables considered are therefore minutes of programming by content category. These categories are the four claimant groups seeking a share of the
2010-2013 satellite royalties: Commercial Television, Devotionals, JSC, and Program
Suppliers.
96. Another explanatory variable included in the model follows the logic of the
Crawford regulated fees regression relied upon in the Cable Proceeding. The models include a control for the number of distant stations so that the regression coefficients of the key explanatory variables might measure the impact on royalties of an increase in the programming minutes of key categories, taking into account a minute of non- compensable network programming.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 39
97. Royalty shares are estimated by the regulated fees regressions in two steps. First, the estimated coefficients from the regression model are used to calculate the so-called
“marginal value” of one program minute of each program category type. Second, these marginal values for each category’s programming minute is used to calculate the so- called “total value” of each program category’s minutes. These total values are converted into royalty shares as each group’s total value divided by the sum of all total values across the four category groups.
Fees-Based Results
98. Table 7 presents the estimates of the key explanatory variables in each of the regulated fees models attempting to measure the impact of an additional minute of programming of each program category type on the natural log of the royalties paid by satellite systems to import distant broadcast signals. Estimates of all the parameters are provided in Appendix F.
99. These estimates calculate the impact of an additional minute of programming of each category type across all four royalty years. Only one estimate is positive and statistically significantly different from zero – the impact of an additional minute of
Program Suppliers programming on log royalties when using the expanded database with monthly minutes and accrued royalty information.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 40
Table 7: Regression Coefficients on Claimant Category Programming Minutes: By Accounting Period and By Month, 2010-2013
Model 1: By Accounting Period Model 2: By Month Estimated Coefficient * 10^6 Estimated Coefficient * 10^6 Claimant Category (Standard Error *10^6) (Standard Error *10^6) 0.57 -2.16 Commercial Television (1.19) (1.10) 0.87 -0.19 Devotionals (3.55) (3.16) 1.13 0.48* Program Suppliers (0.93) (0.19) -0.31 0.28 JSC (0.71) (0.21) Note: * indicates the estimated coefficient is statistically different from zero at the 90% confidence level.
100. Table 8 presents the average marginal value and the resulting royalty shares implied by these estimates when the estimates are positive. When a programming category’s estimated impact on fees paid is negative, the value is set to zero when calculating the category’s average marginal value.
Table 8: Average Marginal Value and Implied Royalties Based on Regulated Fees Regression Analysis, 2010-2013 Commercial Program Data Used Measure Television Devotionals Suppliers JSC Average Marginal Value 6.069 9.233 12.018 0.000 Accounting Period Implied Royalty Share 9.74% 0.82% 89.45% 0.00%
Average Marginal Value 0.000 0.000 0.853 0.503 Monthly Reported Implied Royalty Share 0.00% 0.00% 98.05% 1.95%
101. The results presented in Table 8 provide fees-based estimates to determine the relative share of the royalty pool to allocate to the rights-holders in each claimant group across the 2010-2013 royalty years. I present these share estimates because the Judges
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 41 relied upon fees-based estimates in their determination of 2010-2013 cable royalties.35
However, relative program viewership provides a more reliable measure of program value.
VIII. CONCLUSION: ROYALTY SHARE ALLOCATIONS
102. Based upon the economic theory of consumer behavior, my analysis indicated that relative program viewership provides a reasonable and reliable measure of the relative economic value of distantly retransmitted programing. All else equal, the higher the viewing of distantly retransmitted programming, the higher the value of that programming to consumers, and therefore satellite carriers. Therefore, following this theory, to determine what I believe to be reasonable and reliable relative market values of the 2010-2013 claimant categories, I analyzed data concerning program volume and program viewing patterns of a randomly selected set of stations each year from 2010 to
2013. Model 1 in Table 4 above reports each claimant category’s distant viewing share, and therefore its share of the total 2010-2013 Satellite Royalties for each royalty year. On average across the 2010-2013 royalty years, these shares imply a 69.46% share for
Program Suppliers, 18.00% share for Commercial Television, 0.39% share for the
Devotionals, and a 12.15% share for JSC. I propose the adoption of the shares resulting from Model 1.
35 Cable Determination, 84 Fed. Reg. at 3610.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 42
APPENDIX A: CURRICULUM VITAE
Jeffrey S. Gray, Ph.D. President Analytics Research Group LLC 912 F Street NW Washington, DC 20004
Education & Background Summary Ph.D., Economics, University of Pennsylvania B.A., Economics (with honors) University of California Santa Cruz
Dr. Gray has over 20 years of experience in economic and statistical consulting, survey design, sampling methodologies, and complex database analytics. He is an authority on economic markets, statistical methods, and economic damages. His research has been published in some of the top peer-reviewed journals in the economics profession including The American Economic Review and the Journal of Human Resources. Dr. Gray has presented his findings before a variety of seminars at universities, meetings of professional societies and conferences on specialized topics in the United States and abroad. Dr. Gray has received recognition and financial support to pursue his research from the U.S. Department of Labor, the U.S. Department of Agriculture, and the Research Board of the University of Illinois. Throughout his career Dr. Gray has served as referee for professional journals assessing the appropriate application of economics and statistics.
Dr. Gray has conducted studies for corporations, government agencies and law firms on a variety of economic and statistical issues. Dr. Gray has served as a testifying expert on behalf of both plaintiffs and defendants addressing class certification, liability and/or damages issues. He has provided written or oral expert testimony in state, federal, and international courts and presented analytical findings before the Securities and Exchange Commission, the Texas Commissioner of Insurance, the Government of Singapore, and the New York and Massachusetts State Offices of Attorney General.
In addition to leading the economic and statistical consulting practices at Huron Consulting Group and Deloitte Financial Advisory Services LLP, Dr. Gray has served on the staff of the President’s Council of Economic Advisers and on the faculty of the University of Illinois where he taught graduate and undergraduate courses covering consumer demand analysis, labor economics, and statistics. He earned a Ph.D. in economics from the University of Pennsylvania.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 43
Professional Experience • Analytics Research Group LLC, Washington, DC o Founder and President, Washington DC, 2013 – Present • Deloitte Financial Advisory Services LLP, Washington, DC o Principal and Leader of Economics Practice, Washington DC, 2010 – 2013 • Huron Consulting Group, Boston, MA o Managing Director & National Leader, Economics, 2006 – 2009 • Deloitte Financial Advisory Services LLP/Deloitte & Touche LLP: FAS, Boston, MA o Principal-In-Charge, Boston, MA, 2004 – 2006 o Economist & Principal, Economic Consulting, 2002 – 2006 • Arthur Andersen LLP, Boston, MA & Chicago, IL
o Director, Economic Consulting, 2001 – 2002 o Economist, 1999 – 2002 • Welch Consulting, College Station, TX
o Senior Economist, 1996 – 1999 • University of Illinois, Urbana, IL
o Assistant Professor, 1993 – 1997 • President’s Council of Economic Advisors, Washington, DC
o Staff Economist, 1991 – 1992 • University of Pennsylvania, Philadelphia, PA o Research, Teaching Assistant and Instructor, 1989 – 1991 Professional Affiliations • American Economic Association • American Finance Association • American Statistical Association
Referee Responsibilities • American Economic Review, Demography, Economic Inquiry, International Economic Review, Eastern Economic Journal, Journal of Human Resources, Journal of Labor Economics, Review of Economics and Statistics, Social Science Quarterly, Sociological Forum.
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 44
Publications and Presentations (Prior 10 Years) • Jeffrey S. Gray. Class Action Litigation: Working with Economics and Statistics Experts, invited presentation, Washington, DC, September 2013.
• Jeffrey S. Gray. Patent Infringement Damages: Approaches and Trends, Moderated Panel on Intellectual Property in the Life Sciences, May 2010.
• Jeffrey S. Gray. Institutional Investors: Protecting Your Assets – Prudent Investing, Moderated Panel on Fiduciary Litigation Issues, February 2009.
• Jeffrey S. Gray. Subprime Fallout: Prudent Investing & Economic Damages. Professional Liability Underwriting Society Conference, Boston, MA. October 2008.
• Jeffrey S. Gray with Carl Tannenbaum and Laurence Kotlikoff, Was the Credit Crisis Foreseeable? Moderated Panel, April 2008.
• Eugene Canjels, Jeffrey S. Gray and Michel J. Vanderhart. Does Everyone Overstate the Number of Hours They Work? An Examination of Survey Response Bias Among Salaried and Hourly Workers, White Paper, April 2005.
Expert Testimony (Prior 4 Years) • In the Matter of Distribution of the 2010, 2011, 2012, and 2013 Cable Royalty Funds, before the Copyright Royalty Judges, Washington D.C., Doc No. 14-CRB-0010-CD (2010- 13), (Distribution) trial testimony (2018).
• In the Matter of Distribution of the 2010, 2011, 2012, and 2013 Cable Royalty Funds, before the Copyright Royalty Judges, Washington D.C., Doc No. 14-CRB-0010-CD (2010- 13), (Allocation) trial testimony (2018).
• In the Matter of Distribution of the 2004, 2005, 2006, 2007, 2008 and 2009 Cable Royalty Funds, before the Copyright Royalty Judges, Washington D.C., Doc No. 2012-6 CRB CD 2004-2009 (Phase II), and In the Matter of Distribution of the 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008 and 2009 Satellite Royalty Funds, before the Copyright Royalty Judges, Washington D.C., Doc No. 2012-7 CRB CD 1999-2009 (Phase II), trial testimony (2015).
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 45
APPENDIX B: SATELLITE CARRIER CHANNEL LINEUP CARD
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 46
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 47
APPENDIX C: DISTANTLY RETRANSMITTED STATIONS 2010 2011 2012 2013 Distant Distant Distant Distant Station Subscribers Station Subscribers Station Subscribers Station Subscribers 1 WGN 41,763,771 WGN-DT 66,059,442 WGN-DT 68,041,818 WGN-DT 67,050,270 2 WGN-DT 24,284,458 WPIX-DT 4,650,103 WPIX-DT 3,564,883 WPIX-DT 3,569,531 3 WPIX-DT 5,105,019 WSFL-DT 4,644,588 WNYW-DT 1,216,889 WDCW-DT 2,064,642 4 WSFL-DT 2,348,683 KTLA-DT 1,563,084 WCBS-DT 1,064,886 WNYW-DT 1,204,167 5 WNYW 1,681,082 XETV-DT 1,558,973 WABC-DT 1,044,805 WCBS-DT 1,159,483 6 KTLA-DT 1,587,772 WNYW-DT 1,377,184 WNBC-DT 1,012,781 WABC-DT 1,125,189 7 WABC 1,479,744 WABC-DT 1,196,822 KTLA-DT 972,573 WNBC-DT 1,073,403 8 WCBS 1,443,801 WCBS-DT 1,182,000 KTTV-DT 933,489 KTTV-DT 860,070 9 WNBC 1,418,274 WNBC-DT 1,158,115 WNUV-DT 923,347 KTLA-DT 805,476 10 KTTV 1,246,576 KTTV-DT 1,026,260 WSFL-DT 887,844 KABC-DT 773,201 11 KABC 1,113,234 KABC-DT 913,788 KNBC-DT 812,007 KCBS-DT 732,097 12 KCBS 1,099,423 KCBS-DT 899,494 KABC-DT 805,110 KNBC-DT 714,225 13 KNBC 1,090,087 KNBC-DT 880,373 KCBS-DT 798,920 WWOR-DT 392,127 14 WDCW- 1,064,699 WDCW-DT 678,031 WDCW-DT 469,585 KWGN-DT 379,567 15 KOFY 861,769 WSBK-DT 470,468 WWOR-DT 441,687 WSFL-DT 377,948 16 XETV-DT 820,045 KWGN-DT 462,962 KWGN-DT 421,238 WSBK-DT 360,829 17 WWOR- 638,116 WWOR-DT 461,367 WSBK-DT 401,469 WVLA-DT 265,343 18 KWGN -DT 600,447 WAUG-LD 380,080 WDCW-DT 233,750 WPCW-DT 163,872 19 WSBK-DT 594,729 WNUV-DT 240,714 WPCW-DT 150,596 KXVO-DT 157,534 20 W21AU 490,430 KGO-DT 227,409 WLBT-DT 134,283 WMC-DT 108,279 21 WRTV 468,046 WLBT-DT 187,805 KXVO-DT 133,523 WRC-DT 100,329 22 WTHR 468,046 WPCH-DT 133,605 WVLA-DT 127,949 WCAU-DT 88,728 23 KGO 317,885 KMAX-DT 107,784 WLFL-DT 101,859 WLFL-DT 84,482 24 WSFL-DT 271,414 KXVO-DT 105,166 KGO-DT 93,018 WMUR-DT 83,668 25 KTFF 206,176 KTVU-DT 100,935 XETV-DT 90,639 KGO-DT 78,653 26 WLBT 180,200 KRNS-CD 96,563 WRC-DT 90,084 KMAX-DT 77,254 27 KTVU 155,869 WTIC-DT 91,600 WTVY-DT 85,546 KSHB-DT 76,674 28 WNUV 143,127 WICS-DT 89,442 KTVU-DT 85,149 WNOL-DT 75,247 29 KPIX 130,551 KPIX-DT 87,276 WMC-DT 77,419 KTVU-DT 73,179 30 KMAX 127,171 WTVY-DT 79,964 WUSA-DT 74,681 WLMT-DT 69,262 31 WPCW 119,426 KNTV-DT 79,902 KPIX-DT 73,991 WPSD-DT 67,607 32 KNTV 117,602 WRC-DT 74,280 WCAU-DT 72,301 KPIX-DT 65,197 33 KXVO 109,180 WUSA-DT 74,280 KNTV-DT 68,087 WBDT-DT 60,983 34 KSKN 102,437 WDSU-DT 72,281 KSHB-DT 64,967 KNTV-DT 60,113 35 KSWB-DT 93,534 WWL-DT 72,281 WJHG-DT 62,225 KRNS-HD 56,155 36 WTIC 92,115 WNOL-DT 59,958 WMUR-DT 57,545 KSAT-DT 55,885 37 KRNS-HD 83,584 WLFL-DT 59,926 WPSD-DT 56,400 WHBQ-DT 54,991 38 WABC-DT 53,424 WJHG-DT 58,455 KSAT-DT 52,918 WREG-DT 54,991 39 WNYW- 51,034 WVLA-DT 52,659 WBDT-DT 51,768 WBNX-DT 54,259 40 WNBC -DT 45,301 WGBC-DT 52,390 WHBQ-DT 50,932 KALB-DT2 53,139 41 WCBS-DT 43,819 WTOK-DT 52,390 WREG-DT 50,932 KLAX-DT 53,139 42 WMUR 42,902 KSAT-DT 47,541 KMAX-DT 47,951 WRTV-DT 52,805 43 WRC-DT 34,792 WCAU-DT 45,047 WRTV-DT 47,495 WTHR-DT 52,805 44 WUSA-DT 34,792 WMUR-DT 44,567 WXIN-DT 47,173 WXIN-DT 52,805 45 KREN-DT 28,705 WRTV-DT 39,454 WTHR-DT 45,850 WSYX-DT 47,932 46 KSAT 27,798 WTHR-DT 39,454 WLMT-DT 44,949 WTVH-DT 46,834 47 WFFF 24,992 WXIN-DT 39,454 WDSU-DT 44,616 KCTV-DT 45,274 48 WCAX 24,968 WHBQ-DT 39,184 WBNX-DT 43,052 WHEC-DT 41,899 49 WNNE 24,968 WMC-DT 39,184 KMAX-DT 42,939 WCAX-DT 33,998 50 WVNY 24,968 WREG-DT 39,184 KRNS-CD 42,886 WFFF-DT 33,998 51 KODF-LD 23,221 KSHB-DT 38,254 WSYX-DT 42,186 WNNE-DT 33,998 52 WDSU-DT 23,201 WBDT-DT 33,264 KALB-DT 41,315 WVNY-DT 33,998 53 WWL-DT 23,201 KFVS-DT 29,309 KLAX-DT 41,315 KARE-DT 33,779 54 WICS-DT 22,757 WPSD-DT 29,309 KRNS-CD 38,279 KSTP-DT 33,779
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 48
55 WTTV 20,229 WCAX-DT 28,188 KCTV-DT 38,133 WHAG-DT 33,286 56 WXIN 20,229 WNNE-DT 28,188 WBNS-DT 35,795 WKTV-DT 31,463 57 KARE 20,106 WVNY-DT 28,188 WCAX-DT 32,510 WTTV-DT 30,992 58 KMSP 20,106 WFFF-DT 28,090 WFFF-DT 32,510 KATC-DT 29,922 59 KSTP 20,106 KEYT-DT 27,466 WNNE-DT 32,510 KLFY-DT 29,922 60 KEYT-DT 18,370 KARE-DT 27,104 WVNY-DT 32,510 KBSI-DT 29,235 61 KTTV-DT 18,127 KSTP-DT 27,104 WTOK-DT 31,755 KFVS-DT 29,235 62 WBNS 18,006 WBNX-DT 27,039 WHEC-DT 31,476 WBNS-DT 28,329 63 WSYX 18,006 WBNS-DT 26,415 KARE-DT 31,030 WTTE-DT 28,329 64 WTTE 18,006 WSYX-DT 26,415 KSTP-DT 31,030 WJHG-DT 27,151 65 WGBC-DT 17,758 WTTV-DT 25,640 KFVS-DT 30,045 WLBZ-DT 26,200 66 WTOK-DT 17,758 KBMT-DT 22,920 WNOL-DT 29,956 WVII-DT 26,200 67 KABC-DT 17,192 KFDM-DT 22,920 WTVH-DT 29,114 WCHS-DT 21,167 68 KCBS-DT 14,179 WTTE-DT 22,477 WWL-DT 29,051 WWHO-DT 19,603 69 WAPT 13,637 WKTV-DT 22,107 WKTV-DT 28,484 WUSA-DT 17,221 70 WJTV 13,637 WKEF-DT 19,111 WHAG-DT 26,747 WBNG-DT 16,635 71 WNOL-DT 13,578 KMSP-DT 19,081 KATC-DT 25,944 WAPT-DT 14,751 72 WHBQ-DT 13,200 KCTV-DT 18,490 KLFY-DT 25,944 WJTV-DT 14,751 73 WMC-DT 13,200 WDAF-DT 18,490 WTTE-DT 25,623 WLBT-DT 14,751 74 WREG-DT 13,200 WLBZ-DT 17,096 WGNO-DT 23,520 WEYI-DT 9,507 75 WTVY-DT 12,302 WVII-DT 17,096 WLBZ-DT 22,861 KOLN-DT 8,575 76 KNBC-DT 12,162 WBNG-DT 16,799 WVII-DT 22,861 WPTZ-DT 7,069 77 KSHB-DT 11,619 WHAG-DT 16,492 WBNG-DT 22,053 KHGI-DT 5,376 78 WJHG-DT 9,683 WDTV-DT 15,203 KBSI-DT 21,845 WJRT-DT 5,364 79 KFVS-DT 9,220 WAPT-DT 13,607 WWHO-DT 16,563 KEYC-DT 2,317 80 WPSD-DT 9,220 WJTV-DT 13,607 WDAF-DT 14,940 KEVN-DT 1,491 81 WVLA-DT 8,443 KFXF-DT 13,470 KFXF-DT 14,741 KOTA-DT 1,491 82 KFXF-DT 7,642 WGNO-DT 12,799 WAPT-DT 14,240 KFXF-DT 905 83 KDVR 7,616 KOLN-DT 9,684 WJTV-DT 14,240 84 KBMT-DT 7,527 WJXX-DT 8,519 WTTV-DT 14,148 85 KFDM-DT 7,527 WBOY-DT2 8,119 WDTV-DT 12,344 86 WPIX 7,400 KALB-DT 7,879 KOLN-DT 12,321 87 KCNC 7,380 KHOU-DT 7,814 WNAB-DT 12,183 88 KMGH 7,313 WBOY-DT 7,083 WSMV-DT 12,183 89 WKTV-DT 7,269 WNAB-DT 6,067 KHGI-DT 11,293 90 KUSA 7,250 WSMV-DT 6,067 WGBC-DT 10,364 91 KRMA 6,923 WTVF-DT 6,067 WCHS-DT 9,605 92 KWGN 6,902 WEYI-DT 5,471 WBOY-DT 9,220 93 KTVD 6,892 WHEC-DT 5,466 WEYI-DT 7,733 94 KBDI 6,888 WLMT-DT 4,270 WJRT-DT 7,733 95 WLBZ-DT 5,721 WWHO-DT 3,939 WPTZ-DT 6,498 96 WVII-DT 5,721 WTVH-DT 3,215 KTMF-DT 5,686 97 WBOY-DT 5,394 WPTZ-DT 3,093 WTIC-DT 2,558 98 WDTV-DT 5,394 WJRT-DT 3,012 KBMT-DT 2,258 99 WCAU-DT 5,281 KHGI-DT 2,768 KFDM-DT 2,258 100 KCTV-DT 5,093 WJRT-HD 2,458 KEYC-DT 2,071 101 WDAF-DT 5,093 KBSI-DT 2,325 WISE-DT 1,652 102 WCWJ-DT 4,762 WCWJ-DT 1,427 KEVN-DT 1,288 103 WJXX-DT 4,762 KEVN-DT 949 KOTA-DT 1,288 104 WHAG-DT 4,682 KOTA-DT 949 WNKY-DT 776 105 WRTV-DT 4,675 106 WTHR-DT 4,675 107 WXIN-DT 4,675 108 WLS 4,613 109 WBDT-DT 4,573 110 WKEF-DT 4,573 111 WWNY- 3,888 112 WFLD 3,180 113 KOLN-DT 3,141
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 49
114 WBBM 3,068 115 KARK-DT 3,008 116 KHOU-DT 2,910 117 WMAQ 2,784 118 KSAT-DT 2,334 119 KTBY 2,058 120 KARE-DT 1,959 121 KSTP-DT 1,959 122 WEYI-DT 1,816 123 WJRT-HD 1,731 124 KOCO-DT 1,496 125 WBNX-DT 1,256 126 WPLG 1,098 127 WSEE 1,098 128 WTVJ 1,098 129 WBNS-DT 1,087 130 WSYX-DT 1,087 131 WTTE-DT 1,087 132 WBNG-DT 1,001 133 WXIA 942 134 WGCL 896 135 WSB 850 136 WCAX-DT 423 137 WFFF-DT 423 138 WNNE-DT 423 139 WPTZ-DT 423 140 WVNY-DT 423 141 KEVN-DT 310 142 KOTA-DT 310 143 WJRT-DT 85
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 50
APPENDIX D: ENHANCED VIEWING REGRESSION MODELS
2010: . POISSON DISTANT LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2=="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -5.026E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -5.017E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -5.017E+09 ITERATION 3: LOG PSEUDOLIKELIHOOD = -5.017E+09
POISSON REGRESSION NUMBER OF OBS = 525720 WALD CHI2(109) = . PROB > CHI2 = . LOG PSEUDOLIKELIHOOD = -5.017E+09 PSEUDO R2 = 0.5338
------| ROBUST DISTANT | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | -25.45368 .373798 -68.09 0.000 -26.18631 -24.72105 | QTR | 2 | -.1694059 .0102885 -16.47 0.000 -.189571 -.1492408 3 | -.1969823 .0105229 -18.72 0.000 -.2176069 -.1763577 4 | -.2176362 .0106946 -20.35 0.000 -.2385972 -.1966752 5 | -.332669 .0103377 -32.18 0.000 -.3529304 -.3124076 6 | -.5425481 .0109202 -49.68 0.000 -.5639513 -.5211449 7 | -.6233548 .0111379 -55.97 0.000 -.6451845 -.601525 8 | -.73304 .0115321 -63.57 0.000 -.7556425 -.7104375 9 | -1.015046 .0153652 -66.06 0.000 -1.045161 -.9849304 10 | -1.259539 .0166454 -75.67 0.000 -1.292163 -1.226915 11 | -1.30609 .0170291 -76.70 0.000 -1.339466 -1.272713 12 | -1.331355 .0171573 -77.60 0.000 -1.364983 -1.297727 13 | -.2059547 .0132046 -15.60 0.000 -.2318351 -.1800742 14 | -.2093121 .013518 -15.48 0.000 -.235807 -.1828172 15 | -.2960108 .0139741 -21.18 0.000 -.3233995 -.2686222 16 | -.3465354 .0144288 -24.02 0.000 -.3748154 -.3182555 17 | -.9895111 .0113425 -87.24 0.000 -1.011742 -.9672802 18 | -1.101293 .0114191 -96.44 0.000 -1.123674 -1.078912 19 | -1.276748 .0126568 -100.87 0.000 -1.301555 -1.251941 20 | -1.336466 .013678 -97.71 0.000 -1.363274 -1.309657 21 | -1.590998 .0153523 -103.63 0.000 -1.621088 -1.560908 22 | -1.428608 .0138468 -103.17 0.000 -1.455747 -1.401469 23 | -.5259926 .0131044 -40.14 0.000 -.5516768 -.5003083 24 | -.4033701 .0132104 -30.53 0.000 -.429262 -.3774782 25 | -.1401613 .0138508 -10.12 0.000 -.1673084 -.1130141 26 | -.1476088 .0141664 -10.42 0.000 -.1753745 -.1198431 27 | -.0925725 .0146709 -6.31 0.000 -.121327 -.0638179 28 | -.0174842 .014696 -1.19 0.234 -.0462879 .0113194 29 | .3969263 .0148138 26.79 0.000 .3678918 .4259608 30 | .4999879 .0147949 33.79 0.000 .4709905 .5289853 31 | .6291507 .014854 42.36 0.000 .6000374 .6582639 32 | .752091 .0151408 49.67 0.000 .7224156 .7817665 33 | -.21742 .0118622 -18.33 0.000 -.2406694 -.1941705 34 | -.32587 .0130788 -24.92 0.000 -.3515039 -.300236 35 | -.1759614 .0134857 -13.05 0.000 -.2023929 -.1495298 36 | .0285112 .0119244 2.39 0.017 .0051397 .0518827 37 | .4663382 .0089599 52.05 0.000 .448777 .4838994 38 | .4815019 .0087656 54.93 0.000 .4643218 .4986821 39 | .5821464 .0084846 68.61 0.000 .5655168 .598776 40 | .7000725 .0084594 82.76 0.000 .6834925 .7166526
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 51
41 | .6570021 .0094354 69.63 0.000 .6385091 .6754951 42 | .6108932 .0098553 61.99 0.000 .5915773 .6302092 43 | .7168816 .0087436 81.99 0.000 .6997445 .7340187 44 | .751312 .0087655 85.71 0.000 .7341319 .7684921 45 | .7232291 .0088589 81.64 0.000 .705866 .7405922 46 | .6973021 .0091376 76.31 0.000 .6793928 .7152114 47 | .7033716 .0091628 76.76 0.000 .6854128 .7213304 48 | .6998407 .0092238 75.87 0.000 .6817623 .7179191 49 | .1360903 .0092788 14.67 0.000 .1179043 .1542764 50 | -.2690963 .0106141 -25.35 0.000 -.2898995 -.248293 51 | -.3200881 .0114748 -27.89 0.000 -.3425782 -.297598 52 | -.3111177 .0119765 -25.98 0.000 -.3345913 -.2876441 53 | .3211755 .0095693 33.56 0.000 .30242 .339931 54 | .3753868 .0097342 38.56 0.000 .3563081 .3944655 55 | .4133256 .0097881 42.23 0.000 .3941413 .4325098 56 | .4901472 .0097578 50.23 0.000 .4710223 .5092721 57 | .3679285 .0089674 41.03 0.000 .3503527 .3855043 58 | .3019761 .0089117 33.89 0.000 .2845095 .3194427 59 | .3295078 .0088402 37.27 0.000 .3121813 .3468343 60 | .3415243 .0088075 38.78 0.000 .324262 .3587866 61 | .3835441 .0086446 44.37 0.000 .3666009 .4004872 62 | .2721544 .0089453 30.42 0.000 .2546219 .2896868 63 | .2764489 .0089497 30.89 0.000 .2589079 .29399 64 | .3314245 .0089915 36.86 0.000 .3138015 .3490475 65 | .4072011 .0088297 46.12 0.000 .3898953 .424507 66 | .2838578 .0091493 31.03 0.000 .2659256 .3017901 67 | .2627038 .0089652 29.30 0.000 .2451323 .2802752 68 | .3366773 .0090005 37.41 0.000 .3190365 .354318 69 | .3917893 .0085363 45.90 0.000 .3750585 .4085201 70 | .3497869 .0086667 40.36 0.000 .3328005 .3667733 71 | .4177104 .0086065 48.53 0.000 .4008421 .4345788 72 | .4703894 .008571 54.88 0.000 .4535906 .4871882 73 | .4356678 .0088349 49.31 0.000 .4183516 .452984 74 | .2895361 .0094357 30.69 0.000 .2710425 .3080297 75 | .3084225 .009651 31.96 0.000 .289507 .327338 76 | .3550222 .009467 37.50 0.000 .3364672 .3735772 77 | .5549906 .0100157 55.41 0.000 .5353601 .574621 78 | .4521456 .0106883 42.30 0.000 .4311969 .4730942 79 | .4652726 .0106572 43.66 0.000 .4443848 .4861604 80 | .4763185 .0107843 44.17 0.000 .4551816 .4974554 81 | .4741811 .0096394 49.19 0.000 .4552883 .493074 82 | .3773854 .0098627 38.26 0.000 .3580549 .396716 83 | .3674246 .0098474 37.31 0.000 .3481241 .386725 84 | .3569138 .010009 35.66 0.000 .3372966 .376531 85 | .1081418 .0103514 10.45 0.000 .0878535 .1284302 86 | -.2279022 .0123801 -18.41 0.000 -.2521667 -.2036377 87 | -.3997897 .0133809 -29.88 0.000 -.4260158 -.3735635 88 | -.5315481 .0138079 -38.50 0.000 -.5586112 -.5044851 89 | -.1181229 .0117142 -10.08 0.000 -.1410823 -.0951635 90 | -.1249734 .0119505 -10.46 0.000 -.148396 -.1015509 91 | -.0809102 .0117109 -6.91 0.000 -.1038632 -.0579573 92 | -.0695948 .0110729 -6.29 0.000 -.0912973 -.0478923 93 | .0453935 .0093324 4.86 0.000 .0271024 .0636846 94 | -.015819 .0095892 -1.65 0.099 -.0346134 .0029755 95 | .015178 .00941 1.61 0.107 -.0032653 .0336212 96 | -.0057899 .0094207 -0.61 0.539 -.0242542 .0126743 | PROGRAM_TYPE | MOVIE | .4714104 .0123978 38.02 0.000 .4471112 .4957095 MUSIC | -.1663883 .0238396 -6.98 0.000 -.2131132 -.1196635 MUSIC SPECIAL | -.1201495 .0365922 -3.28 0.001 -.1918688 -.0484302 NEWS | .4543918 .0130902 34.71 0.000 .4287354 .4800481 OTHER | -.3564946 .0129882 -27.45 0.000 -.3819511 -.3310382
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 52
PSEUDO-SPORTS | .7483628 .0135942 55.05 0.000 .7217186 .7750071 PUBLIC AFFAIRS | -1.828424 .0348626 -52.45 0.000 -1.896753 -1.760094 RELIGIOUS | -.383068 .0151299 -25.32 0.000 -.412722 -.353414 SPECIAL | .2085114 .0191065 10.91 0.000 .1710634 .2459595 SPORTS-RELATED | .7122401 .0157478 45.23 0.000 .681375 .7431053 SYNDICATED | .7105643 .0119128 59.65 0.000 .6872158 .7339129 TALK SHOW | .5814114 .0180042 32.29 0.000 .5461238 .616699 TEAM VS. TEAM | .8945432 .0125479 71.29 0.000 .8699498 .9191366 TV MOVIE | .1201773 .0226174 5.31 0.000 .0758481 .1645066 | _CONS | 440.5103 6.315828 69.75 0.000 428.1315 452.8891 ------
. PREDICT DOUBLE DISHAT (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. POISSON DISTANT LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2!="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -3.717E+11 ITERATION 1: LOG PSEUDOLIKELIHOOD = -1.591E+11 (BACKED UP) ITERATION 2: LOG PSEUDOLIKELIHOOD = -1.396E+11 (BACKED UP) ITERATION 3: LOG PSEUDOLIKELIHOOD = -1.064E+11 ITERATION 4: LOG PSEUDOLIKELIHOOD = -8.955E+10 ITERATION 5: LOG PSEUDOLIKELIHOOD = -6.734E+10 ITERATION 6: LOG PSEUDOLIKELIHOOD = -6.620E+10 ITERATION 7: LOG PSEUDOLIKELIHOOD = -6.612E+10 ITERATION 8: LOG PSEUDOLIKELIHOOD = -6.612E+10 ITERATION 9: LOG PSEUDOLIKELIHOOD = -6.612E+10 ITERATION 10: LOG PSEUDOLIKELIHOOD = -6.612E+10
POISSON REGRESSION NUMBER OF OBS = 36999874 WALD CHI2(125) = 2919662.93 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -6.612E+10 PSEUDO R2 = 0.3944
------| ROBUST DISTANT | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | .753644 .00053 1421.93 0.000 .7526052 .7546828 | QTR | 2 | -.1658341 .0094606 -17.53 0.000 -.1843766 -.1472916 3 | -.2662558 .0096536 -27.58 0.000 -.2851766 -.2473351 4 | -.4735252 .0101547 -46.63 0.000 -.493428 -.4536224 5 | -.5613469 .0102528 -54.75 0.000 -.581442 -.5412519 6 | -.7545069 .0108429 -69.59 0.000 -.7757586 -.7332552 7 | -.7860451 .0111502 -70.50 0.000 -.8078991 -.7641911 8 | -.9356478 .0119436 -78.34 0.000 -.9590569 -.9122386 9 | -1.082301 .0124556 -86.89 0.000 -1.106713 -1.057888 10 | -1.32311 .0135206 -97.86 0.000 -1.349609 -1.29661 11 | -1.341128 .0137582 -97.48 0.000 -1.368094 -1.314163 12 | -1.423493 .0143025 -99.53 0.000 -1.451525 -1.395461 13 | -1.203077 .0134976 -89.13 0.000 -1.229531 -1.176622 14 | -1.307868 .0139774 -93.57 0.000 -1.335263 -1.280473 15 | -1.30063 .0141984 -91.60 0.000 -1.328458 -1.272801 16 | -1.281003 .0142178 -90.10 0.000 -1.308869 -1.253137 17 | -1.276282 .0141272 -90.34 0.000 -1.303971 -1.248593 18 | -1.306451 .014345 -91.07 0.000 -1.334567 -1.278336 19 | -1.245699 .014468 -86.10 0.000 -1.274056 -1.217342
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 53
20 | -1.239932 .0145669 -85.12 0.000 -1.268483 -1.211381 21 | -1.158168 .0147291 -78.63 0.000 -1.187036 -1.129299 22 | -1.059392 .0142193 -74.50 0.000 -1.087261 -1.031523 23 | -1.018176 .014036 -72.54 0.000 -1.045686 -.9906657 24 | -.8731739 .0134511 -64.91 0.000 -.8995375 -.8468103 25 | -.7570029 .012209 -62.00 0.000 -.780932 -.7330738 26 | -.6564648 .0118321 -55.48 0.000 -.6796554 -.6332743 27 | -.4922456 .011316 -43.50 0.000 -.5144246 -.4700666 28 | -.4412793 .0109497 -40.30 0.000 -.4627403 -.4198182 29 | -.4126491 .0102229 -40.37 0.000 -.4326857 -.3926125 30 | -.3773585 .0102344 -36.87 0.000 -.3974175 -.3572994 31 | -.4375026 .0105056 -41.64 0.000 -.4580933 -.4169119 32 | -.3307278 .0101807 -32.49 0.000 -.3506816 -.310774 33 | -.2822656 .0098356 -28.70 0.000 -.301543 -.2629882 34 | -.315794 .0099571 -31.72 0.000 -.3353096 -.2962784 35 | -.3508106 .0100554 -34.89 0.000 -.3705189 -.3311023 36 | -.3399136 .0099904 -34.02 0.000 -.3594945 -.3203328 37 | -.3205804 .0095259 -33.65 0.000 -.3392508 -.3019101 38 | -.3213257 .0095729 -33.57 0.000 -.3400882 -.3025631 39 | -.3577112 .0096318 -37.14 0.000 -.3765891 -.3388333 40 | -.3963287 .0096547 -41.05 0.000 -.4152516 -.3774057 41 | -.3002004 .0095616 -31.40 0.000 -.3189408 -.2814599 42 | -.3734744 .0098576 -37.89 0.000 -.3927949 -.3541538 43 | -.3475633 .0097509 -35.64 0.000 -.3666748 -.3284518 44 | -.2637383 .0095291 -27.68 0.000 -.282415 -.2450617 45 | -.0752152 .009149 -8.22 0.000 -.0931469 -.0572835 46 | -.0889711 .0091244 -9.75 0.000 -.1068546 -.0710876 47 | -.1377011 .0091713 -15.01 0.000 -.1556765 -.1197257 48 | -.097484 .0090493 -10.77 0.000 -.1152204 -.0797476 49 | -.3292263 .0095428 -34.50 0.000 -.3479299 -.3105226 50 | -.3536275 .0096435 -36.67 0.000 -.3725284 -.3347266 51 | -.4767927 .0095659 -49.84 0.000 -.4955414 -.4580439 52 | -.3504918 .0095958 -36.53 0.000 -.3692992 -.3316845 53 | -.3342279 .0094699 -35.29 0.000 -.3527886 -.3156673 54 | -.3421408 .0097307 -35.16 0.000 -.3612125 -.323069 55 | -.375404 .0097014 -38.70 0.000 -.3944183 -.3563897 56 | -.4136611 .0097256 -42.53 0.000 -.432723 -.3945993 57 | -.360696 .0101434 -35.56 0.000 -.3805768 -.3408153 58 | -.4856579 .0106671 -45.53 0.000 -.5065649 -.4647509 59 | -.525622 .01077 -48.80 0.000 -.5467309 -.5045132 60 | -.4571446 .0103195 -44.30 0.000 -.4773704 -.4369187 61 | -.1568183 .0092955 -16.87 0.000 -.1750371 -.1385996 62 | -.2375037 .0097254 -24.42 0.000 -.2565651 -.2184422 63 | -.2536289 .0097595 -25.99 0.000 -.2727572 -.2345006 64 | -.2233315 .0097571 -22.89 0.000 -.2424551 -.2042079 65 | -.0886457 .0099301 -8.93 0.000 -.1081083 -.0691832 66 | -.1262083 .0100791 -12.52 0.000 -.1459629 -.1064537 67 | -.0960624 .0097433 -9.86 0.000 -.115159 -.0769658 68 | -.0584509 .0097307 -6.01 0.000 -.0775228 -.0393791 69 | -.076118 .0102217 -7.45 0.000 -.0961522 -.0560838 70 | -.1642816 .0106654 -15.40 0.000 -.1851854 -.1433778 71 | -.0442008 .0100815 -4.38 0.000 -.0639603 -.0244413 72 | -.0212861 .0099748 -2.13 0.033 -.0408364 -.0017359 73 | .1133985 .0098899 11.47 0.000 .0940147 .1327824 74 | .10237 .0098115 10.43 0.000 .0831397 .1216003 75 | .2609977 .0091631 28.48 0.000 .2430384 .278957 76 | .3703416 .0089234 41.50 0.000 .3528521 .3878312 77 | .5708589 .0082183 69.46 0.000 .5547513 .5869664 78 | .5312622 .0082259 64.58 0.000 .5151396 .5473847 79 | .4869105 .0084246 57.80 0.000 .4703987 .5034224 80 | .6246649 .0082492 75.72 0.000 .6084968 .6408331 81 | .8200676 .0082909 98.91 0.000 .8038177 .8363174 82 | .6521948 .0084714 76.99 0.000 .6355912 .6687983
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 54
83 | .651505 .0085495 76.20 0.000 .6347482 .6682618 84 | .6922292 .0085198 81.25 0.000 .6755307 .7089278 85 | .8832863 .0084669 104.32 0.000 .8666915 .8998811 86 | .7243268 .0087032 83.23 0.000 .7072689 .7413848 87 | .7029937 .0087846 80.03 0.000 .6857761 .7202112 88 | .6866395 .0087113 78.82 0.000 .6695657 .7037134 89 | .7984957 .008434 94.68 0.000 .7819654 .8150261 90 | .5117545 .0088803 57.63 0.000 .4943494 .5291596 91 | .4478691 .0089004 50.32 0.000 .4304246 .4653136 92 | .3790545 .009008 42.08 0.000 .3613993 .3967098 93 | .3955332 .0089277 44.30 0.000 .3780352 .4130312 94 | .2642809 .0093706 28.20 0.000 .2459148 .2826469 95 | .1126963 .0092126 12.23 0.000 .0946399 .1307527 96 | -.0052736 .0093172 -0.57 0.571 -.023535 .0129878 | PROGRAM_TYPE | CHILDREN'S SHOW | .2331093 .0165477 14.09 0.000 .2006764 .2655422 CHILDREN'S SPECIAL | .9084399 .0257683 35.25 0.000 .857935 .9589448 DAYTIME SOAP | 2.073639 .0113798 182.22 0.000 2.051335 2.095943 FINANCE | .4155367 .0374801 11.09 0.000 .3420772 .4889963 FIRST-RUN SYNDICATION | 1.94591 .0138936 140.06 0.000 1.918679 1.973141 GAME SHOW | 1.644304 .0113231 145.22 0.000 1.622111 1.666497 HEALTH | 1.168828 .0152216 76.79 0.000 1.138994 1.198661 HOBBIES & CRAFTS | 1.498646 .077746 19.28 0.000 1.346266 1.651025 INSTRUCTIONAL | .8663446 .0231083 37.49 0.000 .8210532 .9116361 MINI-SERIES | 3.059242 .0207275 147.59 0.000 3.018616 3.099867 MOVIE | .0414477 .0126748 3.27 0.001 .0166055 .06629 MUSIC | 1.975656 .0146968 134.43 0.000 1.946851 2.004462 MUSIC SPECIAL | 1.471444 .0234805 62.67 0.000 1.425423 1.517465 NETWORK SERIES | 1.273965 .0113772 111.98 0.000 1.251667 1.296264 NEWS | .8228041 .0110061 74.76 0.000 .8012326 .8443755 OTHER | .1377936 .0117904 11.69 0.000 .114685 .1609023 PELICULA | 3.09926 .0119873 258.55 0.000 3.075765 3.122755 PLAYOFF SPORTS | 2.680955 .0123983 216.24 0.000 2.656655 2.705255 PSEUDO-SPORTS | 2.027365 .017254 117.50 0.000 1.993548 2.061183 PUBLIC AFFAIRS | .7199766 .0193751 37.16 0.000 .6820021 .757951 RELIGIOUS | .3277768 .0173987 18.84 0.000 .2936759 .3618776 SPECIAL | .9611823 .0140621 68.35 0.000 .9336211 .9887436 SPORTING EVENT | 2.16874 .0123544 175.54 0.000 2.144526 2.192954 SPORTS ANTHOLOGY | .5904516 .0274735 21.49 0.000 .5366045 .6442986 SPORTS-RELATED | 1.518072 .0132664 114.43 0.000 1.492071 1.544074 SYNDICATED | .9330672 .0109867 84.93 0.000 .9115336 .9546008 TALK SHOW | 1.286309 .0109014 117.99 0.000 1.264943 1.307675 TEAM VS. TEAM | 2.362981 .011524 205.05 0.000 2.340394 2.385568 TV MOVIE | 1.004579 .0231676 43.36 0.000 .9591717 1.049987 | _CONS | -2.84152 .0141336 -201.05 0.000 -2.869221 -2.813818 ------
. PREDICT DOUBLE DISHAT2 (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. REPLACE DISHAT=DISHAT2 IF CALL2!="WGN" (2837648 REAL CHANGES MADE)
. POISSON DVIEW LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2=="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -4.703E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -4.697E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -4.697E+09 ITERATION 3: LOG PSEUDOLIKELIHOOD = -4.697E+09
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 55
POISSON REGRESSION NUMBER OF OBS = 514695 WALD CHI2(110) = 559167.59 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -4.697E+09 PSEUDO R2 = 0.5305
------| ROBUST DVIEW | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | -24.30902 .37031 -65.65 0.000 -25.03482 -23.58323 | QTR | 2 | -.1612384 .0102029 -15.80 0.000 -.1812357 -.1412411 3 | -.186457 .0104243 -17.89 0.000 -.2068882 -.1660257 4 | -.1932048 .010476 -18.44 0.000 -.2137374 -.1726722 5 | -.3177818 .0101962 -31.17 0.000 -.3377658 -.2977977 6 | -.5195188 .0107149 -48.49 0.000 -.5405197 -.4985179 7 | -.5949713 .010894 -54.61 0.000 -.6163232 -.5736194 8 | -.6992046 .011253 -62.13 0.000 -.7212601 -.6771491 9 | -.9168873 .01469 -62.42 0.000 -.9456791 -.8880955 10 | -1.142433 .0159475 -71.64 0.000 -1.17369 -1.111177 11 | -1.117415 .0159506 -70.05 0.000 -1.148678 -1.086153 12 | -1.155423 .0160226 -72.11 0.000 -1.186827 -1.12402 13 | -.2464716 .0128088 -19.24 0.000 -.2715763 -.2213669 14 | -.2450928 .0130715 -18.75 0.000 -.2707124 -.2194731 15 | -.3293776 .0135093 -24.38 0.000 -.3558552 -.3028999 16 | -.3768333 .0139248 -27.06 0.000 -.4041253 -.3495413 17 | -.9715808 .0111777 -86.92 0.000 -.9934887 -.949673 18 | -1.078627 .0112215 -96.12 0.000 -1.100621 -1.056633 19 | -1.234215 .0123115 -100.25 0.000 -1.258345 -1.210085 20 | -1.270137 .0131866 -96.32 0.000 -1.295982 -1.244292 21 | -1.44774 .0144102 -100.47 0.000 -1.475983 -1.419496 22 | -1.321439 .0130553 -101.22 0.000 -1.347027 -1.295851 23 | -.5060699 .0123906 -40.84 0.000 -.5303551 -.4817847 24 | -.3812616 .0125284 -30.43 0.000 -.4058169 -.3567063 25 | -.1723405 .0133328 -12.93 0.000 -.1984724 -.1462086 26 | -.1831521 .0136897 -13.38 0.000 -.2099834 -.1563209 27 | -.1214439 .0142344 -8.53 0.000 -.1493428 -.093545 28 | -.0697527 .014402 -4.84 0.000 -.0979801 -.0415254 29 | .3445024 .0145751 23.64 0.000 .3159357 .3730691 30 | .4612161 .014441 31.94 0.000 .4329122 .48952 31 | .5749001 .0145516 39.51 0.000 .5463794 .6034208 32 | .699253 .014841 47.12 0.000 .6701652 .7283409 33 | -.2092056 .0117467 -17.81 0.000 -.2322286 -.1861825 34 | -.3170826 .0129817 -24.43 0.000 -.3425262 -.291639 35 | -.1717823 .0134082 -12.81 0.000 -.1980619 -.1455027 36 | .0305039 .0118602 2.57 0.010 .0072583 .0537496 37 | .4663224 .0089428 52.14 0.000 .4487948 .48385 38 | .4948304 .008689 56.95 0.000 .4778002 .5118605 39 | .5870606 .0084462 69.51 0.000 .5705063 .6036148 40 | .7049629 .0084237 83.69 0.000 .6884528 .721473 41 | .6764538 .009281 72.89 0.000 .6582634 .6946442 42 | .6343519 .0096325 65.86 0.000 .6154727 .6532312 43 | .7341352 .0086496 84.87 0.000 .7171823 .7510881 44 | .7535566 .0087484 86.14 0.000 .7364101 .7707031 45 | .7148464 .0088478 80.79 0.000 .6975051 .7321877 46 | .6889194 .009117 75.56 0.000 .6710504 .7067884 47 | .6980315 .0091361 76.40 0.000 .6801251 .7159379 48 | .6945006 .0091945 75.53 0.000 .6764798 .7125214 49 | .1352711 .0092651 14.60 0.000 .1171118 .1534305 50 | -.2699155 .0106035 -25.46 0.000 -.2906979 -.249133 51 | -.3209073 .0114645 -27.99 0.000 -.3433773 -.2984372
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 56
52 | -.3093053 .0119463 -25.89 0.000 -.3327195 -.285891 53 | .3198343 .0095499 33.49 0.000 .3011168 .3385519 54 | .3740456 .0097135 38.51 0.000 .3550075 .3930838 55 | .4121683 .009768 42.20 0.000 .3930233 .4313133 56 | .48899 .0097387 50.21 0.000 .4699024 .5080775 57 | .3671883 .0089566 41.00 0.000 .3496337 .384743 58 | .3012359 .0089011 33.84 0.000 .28379 .3186818 59 | .3287669 .0088296 37.23 0.000 .3114612 .3460727 60 | .3438103 .0087633 39.23 0.000 .3266345 .3609861 61 | .3827836 .0086356 44.33 0.000 .3658582 .3997091 62 | .2713939 .0089371 30.37 0.000 .2538775 .2889104 63 | .2756883 .008943 30.83 0.000 .2581604 .2932162 64 | .3306695 .0089856 36.80 0.000 .3130579 .348281 65 | .4064539 .0088231 46.07 0.000 .3891609 .423747 66 | .2831182 .0091423 30.97 0.000 .2651995 .3010369 67 | .2619892 .0089589 29.24 0.000 .24443 .2795484 68 | .3359692 .0089957 37.35 0.000 .3183379 .3536004 69 | .3910901 .0085293 45.85 0.000 .3743731 .4078072 70 | .35149 .0086371 40.70 0.000 .3345616 .3684185 71 | .4169729 .0086005 48.48 0.000 .4001162 .4338295 72 | .4696584 .0085651 54.83 0.000 .4528711 .4864457 73 | .4351746 .0088313 49.28 0.000 .4178656 .4524837 74 | .2890449 .0094333 30.64 0.000 .270556 .3075338 75 | .3079696 .0096497 31.91 0.000 .2890565 .3268826 76 | .3545711 .0094668 37.45 0.000 .3360165 .3731257 77 | .5544941 .0100137 55.37 0.000 .5348676 .5741207 78 | .4516585 .0106841 42.27 0.000 .4307181 .4725989 79 | .4647718 .0106543 43.62 0.000 .4438898 .4856538 80 | .4808608 .0107403 44.77 0.000 .4598102 .5019115 81 | .4716685 .0096362 48.95 0.000 .452782 .4905551 82 | .3748723 .0098619 38.01 0.000 .3555434 .3942012 83 | .3701721 .0097991 37.78 0.000 .3509663 .3893779 84 | .3544019 .0100074 35.41 0.000 .3347877 .3740161 85 | .1059635 .0103441 10.24 0.000 .0856894 .1262377 86 | -.2273498 .0123518 -18.41 0.000 -.2515589 -.2031407 87 | -.3992338 .0133539 -29.90 0.000 -.4254069 -.3730606 88 | -.5148154 .0136758 -37.64 0.000 -.5416194 -.4880113 89 | -.1175412 .0116897 -10.06 0.000 -.1404526 -.0946298 90 | -.1243113 .0119269 -10.42 0.000 -.1476875 -.100935 91 | -.0823837 .0117079 -7.04 0.000 -.1053308 -.0594367 92 | -.0709534 .0110674 -6.41 0.000 -.0926451 -.0492616 93 | .0464043 .009319 4.98 0.000 .0281394 .0646692 94 | -.0148012 .0095735 -1.55 0.122 -.033565 .0039625 95 | .0161125 .0093933 1.72 0.086 -.002298 .0345231 96 | -.0019957 .0093773 -0.21 0.831 -.0203749 .0163836 | PROGRAM_TYPE | MOVIE | .4722557 .0123627 38.20 0.000 .4480253 .4964861 MUSIC | -.1021752 .0224905 -4.54 0.000 -.1462558 -.0580945 MUSIC SPECIAL | -.1232186 .0366116 -3.37 0.001 -.194976 -.0514611 NEWS | .4495835 .0130609 34.42 0.000 .4239847 .4751823 OTHER | -.2669831 .0128736 -20.74 0.000 -.292215 -.2417512 PSEUDO-SPORTS | .744902 .01356 54.93 0.000 .7183249 .771479 PUBLIC AFFAIRS | -1.550255 .0321361 -48.24 0.000 -1.61324 -1.487269 RELIGIOUS | -.3046212 .0149953 -20.31 0.000 -.3340115 -.2752309 SPECIAL | .2559394 .0185461 13.80 0.000 .2195897 .292289 SPORTS-RELATED | .706214 .0157267 44.91 0.000 .6753903 .7370377 SYNDICATED | .7046757 .0118886 59.27 0.000 .6813744 .7279769 TALK SHOW | .5110064 .017391 29.38 0.000 .4769207 .5450921 TEAM VS. TEAM | .8893037 .0125232 71.01 0.000 .8647588 .9138487 TV MOVIE | .1076293 .0222987 4.83 0.000 .0639246 .151334 | _CONS | 421.175 6.256907 67.31 0.000 408.9116 433.4383
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 57
------
. PREDICT DOUBLE DVHAT (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. POISSON DVIEW LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2!="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -9.211E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -9.211E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -9.211E+09
POISSON REGRESSION NUMBER OF OBS = 2448113 WALD CHI2(125) = 309830.77 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -9.211E+09 PSEUDO R2 = 0.1684
------| ROBUST DVIEW | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | .1108724 .0002787 397.76 0.000 .1103261 .1114187 | QTR | 2 | -.0557542 .0062542 -8.91 0.000 -.0680122 -.0434962 3 | -.0830026 .0063479 -13.08 0.000 -.0954442 -.0705609 4 | -.1352376 .0066071 -20.47 0.000 -.1481872 -.122288 5 | -.1849192 .0065443 -28.26 0.000 -.1977458 -.1720927 6 | -.2585183 .0069233 -37.34 0.000 -.2720878 -.2449488 7 | -.2569606 .0068979 -37.25 0.000 -.2704801 -.243441 8 | -.2370597 .0072245 -32.81 0.000 -.2512195 -.2228999 9 | -.3123494 .0077318 -40.40 0.000 -.3275035 -.2971954 10 | -.3270403 .008345 -39.19 0.000 -.3433962 -.3106843 11 | -.2929912 .0082898 -35.34 0.000 -.309239 -.2767434 12 | -.2926984 .0086637 -33.78 0.000 -.309679 -.2757178 13 | -.3104712 .0080175 -38.72 0.000 -.3261853 -.2947571 14 | -.3273547 .0080302 -40.77 0.000 -.3430935 -.3116159 15 | -.3004884 .0081004 -37.10 0.000 -.3163648 -.2846119 16 | -.2627936 .0081511 -32.24 0.000 -.2787696 -.2468177 17 | -.1598676 .0076008 -21.03 0.000 -.174765 -.1449703 18 | -.1467069 .0077316 -18.97 0.000 -.1618606 -.1315532 19 | -.0889543 .007793 -11.41 0.000 -.1042284 -.0736803 20 | -.0521002 .007785 -6.69 0.000 -.0673584 -.0368419 21 | .004955 .0078146 0.63 0.526 -.0103612 .0202713 22 | .0759126 .0073277 10.36 0.000 .0615507 .0902746 23 | .0742076 .0073333 10.12 0.000 .0598345 .0885807 24 | .0592502 .0075313 7.87 0.000 .0444892 .0740113 25 | -.0024877 .0070689 -0.35 0.725 -.0163424 .0113671 26 | .0272806 .0068479 3.98 0.000 .013859 .0407022 27 | .04476 .0068548 6.53 0.000 .0313249 .0581952 28 | -.0396893 .006882 -5.77 0.000 -.0531778 -.0262007 29 | -.1146324 .0064219 -17.85 0.000 -.1272191 -.1020457 30 | -.0801929 .006402 -12.53 0.000 -.0927407 -.0676452 31 | -.0364478 .0064222 -5.68 0.000 -.0490352 -.0238605 32 | -.007913 .0061159 -1.29 0.196 -.0198999 .004074 33 | .0190506 .0059207 3.22 0.001 .0074461 .0306551 34 | -.0068313 .0060919 -1.12 0.262 -.0187712 .0051087 35 | -.0145825 .0061898 -2.36 0.018 -.0267142 -.0024508 36 | -.0636378 .006312 -10.08 0.000 -.0760091 -.0512666 37 | -.1343217 .0061223 -21.94 0.000 -.1463211 -.1223224 38 | -.1197928 .0061997 -19.32 0.000 -.1319439 -.1076416
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 58
39 | -.1351377 .0062528 -21.61 0.000 -.147393 -.1228825 40 | -.1641403 .0062672 -26.19 0.000 -.1764238 -.1518569 41 | -.0453558 .0062216 -7.29 0.000 -.0575499 -.0331617 42 | -.0538404 .0064523 -8.34 0.000 -.0664866 -.0411941 43 | -.0272391 .0062862 -4.33 0.000 -.0395598 -.0149184 44 | -.0068788 .0061682 -1.12 0.265 -.0189682 .0052106 45 | .0060883 .0060994 1.00 0.318 -.0058663 .018043 46 | -.0057191 .0060291 -0.95 0.343 -.0175359 .0060978 47 | -.0078831 .0060925 -1.29 0.196 -.0198242 .004058 48 | -.0024278 .0059671 -0.41 0.684 -.0141232 .0092676 49 | -.0538656 .0060834 -8.85 0.000 -.0657887 -.0419424 50 | -.0227296 .0060268 -3.77 0.000 -.0345419 -.0109172 51 | -.0869546 .0061946 -14.04 0.000 -.0990959 -.0748134 52 | .0068669 .006381 1.08 0.282 -.0056397 .0193734 53 | .0432963 .0062903 6.88 0.000 .0309675 .0556252 54 | .0754737 .0065708 11.49 0.000 .0625952 .0883523 55 | .0528093 .0065157 8.10 0.000 .0400388 .0655798 56 | -.0234634 .0065782 -3.57 0.000 -.0363564 -.0105704 57 | -.0515755 .0070848 -7.28 0.000 -.0654614 -.0376896 58 | -.110915 .0075784 -14.64 0.000 -.1257683 -.0960616 59 | -.1380699 .007666 -18.01 0.000 -.153095 -.1230449 60 | -.1497064 .007277 -20.57 0.000 -.1639691 -.1354437 61 | -.0724053 .0063635 -11.38 0.000 -.0848774 -.0599331 62 | -.0844791 .0067493 -12.52 0.000 -.0977075 -.0712506 63 | -.0716442 .0067634 -10.59 0.000 -.0849002 -.0583882 64 | -.0720902 .0068701 -10.49 0.000 -.0855552 -.0586251 65 | -.0092243 .0070036 -1.32 0.188 -.0229511 .0045024 66 | .0325023 .0070048 4.64 0.000 .0187731 .0462314 67 | .0171613 .0066798 2.57 0.010 .004069 .0302535 68 | .0440209 .0066822 6.59 0.000 .0309239 .0571178 69 | .0120135 .0072237 1.66 0.096 -.0021447 .0261717 70 | .0309875 .007529 4.12 0.000 .0162309 .0457441 71 | .0880049 .006972 12.62 0.000 .07434 .1016698 72 | .0561 .0069111 8.12 0.000 .0425545 .0696455 73 | .0274744 .007121 3.86 0.000 .0135176 .0414312 74 | .003765 .0070264 0.54 0.592 -.0100065 .0175366 75 | .0677941 .0063912 10.61 0.000 .0552676 .0803206 76 | .1058416 .006219 17.02 0.000 .0936527 .1180306 77 | .2258842 .0055452 40.73 0.000 .2150157 .2367527 78 | .2059783 .0055249 37.28 0.000 .1951497 .2168069 79 | .2050893 .0056566 36.26 0.000 .1940026 .2161761 80 | .2371266 .0055664 42.60 0.000 .2262168 .2480365 81 | .3566204 .0057513 62.01 0.000 .345348 .3678927 82 | .2997762 .0058597 51.16 0.000 .2882913 .3112611 83 | .3130112 .0059415 52.68 0.000 .3013661 .3246563 84 | .3419139 .0059181 57.77 0.000 .3303147 .3535131 85 | .4541891 .0058105 78.17 0.000 .4428008 .4655775 86 | .4021795 .0059317 67.80 0.000 .3905535 .4138055 87 | .38054 .0060604 62.79 0.000 .3686619 .3924181 88 | .3697972 .0059645 62.00 0.000 .358107 .3814873 89 | .3547585 .0057951 61.22 0.000 .3434004 .3661167 90 | .2549953 .0060625 42.06 0.000 .2431131 .2668776 91 | .2171269 .0060782 35.72 0.000 .205214 .2290399 92 | .1972348 .0061761 31.94 0.000 .1851298 .2093397 93 | .1596908 .0060823 26.26 0.000 .1477698 .1716119 94 | .159772 .0063937 24.99 0.000 .1472405 .1723034 95 | .0849943 .0062418 13.62 0.000 .0727606 .097228 96 | .0417409 .0062878 6.64 0.000 .029417 .0540648 | PROGRAM_TYPE | CHILDREN'S SHOW | .0504124 .0087439 5.77 0.000 .0332746 .0675501 CHILDREN'S SPECIAL | .0486478 .0146114 3.33 0.001 .02001 .0772856 DAYTIME SOAP | .3024115 .0056563 53.46 0.000 .2913254 .3134976
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 59
FINANCE | -.1915321 .0214842 -8.92 0.000 -.2336404 -.1494239 FIRST-RUN SYNDICATION | .2693877 .0077258 34.87 0.000 .2542455 .2845299 GAME SHOW | .1148389 .0055494 20.69 0.000 .1039624 .1257155 HEALTH | -.1523359 .0093502 -16.29 0.000 -.1706619 -.1340098 HOBBIES & CRAFTS | -.1401585 .0492398 -2.85 0.004 -.2366667 -.0436502 INSTRUCTIONAL | -.0194058 .0129366 -1.50 0.134 -.0447611 .0059496 MINI-SERIES | .1709869 .0138087 12.38 0.000 .1439224 .1980515 MOVIE | -.1489758 .006495 -22.94 0.000 -.1617058 -.1362458 MUSIC | .5933213 .0099761 59.47 0.000 .5737685 .6128742 MUSIC SPECIAL | .4599082 .0155056 29.66 0.000 .4295178 .4902986 NETWORK SERIES | .1609816 .0056874 28.30 0.000 .1498344 .1721288 NEWS | .0093279 .0054499 1.71 0.087 -.0013537 .0200095 OTHER | .1023661 .0059327 17.25 0.000 .0907381 .1139941 PELICULA | .4786933 .006848 69.90 0.000 .4652714 .4921151 PLAYOFF SPORTS | .8161996 .007472 109.23 0.000 .8015547 .8308445 PSEUDO-SPORTS | -.0823265 .0108631 -7.58 0.000 -.1036177 -.0610354 PUBLIC AFFAIRS | .0533527 .0101231 5.27 0.000 .0335117 .0731936 RELIGIOUS | .0431781 .0092083 4.69 0.000 .0251302 .0612261 SPECIAL | .1027114 .0080391 12.78 0.000 .0869551 .1184677 SPORTING EVENT | .7168695 .0072057 99.49 0.000 .7027467 .7309924 SPORTS ANTHOLOGY | .0110998 .0114865 0.97 0.334 -.0114134 .0336129 SPORTS-RELATED | .3552011 .007589 46.80 0.000 .3403268 .3700753 SYNDICATED | .1103421 .0053446 20.65 0.000 .0998668 .1208174 TALK SHOW | .1370703 .0052733 25.99 0.000 .1267349 .1474057 TEAM VS. TEAM | .6643985 .0059603 111.47 0.000 .6527166 .6760805 TV MOVIE | .0452845 .0127852 3.54 0.000 .0202259 .0703431 | _CONS | 7.944463 .007782 1020.88 0.000 7.929211 7.959716 ------
. PREDICT DVHAT2 (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. REPLACE DVHAT=DVHAT2 IF CALL2!="WGN" (2837648 REAL CHANGES MADE)
. REG TOTAL I.QTR I.PROGRAM_TYPE
SOURCE | SS DF MS NUMBER OF OBS = 2605275 ------+------F(124,2605150) = 2986.68 MODEL | 2.4082E+14 124 1.9421E+12 PROB > F = 0.0000 RESIDUAL | 1.6940E+152605150 650245144 R-SQUARED = 0.1245 ------+------ADJ R-SQUARED = 0.1244 TOTAL | 1.9348E+152605274 742648546 ROOT MSE = 25500
------TOTAL | COEF. STD. ERR. T P>|T| [95% CONF. INTERVAL] ------+------QTR | 2 | -2011.529 212.5585 -9.46 0.000 -2428.136 -1594.922 3 | -2412.492 195.8526 -12.32 0.000 -2796.357 -2028.628 4 | -3876.184 212.4753 -18.24 0.000 -4292.628 -3459.74 5 | -4781.306 198.1577 -24.13 0.000 -5169.688 -4392.923 6 | -5338.008 213.0133 -25.06 0.000 -5755.507 -4920.51 7 | -5554.316 195.2937 -28.44 0.000 -5937.084 -5171.547 8 | -6124.626 213.2609 -28.72 0.000 -6542.61 -5706.642 9 | -6631.419 194.8079 -34.04 0.000 -7013.235 -6249.602 10 | -7457.559 213.9567 -34.86 0.000 -7876.907 -7038.211 11 | -7401.285 202.4482 -36.56 0.000 -7798.077 -7004.494 12 | -8090.935 214.0579 -37.80 0.000 -8510.481 -7671.389
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 60
13 | -7805.506 200.3891 -38.95 0.000 -8198.261 -7412.75 14 | -8179.963 213.9575 -38.23 0.000 -8599.312 -7760.614 15 | -8196.349 208.8392 -39.25 0.000 -8605.667 -7787.032 16 | -8327.82 214.4329 -38.84 0.000 -8748.101 -7907.539 17 | -8825.367 211.7133 -41.69 0.000 -9240.317 -8410.416 18 | -8759.037 214.5341 -40.83 0.000 -9179.517 -8338.558 19 | -9262.327 214.2603 -43.23 0.000 -9682.27 -8842.384 20 | -8860.024 215.6046 -41.09 0.000 -9282.601 -8437.446 21 | -7848.737 216.1332 -36.31 0.000 -8272.35 -7425.123 22 | -7014.46 216.3372 -32.42 0.000 -7438.473 -6590.447 23 | -6335.845 216.483 -29.27 0.000 -6760.144 -5911.546 24 | -5176.66 216.5357 -23.91 0.000 -5601.062 -4752.257 25 | -3920.845 216.3645 -18.12 0.000 -4344.912 -3496.779 26 | -2724.332 216.3645 -12.59 0.000 -3148.399 -2300.265 27 | -1793.527 216.3914 -8.29 0.000 -2217.646 -1369.407 28 | -594.6204 216.3801 -2.75 0.006 -1018.718 -170.5231 29 | 2591.992 214.1212 12.11 0.000 2172.322 3011.662 30 | 2712.043 214.0967 12.67 0.000 2292.421 3131.665 31 | 2171.834 214.118 10.14 0.000 1752.17 2591.498 32 | 1936.772 214.1136 9.05 0.000 1517.117 2356.427 33 | 1669.956 213.9755 7.80 0.000 1250.571 2089.34 34 | 1255.026 213.9602 5.87 0.000 835.6716 1674.381 35 | 730.4967 213.9561 3.41 0.001 311.1503 1149.843 36 | 829.5295 213.947 3.88 0.000 410.2009 1248.858 37 | 1741.357 213.9428 8.14 0.000 1322.036 2160.677 38 | 861.1365 213.8323 4.03 0.000 442.0326 1280.24 39 | 348.2113 213.9314 1.63 0.104 -71.08671 767.5094 40 | 513.4869 213.9194 2.40 0.016 94.21233 932.7614 41 | 1914.171 214.0303 8.94 0.000 1494.679 2333.663 42 | 892.6127 214.0333 4.17 0.000 473.1149 1312.111 43 | 734.0799 214.1424 3.43 0.001 314.3684 1153.791 44 | 1077.691 214.1381 5.03 0.000 657.9876 1497.394 45 | 773.2554 214.1384 3.61 0.000 353.5516 1192.959 46 | 44.39519 214.0635 0.21 0.836 -375.1618 463.9522 47 | 98.37783 214.3444 0.46 0.646 -321.7296 518.4853 48 | 695.7173 214.3601 3.25 0.001 275.579 1115.856 49 | -1174.732 213.7874 -5.49 0.000 -1593.748 -755.716 50 | -1775.51 214.3144 -8.28 0.000 -2195.559 -1355.461 51 | -1852.994 214.8764 -8.62 0.000 -2274.144 -1431.844 52 | -2018.927 214.8635 -9.40 0.000 -2440.052 -1597.802 53 | -972.388 216.7722 -4.49 0.000 -1397.254 -547.5221 54 | -1565.741 216.7554 -7.22 0.000 -1990.574 -1140.908 55 | -1870.696 216.8326 -8.63 0.000 -2295.68 -1445.712 56 | -1708.718 216.8544 -7.88 0.000 -2133.745 -1283.691 57 | -1598.422 214.6471 -7.45 0.000 -2019.122 -1177.721 58 | -2145.985 214.5924 -10.00 0.000 -2566.579 -1725.392 59 | -2259.749 214.6341 -10.53 0.000 -2680.424 -1839.074 60 | -1538.598 214.695 -7.17 0.000 -1959.392 -1117.803 61 | 658.1252 213.6808 3.08 0.002 239.3184 1076.932 62 | -118.4646 213.7166 -0.55 0.579 -537.3416 300.4125 63 | -251.6697 213.7039 -1.18 0.239 -670.5218 167.1825 64 | 782.4847 213.6646 3.66 0.000 363.7095 1201.26 65 | 2261.51 213.2878 10.60 0.000 1843.473 2679.547 66 | 1749.759 213.3975 8.20 0.000 1331.508 2168.011 67 | 2221.563 213.2742 10.42 0.000 1803.553 2639.572 68 | 3599.238 213.3985 16.87 0.000 3180.984 4017.491 69 | 3680.846 214.3836 17.17 0.000 3260.662 4101.031 70 | 3305.464 214.4218 15.42 0.000 2885.205 3725.723 71 | 3351.931 214.5105 15.63 0.000 2931.498 3772.364 72 | 4149.127 214.5249 19.34 0.000 3728.666 4569.588 73 | 5544.224 214.8605 25.80 0.000 5123.105 5965.343 74 | 5266.692 215.0279 24.49 0.000 4845.245 5688.139 75 | 6165.624 214.2986 28.77 0.000 5745.606 6585.642
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 61
76 | 7312.639 214.1969 34.14 0.000 6892.82 7732.457 77 | 11972.74 214.313 55.87 0.000 11552.69 12392.78 78 | 10198.32 214.2636 47.60 0.000 9778.367 10618.27 79 | 10867.09 214.2078 50.73 0.000 10447.25 11286.93 80 | 13190.21 214.6614 61.45 0.000 12769.48 13610.94 81 | 18844.49 216.8975 86.88 0.000 18419.38 19269.6 82 | 15122.98 217.1053 69.66 0.000 14697.46 15548.49 83 | 15953.34 216.1294 73.81 0.000 15529.73 16376.95 84 | 16953.65 217.6373 77.90 0.000 16527.09 17380.21 85 | 21167.67 214.4978 98.68 0.000 20747.26 21588.07 86 | 16568.35 216.7719 76.43 0.000 16143.48 16993.21 87 | 16821.63 215.4556 78.07 0.000 16399.35 17243.92 88 | 16540.53 217.0008 76.22 0.000 16115.21 16965.84 89 | 19942.05 210.8891 94.56 0.000 19528.72 20355.39 90 | 12415.63 213.9767 58.02 0.000 11996.24 12835.01 91 | 10110.85 202.6904 49.88 0.000 9713.588 10508.12 92 | 9247.536 213.3975 43.33 0.000 8829.284 9665.787 93 | 7189.745 209.6839 34.29 0.000 6778.772 7600.718 94 | 4071.32 212.4663 19.16 0.000 3654.894 4487.747 95 | 3876.054 192.3608 20.15 0.000 3499.034 4253.075 96 | 1071.61 212.1024 5.05 0.000 655.8965 1487.323 | PROGRAM_TYPE | CHILDREN'S SHOW | 807.5847 252.1402 3.20 0.001 313.3987 1301.771 CHILDREN'S SPECIAL | 1690.201 604.5753 2.80 0.005 505.2543 2875.147 DAYTIME SOAP | 11120.75 183.6138 60.57 0.000 10760.87 11480.62 FINANCE | 5886.317 424.3085 13.87 0.000 5054.688 6717.947 FIRST-RUN SYNDICATION | 6637.903 358.6718 18.51 0.000 5934.918 7340.887 GAME SHOW | 8725.893 183.0563 47.67 0.000 8367.109 9084.677 HEALTH | 12301.64 376.5321 32.67 0.000 11563.65 13039.63 HOBBIES & CRAFTS | 3838.969 1891.867 2.03 0.042 130.9762 7546.962 INSTRUCTIONAL | 6850.805 362.7544 18.89 0.000 6139.819 7561.791 MINI-SERIES | 7642.043 1591.657 4.80 0.000 4522.45 10761.63 MOVIE | 3076.838 197.0893 15.61 0.000 2690.55 3463.126 MUSIC | 28192.6 336.3385 83.82 0.000 27533.39 28851.81 MUSIC SPECIAL | 11493.66 519.0893 22.14 0.000 10476.27 12511.06 NETWORK SERIES | 10151.87 178.6414 56.83 0.000 9801.739 10502 NEWS | 13166.28 163.7988 80.38 0.000 12845.24 13487.32 OTHER | 4239.884 166.8115 25.42 0.000 3912.94 4566.829 PELICULA | 9581.511 514.2225 18.63 0.000 8573.653 10589.37 PLAYOFF SPORTS | 44757.47 266.7187 167.81 0.000 44234.71 45280.23 PSEUDO-SPORTS | 33879.94 713.9028 47.46 0.000 32480.72 35279.17 PUBLIC AFFAIRS | 4527.655 316.5483 14.30 0.000 3907.232 5148.079 RELIGIOUS | 4741.096 233.269 20.32 0.000 4283.897 5198.295 SPECIAL | 9208.64 244.7138 37.63 0.000 8729.01 9688.271 SPORTING EVENT | 12758.78 185.1446 68.91 0.000 12395.91 13121.66 SPORTS ANTHOLOGY | 4135.254 447.6221 9.24 0.000 3257.931 5012.578 SPORTS-RELATED | 12346.86 214.0748 57.68 0.000 11927.28 12766.44 SYNDICATED | 7698.441 162.6537 47.33 0.000 7379.646 8017.237 TALK SHOW | 8119.179 160.0519 50.73 0.000 7805.483 8432.875 TEAM VS. TEAM | 34588 195.5323 176.89 0.000 34204.77 34971.24 TV MOVIE | 5152.335 494.0849 10.43 0.000 4183.946 6120.724 | _CONS | 2765.397 213.9011 12.93 0.000 2346.158 3184.636
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 62
2011:
. POISSON DISTANT LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2=="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -5.521E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -5.509E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -5.509E+09 ITERATION 3: LOG PSEUDOLIKELIHOOD = -5.509E+09 ITERATION 4: LOG PSEUDOLIKELIHOOD = -5.509E+09
POISSON REGRESSION NUMBER OF OBS = 525660 WALD CHI2(110) = 633519.41 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -5.509E+09 PSEUDO R2 = 0.5624
------| ROBUST DISTANT | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | -10.34143 .1466312 -70.53 0.000 -10.62882 -10.05403 | QTR | 2 | -.2237448 .0115773 -19.33 0.000 -.2464358 -.2010537 3 | -.4111671 .0128312 -32.04 0.000 -.4363158 -.3860183 4 | -.4732579 .0133197 -35.53 0.000 -.499364 -.4471518 5 | -.4975005 .0134809 -36.90 0.000 -.5239225 -.4710784 6 | -.6252281 .0136767 -45.71 0.000 -.6520339 -.5984222 7 | -.5716596 .0145333 -39.33 0.000 -.6001444 -.5431748 8 | -.6588249 .0146682 -44.92 0.000 -.6875741 -.6300757 9 | -.4718975 .0146635 -32.18 0.000 -.5006373 -.4431576 10 | -.5627859 .0157647 -35.70 0.000 -.5936842 -.5318876 11 | -.7496805 .0161906 -46.30 0.000 -.7814136 -.7179475 12 | -.8092849 .016907 -47.87 0.000 -.842422 -.7761477 13 | -.1225202 .0127097 -9.64 0.000 -.1474307 -.0976096 14 | -.1936869 .0131296 -14.75 0.000 -.2194205 -.1679534 15 | -.1930168 .0135197 -14.28 0.000 -.2195149 -.1665188 16 | -.2124731 .0135898 -15.63 0.000 -.2391086 -.1858377 17 | -.4880617 .0131801 -37.03 0.000 -.5138942 -.4622292 18 | -.5024133 .0128709 -39.03 0.000 -.5276398 -.4771868 19 | -.4897258 .013092 -37.41 0.000 -.5153857 -.4640659 20 | -.5627696 .0134831 -41.74 0.000 -.589196 -.5363431 21 | -.9204387 .014967 -61.50 0.000 -.9497736 -.8911039 22 | -1.076904 .0160248 -67.20 0.000 -1.108312 -1.045496 23 | -.6063189 .0139846 -43.36 0.000 -.6337282 -.5789097 24 | -.3885295 .0135875 -28.59 0.000 -.4151606 -.3618985 25 | .0498352 .0146033 3.41 0.001 .0212133 .0784571 26 | .110085 .0137657 8.00 0.000 .0831047 .1370653 27 | -.2953684 .0148841 -19.84 0.000 -.3245407 -.266196 28 | -.006377 .0148116 -0.43 0.667 -.0354072 .0226532 29 | .437128 .0142372 30.70 0.000 .4092236 .4650323 30 | .5038141 .0144365 34.90 0.000 .475519 .5321092 31 | .6043072 .0143925 41.99 0.000 .5760983 .632516 32 | .7195225 .0143499 50.14 0.000 .6913973 .7476478 33 | .8203565 .0094068 87.21 0.000 .8019196 .8387934 34 | .8973175 .0089631 100.11 0.000 .8797502 .9148848 35 | 1.038925 .0089203 116.47 0.000 1.021441 1.056408 36 | 1.143163 .0089022 128.41 0.000 1.125714 1.160611 37 | 1.116622 .0092625 120.55 0.000 1.098468 1.134777 38 | 1.112329 .0091078 122.13 0.000 1.094478 1.13018 39 | 1.178851 .0090526 130.22 0.000 1.161108 1.196594 40 | 1.255153 .0090194 139.16 0.000 1.237475 1.27283 41 | 1.249279 .0092045 135.73 0.000 1.231238 1.267319
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 63
42 | 1.168582 .0095376 122.52 0.000 1.149889 1.187276 43 | 1.198321 .0095507 125.47 0.000 1.179602 1.217041 44 | 1.239588 .0095037 130.43 0.000 1.220961 1.258215 45 | 1.203583 .0090592 132.86 0.000 1.185827 1.221338 46 | 1.164733 .0090785 128.30 0.000 1.14694 1.182527 47 | 1.197492 .0090551 132.25 0.000 1.179745 1.21524 48 | 1.191771 .0091289 130.55 0.000 1.173878 1.209663 49 | .8136104 .009307 87.42 0.000 .795369 .8318517 50 | .481856 .0103396 46.60 0.000 .4615907 .5021212 51 | .4545171 .0109332 41.57 0.000 .4330884 .4759459 52 | .4933756 .0108008 45.68 0.000 .4722063 .5145448 53 | .9434886 .0103036 91.57 0.000 .923294 .9636832 54 | .9572435 .0103021 92.92 0.000 .9370517 .9774352 55 | 1.032463 .0102303 100.92 0.000 1.012412 1.052514 56 | 1.084945 .0101327 107.07 0.000 1.065085 1.104805 57 | 1.046291 .0092453 113.17 0.000 1.028171 1.064412 58 | .9779887 .009175 106.59 0.000 .960006 .9959714 59 | 1.003249 .0092302 108.69 0.000 .9851577 1.02134 60 | 1.036459 .009293 111.53 0.000 1.018245 1.054673 61 | .891985 .0096446 92.49 0.000 .8730819 .9108881 62 | .6894401 .0103131 66.85 0.000 .6692268 .7096534 63 | .6616846 .0104308 63.44 0.000 .6412406 .6821286 64 | .6717509 .0108311 62.02 0.000 .6505223 .6929794 65 | .6702716 .0108118 61.99 0.000 .6490808 .6914625 66 | .5390994 .0112982 47.72 0.000 .5169553 .5612436 67 | .5442434 .0112594 48.34 0.000 .5221754 .5663115 68 | .5172308 .0110142 46.96 0.000 .4956434 .5388182 69 | .389639 .011269 34.58 0.000 .3675521 .4117259 70 | .238023 .0111809 21.29 0.000 .2161088 .2599373 71 | .2651894 .0110962 23.90 0.000 .2434413 .2869375 72 | .3216925 .0105046 30.62 0.000 .3011039 .3422811 73 | .7912123 .0095505 82.85 0.000 .7724937 .8099309 74 | .8676333 .0098744 87.87 0.000 .8482799 .8869867 75 | .9515628 .0099043 96.08 0.000 .9321509 .9709748 76 | .9175216 .0097447 94.16 0.000 .8984224 .9366208 77 | .75554 .0110156 68.59 0.000 .7339498 .7771302 78 | .6287137 .0117889 53.33 0.000 .6056079 .6518194 79 | .6695967 .011938 56.09 0.000 .6461986 .6929948 80 | .6427625 .0119755 53.67 0.000 .619291 .6662341 81 | .6304498 .0107356 58.73 0.000 .6094085 .6514912 82 | .6009962 .0114393 52.54 0.000 .5785756 .6234169 83 | .6536742 .0111985 58.37 0.000 .6317254 .6756229 84 | .6210493 .0113681 54.63 0.000 .5987682 .6433303 85 | .4544326 .0112679 40.33 0.000 .4323479 .4765174 86 | .2327317 .0128813 18.07 0.000 .2074847 .2579786 87 | .1060561 .0134506 7.88 0.000 .0796934 .1324188 88 | -.0172569 .0150234 -1.15 0.251 -.0467022 .0121885 89 | .0267794 .0133636 2.00 0.045 .0005872 .0529716 90 | -.0204795 .0131434 -1.56 0.119 -.0462401 .0052812 91 | .0614683 .0126042 4.88 0.000 .0367646 .0861721 92 | .0973612 .0125634 7.75 0.000 .0727373 .1219851 93 | .4552702 .0110491 41.20 0.000 .4336144 .476926 94 | .4692673 .0111474 42.10 0.000 .4474187 .4911158 95 | .4810014 .0108616 44.28 0.000 .4597131 .5022897 96 | .4172857 .0109936 37.96 0.000 .3957386 .4388328 | PROGRAM_TYPE | MUSIC | -1.370947 .0325617 -42.10 0.000 -1.434766 -1.307127 NETWORK SERIES | -.0340216 .1996588 -0.17 0.865 -.4253456 .3573024 NEWS | .370425 .0066039 56.09 0.000 .3574816 .3833684 OTHER | -.2767211 .0075163 -36.82 0.000 -.2914527 -.2619894 PSEUDO-SPORTS | .1915375 .0141151 13.57 0.000 .1638724 .2192026 PUBLIC AFFAIRS | -1.196966 .0314177 -38.10 0.000 -1.258544 -1.135389
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 64
RELIGIOUS | .1751958 .0100815 17.38 0.000 .1554364 .1949552 SPECIAL | -.187773 .015939 -11.78 0.000 -.2190129 -.1565331 SPORTING EVENT | .5342223 .0444443 12.02 0.000 .447113 .6213316 SPORTS-RELATED | .4735286 .0143885 32.91 0.000 .4453277 .5017295 SYNDICATED | .4466664 .0045444 98.29 0.000 .4377596 .4555732 TALK SHOW | .1005222 .0264892 3.79 0.000 .0486044 .15244 TEAM VS. TEAM | .747614 .006028 124.02 0.000 .7357993 .7594286 TV MOVIE | .387153 .0192357 20.13 0.000 .3494518 .4248542 | _CONS | 185.015 2.477521 74.68 0.000 180.1591 189.8708 ------
. PREDICT DOUBLE DISHAT (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. POISSON DISTANT LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2!="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -6.366E+10 ITERATION 1: LOG PSEUDOLIKELIHOOD = -6.302E+10 ITERATION 2: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 3: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 4: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 5: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 6: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 7: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 8: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 9: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 10: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 11: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 12: LOG PSEUDOLIKELIHOOD = -6.301E+10 ITERATION 13: LOG PSEUDOLIKELIHOOD = -6.301E+10
POISSON REGRESSION NUMBER OF OBS = 41388046 WALD CHI2(124) = 3887865.91 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -6.301E+10 PSEUDO R2 = 0.3800
------| ROBUST DISTANT | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | .8017788 .000449 1785.70 0.000 .8008987 .8026588 | QTR | 2 | -.1549771 .011223 -13.81 0.000 -.1769738 -.1329804 3 | -.1415527 .0111522 -12.69 0.000 -.1634106 -.1196947 4 | -.3691833 .011786 -31.32 0.000 -.3922836 -.3460831 5 | -.5105667 .0118646 -43.03 0.000 -.5338209 -.4873125 6 | -.6351423 .0120892 -52.54 0.000 -.6588367 -.6114479 7 | -.8133737 .0130113 -62.51 0.000 -.8388754 -.787872 8 | -.8783327 .013397 -65.56 0.000 -.9045904 -.8520751 9 | -.9408159 .0139083 -67.64 0.000 -.9680756 -.9135561 10 | -1.115185 .0147436 -75.64 0.000 -1.144082 -1.086288 11 | -.9908166 .014333 -69.13 0.000 -1.018909 -.9627245 12 | -.9708016 .0143566 -67.62 0.000 -.99894 -.9426632 13 | -.8979222 .0140942 -63.71 0.000 -.9255464 -.870298 14 | -.9218285 .0144473 -63.81 0.000 -.9501447 -.8935124 15 | -.9838483 .0146797 -67.02 0.000 -1.01262 -.9550767 16 | -1.04154 .0148072 -70.34 0.000 -1.070561 -1.012518 17 | -1.047713 .0153569 -68.22 0.000 -1.077811 -1.017614
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 65
18 | -1.072133 .0154566 -69.36 0.000 -1.102427 -1.041838 19 | -1.029917 .0148625 -69.30 0.000 -1.059047 -1.000787 20 | -1.113108 .015333 -72.60 0.000 -1.14316 -1.083056 21 | -1.016007 .0152933 -66.43 0.000 -1.045981 -.9860325 22 | -.9284019 .014998 -61.90 0.000 -.9577974 -.8990064 23 | -.7517333 .0141167 -53.25 0.000 -.7794016 -.724065 24 | -.6019811 .0133545 -45.08 0.000 -.6281554 -.5758068 25 | -.7040244 .0135077 -52.12 0.000 -.730499 -.6775499 26 | -.5597281 .0127281 -43.98 0.000 -.5846747 -.5347816 27 | -.3812496 .0123717 -30.82 0.000 -.4054977 -.3570014 28 | -.2084495 .0120067 -17.36 0.000 -.2319821 -.1849169 29 | -.234937 .0112148 -20.95 0.000 -.2569175 -.2129565 30 | -.1893444 .0111299 -17.01 0.000 -.2111587 -.1675301 31 | -.2693724 .0113607 -23.71 0.000 -.2916389 -.2471059 32 | -.2142798 .0113858 -18.82 0.000 -.2365956 -.191964 33 | -.1125003 .0110376 -10.19 0.000 -.1341335 -.0908671 34 | -.1107178 .0111392 -9.94 0.000 -.1325503 -.0888853 35 | -.1545931 .0112523 -13.74 0.000 -.1766471 -.132539 36 | -.1411001 .0111496 -12.66 0.000 -.162953 -.1192472 37 | -.2002329 .0113161 -17.69 0.000 -.222412 -.1780538 38 | -.2840767 .0118023 -24.07 0.000 -.3072088 -.2609445 39 | -.3682628 .0119541 -30.81 0.000 -.3916924 -.3448333 40 | -.3895538 .0126103 -30.89 0.000 -.4142695 -.3648381 41 | -.3435749 .0122037 -28.15 0.000 -.3674936 -.3196562 42 | -.421578 .0119308 -35.34 0.000 -.4449619 -.3981942 43 | -.4281214 .0120224 -35.61 0.000 -.4516849 -.4045579 44 | -.3910654 .0116657 -33.52 0.000 -.4139298 -.3682009 45 | -.2710859 .0110577 -24.52 0.000 -.2927586 -.2494131 46 | -.3372306 .0109987 -30.66 0.000 -.3587876 -.3156736 47 | -.3771513 .0107188 -35.19 0.000 -.3981597 -.3561428 48 | -.3201477 .010961 -29.21 0.000 -.3416309 -.2986646 49 | -.4140252 .0114096 -36.29 0.000 -.4363876 -.3916628 50 | -.3874862 .010884 -35.60 0.000 -.4088185 -.3661539 51 | -.5388974 .0107615 -50.08 0.000 -.5599896 -.5178051 52 | -.5022418 .0106781 -47.03 0.000 -.5231705 -.4813131 53 | -.5291864 .0105283 -50.26 0.000 -.5498215 -.5085513 54 | -.5611133 .0108084 -51.91 0.000 -.5822973 -.5399293 55 | -.6155697 .0109912 -56.01 0.000 -.6371121 -.5940274 56 | -.5889722 .0108599 -54.23 0.000 -.6102572 -.5676872 57 | -.5413687 .0113343 -47.76 0.000 -.5635836 -.5191538 58 | -.7071399 .0117863 -60.00 0.000 -.7302406 -.6840392 59 | -.7205599 .0118581 -60.77 0.000 -.7438013 -.6973185 60 | -.6827491 .0118012 -57.85 0.000 -.705879 -.6596191 61 | -.5345481 .0116555 -45.86 0.000 -.5573924 -.5117038 62 | -.6662682 .0122322 -54.47 0.000 -.690243 -.6422935 63 | -.6885068 .0117075 -58.81 0.000 -.7114531 -.6655605 64 | -.6373116 .0116911 -54.51 0.000 -.6602257 -.6143975 65 | -.448451 .0115664 -38.77 0.000 -.4711208 -.4257811 66 | -.4696026 .0115121 -40.79 0.000 -.4921659 -.4470392 67 | -.4087558 .0112972 -36.18 0.000 -.4308979 -.3866137 68 | -.3910513 .0114083 -34.28 0.000 -.4134111 -.3686915 69 | -.1993769 .0111409 -17.90 0.000 -.2212127 -.1775411 70 | -.2956101 .0117079 -25.25 0.000 -.3185572 -.2726631 71 | -.2582933 .0115378 -22.39 0.000 -.280907 -.2356797 72 | -.1808998 .0112136 -16.13 0.000 -.2028781 -.1589215 73 | -.0218607 .0108238 -2.02 0.043 -.0430749 -.0006465 74 | .0316932 .0105901 2.99 0.003 .010937 .0524494 75 | .3113543 .0099127 31.41 0.000 .2919257 .3307829 76 | .3592574 .0098736 36.39 0.000 .3399056 .3786093 77 | .4284779 .0096138 44.57 0.000 .4096352 .4473207 78 | .3893064 .0097722 39.84 0.000 .3701533 .4084596 79 | .4569396 .0097408 46.91 0.000 .437848 .4760313 80 | .6105785 .0095038 64.25 0.000 .5919514 .6292055
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 66
81 | .6951637 .0098256 70.75 0.000 .6759059 .7144216 82 | .5834352 .0100939 57.80 0.000 .5636514 .6032189 83 | .582527 .0101446 57.42 0.000 .5626439 .6024101 84 | .6157076 .0100853 61.05 0.000 .5959408 .6354744 85 | .82627 .0098998 83.46 0.000 .8068668 .8456732 86 | .6576287 .0101133 65.03 0.000 .6378071 .6774504 87 | .6639743 .0101499 65.42 0.000 .6440809 .6838677 88 | .6591519 .0101818 64.74 0.000 .6391959 .6791079 89 | .8836066 .0096979 91.11 0.000 .864599 .9026141 90 | .6879271 .0098061 70.15 0.000 .6687076 .7071466 91 | .6510809 .0098388 66.17 0.000 .6317972 .6703646 92 | .5857935 .009992 58.63 0.000 .5662095 .6053775 93 | .5080099 .0100044 50.78 0.000 .4884017 .5276181 94 | .3056642 .0102801 29.73 0.000 .2855156 .3258129 95 | .3212943 .01039 30.92 0.000 .3009302 .3416583 96 | .1247555 .0106746 11.69 0.000 .1038336 .1456774 | PROGRAM_TYPE | CHILDREN'S SHOW | .7397097 .0253915 29.13 0.000 .6899432 .7894762 CHILDREN'S SPECIAL | 1.619075 .032258 50.19 0.000 1.55585 1.682299 DAYTIME SOAP | 3.072705 .0160112 191.91 0.000 3.041324 3.104087 FINANCE | .9841277 .0395171 24.90 0.000 .9066756 1.06158 FIRST-RUN SYNDICATION | .2477732 .0666299 3.72 0.000 .117181 .3783654 GAME SHOW | 2.189768 .0159806 137.03 0.000 2.158447 2.22109 HEALTH | 1.813078 .0201207 90.11 0.000 1.773643 1.852514 HOBBIES & CRAFTS | .1076189 .1702114 0.63 0.527 -.2259893 .4412271 INSTRUCTIONAL | 1.408492 .0262254 53.71 0.000 1.357091 1.459893 MINI-SERIES | -20.31795 .0931756 -218.06 0.000 -20.50057 -20.13533 MOVIE | .9087426 .0169133 53.73 0.000 .8755932 .9418921 MUSIC | 3.000517 .0173649 172.79 0.000 2.966483 3.034552 MUSIC SPECIAL | 2.324578 .0236162 98.43 0.000 2.278291 2.370865 NETWORK SERIES | 1.98172 .0160091 123.79 0.000 1.950343 2.013097 NEWS | 1.611243 .0156062 103.24 0.000 1.580655 1.641831 OTHER | .3325712 .0168266 19.76 0.000 .2995918 .3655507 PLAYOFF SPORTS | 3.388887 .0169414 200.04 0.000 3.355682 3.422091 PSEUDO-SPORTS | 1.232258 .1303209 9.46 0.000 .9768342 1.487683 PUBLIC AFFAIRS | 1.645078 .0238955 68.84 0.000 1.598243 1.691912 RELIGIOUS | -.0691968 .0274695 -2.52 0.012 -.1230362 -.0153575 SPECIAL | 2.05373 .0176232 116.54 0.000 2.019189 2.088271 SPORTING EVENT | 2.611987 .0171175 152.59 0.000 2.578438 2.645537 SPORTS ANTHOLOGY | 1.322927 .0285164 46.39 0.000 1.267036 1.378818 SPORTS-RELATED | 2.206471 .0179462 122.95 0.000 2.171297 2.241645 SYNDICATED | 1.354676 .015686 86.36 0.000 1.323932 1.38542 TALK SHOW | 2.026747 .0155793 130.09 0.000 1.996212 2.057282 TEAM VS. TEAM | 3.111492 .0160908 193.37 0.000 3.079955 3.143029 TV MOVIE | 1.59042 .0304305 52.26 0.000 1.530777 1.650063 | _CONS | -4.130501 .0182075 -226.86 0.000 -4.166187 -4.094815 ------
. PREDICT DOUBLE DISHAT2 (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. REPLACE DISHAT=DISHAT2 IF CALL2!="WGN" (3136367 REAL CHANGES MADE)
. POISSON DVIEW LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2=="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -5.211E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -5.203E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -5.203E+09
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 67
ITERATION 3: LOG PSEUDOLIKELIHOOD = -5.203E+09
POISSON REGRESSION NUMBER OF OBS = 514260 WALD CHI2(110) = 634433.51 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -5.203E+09 PSEUDO R2 = 0.5559
------| ROBUST DVIEW | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | -10.42696 .1457136 -71.56 0.000 -10.71256 -10.14137 | QTR | 2 | -.2260427 .011501 -19.65 0.000 -.2485842 -.2035011 3 | -.3880871 .0125758 -30.86 0.000 -.4127351 -.363439 4 | -.4308301 .0129425 -33.29 0.000 -.4561969 -.4054634 5 | -.4572076 .0131196 -34.85 0.000 -.4829216 -.4314936 6 | -.566218 .0131723 -42.99 0.000 -.5920351 -.5404008 7 | -.4884023 .0138953 -35.15 0.000 -.5156365 -.461168 8 | -.5644577 .0139626 -40.43 0.000 -.5918238 -.5370916 9 | -.3596146 .0137906 -26.08 0.000 -.3866436 -.3325856 10 | -.4464478 .0149243 -29.91 0.000 -.4756989 -.4171967 11 | -.5966848 .0150949 -39.53 0.000 -.6262702 -.5670993 12 | -.6333995 .0156892 -40.37 0.000 -.6641499 -.6026492 13 | -.1277048 .0124847 -10.23 0.000 -.1521743 -.1032353 14 | -.1941031 .0128956 -15.05 0.000 -.219378 -.1688283 15 | -.1881841 .0132785 -14.17 0.000 -.2142095 -.1621586 16 | -.1950827 .0132377 -14.74 0.000 -.2210282 -.1691373 17 | -.4738177 .0130087 -36.42 0.000 -.4993144 -.4483211 18 | -.4790184 .0126106 -37.99 0.000 -.5037349 -.454302 19 | -.4484987 .0127034 -35.31 0.000 -.4733969 -.4236004 20 | -.5167388 .0130636 -39.56 0.000 -.5423429 -.4911346 21 | -.8189062 .0141941 -57.69 0.000 -.846726 -.7910863 22 | -.965995 .0152719 -63.25 0.000 -.9959273 -.9360627 23 | -.552257 .0135 -40.91 0.000 -.5787166 -.5257975 24 | -.3515077 .0131962 -26.64 0.000 -.3773717 -.3256437 25 | .0548896 .0140199 3.92 0.000 .0274111 .082368 26 | .1150934 .0132147 8.71 0.000 .089193 .1409938 27 | -.2255505 .0146316 -15.42 0.000 -.2542279 -.1968731 28 | .0683669 .0145155 4.71 0.000 .0399171 .0968168 29 | .5170133 .0140532 36.79 0.000 .4894696 .544557 30 | .5884148 .0142387 41.33 0.000 .5605075 .616322 31 | .6931242 .014085 49.21 0.000 .6655181 .7207304 32 | .7992731 .0140725 56.80 0.000 .7716915 .8268548 33 | .8093685 .0093667 86.41 0.000 .79101 .8277269 34 | .8897014 .0088992 99.97 0.000 .8722592 .9071436 35 | 1.038253 .0088164 117.76 0.000 1.020973 1.055533 36 | 1.142502 .0088004 129.82 0.000 1.125253 1.15975 37 | 1.10944 .0092066 120.50 0.000 1.091396 1.127485 38 | 1.105147 .0090501 122.11 0.000 1.087409 1.122885 39 | 1.172896 .0089908 130.45 0.000 1.155274 1.190517 40 | 1.249198 .0089571 139.46 0.000 1.231642 1.266753 41 | 1.242383 .0091482 135.81 0.000 1.224453 1.260313 42 | 1.161686 .0094829 122.50 0.000 1.1431 1.180272 43 | 1.191397 .0094966 125.46 0.000 1.172784 1.210009 44 | 1.232663 .0094493 130.45 0.000 1.214143 1.251183 45 | 1.196165 .0090043 132.84 0.000 1.178517 1.213813 46 | 1.157315 .0090233 128.26 0.000 1.13963 1.175001 47 | 1.190074 .0089997 132.23 0.000 1.172435 1.207714 48 | 1.184353 .009074 130.52 0.000 1.166568 1.202137 49 | .8061923 .0092504 87.15 0.000 .7880619 .8243228 50 | .4744379 .0102868 46.12 0.000 .4542761 .4945998
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 68
51 | .4471151 .0108813 41.09 0.000 .4257882 .468442 52 | .4859735 .0107481 45.21 0.000 .4649076 .5070394 53 | .9364472 .0102441 91.41 0.000 .9163692 .9565253 54 | .9520643 .0102275 93.09 0.000 .9320187 .97211 55 | 1.024984 .0101737 100.75 0.000 1.005044 1.044924 56 | 1.080457 .0100488 107.52 0.000 1.060761 1.100152 57 | 1.039143 .0091888 113.09 0.000 1.021134 1.057153 58 | .9708386 .0091174 106.48 0.000 .9529688 .9887084 59 | .9960977 .0091738 108.58 0.000 .9781174 1.014078 60 | 1.029308 .0092364 111.44 0.000 1.011205 1.047411 61 | .8849199 .0095898 92.28 0.000 .8661243 .9037154 62 | .6823749 .0102606 66.50 0.000 .6622646 .7024853 63 | .6546195 .0103788 63.07 0.000 .6342773 .6749616 64 | .6696723 .0107364 62.37 0.000 .6486293 .6907153 65 | .6659849 .0107376 62.02 0.000 .6449396 .6870303 66 | .5320486 .0112501 47.29 0.000 .5099989 .5540984 67 | .5396824 .0111924 48.22 0.000 .5177456 .5616192 68 | .5101816 .0109648 46.53 0.000 .488691 .5316721 69 | .3827853 .0112219 34.11 0.000 .3607908 .4047798 70 | .2327295 .0111117 20.94 0.000 .2109509 .2545081 71 | .2584342 .0110479 23.39 0.000 .2367806 .2800878 72 | .314939 .0104538 30.13 0.000 .2944499 .3354281 73 | .7844655 .0094951 82.62 0.000 .7658555 .8030755 74 | .8608867 .0098209 87.66 0.000 .841638 .8801353 75 | .9471685 .0098279 96.38 0.000 .9279062 .9664308 76 | .910454 .009691 93.95 0.000 .8914599 .9294481 77 | .7487837 .0109677 68.27 0.000 .7272874 .7702799 78 | .6219578 .0117444 52.96 0.000 .5989391 .6449765 79 | .6627859 .0118935 55.73 0.000 .6394751 .6860967 80 | .6359517 .0119312 53.30 0.000 .612567 .6593364 81 | .6235868 .0106865 58.35 0.000 .6026417 .6445319 82 | .6006584 .0113279 53.02 0.000 .5784561 .6228607 83 | .6468114 .0111518 58.00 0.000 .6249542 .6686686 84 | .6141865 .0113221 54.25 0.000 .5919955 .6363775 85 | .447624 .0112205 39.89 0.000 .4256322 .4696158 86 | .2259231 .0128402 17.59 0.000 .2007567 .2510894 87 | .0992971 .0134114 7.40 0.000 .0730112 .125583 88 | -.0137024 .0149262 -0.92 0.359 -.0429573 .0155525 89 | .0227702 .0132971 1.71 0.087 -.0032916 .048832 90 | -.0195487 .0130462 -1.50 0.134 -.0451187 .0060213 91 | .0568113 .0125287 4.53 0.000 .0322555 .0813672 92 | .0936066 .0124665 7.51 0.000 .0691728 .1180405 93 | .4501339 .0109905 40.96 0.000 .4285929 .4716749 94 | .4692506 .0110495 42.47 0.000 .447594 .4909073 95 | .4758942 .0108026 44.05 0.000 .4547214 .4970669 96 | .4172884 .0108956 38.30 0.000 .3959335 .4386433 | PROGRAM_TYPE | MUSIC | -1.061109 .0281967 -37.63 0.000 -1.116374 -1.005845 NETWORK SERIES | -.1000416 .2015156 -0.50 0.620 -.495005 .2949217 NEWS | .3620174 .0065607 55.18 0.000 .3491587 .3748762 OTHER | -.2492352 .0073328 -33.99 0.000 -.2636072 -.2348632 PSEUDO-SPORTS | .185811 .0140306 13.24 0.000 .1583114 .2133105 PUBLIC AFFAIRS | -.931265 .0268308 -34.71 0.000 -.9838524 -.8786775 RELIGIOUS | .1105434 .0099661 11.09 0.000 .0910103 .1300765 SPECIAL | -.181015 .0157459 -11.50 0.000 -.2118764 -.1501535 SPORTING EVENT | .5259749 .0444401 11.84 0.000 .438874 .6130758 SPORTS-RELATED | .4835679 .0138662 34.87 0.000 .4563907 .5107452 SYNDICATED | .4386656 .0045028 97.42 0.000 .4298403 .4474909 TALK SHOW | .0924987 .0264799 3.49 0.000 .0405992 .1443983 TEAM VS. TEAM | .7396477 .0059905 123.47 0.000 .7279065 .7513888 TV MOVIE | .3714451 .0191959 19.35 0.000 .3338219 .4090683 |
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 69
_CONS | 186.475 2.462016 75.74 0.000 181.6495 191.3004 ------
. PREDICT DOUBLE DVHAT (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. POISSON DVIEW LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2!="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -8.224E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -8.223E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -8.223E+09 ITERATION 3: LOG PSEUDOLIKELIHOOD = -8.223E+09
POISSON REGRESSION NUMBER OF OBS = 2187960 WALD CHI2(123) = 2027636.08 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -8.223E+09 PSEUDO R2 = 0.1456
------| ROBUST DVIEW | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | .1247979 .0003705 336.86 0.000 .1240717 .125524 | QTR | 2 | -.0215866 .0069596 -3.10 0.002 -.0352272 -.0079461 3 | -.0218102 .0068225 -3.20 0.001 -.0351822 -.0084383 4 | -.0898586 .0072016 -12.48 0.000 -.1039735 -.0757438 5 | -.1710044 .0068823 -24.85 0.000 -.1844935 -.1575154 6 | -.2036282 .0067152 -30.32 0.000 -.2167898 -.1904665 7 | -.2237473 .0071295 -31.38 0.000 -.2377209 -.2097738 8 | -.2129235 .0070975 -30.00 0.000 -.2268344 -.1990126 9 | -.2053642 .007322 -28.05 0.000 -.2197152 -.1910133 10 | -.233296 .0077499 -30.10 0.000 -.2484855 -.2181066 11 | -.1987451 .0076481 -25.99 0.000 -.2137351 -.1837551 12 | -.1589003 .0076796 -20.69 0.000 -.173952 -.1438485 13 | -.1372319 .0075141 -18.26 0.000 -.1519593 -.1225045 14 | -.0737958 .0075566 -9.77 0.000 -.0886064 -.0589851 15 | -.1253711 .0078827 -15.90 0.000 -.1408209 -.1099213 16 | -.1668386 .0081278 -20.53 0.000 -.1827687 -.1509084 17 | -.1231565 .008727 -14.11 0.000 -.140261 -.106052 18 | -.1257057 .0087207 -14.41 0.000 -.142798 -.1086133 19 | -.137424 .0082251 -16.71 0.000 -.1535448 -.1213032 20 | -.1650033 .0086627 -19.05 0.000 -.1819818 -.1480248 21 | -.183469 .0087853 -20.88 0.000 -.2006878 -.1662502 22 | -.1966733 .0089188 -22.05 0.000 -.2141539 -.1791927 23 | -.1599141 .0081126 -19.71 0.000 -.1758145 -.1440136 24 | -.1459229 .0076738 -19.02 0.000 -.1609634 -.1308824 25 | -.0981653 .0077176 -12.72 0.000 -.1132916 -.083039 26 | -.0591321 .0072815 -8.12 0.000 -.0734036 -.0448606 27 | .0105824 .0074185 1.43 0.154 -.0039576 .0251225 28 | -.0019648 .0074876 -0.26 0.793 -.0166403 .0127107 29 | -.0816272 .00691 -11.81 0.000 -.0951706 -.0680838 30 | -.0901145 .0069466 -12.97 0.000 -.1037296 -.0764993 31 | -.1347417 .0072516 -18.58 0.000 -.1489547 -.1205288 32 | -.1181303 .0073698 -16.03 0.000 -.1325748 -.1036858 33 | -.0607419 .0070761 -8.58 0.000 -.0746108 -.0468729 34 | -.0426402 .0071678 -5.95 0.000 -.0566889 -.0285915 35 | -.089783 .0073562 -12.21 0.000 -.1042009 -.0753651 36 | -.1475871 .0074042 -19.93 0.000 -.1620992 -.1330751
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 70
37 | -.1999414 .007399 -27.02 0.000 -.2144432 -.1854395 38 | -.189802 .0078996 -24.03 0.000 -.205285 -.174319 39 | -.195781 .0079081 -24.76 0.000 -.2112806 -.1802815 40 | -.2092678 .0088673 -23.60 0.000 -.2266474 -.1918881 41 | -.1299593 .0083959 -15.48 0.000 -.146415 -.1135037 42 | -.1202644 .0077138 -15.59 0.000 -.1353832 -.1051455 43 | -.1095777 .0077872 -14.07 0.000 -.1248404 -.0943151 44 | -.1047511 .0073899 -14.17 0.000 -.119235 -.0902672 45 | -.1683642 .0071139 -23.67 0.000 -.1823071 -.1544213 46 | -.2209705 .0069943 -31.59 0.000 -.2346791 -.2072619 47 | -.2092409 .006685 -31.30 0.000 -.2223434 -.1961385 48 | -.1831389 .00715 -25.61 0.000 -.1971526 -.1691251 49 | -.1365721 .0074713 -18.28 0.000 -.1512155 -.1219287 50 | -.0953158 .0065112 -14.64 0.000 -.1080775 -.082554 51 | -.1366039 .0067799 -20.15 0.000 -.1498922 -.1233156 52 | -.1341994 .0067463 -19.89 0.000 -.1474218 -.120977 53 | -.1920707 .0067947 -28.27 0.000 -.205388 -.1787535 54 | -.2062143 .0072243 -28.54 0.000 -.2203737 -.1920548 55 | -.221112 .0073199 -30.21 0.000 -.2354587 -.2067653 56 | -.2145479 .0071833 -29.87 0.000 -.2286269 -.2004689 57 | -.2165032 .0074436 -29.09 0.000 -.2310924 -.201914 58 | -.258946 .007706 -33.60 0.000 -.2740495 -.2438425 59 | -.2863092 .0079047 -36.22 0.000 -.3018021 -.2708163 60 | -.2913859 .0079281 -36.75 0.000 -.3069248 -.2758471 61 | -.3159329 .0078935 -40.02 0.000 -.3314039 -.3004619 62 | -.3908796 .0084546 -46.23 0.000 -.4074503 -.3743088 63 | -.3819799 .0077976 -48.99 0.000 -.397263 -.3666969 64 | -.3945939 .0079269 -49.78 0.000 -.4101304 -.3790575 65 | -.28265 .007531 -37.53 0.000 -.2974105 -.2678896 66 | -.2689535 .0073758 -36.46 0.000 -.2834098 -.2544973 67 | -.2555016 .0071667 -35.65 0.000 -.2695479 -.2414552 68 | -.2175151 .0072797 -29.88 0.000 -.231783 -.2032472 69 | -.171296 .0071483 -23.96 0.000 -.1853064 -.1572857 70 | -.1454933 .0076 -19.14 0.000 -.160389 -.1305975 71 | -.1332597 .0074466 -17.90 0.000 -.1478547 -.1186646 72 | -.1517026 .007252 -20.92 0.000 -.1659163 -.1374889 73 | -.1098953 .006981 -15.74 0.000 -.1235778 -.0962127 74 | -.1018535 .006757 -15.07 0.000 -.115097 -.08861 75 | -.0342616 .0063246 -5.42 0.000 -.0466575 -.0218657 76 | -.0539023 .0064597 -8.34 0.000 -.0665632 -.0412415 77 | -.0896361 .0063144 -14.20 0.000 -.1020121 -.0772602 78 | -.0699675 .0064838 -10.79 0.000 -.0826755 -.0572595 79 | -.0016887 .006497 -0.26 0.795 -.0144226 .0110451 80 | .0396102 .0062675 6.32 0.000 .0273261 .0518944 81 | .1602563 .0064635 24.79 0.000 .147588 .1729246 82 | .1518509 .0066852 22.71 0.000 .1387483 .1649536 83 | .1665965 .0067413 24.71 0.000 .1533839 .1798091 84 | .1669991 .0067412 24.77 0.000 .1537867 .1802115 85 | .2680958 .0065173 41.14 0.000 .255322 .2808695 86 | .2221166 .0066297 33.50 0.000 .2091227 .2351106 87 | .2414618 .0066585 36.26 0.000 .2284114 .2545121 88 | .2440394 .0066939 36.46 0.000 .2309196 .2571592 89 | .2476952 .0065046 38.08 0.000 .2349464 .2604441 90 | .1694627 .0064362 26.33 0.000 .156848 .1820774 91 | .1625296 .0064938 25.03 0.000 .149802 .1752572 92 | .150167 .0066427 22.61 0.000 .1371476 .1631864 93 | .1545333 .0065277 23.67 0.000 .1417393 .1673274 94 | .0874403 .0065964 13.26 0.000 .0745117 .1003689 95 | .1399955 .0067355 20.78 0.000 .1267942 .1531968 96 | .0512589 .00688 7.45 0.000 .0377744 .0647435 | PROGRAM_TYPE | CHILDREN'S SHOW | .3139465 .017707 17.73 0.000 .2792415 .3486515
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 71
CHILDREN'S SPECIAL | .088964 .0192678 4.62 0.000 .0511999 .1267282 DAYTIME SOAP | .1825842 .0081666 22.36 0.000 .1665781 .1985904 FINANCE | .0254758 .0158489 1.61 0.108 -.0055875 .0565391 FIRST-RUN SYNDICATION | -.1368764 .0335128 -4.08 0.000 -.2025602 -.0711925 GAME SHOW | .0710015 .0081196 8.74 0.000 .0550874 .0869156 HEALTH | -.4490001 .0131403 -34.17 0.000 -.4747546 -.4232456 HOBBIES & CRAFTS | -1.659664 .008889 -186.71 0.000 -1.677086 -1.642242 INSTRUCTIONAL | .0318355 .0125653 2.53 0.011 .0072079 .0564631 MOVIE | -.0974702 .0085897 -11.35 0.000 -.1143056 -.0806348 MUSIC | .7309865 .0100996 72.38 0.000 .7111917 .7507813 MUSIC SPECIAL | .1673104 .0154979 10.80 0.000 .1369351 .1976857 NETWORK SERIES | .1871668 .008169 22.91 0.000 .1711559 .2031777 NEWS | -.0081006 .0079729 -1.02 0.310 -.0237272 .007526 OTHER | .0160704 .0087776 1.83 0.067 -.0011334 .0332743 PLAYOFF SPORTS | .7671647 .0097446 78.73 0.000 .7480657 .7862638 PSEUDO-SPORTS | .36596 .0213106 17.17 0.000 .3241919 .4077281 PUBLIC AFFAIRS | .0811908 .0156422 5.19 0.000 .0505326 .1118489 RELIGIOUS | -.2660581 .0158764 -16.76 0.000 -.2971752 -.2349411 SPECIAL | .112091 .0095055 11.79 0.000 .0934606 .1307215 SPORTING EVENT | .6256295 .0094393 66.28 0.000 .6071288 .6441303 SPORTS ANTHOLOGY | .1748335 .0154555 11.31 0.000 .1445413 .2051258 SPORTS-RELATED | .3281794 .0101906 32.20 0.000 .3082062 .3481526 SYNDICATED | .0743872 .0078811 9.44 0.000 .0589406 .0898339 TALK SHOW | .1012922 .0078072 12.97 0.000 .0859904 .116594 TEAM VS. TEAM | .5567113 .0083073 67.01 0.000 .5404292 .5729934 TV MOVIE | -.1040049 .0184859 -5.63 0.000 -.1402366 -.0677732 | _CONS | 7.921144 .0104153 760.53 0.000 7.90073 7.941557 ------
. PREDICT DVHAT2 (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. REPLACE DVHAT=DVHAT2 IF CALL2!="WGN" (3136367 REAL CHANGES MADE)
. REG TOTAL I.QTR I.PROGRAM_TYPE
SOURCE | SS DF MS NUMBER OF OBS = 2877908 ------+------F(123,2877784) = 2590.33 MODEL | 1.9667E+14 123 1.5990E+12 PROB > F = 0.0000 RESIDUAL | 1.7764E+152877784 617281509 R-SQUARED = 0.0997 ------+------ADJ R-SQUARED = 0.0996 TOTAL | 1.9731E+152877907 685593943 ROOT MSE = 24845
------TOTAL | COEF. STD. ERR. T P>|T| [95% CONF. INTERVAL] ------+------QTR | 2 | -1233.802 199.9763 -6.17 0.000 -1625.749 -841.856 3 | -1465.133 183.937 -7.97 0.000 -1825.643 -1104.623 4 | -2981.659 199.7801 -14.92 0.000 -3373.221 -2590.097 5 | -3613.664 184.6591 -19.57 0.000 -3975.59 -3251.739 6 | -4135.546 200.2267 -20.65 0.000 -4527.983 -3743.109 7 | -4330.732 183.604 -23.59 0.000 -4690.589 -3970.874 8 | -4884.232 200.4077 -24.37 0.000 -5277.024 -4491.44 9 | -4713.926 182.8181 -25.78 0.000 -5072.243 -4355.609 10 | -5319.998 200.8816 -26.48 0.000 -5713.719 -4926.277 11 | -5561.255 189.2812 -29.38 0.000 -5932.24 -5190.271 12 | -6071.321 201.0402 -30.20 0.000 -6465.353 -5677.29
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 72
13 | -6075.575 189.5582 -32.05 0.000 -6447.102 -5704.047 14 | -6820.299 201.2626 -33.89 0.000 -7214.766 -6425.831 15 | -6878.845 198.0198 -34.74 0.000 -7266.957 -6490.733 16 | -6931.283 201.5526 -34.39 0.000 -7326.319 -6536.247 17 | -7411.434 198.8875 -37.26 0.000 -7801.246 -7021.621 18 | -7362.204 201.503 -36.54 0.000 -7757.143 -6967.265 19 | -7726.836 201.5924 -38.33 0.000 -8121.95 -7331.722 20 | -7264.598 202.7101 -35.84 0.000 -7661.903 -6867.294 21 | -6271.074 203.2038 -30.86 0.000 -6669.346 -5872.801 22 | -5490.605 203.3109 -27.01 0.000 -5889.087 -5092.122 23 | -4895.044 203.3732 -24.07 0.000 -5293.648 -4496.44 24 | -3685.17 203.3691 -18.12 0.000 -4083.766 -3286.573 25 | -2504.605 203.4096 -12.31 0.000 -2903.281 -2105.93 26 | -1408.892 203.4226 -6.93 0.000 -1807.594 -1010.191 27 | -860.6308 203.4804 -4.23 0.000 -1259.445 -461.8164 28 | 488.8094 203.4694 2.40 0.016 90.01659 887.6022 29 | 4700.621 201.3755 23.34 0.000 4305.932 5095.31 30 | 4982.379 201.3755 24.74 0.000 4587.69 5377.067 31 | 4645.18 201.3862 23.07 0.000 4250.47 5039.89 32 | 4482.006 201.3768 22.26 0.000 4087.315 4876.698 33 | 4587.793 201.3054 22.79 0.000 4193.242 4982.345 34 | 4366.059 201.3315 21.69 0.000 3971.456 4760.661 35 | 4037.186 201.2591 20.06 0.000 3642.725 4431.646 36 | 4103.667 201.246 20.39 0.000 3709.232 4498.102 37 | 4664.933 201.2458 23.18 0.000 4270.498 5059.367 38 | 3744.048 201.2231 18.61 0.000 3349.657 4138.438 39 | 3276.086 201.1951 16.28 0.000 2881.751 3670.421 40 | 3525.942 201.144 17.53 0.000 3131.706 3920.177 41 | 5123.284 201.2991 25.45 0.000 4728.745 5517.823 42 | 3855.662 201.3511 19.15 0.000 3461.021 4250.303 43 | 3728.55 201.2958 18.52 0.000 3334.018 4123.083 44 | 4127.605 201.3962 20.49 0.000 3732.876 4522.335 45 | 3326.242 201.2946 16.52 0.000 2931.712 3720.772 46 | 2401.257 201.2924 11.93 0.000 2006.731 2795.783 47 | 2347.553 201.2968 11.66 0.000 1953.019 2742.088 48 | 2897.857 201.2747 14.40 0.000 2503.366 3292.348 49 | 897.547 201.0207 4.46 0.000 503.5534 1291.541 50 | 64.38568 201.4998 0.32 0.749 -330.5469 459.3183 51 | 34.91497 202.0752 0.17 0.863 -361.1454 430.9753 52 | -246.5644 201.9261 -1.22 0.222 -642.3325 149.2037 53 | 1032.298 202.6699 5.09 0.000 635.0718 1429.524 54 | 277.3011 202.7539 1.37 0.171 -120.0893 674.6915 55 | 13.28707 202.8913 0.07 0.948 -384.3728 410.9469 56 | 99.97953 202.9486 0.49 0.622 -297.7926 497.7517 57 | 391.192 201.3944 1.94 0.052 -3.533916 785.918 58 | -271.4331 201.3992 -1.35 0.178 -666.1684 123.3021 59 | -302.3895 201.4376 -1.50 0.133 -697.2001 92.4211 60 | 373.1819 201.5964 1.85 0.064 -21.93992 768.3037 61 | 1980.162 200.6841 9.87 0.000 1586.829 2373.496 62 | 1106.365 200.8117 5.51 0.000 712.7809 1499.948 63 | 980.2805 200.816 4.88 0.000 586.6883 1373.873 64 | 1829.921 200.9735 9.11 0.000 1436.02 2223.822 65 | 2708.349 200.6356 13.50 0.000 2315.11 3101.588 66 | 2372.84 200.7391 11.82 0.000 1979.399 2766.282 67 | 2929.565 200.748 14.59 0.000 2536.106 3323.024 68 | 4170.864 200.8351 20.77 0.000 3777.235 4564.494 69 | 3831.386 201.8598 18.98 0.000 3435.748 4227.024 70 | 3898.265 202.0828 19.29 0.000 3502.189 4294.34 71 | 3497.477 202.111 17.30 0.000 3101.346 3893.607 72 | 4228.77 202.2318 20.91 0.000 3832.403 4625.137 73 | 6066.101 201.8505 30.05 0.000 5670.481 6461.72 74 | 6285.391 202.2386 31.08 0.000 5889.011 6681.772 75 | 7388.793 201.3145 36.70 0.000 6994.223 7783.362
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 73
76 | 8061.793 201.4234 40.02 0.000 7667.01 8456.576 77 | 11196.15 201.3689 55.60 0.000 10801.48 11590.83 78 | 9110.944 201.3574 45.25 0.000 8716.291 9505.598 79 | 9473.548 200.9163 47.15 0.000 9079.759 9867.337 80 | 11035.4 201.8156 54.68 0.000 10639.85 11430.96 81 | 16100.74 204.0031 78.92 0.000 15700.9 16500.58 82 | 12995.4 203.9585 63.72 0.000 12595.65 13395.15 83 | 13512.17 203.0683 66.54 0.000 13114.17 13910.18 84 | 13765.91 204.8169 67.21 0.000 13364.47 14167.34 85 | 18177.32 202.4945 89.77 0.000 17780.43 18574.2 86 | 13713.67 204.0602 67.20 0.000 13313.72 14113.62 87 | 14145.43 203.1457 69.63 0.000 13747.27 14543.58 88 | 13903.76 204.6696 67.93 0.000 13502.62 14304.91 89 | 17902.26 199.8666 89.57 0.000 17510.53 18294 90 | 11800.35 201.276 58.63 0.000 11405.85 12194.84 91 | 9088.92 189.7834 47.89 0.000 8716.951 9460.888 92 | 8866.933 200.6891 44.18 0.000 8473.59 9260.277 93 | 6624.938 197.3999 33.56 0.000 6238.042 7011.835 94 | 3921.649 199.7488 19.63 0.000 3530.149 4313.15 95 | 4036.038 181.6863 22.21 0.000 3679.939 4392.137 96 | 1641.343 199.5103 8.23 0.000 1250.309 2032.376 | PROGRAM_TYPE | CHILDREN'S SHOW | 391.7525 227.8101 1.72 0.085 -54.74732 838.2524 CHILDREN'S SPECIAL | 4339.765 609.9354 7.12 0.000 3144.313 5535.217 DAYTIME SOAP | 12302.9 173.5326 70.90 0.000 11962.78 12643.02 FINANCE | 5913.556 344.6214 17.16 0.000 5238.11 6589.002 FIRST-RUN SYNDICATION | 4206.22 730.3164 5.76 0.000 2774.826 5637.615 GAME SHOW | 9947.746 169.8153 58.58 0.000 9614.914 10280.58 HEALTH | 9687.027 337.3813 28.71 0.000 9025.772 10348.28 HOBBIES & CRAFTS | 5323.328 3589.357 1.48 0.138 -1711.685 12358.34 INSTRUCTIONAL | 7997.567 322.4963 24.80 0.000 7365.486 8629.648 MINI-SERIES | 393.364 8785.988 0.04 0.964 -16826.86 17613.59 MOVIE | 4341.197 186.376 23.29 0.000 3975.906 4706.487 MUSIC | 28136.44 274.867 102.36 0.000 27597.71 28675.17 MUSIC SPECIAL | 15095.7 479.1435 31.51 0.000 14156.6 16034.81 NETWORK SERIES | 10834.2 166.0662 65.24 0.000 10508.72 11159.69 NEWS | 14761.46 153.5787 96.12 0.000 14460.45 15062.47 OTHER | 5011.156 157.3413 31.85 0.000 4702.773 5319.54 PLAYOFF SPORTS | 33270.82 235.6397 141.19 0.000 32808.97 33732.67 PSEUDO-SPORTS | 9070.077 1223.328 7.41 0.000 6672.398 11467.76 PUBLIC AFFAIRS | 8888.009 277.8837 31.98 0.000 8343.366 9432.651 RELIGIOUS | 5698.703 214.4507 26.57 0.000 5278.387 6119.019 SPECIAL | 12243.96 237.4303 51.57 0.000 11778.61 12709.32 SPORTING EVENT | 7794.018 177.0969 44.01 0.000 7446.915 8141.122 SPORTS ANTHOLOGY | 3766.231 343.9122 10.95 0.000 3092.175 4440.287 SPORTS-RELATED | 12971.48 204.2637 63.50 0.000 12571.13 13371.83 SYNDICATED | 8893.532 152.6181 58.27 0.000 8594.406 9192.658 TALK SHOW | 8291.583 150.0093 55.27 0.000 7997.57 8585.596 TEAM VS. TEAM | 31534.14 185.0622 170.40 0.000 31171.42 31896.85 TV MOVIE | 5747.459 566.1434 10.15 0.000 4637.837 6857.08 | _CONS | -207.4533 202.4323 -1.02 0.305 -604.2135 189.3069
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 74
2012: . POISSON DISTANT LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2=="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -5.393E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -5.369E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -5.369E+09 ITERATION 3: LOG PSEUDOLIKELIHOOD = -5.369E+09 ITERATION 4: LOG PSEUDOLIKELIHOOD = -5.369E+09
POISSON REGRESSION NUMBER OF OBS = 526920 WALD CHI2(109) = 775181.92 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -5.369E+09 PSEUDO R2 = 0.6172
------| ROBUST DISTANT | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | -.4906519 .0391296 -12.54 0.000 -.5673445 -.4139594 | QTR | 2 | -.1067903 .0132277 -8.07 0.000 -.1327162 -.0808644 3 | -.2357171 .014067 -16.76 0.000 -.263288 -.2081463 4 | -.2766162 .0146025 -18.94 0.000 -.3052365 -.2479959 5 | -.2267619 .0141651 -16.01 0.000 -.254525 -.1989988 6 | -.321438 .0142487 -22.56 0.000 -.3493649 -.2935111 7 | -.3086555 .0142148 -21.71 0.000 -.3365159 -.280795 8 | -.2957701 .0139249 -21.24 0.000 -.3230624 -.2684778 9 | -.1044102 .0133253 -7.84 0.000 -.1305273 -.0782931 10 | -.0837894 .0132801 -6.31 0.000 -.1098179 -.0577609 11 | -.0477496 .0132565 -3.60 0.000 -.0737319 -.0217673 12 | -.0534219 .013357 -4.00 0.000 -.0796012 -.0272426 13 | -.0530219 .0137533 -3.86 0.000 -.0799779 -.0260658 14 | -.1167485 .0143003 -8.16 0.000 -.1447765 -.0887205 15 | -.0218729 .0138125 -1.58 0.113 -.0489448 .0051991 16 | -.1463164 .0140235 -10.43 0.000 -.1738019 -.1188309 17 | -.3218193 .015385 -20.92 0.000 -.3519734 -.2916652 18 | -.5368787 .0162705 -33.00 0.000 -.5687683 -.5049892 19 | -.5595046 .0167749 -33.35 0.000 -.5923827 -.5266264 20 | -.6269596 .0174363 -35.96 0.000 -.6611341 -.592785 21 | -.8338279 .0182234 -45.76 0.000 -.869545 -.7981107 22 | -.7654795 .0166169 -46.07 0.000 -.7980482 -.7329109 23 | -.3678727 .015169 -24.25 0.000 -.3976034 -.338142 24 | -.3037796 .0155481 -19.54 0.000 -.3342534 -.2733059 25 | -.6368557 .0172032 -37.02 0.000 -.6705734 -.603138 26 | -.7896775 .0193861 -40.73 0.000 -.8276735 -.7516815 27 | -.5966495 .0203789 -29.28 0.000 -.6365913 -.5567076 28 | -.6027881 .02115 -28.50 0.000 -.6442414 -.5613348 29 | -.1336864 .020773 -6.44 0.000 -.1744006 -.0929721 30 | -.0643452 .0215804 -2.98 0.003 -.106642 -.0220485 31 | -.1632813 .0212044 -7.70 0.000 -.2048411 -.1217214 32 | .228361 .0175672 13.00 0.000 .1939299 .2627921 33 | 1.226581 .0116513 105.27 0.000 1.203745 1.249417 34 | 1.334423 .0114683 116.36 0.000 1.311946 1.3569 35 | 1.441008 .0112622 127.95 0.000 1.418934 1.463081 36 | 1.529815 .0111574 137.11 0.000 1.507947 1.551683 37 | 1.523278 .0106268 143.34 0.000 1.50245 1.544106 38 | 1.493752 .0107666 138.74 0.000 1.47265 1.514854 39 | 1.552438 .0107601 144.28 0.000 1.531349 1.573527 40 | 1.619248 .0108301 149.51 0.000 1.598022 1.640475 41 | 1.671165 .0107688 155.19 0.000 1.650059 1.692272 42 | 1.54757 .0110444 140.12 0.000 1.525923 1.569216
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 75
43 | 1.571931 .0109018 144.19 0.000 1.550564 1.593298 44 | 1.59372 .0108766 146.53 0.000 1.572402 1.615038 45 | 1.601224 .0105005 152.49 0.000 1.580644 1.621805 46 | 1.552409 .0106472 145.80 0.000 1.531541 1.573277 47 | 1.586236 .0107131 148.07 0.000 1.565239 1.607233 48 | 1.576082 .0105812 148.95 0.000 1.555343 1.59682 49 | 1.238121 .0106394 116.37 0.000 1.217268 1.258974 50 | .9409665 .0118072 79.69 0.000 .9178248 .9641081 51 | .9152632 .0119361 76.68 0.000 .8918689 .9386574 52 | .9560024 .0117939 81.06 0.000 .9328868 .9791179 53 | 1.255648 .0117215 107.12 0.000 1.232674 1.278622 54 | 1.2906 .0116256 111.01 0.000 1.267814 1.313385 55 | 1.366906 .0114324 119.56 0.000 1.344499 1.389313 56 | 1.403786 .0113636 123.53 0.000 1.381514 1.426058 57 | 1.426136 .0104593 136.35 0.000 1.405636 1.446636 58 | 1.379762 .0105944 130.24 0.000 1.358998 1.400527 59 | 1.431392 .0106159 134.83 0.000 1.410585 1.452198 60 | 1.467571 .0105957 138.51 0.000 1.446803 1.488338 61 | 1.464104 .0107787 135.83 0.000 1.442978 1.48523 62 | 1.385862 .011212 123.60 0.000 1.363887 1.407837 63 | 1.422298 .0111984 127.01 0.000 1.40035 1.444247 64 | 1.407731 .0112631 124.99 0.000 1.385656 1.429806 65 | 1.183589 .0114759 103.14 0.000 1.161097 1.206082 66 | .9168137 .0122432 74.88 0.000 .8928174 .94081 67 | .8819011 .012531 70.38 0.000 .8573408 .9064615 68 | .8815602 .012669 69.58 0.000 .8567294 .9063909 69 | .8533684 .0127252 67.06 0.000 .8284274 .8783094 70 | .6111962 .0136389 44.81 0.000 .5844644 .637928 71 | .5318062 .013649 38.96 0.000 .5050546 .5585577 72 | .505465 .0141007 35.85 0.000 .4778282 .5331018 73 | 1.004426 .0115266 87.14 0.000 .9818348 1.027018 74 | 1.123177 .0113712 98.77 0.000 1.10089 1.145464 75 | 1.185415 .0113581 104.37 0.000 1.163154 1.207677 76 | 1.219792 .0111515 109.38 0.000 1.197935 1.241648 77 | 1.119556 .0118893 94.17 0.000 1.096254 1.142859 78 | 1.046422 .0126585 82.67 0.000 1.021612 1.071232 79 | 1.042315 .012796 81.46 0.000 1.017235 1.067395 80 | 1.093178 .012716 85.97 0.000 1.068255 1.118101 81 | 1.116718 .0129398 86.30 0.000 1.091357 1.14208 82 | 1.081414 .0130019 83.17 0.000 1.05593 1.106897 83 | 1.094171 .0129555 84.46 0.000 1.068779 1.119564 84 | 1.098629 .0125432 87.59 0.000 1.074044 1.123213 85 | 1.025694 .0116837 87.79 0.000 1.002794 1.048593 86 | .8579154 .0119488 71.80 0.000 .8344962 .8813347 87 | .6675407 .0122051 54.69 0.000 .6436191 .6914623 88 | .4273272 .0130482 32.75 0.000 .4017533 .4529011 89 | .5627392 .0133865 42.04 0.000 .5365021 .5889764 90 | .4275362 .0145111 29.46 0.000 .399095 .4559773 91 | .4993203 .0150624 33.15 0.000 .4697985 .5288421 92 | .4280668 .0148898 28.75 0.000 .3988834 .4572503 93 | .248829 .0132295 18.81 0.000 .2228996 .2747584 94 | .1538002 .01317 11.68 0.000 .1279874 .179613 95 | .140341 .0132358 10.60 0.000 .1143994 .1662826 96 | .082164 .0136005 6.04 0.000 .0555075 .1088206 | PROGRAM_TYPE | MOVIE | 2.072897 .0771273 26.88 0.000 1.92173 2.224063 MUSIC | 2.112226 .0823343 25.65 0.000 1.950854 2.273599 NEWS | 2.385201 .0772201 30.89 0.000 2.233852 2.536549 OTHER | 2.286408 .0771503 29.64 0.000 2.135197 2.43762 PUBLIC AFFAIRS | 1.793502 .08269 21.69 0.000 1.631432 1.955571 RELIGIOUS | 1.761983 .0777402 22.67 0.000 1.609615 1.914351 SPECIAL | 1.779417 .0778549 22.86 0.000 1.626824 1.93201
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 76
SPORTING EVENT | 2.038143 .0850302 23.97 0.000 1.871487 2.204799 SPORTS-RELATED | 2.184791 .0779042 28.04 0.000 2.032101 2.33748 SYNDICATED | 2.431012 .0770355 31.56 0.000 2.280026 2.581999 TALK SHOW | 1.910425 .0795996 24.00 0.000 1.754412 2.066437 TEAM VS. TEAM | 2.501179 .0771266 32.43 0.000 2.350013 2.652344 TV MOVIE | 1.885564 .0824227 22.88 0.000 1.724019 2.04711 | _CONS | 16.21078 .6632542 24.44 0.000 14.91083 17.51073 ------
. PREDICT DOUBLE DISHAT (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. POISSON DISTANT LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2!="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -6.307E+10 ITERATION 1: LOG PSEUDOLIKELIHOOD = -6.258E+10 ITERATION 2: LOG PSEUDOLIKELIHOOD = -6.257E+10 ITERATION 3: LOG PSEUDOLIKELIHOOD = -6.257E+10 ITERATION 4: LOG PSEUDOLIKELIHOOD = -6.257E+10
POISSON REGRESSION NUMBER OF OBS = 40836844 WALD CHI2(122) = 3831203.47 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -6.257E+10 PSEUDO R2 = 0.3992
------| ROBUST DISTANT | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | .9095145 .0005043 1803.55 0.000 .9085261 .9105029 | QTR | 2 | -.1232297 .0121186 -10.17 0.000 -.1469817 -.0994778 3 | -.0434989 .0115298 -3.77 0.000 -.0660969 -.020901 4 | -.2783658 .0119978 -23.20 0.000 -.301881 -.2548506 5 | -.4490868 .0124762 -36.00 0.000 -.4735398 -.4246338 6 | -.5779348 .0129556 -44.61 0.000 -.6033273 -.5525423 7 | -.5448877 .0131243 -41.52 0.000 -.5706108 -.5191646 8 | -.5971001 .013669 -43.68 0.000 -.6238908 -.5703094 9 | -.1642713 .0126796 -12.96 0.000 -.1891228 -.1394198 10 | -.2466465 .0131665 -18.73 0.000 -.2724524 -.2208407 11 | .0617468 .0124539 4.96 0.000 .0373376 .0861559 12 | .1360759 .0126427 10.76 0.000 .1112967 .1608552 13 | .1783943 .0130909 13.63 0.000 .1527366 .2040521 14 | .1523399 .0133939 11.37 0.000 .1260884 .1785914 15 | .0179418 .0136156 1.32 0.188 -.0087443 .044628 16 | -.0481199 .0138448 -3.48 0.001 -.0752552 -.0209847 17 | -.1764462 .0142408 -12.39 0.000 -.2043576 -.1485348 18 | -.2736627 .0144958 -18.88 0.000 -.3020739 -.2452515 19 | -.2619406 .014161 -18.50 0.000 -.2896957 -.2341855 20 | -.3694437 .0140025 -26.38 0.000 -.3968882 -.3419993 21 | -.2141078 .0134652 -15.90 0.000 -.240499 -.1877165 22 | -.4996525 .0140023 -35.68 0.000 -.5270966 -.4722085 23 | -.5685853 .0142292 -39.96 0.000 -.5964741 -.5406965 24 | -.476091 .0139191 -34.20 0.000 -.5033719 -.4488101 25 | -.2715586 .0130453 -20.82 0.000 -.297127 -.2459902 26 | -.0998337 .0124877 -7.99 0.000 -.1243091 -.0753582 27 | -.0302023 .0122359 -2.47 0.014 -.0541842 -.0062205 28 | .0869058 .0120643 7.20 0.000 .0632601 .1105514 29 | .3270478 .0107697 30.37 0.000 .3059396 .348156 30 | .3115955 .010752 28.98 0.000 .2905221 .332669
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 77
31 | .2053949 .0109261 18.80 0.000 .1839802 .2268097 32 | .2515761 .0108581 23.17 0.000 .2302947 .2728575 33 | .1867756 .0106706 17.50 0.000 .1658615 .2076897 34 | .2195543 .0107011 20.52 0.000 .1985806 .2405279 35 | .202677 .0108506 18.68 0.000 .1814103 .2239438 36 | .2403984 .010641 22.59 0.000 .2195425 .2612544 37 | .2100978 .0107333 19.57 0.000 .1890609 .2311348 38 | .1176829 .0109267 10.77 0.000 .0962669 .1390989 39 | .1300183 .0109625 11.86 0.000 .1085321 .1515045 40 | .1448302 .0108816 13.31 0.000 .1235027 .1661576 41 | .1902954 .0106517 17.87 0.000 .1694185 .2111723 42 | .0537779 .0109039 4.93 0.000 .0324067 .0751492 43 | .0248526 .0110774 2.24 0.025 .0031413 .0465639 44 | .0477244 .011161 4.28 0.000 .0258493 .0695995 45 | .1142182 .0109281 10.45 0.000 .0927996 .1356368 46 | .0205784 .0111572 1.84 0.065 -.0012893 .0424461 47 | .0513679 .010816 4.75 0.000 .0301689 .0725669 48 | .0456157 .0108325 4.21 0.000 .0243844 .0668469 49 | -.0794547 .011155 -7.12 0.000 -.1013181 -.0575913 50 | -.1588673 .0113038 -14.05 0.000 -.1810224 -.1367123 51 | -.2660632 .0114808 -23.17 0.000 -.2885652 -.2435612 52 | -.271349 .0114247 -23.75 0.000 -.2937411 -.248957 53 | -.2343849 .0113065 -20.73 0.000 -.2565453 -.2122245 54 | -.3276012 .0122001 -26.85 0.000 -.351513 -.3036895 55 | -.3711011 .012905 -28.76 0.000 -.3963944 -.3458078 56 | -.3578462 .011723 -30.53 0.000 -.3808229 -.3348696 57 | -.2066208 .0115257 -17.93 0.000 -.2292107 -.1840309 58 | -.3329043 .0119734 -27.80 0.000 -.3563718 -.3094369 59 | -.3268649 .0120876 -27.04 0.000 -.3505561 -.3031736 60 | -.2779431 .0117949 -23.56 0.000 -.3010606 -.2548257 61 | -.1700763 .011342 -15.00 0.000 -.1923063 -.1478463 62 | -.2320378 .0116994 -19.83 0.000 -.2549682 -.2091073 63 | -.2257133 .0116405 -19.39 0.000 -.2485284 -.2028983 64 | -.1450728 .01137 -12.76 0.000 -.1673577 -.122788 65 | .0693559 .0111209 6.24 0.000 .0475594 .0911525 66 | .0182313 .0113323 1.61 0.108 -.0039795 .0404421 67 | .0807923 .0113115 7.14 0.000 .0586222 .1029624 68 | .1014997 .0113354 8.95 0.000 .0792827 .1237167 69 | .2442319 .0113718 21.48 0.000 .2219435 .2665202 70 | .1514837 .0121236 12.49 0.000 .1277219 .1752455 71 | .2085393 .0112643 18.51 0.000 .1864617 .2306169 72 | .2707248 .0114091 23.73 0.000 .2483633 .2930863 73 | .4501336 .0108917 41.33 0.000 .4287863 .4714809 74 | .489799 .0107667 45.49 0.000 .4686966 .5109014 75 | .7552115 .0100288 75.30 0.000 .7355554 .7748677 76 | .8159177 .009975 81.80 0.000 .796367 .8354684 77 | .8563251 .00968 88.46 0.000 .8373527 .8752975 78 | .7815015 .0098799 79.10 0.000 .7621373 .8008658 79 | .8722098 .0098288 88.74 0.000 .8529456 .8914739 80 | .999069 .009742 102.55 0.000 .979975 1.018163 81 | 1.098948 .0099847 110.06 0.000 1.079378 1.118517 82 | .9875425 .0103074 95.81 0.000 .9673403 1.007745 83 | .9851045 .0102331 96.27 0.000 .965048 1.005161 84 | 1.040454 .0101779 102.23 0.000 1.020505 1.060402 85 | 1.186422 .0100561 117.98 0.000 1.166713 1.206132 86 | 1.020441 .0103341 98.74 0.000 1.000186 1.040695 87 | .9991086 .0103035 96.97 0.000 .9789142 1.019303 88 | 1.010965 .0102611 98.52 0.000 .9908539 1.031077 89 | 1.21099 .0100868 120.06 0.000 1.19122 1.23076 90 | .9732625 .0103622 93.92 0.000 .9529529 .9935721 91 | .9156777 .0103324 88.62 0.000 .8954265 .9359289 92 | .8470443 .0104545 81.02 0.000 .826554 .8675347 93 | .6192952 .0105972 58.44 0.000 .5985252 .6400653
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 78
94 | .2491116 .0116499 21.38 0.000 .2262782 .271945 95 | .2834016 .0113175 25.04 0.000 .2612198 .3055834 96 | .0617937 .0116045 5.32 0.000 .0390493 .0845381 | PROGRAM_TYPE | CHILDREN'S SHOW | .554214 .0214952 25.78 0.000 .5120841 .5963438 CHILDREN'S SPECIAL | 1.416823 .0313444 45.20 0.000 1.35539 1.478257 DAYTIME SOAP | 2.662019 .0176462 150.86 0.000 2.627433 2.696605 FINANCE | .9498977 .0244864 38.79 0.000 .9019051 .9978902 GAME SHOW | 1.958537 .0174318 112.35 0.000 1.924371 1.992702 HEALTH | 1.689925 .0223132 75.74 0.000 1.646192 1.733658 INSTRUCTIONAL | 1.092571 .0311152 35.11 0.000 1.031586 1.153555 MINI-SERIES | .424243 .0887788 4.78 0.000 .2502398 .5982462 MOVIE | 1.041383 .0180699 57.63 0.000 1.005966 1.076799 MUSIC | 2.819802 .0180835 155.93 0.000 2.784359 2.855245 MUSIC SPECIAL | 2.058408 .0250811 82.07 0.000 2.00925 2.107566 NETWORK SERIES | 1.999218 .0172081 116.18 0.000 1.965491 2.032945 NEWS | 1.674438 .0169445 98.82 0.000 1.641227 1.707648 OTHER | .2096004 .0179373 11.69 0.000 .1744439 .2447569 PLAYOFF SPORTS | 3.29401 .0182924 180.08 0.000 3.258158 3.329863 PSEUDO-SPORTS | .7863779 .0851284 9.24 0.000 .6195294 .9532264 PUBLIC AFFAIRS | 1.251909 .0295702 42.34 0.000 1.193953 1.309866 RELIGIOUS | -.1034714 .0254938 -4.06 0.000 -.1534384 -.0535043 SPECIAL | 2.186133 .0194193 112.58 0.000 2.148072 2.224194 SPORTING EVENT | 3.15048 .0172652 182.48 0.000 3.11664 3.184319 SPORTS ANTHOLOGY | 1.575787 .028881 54.56 0.000 1.519181 1.632393 SPORTS-RELATED | 2.12658 .0184779 115.09 0.000 2.090364 2.162796 SYNDICATED | 1.591972 .016987 93.72 0.000 1.558678 1.625266 TALK SHOW | 2.114668 .0168855 125.24 0.000 2.081573 2.147763 TEAM VS. TEAM | 2.995861 .017304 173.13 0.000 2.961945 3.029776 TV MOVIE | 2.26327 .0351785 64.34 0.000 2.194321 2.332218 | _CONS | -5.623185 .0198155 -283.78 0.000 -5.662022 -5.584347 ------
. PREDICT DOUBLE DISHAT2 (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. REPLACE DISHAT=DISHAT2 IF CALL2!="WGN" (3162820 REAL CHANGES MADE)
. POISSON DVIEW LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2=="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -5.047E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -5.034E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -5.034E+09 ITERATION 3: LOG PSEUDOLIKELIHOOD = -5.034E+09
POISSON REGRESSION NUMBER OF OBS = 509265 WALD CHI2(109) = 773306.31 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -5.034E+09 PSEUDO R2 = 0.6042
------| ROBUST DVIEW | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | -.6441931 .0388028 -16.60 0.000 -.7202452 -.568141 | QTR | 2 | -.1069233 .0130776 -8.18 0.000 -.1325548 -.0812917 3 | -.2190309 .0138122 -15.86 0.000 -.2461023 -.1919596
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 79
4 | -.2485574 .0142843 -17.40 0.000 -.2765542 -.2205606 5 | -.2013709 .0138525 -14.54 0.000 -.2285213 -.1742204 6 | -.2972723 .0139496 -21.31 0.000 -.3246129 -.2699316 7 | -.283399 .0139078 -20.38 0.000 -.3106578 -.2561401 8 | -.2703908 .0136072 -19.87 0.000 -.2970604 -.2437212 9 | -.1060834 .0131974 -8.04 0.000 -.1319498 -.080217 10 | -.08174 .0131213 -6.23 0.000 -.1074573 -.0560228 11 | -.0484412 .0131161 -3.69 0.000 -.0741484 -.0227341 12 | -.0542227 .0132161 -4.10 0.000 -.0801257 -.0283197 13 | -.0480284 .0135788 -3.54 0.000 -.0746424 -.0214144 14 | -.0783794 .0139047 -5.64 0.000 -.1056321 -.0511267 15 | .02319 .013353 1.74 0.082 -.0029815 .0493615 16 | -.1001655 .0135721 -7.38 0.000 -.1267663 -.0735647 17 | -.2889118 .0149514 -19.32 0.000 -.318216 -.2596076 18 | -.4541753 .0155282 -29.25 0.000 -.48461 -.4237406 19 | -.46885 .0160119 -29.28 0.000 -.5002327 -.4374674 20 | -.5196725 .0165977 -31.31 0.000 -.5522033 -.4871416 21 | -.5949664 .016615 -35.81 0.000 -.6275312 -.5624015 22 | -.5918378 .0153481 -38.56 0.000 -.6219195 -.561756 23 | -.2852065 .0145006 -19.67 0.000 -.3136271 -.256786 24 | -.2206292 .0148815 -14.83 0.000 -.2497963 -.191462 25 | -.4917764 .016085 -30.57 0.000 -.5233024 -.4602504 26 | -.5779256 .0180686 -31.99 0.000 -.6133395 -.5425117 27 | -.3764724 .01868 -20.15 0.000 -.4130845 -.3398602 28 | -.3882587 .019455 -19.96 0.000 -.4263897 -.3501277 29 | .079534 .0192627 4.13 0.000 .0417797 .1172882 30 | .1423944 .0201123 7.08 0.000 .1029749 .1818138 31 | .0266428 .0196739 1.35 0.176 -.0119173 .065203 32 | .2548241 .0171133 14.89 0.000 .2212826 .2883656 33 | 1.213219 .0115381 105.15 0.000 1.190605 1.235833 34 | 1.322825 .0113418 116.63 0.000 1.300596 1.345055 35 | 1.426153 .011149 127.92 0.000 1.404301 1.448004 36 | 1.51496 .0110438 137.18 0.000 1.493314 1.536605 37 | 1.512017 .0105302 143.59 0.000 1.491378 1.532656 38 | 1.482492 .0106705 138.93 0.000 1.461578 1.503405 39 | 1.541177 .0106635 144.53 0.000 1.520277 1.562077 40 | 1.609693 .0107252 150.09 0.000 1.588672 1.630714 41 | 1.659945 .0106729 155.53 0.000 1.639026 1.680863 42 | 1.536349 .0109513 140.29 0.000 1.514885 1.557813 43 | 1.560712 .0108081 144.40 0.000 1.539529 1.581896 44 | 1.584 .0107756 147.00 0.000 1.56288 1.60512 45 | 1.590088 .0104019 152.86 0.000 1.5697 1.610475 46 | 1.541272 .0105503 146.09 0.000 1.520594 1.56195 47 | 1.575099 .0106172 148.35 0.000 1.55429 1.595909 48 | 1.564945 .010484 149.27 0.000 1.544397 1.585493 49 | 1.226987 .0105429 116.38 0.000 1.206323 1.24765 50 | .9298325 .0117181 79.35 0.000 .9068654 .9527996 51 | .9070966 .0118236 76.72 0.000 .8839227 .9302704 52 | .9448774 .0117047 80.73 0.000 .9219365 .9678183 53 | 1.246369 .0116088 107.36 0.000 1.223616 1.269122 54 | 1.279253 .0115284 110.97 0.000 1.256658 1.301849 55 | 1.355524 .0113332 119.61 0.000 1.333312 1.377737 56 | 1.392404 .011263 123.63 0.000 1.370329 1.414479 57 | 1.414974 .0103606 136.57 0.000 1.394667 1.43528 58 | 1.368711 .0104963 130.40 0.000 1.348138 1.389283 59 | 1.420341 .0105182 135.04 0.000 1.399726 1.440957 60 | 1.45652 .0104974 138.75 0.000 1.435946 1.477095 61 | 1.45307 .0106801 136.05 0.000 1.432137 1.474003 62 | 1.37482 .0111141 123.70 0.000 1.353037 1.396603 63 | 1.411257 .0111004 127.14 0.000 1.3895 1.433013 64 | 1.396647 .0111651 125.09 0.000 1.374764 1.418531 65 | 1.172546 .0113794 103.04 0.000 1.150242 1.194849 66 | .905761 .0121564 74.51 0.000 .8819348 .9295872
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 80
67 | .8708493 .012447 69.96 0.000 .8464536 .895245 68 | .8704815 .0125858 69.16 0.000 .8458138 .8951492 69 | .8480747 .0125984 67.32 0.000 .8233822 .8727672 70 | .6056175 .0135244 44.78 0.000 .5791101 .6321249 71 | .5233906 .0135555 38.61 0.000 .4968222 .5499589 72 | .5020524 .0139798 35.91 0.000 .4746526 .5294523 73 | .993238 .0114383 86.83 0.000 .9708194 1.015657 74 | 1.111988 .0112805 98.58 0.000 1.089879 1.134098 75 | 1.176925 .0112453 104.66 0.000 1.154885 1.198965 76 | 1.2113 .0110369 109.75 0.000 1.189668 1.232932 77 | 1.108224 .0118066 93.86 0.000 1.085083 1.131364 78 | 1.035089 .0125808 82.28 0.000 1.010431 1.059747 79 | 1.033949 .0127006 81.41 0.000 1.009056 1.058842 80 | 1.082046 .0126406 85.60 0.000 1.057271 1.106821 81 | 1.105664 .0128653 85.94 0.000 1.080449 1.13088 82 | 1.070362 .0129285 82.79 0.000 1.045023 1.095702 83 | 1.083154 .0128811 84.09 0.000 1.057908 1.108401 84 | 1.087611 .0124674 87.24 0.000 1.063176 1.112047 85 | 1.014673 .0115985 87.48 0.000 .9919405 1.037406 86 | .8523671 .0118199 72.11 0.000 .8292006 .8755337 87 | .6620276 .0120788 54.81 0.000 .6383536 .6857017 88 | .4275784 .0128905 33.17 0.000 .4023136 .4528433 89 | .5595195 .0132432 42.25 0.000 .5335633 .5854758 90 | .4327367 .0143216 30.22 0.000 .4046668 .4608066 91 | .4988495 .0149116 33.45 0.000 .4696234 .5280756 92 | .4327579 .0147087 29.42 0.000 .4039294 .4615864 93 | .2545741 .0130265 19.54 0.000 .2290428 .2801055 94 | .1593381 .01297 12.29 0.000 .1339173 .1847589 95 | .1374881 .0130989 10.50 0.000 .1118148 .1631615 96 | .0847711 .013428 6.31 0.000 .0584528 .1110894 | PROGRAM_TYPE | MOVIE | 1.959443 .0758018 25.85 0.000 1.810874 2.108012 MUSIC | 2.059764 .080067 25.73 0.000 1.902835 2.216692 NEWS | 2.272988 .0758906 29.95 0.000 2.124245 2.421731 OTHER | 2.173939 .0758432 28.66 0.000 2.025289 2.322589 PUBLIC AFFAIRS | 1.760685 .0797881 22.07 0.000 1.604303 1.917067 RELIGIOUS | 1.691415 .0763641 22.15 0.000 1.541744 1.841086 SPECIAL | 1.678637 .0765151 21.94 0.000 1.52867 1.828603 SPORTING EVENT | 1.923796 .0837522 22.97 0.000 1.759644 2.087947 SPORTS-RELATED | 2.074636 .0765753 27.09 0.000 1.924551 2.224721 SYNDICATED | 2.31853 .0757036 30.63 0.000 2.170154 2.466907 TALK SHOW | 1.981285 .0778713 25.44 0.000 1.82866 2.13391 TEAM VS. TEAM | 2.387558 .0757967 31.50 0.000 2.238999 2.536116 TV MOVIE | 1.799953 .0810725 22.20 0.000 1.641054 1.958853 | _CONS | 18.9267 .6576906 28.78 0.000 17.63765 20.21574 ------
. PREDICT DOUBLE DVHAT (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. POISSON DVIEW LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2!="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -9.383E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -9.383E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -9.383E+09
POISSON REGRESSION NUMBER OF OBS = 2492832 WALD CHI2(122) = 277415.58 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -9.383E+09 PSEUDO R2 = 0.1516
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 81
------| ROBUST DVIEW | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | .1518729 .0003749 405.05 0.000 .151138 .1526077 | QTR | 2 | -.0039834 .0082472 -0.48 0.629 -.0201476 .0121808 3 | .0210191 .007491 2.81 0.005 .0063371 .0357011 4 | -.0972541 .0077844 -12.49 0.000 -.1125112 -.0819969 5 | -.1264829 .0079689 -15.87 0.000 -.1421017 -.1108642 6 | -.1223158 .0080522 -15.19 0.000 -.1380979 -.1065337 7 | -.1093206 .0081754 -13.37 0.000 -.125344 -.0932971 8 | -.0610039 .0083107 -7.34 0.000 -.0772926 -.0447152 9 | .0934541 .0073438 12.73 0.000 .0790605 .1078477 10 | .1122468 .0074938 14.98 0.000 .0975593 .1269344 11 | .2832777 .0072647 38.99 0.000 .2690391 .2975163 12 | .3845684 .0075587 50.88 0.000 .3697536 .3993831 13 | .4647077 .0080509 57.72 0.000 .4489284 .4804871 14 | .5128319 .0082673 62.03 0.000 .4966282 .5290356 15 | .4457705 .0083695 53.26 0.000 .4293665 .4621744 16 | .4150303 .0085569 48.50 0.000 .398259 .4318015 17 | .3981773 .0089287 44.60 0.000 .3806774 .4156771 18 | .3290764 .0091167 36.10 0.000 .3112081 .3469447 19 | .2450867 .0088838 27.59 0.000 .2276748 .2624985 20 | .1337714 .0086822 15.41 0.000 .1167546 .1507881 21 | .1363444 .008324 16.38 0.000 .1200298 .1526591 22 | -.0210968 .0087571 -2.41 0.016 -.0382604 -.0039332 23 | -.0383013 .0089576 -4.28 0.000 -.0558578 -.0207447 24 | .0050863 .008808 0.58 0.564 -.012177 .0223497 25 | .0926387 .0082418 11.24 0.000 .0764852 .1087923 26 | .1359579 .0079401 17.12 0.000 .1203956 .1515201 27 | .1180796 .0078485 15.04 0.000 .1026968 .1334624 28 | .1429091 .0078475 18.21 0.000 .1275283 .1582898 29 | .1719086 .0069414 24.77 0.000 .1583036 .1855135 30 | .1563623 .0069357 22.54 0.000 .1427686 .169956 31 | .1131929 .0070582 16.04 0.000 .0993591 .1270267 32 | .1409665 .0069827 20.19 0.000 .1272807 .1546524 33 | .0940778 .0068559 13.72 0.000 .0806405 .1075151 34 | .1181145 .0069301 17.04 0.000 .1045318 .1316972 35 | .1051008 .0071227 14.76 0.000 .0911406 .119061 36 | .0735705 .0070723 10.40 0.000 .0597091 .0874318 37 | .0599682 .0070614 8.49 0.000 .0461281 .0738083 38 | .0169441 .0072067 2.35 0.019 .0028192 .0310689 39 | .0460977 .0072227 6.38 0.000 .0319416 .0602539 40 | .0514217 .007166 7.18 0.000 .0373766 .0654669 41 | .0635201 .007056 9.00 0.000 .0496905 .0773497 42 | -.0108922 .007333 -1.49 0.137 -.0252646 .0034801 43 | -.0100007 .0074749 -1.34 0.181 -.0246513 .0046499 44 | -.0245846 .0077298 -3.18 0.001 -.0397348 -.0094344 45 | .0208831 .0075997 2.75 0.006 .005988 .0357781 46 | -.0035253 .0077374 -0.46 0.649 -.0186903 .0116396 47 | .0274089 .0074318 3.69 0.000 .0128428 .0419751 48 | -.0046639 .0075141 -0.62 0.535 -.0193912 .0100634 49 | -.0695427 .0076242 -9.12 0.000 -.0844858 -.0545995 50 | -.1110425 .0077558 -14.32 0.000 -.1262436 -.0958413 51 | -.0667955 .0079307 -8.42 0.000 -.0823393 -.0512516 52 | -.0852858 .0078329 -10.89 0.000 -.100638 -.0699337 53 | .0096893 .0075386 1.29 0.199 -.0050861 .0244646 54 | -.0003675 .0086069 -0.04 0.966 -.0172367 .0165017 55 | -.0126113 .0096288 -1.31 0.190 -.0314834 .0062609 56 | -.0486864 .0080066 -6.08 0.000 -.0643791 -.0329938
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 82
57 | -.0305668 .0079887 -3.83 0.000 -.0462243 -.0149092 58 | -.0413328 .0082789 -4.99 0.000 -.0575592 -.0251064 59 | -.0259582 .0084374 -3.08 0.002 -.0424953 -.0094211 60 | -.0681218 .0083215 -8.19 0.000 -.0844316 -.051812 61 | -.0915681 .0079647 -11.50 0.000 -.1071787 -.0759575 62 | -.1027222 .0083567 -12.29 0.000 -.119101 -.0863433 63 | -.1174588 .0083338 -14.09 0.000 -.1337928 -.1011249 64 | -.0976267 .008083 -12.08 0.000 -.1134691 -.0817843 65 | -.0184261 .0077851 -2.37 0.018 -.0336846 -.0031675 66 | .0001508 .0078906 0.02 0.985 -.0153144 .015616 67 | .038129 .0079315 4.81 0.000 .0225836 .0536744 68 | .0532438 .0079526 6.70 0.000 .0376571 .0688305 69 | .1076362 .0079626 13.52 0.000 .0920298 .1232426 70 | .1192366 .0087852 13.57 0.000 .1020181 .1364552 71 | .1389458 .0077119 18.02 0.000 .1238308 .1540608 72 | .1505823 .0080664 18.67 0.000 .1347723 .1663922 73 | .1707514 .0076704 22.26 0.000 .1557176 .1857851 74 | .1589568 .0075686 21.00 0.000 .1441227 .1737909 75 | .2447479 .0068626 35.66 0.000 .2312975 .2581984 76 | .2138525 .0069363 30.83 0.000 .2002576 .2274475 77 | .1250804 .0067259 18.60 0.000 .1118978 .138263 78 | .1120203 .0068973 16.24 0.000 .098502 .1255387 79 | .1816782 .0069065 26.31 0.000 .1681417 .1952147 80 | .1982341 .0069022 28.72 0.000 .184706 .2117621 81 | .3485517 .007088 49.17 0.000 .3346594 .3624439 82 | .3357427 .007384 45.47 0.000 .3212703 .3502152 83 | .341936 .0072732 47.01 0.000 .3276808 .3561912 84 | .3662387 .0072563 50.47 0.000 .3520167 .3804608 85 | .4533292 .0070982 63.87 0.000 .439417 .4672413 86 | .4017521 .0073159 54.91 0.000 .3874131 .416091 87 | .3927986 .007266 54.06 0.000 .3785576 .4070396 88 | .4010923 .0072095 55.63 0.000 .3869619 .4152227 89 | .4597312 .007235 63.54 0.000 .4455509 .4739115 90 | .3856862 .007363 52.38 0.000 .371255 .4001174 91 | .3578412 .0073476 48.70 0.000 .3434401 .3722423 92 | .3508218 .0074567 47.05 0.000 .3362068 .3654367 93 | .1999367 .0074824 26.72 0.000 .1852715 .2146018 94 | .0478096 .008439 5.67 0.000 .0312694 .0643498 95 | .1253595 .0079669 15.73 0.000 .1097446 .1409744 96 | .0468743 .0080451 5.83 0.000 .0311063 .0626424 | PROGRAM_TYPE | CHILDREN'S SHOW | -.0751706 .0113965 -6.60 0.000 -.0975073 -.0528338 CHILDREN'S SPECIAL | -.0529454 .0190949 -2.77 0.006 -.0903707 -.0155201 DAYTIME SOAP | -.1336724 .009239 -14.47 0.000 -.1517806 -.1155642 FINANCE | -.0344178 .012586 -2.73 0.006 -.0590859 -.0097496 GAME SHOW | .0038336 .008971 0.43 0.669 -.0137494 .0214165 HEALTH | -.209272 .0136067 -15.38 0.000 -.2359406 -.1826035 INSTRUCTIONAL | .0145923 .0157813 0.92 0.355 -.0163384 .045523 MINI-SERIES | -.806611 .0647522 -12.46 0.000 -.9335229 -.679699 MOVIE | .0307811 .0090037 3.42 0.001 .0131342 .048428 MUSIC | .4985356 .0099216 50.25 0.000 .4790895 .5179816 MUSIC SPECIAL | .1706805 .0163492 10.44 0.000 .1386367 .2027243 NETWORK SERIES | .1006584 .0086698 11.61 0.000 .083666 .1176509 NEWS | -.0276132 .008458 -3.26 0.001 -.0441907 -.0110358 OTHER | -.155564 .0090014 -17.28 0.000 -.1732064 -.1379216 PLAYOFF SPORTS | .8034099 .0102901 78.08 0.000 .7832416 .8235782 PSEUDO-SPORTS | -.1438221 .0343126 -4.19 0.000 -.2110736 -.0765706 PUBLIC AFFAIRS | .0085465 .0160603 0.53 0.595 -.022931 .0400241 RELIGIOUS | -.1391105 .0117255 -11.86 0.000 -.1620921 -.1161289 SPECIAL | .2334458 .0113592 20.55 0.000 .2111821 .2557095 SPORTING EVENT | .611232 .0088187 69.31 0.000 .5939476 .6285164 SPORTS ANTHOLOGY | .2108568 .0148071 14.24 0.000 .1818353 .2398783
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 83
SPORTS-RELATED | .3156781 .0099466 31.74 0.000 .2961831 .335173 SYNDICATED | .1509241 .0084086 17.95 0.000 .1344436 .1674047 TALK SHOW | .1351793 .0083115 16.26 0.000 .118889 .1514697 TEAM VS. TEAM | .5208119 .0087825 59.30 0.000 .5035986 .5380253 TV MOVIE | .2415148 .024974 9.67 0.000 .1925666 .2904629 | _CONS | 7.348906 .0110818 663.15 0.000 7.327187 7.370626 ------
. PREDICT DVHAT2 (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. REPLACE DVHAT=DVHAT2 IF CALL2!="WGN" (3162820 REAL CHANGES MADE)
. REG TOTAL I.QTR I.PROGRAM_TYPE
SOURCE | SS DF MS NUMBER OF OBS = 2875367 ------+------F(121,2875245) = 2268.82 MODEL | 1.6883E+14 121 1.3953E+12 PROB > F = 0.0000 RESIDUAL | 1.7683E+152875245 614996896 R-SQUARED = 0.0872 ------+------ADJ R-SQUARED = 0.0871 TOTAL | 1.9371E+152875366 673688153 ROOT MSE = 24799
------TOTAL | COEF. STD. ERR. T P>|T| [95% CONF. INTERVAL] ------+------QTR | 2 | -1134.569 199.1348 -5.70 0.000 -1524.866 -744.2713 3 | -1379.708 182.2207 -7.57 0.000 -1736.854 -1022.562 4 | -2494.241 199.2205 -12.52 0.000 -2884.706 -2103.776 5 | -3338.028 184.8252 -18.06 0.000 -3700.279 -2975.777 6 | -3938.017 199.4004 -19.75 0.000 -4328.834 -3547.199 7 | -4205.677 182.8918 -23.00 0.000 -4564.138 -3847.215 8 | -4885.608 199.6598 -24.47 0.000 -5276.934 -4494.282 9 | -4333.481 183.665 -23.59 0.000 -4693.458 -3973.505 10 | -4881.165 200.0895 -24.39 0.000 -5273.333 -4488.997 11 | -5077.686 190.4964 -26.66 0.000 -5451.052 -4704.319 12 | -5450.306 200.0672 -27.24 0.000 -5842.43 -5058.181 13 | -5742.539 192.8851 -29.77 0.000 -6120.587 -5364.491 14 | -6354.61 201.3414 -31.56 0.000 -6749.232 -5959.988 15 | -6225.827 197.6464 -31.50 0.000 -6613.207 -5838.447 16 | -6314.699 201.5508 -31.33 0.000 -6709.731 -5919.666 17 | -6959.225 197.9744 -35.15 0.000 -7347.248 -6571.202 18 | -6963.425 200.7125 -34.69 0.000 -7356.814 -6570.035 19 | -7217.819 200.6939 -35.96 0.000 -7611.172 -6824.466 20 | -6743.525 201.9777 -33.39 0.000 -7139.394 -6347.656 21 | -5804.557 202.4334 -28.67 0.000 -6201.32 -5407.795 22 | -5258.599 202.5906 -25.96 0.000 -5655.669 -4861.528 23 | -4428.084 202.4333 -21.87 0.000 -4824.846 -4031.322 24 | -3499.353 202.5783 -17.27 0.000 -3896.4 -3102.307 25 | -2703.856 202.5096 -13.35 0.000 -3100.767 -2306.944 26 | -1846.148 202.5893 -9.11 0.000 -2243.215 -1449.08 27 | -1399.432 202.8833 -6.90 0.000 -1797.077 -1001.788 28 | -403.8917 202.8853 -1.99 0.047 -801.5399 -6.243593 29 | 3838.12 200.7588 19.12 0.000 3444.639 4231.6 30 | 3958.384 200.7588 19.72 0.000 3564.903 4351.864 31 | 3360.275 200.8099 16.73 0.000 2966.695 3753.855 32 | 3412.745 200.8099 16.99 0.000 3019.164 3806.325 33 | 4075.622 200.7374 20.30 0.000 3682.184 4469.061 34 | 4035.586 200.7355 20.10 0.000 3642.151 4429.021 35 | 3889.886 200.7194 19.38 0.000 3496.483 4283.289 36 | 3991.659 200.7042 19.89 0.000 3598.286 4385.033
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 84
37 | 4566.968 200.4494 22.78 0.000 4174.095 4959.842 38 | 3535.487 200.431 17.64 0.000 3142.649 3928.324 39 | 3086.953 200.4665 15.40 0.000 2694.045 3479.86 40 | 3162.854 200.4368 15.78 0.000 2770.005 3555.703 41 | 4729.955 200.4446 23.60 0.000 4337.091 5122.819 42 | 3206.009 200.6383 15.98 0.000 2812.765 3599.253 43 | 3179.142 200.7079 15.84 0.000 2785.761 3572.522 44 | 3458.359 200.7098 17.23 0.000 3064.974 3851.743 45 | 2880.849 200.7042 14.35 0.000 2487.476 3274.222 46 | 2031.426 200.7344 10.12 0.000 1637.994 2424.858 47 | 2160.986 200.6553 10.77 0.000 1767.709 2554.263 48 | 2570.072 200.63 12.81 0.000 2176.844 2963.299 49 | 575.709 199.8824 2.88 0.004 183.9465 967.4715 50 | -167.7279 200.6781 -0.84 0.403 -561.05 225.5942 51 | -33.54331 201.15 -0.17 0.868 -427.7902 360.7036 52 | -138.1253 201.1314 -0.69 0.492 -532.3357 256.0852 53 | 871.4086 201.4401 4.33 0.000 476.5931 1266.224 54 | -107.1704 201.7488 -0.53 0.595 -502.591 288.2502 55 | -305.7179 201.7372 -1.52 0.130 -701.1156 89.67992 56 | -359.0228 201.647 -1.78 0.075 -754.2439 36.19825 57 | -175.4344 200.3919 -0.88 0.381 -568.1955 217.3268 58 | -967.2291 200.5393 -4.82 0.000 -1360.279 -574.1792 59 | -962.497 200.6436 -4.80 0.000 -1355.751 -569.2426 60 | -439.0783 200.6587 -2.19 0.029 -832.3622 -45.79434 61 | 626.4503 199.852 3.13 0.002 234.7473 1018.153 62 | 13.56247 200.0876 0.07 0.946 -378.6022 405.7272 63 | 201.9397 200.2044 1.01 0.313 -190.4538 594.3332 64 | 824.0171 200.4136 4.11 0.000 431.2135 1216.821 65 | 1648.9 199.765 8.25 0.000 1257.368 2040.433 66 | 1252.55 200.0671 6.26 0.000 860.4256 1644.675 67 | 1594.153 200.1664 7.96 0.000 1201.834 1986.472 68 | 2745.326 200.1502 13.72 0.000 2353.039 3137.614 69 | 2372.028 200.7625 11.82 0.000 1978.541 2765.516 70 | 2216.57 201.1843 11.02 0.000 1822.256 2610.884 71 | 1830.496 201.3464 9.09 0.000 1435.864 2225.128 72 | 2672.684 201.4981 13.26 0.000 2277.755 3067.614 73 | 5188.946 200.8713 25.83 0.000 4795.245 5582.647 74 | 5661.429 201.3453 28.12 0.000 5266.799 6056.058 75 | 7078.99 200.4501 35.32 0.000 6686.114 7471.865 76 | 7878.309 200.5545 39.28 0.000 7485.229 8271.389 77 | 10383.99 200.3642 51.83 0.000 9991.288 10776.7 78 | 8782.286 200.4994 43.80 0.000 8389.315 9175.258 79 | 9173.178 199.9116 45.89 0.000 8781.358 9564.997 80 | 10771.29 200.8773 53.62 0.000 10377.58 11165 81 | 15632.11 202.3413 77.26 0.000 15235.53 16028.69 82 | 12905.87 203.325 63.47 0.000 12507.36 13304.38 83 | 13563.03 201.5428 67.30 0.000 13168.01 13958.05 84 | 13458.73 204.1031 65.94 0.000 13058.7 13858.77 85 | 16738.3 200.5368 83.47 0.000 16345.26 17131.35 86 | 12401.11 203.7551 60.86 0.000 12001.76 12800.47 87 | 12753.11 202.0565 63.12 0.000 12357.09 13149.14 88 | 12576.95 204.071 61.63 0.000 12176.98 12976.92 89 | 16444.26 198.644 82.78 0.000 16054.93 16833.6 90 | 10362.56 200.4559 51.69 0.000 9969.676 10755.45 91 | 7670.589 188.7976 40.63 0.000 7300.552 8040.626 92 | 8932.985 199.9967 44.67 0.000 8540.998 9324.971 93 | 6270.291 196.7314 31.87 0.000 5884.704 6655.877 94 | 3459.032 198.6107 17.42 0.000 3069.762 3848.302 95 | 3325.332 181.209 18.35 0.000 2970.169 3680.496 96 | 1299.767 198.8068 6.54 0.000 910.1129 1689.422 | PROGRAM_TYPE | CHILDREN'S SHOW | 468.3315 238.0134 1.97 0.049 1.833569 934.8294
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 85
CHILDREN'S SPECIAL | 4510.224 629.2136 7.17 0.000 3276.988 5743.461 DAYTIME SOAP | 10801.5 200.3678 53.91 0.000 10408.79 11194.22 FINANCE | 7703.972 258.3187 29.82 0.000 7197.676 8210.267 GAME SHOW | 9254.764 188.7339 49.04 0.000 8884.852 9624.676 HEALTH | 6969.864 393.0563 17.73 0.000 6199.487 7740.24 INSTRUCTIONAL | 8823.995 355.4295 24.83 0.000 8127.366 9520.624 MINI-SERIES | 9355.747 1419.775 6.59 0.000 6573.039 12138.46 MOVIE | 5594.599 201.7152 27.74 0.000 5199.244 5989.953 MUSIC | 21622.66 254.9079 84.83 0.000 21123.05 22122.27 MUSIC SPECIAL | 12412.44 454.3943 27.32 0.000 11521.85 13303.04 NETWORK SERIES | 8963.884 184.3134 48.63 0.000 8602.636 9325.132 NEWS | 14599.41 174.1545 83.83 0.000 14258.08 14940.75 OTHER | 5227.397 177.3536 29.47 0.000 4879.79 5575.004 PLAYOFF SPORTS | 31342.45 245.8267 127.50 0.000 30860.64 31824.26 PSEUDO-SPORTS | 4052.997 806.565 5.03 0.000 2472.157 5633.836 PUBLIC AFFAIRS | 6937.928 374.7658 18.51 0.000 6203.4 7672.456 RELIGIOUS | 3423.466 222.004 15.42 0.000 2988.346 3858.586 SPECIAL | 13132.55 254.9068 51.52 0.000 12632.94 13632.16 SPORTING EVENT | 18527.72 193.6644 95.67 0.000 18148.14 18907.3 SPORTS ANTHOLOGY | 4934.095 381.845 12.92 0.000 4185.693 5682.498 SPORTS-RELATED | 12487.52 211.6606 59.00 0.000 12072.67 12902.37 SYNDICATED | 8866.615 173.0862 51.23 0.000 8527.372 9205.857 TALK SHOW | 8148.213 170.8265 47.70 0.000 7813.399 8483.027 TEAM VS. TEAM | 27099.51 200.4537 135.19 0.000 26706.63 27492.4 TV MOVIE | 11891.44 710.9748 16.73 0.000 10497.95 13284.92 | _CONS | -455.7868 217.5243 -2.10 0.036 -882.1268 -29.44679
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 86
2013: . POISSON DISTANT LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2=="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -5.728E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -5.676E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -5.675E+09 ITERATION 3: LOG PSEUDOLIKELIHOOD = -5.675E+09 ITERATION 4: LOG PSEUDOLIKELIHOOD = -5.675E+09
POISSON REGRESSION NUMBER OF OBS = 525600 WALD CHI2(109) = 751764.88 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -5.675E+09 PSEUDO R2 = 0.6194
------| ROBUST DISTANT | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | 3.149034 .0510656 61.67 0.000 3.048947 3.249121 | QTR | 2 | -.1224915 .0139855 -8.76 0.000 -.1499025 -.0950805 3 | -.3453118 .0144874 -23.84 0.000 -.3737066 -.316917 4 | -.4386833 .0142153 -30.86 0.000 -.4665449 -.4108218 5 | -.4564556 .0133361 -34.23 0.000 -.4825938 -.4303174 6 | -.5116097 .0138168 -37.03 0.000 -.5386901 -.4845292 7 | -.5539322 .0146216 -37.88 0.000 -.5825899 -.5252745 8 | -.5826051 .0143081 -40.72 0.000 -.6106484 -.5545618 9 | -.578401 .0148304 -39.00 0.000 -.6074681 -.5493338 10 | -.7184647 .0151654 -47.38 0.000 -.7481883 -.6887412 11 | -.7716581 .0150284 -51.35 0.000 -.8011132 -.7422029 12 | -.817036 .0150225 -54.39 0.000 -.8464796 -.7875924 13 | -.859858 .0174212 -49.36 0.000 -.8940028 -.8257131 14 | -.8712617 .0173168 -50.31 0.000 -.905202 -.8373214 15 | -.9645652 .0188473 -51.18 0.000 -1.001505 -.9276251 16 | -.9878064 .0184834 -53.44 0.000 -1.024033 -.9515797 17 | -.7266712 .0201167 -36.12 0.000 -.7660992 -.6872432 18 | -.7502991 .0199281 -37.65 0.000 -.7893573 -.7112408 19 | -.8396465 .0198441 -42.31 0.000 -.8785403 -.8007527 20 | -.8995215 .0209925 -42.85 0.000 -.940666 -.858377 21 | -.7758735 .0205748 -37.71 0.000 -.8161994 -.7355477 22 | -.6770824 .0186107 -36.38 0.000 -.7135588 -.6406061 23 | -.004048 .0172174 -0.24 0.814 -.0377935 .0296975 24 | .1395582 .0165908 8.41 0.000 .1070409 .1720755 25 | -.1889075 .018128 -10.42 0.000 -.2244377 -.1533774 26 | -.4241688 .0199983 -21.21 0.000 -.4633646 -.3849729 27 | .214683 .0198554 10.81 0.000 .1757671 .2535989 28 | .3118527 .018954 16.45 0.000 .2747035 .3490019 29 | -.4605461 .0218883 -21.04 0.000 -.5034464 -.4176459 30 | -.6788054 .0239916 -28.29 0.000 -.725828 -.6317827 31 | -.7710884 .0248729 -31.00 0.000 -.8198384 -.7223384 32 | .0122453 .01872 0.65 0.513 -.0244453 .048936 33 | 1.250424 .0136358 91.70 0.000 1.223698 1.27715 34 | 1.386187 .0137821 100.58 0.000 1.359175 1.413199 35 | 1.48756 .013389 111.10 0.000 1.461318 1.513801 36 | 1.591641 .0133845 118.92 0.000 1.565408 1.617874 37 | 1.369486 .0108675 126.02 0.000 1.348186 1.390786 38 | 1.318233 .0110135 119.69 0.000 1.296647 1.339819 39 | 1.365103 .0110507 123.53 0.000 1.343444 1.386762 40 | 1.439519 .0109179 131.85 0.000 1.41812 1.460918 41 | 1.601047 .0105043 152.42 0.000 1.580459 1.621635 42 | 1.511855 .0104674 144.43 0.000 1.491339 1.532371
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 87
43 | 1.546196 .0104592 147.83 0.000 1.525696 1.566695 44 | 1.547249 .0104499 148.06 0.000 1.526768 1.567731 45 | 1.452093 .0107798 134.71 0.000 1.430965 1.473221 46 | 1.428715 .0108372 131.83 0.000 1.407474 1.449955 47 | 1.469636 .0107625 136.55 0.000 1.448542 1.49073 48 | 1.411684 .0106337 132.76 0.000 1.390843 1.432526 49 | .9851085 .0111896 88.04 0.000 .9631772 1.00704 50 | .7198822 .0123408 58.33 0.000 .6956948 .7440697 51 | .7675255 .0121642 63.10 0.000 .7436841 .7913669 52 | .7756883 .0123047 63.04 0.000 .7515715 .7998052 53 | 1.08019 .0111641 96.76 0.000 1.058309 1.102071 54 | 1.080114 .0110844 97.44 0.000 1.058389 1.101839 55 | 1.1363 .0110619 102.72 0.000 1.114619 1.157981 56 | 1.184618 .0109818 107.87 0.000 1.163094 1.206142 57 | 1.242916 .0107166 115.98 0.000 1.221911 1.26392 58 | 1.193824 .0107116 111.45 0.000 1.172829 1.214818 59 | 1.246266 .0107877 115.53 0.000 1.225123 1.26741 60 | 1.264229 .010797 117.09 0.000 1.243067 1.28539 61 | 1.242856 .0110244 112.74 0.000 1.221248 1.264463 62 | 1.148234 .0115166 99.70 0.000 1.125662 1.170807 63 | 1.184489 .0115517 102.54 0.000 1.161848 1.20713 64 | 1.182191 .0115448 102.40 0.000 1.159563 1.204818 65 | .9114441 .0118605 76.85 0.000 .8881979 .9346904 66 | .6929867 .0127389 54.40 0.000 .6680189 .7179544 67 | .6888205 .0126106 54.62 0.000 .6641042 .7135368 68 | .6787232 .0128638 52.76 0.000 .6535106 .7039358 69 | .7366488 .0124323 59.25 0.000 .7122819 .7610158 70 | .6650104 .0129289 51.44 0.000 .6396702 .6903506 71 | .662735 .0126779 52.27 0.000 .6378868 .6875833 72 | .7636105 .0126424 60.40 0.000 .7388319 .7883891 73 | .9914604 .0112183 88.38 0.000 .9694729 1.013448 74 | 1.031921 .0111684 92.40 0.000 1.010032 1.053811 75 | 1.168548 .0111375 104.92 0.000 1.146719 1.190377 76 | 1.188883 .0110074 108.01 0.000 1.167308 1.210457 77 | 1.003311 .0118461 84.70 0.000 .9800928 1.026529 78 | .9097437 .0126852 71.72 0.000 .8848812 .9346063 79 | .863405 .0123802 69.74 0.000 .8391402 .8876697 80 | .8825483 .0123899 71.23 0.000 .8582646 .906832 81 | .9086778 .012193 74.52 0.000 .88478 .9325757 82 | .8471971 .0124948 67.80 0.000 .8227077 .8716865 83 | .8666269 .0125441 69.09 0.000 .8420408 .8912129 84 | .8630239 .0125165 68.95 0.000 .838492 .8875558 85 | .8516002 .0118125 72.09 0.000 .8284482 .8747522 86 | .7959122 .0119514 66.60 0.000 .7724879 .8193365 87 | .4911052 .0127068 38.65 0.000 .4662004 .51601 88 | .2992752 .0136398 21.94 0.000 .2725417 .3260088 89 | .6539492 .0128264 50.98 0.000 .6288099 .6790885 90 | .6665254 .0128754 51.77 0.000 .64129 .6917607 91 | .7006636 .0126447 55.41 0.000 .6758804 .7254469 92 | .6787816 .0126695 53.58 0.000 .6539498 .7036133 93 | .5523316 .0119712 46.14 0.000 .5288685 .5757946 94 | .4017062 .0123275 32.59 0.000 .3775447 .4258677 95 | .3355244 .0124351 26.98 0.000 .3111521 .3598967 96 | .2740678 .0124238 22.06 0.000 .2497177 .298418 | PROGRAM_TYPE | MOVIE | 2.976236 .1760662 16.90 0.000 2.631153 3.32132 MUSIC | 2.059183 .1801489 11.43 0.000 1.706098 2.412269 NETWORK SERIES | 3.202604 .1761719 18.18 0.000 2.857313 3.547894 NEWS | 3.298538 .176035 18.74 0.000 2.953516 3.64356 OTHER | 2.622805 .1760388 14.90 0.000 2.277775 2.967835 PUBLIC AFFAIRS | 1.454167 .1808409 8.04 0.000 1.099725 1.808609 RELIGIOUS | 2.177468 .1763987 12.34 0.000 1.831733 2.523203
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 88
SPECIAL | 2.478927 .1766031 14.04 0.000 2.132792 2.825063 SPORTS-RELATED | 3.034119 .1765324 17.19 0.000 2.688122 3.380117 SYNDICATED | 3.372528 .1759971 19.16 0.000 3.02758 3.717476 TALK SHOW | 2.819346 .1824549 15.45 0.000 2.461741 3.176951 TEAM VS. TEAM | 3.355239 .1760534 19.06 0.000 3.010181 3.700298 TV MOVIE | 3.574084 .1768845 20.21 0.000 3.227397 3.920771 | _CONS | -45.88424 .8786123 -52.22 0.000 -47.60629 -44.1622 ------
. PREDICT DOUBLE DISHAT (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. POISSON DISTANT LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2!="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -5.673E+10 ITERATION 1: LOG PSEUDOLIKELIHOOD = -5.661E+10 ITERATION 2: LOG PSEUDOLIKELIHOOD = -5.661E+10 ITERATION 3: LOG PSEUDOLIKELIHOOD = -5.661E+10 ITERATION 4: LOG PSEUDOLIKELIHOOD = -5.661E+10
POISSON REGRESSION NUMBER OF OBS = 34457464 WALD CHI2(121) = 4052276.00 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -5.661E+10 PSEUDO R2 = 0.4107
------| ROBUST DISTANT | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | .9461416 .0005075 1864.18 0.000 .9451469 .9471364 | QTR | 2 | -.1367482 .0101438 -13.48 0.000 -.1566296 -.1168668 3 | -.0339893 .0098434 -3.45 0.001 -.0532821 -.0146966 4 | -.1751903 .0103125 -16.99 0.000 -.1954024 -.1549782 5 | -.2611455 .0105435 -24.77 0.000 -.2818104 -.2404807 6 | -.5899883 .0118811 -49.66 0.000 -.6132748 -.5667018 7 | -.5576849 .0128839 -43.29 0.000 -.582937 -.5324329 8 | -.8779251 .0135293 -64.89 0.000 -.9044421 -.8514082 9 | -.4661793 .0118052 -39.49 0.000 -.4893171 -.4430415 10 | -.5113994 .0121816 -41.98 0.000 -.535275 -.4875239 11 | -.3695018 .0120342 -30.70 0.000 -.3930885 -.3459152 12 | -.45128 .0126352 -35.72 0.000 -.4760445 -.4265154 13 | -.5747304 .0134124 -42.85 0.000 -.6010181 -.5484426 14 | -.6189451 .0138336 -44.74 0.000 -.6460584 -.5918318 15 | -.8351783 .0144823 -57.67 0.000 -.863563 -.8067936 16 | -.8792289 .0145634 -60.37 0.000 -.9077726 -.8506852 17 | -.9328711 .0149521 -62.39 0.000 -.9621767 -.9035654 18 | -.9663265 .0150997 -64.00 0.000 -.9959213 -.9367317 19 | -.9645528 .0150129 -64.25 0.000 -.9939775 -.9351281 20 | -1.013373 .0151033 -67.10 0.000 -1.042975 -.9837714 21 | -.8965859 .0147367 -60.84 0.000 -.9254694 -.8677025 22 | -1.030015 .0149764 -68.78 0.000 -1.059369 -1.000662 23 | -.9537342 .0146082 -65.29 0.000 -.9823657 -.9251027 24 | -.9137255 .0145914 -62.62 0.000 -.9423242 -.8851268 25 | -.8940635 .0141511 -63.18 0.000 -.9217991 -.8663279 26 | -.7157619 .0132547 -54.00 0.000 -.7417407 -.6897831 27 | -.7177366 .0129965 -55.23 0.000 -.7432092 -.692264 28 | -.6385661 .0128762 -49.59 0.000 -.6638031 -.6133292 29 | -.1815789 .0103268 -17.58 0.000 -.201819 -.1613388 30 | -.1765399 .0102909 -17.15 0.000 -.1967097 -.15637
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 89
31 | -.1987632 .0104902 -18.95 0.000 -.2193235 -.1782029 32 | -.2845572 .0107061 -26.58 0.000 -.3055408 -.2635736 33 | -.2802795 .0104778 -26.75 0.000 -.3008156 -.2597433 34 | -.2698114 .0104493 -25.82 0.000 -.2902916 -.2493311 35 | -.2356835 .0104383 -22.58 0.000 -.2561422 -.2152248 36 | -.2346008 .0103435 -22.68 0.000 -.2548737 -.2143279 37 | -.2689478 .0103161 -26.07 0.000 -.289167 -.2487286 38 | -.3432574 .0104317 -32.91 0.000 -.3637032 -.3228117 39 | -.3536607 .010396 -34.02 0.000 -.3740365 -.3332849 40 | -.3200659 .0103813 -30.83 0.000 -.340413 -.2997188 41 | -.0564619 .0095957 -5.88 0.000 -.0752691 -.0376548 42 | -.1258006 .0097332 -12.92 0.000 -.1448773 -.1067239 43 | -.1568852 .0098125 -15.99 0.000 -.1761173 -.1376531 44 | -.1049221 .0096105 -10.92 0.000 -.1237583 -.0860859 45 | .1634594 .0091618 17.84 0.000 .1455027 .1814162 46 | .0739028 .0094037 7.86 0.000 .0554719 .0923338 47 | .0376346 .0094182 4.00 0.000 .0191753 .0560939 48 | -.0058108 .0095108 -0.61 0.541 -.0244516 .01283 49 | -.2779083 .010254 -27.10 0.000 -.2980058 -.2578107 50 | -.4302811 .0105849 -40.65 0.000 -.4510272 -.4095351 51 | -.5137686 .0103694 -49.55 0.000 -.5340922 -.493445 52 | -.4152492 .0101078 -41.08 0.000 -.4350601 -.3954383 53 | -.3921854 .0096948 -40.45 0.000 -.4111868 -.3731839 54 | -.5127559 .00995 -51.53 0.000 -.5322575 -.4932544 55 | -.5194607 .0099215 -52.36 0.000 -.5389064 -.5000149 56 | -.4970621 .0098585 -50.42 0.000 -.5163843 -.4777398 57 | -.4086712 .0097218 -42.04 0.000 -.4277256 -.3896167 58 | -.4970622 .0100964 -49.23 0.000 -.5168507 -.4772737 59 | -.49793 .010081 -49.39 0.000 -.5176884 -.4781715 60 | -.4752517 .009993 -47.56 0.000 -.4948375 -.4556658 61 | -.4662921 .0100557 -46.37 0.000 -.4860008 -.4465834 62 | -.5671531 .0105876 -53.57 0.000 -.5879044 -.5464019 63 | -.5410065 .0104476 -51.78 0.000 -.5614835 -.5205295 64 | -.4346202 .0102889 -42.24 0.000 -.454786 -.4144544 65 | -.2464082 .0100288 -24.57 0.000 -.2660644 -.2267521 66 | -.291739 .0100871 -28.92 0.000 -.3115094 -.2719687 67 | -.2637742 .0100179 -26.33 0.000 -.283409 -.2441394 68 | -.2291802 .0099005 -23.15 0.000 -.2485848 -.2097756 69 | -.0618048 .0098832 -6.25 0.000 -.0811756 -.0424341 70 | -.1954427 .0102847 -19.00 0.000 -.2156004 -.175285 71 | -.0710048 .0099279 -7.15 0.000 -.0904631 -.0515465 72 | -.0211385 .009967 -2.12 0.034 -.0406735 -.0016035 73 | -.1397975 .0105179 -13.29 0.000 -.1604122 -.1191828 74 | -.1247223 .010593 -11.77 0.000 -.1454843 -.1039603 75 | .1235076 .0098271 12.57 0.000 .1042469 .1427683 76 | .1911362 .0097035 19.70 0.000 .1721177 .2101548 77 | .2989976 .0094114 31.77 0.000 .2805516 .3174437 78 | .2629652 .0095246 27.61 0.000 .2442973 .2816332 79 | .1987994 .0095936 20.72 0.000 .1799963 .2176024 80 | .3034718 .0093227 32.55 0.000 .2851995 .321744 81 | .5017072 .0091814 54.64 0.000 .4837119 .5197025 82 | .4067768 .0093685 43.42 0.000 .3884149 .4251388 83 | .4102745 .0093276 43.99 0.000 .3919928 .4285562 84 | .434298 .0092461 46.97 0.000 .4161759 .4524201 85 | .5988711 .0091392 65.53 0.000 .5809586 .6167837 86 | .4279911 .0093329 45.86 0.000 .4096989 .4462834 87 | .398228 .0093402 42.64 0.000 .3799215 .4165344 88 | .403621 .0094037 42.92 0.000 .3851901 .4220519 89 | .8178371 .0090547 90.32 0.000 .8000903 .8355839 90 | .5685944 .0094507 60.16 0.000 .5500715 .5871174 91 | .5149372 .0094552 54.46 0.000 .4964053 .5334692 92 | .5049127 .0094399 53.49 0.000 .4864109 .5234145 93 | .5011368 .0090635 55.29 0.000 .4833726 .518901
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 90
94 | .1809498 .0095094 19.03 0.000 .1623118 .1995878 95 | .163294 .0095388 17.12 0.000 .1445982 .1819898 96 | .0056805 .0097217 0.58 0.559 -.0133737 .0247346 | PROGRAM_TYPE | CHILDREN'S SHOW | .1717676 .0138756 12.38 0.000 .144572 .1989632 CHILDREN'S SPECIAL | .8426751 .0277286 30.39 0.000 .788328 .8970223 DAYTIME SOAP | 1.5184 .0104365 145.49 0.000 1.497945 1.538855 FINANCE | -.2424335 .0246902 -9.82 0.000 -.2908254 -.1940415 GAME SHOW | .960462 .0103442 92.85 0.000 .9401877 .9807362 HEALTH | .6372538 .015328 41.57 0.000 .6072115 .6672962 INSTRUCTIONAL | .8846788 .022266 39.73 0.000 .8410383 .9283193 MOVIE | .0093621 .0120363 0.78 0.437 -.0142287 .0329529 MUSIC | 1.51663 .0112297 135.06 0.000 1.49462 1.53864 MUSIC SPECIAL | 1.220433 .0173671 70.27 0.000 1.186394 1.254472 NETWORK SERIES | 1.131794 .0101343 111.68 0.000 1.111931 1.151657 NEWS | .5127534 .0097487 52.60 0.000 .4936463 .5318606 OTHER | -.6084068 .0110416 -55.10 0.000 -.6300478 -.5867657 PLAYOFF SPORTS | 1.978716 .0119241 165.94 0.000 1.955345 2.002087 PSEUDO-SPORTS | 1.21097 .0404692 29.92 0.000 1.131652 1.290288 PUBLIC AFFAIRS | .2223115 .0250282 8.88 0.000 .1732571 .2713659 RELIGIOUS | -2.002624 .0353656 -56.63 0.000 -2.071939 -1.933309 SPECIAL | 1.086172 .0119782 90.68 0.000 1.062695 1.109649 SPORTING EVENT | 1.302535 .0109151 119.33 0.000 1.281142 1.323928 SPORTS ANTHOLOGY | -.0992098 .0309291 -3.21 0.001 -.1598297 -.03859 SPORTS-RELATED | .9722406 .0122265 79.52 0.000 .9482771 .996204 SYNDICATED | .3054075 .0097598 31.29 0.000 .2862785 .3245364 TALK SHOW | .933061 .0095819 97.38 0.000 .9142809 .9518412 TEAM VS. TEAM | 1.735949 .0102366 169.58 0.000 1.715885 1.756012 TV MOVIE | .9715364 .0300854 32.29 0.000 .9125701 1.030503 | _CONS | -4.443226 .0133327 -333.26 0.000 -4.469358 -4.417094 ------
. PREDICT DOUBLE DISHAT2 (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. REPLACE DISHAT=DISHAT2 IF CALL2!="WGN" (2803509 REAL CHANGES MADE)
. POISSON DVIEW LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2=="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -5.056E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -5.035E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -5.034E+09 ITERATION 3: LOG PSEUDOLIKELIHOOD = -5.034E+09
POISSON REGRESSION NUMBER OF OBS = 490290 WALD CHI2(109) = 745794.20 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -5.034E+09 PSEUDO R2 = 0.5935
------| ROBUST DVIEW | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | 3.124132 .0504452 61.93 0.000 3.025261 3.223002 | QTR | 2 | -.1144239 .0138251 -8.28 0.000 -.1415205 -.0873272 3 | -.3192492 .0142445 -22.41 0.000 -.347168 -.2913304 4 | -.4095121 .013945 -29.37 0.000 -.4368437 -.3821805
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 91
5 | -.4225764 .0130215 -32.45 0.000 -.448098 -.3970548 6 | -.4770324 .0135026 -35.33 0.000 -.503497 -.4505678 7 | -.5063322 .0142387 -35.56 0.000 -.5342396 -.4784249 8 | -.5352223 .0139186 -38.45 0.000 -.5625022 -.5079424 9 | -.5257934 .0144215 -36.46 0.000 -.554059 -.4975279 10 | -.6450434 .0146262 -44.10 0.000 -.6737102 -.6163766 11 | -.6929583 .014447 -47.97 0.000 -.7212738 -.6646428 12 | -.7333807 .0144108 -50.89 0.000 -.7616254 -.705136 13 | -.7117742 .0164509 -43.27 0.000 -.7440174 -.679531 14 | -.6906884 .0161239 -42.84 0.000 -.7222907 -.6590861 15 | -.7000199 .0172214 -40.65 0.000 -.7337732 -.6662667 16 | -.7317079 .0169086 -43.27 0.000 -.7648481 -.6985676 17 | -.5019332 .0186536 -26.91 0.000 -.5384935 -.4653729 18 | -.4453761 .0179033 -24.88 0.000 -.480466 -.4102862 19 | -.4946686 .0174956 -28.27 0.000 -.5289594 -.4603778 20 | -.5119021 .018661 -27.43 0.000 -.548477 -.4753272 21 | -.446911 .0183274 -24.38 0.000 -.482832 -.41099 22 | -.3774961 .0162235 -23.27 0.000 -.4092935 -.3456986 23 | .155118 .0156383 9.92 0.000 .1244674 .1857686 24 | .2749494 .0151977 18.09 0.000 .2451625 .3047364 25 | -.0264585 .0162542 -1.63 0.104 -.0583162 .0053992 26 | -.2100475 .0180067 -11.66 0.000 -.24534 -.174755 27 | .2878029 .0184659 15.59 0.000 .2516104 .3239953 28 | .3851849 .0175157 21.99 0.000 .3508548 .419515 29 | -.1840772 .0197793 -9.31 0.000 -.222844 -.1453105 30 | -.2009553 .0203568 -9.87 0.000 -.240854 -.1610566 31 | -.2564291 .02165 -11.84 0.000 -.2988623 -.2139958 32 | .1178233 .0175111 6.73 0.000 .0835021 .1521444 33 | 1.228413 .0132168 92.94 0.000 1.202508 1.254317 34 | 1.382604 .013293 104.01 0.000 1.35655 1.408657 35 | 1.465026 .0129689 112.96 0.000 1.439608 1.490445 36 | 1.570039 .0129592 121.15 0.000 1.54464 1.595439 37 | 1.381577 .010744 128.59 0.000 1.360519 1.402635 38 | 1.34243 .0108248 124.01 0.000 1.321214 1.363646 39 | 1.379476 .0109142 126.39 0.000 1.358085 1.400868 40 | 1.458273 .0107576 135.56 0.000 1.437188 1.479357 41 | 1.589232 .0104778 151.68 0.000 1.568696 1.609768 42 | 1.498226 .0104379 143.54 0.000 1.477768 1.518684 43 | 1.532517 .0104242 147.02 0.000 1.512086 1.552948 44 | 1.532034 .0104133 147.12 0.000 1.511624 1.552443 45 | 1.449899 .0107053 135.44 0.000 1.428917 1.470881 46 | 1.426521 .0107634 132.53 0.000 1.405425 1.447617 47 | 1.467442 .0106883 137.29 0.000 1.446493 1.488391 48 | 1.407553 .0105699 133.17 0.000 1.386836 1.428269 49 | .9807701 .0111266 88.15 0.000 .9589623 1.002578 50 | .7155438 .0122821 58.26 0.000 .6914714 .7396163 51 | .7631806 .0121047 63.05 0.000 .7394559 .7869053 52 | .7713434 .0122457 62.99 0.000 .7473423 .7953446 53 | 1.078575 .0110712 97.42 0.000 1.056876 1.100274 54 | 1.075737 .0110172 97.64 0.000 1.054144 1.097331 55 | 1.131961 .0109948 102.95 0.000 1.110412 1.153511 56 | 1.18028 .0109153 108.13 0.000 1.158886 1.201673 57 | 1.238787 .0106498 116.32 0.000 1.217914 1.25966 58 | 1.189614 .0106439 111.76 0.000 1.168752 1.210475 59 | 1.242056 .0107211 115.85 0.000 1.221043 1.263069 60 | 1.260019 .0107307 117.42 0.000 1.238987 1.281051 61 | 1.241156 .0109369 113.48 0.000 1.21972 1.262592 62 | 1.143917 .0114508 99.90 0.000 1.121474 1.166361 63 | 1.180159 .0114866 102.74 0.000 1.157646 1.202673 64 | 1.177861 .0114809 102.59 0.000 1.155359 1.200363 65 | .9073184 .0117993 76.90 0.000 .8841921 .9304447 66 | .6888819 .0126811 54.32 0.000 .6640275 .7137364 67 | .6847244 .0125508 54.56 0.000 .6601252 .7093236
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 92
68 | .6746297 .0128052 52.68 0.000 .6495321 .6997273 69 | .7355873 .0123501 59.56 0.000 .7113815 .7597931 70 | .6612228 .0128707 51.37 0.000 .6359967 .6864488 71 | .6619897 .0125959 52.56 0.000 .6373022 .6866771 72 | .7598892 .0125825 60.39 0.000 .735228 .7845505 73 | .9876605 .0111522 88.56 0.000 .9658025 1.009518 74 | 1.031078 .0110761 93.09 0.000 1.009369 1.052786 75 | 1.15943 .0110699 104.74 0.000 1.137733 1.181127 76 | 1.17978 .0109398 107.84 0.000 1.158339 1.201222 77 | .9938916 .011764 84.49 0.000 .9708345 1.016949 78 | .9002757 .0126111 71.39 0.000 .8755583 .924993 79 | .8593162 .0123202 69.75 0.000 .835169 .8834633 80 | .878467 .0123301 71.25 0.000 .8543004 .9026335 81 | .904177 .0121313 74.53 0.000 .8804 .9279539 82 | .8426913 .0124358 67.76 0.000 .8183176 .8670651 83 | .8644241 .012468 69.33 0.000 .8399873 .888861 84 | .8613698 .0124352 69.27 0.000 .8369973 .8857422 85 | .8469955 .0117478 72.10 0.000 .8239702 .8700208 86 | .7936476 .0118676 66.87 0.000 .7703875 .8169078 87 | .4888352 .0126325 38.70 0.000 .4640759 .5135945 88 | .3224027 .0133939 24.07 0.000 .2961512 .3486541 89 | .6492556 .0127654 50.86 0.000 .6242358 .6742755 90 | .6677248 .0127747 52.27 0.000 .6426868 .6927629 91 | .6959352 .0125845 55.30 0.000 .6712701 .7206003 92 | .676882 .0125876 53.77 0.000 .6522107 .7015533 93 | .5505227 .0118913 46.30 0.000 .5272161 .5738292 94 | .4052951 .0122092 33.20 0.000 .3813655 .4292246 95 | .3335128 .0123425 27.02 0.000 .3093219 .3577037 96 | .2697201 .0123453 21.85 0.000 .2455237 .2939166 | PROGRAM_TYPE | MOVIE | 2.267832 .1081623 20.97 0.000 2.055838 2.479826 MUSIC | 1.705145 .1121473 15.20 0.000 1.48534 1.92495 NETWORK SERIES | 2.500084 .1083261 23.08 0.000 2.287769 2.7124 NEWS | 2.585038 .1081143 23.91 0.000 2.373138 2.796938 OTHER | 2.004251 .1081251 18.54 0.000 1.79233 2.216173 PUBLIC AFFAIRS | 1.258734 .1131194 11.13 0.000 1.037024 1.480443 RELIGIOUS | 1.554103 .1086272 14.31 0.000 1.341197 1.767008 SPECIAL | 1.888516 .1089156 17.34 0.000 1.675045 2.101986 SPORTS-RELATED | 2.320136 .1089215 21.30 0.000 2.106654 2.533618 SYNDICATED | 2.658037 .1080529 24.60 0.000 2.446257 2.869816 TALK SHOW | 2.180242 .1164815 18.72 0.000 1.951942 2.408541 TEAM VS. TEAM | 2.643729 .1081436 24.45 0.000 2.431772 2.855687 TV MOVIE | 2.873031 .1094839 26.24 0.000 2.658446 3.087615 | _CONS | -44.74714 .8571097 -52.21 0.000 -46.42704 -43.06723 ------
. PREDICT DOUBLE DVHAT (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. POISSON DVIEW LSUBS I.QTR I.PROGRAM_TYPE [W=MIN] IF CALL2!="WGN",VCE(ROBUST) (FREQUENCY WEIGHTS ASSUMED)
ITERATION 0: LOG PSEUDOLIKELIHOOD = -7.317E+09 ITERATION 1: LOG PSEUDOLIKELIHOOD = -7.317E+09 ITERATION 2: LOG PSEUDOLIKELIHOOD = -7.317E+09
POISSON REGRESSION NUMBER OF OBS = 2226195 WALD CHI2(121) = 236095.16 PROB > CHI2 = 0.0000 LOG PSEUDOLIKELIHOOD = -7.317E+09 PSEUDO R2 = 0.1232
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 93
------| ROBUST DVIEW | COEF. STD. ERR. Z P>|Z| [95% CONF. INTERVAL] ------+------LSUBS | .1438816 .0003773 381.38 0.000 .1431421 .144621 | QTR | 2 | -.0403642 .0057103 -7.07 0.000 -.0515562 -.0291722 3 | .0081251 .0054409 1.49 0.135 -.0025388 .0187891 4 | -.003631 .0057158 -0.64 0.525 -.0148337 .0075717 5 | -.0319958 .0058144 -5.50 0.000 -.0433917 -.0205998 6 | -.0662635 .0066012 -10.04 0.000 -.0792016 -.0533253 7 | -.0517008 .0081796 -6.32 0.000 -.0677326 -.0356691 8 | -.1605779 .0078915 -20.35 0.000 -.176045 -.1451108 9 | -.1562441 .0063311 -24.68 0.000 -.1686528 -.1438353 10 | -.1077625 .0063787 -16.89 0.000 -.1202645 -.0952604 11 | -.0043812 .0063003 -0.70 0.487 -.0167296 .0079671 12 | .0452067 .0066381 6.81 0.000 .0321961 .0582172 13 | .0515193 .0070213 7.34 0.000 .0377577 .0652808 14 | .0852137 .007347 11.60 0.000 .0708139 .0996136 15 | .0629298 .0076269 8.25 0.000 .0479814 .0778783 16 | .0538469 .007559 7.12 0.000 .0390315 .0686623 17 | .0211679 .0080619 2.63 0.009 .0053669 .036969 18 | .0256899 .0079999 3.21 0.001 .0100104 .0413695 19 | .0310415 .0077456 4.01 0.000 .0158603 .0462226 20 | .0255476 .0076596 3.34 0.001 .0105352 .0405601 21 | .0518414 .0073143 7.09 0.000 .0375056 .0661773 22 | -.0599998 .0078077 -7.68 0.000 -.0753025 -.044697 23 | -.064715 .0076908 -8.41 0.000 -.0797887 -.0496412 24 | .0528757 .0071833 7.36 0.000 .0387967 .0669547 25 | .0695244 .0068362 10.17 0.000 .0561257 .082923 26 | .0939139 .0062436 15.04 0.000 .0816766 .1061513 27 | .0323004 .0062322 5.18 0.000 .0200855 .0445153 28 | .0462803 .006445 7.18 0.000 .0336482 .0589123 29 | .049229 .0054723 9.00 0.000 .0385036 .0599545 30 | .0548993 .0054102 10.15 0.000 .0442955 .0655031 31 | .0709653 .0055669 12.75 0.000 .0600544 .0818762 32 | .0555066 .0056508 9.82 0.000 .0444313 .0665819 33 | .0671678 .0055794 12.04 0.000 .0562325 .0781032 34 | .0744866 .0055976 13.31 0.000 .0635155 .0854577 35 | .0974672 .0055515 17.56 0.000 .0865865 .108348 36 | .0743489 .0055541 13.39 0.000 .0634631 .0852348 37 | .0060866 .0056296 1.08 0.280 -.0049472 .0171204 38 | -.0245117 .0057273 -4.28 0.000 -.0357371 -.0132863 39 | -.0562354 .0058107 -9.68 0.000 -.0676242 -.0448466 40 | -.0527522 .0059214 -8.91 0.000 -.064358 -.0411464 41 | -.0038394 .0054389 -0.71 0.480 -.0144994 .0068206 42 | -.0144533 .0055183 -2.62 0.009 -.0252691 -.0036376 43 | -.002349 .0056115 -0.42 0.676 -.0133474 .0086493 44 | -.0099698 .0055977 -1.78 0.075 -.020941 .0010014 45 | .1161506 .0057318 20.26 0.000 .1049165 .1273847 46 | .0745052 .0059537 12.51 0.000 .0628361 .0861742 47 | .0711979 .0058975 12.07 0.000 .0596391 .0827567 48 | .0686555 .0059085 11.62 0.000 .057075 .080236 49 | -.0632198 .0064554 -9.79 0.000 -.0758722 -.0505674 50 | -.1202913 .0065273 -18.43 0.000 -.1330845 -.1074981 51 | -.075557 .0061141 -12.36 0.000 -.0875405 -.0635735 52 | -.099436 .0059913 -16.60 0.000 -.1111787 -.0876932 53 | -.0817094 .0060283 -13.55 0.000 -.0935247 -.0698941 54 | -.1303938 .006266 -20.81 0.000 -.1426749 -.1181126 55 | -.1307452 .0061853 -21.14 0.000 -.1428681 -.1186222 56 | -.1676764 .0062616 -26.78 0.000 -.1799488 -.1554039 57 | -.2834885 .0061853 -45.83 0.000 -.2956115 -.2713655
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 94
58 | -.2269811 .0063858 -35.54 0.000 -.239497 -.2144651 59 | -.2236652 .0064077 -34.91 0.000 -.2362241 -.2111062 60 | -.2328892 .0063291 -36.80 0.000 -.245294 -.2204844 61 | -.3213019 .0062206 -51.65 0.000 -.3334941 -.3091097 62 | -.3156602 .0067162 -47.00 0.000 -.3288236 -.3024968 63 | -.3111057 .0065789 -47.29 0.000 -.3240001 -.2982114 64 | -.2701191 .0064481 -41.89 0.000 -.2827572 -.257481 65 | -.18445 .0061395 -30.04 0.000 -.1964832 -.1724167 66 | -.1702436 .0060778 -28.01 0.000 -.1821559 -.1583313 67 | -.1569216 .0060498 -25.94 0.000 -.1687791 -.1450641 68 | -.1379136 .0058737 -23.48 0.000 -.1494258 -.1264015 69 | -.0637328 .0057119 -11.16 0.000 -.0749279 -.0525377 70 | -.0660654 .0059463 -11.11 0.000 -.0777199 -.0544109 71 | -.0550917 .0057317 -9.61 0.000 -.0663256 -.0438578 72 | -.0322436 .0058708 -5.49 0.000 -.0437503 -.020737 73 | -.109195 .0065371 -16.70 0.000 -.1220075 -.0963825 74 | -.0929876 .0066532 -13.98 0.000 -.1060277 -.0799476 75 | -.0404637 .0060745 -6.66 0.000 -.0523695 -.0285579 76 | -.0187065 .0060024 -3.12 0.002 -.030471 -.0069419 77 | .0560817 .005903 9.50 0.000 .0445121 .0676513 78 | .094926 .0059303 16.01 0.000 .0833028 .1065491 79 | .0142654 .0059521 2.40 0.017 .0025995 .0259313 80 | .0022246 .0057521 0.39 0.699 -.0090493 .0134984 81 | .1626321 .0058247 27.92 0.000 .1512158 .1740483 82 | .1451897 .0059709 24.32 0.000 .1334869 .1568924 83 | .1447676 .0059707 24.25 0.000 .1330653 .1564699 84 | .1489201 .0059114 25.19 0.000 .1373339 .1605062 85 | .2247551 .0057503 39.09 0.000 .2134848 .2360254 86 | .1601844 .0058386 27.44 0.000 .148741 .1716279 87 | .139017 .0058443 23.79 0.000 .1275623 .1504717 88 | .1545366 .0059062 26.17 0.000 .1429607 .1661125 89 | .3029651 .005854 51.75 0.000 .2914916 .3144386 90 | .2427388 .0061362 39.56 0.000 .230712 .2547656 91 | .2236998 .0061354 36.46 0.000 .2116746 .235725 92 | .2243862 .0061228 36.65 0.000 .2123857 .2363868 93 | .0525787 .0053541 9.82 0.000 .042085 .0630725 94 | -.0253615 .0054584 -4.65 0.000 -.0360598 -.0146633 95 | .0263231 .0055771 4.72 0.000 .0153922 .037254 96 | -.0093492 .005579 -1.68 0.094 -.0202838 .0015854 | PROGRAM_TYPE | CHILDREN'S SHOW | -.0369056 .0083952 -4.40 0.000 -.05336 -.0204512 CHILDREN'S SPECIAL | .1262423 .0166034 7.60 0.000 .0937003 .1587844 DAYTIME SOAP | -.1649498 .0063768 -25.87 0.000 -.1774481 -.1524516 FINANCE | .0858773 .011404 7.53 0.000 .063526 .1082287 GAME SHOW | .2490914 .0057504 43.32 0.000 .2378209 .2603619 HEALTH | -.1121719 .0099416 -11.28 0.000 -.1316572 -.0926866 INSTRUCTIONAL | .2632866 .0111232 23.67 0.000 .2414855 .2850877 MOVIE | .2166601 .0065294 33.18 0.000 .2038627 .2294575 MUSIC | .347307 .0068479 50.72 0.000 .3338853 .3607286 MUSIC SPECIAL | .4133941 .0116583 35.46 0.000 .3905442 .436244 NETWORK SERIES | .3036001 .0057605 52.70 0.000 .2923097 .3148906 NEWS | .1389507 .0054326 25.58 0.000 .1283029 .1495984 OTHER | .0194778 .006085 3.20 0.001 .0075514 .0314041 PLAYOFF SPORTS | .745749 .0081007 92.06 0.000 .729872 .7616261 PSEUDO-SPORTS | .1643713 .0183304 8.97 0.000 .1284443 .2002982 PUBLIC AFFAIRS | .0888862 .0131758 6.75 0.000 .0630621 .1147103 RELIGIOUS | -.0601195 .0153305 -3.92 0.000 -.0901668 -.0300722 SPECIAL | .1974214 .0069015 28.61 0.000 .1838948 .210948 SPORTING EVENT | .4183168 .0063094 66.30 0.000 .4059506 .4306829 SPORTS ANTHOLOGY | .185243 .0156631 11.83 0.000 .1545439 .2159421 SPORTS-RELATED | .3816731 .0074327 51.35 0.000 .3671053 .3962408 SYNDICATED | .1220421 .0053689 22.73 0.000 .1115193 .132565
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 95
TALK SHOW | .2426192 .0052636 46.09 0.000 .2323027 .2529357 TEAM VS. TEAM | .4832244 .00576 83.89 0.000 .4719351 .4945138 TV MOVIE | .1724719 .0154323 11.18 0.000 .1422252 .2027186 | _CONS | 7.576474 .0083133 911.37 0.000 7.56018 7.592768 ------
. PREDICT DVHAT2 (OPTION N ASSUMED; PREDICTED NUMBER OF EVENTS)
. REPLACE DVHAT=DVHAT2 IF CALL2!="WGN" (2803509 REAL CHANGES MADE)
. REG TOTAL I.QTR I.PROGRAM_TYPE
SOURCE | SS DF MS NUMBER OF OBS = 2429800 ------+------F(120,2429679) = 2263.08 MODEL | 1.4534E+14 120 1.2112E+12 PROB > F = 0.0000 RESIDUAL | 1.3004E+152429679 535199874 R-SQUARED = 0.1005 ------+------ADJ R-SQUARED = 0.1005 TOTAL | 1.4457E+152429799 594990701 ROOT MSE = 23134
------TOTAL | COEF. STD. ERR. T P>|T| [95% CONF. INTERVAL] ------+------QTR | 2 | -703.2218 201.9944 -3.48 0.000 -1099.124 -307.3199 3 | -643.0193 183.152 -3.51 0.000 -1001.991 -284.0477 4 | -2242.58 202.0574 -11.10 0.000 -2638.606 -1846.555 5 | -3268.415 187.6459 -17.42 0.000 -3636.194 -2900.635 6 | -3878.927 202.2943 -19.17 0.000 -4275.417 -3482.438 7 | -4227.365 185.0062 -22.85 0.000 -4589.971 -3864.76 8 | -5064.761 202.3736 -25.03 0.000 -5461.406 -4668.115 9 | -4449.136 186.5335 -23.85 0.000 -4814.735 -4083.536 10 | -4960.724 203.169 -24.42 0.000 -5358.928 -4562.52 11 | -5314.615 192.7359 -27.57 0.000 -5692.371 -4936.86 12 | -5748.563 202.7493 -28.35 0.000 -6145.944 -5351.181 13 | -6157.166 196.5971 -31.32 0.000 -6542.489 -5771.843 14 | -6652.608 203.9765 -32.61 0.000 -7052.395 -6252.821 15 | -6561.772 200.4412 -32.74 0.000 -6954.63 -6168.914 16 | -6576.713 204.2625 -32.20 0.000 -6977.061 -6176.366 17 | -7357.472 201.1357 -36.58 0.000 -7751.691 -6963.253 18 | -7187.957 203.3808 -35.34 0.000 -7586.576 -6789.338 19 | -6804.625 203.3629 -33.46 0.000 -7203.209 -6406.041 20 | -6289.26 204.6559 -30.73 0.000 -6690.379 -5888.142 21 | -5315.89 205.0568 -25.92 0.000 -5717.794 -4913.986 22 | -4818.634 205.1919 -23.48 0.000 -5220.803 -4416.465 23 | -3851.317 205.1772 -18.77 0.000 -4253.457 -3449.177 24 | -3009.212 205.3061 -14.66 0.000 -3411.604 -2606.819 25 | -2734.585 205.0803 -13.33 0.000 -3136.535 -2332.635 26 | -1865.68 205.2066 -9.09 0.000 -2267.877 -1463.482 27 | -928.6863 205.3899 -4.52 0.000 -1331.243 -526.1293 28 | 207.641 205.3729 1.01 0.312 -194.8826 610.1646 29 | 4171.835 203.469 20.50 0.000 3773.043 4570.627 30 | 4285.637 203.4898 21.06 0.000 3886.804 4684.47 31 | 3695.606 203.5283 18.16 0.000 3296.698 4094.515 32 | 3300.325 203.4868 16.22 0.000 2901.498 3699.152 33 | 3967.887 203.4353 19.50 0.000 3569.161 4366.613 34 | 3828.527 203.4605 18.82 0.000 3429.752 4227.303 35 | 3603.372 203.3955 17.72 0.000 3204.724 4002.02 36 | 3645.825 203.4046 17.92 0.000 3247.159 4044.491 37 | 4035.531 203.3024 19.85 0.000 3637.065 4433.997 38 | 3073.574 203.2276 15.12 0.000 2675.255 3471.893
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 96
39 | 2725.932 203.2495 13.41 0.000 2327.57 3124.294 40 | 2799.303 203.2404 13.77 0.000 2400.959 3197.647 41 | 4397.58 203.293 21.63 0.000 3999.133 4796.028 42 | 3097.781 203.5235 15.22 0.000 2698.882 3496.68 43 | 2859.611 203.4021 14.06 0.000 2460.95 3258.272 44 | 2990.655 203.3773 14.70 0.000 2592.043 3389.267 45 | 2936.527 203.3631 14.44 0.000 2537.942 3335.112 46 | 2145.338 203.309 10.55 0.000 1746.86 2543.817 47 | 2190.503 203.3211 10.77 0.000 1792.001 2589.005 48 | 2385.475 203.2878 11.73 0.000 1987.038 2783.912 49 | 506.377 202.6024 2.50 0.012 109.2835 903.4705 50 | -162.891 203.4355 -0.80 0.423 -561.6175 235.8355 51 | 355.9721 203.9273 1.75 0.081 -43.7183 755.6626 52 | 255.3951 203.8364 1.25 0.210 -144.117 654.9073 53 | 1630.648 204.4262 7.98 0.000 1229.98 2031.317 54 | 707.2594 204.6893 3.46 0.001 306.0756 1108.443 55 | 354.8481 204.8018 1.73 0.083 -46.5562 756.2524 56 | 455.2417 204.8032 2.22 0.026 53.83467 856.6487 57 | 821.2846 203.3211 4.04 0.000 422.7824 1219.787 58 | -40.54262 203.3634 -0.20 0.842 -439.1277 358.0425 59 | 31.27672 203.3118 0.15 0.878 -367.2072 429.7606 60 | 534.2764 203.4969 2.63 0.009 135.4296 933.1232 61 | 1760.381 202.6862 8.69 0.000 1363.123 2157.639 62 | 935.0568 202.848 4.61 0.000 537.4818 1332.632 63 | 1114.713 202.8584 5.50 0.000 717.1181 1512.309 64 | 1897.958 203.1495 9.34 0.000 1499.792 2296.123 65 | 2594.49 202.6489 12.80 0.000 2197.305 2991.674 66 | 2545.116 202.8146 12.55 0.000 2147.607 2942.626 67 | 3067.614 202.8221 15.12 0.000 2670.09 3465.139 68 | 3823.166 203.0274 18.83 0.000 3425.24 4221.093 69 | 2938.222 203.5461 14.44 0.000 2539.279 3337.166 70 | 2756.713 203.8026 13.53 0.000 2357.267 3156.159 71 | 2995.1 203.9133 14.69 0.000 2595.437 3394.763 72 | 4177.738 204.0758 20.47 0.000 3777.756 4577.719 73 | 5437.149 203.81 26.68 0.000 5037.688 5836.609 74 | 5601.568 204.2257 27.43 0.000 5201.293 6001.843 75 | 6293.684 203.1996 30.97 0.000 5895.42 6691.948 76 | 7165.427 203.3164 35.24 0.000 6766.934 7563.921 77 | 9387.859 203.2457 46.19 0.000 8989.505 9786.214 78 | 7821.681 203.5545 38.43 0.000 7422.721 8220.64 79 | 7928.778 202.5281 39.15 0.000 7531.83 8325.726 80 | 9557.751 203.7936 46.90 0.000 9158.323 9957.179 81 | 13757.6 205.7672 66.86 0.000 13354.3 14160.89 82 | 10933.93 206.8009 52.87 0.000 10528.61 11339.26 83 | 11833.45 204.9667 57.73 0.000 11431.73 12235.18 84 | 11495.01 207.7543 55.33 0.000 11087.82 11902.2 85 | 15587.68 202.4936 76.98 0.000 15190.8 15984.56 86 | 11429.3 207.0942 55.19 0.000 11023.4 11835.2 87 | 11684.11 205.3418 56.90 0.000 11281.64 12086.57 88 | 11334.22 207.5208 54.62 0.000 10927.48 11740.95 89 | 17974.1 198.8672 90.38 0.000 17584.33 18363.88 90 | 10441.94 203.6703 51.27 0.000 10042.75 10841.12 91 | 7821.802 193.1452 40.50 0.000 7443.244 8200.36 92 | 8818.162 203.1754 43.40 0.000 8419.945 9216.379 93 | 6824.471 199.9592 34.13 0.000 6432.558 7216.384 94 | 3578.394 201.5566 17.75 0.000 3183.35 3973.438 95 | 3665.891 183.1655 20.01 0.000 3306.893 4024.889 96 | 1759.359 201.7407 8.72 0.000 1363.955 2154.764 | PROGRAM_TYPE | CHILDREN'S SHOW | 517.3722 262.4616 1.97 0.049 2.956667 1031.788 CHILDREN'S SPECIAL | 4786.64 656.4052 7.29 0.000 3500.109 6073.171 DAYTIME SOAP | 10840.93 202.4954 53.54 0.000 10444.05 11237.82
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 97
FINANCE | 6693.552 263.675 25.39 0.000 6176.758 7210.345 GAME SHOW | 9463.336 189.0891 50.05 0.000 9092.728 9833.944 HEALTH | 5523.091 337.5952 16.36 0.000 4861.416 6184.766 INSTRUCTIONAL | 8457.582 359.8697 23.50 0.000 7752.25 9162.914 MOVIE | 4182.863 202.8368 20.62 0.000 3785.31 4580.416 MUSIC | 25195.92 262.7388 95.90 0.000 24680.96 25710.88 MUSIC SPECIAL | 13129.72 404.8202 32.43 0.000 12336.29 13923.16 NETWORK SERIES | 10993.32 184.9207 59.45 0.000 10630.88 11355.76 NEWS | 13558.61 173.9608 77.94 0.000 13217.66 13899.57 OTHER | 4788.031 177.4436 26.98 0.000 4440.248 5135.815 PLAYOFF SPORTS | 33761.46 250.085 135.00 0.000 33271.3 34251.61 PSEUDO-SPORTS | 2225.757 689.3521 3.23 0.001 874.6512 3576.863 PUBLIC AFFAIRS | 6277.813 366.7697 17.12 0.000 5558.957 6996.669 RELIGIOUS | 2297.807 224.6587 10.23 0.000 1857.484 2738.13 SPECIAL | 12099.48 241.6763 50.06 0.000 11625.8 12573.16 SPORTING EVENT | 8456.23 206.3147 40.99 0.000 8051.86 8860.599 SPORTS ANTHOLOGY | 3932.692 386.9434 10.16 0.000 3174.296 4691.087 SPORTS-RELATED | 13414.3 215.5236 62.24 0.000 12991.88 13836.72 SYNDICATED | 8640.874 172.939 49.96 0.000 8301.92 8979.829 TALK SHOW | 7190.433 170.6396 42.14 0.000 6855.985 7524.88 TEAM VS. TEAM | 27458.73 200.9249 136.66 0.000 27064.93 27852.54 TV MOVIE | 16491.05 753.7467 21.88 0.000 15013.73 17968.37 | _CONS | 10.16624 218.0302 0.05 0.963 -417.1652 437.4977 ------
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 98
APPENDIX E: SHARES BASED ON TOTAL VIEWING REGRESSIONS
Table Appendix E: Viewing Shares of Compensable Programming, by Claimant Category Year Claimant Category Model 4 Commercial Television 19.42% Devotionals 0.42% Program Suppliers 71.95% 2010 JSC 8.21% Total 100%
Commercial Television 21.24% Devotionals 0.50% Program Suppliers 70.90% 2011 JSC 7.36% Total 100%
Commercial Television 20.45% Devotionals 0.32% Program Suppliers 72.21% 2012 JSC 7.03% Total 100%
Commercial Television 20.43% Devotionals 0.24% Program Suppliers 71.92% 2013 JSC 7.40% Total 100%
Commercial Television 20.38% Devotionals 0.37% Program Suppliers 71.74% JSC 7.50%
Average: 2010 Average: 2013 through Total 100%
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 99
APPENDIX F: FEES-BASED REGRESSION MODELS
Table F-1 Fees Regression Using Accounting Period Data
. XTREG LFEES MINCOM DEV PS SPORTS LAG_SUBS NDIST,FE CLUSTER(SYS_ID)
FIXED-EFFECTS (WITHIN) REGRESSION NUMBER OF OBS = 28 GROUP VARIABLE: ID NUMBER OF GROUPS = 6
R-SQ: WITHIN = 0.2773 OBS PER GROUP: MIN = 1 BETWEEN = 0.6431 AVG = 4.7 OVERALL = 0.7554 MAX = 7
F(3,5) = . CORR(U_I, XB) = 0.8178 PROB > F = .
(STD. ERR. ADJUSTED FOR 6 CLUSTERS IN SYS_ID) ------| ROBUST LFEES | COEF. STD. ERR. T P>|T| [95% CONF. INTERVAL] ------+------MINCOM | 5.69E-07 1.19E-06 0.48 0.654 -2.50E-06 3.64E-06 DEV | 8.65E-07 3.55E-06 0.24 0.817 -8.25E-06 9.98E-06 PS | 1.13E-06 9.30E-07 1.21 0.280 -1.27E-06 3.52E-06 SPORTS | -3.05E-07 7.06E-07 -0.43 0.684 -2.12E-06 1.51E-06 LAG_SUBS | 9.64E-09 4.19E-09 2.30 0.070 -1.12E-09 2.04E-08 NDIST | -.043663 .0424443 -1.03 0.351 -.1527696 .0654437 _CONS | 13.9723 .0664051 210.41 0.000 13.8016 14.143 ------+------SIGMA_U | 2.7152556 SIGMA_E | .10187303 RHO | .99859432 (FRACTION OF VARIANCE DUE TO U_I) ------
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 100
Table F-2 Fees Regression Using Monthly Data
. xtreg lfees mincom dev ps sports lag_subs ndist,fe cluster(sys_id)
Fixed-effects (within) regression Number of obs = 197 Group variable: id Number of groups = 6
R-sq: within = 0.1762 Obs per group: min = 10 between = 0.7207 avg = 32.8 overall = 0.8159 max = 47
F(5,5) = . corr(u_i, Xb) = 0.8663 Prob > F = .
(Std. Err. adjusted for 6 clusters in sys_id) ------| Robust lfees | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------mincom | -2.16e-06 1.10e-06 -1.96 0.108 -5.00e-06 6.79e-07 dev | -1.94e-07 3.16e-06 -0.06 0.953 -8.33e-06 7.94e-06 ps | 4.77e-07 1.89e-07 2.52 0.053 -1.00e-08 9.64e-07 sports | 2.82e-07 2.07e-07 1.36 0.232 -2.51e-07 8.14e-07 lag_subs | 4.90e-08 6.10e-09 8.04 0.000 3.34e-08 6.47e-08 ndist | .0011795 .0025725 0.46 0.666 -.0054333 .0077923 _cons | 12.07874 .0462067 261.41 0.000 11.95997 12.19752 ------+------sigma_u | 2.6650684 sigma_e | .10868018 rho | .99833979 (fraction of variance due to u_i) ------
Jeffrey S. Gray, Ph.D., Written Direct Testimony, 2010-13 Satellite Allocation | 101
Before the COPYRIGHT ROYALTY JUDGES Washington, D.C.
______) In the Matter of ) ) Distribution of the ) Docket No. 14-CRB-0011-SD (2010-13) ) 2010, 2011, 2012, and 2013 ) Satellite Royalty Funds ) ______)
DIRECT TESTIMONY OF
HOWARD B. HOMONOFF
MARCH 22, 2019
TESTIMONY OF HOWARD B. HOMONOFF
COPYRIGHT ROYALTY JUDGES
2010-2013 SATELLITE ALLOCATION COPYRIGHT ROYALTY DISTRIBUTION
PROCEEDING
MARCH 22, 20191
I. Introduction/Personal Background and Experience
1. I have been a lawyer, business executive, strategic consultant and columnist in the media and entertainment industry for roughly the last 30 years. I currently serve as Managing Director of Homonoff Media Group, LLC, a New York-based media consulting
firm that provides strategic business consulting services to leading executives and companies throughout the media world. My work in strategy consulting leverages my more than 20 years of experience in the cable programming network, broadcasting, and digital media industries. That experience includes work as a senior executive for cable operators and programming networks, with specific responsibilities related to the acquisition and distribution of programming; as a consultant and business advisor to a variety of media and communications clients, again, with a focus on programming acquisition and distribution; and as an academic and lecturer with a specific focus around content acquisition and distribution. Also, I worked on Capitol Hill and in private law practice in the regulation of the cable and broadcasting businesses, and have testified in administrative and court proceedings, including a previous cable royalty distribution proceeding before the Copyright Royalty Judges (“Judges”)2 as an industry expert.
2. In addition to my work at Homonoff Media Group, I am a weekly contributor to Forbes.com, where I regularly cover innovative topics and trends related to the media industry; an Adjunct Professor of Media Economics and Media Management and Leadership
1 While the opinions presented in this testimony are completely based upon the information and documents made available to me to date, I reserve the right to amend my testimony based upon my review of any additional information or documentation and pursuant to the regulations of the Copyright Royalty Judges.
2 See Written Direct Testimony of Howard B. Homonoff, Docket No. 2007-3 CRB CD 2004-2005 (filed June 1, 2009; corrected September 28, 2009) (“2009 Testimony”).
Testimony of Howard B. Homonoff March 22, 2019 1
in The New School’s graduate program for media management; and Senior Fellow at Columbia Business School’s Institute on Tele-Information (CITI). In academia, I have previously served as the Program Director and Associate Professor for the Paul F. Harron MBA/MS Program in Television Management at Drexel University; as a faculty member at the Practicing Law Institute on the Cable and Broadband Industry; and as a Lecturer at the Wharton School at the University of Pennsylvania and Duke University Fuqua School of Business, both related to the latest developments in the media industry.
3. Prior to Homonoff Media Group, I served as Senior Vice President and part of
the leadership team at MediaLink, a strategic advisory firm in which I provided consulting
services to C-suite executives in media, technology, finance, and marketing. Prior to that, I
served as one of the senior Directors in the Entertainment, Media & Communications
advisory practice at PwC (formerly PricewaterhouseCoopers). My work in strategy
consulting has included helping create growth strategies for traditional and digital media
companies; advising on strategic partnerships among media companies, brands, agencies and
tech firms; and intellectual property exploitation and development. Among my many client
assignments over my career, I have worked with global companies such as
Comcast/NBCUniversal, Fox Cable Networks, The Walt Disney Company, Warner Bros.,
Procter & Gamble, and Verizon; digital-first publishers such as Awesomeness TV; and
advertising industry clients such as the Association of National Advertisers and the American
Association of Advertising Agencies.
4. As an executive in the media industry, I have served as Vice President and
General Manager of CNBC Strategic Ventures, overseeing CNBC’s business development
efforts and managing the exploitation of CNBC content on new media platforms; as General
Counsel of NBC Cable Networks (which included CNBC, MSNBC, Shop-at-Home and
CNBC World), counseling the company on a range of strategic and operational matters and
negotiating numerous programming affiliation agreements; and as Director of Corporate and
Testimony of Howard B. Homonoff March 22, 2019 2
Legal Affairs at Continental Cablevision, then the nation’s third-largest cable operator,
working as part of a senior management team on strategy and execution for issues such as
retransmission consent for broadcast television stations. In these positions, I was involved
directly in the drafting, negotiation, execution, and implementation of agreements for carriage
of cable programming networks, as well as broadcast television stations. Prior to working in
the industry, I served as Counsel for the U.S. House of Representatives’ Subcommittee on
Telecommunications and Finance, where I was responsible for the oversight of the Federal
Communications Commission (“FCC”) and drafting and passage of major
telecommunications and securities legislation.
5. I have served as an expert witness for various major media companies
regarding digital media, broadcasting, and broadband in proceedings before the FCC, the
Judges, and state and federal courts. I am a graduate of Cornell University with a B.A. in
Government with Distinction in All Subjects and received my J.D. from New York
University School of Law.
6. A more detailed summary of my background is attached hereto as Exhibit 1.
II. Purpose and Summary of Testimony
7. In the course of my preparation for this proceeding, counsel for the Motion
Picture Association of America, Inc., and its represented claimants (“Program Suppliers”) provided me with documents from prior proceedings before the Judges. I understand that for the purposes of this proceeding, Program Suppliers (whose content is also referenced herein as Program Suppliers/Entertainment (“PSE”) content) are seeking an allocation of the 2010-
2013 statutory license royalties for series, movies, non-team sports, and special programs aired on broadcast television signals distantly retransmitted by satellite systems. My testimony is based upon my experience, as well as research that I conducted from publicly available sources.
Testimony of Howard B. Homonoff March 22, 2019 3
8. I have prepared this testimony at the request of Program Suppliers, who asked
that I provide an industry expert perspective on the process by which satellite operators
(sometimes referred to as “satellite providers”) negotiate for carriage of programming on their direct-to-home satellite systems; the factors that influence their programming carriage decisions; and an analysis of actual marketplace behavior of satellite operators with regard to acquisition and value of programming.
9. I would like to add several clarifying points on terminology used in this report.
For example, throughout my testimony, I make use of the FCC term “multichannel video programming distributors” (or “MVPDs”) to refer collectively to satellite and cable operators.
My testimony is generally focused on the decision-making and marketplace concerning satellite operators, but where appropriate I note the applicability of the analysis for MVPDs in general or only for cable operators. The relevant period for my analysis is 2010-2013.
10. Several other terminology points are worth noting. I refer to the channels carried by satellite operators and all MVPDs as “cable networks.” In business parlance, these are sometimes interchangeably referred to as “cable networks” or “cable programming networks,” but, in my testimony, I consistently use the term “cable networks.” I also use the term “license fees” to refer to the monthly payments that satellite operators make to cable networks for the right to deliver those networks to their subscribers. I use the term
“subscriber fees” only when referring to the monthly direct payments consumers pay to satellite operators and MVPDs for their receipt of multichannel video services.
11. I would also like to add an explanatory note with respect to the approach I have taken herein. Overall, my analysis is drawn from my direct, personal business experience of negotiating for and advising executives of cable programming creators and distributors throughout my media career, and is informed by the specific research reflected.
First, I have analyzed the process by which satellite operators (similar to their competitors in
Testimony of Howard B. Homonoff March 22, 2019 4
cable) make decisions about what programming to deliver to their subscribers. Second, I
have relied upon what in my experience are the most important business metrics that the
satellite industry uses to determine the value it places on that programming—which channels
they decide to carry, including the mix of programming categories contained in that
programming and the per month wholesale prices (what I refer to herein as “license fees” but
also called “affiliate fees” or “subscriber fees”) that satellite operators pay for that
programming they have decided to carry.
12. I am aware that in prior proceedings before the Judges, other experts have
focused on the programming expenditures of cable networks rather than that of MVPDs.3
However, cable network expenditures for the production or acquisition of programming are
not as useful a benchmark for assessing the value MVPDs place on cable network
programming as the direct expenditures made by the MVPDs. MVPDs do not directly pay
for the individual programming on the cable networks they carry. Rather, MVPDs are
focused on their wholesale programming costs, as they must directly balance the prices their
customers pay for the retail distribution of programming against the MVPDs’ wholesale cost
of acquisition. In my experience, the value of programming to cable networks is as much due
to its attractiveness to advertisers as to the public, given that cable networks receive a far
greater percentage of advertising revenues than MVPDs. Accordingly, I have chosen to
focus my analysis on MVPD and satellite operator direct payments related to programming.
13. I would also take note that other experts and prior determinations of the
Judges have used cable operator surveys, such as the Bortz survey, as a measuring stick for
assessing relative value of programming genres.4 In my direct experience over several
3 See, e.g., Rebuttal Testimony of James Trautman, Docket No. 2007-3 CRB CD 2004-2005 at 10-14 (December 11, 2009); Written Direct Testimony of Dr. Mark A. Israel, Docket No. 14-CRB-0010-CD (2010-13) at 24-30 (December 22, 2016); see also 84 Fed. Reg. 3552, 3601-02 (February 12, 2019).
4 See, e.g., 75 Fed. Reg. at 57063, 57065-70 (September 17, 2010).
Testimony of Howard B. Homonoff March 22, 2019 5
decades in the distribution of cable programming networks, I have never heard any discussion
of the Bortz survey in negotiations between cable network and MVPD executives, reinforcing
my view expressed in this testimony that the real-world decisions by satellite operators and
MVPDs on how they spend money on this programming remain the best reflector of their assessments of market value.5
14. In summary, I provide the following findings and opinions related to this
proceeding. First, the process by which satellite operators make their programming decisions
is not very different from that undertaken by cable operators. As I outlined in my testimony
in the 2004-2005 Cable Phase I proceeding, these decisions are typically driven by
programming executives at corporate headquarters (“Corporate Programming Executives”).
Second, in this process, Corporate Programming Executives determine which cable networks
and which programming genres of cable networks to carry, and how much money to spend as
a relative matter for each different network and type of programming carried. These
marketplace decisions provide the evidence of how they value this programming in relation
to its ability to recruit and retain subscribers, and at prices sustainable for their business.
Third, based on my review of the 2010-2013 time period, and relative to the other program
categories at issue in this proceeding, PSE content has clearly demonstrated the highest
marketplace value to satellite operators in attracting and retaining subscribers. PSE content is
the most widely-distributed to satellite subscribers among the programming categories under
consideration in this proceeding and represents the largest percentage of monthly license fees
paid by MVPDs as a whole and by satellite operators to the most widely-distributed cable
networks. Specifically, 80% of the top 50 most widely distributed cable networks (“Top 50
Networks”) would be categorized as Program Suppliers’ programming networks; 91.6% of
the actual programming on the Top 50 Networks is PSE content; and 58.5% of the total
5 My position that the satellite operators’ marketplace behavior is the relevant perspective is consistent with the Judges’ determination that cable systems are the “buyers” of programming in the hypothetical market. See 84 Fed. Reg. at 3555. Testimony of Howard B. Homonoff March 22, 2019 6
license fees paid by MVPDs to the Top 50 Networks are to cable networks appropriately
categorized as Program Suppliers.6
III. The Satellite Operator Programming Decision-Making Process Demonstrates the Centrality of Corporate Programming Executives.
15. As the Judges and their predecessors have recognized, most of the
programming negotiations that cable operators conduct concern the carriage of whole cable
networks. Satellite operators, like their cable counterparts, do not typically acquire either
individual programs or categories of programming for retransmission.7 Instead, they acquire
bundles of programming in the form of nationally-distributed cable networks and local
broadcast stations. Accordingly, the Judges have analyzed a “hypothetical marketplace” in
which broadcasters would negotiate with cable operators for the cable operators’ right to
carry their broadcast signals in distant (non-local) markets. The Judges and their
predecessors envisioned that hypothetical market operating “in the same manner as cable networks currently offer programming packages….”8 The Judges’ reliance on the cable
network marketplace for guidance on a hypothetical distant signal marketplace is consistent
with my experience. A hypothetical marketplace for the acquisition of programming on
distant signals is closely analogous to the market for cable networks, which represents a large
majority of the programming MVPDs provide to their subscribers. Therefore, in the 2004-
6 I have chosen in this testimony on the Top 50 Networks in order to focus on those nationally-based services that the MVPD marketplace has determined are most valued by their subscribers. I would note that the fuller list of all possible programming networks in 2010-2013 (269 identified by S&P Market Intelligence (formerly SNL Kagan)) would ultimately provide a similar picture of predominance by PSE programming networks. PSE programming networks accounted for roughly 66% of that larger list of 269 programming networks. However, even that percentage is skewed by the presence of 48 regional sports networks (“RSNs”) among the 219 programming networks below the Top 50 Networks. While any satellite operator may choose to carry almost any of the 179 PSE networks among the total of 269, each subscriber is only generally granted access to a tiny handful of RSNs based on geography. Thus, choices made among the nationally distributed cable networks provide a truer picture of the overall cable network marketplace.
7 See Report of the Copyright Arbitration Royalty Panel to the Librarian of Congress, Docket No. 2001-8 CARP CD 98-99 at 11 (Oct. 21, 2003) (“1998-99 CARP Report”); see also 84 Fed. Reg. at 3557-58 (recognizing that cable operators are required by law to acquire and retransmit entire signals, and not individual programs or categories of programming).
8 1998-99 CARP Report at 12. Testimony of Howard B. Homonoff March 22, 2019 7
2005 Cable Phase I proceeding, I presented testimony examining the cable network marketplace as a guide in analyzing the distant signal programming marketplace. In this testimony, I am presenting a similar analysis focused on the 2010-2013 time frame, albeit noting appropriate differences in approach or programming value equation for satellite operators as opposed to cable operators.
16. The process by which cable and satellite operators construct their programming line-ups is fundamentally consistent. Both cable and satellite operators typically have a senior executive at their “corporate” or “headquarters” location, often with a title such as Executive Vice President or Senior Vice President, who has overall responsibility for programming acquisition operations (hereinafter referred to as “Corporate
Programming Executives”). These Corporate Programming Executives and the teams that report to them are responsible for the full range of activities in the lifecycle of programming acquisition negotiations and implementation. The only significant point of differentiation between cable and satellite operators in this area is that cable operators have cable systems managed locally throughout the country, while satellite operators deliver all of their programming through a centralized system (although different regions and subscribers may still receive different programming).
17. The Corporate Programming Executives are generally responsible for fielding inquiries from all of the programmers (i.e., cable networks) interested in launching or continuing the carriage of their programming by the MVPD, and thus, the Corporate
Programming Executives see first-hand the offerings from virtually all programmers. This team evaluates proposals from large media companies with ownership of multiple, well- established cable networks, such as The Walt Disney Company, Fox, NBC Universal, Turner
Broadcasting (now part of Warner Media) and others, as well as from start-up networks testing the waters for their first carriage and, potentially, investment funding, as well. The
Testimony of Howard B. Homonoff March 22, 2019 8
Corporate Programming Executives screen submitted materials, hear live pitches, and
determine which programming and which programmers are the most promising candidates
for carriage either across an entire subscriber “footprint” or for a more limited number of
subscribers.
18. Particularly with new cable networks, and depending on such factors as the
content of the cable network in question, relationships in the industry, and the quality of
initial presentations, the Corporate Programming Executives will seek input from other
interested stakeholders on the value of adding the particular cable network on one or more
systems. Those stakeholders generally include corporate executives in senior management,
marketing, research, finance, and public relations, and may include (in the case of cable
operators only) general managers and other personnel in the field with local programming
responsibilities. Corporate Programming Executives may also conduct their own formal or
informal research into whether and to what extent a cable network is carried by competitors,
as well as other marketplace developments such as investments made by cable networks on
new programming or shifts in programming formats. Corporate Programming Executives
will also leverage lessons learned from prior experience with a programming category,
network owner, or even particular talent. All of this input is synthesized as part of the
process that Corporate Programming Executives undertake in weighing the relative costs and
benefits of carrying any particular cable network.
19. Once an MVPD has preliminarily decided to pursue a carriage agreement with
a cable network, the Corporate Programming Executives generally take the lead in the
negotiating process. This often begins with the drafting or revising of a term sheet between the MVPD and the cable network until the parties have resolved key business terms such as price (monthly license fees paid by the MVPD to the cable network), tiering, launch and distribution commitments, timing, marketing support, and potential advertising partnerships.
Testimony of Howard B. Homonoff March 22, 2019 9
The Corporate Programming Executives usually work closely in this process with senior
corporate executives such as the Chief Executive Officer, Chief Operating Officer, Chief
Financial Officer, Chief Marketing Officer, and others. After the resolution of those
fundamental business terms and conditions, the legal and business affairs team for the cable
network will generally work through the negotiation of a long-form agreement with the
MVPD’s corporate programming group and their attorneys.
20. After a programming affiliation agreement has been formally executed, the
Corporate Programming Executives disseminate information on the key rights and responsibilities under the agreement, arrange the actual launch and/or additional rollout of the cable network to subscribers, and monitor compliance with the agreement. For cable operators only, implementation at the local level typically follows the framework established
at corporate headquarters. For satellite operators, they will execute programming decisions
entirely at a centralized corporate level.
21. In addition to the negotiation and implementation of specific programming
agreements, the Corporate Programming Executives work to develop the MVPD’s overall
channel line-up strategy, in concert with a corporate strategy or product development group.
The challenge is to develop and maintain an overarching approach to the delivery of
programming to subscribers that is sensitive to changes in subscriber demands, keeps abreast of developing trends in programming content, and supports the financial and operational goals of the MVPD as a whole. Each company’s strategic approach becomes most evident in its choice of the overall mix of cable networks and genres delivered to subscribers on everything from the most inexpensive tier of broadcast basic service all the way through to the more expensive and niche-oriented digital tiers in areas such as sports, ethnic programming, or video-on-demand.
Testimony of Howard B. Homonoff March 22, 2019 10
III. Subscriber Impact is a Key Factor that Influences MVPD, and Especially Satellite Operator, Programming Decisions.
22. While several factors affect satellite operator programming carriage decisions, ultimately, what counts is the impact of such decisions on subscriber behavior. These programming decisions, as dictated by the technology and business objectives, are based on their understanding of what programming their subscribers value and what will attract and retain those subscribers. As satellite operators make determinations of what programming to carry and what to pay for it, satellite operators depend even more on the impact on video subscribers than their cable counterparts. While cable operators are most likely to provide additional services (such as broadband and telephone service), satellite operators do not have such supplementary services and thus rely, to a far greater extent than cable operators, on video subscriber growth to be financially successful. Throughout this section I will focus specifically on the market perspective of satellite operators.
23. Adding and retaining subscribers is critical to enhancing the value of a satellite company, thus, actual satellite operator behavior in the market should follow the preferences of their subscribers. This could not be clearer from reviewing the financial statements of the two national satellite operators. As DIRECTV stated in its 2011 10-K: “Our vision is to provide customers with the best video experience in the United States both inside and outside of the home by offering subscribers unique, differentiated and compelling programming. . . .”9 DISH Network Corporation (“DISH”) made a similar point in its 2012
10-K: “Our business strategy is to be the best provider of video services in the United States
9 See DIRECTV Annual Report Pursuant to Section 13 or 15(d) of the Securities and Exchange Act of 1934, Form 10-K, at 4 (for the fiscal year ended December 31, 2011), available at https://www.sec.gov/Archives/edgar/data/1465112/000104746912001379/a2207454z10-k.htm (last visited March 21, 2019) (“DIRECTV 2011 10-K”).
Testimony of Howard B. Homonoff March 22, 2019 11
by providing high-quality products, outstanding customer service, and great value.”10 This
emphasis is entirely consistent with my experience interacting with these companies.
24. The size of a satellite operator’s subscriber base is critical to its valuation in the financial marketplace. In its 10-Ks in both 2011 and 2014 (at the beginning and end of
the period under examination in this proceeding), DIRECTV identified its top-listed “Key
Strength” as a “Large Subscriber Base.”11 I made a similar point with respect to the
importance of subscribers to cable operators in my testimony in the 2004-2005 Cable Phase I
proceeding, noting the perspective from one market analyst firm that identified “8 Metrics
[that] Matter Most” for the valuation of Time Warner Cable, with the top 5 all related to the
attraction and retention of subscribers—whether video, high speed data, or voice (the cable
“triple play”).12 The importance of attracting and retaining subscribers for both cable
operators and satellite operators was no less important in 2010-2013 than it was in 2004-2005
or than it is today. As I note above, the fact that satellite operators lack additional
competitive services places generation of revenue all the more critically on the numbers of video subscribers. Notably, most market analysts have attributed the most recent fall-off in stock performance of DISH quite directly to “continued subscriber losses.”13
25. Beyond the gross numbers of subscribers (and the rates of subscriber growth
and subscriber losses), the revenue generated on a per-subscriber basis (generally referred to
10 See DISH Network Corporation Annual Report Pursuant to Section 13 or 15(d) of the Securities and Exchange Act of 1934, Form 10-K, at 1 (for the fiscal year ended December 31, 2011), available at https://www.sec.gov/Archives/edgar/data/1001082/000110465913011967/a13-1215_110k.htm (last visited March 21, 2019) (“DISH 2012 10-K”).
11 See DIRECTV 2011 10-K at 3; see also, DIRECTV Annual Report Pursuant to Section 13 or 15(d) of the Securities and Exchange Act of 1934, Form 10-K, at 4 (for the fiscal year ended December 31, 2014), available at https://www.sec.gov/Archives/edgar/data/1465112/000104746915001196/a2223104z10-k.htm (last visited March 21, 2019) (“DIRECTV 2014 10-K”).
12 See Media Metrics - Time Warner Cable, Soleil Equity Research, March 24, 2009 at 3.
13 “Subscriber Losses Will Continue To Be A Drag On DISH Network’s Revenues In 2019,” Forbes (February 14, 2019), available at https://www.forbes.com/sites/greatspeculations/2019/02/14/subscriber-losses-will- continue-to-be-a-drag-on-dish-networks-revenues-in-2019/#4ec8c8916762 (last visited March 21, 2019).
Testimony of Howard B. Homonoff March 22, 2019 12
as “average revenue per subscriber” or “ARPU”) is a crucial measurement of a satellite
operator’s financial well-being. As DIRECTV noted in 2011: “Our revenue growth in
DIRECTV U.S. has been generated by increases in the total number of subscribers and in
ARPU.”14 Revenue generated by monthly subscriber fees overwhelmingly dominated the overall revenue picture at DISH and DIRECTV during the 2010-2013 time frame. Between
2010 and 2013, DISH earned revenues totalling roughly $52.3 billion, and over 99% of all of
its revenues came from subscribers.15 DIRECTV was not separately breaking out different categories of revenue during this period of time, but on its 2010 10-K, DIRECTV reported total revenues of $20.268 billion for its U.S. business. Given its reported ARPU for its 19.2 million average subscribers was $89.71 per month, this means that the overwhelming percentage of the company’s U.S. revenues derived from its subscribers, just as with DISH.16
26. In addition to the essential need for growing the numbers of subscribers and
revenues from those subscribers, cable and satellite operators must carefully manage the costs
associated with acquiring the rights to deliver programming to those subscribers, referred to
as the “affiliate fees” paid to the programming networks. In its 2010 10-K, DIRECTV identified one of its “key pillars” of growth as “Strategically Manag[ing] Content Cost
Growth.”17 The company noted that “[p]rogramming costs are DIRECTV’s largest expense and as a result, we must manage these costs as effectively as possible.”18 DIRECTV identifies these “rising programming costs” as a “significant pressure” on the company’s goal
of “maintain[ing] strong margins.”19
14 See DIRECTV 2011 10-K at 38.
15 See S&P Global Market Intelligence LLC, Technology, Media, & Telecommunications Database.
16 See DIRECTV 2011 10-K at 45.
17 See DIRECTV 2011 10-K at 6.
18 See id.
19 See id. Testimony of Howard B. Homonoff March 22, 2019 13
27. There is a direct correlation between a satellite operators’ growth in the size of
its subscriber base and its programming costs. Generally, a greater number of subscribers
translates directly to an MVPD’s ability to obtain “volume discounts” in cable programming network affiliation agreements. For a cable network, more subscribers delivered by the
MVPD means more value to the cable network by increasing the opportunity for the programming network to grow its viewership and advertising revenues. As a consequence, cable networks routinely grant lower pricing on a per-subscriber basis to MVPDs that have the largest numbers of subscribers. Thus, for a satellite operator, in addition to enhancing its market value and revenues, providing programming most attractive to subscribers will also provide the opportunity to pay a lower wholesale (monthly) license fee for a cable network than a competitor that attracts fewer subscribers. This in turn is directly linked to the strategic importance for the satellite operators, in particular, to manage their programming costs effectively and only spend in a fashion that they believe will maximize subscriber value.
IV. Additional Factors That Drive Satellite Operator Programming Carriage and Spending Decisions.
28. The threat of and need to respond to competition is another key factor in determining the value of cable networks in the eyes of satellite operators. To the extent that cable networks might be available from one or more competitors, this would in my experience only heighten the value placed on those networks in the eyes of satellite operators.
The most significant opportunities for the satellite operators to continue to grow their business in the video sphere are to keep subscribers away from their competitors and to generate the highest possible revenue from those subscribers that they retain.
29. In terms of the 2010-2013 timeframe, the competition for video subscribers increased significantly by comparison with the 2004-2005 period addressed in my testimony in the 2004-2005 Cable Phase I proceeding. For example, while DISH and DIRECTV had competed from their inception with cable operators, the telephone companies did not begin to
Testimony of Howard B. Homonoff March 22, 2019 14
acquire significant numbers of video customers until the 2010-2013 time period.
Competition for subscribers and on maintaining ARPU sharpened as telephone companies
(Verizon and AT&T, most prominently) entered the video marketplace and began to
negotiate directly with cable networks and to compete for video subscribers alongside
satellite and incumbent cable operators. As DISH pointed out in its 2013 10-K, by
September of that year, AT&T had 5.3 million video subscribers and Verizon had 5.1 million,
collectively accounting for 10% of the total MVPD market.20 According to analysts’ reports
at the time, the telephone companies were providing a “very comparable” video service and, thus, an outlet for subscribers seeking alternatives to cable and satellite operator video packages.21
30. Another source of competition that effectively did not exist for satellite
operators in the prior proceeding period is the emergence of an explosive amount of video
programming available over the internet. DIRECTV and DISH both called out this newly
emerging threat in their financial filings during this time period.22 YouTube had little to no
market impact for most of 2004-2005, but was already a major force competing for the time
and attention of video subscribers by 2010-2013.23 And by 2010-2013, Netflix, Hulu, and
Amazon all provided significant opportunities for viewing of the types of content that
20 See DISH Network Corporation Annual Report Pursuant to Section 13 or 15(d) of the Securities and Exchange Act of 1934, Form 10-K, at 4 (for the fiscal year ended December 31, 2013), available at https://dish.gcs-web.com/static-files/6e233fbf-5073-4445-a7a7-a4692b717bdb (last visited March 21, 2019) (“DISH 2013 10-K”).
21 See Time Warner Cable, Credit Suisse Equity Research, March 27, 2009 at 3.
22 See DIRECTV 2010 10-K at 8; DISH 2013 10-K at 21.
23 Just to give some sense of the scale of YouTube’s presence in the video consumption market, by May of 2010 YouTube was receiving over 2 billion views per day, an unavoidable competitive threat to viewing by MVPD- provided cable networks. See https://www.theatlantic.com/technology/archive/2011/08/infographic-the-history- of-video-advertising-on-youtube/242836/ (last visited March 21, 2019). Testimony of Howard B. Homonoff March 22, 2019 15
satellite operators licensed from cable networks, making it all the more critical how satellite
operators allocated their programming expenditures among the various network options.24
31. Avoiding negative subscriber impact is an important facet of satellite operator
experience with difficult programming negotiations. High-profile programming negotiation battles involving cable and satellite operators on the one hand, and cable programming networks on the other, have for many years generated consternation among subscribers and fears and threats of subscriber defections. These challenges are only heightened with increased competition, and they reinforce the importance of carrying programming that will maximize the odds of attracting and retaining subscribers and reduce the risk of defections and reduced revenue streams.
32. Satellite operator programming decisions are also limited by the capacity available for programming carriage – satellite operators simply could not carry every available programming network even if they wanted to do so. In 2008, the average number of programming networks received by cable or satellite subscribers stood at roughly 129, which increased to roughly 189 by 2014. Yet, the number of potential programming networks to carry was in the hundreds.25 Thus, an important issue for satellite operators in deciding whether and on what terms to carry cable networks, as well as distant signals, is how to efficiently allocate their costs because they simply are unable to carry all of the available networks.
33. Within the portion of their services allocated to video programming, the satellite operator must determine what bandwidth to dedicate to 24-hour per-day
24 For a discussion of the technological changes driving these developments, see, e.g., The Evolution of Video Streaming and Content Delivery, Brookings Institution, May 2014, available at https://www.brookings.edu/wp- content/uploads/2016/06/West_Evolution-of-VideoStreaming-and-Digital-Content-Delivery_Final.pdf (last visited March 21, 2019).
25 Megan Geuss, “On Average, Americans Get 189 Cable TV Channels And Watch Only 17” (May 6, 2014), available at https://arstechnica.com/information-technology/2014/05/on-average-americans-get-189-cable-tv- channels-and-only-watch-17/ (last visited March 21, 2019). Testimony of Howard B. Homonoff March 22, 2019 16
programming services as opposed to video-on-demand channels, and then which cable
networks will fill those scarce 24-hour-per day slots. Carrying one 24-hour per-day channel
(whether a cable network or distant signal) means an operator pays an opportunity cost of other cable networks or other services that must make way.
34. In sum, satellite operators have a complex matrix of factors to consider in determining what programming they will carry on their systems to attract and retain subscribers. Given the centrality of the subscriber to the satellite operators’ financial health, the perceived programming preferences of the subscriber is inevitably a critical factor in the satellite operator’s choice of what programming should be carried and how it should be carried—that is, to how many subscribers, on what tier of service, for how long, and at what price. In fact, the factors that come into play in the programming carriage decision-making process can and should be viewed as proxies for weighing the value for subscribers of the carriage of a particular cable network and the type of programming it delivers.
V. Actual Marketplace Decisions by MVPDs Indicate Value of Program Suppliers Programming to These Distributors.
A. Distribution of Programming Network Categories
35. A key path to understanding the relative value of any particular category of
programming to the attraction and retention of satellite operator subscribers is to observe the
actual marketplace decisions made by these satellite operators and their MVPD competitors.
Because these decisions are made on a channel-by-channel basis, looking at the overall
channel line-up of a satellite operator generally reflects what programming they perceive to
be most valuable to their subscribers. The foundation of those cable programming decisions
is the 24-hour-per-day-cable network, at least since the late 1970s and early 1980s with the
launch of CNN, USA, ESPN and other cable networks. As the 1998-99 CARP Report
acknowledged, the relative program value seen in the cable network marketplace is a very
Testimony of Howard B. Homonoff March 22, 2019 17
helpful guidepost for a hypothetical relative program value in the broadcast distant signal
marketplace.26
36. Satellite operators typically build their programming line-ups with a variety of
different genres of content they perceive, on aggregate, as having the most appeal to
subscribers. By reviewing the most prevalent programming offered on the cable networks
provided by satellite operators and their competitors on their systems, we can understand how satellite operators (conducting their business in this competitive environment) value different programming options to deliver to subscribers. Carriage decisions about cable network and distant signal programming are not made in a vacuum. The relative importance and value to the distributors of programming carried on distant signals can effectively be understood by examining the entirety of the cable network line-ups assembled by MVPDs.
37. Since the early 1980s, the most prevalent and widely-distributed genres of cable network programming have included a general entertainment category, which includes subcategories such as arts, film, family/kids, women’s entertainment, music and international programming. Satellite operators, like their competitors, have generally favored these popular genres in filling their programming line-ups. This historic pattern was confirmed in my analysis for this testimony.
38. Given that the focus of this proceeding is 2010-2013, I examined the Top 50
Networks for those years to provide a sample of how cable and satellite operators filled out their cable network line-ups amongst a variety of program categories. These Top 50
Networks were carried on average in 93.4 million cable and satellite households during 2010-
2013.27 In addition, out of annual average license fees during 2010-2013 of roughly $28
billion per year paid by MVPDs to cable networks, over 75% ($21.2 billion) went to these
Top 50 Networks, even as they accounted for a small fraction of the total number of cable
26 See note 7, supra.
27 See S&P Global Market Intelligence LLC, Technology, Media, & Telecommunications Database. Testimony of Howard B. Homonoff March 22, 2019 18
networks available for carriage.28 Upon review, these cable network carriage choices during
2010-2013, as well as the relative amounts spent on different programming content genres,
are consistent with my own experience.
39. S&P Global Market Intelligence LLC (hereafter “S&P), formerly known as
SNL Kagan, maintains a database of all cable networks carried in U.S. multichannel video
(cable, satellite and telco) households.29 The single most widely distributed cable network
during the years 2010-2013 was The Weather Channel, carried by cable and satellite
operators in an average of 100.2 million homes in the U.S. between 2010 and 2013.30 The
Top 50 Networks also included The Food Network, Discovery Channel, TNT, TBS, and the
Cartoon Network among the top 10 most widely distributed networks. Number 50 on this list
was Oxygen, which was carried in an average of 77.2 million U.S. households during this
time period.31 A list of the Top 50 Networks and their average subscribing households over
the 2010-2013 time period, as reported by S&P, is provided in Table 1.
28 See note 6 above for a fuller explanation of the relative importance of the Top 50 Networks.
29 See S&P Global Market Intelligence LLC, Technology, Media, & Telecommunications Database.
30 See id.
31 See id.
Testimony of Howard B. Homonoff March 22, 2019 19
Table 1:
Top 50 Networks
2013 2012 2011 2010 2010-13 Average Average Average Average Average SNL Kagan Content Subscribers Subsribers Subscribers Subscribers Subscribers Cable Network Subdivisions (in millions) (in millions) (in millions) (in millions) (in millions) The Weather Channel (US) News 99.7 100.3 100.6 100.2 100.2 Food Network Niche Networks 99.1 99.5 99.9 99.7 99.6 Discovery Channel (US) Arts & Entertainment 98.7 99.4 100.1 100.2 99.6 TNT (US) General/Variety 98.6 99.8 100.2 99.8 99.6 TBS General/Variety 99.0 99.5 100.0 99.0 99.4 Cartoon Network Family/Kids 98.5 98.8 99.1 98.8 98.8 Nickelodeon/Nick At Nite (US) Family/Kids 98.7 99.4 99.9 100.0 99.5 USA (US) General/Variety 98.6 99.5 100.0 99.6 99.4 CNN (US) News 98.5 99.1 99.6 99.7 99.2 HLN (US) News 98.0 98.8 99.5 100.2 99.1 A&E (US) Arts & Entertainment 98.3 98.9 99.4 99.4 99.0 Lifetime Television Women's 98.2 98.8 99.3 99.2 98.9 C-SPAN (US) News 99.4 99.3 99.0 97.7 98.9 ESPN (US) Sports 97.8 98.8 99.4 99.4 98.9 ESPN2 (US) Sports 97.7 98.7 99.3 99.2 98.8 HGTV (US) Niche Networks 98.1 98.7 99.1 99.0 98.7 TLC (US) Arts & Entertainment 97.9 98.6 99.1 99.1 98.7 History (US) Arts & Entertainment 98.2 98.7 99.0 98.7 98.6 Disney Channel (US) Family/Kids 97.9 98.9 99.0 98.6 98.6 Paramount Network (US) General/Variety 97.5 98.4 99.1 99.1 98.5 M TV (US) Music 97.4 98.3 98.9 98.9 98.4 Comedy Central (US) Niche Networks 97.8 98.3 98.6 98.5 98.3 FOX News Channel (US) News 97.2 97.9 98.4 98.4 98.0 VH1 (US) Music 96.7 97.6 98.3 98.3 97.7 Freeform (US) Family/Kids 96.4 97.3 98.0 98.5 97.5 Syfy (US) Niche Networks 97.4 97.5 97.6 97.3 97.4 E! (US) Arts & Entertainment 96.5 97.6 98.0 97.5 97.4 CNBC (US) News 96.2 97.5 98.1 97.9 97.4 FX Network (US) General/Variety 97.3 98.0 97.0 95.8 97.0 TV Land / TV Land Classic (US) General/Variety 96.2 96.5 97.1 97.5 96.8 Animal Planet (US) Arts & Entertainment 96.3 96.7 96.9 96.7 96.6 AMC (US) Film 98.2 93.9 96.5 95.8 96.1 MSNBC (US) News 95.6 95.4 95.2 94.1 95.1 Travel Channel (US) Niche Networks 94.3 94.7 95.2 95.4 94.9 Bravo (US) General/Variety 94.2 94.8 94.3 93.1 94.1 truTV (US) General/Variety 91.5 92.0 92.3 92.4 92.0 CMT (US) Music 91.2 91.7 91.7 90.9 91.4 BET (US) International/Ethnic 91.2 91.3 90.9 90.1 90.9 Hallmark Channel (US) Family/Kids 86.6 86.9 87.2 87.8 87.1 Golf Channel (US) Niche Networks 82.9 84.1 83.7 82.5 83.3 LMN (US) Film 84.1 83.6 81.3 77.4 81.6 TCM (US) Film 81.7 81.3 80.9 80.3 81.1 FOX Sports 1 (US) Sports 84.5 79.9 78.0 76.0 79.6 POP (US) General/Variety 78.0 79.0 79.7 80.6 79.3 M TV2 (US) Music 80.5 79.1 78.1 77.6 78.8 Disney XD (US) Family/Kids 80.6 79.4 78.1 76.3 78.6 OWN: Oprah Winfrey Network (US) Arts & Entertainment 81.0 78.3 76.3 74.8 77.6 National Geo Channel Arts & Entertainment 84.6 81.4 74.1 69.8 77.5 WE Tv (US) Women's 82.8 74.2 76.8 75.9 77.4 Oxygen (US) Women's 78.3 78.0 76.5 76.1 77.2
*Data compiled by Howard Homonoff from S&P Global Market Intelligence (formerly known as SNL Kagan).
Testimony of Howard B. Homonoff March 22, 2019 20
40. Analyzing the genres of content from among these most widely distributed
networks provides a key window into how satellite operators value the different genres
available to them to be carried in order to maximize subscriber value and impact. According
to my review of the content categories of these networks, 40 networks—or 80%—of the Top
50 Networks should be labeled as what I understand to be PSE networks, that is,
programming most analogous to the content claimed by the Program Suppliers claimant
group in this proceeding.32 This is a greater concentration of PSE content than I noted in my
report for the 2004-2005 time period (PSE programming comprised 74% of the 50 most
widely carried cable networks during that time period).33 The cable networks that I included
in the PSE category appear in yellow on Table 2, while cable networks in the News category
appear in orange, and cable networks in the Sports category appear in blue.
32 As I discuss later, this is a conservative estimate, as there are cable networks attributed to News and Sports which, based on their content, could also fall within PSE programming networks, such as MSNBC, CNN, and ESPN. See text, infra, at ¶¶ 44-50 and related footnotes.
33 See 2009 Testimony at 15.
Testimony of Howard B. Homonoff March 22, 2019 21
Table 2:
Top 50 Cable Networks, Categorized By CRB Program Category CRB Program SNL Kagan Content Cable Network Category Subdivisions The Weather Channel (US) News News Food Network PSE Niche Networks Discovery Channel (US) PSE Arts & Entertainment TNT (US) PSE General/Variety TBS PSE General/Variety Cartoon Network PSE Family/Kids Nickelodeon/Nick At Nite (US) PSE Family/Kids USA (US) PSE General/Variety CNN (US) News News HLN (US) News News A&E (US) PSE Arts & Entertainment Lifetime Television PSE Women's C-SPAN (US) News News ESPN (US) Sports Sports ESPN2 (US) Sports Sports HGTV (US) PSE Niche Networks TLC (US) PSE Arts & Entertainment History (US) PSE Arts & Entertainment Disney Channel (US) PSE Family/Kids Paramount Network (US) PSE General/Variety M TV (US) PSE Music Comedy Central (US) PSE Niche Networks FOX News Channel (US) News News VH1 (US) PSE Music Freeform (US) PSE Family/Kids Syfy (US) PSE Niche Networks E! (US) PSE Arts & Entertainment CNBC (US) News News FX Network (US) PSE General/Variety TV Land / TV Land Classic (US) PSE General/Variety Animal Planet (US) PSE Arts & Entertainment AMC (US) PSE Film MSNBC (US) News News Travel Channel (US) PSE Niche Networks Bravo (US) PSE General/Variety truTV (US) PSE General/Variety CMT (US) PSE Music BET (US) PSE International/Ethnic Hallmark Channel (US) PSE Family/Kids Golf Channel (US) PSE Niche Networks LMN (US) PSE Film TCM (US) PSE Film FOX Sports 1 (US) Sports Sports POP (US) PSE General/Variety M TV2 (US) PSE Music Disney XD (US) PSE Family/Kids OWN: Oprah Winfrey Network (US) PSE Arts & Entertainment National Geo Channel PSE Arts & Entertainment WE Tv (US) PSE Women's Oxygen (US) PSE Women's
Testimony of Howard B. Homonoff March 22, 2019 22
41. Within that PSE category, I include 8 categories that S&P uses to subdivide
the cable programming content marketplace, such as 9 networks under “General/Variety”
(including TNT, USA, FX, E!, and Bravo); 8 under “Arts & Entertainment” (networks such as Discovery Channel, A&E, TLC, and History); 6 under “Family/Kids” (including
Nickelodeon/Nick at Nite, Disney Channel, and Cartoon Network); 6 under “Niche
Networks” (such as HGTV, Food Network, Travel Channel and The Golf Channel34); 4
under “Music” (MTV, VH1, CMT and MTV2); 3 under “Women’s” (Lifetime, Oxygen and
WETv); 3 under “Film” (AMC, LMN and TCM); and 1 under “International/Ethnic/Foreign
Language” (BET). Figure 1 below delineates this breakdown of what constitutes the PSE cable networks. As I discuss below, the preponderance of the programs carried on these networks during the 2010-2013 time period appears to fall within the definition of “Program
Suppliers” programming as I understand it is used in this proceeding.
34 I have categorized Golf Channel as PSE/Niche Networks and not Sports because golf programming is not part of the “live team sports” that falls within the definition of JSC/Sports under the program category definitions adopted by the Judges in this proceeding. See Amended Notice Of Participant Groups, Commencement Of Voluntary Negotiation Period (Allocation), And Scheduling Order at Exhibit A (December 1, 2015). I would note that in this proceeding Program Suppliers’ claims appropriately include both non-live team sports as well as syndicated news programming.
Testimony of Howard B. Homonoff March 22, 2019 23
Figure 1:
42. The second largest genre among the Top 50 Networks was News, which included The Weather Channel, CNN, MSNBC, and Fox News Channel. The News genre accounted for 7 of the Top 50 Networks or 14% of the Top 50. The third most widely carried genre was Sports (consisting of ESPN, ESPN2, and Fox Sports 1within the Top 50
Networks). This group of 3 networks accounted for 6% of the Top 50 Networks. Figure 2 illustrates the average relative mix of programming categories among the Top 50 Networks for the 2010-2013 time period.
Testimony of Howard B. Homonoff March 22, 2019 24
Figure 2:
*Network programming categorization is as applied by Howard Homonoff from S&P Global Market Intelligence (formerly known as SNL Kagan) network categorization.
43. The above analysis of the most widely distributed cable networks in 2010-
2013 demonstrates that the satellite operators operate as part of a marketplace in which
MVPD cable network carriage choices are heavily weighted towards the Program Suppliers category. All other categories fall significantly behind in their proportion of the most-widely distributed cable networks by MVPDs and satellite operators specifically.
B. Distribution of Individual Programming Categories
44. As the Judges have previously indicated,35 and as I have reiterated above, focusing on the marketplace decisions concerning carriage of whole cable networks is the most useful proxy for determining relative value in the distant signal content market.
Nonetheless, looking at the distribution of genres of programs, regardless of the networks on which they are aired, is useful, as well. In constructing the programming for a “whole cable network” as opposed to just an individual “show,” cable networks often need to look beyond
35 See note 7, supra.
Testimony of Howard B. Homonoff March 22, 2019 25
the category of programming that primarily defines them and their brand. To that end, I analyze distribution of the programs on cable networks across the CRB program categories.
45. In my analysis for this testimony and consistent with my experience in the market, it is clear that most Sports cable networks include within their program schedule a significant amount of programming that is not “live team sports” and that therefore would fall outside of the definition of the Joint Sports Claimants program category in this proceeding, which is limited to live team sports. Among the many programs that do not fit into the
“Sports” programming definition are golf programming on Golf Channel; live non-team sports such as tennis tournaments on ESPN and ESPN2; and signature live highlights shows, pre-produced interviews, and non-fiction programming that also run consistently on ESPN and ESPN2. Thus, in my analysis, I have included, as Program Suppliers content, several programming sub-categories such as “Sports-related,” “Pseudo-sports,” and “Sporting
Events,” all of which have been considered Program Suppliers content in prior proceedings of the Judges and their predecessors.
46. My analysis demonstrates similar results at News cable networks, whose lineups include a significant mix of live talk shows and pre-produced non-fiction programming that falls within Program Suppliers’ content. However, overall, I have taken a conservative approach to what I have placed in the Program Suppliers category. For example, two categories that Gracenote does not define as “News” are “Finance” and “Public
Affairs,” but I have chosen for purposes of my analysis to include those under “News” programming. I base this, in part, on my own direct work experience at CNBC and MSNBC, particularly CNBC, which dominates the Finance category. However, I am aware that
Program Suppliers’ content does include “syndicated news programming” similar to that which I attributed to the “News” category.
Testimony of Howard B. Homonoff March 22, 2019 26
47. For the present analysis, I drew from data supplied by Gracenote, which provides historical data drawn from its electronic program guides.36 I reviewed the
individual programming schedules for the Top 50 Networks during 2010-2013, and then
selected five weeks from each year to sample the mix of programming. The selected weeks
roughly correlate to the weeks I used for my analysis in my testimony in the 2004-2005 Cable
Phase I proceeding.37 For the selected weeks, I then aggregated the minutes of programming
in each genre for each network, and then classified them based on the program categories
used in this proceeding. In the 2010-2013 time period, the Program Suppliers’ category
content comprised the vast majority of individual programs on the Top 50 Networks,
including very substantial amounts of Program Suppliers’ content on some cable networks
that are generally considered to be “Sports” or “News.”38
48. In terms of the categorization of individual programs by genre, I have grouped
within the “Program Suppliers” category a broad range of 25 different produced-
programming genres that Gracenote classifies as “Network Series,” “Special,” “Hobbies &
Crafts,” “Syndicated,” “Movie,” “TV Movie” and “Other,” all of which have always been
categorized in prior proceedings as Program Suppliers content.39
49. According to the Gracenote data, for the selected weeks of analysis, ESPN
averaged over 80% of its programming time with PSE content, and ESPN 2 was very close to
36 I relied upon Tribune Media Services (“TMS”) for a similar analysis for the 2004-05 time period in my 2009 Testimony. The Tribune Company purchased Gracenote in December 2013, combining the Gracenote and TMS market offerings. A full listing of the Gracenote programming categories appearing in my data selection is attached to my testimony as Exhibit 2.
37 The weeks selected for my analysis for each of the years between 2010-2013 were: February 7-13, 2010, February 28-March 6, 2010, July 25-31, 2010, October 10-18, 2010, November 28-December 3, 2010; February 8-12, 2011, February 27-March 5, 2011, July 31-August 6, 2011, October 9-15, 2011, November 27-December 3, 2011; February 5-14, 2012, February 28-March 3, 2012, July 29-August 4, 2012, October 7-13, 2012, November 25-December 1, 2012; February 3-9, 2013, February 24-March 2, 2013, July 28-August 3, 2013, October 6-12, 2013, November 24-30, 2013.
38 See Exhibit 3.
39 See Exhibit 2 for a full listing of all Gracenote programming categories appearing in my data selection. Testimony of Howard B. Homonoff March 22, 2019 27
that 80% figure.40 Only roughly 20% of the content on these two most prominent sports
cable networks was “live team sports.” And Fox Sports 1 had an even higher content of PSE
programming – 86%. In terms of News cable networks, PSE content comprised nearly 82%
of programming on Fox News Channel and over 40% of the programming on CNN.41
50. Aggregating the minutes of programing across the Top 50 Networks in 2010-
2013 during the selected 5-week period results in approximately 91.6 % of the programs falling within the Program Suppliers category. In contrast to the preponderance of Program
Suppliers-type programming, content in the News category (including Gracenote’s “News,” as well as “Finance” and “Public Affairs”) comprised 7.1% of the Top 50 Networks’ programming during the selected weeks. Sports programming comprised approximately 1% of the Top 50 Network programming, and “Devotional” programming at 0.3%. Figure 3 below provides a graphic view of the relative share of individual programs, by genre, delivered to subscribers to the Top 50 Networks.
40 See Exhibit 3.
41 See id. Testimony of Howard B. Homonoff March 22, 2019 28
Figure 3:
* Network programming categorization is as applied by Howard Homonoff from categorization supplied by Gracenote.
C. Distribution of Satellite Operator License Fee Expenditures for Cable Networks
51. As I noted earlier, one of the most critical factors driving the decision of
whether and how to carry a particular cable network is the cost of monthly license fees paid
by the satellite operator (or competitive MVPDs) for the right to carry a cable network to
their subscribers. In this spending area, the Judges’ predecessors acknowledged the validity
of using the cable network marketplace as a guide to the value reasonably placed on different
categories of programming in the hypothetical distant signal marketplace.42
52. The 1998-99 CARP Report approached the hypothetical distant signal market by looking at the relative license fees for a dozen different cable networks. However, when
Corporate Programming Executives are assembling packages of cable networks, they must address how they spend their budgets on a broader range of networks that offer a large mix of
42 1998-99 CARP Report.
Testimony of Howard B. Homonoff March 22, 2019 29
different programming choices – in fact as noted above, S&P tracks data from 269 different cable networks that a satellite operator could have chosen to carry during the 2010-2013 time period. Certain individual networks may be relatively more expensive than others (for example, ESPN was clearly the most expensive individual basic cable network during 2010-
2013, with average monthly license fees of $4.93 per subscriber during this time period). But a satellite operator spreads its license fee expenses amongst a wide variety of cable networks.
It is in the aggregate mix of these programming choices and the monies spent that determines the priorities and the relative value the operator places on different genres of programming.
Consequently, I reviewed the average monthly per subscriber license fees for the Top 50
Networks during 2010-2013. Given the need to understand the full marketplace that satellite operators compete in, I have analyzed payments made throughout the MVPD industry to these Top 50 Networks.
53. Between 2010-2013, the Top 50 Networks earned on average $18.97 per subscriber per month.43 Given that a cable network among the Top 50 networks was carried on average in 93.4 million homes during this period, this means that in the aggregate these cable networks earned an average of $21.2 billion per year between 2010 and 2013 in monthly license fees, approximately 75.25% of the total annual license fees of $28.25 billion earned by all cable networks on average each year between 2010 and 2013.44
54. The license fees for the Top 50 Networks in 2010-2013 identified on Exhibit 4 indicate that among the programming categories at issue in this proceeding, by far the largest percentage of license fees are spent on the PSE category of programming. For example, between 2010-2013, out of the aggregate $18.97 per month paid in license fees by cable and satellite operators for the Top 50 Networks, the 40 cable networks in the PSE category earned
43 See Exhibit 4, prepared with data from S&P Global Market Intelligence, Technology, Media, & Telecommunications Database.
44 See S&P Global Market Intelligence LLC, Technology, Media, & Telecommunications Database.
Testimony of Howard B. Homonoff March 22, 2019 30
an average total of approximately $11.10 per month—roughly 58.5% of the total license fees
paid to the Top 50 Networks during that time period. By comparison, the aggregated license
fees for the Sports category in this time frame were $5.97 per month (of which $4.93 was for
one network - ESPN), representing 31.5% of the total license fees paid to the Top 50
Networks. The average monthly license fees for all of these Sports cable networks among the
Top 50 Networks equals roughly 53.7% of aggregate license fees paid for PSE cable
networks.45
55. For the “News” category, there was an even greater disparity with the PSE
networks. The aggregated license fees paid to the News cable networks between 2010-2013
were $1.89 per month, representing roughly 10% of the total license fees paid to the Top 50
Networks, and only 17% of the aggregate subscriber fees paid for PSE cable networks.46
Figure 4 demonstrates the mix of license fees across the different cable network programming classifications.
45 Again, it is important to keep in mind that the majority of programming on ESPN is not live team sports and would fit within the definition of “Program Suppliers” rather than “Sports” programming used in this proceeding. See ¶¶ 44-50 above; see also Exhibit 3.
46 See Exhibits 4 and 5.
Testimony of Howard B. Homonoff March 22, 2019 31
Figure 4:
Top 50 Networks 2010-2013 Average Monthly Per Sub License Fees
12.00 11.10
10.00
8.00 5.97 6.00
4.00 1.89 2.00
0.00 PSE License Fees/Mo. News License Fees/Mo. Sports License Fees/Mo.
* Network programming categorization is as applied by Howard Homonoff from SNL / Kagan network categorization.
56. This review of monthly per subscriber license fees earned by the Top 50
Networks provides key insight into how satellite operators place relative value on different types of programming genres for attracting and retaining subscribers. We can see that PSE- based cable networks are those that the MVPDs have chosen to not only distribute to the highest number of their subscribers, but upon which they have chosen to spend a significantly larger portion of license fees compared to Sports and News cable networks. Based on this behavior in the cable network marketplace involving the impact on the single most important commodity in the MVPD business—their monthly subscribers—these distributors of cable network programing have definitely demonstrated the highest value attributed to Program
Suppliers’ programming compared with competition from News and Sports programming.
VI. Conclusion
57. My analysis for this testimony supports my direct experience working in the cable network distribution world for the last several decades—–that among the programming genres under review in this proceeding, PSE programming is collectively the most valued by
Testimony of Howard B. Homonoff March 22, 2019 32
satellite operators in their real-world marketplace decisions. The foundations of that
conclusion rest on the following observations. First, the processes by which satellite
operators make their programming decisions and the factors that influence those decisions
indicate the central importance of subscriber attraction and retention. Second, the most
important indicia of value for satellite operators in their acquisition of the rights to cable network programming are what cable networks they choose to carry and the price (monthly license fees) that they pay for those rights. Third, the calculations of the collective programming decisions by MVPDs in the cable network marketplace specifically point to the predominance and high market value of PSE programming: (1) Program Suppliers’ cable networks account for 80% of the most widely distributed cable networks, far beyond the 14% and 6% respectively representing carriage of News and Sports cable networks; (2) individual programming choices among the Top 50 Networks demonstrates an even heavier weight on
PSE content at 91.6% versus 7.1% and 1% respectively for News and Sports; and (3) PSE cable networks receive 58.5% of the total average monthly license fees for the Top 50
Networks, compared to 31.5% for Sports and 10% for News. In conclusion, in both how cable network content is carried and how it is paid for, PSE cable networks were clearly
valued collectively at a significantly higher level than either Sports or News cable networks
during the 2010-2013 period.
Testimony of Howard B. Homonoff March 22, 2019 33
DECLARATION OF HOWARD B. HOMONOFF
I declare under penalty of perjury that the foregoing testimony is true and correct, and of my personal knowledge.
Executed on March 22, 2019
Testimony of Howard B. Homonoff March 22, 2019 34
EXHIBIT 1
HOWARD B. HOMONOFF 201.739.3968 | [email protected]
SENIOR MEDIA INDUSTRY STRATEGY CONSULTANT AND REVENUE LEADER
Accomplished, high-performing, and target-driven executive with 20+ years of immersive experiences in advising C-suite executives, directing operations, business development, and strategic planning. Accomplishments include: • Led Association of National Advertisers (ANA), 4As, and SAG-AFTRA to build and demonstrate implementation of new data analytics platform to compensate talent on most relevant exposure metrics. Resulted in over 30,000 video commercials per year now embedded with mandatory digital fingerprint identification. • At CNBC, delivered bottom-line growth of 40% in streaming video business and 3x increase of ancillary revenue through creating and leading new business unit and assuming responsibility for P&L management of businesses such as streaming video, satellite radio, interactive TV, publishing, and broadcast syndication. • Facilitated launch of first-generation, multi-billion-dollar broadband business at Continental Cablevision, developing strategy and leading on industry partnerships, government relations, marketing, and technology integration. • Originated extensive thought leadership as Forbes contributing writer on media, marketing, advertising and entertainment, and created and drove distribution for related newsletter to 2000 industry insiders.
P ROFESSIONAL E XPERIENCE
Homonoff Media Group, LLC | New York, NY 2018 – present, Principal / Managing Director 2013 – 2014, 2002 – 2006
Serves an instrumental role in pivoting company’s direction by providing strategic business development, marketing advice, and expert industry witness services to traditional and newly emerging media and technology companies. Launches and administers multiple linear TV, VOD, and digital media properties in verticals such as documentaries, parenting, news, and ethnic media. Consistently thrives in producing unique out-of-home digital content network for major financial institutions, delivering IPTV content, and advertising outside of traditional distribution channels. Facilitated launch of Verizon FiOS TV inside one of the leading global communication companies, ultimately accumulating millions of new subscribers and generating multi-billion-dollar incremental revenue.
MediaLink, LLC | New York, NY 2015 - 2018 Senior Vice President
Displayed extensive involvement as member of the firm’s strategic advisory leadership team in winning business, leading engagements and business development efforts in media, technology, advertising, entertainment, and finance. Attained an outstanding reputation for growing one of the company’s marquee accounts by 25% year over year. Unfailingly secured millions of dollars in revenue with new clients in the digital media, advanced TV, gaming, and global technology while constantly exercising integrity in client relationships. Fulfilled a vital role in establishing new client strategies on thought leadership, business acceleration and event planning. Leveraged subject matter expertise in administering new marketing approach by digital-first branded content company.
PwC (PricewaterhouseCoopers LLP)| New York, NY 2006 - 2013 Director
Proactively maintained and continuously developed Entertainment, Media, and Communications advisory practice. Achieved the highest standard of excellence in leading a 3-year effort of designing, constructing, and operating unique tech platform and new framework for calculating talent costs, integrating set-top box data, and mandating industry- wide meta-tagging and tracking of television advertising on media industry consortium - the Association of National Advertisers (ANA), the 4As, and SAG-AFTRA. Strengthened and refined professional effectiveness, including adoption by the video commercial industry of a universal form of digital identification for over 30,000 commercials produced each year, permitting tracking and measurement of commercial views across all media platforms. Rendered outstanding supervision of major Hollywood studio by developing strategic alternatives for monetizing content library on digital media platforms, benchmarking alternative approaches by market competitors. Optimized highly sophisticated skills and strategies in selling, leading the teams, and in delivering multiple multi- million-dollar consulting engagements.
Howard Homonoff Resume P.2 [email protected]; 201.739.3968
CNBC, Inc. | Fort Lee, NJ 2000 - 2001 Vice President and General Manager, CNBC Strategic Ventures
Spearheaded overall strategy, sales, and business development. Possessed expertise in analyzing incoming strategic partnerships and business development opportunities, negotiating such deals, and selling them inside NBC and General Electric (Parent Company). Played a vital role in managing the resulting businesses and relationships primarily involving the exploitation of CNBC’s brand and its content. Successfully created new business unit and assumed responsibility for P&L management of early stage digital businesses, including web-based streaming media and broadcast syndication. Delivered exceptional performance which led to bottom-line growth of 40% for CNBC/Dow Jones Business Video and highly recognized for invaluable contribution towards a 3x increase of ancillary revenue in areas such as satellite radio, interactive TV, and publishing. Explored new approaches and strategies in developing business plan for digital cable service CNBC World, which generated an asset value of hundreds of millions of dollars.
CNBC, Inc. | Fort Lee, NJ 1996 - 2000 General Counsel, NBC Cable Networks
Oversaw all legal and business affairs for CNBC, MSNBC, and digital media properties to include content distribution, production, and IP licensing and patent-related transactions. Demonstrated an exceptional mastery of professional skills in handling programming agreement negotiations ensuring incremental delivery of $1 billion. Meticulously designed industry-leading employee training and effectively developed state-of-the-art policies and procedures in areas of insider trading and portfolio management for news organization and senior executives.
A C A D E M I C Q UALIFICATIONS
NEW YORK UNIVERSITY SCHOOL OF LAW| New York, NY Juris Doctor Program
CORNELL UNIVERSITY | Ithaca, NY Bachelor of Arts in Government With distinction in all subjects
C ERTIFICATIONS AND P ROFESSIONAL A FFILIATIONS
Bar Membership
National Association of Television Program Executives (NATPE) | 2017 – Present Moderator
Forbes.com | 2014 – Present Weekly Columnist, Media and Entertainment
The New School | New York, NY | 2014 – Present Adjunct Professor in Media Management, Graduate School of Media Studies
Columbia Business School | New York, NY | 2013 – Present Senior Fellow, Institute on Tele-Information
Executive Producer/Host | 2013 – 2016 Media Reporter, NY cable television program/MNN network
The Wharton School | 2015 Presenter, Future of Advertising Annual Meeting
Drexel University | 2005-2007 Program Director/Professor, MBA/MS Television Management Program
Practicing Law Institute on Cable and Broadband Telecom | 1998 - 2011 Faculty Member
EXHIBIT 2
Homonoff Exhibit 2
Gracenote Program Types In Homonoff Data Selection, 2010-13
Gracenote Program Type Arts Cartoon Children's Show Children's Special Daytime Soap Fianance Game Show Health Hobbies & Crafts Instructional Mini-series Movie Music Music Special Network Series News Other Playoff Sports Pseudo-sports Public Affairs Religious Special Sporting Event Sports Anthology Sports-related Syndicated TV Movie Talk Show Team vs. Team
Exhibit 2, Testimony of Howard B. Homonoff, 2010-13 Satellite Allocation 1 EXHIBIT 3 Homonoff Exhibit 3
Distribution Of Individual Programming Categories On Top 50 Networks, 2010-13
Programming Minutes Percentage Share Of CRB Program In Homonoff Gracenote Programming Cable Network Category Data Selection Minutes AETV PSE 201,600 100.00% AMC PSE 201,122 100.00% APL PSE 201,480 100.00% BET News 150 0.07% BET Devotional 300 0.15% BET PSE 201,420 99.78% BRAVO PSE 201,615 100.00% CMTV PSE 201,771 100.00% CNBC News 115,530 57.31% CNBC PSE 86,070 42.69% CNN News 119,670 59.34% CNN PSE 81,990 40.66% COMEDY News 92 0.05% COMEDY PSE 201,913 99.95% CSPAN News 149,243 74.27% CSPAN PSE 51,700 25.73% DISN PSE 201,570 100.00% DSC Devotional 9,108 4.52% DSC PSE 192,369 95.48% DXD PSE 201,600 100.00% E News 1,110 0.55% E Devotional 60 0.03% E PSE 200,400 99.42% ESPN Sports 39,864 19.75% ESPN PSE 161,936 80.25% ESPN2 Sports 40,637 20.15% ESPN2 PSE 161,027 79.85% FNC News 36,660 18.18% FNC PSE 164,940 81.82% FOOD News 450 0.22% FOOD PSE 201,150 99.78% FREEFR News 360 0.18% FREEFR Devotional 13,050 6.48% FREEFR PSE 187,833 93.34% FS1 Sports 4,170 13.78% FS1 PSE 26,100 86.22% FX Sports 630 0.31% FX PSE 200,766 99.69% GOLF PSE 201,600 100.00% HALL PSE 201,900 100.00% HGTV PSE 201,600 100.00%
Exhibit 3, Testimony of Howard B. Homonoff, 2010-13 Satellite Allocation 1 Homonoff Exhibit 3
Distribution Of Individual Programming Categories On Top 50 Networks, 2010-13
Programming Minutes Percentage Share Of CRB Program In Homonoff Gracenote Programming Cable Network Category Data Selection Minutes HISTOR Devotional 1,080 0.54% HISTOR PSE 200,520 99.46% HLN News 103,440 51.31% HLN PSE 98,160 48.69% LIFE Devotional 2,850 1.41% LIFE PSE 198,751 98.59% LIFEMO PSE 201,600 100.00% MSNBC News 132,660 65.81% MSNBC PSE 68,910 34.19% MTV News 22 0.01% MTV PSE 201,768 99.99% MTV2 PSE 201,630 100.00% NGC Devotional 1,140 0.57% NGC PSE 200,460 99.43% NIK PSE 201,606 100.00% OWN News 720 0.47% OWN PSE 152,040 99.53% OXYGEN Devotional 2,341 1.16% OXYGEN PSE 199,559 98.84% PAR PSE 201,353 100.00% POP Devotional 60 0.03% POP PSE 201,480 99.97% SYFY PSE 201,330 100.00% TBS News 15 0.01% TBS Sports 8,713 4.32% TBS PSE 192,864 95.67% TCM PSE 201,525 100.00% TLC News 120 0.06% TLC Devotional 1,240 0.62% TLC PSE 200,222 99.33% TNT Sports 4,866 2.42% TNT PSE 196,427 97.58% TOON Devotional 1,005 0.50% TOON PSE 200,595 99.50% TRAV PSE 201,600 100.00% TRUTV PSE 201,600 100.00% TVLAND PSE 201,620 100.00% USA Devotional 1,410 0.70% USA PSE 200,523 99.30% VH1 PSE 202,040 100.00% WE Devotional 120 0.06%
Exhibit 3, Testimony of Howard B. Homonoff, 2010-13 Satellite Allocation 2 Homonoff Exhibit 3
Distribution Of Individual Programming Categories On Top 50 Networks, 2010-13
Programming Minutes Percentage Share Of CRB Program In Homonoff Gracenote Programming Cable Network Category Data Selection Minutes WE PSE 201,600 99.94% WEATH News 39,240 19.47% WEATH PSE 162,330 80.53% TOTAL 9,859,711
PSE Share Of Programming 9,027,585 91.6% News Share Of Programming 699,482 7.1% Sports Share Of Programming 98,880 1.0% Devotional Share Of Programming 33,764 0.3% 9,859,711 100.0%
Exhibit 3, Testimony of Howard B. Homonoff, 2010-13 Satellite Allocation 3 EXHIBIT 4 Homonoff Exhibit 4
Top 50 Networks, Average License Fees Per Subscriber/Month, 2010-13 2010-13 2013 Average 2012 Average 2011 Average 2010 Average Average CRB Program License Fees License Fees License Fees License Fees Cable Network License Fees Category per Sub/Month per Sub/Month per Sub/Month per Sub/Month per Sub/Month ($) ($) ($) ($) ($) The Weather Channel (US) News 0.13 0.13 0.12 0.12 0.12 Food Network PSE 0.18 0.17 0.16 0.15 0.17 Discovery Channel (US) PSE 0.40 0.38 0.36 0.34 0.37 TNT (US) PSE 1.39 1.31 1.22 1.09 1.25 TBS PSE 0.70 0.66 0.60 0.53 0.62 Cartoon Network PSE 0.22 0.21 0.20 0.19 0.20 Nickelodeon/Nick At Nite (US) PSE 0.62 0.55 0.51 0.47 0.54 USA (US) PSE 0.88 0.81 0.76 0.72 0.79 CNN (US) News 0.59 0.57 0.55 0.52 0.56 HLN (US) News 0.00 0.00 0.00 0.00 0.00 A&E (US) PSE 0.29 0.28 0.27 0.26 0.28 Lifetime Television PSE 0.32 0.31 0.30 0.29 0.30 C-SPAN (US) News 0.06 0.06 0.06 0.06 0.06 ESPN (US) Sports 5.54 5.04 4.77 4.39 4.93 ESPN2 (US) Sports 0.70 0.67 0.64 0.58 0.65 HGTV (US) PSE 0.19 0.17 0.15 0.14 0.16 TLC (US) PSE 0.20 0.19 0.19 0.18 0.19 History (US) PSE 0.26 0.25 0.24 0.23 0.24 Disney Channel (US) PSE 1.06 1.01 0.96 0.90 0.98 Paramount Network (US) PSE 0.32 0.25 0.23 0.21 0.25 MTV (US) PSE 0.44 0.40 0.37 0.35 0.39 Comedy Central (US) PSE 0.19 0.17 0.16 0.15 0.17 FOX News Channel (US) News 0.96 0.90 0.79 0.70 0.84 VH1 (US) PSE 0.21 0.19 0.18 0.16 0.18 Freeform (US) PSE 0.27 0.26 0.24 0.22 0.25 Syfy (US) PSE 0.28 0.27 0.27 0.24 0.26 E! (US) PSE 0.24 0.23 0.22 0.21 0.22 CNBC (US) News 0.34 0.31 0.31 0.30 0.31 FX Network (US) PSE 0.53 0.48 0.45 0.43 0.47 TV Land / TV Land Classic (US) PSE 0.16 0.13 0.13 0.12 0.13 Animal Planet (US) PSE 0.11 0.10 0.10 0.09 0.10 AMC (US) PSE 0.35 0.34 0.26 0.25 0.30 MSNBC (US) News 0.23 0.21 0.20 0.18 0.20 Travel Channel (US) PSE 0.13 0.12 0.11 0.11 0.12 Bravo (US) PSE 0.25 0.24 0.23 0.22 0.24 truTV (US) PSE 0.15 0.13 0.13 0.11 0.13 CMT (US) PSE 0.11 0.10 0.10 0.09 0.10 BET (US) PSE 0.21 0.19 0.18 0.17 0.19 Hallmark Channel (US) PSE 0.07 0.07 0.06 0.06 0.06 Golf Channel (US) PSE 0.31 0.30 0.29 0.27 0.29 LMN (US) PSE 0.10 0.10 0.10 0.09 0.10 TCM (US) PSE 0.28 0.28 0.27 0.26 0.27 FOX Sports 1 (US) Sports 0.24 0.22 0.22 0.21 0.22 POP (US) PSE 0.02 0.02 0.02 0.02 0.02 MTV2 (US) PSE 0.07 0.06 0.06 0.05 0.06 Disney XD (US) PSE 0.17 0.16 0.15 0.14 0.15 OWN: Oprah Winfrey Network (US) PSE 0.18 0.01 0.02 0.02 0.06 National Geo Channel PSE 0.24 0.23 0.22 0.21 0.23 WE Tv (US) PSE 0.12 0.12 0.11 0.11 0.12 Oxygen (US) PSE 0.14 0.12 0.12 0.12 0.13 TOTAL Average Monthly License 18.97 Fees Per Sub/Month
PSE Average License Fees Per 11.10 58.5% Sub/Month News Average License Fees Per 1.89 10.0% Sub/Month Sports Average License Fees Per 5.97 31.5% Sub/Month
Exhibit 4, Testimony of Howard B. Homonoff, 2010-13 Satellite Allocation 1 Before the COPYRIGHT ROYALTY JUDGES Washington, D.C.
______) In the Matter of ) ) Distribution of the ) Docket No. 14-CRB-0011-SD (2010-13) ) 2010, 2011, 2012, and 2013 ) Satellite Royalty Funds ) ______)
DIRECT TESTIMONY OF JONDA K. MARTIN
MARCH 22, 2019 DIRECT TESTIMONY OF JONDA K. MARTIN
I. BIOGRAPHICAL INFORMATION
My name is Jonda K. Martin. I am the President of Cable Data Corporation
(“CDC”), which is located in Mount Airy, Maryland. In my more than 25 years at CDC,
I have been actively involved in all aspects of the company, including research, data entry, report generation, and administration. I received a Bachelor of Science/Business
Administration degree from American University in Washington, D.C., with concentrations in international business and management of information systems. I also received an MBA from University of Maryland. I have previously testified before the
Copyright Arbitration Royalty Panel (“CARP”) regarding CDC’s operations in connection with the CARP’s distribution of 1998 and 1999 cable compulsory license royalties, and before the Copyright Royalty Judges (“Judges”) in connection with Phase I and Phase II proceedings regarding the distribution of the 2000-2003 cable royalty funds, a Phase I proceeding regarding the distribution of the 2004 and 2005 cable royalty funds, and a Phase II proceeding regarding the 2004-2009 cable and 1999-2009 satellite royalty funds. I also submitted testimony in the Allocation Phase of the 2010-2013 cable proceeding.
II. PURPOSE OF TESTIMONY
The purpose of my testimony is to provide the Judges with an overview of CDC’s operations and describe its data collection process and methodologies in relevant detail. I will also provide an overview of how the Section 119 royalty scheme works and describe
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation | 1 the carriage data that CDC supplied to Program Suppliers in connection with this proceeding, which I understand were utilized by Dr. Jeffrey S. Gray in his analyses. I will also describe the local county analysis that CDC performed for Program Suppliers in connection with this proceeding, which I understand was provided to Nielsen.
III. CABLE DATA CORPORATION
CDC was established in 1979 to collect and analyze information on Statements of
Account (“SOAs”) that cable and satellite systems file with the Licensing Division of the
Copyright Office (“Licensing Division”). CDC makes the collected information available to users by purchase, either on an as needed basis or by subscription. CDC is the only company providing such a service. Numerous parties involved in the cable and satellite industries rely on data collected by CDC. This is particularly true for parties involved in copyright compulsory license proceedings. As a result, CDC data have been presented over the years to the Copyright Royalty Tribunal, the CARP, and the Judges in virtually all of the cable and satellite copyright royalty distribution proceedings and rate adjustment proceedings. In this section of my testimony, I will provide an overview of
CDC’s operations and its data collection methodologies.
Data collection is an integral part of CDC’s operations. CDC has two full-time employees who spend the vast majority of each work day on-location in the Licensing
Division of the Copyright Office. Those employees record data and other information from each publicly-available, filed SOA into our online database via laptop computers.1
1 Once SOAs are filed at the Copyright Office they are subject to review by the Licensing Division before they are made available for public inspection. The Licensing Division’s initial
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation | 2
CDC produces standard reports and customized reports which summarize the SOA data.
To keep CDC data as consistent as possible with the SOAs on file with the Licensing
Division, CDC performs regular system updates to account for modifications made to a system’s filing, for reasons such as additional royalty payments and refunds issued by the
Licensing Division.
After the SOA data is entered in CDC’s computer system, CDC produces both standardized and customized reports of aggregated cable and satellite system data for its clients. Both CDC’s standardized and customized reports are derived from the same database and rely on the same CDC data protocols.
IV. OVERVIEW OF THE SECTION 119 ROYALTY SCHEME
In 1988, Congress enacted the Section 119 statutory license, which allows satellite carriers to retransmit broadcast signals to their subscribers, provided that they comply with the statute and remit royalties to the Copyright Office on semi-annual basis.
According to the Licensing Division’s most recent Report Of Receipts, Section 119 royalties collected by the Copyright Office totaled approximately $96.4 million in 2010,
$96.2 million in 2011, $89.7 million in 2012, and $87.6 million in 2013.2 While the
Section 119 and Section 111 licenses are similar in some ways, there are some important differences in the way royalties are calculated by cable operators and satellite carriers, which I explain below.
review typically causes a several month delay between the date the SOAs are filed with the Office and the date the filings are available for CDC’s on-site employees to access.
2 See Licensing Division Report Of Receipts (February 28, 2019), available at https://www.copyright.gov/licensing/receipts.pdf (last visited March 21, 2019).
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation | 3
First, while Section 111 royalties are calculated based on a percentage of the cable systems’ gross receipts from tiers of service containing distant broadcast signals, Section
119 royalties are calculated based on the number of subscribers receiving distant signals per month, with different rates established for private home viewing and viewing in commercial establishments. The Section 119 royalty rates for the 2010-2013 royalty years were as follows:
Section 119 Royalty Rates Per-Subscriber, Per-Month, 2010-20133
Royalty Year Private Home Viewing Viewing In Commercial Establishments 2010 $0.25 $0.50
2011 $0.25 $0.50
2012 $0.26 $0.51
2013 $0.27 $0.52
Second, because Section 119’s royalty rates are calculated on a per-subscriber, per-month basis, unlike cable operators, satellite carriers are not required to pay a minimum fee for the privilege of retransmitting broadcast signals. Satellite carriers also do not calculate their royalties using subscriber groups.
Finally, while both the Section 111 and Section 119 licenses allow cable operators and satellite carriers to retransmit non-network programming, Section 119 also allows satellite carriers to retransmit network programming to subscribers in unserved
3 See 37 C.F.R. § 386.2.
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation | 4 households. As a result, network programming on distant signals is compensable under the Section 119 license.
V. CDC CARRIAGE DATA PROVIDED TO PROGRAM SUPPLIERS
CDC provided Program Suppliers with customized data reports for each of the
2010-2013 satellite royalty years. For each royalty year, these data reports listed all broadcast stations carried as full-time distant signals by satellite systems, the number of distant subscribers to which each station was available, and CDC’s calculation of the distant fees generated by that station. I understand that Dr. Jeffrey Gray utilized the customized CDC data reports to select the satellite stations for each royalty year at issue in this proceeding for Program Suppliers’ Nielsen distant viewing studies. CDC also provided Program Suppliers with a set of customized data reports for each accounting period during the 2010-2013 satellite royalty years showing the subscribers reported on a monthly basis by each satellite carrier who submitted SOAs, and the royalty fees generated by such carriage. I also understand that Dr. Gray utilized these CDC data in his economic analysis.
VI. CDC’s LOCAL COUNTY ANALYSIS After Dr. Gray selected stations for each of the 2010-2013 satellite royalty years,
Program Suppliers’ counsel sent the lists of Dr. Gray’s stations to CDC. CDC then analyzed each of these stations in order to determine which counties fell within the station’s local service area. We refer to this exercise as local county analysis.
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation | 5
CDC based identification of the counties local to each of the 2010-2013 satellite stations on the FCC signal carriage rules.4 I explain how CDC conducted its local county analysis for commercial stations below. In addition, Appendix A to my testimony provides illustrations of how the local county analysis was performed for 2010-2013.
A. Local County Analysis Of Commercial Stations
For our local county analysis of commercial stations, CDC employed the following general steps. First, we identified the counties that constituted each station’s
Designated Market Area (“DMA”). All such counties are considered local for that station. Second, we identified the counties in which each station was deemed
“significantly viewed” per the FCC. All such counties are considered local for that station pursuant to the FCC’s signal carriage rules. Lastly, we looked at other factors that would qualify a county as local to the station in question.
Once CDC completed the local county analysis, I sent the results to Program
Suppliers’ counsel. I understand that the results of CDC’s local county analysis were provided to Nielsen.
Thank you for the opportunity to present this information in this proceeding. I hope that it will assist you in your deliberations.
4 The signal carriage rules, now rescinded, were found at Sections 76.57 through 76.63 of the regulations of the FCC. 47 C.F.R. §§ 76.57-76.63 (1976).
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation | 6
APPENDIX A
LOCAL COUNTY ANALYSIS OF COMMERCIAL STATIONS
A. Local County Analysis Illustration For Commercial Station KXVO, Channel 38, Licensed To Omaha, Nebraska.
Described below are the steps CDC employed for the county analysis of commercial stations. The steps consist principally of Designated Market Area (“DMA”) analysis, significantly viewed (“SV”) analysis, 35-mile zone analysis, and the Grade B Contour analysis. I selected for the purpose of illustration, commercial station KXVO, Channel 38, licensed to Omaha, Nebraska. The analysis applied to KXVO is repeated for all of the commercial stations in each sample for which CDC conducted a county analysis.
1. DMA Analysis
Nielsen groups counties by DMAs.1 Each DMA consists of a group of counties forming an exclusive geographic area in which Nielsen has determined that the home market television stations hold a dominance of viewing. Although a few counties are split between DMAs, as a rule each county is assigned to one and only one DMA. Attachment 1 is a page with the Omaha market (among others) from Nielsen’s 2011 DMA report titled “U.S. TV Household Estimates” (“the DMA book”), published in September 2010, which shows all DMAs and the counties associated with each. This page provides a good example of how DMAs are used to identify local carriage for station KXVO. Again, the objective in determining the counties where a station is local is to enable Nielsen to exclude viewing from those counties, with the result that only distant viewing for KXVO will be captured. The Omaha DMA market consists of twenty-four counties: nine Iowa counties, one Missouri county and fourteen Nebraska counties: Iowa: Cass, Crawford, Fremont, Harrison, Mills, Montgomery, Page, Pottawattamie, Shelby Missouri: Atchison Nebraska: Burt, Cass, Colfax, Cuming, Dodge, Douglas, Johnson, Nemaha, Otoe, Platte, Richardson, Sarpy, Saunders, Washington Because KXVO is licensed to Omaha, a system serving communities in any of these twenty-four counties must carry KXVO to its subscribers as a local signal. CDC concluded that these twenty-four counties were within KXVO’s local service area. 2. Significantly Viewed Analysis
Besides the DMA criterion, stations are considered local in counties and/or communities in which the Federal Communications Commission (“FCC”) has deemed the station is
1 The definition of “local market” in Sections 119(d)(11), 122(j)(2), and 47 C.F.R. § 76.55(e)(2) define a station’s market as its Nielsen DMA.
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation, Appendix A | 1
“significantly viewed” (SV) meaning the station reaches certain FCC-defined viewing thresholds within the county or community. Because a system serving County X must carry stations that are significantly viewed in County X (or Community X), such carriage is considered local. Attachment 2 lists selected counties in Nebraska and the TV stations significantly viewed in each as reported on the FCC’s website: https://transition.fcc.gov/mb/significantviewedstations041916.pdf. In the case of KXVO, six counties outside the Omaha DMA are considered SV: Butler, NE Gage, NE Jefferson, NE Lancaster, NE Saline, NE Seward, NE If a system serves communities located in these SV counties, that system must carry KXVO as a local signal and consequently, those SV counties were considered local to KXVO. B. Other Criteria For Determining Whether a Station Is Local
Besides the DMA and SV criteria, which identify the vast majority of local counties, CDC also examined other criteria to see if there are any additional counties that would be considered local. These criteria include the station’s 35-Mile Specified Zone and Grade B Contour (TV Service Contour). 1. 35-Mile Specified Zone
For all television markets, major and smaller, a system’s carriage of a TV station to subscribers located inside the station’s 35 mile specified zone means the station is local to those subscribers.
A copy of the 35-Mile Specified Zone for Omaha is shown as Attachment 3. FCC rules require a system serving communities located within that specified zone to carry KXVO as a local signal. Review of the zone indicated that the counties within the specified zone had already been classified as local due to the SV/DMA criteria. 2. The Grade B Contour
Another criterion, in some situations, is the Grade B contour. The contour is an irregular and oddly-shaped circle surrounding the TV station’s transmitter site. The Grade B is a measure of estimated signal strength based on the station’s antenna size, power, and direction. The Grade B, in other words, is a measure of how clear a picture can be expected to be on a person’s television set.
The Grade B contour can be used as a criterion in two circumstances relevant to the local county analysis CDC performed for 2010-2013. The first circumstance has to do with stations licensed to smaller markets. If a system serves communities located in a smaller market, (i.e., located within the 35-mile zone of such a market), the system can carry as local any station from another smaller market whose Grade B encompasses the communities served by the system. The second circumstance relates to systems serving communities located outside all television markets. In the case of a system serving such an area, the system may carry as local all stations whose Grade Bs encompass the communities served by the system. In both circumstances, if the
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation, Appendix A | 2
station is local per the Grade B criterion, the system operator does not have to account for the station in its royalty calculation. Attachment 3 further shows the Grade B contour of KXVO that CDC downloaded from the FCC’s website shaded in yellow: https://www.fcc.gov/media/television/tv-service-contour- data-points. Systems serving communities that fall “outside all (television) markets” must carry as a local signal any station whose Grade B encompasses the communities of the system. To identify any such counties, CDC looked to see if there were any additional counties within KXVO’s Grade B contour that were outside all markets and not already classified local by a previous standard. That review indicated that the counties within the Grade B contour had either already been classified as local due to the SV/DMA criteria or were not outside all markets. Once CDC identified all the local counties for KXVO, I provided those counties to Program Suppliers’ counsel. KXVO’s local counties were the twenty-four counties in the Omaha DMA, plus the six SV counties, for a total of 30 counties in which KXVO was a local signal. C. County Analysis For “Partially-Local” Stations
In the course of CDC’s analyses, we may find that an entire county is neither wholly distant nor wholly local. An example would be a county that is neither SV nor DMA for a station, but which falls partially within the 35-mile zone of the station’s market. Another example might be a county located outside all television markets and partially covered by a station’s Grade B. In these few cases, CDC relies on the location of a majority of the county’s population to designate the county as local or distant. Because the entire county must be classified as either local or distant for purposes of the Nielsen Studies, it is reasonable to assume that viewing will track with population. For example, are more people (i.e., viewers) located inside the 35-mile zone (or Grade B) or outside? If, in our example, most of the population is within the station’s Grade B contour, we consider the county local. CDC relies on mapping, distance calculations and census data to measure coverage of a county in relation to the Grade B (or 35-mile zone).
Jonda Martin Written Direct Testimony, 2010-13 Satellite Allocation, Appendix A | 3
MARTIN APPENDIX A ATTACHMENT 1 U.S. TV HOUSEHOLD ESTIMATES BY COUNTY WITHIN DESIGNATED MARKET AREA (DMA) DESIGNATED MARKET AREA** % TV % OF DESIGNATED MARKET AREA** % TV % OF STATE COUNTY TOTAL TV PENE- U.S. TV STATE COUNTY TOTAL TV PENE- U.S. TV COUNTY SIZE * HOUSEHOLDS HOUSEHOLDS TRATION HOUSEHOLDS COUNTY SIZE * HOUSEHOLDS HOUSEHOLDS TRATION HOUSEHOLDS 622 NEW ORLEANS (CONT'D) 633 ODESSA-MIDLAND (CONT'D) LOUISIANA TEXAS ST CHARLES M B 17,800 17,710 BREWSTER D 4,000 3,510 ST JAMES B 7,200 7,150 CRANE D 1,500 1,490 ST JOHN BAPT M B 16,100 16,030 ECTOR M C 49,400 49,090 ST TAMMANY M B 87,700 87,140 GLASSCOCK D 400 400 TANGIPAHOA C 45,700 45,070 HOWARD D 11,400 11,330 TERREBONNE C 39,100 38,860 JEFF DAVIS D 1,000 910 WASHINGTON D 17,900 17,450 LOVING D 100 100 MISSISSIPPI MARTIN D 1,700 1,690 HANCOCK B 17,000 16,820 MIDLAND M C 49,700 49,360 PEARL RIVER D 22,500 22,230 PECOS D 5,500 5,380 PRESIDIO D 2,800 2,720 REAGAN D 1,100 1,090 501 NEW YORK 7,628,500 7,515,330 99 6.484 REEVES D 3,500 3,460 CONNECTICUT TERRELL D 400 380 FAIRFIELD A 331,800 329,210 UPTON D 1,300 1,280 NEW JERSEY WARD D 4,100 4,060 BERGEN M A 335,900 334,340 WINKLER D 2,600 2,580 ESSEX M A 276,300 273,670 HUDSON M A 230,900 227,700 650 OKLAHOMA CITY 709,800 704,670 99 .608 HUNTERDON M A 47,900 47,610 MIDDLESEX M A 281,200 279,650 OKLAHOMA MONMOUTH M A 236,200 235,140 ALFALFA D 1,900 1,860 MORRIS M A 178,700 178,340 BECKHAM D 8,300 8,220 OCEAN M A 228,300 226,070 BLAINE D 3,900 3,860 PASSAIC M A 160,000 158,470 CADDO D 11,000 10,910 SOMERSET M A 118,000 117,340 CANADIAN M B 42,100 41,830 SUSSEX M A 54,900 54,500 CLEVELAND M B 97,000 96,490 UNION M A 184,800 184,020 CUSTER D 10,800 10,710 WARREN A 42,200 41,950 DEWEY D 1,900 1,880 NEW YORK ELLIS D 1,800 1,760 BRONX M A 483,800 475,760 GARFIELD D 24,100 23,930 DUTCHESS A 105,900 104,530 GARVIN D 10,900 10,780 KINGS M A 912,900 879,220 GRADY M D 20,400 20,230 NASSAU M A 449,200 447,680 GRANT D 1,700 1,680 NEW YORK M A 772,000 744,390 GREER D 2,100 2,090 ORANGE A 129,900 128,410 HARMON D 1,100 1,080 PUTNAM M A 34,500 34,340 HARPER D 1,500 1,470 QUEENS M A 790,300 779,680 HUGHES D 5,200 5,190 RICHMOND M A 175,300 174,440 KAY D 18,400 18,220 ROCKLAND M A 97,400 95,530 KINGFISHER D 5,500 5,450 SUFFOLK M A 500,800 498,680 KIOWA D 3,700 3,670 SULLIVAN C 29,700 28,880 LINCOLN M D 12,500 12,360 ULSTER C 70,200 68,370 LOGAN M B 15,100 14,940 WESTCHESTER M A 346,100 344,150 MAJOR D 3,000 2,950 PENNSYLVANIA MCCLAIN M B 12,900 12,710 PIKE M A 23,400 23,260 MURRAY D 5,300 5,270 NOBLE D 4,400 4,350 OKFUSKEE D 3,900 3,760 544 NORFOLK-PORTSMTH-NEWPT NWS 721,300 716,050 99 .618 OKLAHOMA M B 295,500 293,860 NORTH CAROLINA PAYNE C 31,100 30,820 CAMDEN D 4,000 3,980 POTTAWATOMIE M B 26,000 25,820 CHOWAN D 6,000 5,900 ROGER MILLS D 1,500 1,470 CURRITUCK M B 9,600 9,530 SEMINOLE D 9,400 9,330 DARE D 14,900 14,690 WASHITA D 4,700 4,620 GATES D 4,500 4,460 WOODS D 3,400 3,370 HERTFORD D 8,700 8,580 WOODWARD D 7,800 7,730 PASQUOTANK D 16,000 15,900 PERQUIMANS D 5,500 5,430 652 OMAHA 420,600 418,290 99 .361 VIRGINIA ACCOMACK D 15,700 15,470 IOWA CHSPEAKE CITY M B 80,700 80,180 CASS D 5,800 5,780 GLOUCESTER M B 14,800 14,660 CRAWFORD D 6,200 6,180 HAMPTON CITY M B 53,000 52,700 FREMONT D 2,900 2,880 ISLE OF WIGHT M B 14,300 14,150 HARRISON M D 6,000 5,980 JAMES CITY M B 31,400 31,150 MILLS M D 5,600 5,580 MATHEWS M B 4,000 3,940 MONTGOMERY D 4,500 4,480 NORFOLK CITY M B 86,200 85,610 PAGE D 5,900 5,840 NORTHAMPTON D 5,600 5,550 POTTAWATTAMIE M B 36,100 35,890 NWPRT NWS CTY M B 73,100 72,700 SHELBY D 4,700 4,680 PRTSMUTH CITY M B 37,300 37,040 MISSOURI SOUTHAMPTON D 10,400 10,270 ATCHISON D 2,600 2,570 SUFFOLK CITY M B 32,000 31,620 NEBRASKA SURRY M D 2,800 2,780 BURT D 2,800 2,790 VIRGINIA BCH M B 163,000 162,150 CASS M B 9,800 9,780 YORK M B 27,800 27,610 COLFAX D 3,500 3,470 CUMING D 3,500 3,490 DODGE D 14,500 14,440 740 NORTH PLATTE 15,500 15,350 99 .013 DOUGLAS M B 205,900 204,540 JOHNSON D 1,800 1,790 NEBRASKA NEMAHA D 2,800 2,780 LINCOLN M D 15,000 14,850 OTOE D 5,900 5,880 LOGAN M D 300 300 PLATTE D 12,900 12,830 MCPHERSON M D 200 200 RICHARDSON D 3,300 3,260 SARPY M B 58,500 58,320 633 ODESSA-MIDLAND 148,000 146,310 99 .126 SAUNDERS M D 7,700 7,680 WASHINGTON M B 7,400 7,380 NEW MEXICO LEA-S D 2,100 2,090 TEXAS ANDREWS D 5,400 5,390
M METRO COUNTY OF DMA MARKET 42 * SEE PAGE A FOR COUNTY SIZE DEFINITIONS NM METRO COUNTY OF NON-DMA MARKET ** SEE PAGE A FOR DMA CODE AND NAME DEFINITION MARTIN APPENDIX A ATTACHMENT 2 Significantly-Viewed Counties---KXVO, Omaha
Nebraska
Butler Lancaster KMTV, 3, Omaha, NE +KLKE, 24, Albion, NE (formerly KBGT) WOWT, 6, Omaha, NE (formerly WOW) KOLN, 10, Lincoln, NE KETV, 7, Omaha, NE KMTV, 3, Omaha, NE +KXVO, 15, Omaha, NE WOWT, 6, Omaha, NE (formerly WOW) +KPTM, 42, Omaha, NE KETV, 7, Omaha, NE KOLN, 10, Lincoln, NE +KXVO, 15, Omaha, NE +KPTM, 42, Omaha, NE
Gage Saline KOLN, 10, Lincoln, NE KSNB-TV, 4, Superior, NE (formerly KMTV, 3, Omaha, NE KHTL) KETV, 7, Omaha, NE KOLN, 10, Lincoln, NE +KXVO, 15, Omaha, NE KMTV, 3, Omaha, NE +KPTM, 42, Omaha, NE KETV, 7, Omaha, NE +KXVO, 15, Omaha, NE +KPTM, 42, Omaha, NE
Jefferson Seward KSNB-TV, 4, Superior, NE (formerly KOLN, 10, Lincoln, NE KHTL) KMTV, 3, Omaha, NE KHAS-TV, 5, Hastings, NE WOWT, 6, Omaha, NE (formerly WOW) KOLN, 10, Lincoln, NE KETV, 7, Omaha, NE +KXVO, 15, Omaha, NE +KXVO, 15, Omaha, NE +KPTM, 42, Omaha, NE +KPTM, 42, Omaha, NE
MARTIN APPENDIX A ATTACHMENT 3 KXVO-DT, CW, OMAHA, NE
CHANNEL 38, VIRTUAL CHANNEL 15
CERTIFICATE OF SERVICE
I hereby certify that on this 22nd day of March, 2019, a copy of the foregoing pleading
was provided to each of the parties on the attached service list, either electronically via the
Copyright Royalty Judges’ eCRB electronic filing system, or, for those parties not receiving
service through eCRB, by Federal Express overnight mail.
/s/ Lucy Holmes Plovnick Lucy Holmes Plovnick
Certificate Of Service & Service List, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 1
SERVICE LIST
JOINT SPORTS CLAIMANTS BROADCASTER CLAIMANTS GROUP
Robert Alan Garrett John I. Stewart, Jr. Daniel A. Cantor David Ervin Michael Kientzle Ann Mace Bryan L. Adkins Brendan Sepulveda ARNOLD & PORTER KAYE SCHOLER CROWELL & MORING LLP LLP 1001 Pennsylvania Ave., NW 601 Massachusetts Avenue, N.W. Washington, DC 20004-2595 Washington, DC 20001 Phone: (202) 624-2685 Phone: (202) 942-5000 Fax: (202) 628-5116 Fax: (202) 942-5999 [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected]
SETTLING DEVOTIONAL CLAIMANTS
Arnold P. Lutzker Benjamin Sternberg Jeannette M. Carmadella LUTZKER & LUTZKER LLP 1233 20th Street, NW, Suite 703 Washington, DC 20036 Phone: (202) 408-7600 Fax: (202) 408-7677 [email protected]
Matthew J. MacLean Michael A. Warley Jessica T. Nyman PILLSBURY WINTHROP SHAW PITTMAN LLP 2300 N Street, NW Washington, DC 20037 Phone: (202) 663-8525 Fax: (202) 663-8007 [email protected]
Certificate Of Service & Service List, Program Suppliers’ WDS-A, 2010-13 Satellite Allocation | 2
Proof of Delivery
I hereby certify that on Friday, March 22, 2019 I provided a true and correct copy of the Program Suppliers' Allocation Phase Written Direct Statement to the following:
American Society of Composers, Authors and Publishers (ASCAP), represented by Sam Mosenkis served via Electronic Service at [email protected]
Major League Soccer, LLC, represented by Edward S. Hammerman served via Electronic Service at [email protected]
Motion Picture Association of America (MPAA)-Represented Program Suppliers, represented by Gregory Olaniran served via Email
Multigroup Claimants, represented by Brian D Boydston served via Electronic Service at [email protected]
Settling Devotional Claimants, represented by Jeannette M. Carmadella served via Email
Broadcaster Claimants Group, represented by John Stewart served via Electronic Service at [email protected]
National Public Radio, Inc. (NPR) (submitted comment), represented by Gregory A Lewis served via Electronic Service at [email protected]
Spanish Language Producers (SLP), represented by Brian Boydston served via Email
Devotional Claimants, represented by Clifford M Harrington served via Electronic Service at [email protected]
SESAC, Inc., represented by John C. Beiter served via Electronic Service at [email protected]
Joint Sports Claimants, represented by Michael E Kientzle served via Electronic Service at [email protected]
Joint Sports Claimants (JSC), represented by Michael J. Mellis served via Email Spanish Language Producers, represented by Brian D Boydston served via Electronic Service at [email protected]
American Society of Composers, Authors and Publishers, represented by Samuel Mosenkis served via Email
American Society of Composers, Authors and Publishers (ASCAP) and Broadcast Music, Inc. (BMI), represented by Jackson Wagener served via Email
Broadcast Music, Inc. (BMI), represented by Jennifer T. Criss served via Electronic Service at [email protected]
Broadcast Music, Inc., represented by Joseph J. DiMona served via Email
Signed: /s/ Lucy H Plovnick