**Before the**

**FEDERAL COMMUNICATIONS COMMISSION**

**Washington, D.C. 20554**

In the Matter of )

)

Federal-State Joint Board on ) CC Docket No. 96-45

Universal Service )

)

Forward-Looking Mechanism ) CC Docket No. 97-160

for High Cost Support for )

Non-Rural LECs )

**TENTH REPORT AND ORDER**

**Adopted: October 21, 1999 Released: November 2, 1999**

By the Commission: Commissioner Tristani issuing a separate statement;
Commissioner Furchtgott-Roth dissenting and issuing a statement.

**TABLE OF CONTENTS**

[Paragraph Number]{.underline}

I. INTRODUCTION 1

II\. PROCEDURAL HISTORY 3

A. *Universal Service Order* 3

B. *1997 Further Notice* and the Input Value Development Process 5

C. *Platform Order* and *Second Recommended Decision* 8

D. *Inputs Further Notice* and *Seventh Report and Order* 10

III\. ESTIMATING FORWARD-LOOKING ECONOMIC COST 12

A. Designing a Forward-Looking Wireline Local Telephone Network 12

B. Synthesis Model 17

1\. Historical Background 17

2\. Validation 21

C. Selecting Forward-Looking Input Values 29

IV\. DETERMINING CUSTOMER LOCATIONS 33

A. Background 33

B. Customer Location Data 36

1\. Geocode Data 36

2\. Road Surrogate Customer Locations 40

3\. Methodology for Estimating the Number of Customer Locations 48

V. OUTSIDE PLANT INPUT VALUES 63

A. Introduction 63

B. Engineering Assumptions and Optimizing Routines 66

1\. Optimization 67

2\. T-1 Technology 77

3\. Distance Calculations and Road Factor 80

C. Cable and Structure Costs 83

1\. Background 83

2\. Nationwide Values 90

3\. Preliminary Issues Cable Costs 94

4\. Cost Per Foot of Cable 101

5\. Cable Fill Factors 186

6\. Structure Costs 209

7\. Plant Mix 227

D. Structure Sharing 241

1\. Background 241

2\. Discussion 243

E. Serving Area Interfaces 250

1\. Background 250

2\. Discussion 253

F. Digital Loop Carriers 269

1\. Background 269

2\. Discussion 274

VI\. SWITCHING AND INTEROFFICE FACILITIES 286

A. Introduction 286

B. Switch Costs 290

1\. Background 290

2\. Discussion 296

C. Use of the Local Exchange Routing Guide (LERG) 320

D. Other Switching and Interoffice Transport Inputs 324

VII\. EXPENSES 338

A. Introduction 338

B. Plant-Specific Operations Expenses 341

1\. Background 341

2\. Discussion 346

C. Common Support Services Expenses 377

1\. Background 377

2\. Discussion 382

D. GSF Investment 409

1\. Background 409

2\. Discussion 411

VIII\. CAPITAL COSTS 419

A. Depreciation 419

1\. Background 419

2\. Discussion 422

B. Cost of Capital 432

C. Annual Charge Factors 436

IX\. PROPOSED MODIFICATION TO PROCEDURES FOR DISTINGUISHING RURAL AND
NON-RURAL COMPANIES 440

A. Background 440

B. Discussion 443

1\. Annual Filing Requirement 447

2\. Statutory Terms 450

3\. Identification of Rural Telephone Companies 458

X. PROCEDURAL MATTERS AND ORDERING CLAUSE 460

A. Final Regulatory Flexibility Analysis 460

B. Paperwork Reduction Act Analysis 472

C. Ordering Clauses 473

Appendix A (Input Values) A-1

Appendix B (Methodology for Estimating Outside Plant Costs) B-1

Appendix C (Description of Methodology for Estimating Switching Costs)
C-1

Appendix D (Expenses) D-1

Appendix E (Parties Filing Comments and Reply Comments) E-1

**I. INTRODUCTION**

Number range level0Number range level1Number range level2Number range
level3Number range level4Number range level5Number range level6Number
range level71. In the Telecommunications Act of 1996 (1996 Act),[^1]
Congress directed this Commission and the states to take the steps
necessary to establish explicit support mechanisms to ensure the
delivery of affordable telecommunications service to all Americans. In
response to this directive, the Commission has taken action to put in
place a universal service support system that will be sustainable in an
increasingly competitive marketplace. In the *Universal Service Order*,
the Commission adopted a plan for universal service support for rural,
insular, and high-cost areas to replace longstanding federal support to
incumbent local telephone companies with explicit, competitively neutral
federal universal service support mechanisms.[^2] The Commission adopted
the recommendation of the Federal-State Joint Board on Universal Service
(Joint Board) that an eligible carrier\'s level of universal service
support should be based upon the forward-looking economic cost of
constructing and operating the network facilities and functions used to
provide the services supported by the federal universal service support
mechanisms.[^3]

2\. In this Report and Order, we complete the selection of a model to
estimate forward-looking cost by selecting input values for the
synthesis model we previously adopted.[^4] These input values include
such things as the cost of switches, cables, and other network
components necessary to provide supported services, in addition to
various capital cost parameters. The forward-looking cost of providing
supported services estimated by the model will be used as part of the
Commission\'s methodology to determine high-cost support for non-rural
carriers beginning January 1, 2000. This methodology is established in a
companion order.[^5]

**II. PROCEDURAL HISTORY**

**A. *Universal Service Order***

3\. Prior to the 1996 Act, three explicit interstate universal service
programs provided assistance to small incumbent local exchange carriers
(LECs) and LECs that served rural and high-cost areas: high-cost loop
support;[^6] dial equipment minutes (DEM) weighting; and the Long-Term
Support (LTS) program.[^7] Other mechanisms also have historically
contributed to maintaining affordable rates in rural areas, including
support implicit in geographic toll rate averaging, intrastate rates,
and interstate access charges. Section 254 of the Communications Act of
1934, as amended, directed the Commission to reform universal service
support mechanisms to ensure that they are compatible with the
pro-competitive goals of the 1996 Act, and it required the Commission to
institute a Joint Board on universal service and to implement the
recommendations from the Joint Board by May 8, 1997.[^8] After receiving
the recommendations of the Joint Board on November 7, 1996,[^9] the
Commission adopted the *Universal Service Order* on May 7, 1997.

4\. In the *Universal Service Order*, the Commission adopted a
forward-looking economic cost methodology to calculate support for
non-rural carriers. Under this methodology, a forward-looking economic
cost mechanism selected by the Commission, in consultation with the
Joint Board, would be used to estimate non-rural carriers\'
forward-looking economic cost of providing the supported services in
high-cost areas.[^10]

**B. *1997 Further Notice* and the Input Value Development Process**

5\. In a July 18, 1997 *Further Notice of Proposed Rulemaking*, the
Commission established a multi-phase plan to develop a federal universal
service support mechanism that would send the correct signals for entry,
investment, and innovation.[^11] The *1997 Further Notice* divided
questions related to the cost models into \"platform design\" issues and
\"input value\" issues.[^12] The *1997 Further Notice* subdivided each
of the platform and input issues into the following four topic groups:
(1) customer location; (2) outside plant design; (3) switching and
interoffice; and (4) general support facilities (GSF) and expense
issues.[^13]

6\. After reviewing the comments received in response to the *1997
Further Notice*, the Common Carrier Bureau (Bureau) released two public
notices to guide parties wishing to submit cost models for consideration
as the high-cost federal mechanism.[^14]

7\. In addition to the *1997 Further Notice*, the Bureau has solicited
comment and allowed interested parties the opportunity to participate in
the development of the input values to be used in the forward-looking
cost model. On May 4, 1998, the Bureau released a *Public Notice* to
update the record on several input-related issues.[^15] The Bureau also
issued data requests designed to acquire information that could be
useful in determining the final input values,[^16] and conducted a
series of public workshops designed to elicit further comment from
interested parties in selecting final input values.[^17] Finally, the
Bureau conducted numerous *ex parte* meetings with interested parties
throughout this proceeding.[^18]

**C. *Platform Order* and *Second Recommended Decision***

8\. In the *Platform Order*, released on October 28, 1998, the
Commission adopted the forward-looking cost model platform to be used in
determining federal universal service high-cost support for non-rural
carriers.[^19] The model platform that the Commission adopted combined
elements from each of the three models under consideration in this
proceeding: (1) the BCPM, Version 3.0 (BCPM);[^20] (2) the HAI Model,
Version 5.0a (HAI);[^21] and (3) the Hybrid Cost Proxy Model, Version
2.5 (HCPM).[^22] In the *Platform Order*, the Commission also specified
several issues that would be addressed in the inputs stage of this
proceeding. These issues include: (1) the geocode data source to
determine customer locations;[^23] (2) the road surrogate method to
determine the location of non-geocoded customer locations;[^24] and (3)
the use of the local exchange routing guide (LERG) to identify the
existing host-remote switch relationships.[^25]

9\. On November 25, 1998, the Joint Board released the *Second
Recommended Decision*, in which it recommended that the Commission
compute federal high-cost support for non-rural carriers through a
two-step process.[^26] First, the Joint Board recommended that the
Commission should estimate the total support amount necessary in those
areas considered to have high costs relative to other areas. Second, the
Joint Board recommended that the Commission should consider, in a
consistent manner across all states, any particular state\'s ability to
support high-cost areas within the state.[^27] The Joint Board
recommended that federal support should be provided to the extent that
the state would be unable to support its high-cost areas through its own
reasonable efforts.[^28] In addition, the Joint Board recommended that
the Commission continue to work with the Joint Board to select the input
values to complete a forward-looking cost model and to finalize the
methodology for distributing federal high-cost support.[^29]

**D. *Inputs Further Notice* and *Seventh Report and Order***

10\. On May 28, 1999, the Commission released the *Inputs Further
Notice* and the *Seventh Report and Order*.[^30] In the *Inputs Further
Notice*, we proposed and sought comment on a complete set of input
values for use in the model, such as the cost of switches, cables, and
other network components.[^31] For the most important inputs, we
provided a detailed description of the methodology that was used to
arrive at the proposed values.[^32]

11\. In the *Seventh Report and Order*, we adopted revisions to the
federal support mechanisms, in light of the Joint Board\'s
recommendations, to permit rates to remain affordable and reasonably
comparable across the nation, consistent with the 1996 Act. To
accomplish these goals, we established and sought comment on a
methodology for determining non-rural carriers\' support amounts, based
on the forward-looking costs estimated using a national cost model, and
a national cost benchmark, that will begin on January 1, 2000.[^33]

**III. ESTIMATING FORWARD-LOOKING ECONOMIC COST**

**A. Designing a Forward-Looking Wireline Local Telephone Network**

12\. To understand the assumptions made in the mechanism, it is
necessary to understand the layout of the current wireline local
telephone network.[^34] In general, a telephone network must allow any
customer to connect to any other customer. In order to accomplish this,
a telephone network must connect customer premises to a switching
facility, ensure that adequate capacity exists in that switching
facility to process all customers\' calls that are expected to be made
at peak periods, and then interconnect that switching facility with
other switching facilities to route calls to their destinations. A wire
center is the location of a switching facility. The wire center
boundaries define the area in which all customers are connected to a
given wire center. The *Universal Service Order* required the models to
use existing incumbent LEC wire center locations in estimating
forward-looking cost.[^35]

13\. Within the boundaries of each wire center, the wires and other
equipment that connect the central office to the customers\' premises
are known as outside plant. Outside plant can consist of either copper
cable or a combination of optical fiber and copper cable, as well as
associated electronic equipment. Copper cable generally carries an
analog signal that is compatible with most customers\' telephone
equipment. The range of an analog signal over copper is limited,
however, so thicker, more expensive cables or loading coils must be used
to carry signals over greater distances. Optical fiber cable carries a
digital signal that is incompatible with most customers\' telephone
equipment, but the quality of a signal carried on optical fiber cable is
superior at greater distances when compared to a signal carried on
copper wire. Generally, when a neighborhood is located too far from the
wire center to be served by copper cables alone, an optical fiber cable
will be deployed to a point within the neighborhood, where a piece of
electronic equipment will be placed that converts the digital light
signal carried on optical fiber cable to an analog, electrical signal
that is compatible with customers\' telephones. This equipment is known
as a digital loop carrier remote terminal, or DLC, which is connected to
a serving area interface (SAI). From the SAI, copper cables of varying
gauge extend to all of the customer premises in the neighborhood. Where
the neighborhood is close enough to the wire center to be served
entirely on copper cables, copper trunks connect the wire center to the
SAI, and copper cables will then connect the SAI to the customers in the
serving area. The portion of the loop plant that connects the central
office with the SAI or DLC is known as the feeder plant, and the portion
that runs from the DLC or SAI throughout the neighborhood is known as
the distribution plant.

14\. The model\'s estimate of the cost of serving the customers located
within a given wire center\'s boundaries includes the calculation of
switch size, the lengths, gauge, and number of copper and fiber cables,
and the number of DLCs required. These factors depend, in turn, on how
many customers the wire center serves, where the customers are located
within the wire center boundaries, and how they are distributed within
neighborhoods. Particularly in rural areas, some customers may not be
located in neighborhoods at all but, instead, may be scattered
throughout outlying areas. In general, the model divides the area served
by the wire center into smaller areas known as serving areas. For
serving areas sufficiently close to the wire center, copper feeder cable
extends from the wire center to a SAI where it is cross-connected to
copper distribution cables. If the feeder is fiber, it extends to a DLC
terminal in the serving area, which converts optical digital signals to
analog signals. Individual circuits from the DLC are cross-connected to
copper distribution cables at the adjacent SAI.

15\. The model assumes that wire centers are interconnected with one
another using optical fiber networks known as Synchronous Optical
Network (SONET) rings.[^36] The infrastructure to interconnect the wire
centers is known as the interoffice network, and the carriage of traffic
among wire centers is known as transport. In cases where a number of
wire centers with relatively few people within their boundaries are
located in close proximity to one another, it may be more economical to
use the processor capacity of a single switch to supervise the calls of
the customers in the boundaries of all the wire centers. In that case, a
full-capacity switch (known as a host) is placed in one of the wire
centers and less expensive, more limited-capacity switches (known as
remotes) are placed in the other wire centers. The remotes are then
connected to the host with interoffice facilities. Switches that are
located in wire centers with enough customers within their boundaries to
merit their own full-capacity switches and that do not serve as hosts to
any other wire centers are called stand-alone switches.

16\. There are also a number of expenses and general support facilities
(GSF) costs associated with the design of a forward-looking wireline
telephone network.[^37] GSF costs include the investment related to
vehicles, land, buildings, and general purpose computers. Expenses
include: plant-specific expenses, such as maintenance of facilities and
equipment expenses; plant non-specific expenses, such as engineering,
network operations, and power expenses; customer services expenses, such
as marketing, billing, and directory listing expenses; and corporate
operations expenses, such as administration, human resources, legal, and
accounting expenses.[^38]

**B. Synthesis Model**

**1. Historical Background**

17\. The synthesis model adopted in the *Platform Order* allows the user
to estimate the cost of building a telephone network to serve
subscribers in their actual geographic locations, to the extent these
locations are known.[^39] To the extent that the actual geographic
locations of customers are not available, the Commission determined that
the synthesis model should assume that customers are located along
roads.[^40]

18\. Once the customer locations have been determined, the model employs
a clustering algorithm to group customers into serving areas in an
efficient manner that takes into consideration relevant engineering
constraints.[^41] After identifying efficient serving areas, the model
designs outside plant to the customer locations.[^42] In doing so, the
model employs a number of cost minimization principles designed to
determine the most cost-effective technology to be used under a variety
of circumstances, such as varying terrain and density.[^43]

19\. The Commission concluded that the federal universal service
mechanism should incorporate, with certain modifications, the HAI 5.0a
switching and interoffice facilities module to estimate the cost of
switching and interoffice transport.[^44] The Commission noted that it
would consider adopting the LERG at the inputs stage of this proceeding
to determine the deployment of host and remote switches.[^45] In
addition, the Commission adopted the HAI platform module for calculating
expenses and capital costs, such as depreciation.[^46]

20\. The Commission noted that technical improvements to the cost model
will continue, both before implementation of the model for non-rural
carriers and on an ongoing basis, as necessary.[^47] The Commission
therefore delegated to the Bureau the authority to make changes or
direct that changes be made to the model platform as necessary and
appropriate to ensure that the platform of the federal mechanism
operates as described in the *Platform Order*.[^48] As contemplated in
the *Platform Order*, Commission staff and interested parties have
continued to review the model platform to ensure that it operates as
intended. As a result, some refinements have been made to the model
platform adopted in the *Platform Order*.[^49] All changes to the model
platform are posted on the Commission\'s Web site.[^50]

**2. Validation**

21\. In the *Universal Service Order*, the Commission concluded that
high-cost support should be based on forward-looking costs.[^51] Since
that time, the Commission has continued to work to adopt a cost model
that is reasonably accurate and verifiable.[^52] Although we have
remained confident in our ability to adopt a model, in the *Inputs
Further Notice*, we sought comment on how the Commission might determine
support levels without using a model, \"\[i\]n the unlikely event that
the model is not ready for timely implementation.\"[^53] A few
commenters offered concrete suggestions in response to this request,
virtually all of which involved the use of carriers\' book costs in lieu
of the model.[^54]

22\. As an initial matter, we affirm the Commission\'s decision to base
support calculations on forward-looking costs. We have repeatedly
articulated our reasons for believing that forward-looking costs
represent a superior method for determining support amounts. The most
significant of these is that forward-looking costs are the basis of
economic decisions in a competitive market, and therefore send the
correct signals for entry and investment.

23\. Moreover, the Commission and its staff have undertaken a thorough
review of the model and its input values over the past six months. In so
doing, the staff has coordinated extensively with and received
substantial input from the Joint Board staff and interested outside
parties. As a result of this examination of the model, we are convinced
that it generates reasonably accurate estimates of forward-looking costs
and that the model is the best basis for determining non-rural
carriers\' high-cost support in a competitive environment.

24\. After this review of the model, we find that none of the criticisms
of the model undermine our decision to use it for calculating non-rural
carriers\' high-cost support. For example, some parties have observed
that the model seems to generate unexpectedly high cost estimates for
certain states, such as Mississippi and Alabama.[^55] Because of the
high levels of cost estimated in these states, they receive larger
shares of forward-looking support than they receive under the current
mechanism, and also receive higher levels of support than some other
states (such as the sparsely populated Western and Midwestern states)
that many parties expected to lead the list of high-cost states. After
further review, however, we have found several factors that explain the
model\'s results.

25\. We first sought to verify the model\'s results by determining
whether the model generated higher costs in areas where customers are
more dispersed. Although there are other relevant factors, most people
agree that telephone plant costs tend to be highest when customers are
spread thinly over a large area. We used a \"minimum spanning tree\"
measurement to determine relative dispersion of customers.[^56] We found
that the model\'s cost estimates were highly correlated with dispersion
of customers. This provides a preliminary, objective check on the
model\'s accuracy.

26\. This analysis does not, however, explain why the model estimates
higher costs in some states relative to others in a distribution that
differs from carriers\' book costs and from some observers\'
expectations. In researching this issue, we discovered that significant
differences exist among the states in the territory served by larger
carriers, which are typically considered non-rural carriers under the
Act.[^57] It is important to remember that the present model runs only
cover the territory served by non-rural carriers. The costs estimated by
the model will be significantly affected by the type of territory served
by those carriers in the state whose costs are being calculated, and to
the extent that a rural territory is being served by a rural carrier
that is not receiving high-cost support under this mechanism, the cost
of serving that territory will not be reflected in the level of support
for that state determined in this phase of the proceeding. In general,
we found that the states where the model estimated the highest costs
were those states in which the territory served by the non-rural
carriers, which are typically larger carriers, included more rural areas
than in other states. We also found that some states that are generally
perceived as rural are served primarily by small carriers, so that the
remaining territory in the state, which would be served by the non-rural
carrier, is less rural than the state as a whole. For example, in
Mississippi, the large incumbent LEC serves the vast majority of the
state\'s territory, including many very rural areas. By contrast, in
Montana for example, the large incumbent LEC serves less than a third of
the state\'s territory, and its serving area includes all but one of the
largest cities in the state. Small rural carriers serve the most
sparsely populated rural areas in Montana. As a result, considering only
the non-rural carriers\' territory, Mississippi appears to be a
considerably more rural state than Montana. As discussed above, our
analysis showed that the model\'s cost estimates were highly correlated
with dispersion \-- that is, the areas with the most dispersed customers
were estimated by the model to have the highest costs. Although this
results in relative cost estimates among states that differ from some
people\'s expectations, we believe that this may primarily reveal that
those expectations were based on a lack of information or incorrect
premises about non-rural carriers\' service territories.

27\. Moreover, our investigation revealed that most of the variations
between carriers\' book cost levels and the model\'s estimated
forward-looking costs can be explained by three factors. The first is
the percentage of business lines in the study area. Study areas with a
lower percentage of business lines tend to have lower book costs
relative to forward-looking costs. The second factor is the percentage
of customers in rural areas. Study areas with a higher percentage of
rural customers also tend to have lower relative book costs. These two
factors, taken together, suggest that the book cost of the existing
network is more likely to be below the model\'s estimate of the cost of
a forward-looking network in rural areas with fewer business customers.
This may suggest that these areas are served by networks of a different
quality standard than that assumed in the model, or that the networks in
these areas have not been upgraded or experienced much growth in some
time and therefore are substantially depreciated on carriers\' books.
The third factor is discrepancies in line counts between the data used
in the model and the most current carrier-reported data. We have taken
steps to correct these discrepancies in the line count data that we
adopt in this Order.[^58]

28\. We believe that the model, as used in the methodology we set out in
the companion *Methodology Order*, is the best way to generate non-rural
carriers\' support amounts for the funding year beginning January 1,
2000. We also recognize, however, that the model must evolve as
technology and other conditions change. We therefore have committed to
initiating a proceeding to study how the model should be used in the
future (e.g., how often inputs data should be updated) and how the model
itself should change to reflect changing circumstances. We anticipate
releasing a further notice of proposed rulemaking on these issues in
early 2000, and hope to reach significant decisions on these issues
during the course of that year.

**C. Selecting Forward-Looking Input Values**

29\. In the *Universal Service Order*, the Commission adopted ten
criteria to be used in determining the forward-looking economic cost of
providing universal service in high-cost areas.[^59] These criteria
provide specific guidance for our selection of input values for use in
the synthesis model. Rather than reflecting existing incumbent LEC
facilities, the technology assumed in the model \"must be the
least-cost, most-efficient, and reasonable technology for providing the
supported services that is currently being deployed.\"[^60] As noted
below, existing LEC plant in a particular area may not reflect
forward-looking technology or design choices.[^61] Similarly, the input
values we adopt in this Order are not intended to replicate any
particular company\'s embedded or book costs. Criterion three directs
that \"costs must not be the embedded cost of the facilities, functions,
or elements.\"[^62] Rather, the model \"must be based upon an
examination of the current cost of purchasing facilities and
equipment.\"[^63]

30\. As discussed below, we generally adopt nationwide, rather than
company-specific, input values in the federal mechanism. In many cases,
the only data for various inputs on the record in this proceeding are
embedded cost, company-specific data. We have used various techniques to
convert these data to forward-looking values. For example, we modify the
switching data to adjust for the effects of inflation and the cost
changes unique to the purchase and installation of digital
switches.[^64] Where possible, we have tried to account for variations
in costs by objective means. For example, the model reflects differences
in structure costs by using different values for the type of plant, the
density zone, and geological conditions. There may be additional
modifications we can make in the future to more accurately reflect
variations in forward-looking costs based on objective criteria. For
example, we do not adjust our maintenance expense estimates to reflect
regional wage differences, as discussed below, because we have not found
and no party has suggested a specific data source or methodology that
would be useful in making such adjustments.[^65] We certainly remain
open to considering data sources in the future of the model proceeding
that would permit us to vary these or other input values to reflect
differences in forward-looking cost that can be measured objectively.

31\. Although the BCPM sponsors have provided nationwide default values,
they and other LECs generally advocate company-specific input values.
For purposes of determining federal universal service support amounts,
however, we believe that nationwide default values generally are more
appropriate than company-specific values. Under the new federal
universal service support mechanism, support is based on the estimated
costs that an efficient carrier would incur to provide the supported
services, rather than on the specific carrier\'s book costs. We also
believe that it would be administratively unworkable to use
company-specific values in the federal nationwide model. Finally, we
note that, for most inputs, we have no means of adopting
company-specific input values, except possibly by relying on embedded
data for each company. We make no finding as to whether nationwide
values would be appropriate for purposes other than determining federal
universal service support.[^66]

32\. For universal service purposes, we find that using nationwide
averages is appropriate. The Commission has not considered what type of
input values, company-specific or nationwide, nor what specific input
values, would be appropriate for any other purposes. The federal cost
model was developed for the purpose of determining federal universal
service support, and it may not be appropriate to use nationwide values
for other purposes, such as determining prices for unbundled network
elements. We caution parties from making any claims in other proceedings
based upon the input values we adopt in this Order.

**IV. DETERMINING CUSTOMER LOCATIONS**

**A. Background**

33\. The determination of customer locations relative to the wire center
heavily influences a forward-looking cost model\'s design of outside
plant facilities. This is because assumptions about the locations of
customers will determine the predicted loop length, which in turn will
have a large impact on the cost of service and the technologies employed
by the model.[^67] Each of the models under consideration in the
*Platform Order* provided a methodology for determining customer
locations.[^68] The Bureau sought comment on these proposals and
solicited alternative proposals from interested parties for locating
customers.[^69]

34\. In the *Platform Order*, the Commission concluded that HAI\'s
proposal to use actual geocode data, to the extent that they are
available, and BCPM\'s proposal to use road network information to
create \"surrogate\" customer locations where actual data are not
available, provided the most reasonable method for determining customer
locations.[^70] The Commission concluded that \"the source or sources of
geocode data to use in determining customer location will be decided at
the inputs phase of this proceeding.\"[^71] The Commission also
concluded that \"the selection of a precise algorithm for placing road
surrogates pursuant to these conclusions should be conducted in the
inputs stage of this proceeding as part of the process of selecting a
geocode data set for the federal mechanism.\"[^72]

35\. In the *Inputs Further Notice,* we tentatively concluded that no
source of actual geocode data had been made sufficiently available for
review to be used in the model at that time.[^73] Therefore, we
tentatively concluded that a road surrogate algorithm would be used to
locate customers in the federal mechanism until a source of actual
geocode data is selected by the Commission. In doing so, we tentatively
adopted the road surrogate algorithm proposed by PNR Associates (PNR) to
develop road surrogate customer locations.[^74]

**B. Customer Location Data**

**1. Geocode Data**

36\. While we affirm our conclusion in the *Platform Order* that geocode
data should be used to locate customers in the federal mechanism, we
conclude that no source of actual geocode data has yet been made
adequately accessible for public review. We conclude below that we will
use an algorithm based on the location of roads to create surrogate
geocode data on customer locations for the federal mechanism until a
source of actual geocode data is identified and selected by the
Commission. We reiterate our expectation that a source of accurate and
verifiable actual geocode data will be identified in the future for use
in the federal mechanism.[^75]

37\. In the *Platform Order*, we concluded that a model is most likely
to select the least-cost, most-efficient outside plant design if it uses
the most accurate data for locating customers within wire centers, and
that the most accurate data for locating customers within wire centers
are precise latitude and longitude coordinates for those customers\'
locations.[^76] We noted that commenters generally support the use of
accurate geocode data in the federal mechanism where available.[^77] We
further noted that the only actual geocode data in the record were those
prepared for HAI by PNR, but also noted that \"our conclusion that the
model should use geocode data to the extent that they are available is
not a determination of the accuracy or reliability of any particular
source of the data.\"[^78] Although commenters supported the use of
accurate geocode data, several commenters questioned whether the PNR
geocode data were adequately available for review by interested
parties.[^79]

38\. In the *Universal Service Order*, the Commission required that the
\"model and all underlying data, formulae, computations, and software
associated with the model must be available to all interested parties
for review and comment.\"[^80] In an effort to comply with this
requirement, the Commission has made significant efforts to encourage
parties to submit geocode data on the record in this proceeding.[^81]
PNR took initial steps to comply with this requirement in December 1998
by making available the \"BIN\" files[^82] derived from the geocoded
points to interested parties pursuant to the *Protective Order*.[^83]
PNR also has continued to provide access to the underlying geocode data
at its facility in Pennsylvania. Several commenters argue, however, that
the availability of the BIN data alone is not sufficient to comply with
the requirements of criterion eight, particularly in light of the
expense and conditions imposed by PNR in obtaining access to the geocode
point data.[^84] In addition, PNR acknowledges that its geocode database
relies on third-party data that PNR is not permitted to disclose.[^85]

39\. Consistent with our tentative conclusion in the *Inputs Further
Notice*, we conclude that interested parties have not had an adequate
opportunity to review and comment on the accuracy of the PNR actual
geocode data set. The majority of commenters addressing this issue
support this conclusion.[^86] We note that a nationwide customer
location database will, by necessity, be voluminous, relying on a
variety of underlying data sources. In light of the concerns expressed
by several commenters relating to the conditions and expense in
obtaining geocode data from PNR, we find that no source of actual
geocode data has been made sufficiently available for review. While PNR
has made some effort to satisfy the requirements of criterion eight, we
prefer to adopt a data set that is more readily available for meaningful
review. In particular, we note that the geocode points are available
only on-site at PNR\'s facilities, making it difficult for parties to
verify the accuracy of those points. We recognize, however, that more
comprehensive actual geocode data are likely to be available in the
future, and we encourage parties to continue development of an actual
geocode data source that complies with the criteria outlined in the
*Universal Service Order* for use in the federal mechanism.[^87]

**2. Road Surrogate Customer Locations**

40\. We conclude that PNR\'s road surrogating algorithm should be used
to develop geocode customer locations for use in the federal universal
service mechanism to determine high-cost support for non-rural carriers
beginning January 1, 2000. In the *Platform Order,* we concluded that,
in the absence of actual geocode customer location data, associating
road networks and customer locations provides the most reasonable
approach for determining customer locations.[^88]

41\. As we noted in the *Platform Order*, \"associating customers with
the distribution of roads is more likely to correlate to actual customer
locations than uniformly distributing customers throughout the Census
Block, as HCPM proposes, or uniformly distributing customers along the
Census Block boundary, as HAI proposes.\"[^89] We therefore concluded in
the *Platform Order* that the selection of a precise algorithm for
placing road surrogates should be conducted in the inputs stage of this
proceeding.[^90] In the *Inputs Further Notice*, we tentatively adopted
the PNR road surrogate algorithm to determine customer locations.[^91]

42\. Currently, there are two road surrogating algorithms on the record
in this proceeding - those proposed by PNR and Stopwatch Maps. On March
2, 1998, AT&T provided a description of the road surrogate methodology
developed by PNR for locating customers.[^92] On January 27, 1999, PNR
made available for review by the Commission and interested parties,
pursuant to the terms of the *Protective Order*, the road surrogate
point data for all states except Alaska, Iowa, Virginia, Puerto Rico and
eighty-four wire centers in various other states.[^93] On February 22,
1999, PNR filed a more detailed description of its road surrogate
algorithm.[^94] Consistent with the conditions set forth in the *Inputs
Further Notice*, PNR has now made available road surrogate data for all
fifty states and Puerto Rico.[^95]

43\. In general, the PNR road surrogate algorithm utilizes the Census
Bureau\'s Topologically Integrated Geographic Encoding and Referencing
(TIGER) files, which contain all the road segments in the United
States.[^96] For each Census Block, PNR determines how many customers
and which roads are located within the Census Block.[^97] For each
Census Block, PNR also develops a list of road segments. The total
distance of the road segments within the Census Block is then computed.
Roads that are located entirely within the interior of the Census Block
are given twice the weight as roads on the boundary. This is because
customers are assumed to live on both sides of a road within the
interior of the Census Block. In addition, the PNR algorithm excludes
certain road segments along which customers are not likely to
reside.[^98] For example, PNR excludes highway access ramps, alleys, and
ferry crossings.[^99] The total number of surrogate points is then
divided by the computed road distance to determine the spacing between
surrogate points. Based on that distance, the surrogate customer
locations are uniformly distributed along the road segments.[^100] In
order to ensure that its road surrogate data set includes all currently
served customers, PNR has made minor adjustments to its methodology in
some instances. For example, Census Blocks that are not assigned to any
current wire center have been assigned to the nearest known wire center,
based on the \"underpinned of the census block in relation to the wire
center\'s central office location.\"[^101]

44\. Stopwatch Maps has compiled road surrogate customer location files
for six states suitable for use in the federal mechanism.[^102] We
conclude, however, that until a more comprehensive data set is made
available, the Stopwatch data set will not comply with the *Universal
Service Order\'s* criterion that the underlying data are available for
review by the public. Only GTE endorses the use of the Stopwatch data
set.[^103] In addition, we note that the availability of customer
locations for only six states is of limited utility in a nationwide
model designed to be implemented on January 1, 2000.

45\. AT&T and MCI contend that the exclusive use of a road surrogate
algorithm to locate customers produces a 2.7 percent upward bias in loop
cost on average on a study area basis when compared to a data set
consisting of PNR actual geocode data, where available, and surrogate
locations where actual data are unavailable.[^104] AT&T and MCI argue
that this occurs because the road surrogate methodology uniformly
disperses customers along roads, failing to take into consideration
actual, uneven customer distributions that tend to cluster customer
locations more closely.[^105] AT&T and MCI therefore suggest a downward
adjustment to produce more accurate outside plant cost estimates.[^106]
GTE disagrees and contends that, because the PNR actual geocode data
create serving areas that are too dense, it is not surprising that AT&T
and MCI have found that the use of road surrogate data produces costs
that are slightly higher.[^107] GTE argues that there is no evidence to
conclude, therefore, that a uniform dispersion of customers is likely to
overstate outside plant costs.[^108] Sprint contends that the decision
to optimize distribution plant in the model mitigates any concern that
the road surrogate algorithm overstates the amount of outside
plant.[^109]

46\. We agree with GTE and Sprint that there should be no downward
adjustment in cost to reflect the exclusive use of a road surrogate
algorithm. In doing so, we note that, although the Commission has gone
to great lengths to identify a source of actual, nationwide customer
locations, no satisfactory data source has been identified. In fact,
only one source of such data, the PNR geocode data, has been placed on
the record. As noted above, however, we have rejected the PNR geocode
data set at this time because it has not been made adequately available
for review. In the absence of a reliable source of actual customer
locations by which to compare the surrogate locations, it is impossible
to substantiate AT&T and MCI\'s contention that the road surrogate
algorithm overstates the dispersion of customer locations in comparison
to actual locations.[^110] Although LECG has made comparisons between
Ameritech geocode locations and the PNR road surrogate locations, the
validity of that comparison is dependent on the accuracy of the geocode
data used in that comparison.[^111] As Ameritech has not filed that data
on the record, we have no way of verifying the accuracy of its geocoded
locations. In addition, we note that Ameritech agrees that the PNR road
surrogate \"is a reasonable method for locating customers in the absence
of actual geocode data.\"[^112] Having no reliable evidence that the PNR
road surrogate algorithm systematically overstates customer dispersion,
we conclude that no downward adjustment to the outside plant cost
estimate is required.

47\. We also disagree with Bell Atlantic\'s contention that road
surrogate data is inherently random and likely to misidentify high-cost
areas.[^113] As noted in the *Platform Order*, we believe that it is
reasonable to assume that customers generally reside along roads and,
therefore, associating customers with the distribution of roadways is a
reasonable method to estimate customer locations. We note that PNR\'s
methodology of excluding certain road segments is consistent with the
Commission\'s conclusion in the *Platform Order* that certain types of
roads and road segments should be excluded because they are unlikely to
be associated with customer locations.[^114] In addition, we note that
PNR\'s reliance on the Census Bureau\'s TIGER files ensures a degree of
reliability and availability for review of much of the data underlying
PNR\'s road surrogate algorithm, in compliance with criterion eight of
the *Universal Service Order*.[^115] The PNR road surrogate algorithm is
also generally supported by commenters addressing this issue.[^116]
While AT&T and MCI advocate the use of actual geocode data points, AT&T
and MCI endorse the PNR road surrogate algorithm to identify surrogate
locations in the absence of actual geocode data.[^117] We therefore
affirm our tentative conclusion in the *Inputs Further Notice* and adopt
the PNR road surrogate algorithm and data set to determine customer
locations for use in the model beginning on January 1, 2000.

**3. Methodology for Estimating the Number of Customer Locations**

48\. In addition to selecting a source of customer data, we also must
select a methodology for estimating the number of customer locations
within the geographic region that will be used in developing the
customer location data. In addition, we must determine how demand for
service at each customer location should be estimated and how customer
locations should be allocated to each wire center. In the *Inputs
Further Notice*, we tentatively concluded that PNR\'s methodology for
estimating the number of customer locations based on households should
be used for developing the customer location data.[^118] In addition, we
also tentatively concluded that we should use PNR\'s methodology for
estimating the demand for service at each location, and for allocating
customer locations to wire centers.[^119] We now affirm these tentative
conclusions.

49\. In the *Universal Service Order*, the Commission concluded that a
\"model must estimate the cost of providing service for all businesses
and households within a geographic region.\"[^120] The Commission has
sought comment on the appropriate method for defining \"households,\" or
residential locations, for the purpose of calculating the
forward-looking cost of providing supported services.[^121] Interested
parties have proposed alternative methods to comply with this
requirement.[^122]

50\. AT&T, MCI, and Ameritech support the methodology devised by PNR,
which is based upon the number of households in each Census Block, while
BellSouth, GTE, SBC, USTA, and US West propose that we use a methodology
based upon the number of housing units in each Census Block.[^123] A
household is an occupied residence, while housing units include all
residences, whether occupied or not.[^124]

51\. In the *Inputs Further Notice*, we tentatively adopted the use of
the PNR National Access Line Model, as proposed by AT&T and MCI, to
estimate the number of customer locations within Census Blocks and wire
centers.[^125] The PNR National Access Line Model uses a variety of
information sources, including: survey information; the LERG; Business
Location Research (BLR) wire center boundaries; Dun & Bradstreet\'s
business database; Metromail\'s residential database; Claritas\'s
demographic database; and U.S. Census Bureau estimates. PNR\'s model
uses these sources in a series of steps to estimate the number of
residential and business locations, and the number of access lines
demanded at each location.[^126] The model makes these estimates for
each Census Block, and for each wire center in the United States.[^127]
In addition, each customer location is associated with a particular wire
center.[^128] We conclude that PNR\'s process for estimating the number
of customer locations should be used for developing the customer
location data. We also conclude that we should use PNR\'s methodology
for estimating the demand for service at each location, and for
allocating customer locations to wire centers.[^129] We believe that the
PNR methodology is a reasonable method for determining the number of
customer locations to be served in calculating the cost of providing
supported services.

52\. PNR\'s process for estimating the number of customer locations
results in an estimate of residential locations that is greater than or
equal to the Census Bureau\'s estimate of households, by Census Block
Group, and its estimate is disaggregated to the Census Block level.
PNR\'s estimate of demand for both residential and business lines in
each study area will also be greater than or equal to the number of
access lines in the Automated Reporting and Management Information
System (ARMIS) for that study area.

53\. The BCPM model relied on many of the same data sources as those
used in PNR\'s National Access Line Model. For example, BCPM 3.1 used
wire center data obtained from BLR and business line data obtained from
PNR.[^130] In estimating the number of residential locations, however,
the BCPM model used Census Bureau data that include household and
housing unit counts from the 1990 Census, updated based upon 1995 Census
Bureau statistics regarding household growth by county. In addition,
rather than attempting to estimate demand by location at the Block
level, the BCPM model builds two lines to every residential location and
at least six lines to every business.

54\. A number of commenters contend that the total cost estimated by the
model should include the cost of providing service to all possible
customer locations, even if some locations currently do not receive
service.[^131] Some commenters further contend that, if total cost is
based on a smaller number of locations, support will not be sufficient
to enable carriers to meet their carrier-of-last-resort obligations.
These commenters argue that basing the estimate of residential locations
on households instead of housing units will underestimate the cost of
building a network that can provide universal service.[^132] They
therefore assert that residential locations should be based on the
number of housing units \-- whether occupied or unoccupied.[^133] These
commenters contend that only this approach reflects the obligation to
provide service to any residence that may request it in the
future.[^134]

55\. Some commenters also contend that the PNR National Access Line
Model has not been made adequately available for review.[^135] As noted
above, the National Access Line Model is a multi-step process used to
develop customer location counts and demand and associate those customer
locations with Census Blocks and wire centers.[^136] As a result, PNR
contends that the National Access Line Model cannot be provided in a
single, uniform format.[^137] The HAI sponsors have provided a
description of the National Access Line Model process in the HAI model
documentation.[^138] PNR has made the National Access Line Model process
available for review through on-site examination and has provided more
detailed explanation of the National Access Line Model upon request from
interested parties. PNR notes that several parties have taken advantage
of this opportunity.[^139] PNR also notes that the National Access Line
Model computer code is available for review on-site.[^140] PNR also has
filed with the Commission the complete output of the National Access
Line Model process.[^141] In addition, Bell Atlantic and Sprint argue
that the National Access Line Model produces line counts that vary
significantly from actual line counts.[^142]

56\. In adopting the PNR approach for developing customer location
counts, we note that the synthesis model currently calculates the
average cost per line by dividing the total cost of serving customer
locations by the current number of lines. Because the current number of
lines is used in this average cost calculation, we agree with AT&T and
MCI that the total cost should be determined by using the current number
of customer locations. As AT&T and MCI note, \"the key issue is the
consistency of the numerator and denominator\" in the average cost
calculation. According to AT&T and MCI, other proposed approaches result
in inconsistency because they use the highest possible cost in the
numerator and divide by the lowest possible number of lines in the
denominator, and therefore result in larger than necessary support
levels.[^143] AT&T and MCI also assert that, in order to be consistent,
housing units must be used in the determination of total lines if they
are used in the determination of total costs.[^144] MCI points out that
\"\[i\]f used consistently in this manner, building to housing units as
GTE proposes is unlikely to make any difference in cost per
line.\"[^145] Although SBC advocates the use of housing units, it agrees
that the number of lines resulting from this approach should also be
used in the denominator of any cost per line calculation to prevent the
distortion noted by AT&T and MCI.[^146] We agree with AT&T and MCI that,
as long as there is consistency in the development of total lines and
total cost, it makes little difference whether households or housing
units are used in determining cost per line. For the reasons discussed
below, we believe that PNR\'s methodology based on households is less
complex and more consistent with a forward-looking methodology than
housing units.

57\. To the extent that the PNR methodology includes the cost of
providing service to all currently served households, we conclude that
this is consistent with a forward-looking cost model, which is designed
to estimate the cost of serving current demand. As noted by AT&T and
MCI, adopting housing units as the standard would inflate the cost per
line by using the highest possible numerator (all occupied and
unoccupied housing units) and dividing by the lowest possible
denominator (the number of customers with telephones).[^147]

58\. If we were to calculate the cost of a network that would serve all
potential customers, it would not be consistent to calculate the cost
per line by using current demand. In other words, it would not be
consistent to estimate the cost per line by dividing the total cost of
serving all potential customers by the number of lines currently served.
The level and source of future demand, however, is uncertain. Future
demand might include not only demand from currently unoccupied housing
units, but also demand from new housing units, or potential increases in
demand from currently subscribing households. We also recognize that
population or demographic changes may cause future demand levels in some
areas to decline. Given the uncertainty of future demand, we noted in
the *Inputs Further Notice* that we are concerned that including such a
highly speculative cost of future demand may not reflect forward-looking
cost and may perpetuate a system of implicit support. Ameritech and AT&T
and MCI also note that adopting the proposed conservative fill factors
will ensure sufficient plant to deal with any customer churn created as
a result of temporarily vacant households.[^148]

59\. In addition, we do not believe that including the cost of providing
service to all housing units would necessarily promote universal service
to unserved customers. We note that there is no guarantee that carriers
would use any support derived from the cost of serving all housing units
to provide service to these customers. Many states permit carriers to
charge substantial line extension or construction fees for connecting
customers in remote areas to their network. If that fee is unaffordable
to a particular customer, raising the carrier\'s support level by
including the costs of serving that customer in the model\'s
calculations would have no effect on whether the customer actually
receives service. In fact, as long as the customer remains unserved, the
carrier would receive a windfall. We recognize that providing service to
currently unserved customers in such circumstances is an important
universal service goal and the Commission is addressing this issue more
directly in another proceeding.[^149]

60\. We also find that interested parties have been given a reasonable
opportunity to review and understand the National Access Line Model
process for developing customer counts. The HAI sponsors have documented
the process by which the National Access Line Model derives customer
location counts and PNR has made itself available to respond to
inquiries from interested parties. The National Access Line Model is a
commercially licensed product developed by PNR, and we do not find it
unreasonable for PNR to place some restriction on its distribution to
the public. In addition, we agree that the National Access Line Model is
more correctly characterized as a process consisting of several steps,
and therefore we find no practical alternative to on-site review. Even
if it were possible for PNR to turn the National Access Line Model over
to the public in a single format, we believe that this would be of
limited utility without a detailed explanation of the entire process. We
therefore conclude that PNR has made reasonable efforts to ensure that
interested parties understand the underlying process by which the
National Access Line Model develops customer counts and has made that
process reasonably available to interested parties. In addition, unlike
the case with PNR\'s geocode data points, PNR\'s road surrogate customer
location points are available for review and comparison by interested
parties.

61\. In response to Bell Atlantic and Sprint\'s concern regarding the
line counts generated by the National Access Line Model, we note that
the line count data proposed in the *Inputs Further Notice* had been
trued up by PNR to 1996 ARMIS line counts. We subsequently have modified
those data to reflect the most currently available ARMIS data.
Accordingly, the input values that we adopt in this Order will true up
the line counts generated by the National Access Line Model to 1998
ARMIS line counts. While the Commission has requested line count data
from the non-rural LECs,[^150] no party has suggested, and we have not
been able to discern, any feasible way of associating such data with
wire centers used in the model. The Commission intends to continue to
review this issue in addressing future refinements to the
forward-looking cost model.

62\. In the *Inputs Further Notice*, we also noted that the accuracy of
wire center boundaries is important in estimating the number of customer
locations.[^151] PNR currently uses BLR wire center information to
estimate wire center boundaries.[^152] As noted above, the BCPM model
also uses BLR wire center boundaries, as does Stopwatch Maps in its road
surrogate customer location files.[^153] A few commenters support the
use of BLR wire center boundaries, noting widespread use by the model
proponents.[^154] Others advocate the use of actual wire center
boundaries.[^155] These commenters acknowledge, however, that this
information is generally considered confidential and may not be released
publicly by the incumbent LEC.[^156] We conclude that the BLR wire
center boundaries are the best available data that are open to
inspection and that they provide a reasonably reliable estimation of
wire center boundaries. We note that both the BCPM and HAI proponents
have utilized the BLR wire center data in their respective models. While
use of actual wire center boundaries may be preferable, we agree that
such information is currently unavailable or proprietary. We therefore
approve the use of the BLR wire center boundaries in the current
customer location data set.

**V. OUTSIDE PLANT INPUT VALUES**

**A. Introduction**

63\. In this section, we consider inputs to the model related to outside
plant. The *Universal Service Order\'s* first criterion specifies that
\"\[t\]he technology assumed in the cost study or model must be the
least-cost, most efficient, and reasonable technology for providing the
supported services that is currently being deployed.\"[^157] Thus, while
the model uses existing incumbent LEC wire center locations in designing
outside plant, it does not necessarily reflect existing incumbent LEC
loop plant.[^158] Indeed, as the Commission stated in the *Platform
Order*, \"\[e\]xisting incumbent LEC plant is not likely to reflect
forward-looking technology or design choices.\"[^159] The *Universal
Service Order\'s* third criterion specifies that \"\[o\]nly long-run
forward-looking costs may be included.\"[^160] We select input values
consistent with these criteria.

64\. As the Commission noted in the *Platform Order*, outside plant, or
loop plant, constitutes the largest portion of total network investment,
particularly in rural areas.[^161] Outside plant investment includes the
copper cables in the distribution plant and the copper and optical fiber
cables in the feeder plant that connect the customers\' premises to the
central office. Cable costs include the material costs of the cable, as
well as the costs of installing the cable.[^162]

65\. Outside plant consists of a mix of aerial, underground, and buried
cable.[^163] Aerial cable is strung between poles above ground.
Underground cable is placed underground within conduits for added
support and protection. Buried cable is placed underground but without
any conduit. A significant portion of outside plant investment consists
of the poles, trenches, conduits, and other structure that support or
house the copper and fiber cables. In some cases, electric utilities,
cable companies, and other telecommunications providers share structure
with the LEC and, therefore, only a portion of the costs associated with
that structure are borne by the LEC. Outside plant investment also
includes the cost of the SAIs and DLCs that connect the feeder and
distribution plant.

**B. Engineering Assumptions and Optimizing Routines**

66\. As noted in the *Inputs Further Notice*, the model determines
outside plant investment based on certain cost minimization and
engineering considerations that have associated input values.[^164] In
the *Inputs Further Notice*, we recognized that it was necessary to
examine certain input values related to the engineering assumptions and
optimization routines in the model that affect outside plant
costs.[^165] Specifically, we tentatively concluded that: (1) the
optimization routine in the model should be fully activated; (2) the
model should not use T-1 feeder technology; and (3) the model should use
rectilinear distances and a \"road factor\" of one.[^166]

**1. Optimization**

67\. When running the model, the user has the option of optimizing
distribution plant routing via a minimum spanning tree algorithm
discussed in the model documentation.[^167] The algorithm functions by
first calculating distribution routing using an engineering rule of
thumb and then comparing the cost with the spanning tree result,
choosing the routing that minimizes annualized cost.[^168] The user has
the option of not using the distribution optimization feature, thereby
saving a significant amount of computation time, but reporting network
costs that may be significantly higher than with the optimization. The
user also has the option of using the optimization feature only in the
lowest density zones.

68\. In reaching our tentative conclusion that the model should be run
with the optimization routine fully activated in all density zones, we
recognized that using full optimization can substantially increase the
model\'s run time.[^169] We noted that a preliminary analysis of
comparison runs with full optimization versus runs with no optimization
indicated that, for clusters with line density greater than 500, the
rule of thumb algorithm results in the same or lower cost for nearly all
clusters.[^170] Accordingly, we sought comment on whether an acceptable
compromise to full optimization would be to set the optimization factor
at \"‑p500,\" as described in the model documentation.[^171]

69\. We adopt our tentative conclusion that the model should be run with
the optimization routine fully activated in all density zones when the
model is used to calculate the forward-looking cost of providing the
services supported by the federal mechanism. The first of the ten
criteria pronounced by the Commission to ensure consistency in
calculations of federal universal support specifies that \"\[t\]he
technology assumed in the cost study or model must be the least-cost,
most efficient, and reasonable technology for providing the supported
services that is currently being deployed.\"[^172] As we explained in
the *Inputs Further Notice*, running the model with the optimization
routine fully activated complies with this requirement.[^173] In
contrast, running the model with the optimization routine disabled may
result in costs that are significantly higher than with full
optimization. The majority of commenters that address the optimization
issue support the use of full optimization.[^174] GTE opposes any
implementation of optimization.[^175]

70\. We agree with AT&T and MCI and GTE that it is inappropriate to
deviate from full optimization merely to minimize computer run
time.[^176] While the rule of thumb algorithm generally results in costs
that are approximately the same as the spanning tree algorithm for dense
clusters, for some dense clusters the spanning tree algorithm will
result in lower costs. For this reason, we believe that any choice in
maximum density clusters in which the minimum spanning tree algorithm is
not applied may result in an arbitrary overestimate of costs for some
clusters. Accordingly, running the model with full optimization is
consistent with ensuring that the model uses the least-cost, most
efficient, and reasonable distribution plant routings for providing the
supported services.

71\. As explained above, the model seeks to minimize costs by selecting
the lower of the cost estimates from the spanning tree algorithm and the
rule of thumb algorithm. Both GTE and US West challenge the selection of
the routing that minimizes annualized cost on the basis of a comparison
between an engineering rule of thumb and the spanning tree result.[^177]
US West claims that use of the rule of thumb approach is inappropriate
because combining it with the spanning tree analytical approach to
determine the amount of needed plant biases the results downward and
will produce inappropriately low results.[^178]

72\. We find that US West\'s concerns are misplaced. Contrary to US
West\'s characterization, the rule of thumb used in the model is not an
averaging methodology.[^179] Instead, it is a methodology that
determines a sufficient amount of investment to serve each customer in
every cluster using a standardized approach to network design. This
approach connects every populated microgrid cell to the SAI using routes
which are placed along the vertical and horizontal boundaries of the
microgrid cells constructed in the distribution algorithm.[^180] The
rule-of-thumb algorithm is somewhat similar in its functioning to the
so-called "pinetree" methodology proposed by both the early HAI and BCPM
models for building feeder plant. Thus, the rule of thumb provides an
independent calculation of sufficient outside plant for each cluster.
The minimum spanning tree algorithm connects drop terminal points to the
SAI using a more sophisticated algorithm in which routes are not
restricted to following the vertical and horizontal boundaries of
microgrid cells. The algorithm \"chooses\" a path independently of the
set route structure defined by the rule-of-thumb,but still connects all
drop terminals to the SAI. Since both the rule of thumb algorithm and
the spanning tree algorithm use currently available technologies and
generate investments that are sufficient to provide supported services,
an approach which selects the minimum cost based on an evaluation of
both of the algorithms is fully consistent with cost minimization
principles.[^181]

73\. We also disagree with GTE\'s assertion that the optimization
routine should be disabled because it disproportionately affects lower
density areas where universal service is needed most.[^182] The task of
the model is to estimate the cost of the least-cost, most-efficient
network that is sufficient to provide the supported services. Moreover,
we note that the model does not determine the level of high-cost support
amounts. We have taken steps in our companion order to ensure that
sufficient support is provided for rural and high-cost areas.

74\. We also reject GTE\'s claim that the optimization routine does not
work as intended.[^183] GTE bases this contention on the observation
that in some instances when the optimization factor is increased from
-p100 to -p200 (i.e. going from density zones less than or equal to 100
lines per square mile to density zones less than or equal to 200 lines
per square mile), both loop investment and universal service
requirements increase. This, according to GTE, would not happen if the
optimization worked properly.[^184]

75\. We disagree. Optimizing the distribution plant is not synonymous
with optimizing the entire network. Because the model\'s optimization
routine optimizes distribution and feeder sequentially, and the starting
point for the optimization of feeder plant is the distribution plant
routing chosen, there are occasions when the optimal feeder plant will
be more costly than it would be if distribution plant and feeder plant
had been optimized simultaneously. In some cases, the lower distribution
investment produced by the optimization routine may be offset by higher
feeder investment, resulting in higher total outside plant costs than
produced by the rule of thumb algorithm.[^185] Contrary to GTE\'s
assertion, this phenomenon does not demonstrate that the optimization
works improperly. To the contrary, it demonstrates that optimization
occurs properly within the constraints of the model\'s design.

76\. Moreover, we conclude that such rare occurrences do not outweigh
the benefits of the optimization routine. The magnitude of the
difference between the network cost produced by the optimization routine
in these instances and the rule of thumb algorithm is *de minimis*.
Furthermore, altering the model to optimize distribution investment and
feeder investment simultaneously would greatly add to the complexity of
the model.

**2. T-1 Technology**

77\. A user of the model also has the option of using T-1 on copper
technology as an alternative to analog copper feeder or fiber feeder in
certain circumstances.[^186] T-1 is a technology that allows digital
signals to be transmitted on two pairs of copper wires at 1.544 Megabits
per second (Mbps). If the T-1 option is enabled, the optimizing routines
in the model will choose the least cost feeder technology among three
options: analog copper; T-1 on copper; and fiber.[^187] For serving
clusters with loop distances below the maximum copper loop length, the
model could choose among all three options; between 18,000 feet and the
fiber crossover point, which earlier versions of the model set at 24,000
feet, the model could choose between fiber and T-1, and above the fiber
crossover point, the model would always use fiber. In the HAI model, T-1
technology is used to serve very small outlier clusters in locations
where the copper distribution cable would exceed 18,000 feet.

78\. In the *Inputs Further Notice*, we tentatively concluded that the
T-1 option in the model should not be used at this time.[^188] We noted
that the only input values for T-1 costs on the record were the HAI
default values and tentatively found that, because the model and HAI
model use T-1 differently, it would be inappropriate to use the T-1
technology in the model based on these input values.[^189] We also noted
that the BCPM sponsors and other LECs maintained that T-1 was not a
forward-looking technology and therefore should not be used in the
model.[^190] Other sources indicated that advanced technologies, such as
HDSL, could be used to transmit information at T-1 or higher
rates.[^191] We sought comment on this issue.[^192] We also sought
comment on the extent to which HDSL technology presently is being used
to provide T-1 service.[^193]

79\. We conclude that the T-1 option should not be employed in the
current version of the model. We agree with those commenters addressing
this issue that traditional T-1 using repeaters at 6000 foot intervals
is not a forward-looking technology.[^194] While HDSL and other DSL
variants are forward-looking technologies, we do not at this time have
sufficient information to determine appropriate input values for these
technologies for use in the model. We conclude, therefore, that use of
T-1 in the optimization routine as an alternative to analog copper or
digital fiber feeder for certain loops under 24,000 feet is not
appropriate at this time.[^195] Accordingly, the model will be run for
universal service purposes with the T-1 option disabled.

**3. Distance Calculations and Road Factor**

80\. In the distribution and feeder computations within the model, costs
for cable and structure are computed by multiplying the route distances
by the cost per foot of the cable or the structure facility, which
depends on capacity and terrain factors. Distances between any two
points in the network are computed using either of two distance
functions.[^196] The model allows a separate road factor for each
distance function, and every distance measurement made in the model is
multiplied by the designated factor. Road factors could be computed by
comparing average distances between geographic points along actual roads
with distances computed using either of the two distance functions.
Given sufficient data, these factors could be computed at highly
disaggregated levels, such as the state, county, or individual wire
center.

81\. In the *Inputs Further Notice*, we tentatively concluded that the
model should use rectilinear distance in calculating outside plant
distances, rather than airline distance, because rectilinear distance
more accurately reflects the routing of telephone plant along roads and
other rights of way.[^197] We also tentatively concluded that the road
factor in the model, which reflects the ratio between route distance and
road distance, should be set equal to one.[^198] In addition, we asked
whether we should use airline miles with wire center specific road
factors as an alternative to rectilinear distance.[^199]

82\. We reaffirm our tentative conclusion that the model should use
rectilinear distance rather than airline distance in calculating outside
plant distances.[^200] As we noted in the *Inputs Further Notice*,
research suggests that, on average, rectilinear distance closely
approximates road distances.[^201] We agree with SBC that the
calculation of outside plant distances should reflect the closest
approximation to actual route conditions and road distance.[^202] We
also conclude that it would be inappropriate to use airline distance in
the model without simultaneously developing a process for determining
accurate road factors (which would be uniformly greater than or equal to
1 in this case). While the use of geographically disaggregated road
factors may merit further investigation, we note that the absence of
such a data set on the record at this time precludes our ability to
adopt that approach.[^203] We therefore conclude that the model should
use a rectilinear distance metric with a road factor of one.

**C. Cable and Structure Costs**

**1. Background**

83\. The model uses several tables to calculate cable costs, based on
the cost per foot of cable, which may vary by cable size (i.e., gauge
and pair size) and the type of plant (i.e., underground, buried, or
aerial). There are four separate tables for copper distribution and
feeder cable of two different gauges, and one table for fiber cable. The
engineering assumptions and optimizing routines in the model, in
conjunction with the input values in the tables, determine which type of
cable is used.

84\. The model also uses structure cost tables that identify the per
foot cost of loop structure by type (aerial, buried, or underground),
loop segment (distribution or feeder), and terrain conditions (normal,
soft rock, or hard rock) for each of the nine density zones.[^204]

85\. After the model has grouped customer locations in clusters, it
determines, based on cost minimization and engineering considerations,
the appropriate technology type for the cluster and the correct size of
cables in the distribution network. Every customer location is connected
to the closest SAI by copper cable. The copper cable used in the local
loop typically is either 24- or 26-gauge copper. Twenty-four gauge
copper is thicker and, therefore, is expected to be more expensive than
26-gauge copper. Twenty-four gauge copper also can carry signals greater
distances without degradation than 26-gauge copper and, therefore, is
used in longer loops. In the model, if the maximum distance from the
customer to the SAI is less than or equal to the copper gauge crossover
point, then 26-gauge cable is used. Feeder cable is either copper or
fiber. Fiber is used for loops that exceed 18,000 feet, the maximum
copper loop length permitted in the model, as determined in the
*Platform Order*.[^205] When fiber is more cost effective, the model
will use it to replace copper for loops that are shorter than 18,000
feet.

86\. In the *1997 Further Notice*, the Commission sought comment on the
input values that the model should use for cable and installation
costs.[^206] The Commission specifically sought comment on the accuracy
of the default values in the BCPM and HAI models and encouraged
companies to submit data to support their positions.[^207] The
Commission tentatively concluded that cable material and installation
costs should be separately identified by both density zone and terrain
type.[^208] Because the Commission had received no documentation
confirming that feeder and distribution cable installation costs should
differ, the Commission tentatively concluded that the federal mechanism
should adopt HAI\'s assumption that such costs are identical.[^209]

87\. The Commission also sought comment and adopted tentative findings
and conclusions relating to the cost of outside plant structure in the
*1997 Further Notice*.[^210] The Commission directed the HAI and BCPM
sponsors to justify fully their default values for their mix of aerial,
underground, and buried structure (i.e., plant mix) and sought comment
on the input values that will accurately reflect the impact of varying
terrain conditions on costs.[^211] The Commission noted that \"recent
installations of outside structure may more closely meet forward-looking
design criteria than do historical installations.\"[^212] The Commission
found that an efficient carrier will vary its plant mix according to the
population density of an area and tentatively concluded that the
assignment of plant mix defined by the model should reflect both terrain
factors and line density zones.[^213]

88\. In the *Inputs Public Notice*, the Bureau sought comment on the
analysis of David Gabel and Scott Kennedy of data from the Rural
Utilities Service (RUS) regarding cable and structure costs.[^214] On
December 11, 1998, the Bureau held a public workshop designed to elicit
comment on the input values for materials costs.[^215] At the workshop,
Dr. Gabel presented the methodology used by the Commission staff to
derive preliminary values for cable costs for non-rural LECs based on
his earlier analysis of the RUS data.

89\. We sought to supplement the record with respect to cable and
structure costs by requesting additional data from LECs, including
competitive LECs, in the form of a voluntary survey of structure and
cable costs.[^216] Ten companies eventually responded to the
survey.[^217]

**2. Nationwide Values**

90\. As discussed in this section, we adopt nationwide average values
for estimating cable and structure costs in the model rather than
company-specific values.[^218] In reaching this conclusion, we reject
the explicit or implicit assumption of most LEC commenters that
company-specific values, which reflect the costs of their embedded
plant, are the best predictor of the forward-looking cost of
constructing the network investment predicted by the model. We find
that, consistent with the *Universal Services Order\'s* third criterion,
the forward-looking cost of constructing a plant should reflect costs
that an efficient carrier would incur, not the embedded cost of the
facilities, functions, or elements of a carrier.[^219] We recognize that
variability in historic costs among companies is due to a variety of
factors and does not simply reflect how efficient or inefficient a firm
is in providing the supported services. We reject arguments of the LECs,
however, that we should capture this variability by using
company-specific data rather than nationwide average values in the
model. We find that using company-specific data for federal universal
service support purposes would be administratively unmanageable and
inappropriate. Moreover, we find that averages, rather than
company-specific data, are better predictors of the forward-looking
costs that should be supported by the federal high-cost mechanism.
Furthermore, we note that we are not attempting to identify any
particular company\'s cost of providing the supported services. We are
estimating the costs that an efficient provider would incur in providing
the supported services.

91\. AT&T and MCI agree that nationwide input values generally should be
used for the input values in the model.[^220] AT&T and MCI concur with
our tentative conclusion that the use of nationwide values is more
consistent with the forward-looking nature of the high-cost model
because it mitigates the rewards to less efficient companies.
Additionally, AT&T and MCI maintain that developing separate inputs
values on a state-specific, study-area specific, or holding
company-specific basis is not practicable. As AT&T and MCI contend,
doing so would be costly and administratively burdensome.

92\. While reliance on company-specific data may be appropriate in other
contexts, we find that for federal universal service support purposes it
would be administratively unmanageable and inappropriate. The incumbent
LECs argue that virtually all model inputs should be company-specific
and reflect their individual costs, typically by state or by study
area.[^221] For example, GTE claims that the costs that an efficient
carrier incurs to provide basic service vary among states and even among
geographic areas within a state.[^222] GTE asserts that the only way for
the model to generate accurate estimates, i.e., estimates that reflect
these differences, is to use company-specific inputs rather than
nationwide input values. As parties in this proceeding have noted,
however, selecting inputs for use in the high-cost model is a complex
process. Selecting different values for each input for each of the fifty
states, the District of Columbia, and Puerto Rico, or for each of the 94
non-rural study areas, would increase the Commission\'s administrative
burden significantly. Unless we simply accept the data the companies
provide us at face value, we would have to engage in a lengthy process
of verifying the reasonableness of each company\'s data. For example, in
a typical tariff investigation or state rate case, regulators examine
company data for one time high or low costs, pro forma adjustments, and
other exceptions and direct carriers to adjust their rates accordingly.
Scrutinizing company-specific data to identify such anomalies and to
make the appropriate adjustments to the company-proposed input values to
ensure that they are reasonable would be exceedingly time consuming and
complicated given the number of inputs to the model.

93\. Where possible, we have tried to account for variations in costs by
objective means. As explained below, the model reflects differences in
structure costs by using different values for the type of plant, the
density zone, and geological conditions. As discussed below, we sought
comment in the *Inputs Further Notice* on alternatives to nationwide
plant mix values, but the algorithms on the record produce biased
results. We continue to believe that varying plant mix by state, study
area, or region of the country may more accurately reflect variations in
forward-looking costs and intend to seek further comment on this issue
in the future of the model proceeding.

**3. Preliminary Cable Cost Issues**

94\. [Use of 24-gauge and 26-gauge Copper]{.underline}. In the *Inputs
Further Notice*, we tentatively concluded that the model should use both
24-gauge and 26-gauge copper in all available pair-sizes.[^223] We based
our tentative conclusion on a preliminary analysis of the results of the
structure and cable cost survey, in which it appeared that a significant
amount of 24-gauge copper cable in larger pair sizes currently is being
deployed. We also noted that, while HAI default values assume that all
copper cable below 400 pairs in size is 24-gauge and all copper cable of
400 pairs and larger is 26-gauge, the BCPM default values include
separate costs for 24- and 26-gauge copper of all sizes.[^224]

95\. We conclude that the model should use both 24-gauge and 26-gauge
copper in all available pair sizes. No commenter refuted our observation
that a significant amount of 24-gauge copper cable in larger pair sizes
currently is being deployed. Those commenters addressing this issue
concur with our tentative conclusion.[^225] SBC confirms our analysis of
the survey data and notes that it deploys 24-gauge cable in sizes from
25 to 2400 pairs.[^226] GTE explains, and we agree, that the model
should use both 24-gauge and 26-gauge copper in all available pair sizes
in order to stay within transmission guidelines when modeling 18
kilofoot loops.[^227]

96\. [Distinguishing Feeder and Distribution Cable Costs]{.underline}.
In the *Inputs Further Notice*, we reaffirmed the Commission\'s
tentative conclusion in the *1997 Further Notice* that the same input
values should be used for copper cable whether it is used in feeder or
in distribution plant.[^228] We adopt this tentative conclusion. Those
commenters addressing this issue agree with our tentative
conclusion.[^229] GTE contends that it is both unnecessary and
inappropriate to have different costs for feeder and distribution cable
material.[^230] GTE explains that, although quantities of material and
labor related to cable size may differ between feeder and distribution,
the unit costs for each remain the same.[^231] Similarly, Sprint agrees
that the material cost of cable is the same whether it is used for
distribution or feeder.[^232] In sum, we find that the record
demonstrates that it is appropriate to use the same input values for
copper cable whether it is used in feeder or in distribution plant.

97\. [Distinguishing Underground, Buried, and Aerial Installation
Costs]{.underline}. In the *Inputs Further Notice*, we also tentatively
concluded that we should adopt separate input values for the cost of
aerial, underground, and buried cable.[^233] We reached this tentative
conclusion on the basis of our analysis of cable cost data supplied to
us in response to data requests and through *ex parte* presentations. We
found considerable differences in the per foot cost of cable, depending
upon whether the cable was strung on poles, pulled through conduit, or
buried.

98\. We conclude that separate input values for the cost of aerial,
underground, and buried cable should be adopted. Those commenters
addressing this issue confirm our analysis of the data, i.e., that there
are differences, some significant, in placement costs for aerial,
underground, and buried cable.[^234] GTE explains that, from a material
perspective, the cable may have different protective sheathing,
depending on construction applications.[^235] GTE adds that labor costs
also differ depending on the type of placement.[^236] Both SBC and
Sprint identify the cost of labor as varying significantly depending
upon the type of placement.[^237] Based upon a review of the record in
this proceeding, we conclude that separate input values for the cost of
aerial, underground, and buried cable are, therefore, warranted.

99\. [Deployment of Digital Lines]{.underline}. We also conclude that
two inputs, \"pct_DS1\" and \"pct_1sa\", should be modified to provide
more accurate deployment of digital lines in the distribution plant. The
model can deploy a portion of distribution plant on digital DS1 circuits
by specifying these two user adjustable inputs. The input \"pct_DS1\"
determines the percentage of switched business traffic carried on DS1
circuits, and the input \"pct_1sa\" determines the percentage of special
access lines carried on DS1 circuits. Previously, we used default values
for the inputs \"pct_DS1\" and \"pct_1sa.\" We now adopt more accurate
values for these inputs using 1998 line count data, following the
methodology described below.

100\. Initially the model determines the number of special access lines
from a \"LineCount\" table in the database \"hcpm.mdb,\" which provides
for each wire center the number of residential lines, business lines,
special access lines, public lines, and single business lines.[^238] The
Commission required incumbent LECs to provide line counts for business
switched and non-switched access lines on a voice equivalent basis[^239]
and on a facilities basis.[^240] Upon receipt of those filings, we
determined industry totals for each of the line count items
requested.[^241] By applying the model\'s engineering conventions to the
totals, the model determines the percentage of switched and non-switched
lines provided as DS1-type service.[^242] Thus, using the channel and
facility counts submitted in response to the *1999 Data Request*, it is
possible to determine the \"pct_DS1\" input value using the following
formula: (1-pct_DS1)\*channels + pct_DS1\*channels/12 =
facilities.[^243] A similar calculation is performed to solve for the
\"pct_1sa\" input value. For both switched business and special access
lines, the number of digital lines is then determined by multiplying the
respective line count by the input value \"pct_DS1\" or \"pct_1sa.\"
Since 24 communications channels can be carried by two pairs of copper
wires, the number of copper cables required to carry digital traffic is
computed by dividing the number of digital channels by 12. These
percentages are used to adjust the wire center cable requirements by
reducing the facilities needed to serve multi-line business and special
access customers.

**4. Cost Per Foot of Cable**

**a. Background**

101\. In the *Inputs Further Notice*, we tentatively concluded that we
should use, with certain modifications, the estimates in the NRRI Study,
*Estimating the Cost of Switching and Cables Based on Publicly Available
Data*, for the per-foot cost of aerial, underground, and buried 24-gauge
copper cable.[^244] Concomitantly, we tentatively concluded that we
should use, with certain modifications, the estimates in the NRRI Study
for the per-foot cost of aerial, underground, and buried fiber
cable.[^245]

102\. In reaching these conclusions, we rejected the default input
values for cable costs provided by both the HAI and BCPM sponsors which
are based upon the opinions of their respective experts, because they
lacked additional support that would have enabled us to substantiate
those opinions.[^246] We also noted that we had received cable cost data
from a number of LECs, including data received in response to the
structure and cable cost survey, and were in the process of scrutinizing
it.[^247]

103\. The HAI sponsors supported using the publicly available RUS data
in the NRRI Study to estimate cable costs and structure costs.[^248] In
contrast, Sprint questioned the reliability and suitability of these
data, and urged us instead to use the cable cost data provided by
incumbent LECs.[^249] Sprint pointed out that the RUS data only reflect
information from the two lowest density zones.[^250] Sprint explained
that because longer loops are used in sparsely populated areas,
lower-gauge copper often is used. We explained that Sprint had
mischaracterized the analysis of the RUS data in the NRRI Study. We
noted for example, that Sprint challenged the validity of the study
because some of the observations have zero values for labor or material,
while failing to recognize that these values were excluded from Gabel
and Kennedy\'s regression analysis.[^251] Similarly, we found that
Sprint\'s complaint that Gabel and Kennedy do not analyze separately the
components of total cable costs, labor and material, overlooked the fact
that Gabel and Kennedy\'s regression analysis is designed to explain the
variation in total costs.[^252]

104\. Moreover, in reaching our tentative conclusion to use the NRRI
Study and the underlying data from the two lowest density zones, i.e.,
rural areas, to estimate cable costs for non-rural LECs, we noted that
none of the parties proposed cable cost values that vary by density
zones. Nor did the models considered by the Commission have the
capability of varying cable costs by density zones.[^253]

**b. Discussion**

105\. We affirm our tentative conclusion that we should use, with
certain modifications as described more fully below, the estimates in
the NRRI Study for the per-foot cost of aerial, underground, and buried
24-gauge copper cable and for the per-foot cost of aerial, underground,
and buried fiber cable. We conclude that, on balance, these estimates,
as modified in the *Inputs Further Notice*, and further adjusted herein,
are the most reasonable estimates of the per-foot cost of aerial,
underground, and buried 24-gauge copper cable and fiber cable on the
record before us. In reaching this conclusion, we reject, for the
reasons enumerated below, the arguments of those commenters who contend
that we should use company-specific data to develop the inputs for the
per-foot cost of cable to be used in the model.[^254]

106\. [Company-specific data]{.underline}. As we discussed above, we
have determined to use nationwide average input values for estimating
outside plant costs.[^255] In reaching this conclusion, we determined
that the use of company-specific inputs was inappropriate because of the
difficulty in verifying the reasonableness of each company\'s data,
among other reasons. We have examined cable cost and structure cost data
received from a number of non-rural LECs, as well as AT&T, in response
to the structure and cable cost survey and through a series of *ex
parte* filings. In addition, we have examined additional
company-specific data submitted by certain parties with their comments.
As discussed more fully below, we conclude that these data are not
sufficiently reliable to use to estimate the nationwide input values for
cable costs or structure costs to be used in the model.[^256]

107\. We conclude that the cable cost and structure cost data received
in response to the structure and cable cost survey, in the *ex parte*
filings, and in the comments are not verifiable. We find that with
regard to the survey data, notwithstanding our request, most respondents
did not trace the costs submitted in response to the survey from dollar
amounts set forth in contracts by providing copies of these contracts
and all of the interim calculations for a single project or a randomly
selected central office. With regard to the *ex parte* data and data
submitted with the comments, we find that, because most respondents did
not document in sufficient detail the methodology, calculations,
assumptions, and other data used to develop the costs they submitted,
nor did they submit contracts or invoices setting forth in detail the
cable and structure costs they incurred, these data cannot be
substantiated.[^257] Moreover, we note that the structure and cable
costs reported in the survey by some parties differ significantly from
those reported by the same parties in the *ex parte* filings. These
differences are not explained, and render those sets of data unreliable.

108\. We find this lack of back-up information particularly unsettling
given the magnitude of certain of the costs reported. We agree with AT&T
and MCI that the cable installation costs submitted by the incumbent
LECs appear to be high.[^258] We also agree with AT&T and MCI that this
is because the loading factors employed in calculating these costs
appear to be overstated. Because of the lack of back-up information to
explain these loading costs, however, there is no evidence on the record
to controvert our initial assessment. Accordingly, the level of these
costs remains suspect.

109\. Moreover, we find additional deficiencies beyond the critical lack
of substantiating data, impugning the reliability of the LEC survey data
and the *ex parte* data we have received. As discussed above, the task
of the model is to calculate forward-looking costs of constructing a
wireline local telephone network. To that end, the survey directed
respondents to submit cable and structure costs for growth projects for
which expenditures were at least \$50,000.[^259] We believed that such
projects would best reflect the costs that a LEC would incur today to
install cable if it were to construct a local telephone network using
current technology. In contrast, absent from the data would be costs
associated with maintenance or projects of smaller scale which do not
represent the costs of installing cable during such construction using
current technology. Thus, the data would capture the economies of scale
enjoyed on large projects which, should result in lower cable costs on a
per-foot basis. Notwithstanding the survey directions, several of the
respondents submitted data representing projects that were not growth
projects or projects for which expenditures were less than the \$50,000
minimum we established.

110\. Conversely, some respondents included costs that should have been
excluded under the definitions employed in the survey. For example, some
respondents included costs for terminating structures, such as
cross-connect boxes, in the cable costs they reported. Similarly, some
respondents reported underground structure costs on a \"per duct foot\"
basis contrary to the instructions set forth in the survey directing
that such costs be reported on a \"per foot\" basis. We find that these
inconsistencies render the use of the survey data inappropriate.

111\. In sum, we find that certain of the concerns we identified with
regard to using company-specific data, rather than nationwide average
inputs for model inputs, have been borne out in our review of the cable
cost and structure cost data we have reviewed. Specifically, we find
that we are unable to verify the reasonableness of such data.
Accordingly, we find that we are unable to use the company-specific data
we have received for the estimation of cable cost and structure cost
inputs for the model.

112\. In reaching this conclusion, we reject the contention that the
inability to link the costs submitted in response to the cable and
structure cost survey to contracts is irrelevant because the survey
request was not intended to create such a trail.[^260] This claim
ignores the fact that the reasonableness of the survey data was placed
into question by the presence of data received on the record that was
inconsistent with the survey data. For this reason, as GTE attests, we
attempted to create such a trail by requesting contracts and other
supporting data in an effort to verify the reasonableness of the
company-specific data received in response to the survey as well as in
*ex parte* filings.[^261]

113\. [Methodology]{.underline}. As we explained in the *Inputs Further
Notice*, our tentative decision to rely on the NRRI Study was predicated
on our inability to substantiate the default input values for cable
costs and structure costs provided by the HAI and BCPM sponsors.[^262]
For that reason, we tentatively concluded, in the absence of more
reliable evidence of cable and structure costs for non-rural LECs, to
use estimates in Gabel and Kennedy\'s analysis of RUS data, subject to
certain modifications, to estimate cable and structure costs for
non-rural LECs. As we explained, Gabel and Kennedy first developed a
data base of raw data from contracts for construction related to the
extension of service into new areas, and reconstruction of existing
exchanges, by rural-LECs financed by the RUS. Gabel and Kennedy then
performed regression analyses, using data from the HAI model on line
counts and rock, soil, and water conditions for the geographic region in
which each company in the database operates to estimate cable and
structure costs.[^263] Regression analysis is a standard method used to
study the dependence of one variable, the dependent variable, on one or
more other variables, the explanatory variables. It is used to predict
or forecast the mean value of the dependent variable on the basis of
known or expected values of the explanatory variables.[^264]

114\. Those commenters advocating the use of company-specific data
provide a litany of alleged weaknesses and flaws in the NRRI Study, and
the modifications we proposed, to discredit its use to estimate the
input values for cable costs and structure costs. In sum, they argue
that the overall approach we proposed is unsuitable for estimating the
cable and structure costs of non-rural LECs and generally leads to
estimates which understate actual forward-looking costs.[^265] As
discussed below, we find the contentions in support of this claim
unpersuasive. Significantly, we note that these commenters provide no
evidence that substantiates the reasonableness of the company-specific
cable costs and structure costs submitted on the record to permit their
use as an alternative in the estimation of cable and structure cost
inputs to be used in the model.[^266]

115\. For similar reasons, we reject AT&T and MCI\'s recommendation that
we rely on the RUS data to develop cost estimates for the material cost
of cable and then adopt \"reasonable\" values for the costs of cable
placing, splicing, and engineering based on the expert opinions
submitted by AT&T and MCI in this proceeding.[^267] We find that the
expert opinions on which AT&T and MCI\'s proposed methodology relies
lack additional support that would permit us to substantiate those
opinions. Moreover, as discussed in more detail below, we reject AT&T
and MCI\'s contentions, often analogous to those raised by the non-rural
LECs, that the approach we proposed to estimate cable and structure
costs is flawed in certain respects.

116\. We reject the contentions of the commenters, either express or
implied, that it is inappropriate to employ the NRRI Study because the
RUS data set on which it relies is not a sufficiently reliable data
source for structure and cable costs. We find that the RUS data set is a
reasonably reliable source of absolute cable costs and structure costs,
and more reliable and verifiable than the company-specific data we have
reviewed. As explained in the NRRI Study, and noted above, the RUS data
reflect contract costs for construction related to the extension into
new areas, and reconstruction of existing exchanges, by rural LECs
financed by the RUS.[^268] Thus, the RUS data reflect actual costs
derived from contracts between LECs and vendors. These costs are not
estimates, but actual costs. Nor do they reflect only the opinions of
outside plant engineers. In sum, we conclude that these are verifiable
data.

117\. We also note that the RUS data reflect the costs from 171
contracts covering 57 companies operating in 27 states adjusted to 1997
dollars.[^269] These companies operate in areas that have different
terrain, weather, and density characteristics. This fact makes the RUS
data sample suitable for econometric analysis. Moreover, we find that,
because the costs are for construction that must abide by the
engineering standards established by the RUS, these data are consistent.
We note also that the imposition of consistent engineering requirements
mitigate the impact of any inefficiencies or inferior technologies that
may otherwise be reflected in the data.

118\. Finally, as noted above, the RUS data reflect costs for additions
to existing plant or new construction. The use of such costs is
consistent with the objective of the model to identify the cost today of
building an entire network using current technology.

119\. In reaching our conclusion to use the NRRI Study and thus the
underlying RUS data, we have considered and rejected the contentions of
the commenters that the RUS data set is flawed thereby rendering use of
the NRRI Study inappropriate. GTE claims that because certain high-cost
observations were removed from the RUS data, the NRRI Study\'s results
are unrepresentative of rural companies\' costs, and are even less
representative of non-rural companies\' costs.[^270] We disagree. Gabel
and Kennedy omitted data reflecting certain contracts from the RUS data
they used to develop cost estimates because estimates produced using the
data were inconsistent with the values of such estimates suggested by *a
priori* reasoning or evidence.[^271] For example, they excluded certain
observations from the buried copper and structure regression analysis
because buried copper cable and structure estimates obtained from this
analysis would otherwise be higher in low density areas than in higher
density areas. Such a result is contrary to the information contained in
the more than 1000 observations reflected in the data from which Gabel
and Kennedy developed their buried copper cable and structure regression
equation. Thus, removing the observations does not render the remaining
data set less representative of rural companies\' costs or, as adjusted
below, the estimates of the costs of non-rural companies. Moreover, we
note that the evidence supplied on the record in this proceeding
demonstrates that structure costs increase as population density
increases. Thus, we find that the RUS data set is not flawed as GTE
contends. We conclude that the removal of certain high cost observations
was reasonable.

120\. We also disagree with GTE\'s and Bell Atlantic\'s assertion that
the NRRI Study is flawed because the RUS company contracts do not
reflect actual unit costs for work performed, but rather the total cost
for a project.[^272] Both commenters claim that this alleged failure
results in unexplained variations in the RUS data which undermine the
validity of the estimates produced. Contrary to GTE\'s and Bell
Atlantic\'s contention, the contracts from which Gabel and Kennedy
developed their data base for developing structure and cable costs do
set forth per unit costs for materials and per unit costs for specific
labor tasks.[^273]

121\. We also disagree with AT&T and MCI\'s claim that the RUS data are
defective because they consist of primarily small cables.[^274] AT&T and
MCI claim that 74 percent of the RUS data are for cables of 50 pairs or
less, and 95 percent are for cable sizes of 200 pairs or less. As a
result, AT&T and MCI contend that the RUS data are inaccurate,
especially for cable sizes above 200 pairs. We disagree with AT&T and
MCI\'s analysis. We note that, for the buried copper cable and structure
regression equations we proposed and adopt, approximately 39 percent of
the observations are for cable sizes of 50 pairs or less, and
approximately 76 percent are for 200 pairs or less. For the underground
copper cable regression equation we proposed and adopt, approximately 10
percent of the observations are for cable sizes of 50 pairs or less, and
approximately 33 percent are for 200 pairs or less. For the aerial
copper cable regression equation we proposed and adopt, approximately 40
percent of the observations are for cable sizes of 50 pairs or less, and
approximately 76 percent are for 200 pairs or less. Thus, the proportion
of the observations reflected in the copper cable cost estimates we
adopt are significantly greater for relatively large cables than what
AT&T and MCI contend.

122\. Finally, we reject the contention that it is inappropriate to use
the NRRI Study because the RUS data base is not designed for the purpose
of developing input values for the model.[^275] In the NRRI Study, Gabel
and Kennedy explain that they began developing the data base as an
outgrowth of the Commission\'s January 1997 workshop on cost proxy
models when it became apparent that costs used as inputs in such models
should be able to be validated by regulatory commissions. For this
reason, they prepared data that is in the public domain to provide
independent estimates of structure and cable costs.[^276]

123\. We also find unpersuasive the contention that there are
econometric flaws in the NRRI Study which render it unsuitable for
developing input values.[^277] We disagree with the contentions of
several commenters that the structure cost and cable cost regression
equations that we develop from the RUS data are flawed because they are
based on a relatively small number of observations.[^278] As a general
rule of thumb, in order to obtain reliable estimates for the intercept
and the slope coefficients in a regression equation, the number of
observations on which the regression is based should be at least 10
times the number of independent variables in the regression
equation.[^279] Ameritech claims that the sample size used to estimate
the costs of buried placement is too small because it contains only 26
observations in density zone one.[^280] Ameritech\'s criticism ignores
the fact that we use a single regression equation to estimate buried
copper cable and structure costs for density zones one and two based on
1,131 observations (1,105 in zone two and 26 in zone one). There are
four independent variables in the buried copper cable and structure
regression equation, i.e., the variables that indicate the size of the
cable, presence of a high water table, combined rock and soil type, and
density zone. This suggests that approximately 40 observations are
needed to obtain reliable estimates for the parameters in this
regression equation. The total number of observations used to estimate
this regression equation, 1,131, readily exceeds the number suggested
for estimating reliably this regression equation. The number of
observations for density zone one alone, 26, provides 65 percent of the
suggested number of observations. Similarly, AT&T and MCI claim that the
sample size for underground cable is too small because it contains only
80 observations.[^281] There is one independent variable in the adopted
underground copper cable equation, i.e., the variable that indicates the
size of the cable. Based on the rule of thumb noted above, 10
observations are needed to reliably estimate this regression equation.
The number of observations used to estimate the adopted underground
copper cable regression equation, 81, is more than eight times this
suggested number.[^282] Moreover, we note that Ameritech does not
provide any evidence that suggest that a sample that has 26 observations
in density zone 1 produces biased estimates of buried structure and
cable costs for density zone one. Similarly AT&T and MCI do not provide
any evidence to support their allegation that a sample size of 80
observations produces biased estimates of underground copper cable
costs. Finally, we note that GTE contends that the regression results
for aerial structure are undermined because the sample size for poles is
based only on 19 observations.[^283] While a sample of this size fails
to satisfy the general rule of thumb we noted above, we find that the
estimates produced are reasonable. As we pointed out in the *Inputs
Further Notice*, the average material price reported in the NRRI Study
for a 40-foot, class four pole is \$213.94. This is close to our
calculations of the unweighted average material cost for a 40-foot,
class four pole, \$213.97, and the weighted average material cost, by
line count, \$228.22, based on data submitted in response to the *1997*
*Data Request*. Moreover, we note that GTE does not provide any evidence
that suggests that a sample size of 19 poles for developing aerial
structure costs produces biased estimates as GTE seems to allege.

124\. We also disagree with GTE\'s contention that the NRRI Study
contains three methodological errors that make its results unreliable.
First, GTE asserts that the most serious of these flaws is that the NRRI
Study improperly averages ordinal or categorical data, i.e., qualitative
values, for the costs of placing structure in different types of
soil.[^284] Contrary to GTE\'s claim, the independent variables that
indicate soil type, rock hardness, and the presence of a high water
table used in the regression equations for aerial and underground
structure and buried structure and cable costs in the NRRI Study and
proposed in the *Inputs Further Notice* do not reflect an incorrect
averaging of ordinal data. The variables for soil, rock, and water
indicate the average soil, rock, and water conditions in the service
areas of RUS companies. They are based on averages of data obtained from
the HAI database for the Census Block Groups in which the RUS companies
operate. In general, the magnitude of the t-statistics for the
coefficients of the independent variables for soil, rock, and water in
the structure regression equations indicate that these variables have a
statistically significant impact on structure costs. The magnitude of
the F-statistic indicates that the independent variables in the
structure regression equations, including those that indicate water,
rock, and soil type, jointly provide a statistically significant
explanation of the variation in structure costs. These statistical
findings justify use of these variables in the structure regression
equations. We also note that HAI uses as cardinal values, i.e.,
quantitative, not ordinal values, the soil and rock data from which the
averages reflected in the rock and soil variables in the NRRI Study are
calculated. For example, HAI uses a multiplier of between 1 and 4 to
calculate the increase in placement cost attributable to the soil
condition. Moreover, and more importantly, we note that no commenter has
demonstrated the degree of, or even the direction of, any bias in the
cost estimates derived in the NRRI Study or in the regression equations
proposed in the *Inputs Further Notice* as a result of the use of soil,
water, and rock variables based on averages of HAI data.

125\. GTE also claims that the NRRI Study is flawed because it relies on
the HAI model\'s values relating to soil type which GTE claims were
\"made up.\"[^285] GTE contends that this renders the variable relating
to soil type judgmental and biased. We find GTE\'s concern misplaced. As
explained above, the econometric analyses of the data demonstrate a
statistically significant relationship between the geological variables
developed from the HAI data and the structure costs. Finally, we
disagree with GTE\'s claim that the NRRI Study is flawed because of a
mismatch in the geographic coverage of the RUS data and the HAI model
variables.[^286] GTE does not provide any evidence showing that the
alleged mismatch introduces an upward or downward bias on the cost
estimates obtained from the regression equations. Moreover, and more
importantly, the t-statistics for the coefficients of the variables that
measure rock and soil type generally indicate that these geological
variables provide a statistically significant explanation of variations
in RUS companies\' structure costs.

126\. We also reject the claims that the derivation of the equations for
24-gauge buried copper cable, buried structure, and buried fiber cable
from the NRRI Study regression equations for 24-gauge buried copper
cable and structure and buried fiber cable and structure, respectively,
is inappropriate.[^287] As we explained in the *Inputs Further Notice*,
we modified the regression equations in the NRRI Study for 24-gauge
buried copper cable and structure and buried fiber cable and structure,
as modified by the Huber methodology described below, to estimate the
cost of 24-gauge buried copper cable, buried structure and buried fiber
cable because the regression equations for buried copper cable and
structure and buried fiber cable and structure provide estimates for
labor and material costs for both buried cable and structure
combined.[^288] In layman\'s terms, we split the modified 24-gauge
buried copper cable and structure regression equation into two separate
equations, one for 24-gauge buried copper cable and one for buried
structure costs. We also split the modified buried fiber cable and
structure regression equation to obtain an equation for buried fiber
cable.[^289] We did this because the model requires a separate input for
labor and material costs for cable and a separate input for labor and
material costs for structure. In contrast, the RUS data and buried cable
and structure regression equations developed from these data, reflect
labor and material costs for buried cable and structure combined.

127\. Significantly, the criticisms of our development of the 24-gauge
buried copper cable equation, buried structure equation and buried fiber
cable equation in this manner ignore the fact that reliable, alternative
data for buried cable costs and buried structure costs is not available
on the record.[^290] Given that the model requires a separate input
reflecting labor and material costs for both copper and fiber cable and
a separate input reflecting labor and material costs for structure, and
that the only reliable data on the record does not separate such costs
between cable and structure, we find it necessary to split the
regression equation.

128\. Contrary to the assertions of the commenters, either express or
implied, the steps we took to derive these equations were not
arbitrary.[^291] We used a single buried structure equation to estimate
the cost for buried structure without distinguishing between the
equation for buried copper structure and the equation for buried fiber
structure because the model does not distinguish between buried copper
structure costs and buried fiber structure costs. We find that this is
reasonable because the intercept and the coefficients for the variables
that primarily explain the variation in structure costs, i.e., the
variables that indicate density zone, the combined soil and rock type,
and the presence of a high water table, in the combined regression
equation for buried fiber cable and structure are not statistically
different from the intercept and the coefficients for these variables in
the combined regression equation for 24-gauge buried copper cable and
structure.[^292] We also find that it is reasonable to develop a
separate structure equation from the regression equation for the
combined cost of 24-gauge buried copper cable and structure rather than
from the regression equation for the combined cost of buried fiber cable
and structure because the water and soil and rock type indicator
variables in the regression equation for the combined cost of 24-gauge
buried copper cable and structure are statistically significant. In
contrast, these variables are not statistically significant in the
buried fiber cable and structure regression equation.[^293] In addition,
we note that the number of observations used to estimate the 24-gauge
buried copper cable and structure regression equation, 1,131, exceeds
the number of observations used to estimate the buried fiber cable and
structure regression equation, 707 observations.

129\. We note that we included in the separate buried cable equations
the variable for cable size and its coefficient reflected in the
combined cable and structure regression equations. We find that this is
reasonable because the cable size variable and its coefficient explain
the variation in cable costs. We also note that we excluded from the
separate buried cable equations the independent variables in the
combined cable and structure regression equations that indicate density
zone, the presence of a high water table, and the soil and rock type. We
find that this is reasonable because these variables and their
coefficients explain primarily the variation in buried structure costs.
Conversely, we excluded from the separate buried structure equation the
variable for cable size and its coefficient reflected in the combined
24-gauge buried copper cable and structure regression equation because
this variable and its coefficient explain the variation in cable costs.

130\. We also included in the separate structure equation the variables
and the coefficients for the variables that indicate density zone, the
combined soil and rock type, and the presence of a high water table in
the combined regression equation for 24-gauge buried copper cable and
structure. Again, we find this is reasonable because these independent
variables and coefficients primarily explain the variation in structure
costs.

131\. Finally, because the estimated intercepts in the regression
equations for the cost of buried cable and structure reflect the fixed
cost for both buried cable and structure in density zone one, we
included in the separate equations for buried cable an intercept
reflecting the fixed cost of cable. Similarly, we included in the
equation for buried structure an intercept reflecting the fixed cost of
structure in density zone one. Specifically, we allocated an estimate of
the portion of the combined fixed cable and structure costs that
represents the fixed copper cable costs reflected in the intercept in
the 24-gauge buried copper cable and structure cost regression equation
to the intercept in the equation for 24-gauge buried copper cable.
Correspondingly, we allocated an estimate of the portion of fixed cable
and structure cost that represents the fixed costs of buried structure
reflected in the intercept in the buried 24-gauge copper cable and
structure cost regression equation to the intercept in the equation for
structure costs. We also allocated to the intercept in the separate
buried fiber cable equation the remaining portion of the fixed costs
reflected in the intercept in the combined buried fiber cable and
structure regression equation after subtracting from the value of this
intercept the estimate for fixed structure costs in density zone 1 in
the separate buried structure equation. The sum of the particular values
that we adopt for the fixed cable cost in the separate 24-gauge copper
cable equation, \$.46, and the fixed structure cost in density zone 1 in
the separate structure equation, \$.70, equals the 24 gauge buried
copper cable and structure fixed costs reflected in the intercept in the
combined copper cable and structure regression equation of \$1.16. The
sum of the particular values that we adopt for the fixed cable cost in
density zone 1 in the separate fiber cable equation, \$.47, and the
fixed structure cost in the separate structure equation of \$.70 equals
the buried fiber cable and structure fixed costs reflected in the
intercept in the combined fiber cable and structure regression equation,
\$1.17. We find that these values are reasonable. We note that
\$.46[^294] lies between AT&T and MCI\'s estimate of the fixed cost for
a 24-gauge buried copper cable of \$.12[^295] and the HAI default value
for the installed cost of a 6-pair 24-gauge buried copper cable of
\$.63.[^296] Moreover, we note that we could have used relatively higher
or lower values for the fixed structure and cable costs in the separate
structure and cable equations. However, we note that the sum of the
fixed costs reflected in the buried structure cost estimates (excluding
LEC engineering costs) developed from the separate buried structure
equation and the fixed costs reflected in the buried cable cost
estimates (excluding LEC engineering and splicing costs) developed from
the separate buried copper or fiber cable equation is not affected by
the relative values that we use for the fixed cost in these separate
equations.[^297]

132\. Finally, we note that GTE contends that the proposed equations for
buried cable and buried structure are questionable because the buried
structure costs would not vary with the presence of water.[^298] As
discussed below, we have modified the regression equation for buried
copper cable and structure by adding the variable that indicates the
presence of a high water table. We obtain structure cost estimates used
as input values by setting the coefficient for the water indicator
variable equal to zero. These structure cost estimates, therefore,
assume that a high water table is not present. The model adjusts these
estimates to reflect the impact on these costs of a high water table.
GTE also claims that the proposed equations are questionable because the
costs for buried structure derived from the buried structure equation
would not vary with cable size. We reject this contention. GTE has not
provided any evidence that demonstrates that buried structure costs vary
with cable size. To the contrary, GTE states that it cannot produce such
evidence because it is not able to separate actual costs of buried
structure from total costs of buried plant.

133\. In sum, we find that the regression equations we proposed and
tentatively adopted in the *Inputs Further Notice* are an appropriate
starting point for estimating cable costs and structure costs for
non-rural LECs for purposes of developing inputs for the model,
particularly given the absence of more reliable cable and structure cost
data from any other source.[^299] We find, however, that certain
commenters\' criticisms of the regression equations we proposed have
merit. We make the following adjustments to improve the regression
equations consistent with those criticisms.[^300]

134\. First, we remove the independent variable that indicates whether
two or more cables are placed at the same location from the regression
equations for 24-gauge aerial copper cable, 24-gauge buried copper cable
and structure, aerial fiber cable, and buried fiber cable and
structure.[^301] As a result, the regression equations we adopt do not
have this variable as an independent variable. We do not include this
independent variable in any of the cable and structure equations because
the model does not use a different cable cost if the outside plant
portion of the network it builds requires more than one cable.

135\. We also remove from the regression equation for 24-gauge
underground copper cable the variable that is the mathematical square of
the number of copper cable pairs. We remove this variable because its
use results in negative values for the largest cable sizes, as some
parties point out.[^302] We note that none of the other proposed cable
and structure regression equations had this variable as an independent
variable.

136\. We add the variable that indicates the presence of a high water
table to the regression equations for buried copper cable and structure
and underground structure costs. With this change, all of the regression
equations for structure costs adopted in this Order have this variable
as an independent variable.[^303] We include this variable in the
structure equations because the model applies a cost multiplier to all
structure costs when the water table depth is less than the critical
water depth. To develop structure cost inputs, we set the value of the
water indicator variable equal to zero in the structure regression
equations, thereby developing structure costs that assume that there is
no water in the geographic area where the structure is installed. The
multiplier in the model then adjusts these costs to reflect the impact
on these costs of a high water table when it determines that the water
table depth is less than the critical water depth.

137\. We reduce the value of the intercept to \$.46 from \$.80 in the
equation proposed in the *Inputs Further Notice* for calculating the
labor and material costs for buried copper cable (excluding structure,
LEC engineering, and splicing costs). We now estimate the buried
24-gauge copper cable and structure regression equation after removing
the multi-cable variable and adding the water indicator variable. The
value of the intercept in this regression equation of \$1.16 is less
than the intercept in the proposed regression equation of \$1.51. As we
did in the *Inputs Further Notice*, we derive the buried copper cable
equation from the regression equation for 24-gauge buried copper cable
and structure costs. The value of the intercept in the buried copper
cable and structure regression equation represents the fixed cost for
both buried copper cable and buried copper cable structure in density
zone 1. We assume, as we did in the *Inputs Further Notice*, that \$.70
is the fixed cost for buried copper cable structure in density zone 1.
Accordingly, the fixed labor and material cost for buried copper cable
is \$1.16 minus \$.70, or \$.46.

138\. We also reduce the value of the intercept to \$.47 from \$.60 in
the equation proposed in the *Inputs Further Notice* for calculating the
labor and material costs for buried fiber cable (excluding structure,
LEC engineering, and splicing costs). We now estimate the buried fiber
cable and structure regression equation after removing the multi-cable
variable. The value of the intercept in this regression equation,
\$1.17, is greater than the value of the intercept in the proposed
regression equation, \$1.14. As we did in the *Inputs Further Notice*,
we derive the buried fiber cable equation from the regression equation
for buried fiber cable and structure costs. The value of the intercept
in the buried fiber cable and structure regression equation represents
the fixed cost for both buried fiber cable and buried fiber cable
structure in density zone 1. We assume that \$.70 is the fixed cost for
buried fiber cable structure in density zone 1. Accordingly, the fixed
labor and material cost for buried fiber cable in density zone 1 is
\$1.17 minus \$.70 or \$.47

139\. [Huber Adjustment]{.underline}. In the *Inputs Further Notice,* we
tentatively concluded that one substantive change should be made to
Gabel and Kennedy\'s analysis.[^304] As we explained, we tentatively
concluded that the regression equations in the NRRI Study should be
modified using the Huber regression technique[^305] to mitigate the
influence of outliers in the RUS data.[^306] Statistical outliers are
values that are much higher or lower than other data in the data set.
The Huber algorithm uses a standard statistical criterion to determine
the most extreme outliers and exclude those outliers. Thereafter, the
Huber algorithm iteratively performs a regression, then for each
observation calculates an observation weight based on the absolute value
of the observation residual. Finally, the algorithm performs a weighted
least squares regression using the calculated weights. This process is
repeated until the values of the weights effectively stop
changing.[^307]

140\. We affirm our tentative conclusion to modify the regression
equations in the NRRI Study using the Huber methodology to develop input
values for cable and structure costs. The cable and structure cost
inputs used in the model should reflect values that are typical for
cable and structure for a number of different density and terrain
conditions. If they do not reflect values that are typical, the model
may substantially overestimate or underestimate the cost of building a
local telephone network. As discussed below, application of the Huber
methodology minimizes this risk, thereby producing estimates that are
consistent with the goal of developing cable and structure cost inputs
that reflect values that are typical for cable and structure for
different density and terrain conditions.

141\. The commenters attest to the fact that there are significant
variances in the RUS structure and cable cost data.[^308] We find that
the presence of these outliers warrants the use of the Huber
methodology. By relying on the Huber methodology to identify and to
exclude or give less than full weight to these data outliers in the
regressions, we decrease the likelihood that the cost estimates produced
reflect measurement error or data anomalies that may represent unusual
circumstances that do not reflect the typical case. We note that we are
not readily able to ascertain the specific circumstances that may
explain why some data points are outliers relative to more clustered
data points because of the multivariate nature of the database. Such
occurrences are expected when dealing with such a database. Not only are
there many observations, but these observations reflect the
circumstances surrounding the construction work of many different
contractors done for a large number of companies on different projects
over a number of years. We also note that the task of identifying
structure cost outliers without using a statistical approach such as
Huber is especially difficult because these costs are a function of
different geological conditions and population densities. Given that it
is not feasible, as a practical matter, to determine why particular data
points are outliers and our objective is to develop typical cable and
structure costs, we conclude that use of the Huber methodology is
appropriate.[^309]

142\. We find the comments opposing application of the Huber methodology
unpersuasive. In the first instance, we reject the assertions of the
commenters, either express or implied, that the application of robust
regression analysis is not the preferred method of dealing with outliers
in a regression.[^310] There is no preferred method. The use of robust
regression techniques is a matter of judgement for the estimator. As we
explained above, the goal of our analysis is to estimate values that are
typical for cable and structure costs for different density and terrain
conditions. We determined that we should mitigate the effects of
outliers occurring in the data to ensure that the estimates we produce
reflect typical costs. Noting that such outliers have an undue influence
on ordinary least squares regression estimates because the residual
associated with each outlier is squared in calculating the regression,
we determined, in our expert opinion, to employ the Huber methodology to
diminish the destabilizing effects of these outliers. Thus, while it can
be argued that we could have produced a different estimate, the
commenters have not established that application of the Huber
methodology produces an unreasonable estimate.

143\. Bell Atlantic and GTE assert that the probability distribution of
the error term must be symmetric about its mean and have fatter tails
than in the normal distribution in order to use the Huber
methodology.[^311] We disagree. The Huber methodology in effect fits a
line or a plane to a set of data. The algebraic expression of this line
or plane explains or predicts the effects on a dependent variable, e.g.,
24-gauge aerial copper cable cost, of changes in independent variables,
e.g., aerial copper cable size. It does this by assigning zero or less
than full weight to observations that have extremely high or extremely
low values. The assignment of weights to observations depends on the
values of the observations. It does not depend on the probability of
observing these values. The error term to which Bell Atlantic and GTE
refer is the difference between the predicted or estimated values of the
dependent variable and the observed values of the dependent variable.
Given that the error term is the difference between the predicted and
observed values of the dependent variable, and that the assignment of
weights by the Huber methodology does not depend on the probability of
observing particular values of this variable, this assignment of weights
does not depend on the probability of observing particular values of the
error term. It, therefore, does not depend on whether the probability
distribution of the error term is symmetric about its mean and has
fatter tails than in the normal distribution.

144\. Bell Atlantic also argues that the Huber methodology should not be
used unless there is evidence that outliers in the RUS data are
erroneous.[^312] We disagree. We believe that use of the Huber
methodology with RUS data ensures that cost estimates reflect typical
costs regardless of whether there is evidence that outliers in the RUS
data are erroneous. The RUS data, as Bell Atlantic and other parties
point out, have a number of high values and low values.[^313] These
outliers may reflect unusual circumstances that are unlikely to occur in
the future. The Huber methodology dampens the effects of anomalistically
high or low values that may reflect unusual circumstances.
Notwithstanding the dispersion in the RUS data, we believe that there
are relatively few errors in these data. As we explained, the RUS data
are derived from contracts. Gabel and Kennedy determined that the values
reflected in the RUS data are within one percent of the values set forth
on the contracts.[^314] There are likely to be few errors in the
contracts themselves because these are binding agreements that involve
substantial sums of money between RUS companies and contractors. These
parties have an obvious interest in ensuring that these values are
correctly reflected in these contracts. While we believe that errors in
these contracts are likely to be infrequent, outlier observations in the
RUS data may reflect large errors. The Huber methodology dampens the
effects of outlier observations that may reflect large errors.

145\. We find that the estimates produced by applying the Huber
methodology are reasonable. As we explain more fully in Appendix B, the
estimates resulting from application of the Huber methodology reflect
most of the information represented in nearly all of the cable and
structure cost observations in the RUS data. Approximately 80 percent of
the cable and structure observations are assigned a weight of at least
80 percent in each structure and regression equation that we adopt. This
large majority comprises closely clustered observations that clearly
represent typical costs. Conversely, approximately 20 percent of the
cable and structure observations are assigned a weight of less than .8
in each of these regression equations. This small minority comprises
observations that have extremely high and extremely low values that do
not represent typical costs. We also note that because the Huber
methodology treats symmetrically observations that have high or low
values, it excludes or assigns less than full weight to data outliers
without regard to whether these are high or low cost observations.

146\. [Buying Power Adjustment]{.underline}. In the *Inputs Further
Notice*, we tentatively concluded that we should make three adjustments
to the regression equations in the NRRI Study, as modified by the Huber
methodology described above, to estimate the cost of 24-gauge aerial
copper cable, 24-gauge underground copper cable, and 24-gauge buried
copper cable.[^315] We further tentatively concluded that these
adjustments should be made in the estimation of the cost of aerial fiber
cable, buried fiber cable, and underground fiber cable.[^316] The first
of these adjustments was to adjust the equation to reflect the superior
buying power that non-rural LECs may have in comparison to the LECs
represented in the RUS data. We noted that Gabel and Kennedy determined
that Bell Atlantic\'s material costs for aerial copper cable are
approximately 15.2 percent less than these costs for the RUS companies
based on data entered into the record in a proceeding before the Maine
Public Utilities Commission (the \"Maine Commission).[^317] Similarly,
Gabel and Kennedy determined that Bell Atlantic\'s material costs for
aerial fiber cable are approximately 33.8 percent less than these costs
for the RUS companies.[^318] We also noted that Gabel and Kennedy
determined that Bell Atlantic\'s material costs for underground copper
cable are approximately 16.3 percent less than these costs for the RUS
companies and 27.8 percent less for underground fiber cable. We
tentatively concluded that these figures represent reasonable estimates
of the difference in the material costs that non‑rural LECs pay in
comparison to those that the RUS companies pay for cable.[^319]
Accordingly, to reflect this degree of buying power in the copper cable
cost estimates that we derived for non‑rural LECs, we proposed to reduce
the regression coefficient for the number of copper pairs by 15.2
percent for aerial copper cable, and 16.3 percent for 24-gauge
underground copper cable.

147\. We also proposed to reduce the regression coefficient for the
number of fiber strands by 33.8 percent for aerial fiber cable and 27.8
percent for underground fiber cable.[^320] As we explained, this
coefficient measures the incremental or additional cost associated with
one additional copper pair or fiber strand, as applicable, and
therefore, largely reflects the material cost of the cable. Because the
NRRI Study did not include a recommendation for such an adjustment for
buried copper cable or buried fiber, we tentatively concluded we should
reduce the coefficient by 15.2 percent for buried copper cable and 27.8
percent for buried fiber cable.[^321] We explained that the level of
these adjustments reflect the lower of the reductions used for aerial
and underground copper cable and aerial and underground fiber cable,
respectively.

148\. We adopt the tentative conclusion in the *Inputs Further Notice*
and select buying power adjustments of 15.2 percent, 16.3 percent and
15.2 percent for 24-gauge aerial copper cable, 24-gauge underground
copper cable, and 24-gauge buried copper cable, respectively.
Correspondingly, we adopt buying power adjustments of 33.8 percent, 27.8
percent, and 27.8 percent for aerial fiber cable, underground fiber
cable, and buried fiber cable, respectively. We find that, based on the
record before us, the buying power adjustment is appropriate and the
levels of the adjustments we proposed for the categories of copper and
fiber cable we identified are reasonable.

149\. As we explained in the *Inputs Further Notice*, the buying power
adjustment is intended to reflect the difference in the materials prices
that non‑rural LECs pay in comparison to those that the RUS companies
pay.[^322] Because non-rural LECs pay less for cable, a downward
adjustment to the estimates developed from data reflecting the costs of
rural-LECs is necessary to derive estimates representative of cable
costs for non-rural LECs. The commenters generally concede that such
differences exist.[^323] There is, however, disagreement among the
commenters that an adjustment is necessary in this instance to reflect
this difference.

150\. Those commenters advocating the use of company-specific data
oppose the buying power adjustment as unnecessary. GTE and Sprint
contend that the use of a more representative data set, i.e.,
company-specific data, would account for any differences in buying
power.[^324] As we explained above, however, the RUS data are the most
reliable data on the record before us for estimating cable and structure
costs. Because there is a difference in the material costs that
non‑rural LECs pay in comparison to those that the RUS companies pay, a
downward adjustment to the RUS cable estimates is necessary to obtain
representative cable cost estimates for non-rural LECs.

151\. We note that AT&T and MCI support the proposed adjustment for
aerial and underground copper and fiber cable.[^325] AT&T and MCI
oppose, however, the use of the lower of the reductions adopted for
aerial and underground cable categories, for the buried cable category.
Although AT&T and MCI agree that an adjustment is appropriate for buried
cable, they contend that the buying power adjustment should be set at
the higher figures of 16.3 percent for buried copper cable and 33.8
percent for buried fiber cable, or at the very least, at the average of
the higher and lower values for aerial and underground cable. We
disagree. We find that AT&T and MCI offer no support to demonstrate why
the higher values should be used. As explained below, the levels of the
adjustments we proposed and adopt are the most conservative based on the
available record evidence.

152\. Apart from opposing the buying power adjustment on the ground that
as a general matter the adjustment is unnecessary, those opposing the
adjustment take issue with the adjustment on methodological grounds. GTE
contends that the adjustment cannot properly convert RUS data into costs
for non-rural carriers because the RUS data do not reflect the cost
structure of rural carriers.[^326] As we explained above, the assertion
that the RUS data does not reflect the cost structure of rural carriers
is without merit. GTE also contends that the application of the
adjustment factors to the coefficients in the regression equations is
contrary to the fundamentals of sound economic analysis.[^327] The
solution GTE recommends is that additional observations for non-rural
companies be added to the data set. This solution echoes GTE\'s
assertion that company-specific data should be used. Reliable
observations for non-rural LECs are not available, however, as explained
above.

153\. GTE also identifies what it considers flaws in the development of
the buying power adjustment.[^328] GTE argues that because the
adjustment to the RUS data was developed using only one larger
company\'s data (Bell Atlantic\'s) reflecting costs for a single year,
the adjustment is not proper.[^329] We disagree for several reasons.
First, we note that although we specifically requested comment on this
adjustment and its derivation in the *Inputs Further Notice,*[^330] GTE
and other parties challenging the use of Bell Atlantic\'s data have not
provided any alternative data for measuring the level of market power,
despite their general agreement that such market power exists.[^331]
These parties failed to submit comparable verifiable data to show that
the buying power adjustment we proposed was inaccurate. Under these
circumstances, we cannot give credence to the unsupported claims that
the Bell Atlantic data is not representative.

154\. Equally important, we have reason to conclude that the adjustment
we adopt is a conservative one. The buying power adjustment we proposed
and adopt is based upon a submission by Bell Atlantic to the Maine
Commission in a proceeding to establish permanent unbundled network
element (UNE) rates.[^332] In that context, it was in Bell Atlantic\'s
interests to submit the highest possible cost data in order to ensure
that the UNE rates would give it ample compensation. But in the context
of the adjustment we consider here for buying power, a relatively higher
cost translates into a [reduced]{.underline} adjustment because the
greater the LEC costs, the less the differential between LEC and rural
carrier costs. Therefore, given the source of this data, we conclude
that it is likely to produce a conservative buying power adjustment, not
an excessive one. Nevertheless, in the proceeding on the future of the
model, we intend to seek further comment on the development of an
appropriate buying power adjustment to reflect the forward-looking costs
of the competitive efficient firm. In sum, we find that GTE\'s
criticisms are not persuasive, and that the adjustment is a reasonable
one, supported by the record.

155\. GTE also asserts a litany of other concerns that, according to
GTE, render the buying power adjustment invalid.[^333] We find these
concerns unpersuasive. GTE claims that the adjustment is suspect because
some RUS observations used in the determination of material costs are
not used in the regression.[^334] We disagree. As discussed above, we
apply the Huber methodology to RUS cable costs that reflect both labor
and material costs.[^335] The observations in the RUS database to which
the Huber methodology assigns zero or less than full weight are those
with the highest and the lowest values. As described more fully below, a
statistical analysis demonstrates that this assignment of weights to
these observations has little impact on the level of material costs
reflected in the cable cost estimates derived by using this methodology.
Therefore, material cost averages based on all of the RUS data are not
likely to vary significantly from material cost averages based on a
subset of these data.

156\. Specifically, with one exception, the value of the regression
coefficient for the variable representing the size of the cable in the
cable cost regression equations derived by using the Huber methodology
lies inside the 95 percent confidence interval surrounding the value of
this coefficient in these regression equations in the NRRI Study
obtained by using ordinary least squares.[^336] The coefficient for the
variable that represents cable size represents the additional cost for
an additional pair of cable and therefore represents cable material
costs. The values of the coefficient for the cable size variable
obtained by using Huber and ordinary least squares are based on a sample
of RUS companies\' cable costs drawn from a larger population of such
costs. The values of the coefficient obtained from this sample by using
the Huber methodology and ordinary least squares are estimates of the
true values of this coefficient theoretically obtained from the
population of cable costs by using these techniques. Generally speaking,
a 95 percent confidence interval associated with a coefficient estimate
contains, with a probability of 95 percent, the true value of the
coefficient.[^337] The fact that the value of the cable size coefficient
obtained by using the Huber methodology lies within an interval that
contains with 95 percent certainty the true value of the ordinary least
squares cable size coefficient supports the conclusion that the Huber
methodology does not by its weighting methodology have a statistically
significant impact on the level of the material costs reflected in the
cable cost estimates derived by using this methodology.[^338]

157\. GTE also claims that some RUS observations appear to be from
rescinded contracts or contracts excluded from the NRRI Study per-foot
cable cost calculation.[^339] However, GTE offers no evidence that this
is the case. Finally, GTE claims that some RUS observations are for
technologies that may not be appropriate for a forward-looking cost
model.[^340] On the contrary, loading coils were excluded from the RUS
data base. Thus, we find that the RUS data do not reflect any
non-forward-looking technologies.

158\. GTE and Sprint each attempt to impugn the validity of the buying
power adjustment, claiming that there may be an incongruity between the
data submitted to the Maine Commission by Bell Atlantic and the RUS
data.[^341] We find this claim unpersuasive. Both GTE and Sprint assert
that it is unknown whether the underlying data include such items as
sales tax or shipping costs and, if so, whether the level of these items
is comparable between Maine and the states included in the RUS data.
Significantly, neither claim that such an incongruity exists in fact,
nor do they provide viable alternatives for the calculation of the
adjustment. We note that the RUS data reflect the same categories of
costs as those reflected in the Bell Atlantic data. More importantly,
this data reflects the best available evidence on the record on which to
base the buying power adjustment.

159\. BellSouth claims that the buying power adjustment is flawed
because it does not take into account the exclusion of RUS data
resulting from the Huber adjustment.[^342] Bell Atlantic makes a similar
claim.[^343] Both parties argue that because the Huber methodology
excludes high cost data from the regression analysis, it is
inappropriate to apply a discount which essentially has the same effect.
In sum, these commenters claim that we are adjusting for high material
costs twice. We disagree. This contention ignores the fact that the
application of the Huber methodology and the buying power adjustment are
fundamentally different adjustments. The Huber adjustment gives reduced
weight to observations that are out of line with other data provided by
the RUS companies. The Huber adjustment provides coefficient estimates
that can be used to estimate the cost incurred by a typical RUS company.
The adjustment is designed to dampen the effect of outlying observations
that otherwise would exhibit a strong influence on the analysis. The
large buying power adjustment, on the other hand, adjusts for the
greater buying power of the non‑rural companies. None of the RUS
companies have the buying power of, for example, Bell Atlantic or GTE,
and therefore have to pay more for material. The buying power adjustment
could only duplicate the Huber adjustment if some of the RUS companies
have the buying power of a company as large as Bell Atlantic. Because
none of the firms in the RUS data base are close to the size of Bell
Atlantic, the commenters are incorrect when they assert that, since the
Huber methodology excludes high cost data from the regression analysis,
it is inappropriate to apply the buying power adjustment.

160\. We also reject BellSouth\'s argument that, to determine the size
of the buying power adjustment, we should use a weighted average of the
cable price differentials between Bell Atlantic and the RUS companies
that is based on the miles of cable installed, not the number of
observations, for each cable size.[^344] In the NRRI Study, this
weighted average price differential is determined by: (1) calculating
the price differential between Bell Atlantic\'s average cable price and
the RUS companies\' average cable price for each cable size; (2)
weighting the price differential for each cable size by the number of
observations used to calculate the RUS companies\' average cable price;
and (3) summing these weighted price differentials.[^345] The average
measures the central tendency of the data. In general, the average more
reliably measures this central tendency the larger the number of
observations from which this average is calculated. In the NRRI Study,
the average cable prices calculated for the RUS companies that reflect a
relatively large number of observations are more reliable than those
that reflect relatively few observations. Accordingly, weighting the
price differentials for each cable size by the number of observations
reflected in the average cable price calculated for the RUS companies
provides a weighted average that reliably measures the central tendency
of the price. In contrast, use of the miles of cable installed as
weights to determine the average cable price differentials could result
in a less reliable measure of central tendency because price
differentials based on a small number of observations but reflecting a
high percentage of cable miles purchased would have a greater impact on
the weighted average than price differentials based on a large number of
observations of cable purchase prices. Moreover, use of the number of
miles of cable installed as the weights would result in a weighted
average price differential that reflects RUS companies\' relative use of
different size cables. The RUS companies\' relative use of different
size cables is irrelevant for use in a model used to calculate non-rural
LECs\' cost of constructing a network.

161\. We also reject Bell Atlantic\'s contention that the buying power
adjustment is flawed because it should have been applied to the material
costs rather than the regression coefficient of copper cable pairs or
the number of fiber strands.[^346] Bell Atlantic has provided no
evidence that demonstrates that applying the discount to the coefficient
is incorrect. It is an elementary proposition of statistics that the
result of applying the discount to the regression coefficient is equal
to applying the discount to the material costs.[^347] Significantly,
Bell Atlantic has not demonstrated that applying the discount to the
regression coefficient does not produce the same result as applying the
discount to the material costs.

162\. Finally, we disagree with Sprint that, because buying power
equates to company size, it is inappropriate to apply this adjustment
uniformly to all carriers.[^348] We are estimating the costs that an
efficient provider would incur to provide the supported services.[^349]
We are not attempting to identify any particular company\'s cost of
providing the supported services. We find, therefore, that applying the
buying power adjustment as we propose is appropriate for the purpose of
calculating universal service support.

163\. In sum, we find unpersuasive the criticisms of the buying power
adjustment we proposed. We conclude that, based on the record before us,
a downward adjustment to the estimates developed from data reflecting
the cable costs of rural LECs is necessary to derive estimates
representative of cable costs for non-rural LECs and that the levels we
have proposed for this adjustment are reasonable.

164\. [LEC Engineering]{.underline}. The second adjustment we proposed
to the regression equations used to estimate cable costs was to account
for LEC engineering costs, which were not included in the RUS
data.[^350] As we noted, the BCM2 default values include a loading of
five percent for engineering.[^351] In contrast, the HAI sponsors
claimed that engineering constitutes approximately 15 percent of the
cost of installing outside plant cables.[^352] This percentage includes
both contractor engineering and LEC engineering. The cost of contractor
engineering already is reflected in the RUS cable cost data. In the
*Inputs Further Notice*, we tentatively concluded that we should add a
loading of 10 percent to the material and labor costs of cable (net of
LEC engineering and splicing costs) to approximate the cost of LEC
engineering.[^353]

165\. We affirm our tentative conclusion to add a loading of 10 percent
to the material and labor for the cost of cable (net of LEC engineering
and splicing costs) to approximate the cost of LEC engineering. We find
that, based on the record before us, the proposed LEC engineering
adjustment, as modified below, is appropriate. We also find that the
level of the adjustment we proposed is reasonable. We note that there is
a general consensus among the commenters that the proposed adjustment is
necessary.[^354] We reject, however, the contentions of those commenters
that advocate that the level of the LEC adjustment be based on
company-specific data. As we explained above, we find such data to be
unreliable. For similar reasons, we reject the LEC engineering
adjustment proposed by AT&T and MCI. As we explained, AT&T and MCI\'s
proposal is based on expert opinions which we find to be unsupported
and, therefore, unreliable.[^355] Accordingly, the level of the
adjustment that we proposed, which, as we explained in the *Inputs
Further Notice* represents the mid-point between the HAI default loading
and the BCPM default loading, is the most reasonable value on the record
before us.

166\. Sprint contends that we should calculate the loadings for LEC
engineering on a flat dollar basis rather than on a fixed percentage of
the labor and material costs of cable.[^356] We find persuasive
Sprint\'s contention that LEC engineering costs do not vary with the
size of the cable and therefore do not vary with the cost of the cable.
Accordingly, we find it reasonable to apply the loading for LEC
engineering in the manner that Sprint recommends.

167\. We also find that the commenters are correct that the loading for
LEC engineering should not reflect any adjustment for buying power
because the buying power differential between non-rural and rural LECs
only relates to materials.[^357] We adjust our calculation accordingly.
Similarly, we also find it appropriate to include in the loading for LEC
engineering an allowance for LEC engineering associated with
splicing.[^358] We find that this is appropriate because the loading for
LEC engineering is based on BCPM and HAI default values for this loading
that are expressed as a percentage of cable costs inclusive of
engineering.[^359]

168\. [Splicing Adjustment]{.underline}. The third adjustment to the
regression equations that we proposed in the *Inputs Further Notice* was
to account for splicing costs, which also were not included in the RUS
data.[^360] As we explained, Gabel and Kennedy determined that the ratio
of splicing costs to copper cable costs (excluding splicing and LEC
engineering costs) is 9.4 percent for RUS companies in the NRRI
Study.[^361] Similarly, Gabel and Kennedy determined that the ratio of
splicing costs to fiber cable costs (excluding splicing and LEC
engineering costs) is 4.7 percent.[^362] Thus, we tentatively concluded
that we should adopt a loading of 9.4 percent for splicing costs for
24-gauge aerial copper cable, 24-gauge underground copper cable, and
24-gauge buried copper cable.[^363] Correspondingly, we tentatively
concluded that we should adopt a loading of 4.7 percent for splicing
costs for aerial fiber cable, underground fiber cable, and buried fiber
cable.[^364]

169\. We affirm these tentative conclusions. We find that, based on the
record before us, the splicing cost adjustment is appropriate and the
levels of the adjustments proposed are reasonable. In reaching this
conclusion, we reject the claims of those commenters that advocate the
use of company-specific data to develop the splicing loadings.[^365] For
the reasons enumerated above, we find such data unreliable.

170\. We disagree with GTE\'s claim that, because the splicing factor is
based on the RUS data, it is flawed.[^366] This contention echoes GTE\'s
assertion that we should use company-specific data. As we explained
above, however, we conclude that such data are not reliable. We also
disagree with GTE\'s contention that an analysis of the source contract
data shows that some splicing costs are invalid.[^367] GTE is mistaken.
The RUS cost data from which the regression equations in the NRRI Study
and in this Order are derived exclude splicing costs. Cable cost
estimates obtained by using this methodology and these data are net of
LEC engineering and splicing costs. We add to these cable cost estimates
a loading factor for splicing that Gabel and Kennedy developed
separately using the RUS data in the NRRI Study without using the
regression analysis. In the NRRI Study, Gabel and Kennedy determined the
ratio of splicing to cable costs by comparing the cost for splicing and
the cost for cable (exclusive of splicing and LEC engineering costs)
reflected in the contracts included in the RUS data base. Some of the
splicing costs reflected in this database are relatively high and some
are relatively low. None of these high or low values is likely to
influence significantly this ratio because it reflects a large number of
observations. Accordingly, we find it reasonable to apply the splicing
ratios developed in the NRRI Study to the cable cost estimates developed
separately in this Order by using the Huber methodology with the RUS
data.

171\. We also disagree with AT&T and MCI\'s contention that, rather than
adopting the proposed splicing loadings or the incumbent LEC\'s loading
factors, we should adopt \"reasonable values for the costs of cable
placing, splicing, and engineering based on the expert opinions
submitted in this proceeding.\"[^368] As discussed above, we find that
these expert opinions are unsupported, and therefore unreliable.

172\. For the same reason, we also find unpersuasive AT&T and MCI\'s
claim that the loading of 9.4 percent for splicing copper cable is
excessive.[^369] AT&T and MCI estimates that splicing costs vary between
3.4 and 6.9 percent of cable investment in contrast to the proposed rate
of 9.4 percent. We find that these estimates, which rely on assumptions
concerning the per-hour cost of labor, the number of hours required to
set up and close the splice, the number of splices per hour, and the
distance between splices, are unreliable. AT&T and MCI have provided no
evidence other than the unsupported opinions of their experts to
substantiate these data. In contrast, Bell Atlantic supports the use of
the 9.4 percent loading indicating, that this level is consistent with
its own data.[^370]

173\. While Sprint agrees that a splicing loading is required in the
NRRI regression, Sprint recommends that a flat dollar \"per pair per
foot\" cost additive should be employed rather than the adjustment we
proposed.[^371] We disagree. We find that Sprint\'s flat dollar \"per
pair per foot\" cost additive ignores the differences in set-up costs
among different cable sizes. In contrast, the percent loading for
splicing costs we adopt herein implicitly recognizes such differences
because these loadings are applied to cable costs estimates (exclusive
of splicing and LEC engineering costs) derived from regression equations
that have an intercept term that provides a measure of the fixed cost of
cable. Accordingly, we conclude that the percent loading approach is
more reasonable.

174\. Sprint also asserts that underground splicing costs are higher due
to the need to work in manholes.[^372] We agree. The dollar amounts
associated with the fixed percentage loadings adopted in this Order for
underground copper and fiber cable are generally larger than for aerial
and buried copper cable and fiber cable. The dollar amounts that we
adopt for splicing are generally larger for underground cable because
the costs that we develop from RUS data for underground cable net of
splicing and engineering costs are generally larger than the costs that
we develop for aerial and buried cable net of splicing and engineering
costs. As a result, when the fixed percentage is applied to these cable
costs, the dollar amount for splicing is generally larger for
underground cable than for aerial and buried cable.[^373]

175\. We disagree with those commenters who argue that the splicing
costs do not vary with the cost of cable (net of splicing costs).[^374]
We find that cable costs increase as the size of the cable increases.
Splicing costs increase as the size of the cable increases because
larger cables require more splicing than small cables. Therefore,
splicing costs increase as the cost of the cable increases.

176\. Finally, we disagree with SBC\'s claim that the 14 percent
splicing factor for fiber cable is more appropriate than the 4.7 percent
we proposed.[^375] We find that the 14 percent factor SBC proposes is
unsupported. SBC asserts that this factor is based on an average cost
ratio from an analysis using various lengths of underground fiber
placement, including placing labor and comparing it to associated
splicing costs from current cost dockets. However, SBC has not provided
this analysis on the record.

177\. [26-Gauge Copper Cable]{.underline}. In the *Inputs Further
Notice*, we explained that, because the NRRI Study did not provide
estimates for 26-gauge copper cable, we must either use another data
source or find a method to derive these estimates from those for
24-gauge copper cable.[^376] To that end, we tentatively concluded that
we should derive cost estimates for 26-gauge cable by adjusting our
estimates for 24-gauge cable.[^377] We proposed to estimate these ratios
using data on 26-gauge and 24-gauge cable costs submitted by Aliant and
Sprint and the BCPM default values for these costs.[^378] We noted, that
while we would prefer to develop these ratios based on data from more
than these three sources, we tentatively concluded that these were the
best data available on the record for this purpose.

178\. We affirm our tentative conclusion to derive cost estimates for
26-gauge cable by adjusting our estimates for 24-gauge cable. As we
explained in the *Inputs Further Notice*, we agree with the BCPM
sponsors that the cost of copper cable should not be estimated based
solely on the relative weight of the cable.[^379] Instead, we proposed
to use the ordinary least squares regression technique to estimate the
ratio of the cost of 26-gauge to 24-gauge cable for each plant type
(i.e., aerial, underground, buried). We conclude that, based on the
record before us, this approach, adjusted as described more fully below,
is reasonable.

179\. Consistent with their position on estimating the costs of 24-gauge
cable, many commenters advocate that we use company-specific data to
estimate the costs of 26-gauge cable.[^380] As we explained above, we
have determined that such data are not sufficiently reliable to employ
in the model.[^381] Accordingly, we reject the use of company-specific
data to estimate the costs of 26-gauge cable. We note that AT&T and MCI
endorse the derivation of cost estimates for 26-gauge cable from
estimates for 24-gauge cable.[^382] Notwithstanding their support of the
general approach we proposed, AT&T and MCI oppose estimating the ratio
of costs of 26-gauge cable to 24-gauge cable using the cable costs
submitted by Aliant and Sprint and the BCPM default values. Instead,
AT&T and MCI advocate the use of the relative weight of copper to adjust
the cost of the 24-gauge copper.[^383] AT&T and MCI claim that this
approach is the most logical because 26-gauge copper costs are directly
proportional to the weight of the metallic copper in the cable. We
reject AT&T and MCI\'s recommended approach. We find that, because AT&T
and MCI have provided no evidence that the weight differential is
approximately equal to the price differential, there is insufficient
evidence on the record demonstrating the reasonableness of this
approach.

180\. Many of those commenters advocating the use of company-specific
data contend that there are flaws in the methodology adopted herein to
derive cost estimates for 26-gauge cable by adjusting our estimates for
24-gauge cable. Bell Atlantic and GTE contend that our methodology
results in biased estimates due to statistical error.[^384] We agree and
modify our proposed methodology as explained below.

181\. As we explained in Appendix D of the *Inputs Further Notice*, in
order to derive the 26-gauge copper cable costs, we first estimated the
cost for 24-gauge copper cable for each cable size from the RUS data
using the Huber methodology.[^385] More specifically, we obtained an
estimate of the expected or mean value of the cost for 24-gauge copper
cable (for given values of the independent variables in the regression
equation). We then obtained values for the ratio of 24-gauge copper
cable to 26-gauge copper cable for each cable size using *ex parte* data
obtained from Aliant and Sprint and BCPM default values for the costs
and employing ordinary least squares regression analysis. As a result,
we obtained an estimate of the expected value of the ratio of 24-gauge
copper cable to 26-gauge copper cable (for given values of the
independent variables in the regression equation). Finally, we
multiplied the reciprocal of this ratio by the cost of 24-gauge copper
cable obtained by using the Huber methodology with RUS data to obtain
the proposed 26-gauge copper cable cost for each copper cable size. Bell
Atlantic and GTE contend, and we agree, that this is a biased estimate
of the expected value of the cost for 26-gauge copper cable because the
expected value of the ratio of two random variables, e.g., 26-gauge
copper cable cost and 24-gauge copper cable, does not equal the ratio of
the expected value of the first random variable to the expected value of
the second random variable. We note that the magnitude of the bias is
larger as the difference grows between the expected value of the ratio
of 26-gauge copper cable cost to 24-gauge copper cable cost and the
ratio of the expected value of 26-gauge copper cable cost to the
expected value of 24-gauge copper cable cost.

182\. Accordingly, we modify the methodology tentatively adopted in the
*Inputs Further Notice* to derive estimates of 26-gauge copper cable
costs from 24-gauge copper cable costs that are not biased. As explained
in more detail in Appendix B, in addition to estimating the expected
value of the cost for 24-gauge copper cable for each cable size using
the RUS data, we also estimate the expected value of the costs of
24-gauge and 26-gauge copper cable for each cable size using the data
submitted by Aliant and Sprint and the BCPM default values, as well as
data submitted by BellSouth,[^386] hereinafter identified in the
aggregate as \"the non-rural LEC data.\" We divide the estimate of the
expected value for 24-gauge copper cable cost derived from the non-rural
LEC data into the estimate of the expected value for 26-gauge copper
cable cost derived from these data for each cable size. The result is a
ratio of an estimate of the expected value for 26-gauge copper cable
cost to an estimate of the expected value for 24-gauge cable cost for
each cable size. Finally, we multiply this ratio by the estimate of the
expected value of the cost for 24-gauge copper cable derived from the
RUS data to obtain an estimate of the expected value of the cost for
26-gauge copper cable for each cable size. We find that this adjustment
eliminates the bias identified by the commenters. We conclude,
therefore, that these estimates are reasonable and adopt them as inputs
for 26-gauge copper cable costs.

183\. We note that, in adopting these modifications, we find that it is
reasonable to rely on the non-rural LEC data for calculating the ratio
of the cost for 24-gauge copper cable to that for 26-gauge copper cable,
but not for calculating the absolute cost for 24-gauge copper cable and
26-gauge copper cable. As discussed above, we find that the non-rural
LEC data are not a reliable measure of absolute costs. Notwithstanding
this finding, we conclude that it is reasonable to use the non-rural LEC
data to determine the relative value of the cost for 24-gauge copper
cable to that for 26-gauge copper cable. We find that it is reasonable
to conclude that each LEC used the same methodology to develop both
24-gauge and 26-gauge copper cable costs. Accordingly, any bias in the
costs for 24-gauge and 26-gauge copper cable that results from using a
given methodology is likely to be in the same direction and of a similar
magnitude. As a consequence, the estimate of the expected value of the
cost for 26-gauge copper cable for each cable size and the estimate of
the expected value of the cost for 24-gauge copper cable obtained from
non-rural LEC data are likely to be biased by approximately the same
factor. The ratios of the estimates of these expected values are not
likely to be affected significantly because the bias in one estimate
approximately cancels the bias in the other estimate when the ratio is
calculated.

184\. GTE also contends that the proposed methodology systematically
reduces the amount of labor associated with placing cable.[^387] We
conclude that the adjustments made in response to GTE and Bell
Atlantic\'s criticisms discussed above render this criticism irrelevant.
We find that no systematic bias will result because the ratio of the
24-gauge cost of copper cable to the cost of 26-gauge copper cable
represents the installed cost of 26-gauge copper cable including all
labor and materials divided by the installed cost of 24-gauge copper
cable including all labor and materials. Moreover, this ratio is applied
to the installed cost of 24-gauge copper cable which includes all labor
and material costs.

185\. BellSouth claims that neither the data used to develop the
ordinary least squares regression equation we employ in the *Inputs
Further Notice* to estimate the cost of 26-gauge copper cable or the
computations used to derive that equation have been provided.[^388]
BellSouth contends that, as a result, it is not possible to confirm or
contradict the discount value. We disagree. Contrary to BellSouth\'s
assertion, the data are available. As we explained, the regression
equation uses *ex parte* data submitted by Aliant and Sprint. These data
are available subject to the Commission\'s rules regarding the treatment
of confidential material. We also note that the BellSouth data we employ
in the adjusted methodology we adopt herein are publicly available.
Moreover, the BCPM data are publicly available.

**5. Cable Fill Factors**

**a. Background**

186\. As we explained in the *Inputs Further Notice*, in determining
appropriate cable sizes, network engineers include a certain amount of
spare capacity to accommodate administrative functions, such as testing
and repair, and some expected amount of growth.[^389] The percentage of
the total usable capacity of cable that is expected to be used to meet
current demand is referred to as the cable fill factor.[^390] If cable
fill factors are set too high, the cable will have insufficient capacity
to accommodate small increases in demand or service outages. In
contrast, if cable fill factors are set too low, the network could have
considerable excess capacity. While carriers may choose to build excess
capacity for a variety of reasons, it is necessary to determine the
appropriate cable fill factors for use in the federal mechanism. We also
explained that, if the fill factors are too low, the resulting excess
capacity would increase the model\'s cost estimates to levels higher
than an efficient firm\'s costs, potentially resulting in excessive
universal service support payments. Accordingly, as discussed more fully
below, we tentatively selected the HAI defaults for distribution fill
factors, the average of the HAI and BCPM default values for copper
feeder fill factors, and fiber fill factors of 100 percent.[^391]

187\. [Variance Among Density Zones]{.underline}. As a preliminary
matter, we noted that both the HAI and BCPM sponsors provided default
fill factors for copper cable that vary by density zone, and that both
agreed that fill factors should be lower in the lowest density
zones.[^392] We explained that the HAI sponsors claimed that an outside
plant engineer is more interested in providing a sufficient number of
spares than in the ratio of working pairs to spares, so the appropriate
fill factor will vary with cable size.[^393] Because smaller cables are
used in lower density zones, HAI recommended that lower fill factors be
used in the lowest density zones to ensure there will be enough spares
available. Similarly, the BCPM sponsors claimed that less dense areas
require lower fill ratios because the predominant plant type is buried
and it is costly to add additional capacity after installation.[^394] We
tentatively agreed with the HAI and BCPM sponsors that fill factors for
copper cable should be lower in the lowest density zones, and reflected
this relationship in the fill factors that we proposed in the *Inputs
Further Notice*.[^395]

188\. [Distribution Fill Factors]{.underline}. We also noted in the
*Inputs Further Notice* that the fill factors proposed by the HAI
sponsors for distribution cable were somewhat lower than for copper
feeder cable.[^396] In contrast, the BCPM default fill factors for
distribution cable are set at 100 percent for all density zones.[^397]
We explained that this difference is related to the differences between
certain assumptions that were made in the HAI and BCPM models. The HAI
proponents claimed that the level of spare capacity provided by their
default values is sufficient to meet current demand plus some amount of
growth.[^398] This is consistent with the HAI model\'s approach of
designing plant to meet current demand, which on average is 1.2 lines
per household as defined by HAI. BCPM, on the other hand, designs
outside plant with the assumption that every residential location has
two lines, which is more than current demand. This reflects the practice
of incumbent LECs to build enough distribution plant to meet not only
current demand, but also anticipated future demand because it is costly
to add distribution plant at a later point in time.[^399]

189\. We also noted that, in a meeting with Commission staff, Ameritech
raised the issue of whether industry practice is the appropriate
guideline for determining fill factors to use in estimating the
forward-looking economic cost of providing the services supported by the
federal mechanism.[^400] Ameritech claimed that forward-looking fill
factors should reflect enough capacity to provide service for new
customers for a few years until new facilities are built, and should
account for the excess capacity required for maintenance and testing,
defective copper pairs, and churn.[^401]

190\. We tentatively concluded that the fill factors selected for use in
the federal mechanism generally should reflect current demand,[^402] and
not reflect the industry practice of building distribution plant to meet
\"ultimate\" demand. We also tentatively selected the HAI defaults for
distribution fill factors and tentatively concluded that they reflect
the appropriate fill needed to meet current demand.[^403]

191\. [Feeder Fill Factors]{.underline}. In the *Inputs Further Notice*
we explained that, in contrast to distribution plant, feeder plant
typically is designed to meet only current and short term capacity
needs.[^404] We noted that the BCPM copper feeder default fill factors
are slightly higher than HAI\'s, but both the HAI and BCPM default
values appear to reflect current industry practice of sizing feeder
cable to meet current, rather than long term, demand.[^405] We
tentatively selected copper feeder fill factors that are the average of
the HAI and BCPM default values because both the HAI and BCPM default
values assume that copper feeder fill reflects current demand.[^406]

192\. [Fiber Fill Factors]{.underline}. We also explained in the *Inputs
Further Notice* that, because of differences in technology, fiber fill
factors typically are higher than copper feeder fill factors.[^407]
Standard fiber optic multiplexers operate on four fiber strands: primary
optical transmit, primary optical receive, redundant optical transmit,
and redundant optical receive. In determining appropriate fiber cable
sizes, network engineers take into account this 100 percent redundancy
in determining whether excess capacity is needed that would warrant
application of a fill factor.[^408] Both the HAI and BCPM models use the
standard practice of providing 100 percent redundancy for fiber and set
the default fiber fill factors at 100 percent. Accordingly, we
tentatively concluded that the input value for fiber fill in the federal
mechanism should be 100 percent.[^409]

**b. Discussion**

193\. We affirm our tentative conclusion that fill factors for copper
cable should be lower in the lowest density zones. Significantly, those
commenters addressing this issue agree that lower density zones should
utilize lower copper cable fill factor inputs.[^410] We also reject, at
the outset, certain assertions made by GTE and others, challenging the
overall approach we proposed and adopt herein for determining the
appropriate cable fill factors to use in the federal mechanism and
reject GTE\'s assertions that the model is flawed.

194\. We disagree with GTE\'s assertion that the use of generalized fill
factors are not proper inputs for a cost model that seeks to estimate
the forward-looking costs of building a network. GTE claims that the use
of generalized fill factors disregards how actual distribution plant is
designed and that different levels of utilization are observed in
different parts of the local network.[^411] However, we find that GTE\'s
concerns are misplaced. Contrary to GTE\'s implication, generalized fill
factors are an administrative input and are not the sole determinate of
the effective fill factor. As we explained in the *Inputs Further
Notice*, the effective fill factor will vary with the number of customer
locations and the available discrete size of cable.[^412] Thus, the
effective fill factor will reflect how distribution plant is designed
and different levels of utilization that are observed in different parts
of the local network.

195\. Similarly, we disagree with GTE\'s assertion that company-specific
information should be used to determine appropriate fill factor
inputs.[^413] We note that the final effective fill factors are the
result of the input of the administrative fill factors and
company-specific customer location data. We also disagree with the
contention that administrative fill factors must be
company-specific.[^414] The administrative fill factors are determined
per engineering standards and density zone conditions. These factors are
independent of an individual company\'s experience and measured
effective fill factors. The administrative fill factors would be the
same for every efficient competitive firm.

196\. We reject GTE\'s contention that the model should be modified to
accept the number of pairs per location to determine the required amount
of distribution plant rather than using fill factors.[^415] GTE claims
that this is necessary because using fill factor inputs produces
anomalous results. GTE contends that the use of fill factors causes the
number of implicit lines per location to decrease as density increases,
in contrast to what occurs in reality. There are, according to GTE,
always more business customers in higher density zones; therefore, the
number of lines that must be provisioned per location should increase as
density increases.

197\. We find that there is no need to modify the model to accept pairs
per location rather than fill factors, as GTE contends. The number of
implicit lines per location does not decrease in the model as GTE
claims. On the contrary, the number of implicit lines per location
increases as a function of the number of business lines. The model will
build to the level of business demand. With business demand increasing
as a function of density, the model generates a higher number of lines
per location as density increases. In sum, the anomaly that GTE
identifies does not exist. GTE\'s claim reflects a misunderstanding of
the model\'s operation.

198\. Finally, we disagree with GTE\'s assertion that there is an error
in the way the model calculates density zones that prevents correct
application of zone-specific inputs.[^416] As GTE explains, after the
model has assigned customer locations to clusters, it constructs a
\"convex hull\" around all locations in the cluster. The model then
calculates density as the lines in the cluster divided by the area
within the convex hull. GTE claims that the calculated densities will be
higher than those observed in the real world because the denominator
excludes all land not contained in the convex hull. While we agree with
GTE\'s description of how the model determines cluster density, we find
GTE\'s claim that this methodology is erroneous to be misplaced. In sum,
GTE argues that the model employs a restricted definition of area which
causes the model to use excessively high utilization factors.[^417] In
other words, the issue is whether the model should recognize all of the
area around a cluster. We conclude that it should not. If the land
outside the convex hull were included in the denominator, as GTE implies
it should, the denominator would recognize unoccupied areas where no
customers reside. As a result, the model would select density zone fill
factors that are lower than needed to service the customers in that
cluster. There would be a downward bias in the model fill factors. Thus,
there is not an error in the way the model calculates density zones, as
GTE contends. The model generates density values that correspond to the
way the population is dispersed. To do otherwise would introduce a bias
and distort the forward-looking cost estimates generated by the model.

199\. [Distribution Fill Factors]{.underline}. We also affirm our
tentative conclusion that the fill factors selected for use in the
federal mechanism generally should reflect current demand and not
reflect the industry practice of building distribution plant to meet
ultimate demand. As we explained in the *Inputs Further Notice*, the
fact that industry may build distribution plant sufficient to meet
demand for ten or twenty years does not necessarily suggest that these
costs should be supported today by the federal universal service support
mechanism.[^418]

200\. We find unpersuasive GTE\'s assertion that the input values for
distribution fill factors should reflect ultimate demand.[^419] In
concluding that the fill factors should reflect current demand, we
recognized that correctly forecasting ultimate demand is a speculative
exercise, especially because of rapid technological advances in
telecommunications. For example, we note that ultimate demand decreases
substantially when computer modem users switch from dedicated lines
serving analog modems to digital subscriber lines where one pair of
copper wire provides the same function as a voice line and a separate
dedicated line. Given this uncertainty, we find that basing the fill
factors on current demand rather than ultimate demand is more reasonable
because it is less likely to result in excess capacity, which would
increase the model\'s cost estimates to levels higher than an efficient
firm\'s costs and could potentially result in excessive universal
service support payments.

201\. Significantly, we note that, contrary to GTE\'s inference, current
demand as we define it includes an amount of excess capacity to
accommodate short-term growth.[^420] We find that GTE has not provided
any evidence that demonstrates that the level of excess capacity to
accommodate short-term growth is unreasonable. Rather, GTE claims that,
if distribution is not built to reflect ultimate demand there will be
delays in service and increased placement costs due to the need to
reinforce distribution plant in established neighborhoods on a regular
basis.[^421] GTE also contends that telephone companies do not design
distribution plant with the expectation that it will require
reinforcement because that is rarely the least-cost method of placing
plant.[^422] GTE also claims that, in a competitive environment,
facilities-based competitors would build plant to serve ultimate
demand.[^423] We find, however, that these unsupported claims do not
demonstrate that reflecting ultimate demand in the fill factors more
closely represents the behavior of an efficient firm and will not result
in the modeling of excess capacity. Finally, we find that we did not
misinterpret the meaning of building distribution plant to serve
\"ultimate demand,\" as GTE asserts.[^424] Rather, we refused to engage
in the highly speculative activity of defining \"ultimate demand.\"
Moreover, we believe that universal service support will be determined
more accurately considering current demand, and not ultimate demand.
Although firms may have installed excess capacity, it does not follow
that the cost of this choice should be supported by the universal
service support mechanism. As growth occurs, however, we anticipate that
the requirement for new capacity will be reflected in updates to the
model.[^425]

202\. Concomitantly, we adopt the proposed values for distribution fill
factors. As we explained in the *Inputs Further Notice*, the model
designs outside plant to meet current demand in the same manner as the
HAI model.[^426] Accordingly, it is appropriate to choose fill factors
that are set at less than 100 percent. We conclude that, based on the
record before us, the proposed values reflect the appropriate fill
factors needed to meet current demand.

203\. There is divergence among the commenters with regard to the
adoption of the proposed values for the distribution fill factors.
Sprint does not object to the use of the proposed values, stating that
\"they appear to reasonably represent realistic, forward-looking
practices.\"[^427] As noted above, Ameritech contends that the copper
distribution and feeder fill factors are reasonable estimates to use if
company-specific or state-specific fill factors are not used.[^428] In
contrast, SBC disagrees with the HAI proponents\' claim that the level
of spare capacity provided in the proposed values is sufficient to meet
current demand plus some amount of growth.[^429] SBC, however, offers no
controverting evidence demonstrating that the proposed values are
insufficient to meet current demand plus short-term growth. We find that
the lone fact that SBC disagrees is insufficient to controvert our
conclusion that the proposed values reflect the appropriate fill needed
to meet current demand. BellSouth contends that the proposed values will
significantly understate distribution cable requirements.[^430]
BellSouth submits instead projected fill factors for its distribution
copper, feeder copper, and fiber cables determined by BellSouth network
engineers. We find these estimates unsupported. Similarly, Bell Atlantic
contends that the proposed fill factors for feeder and distribution are
too high and recommends we adopt its proposed fill factors.[^431] We
find these recommended fill factors unsupported. We, therefore, select
the proposed values for distribution fill factors.

204\. We also disagree with AT&T and MCI\'s contention that the proposed
values for the distribution fill factors are too low. AT&T and MCI claim
that distribution fill factors of 1.2 lines per household are more than
adequate in a forward-looking cost study.[^432] We disagree. We find
that 1.2 lines per household are inadequate because they simply reflect
the existing provision of telephone service and are less than current
demand as we define it herein.[^433] Moreover, AT&T and MCI\'s claim is
belied by their own assertions. AT&T and MCI contend that the \"proposed
conservative fill factors will ensure sufficient plant capacity to
accommodate potentially unaccounted service needs in the PNR
data.\"[^434] AT&T and MCI also state that \"\[t\]he fill levels used in
HAI provides more than enough spare capacity for service work, churn,
and unforeseen spikes in demand.[^435] In sum, AT&T and MCI attest to
the reasonableness of not only use of the HAI default values for
distribution plant, but also the use of the average of the HAI and BCPM
default values for copper feeder.

205\. We also disagree with AT&T and MCI\'s claim that higher factors
are appropriate because the model\'s sizing algorithm produces effective
fill factors that are lower than optimal values.[^436] As we explained
in the *Inputs Further Notice*, because cable and fiber are available
only in certain sizes, the effective fill factor may be lower than the
administrative fill factor adopted as an input.[^437] We find that AT&T
and MCI\'s claim

ignores this fact.

206\. Finally, we note that AT&T and MCI also claim that the factor
should be higher because universal service support does not include
residential second lines or multiple business lines. The Commission has
never acted on the recommendation in the *First Recommended Decision*
that only primary residential lines should be supported.[^438] Moreover,
we also note that AT&T and MCI\'s claim ignores the sixth criterion,
which requires that:

The Cost Study or model must estimate the cost of providing service for
all businesses and households. . . Such inclusion of multi-line business
services and multiple residential lines will permit the cost study or
model to reflect the economies of scale associated with the provision of
these services.[^439]

In sum, we find AT&T and MCI\'s claim in this regard unpersuasive.

207\. [Feeder Fill Factors]{.underline}. We also affirm our tentative
conclusion to adopt copper feeder fill factors that are the average of
the HAI and BCPM default values. The divergence among the commenters
noted above with regard to the use of the average of the HAI and BCPM
default values for the distribution fill factors is reflected in the
comments regarding the proposed feeder fill factors. Sprint finds that
use of the average of the HAI and BCPM default values for feeder fill
factors is reasonable.[^440] Ameritech\'s conditional support was noted
above. In contrast, BellSouth contends that the average of the HAI and
BCPM default values will significantly understate copper feeder cable
requirements.[^441] As noted above, BellSouth advocates the use of
projected fill factors for copper feeder determined by BellSouth network
engineers. Similarly, Bell Atlantic contends that the feeder fill
factors are too high.[^442] We reject the use of these fill projections
for copper feeder for the reasons enumerated above. We also reject, for
the reasons enumerated above, AT&T and MCI\'s contention that feeder
fill factors based on the average of the HAI and BCPM default values are
too low.

208\. [Fiber Fill Factors]{.underline}. Finally, we affirm our tentative
conclusion that the input value for fiber fill in the federal mechanism
should be 100 percent. The majority of commenters addressing this
specific issue agree with our tentative conclusion.[^443] AT&T and MCI
contend that fiber feeder fill factors of 100 percent are appropriate
because the allocation of four fibers per integrated DLC site equates to
an actual fill of 50 percent, since a redundant transmit and a redundant
receive fiber are included in the four fibers per site.[^444] AT&T and
MCI explain that, because fiber capacity can easily be upgraded, 100
percent fill factors applied to four fibers per site are sufficient to
meet unexpected increases in demand, to accommodate customer churn, and,
to handle maintenance issues. Similarly, SBC asserts that fiber fill
factors of 100 percent can be obtained because they are not currently
subject to daily service order volatility and are more easily
administered.[^445] In contrast, BellSouth advocates that we employ
projected fills estimated by BellSouth engineers.[^446] As noted above,
these estimates are unsupported and we reject them accordingly. In sum,
we find that the record demonstrates that it is appropriate to use 100
percent as the input value for fiber fill in the federal mechanism.

**6. Structure Costs**

**a. Background**

209\. Outside plant structure refers to the set of facilities that
support, house, guide, or otherwise protect distribution and feeder
cable.[^447] We explained that aerial structure consists of telephone
poles and associated hardware such as anchors and guys. Buried structure
consists of trenches.[^448] Underground structure consists of trenches,
conduit, manholes, and pullboxes. Underground cable is placed
underground within conduits for added support and protection. Structure
costs include the initial capital outlay for physical material
associated with outside plant structure, including manholes; conduit,
trenches, poles, anchors and guys, and other facilities; the capitalized
cost for supplies, delivery, provisioning, right of way fees, taxes, and
any other capitalized costs directly attributable to these assets; and
the capitalized cost for the labor, engineering, and materials required
to install these assets. For example, buried and underground structure
costs include capitalized labor, engineering, and material costs for
such activities as plowing or trenching, backfilling, boring cable, and
cutting and restoring asphalt, concrete, or sod, or any combination of
such activities. Generally, the type of structure that is placed will
vary depending on the type of plant installed, i.e., the plant mix.

210\. As noted above, the model uses structure cost tables that identify
the per-foot cost of structure by type (aerial, buried, or underground),
loop segment (distribution or feeder), and terrain conditions (normal,
soft rock, or hard rock), for each of the nine density zones. For aerial
structure, the cost per foot that is entered in the model is calculated
by dividing the total installed cost per telephone pole by the distance
between poles. We tentatively concluded that we should use, with certain
modifications, the estimates in the NRRI Study for the per-foot cost of
aerial, underground, and buried structure.[^449] We noted that, in
general, these estimates are derived from regression equations that
measure the effect on these costs of density, water, soil, and rock
conditions.

211\. In the *Inputs Further Notice*, we rejected the HAI and BCPM
sponsors\' default input values for structure costs because they were
based upon the opinions of their respective experts and lacked
supporting data that allowed us to substantiate these values.[^450] As
noted above, we have received other structure cost data from a number of
LECs, as well as AT&T, including data received in response to the
structure and cable cost survey and data submitted in *ex parte*
filings.

212\. In the *Inputs Further Notice*, we tentatively decided to use the
regression equation for aerial structure in the NRRI Study as a starting
point for aerial structure input values.[^451] We proposed to use this
equation to develop proposed input values for the labor and material
cost for a 40-foot, class-four telephone pole. We developed separate
pole cost estimates for normal bedrock, soft bedrock, and hard
bedrock.[^452] The regression coefficients estimate the combined cost of
material and supplies. The NRRI Study reports that the average material
price for a 40-foot, class-four pole is \$213.94.[^453] We noted that
this estimate is very close to results obtained from the data submitted
in response to the *1997 Data Request*.

213\. We also tentatively concluded that we should add to these
estimates the cost of anchors, guys, and other materials that support
the poles, because the RUS data from which this regression equation was
derived do not include these costs.[^454] As we noted, Gabel and Kennedy
used the RUS data to develop the following cost estimates for anchors,
guys and other pole-related items: \$32.98 in rural areas; \$49.96 in
suburban areas; and \$60.47 in urban areas.[^455] We tentatively
concluded that these are reasonable estimates for the cost of anchors,
guys, and other pole-related items.[^456]

214\. We also explained, in the *Inputs Further Notice*, that in order
to obtain proposed input values that can be used in the model, it is
necessary to convert the estimated pole costs into per-foot costs for
each of the nine density zones.[^457] For purposes of this computation,
we proposed to use, for density zones 1 and 2, the per-pole cost that we
have estimated for rural areas, based on the NRRI Study; for density
zones 3 through 7, the per-pole cost for suburban areas; and for density
zones 8 and 9, the per-pole cost for urban areas. We then divided the
estimated cost of a pole by the estimated distance between poles. We
proposed to use the following values for the distance between poles: 250
feet for density zones 1 and 2; 200 feet for zones 3 and 4; 175 feet for
zones 5 and 6; and 150 feet for zones 7, 8, and 9. For the most part,
these values are consistent with both the HAI and BCPM defaults.

215\. We also tentatively concluded that we should adopt a methodology
to estimate the cost of underground structure that is similar to the one
we proposed for the cost of aerial structure.[^458] We tentatively
concluded that we should use the equation set forth in Appendix D of the
*Inputs Further Notice* as a starting point for this estimate.[^459] We
proposed to use this equation to develop proposed input values for the
labor and material cost for underground cable structure. We developed
separate cost estimates for underground structure in normal bedrock,
soft bedrock, and hard bedrock for density zones 1 and 2.[^460]

216\. We also tentatively concluded that we should use the modified
equation for estimating the cost of 24-gauge buried copper cable and
structure to estimate the cost of buried structure.[^461] We determined
that it is necessary to modify this equation because estimates derived
from it include labor and material costs for both buried cable and
structure.[^462]

217\. Finally, we determined that, because the RUS data are from
companies that operate only in density zones 1 and 2, we were unable to
develop estimates from the regression equation for density zones 3
through 9 for underground and buried structure.[^463] We tentatively
concluded, therefore, that we should derive cost estimates for density
zones 3 through 9 by extrapolating from the estimates for density zone
2. We sought comment on alternative methods for estimating structure
costs for density zones 3 through 9.

**b. Discussion**

218\. We affirm our tentative conclusions to use the regression equation
for aerial structure in the NRRI Study as a starting point for the cost
estimate for aerial structure; to use the regression equation for
underground structure in the *Inputs Further Notice* as a starting point
for the cost estimate for underground structure for density zones 1 and
2; and to use the regression equation for the cost of 24-gauge buried
copper cable and structure, as modified below, to estimate the cost of
buried structure for density zones 1 and 2.[^464] Concomitantly, we
affirm our tentative conclusion to add to the estimates for aerial
structure the costs of anchors, guys, and other materials that support
the poles. As we explained in the *Inputs Further Notice*, the RUS data
from which this regression equation was derived do not include these
costs. We also adopt the following values we proposed in the *Inputs
Further Notice* for the distance between poles: 250 feet for density
zones 1 and 2; 200 feet for zones 3 and 4; 175 feet for zones 5 and 6;
and 150 feet for zones 7, 8, and 9.

219\. As noted above, several commenters advocate that the input values
we adopt for structure costs reflect company-specific data. For the
reasons enumerated above, we reject the use of the company-specific data
we have received to estimate the nationwide average input values for
structure costs to be used in the model.

220\. Notwithstanding this conclusion, we find that it is unnecessary to
extrapolate cost estimates for underground and buried structure for
density zones 3 through 9 as we proposed. At the time of the *Inputs
Further Notice*, we believed the extrapolated data were the best data
available to us at the time for density zones 3 through 9 although we
noted our preference to use data specific to those density zones.[^465]
Upon further examination, we find that cost data, which include values
for density zones 3 through 9, submitted by various state commissions
for use in this proceeding are more reliable than the extrapolated
data.[^466] Specifically, we reviewed structure cost data from North
Carolina, South Carolina, Indiana, Nebraska, New Mexico, Montana,
Minnesota, and Kentucky. These data reflect structure costs designed for
use in the HAI and BCPM models.[^467]

221\. The structure costs submitted by the state commissions have values
for normal rock, soft rock, and hard rock for density zones 3 through 9.
We adopt as the buried and underground structure cost input values for
these density zones weighted average structure costs developed from
these data based on the number of access lines for the companies to
which the state decisions regarding the submitted structure costs apply.
We find that these weighted averages represent reasonable estimates for
buried and underground structure costs in normal, soft, and hard rock
conditions for density zones 3 through 9.

222\. Apart from the criticism of the extrapolation of structure costs
for density zones 3 through 9 from the estimates for density zone
2,[^468] the comments we have received regarding the values we proposed
for structure costs vary as to the type of structure the commenters
address and vary as to the position they take on the reasonableness of
the estimates.[^469] BellSouth states that the values we adopt for
aerial structures are \"fairly representative of BellSouth\'s values\"
but claims that, based on a comparison to its actual data, the values
for underground and buried structure are too low.[^470] Cincinnati Bell
states that the values we adopt for underground structure never vary
from Cincinnati Bell\'s actual costs by more than 15 percent.[^471]
Sprint claims that our proposed cost of poles are understated but the
costs of anchor and guys appear to be reasonable.[^472] SBC claims that
its actual weighted cost of a 40 foot pole is inconsistent with the
loaded cost from the NRRI Study. SBC asserts, however, that the
NRRI-specified cost is more closely aligned with SBC\'s anchor and guy
costs.[^473] We find that, given this divergence of positions, the
support in the record for some of our proposed values, and lack of
back-up data to support the arguments opposing our proposals, on
balance, the structure cost estimates we adopt for aerial, underground,
and buried structure for density zones 1 and 2 are reasonable. Moreover,
we find it is reasonable to use the values we adopt for density zones 3
through 9. As we discussed above, these values reflect cost data for
density zones 3 through 9 and have been submitted to us by state
commissions for use in this proceeding. These values are more reliable
than those derived through the extrapolation of data reflecting density
zones 1 and 2, and for the reasons discussed above, the company-specific
data submitted on the record.

223\. In reaching these conclusions, we note that AT&T and MCI advocate
that we adjust the regressions used to estimate structure costs to
reflect the buying power of large non-rural LECs.[^474] We find that,
because AT&T and MCI did not provide any data to support such a
determination, the record is insufficient to determine that such an
adjustment is necessary. We also reject AT&T and MCI\'s claim that the
costs of underground structure are excessive because they fail to
exclude manhole costs from the costs of underground distribution.[^475]
Contrary to AT&T and MCI\'s assertion, we find that manhole costs are
necessary to allow for splicing when the length of the distribution
cable exceeds minimum distance criteria adopted by the model.

224\. Finally, we note, as described more fully above, that we have made
adjustments to certain of the regression equations in the *Inputs
Further Notice* from which we estimate structure costs in order to
address certain of the criticisms reflected in the comments and improve
the regression equations accordingly.[^476]

225\. [LEC Loading Adjustment]{.underline}. In the *Inputs Further
Notice*, we tentatively concluded that we should add a loading of ten
percent to the material and labor cost (net of LEC engineering) for
aerial, underground, and buried structure because the cost of LEC
engineering was not reflected in the data from which Gabel and Kennedy
derived their estimates.[^477] We find that, based on the record before
us, the LEC engineering adjustment is appropriate and the proposed level
of the adjustment is reasonable. In reaching this conclusion, we reject
at the outset the position of those commenters advocating that the
adjustment be based on company-specific data. As we explained above, we
find such data are not the most reliable data on the record.

226\. As with the LEC adjustment proposed for cable costs discussed
above, there is a general consensus on the record among the commenters
that an adjustment is necessary. We find, therefore, that an adjustment
to reflect the cost of LEC engineering is appropriate. Beyond the
general claim that we should adopt company-specific data, there is
divergence among the commenters regarding the appropriate level of this
adjustment. GTE claims that the adjustment should be greater than 10
percent based on a comparison to its data for buried plant.[^478] SBC
agrees that 10 percent is appropriate for aerial and buried structure
but too low for underground structure.[^479] SBC proposes a loading
factor of 20 percent instead for underground structure. Based on our
review of the information, it is our judgement that the 10 percent
adjustment is the most reasonable value on the record before us to
reflect the cost of LEC engineering.[^480]

**7. Plant Mix**

**a. Background**

227\. In the *Inputs Further Notice*, we explained that plant mix, i.e.,
the relative proportions of different types of plant in any given area,
plays a significant part in determining total outside plant
investment.[^481] This is because the costs of cable and outside plant
structure differ for aerial, buried, and underground cable and
structure. The model provides three separate plant mix tables, for
distribution, copper feeder, and fiber feeder, which can accept
different plant mix percentages for each of the nine density zones.

228\. [Distribution Plant]{.underline}. In the *Inputs Further Notice*,
we tentatively selected input values for distribution plant mix that
more closely reflected the assumptions underlying BCPM\'s default values
than HAI\'s default values.[^482] Specifically, we tentatively proposed
input values, for the lowest to the highest density zones, that range
from zero percent to 90 percent for underground plant; 60 to zero
percent for buried plant; and 40 to ten percent for aerial plant. We
tentatively selected input values that more closely reflected the
assumptions underlying the BCPM default values because the model does
not design outside plant that contains either riser cable or block
cable, so we did not believe it would be appropriate to assume that
there is as high a percentage of aerial plant in densely populated areas
as the HAI default values assume. Moreover, although our proposed plant
mix values assumed somewhat less underground structure in the lower
density zones than the BCPM default values, we disagreed with HAI\'s
assumption that there is very little underground distribution plant and
none in the six lowest density zones.

229\. [Feeder Plant]{.underline}. We tentatively selected input values
for feeder plant mix that generally reflect the assumptions underlying
the BCPM and HAI default plant mix percentages, with certain
modifications.[^483] We tentatively proposed input values, for the
lowest to the highest density zones, that range from five percent to 95
percent for underground plant; 50 to zero percent for buried plant; and
45 to five percent for aerial plant. Based on our preliminary review of
the structure and cable survey data,[^484] the proposed values assume
that there is no buried plant in the highest density zone. In contrast
to the BCPM defaults, the proposed values assume there is some aerial
plant in the three highest density zones. We tentatively found that it
is reasonable to assume that there is some aerial feeder plant in all
density zones, as HAI does, particularly in light of our assumption that
there is no buried feeder in the highest density zone, where aerial
placement would be the only alternative to underground plant. Although
the HAI sponsors had proposed plant mix values that vary between copper
feeder and fiber feeder, they offered no convincing rationale for doing
so. We tentatively concluded that, like the BCPM defaults, our proposed
plant mix ratios should not vary between copper feeder and fiber feeder.

230\. Finally, we sought comment on alternatives to the nationwide plant
mix input values we tentatively adopted. As we explained, the Commission
tentatively concluded, in the *1997 Further Notice*, that plant mix
ratios should vary with terrain as well as density zones.[^485] Because
the model does not provide separate plant mix tables for different
terrain conditions, however, the nationwide plant mix values we proposed
do not vary by terrain. We noted that one method of varying plant mix by
terrain would be to add separate plant mix tables, as there are in BCPM,
to the model. We observed that, while the BCPM model provides separate
plant mix tables, the BCPM default values reflect only slightly more
aerial and less buried plant in hard rock terrain than in normal and
soft rock terrain. We suggested that another method of varying plant mix
would be to use company-specific or state-specific input values for
plant mix, as advocated by the BCPM sponsors and other LECs.

231\. We also noted that, although we had generally chosen not to use
study area specific input values in the federal mechanism, and we
recognized that historical plant mix ratios may not reflect an efficient
carrier\'s plant type choice today, historical plant mix might reflect
terrain conditions that will not change over time. We explained that our
analysis of current ARMIS data reveals a great deal of variability in
plant mix ratios among the states. To that end, we recognized that US
West had proposed an algorithm in certain state proceedings for
adjusting plant mix to reflect its actual sheath miles as reported in
ARMIS.[^486] We sought comment on a modified version of this algorithm
as an alternative to nationwide plant mix values.[^487]

**b. Discussion**

232\. As explained above, although we tentatively chose to adopt
nationwide plant mix values, we presented and sought comment on an
alternative algorithm based on sheath miles reported in ARMIS to develop
plant mix values. Consistent with that alternative, GTE asserts that
company-specific plant mix should be used instead of nationwide input
values.[^488] Similarly, Sprint contends that company-specific or
state-specific plant mix values should be used.[^489] US West asserts
that the model should utilize study-area specific plant mix values that
are available in ARMIS as a starting point for plant mix inputs in the
model.[^490]

233\. We find, however, as discussed more fully below, because companies
do not report aerial and buried route miles in ARMIS, that it is not
possible to develop plant mix factors directly from these data at this
time. Moreover, we note that the record does not reflect
company-specific plant mix values for all companies, nor has any
commenter presented a methodology that recognizes the fact that plant
mix varies across density zones and allocates it accordingly. In sum, we
conclude that neither company-specific nor ARMIS-derived data represent
reasonable alternatives to the use of nationwide inputs. We find,
therefore, that the use of nationwide inputs is the most reasonable
approach in developing plant mix values on the record before us.

234\. US West claims that the plant mix algorithm we proposed places too
much plant in aerial. US West traces this flaw to several alleged errors
in the plant mix algorithm.[^491] US West claims that the algorithm
erroneously double weights the model plant mix. This is not an error as
US West claims. Because the model results used in US West\'s analysis
are based on the low aerial distribution input, we find that the double
weight should result in low levels of aerial construction rather than
high levels of aerial construction. US West also identifies several
formulaic errors.[^492] We find these errors attributable, however, to
US West\'s lack of understanding of how the proposed algorithm
works.[^493] We agree, however, with US West that the high aerial
results do appear to be a function of incorrectly weighting aerial
plant. We find that this problem is a function of treating the aerial
plant mix factor as a residual rather than directly estimating an aerial
factor. Given this flaw, we conclude that we should not adopt the plant
mix algorithm on which we sought comment.

235\. As noted above, we sought comment on alternatives to nationwide
plant mix input values.[^494] US West has proposed two algorithms. As
explained below, we find that each of these has its own biases and,
therefore, that neither is a reasonable alternative to what we have
proposed. In brief, US West\'s first algorithm takes the geometric mean
of the national default and a structure ratio to determine the plant mix
factor. It defines the structure ratio for underground plant as the
ratio of ARMIS trench miles to model route miles; for buried and aerial
plant the structure ratio is defined as the relative sheath miles of the
structure type multiplied by the model route miles less the ARMIS trench
miles. We find that the final result of this algorithm places too much
underground structure because, for all but the lowest density zone, the
underground plant mix factor is significantly higher than the ARMIS
ratio. The second algorithm US West proposes starts with the relative
share of ARMIS sheath miles for all three structure types. It then
establishes two series of fractions that sum to one. In the first
series, the fractions increase as the density zone increases. This
series is applied to underground structure and thus places more
underground structure in the higher density zones. In the second series,
the fractions decrease as the density zones increase. This series is
applied to aerial structure, with the result that the percentage of
aerial cable declines as density increases. For buried structure, the
ARMIS ratio is used for all density zones. We find that this algorithm
is flawed because it does not recognize the difference between sheath
and route miles. As a consequence, the algorithm produces a biased
result. Specifically, it constructs too much underground cable. We find
that, until this problem is resolved, relying directly on ARMIS
information leads to unreasonable results.

236\. [Distribution Plant]{.underline}. We adopt the proposed input
values for distribution plant mix which are set forth in Appendix A. We
conclude that these values for the lowest to the highest density zones,
which range from zero percent to 90 percent for underground plant; 60 to
zero percent for buried plant; and 40 to ten percent for aerial plant,
are the most reasonable estimates of distribution plant mix on the
record before us.

237\. There is divergence among the commenters with regard to the
appropriateness of the input values for the distribution plant mix
proposed in the *Inputs Further Notice*. SBC supports the proposed
distribution plant mix, noting that it \"closely aligns with the
embedded plant and future outside plant design.\"[^495] AT&T and MCI
advocate the use of the HAI default values for plant mix because,
according to AT&T and MCI, they more properly reflect the use of aerial
and underground cable than the proposed distribution plant mix
inputs.[^496] AT&T and MCI claim that the proposed inputs reflect too
much underground and too little aerial cable. As we explained in the
*Inputs Further Notice*, the model does not design outside plant that
contains either riser cable or block cable. Accordingly, use of the HAI
default values, which assume a high percentage of aerial plant in
densely populated areas, would be inconsistent with the model platform.
AT&T and MCI ignore this fact.

238\. In the *Inputs Further Notice,* we stated that we disagreed with
HAI\'s assumption that there is very little underground distribution
plant and none in the six lowest density zones.[^497] In support of the
HAI values for underground distribution plant, AT&T and MCI proffer the
distribution plant mix values for BellSouth, notably the only company to
provide such data, showing that its underground distribution plant mix
value is very low. We find that, because we are not adopting a
company-specific algorithm, it is not necessary to address this issue.
As noted above, we will not adopt an alternative algorithm until the
issue of underground structure distances has been resolved. We adhere to
employing a national value because we find that, though it may not be
exact for every company, it will be reasonable for all companies.

239\. [Feeder Plant]{.underline}. We also adopt the proposed input
values for feeder plant mix which are set forth in Appendix A. We
conclude that these values for the lowest to the highest density zones,
which range from five percent to 95 percent for underground plant; 50 to
zero percent for buried plant; and 45 to five percent for aerial plant,
are the most reasonable estimates of distribution plant mix on the
record before us. GTE\'s and Sprint\'s comments specifically address the
specific issue of feeder plant mix inputs. As noted above, both carriers
advocate the use of company-specific data for plant mix.[^498] We reject
the use of such data for feeder plant mix for the reasons we enumerate
above.

240\. Finally, we affirm our tentative conclusion that the plant mix
ratios should not vary between copper feeder and fiber feeder. In
reaching our tentative conclusion, we noted that, although the HAI
sponsors proposed plant mix values that vary between copper feeder and
fiber feeder, they have offered no convincing rationale for doing so. We
find such support still lacking. GTE claims that a distinction is
necessary because the existing plant mix indicates that the trend for
more out-of-sight construction has already resulted in differing copper
and fiber feeder plant mixes.[^499] In contrast, SBC contends that plant
mix ratios should not vary between copper feeder and fiber feeder
because existing structure is used whenever available for fiber and
copper placement so the mix ratio would not differ.[^500] We find
neither of these claims to be persuasive. Accordingly, we conclude that,
given the absence of controverting evidence, it is reasonable to assume
that plant mix ratios should not vary between copper feeder and fiber
feeder in the model.

**D. Structure Sharing**

**1. Background**

241\. Outside plant structures are generally shared by LECs, cable
operators, electric utilities, and others, including competitive access
providers and interexchange carriers. To the extent that several
utilities place cables in common trenches, or on common poles, it is
appropriate to share the costs of these structures among the various
users and assign a portion of the cost of these structures to the
telephone company.

242\. In the *Inputs Further Notice*, the Commission tentatively adopted
structure sharing values for aerial, buried, and underground
structure.[^501] Several comments relating to these values were filed in
response to the *Inputs Further Notice*. Both the BCPM and HAI models
varied the percentage of costs they assume will be shared depending on
the type of structure (aerial, buried, or underground) and line
density.[^502] Commenters differ significantly, however, on their
assumptions as to the extent of sharing and, therefore, the percentage
of structure costs that should be attributed to the telephone company in
a forward-looking cost model.[^503]

**2. Discussion**

243\. We adopt the following structure sharing percentages that
represent what we find is a reasonable share of structure costs to be
incurred by the telephone company. For aerial structure, we assign 50
percent of structure cost in density zones 1-6 and 35 percent of the
costs in density zones 7-9 to the telephone company.[^504] For
underground and buried structure, we assign 100 percent of the cost in
density zones 1-2, 85 percent of the cost in density zone 3, 65 percent
of the cost in density zones 4-6, and 55 percent of the cost in density
zones 7-9 to the telephone company.[^505] In doing so, we adopt the
sharing percentages we proposed in the *Inputs Further Notice*, except
for buried and underground structure sharing in density zones 1 and 2,
as explained below.

244\. Commenters continue to diverge sharply in their assessment of
structure sharing.[^506] As noted by US West, \"\[s\]ince
forward-looking sharing percentages for replacement of an entire network
are not readily observable, there is room for reasonable analysts to
differ on the precise values for those inputs.\"[^507] While commenters
engage in lengthy discourse on topics such as whether the model should
assume a \"scorched node\" approach in developing structure sharing
values, little substantive evidence that can be verified has been added
to the debate.[^508] AT&T and MCI contend that the structure sharing
percentages proposed in the *Inputs Further Notice* assign too much of
the cost to the incumbent LEC and fail to reflect the greater potential
for sharing in a forward-looking cost model.[^509] In contrast, several
commenters contend that the proposed values assign too little cost to
the incumbent LEC and reflect unrealistic opportunities for
sharing.[^510] In support of this contention, some LEC commenters
propose alternative values that purport to reflect their existing
structure sharing percentages, but fail to substantiate those values.
SBC, however, claims that the structure sharing percentages we propose
reflect its current practice and concurs with the structure sharing
values that we adopt in this Order.[^511]

245\. More than with other input values, our determination of structure
sharing percentages requires a degree of predictive judgement. Even if
we had accurate and verifiable data with respect to the incumbent LECs\'
existing structure sharing percentages, we would still need to decide
whether or not those existing percentages were appropriate starting
points for determining the input values for the forward-looking cost
model.[^512] AT&T and MCI argue that past structure sharing percentages
should be disregarded in predicting future structure sharing
opportunities. Incumbent LEC commenters argue that sharing in the future
will be no more, and may be less, than current practice.

246\. In the *Inputs Further Notice*, we relied in part on the
deliberations of a state commission faced with making similar predictive
judgment relating to structure sharing.[^513] The Washington Utilities
and Transportation Commission, conducted an examination of these issues
and adopted sharing percentages similar to those we proposed.[^514]

247\. In developing the structure sharing percentages adopted in this
Order, we find the sharing percentages proposed by the incumbent LECs to
be, in some instances, overly conservative. While we do not necessarily
agree with AT&T and MCI as to the extent of available structure sharing,
we do agree that a forward-looking mechanism must estimate the structure
sharing opportunities available to a carrier operating in the
most-efficient manner. As discussed in more detail in this Order, the
forward-looking practice of a carrier does not necessarily equate to the
historical practice of the carrier.[^515] Given the divergence of
opinion on this issue, and of AT&T and MCI\'s contention that further
sharing opportunities will exist in the future, we have made a
reasonable predictive judgment, and also anticipate that this issue will
be revisited as part of the Commission\'s process to update the model in
a future proceeding.

248\. In the 1997 *Further Notice*, the Commission tentatively concluded
that 100 percent of the cost of cable buried with a plow should be
assigned to the telephone company.[^516] In the *Inputs Further Notice*,
we sought comment on the possibility that some opportunities for sharing
existed for buried and underground structure in the least dense areas
and proposed assignment of 90 percent of the cost in density zones 1-2
to the telephone company.[^517] Several commenters contend that there
are minimal opportunities for sharing of buried and underground
structure, particularly in lower density areas.[^518] In addition,
several commenters contend that, to the extent sharing is included in
the RUS data, it is inappropriate to count that sharing again in the
calculation of structure cost.[^519] While we agree that structure
sharing should not be double counted, we note that the RUS data includes
little or no sharing of underground or buried structure in density zones
1-2.[^520] This does, however, support the contention of commenters that
there is, at most, minimal sharing of buried and underground structure
in these density zones.[^521] We therefore modify our proposed input
value in this instance and assign 100 percent of the cost of buried and
underground structure to the telephone company in density zones 1-2.

249\. We believe that the structure sharing percentages that we adopt
reflect a reasonable percentage of the structure costs that should be
assigned to the LEC. We note that our conclusion reflects the general
consensus among commenters that structure sharing varies by structure
type and density. While disagreeing on the extent of sharing, the
majority of commenters agree that sharing occurs most frequently with
aerial structure and in higher density zones.[^522] The sharing values
that we adopt reflect these assumptions. SBC also concurs with our
proposed structure sharing values.[^523] In addition, as noted above,
the Washington Utilities and Transportation Commission has adopted
structure sharing values that are similar to those that we adopt.[^524]
We also note that the sharing values that we adopt fall within the range
of default values originally proposed by the HAI and BCPM sponsors.

**E. Serving Area Interfaces**

**1. Background**

250\. A serving area interface (SAI) is a centrally located piece of
network equipment that acts as a physical interface between a feeder
cable connecting a wire center and neighborhood distribution copper
cables.[^525] The model includes appropriate investment for SAIs in all
serving areas, whether served by copper or fiber feeder cable.

251\. As we explained in the *Inputs Further Notice*, both the sponsors
of BCPM and HAI submitted default input values for indoor and outdoor
SAI costs.[^526] In addition, Sprint submitted cost estimates for a 7200
pair indoor SAI.[^527] Because the cost of an SAI depends on the cost of
its components, we tentatively concluded that, in the absence of
contract data between the LECs and suppliers, it was necessary to
evaluate the cost of these components.[^528] We posted preliminary
ranges of SAI input values on the Commission\'s Web site to elicit
comment and empirical data from interested parties on the cost of
SAIs.[^529] The Bureau also conducted a workshop on December 11, 1998,
to discuss the posted preliminary inputs.[^530] Accordingly, our
analysis began with a review of the data and justifications submitted by
the HAI sponsors and Sprint regarding the cost of the components that
comprise a 7200 pair indoor SAI.[^531] Specifically, we reviewed the
cost of the following SAI components for a 7200 pair indoor SAI:
building entrance splicing and distribution splicing; protectors; tie
cables; placement of feeder blocks; placement of cross-connect
jumpers/punch down; and placement of distribution blocks. Of these, we
tentatively concluded that protector and splicing costs are the main
drivers of SAI costs, and cross-connect costs and feeder block and
distribution block installation costs greatly contribute to the
difference in Sprint\'s and the HAI proponents\' indoor SAI costs.[^532]

252\. In the *Inputs Further Notice*, we also proposed to determine the
costs of the other SAI sizes by extrapolating from the cost of the 7200
pair indoor SAI because we did not have similar component-by-component
data for other SAI sizes.[^533] We found that this appeared to be a
reasonable approach because of the linear relationship between splicing
and protection costs, which are the main drivers of cost, and the number
of pairs in the SAI.[^534]

**2. Discussion**

253\. We affirm our approach to derive the cost of an SAI on the basis
of the cost of its components and adopt a total cost of \$21,708 for the
7200 pair indoor SAI. We find that there remains an absence of contract
data between the LECs and suppliers with regard to SAIs on the record
before us.[^535] Accordingly, we affirm, as discussed in more detail
below, our tentative conclusions with respect to the following issues:
(1) the cost per pair for protector material; (2) the appropriate
splicing rate and corresponding labor rate; (3) the methodology employed
in cross-connecting in a SAI; and (4) the appropriate feederblock and
distribution installation rate.

254\. Based on the record before us, we conclude that \$4 per pair is a
reasonable estimate of the cost for protected material. As we explained
in the *Inputs Further Notice*, this estimate is based on an analysis of
*ex parte* submissions, which is the only evidence we have available to
evaluate the cost of SAI components.[^536] We also noted that Sprint has
agreed that \$4 is a reasonable estimate of the cost.[^537] SBC and AT&T
and MCI concur with our tentative conclusion to adopt the \$4 per pair
cost.[^538] In sum, the record fully supports our conclusion that \$4
per pair is a reasonable estimate of the cost for protector material.

255\. We also conclude that the record demonstrates that a splicing rate
of 250 pairs is reasonable, and adopt it accordingly. As we explained in
the *Inputs Further Notice*, the HAI sponsors proposed a splicing rate
of 300 pairs per hour, while Sprint argued for a splicing rate of 100
pairs per hour.[^539] We believed that HAI\'s proposed rate was a
reasonable splicing rate under optimal conditions, and therefore, we
tentatively concluded that Sprint\'s proposed rate was too low.[^540] We
noted that the HAI sponsors submitted a letter from AMP Corporation, a
leading manufacturer of wire connectors, in support of the HAI
rate.[^541] We recognized, however, that splicing under average
conditions does not always offer the same achievable level of
productivity as suggested by the HAI sponsors. For example, splicing is
not typically accomplished under controlled lighting or on a worktable.
Having accounted for such variables, we proposed a splicing rate of 250
pairs per hour.

256\. AT&T and MCI, the proponents of the 300 pairs per hour rate,
support our tentative conclusion.[^542] Sprint takes issue with the
splicing rate we proposed.[^543] Sprint impugns the evidence, appearing
in the form of a letter from AMP Corporation on which we relied in part,
to determine a reasonable splicing rate.[^544] In sum, Sprint contends
the letter represents an \"unsupported claim of someone trying to sell
equipment.\"[^545] While Sprint is correct that the proponent is an
equipment manufacturer, neither Sprint nor any other commenter provided
evidence from any other equipment manufacturer to refute AMP.

257\. Sprint also questions the fact that we did not utilize the data
available from the NRRI Study to determine the splicing rate.[^546]
Sprint maintains that an analysis of that data results in a splicing
rate of 58.8 pairs per hour, substantially less than the 300 pairs per
hour we recognized as a ceiling in our analysis. We based our proposed
splicing rate on an analysis of such rates as they relate specifically
to the installation of a complete and functional SAI. In contrast,
although the data to which Sprint refers is for modular splicing, it is
not clear, nor does Sprint claim, that such data specifically relates to
the installation of SAIs. In sum, the validity of this data as a measure
in the derivation of splicing rates for SAI installation is not
established on the record. Sprint\'s critique ignores this fact.
Accordingly, we reject the use of the data available from the NRRI Study
to determine the splicing rate.

258\. We also conclude that the \$60 per hour labor rate we proposed for
splicing is reasonable and adopt it accordingly. Those commenters
addressing this specific issue agree.[^547] As we explained in the
*Inputs Further Notice*, this rate, which equates with the prevalent
labor rate for mechanical apprentices, is well within the range of
filings on the record.[^548]

259\. We also conclude that the model should assume that a \"jumper\"
method will be used half the time and a \"punch down\" method will be
used the remainder of the time to cross-connect an SAI. A cross-connect
is the physical wire in the SAI that connects the feeder and
distribution cable.

260\. In the *Inputs Further Notice*, we tentatively concluded that
neither the jumper method nor the punch down method is used exclusively
in SAIs.[^549] We reached this tentative conclusion based on the
conflicting assertions of Sprint and the HAI sponsors. We noted that,
Sprint asserted that the \"jumper\" method generally will be employed to
cross-connect in a SAI.[^550] In contrast, the HAI sponsors claimed that
the \"punch down\" method is generally used to cross-connect.[^551] We
also noted that, in buildings with high churn rates, such as commercial
buildings, carriers may be more likely to use the jumper method. On the
other hand, in residential buildings, where changes in service are less
likely, carriers may be more likely to use the less expensive punch down
method. Thus, we tentatively concluded that it appeared that both
methods are commonly used, and that neither is used substantially more
than the other.[^552]

261\. Based on the record before us, we affirm our tentative conclusion
to assume that the \"jumper\" method and the \"punch down\" method will
be used an equal portion of the time.[^553] SBC challenges this
conclusion, pointing out that it uses the \"jumper\" method in
applications involving hard lug or insulation displacement contact and
that it is currently replacing existing \"punch down\" interfaces.[^554]
We conclude that SBC\'s sole claim is not sufficient to demonstrate that
the \"jumper\" method is used substantially more than the \"punch down\"
method. We note also that Sprint contends that the cross-connect
proposed by AT&T and MCI is not an SAI, but a building entrance
terminal.[^555] We disagree. The design meets the SAI definition of
providing an interface between distribution and feeder facilities. In
sum, we find that the record demonstrates that it is reasonable for the
model to assume that a \"jumper\" method will be used half the time and
a \"punch down\" method will be used the remainder of the time to
cross-connect an SAI.

262\. We also adopt a feeder block and distribution installation rate of
200 pairs per hour. As we explained in the *Inputs Further Notice*, we
derived this installation factor based on a comparison of Sprint\'s
proposed installation rate of 60 pairs per hour with HAI\'s proposed 400
pair per hour rate.[^556] We concluded that, because neither feeder
block installation nor distribution block installation is a complicated
procedure, Sprint\'s rate of 60 pairs per hour is too low. We also
recognized that installation conditions are not always ideal. As we
explained, feeder block and distribution block installations are not
typically accomplished under controlled lighting or on a worktable. We
proposed a rate of 200 pairs per hour to recognize these
variables.[^557]

263\. We note that our proposed feeder block and distribution block
rates are unchallenged. Significantly, SBC attests that this
installation rate aligns with time-in-motion studies performed in
cross-connect building applications.[^558] We conclude, therefore, that
our proposed rate is reasonable, and adopt input values based upon it
accordingly.

264\. We also adopt the cost estimates for other size indoor and outdoor
SAIs tentatively adopted in the *Inputs Further Notice*.[^559] We
conclude that, based on the record before us, the derivation of the
costs of the other SAI sizes from the cost of the 7200 pair indoor SAI
is reasonable.

265\. GTE takes issue with the derivation of the costs of the other SAIs
from the cost of the 7200 pair indoor SAI.[^560] First, GTE contends
that there is no need to extrapolate the costs of other SAIs because the
costs of individual SAI sizes and associated labor are readily
available.[^561] We disagree. We concluded that it was necessary to
extrapolate the costs of other SAI sizes from the cost of a 7200 pair
SAI because of the lack of component-by-component data for other SAI
sizes on the record. As noted above, we find the record still lacks such
data. We also disagree with GTE\'s contention that SAI costs are not
subject to a linear relationship across all sizes as we
determined.[^562] We find GTE\'s contention, which relies on GTE\'s SAI
estimates, unpersuasive given the lack of substantiating data supporting
these estimates.[^563] In sum, the record demonstrates that the
derivation of the costs of the other SAIs from the cost of the 7200 pair
indoor SAI is reasonable.

266\. US West contends that the costs of a SAI should be determined by
the actual cable sizes for the cables entering and leaving the SAI
rather than the number of cable pairs entering and leaving the
interface.[^564] We agree. The model has been revised to calculate the
costs of an SAI on the basis of actual cable sizes for the cables
entering and leaving the SAI.

267\. US West raises an additional issue concerning the sizing of SAIs.
US West notes that some clusters created by the clustering module exceed
the default line limit of 1800 lines and gives as an example a specific
cluster containing 7,900 lines.[^565] The largest SAI can accommodate
only 7200 lines, counting both feeder side and distribution side lines.
Therefore, US West contends that, in situations such as this,
insufficient SAI plant is deployed by the model. We agree with this
analysis. There is no way to guarantee that the line limit of 1800 lines
will not be exceeded for some clusters, even though modifications have
been made to the cluster algorithm to mitigate this possibility to the
greatest possible extent. Therefore, in the current version of the
model, we modify the input table for SAI costs so as to allow for
serving areas (clusters) in which the capacity of feeder cable plus
distribution cable meeting at the interface may exceed 7200. We do this
by allowing for line increments of 1800 up to a total line capacity of
28,800. The values in the input table assume that, whenever more than
7200 lines are required in an SAI, two or more standard SAIs are built,
one with full capacity of 7200 and the others with capacities equal to
1800, 3600, 5400 or 7200. The input values for each of the
multiply-placed SAIs are then summed.

268\. A related issue is raised by US West with respect to drop terminal
capacity in the model.[^566] In previous versions of the model, drop
terminals were sized for residential housing units and small business
locations, with a maximum line capacity per drop location equal to 25
lines. For medium size and larger business locations with line demand
greater than 25 lines, no specific provision for additional drop
terminal capacity was provided, except in situations in which a single
business accounted for all of the lines in a single cluster. Again, we
agree with the US West analysis of this issue. Accordingly, we have
modified the input table for drop terminal costs by adding additional
line sizes equal to 50, 100, 200, 400, 600, 900, 1200, 1800, 2400, 3600,
5400, and 7200. At any location requiring a drop terminal with capacity
exceeding 25 lines, the model will assume that the location will be
served by an indoor SAI, and the cost of the corresponding interface is
equal to the corresponding value from the table for SAI costs.

**F. Digital Loop Carriers**

**1. Background**

269\. A digital loop carrier (DLC) is a piece of network equipment that
converts an optical digital signal carried on optical fiber cable to an
analog, electrical signal that is carried on copper cable and is
compatible with customers\' telephones.[^567] Because of the high cost
of DLCs, a single DLC is shared among a number of customers where
possible. The model uses fiber cable and DLCs whenever it calculates
that this configuration is cheaper than using copper cable or when the
distance between the customer and the wire center exceeds the maximum
copper loop length. When using DLCs, the model determines the size and
number of DLCs that should be installed at a location, based on cost
minimization and engineering constraints. In designing outside plant,
the model uses five different sizes of DLCs.[^568] In order to run the
model, a user must input the fixed and per-line cost for each of these
DLC sizes. The total cost of a particular DLC is determined by
multiplying the number of lines connected to the DLC times the per-line
cost of the DLCs, and then adding the fixed cost of the DLC.

270\. In the *Inputs Further Notice*, we tentatively concluded that we
should estimate the costs for DLCs based on an average of the contract
data submitted on the record, adjusted for cost changes over time.[^569]
These contract data included data submitted to the Commission in
response to the *1997 Data Request*,[^570] and in *ex parte* submissions
following the December 11, 1998 workshop we sponsored, to estimate the
costs of DLCs in the model.[^571] We found these data to be the most
reliable proffered at that time.[^572] We rejected use of the BCPM and
HAI default values because these values are based on the opinions of
experts without data to enable us to substantiate those opinions.[^573]
Additionally, we rejected data submitted by the HAI sponsors following
the workshop.[^574] We found the data submitted by the HAI sponsors to
be significantly lower than the contract data on the record, and
concluded that it would be inappropriate to use the data submitted by
the HAI sponsors, especially as no support was provided to justify use
of the data.[^575]

271\. In reaching our tentative conclusion to use the contract data, we
noted that, although we would have preferred to have a larger sampling
of data, the contract data represent the costs incurred by several of
the largest non-rural carriers, as well as two of the smallest non-rural
carriers.[^576] We noted that, throughout this proceeding, the
Commission had repeatedly requested cost data on DLCs, largely to no
avail.[^577] Finally, we stated our belief that the data on which we
relied was the best data available on the record to determine the cost
of DLCs.[^578]

272\. In the *Inputs Further Notice*, we also recognized that the cost
of purchasing and installing a DLC changes over time.[^579] We explained
that such changes occur because of improvements in the methods and
components used to produce DLCs, changes in both capital and labor
costs, and changes in the functionality requirements of DLCs.
Accordingly, we tentatively concluded that it is appropriate to adjust
the contract data, which represents the years 1995-1998, to reflect 1999
prices.[^580] We proposed a 2.6 percent annual reduction in both fixed
DLC cost and per-line DLC cost in order to capture changes in the cost
of purchasing and installing DLCs over time.[^581] We based this rate on
the change in cost calculated for electronic digital switches over a
four year period. We noted our belief that the change in the cost of
these switches over time is a reasonable proxy for changes in DLC cost,
because they are both types of digital telecommunications equipment. We
also noted that the 2.6 percent figure is a conservative estimate, based
on the change in cost of remote switches. Our analysis suggested that
the change in cost of host switches over the past four years is much
higher. Finally, we noted that use of the current consumer price index
results in a similar figure over four years.[^582] The indexed amount is
based on the effective date of the contracts.

273\. Finally, we also sought comment on the extent, if any, to which we
should increase our proposed estimates for DLCs to reflect material
handling and shipping costs.[^583] We did this in response to comments
submitted by ATU. It was unclear whether the DLC data submitted by other
parties included these costs. ATU suggested that these costs could
represent up to 10 percent of the material cost of a DLC.[^584]

**2. Discussion**

274\. We adopt an average of the contract data submitted on the record,
adjusted for cost changes over time, as the cost estimates for DLCs.
This decision is predicated on two conclusions. The first is our
determination that the contract data submitted to the Commission in
response to the *1997 Data Request*, and in *ex parte* submissions
following the December 11, 1998, workshop, remains the most reliable
data on the record. Significantly, no additional information has been
proffered nor has any alternative method been proposed, on which to base
our estimate of DLC costs. The second is that we conclude that it is
reasonable to reduce both the fixed DLC cost and per-line DLC cost
reflected in this data by a factor of 2.6 percent per year in order to
capture changes in the cost of purchasing and installing DLCs over time.

275\. As we explained in the *Inputs Further Notice*, the contract data
submitted to the Commission in response to the *1997 Data Request*, and
in *ex parte* submissions following the December 11, 1998, workshop, is
the most reliable data because, not only is it the only data on the
record, but it reflects the actual costs incurred in purchasing
DLCs.[^585] Moreover, although we would have preferred a larger sample,
the contract data is sufficiently representative of non-rural carriers
because it reflects the costs incurred by several of the largest
non-rural carriers, as well as two of the smallest non-rural carriers.

276\. GTE, Bell Atlantic and Sprint support the use of the contract data
in estimating the cost of DLCs.[^586] Only AT&T and MCI and SBC
challenge the use of these data.[^587] SBC contends that the contract
data is not the most reliable data on DLC costs because labor costs
associated with testing, turn-up, and delivery of derived facilities are
not factored into the input values.[^588] We disagree. The data we
identify as \"contract data\" include these costs. As we explained in
the *Inputs Further Notice* and noted above, we sponsored a workshop on
December 11, 1998, to further develop the record on DLC costs in this
proceeding. During the workshop, we presented a template of the
components of a typical DLC to the attendees. The template provided the
respondents the opportunity to identify their contract costs with regard
to each of the components. In addition, we requested that the
respondents identify, and thereby include, other costs associated with
DLC acquisition, including labor costs associated with testing, turn-up,
and delivery of the DLC. Using this opportunity to submit DLC cost data,
GTE and Aliant included such costs in their submissions. Sprint
submitted similar data in a September 9, 1998 *ex parte* filing. These
costs were identified and added to the analysis of US West\'s and
BellSouth\'s contract data. We derived these costs from *ex parte*
filings made by these carriers in this proceeding.

277\. AT&T and MCI allege that the contract data overstates the actual
costs of DLC equipment and therefore, should not be adopted.[^589] AT&T
and MCI instead advocate use of the HAI default values.[^590] AT&T and
MCI argue that the contract costs are not only unsupported by any
verifiable evidence but, more importantly, are refuted by the contract
information from which they were derived. In support, AT&T and MCI
submit an analysis of the DLC cost submissions of Bell Atlantic,
BellSouth, and Sprint. In each instance, AT&T and MCI assert that these
data demonstrate DLC costs that are far below those proposed by the
incumbent LECs and the Commission and that are fully consistent with the
HAI default values.

278\. We disagree with AT&T and MCI\'s analysis. For example, AT&T and
MCI claim that information provided by Bell Atlantic shows that total
DLC common equipment costs for DLC systems capable of serving 672, 1344,
and 2016 lines are similar to, and uniformly less than, the
corresponding HAI values.[^591] In reaching this conclusion, however,
AT&T and MCI omit the costs for line equipment. As Bell Atlantic points
out, the cost of digital line carrier equipment should include these
costs, and we agree.[^592]

279\. Similarly, AT&T and MCI assert that certain of Sprint\'s costs are
significantly inflated and, once adjusted, are similar to and uniformly
less than the corresponding HAI values.[^593] We find, however, these
adjustments to be unsupported. AT&T and MCI reduce the supply expenses
associated with Sprint\'s DLC costs, more than 66 percent, based on the
experience of AT&T and MCI\'s engineering team members.[^594] AT&T and
MCI offer no evidence, however, other than the opinions of their experts
to substantiate this proposed adjustment.

280\. AT&T and MCI also contend that Sprint applies excessive mark-ups
for sales tax.[^595] AT&T and MCI argue that, because Sprint operates
its own logistics company, there is no reason to apply sales tax to both
supply expense and materials. We find that AT&T and MCI offer no support
to demonstrate that this results in an excessive mark-up for sales tax.
We reach the same conclusion with regard to AT&T and MCI\'s proposed
reduction to Sprint\'s labor costs. AT&T and MCI contend that Sprint\'s
labor costs are inflated and propose reductions in such costs through a
reduction in the number of labor hours associated with DLC
installation.[^596] AT&T and MCI provide no support for such a reduction
and, therefore, we decline to reduce Sprint\'s labor costs.[^597]

281\. Significantly, AT&T and MCI offer no evidence to controvert our
tentative conclusion that the HAI values they employ as a comparative
benchmark, and advocate that we adopt, are not more reliable than the
contract data. We rejected the use of the HAI and the BCPM default
values because they are based on the opinions of experts without
substantiating data.[^598] Similarly, we rejected data submitted by the
HAI sponsors following the December 11, 1998, workshop. We found that
data to be significantly lower than the contract data on the record, and
concluded that it would be inappropriate to use because it also lacked
support.[^599] AT&T and MCI have not provided any additional evidence to
substantiate the HAI data.

282\. We also affirm our tentative conclusion that it is reasonable to
reduce both the fixed DLC costs and per-line DLC costs reflected in the
contract data in order to capture changes in the cost of purchasing and
installing DLCs. As we explained in the *Inputs Further Notice*, this
reduction recognizes the fact that the cost of purchasing and installing
a DLC diminishes over time because of improvements in the methods and
components used to produce DLCs, changes in both capital and labor
costs, and changes in the functionality requirements of DLCs.[^600] The
premise that overall DLC costs move downward over time is not disputed
on the record.

283\. We also conclude that the 2.6 percent reduction we proposed in
both the fixed DLC costs and per-line DLC costs is appropriate. As we
explained in the *Inputs Further Notice*, this is a conservative
estimate, based on the change in cost of remote switches, which is a
reasonable proxy for changes in DLC cost.[^601] More importantly, a
comparison of data submitted on the record by Sprint for the years 1997,
1998, and 1999 demonstrates that an overall reduction of 2.6 percent is
considerably less than Sprint\'s actual experience. An analysis
undertaken by staff produces an average reduction in DLC costs for
Sprint of 9.2 percent per year. We note that this estimate reflects both
material and labor costs.

284\. Only SBC and GTE specifically address the 2.6 percent
reduction.[^602] SBC supports the 2.6 percent reduction in fixed and
per-line DLC costs as it applies to material costs only. In contrast,
GTE opposes the adjustment.[^603] GTE suggests that, as the inputs are
adjusted over time, the cost of current technology will be reflected in
the revised data.[^604] GTE is correct that the current cost of
technology would be reflected in revised data. The adjustment we
proposed and adopt updates cost to current cost. Implicit in SBC\'s
comment is the premise that labor costs will not decrease over time.
Although this may be a reasonable assumption, the 2.6 percent reduction
we adopt is applied to the overall cost of a DLC. As we explained above,
the 2.6 percent reduction is a conservative estimate compared to the
actual reductions we have observed in the Sprint data. As a result, we
conclude that increases in labor will be offset by reductions in other
factors in the cost of DLCs.

285\. Finally, as noted above, we sought comment on the extent, if any,
to which we should increase our proposed estimates for DLCs to reflect
material handling and shipping costs because it was unclear whether the
DLC data submitted by other parties include these costs. On further
analysis, we note that material handling and shipping costs are
reflected in the proposed DLC estimates we adopt herein. Moreover, we
conclude that it is appropriate to include these costs in the cost
estimates for DLCs. We note that no comments were filed opposing the
inclusion of such costs.

**VI. SWITCHING AND INTEROFFICE FACILITIES**

**A. Introduction**

286\. The central office switch provides the connection between a
subscriber\'s local loop and the outside world. Modern digital switches
connect telephones, fax machines, and computers to other subscribers on
the public switched network.[^605] In order to accomplish this, a
telephone network must connect customer premises equipment to a
switching facility, ensure that adequate capacity exists in that
switching facility to process calls, and interconnect the switching
facility with other switching facilities to route calls to their
destination. A wire center is the location of the switching facility and
the wire center boundaries define the area in which all customers are
connected to a given wire center. The infrastructure to interconnect the
wire centers is known as the \"interoffice\" network, and the carriage
of traffic between wire centers is known as \"transport.\"

287\. In the *Universal Service Order*, the Commission stated that
\"\[a\]ny network function or element, such as . . . switching,
transport or signaling, necessary to provide supported services must
have an associated cost.\"[^606] In the *1997 Further Notice*, the
Commission sought comment on issues that affect the input values
relating to the forward-looking economic cost of switching and
interoffice transport.[^607] The *Switching and Transport Public Notice*
established several guidelines relating to switching, the design of the
interoffice network, and interoffice cost attributable to providing
supported services.[^608] In the *Platform Order*, the Commission
concluded that the federal mechanism should incorporate, with certain
modifications, the HAI 5.0a switching and interoffice facilities
module.[^609]

288\. Both HAI and BCPM sponsors have provided default input values for
estimating the forward-looking economic cost of switching and
interoffice network.[^610] On December 1, 1998, the Bureau held a public
workshop designed to elicit comment on the switching inputs values to be
used in the federal mechanism.[^611]

289\. In the *Inputs Further Notice*, we tentatively adopted input
values associated with switching and interoffice facilities, including
values associated with the installation and purchase of new
switches.[^612] In addition, we tentatively adopted the Local Exchange
Routing Guide (LERG) database to identify host-remote switch
relationships.[^613]

**B. Switch Costs**

**1. Background**

290\. In the *Inputs Further Notice*, we tentatively concluded that we
should use publicly available data on the cost of purchasing and
installing switches that was compiled by the Commission, in conjunction
with the work of Gabel and Kennedy,[^614] and the Bureau of Economic
Analysis (BEA) of the U.S. Department of Commerce.[^615] This
information was gathered from depreciation reports filed by LECs at the
Commission. In order to better estimate the costs of small switches, we
tentatively concluded in the *Inputs Further Notice* to augment the
depreciation data with data compiled by the Commission, in conjunction
with Gabel and Kennedy and the U.S. Department of Agriculture Rural
Utility Service (RUS).[^616] This information was gathered from reports
made to RUS by LECs.

291\. In order to make the RUS data comparable with the depreciation
data, we proposed a series of adjustments to the RUS data. The cost
figures reported in the depreciation information reflect the costs of
purchasing and installing new switches. While the RUS cost data also
contain information on purchasing and installing new switches, they do
not include: (1) the cost associated with purchasing and installing the
main distribution frame (MDF); (2) the cost associated with purchasing
and installing power equipment; (3) the cost of connecting each remote
switch to its respective host switch; and (4) LEC engineering
costs.[^617] In order to make the depreciation and RUS information
comparable, we proposed in the *Inputs Further Notice* to add estimates
of these four components to the switch costs reported in the RUS
information.[^618]

292\. In order to account for the cost of MDF omitted from the RUS
information, we tentatively concluded that \$12 per line was a
reasonable cost for purchasing and installing MDF equipment.[^619] In
order to account for the cost of power equipment omitted from the RUS
information, we tentatively concluded that the cost of purchasing and
installing switches with 0-999 lines should be increased by \$12,000,
the cost of purchasing and installing switches with 1,000-4,999 lines
should be increased \$40,000, and the cost of purchasing and installing
switches with 5,000-25,000 lines should be increased by \$74,500.[^620]
We tentatively concluded that \$27,598 should be added to the cost of
each RUS remote switch in order to account for cost of connecting the
remote switch to the host switch, a cost omitted from the RUS
information.[^621] We further proposed in the *Inputs Further Notice*
that, in order to account for the LEC engineering costs omitted in the
RUS information, we should add, after making the above adjustments for
power, MDF, and remote connection costs, eight percent to the total cost
of each RUS switch.

293\. In order to determine the reasonable forward-looking cost of
switches, based on the selected data set, we tentatively concluded in
the *Inputs Further Notice* that we should employ regression
analysis.[^622] We tentatively concluded that the cost of a switch
should be estimated as a linear function of the number of lines
connected to the switch and the type of switch installed (i.e., host or
remote).[^623]

294\. In order to capture changes in the cost of purchasing and
installing switching equipment over time, we tentatively concluded in
the *Inputs Further Notice* that we should modify the data to adjust for
the effects of inflation, and explicitly incorporate variables in the
regression analysis that capture cost changes unique to the purchase and
installation of digital switches.[^624]

295\. In the *Inputs Further Notice*, we tentatively concluded that in
order to capture the costs associated with the purchase and installation
of new switches, and to exclude the costs associated with upgrading
switches, we should exclude switch cost data that contained costs
reported more than three years after installation. We tentatively
concluded that this restriction eliminates switch cost data that contain
a significant amount of upgrade costs and, therefore, do not solely
represent the purchase and installation costs of new switches.[^625]

**2. Discussion**

296\. [Switch Cost Estimates]{.underline}. We adopt the fixed cost (in
1999 dollars) of a remote switch as \$161,800 and the fixed cost (in
1999 dollars) of both host and stand-alone switches as \$486,700. We
adopt the additional cost per line (in 1999 dollars) for remote, host,
and stand-alone switches as \$87.[^626]

297\. For the reasons set forth below, we affirm our tentative
conclusion to use the publicly available data from LEC depreciation
filings, and to supplement the depreciation data with data from LEC
reports to the RUS. We also affirm our tentative conclusion that we
should not rely on the BCPM and HAI default values, because these values
are largely based on non-public information or opinions of their
experts, without data that enable us adequately to substantiate those
opinions.

298\. [Switch Cost Data]{.underline}. The depreciation data contains for
each switch reported: the model designation of the switch; the year the
switch was first installed; and the lines of capacity and book-value
cost of purchasing and installing each switch at the time the
depreciation report was filed with the Commission.[^627] The RUS data
contains, for each switch reported: the switch type (i.e., host or
remote); the number of equipped lines; cost at installation; and year of
installation.[^628]

299\. The sample that we use to estimate switch costs includes 1,085
observations. The sample contains 946 observations selected from the
depreciation data, which provide information on the costs of purchasing
and installing switches gathered from 20 states. All observations in the
depreciation data set are for switches with 1,000 lines or more. In
order to better estimate the cost of small switches, we augmented the
depreciation data set by adding data from RUS. The RUS sample contains
139 observations which provide information from across the nation on the
costs of small switches purchased and installed by rural carriers. Over
80 percent of the observations of switch costs in the RUS data set
measure the costs for switches with 1,000 lines of capacity or less. The
combined sample represents purchases of both host and remote switches,
with information on 490 host switches and 595 remote switches, and
covers switches installed between 1989 and 1996. This set of data
represents the most complete public information available to the
Commission on the costs of purchasing and installing new switches.

300\. The depreciation data set proposed in the *Inputs Further Notice*
excluded 26 observations that had been deemed to be outliers by the
Bureau of Economic Analysis. Bell Atlantic criticizes the Commission for
excluding these outliers.[^629] The excluded observations were not
available in electronic form prior to the release of the *Inputs Further
Notice*. Subsequently, the Bureau obtained these outlying observations
from the Bureau of Economic Analysis and reinserted them into the data
set used to derive the input values we adopt herein. In addition,
several commenters recommend that the depreciation data set also should
include switches with fewer than 1,000 lines of capacity. This
information, however, is not available in electronic format and,
therefore, would be unduly burdensome to include.[^630]

301\. In response to the *1997 Data Request*, the Commission received a
second set of information pertaining to 1,486 switches. Upon analysis,
however, we have identified one or more problems with most of the data
submitted: missing switch costs; zero or negative installation costs;
zero or blank line counts; unidentifiable switches; or missing or
inconsistent Common Language Local Identification (CLLI) codes. After
excluding these corrupted observations, 302 observations remained. The
remaining observations represented switches purchased by only four
companies. We affirm our tentative conclusion that the data set we use
is superior to the data set obtained from the data request, both in
terms of the number of usable observations and the number of companies
represented in the data set.

302\. Following the December 1, 1998, workshop, three companies
voluntarily submitted further data regarding the cost of purchasing and
installing switches: BellSouth provided data on switch investments for
its entire operating region; Sprint provided similar data for its
operations in Nevada, Missouri, and Kansas; and GTE provided switch
investment information for California.[^631] When consolidated, this
information forms a data set of approximately 300 observations
representing the costs of new switches.[^632] As AT&T has noted,
however, the information submitted contains some inconsistencies.[^633]
Considering these inconsistencies, the limited number of companies
represented, and the size of this voluntarily submitted data set, we
conclude that the data set we use is preferable.

303\. BellSouth suggests that we merge either the information received
in response to the *1997 Data Request,* the information from the
voluntary submissions, or both, with the data set we use.[^634] We
reject this suggestion because there are significant inconsistencies
between the different data sets. For example, in its voluntary
submission, GTE provides the amount of total investment for each of its
California switches at the time these switches were installed, but
reports associated line counts only for October 1998. This information
is not consistent with the data set used by the Commission, which
contains aggregate investment and line counts measured at the same point
in time. Second, our analysis of the information provided in both the
voluntary submissions and the data request reveals, based on simple
linear regression, inconsistencies between these two data sets and the
data set employed by the Commission.[^635] Our analysis reveals that
both alternative data sets contain information that is inconsistent with
the comments in this proceeding.[^636]

304\. [Adjustments to the Data]{.underline}. As discussed above, in the
*Inputs Further Notice*, we proposed certain adjustments to the RUS data
to account for the cost of MDF and power equipment, which were omitted
from the RUS information.[^637] Specifically, we proposed increasing the
cost of purchasing and installing switches by \$12 per line for MDF and
\$12,000, \$40,000, or \$74,500, depending upon switch size, for power
costs. Commenters who address this issue agree that the RUS data must be
modified to account for the costs of MDF and power to make the RUS data
consistent with the depreciation data, which include these costs.[^638]
Some commenters who address these adjustments claim that we should use
different values for MDF and power costs, but provide little or no
information we can use to verify their suggested values.[^639] Sprint,
for example, claims our power costs are too low and provides a breakdown
of power costs, but does not supply any data to support their higher
proposed values for power costs.[^640] AT&T and MCI claim our proposed
power costs should be reduced because they are substantially higher than
those proposed by their experts.[^641]

305\. We find that we need not attempt to resolve disagreement over the
reasonableness of our proposed values, in the absence of any additional
information, because we adopt an alternative methodology for estimating
MDF and power costs. We find that we should adjust the RUS data for MDF
and power equipment costs in a way that is more consistent with the way
in which these costs are estimated in the depreciation data set. In the
depreciation data, MDF and power equipment costs are estimated as a
percentage of the total cost of the switch, as are all other components
of the switch. Based on the estimates of Technology Futures, Inc., we
find that these costs were eight percent of total cost.[^642] Because we
are adjusting the RUS data so that they are comparable with the
depreciation data, we find it is appropriate to use a comparable method
to estimate the portion of total costs attributable to MDF and power
equipment. Accordingly, in order to account for the cost of MDF and
power equipment omitted from the RUS information, we conclude that the
cost of switches reported in the RUS data should be increased by eight
percent.

306\. In the *Inputs Further Notice*, we tentatively concluded, based on
an estimate provided by Gabel and Kennedy, that \$27,598 should be added
to the cost of each remote switch reported in the RUS data.[^643] SBC
recommends that remote termination costs should be added to remote
switch costs on a per-line basis, but provides no estimates of the
per-line cost of remote termination.[^644] Sprint provides remote
termination estimates of \$22,636 for termination of remote switches
with less than 641 lines and \$46,332 for termination of remote switches
with between 641 and 6,391 lines.[^645] Using Sprint\'s methodology, the
average cost of terminating a RUS remote switch on a RUS host switch is
\$29,840.[^646] Sprint\'s estimate is consistent in magnitude with Gabel
and Kennedy\'s estimate. Therefore, because Sprint\'s tiered estimates
captures differences between remote termination costs associated with
remote switch size, we adopt Sprint\'s estimates.

307\. Based upon Gabel and Kennedy recommendations, derived from data
analysis undertaken by RUS, we conclude that the cost of switches
reported in the RUS data should be increased by eight percent in order
to account for the cost of LEC engineering.[^647] We conclude, however,
that this adjustment should not be added to the cost of power and MDF,
because these estimates already include the costs of LEC engineering.

308\. [Methodology]{.underline}. Consistent with our tentative
conclusions in the *Inputs Further Notice,* we employ regression
analysis. In this Order, we also adopt our tentative conclusion to use a
linear function based on examination of the data and statistical
evidence.

309\. Sprint recommends using a non-linear function, such as the log-log
function, to take into account the declining marginal cost of a switch
as the number of lines connected to it increases.[^648] We affirm our
tentative conclusion that the linear function we adopt provides a better
fit with the data than the log-log function. A discussion of the effect
of time and type of switch on switch cost is presented below.

310\. Based upon an analysis of the data and the record, we conclude
that the fixed cost (i.e., the base getting started cost of a switch,
excluding costs associated with connecting lines to the switch) of host
switches and remote switches differ, but that the per-line variable cost
(i.e., the costs associated with connecting additional lines to the
switch) of host and remote switches are approximately the same. This is
consistent with statistical evidence[^649] and the comments of Sprint,
BellSouth, and the HAI sponsors.[^650]

311\. [Accounting for Changes in Cost Over Time]{.underline}. We
recognize that the cost of purchasing and installing switching equipment
changes over time. Such changes result, for example, from improvements
in the methods used to produce switching equipment, changes in both
capital and labor costs, and changes in the functional requirements that
switches must meet for basic dial tone service. In order to capture
changes in the cost of purchasing and installing switching equipment
over time, we affirm our tentative conclusion in the *Inputs Further
Notice* to modify the data to adjust for the effects of inflation, and
explicitly incorporate variables in the regression analysis that capture
cost changes unique to the purchase and installation of digital
switches.

312\. To the extent that the general level of prices in the economy
changes over time, the purchasing power of a dollar, in terms of the
volume of goods and services it can purchase, will change. In order to
account for such economy-wide inflationary effects, we multiply the cost
of purchasing and installing each switch in the data set by the
gross-domestic-product chain-type price index[^651] for 1997 and then
divide by the gross-domestic-product chain-type price index for the year
in which the switch was installed, thereby converting all costs to 1997
values.[^652]

313\. In order to account for cost changes unique to switching
equipment, we enter time terms directly into the regression
equation.[^653] US West agrees that the costs of the equipment, such as
switches and multiplexers, used to provide telecommunications services
are declining, and that the per-unit cost of providing more services on
average is declining.[^654] Bell Atlantic and GTE, however, contend that
the cost of switches is not currently declining and therefore pricing
declines should not be expected to continue into the future.[^655] As
evidence, they cite their own fixed-cost contracts. As AT&T notes,
however, \"\[i\]f Bell Atlantic in fact agreed to switching contracts
that 'effectively froze prices on switching equipment,' those prices
would reflect its idiosyncratic business judgement . . .\"[^656] GTE
expresses concern that, under certain specifications of time, the
regression equation produces investments for remote switch \"getting
started\" costs that are negative and that such specifications overstate
the decline in switch costs.[^657] As noted in the *Inputs Further
Notice*, the HAI sponsors also caution that the large percentage price
declines in switch prices seen in recent years may not continue.[^658]
We affirm our tentative conclusion that the reciprocal form of time in
the regression equation satisfies these concerns by yielding projections
of switch purchase and installation costs that are positive yet
declining over time.[^659]

314\. Ameritech and GTE advocate the use of the Turner Price Index to
convert the embedded cost information contained in the depreciation data
to costs measured in current dollars.[^660] We note, however, that this
index and the data underlying it are not on the public record. We prefer
to rely on public data when available. Moreover, we affirm our tentative
conclusion that it is not necessary to rely on this index to convert
switch costs to current dollars. Rather, as described in the preceding
paragraph, we will account for cost changes over time explicitly in the
estimation process, rather than adopting a surrogate such as the Turner
Price Index.

315\. [Treatment of Switch Upgrades]{.underline}. The book-value costs
recorded in the depreciation data include both the cost of purchasing
and installing new equipment and the cost associated with installing and
purchasing subsequent upgrades to the equipment over time. Upgrades
costs will be a larger fraction of reported book-value costs in
instances where the book-value costs of purchasing and installing
switching equipment are reported well after the initial installation
date of the switch. We affirm our tentative conclusion that, in order to
estimate the costs associated with the purchase and installation of new
switches, and to exclude the costs associated with upgrading switches,
we should remove from the data set those switches installed more than
three years prior to the reporting of their associated book-value
costs.[^661] We believe that this restriction will eliminate switches
whose book values contain a significant amount of upgrade costs, and
recognizes that, when ordering new switches, carriers typically order
equipment designed to meet short-run demand.

316\. Bell Atlantic criticizes the Commission for excluding a large
percentage of the observations from the initial depreciation data
set.[^662] As noted in the preceding paragraph, however, the
observations that have been excluded do not accurately represent the
price of a new switch.

317\. We reject the suggestions of Ameritech, Bell Atlantic, BellSouth,
GTE, and Sprint that the costs associated with purchasing and installing
switching equipment upgrades should be included in our cost
estimates.[^663] The model platform we adopted is intended to use the
most cost-effective, forward-looking technology available at a
particular period in time. The installation costs of switches estimated
above reflect the most cost-effective forward-looking technology for
meeting industry performance requirements. Switches, augmented by
upgrades, may provide carriers the ability to provide supported
services, but do so at greater costs. Therefore, such augmented switches
do not constitute cost-effective forward-looking technology. In
addition, as industry performance requirements change over time, so will
the costs of purchasing and installing new switches. The historical cost
data employed in this analysis reflect such changes over time, as do the
time-trended cost estimates.

318\. [Additional Variables]{.underline}. Several parties contend that
additional independent variables should be included in our regression
equation. Some of the recommended variables include minutes of use,
calls, digital line connections, vertical features, and regional, state,
and vendor-specific identifiers.[^664] For the purposes of this
analysis, our model specification is limited to include information that
is in both the RUS and depreciation data sets. Neither data set includes
information on minutes of use, calls, digital line connections, vertical
features, or differences between host and stand-alone switches. State
and regional identifiers are not included in the regression because we
only have depreciation data on switches from 20 states. Thus, we could
not accurately estimate region-wide or state-wide differences in the
cost of switching. Our model specification also does not include
vendor-specific variables, because the model platform does not
distinguish between different vendors\' switches.[^665]

319\. [Switch Cost Estimates]{.underline}. A number of commenters
criticize the switch cost estimates contained in the *Inputs Further
Notice* and suggest that they should be dismissed or substantially
revised. For example, Sprint suggests that we dismiss the results
because the data are collinear and the model is mis-specified.[^666]
Bell Atlantic and BellSouth suggest that the Commission underestimates
the cost of switches, while AT&T and MCI suggest that the Commission
overestimates the cost of switches.[^667] The Commission\'s estimates,
however, are based upon the most complete, publicly-available
information on the costs of purchasing and installing new switches and
therefore represent the Commission\'s best estimates of the cost of host
and remote switches. In the preceding paragraphs and in Appendix C, we
have addressed the specific objections that have been raised by parties
with regard to the methodology, data set, or other aspects of the
approach we adopt to derive switch cost estimates, and for the reasons
given there, we reject those objections. We conclude that the remaining
evidence provided as grounds for dismissing or substantially revising
these estimates is largely anecdotal or unconfirmed and undocumented and
does not lead us to believe that our estimates should be altered. We
conclude, therefore, that the switch cost estimates we adopt are the
best estimates of forward-looking cost.

**C. Use of the Local Exchange Routing Guide (LERG)**

320\. In the *Inputs Further Notice*, we tentatively concluded that the
Local Exchange Routing Guide (LERG) database should be used to determine
host-remote switch relationships in the federal high-cost universal
service support mechanism.[^668] We now affirm that conclusion. In the
*1997 Further Notice*, the Commission requested \"engineering and cost
data to demonstrate the most cost-effective deployment of switches in
general and host-remote switching arrangements in particular.\"[^669] In
the *Switching and Transport Public Notice*, the Bureau concluded that
the model should permit individual switches to be identified as host,
remote, or stand-alone switches.[^670] The Bureau noted that, although
stand-alone switches are a standard component of networks in many areas,
current deployment patterns suggest that host-remote arrangements are
more cost-effective than stand-alone switches in certain cases.[^671] No
party has placed on the record in this proceeding an algorithm that will
determine whether a wire center should house a stand-alone, host, or
remote switch.[^672] We therefore affirm our conclusion to use the LERG
to determine host-remote switch relationships.

321\. In the *Platform Order*, we concluded that the federal mechanism
should incorporate, with certain modifications, the HAI 5.0a switching
and interoffice facilities module.[^673] In its default mode, HAI
assumes a blended configuration of switch technologies, incorporating
both hosts and remotes, to develop switching cost curves.[^674] HAI also
allows the user the option of designating, in an input table, specific
wire center locations that house host, remote, and stand-alone switches.
When the host-remote option is selected, switching curves that
correspond to host, remote, and stand-alone switches are used to
determine the appropriate switching investment. The LERG database could
be used as a source to identify the host-remote switch relationships. In
the *Platform Order*, we stated that \"\[i\]n the inputs stage of this
proceeding we will weigh the benefits and costs of using the LERG
database to determine switch type and will consider alternative
approaches by which the selected model can incorporate the efficiencies
gained through the deployment of host-remote configurations.\"[^675]

322\. The majority of commenters throughout this proceeding have
supported the use of the LERG database as a means of determining the
deployment of host and remote switches.[^676] These commenters contend
that the use of the LERG to determine host-remote relationships will
incorporate the accumulated knowledge and efficiencies of many LECs and
engineering experts in deploying the existing switch
configurations.[^677] Sprint contends that there are many intangible
variables that can not be easily replicated in determining host-remote
relationships.[^678] Commenters also contend that an algorithm that
realistically predicts this deployment pattern is not feasible using
publicly available data and would be unnecessarily \"massive and
complex.\"[^679] AT&T and MCI argue, however, that use of the LERG to
identify host-remote relationships may reflect the use of embedded
technology, pricing, and engineering practices.[^680]

323\. We conclude that the LERG database is the best source set forth in
this proceeding to determine host-remote switch relationships in the
federal high-cost universal service support mechanism. As noted above,
no algorithm has been placed on the record to determine whether a wire
center should house a stand-alone, host, or remote switch. In addition,
many commenters contend that development of such an algorithm
independently would be difficult using publicly available data.[^681]
While GTE suggests that the best source of host-remote relationships
would be a file generated by each company, we note that no such
information has been submitted in this proceeding.[^682] In addition,
GTE\'s proposal would impose administrative burdens on carriers. We
conclude that the use of the LERG to identify the host-remote switch
relationships is superior to HAI\'s averaging methodology which may not,
for example, accurately reflect the fact that remote switches are more
likely to be located in rural rather than urban areas. We therefore
conclude that use of the LERG is the most feasible alternative currently
available to incorporate the efficiencies of host-remote relationships
in the federal high-cost universal service support mechanism.

**D. Other Switching and Interoffice Transport Inputs**

324\. [General]{.underline}. In the *Inputs Further Notice*, we proposed
several minor modifications to the switching inputs to reflect the fact
that the studies on which the Commission relied to develop switch costs
include all investments necessary to make a switch operational.[^683]
These investments include telephone company engineering and
installation, the main distribution frame (MDF), the protector frame
(often included in the MDF), and power costs.[^684] To avoid double
counting these investments, both as part of the switch and as separate
input values, the commenters agree that the MDF/Protector investment per
line and power input values should be set at zero.[^685] In addition,
commenters agree that the Switch Installation Multiplier should be set
at 1.0.[^686] We agree that including these investments both as part of
the switch cost and as separate investments would lead to double
counting of these costs. We therefore adopt these values.

325\. [Analog Line Offset]{.underline}. In the *Inputs Further Notice*,
we tentatively concluded that the \"Analog Line Circuit Offset for
Digital Lines\" input should be set at zero.[^687] We now affirm that
conclusion. AT&T and MCI contend that the switch investment in the model
should be adjusted downward to reflect the cost savings associated with
terminating digital, rather than analog, lines.[^688] AT&T and MCI
assert that this cost savings is due primarily to the elimination of a
MDF and protector frame termination. AT&T and MCI further contend that
the model produces, on average, 40 percent digital lines, while the data
used to determine switch costs reflect the use of only approximately 18
percent digital lines.[^689] In contrast, GTE contends that the model
may calculate more analog lines than carriers have historically placed
due to the use of an 18,000 feet maximum copper loop length.[^690]

326\. AT&T and MCI suggest that the analog line offset input should
reflect a \$12 MDF and \$18 switch port termination savings per line in
switch investment for terminating digital lines in the model.[^691]
Several commenters disagree and recommend setting the analog line offset
to zero.[^692] Sprint contends that the analog line offset is inherent
in the switching curve in the model, thus making this input unnecessary
and, therefore, justified only if the switch cost curve is based on 100
percent of analog line cost.[^693] Sprint argues that an unknown mixture
of analog and digital lines are taken into consideration in developing
the switch curve.[^694]

327\. The record contains no basis on which to quantify savings beyond
those taken into consideration in developing the switch cost. We also
note that the depreciation data used to determine the switch costs
reflect the use of digital lines. The switch investment value will
therefore reflect savings associated with digital lines. AT&T and MCI\'s
proposed analog line offset per line is based on assumptions that are
neither supported by the record nor easily verified. For example, it is
not possible to determine from the depreciation data the percentage of
lines that are served by digital connections. It is therefore not
possible to verify AT&T and MCI\'s estimate of the digital line usage in
the \"historical\" data. In the absence of more explicit support of AT&T
and MCI\'s position, we conclude that the Analog Line Circuit Offset for
Digital Lines should be set at zero.

328\. [Switch Capacity Constraints]{.underline}. In the *Inputs Further
Notice*, we proposed to adopt the HAI default switch capacity constraint
inputs as proposed in the HAI 5.0a model documentation.[^695] We now
adopt that proposal. The forward-looking cost mechanism contains switch
capacity constraints based on the maximum line and traffic capabilities
of the switch. In their most recent filings on this issue, AT&T and MCI
recommend increasing the switch line and traffic capacity constraints
above the HAI input default values for those inputs.[^696] AT&T and MCI
contend that the default input values no longer reflect the use of the
most current technology.[^697] For example, AT&T and MCI recommend that
the maximum equipped line size per switch should be increased from
80,000 to 100,000 lines.[^698]

329\. We conclude that the original HAI switch capacity constraint
default values are reasonable for use in the federal mechanism. We note
that Sprint, the only commenter to respond to this issue, supports this
conclusion.[^699] We also note that the HAI model documentation
indicates that the 80,000 line assumption was based on a conservative
estimate \"recognizing that planners will not typically assume the full
capacity of the switch can be used.\"[^700] AT&T and MCI therefore
originally supported the 80,000 line limitation as the maximum equipped
line size value with the knowledge that the full capacity of the switch
may be higher.[^701]

330\. [Switch Port Administrative Fill]{.underline}. In the *Inputs
Further Notice*, we proposed a switch port administrative fill factor of
94 percent.[^702] We now adopt that proposed value. The HAI model
documentation defines the switch port administrative fill as \"the
percent of lines in a switch that are assigned to subscribers compared
to the total equipped lines in a switch.\"[^703] HAI assigns a switch
port administrative fill factor of 98 percent in its default input
values.[^704] The BCPM default value for the switch percent line fill is
88 percent.[^705]

331\. Bell Atlantic contends that switches have significant unassigned
capacity due to the fact that equipment is installed at intervals to
handle growth.[^706] Sprint recommends an average fill factor of 80
percent.[^707] US West contends that its actual average fill factor is
78 percent.[^708] AT&T and MCI contend that the switching module
currently applies the fill factor input against the entire switch when
it should be applied only to the line port portion of the switch.[^709]
AT&T and MCI therefore contend that, either the formula should be
modified, or the input needs to be adjusted upward so that the overall
switching investment increase attributable to line fill will be the same
as if the formula were corrected.[^710]

332\. We note that the switch port administrative fill factor of 94
percent has been adopted in several state universal service proceedings
and is supported by the Georgetown Consulting Group, a consultant of
BellSouth.[^711] We also note that this value falls within the range
established by the HAI and BCPM default input values. The BCPM model
documentation established a switch line fill default value of 88 percent
that included \"allowances for growth over an engineering time horizon
of several years.\"[^712] Sprint has provided no substantiated evidence
to support its revised value of 80 percent. US West\'s average fill
factor of 78 percent is based on data that include switches with
unreasonably low fill factors.[^713] Regarding AT&T and MCI\'s
contention that the switching module currently applies the fill factor
input against the entire switch rather than the line port portion of the
switch, we note that this occurs only when the host-remote option is not
utilized in the switch module. As noted above, we are using the
host-remote option and therefore no adjustment to the switch fill factor
is required. We therefore adopt a switch port administrative fill factor
of 94 percent.

333\. [Trunking]{.underline}. In the *Inputs Further Notice*, we
tentatively concluded that the switch module should be modified to
disable the computation that reduces the end office investment by the
difference in the interoffice trunks and the 6:1 line to trunk ratio. In
addition, we tentatively adopted the proposed input value of \$100.00
for the trunk port investment.[^714] We now affirm these tentative
conclusions and adopt this approach.

334\. The HAI switching and interoffice module developed switching cost
curves using the Northern Business Information (NBI) publication, \"U.S.
Central Office Equipment Market: 1995 Database.\"[^715] These investment
figures were then reduced per line to remove trunk port investment based
on NBI\'s implicit line to trunk ratio of 6:1.[^716] The actual number
of trunks per wire center is calculated in the transport calculation,
and port investment for these trunks is then added back into the
switching investments.

335\. Sprint notes that, under the HAI trunk investment approach,
raising the per-trunk investment leads to a decrease in the switch
investment per line, \"despite a reasonable and expected increase\" in
the investment per line.[^717] GTE also notes that the selection of the
trunk port input value creates a dilemma in that it is used to reduce
the end office investment, as noted above, and to develop a tandem
switch investment.[^718] GTE and Sprint recommend that the switch module
be modified by disabling the computation that reduces the end office
investment by the difference in the computed interoffice trunks and the
6:1 line to trunk ratio.[^719] MCI agrees that the trunk port
calculation should be deactivated in the switching module.[^720]

336\. In the *Inputs Further Notice*, we agreed with commenters that the
trunk port input creates inconsistencies in reducing the end office
investment.[^721] Consistent with the suggestions made by GTE and MCI,
we conclude that the switch module should be modified to disable the
computation that reduces the end office investment by the difference in
the computed interoffice trunks and the 6:1 line to trunk ratio. Sprint,
the only commenter to address this issue in response to the *Inputs
Further Notice*, agrees with our conclusion.[^722]

337\. Because the trunk port input value is also used to determine the
tandem switch investment, we must determine the trunk port
investment.[^723] In the *Inputs Further Notice*, we proposed an input
value for trunk port investment per end of \$100.00.[^724] SBC and
Sprint contend that this value should be higher \-- ranging from
\$150.00 to \$200.00.[^725] BellSouth has filed information on the
record that supports our proposed trunk port investment value.[^726]
BellSouth notes that the four states that have issued orders addressing
the cost of the trunk port for universal service[^727] have chosen
estimates of the cost of the trunk port that range from \$62.73 to
\$110.77.[^728] We conclude that the record supports the adoption of a
trunk port investment per end of \$100.00, as supported by the HAI
default values. As noted above, this value is consistent with the
findings of several states and BellSouth. In addition, we note that SBC
and Sprint provide no data to support their higher proposed trunk port
investment value. We therefore adopt the HAI suggested input value of
\$100.00 for the trunk port investment, per end.

**VII. EXPENSES**

**A. Introduction**

338\. In this section, we consider the inputs to the model related to
expenses and general support facilities (GSF) investment. Consistent
with the *Universal Service Order\'s* seventh criterion, we select input
values that result in a reasonable allocation of joint and common costs
for non-network-related costs, such as GSF, plant non-specific expenses,
corporate operations expenses, and customer services expenses. The
Commission\'s methodology for estimating these types of expenses is
designed to \"ensure that the forward-looking economic cost \[calculated
by the model\] does not include an unreasonable share of the joint and
common costs for non-supported services.\"[^729] Consistent with the
*Universal Service Order\'s* first and third criteria, we also select
input values for plant-specific operations expenses that reflect the
cost of maintaining a forward-looking network.[^730]

339\. GSF costs include the investment and expenses related to vehicles,
land, buildings, and general purpose computers. Other expenses include:
plant-specific operations expenses,[^731] plant non-specific
expenses,[^732] corporate operations expenses,[^733] and customer
services expenses.[^734] For purposes of this Order, costs associated
with common support services (often called overhead expenses) refer to
plant non-specific expenses, corporate operations expenses, and customer
services expenses.

340\. In the *Platform Order*, the Commission adopted HAI\'s algorithm
for calculating expenses and GSF costs, as modified to provide some
additional flexibility in calculating expenses offered by the BCPM
sponsors.[^735] With this added flexibility, the model allows the user
to estimate expenses as either a per-line amount or as a percentage of
investment. We noted that many of the questions regarding how best to
calculate expenses would be resolved in the input selection phase of
this proceeding.[^736] In the *Inputs Further Notice*, we tentatively
concluded that the input values for plant-specific operations expenses
should be calculated as a percentage of investment,[^737] and that the
input values for common support services expenses should be estimated on
a per-line basis.[^738] In addition, we tentatively concluded that we
should adopt input values that reflect the average expenses that will be
incurred by non-rural carriers, rather than company-specific expense
estimates.[^739] As described below, we proposed methodologies for
calculating these expenses. In addition, we proposed a methodology for
estimating the GSF investment that should be allocated to the supported
services.[^740]

**B. Plant-Specific Operations Expenses**

**1. Background**

341\. Plant-specific operations expenses are the expense costs related
to the maintenance of specific kinds of telecommunications plant.[^741]
In the *Inputs Further Notice,* we proposed a methodology for estimating
expense-to-investment ratios consisting of four steps.[^742] First, we
obtained account-specific current cost to book cost (current-to-book)
ratios for the related investment accounts, for the years ending 1995
and 1996, from Ameritech, Bell Atlantic, BellSouth, GTE, and SBC.[^743]
Second, we calculated two sets of composite current-to-book ratios (year
end 1995 and 1996) for each account based on composite current-to-book
ratios for each of the five companies.[^744] Third, we applied these
composite current-to-book ratios to the year-end 1995 and 1996
investment account balances from the ARMIS 43-03 reports for all
ARMIS-filing companies and averaged the 1995 and 1996 adjusted balances
for each account.[^745] Fourth, we calculated expense-to-investment
ratios for each plant-specific operations expense account by dividing
the total 1996 account balance for all ARMIS-filing companies by the
current average investment calculated previously.[^746] We tentatively
concluded that these expense-to-investment ratios should be applied to
the model-derived investment balances to obtain forward-looking
plant-specific operations expense estimates.

342\. In the *Inputs Further Notice*, we proposed adopting input values
that reflect the average expenses that will be incurred by non-rural
carriers, rather than a set of company-specific maintenance expense
estimates, for several reasons.[^747] We stated that using nationwide
expense-to-investment ratios is consistent with the views of the states
as reflected in the state Joint Board staff recommendations.[^748] In
addition, our proposed methodology requires some method of converting
booked cost investment to current investment in order to estimate
forward-looking plant specific operations expenses based on present day
replacement cost, rather than historic, financial account balances. We
noted that we have not been able to obtain current-cost-to-book-cost
ratios for each non-rural ARMIS reporting firm, which would be necessary
to calculate company or study area specific expense-to-investment
ratios.[^749] We tentatively concluded that averages are more consistent
with the forward-looking nature of the high-cost model because less
efficient firms are not rewarded if they have higher than average costs.
In seeking comment on these proposals and tentative conclusions, we
requested that parties advocating the use of company-specific values or
other alternatives to nationwide or regional estimates identify the
method and data readily available that could be used to estimate
plant-specific expenses and indicate how their proposal is consistent
with the goal of estimating forward-looking costs.[^750]

343\. In reaching our tentative conclusions, we recognized that parties
have argued that maintenance expenses vary widely by geographic area and
type of plant, while others have argued that plant-specific expenses are
highly dependent on regional wage differences.[^751] We explained that
the synthesis model takes into account the variance in maintenance cost
by type of plant installed because, as investment in a particular type
of plant varies, the associated expense cost also varies.[^752] We noted
that we had been unable to verify significant regional differences among
study areas or companies based solely on labor rate variations using the
publicly available ARMIS expense account data for plant-specific
maintenance costs. Nonetheless, we sought comment on the degree to which
regional wage rate differentials exist and are significant, and asked
parties to suggest independent data sources on variations of wage rates
between regions and a methodology that permits such distinctions without
resorting to self-reported information from companies.[^753] In
addition, we sought specific comment on a possible method of estimating
regional wage differences by using indexes calculated by the
President\'s Pay Agent.[^754]

344\. We also tentatively concluded that we should not adopt different
expense estimates for small, medium, and large non-rural companies on a
per-line basis.[^755] We explained that we had tested whether
significant differences in maintenance expenses per line could be
discerned from segmenting companies into carriers serving less than
500,000 access lines, carriers serving between 500,000 and 5,000,000
access lines, and carriers serving over 5,000,000 access lines.[^756]
Because we found no significant differences in the expense factor
per-line or per-investment estimates based on these criteria, we
determined that economies of scale should not be a factor in estimating
plant-specific expenses.[^757]

345\. Finally, we noted that we used data from 1995 and 1996 in the
proposed methodology and tentatively concluded that it is appropriate to
adjust these data to account for inflation and changes in productivity
by obtaining revised 1997 current-to-book ratios from those companies
providing data.[^758] In addition, we tentatively concluded that we
should use the most current ARMIS data available for the maintenance
factor methodology. We sought comment on using the most current data
available in the final computation of expense estimates.[^759]

**2. Discussion**

346\. Consistent with our tentative conclusions, we adopt input values
that reflect the average expenses that will be incurred by non-rural
carriers, rather than a set of company-specific maintenance expense
estimates. We adopt our proposed four-step methodology for estimating
expense-to-investment ratios using revised current-to-book ratios and
1997 and 1998 ARMIS data. We clarify that the ARMIS investment and
expense balances used to calculate the expense-to-investment ratios in
steps three and four should be based on the accounts for all *non-rural*
ARMIS-filing companies. Although some rural companies file ARMIS
reports, the mechanism we adopt today will be used, beginning January 1,
2000, to determine high-cost support only for non-rural carriers. We
find, therefore, that it is appropriate to include only data from the
non-rural ARMIS-filing companies in calculating these
expense-to-investment ratios.[^760]

347\. [Current Data]{.underline}. Parties commenting on whether we
should update our methodology using more current ARMIS data agree that
we should use the most currently available data.[^761] We obtained
account-specific current-to-book ratios for the related plant investment
accounts, for the years ending 1997 and 1998, from Ameritech, Bell
Atlantic, BellSouth, GTE, and SBC.[^762] Accordingly, we adopt input
values using these updated current-to-book ratios and 1997 and 1998
ARMIS data to calculate the expense-to-investment ratios that we use to
obtain plant-specific operations expense estimates for use in the
federal mechanism. These input values and the non-proprietary data used
to calculate the expense-to-investment ratios are set forth in Appendix
D.[^763]

348\. [Nationwide Estimates]{.underline}. As discussed in this section,
we adopt nationwide average values for estimating plant-specific
operations expenses rather than company-specific values for several
reasons. We reject the explicit or implicit assumption of most LEC
commenters that the cost of maintaining incumbent LEC embedded plant is
the best predictor of the forward-looking cost of maintaining the
network investment predicted by the model. We find that, consistent with
the *Universal Service Order\'s* criteria, forward-looking expenses
should reflect the cost of maintaining the least-cost, most-efficient,
and reasonable technology being deployed today, not the cost of
maintaining the LECs\' historic, embedded plant. We recognize that
variability in historic expenses among companies is due to a variety of
factors and does not simply reflect how efficient or inefficient a firm
is in providing the supported services. We reject arguments of the LECs,
however, that we should capture this variability by using
company-specific data in the model. We find that using company-specific
data for federal universal service support purposes would be
administratively unmanageable and inappropriate. Moreover, we find that
averages, rather than company-specific data, are better predictors of
the forward-looking costs that should be supported by the federal
high-cost mechanism. In addition, we find that using nationwide averages
will reward efficient companies and provide the proper incentives to
inefficient companies to become more efficient over time, and that this
reward system will drive the national average toward the cost that the
competitive firm could achieve. Accordingly, we affirm our tentative
conclusion that we should adopt nationwide average input values for
plant-specific operations expenses.

349\. AT&T and MCI agree with our tentative conclusion that we should
adopt input values that reflect the average expenses incurred by
non-rural carriers, rather than company-specific expenses. They argue
that the universal service support mechanism should be based on the
costs that an efficient carrier *could* achieve, not on what any
individual carriers *has* achieved.[^764] In contrast, incumbent LEC
commenters argue that we should use company-specific values.[^765]

350\. BellSouth, for example, contends that the approach suggested by
AT&T and MCI conflicts with the third criterion for a cost proxy model,
which states that \"\[t\]he study or model, however, must be based upon
an examination of the current cost of purchasing facilities and
equipment . . ..\"[^766] BellSouth argues that the \"only logical
starting point for estimating forward-looking expenses is the current
actual expenses of the ILECs.\"[^767] We agree that we should start with
current actual expenses, as we do, in estimating forward-looking
maintenance expenses. We do not agree with the inferences made by the
incumbent LEC commenters, however, that our input values should more
closely match their current maintenance expenses.

351\. BellSouth\'s reliance on criterion three fails to quote the first
part of that criterion, which states:

Only long-run forward-looking economic cost may be included. The
long-run period must be a period long enough that all costs may be
treated as variable and avoidable. The costs must not be the embedded
cost of facilities, functions, or elements.[^768]

Thus, the model\'s forward-looking expense estimates should not reflect
the cost of maintaining the incumbent LEC\'s embedded plant. The
*Universal Service Order\'s* first criterion specifies that \"\[t\]he
technology assumed in the cost study or model must be the least-cost,
most efficient, and reasonable technology for providing the supported
services that is currently being deployed.\"[^769] As we explained in
the *Inputs Further Notice*, while the synthesis model uses existing
incumbent LEC wire center locations in designing outside plant, it does
not necessarily reflect existing incumbent LEC loop plant.[^770] Indeed,
as the Commission stated in the *Platform Order*, \"\[e\]xisting
incumbent LEC plant is not likely to reflect forward-looking technology
or design choices.\"[^771] Thus, for example, the model may design
outside plant with more fiber and DLCs and less copper cable than has
been deployed historically in an incumbent LEC\'s network. We find that
the forward-looking maintenance expenses also should reflect changes in
technology.

352\. GTE argues that expense-to-investment ratios should not be
developed as national averages, because no national average can reflect
the composition of each company\'s market demographics and plant.[^772]
GTE argues further that costs vary by geographic area and that this
variability reflects operating difficulties due to terrain, remoteness,
cost of labor, and other relevant factors.[^773] GTE contends that
\"\[u\]sing national average operating expenses will either understate
or overstate the forward-looking costs of providing universal service
for each carrier, depending on the variability of each company to the
average.\"[^774] GTE claims that the use of the national average
penalizes efficient companies that operate in high-cost areas.[^775]

353\. Similarly, Sprint contends that the use of nationwide estimated
data does not accurately depict the realities of operating in Sprint\'s
service territories.[^776] Sprint claims that the national averages are
far below Sprint\'s actual costs, because the Commission\'s methodology
for estimating plant-specific expense inputs is heavily weighted toward
the Bell companies\' urban operating territories.[^777] According to
Sprint, the Bell companies have a much higher access line density than
Sprint, and the expense data from such companies with a higher density
of customers will result in expense levels that are much lower than the
expense levels experienced by smaller carriers.[^778] AT&T and MCI
respond by showing that a particular small carrier, serving a lower
density area than Sprint, has plant-specific expenses that, on a
per-line basis, are less than half of Sprint\'s expenses.[^779] AT&T and
MCI claim that \"the most significant driver of cost differences between
carriers in the ARMIS study area data is *efficiency*.\"[^780] Like
other LECs, SBC argues that the costs for LECs vary dramatically, based
on various factors including size, operating territories, vendor
contracts, relationships with other utility providers and the
willingness to accept risk.[^781] SBC asserts that \"\[t\]hese
differences are not in all instances attributable to inefficient
operations.\"[^782]

354\. We agree with SBC that not all variations in costs among carriers
are due to inefficiency. Although we believe that some cost differences
are attributable to efficiency, we are not convinced by AT&T and MCI\'s
example that Sprint is less efficient than the small carrier they
identify. Sprint could have higher maintenance costs because it provides
higher quality service. But we also are not convinced by Sprint\'s
argument that maintenance expenses necessarily are inversely
proportional to density. Sprint provides no evidence linking higher
maintenance costs with lower density zones, and we can imagine
situations where there are maintenance costs in densely populated urban
areas that are not faced by carriers in low density areas. For example,
busy streets may need to be closed and traffic re-routed, or work may
need to be performed at night and workers compensated with overtime pay.

355\. We cannot determine from the ARMIS data how much of the
differences among companies are attributable to inefficiency and how
much can be explained by regional differences or other factors.
BellSouth\'s consultant concedes that there is nothing in the ARMIS
expense account data that would enable the Commission to identify
significant regional differences.[^783] GTE concedes that it may be
difficult to analyze some data because companies have not been required
to maintain a sufficient level of detail in their publicly available
financial records.[^784] GTE\'s proposed solution for reflecting
variations among states is simply to use company-specific data.[^785]
Indeed, none of the LECs propose a specific alternative to using
self-reported information from companies.[^786] For example, SBC argues
we should use company-specific expenses provided pursuant to the
*Protective Order* to develop company-specific costs, because these are
the costs that will be incurred by the providers of universal
service.[^787]

356\. While reliance on company-specific data may be appropriate in
other contexts, we find that, for federal universal service support
purposes, it would be administratively unmanageable and inappropriate.
The incumbent LECs argue that virtually all model inputs should be
company-specific and reflect their individual costs, typically by state
or by study area.[^788] As parties in this proceeding have noted,
selecting inputs for use in the high-cost model is a complex
process.[^789] Selecting different values for each input for each of the
fifty states, the District of Columbia, and Puerto Rico, or for each of
the 94 non-rural study areas, would increase the Commission\'s
administrative burden significantly.[^790] Unless we simply accept the
data the companies provide us at face value, we would have to engage in
a lengthy process of verifying the reasonableness of each company\'s
data. For example, in a typical tariff investigation or state rate case,
regulators examine company data for one-time high or low costs, pro
forma adjustments, and other exceptions and direct carriers to adjust
their rates accordingly. Scrutinizing company-specific data to identify
such anomalies and to make the appropriate adjustments to the
company-proposed input values would be exceedingly time consuming and
complicated given the number of inputs to the model.[^791] We recognize
that such anomalies invariably exist in the ARMIS data, but we find
that, by using averages, high and low values will cancel each other out.

357\. Where possible, we have tried to account for variations in cost by
objective means. As we stated in the *Inputs Further Notice*, we believe
that expenses vary by the type of plant installed.[^792] The model takes
this variance into account because, as investment in a particular type
of plant varies, the associated expense cost also varies. The model
reflects differences in structure costs by using different values for
the type of plant, the density zone, and soil conditions.

358\. As discussed above, we cannot determine from the ARMIS data how
much of the differences among companies are attributable to inefficiency
and how much can be explained by regional differences or other factors.
To the extent that some cost differences are attributable to
inefficiency, using nationwide averages will reward efficient companies
and provide the proper incentives to inefficient companies to become
more efficient over time. We find that it is reasonable to use
nationwide input values for maintenance expenses because they provide an
objective measure of forward-looking expenses. In addition, we find that
using nationwide averages in consistent with our forward-looking
economic cost methodology, which is designed to send the correct signals
for entry, investment, and innovation.

359\. Bell Atlantic contends that using nationwide averages for plant
specific expenses, rather than ARMIS data disaggregated to the study
area level, defeats the purpose of a proxy model because it averages
high-cost states with low-cost states.[^793] Bell Atlantic argues that
we should use the most specific data inputs that are available, whether
region-wide, company specific, or study-area specific.[^794] Conceding
that data are not always available at fine levels of disaggregation,
Bell Atlantic contends there is no reason to throw out data that more
accurately identify the costs in each area.[^795] Bell Atlantic argues
that, even if the Commission does not have current-to-book ratios for
all of the ARMIS study areas, it could use average current-to-book
ratios and apply them to company-specific ARMIS data.[^796]

360\. Contrary to Bell Atlantic\'s contention, we do not find that using
nationwide average input values in the federal high-cost mechanism is
inconsistent with the purpose of using a cost model. In addition to the
administrative difficulties outlined above, we find that nationwide
values are generally more appropriate than company-specific input values
for use in the federal high-cost model. In using the high-cost model to
estimate costs, we are trying to establish a national benchmark for
purposes of determining support amounts. The model assumes, for example,
that all customers will receive a certain quality of service whether or
not carriers actually are providing that quality of service.[^797]
Because differences in service quality can cause different maintenance
expense levels, by assuming a consistent nationwide quality of service,
we control for variations in company-specific maintenance expenses due
to variations in quality of service. Clearly, we are not attempting to
identify any particular company\'s cost of providing the supported
services. We are, as AT&T and MCI suggest,[^798] estimating the costs an
efficient provider would incur in providing the supported services. We
are not attempting to replicate past expenses, but to predict what
support amounts will be sufficient in the future. Because high-cost
support is portable, a competitive eligible telecommunications carrier,
rather than the incumbent LEC, may be the recipient of the support. We
find that using nationwide averages is a better predictor of the
forward-looking costs that should be supported by the federal high-cost
mechanism than any particular company\'s costs.[^799]

361\. [Estimating regional wage differences]{.underline}. We do not
adjust our nationwide input values for plant-specific operations
expenses to reflect regional wage differences. Most LEC commenters
advocate the use of company-specific data to reflect variations in wage
rates.[^800] GTE, for example, claims that regional wage rate
differentials are reflected in the company-specific data available from
ARMIS.[^801] GTE complains that our proposed input values suggest there
is no difference in labor and benefits costs between a company operating
in Los Angeles and one operating in Iowa.[^802] As discussed above, the
publicly available ARMIS expense account data for plant-specific
maintenance expenses do not provide enough detail to permit us to verify
significant regional differences among study areas or companies based
solely on labor rate variations.[^803] For the reasons discussed above,
we find that we should not use company-specific ARMIS data to estimate
these expenses, but instead use input values that reflect nationwide
averages.[^804]

362\. Although they would prefer that we use company-specific data, some
LEC commenters suggest that the wage differential indexes used by the
President\'s Pay Agent, on which we sought comment, would be an
appropriate method of disaggregating wage-related ARMIS expense
data.[^805] GTE, on the other hand, contends that these indexes are not
relevant to the telecommunications industry, because they are designed
for a specific labor sector, that is, federal employees.[^806] GTE
claims that there are numerous publicly available sources of labor
statistics and that, if we adopt an index factor, it should be specific
to the telecommunications industry.[^807]

363\. We agree with GTE that, if we were to use an index to adjust our
input values for regional wage differences, it would be preferable to
use an index specific to the telecommunications industry. We looked at
other publicly available sources of labor statistics, however, and were
unable to find a data source that could be adapted easily for making
meaningful adjustments to the model input values for regional wage
differences. Specifically, we looked at U.S. Department of Labor, Bureau
of Labor Statistics (BLS) information on wage rate differentials for
communications workers comparing different regions of the country.[^808]
The Employment Cost Indexes calculated by BLS identify changes in
compensation costs for communications workers as compared to other
industry and occupational groups. In a number of the indexes,
communications is not broken out separately, but is included with other
service-producing industries: transportation, communication, and public
utilities; wholesale and retail trade; insurance, and real estate; and
service industries. In making regional comparisons, the Employment Cost
Indexes divide the nation into four regions: northeast, south, midwest,
and west. There also are separate indexes comparing metropolitan areas
to other areas.

364\. We find that the regions used in the BLS data are too large to
make any significant improvement over our use of nationwide average
numbers. For example, Wyoming is in the same region as California, but
we have no reason to believe that wages in those two states are more
comparable than wages rates in California and Iowa. That is, there is no
simple way to use the BLS data to make the type of regional wage
adjustments suggested by GTE. We note that no party has suggested a
specific data source or methodology that would be useful in making such
adjustments. Accordingly, we decline to adopt a method for adjusting our
nationwide input values for plant-specific operations expenses to
reflect regional wage differences.

365\. [Methodology]{.underline}. As discussed in this section, we adopt
our proposed methodology for calculating expense-to-investment ratios to
estimate plant-specific operations expenses. We reject arguments of some
LEC commenters that this methodology inappropriately reduces these
expense estimates.

366\. Several LEC commenters generally support our methodology for
calculating expense-to-investment ratios to estimate plant-specific
operations expenses, although, as discussed above, only if we use
company-specific input values. For example, GTE agrees with our
tentative conclusion that input values for each plant-specific
operations expense account can be calculated as the ratio of booked
expense to current investment, but only if this calculation is performed
on a company-specific basis.[^809] BellSouth states that \"\[t\]he
methodology proposed by the Commission for plant-specific expenses is
very similar to the methodology employed by BellSouth.\"[^810]

367\. Other LEC commenters object to our use of current-to-book ratios
to convert historic account values to current cost. Although their
arguments differ somewhat, they essentially claim that the effect of our
methodology is to reduce forward-looking maintenance expenses and that
this is inappropriate because the input values are lower than their
current maintenance expenses.[^811] AT&T and MCI counter that, if there
is any problem with our maintenance expense ratios, it is that they
reflect the servicing of too much embedded plant, which has higher
maintenance costs, and too little forward-looking plant, which has lower
maintenance costs.[^812]

368\. US West asserts that, while in theory it is correct to adjust
expense-to-investment ratios using current-to-book ratios, in practice
there is a problem because the current-to-book ratio is based on
reproduction costs and the model estimates replacement costs.[^813] US
West defines reproduction cost as the cost of reproducing the existing
plant using today's prices and replacement cost as the cost of replacing
the existing plant with equipment that harnesses new technologies and is
priced at today's prices.[^814] US West claims that our methodology
actually increases the mismatch between historic and forward-looking
investment levels because the reproduction costs are not the same as the
replacement costs.[^815] We agree that reproduction costs are not the
same as replacement costs because the mix of equipment and technology
will differ, but we disagree with US West\'s characterization of this as
a mismatch.

369\. US West estimates that applying current-to-to book ratios to
existing investment would generate reproduction costs that are 141
percent higher than historic costs.[^816] US West claims that, in
contrast, forward-looking models generally show that the cost of
replacing those facilities would be slightly less than historic costs,
if new technologies were deployed. US West\'s claim that our methodology
results in a mismatch because of these cost differences, however, is
wrong. Rather, the differences between reproduction costs and
replacement costs merely show that the mix of technologies has changed.
The hypothetical example US West uses to illustrate its argument fails
to account for changes in technology. The following hypothetical example
illustrates how changes in the mix of technology will change maintenance
expenses.[^817] If historic investment on a company\'s books consists of
100 miles of copper plant, at a cost of \$10 per mile, and 10 miles of
fiber plant, at a cost of \$1 per mile, then the historic cost is
\$1010. If current maintenance costs are \$10 for the copper plant and
\$0.10 for the fiber plant, the total maintenance expense is \$10.10. If
the price of copper increases to \$15 per mile and the price of fiber
decreases to 80 cents per mile, then the reproduction costs would
increase to \$1508. If the forward-looking model designs a network with
60 miles of copper and 50 miles of fiber, the resulting replacement cost
is \$940.[^818] Using our methodology, we use the current-to-book ratios
of 1.5 (\$15/\$10) and .8 (80 cents divided by \$1) to revalue the
copper and fiber investment, respectively, at current prices, and the
resulting maintenance expense for the forward-looking plant would be
\$6.58 rather than \$10.10.[^819] This does not result in a mismatch. In
our hypothetical example, the maintenance costs for fiber were
substantially less on a per-mile basis than they were for copper. Thus,
we would expect the forward-looking plant with considerably more fiber
and less copper to have lower maintenance costs than the current plant,
which has more copper. Because the mix of plant changes, the Commission
should not, as US West suggests, simply adjust book investment to
current dollars to derive maintenance expenses for the forward-looking
plant estimated by the model.

370\. Sprint argues that we should simply divide the current year\'s
actual expense for each account by the average plant balance associated
with that expense.[^820] Sprint claims that, when this ratio is applied
to the investment calculated by the model, forward-looking expense
reductions occur in two ways: (1) the investment base is lower due to
the assumed economies of scale in reconstructing the forward-looking
network all at one time; and (2) greater use of fiber in the
forward-looking network reduces maintenance costs because less
maintenance is required of fiber than of the copper in embedded
networks.[^821] Sprint claims that reducing maintenance for a
current-to-book ratio as well as for technological factors constitutes a
\"double-dip\" in maintenance expense reduction.[^822]

371\. Sprint\'s claim that our methodology constitutes a \"double dip\"
in reducing maintenance expenses is misleading because the effect of
using current-to-book ratios depends upon whether current costs have
risen or fallen relative to historic costs. Current-to-book ratios are
used to restate a company\'s historic investment account balances, which
reflect investment decisions made over many years, in present day
replacement costs. Thus, if current costs are higher than historic costs
for a particular investment account, the current-to-book ratio will be
greater than one, and the expense-to-investment ratio for that account
will decrease when the investment (the denominator in the ratio) is
adjusted to current replacement costs.[^823] Sprint calls this double
dipping because copper costs have risen and the model uses less copper
plant than that which is reflected on Sprint\'s books. If current costs
are lower than historic cost, however, the current-to-book ratio will be
less than one and the adjusted expense-to-investment ratio for that
account will increase when the investment (the denominator in the ratio)
is adjusted to current replacement costs. Fiber cable and digital
switching costs, for example, have fallen relative to historic costs.
Sprint essentially is arguing that our methodology is wrong because it
understates Sprint\'s historical costs. The input values we select are
not intended to replicate a particular company\'s historic costs, for
the reasons discussed above.[^824]

372\. SBC disputes our assumption that the model takes into account
variations in the type of plant installed because, as investment in a
particular type of plant varies, so do the associated expense
costs.[^825] SBC argues that expenses do not vary simply because
investment varies.[^826] Nonetheless, SBC believes that developing a
ratio of expense to investment and applying it to forward-looking
investments is a reasonable basis for identifying forward-looking plant
specific expenses.[^827] SBC complains that our methodology is
inconsistent, however, because it has defined two completely different
sets of forward-looking investments: one based on historical ARMIS
investments adjusted to current amounts; and another derived on a
bottom-up basis employing the cost model.[^828] Until we reconcile these
\"inconsistencies,\" SBC recommends that we use unadjusted historical
investment amounts in developing plant specific expense factors, because
they are closer to SBC\'s historical plant specific expenses.[^829]

373\. Although they characterize the issue somewhat differently, US
West, Sprint, and SBC essentially argue that our methodology is wrong
because it understates their historical costs. AT&T and MCI counter that
a forward-looking network often will result in lower costs than an
embedded network and that the trend in the industry has been to develop
equipment and practices to minimize maintenance expense.[^830] AT&T and
MCI claim that, if there is any problem with our maintenance expense
ratios, it is that they reflect the servicing of too much embedded
plant, which has higher maintenance costs, and too little
forward-looking plant, which has lower maintenance costs.[^831] AT&T and
MCI further claim that, if our analysis had been based exclusively on
financial information that reflected equipment consistent with the
most-efficient forward-looking practices, the maintenance expenses would
have been lower.[^832]

374\. None of the commenters provide a compelling reason why we should
not use current-to-book ratios to adjust historic investment to current
costs. SBC in fact suggests that the Commission consider using the
Telephone Plant Index (TPI) in future years to convert expense estimates
to current values.[^833] SBC appears to be confusing the effect of
measuring inputs in current dollars, which it recognizes is reasonable,
and the end result of the calculation, which includes the impact of
measuring all inputs in current dollars, changes in the mix of inputs,
the impact of least-cost optimal design used by the model, and the
model\'s engineering criteria. The relationship between maintenance
costs and investment in the Commission\'s methodology is related to all
of these factors.

375\. Sprint also claims that our methodology understates maintenance
costs, because it assumes new plant and the average maintenance rate
will be higher than the rate in an asset\'s first year.[^834] AT&T and
MCI dispute Sprint\'s claim that maintenance costs per unit of plant
increase over time.[^835] Sprint provides an example which purports to
show that an asset with a ten year life, a ten percent maintenance fee
in the first year, and annual costs increasing annually at three
percent, would result in an average maintenance rate of 11.55
percent.[^836] Sprint\'s example, however, does not consistently apply
our methodology. Sprint\'s example fails to apply the current-to-book
ratio to the total and average plant in service estimates used in the
example. When the current-to-book ratio is applied to the total and
average plant in service estimates, the resulting maintenance rate is
ten percent for all years.

376\. BellSouth argues that the investment calculated by the model is
unrealistically low because sharing assigned to the telephone company is
unrealistically low and fill factors are unrealistically high.[^837]
BellSouth argues that, because it has shared in cost of trenching, this
does not mean the maintenance cost for buried cable would be less, and
in fact, the costs may be higher.[^838] BellSouth apparently is confused
about the Commission\'s methodology, because the sharing percentages
apply only to the costs of structure, not the costs of the cable.

**C. Common Support Services Expenses**

**1. Background**

377\. Common support services expenses include corporate operations
expenses, customer service expenses, and plant non-specific expenses.
Corporate operations expenses are those costs associated with general
administrative, executive planning, human resources, legal, and
accounting expenses for total company operations. Customer services
expenses include marketing, billing, operator services, directory
listing, and directory assistance costs.[^839] Plant non-specific
expenses are common network operations and maintenance types of
expenses, including engineering, network operations, power, and testing
expenses, that are considered general or administrative overhead to
plant operations.[^840]

378\. In the *Inputs Further Notice*, we proposed a methodology using
regression analysis to estimate common support services expenses on a
per-line basis. We noted that, unlike plant-specific expenses, common
support services expenses are costs that cannot readily be associated
with any particular maintenance expense or investment account.[^841] In
the regression methodology, we used publicly available 1996 ARMIS
expense data[^842] and minutes of use information from NECA,[^843] by
study area, to estimate the portion of these company-wide expenses that
should be supported by the federal high-cost mechanism.[^844]
Specifically, we used the average of the estimates from two
specifications that estimated total expenses per line as a function of
the percentage of switched lines, the percentage of special lines, and
toll minutes per line, either in combination (Specification 1) or
separated between intrastate and interstate toll minutes (Specification
2).[^845] The specifications were designed to separate the portion of
expenses attributable to special access lines and toll usage, which are
not supported by the federal high-cost mechanism, from the portion of
expenses attributable to switched lines and local usage, which are
supported.

379\. As with plant-specific operations expenses, we tentatively
concluded that input values for corporate operations, customer service,
and plant non-specific expenses should be estimated on a nationwide
basis, rather than a more disaggregated basis.[^846] In reaching this
tentative conclusion, we recognized that parties have argued that these
types of expenses may vary as a result of company-specific plant
configurations, geographic and labor demographic variables, one-time
exogenous costs, and non-recurring adjustments such as re-engineering
expenses.[^847] We observed that we had not been able to distinguish
significant differences in regional wage differentials for
administrative services based solely on ARMIS expense data for these
accounts.[^848] Moreover, costs associated with corporate overhead and
customer service accounts are not directly linked to a specific
company\'s investment levels. We tentatively concluded that these types
of administrative and service expenses are less dependent on carrier
physical plant or geographic differentials than on factors that also
correlate to company size (number of lines) and demand (minutes of
use).[^849]

380\. After estimating common support services expenses using the
regression methodology, we made certain adjustments to remove additional
portions of those expenses attributable to services that are not
supported by the federal universal service support mechanism. The
expenses we removed were associated with services that could be
identified and estimated from ARMIS expense data.[^850] We tentatively
concluded that 95.6 percent of marketing expenses should be attributed
to non-supported services, based on an Economics and Technology, Inc.
(ETI) analysis.[^851] In addition, we adjusted the estimates for
non-supported service costs related to coin operations and collection,
published directory, access billing, interexchange carrier office
operation, and service order processing.[^852] We noted that
non-recurring expenses for corporate operations can be significant and
that our estimates should be adjusted to account for these one-time
charges.[^853] We explained, however, that we had been unable to find an
objective public data source or discern a systematic method for
excluding these costs from the ARMIS expense data used in the regression
methodology.[^854] We sought comment on how to identify, estimate, and
remove these one-time non-recurring expenses.[^855]

381\. We also adjusted our estimates for common support services
expenses by converting the values, which were based on 1996 ARMIS data,
to 1999 values.[^856] Specifically, we reduced the estimated expenses by
a 6.0 percent productivity factor for each year (1997 and 1998) and
added an inflation factor based on the fixed weighted Gross Domestic
Product Price Index (GDP-PI) for 1997 (2.1120 percent) and for 1998
(2.1429 percent).[^857] That is, we proposed a net reduction of 3.888
percent for 1997 and 3.8571 percent for 1998, and sought comment on this
method for converting expenses to 1999 values.[^858]

**2. Discussion**

382\. Consistent with our tentative conclusions, we adopt input values
that estimate the average common support services expenses that will be
incurred by non-rural carriers on a per-line basis, rather than a set of
company-specific common support services expenses.[^859] We affirm our
tentative conclusion that input values for corporate operations,
customer service, and plant non-specific expenses should be estimated on
a nationwide basis, rather than a more disaggregated basis. As noted
above, we find that for universal service purposes nationwide averages
are more appropriate than company-specific values.[^860] We conclude
that we should use Specification 1 of our proposed regression
methodology to estimate expenses for ARMIS accounts 6510 (Other
Property, Plant, and Equipment); 6530 (Network Operations); 6620
(Service Expense/Customer Operations); and 6700 (Executive, Planning,
General, and Administrative).[^861] As discussed below, we use an
alternative methodology to estimate expenses for ARMIS account 6610
(Marketing).[^862] We conclude that we should use 1998 ARMIS data in
both methodologies, and an estimate of 1998 Dial Equipment Minutes of
Use (DEMs) in the regression equation, to calculate these input values.
We clarify that the ARMIS data we use to calculate these estimates are
based on ARMIS accounts for all *non-rural* ARMIS-filing companies. We
find that it is appropriate to include only data from the non-rural
ARMIS-filing companies in calculating the expense per line for common
support services expenses.[^863]

383\. [Current Data and Use of Productivity Factor]{.underline}. The
input values we adopt in this Order are explained more fully in Appendix
D, which contains a summary of the per-line, per-month input values for
plant non-specific expenses, corporate operations expenses, and customer
services expenses, including regression results, calculations, and
certain adjustments made to the data based on the methodologies
described below.[^864] Because we used 1996 ARMIS data in our regression
methodology to estimate our proposed input values for common support
services expenses, we proposed a method of converting those estimates to
1999 values.[^865] Specifically, we proposed using a productivity factor
of 6.0 percent for the years 1997 and 1998 to reduce the estimated input
values.[^866] We further proposed adjusting the expense data for those
years with an inflation factor based on the Gross Domestic Product Price
Index (GDP-PI) in order to bring the input values up to current
expenditure levels.[^867]

384\. AT&T and MCI claim that the 6.0 productivity factor is too
low,[^868] while most LEC commenters contend that it is too high.[^869]
Sprint argues that expenses should not be adjusted for a productivity or
an inflation factor and that we should use 1998 data.[^870] GTE argues
that no productivity adjustments are necessary, if we use current,
company-specific ARMIS data to develop input values.[^871] Although we
generally decline to adopt company-specific input values for common
support services expenses, we agree that using the most currently
available ARMIS data (1998) obviates the need to adjust our estimates
for either productivity gains or an inflation factor at this time. We
believe, however, that there should be an incentive for increased
productive efficiency among carriers receiving high-cost universal
service support. Accordingly, we believe that a reasonable productivity
measure or some other type of efficiency incentive to decrease costs
associated with common support services expenses should be incorporated
into the universal service high-cost support mechanism in the future. We
intend to address this issue in the proceeding on the future of the
model.

385\. The input values we adopt in this Order are estimates of the
portion of company-wide expenses that should be supported by the federal
high-cost mechanism.[^872] We derive the estimates using standard
economic analysis and forecasting methods. The analysis relies on
publicly available 1998 ARMIS expense data and the most current minutes
of use information from NECA. This data is organized by study area. The
estimate of 1998 DEMs is based on a calculated growth rate of 1997 to
1996 DEMs reported by NECA.[^873] As a result of deleting rural
ARMIS-filing companies and including company study area changes since
1996, pooling of the 1998 data sets provides expense, minutes of use,
and line count data for 80 study areas.[^874] This is in comparison to
the 91 study areas resulting from pooling the 1996 data described in the
*Inputs Further Notice*.[^875]

386\. Some parties object to our using data at the study area level,
because they claim that ARMIS-filing companies report data in two
distinct ways. Ameritech and US West argue that parent companies
generally assign a significant portion of plant non-specific and
customer operations expenses across their operating companies on the
basis of an allocation mechanism.[^876] As a result, they claim that a
simple regression on the study area observations will produce
coefficients that reflect a blend of two relationships: the cost-based
relationship and the allocation-based relationship, of which only the
former is appropriate to measure.[^877] They argue further that it is
necessary to model the allocation method explicitly, to net out the
latter data, or to aggregate the data to the parent company level.
Although we acknowledge that our accounting rules provide carriers with
some flexibility, we expect that the allocation mechanism used by the
parent company represents underlying cost differences among its study
areas.[^878] We find that it is reasonable to assume that the companies
use allocation mechanisms that are based on cost relationships to
allocate costs among their study areas. Accordingly, we find that it is
reasonable to use ARMIS data at the study area level in the regression
methodology.

387\. [Regression Methodology]{.underline}. As described in the *Inputs
Further Notice*, we adopt standard multi‑variate regression analysis to
determine the portion of corporate operations expenses, customer
services expenses, and plant non-specific expenses attributable to the
services that should be supported by the federal high-cost
mechanism.[^879] We adopt an equation (Specification 1) which estimates
total expenses per line as a function of the percentage of switched
lines, the percentage of special lines, and toll minutes per line.[^880]
We use this regression methodology to estimate the expenses attributable
to universal service for the following accounts:

Other Property, Plant, and Equipment (6510);

Network Operations (6530);

Service Expense/Customer Operations (6620); and

Executive, Planning, General and Administrative (6700).

We adopt this specification, rather than an average of the two
specification estimates suggested in the *Inputs Further Notice*, to
separate the portion of expenses that could be estimated as attributable
to special access lines and toll usage, which are not supported by the
federal high-cost mechanism, from switched lines and local usage.[^881]
As explained below, we use an adjusted weighted average of study areas
to estimate the support expense attributable to Account 6610, Marketing.

388\. Several parties contend that our regression analysis is
flawed.[^882] Sprint, for example, claims that we have exaggerated the
significance of our statistical findings beyond a level justified by the
regression result; and have made the often-committed error of
interpreting our regression results in a way that implies
causality.[^883] US West argues that, although there is a causal
relationship between the level of expenses and the variables we use in
the regression, the coefficient of determination or R^2^ is fairly low,
which implies that the causal relationship only explains a small portion
of the total costs.[^884] GTE claims that our regression is
mis-specified because it utilizes only the mix of output as explanatory
variables, and excludes important variables related to differences in
input prices and production functions.[^885] Because of this
mis-specification and the omitted variables, GTE also claims that our
equations have a low predictive ability, as measured by the R^2^s.[^886]

389\. We disagree with commenters who claim that there is little
explanatory value in our regression analysis.[^887] In accounts 6620,
6700, 6530 the regressions explain a high degree of the variability in
the expense variables.[^888] Only account 6510 (Other Property, Plant,
and Equipment) has a low R^2^, which is not surprising given the
reported data in this account. Based on the 1998 ARMIS data, the
resulting regression coefficient for this expense category is negative
due to the numerous negative expenses reported by carriers in 1998.
Because the ARMIS reports represent actual 1998 expenses incurred by the
non-rural telecommunications companies within their various study areas,
we find that it is appropriate to include this negative expense in our
calculations. We note, however, that inclusion of this account in our
calculations represents less than one percent of the total expense input
for common support services expenses.[^889]

390\. We believe that our regressions represent a cost‑causative
relationship, and that common support services expenses are a function
of the number of total lines served, plus the volume of minutes. Because
in the long run, all costs are variable, we disagree with commenters who
suggest that our methodology is flawed because we do not include an
intercept term in our regression equation to represent fixed or start-up
costs.[^890] As discussed above, the model is intended to estimate
long-run forward-looking cost over a time period long enough so that all
costs may be treated as variable and avoidable.[^891] Moreover, the
federal high-cost mechanism calculates support on a per-line basis,
which is distributed to eligible carriers based upon the number of lines
they serve. We would not provide support to carriers with no lines. Nor
would we vary support, which is portable, between an incumbent and a
competitive eligible telecommunications carrier, based on differences in
their fixed or start-up costs. We explicitly assume, therefore, that if
a company has zero lines and zero minutes, it should have zero expenses.
Thus, we have no constant or fixed cost in our regressions. We also
believe that these expenses are driven by the number of channels, not
the number of physical lines.

391\. That is, our assumptions imply that expenses are a linear function
of lines and minutes.[^892] We next need to separate out the common
support services expenses related to special access lines and toll
minutes, because these services are not supported by the federal
high-cost mechanism. Therefore, we split the lines variable into
switched and special access lines, and we split the minutes variable
into local and toll minutes. In this modified equation, expenses are a
function of switched lines, plus special access lines, plus local
minutes, plus toll minutes.[^893] We believe that changes in local
minutes, however, should not cause changes in common support services
expenses that are not already reflected in the expenses associated with
switched lines. We find that it is reasonable to assume that local calls
do not increase these overheard costs in the same way that toll minutes
do. For example, in most jurisdictions local calls are a flat-rated
service and additional local calling requires no additional information
on the customer\'s bill. With toll calling, however, even subscribers
that have some kind of a calling plan receive detailed information about
those calls. It is reasonable to assume that adding an additional line
on a subscriber\'s bill for a toll call causes overhead costs that are
not caused by local calls. Moreover, toll calling outside a carrier\'s
serving area involves the costs associated with completing that call on
another carrier\'s network. As discussed below, we tested our assumption
that local calls do not affect costs in the same way that toll calls do
by running the regressions to include local minutes. Based on theory and
our analysis, we decided to drop the local minutes variable, so that
expenses are a function of switched lines, plus special access lines,
plus toll minutes.[^894] Because we are calculating a per-line expense
estimate, we divide all the variables by the total number of lines to
derive our final equation: expenses divided by total lines equals the
percentage of switched lines, plus the percentage of special lines, plus
toll minutes divided by total lines.[^895]

392\. US West claims that our regressions may not be based on
appropriate cost-causative relationships, because we count special
access lines by channels and not by physical pairs.[^896] The ARMIS data
used in the regressions count special lines as channels. That is,
special access lines are counted as DS0 equivalents: a DS1 has 24
channels, and a DS3 has 672 channels. US West contends that it is far
from clear how this method of counting special access lines reflects how
these services cause expenses, because it is clear that DS1s and DS3s
are not priced as if they cause 24 and 672 times the amount of expenses
as a narrowband line.[^897]

393\. The fact that DS1s and DS3s are priced differently in the current
marketplace does not imply that it is improper to count lines as
channels. US West\'s suggested alternative, counting special lines as
physical pairs, would assume that a residential customer with two lines
causes the same amount of overhead expenses as a special access customer
with one DS1 line. To the contrary, we find that it is reasonable to
assume that more overhead expenses are devoted to winning and keeping
the DS1 customer than the residential customer. Further, we expect that
more overhead expenses are related to customers using higher capacity
services than those using lower capacity services. Accordingly, we find
that it is reasonable to use channel counts in our regression
equations.[^898]

394\. Some commenters also criticized our regression analysis on the
grounds that variables are highly correlated and that the predicted
coefficients are not stable.[^899] In particular, US West claims that
the confidence intervals and standard errors are large and that a
dividing-the-sample experiment leads to drastically different
results.[^900] While these commenters are correct that the correlation
values are high for the raw variables, the values are not high once the
variables under consideration are adjusted by dividing by total
lines.[^901] We find that the correlation values are all very
reasonable. We note, in particular, the ‑1 correlation between switched
lines and special lines. The fact that switched lines plus special lines
equals one is the reason the regression cannot be run with a separate
constant. We note that our parameterization has switched lines, special
lines, and toll minutes as explanatory variables. We have chosen not to
include local minutes in our regressions for theoretical reasons. So,
the key correlation values are the correlations of toll minutes with
special lines and with switched lines. We find that those values are
reasonable.

395\. Several commenters suggested that we use local minutes as an
explanatory variable.[^902] Despite our tentative conclusion that our
regressions should not include local minutes as a variable, in response
to these comments, we re‑ran each of the regressions with local minutes
per line as an additional variable. In three of the four regressions,
the coefficient for local minutes was not significant at the five
percent level, and for account 6700, its sign was the opposite of what
was expected.[^903] The resulting difference in the estimated expenses
attributable to supported services was very small in magnitude as well.
If we used the local minutes variable in our parameterization, after
summing across all expense accounts, our per-line, per-month estimate
for a switched line would be approximately \$0.01 more.[^904] Given our
belief that local minutes should not influence these expenses, the lack
of significance in the coefficients, and the overall lack of impact when
the variable was consistently included in the regressions, we conclude
that we should not include local DEMs per line in our specifications.

396\. Except for the inclusion of local minutes as a variable, no
commenters have suggested a better parameterization or methodology for
using the ARMIS data to estimate expense inputs for these accounts.
Further, no commenters have suggested an alternative publicly available
data set to use for our estimation of expense input values. We
acknowledge that there is substantial variation in the underlying
expense data taken from the ARMIS reports. Common support services
expenses often contain charges unrelated to the specified relationships
in the regression equation. For example, there are many one-time
expenses and non-recurring charges associated with these accounts. We
have tried to limit the effect of this problem by making adjustments to
the expense data, as discussed below. Given the data limitations and the
parameterization we have chosen, we find that the estimated coefficients
are the best estimate of the applicable expenses, regardless of the
resulting standard errors.

397\. [Removal of One-Time Expenses]{.underline}. In the *Inputs Further
Notice*, we discussed our efforts to adjust estimates of common support
services expenses to account for one-time and non-recurring
expenses.[^905] We sought comment on the need for information about and
estimates of various types of exogenous costs and common support service
expenses that are recovered through non-recurring charges and tariffs.
These expenses include specific one-time charges for the cost of mergers
or acquisitions and process re-engineering, and network and
interexchange carrier connection, disconnection, and re-connection
(i.e., churn) costs.

398\. In the *Inputs Further Notice*, we tentatively concluded that we
should not use an analysis submitted by AT&T and MCI to estimate
one-time and non-recurring expenses for corporate and network operations
expenses.[^906] This analysis averaged five years (1993-1997) of data
from Security and Exchange Commission (SEC) 10-K and 10-Q filings for
all tier one companies to identify and calculate a percentage estimate
of corporate and network operations expenses classified as one-time and
non-recurring charges associated with these types of activities. Our
tentative conclusion not to rely on the AT&T and MCI analysis to make
these adjustments was based on the fact that we were using 1996 ARMIS
data to estimate the expense inputs. Because the SEC reports do not
indicate whether the one-time expenses were actually made solely during
a specific year indicated, we tentatively concluded that we could not
use the analysis\' five year average or the actual 1996 SEC figures to
make adjustments to the 1996 ARMIS data. In the *Inputs Further Notice,*
we noted however that the AT&T and MCI analysis indicates that one-time
expenses for corporate and network operations can be significant.[^907]
We sought comment on how to identify and estimate one-time and
non-recurring expenses associated with these common support services.

399\. AT&T and MCI disagree with our tentative decision to reject their
one-time cost estimates and argue that it is better to estimate one-time
costs through use of the SEC reports, although these reports may
imperfectly establish the precise date of the occurrence, than to fail
to exclude these costs at all.[^908] Although some LEC commenters may
agree that we should adjust our estimates to exclude one-time and
non-recurring expenses, they provide no data or methodology to
accomplish this, other than suggesting that we should get this
information from the companies.[^909] GTE claims that unless companies
implement specific tracking mechanisms, these data are not generally or
easily identified after the fact.[^910]

400\. We now reconsider our tentative conclusion not to use the analysis
submitted by AT&T and MCI to adjust our network and corporate operations
expense estimates to account for one-time and non-recurring expenses. We
do so for a number of reasons. First, we received no additional
information on publicly available data sources or other reasonable
methods to estimate these one-time and non-recurring costs at this time.
Second, the problems associated with determining the actual costs of
1996 one-time expenses based on the SEC reports are obviated because we
are using 1998 expense data to estimate the forward-looking input
values. We find that using the estimated average of one-time costs over
the five preceding years (1993-1997) to adjust 1998 data is a reasonable
method to determine the impact of costs related to mergers and
acquisitions and work force restructuring. Further, we believe any
adjustments for one-time costs based on the AT&T and MCI analysis may be
biased downward after comparing the number of companies involved in
these types of activities in 1998 and 1999 to those in 1993-1997.[^911]
Accordingly, we adjust downward estimated expenses in account 6530
(Network Operations) by 2.6 percent and in account 6700 (Executive,
Planning, General, and Administrative) by 20 percent.

401\. [Removal of Non-Supported Expenses]{.underline}. In the *Inputs
Further Notice*, we also discussed our efforts to adjust marketing and
other customer service expenses to account for recurring expenses that
are not related to services supported by the federal high-cost
mechanism.[^912] The non-supported expenses we attempted to identify
include vertical features expenses, billing and collection expenses not
related to supported services, operational support systems and other
expenses associated with providing unbundled network elements and
wholesale services to competitive local exchange carriers. We proposed
adjustments to extract non-supported service costs related to marketing,
which is discussed separately below,[^913]coin operations, published
directory, access billing, interexchange carrier office operation, and
service order processing.[^914] Specifically, we made percentage
reductions to the regression coefficient results for specific expense
accounts based on a time trend analysis of average ARMIS 43-04 expense
data for five years (1993-1997).

402\. Some commenters argue that our proposed methodology removes
non-supported services twice because these expenses were already taken
out by the regression when expenses are subdivided among switched lines,
special lines, and toll minutes.[^915] Although we agree, as discussed
below, that our methodology double counted the marketing expenses
associated with special access lines, we do not agree with the theory
that combining a percentage reduction with the regression methodology
invariably removes expenses twice. For example, vertical features
associated with switched lines such as call waiting are not supported,
but the expenses associated with call waiting are not removed using the
regression analysis. If we had the data to separately identify and
remove vertical features expenses from switched lines, we believe that
it would be appropriate to do so and to continue using the regression
analysis to separate the remaining expenses. Nonetheless, upon further
analysis, we find that we should not adopt our proposed method of
removing these non-supported recurring expenses. We find that this
method is not sufficient to adequately identify non-supported common
support service expenses due to differences in account classifications
from the ARMIS 43-03 and ARMIS 43-04 reports. Therefore, we do not
utilize the time trend analysis or take reductions for these
non-supported expenses in the input values at this time. We recognize
that this causes an overstatement of in our estimate of the expenses
attributable to supported services in account 6620 (Service Expense and
Customer Operations). Unlike the case with marketing, however, we do not
have an alternative source of information on which to base a methodology
for removing the non-supported expenses in this account. We plan to seek
comment on a verifiable and systematic method to identify and remove
these costs in the proceeding on the future of the model.

403\. [Marketing]{.underline}. As explained in the *Inputs Further
Notice,* we made an adjustment to the Account 6610 (Marketing)[^916]
regression coefficient based on an analysis made by Economics and
Technology, Inc. (ETI).[^917] The ETI analysis offered a method for
disaggregating product management, sales, and advertising expenses for
basic (residential) telephone service from total marketing costs. Based
on information from the New England Telephone Cost Study, ETI attributed
an average of 95.6 percent of company marketing costs to non-supported
customers or activities, such as vertical and new services. Relying on
this analysis, we reduced the input estimate to reflect 4.4 percent of
marketing expenses determined by the regression. In the *Inputs Further
Notice*, we tentatively concluded that this was the most accurate method
on the record for apportioning marketing expenses between supported and
non-supported services.[^918]

404\. We agree with commenters that, in making this adjustment to the
post-regression analysis input estimate, we incorrectly estimated
marketing expenses because reductions were taken twice for special
access lines.[^919] We agree with the commenters that any adjustments to
exclude expenses based on the type of service should be made from total
relevant marketing expenses rather than the regression results.
Therefore, we do not use the regression methodology to estimate
marketing expenses. Instead, using the 1998 ARMIS data, we adjust the
total weighted average of relevant expenses for all study areas.

405\. Commenters also point out that the adjustment figure of 4.4
percent based on the ETI Study as initially reported was determined
under the assumption that only expenses attributable to residential
local service would be supported.[^920] Further, the ETI estimate of
costs associated with the marketing of supported services was calculated
by taking a percentage of expenses only from Account 6611, Product
Management. Specifically, the ETI estimate did not include any relevant
expenses from Account 6613, Product Advertising. As noted in the *Inputs
Further Notice*, funding support for marketing is to be based on those
expenses associated with advertising. Section 214 of the Communications
Act requires eligible telecommunications carriers to advertise the
availability of residential local exchange and universal service
supported services.[^921] Moreover, we note that under the current high
cost loop support mechanism, carriers receive no support for
marketing.[^922]

406\. We received further documentation and an alternative analysis from
ETI which included an estimate for advertising expenditures.[^923] The
revised analysis included proportional allocations of advertising costs
based on the percentage of lines estimated for primary line residential
service and single-line business service. ETI also used line count
source material from the Preliminary Statistics of Common Carriers 1998
rather than relying on 1996 data used in its original analysis.

407\. Based on the new information provided and the lack of any
reasonable alternative presented by the commenters, we calculate an
input estimate of supported advertising expenses using the ETI study and
1998 ARMIS expenses.[^924] By adding a proportional allocation for
multi-line business advertising expenses to the ETI alternative analysis
(which only included an estimate representing primary line and single
line business advertising costs), we conclude that 34.4 percent of
Account 6613, Product Advertising, would be the most appropriate expense
amount for the advertising of universal service.[^925] Because the
additional data provided by ETI allowed for the calculation and estimate
of supported and non-supported advertising expenditures, we did not
allocate costs associated with product management or sales. As
previously mentioned, these marketing activities are not specifically
required for support under Section 214 of the Communications Act and
currently receive no high cost loop support. Taking 34.84 percent of
total 1998 advertising expenses for the 80 non-rural high cost study
areas and dividing by total lines per month, the average per line per
month input value for advertising support is \$0.09. This level of
advertising expenses represents 5.82 percent of total 1998 marketing
costs for non-rural carriers.

408\. [Local Number Portability]{.underline}. There is an additional
input value that we estimate separately from our consideration of other
expense input values. Specifically, the synthesis model has a
user-adjustable input for the per-line costs associated with local
number portability (LNP). In the *Inputs Further Notice*, we proposed a
per-line monthly LNP cost of \$0.39, based on a weighted average of the
LNP rates filed by the LECs available at that time.[^926] AT&T and MCI
point out that the Commission suspended and investigated some of those
rates, and that the rates we approved are generally lower than the rates
we used to estimate our LNP input value.[^927] They argue that we should
use the line-weighted nationwide average of approved LNP rates, which
they estimate currently is \$.032.[^928] GTE claims that there is no
justification for using the nationwide average LNP rate, as suggested by
AT&T and MCI, because the approved LNP rates provide the best
representation of each company\'s LNP costs.[^929] We agree with GTE and
in this instance depart from our general practice of using nationwide
input values in the federal universal service support mechanism. Because
the Commission has investigated and approved LNP rates for most LECs, we
find that it is appropriate to use the company-specific input values
listed in Appendix D.[^930] For those carriers that have not yet filed
an LNP tariff, we will use the line-weighted nationwide average of
approved LNP rates.

**D. GSF Investment**

**1. Background**

409\. GSF investment includes buildings, motor vehicles, and general
purpose computers. The synthesis model platform uses a three-step
algorithm to estimate GSF investment. First, for each study area, the
model calculates a GSF investment ratio for each GSF account by dividing
the ARMIS investment for the account by the ARMIS total plant in service
(TPIS) less GSF investment. The values proposed in the *Inputs Further
Notice* used 1996 ARMIS data in this step.[^931] Second, the model
calculates a preliminary estimate for GSF investment for each account by
multiplying the model\'s estimate of TPIS by the GSF investment ratios
developed in step one.[^932] Third, the model reduces the preliminary
GSF investment estimates for each account by multiplying these estimates
by one of two factors.[^933]

410\. In the *Inputs Further Notice*, we tentatively concluded that the
model\'s preliminary estimates of GSF investment should be reduced in
the third step of the algorithm, because only a portion of GSF
investment is related to the cost of providing the services supported by
the federal mechanism, but that we should not use the same factors as
those used in the HAI model.[^934] We noted that the HAI sponsors used
one factor for some accounts and a different factor for others, but had
not explained why either particular factor should be used.[^935] Rather
than using two different factors, we proposed using a factor that
reflects the percentage of customer operations, network operations, and
corporate operations used to provide the supported services.
Specifically, we proposed calculating preliminary GSF investment on a
study area specific basis (steps one and two), and then multiplying
these estimates by a nationwide allocation factor derived from the
regression methodology that we used to estimate the portion of common
support services expenses attributable to switched lines and local
usage.[^936]

**2. Discussion**

411\. We conclude that the model\'s preliminary estimates of GSF
investment should be reduced in the third step of the algorithm, because
we find that only a portion of GSF investment is related to the cost of
providing the services supported by the federal mechanism. In response
to certain comments, however, we modify our proposed allocation factor,
as discussed below. Although we reject commenters\' arguments that the
preliminary GSF investment should not be reduced at all, we agree that
we should not exclude facility-related maintenance expenses in our
proposed allocation factor. In addition, we modify our method of
calculating the denominator of our allocation factor so that both the
numerator and denominator are simple averages. Finally, we clarify that
the ARMIS TPIS used in the first step of the algorithm excludes ARMIS
GSF investment.

412\. [Reduction of Preliminary GSF Estimate]{.underline}. Several LEC
commenters argue that the preliminary GSF investment should not be
reduced by an allocator in the third step of the algorithm.[^937] SBC
contends that the factor we use to reduce our preliminary GSF investment
estimates substantially underestimates the GSF amounts related to the
supported services.[^938] SBC claims that the ratios used to estimate
the preliminary GSF investment already provides a reasonable basis for
allocating GSF to supported services, because the GSF ratio (derived
from the ARMIS accounts) is only applied to investment identified by the
model as associated with supported services.[^939] BellSouth also claims
that the TPIS calculated by the model is the investment necessary to
provide the supported services and that no further reductions in the
preliminary GSF investment estimate are appropriate.[^940] Sprint
similarly claims that by applying a book GSF ratio to the
forward-looking plant necessary to provide supported services, the
modeled GSF plant also has been converted to a forward-looking level
necessary to provide the supported services. Sprint contends that
applying an additional allocator is not necessary and has the effect of
reducing GSF plant twice.[^941]

413\. We disagree with SBC\'s contention that only a portion of GSF is
assigned to supported services in deriving our preliminary estimates of
GSF investment.[^942] To the contrary, the GSF ratio is applied to all
model investment, which includes the investment required to provide both
supported and non-supported services. As discussed above, the model
estimates the cost of providing services for all businesses and
households within a geographic region, including the provision of
special access, private lines, and toll services.[^943] Because these
services are not supported by the federal high-cost mechanism, the
preliminary GSF investment estimate must be adjusted to reflect the
portion of GSF investment attributable to the supported services. Thus,
BellSouth\'s assertion that the TPIS calculated by the model is the
investment necessary to provide the supported services is wrong. For the
same reasons, we reject Sprint\'s argument that, by applying the book
GSF ratio, the modeled GSF plant has somehow been converted to a
forward-looking level necessary to provide the supported services. On
the contrary, the conversion estimates the amount of GSF investment
attributable to all services, supported and non-supported. The second
reduction is required to estimate the amount of GSF investment that
should be supported by the federal universal service support mechanism.

414\. [Allocation Factor]{.underline}. Assuming that we use an allocator
to reduce preliminary GSF investment, several commenters criticize the
particular allocator that we proposed in the *Inputs Further Notice.*
For example, GTE questions why we used only expenses for customer
operations, network operations, and corporate operations in the
allocation calculation and excluded plant-specific expenses.[^944] GTE
argues that plant-specific operations also use GSF investments and
should be counted in the calculation. SBC also argues that GSF
investment supports all aspects of a LEC\'s operations, and contends
that it makes no sense to exclude facility-related maintenance expenses
in our proposed allocation factor.[^945] We agree that expenses for
plant-specific operations expenses should be included in our calculation
of the nationwide allocation factor derived from the regression
methodology. Accordingly, the allocation factor we adopt to estimate GSF
investment includes plant-specific operations expenses.[^946]

415\. GTE also contends that the forward-looking way to calculate a GSF
investment ratio is to convert all ARMIS investments to current values
using current-to-book ratios, before calculating an adjusted ARMIS GSF
to TPIS investment ratio.[^947] Although we concede there is some logic
to GTE\'s argument that we should convert ARMIS GSF investments to
current values by using current-to-book ratios, we note that this would
require a change in the model platform. As we explain above, the model
platform uses a three-step algorithm to estimate GSF investment.[^948]
Although we can easily change the input value for the factor used in
step three, we could not adjust the ARMIS data by applying a
current-to-book factor without modifying the model platform.[^949]
Proposals to change the model platform are properly addressed in
response to pending petitions for reconsideration of the *Platform
Order* or the proceeding on the future of the model.

416\. Finally, GTE claims that our estimation of the universal service
portion of the GSF investment is flawed because our regression
methodology uses a wrong specification and incorrectly excludes
expenses.[^950] GTE also claims that the calculation allocator itself is
flawed because the numerator is a simple average of expenses derived
from the regression results, but the denominator is a weighted average
of the total expenses developed from ARMIS data.[^951] GTE argues that
the type of average in the numerator and denominator should match.[^952]
While we do not agree that our regression methodology is flawed, we find
that GTE has pointed out an inconsistency in our GSF methodology.
Specifically, we agree that we should use the same type of average in
both the numerator and denominator of our allocation factor. As a
result, we use the simple average of total expenses in the denominator
of the allocation factor we adopt for estimating the portion of GSF
attributable to supported services.[^953]

417\. [Clarification]{.underline}. BellSouth claims that the algorithm
used to estimate GSF investment contains an error in consistency.
BellSouth suggests that in step one we should determine the ratio of
ARMIS-based GSF investment to the ARMIS-based TPIS less GSF
investment.[^954] In step two, this ratio is multiplied by the TPIS
investment determined by the model, which excludes GSF. We clarify that
the model calculates GSF investment as BellSouth suggests it should.
That is, the model uses ARMIS-based TPIS less GSF investment.[^955] US
West claims that in the second step of the algorithm the synthesis model
includes only fifty percent of the building investment and no land
investment.[^956] The synthesis model incorporates the HAI switching and
expense modules and calculates the investment related to wire center
buildings and land in the switching module. So, US West is mistaken that
fifty percent of the building and land investment is eliminated, because
this investment is added back in calculating switching costs.[^957]

418\. For the reasons stated above, we adopt input values for GSF
investment that reflect the portion of GSF investment attributable to
the cost of providing the services supported by the federal mechanism.
Specifically, we calculate preliminary GSF investment on a study area
specific basis, using 1998 ARMIS data, and then multiply these estimates
by a nationwide allocation factor derived from the regression
methodology that we used to estimate the portion of common support
services expenses attributable to switched lines and local usage and the
portion of plant-specific operations expenses attributable to the
supported services.[^958] The allocation factor is the sum of plant
specific operations expenses, customer operations expenses, network
operations expenses, and corporate operations expenses attributable to
the supported services, divided by the sum of those expenses calculated
on a total regulated basis.

**VIII. CAPITAL COSTS**

**A. Depreciation**

**1. Background**

419\. We now consider the inputs related to the calculation of
depreciation expenses. The model uses \"adjusted projection lives\" to
recover the current costs of the assets.[^959] Under this approach, the
annual depreciation charges associated with an asset are computed by
dividing the asset\'s current cost by its adjusted projection
life.[^960] A shorter life will increase the annual depreciation
expense.

420\. In the *Universal Service Order*, the Commission concluded that
\"economic lives and future net salvage percentages used in calculating
depreciation expense should be within the FCC-authorized range\" and use
currently authorized depreciation lives.[^961] In the *1997 Further
Notice*, the Commission tentatively concluded that it should adopt
depreciation expenses that reflect a weighted average of the rates
authorized for carriers that are required to submit their rates to
us.[^962] The Commission also sought comment on whether adjusted
projected asset lives should reflect the lives of facilities and
equipment dedicated to providing only the services supported by
universal service or whether the asset lives should reflect a decision
to replace existing plant with plant that can provide broadband
services.[^963] The *May 4 Public Notice* requested further information
on these issues.[^964]

421\. In the *Inputs Further Notice*, we tentatively adopted a method of
depreciation that should be used in the model, i.e., how depreciation
allowances should be allocated over the life of an asset.[^965] Because
the Commission\'s depreciation accounting rules require the use of
straight-line equal-life-group depreciation, rather than a more
accelerated depreciation method, we tentatively concluded that this
method, which is used for all Commission-proposed depreciation, is also
appropriate for use in the high-cost support mechanism.[^966]

**2. Discussion**

**a. Method of Depreciation**

422\. For the reasons explained below, we adopt a straight-line
equal-life-group method of depreciation. Further, we select curve shapes
to be used to distribute equal-life groups in each plant account.[^967]

423\. Most commenters support our tentative conclusion to use the
straight-line equal-life-group method of depreciation.[^968] Ameritech
argues, however, that the Commission\'s adoption of a straight-line
depreciation method in other contexts need not limit us to that method
for use in this model, and that \"the method of depreciation for a
specific study area needs to be consistent with any study that underlie
\[sic\] the development of economic lives or net salvage.\"[^969]
Although Ameritech may correctly assert that there is no requirement
that we adopt a method of depreciation simply because it is the method
previously adopted by the Commission in another context, we believe that
the Commission\'s adoption, in other proceedings, of the straight-line
equal-life-group method reflects the well-considered conclusion that
this method of depreciation is best-suited to determining the economic
costs of providing local service. The straight-line equal-life-group
depreciation method is also consistent with our method of developing
economic lives and net salvage for the same plant accounts. Because the
Commission consistently uses a straight-line equal-life-group
depreciation method in all other Commission-proposed depreciation, and
in light of the general support received in favor of straight-line
equal-life-group depreciation, we conclude that straight-line
equal-life-group depreciation is appropriate for use in the high-cost
support mechanism.[^970]

424\. In using the straight-line equal-life-group method of depreciation
in other contexts, the Commission has acknowledged that the method
necessarily requires the selection of a curve shape for the distribution
of the equal-life groups.[^971] The HAI model assumed a single curve
shape for all plant accounts.[^972] Because the curve shapes are not
easily averaged across all categories, however, we believe that use of
the single HAI curve shape will unduly distort the model input values.
We, therefore, determine that separate curve shapes should be adopted
for each plant account category. Actuaries have developed generic,
standardized curve shapes, called Gompertz-Makeham (GM) standard curves,
that describe generalized mortality patterns. GM standard curve shapes
are recognizable to many knowledgeable parties concerned with
depreciation methods and are normally more immediately meaningful to
them than nonstandard curve shapes, which are identified by the values
for three variables.[^973] For convenience purposes, GM standard curves
are often substituted for nonstandard curves. USTA has developed
nonstandard curve shapes for most plant accounts based on mortality data
provided by its members, using the same methodology approved in other
Commission proceedings.[^974] For the remaining plant accounts, the
Commission has developed composite curves, also nonstandard, utilizing
data from available depreciation studies. Because the GM standard curves
are recognizable and convenient to parties interpreting the data inputs
in the high-cost model, and because the standardized curves will not
vary significantly from the nonstandardized curves, we conclude that GM
standard curves will be more useful in the high-cost inputs model than
nonstandard curves. For each plant category, therefore, we adopt the GM
standard curve shape nearest that developed by USTA or the
Commission.[^975]

**b. Depreciation Lives and Future Net Salvage Percentages**

425\. We adopt the tentative conclusion of the *Inputs Further Notice*
that we should use HAI\'s input values with respect to depreciation
lives and future net salvage percentages. As explained below, we reject
the objections by some commenters that the HAI input values are not
appropriate for determining depreciation rates in a competitive
environment.

426\. In estimating depreciation expenses, the model uses the projected
lives and future net salvage percentages for the asset accounts in Part
32 of the Commission\'s rules.[^976] Traditionally, the projected lives
and future net salvage values used in setting a carrier\'s rates have
been determined in a triennial review process involving the state
commission, the Commission, and the carrier. In order to simplify this
process, the Commission has prescribed ranges of acceptable values for
projected lives and future net salvage percentages.[^977] The
Commission\'s prescribed ranges reflect the weighted average asset life
for regulated telecommunications providers. These ranges are treated as
safe harbors, such that carriers that incorporate values within the
ranges into their depreciation filings will not be challenged by the
Commission. Carriers that submit life and salvage values outside of the
prescribed range must justify their submissions with additional
documentation and support.[^978] Commission-authorized depreciation
lives are not only estimates of the physical lives of assets, but also
reflect the impact of technological obsolescence and forecasts of
equipment replacement. We believe that this process of combining
statistical analysis of historical information with forecasts of
equipment replacement generates forward-looking projected lives that are
reasonable estimates of economic lives and, therefore, are appropriate
measures of depreciation.

427\. We disagree with comments by incumbent LECs that the Commission\'s
prescribed ranges are not appropriate for determining depreciation rates
in a competitive environment.[^979] These parties argue that rapid
changes in technology and competition in local telecommunications
markets will diminish asset lives significantly below the Commission\'s
prescribed range by causing existing equipment to become obsolete more
quickly.[^980] We agree with GSA, AT&T and MCI that there is no evidence
to support the claim that increased competition or advances in
technology require the use of shorter depreciation lives in the model
than are currently prescribed by the Commission.[^981] The Commission\'s
prescribed lives are not based solely on the engineered life of an
asset, but also consider the impacts of technological change and
obsolescence. We note that the depreciation values we adopt are
generally at the lower end of the prescribed range. We also find
compelling the data presented in GSA\'s comments showing that, although
the average depreciation rate for an incumbent LEC\'s Total Plant in
Service is approximately seven percent, incumbent LECs are retiring
plant at a four percent rate.[^982] This difference has allowed
depreciation reserves to increase so that the depreciation reserve-ratio
is currently greater than 50 percent.[^983] We conclude that the
existence of this difference implies that the prescribed lives are
shorter than the engineered lives of these assets. In addition, this
difference provides a buffer against technological change and
competitive risk for the immediate future. We, therefore, conclude that
the Commission\'s prescribed ranges are appropriate to determine
depreciation rates for use in the federal high-cost mechanism.

428\. We also decline to adopt the values for projected lives and net
salvage percentages submitted by several incumbent LEC commenters. These
commenters propose adoption of default values for projected lives and
salvage based LEC industry date surveys[^984] or on similar values
currently used by LECs for financial reporting purposes.[^985] The LEC
industry data survey\'s projected lives generally fall outside of the
Commission\'s prescribed ranges.[^986] This is significant because the
values that fall outside of the prescribed ranges represent accounts
that reflect the overwhelming majority of plant investment, thus
potentially triggering a dramatic distortion of the estimated cost of
providing the supported services. Moreover, these commenters assert that
technological advances and competition will have the effect of
displacing current technologies, but offer no specific evidence that
this displacement will occur at greater rates than the forward-looking
Commission-authorized depreciation lives take into account. The record
is particularly silent regarding the displacement of technologies
associated with the provision of services supported by the federal
high-cost mechanism. We do not believe that the LEC industry data
survey\'s projected lives have been adequately supported by the record
in this proceeding to justify their adoption.

429\. We also agree with GSA\'s comments that the projected-life values
currently used by LECs for financial reporting purposes are
inappropriate for use in the model.[^987] In addition, the commenters
proposing these values have not explained why the values used for
financial reporting purposes would also reflect economic depreciation.
The depreciation values used in the LECs\' financial reporting are
intended to protect investors by preferring a conservative
understatement of net assets, partially achieving this goal by erring on
the side of over-depreciation. These preferences are not compatible with
the accurate estimation of the cost of providing services that are
supported by the federal high-cost mechanism. We, therefore, decline to
adopt the projected life values used by LECs for financial reporting
purposes.

430\. In the *1997 Further Notice*, the Commission tentatively concluded
that it should adopt depreciation expenses that reflect a weighted
average of the rates authorized for carriers that are required to submit
their rates to us.[^988] The values submitted by the HAI sponsors
essentially reflect such a weighted average. The HAI values represent
the weighted average depreciation lives and net salvage percentages from
76 study areas.[^989] According to the HAI sponsors, these depreciation
lives and salvage values reflect the experience of the incumbent LEC in
each of these study areas in retiring plant and its projected plans for
future retirements.[^990]

431\. In the *Inputs Further Notice*, we tentatively concluded that
HAI\'s values represent the best forward-looking estimates of
depreciation lives and net salvage percentages.[^991] Generally, these
values fall within the ranges prescribed by the Commission for projected
lives and net salvage percentages. Although the HAI values for four
account categories fall outside of the Commission\'s prescribed
ranges,[^992] these values still reflect the weighted average of
projected lives and net salvage percentages that were approved by the
Commission and, therefore, are consistent with the approach proposed in
the *1997 Further Notice*. As noted above, the fact that an approved
value falls outside of the prescribed range simply means that the
carrier proposing the value was required to provide additional
justification to the Commission for this value. We are satisfied that
HAI calculated its proposed rates using the proper underlying
depreciation factors and that HAI\'s documentation supports the
selection of these values. We, therefore, adopt HAI\'s values for
estimating the depreciation lives and net salvage percentages.

**B. Cost of Capital**

432\. We now adopt the conclusions that we tentatively reached in the
*Inputs Further Notice* regarding the cost of capital. For the reasons
discussed below, we do not find that any commenter has provided a
compelling argument for altering the current federal rate of return of
11.25 percent, absent the adoption of a different rate of return by the
Commission in a rate prescription order.

433\. The cost of capital represents the annual percentage rate of
return[^993] that a company\'s debt-holders and equity holders require
as compensation for providing the debt and equity capital that a company
uses to finance its assets.[^994] In the *Universal Service Order*, the
Commission concluded that the current federal rate of return of 11.25
percent is a reasonable rate of return by which to determine
forward-looking costs.[^995]

434\. GSA, AT&T and MCI comment that the cost of capital for incumbent
LECs is well below 11.25 percent.[^996] Bell Atlantic advocates a cost
of capital rate in the range of 12.75 to 13.15 percent.[^997] GTE and
USTA dispute the lower cost of capital asserted by AT&T and MCI and
GSA.[^998] All commenters addressing this issue agreed that, if a
different rate of return is adopted in a rate prescription order, that
value should be adopted in the model.[^999]

435\. We find that the commenters proposing an adjustment to the cost of
capital have failed to make an adequate showing to justify rates that
differ from the current 11.25 percent federal rate of return. We
conclude, therefore, that the current rate is reasonable for determining
the cost of providing services supported by the federal high-cost
mechanism. If the Commission, in a rate prescription order, adopts a
different rate of return, we conclude the federal mechanism should use
the more recently determined rate of return.

**C. Annual Charge Factors**

436\. We also now adopt our tentative conclusion in the *Inputs Further
Notice* to use HAI\'s annual charge factor methodology. As explained
below, we find this appropriate because the synthesis model uses a
modified version of HAI\'s expense module.

437\. Incumbent LECs develop cost factors, called \"annual charge
factors,\" to determine the dollar amount of recurring costs associated
with acquiring and using particular pieces of investment for a period of
one year. Incumbent LECs develop these annual charge factors for each
category of investment required. The annual charge factor is the sum of
depreciation, cost of capital, adjustments to include taxes on equity,
and maintenance costs.

438\. To develop annual charge factors, the BCPM proponents proposed a
model with user-adjustable inputs to calculate the depreciation and cost
of capital rates for each account.[^1000] The BCPM proponents stated
that this account-by-account process was designed to recognize that all
of the major accounts have, among other things, differing economic lives
and salvage values that lead to distinct capital costs.[^1001] HAI\'s
model is also user adjustable and reflects the sum for the three inputs:
depreciation, cost of capital, and maintenance costs.[^1002] In the
*Inputs Further Notice*, the Commission tentatively adopted HAI\'s
annual charge factor methodology, and invited comment on this tentative
decision.[^1003] GTE argues that the annual charge factors should be
company specific, in order to make the cost calculations in the
optimization phase and the expense module comparable.[^1004] We do not
believe it would be appropriate to adopt GTE\'s proposal of using
company-specific annual charges, because we are adopting nationwide
averages for all other inputs, including those that make up the annual
charge. Adopting company-specific annual charges would therefore result
in likely inconsistencies between various related inputs and in the
model as a whole. AT&T and MCI support the use of the HAI annual charge
factor methodology.[^1005]

439\. Because the synthesis model uses HAI\'s expense module, with
modifications, we adopt HAI\'s annual charge factor methodology,
utilizing the capital cost and expense inputs adopted above.[^1006] We
believe that HAI\'s annual charge factor methodology is consistent with
other inputs used in the model adopted by the Commission, and is,
therefore, easier to implement and yields more reasonable results.

**IX. PROPOSED MODIFICATION TO PROCEDURES FOR DISTINGUISHING RURAL AND
NON-RURAL COMPANIES**

**A. Background**

440\. In the *Universal Service Order*, the Commission determined that
rural and non-rural carriers will receive federal universal service
support determined by separate mechanisms until at least January 1,
2001.[^1007] The Commission stated that it would define rural carriers
as those carriers that meet the statutory definition of a rural
telephone company in section 153(37) of the Communications Act.[^1008]
Under this definition, a \"local exchange carrier operating entity\" is
deemed a \"rural telephone company\" to the extent that such entity\--

\(A\) provides common carrier service to any local exchange carrier
study area that does not include either\--

\(i\) any incorporated place of 10,000 inhabitants or more, or any part
thereof, based on the most recently available population statistics of
the Bureau of the Census; or

\(ii\) any territory, incorporated or unincorporated, included in an
urbanized area, as defined by the Bureau of the Census as of August 10,
1993;

\(B\) provides telephone exchange service, including exchange access, to
fewer than 50,000 access lines;

\(C\) provides telephone exchange service to any local exchange carrier
study area with fewer than 100,000 access lines; or

\(D\) has less than 15 percent of its access lines in communities of
more than 50,000 on the date of enactment of the Telecommunications Act
of 1996.

441\. In addition, the Commission determined that LECs should
self-certify their status as a rural company each year to the Commission
and their state commission.[^1009] On September 23, 1997, the Bureau
released a Public Notice requiring carriers seeking to be classified as
rural telephone companies to file a letter with the Commission by April
30 of each year certifying that they meet the statutory
definition.[^1010] The *Self-Certification Public Notice* requires a LEC
certifying as a rural carrier to explain how it meets at least one of
the four criteria set forth in the statutory definition.[^1011] On June
22, 1998, the Accounting Policy Division (Division) released a Public
Notice with a list of the approximately 1,400 carriers that had
certified as rural carriers as of April 30, 1998.[^1012] On March 16,
1999, the Bureau released a Public Notice revising the annual deadline
for LECs seeking to be classified as rural carriers to July 1 of each
year. In the *Inputs Further Notice*, the Commission extended the July
1, 1999, recertification filing deadline to October 15, 1999.[^1013] On
September 27, 1999, the Division released a Public Notice further
extending the deadline to December 1, 1999, in consideration of the
possibility that certain carriers might not be required to file the
certification letter in light of the action we take in this
Order.[^1014]

442\. Because a vast majority of the carriers certifying as rural
telephone companies serve fewer than 100,000 access lines, we
tentatively concluded in the *Inputs Further Notice* that we should
adopt new filing requirements for carriers filing rural
self-certification letters.[^1015] We proposed that carriers who serve
fewer than 100,000 access lines should not have to file the annual rural
certification letter unless their status has changed since their last
filing.[^1016] We also sought comment on certain terms relevant to the
definition of a rural telephone company in section 153(37) of the
Act.[^1017] In addition, we sought comment on whether the Commission
should reconsider its use of section 153(37) to distinguish rural
telephone companies from non-rural telephone companies.[^1018]

**B. Discussion**

443\. Consistent with our tentative conclusion in the *Inputs Further
Notice*, we eliminate the annual filing requirements for carriers
serving fewer than 100,000 access lines that have self-certified as
rural, unless changes occur in their status as rural carriers. In
addition, we will require carriers serving study areas with more than
100,000 access lines to file rural self-certifications that are
consistent with the statutory interpretation discussed below.
Thereafter, such carriers also will be required to file only in the
event of a change in their status.

444\. As discussed below, we interpret \"local exchange operating
company\" in section 153(37) of the Act to refer to the legal entity
that provides local exchange service. In addition, we interpret
\"communities of more than 50,000\" in that section to refer to legally
incorporated localities, consolidated cities, and census-designated
places with populations of more than 50,000 according to Census Bureau
statistics.

445\. With respect to our request for comment on whether we should
reconsider our use of section 153(37) to distinguish rural telephone
companies from non-rural companies, we conclude below that we should not
use an alternative definition of rural telephone company to determine
which companies are subject to the rural or non-rural high-cost support
mechanisms.

446\. Because of settled expectations in this ongoing proceeding, the
Commission will accept a carrier\'s current rural self-certification for
purposes of calculating support based on that status for calendar year
2000. We will require carriers serving study areas with more than
100,000 access lines to certify their rural status by July 1, 2000, for
purposes of receiving support beginning January 1, 2001.

**1. Annual Filing Requirement**

447\. [Carriers serving study areas with fewer than 100,000 access
lines]{.underline}. We adopt the proposed change in the annual
self-certification requirement for rural carriers and will require that
carriers serving fewer than 100,000 access lines file a rural
self-certification letter only if their status has changed since their
last filing. All commenters addressing this issue urge the Commission to
eliminate annual filing requirements.[^1019] We believe that this is a
better approach because the overwhelming majority of the companies that
filed rural certification letters qualified as rural telephone companies
under the 50,000- or 100,000-line thresholds identified in the statute.
Access line counts can be verified easily with publicly available data.
Further, this relaxation in filing requirements will lessen the burden
on rural carriers. We estimate that this change will eliminate the
filing requirement for approximately 1,380 of the carriers that filed in
1998, many of which are small businesses on which even limited
regulatory requirements may be unduly burdensome. We, therefore,
conclude that carriers serving study areas with fewer than 100,000
access lines that already have certified their rural status need not
re-certify for purposes of receiving support beginning January 1, 2000,
and need only file thereafter if their status changes. As explained
below, we must determine the status for carriers serving study areas
with more than 100,000 access lines.

448\. We believe, as GTE suggests, that carriers generally (although not
uniformly) have filed for rural status in this proceeding on a study
area basis. Indeed, the synthesis model that has been posted on the
Commission\'s Web site \-- allowing carriers to determine how the
Commission has been treating them throughout this proceeding \--
estimates cost on a study area basis.[^1020] Not all carriers, however,
have uniformly filed for rural status on a study area basis, as we noted
in the *Inputs Further Notice*, resulting in inconsistencies that must
be resolved in order to assure equitable treatment of all carriers.
These inconsistencies will be addressed below.

449\. [Carriers serving study areas with more than 100,000 access
lines]{.underline}. For purposes of calculating high cost support using
the model for the year 2000, we will continue to treat carriers as rural
if they have previously self-certified as rural carriers. We will then
require rural carriers serving study areas with more than 100,000 access
lines to file certification letters by July 1, 2000, for their year 2001
status. Commenters that address the issue broadly support
re-certification requirements that require these carriers to re-certify
only if their status has changed, rather than require them to re-certify
each year.[^1021] Finding that the relaxed re-certification requirements
will reduce administrative burdens for carriers subject to rural
certification and for the Commission, we conclude that certified rural
carriers with more than 100,000 access lines need only re-certify their
status if it changes. Therefore, in 2001 and subsequent years, a carrier
serving study areas with more than 100,000 access lines and claiming
rural status will be required to file only if its status changes.

**2. Statutory Terms**

450\. As noted in the *Inputs Further Notice*, carriers\' line counts
are readily available to the Commission, but information about service
territories and communities served are not. As a result, the Commission
can easily determine whether a carrier satisfies criteria (B) or (C) of
the rural telephone company definition,[^1022] because these criteria
are based on information that can be verified easily with publicly
available data \-- the number of access lines served by a carrier. In
contrast, criteria (A) and (D) require additional information and
analysis to verify a carrier\'s self-certification as a rural
company.[^1023] Specifically, under criterion (A), a carrier is rural if
its study area does not include \"any incorporated place of 10,000
inhabitants or more\" or \"any territory . . . in an urbanized area,\"
based upon Census Bureau statistics and definitions.[^1024] Under
criterion (D), a carrier is rural if it had \"less than 15 percent of
its access lines in communities of more than 50,000 on the date of
enactment of the \[1996 Act\].\"[^1025]

451\. We conclude that criterion (A), by referencing Census Bureau
sources, can be applied consistently without further interpretation by
the Commission. We will require, however, that carriers self-certifying
as rural telephone companies pursuant to criterion (A) include with
their self-certification letter a description of the study areas in
which they provide service and the basis for their assertion that they
meet the requirements of criterion (A).

452\. In the *Inputs Further Notice*, we sought comment on the meaning
of the term \"local exchange operating entity.\" Commenters have offered
three different interpretations of this term. Many commenters suggest
that we should interpret the term as applying at the study area
level.[^1026] Although in most cases an operating entity will provide
service to only one study area within a state, that is not always the
case. As a result, the study area approach could mean classifying a
carrier at an organizational level smaller than the actual legal entity
responsible for the provision of the local exchange services (e.g., a
\"division\" of a company). In contrast, AT&T and MCI argue that the
term should mean the holding company within a state whose affiliates
provide the local exchange services.[^1027] The third interpretation has
been proposed by RTC and Citizens Utilities, who argue that the most
natural understanding of \"local exchange operating entity\" is the
legal entity responsible for the provision of local exchange services,
regardless of whether that entity serves a single or multiple study
areas.[^1028] We conclude that this interpretation is the most
reasonable one.

453\. We believe that it is most logical to classify the carrier at the
actual corporate level through which it offers its local exchange
services. As RTC and Citizens Utilities point out, it is that entity
that has legal responsibility for the provision of the local exchange
services.[^1029] The holding company interpretation proposed by MCI and
AT&T seems to rest upon the concern that study area designations will be
manipulated and, as a result, carriers will inappropriately be eligible
for support as rural carriers, when they should not be.[^1030] We do not
believe that the potential for manipulation of the federal universal
service support mechanism by rural carriers poses the threat that AT&T
and MCI suggest; to the contrary, the study area waiver process provides
the Commission with oversight over the creation, division, and
combination of study areas.[^1031]

454\. On the other hand, if a carrier should be operating within
multiple study areas, we see no basis for interpreting the term \"local
exchange operating entity\" in a manner that would ignore the legal
entity responsible for the provision of services by designating a
subunit of the legal entity as the local exchange operating entity for a
particular study area. Rather, it is more reasonable to have the term
local exchange operating entity be synonymous with the corporate entity
bearing legal responsibility for the services provided.[^1032]

455\. Although we adopt Citizen Utilities\' interpretation of \"local
exchange operating entity,\" we reject its proposed interpretation of
criterion (C). Citizens Utilities proposes that a local exchange carrier
operating entity be considered a rural carrier for each of its study
areas, regardless of whether those study areas have fewer than 100,000
access lines, if any [single]{.underline} study area in which it
operates contains fewer than 100,000 access lines.[^1033] Under this
interpretation, which only Citizens Utilities supports, an incumbent LEC
offering service to a significant portion of a state, including major
urban areas, could be certified as a rural carrier for all study areas
that it serves within the state if it merely has one outlying study area
with less than 100,000 access lines. We find this interpretation to be
inconsistent with the statutory language that an entity is an rural
telephone company only \"to the extent\" that it serves a study area
with fewer than 100,000 lines. Essentially, Citizens Utilities\'
interpretation would read that limiting language out of section 153(37).
The effect of such a reading would be to permit some of the largest LECs
in the nation to claim rural status for all of their study areas if they
happen to serve a rural study area within in the state. Such an
interpretation would undermine not only the Commission\'s universal
service support mechanisms, but also the fundamental procompetitive
policies underlying the 1996 Act.[^1034] We do not believe that this
could be what Congress intended when it specified that carriers would be
deemed rural telephone companies \"to the extent\" that they satisfied
the various criteria, including criterion (C) pertaining to serving
study areas with less than 100,000 access lines. Accordingly, consistent
with the language of the statutory provision, its purpose, and its
context in the Act, we adopt the interpretation that a LEC may be
properly considered a rural carrier with respect to those study areas to
which its operating company provides service to fewer than 100,000
access lines. In contrast, a LEC will be deemed a non-rural carrier for
study areas serving more than 100,000 access lines unless it satisfies
one of the other criteria under section 153(37).

456\. We also sought comment in the *Inputs Further Notice* regarding
the proper interpretation of \"communities of more than 50,000.\" GTE
offers an interpretation of this phrase based on the definition of
\"rural area\" in section 54.5 of the Commission\'s rules.[^1035] GTE
calculates its percentages of rural and non-rural lines by determining
whether each of its wire centers is associated with a metropolitan
statistical area (MSA). The lines in each wire center associated with an
MSA are considered to be urban, unless the wire center has rural
pockets, as defined by the most recent Goldsmith Modification.[^1036]
The approach suggested by GTE in its comments has merit because it
prevents rural treatment of a suburban area adjacent to a
census-designated place. At this time, however, there is no information
on the record to indicate that this circumstance presents a serious
problem in our determination of a carrier\'s status as a rural or
non-rural company. Other commenters addressing the issue support the
definition of \"communities of more than 50,000\" by using Census Bureau
statistics for legally incorporated localities, consolidated cities, and
census-designated places,[^1037] and some specifically reject the use of
the Commission\'s definition in section 54.5 because of the added
complication of its use.[^1038]

457\. Because GTE\'s approach is more complicated and difficult to
administer and because the consequences of the approach would reach only
a few, if any, rural carriers\' study areas, we decline to adopt GTE\'s
interpretation of \"communities of more than 50,000.\" Instead, we now
adopt the use of Census Bureau statistics for legally incorporated
localities, consolidated, cities, and census-designated places for
identifying communities of more than 50,000, as Census Bureau statistics
are widely available and may be consistently applied by the Commission.
We further require that, when a carrier files for rural certification
under criterion (D), it must include in its certifying letter a list of
all communities of more than 50,000 to which it provides service, the
population of those communities, the number of access lines serving
those communities, and the total number of access lines the carrier
serves.

**3. Identification of Rural Telephone Companies**

458\. States apply the definition of rural telephone company in
determining whether a rural telephone company is entitled to an
exemption under section 251(f)(1) of the Act and in determining, under
section 214(e)(2) of the Act, whether to designate more than one carrier
as an eligible telecommunications carrier in an area served by a rural
telephone company.[^1039] Although the Commission used the rural
telephone company definition to distinguish between rural and non-rural
carriers for purposes of calculating universal service support, there is
no statutory requirement that it do so. The Commission adopted the Joint
Board\'s recommendation to allow rural carriers to receive support based
on embedded costs for at least three years, because, as compared to
large LECs, rural carriers generally serve fewer subscribers, serve more
sparsely populated areas, and do not generally benefit as much from
economies of scale and scope.[^1040] The Commission also noted that, for
many rural carriers, universal service support provides a large share of
the carriers\' revenues, and thus, any sudden change in the support
mechanisms may disproportionately affect rural carriers\'
operations.[^1041]

459\. In the *Inputs Further Notice*, we sought comment on whether to
reconsider the means of distinguishing rural and non-rural carriers.
Commenters generally oppose any reconsideration of our decision to use
the definition of rural telephone company to distinguish between rural
and non-rural carriers for the purpose of evaluating universal service
support on the grounds that changing the definition at this time could
disrupt the settled expectations that they have developed.[^1042] We
agree that we should not change our reliance on the statutory definition
of rural telephone company to distinguish between rural and non-rural
carriers for universal service purposes. Accordingly, we will leave in
place the Commission\'s decision to use the definition of rural
telephone company in section 153(37) of the Communications Act to
distinguish rural telephone companies from non-rural ones.

**X. PROCEDURAL MATTERS AND ORDERING CLAUSE**

**A. Final Regulatory Flexibility Analysis**

460\. As required by the Regulatory Flexibility Act (RFA),[^1043] an
Initial Regulatory Flexibility Analysis (IRFA) was incorporated in the
*Inputs Further Notice*.[^1044] The Commission sought written public
comment on the proposals in the *Inputs Further Notice*, including
comments on the IRFA. The Final Regulatory Flexibility Analysis (FRFA)
in this Order conforms to the RFA, as amended.[^1045]

461\. *Need for and Objectives of This Order*. In the *Universal Service
Order*, the Commission adopted a plan for universal service support for
rural, insular, and high-cost areas to replace longstanding federal
subsidies to incumbent local telephone companies with explicit,
competitively neutral federal universal service mechanisms.[^1046] In
doing so, the Commission adopted the recommendation of the Joint Board
that an eligible carrier\'s support should be based upon the
forward-looking economic cost of constructing and operating the networks
facilities and functions used to provide the services supported by the
federal universal service mechanism.

462\. In the *Universal Service Order*, the Commission also determined
that rural and non-rural carriers will receive federal universal service
support determined by separate mechanisms until at least January 1,
2001.[^1047] The Commission stated that it would define rural carriers
as those carriers that meet the statutory definition of a rural
telephone company in section 153(37) of the Communications Act.[^1048]
We have found that carriers self-certifying as rural have not always
applied section 153(37) uniformly.[^1049] In section IX of this Order,
we clarify our interpretation of section 153(37). We also address the
possibility that our annual self-certification requirements may be
modified or eliminated in order to reduce the reporting burden on filing
entities.

463\. Our plan to adopt a mechanism to estimate forward-looking costs
for larger, non-rural carriers has proceeded in two stages. On October
28, 1998, the Commission completed the first stage of this proceeding:
the selection of the model platform. The platform encompasses the
aspects of the model that are essentially fixed, primarily assumptions
about the design of the network and network engineering. In this Order,
we complete the second stage of this proceeding, by selecting input
values for the cost model, such as the cost of cables, switches and
other network components, in addition to various capital cost
parameters.

464\. *Summary and Analysis of the Significant Issues Raised by Public
Comments in Response to the IRFA.* No comments were received
specifically in response to the IRFA. We received several comments,
however, addressing concerns that may affect small entities. These
comments universally supported our proposal, adopted in this
Order,[^1050] to reduce the burden of carriers self-certifying as rural
by eliminating the annual filing requirement.

465\. *Description and Estimate of the Number of Small Entities to which
the Order will Apply*. The RFA generally defines \"small entity\" as
having the same meaning as the term \"small business,\" \"small
organization,\" and \"small government jurisdiction.\"[^1051] In
addition, the term \"small business\" has the same meaning as the term
\"small business concern\" under the Small Business Act, unless the
Commission has developed one or more definitions that are appropriate to
its activities.[^1052] Under the Small Business Act, a \"small business
concern\" is one that: (1) is independently owned and operated; (2) is
not dominant in its field of operation; and (3) meets any additional
criteria established by the SBA.[^1053] The SBA has defined a small
business for Standard Industrial Classification (SIC) category 4813
(Telephone Communications, Except Radiotelephone) to be small entities
when they have no more than 1,500 employees.[^1054]

466\. We have included small incumbent LECs in this present RFA
analysis. As noted above, a \"small business\" under the RFA is one
that, *inter alia*, meets the pertinent small business size standard
(*e.g.*, a telephone communications business having 1,500 or fewer
employees), and \"is not dominant in its field of operation.\"[^1055]
The SBA\'s Office of Advocacy contends that, for RFA purposes, small
incumbent LECs are not dominant in their field of operation because any
such dominance is not \"national\" in scope.[^1056] We have therefore
included small incumbent LECs in this RFA analysis, although we
emphasize that this RFA action has no effect on Commission analyses and
determinations in other, non-RFA contexts.

467\. [Local Exchange Carriers]{.underline}. Neither the Commission nor
SBA has developed a definition of small providers specifically directed
toward LECs. The closest applicable definition under SBA rules is for
telephone communications companies other than radiotelephone (wireless)
companies. The most reliable source of information regarding the number
of LECs nationwide of which we are aware appears to be the data that we
collect annually in connection with the Telecommunications Relay Service
(TRS).[^1057] According to our most recent data, 1,410 companies
reported that they were engaged in the provision of local exchange
service as incumbents.[^1058] Although it seems certain that some of
these carriers are not independently owned and operated, or have more
than 1,500 employees, we are unable at this time to estimate with
greater precision the number of LECs that would qualify as small
business concerns under SBA\'s definition. Consequently, we estimate
that there are fewer than 1,410 small entity LECs that may be affected
by this Order. We also note that, with the exception of our
clarification of the definition of rural carrier under section 153(37)
and the modification of reporting requirements, the rules adopted by
this Order apply only to larger, non-rural LECs.

468\. *Description of Projected Reporting, Recordkeeping, and Other
Compliance Requirements.* This Order imposes no new reporting,
recordkeeping, or other compliance requirements. As discussed more fully
in section IX, above, this Order immediately eliminates the requirement
that carriers serving study areas with fewer than 100,000 access lines
must annually file letters certifying themselves as rural carriers in
order to remain in the rural carrier universal service support
mechanism. Further, this Order eliminates, after the July 1, 2000,
filing deadline, the requirement that rural carriers serving study areas
with more than 100,000 access lines must file annual self-certification
letters. All rural carriers must, however, notify the Commission in the
event of a change in rural status.

469\. The overall effect of this Order will be to reduce reporting,
recordkeeping, and other compliance requirements for small
entities.[^1059] This benefit will apply to all carriers deemed rural
under section 153(37), regardless of whether they are a small or large
entity. Carriers serving study areas with fewer than 100,000 access
lines\--which are more likely to be small entities than those serving
study areas with more than 100,000 access lines\--will be most
immediately benefited, as no further filings will be required of them
unless and until their rural status changes. The largest carriers will
generally be non-rural and not affected by this change in reporting. To
the extent that large and small entities are treated differently,
therefore, small entities will not carry a disproportionately high cost
of compliance.

470\. *Steps Taken to Minimize Significant Economic Impact on Small
Entities and Significant Alternatives Considered.* As noted above and
discussed more fully in section IX, with respect to reporting
requirements affecting small entities, we eliminate the burden of an
annual filing requirement for rural carriers. For carriers serving study
areas with fewer than 100,000 access lines, this change is effective
immediately. Rural carriers serving study areas with more than 100,000
access lines will be required to file a self-certification letter by
July 1, 2000, but will not be required to refile additional annual
certifications unless their status changes. These changes have at their
heart consideration of the resources of small entities, and will reduce,
if not eliminate, the costs of compliance for small entities. The
alternative to this approach would have been to require additional
unnecessary self-certification letters from the vast majority of filing
carriers, even though the data supporting those self-certifications are
easily verified by publicly available documentation.[^1060] The other
changes to Commission rules that we adopt in this Order affect only
larger, non-rural LECs, and should have no direct affect on small
entities.

471\. *Report to Congress*. The Commission will send a copy of this
Order, including this FRFA, in a report to be sent to Congress pursuant
to the Small Business Regulatory Enforcement Fairness Act of
1996.[^1061] In addition, the Commission will send a copy of the this
Order, including FRFA, to the Chief Counsel for Advocacy of the Small
Business Administration. A copy of this Order and FRFA (or summaries
thereof) will also be published in the Federal Register.[^1062]

**B. Paperwork Reduction Act Analysis**

472\. The decision herein has been analyzed with respect to the
Paperwork Reduction Act of 1995, Pub. L. 104-13, and has been approved
in accordance with the provisions of that Act. On August 4, 1999, the
Office of Management and Budget approved the proposed requirements
contained in the *Inputs Further Notice* under OMB control number
3060-0793.

**C. Ordering Clauses**

473\. IT IS ORDERED, pursuant to Sections 1, 4(i) and (j), 201-209,
218-222, 254, and 403 of the Communications Act, as amended, 47 U.S.C.
§§ 151, 154(i), 154(j), 201-209, 218-222, 254, and 403 that this Report
and Order IS HEREBY ADOPTED.

474\. IT IS FURTHER ORDERED that the Commission\'s Office of Public
Affairs, Reference Operations Division, SHALL SEND a copy of this Report
and Order, including the Final Regulatory Flexibility Analysis, to the
Chief Counsel for Advocacy of the Small Business Administration.

FEDERAL COMMUNICATIONS COMMISSION

Magalie Roman Salas

Secretary

**Separate Statement of**

**Commissioner Gloria Tristani**

*Re: Federal-State Joint Board on Universal Service; Ninth Report &
Order and Seventeenth Order on Reconsideration. CC Docket No. 96-45*

*Federal-State Joint Board on Universal Service; Forward-Looking
Mechanism for High Cost Support for Non-Rural LECs. CC Docket Nos. 96-45
& 97-160.*

In adopting these Orders, the Commission has taken an important step
towards fulfilling its mandate under the 1996 Act to ensure that all
Americans have access to telecommunications and information services.
The new high-cost mechanism, together with the selected inputs,
establishes a specific, predictable, and sufficient mechanism to
preserve and advance universal service. I believe that the mechanism
will provide sufficient resources to the states to ensure reasonable
comparability of rates among states. Moreover, I am pleased that the
Commission will be ready to provide forward-looking support to non-rural
carriers based on this mechanism, effective January 1, 2000.

I commend my fellow Joint Board members, the Joint Board staff, and the
Common Carrier Bureau for their outstanding cooperation in developing
the model and model inputs. I likewise commend the outside parties who
worked with the Joint Board and the Bureau throughout this process. I
look forward to continued cooperation as we confront the other pieces of
universal service reform, including adjusting interstate access charges
to account for explicit support, selecting an appropriate methodology
for rural carriers serving high cost areas, and addressing the needs of
unserved and underserved areas.

**DISSENTING STATEMENT OF COMMISSION FURCHTGOTT-ROTH**

Re: *Federal-State Joint Board on Universal Service*, Ninth Report &
Order and Eighteenth Order on Reconsideration, CC Docket No. 96-45;
*Federal-State Joint Board on Universal Service, Forward-Looking
Mechanism for High Cost Support for Non-Rural LECs*, Tenth Report and
Order, CC Docket Nos. 96-45, 97-160.

In the companion orders that it releases today, the Commission finalizes
its implementation of a computer model that it will use to determine the
total cost of providing service to every resident in the country. It
plans to use this model to distribute universal service support among
"non-rural carriers," the term that is used to describe the large
telephone companies that serve rural areas. As I have said at earlier
stages in this proceeding, this Commission's approach to universal
service is fundamentally at odds with the Telecommunications Act
generally and specifically with its express directive that the
Commission "preserve and advance" universal service. Moreover, its
adoption of this unwieldy model is inconsistent with the Act's mandate
that universal service support be "specific" and "predictable." Finally,
as a consequence of the Commission's action today, consumers will now
pay higher bills for dubious subsidies to large companies. I therefore
dissent from these orders.

**The Orders Are Inconsistent With Congress's Objective of Preserving
Universal Service Support for Rural Carriers.** By way of background,
four years ago, universal service was a \$2 billion per year program
targeted mostly at small, rural telephone companies. Today, as a result
of the Commission's unwarranted interference in the existing universal
service system and the new programs that it has dreamed up, the program
costs taxpayers more than \$5 billion a year.

I believe that this proceeding illustrates, yet again, that this
Commission has its universal service priorities entirely backward.
Section 254 of the Telecommunications Act of 1996 was drafted with rural
carriers in mind. The primary objective of that provision was to ensure
that rural carriers continued to receive sufficient funding to enable
them to provide local service at rates comparable to those in urban
areas. In light of this objective, the Commission should have turned
first to the matter of preserving rural universal service. Instead, the
Commission has squandered a tremendous amount of its employees' time and
taxpayers' money coming up with an entirely new approach to universal
service. And the matter of universal service support for rural carriers
has been this Commission's very last priority.

I am relieved to see that the Commission has in these orders taken steps
to ensure that funding for rural carriers will not decrease -- at least
in the near term. I have little confidence, however, that rural carriers
can count on this promise for long. This Commission has so substantially
increased universal service funding for other, less essential programs
that, if and when it finally turns to addressing the issue of rural
universal service support, I question whether there will be any money
left for rural telephone companies.

**The Commission's Model Is Unwieldy, Easily Manipulated, and Will
Require Constant Maintenance.** Not only does the Commission have its
universal service priorities wrong, but also the model on which it
relies is inconsistent with the Telecommunications Act's requirement
that universal service support be "specific" and "predictable." The
model is an immensely complicated computer program that requires around
180 hours -- more than one week -- to run. Since issuing an October 1998
NPRM in which it proposed this model, the Commission has made numerous
changes to the model platform, and each change has required interested
parties to go back to their computers and spend days testing the model.
Only in the last few weeks has the Commission decided on final input
values. In my view, it is unclear whether interested parties have even
had the opportunity meaningfully to comment on a final version of the
model, as the Administrative Procedure Act requires.

The model is also completely dependent on hundreds of assumptions about
the local exchange markets and costs. The bottom line is that, simply by
making different assumptions about local exchange networks, or by
picking different input values for costs, the Commission is able to push
the end result in whatever direction it chooses. I do not believe that a
system that can be manipulated in this way will generate the "specific"
and "predictable" universal service support that the 1996 Act requires.
In addition, the fact that the Commission has found it necessary to
tinker with this model so extensively reflects its fundamental lack of
confidence in its model.

The model is also going to be enormously time-consuming and expensive to
maintain. Each time technology or prices change, the Commission's staff
will be required to adjust the model. I am opposed to wasting resources
on this effort.

**The Commission's Approach to Universal Service Means that Consumers
Will Pay More.** As a final matter, I want to point out what the
Commission's current approach to high-cost universal service will mean
for consumers. According to the model, carriers in a few states
(primarily Mississippi and Alabama) should receive significantly more
funding than they currently do, and the Commission plans to increase
subsidies for carriers in these states. But the model also says that
carriers in many other states should receive *less* universal service
funding than they now do. The Commission, however, does not plan to
follow the model's guidance with respect to these carriers. Instead,
because it committed to Congress in April 1998 that universal service
support would not decrease for any state, the Commission plans to
continue distributing current levels of universal service support to
carriers in all states.

The result of this so-called "hold harmless" requirement is that all
carriers will receive as much or more universal service funding as they
did before the issuance of these two orders. In other words, the bill
for high-cost universal service support will go up, and consumers' phone
bills are going to increase correspondingly. I predict that these will
be only the first of several increases that consumers can expect to see
in the upcoming months as a result of this Commission\'s misguided
universal service policies.

[^1]: ^?^ Pub. L. No. 104-104, 110 Stat. 56. The 1996 Act amended the
    Communications Act of 1934, 47 U.S.C. §§ 151 *et. seq.* (Act).
    Hereinafter, all citations to the Act will be to the relevant
    section of the United States Code unless otherwise noted.

[^2]: ^?^ *Federal‑State Joint Board on Universal Service*, Report and
    Order, CC Docket No. 96‑45, 12 FCC Rcd 8776 (1997) (*Universal
    Service Order*), as corrected by *Federal‑State Joint Board on
    Universal Service*, Errata, CC Docket No. 96‑45, FCC 97‑157 (rel.
    June 4, 1997). *See also* *Texas Office of Public Utility Counsel v.
    FCC and USA*, 183 F.3d 393 (5th Cir. 1999) (affirming in relevant
    part the Commission\'s decisions regarding implementation of the
    high-cost support system).

[^3]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8888, para. 199. The
    Commission also determined that high-cost support for rural carriers
    should continue essentially unchanged and should not be based on
    forward-looking costs until 2001, at the earliest. *Universal
    Service Order*, 12 FCC Rcd at 8889, para. 203. The Commission
    adopted the Joint Board\'s recommendation to define \"rural
    carriers\" as those carriers that meet the statutory definition of a
    \"rural telephone company.\" *Universal Service Order*, 12 FCC Rcd
    at 8943, para. 310 (*citing* 47 U.S.C. § 153(37)).

[^4]: ^?^ *Federal-State Joint Board on Universal Service*, Fifth Report
    and Order, CC Docket Nos. 96-45, 97-160, 13 FCC Rcd 21323 (1998)
    (*Platform Order*).

[^5]: ^?^ *Federal-State Joint Board on Universal Service*, Ninth Report
    and Order and Eighteenth Order on Reconsideration, CC Docket No.
    96-45, FCC 99-306 (adopted Oct. 21, 1999) (*Methodology Order*).

[^6]: ^?^ Although the existing high-cost loop fund has historically
    been known as the \"Universal Service Fund,\" we will avoid this
    terminology because of the confusion it may create with the new
    universal service support mechanisms that the Commission has created
    pursuant to section 254 of the Communications Act.

[^7]: ^?^ The Commission\'s rules governing these programs are set forth
    at 47 C.F.R. §§ 36.601 *et. seq.* (high-cost loop fund); 47 C.F.R. §
    36.125(b) (DEM weighting); and 47 C.F.R. §§ 69.105, 69.502,
    69.603(e), 69.612 (LTS).

[^8]: ^?^ 47 U.S.C. § 254(a).

[^9]: ^?^ *Federal-State Joint Board on Universal Service*, First
    Recommended Decision, CC Docket No. 96-45, 12 FCC Rcd 87 (1996)
    (*First Recommended Decision*).

[^10]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8890, para. 206. In
    the *Universal Service Order*, the Commission concluded that the
    federal universal service support mechanism would support 25 percent
    of the difference between the forward-looking economic cost of
    providing the supported service and a nationwide revenue benchmark.
    *See Universal Service Order*, 12 FCC Rcd at 8888, para. 201. In
    response to issues raised by commenters and state Joint Board
    members, the Commission referred back to the Joint Board questions
    related to how federal support should be determined. *See
    Federal-State Joint Board on Universal Service*, Order and Order on
    Reconsideration, CC Docket No. 96-45, 13 FCC Rcd 13749 (1998)
    (*Referral Order*). *See also Federal-State Joint Board on Universal
    Service*, Second Recommended Decision, CC Docket No. 96-45, 13 FCC
    Rcd 24744 (1998) (*Second Recommended Decision*).

[^11]: ^?^ *Federal-State Joint Board on Universal Service,
    Forward-Looking Mechanism for High Cost Support for Non-Rural LECs*,
    Further Notice of Proposed Rulemaking, CC Docket Nos. 96-45, 97-160,
    12 FCC Rcd 18514 at 18519, para. 5 (1997) (*1997 Further Notice*).

[^12]: ^?^ Generally, there is a platform component for each portion of
    the local exchange network being modeled. Examples of platform
    design issues are the establishment of switch capacity limitations
    and the routing of feeder and distribution cables. Examples of input
    values are the price of various network components, their associated
    installation and placement costs, and capital cost parameters such
    as debt-equity ratios. *See 1997 Further Notice*, 12 FCC Rcd at
    18516-18, paras. 17-18.

[^13]: ^?^ *See generally 1997 Further Notice*.

[^14]: ^?^ *Guidance to Proponents of Cost Models in Universal Service
    Proceeding: Switching, Interoffice Trunking, Signaling, and Local
    Tandem Investment*, Public Notice, CC Docket Nos. 96-45, 97-160, DA
    97-1912 (rel. Sept. 3, 1997) (*Switching and Transport Public
    Notice*); *Guidance to Proponents of Cost Models in Universal
    Service Proceeding: Customer Location and Outside Plant*, Public
    Notice, CC Docket Nos. 96-45, 97-160, DA 97-2372 (rel. Nov. 13,
    1997) (*Customer Location & Outside Plant Public Notice*).

[^15]: ^?^ *Common Carrier Bureau Requests Further Comment On Selected
    Issues Regarding The Forward-Looking Economic Cost Mechanism For
    Universal Service*, Public Notice, CC Docket Nos. 96-45, 97-160, DA
    98-848 (rel. May 4, 1998) (*Inputs Public Notice*).

[^16]: ^?^ *Federal-State Joint Board on Universal Service*, Order, CC
    Docket No. 96-45, 12 FCC Rcd 9803 (1997) (*1997 Data Request*).

[^17]: ^?^ *Common Carrier To Hold Three Workshops On Input Values To Be
    Used To Estimate Forward-Looking Economic Costs For Purposes Of
    Universal Service Support*, Public Notice, CC Docket Nos. 96-45,
    97-160, DA 98-2406 (rel. Nov. 25, 1998) (*Workshop Public Notice*).

[^18]: ^?^ *See, e.g.*, Letter from W. Scott Randolph, GTE, to Magalie
    Roman Salas, FCC, dated March 2, 1999; Letter from Pete Sywenki,
    Sprint, to Magalie Roman Salas, FCC, dated, February 26, 1999;
    Letter from Chris Frentrup, MCI, to Magalie Roman Salas, FCC, dated
    February 9, 1999.

[^19]: ^?^ *See Platform Order*.

[^20]: ^?^ Submission in CC Docket Nos. 96-45 and 97-160 by BellSouth
    Corporation, BellSouth Telecommunications, Inc., U S WEST, Inc., and
    Sprint Local Telephone Company (BCPM proponents), dated Dec. 11,
    1997 (BCPM Dec. 11, 1997 submission).

[^21]: ^?^ Letter from Richard N. Clarke, AT&T, to Magalie Roman Salas,
    FCC, dated Dec. 11, 1997 (HAI Dec. 11, 1997 submission). HAI was
    submitted by AT&T and MCI (HAI sponsors). *See also* Letter from
    Richard Clarke, AT&T, to Magalie Roman Salas, FCC, dated February 3,
    1998 (HAI Feb. 3 submission).

[^22]: ^?^ HCPM was developed by Commission staff members William
    Sharkey, Mark Kennet, C. Anthony Bush, Jeffrey Prisbrey, and
    Commission contractor Vaikunth Gupta of Panum Communications.
    *Common Carrier Bureau Announces Release of HCPM Version 2.0*,
    Public Notice, DA 97-2712 (rel. Dec. 29, 1997). *See also* United
    States Government Memo from W. Sharkey, FCC, to Magalie Roman Salas,
    FCC, dated Feb. 6, 1998.

[^23]: ^?^ *Platform Order*, 13 FCC Rcd at 21338, para. 34.

[^24]: ^?^ *Platform Order*, 13 FCC Rcd at 21341, para. 41.

[^25]: ^?^ *Platform Order*, 13 FCC Rcd at 21355, para. 76. The LERG is
    a database of switching information maintained by Telcordia
    Technologies (formerly Bellcore) that includes the existing
    host-remote relationships.

[^26]: ^?^ *Second Recommended Decision*, 13 FCC Rcd at 24746, para. 5.

[^27]: ^?^ *Second Recommended Decision*, 13 FCC Rcd at 24746, para. 5.

[^28]: ^?^ *Second Recommended Decision*, 13 FCC Rcd at 24746-47, para.
    5.

[^29]: ^?^ *Second Recommended Decision*, 13 FCC Rcd at 24757, para. 28.

[^30]: ^?^ *Federal-State Joint Board on Universal Service,
    Forward-Looking Mechanism for High Cost Support for Non-Rural LECs*,
    Further Notice of Proposed Rulemaking, CC Docket Nos. 96-45, 97-160,
    FCC 99-120 (rel. May 28, 1999) (*Inputs Further Notice*);
    *Federal-State Joint Board on Universal Service, Access Charge
    Reform*, Seventh Report and Order and Thirteenth Order on
    Reconsideration in CC Docket No. 96-45; Fourth Report and Order in
    CC Docket No. 96-262; and Further Notice of Proposed Rulemaking, CC
    Docket Nos. 96-45, 96-262, 14 FCC Rcd 8078 (1999) (*Seventh Report
    and Order*). *See also* *Common Carrier Bureau Releases Preliminary
    Results Using Proposed Input Values In The Forward-Looking Cost
    Model For Universal Service*, Public Notice, CC Docket Nos. 96-45,
    97-160, DA 99-1165 (rel. June 16, 1999) (*Preliminary Input Values
    Public Notice)*; *Common Carrier Bureau Releases Revised Spreadsheet
    For Estimating Universal Service Support Using Proposed Input Values
    In The Forward-Looking Cost Model*, Public Notice, CC Docket Nos.
    96-45, 97-160, DA 99-1322 (rel. July 2, 1999).

[^31]: ^?^ *See Inputs Further Notice* at Appendix A.

[^32]: ^?^ *See generally Inputs Further Notice*.

[^33]: ^?^ *See generally Seventh Report and Order*.

[^34]: ^?^ We also note that technologies such as wireless services are
    likely to become more important over time in providing universal
    service. We will continue to review suggestions for incorporating
    such technologies into the forward-looking mechanism for future
    years. *See, e.g.*, Letter from David L. Sieradzki, on behalf of
    Western Wireless, to Magalie Roman Salas, FCC, dated January 26,
    1999 (submitting the \"Wireless Cost Model\"). In addition, we
    intend to initiate a proceeding in the near future to consider how
    changes in technologies and other related factors should be
    accounted for in the model.

[^35]: ^?^ The *Universal Service Order* established ten criteria to
    ensure consistency in calculations of federal universal service
    support. *Universal Service Order*, 12 FCC Rcd at 8913, para. 250.
    Criterion one requires that a model must include incumbent LECs\'
    wire centers as the center of the loop network and the outside plant
    should terminate at incumbent LECs\' current wire centers.

[^36]: ^?^ SONET is a set of standards for optical (fiber optic)
    transmission. It was developed to meet the need for transmission
    speeds above the T3 level (45 Mbps) and is generally considered the
    standard choice for transmission devices used with broadband
    networks. BCPM Dec. 11 submission, Model Methodology at 68.

[^37]: ^?^ *See Platform Order*, 13 FCC Rcd at 21357-61, paras. 81-91.

[^38]: ^?^ *Platform Order*, 13 FCC Rcd at 21357-58, para. 82.

[^39]: ^?^ *Platform Order*, 13 FCC Rcd at 21337, para. 33. *See also*
    discussion of customer location data, *infra* section IV.

[^40]: ^?^ *Platform Order*, 13 FCC Rcd at 21340-41, para. 40. *See
    also* discussion of road surrogating method, *infra*.

[^41]: ^?^ *Platform Order*, 13 FCC Rcd at 21342, para. 44.

[^42]: ^?^ *Platform Order*, 13 FCC Rcd at 21346, para. 55.

[^43]: ^?^ *Platform Order*, 13 FCC Rcd at 21348, para. 61.

[^44]: ^?^ *Platform Order*, 13 FCC Rcd at 21354-55, para. 75.

[^45]: ^?^ *Platform Order*, 13 FCC Rcd at 21355, para. 76.

[^46]: ^?^ *Platform Order*, 13 FCC Rcd at 21357, para. 81.

[^47]: ^?^ *Platform Order*, 13 FCC Rcd at 21329, para. 13.

[^48]: ^?^ *Platform Order*, 13 FCC Rcd at 21329, para. 13.

[^49]: ^?^ *Common Carrier Bureau To Post On The Internet Modifications
    To The Forward-Looking Economic Cost Model For Universal Service
    Support*, Public Notice, CC Docket Nos. 96-45, 97-160, DA 98-2533
    (rel. Dec. 15, 1998).

[^50]: ^?^ Model platform changes can be found at
    http://www.fcc.gov/ccb/apd/hcpm. Changes to the model are detailed
    in the \"History.doc\" file. The model platform was not modified
    after June 2, 1999, in order to allow parties an opportunity to
    evaluate the model platform, the proposed inputs to the model, and
    issues related to the methodology for determining high-cost support.
    After release of this Order, we will post a revised model platform
    on the Commission\'s Web site, including the input values adopted
    herein.

[^51]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8899-8900, paras.
    224-226.

[^52]: ^?^ *See, e.g.*, *1997 Further Notice*; *Inputs Public Notice*;
    *Workshop Public Notice*; *Inputs Further Notice*.

[^53]: ^?^ *Inputs Further Notice* at para. 243.

[^54]: ^?^ *See, e.g.*, Bell Atlantic *Inputs Further Notice* comments
    at 5-6, GTE *Inputs Further Notice* comments at 89-91, USTA *Inputs
    Further Notice* comments at 4-5. *But see* US West *Inputs Further
    Notice* comments at 70-71.

[^55]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 4.

[^56]: ^?^ *See* C.A. Bush et al., *The Hybrid Cost Proxy Model Customer
    Location and Loop Design Modules*, Dec. 15, 1998 at 13-14 (*HCPM
    Dec. 15, 1998 documentation*).

[^57]: ^?^ *See* 47 U.S.C. § 153(37).

[^58]: ^?^ *See infra* para. 61.

[^59]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913-16, para. 250.

[^60]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250
    (criterion one).

[^61]: ^?^ *See infra* paras. 63, 351.

[^62]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250
    (criterion three).

[^63]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250
    (criterion three).

[^64]: ^?^ *See infra* para. 311.

[^65]: ^?^ *See infra* paras. 361-64.

[^66]: ^?^ State commissions, for example, may find that it is not
    appropriate to use nationwide values in determining state universal
    service support or prices for unbundled network elements and may
    choose instead to use statewide or company-specific values.

[^67]: ^?^ *See 1997 Further Notice*, 12 FCC Rcd at 18535, para. 44.

[^68]: ^?^ *Platform Order*, 13 FCC Rcd at 21337, para. 31.

[^69]: ^?^ *See, e.g.*, *1997 Further Notice*, 12 FCC Rcd at 18535,
    para. 44; *Inputs Public Notice* at 3-4; *Common Carrier Bureau
    Seeks Comment On Model Platform Development*, Public Notice, CC
    Docket Nos. 96-45, 97-160, DA 98-1587 (rel. Aug. 7, 1998) (*Platform
    Public Notice*) at 2-4.

[^70]: ^?^ *Platform Order*, 13 FCC Rcd at 21337, para. 31. The term
    \"geocode data\" refers to the identification of each customer
    location by precise latitude and longitude coordinates. Surrogating
    methods, and customer location data provided by the Census Bureau,
    constitute geocode data. For purposes of clarity, however, we will
    use the term \"geocode\" data to refer only to actual precise
    latitude and longitude data, unless we specifically refer to the
    data as \"surrogate geocode\" data.

[^71]: ^?^ *Platform Order*, 13 FCC Rcd at 21337-38, para. 34.

[^72]: ^?^ *Platform Order*, 13 FCC Rcd at 21340-41, para. 40.

[^73]: ^?^ *Inputs Further Notice* at paras. 25-28.

[^74]: ^?^ *Inputs Further Notice* at para. 29.

[^75]: ^?^ In the upcoming proceeding on future changes to the model,
    *see supra* note 34, we intend to consider alternatives for
    obtaining customer location data.

[^76]: ^?^ *Platform Order*, 13 FCC Rcd at 21337, para. 33.

[^77]: ^?^ *Platform Order*, 13 FCC Rcd at 21337-38, para. 34.

[^78]: ^?^ *Platform Order*, 13 FCC Rcd at 21338, para. 34.

[^79]: ^?^ *Platform Order*, 13 FCC Rcd at 21338, para. 34.

[^80]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8915, para. 250
    (criterion eight).

[^81]: ^?^ *See* *Federal-State Joint Board on Universal Service*,
    Protective Order, CC Docket Nos. 96-45, 97-160, 13 FCC Rcd 13910
    (1998) (*Protective Order*). *See also Inputs Public Notice* at 3-4.

[^82]: ^?^ BIN files are the output of the clustering routine in the
    synthesis model platform derived from the actual geocode customer
    locations and, as such, do not reveal the actual geocoded customer
    locations. The BIN files allow users to run all aspects of the model
    except for the clustering. PNR has made the BIN files available to
    interested parties for a fee of \$25.00, pursuant to the terms of
    the *Protective Order*. *See* Letter from William M. Newman, PNR, to
    Magalie Roman Salas, FCC, dated December 17, 1998 (PNR Dec. 17 *ex
    parte*).

[^83]: ^?^ *See* PNR Dec. 17 *ex parte*.

[^84]: ^?^ *See, e.g.*, Bell Atlantic Petition for Reconsideration at
    5-6; BellSouth Petition for Reconsideration at 3-4; GTE Petition for
    Reconsideration at 21; Sprint *Inputs Further Notice* comments at
    11.

[^85]: ^?^ Letter to Thomas W. Mitchell, on behalf of GTE, from Charles
    A. White, PNR, dated April 29, 1999 (PNR April 29 *ex parte*) at 1.

[^86]: ^?^ *See, e.g.*, Ameritech *Inputs Further Notice* comments at 2;
    GTE *Inputs Further Notice* comments at 36-37; SBC *Inputs Further
    Notice* comments at 4; Sprint *Inputs Further Notice* comments at
    11.

[^87]: ^?^ We note that AT&T and MCI have suggested that the Commission
    condition receipt of universal service funding on the provision of
    customer location data by the carrier. *See* AT&T/MCI *Inputs
    Further Notice* comments at 5. We decline to adopt this suggestion
    at this time, but will consider this and other alternatives to
    obtaining customer location data in the upcoming proceeding on
    future changes to the model.

[^88]: ^?^ *Platform Order*, 13 FCC Rcd at 21340-41, para. 40.

[^89]: ^?^ *Platform Order*, 13 FCC Rcd at 21340-41, para. 40.

[^90]: ^?^ *Platform Order*, 13 FCC Rcd at 21341, para. 41.

[^91]: ^?^ *Inputs Further Notice* at para. 34.

[^92]: ^?^ Letter from Michael Liebermann, AT&T, to Magalie Roman Salas,
    FCC, dated March 2, 1998 (AT&T March 2 *ex parte*).

[^93]: ^?^ Letter from William M. Newman, PNR, to Magalie Roman Salas,
    FCC, dated January 27, 1999 (PNR Jan. 27 *ex parte*). PNR has made
    available by mail to interested parties the road surrogate point
    data for a fee of \$25.00, pursuant to the terms of the *Protective
    Order.*

[^94]: ^?^ Letter from Charles A. White, PNR, to Magalie Roman Salas,
    FCC, dated February 22, 1999 (PNR Feb. 22 *ex parte*).

[^95]: ^?^ Letter from Charles A. White, PNR, to Magalie Roman Salas,
    FCC, dated July 29, 1999 (PNR July 29 *ex parte*).

[^96]: ^?^ PNR Feb. 22 *ex parte* at 1. A road segment is a length of
    road between two intersections. The Census Bureau classifies and
    numbers each of these road segments. PNR uses a slightly modified
    version of the Census Bureau road classifications. *Id.* at 2

[^97]: ^?^ The PNR National Access Line Model is used to determine the
    number of residential and business customer locations in a given
    wire center. *See* PNR Feb. 22 *ex parte* at 1.

[^98]: ^?^ PNR Feb. 22 *ex parte* at 2.

[^99]: ^?^ PNR Feb. 22 *ex parte* at 2.

[^100]: ^?^ PNR Feb. 22 *ex parte* at 2.

[^101]: ^?^ *See* PNR July 29 *ex parte*. PNR has also filled in the
    states and wire centers that were missing from earlier versions of
    its road surrogate customer location data set.

[^102]: ^?^ *See* Letter from Pete Sywenki, Sprint, to Magalie Roman
    Salas, FCC, dated December 11, 1998 (Sprint Dec. 11 *ex parte*).

[^103]: ^?^ GTE *Inputs Further Notice* comments at 38.

[^104]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 3. Because the
    PNR actual geocode data set does not provide a complete data set of
    customer locations, AT&T and MCI compare a combination of actual and
    surrogate data with the use of all surrogate data. The percentage of
    actual geocoded customer data varies in different areas.

[^105]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 3 (contending
    that customers tend to cluster unevenly along roads and even leave
    stretches unpopulated). *See also* Ameritech *Inputs Further Notice*
    comments at 5 (contending that PNR surrogate locations tend to
    spread customers more evenly than when compared to Ameritech\'s
    geocoded customer data).

[^106]: ^?^ AT&T/MCI *Inputs Further Notice* reply comments at 10.

[^107]: ^?^ GTE *Inputs Further Notice* reply comments at 4-5.

[^108]: ^?^ GTE *Inputs Further Notice* reply comments at 5.

[^109]: ^?^ Sprint *Inputs Further Notice* comments at 13.

[^110]: ^?^ As noted above, AT&T and MCI rely on the PNR actual geocode
    data that we have rejected for lack of a meaningful verification
    process. In the absence of a verifiable, actual geocode data source,
    it is impossible to make the type of comparison suggested by AT&T
    and MCI to determine the accuracy of the road surrogate algorithm.

[^111]: ^?^ Letter from Celia Nogales, Ameritech, to Magalie Roman
    Salas, FCC, dated July 14, 1999 (Ameritech July 14 *ex parte*). LECG
    is an economic consulting firm.

[^112]: ^?^ Ameritech *Inputs Further Notice* comments at 3.

[^113]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 8. As
    noted, the decision to use a surrogating algorithm based on roads
    was made by the Commission in the *Platform Order*. Our purpose in
    this Order is not to revisit that decision but to select the road
    surrogate algorithm that will be used in the federal mechanism.

[^114]: ^?^ *Platform Order*, 13 FCC Rcd at 21341, para. 41.

[^115]: ^?^ We also note that PNR has made the road surrogate data
    points available to interested parties pursuant to the provisions of
    the *Protective Order* in this proceeding. *See* PNR Jan. 27 *ex
    parte*; PNR Feb. 9 *ex parte*; PNR Feb. 22 *ex parte*.

[^116]: ^?^ *See, e.g.*, Ameritech *Inputs Further Notice* at 3;
    AT&T/MCI *Inputs Further Notice* comments at 6-7; Sprint *Inputs
    Further Notice* at 12.

[^117]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 6-7.

[^118]: ^?^ *Inputs Further Notice* at para. 43.

[^119]: ^?^ *Inputs Further Notice* at para. 43.

[^120]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8915, para. 250
    (criterion 6).

[^121]: ^?^ *Inputs Public Notice* at 4-6. *See also Inputs Further
    Notice* at para. 46.

[^122]: ^?^ We note that the question of which residential and business
    locations should be included for purposes of estimating the
    forward-looking cost of providing the supported services is distinct
    from the question of which lines should be supported. *See Universal
    Service Order*, 12 FCC Rcd at 8829, paras. 95-96 (declining to adopt
    the Joint Board\'s recommendation to restrict universal service
    high-cost support to primary residential lines and single-line
    businesses).

[^123]: ^?^ Ameritech *Inputs Further Notice* comments at 6; AT&T/MCI
    *Inputs Further Notice* comments at 7-8; BellSouth *Inputs Further
    Notice* comments at B-2; GTE *Inputs Further Notice* comments at 40;
    SBC *Inputs Further Notice* comments at 6; USTA *Inputs Further
    Notice* comments at 2-3; US West *Inputs Further Notice* comments at
    45-46.

[^124]: ^?^ These definitions reflect the Census Bureau\'s methodology
    for housing unit and household estimates. *See*
    http://www.census.gov/population/methods/sthhmet.txt.

[^125]: ^?^ HAI Dec. 11, 1997 submission, Model Description at 21.

[^126]: ^?^ *See Inputs Further Notice* at Appendix B.

[^127]: ^?^ HAI Dec. 11, 1997 submission, Model Description at 21.

[^128]: ^?^ Customer locations in unserved areas, as reflected by BLR
    wire center boundaries, are not associated with particular wire
    centers. *See* Letter from Charles A. White, PNR, to Magalie Roman
    Salas, FCC, dated April 12, 1999. PNR has, however, taken steps to
    assign such customer locations to the nearest wire center. PNR July
    29 *ex parte*.

[^129]: ^?^ *See* *Inputs Further Notice* at Appendix B for a complete
    description of the PNR methodology for estimating the number of
    customer locations.

[^130]: ^?^ BCPM April 30, 1998 documentation, Model Methodology at
    26-27.

[^131]: ^?^ BellSouth *Inputs Further Notice* at B-2; GTE *Inputs
    Further Notice* at 40; SBC *Inputs Further Notice* comments at 6;
    USTA *Inputs Further Notice* comments at 2-3; US West *Inputs
    Further Notice* comments at 45-46.

[^132]: ^?^ BellSouth *Inputs Further Notice* comments at B-2; GTE
    *Inputs Further Notice* comments at 40; PRTC *Inputs Further Notice*
    comments at 5.

[^133]: ^?^ *See*, *e.g.*, BellSouth *Inputs Further Notice* at B-2; GTE
    *Inputs Further Notice* at 40; SBC *Inputs Further Notice* comments
    at 6; USTA *Inputs Further Notice* comments at 2-3; US West *Inputs
    Further Notice* comments at 45-46.

[^134]: ^?^ *See*, *e.g.*, BellSouth *Inputs Further Notice* comments at
    B-2; GTE *Inputs Further Notice* comments at 40; US West *Inputs
    Further Notice* comments at 45-46.

[^135]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 14-15; GTE
    *Inputs Further Notice* comments at 37-38; Sprint *Inputs Further
    Notice* comments at 13-14; US West *Inputs Further Notice* reply
    comments at 12.

[^136]: ^?^ HAI has provided a complete description of the process by
    which PNR\'s National Access Line Model develops customer counts.
    *See* HAI Dec. 11, 1997, Model Description at 21.

[^137]: ^?^ PNR *Inputs Further Notice* reply comments at 2.

[^138]: ^?^ *See* HAI Dec. 11, 1997, Model Description at 21.

[^139]: ^?^ PNR *Inputs Further Notice* reply comments at 2.

[^140]: ^?^ PNR *Inputs Further Notice* reply comments at 2-3.

[^141]: ^?^ Letter from Charles White, PNR, to Magalie Roman Salas, FCC,
    dated October 6, 1999.

[^142]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 14-15;
    Sprint *Inputs Further Notice* comments at 13-14.

[^143]: ^?^ AT&T and MCI *ex parte*, Dec. 23, 1997.

[^144]: ^?^ Letter from Chris Frentrup, MCI, to Magalie Roman Salas,
    FCC, dated March 5, 1999 (MCI March 5 *ex parte*).

[^145]: ^?^ MCI March 5 *ex parte* (Issues 1 and 2).

[^146]: ^?^ SBC *Inputs Further Notice* comments at 6.

[^147]: ^?^ AT&T and MCI *ex parte*, Dec. 23, 1997.

[^148]: ^?^ Ameritech *Inputs Further Notice* comments at 7; AT&T/MCI
    *Inputs Further Notice* comments at 8. *See infra* section V for
    discussion of fill factors.

[^149]: ^?^ *See Federal-State Joint Board on Universal Service:
    Promoting Deployment and Subscribership in Unserved and Underserved
    Areas, Including Tribal and Insular Areas*, Further Notice of
    Proposed Rulemaking, CC Docket No. 96-45, FCC 99-204 (rel. Sept. 3,
    1999) at paras. 120-121.

[^150]: ^?^ *See* *Federal-State Joint Board on Universal Service,
    Forward-Looking Mechanism for High Cost Support for Non-Rural LECs*,
    Order, CC Docket Nos. 96-45, 97-160, DA 99-1406 (rel. July 19,
    1999).

[^151]: ^?^ *Inputs Further Notice* at para. 47.

[^152]: ^?^ HAI Dec. 11, 1997 submission, Model Description at 21.

[^153]: ^?^ *See* Sprint Dec. 11, 1998 *ex parte*, attachment at 1.

[^154]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 8; PNR *Inputs
    Further Notice* reply comments at 3.

[^155]: ^?^ PRTC *Inputs Further Notice* comments at 6; SBC *Inputs
    Further Notice* comments at 6.

[^156]: ^?^ SBC *Inputs Further Notice* comments at 6; PNR *Inputs
    Further Notice* reply comments at 3.

[^157]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250.

[^158]: ^?^ *Inputs Further Notice* at para. 11; *Universal Service
    Order*, 12 FCC Rcd at 8913, para. 250.

[^159]: ^?^ *Platform Order*, 12 FCC Rcd at 21350, para. 66. \"Instead,
    incumbent LECs\' existing plant will tend to reflect choices made at
    a time when different technology options existed or when the
    relative cost of equipment to labor may have been different than it
    is today.\" *Id.*

[^160]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250.

[^161]: ^?^ *Platform Order*, 13 FCC Rcd at 21335, para. 27.

[^162]: ^?^ As discussed below, cable installation costs for buried
    cable often are included with the structure costs.

[^163]: ^?^ The phrase \"plant mix\" refers to the ratio of outside
    plant that is aerial, underground, or buried in a network or
    particular area.

[^164]: ^?^ *See* *Inputs Further Notice* at paras. 56-63.

[^165]: ^?^ *Inputs Further Notice* at para. 56.

[^166]: ^?^ *Inputs Further Notice* at paras. 58, 61-62.

[^167]: ^?^ The model uses a minimum spanning tree algorithm based on
    the Prim algorithm. The model always optimizes feeder plant. *See*
    HCPM Dec. 15, 1998 documentation at 13.

[^168]: ^?^ HCPM Dec. 15, 1998 documentation at 11.

[^169]: ^?^ *Inputs Further Notice* at para. 58.

[^170]: ^?^ *See Inputs Further Notice* at para. 58 n. 135. Since, under
    full optimization, the model chooses the least cost of the full
    optimization algorithm or the rule of thumb algorithm, a comparison
    run as described above can show how well the full optimization
    performs as a function of density.

[^171]: ^?^ *See* HCPM Dec. 15, 1998 documentation at 30-31; *see also*
    Design History of HCPM, April 6, 1999 at
    http://www.fcc.gov/ccb/apd/hcpm.

[^172]: ^?^ *Universal Service Order,* 12 FCC Rcd at 8913, para. 250.

[^173]: ^?^ *Inputs Further Notice* at para. 58.

[^174]: ^?^ *See e.g.*, AT&T and MCI *Inputs Further Notice* comments at
    9-10; US West *Inputs Further Notice* comments at 21; SBC *Inputs
    Further Notice* comments at 7. We note that SBC supports full
    optimization so long as its application produces a significant
    difference in the results. As we explain, application of full
    optimization does produce a significant difference in the results.
    Moreover, SBC states that the optimization routine offers \"the most
    cost effective design.\" *Id.*

[^175]: ^?^ GTE *Inputs Further Notice* comments at 33-35; GTE *Inputs
    Further Notice* reply comments at 9-11.

[^176]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 10; GTE
    *Inputs Further Notice* comments at 33. We note that although GTE
    opposes any implementation of optimization, GTE also specifically
    addressed whether the compromise to full optimization on which we
    sought comment was acceptable.

[^177]: ^?^ US West *Inputs Further Notice* comments at 18-21; GTE
    *Inputs Further Notice* comments at 34-35.

[^178]: ^?^ US West contends that, because the optimization algorithm
    functions by choosing between the lowest value produced by the rule
    of thumb or the spanning tree, the optimization algorithm retains
    those instances where the rule of thumb underestimates the amount of
    plant needed while eliminating all estimates that exceed the more
    analytically derived results, thereby biasing the results downward.
    In order to remedy this flaw, US West recommends that the model be
    modified to consider only the minimum spanning tree results for
    distribution design.

[^179]: ^?^ *See* US West *Inputs Further Notice* comments at 18-21.

[^180]: ^?^ Because the optimization routine allows for the possibility
    of some, but not all possible junction nodes (also called Steiner
    nodes), it is possible that the \"rule of thumb\" can provide a
    feasible lower cost result than the optimization routine in certain
    cases. As explained in the model documentation, junction nodes can
    sometimes reduce the cost of constructing a communications network.
    HCPM Dec.15, 1998 documentation at 14.

[^181]: ^?^ US West\'s recommendation that only the minimum spanning
    tree results be recognized would have us ignore accepted practices
    in cost minimization. Because it is not possible in the general case
    to solve for the optimal solution, it is accepted practice in cost
    minimization analysis to examine the results of various available
    alternative cost minimization methodologies and choose the lowest
    cost result, provided that each alternative meets the appropriate
    design standards. This is the same principle on which Branch and
    Bond algorithms work. *See e.g.,* Mark S. Daskin, *Network and
    Discrete Location: Models, Algorithms, and Applications* (1995). In
    so doing, the result that is chosen is the result that is closer to
    the least cost, while providing a sufficient amount of plant to
    provide the supported services. For these reasons, the optimization
    algorithm employed in the model produces results superior to those
    produced by the application of only a single cost minimization
    methodology.

[^182]: ^?^ GTE asserts that an analysis of GTE\'s service area in
    Oregon reveals that a majority of the cost impact occurs when the
    spanning tree algorithm optimizes clusters with less than 100 lines
    per square mile. GTE *Inputs Further Notice* reply comments at 11.

[^183]: ^?^ GTE *Inputs Further Notice* reply comments at 11.

[^184]: ^?^ GTE also claims that there are numerous cases where the
    optimization routine has resulted in increased costs at the wire
    center level. GTE *Input Further Notice* comments at 34-35.
    Specifically, GTE contends that when the optimization logic is
    applied to clusters with fewer than 100 lines per square mile for
    GTE\'s Florida serving area, total monthly costs for eight wire
    centers were higher than without optimization.

[^185]: ^?^ This situation can occur because the minimum spanning tree
    algorithm may increase the distance of some customers in a cluster
    from the serving area interface in order to achieve lower overall
    costs through more efficient routing. In some cases, this increased
    distance might cause a cluster that fell within the maximum copper
    distance constraint under the rule of thumb algorithm to exceed that
    constraint. The increased cost of serving the cluster with the fiber
    feeder system could then increase total cost even though the
    optimization worked as intended in the distribution portion of the
    model.

[^186]: ^?^ *See Inputs Further Notice* at para. 59.

[^187]: ^?^ HCPM Dec. 15, 1998 documentation at 10.

[^188]: ^?^ *Inputs Further Notice* at para. 61.

[^189]: ^?^ *Inputs Further Notice* at para. 61.

[^190]: ^?^ *Inputs Further Notice* at para. 59.

[^191]: ^?^ HDSL (high data rate digital subscriber line) transmits
    1.544 Mbps or 2.048 Mbps in bandwidths ranging from 80 kilohertz
    (kHz) to 240 kHz, rather than in a bandwidth of 1.5 megahertz (mHz)
    required for traditional T-1 services. *See*
    www.adsl.com/general_tutorial.

[^192]: ^?^ *Inputs Further Notice* at para. 60.

[^193]: ^?^ *Inputs Further Notice* at para. 60.

[^194]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 62; SBC
    *Inputs Further Notice* comments at 7; AT&T and MCI *Inputs Further
    Notice* comments at 11; AT&T and MCI *Inputs Further Notice* reply
    comments at 12-13. We note that, notwithstanding their support for
    the decision to not use T-1, AT&T and MCI encourage the Commission
    to modify the model to use T-1 technology in the same manner as does
    the HAI model, i.e., as a distribution alternative where, after
    using a fiber fed integrated digital loop carrier to link a main
    cluster of customer locations with a serving wire center, outlying
    customer locations beyond 18,000 feet from the main cluster\'s
    center are served by copper T-1 distribution loops. This
    recommendation, which would represent a platform change, will be
    considered in the upcoming proceeding on future changes to the
    model.

[^195]: ^?^ SBC and GTE responded to our inquiry regarding the use and
    extent of advanced technologies to transmit information at T-1 on
    higher rates. SBC maintains that it is not reasonable to expect that
    HDSL will be used on T-1 technology. SBC *Inputs Further Notice*
    comments at 7. SBC explains that HDSL is being considered primarily
    for small pair gain (DLC) activation to meet specific customer needs
    or HI-CAP provisioning, and not for normal DLC activation. GTE
    maintains that HDSL can be and is used to provide 1.544 Megabit per
    second data rates over embedded copper plant, but its use is not an
    appropriate forward-looking technology. GTE *Inputs Further Notice*
    comments at 62. GTE adds that predominant uses of HDSL are to
    provision \"short fuse\" 1.544 Mbps service requests and extend the
    life of the embedded copper network. In sum, SBC and GTE assert
    that, even if augmented by advanced technology such as HDSL, T-1 is
    still not a forward-looking technology.

[^196]: ^?^ A rectilinear measurement computes the distance between two
    points by constructing a rectangle with the two points as opposite
    vertices and measuring the distance of two adjacent sides of the
    rectangle. The airline distance is the length of the diagonal line
    that directly connects the two points.

[^197]: ^?^ *Inputs Further Notice* at para. 62.

[^198]: ^?^ *Inputs Further Notice* at para. 62.

[^199]: ^?^ *Inputs Further Notice* at para. 63.

[^200]: ^?^ As BellSouth attests, cable rarely follows a straight-line
    \"as the crow flies\" route. BellSouth *Inputs Further Notice*
    comments, Attachment B at B-3.

[^201]: ^?^ *Inputs Further Notice* at para. 62 n. 142 *citing* Robert
    F. Love *et al.*, *Facilities Location Models and Methods*, Chapter
    10 (1988).

[^202]: ^?^ SBC *Inputs Further Notice* comments at 7.

[^203]: ^?^ We make no finding as to whether using airline miles with
    geographically disaggregated road factors, if available, would be a
    more appropriate method of calculating distances and intend to
    explore this issue further in the future of the model proceeding.

[^204]: ^?^ The nine density zones (measured in terms of the number of
    lines per square mile) are as follows: (1) zero - 4.99; (2) 5 -
    99.99; (3) 100 - 199.99; (4) 200 - 649.99; (5) 650 - 849.99; (6)
    850 - 2549.99; (7) 2550 - 4999.99; (8) 5000 - 9,999.99; (9) 10,000+.

[^205]: ^?^ *Platform Order*, 13 FCC Rcd at 21352-53, para. 70.

[^206]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18544.

[^207]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18544. The BCPM and HAI
    default values are the default input values for the user-adjustable
    input values in the BCPM and HAI models, respectively. Although we
    had chosen a model platform and were no longer considering adoption
    of the BCPM and HAI models, we continued to consider the BCPM and
    HAI default input values for the inputs to be used in the model. As
    we explained in the *Inputs Further Notice*, for some inputs, these
    were the only values on the record. *Inputs Further Notice* at para.
    51 n. 125. We also noted that although the BCPM model includes
    nationwide default values, the BCPM sponsors generally advocated the
    use of company-specific values and, in some cases, proposed such
    values.

[^208]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18544.

[^209]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18544.

[^210]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18541.

[^211]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18541.

[^212]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18541.

[^213]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18541.

[^214]: ^?^ *Inputs Public Notice* at 7. *See* David Gabel and Scott
    Kennedy, *Estimating the Cost of Switching and Cables Based on
    Publicly Available Data*, National Regulatory Research Institute
    NRRI 98-09, April 1998, (NRRI Study). Dr. Gabel and Mr. Kennedy are
    consultants for the Commission in this proceeding.

[^215]: ^?^ *See Workshop Public Notice*.

[^216]: ^?^ After numerous discussions with industry during development
    of the survey, we distributed a final version on December 14, 1998,
    and requested responses by January 14, 1999.

[^217]: ^?^ BellSouth, Ameritech, Pacific Bell, Nevada Bell,
    Southwestern Bell, Sprint, GTE, Aliant, SNET, and AT&T submitted
    data in response to the structure and cable cost survey. Several
    companies requested additional time to complete and submit their
    data. After receiving and reviewing the data, staff found that,
    despite detailed survey instructions, further discussions with a
    number of companies were required before we could assemble the data
    for comparison and analysis. In a number of cases, respondents filed
    revised data or clarified the data they had submitted.

[^218]: ^?^ *See also* *supra* paragraphs 29-32 and *infra* paragraph
    348 for further discussion of the adoption of nationwide average
    values for estimating costs and expenses in the model.

[^219]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250.

[^220]: ^?^ AT&T and MCI *Inputs Further Notice* reply comments at 3.

[^221]: ^?^ *See, e.g.,* Bell Atlantic *Inputs Further Notice* comments
    at 20-21; BellSouth *Inputs Further Notice* comments, Attachment B
    at B-16, B-18; GTE *Inputs Further Notice* comments at 10-11;
    Ameritech *Inputs Further Notice* comments at 8; Sprint *Inputs
    Further Notice* comments at 3-7.

[^222]: ^?^ GTE *Inputs Further Notice* comments at 10-11. *See also*
    BellSouth *Inputs Further Notice* comments, Attachment A at A-5,
    A-8 - A-14.

[^223]: ^?^ *Inputs Further Notice* at para. 65.

[^224]: ^?^ *Inputs Further Notice* at para. 65 n. 145 *citing* HAI
    Inputs Portfolio at 20.

[^225]: ^?^ *See e.g.*, AT&T and MCI *Inputs Further Notice* comments at
    13; GTE *Inputs Further Notice* comments at 47-48; Sprint *Inputs
    Further Notice* comments at 17-18; SBC *Inputs Further Notice*
    comments at 7-8.

[^226]: ^?^ SBC *Inputs Further Notice* comments at 8.

[^227]: ^?^ GTE *Inputs Further Notice* comments at 47. GTE asserts that
    it believes that, even for 12 kilofoot loops, a significant amount
    of 24-gauge cable will continue to be deployed in the network
    because of certain cost-saving reasons related to its larger
    diameter.

[^228]: ^?^ *Inputs Further Notice* at para. 66.

[^229]: ^?^ *See e.g*., GTE *Inputs Further Notice* comments at 48;
    Sprint *Inputs Further Notice* comments at 18; SBC *Inputs Further
    Notice* comments at 8.

[^230]: ^?^ GTE *Inputs Further Notice* comments at 47. *See also* SBC
    *Inputs Further Notice* comments at 8. SBC contends that the same
    input values should be used as long as density values, which reflect
    costs differences in varying degrees of urban and suburban
    construction, are properly reflected.

[^231]: ^?^ GTE *Inputs Further Notice* comments at 47.

[^232]: ^?^ Sprint *Inputs Further Notice* comments at 17. Sprint
    contends however that in actual practice, splicing costs may be
    somewhat higher for distribution cable due to such factors as more
    frequent tapering of cable sizes and branch splices, but this
    difference is not material for modeling purposes.

[^233]: ^?^ *Inputs Further Notice* at para. 68.

[^234]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 48; SBC
    *Inputs Further Notice* comments at 8; Sprint *Inputs Further
    Notice* comments at 18. *See also* AT&T and MCI *Inputs Further
    Notice* comments at 13.

[^235]: ^?^ GTE *Inputs Further Notice* comments at 48.

[^236]: ^?^ GTE *Inputs Further Notice* comments at 48.

[^237]: ^?^ SBC *Inputs Further Notice* comments at 8; Sprint *Inputs
    Further Notice* comments at 18.

[^238]: ^?^ By model convention, business lines are reported as switched
    business lines.

[^239]: ^?^ For example, DS1 service provides 24 voice equivalent
    channels using two copper pairs.

[^240]: ^?^ *See Federal-State Joint Board on Universal Service,
    Forward-Looking Mechanism for High Cost Support for Non-Rural LECs*,
    Order, CC Docket Nos. 96-45, 97-160, DA 99-1406 (rel. July 19, 1999)
    (*1999 Data Request*).

[^241]: ^?^ For these line count totals, we only use data from the
    responses that we found to be consistent with the definitions
    prescribed in the *1999 Data Request*. Submissions in which
    companies reported more facilities than channels are inconsistent
    with those definitions and do not reflect current industry practice.

[^242]: ^?^ We note that only DS0 or DS1 service is provided under the
    model\'s conventions. The model does not allow for the deployment of
    DS2 or DS3 services.

[^243]: ^?^ This equation is applied separately for switched and
    non-switched lines.

[^244]: ^?^ *Inputs Further Notice* at para. 72; *See also* *Inputs
    Further Notice* at 77, 82-83. As noted in paragraph 88 *supra*, this
    study provides a methodology for estimating cable and structure
    costs.

[^245]: ^?^ *Inputs Further Notice* at paras. 90, 92, 94.

[^246]: ^?^ *Inputs Further Notice* at para. 69.

[^247]: ^?^ *Inputs Further Notice* at para. 69.

[^248]: ^?^ *See Inputs Further Notice* at para. 71 n. 152 *citing*
    Letter from Chris Frentrup, MCI Worldcom, to Magalie Roman Salas,
    FCC, dated Feb. 9, 1999 (MCI Feb. 9, 1999 *ex parte*).

[^249]: ^?^ *See Inputs Further Notice* at para. 71 n. 153 *citing*
    Letter from Pete Sywenki, Sprint, to Magalie Roman Salas, FCC, dated
    Jan 29, 1999 (Sprint Jan. 29, 1999 *ex parte*).

[^250]: ^?^ *See Inputs Further Notice* at para 71 n. 154 *citing*
    Sprint Jan. 29, 1999 *ex parte* at 8-9.

[^251]: ^?^ *Inputs Further Notice* at para. 73 n. 156 *citing* Sprint
    Jan. 29 *ex parte*, Attachment at 5.

[^252]: ^?^ *Inputs Further Notice* at para. 73 n. 157 *citing* Sprint
    Jan. 29 *ex parte*, Attachment at 7.

[^253]: ^?^ *Inputs Further Notice* at para. 73.

[^254]: ^?^ *See e.g.*, Bell Atlantic *Inputs Further Notice* comments
    at 18; GTE *Inputs Further Notice* comments at 48; BellSouth *Inputs
    Further Notice* comments, Attachment B at B-7 - B-11.

[^255]: ^?^ *See supra* paragraph 29-32 and 90-93.

[^256]: ^?^ The following discussion reaches conclusions with regard to
    the use of company-specific data in the estimation of cable costs
    inputs. Such information was received initially, in conjunction with
    structure costs data, in response to our survey on cable and
    structure costs. Because we find that the data for cable costs and
    structure costs suffers from the same deficiencies, we also reach
    conclusions with regard to the use of such data in the estimation of
    structure cost inputs.

[^257]: ^?^ In reaching this conclusion we also take note of AT&T and
    MCI\'s inability to link the incumbents LECs actual contract costs
    and the data they submitted to the Commission. AT&T and MCI *Inputs
    Further Notice* comments at 15 (Proprietary Version).

[^258]: ^?^ AT&T and MCI *Input Further Notice* comments at 15
    (Proprietary Version).

[^259]: ^?^ *Inputs Further Notice*, Appendix C, section III.C.

[^260]: ^?^ GTE *Inputs Further Notice* reply comments at 27.

[^261]: ^?^ As GTE explains in its comments, GTE submitted additional
    information as a follow-up to our original request. GTE submitted
    such information in response to a request from the Bureau.

[^262]: ^?^ *Inputs Further Notice* at paras. 69-74, 105. As noted
    above, we had received data in response to the cable and structure
    cost survey and, at the time of the *Inputs Further Notice*, were in
    the process of scrutinizing it.

[^263]: ^?^ NRRI Study at 34-36.

[^264]: ^?^ For a discussion of regression analysis, *See* William H.
    Greene, *Econometric Analysis* (1990).

[^265]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 13-33;
    Bell Atlantic *Inputs Further Notice* comments at 15-19; BellSouth
    *Inputs Further Notice* comments, Attachment A at A-2 - A-5,
    Attachment B at B-1 - B-14; US West *Inputs Further Notice*
    comments, Attachment A at 2-29; Sprint *Inputs Further Notice*
    comments at 5-7, 17-33.

[^266]: ^?^ As discussed in more detail below, we have relied on
    contract data in the estimation of input values for the costs of
    DLCs and *ex parte* data in the estimation of input values for the
    costs of SAIs. As explained in paragraphs 253-254 and 274-275, such
    data is the only reliable data available on the record for the
    determination of such costs.

[^267]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 15-16.

[^268]: ^?^ NRRI Study at 2.

[^269]: ^?^ NRRI Study at 2.

[^270]: ^?^ GTE *Inputs Further Notice* comments at 15-16.

[^271]: ^?^ NRRI Study at 37-40.

[^272]: ^?^ GTE *Inputs Further Notice* comments at 17-19*;* Bell
    Atlantic *Inputs Further Notice* comments at 16, Attachment C at 9.

[^273]: ^?^ NRRI Study at 8-9 and 67-73.

[^274]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 14.

[^275]: ^?^ *See e.g.*, Bell Atlantic *Input Further Notice* comments at
    16, Attachment C at 9.

[^276]: ^?^ NRRI Study at 1-2.

[^277]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 19-22;
    Bell Atlantic *Inputs Further Notice* comments at 16-17, Attachment
    C at 13-14.

[^278]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 15;
    Ameritech *Inputs Further Notice* comments at 26; AT&T and MCI
    *Inputs Further Notice* comments at 14.

[^279]: ^?^ Richard W. Madsen and Melvin L. Moeschberger, *Statistical
    Concepts with Applications to Business and Economics*, 490 (2nd
    Edition 1986).

[^280]: ^?^ Ameritech *Inputs Further Notice* comments at 16.

[^281]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 14.

[^282]: ^?^ The *Inputs Further Notice* indicated that 80 observations
    were used to estimate the proposed underground copper cable costs.
    However, 81 observations were used to develop these proposed costs.
    Eighty one observations are used to estimate the adopted underground
    copper cable costs.

[^283]: ^?^ GTE *Inputs Further Notice* comments at 15.

[^284]: ^?^ GTE *Inputs Further Notice* comments at 19-21. *See also*
    Bell Atlantic *Inputs Further Notice* comments at 16-17, Attachment
    C at 13-14.

[^285]: ^?^ GTE *Inputs Further Notice* comments at 21.

[^286]: ^?^ GTE *Inputs Further Notice* comments at 22.

[^287]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 52-53;

[^288]: ^?^ *Inputs Further Notice* at paras. 83, 113. *See also Inputs
    Further Notice*, Appendix D, sections I.C., III.C.

[^289]: ^?^ *Inputs Further Notice* at para. 94. *See also* *Inputs
    Further Notice*, Appendix D, section II.C.

[^290]: ^?^ Moreover, at least one LEC commenter states that it is not
    able to separate buried structure costs from total buried plant
    costs. GTE *Inputs Further Notice* comments at 53. This inability
    may reflect the fact that under current FCC accounting guidelines
    these costs are not identified separately.

[^291]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 52;
    BellSouth *Inputs Further Notice* comments, Attachment A at A-16.

[^292]: ^?^ That is, the values of the intercept and the coefficients
    for the variables that indicate density zone, the combined soil and
    rock type, and the presence of a high water table in the combined
    regression equation for buried fiber cable and structure lie within
    the 95 percent confidence interval surrounding the values of the
    intercept and the coefficients for the respective variables in the
    combined regression equation for 24-gauge buried copper cable and
    structure.

[^293]: ^?^ Nevertheless, the value of the F-statistic for the
    regression equation for the combined cost of buried fiber cable and
    structure, 172.80, indicates that the regression equation is
    statistically significant.

[^294]: ^?^ This estimate of the fixed cost for a 24-gauge buried copper
    cable excludes fixed costs for structure, LEC engineering, and
    splicing, but includes fixed costs for contractor engineering.

[^295]: ^?^ *See* AT&T and MCI *Inputs Further Notice* comments,
    Appendix A at A-7. The AT&T and MCI estimate of the fixed cost for a
    24-gauge buried copper cable excludes fixed costs for structure,
    splicing, and contractor and LEC engineering.

[^296]: ^?^ *See* HAI Model, Release 5.0a, Model Description, Appendix B
    at 15. A 6-pair 24-gauge buried copper cable is the smallest buried
    cable for which HAI has a default value. The HAI default value for
    the installed cost of a 6-pair 24-gauge buried copper cable excludes
    fixed and variable costs for structure, but includes fixed and
    variable costs for material, contractor and LEC engineering, and
    splicing. Fixed cable costs do not vary with cable size. A large
    percentage of the installed cable cost for a small cable is a fixed
    cost.

[^297]: ^?^ The sum of the fixed costs reflected in the buried structure
    cost estimates, including LEC engineering costs, developed from the
    separate buried structure equation and the fixed costs reflected in
    the buried copper or fiber cable cost estimates, including LEC
    engineering and splicing costs, developed from the separate buried
    cable equation is affected slightly by the relative values used for
    the fixed cost in these separate equations. The relative values used
    for these fixed costs affects slightly the sum of these fixed costs
    because a splicing loading of 9.4 or 4.7 percent is applied to the
    fixed cost reflected in the separate buried copper or fiber cable
    cost estimates (excluding LEC engineering and splicing costs), while
    a loading of 10 percent for LEC engineering is applied to the fixed
    cost reflected in the separate buried structure cost estimates
    (excluding LEC engineering costs).

[^298]: ^?^ GTE *Inputs Further Notice* comments at 52.

[^299]: ^?^ We note that the regression equations in the NRRI Study are
    a starting point because, as we explained in the *Inputs Further
    Notice,* and discuss in more detail below, we proposed to modify the
    regression equations used to estimate cable costs to capture the
    buying power of the non-rural LECs reflected in the price they pay
    for cable.

[^300]: ^?^ We set forth in Appendix B the regression equations that we
    adopt in this Order. We also set forth in Appendix B the adjustments
    we make to those equations to reflect the buying power of large
    LECs, splicing costs, LEC engineering costs, and to separate the
    buried cable and structure regression equations into separate
    equations for buried cable and buried structure.

[^301]: ^?^ *See* Bell Atlantic *Inputs Further Notice* comments,
    Attachment C at 25; Ameritech *Inputs Further Notic*e comments at
    13-14; US West *Inputs Further Notice* comments, Attachment A at 9,
    11.

[^302]: ^?^ *See e.g.*, Ameritech *Inputs Further Notice* comments at
    10-11; GTE *Inputs Further Notice* comments at 30-31; Bell Atlantic
    *Inputs Further Notice* comments at 25-26; US West *Inputs Further
    Notice* comments, Attachment A at 9.

[^303]: ^?^ *See* Bell Atlantic *Inputs Further Notice* comments,
    Attachment C at 25-26; Ameritech *Inputs Further Notice* comments at
    13; US West *Inputs Further Notice* comments, Attachment A at 9; GTE
    *Inputs Further Notice* comments at 30-31.

[^304]: ^?^ *Inputs Further Notice* at para. 75.

[^305]: ^?^ We used Stata Statistical Software: Release 5 (Stata) to
    perform the calculations needed to estimate the regression equations
    adopted in this Order for cable and structure costs. Stata has a
    robust regression methodology that uses formulas developed by P.J.
    Huber, R.D. Cook, A.E. Beaton and J.W. Tukey. We used this
    methodology to estimate the regression equations for cable and
    structure costs. We refer to this robust regression methodology as
    the Huber methodology. *See Stata Reference Manual, Release 5*,
    Volume 3, P-Z, Stata Press, College Station, TX, 168-173.

[^306]: ^?^ *Inputs Further Notice* at para. 76.

[^307]: ^?^ As noted in the *Inputs Further Notice*, we used the robust
    regression parameter estimates for cable, conduit, and buried
    structure. The use of robust estimation did not improve the
    statistical properties of the estimators for pole costs, so we
    tentatively concluded that the ordinary least squares technique is
    appropriate for pole costs. The value of the F-statistic was not
    statistically significant at the five percent level. *Inputs Further
    Notice* at para. 76 n. 161.

[^308]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 23-26;
    Bell Atlantic *Inputs Further Notice* comments at 17, Attachment C
    at 29-34; US West *Inputs Further Notice* comments, Attachment A at
    11-13; BellSouth *Inputs Further Notice* comments, Attachment A at
    A-17.

[^309]: ^?^ For example, for one to determine why the reported structure
    cost for a single project is an outlier, one would have to interview
    the LEC engineers and contractors to verify the reported cost,
    identifying with precision whether unusual circumstances surrounded
    the project thereby leading to atypical costs.

[^310]: ^?^ *See e.g*., GTE *Inputs Further Notice* comments at 23-26;
    Bell Atlantic *Inputs Further Notice* comments at 17, Attachment C
    at 29-34; US West *Inputs Further Notice* comments, Attachment A at
    11-13; BellSouth *Inputs Further Notice* comments, Attachment A at
    A-17.

[^311]: ^?^ *See* Bell Atlantic *Inputs Further Notice* comments at 17,
    Attachment C at 30, 31; *See* GTE *Inputs Further Notice* comments
    at 25.

[^312]: ^?^ *See* Bell Atlantic *Inputs Further Notice* comments at 17.

[^313]: ^?^ Bell Atlantic *Inputs Further Notice* comments, Attachment C
    at 23, 24. *See also* GTE *Inputs Further Notice* comments at 17,
    18; AT&T *Inputs Further Notice* comments at 14.

[^314]: ^?^ NRRI Study at 34.

[^315]: ^?^ *Inputs Further Notice* at paras. 77-81; 82; 83-84.

[^316]: ^?^ *Inputs Further Notice* at paras. 90-95.

[^317]: ^?^ *Inputs Further Notice* at para. 79 n. 163 *citing* NRRI
    Study at 47.

[^318]: ^?^ *Inputs Further Notice* at para. 91 n. 174 *citing* NRRI
    Study at 47.

[^319]: ^?^ *Inputs Further Notice* at paras. 79, 82.

[^320]: ^?^ *Inputs Further Notice* at paras. 91, 93.

[^321]: ^?^ *Inputs Further Notice* at paras. 84, 95.

[^322]: ^?^ *Inputs Further Notice* at para. 79.

[^323]: ^?^ *See e.g.*, SBC *Inputs Further Notice* comments at 8;
    Sprint *Inputs Further Notice* comments at 22; Sprint *Inputs
    Further Notice* reply comments at 15; AT&T and MCI *Inputs Further
    Notice* comments at 21.

[^324]: ^?^ GTE *Inputs Further Notice* comments at 26-27; Sprint
    *Inputs Further Notice* reply comments at 14.

[^325]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 21.

[^326]: ^?^ GTE *Inputs Further Notice* comments at 26.

[^327]: ^?^ GTE *Inputs Further Notice* comments at 27.

[^328]: ^?^ GTE *Inputs Further Notice* comments at 28.

[^329]: ^?^ GTE *Inputs Further Notice* comments at 28.

[^330]: ^?^ *Inputs Further Notice* at para. 79.

[^331]: ^?^ Such agreement is consistent with representations by parties
    in merger contexts that a merger will produce costs savings.

[^332]: ^?^ NRRI Study at 47. *See Inputs Further Notice* at para. 79.

[^333]: ^?^ GTE *Inputs Further Notice* comments at 28-29.

[^334]: ^?^ GTE *Inputs Further Notice* comments at 29.

[^335]: ^?^ *See supra* paras. 139-145.

[^336]: ^?^ We set forth in Appendix B a table that shows the value of
    this regression coefficient derived by using the Huber methodology
    and the 95 percent confidence interval surrounding the value of this
    coefficient obtained by using ordinary least squares. We also
    discuss in more detail the statistical evidence on the impact of the
    Huber methodology on the level of the material costs reflected in
    the cable cost estimates.

[^337]: ^?^ As a general matter, 95 percent of the confidence intervals
    associated with different estimates of a given coefficient derived
    from a large number of samples of a given population can be expected
    to contain the true value of the coefficient.

[^338]: ^?^ The one exception is that the value of the cable size
    coefficient obtained by using the Huber methodology for buried
    copper cable lies outside the 95 percent confidence interval
    associated with the cable size coefficient for buried copper cable
    obtained in the NRRI Study using ordinary least squares. This
    suggests that the assignment of weights by the Huber methodology
    does have a statistically significant impact on the level of the
    buried material costs reflected in the buried cable cost estimates.
    We find that this does not lead to an unreasonable estimate for
    buried cable costs. As we explained, application of the Huber
    methodology results in a better estimate of the expected value or
    tendency of the material costs for the RUS companies. Moreover, as
    noted above, the level of the buying power adjustment we adopt for
    buried copper cable is the most conservative estimate on the record
    before us.

[^339]: ^?^ GTE *Inputs Further Notice* comments at 29.

[^340]: ^?^ GTE *Inputs Further Notice* comments at 29.

[^341]: ^?^ GTE *Inputs Further Notice* comments at 28-29; Sprint
    *Inputs Further Notice* comments at 22-23.

[^342]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment A at
    A-5, A-18.

[^343]: ^?^ Bell Atlantic *Inputs Further Notice* comments, Attachment C
    at 22-23, 27.

[^344]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment A at
    A-18.

[^345]: ^?^ NRRI Study at 47 n. 47.

[^346]: ^?^ Bell Atlantic *Inputs Further Notice* comments, Attachment C
    at C-27.

[^347]: ^?^ E(aX) = aE(X) where \"a\" is the discount factor and X is
    the price of cable. *See*, *e.g.*, Gerald Keller and Brian Warrick,
    *Statistics for Management Economics* at 206 (Fourth Edition,
    Duxbury, 1997).

[^348]: ^?^ Sprint *Inputs Further Notice* comments at 22. *See also*
    Cincinnati Bell *Inputs Further Notice* comments at 3-5.

[^349]: ^?^ *See supra* at paragraph 29.

[^350]: ^?^ *See Inputs Further Notice* at paras. 80, 91. It should be
    noted that the LEC Engineering Adjustment as well as the Splicing
    Adjustment discussed *infra* in paragraphs 168-176 would be required
    in the estimation of costs for rural LECs from the RUS data base
    because such costs were not reflected in the RUS data. These
    adjustments are part of the process in developing estimates from the
    data.

[^351]: ^?^ *Inputs Further Notice* at para. 80.

[^352]: ^?^ *Inputs Further Notice* at para. 80.

[^353]: ^?^ *Inputs Further Notice* at paras. 80, 82, 84, 91, 93, 95.

[^354]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 31-32;
    AT&T and MCI *Inputs Further Notice* comments at 16-18; BellSouth
    *Inputs Further Notice* comments, Attachment B at B-8 - B-9;
    BellSouth *Inputs Further Notice* reply comments at 6-7; Sprint
    *Inputs Further Notice* comments at 24-25; Bell Atlantic *Inputs
    Further Notice* reply comments, Attachment A at 1.

[^355]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 16.

[^356]: ^?^ Sprint *Inputs Further Notice* comments at 24.

[^357]: ^?^ *See* *e.g.,* GTE *Inputs Further Notice* comments at 26-28;
    BellSouth *Inputs Further Notice* comments, Attachment B at B-9.

[^358]: ^?^ AT&T and MCI develop equations for engineering costs that
    reflect engineering costs associated with splicing. *See* AT&T and
    MCI *Inputs Further Notice* comments, Exhibit A at A-7.

[^359]: ^?^ We develop the flat cost-per-foot loading for LEC
    engineering for each type of cable by first estimating the RUS
    companies\' total cable cost inclusive of splicing and exclusive of
    LEC engineering costs based on: (1) the regression equations we
    adopt in this Order; (2) the number of feet of cable that was placed
    pursuant to the contracts from which the data used to develop these
    regression equation are derived; and (3) the loadings that we adopt
    in this Order for splicing costs, 9.4 percent for copper cable and
    4.7 percent for fiber cable. We then compute for each type of cable
    the total LEC engineering cost based on the total cable cost
    inclusive of LEC splicing costs and the loading that we adopt in
    this Order for LEC engineering, 10 percent. Finally, for each type
    of cable, we compute the flat cost per foot loading for LEC
    engineering by dividing the total LEC engineering costs by the total
    number of feet of cable placed pursuant to the RUS contracts.

    Based on this methodology, we derive values for LEC engineering
    costs of \$.19, \$1.50, \$.16, \$.19, \$.65, and \$.14 per foot for
    24-gauge aerial copper cable costs, 24-gauge underground copper
    cable costs, 24-gauge buried copper cable costs, aerial fiber cable
    costs, underground fiber cable costs, and buried fiber cable costs,
    respectively. We add these LEC engineering costs to the cable cost
    estimates derived by using the Huber methodology.

[^360]: ^?^ *See Inputs Further Notice* at paras. 81, 91.

[^361]: ^?^ *Inputs Further Notice* at para. 81 n. 164 *citing* NRRI
    Study at 29.

[^362]: ^?^ *Inputs Further Notice* at para. 91 n. 176 *citing* NRRI
    Study at 29.

[^363]: ^?^ *Inputs Further Notice* at paras. 81, 82, 84.

[^364]: ^?^ *Inputs Further Notice* at paras. 91, 93, 95.

[^365]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 32 and
    50; Sprint *Inputs Further Notice* comments at 27; BellSouth *Inputs
    Further Notice* comments, Attachment A at A-9 - A-11, and Attachment
    B at B-8; BellSouth *Inputs Further Notice* reply comments at 6-7.

[^366]: ^?^ GTE *Inputs Further Notice* comments at 49.

[^367]: ^?^ GTE *Inputs Further Notice* comments at 49.

[^368]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 16.

[^369]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 16-18.

[^370]: ^?^ Bell Atlantic *Inputs Further Notice* reply comments,
    Attachment A at 1.

[^371]: ^?^ Sprint *Inputs Further Notice* comments at 25. We note that
    Sprint advocates the use of company-specific data in the first
    instance.

[^372]: ^?^ Sprint *Inputs Further Notice* comments at 25.

[^373]: ^?^ There is one instance where the underground cable costs that
    we develop from RUS data (net of splicing and engineering costs) are
    not the largest for a given cable size. For the largest fiber cable
    size, 288 pairs, the costs that we develop for buried cable, \$12.07
    per foot, are greater than those for underground cable, \$11.96 per
    foot. However, the model is unlikely to frequently place the largest
    fiber cable size in the network it builds in high-cost areas because
    most high-cost areas are in the lowest density zones where use of
    such a cable provides too much capacity relative to demand.

[^374]: ^?^ *See e.g.*, Sprint *Inputs Further Notice* reply comments at
    16; GTE *Inputs Further Notice* reply comments 26-27.

[^375]: ^?^ SBC *Inputs Further Notice* comments at 9.

[^376]: ^?^ *Inputs Further Notice* at para. 85.

[^377]: ^?^ *Inputs Further Notice* at para. 86.

[^378]: ^?^ We did not use the HAI default values in addition to these
    data to estimate these ratios because the HAI defaults do not have
    separate values for 26-gauge and 24-gauge cable costs for each
    different cable size.

[^379]: ^?^ *Inputs Further Notice* at para. 86.

[^380]: ^?^ *See e.g.*, BellSouth *Inputs Further Notice* comments at
    6-7, Attachment B at B-8 - B-9; GTE *Inputs Further Notice* comments
    at 48.

[^381]: ^?^ *See* *supra* paragraph 92.

[^382]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 19-20.

[^383]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 19-20.

[^384]: ^?^ Bell Atlantic *Inputs Further Notice* comments, Attachment C
    at 26-27; GTE *Inputs Further Notice* comments at 29-30.

[^385]: ^?^ *Inputs Further Notice*, Appendix D.

[^386]: ^?^ BellSouth *Inputs Further Notice* reply comments, Attachment
    A at A-22 - A-23.

[^387]: ^?^ GTE *Inputs Further Notice* comments at 48-50.

[^388]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment A at
    A-19.

[^389]: ^?^ *Inputs Further Notice* at para. 96.

[^390]: ^?^ We note that the actual fill factor may be lower than the
    fill factor used to design the network (sometimes referred to as
    administrative fill), because cable and fiber are available only in
    certain sizes. For example, assume a neighborhood with 100
    households has a current demand of 120 telephones. Dividing the
    120-pair demand by an 80 percent administrative fill factor
    establishes a need for 150 pairs. Cable is not sold, however, in
    150-pair units. The company would purchase the smallest cable that
    is sufficient to provide 150 pairs, which is a 200 pair cable. The
    fill factor that occurs and is measurable, known as the effective
    fill, would be the number of pairs needed to meet demand, 120 pairs,
    divided by the number of pairs installed, 200 pairs, or 60 percent.

[^391]: ^?^ *Inputs Further Notice* at paras. 100, 101, 102.

[^392]: ^?^ *Inputs Further Notice* at para. 97. As explained below,
    default values in BCPM 3.1 for distribution cable do not vary by
    density zone.

[^393]: ^?^ *Inputs Further Notice* at para. 97 n. 187 *citing* HAI Dec.
    11, 1997 submission, Inputs Portfolio at 39, 63.

[^394]: ^?^ *Inputs Further Notice* at para. 97 n. 188 *citing* BCPM 3.1
    May 26, 1998 (Preliminary Edition) Loop Inputs Documentation at 51.

[^395]: ^?^ *Inputs Further Notice* at para. 97.

[^396]: ^?^ *Inputs Further Notice* at para. 98 n. 189 *citing* HAI Dec.
    11, 1997 submission, Inputs Portfolio at 39, 63. HAI 5.0 default
    values range from 50 percent in the lowest density zone to 75
    percent in the highest density zone for distribution cable sizing
    fill factors, and range from 65 percent in the lowest density zone
    to 75 percent in the highest density zone for copper feeder cable
    sizing fill factors.

[^397]: ^?^ *Inputs Further Notice* at para. 98 n. 190 *citing* BCPM
    Dec. 11, 1997 submission. We noted that earlier versions of BCPM,
    however, had lower fill factors for distribution than for feeder.
    *See, e.g., 1997 Further Notice* at para. 118. Default values in
    BCPM 3.1 range from 75 to 85 percent for feeder cable.

[^398]: ^?^ *Inputs Further Notice* at para. 98 n. 191 *citing* HAI Dec.
    11, 1997 submission, Inputs Portfolio at 39, 63.

[^399]: ^?^ For example, in an *ex parte* meeting on March 24, 1999,
    Ameritech representatives said that Ameritech designs distribution
    plant to meet \"ultimate\" demand and designs feeder plant that is
    \"growable.\" *See* Letter from Celia Nogales, Ameritech, to Magalie
    Roman Salas, FCC, dated March 25, 1999 (Ameritech March 25 *ex
    parte*).

[^400]: ^?^ *Inputs Further Notice* at para. 99.

[^401]: ^?^ *Inputs Further Notice* at para. 99 n. 194. Ameritech filed
    data, subject to the protective order in this proceeding, showing
    how these considerations are used to calculate the actual and
    forward-looking fill factors in Ameritech\'s territory. *See*
    Ameritech March 25 *ex parte*.

[^402]: ^?^ We define \"current demand\" to include a reasonable amount
    of excess capacity to accommodate short term growth. *Inputs Further
    Notice* at para. 100 n. 195.

[^403]: ^?^ *Inputs Further Notice* at para. 100.

[^404]: ^?^ *Inputs Further Notice* at para. 101 *citing* Ameritech
    March 25 *ex parte*.

[^405]: ^?^ *Inputs Further Notice* at para. 101.

[^406]: ^?^ *Inputs Further Notice* at para. 101.

[^407]: ^?^ *Inputs Further Notice* at para. 102.

[^408]: ^?^ That is, fiber plant with a 100 percent fill factor has an
    actual utilization of 50 percent; whereas copper plant with a 50
    percent fill factor has an actual utilization of 50 percent.

[^409]: ^?^ *Inputs Further Notice* at para. 102.

[^410]: ^?^ Sprint *Inputs Further Notice* comments at 29; SBC *Inputs
    Further Notice* comments at 9; GTE *Inputs Further Notice* comments
    at 54.

[^411]: ^?^ GTE *Inputs Further Notice* comments at 53.

[^412]: ^?^ *Inputs Further Notice* at para. 96 n. 135.

[^413]: ^?^ GTE *Inputs Further Notice* comments at 54. Ameritech
    contends that the nationwide fill factors proposed by the Commission
    are reasonable estimates to use if company-specific or
    state-specific fill factors are not used. Ameritech *Inputs Further
    Notice* comments at 20.

[^414]: ^?^ *See e.g.*, GTE *Inputs Further Notice* comments at 54;
    BellSouth *Inputs Further Notice* comments, Attachment B at B-12

[^415]: ^?^ GTE *Inputs Further Notice* comments at 54.

[^416]: ^?^ GTE *Inputs Further Notice* comments at 55.

[^417]: ^?^ We note that GTE did not assert that this bias will increase
    structure costs.

[^418]: ^?^ *Inputs Further Notice* at para. 100.

[^419]: ^?^ GTE *Inputs Further Notice* comments at 55-56.

[^420]: ^?^ GTE *Inputs Further Notice* comments at 55-56.

[^421]: ^?^ GTE *Inputs Further Notice* comments at 55.

[^422]: ^?^ GTE *Inputs Further Notice* comments at 55.

[^423]: ^?^ GTE *Inputs Further Notice* comments at 55.

[^424]: ^?^ GTE *Inputs Further Notice* comments at 56.

[^425]: ^?^ We anticipate beginning a proceeding in the near future to
    determine how to incorporate changed circumstances such as these
    into the modeling process.

[^426]: ^?^ *Inputs Further Notice* at para. 100.

[^427]: ^?^ Sprint *Inputs Further Notice* comments at 29.

[^428]: ^?^ Ameritech *Inputs Further Notice* comments at 20.

[^429]: ^?^ SBC *Inputs Further Notice* comments at 9.

[^430]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment B at
    B-11.

[^431]: ^?^ Bell Atlantic *Inputs Further Notice* comments, Attachment D
    at 7 (Proprietary Version); Bell Atlantic *Inputs Further Notice*
    reply comments, Attachment A at A-1.

[^432]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 22-23.

[^433]: ^?^ *See* FCC, Common Carrier Bureau, Industry Analysis
    Division, *Trends in Telephone Service* at 20-6 (rel. Sept. 1999).

[^434]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 8.

[^435]: ^?^ AT&T and MCI *Inputs Further Notice* reply comments at 20.

[^436]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 22.

[^437]: ^?^ *Inputs Further Notice* at para. 96 n. 185.

[^438]: ^?^ *See First Recommended Decision*, 12 FCC Rcd at 91-92,
    132-134, paras. 4, 89-92.

[^439]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8915, para. 250.

[^440]: ^?^ Sprint *Inputs Further Notice* comments at 29.

[^441]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment B at
    B-11.

[^442]: ^?^ Bell Atlantic *Inputs Further Notice* comments, Attachment D
    at 7 (Proprietary Version); Bell Atlantic *Inputs Further Notice*
    reply comments, Attachment A at A-1.

[^443]: ^?^ *See e.g.*, AT&T and MCI *Inputs Further Notice* comments at
    22; Sprint *Inputs Further Notice* comments at 30; GTE *Inputs
    Further Notice* comments at 56; SBC *Inputs Further Notice* comments
    at 9-10.

[^444]: ^?^ We note that GTE agrees with a fill factor of 100 percent
    for fiber as it relates to 100 percent redundancy only if it
    provides fibers for redundant optical transmit and receive and does
    not equate to 100 percent fiber utilization. We note that a fill
    factor of 100 percent for fiber does not equate to 100 percent fiber
    utilization.

[^445]: ^?^ SBC *Inputs Further Notice* comments at 9-10.

[^446]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment B at
    B-9 - B-10.

[^447]: ^?^ *Inputs Further Notice* at para. 104.

[^448]: ^?^ When a plow is used to place buried cable, a separate trench
    is not required.

[^449]: ^?^ *Inputs Further Notice* at para. 106.

[^450]: ^?^ *Inputs Further Notice* at para. 105.

[^451]: ^?^ *Inputs Further Notice* at para. 107. This regression
    equation was set forth in Appendix D, section III.A of the *Inputs
    Further Notice*.

[^452]: ^?^ *See* *Inputs Further Notice*, Appendix D, section III.A.

[^453]: ^?^ *Inputs Further Notice* at para. 107 n. 206 *citing* NRRI
    Study at 51, Table 2-11.

[^454]: ^?^ *Inputs Further Notice* at para. 108.

[^455]: ^?^ *Inputs Further Notice* at para. 108 n. 208 *citing* NRRI
    Study at 55, Table 2-14.

[^456]: ^?^ *Inputs Further Notice* at para. 108.

[^457]: ^?^ *Inputs Further Notice* at para. 110.

[^458]: ^?^ *Inputs Further Notice* at para. 111.

[^459]: ^?^ *See* *Inputs Further Notic*e, Appendix D, section III.B.
    This regression equation is based on the RUS data, but was developed
    after the publication of that report. The NRRI Study does not set
    forth a regression equation for estimating the cost of underground
    structure.

[^460]: ^?^ This regression equation was developed using underground
    cost data for density zones 1 and 2. The variable in this equation
    that represents the density zone of the geographic area in which the
    underground costs are incurred is not statistically significant at
    any standard level of significance.

[^461]: ^?^ This equation is set forth in Appendix D, section III.C of
    the *Inputs Further Notic*e.

[^462]: ^?^ *See* *Inputs Further Notice*, Appendix D, section III.C.

[^463]: ^?^ *Inputs Further Notice* at para. 112.

[^464]: ^?^ *See* paragraphs 126-132 for a discussion of the development
    of the equation for buried structure.

[^465]: ^?^ *Inputs Further Notice* at para. 112.

[^466]: ^?^ In the *Universal Service Order*, we determined that states
    could submit their own cost studies to serve as the basis for
    calculating federal universal service support in their states, if
    those studies met the criteria for forward-looking economic cost
    determinations. In sum, we required that such cost studies must be
    based on forward-looking economic cost principles and supported by
    publicly available data and computations. In order for the
    Commission to accept a state cost study for these purposes, we also
    required that the study be the same cost study that is used by the
    state to determine intrastate universal service support levels
    pursuant to 254(f) of the Act. *See Universal Service Order*, 12 Fcc
    Rcd at 8912-16, paras. 248, 250-51. The Commission subsequently
    adopted the Joint Board\'s recommendation to estimate
    forward-looking costs using a single national model. *See Seventh
    Report & Order*, 14 FCC Rcd at 8103.

[^467]: ^?^ The RUS data underlying the NRRI Study reflect structure
    costs for density zones 1 and 2.

[^468]: ^?^ *See* GTE *Inputs Further Notice* comments at 53.

[^469]: ^?^ GTE contends that the model should rely on two sizes of
    poles in estimating aerial costs. GTE *Inputs Further Notice*
    comments at 51. GTE also recommends that the calculation of the
    number of poles for a given length of facility be modified. We find
    that there is insufficient evidence on the record at this time with
    regard to the type of pole used in a particular density zone to make
    a determination as to GTE\'s first recommendation. We may evaluate
    this, among other factors, and provide parties an opportunity to
    submit additional evidence on the record in the upcoming proceeding
    on future changes to the model. We also find that GTE\'s second
    recommendation represents a platform change which may be considered
    in the upcoming proceeding on future changes to the model.

[^470]: ^?^ GTE *Inputs Further Notice* comments at 51.

[^471]: ^?^ Cincinnati Bell *Inputs Further Notice* comments at 4.

[^472]: ^?^ Sprint *Inputs Further Notice* comments at 30-31.

[^473]: ^?^ SBC *Inputs Further Notice* comments at 10.

[^474]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 23.

[^475]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 24.

[^476]: ^?^ *See supra* at paragraphs 133-138.

[^477]: ^?^ *Inputs Further Notice* at paras. 109, 111, 114. We note
    that this adjustment is consistent with that made to aerial,
    underground, and buried cable.

[^478]: ^?^ GTE *Inputs Further Notice* comments at 53.

[^479]: ^?^ SBC *Inputs Further Notice* comments at 10-11.

[^480]: ^?^ *See* *supra* paragraph 165.

[^481]: ^?^ *Inputs Further Notice* at para. 116.

[^482]: ^?^ In the *Inputs Further Notice*, we distinguished the BCPM
    default values for distribution plant from those reflected in the
    HAI model. *Inputs Further Notice* at para. 47. As we explained, the
    BCPM default values for distribution plant assume that there is no
    underground plant in the lowest density zone and the percentage
    increases with each density zone to 90 percent underground
    distribution plant in the highest density zone. In contrast, the HAI
    default values for distribution plant mix place no underground
    structure in the six lowest density zones and assume that only 10
    percent of the structure in the highest density zone is underground.
    The BCPM default values assume there is no aerial plant in the
    highest density zone in normal and soft rock terrain, and 10 percent
    aerial plant in hard rock terrain. In contrast, the HAI default
    values assume that there is significantly more aerial cable, 85
    percent, in the highest density zone, but notes that this includes
    riser cable within multi-story buildings and \"block cable\"
    attached to buildings, rather than to poles.

[^483]: ^?^ As we explained in the *Inputs Further Notice*, the default
    plant mix percentages for feeder plant are generally similar in the
    BCPM and the HAI models. *Inputs Further Notice* at para. 120.
    Although the BCPM default values vary between normal or soft rock
    terrain and hard rock terrain, as noted above, and the HAI default
    values differ between copper and fiber feeder, the plant mix ratios
    across density zones are similar. For example, both the BCPM default
    values and the HAI default values assume that there is only five or
    ten percent of underground feeder plant in the lowest density zone.
    The HAI defaults assume there is somewhat more aerial feeder cable
    than the BCPM defaults, except for fiber feeder cable in the four
    lowest density zones. The BCPM defaults assume there is no aerial
    feeder plant in the three highest density zones, except in hard rock
    terrain. Despite these differences, the relative amounts of aerial
    and buried plant across density zones are generally similar.

[^484]: ^?^ *See Inputs Further Notice*, Appendix C.

[^485]: ^?^ *1997 Further Notice* at para. 122.

[^486]: ^?^ Structure distance, also known as route distance, measures
    the distance of the pole line or the trench. Sheath distance
    measures cable distance. If there is only one cable along a
    particular route then structure distance and sheath distance are
    equal. When, however, there is more than one cable along a route,
    sheath distance will be a multiple of the structure distance.

[^487]: ^?^ The proposed algorithm uses ARMIS 43-08 data on buried and
    aerial sheath distances and trench distances to allocate model
    determined structure distance between aerial, buried, and
    underground structures. The first step is to set the underground
    structure distance equal to the ARMIS trench distance and to
    allocate that distance among the density zones on the basis of the
    nationwide plant mix defaults. Then an initial estimate of aerial
    plant is calculated as the sum of the synthesis model structure
    distances by density zone multiplied by the nationwide aerial plant
    mix defaults. A second estimate of aerial plant is calculated by
    multiplying structure distance less trench miles by the aerial
    percentage of total ARMIS sheath miles. Then an adjustment ratio is
    calculated by dividing the second estimate by the initial estimate.
    This adjustment ratio is then applied to each density zone to adjust
    the nationwide default so that the final synthesis model plant mix
    reflects the study area specific plant mix. The buried plant mix
    percentage is determined as a residual equal to one minus sum of the
    underground and aerial percentages.

[^488]: ^?^ GTE *Inputs Further Notice* comments at 58.

[^489]: ^?^ Sprint *Inputs Further Notice* comments at 34.

[^490]: ^?^ US West *Inputs Further Notice* comments at 32-36.

[^491]: ^?^ US West *Inputs Further Notice* comments, Attachment D.

[^492]: ^?^ US West *Inputs Further Notice* comments, Attachment D.

[^493]: ^?^ For example, the ARMIS buried ratio is not the ratio of
    model buried to the sum of model underground and model aerial as US
    West claims, but rather the ratio of model buried to the sum of
    model buried and model aerial. US West claims that the underground
    ratio is the ratio of ARMIS to model sheath miles. This is
    incorrect. It is the ratio of ARMIS trench miles to model route
    miles.

[^494]: ^?^ *Inputs Further Notice* at para. 49.

[^495]: ^?^ SBC *Inputs Further Notice* comments at 11.

[^496]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 25.

[^497]: ^?^ *Inputs Further Notic*e at para. 119.

[^498]: ^?^ GTE *Inputs Further Notice* comments at 58; Sprint *Inputs
    Further Notice* comments at 34.

[^499]: ^?^ GTE *Inputs Further Notice* comments at 59.

[^500]: ^?^ SBC *Inputs Further Notice* comments at 11.

[^501]: ^?^ *Inputs Further Notice* at para. 129.

[^502]: ^?^ *See* HAI Dec. 11, 1997 submission, Appendix B at 57; BCPM
    Jan. 31, 1997 submission, Attachment 9. The BCPM sponsors assume
    that an efficient telephone company will benefit only marginally
    from sharing. The HAI sponsors assume that utilities will engage in
    substantial sharing with telephone companies, and generally assigns
    between 25% and 50% of the cost of shared facilities to the LEC.

[^503]: ^?^ *See, e.g.*, AT&T/MCI *Inputs Further Notice* comments at
    28-31; Bell Atlantic *Inputs Further Notice* comments at 18; GTE
    *Inputs Further Notice* comments at 57; SBC *Inputs Further Notice*
    comments at 11.

[^504]: ^?^ The model uses nine density zones, ranging from the lowest
    density zone (1) to the highest density zone (9). The nine density
    zones (measured in terms of the number of lines per square mile) are
    as follows: (1) zero - 4.99; (2) 5 - 99.99; (3) 100 - 199.99; (4)
    200 - 649.99; (5) 650 - 849.99; (6) 850 - 2549.99; (7) 2550 -
    4999.99; (8) 5000 - 9,999.99; (9) 10,000+.

[^505]: ^?^ *See* Appendix A for a complete list of the input values
    that we adopt in this Order.

[^506]: ^?^ *See, e.g.*, AT&T/MCI *Inputs Further Notice* comments at
    28-31; Bell Atlantic *Inputs Further Notice* comments at 18; GTE
    *Inputs Further Notice* comments at 57; SBC *Inputs Further Notice*
    comments at 11.

[^507]: ^?^ US West *Inputs Further Notice* comments at 28.

[^508]: ^?^ In general, the \"scorched node or utilities\" debate
    concerns whether the model should assume that all utilities are
    non-existent in developing structure sharing percentages. Commenters
    contend that if the model assumes that everything is in place except
    for the telecommunications network, then the sharing percentages
    used in the model should reflect fewer opportunities for sharing
    because it would not be possible to coordinate sharing with other
    utilities in the development of a new network. In particular,
    opportunities for sharing of underground and buried structure would
    be limited. *See* BellSouth *Inputs Further Notice* comments at 8-9;
    GTE *Inputs Further Notice* comments at 18-21; US West *Inputs
    Further Notice* comments at 28-29. While this may provide an
    interesting topic for academic debate, we do not believe it to be
    particularly useful or relevant in determining the structure sharing
    values in this proceeding. We note that, as part of the logical
    argument that the entire telephone network is to be rebuilt, it is
    also necessary to assume that the telephone industry will have at
    least the same opportunity to share the cost of building plant that
    existed when the plant was first built. We also note that cable and
    electric utilities continue to deploy service to new customers and
    replace existing technologies which provides an opportunity for
    carriers to share structure.

[^509]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 28.

[^510]: ^?^ *See, e.g.*, BellSouth *Inputs Further Notice* comments,
    Attachment B at B-13; Sprint *Inputs Further Notice* comments at
    36-39; US West *Inputs Further Notice* comments at 29-32.

[^511]: ^?^ SBC *Inputs Further Notice* comments at 11.

[^512]: ^?^ In contrast, when developing inputs for tangible components
    of the network, we generally begin our analysis with an estimation
    of the cost of today\'s technology at today\'s prices.

[^513]: ^?^ *Inputs Further Notice* at para. 130.

[^514]: ^?^ *See* Washington USF Proceeding, Tenth Supplemental Order,
    Docket No. UT-980311(a) at para. 108. *See also* Washington
    Utilities and Transportation Commission, Eighth Supplemental Order,
    Docket No. UT-960369 at paras. 73-76 (1998).

[^515]: ^?^ *See* Washington Utilities and Transportation Commission,
    Eighth Supplemental Order, Docket No. UT-960369 (1998) at para. 73
    (proposing a range of sharing values \"which reflects the balance
    between maximum achievable structure sharing and the amount of
    structure sharing achieved historically.\").

[^516]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18547, para. 80.

[^517]: ^?^ *Inputs Further Notice* at paras. 129-132.

[^518]: ^?^ *See, e.g.*, Bell Atlantic *Inputs Further Notice* comments
    at 19; BellSouth *Inputs Further Notice*, Attachment B at B-14; GTE
    *Inputs Further Notice* comments at 56-57.

[^519]: ^?^ Ameritech *Inputs Further Notice* comments at 12; Sprint
    *Inputs Further Notice* at 38; US West *Inputs Further Notice*
    comments, Attachment A at 8.

[^520]: ^?^ NRRI Study at 30-31.

[^521]: ^?^ *See* GTE *Inputs Further Notice* comments at 57; Sprint
    *Inputs Further Notice* comments at 39 (noting that the RUS data
    demonstrates that there are few sharing opportunities in rural
    areas).

[^522]: ^?^ *See, e.g.*, HAI Dec. 11, 1997 submission, Appendix B at 57;
    BCPM Jan. 31, 1997 submission, Attachment 9; Montana State Cost
    Study at 46-47.

[^523]: ^?^ SBC *Inputs Further Notice* comments at 11.

[^524]: ^?^ *See* Washington USF Proceeding, Docket No. UT-980311(a),
    Appendix D.

[^525]: ^?^ Generally, when a neighborhood is located near a wire
    center, copper feeder cable, using analog transmission, is deployed
    to connect the wire center to the SAI. From the SAI, copper cables
    of varying gauge extend to all of the customer premises in the
    neighborhood.

[^526]: ^?^ *Inputs Further Notice* at para. 134.

[^527]: ^?^ *Inputs Further Notice* at para. 134 n. 242 *citing* *Indoor
    SAI Cost Analysis,* submitted by Sprint - Local Telecommunications
    Division, July 30, 1998.

[^528]: ^?^ *Inputs Further Notice* at para. 134.

[^529]: ^?^ *Workshop Public Notice* at 2. We used BCPM default inputs
    as the low end of the ranges for both indoor and outdoor SAIs, and
    Sprint\'s cost estimates as the high end of the range for indoor
    SAIs. The high end of the range for outdoor SAIs represented our
    analysis of state-approved SAI parameters. Our preliminary ranges
    for SAI costs did not include HAI inputs because staff concluded
    that HAI had not included all of the materials and splicing required
    to install this equipment.

[^530]: ^?^ *See* *Common Carrier Bureau Releases Preliminary Common
    Input Values to Facilitate Selection of Final Input Values for the
    Forward-Looking Cost Model for Universal Service*, Public Notice, CC
    Docket Nos. 96-45, 97-160, DA 99-295 (rel. Feb. 5, 1999)
    (*Preliminary Input Values Public Notice*); *Workshop Public
    Notice*. S*ee also* Preliminary Input Values Handouts, dated
    December 11, 1998.

[^531]: ^?^ We noted that the BCPM defaults do not specify estimates for
    the cost of SAI components. *Inputs Further Notice* at para. 134 n.
    243.

[^532]: ^?^ *Inputs Further Notice* at para. 136. *See* *Inputs Further
    Notice,* Appendix D, section IV for a breakdown of costs for each
    component calculated to derive the proposed cost of a 7200 pair DLC.

[^533]: ^?^ *Inputs Further Notic*e at para. 141.

[^534]: ^?^ As we explained in the *Inputs Further Notice*, we relied on
    HAI data to determine the relationship in cost among the various
    sizes of SAI. Specifically, we developed a ratio of our proposed
    cost for a 7200 pair indoor SAI to the cost proposed by HAI. We then
    proposed to apply this ratio, 2.25, to the values submitted by the
    HAI sponsors for other sizes of indoor and outdoor SAIs. Applying
    this factor, we tentatively adopted the cost estimates for indoor
    and outdoor SAIs. We proposed to use the HAI, rather than BCPM data,
    in this manner because BCPM had not submitted estimates for all of
    the SAI sizes used in the model. We noted that using the BCPM data
    in this way would result in roughly the same cost estimates for
    indoor and outdoor SAIs. *Inputs Further Notice* at para. 141.

[^535]: ^?^ BellSouth and Bell Atlantic submitted SAI costs in their
    comments. However, neither party provided any support for these
    values which reflect total SAI costs. *See* BellSouth *Inputs
    Further Notice* comments at Exhibit 1; Bell Atlantic *Inputs Further
    Notice* comments, Attachment D at 7.

[^536]: ^?^ *Inputs Further Notice* at para. 134-135.

[^537]: ^?^ *See* Letter from Pete Sywenki, Sprint, to Magalie Roman
    Salas, FCC, dated February 4, 1999 (Sprint Feb. 4, 1999 *ex parte)*.

[^538]: ^?^ SBC *Inputs Further Notice* comments at 12. AT&T and MCI
    support the SAI costs tentatively adopted. AT&T and MCI *Inputs
    Further Notice* reply comments at 28.

[^539]: ^?^ *Inputs Further Notice* at para. 138 n. 250 *citing* Letter
    from Chris Frentrup, MCI WorldCom, to Magalie Roman Salas, FCC,
    dated January 21, 1999; Letter from Kenneth T. Cartmell, U S West,
    dated February 8, 1999, to Magalie Roman Salas, FCC; Letter from
    Pete Sywenki, Sprint, to Magalie Roman Salas, FCC dated February 4,
    1999. On January 20, 1999, the sponsors of HAI provided a
    demonstration of splicing, in support of their splicing rate.

[^540]: ^?^ *Inputs Further Notice* at para. 138.

[^541]: ^?^ *Inputs Further Notice* at para. 138 n. 251 *citing*
    attachment to letter from Chris Frentrup, MCI WorldCom, to Magalie
    Roman Salas, FCC, dated January 21, 1999.

[^542]: ^?^ AT&T and MCI *Inputs Further Notice* reply comments at 29.

[^543]: ^?^ In its February 9 *ex parte* noted above, US West proposed a
    splicing rate of 150 pairs per hour, slightly higher than Sprint\'s
    proposed rate.

[^544]: ^?^ Sprint *Inputs Further Notice* comments at 40. The letter
    from AMP Corporation was submitted by the HAI sponsors. *See Inputs
    Further Notice* at para. 138 n. 251.

[^545]: ^?^ Sprint *Inputs Further Notice* comments at 40.

[^546]: ^?^ Sprint *Inputs Further Notice* comments at 40.

[^547]: ^?^ *See e.g.*, SBC *Inputs Further Notice* comments at 12; AT&T
    and MCI *Inputs Further Notice* reply comments at 28.

[^548]: ^?^ *Inputs Further Notice* at para. 138.

[^549]: ^?^ *Inputs Further Notice* at para. 139.

[^550]: ^?^ *Inputs Further Notice* at para. 139.

[^551]: ^?^ *Inputs Further Notice* at para. 139.

[^552]: ^?^ *Inputs Further Notice* at para. 139.

[^553]: ^?^ *See* *Inputs Further Notice*, Appendix D, section IV to see
    how this conclusion is used to determine proposed costs for a 7200
    pair SAI.

[^554]: ^?^ SBC *Inputs Further Notice* comments at 12.

[^555]: ^?^ Sprint *Inputs Further Notice* comments at 40-41.

[^556]: ^?^ *Inputs Further Notice* at para. 140.

[^557]: ^?^ *See* *Inputs Further Notice*, Appendix D, section IV to see
    how this value is used in the calculation of a 7200 pair SAI.

[^558]: ^?^ SBC *Inputs Further Notice* comments at 12.

[^559]: ^?^ *Inputs Further Notice* at para. 141. These cost estimates
    are contained in Appendix A of the *Inputs Further Notice*.

[^560]: ^?^ GTE *Inputs Further Notice* comments at 61.

[^561]: ^?^ GTE *Inputs Further Notice* comments at 61.

[^562]: ^?^ GTE *Inputs Further Notice* comments at 61.

[^563]: ^?^ We note that in contrast to GTE\'s claim, the SAI costs
    reflected in BellSouth\'s comments reflect linearity.

[^564]: ^?^ US West *Inputs Further Notice* comments at 15-16.

[^565]: ^?^ US West *Inputs Further Notice* comments at 14; US West
    *Inputs Further Notice* comments at 16; Letter from Kenneth T.
    Cartmell, US West, to Magalie Roman Salas, FCC, dated September 24,
    1999 (US West September 24 *ex parte*) at 12.

[^566]: ^?^ US West September 24 *ex parte* at 12.

[^567]: ^?^ Optical fiber cable carries a digital signal that is
    incompatible with most customers\' telephone equipment, but the
    quality of the signal degrades less with distance compared to a
    signal carried on copper wire. Generally, when a neighborhood is
    located too far from the wire center to be served by copper cables
    alone, an optical fiber cable will be deployed to a point within the
    neighborhood, where a DLC will be placed to convert incoming digital
    signals to analog signals and outgoing analog signals to digital.
    From the DLC, copper cables of varying gauge extend to all of the
    customer premises in the neighborhood.

[^568]: ^?^ The current version of the model supports a fifth DLC size
    in addition to those already supported. DLC capacities currently
    supported are 2016, 1344, 672, 96, and 24 line facilities.

[^569]: ^?^ *Inputs Further Notice* at para. 144.

[^570]: ^?^ In response to the *1997 Data Request*, Ameritech, Bell
    Atlantic (including NYNEX), BellSouth, SBC, US West, GTE, Sprint,
    ATU, and PRTC originally submitted data to the Commission on DLC
    costs in 1997. Bell South, US West and ATU resubmitted their data on
    the record of this proceeding subject to the *Protective Order*.
    *See* Letter from William W. Jordan, BellSouth, to Magalie Roman
    Salas, FCC, dated March 15, 1999; Letter from Robert B. McKenna, US
    West, to Magalie Roman Salas, FCC, dated March 8, 1999; Letter from
    Alane C. Weixel, counsel for ATU, to Magalie Roman Salas, FCC, dated
    May 6, 1999 (ATU May 6, 1999 *ex parte*).

[^571]: ^?^ Letter from W. Scott Randolph, GTE, to Magalie Roman Salas,
    FCC, dated February 11, 1998; Letter from Robert A. Mazer and Albert
    Shuldiner, Counsel for Aliant, to Magalie Roman Salas, FCC, dated
    February 8, 1998.

[^572]: ^?^ *Inputs Further Notice* at para. 143.

[^573]: ^?^ *Inputs Further Notice* at para. 143.

[^574]: ^?^ *Inputs Further Notice* at para. 144.

[^575]: ^?^ *Inputs Further Notice* at para. 144.

[^576]: ^?^ *Inputs Further Notice* at para. 144.

[^577]: ^?^ *Inputs Further Notice* at para. 144. In addition to the
    data submitted in response to the *1997 Data Request*, and following
    the December 11, 1998, workshop, the Bureau requested further data
    on DLC costs in the *1997 Further Notice* and in the *Inputs Public
    Notice*. *See also Preliminary Input Values Public Notice*.

[^578]: ^?^ *Inputs Further Notice* at para. 144. Only US West,
    BellSouth, and ATU presented their contract data from the *1997 Data
    Request* in a useable format Some of the data and comments that were
    submitted in response to the *1997 Data Request*, but not re-filed
    on the record under the *Protective Order*, could not be used
    because the data were either inadequate or presented in a format
    from which we could not extract relevant information. *Inputs
    Further Notice* at para. 144 n. 262.

[^579]: ^?^ *Inputs Further Notice* at para. 145.

[^580]: ^?^ *Inputs Further Notice* at para. 146.

[^581]: ^?^ *Inputs Further Notice* at para. 146.

[^582]: ^?^ *Inputs Further Notice* at para. 146.

[^583]: ^?^ *Inputs Further Notice* at para. 145.

[^584]: ^?^ ATU May 6, 1999 *ex parte*. ATU also suggested that costs
    for placement, installation, and testing should be added to the DLC
    material costs it submitted. We note that these site preparation
    costs are already separately accounted for in the model.

[^585]: ^?^ *Inputs Further Notice* at para. 143.

[^586]: ^?^ GTE *Inputs Further Notice* comments at 62; Bell Atlantic
    *Inputs Further Notice* comments, Attachment D at 8-9, Chart 12.
    Sprint attests to the reasonableness of the proposed inputs based on
    the contract data. Sprint *Inputs Further Notice* comments at 41.
    Sprint explains that it demonstrated in a June 24, 1999 *ex parte*
    that the proposed inputs are in line with Sprint\'s actual costs
    including material and handling.

[^587]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 32-35
    (Proprietary Version); SBC *Inputs Further Notice* comments at 13.

[^588]: ^?^ SBC *Inputs Further Notice* comments at 13.

[^589]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 32-35
    (Proprietary Version)

[^590]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 34.

[^591]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 33-34
    (Proprietary Version).

[^592]: ^?^ Bell Atlantic *Inputs Further Notice* reply comments 6-7.

[^593]: ^?^ AT&T and MCI *Inputs Further Notice* comments at 34.

[^594]: ^?^ AT&T and MCI *Inputs Further Notice* comments, Attachment B
    at B-4 (Proprietary Version)

[^595]: ^?^ AT&T and MCI *Inputs Further Notice* comments, Attachment B
    at B-4 (Proprietary Version).

[^596]: ^?^ AT&T and MCI *Inputs Further Notice* comments, Attachment B
    at B-4 (Proprietary Version).

[^597]: ^?^ AT&T and MCI also claim that Sprint fails to make use of
    forward-looking technology such as GR303-capable hardware. AT&T and
    MCI *Inputs Further Notice* comments, Attachment B at B-4
    (Proprietary Version). Contrary to AT&T and MCI\'s assertion, the
    data supplied by Sprint and reflected in the contract data adopted
    herein reflects the cost of GR303-capable hardware. *See* Sprint
    Sept. 9, 1998 *ex parte*.

[^598]: ^?^ *Inputs Further Notice* at para. 143.

[^599]: ^?^ *Inputs Further Notice* at para. 144.

[^600]: ^?^ *Inputs Further Notice* at para. 146.

[^601]: ^?^ None of the commenters challenge the use of this proxy for
    estimating the change in DLC costs.

[^602]: ^?^ SBC *Inputs Further Notice* comments at 13; GTE *Inputs
    Further Notic*e comments at 61-62.

[^603]: ^?^ GTE *Inputs Further Notice* comments at 61-62.

[^604]: ^?^ GTE *Inputs Further Notice* comments at 62.

[^605]: ^?^ The functions performed by the switch for local service
    include: line termination; line monitoring; usage call processing,
    routing, and completion; interconnection to other carriers; billing
    and maintenance; and vertical services and features. We note that
    not all of these functions are supported by universal service.

[^606]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250
    (criterion two).

[^607]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18560-66, paras.
    121-38.

[^608]: ^?^ *Switching and Transport Public Notice* at 2-6. The Bureau
    guidelines established that: (1) the models permit individual
    switches to be identified as host, remote, or stand-alone; (2)
    switching investment costs should be separately estimated for host,
    remote, and stand-alone switches; (3) models should include switch
    capacity constraints; (4) all of the line-side port costs and a
    percentage of usage costs should be assigned to the cost of
    providing the supported service; and (5) models should accommodate
    an interoffice network that is capable of connecting switches
    designated as hosts and remotes in a way that is compatible with
    capabilities of equipment and technology that are available today
    and current engineering practices. *Id.*

[^609]: ^?^ *Platform Order*, 13 FCC Rcd at 21354, para. 75.

[^610]: ^?^ *See* Letter from Richard N. Clarke, AT&T, to Magalie Roman
    Salas, FCC, dated February 3, 1998 (HAI Feb. 3 submission) App. B;
    BCPM April 30, 1998 submission, Switch Model Inputs.

[^611]: ^?^ *See Workshop Public Notice*. The December 1, 1998 workshop
    addressed issues relating to switching and expenses.

[^612]: ^?^ *See Inputs Further Notice* at paras. 147-91, App. A.

[^613]: ^?^ *Inputs Further Notice* at paras. 174-78.

[^614]: ^?^ *See* NRRI Study, *supra* note 214.

[^615]: ^?^ *Inputs Further Notice* at para. 152.

[^616]: ^?^ *Inputs Further Notice* at para. 162.

[^617]: ^?^ Letter from W. Scott Randolph, GTE, to Magalie Roman Salas,
    FCC dated December 18, 1998 (GTE Dec. 18 *ex parte*) at 5 and 6;
    NRRI Study at 97 and 102; Letter from Pete Sywenki, Sprint, to
    Magalie Roman Salas, FCC, dated December 22, 1998 (Sprint Dec. 22
    *ex parte*) at 13-21; Letter from Richard Clarke, AT&T, to Magalie
    Roman Salas, FCC, dated January 7, 1999 (AT&T Jan. 7 *ex parte*) at
    1.

[^618]: ^?^ *Inputs Further Notice* at paras. 157-161.

[^619]: ^?^ *Inputs Further Notice* at para. 158.

[^620]: ^?^ *Inputs Further Notice* at para. 159.

[^621]: ^?^ *Inputs Further Notice* at para. 160.

[^622]: ^?^ *Inputs Further Notice* at para. 163.

[^623]: ^?^ *Inputs Further Notice* at para. 164. In order to estimate
    the forward-looking cost of purchasing and installing a switch,
    switch costs also are estimated as a function of the date of
    installation. By including information on installation dates, the
    model produces forward-looking estimates that account for historical
    pricing trends.

[^624]: ^?^ *Inputs Further Notice* at para. 166.

[^625]: ^?^ *Inputs Further Notice* at para. 170.

[^626]: ^?^ *See* Appendix C for regression results, and an explanation
    of how cost estimates are derived from these results.

[^627]: ^?^ Until 1996, large incumbent LECs were required to file
    depreciation rate reports with the Commission pursuant to 47 C.F.R.
    § 43.43. Prior to filing these reports, companies generally would
    submit depreciation rate studies that included data for each digital
    switch in operation. *See* Appendix C for a further description of
    the data set.

[^628]: ^?^ Many small telephone companies receive financial assistance
    from RUS, which requires these companies to report the payments made
    for new switches. *See* Appendix C for a further description of the
    RUS data.

[^629]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 10 and 11.

[^630]: ^?^ The Bureau of Economic Analysis, in creating the electronic
    data set from depreciation filings, did not include observations for
    switches with fewer than 1,000 lines.

[^631]: ^?^ BellSouth January 29, 1999 *ex parte*; Sprint February 5,
    1999 *ex parte*; and GTE February 22, 1999 *ex parte*.

[^632]: ^?^ Some of the switch cost values provided in the voluntary
    submissions include the costs associated with upgrading switching
    equipment. The voluntary information does not, however, contain
    information that would allow us to identify the upgrade components
    associated with these additional costs. For example,
    post-installation investments are not identified as investments in
    additional line capacity, additional software, and so forth. After
    removing the information where new switch costs and the costs
    associated with post installation upgrades are inextricably linked,
    using the process outlined in Appendix C, fewer than 300
    observations remain.

[^633]: ^?^ AT&T points out that the data submitted by Sprint contains
    records that are either missing or inconsistent with other records,
    records that are old or do not reflect equipment used exclusively to
    provide end office switching, and records that contain ambiguous
    information. *See* AT&T Mar. 10, 1999 *ex parte*.

[^634]: ^?^ BellSouth *Inputs Further Notice* comments at B-14 and B-15.

[^635]: ^?^ A year-by-year analysis of the deprecation data and the RUS
    data reveals that the fixed cost of a host switch is significantly
    more than the fixed cost of a remote switch. Our analysis examining
    the deprecation data reveals that the difference is statistically
    significant and positive in four of the seven years covered by the
    Commission data set. In 1995, there are only nine observations
    including only one host switch, and therefore, there is insufficient
    data to draw any conclusion for 1995. In the other two remaining
    years, 1993 and 1994, the difference has a large positive magnitude
    but is not statistically significant (the \"t-statistics\" for these
    years are 0.68 and 0.99). In contrast, the fixed cost of host
    switches in the data from the *1997 Data Request* do not differ
    statistically from the fixed costs of remote switches, nor is there
    a large difference in the magnitudes of the estimated costs.
    Similarly, year-by-year analysis of the voluntary data provided by
    the carriers does not reveal any systematic difference between host
    fixed costs and remote fixed costs.

[^636]: ^?^ As noted in the previous footnote, the fixed cost of host
    switches exceeds the fixed cost of remote switches in the data set
    we have chosen. This is consistent with comments from this
    proceeding. *See* BellSouth *Inputs Further Notice* comments at
    B-15; Sprint *Inputs Further Notice* comments at 46; and Letter from
    Richard Clarke, AT&T, to Magalie Roman Salas, FCC, dated January 7,
    1999 (AT&T Jan. 7 *ex parte*) at 1.

[^637]: ^?^ *See supra* para. 291.

[^638]: ^?^ *See, e.g.,* AT&T/MCI *Inputs Further Notice* comments at
    38; Sprint *Inputs Further Notice* comments at 44; *but cf.* GTE
    *Inputs Further Notice* comments at 65. GTE appears to be confused
    about our use of the power adjustment to make the RUS data
    comparable to the depreciation data and incorrectly assumes we only
    use the depreciation data for switches with more than 25,000 lines.

[^639]: ^?^ SBC claims that our proposed \$12 per line for MDF is too
    low and argues a more reasonable estimate is \$30 per line. SBC
    *Inputs Further Notice* comments at 13. Sprint, AT&T and MCI, on the
    other hand, agree that \$12 cost per line for MDF is reasonable.
    AT&T/MCI *Inputs Further Notice* comments at 38; Sprint *Inputs
    Further Notice* comments at 44.

[^640]: ^?^ Sprint *Inputs Further Notice* comments at 44, attachment 7.
    GTE also claims its power investment is higher than our proposed
    values, but offers no data to support this claim. GTE *Inputs
    Further Notice* comments at 66.

[^641]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 38.

[^642]: ^?^ Lawrence K. Vanston, Ray L. Hodges, Adrian J. Poitras,
    Technology Futures, Inc., *Transforming the Local Exchange Network:
    Analyses and Forecast of Technology Change* 149 (2d ed. 1997) (TFI
    Study). The terminology used in the TFI study differs somewhat. What
    TFI calls \"shell\" is \"the common equipment, such as cabling and
    power equipment, that is not modular and lasts the life of the
    switch entity.\" TFI Study at 136. This includes MDF and power
    investment.

[^643]: ^?^ *Inputs Further Notice* at para. 160 (citing NRRI Study at
    102-104).

[^644]: ^?^ SBC *Inputs Further Notice* comments at 13.

[^645]: ^?^ *See* Sprint *Inputs Further Notice* comments at 45. Sprint
    also provided an estimate of the cost of terminating remote switches
    with over 6,390 lines. We note, however, that there are no remote
    switches in the RUS data with over 6,390 lines.

[^646]: ^?^ Sprint estimates the average cost of terminating its own
    remotes on its own host switches as \$61,700. Its tiered cost
    estimates indicate, however, that for remotes in the RUS data set,
    which do not include any remote switches with over 6,390 lines, the
    average cost is \$29,840. *See* Sprint *Inputs Further Notice*
    comments at 45.

[^647]: ^?^ *Id*.

[^648]: ^?^ Sprint Dec. 22 *ex parte* at 12. Sprint criticized the
    Commission\'s preliminary switch regression presented in the
    December 1998 workshop based on the \"R-squared\" statistical
    goodness of fit criterion. After adjusting for data transformations
    associated with moving to a log-log specification, however, the
    R-squared of a log-log regression (0.56) suggested by Sprint is
    lower than the R-squared in the linear regression (0.73).
    Specifically, we note that the R-squared measure resulting from a
    regression employing a log-log functional form is not directly
    comparable to the R-squared measure from a linear regression. In
    order for the two measures to be comparable, the R-squared measure
    computed from the log-log regression must be computed using observed
    and predicted cost measures, not the logs of these measures. We also
    note that the log-log regression we employed is of the form:

    Ln(Cost) = a~1~ + a~2~\*Ln(Lines) + a~3~\*Host + a~4~\*Ln(Time) +
    a~5~\*Ln(Lines)\*Ln(Time) + a~6~\*Host\*Ln(Time) + e

    where Ln(x) denotes the natural log of x. Because Sprint did not
    make these necessary adjustments, we believe that its criticism of
    the use of a linear function is misplaced. For a discussion of the
    \"R-squared\" statistical goodness of fit criterion and a discussion
    of log-log specifications, see William H. Greene, *Econometric
    Analysis*, 192-193 and 251 (1990).

[^649]: ^?^ *See* General Wald Test for omitted variables in Ramu
    Ramanathan, *Introductory Econometrics with Applications* 170
    (1989).

[^650]: ^?^ *See* Sprint *Inputs Further Notice* comments at 46. *See
    also* Letter from Richard Clarke, AT&T, to Magalie Roman Salas, FCC,
    dated January 7, 1999 (AT&T Jan. 7 *ex parte*) at 1.

    The primary difference between a host switch and remote switch is in
    the extent and complexity of the \`getting started equipment,\'
    associated with each type of switch (e.g., switch central processor
    functions, SS7 non-scaleable equipment, maintenance and testing,
    call recording for billing purposes, etc.). Because most of these
    functions for lines terminating a remote switch are performed at
    that switch\'s host, very little of this type of \`getting started\'
    equipment is required at the remote. In contrast, the scaleable
    equipment used to terminate lines and trunks and to perform basic
    call processing is essentially the same at the host and remote. In
    fact, the line units used by Lucent 5E Remote Switching Modules are
    identical to those used by 5E host or stand-alone switches.
    Similarly, the line cards used in Nortel DMS 100 host or stand-alone
    switches are the same as those used in DMS 100 remotes, or in DMS 10
    host or remote switches.

    *Id.* BellSouth notes in its *Inputs Further Notice* comments that
    \"BellSouth finds that the per line costs are slightly different
    because hosts\' lines also bear the costs of some umbilical trunking
    and control that is not provided at the remotes. Still it is a
    reasonable simplification to allow host and remote per line costs to
    be the same.\" BellSouth *Inputs Further Notice* comments at B-15.

[^651]: ^?^ The gross-domestic-product chain-type price index, which
    tracks economy-wide inflation, is published monthly by the Bureau of
    Economic Analysis of the U.S. Department of Commerce in the *Survey
    of Current Business*.

[^652]: ^?^ Switch costs are adjusted after estimation for both realized
    and expected inflation between 1997 and 1999. *See* Appendix C for
    an explanation of these adjustments.

[^653]: ^?^ Time was added to the regression in reciprocal form as an
    independent variable to measure fixed cost changes unique to remote
    switches. Then, a time term was added in conjunction with the host
    identifier variable to measure the fixed cost changes unique to host
    switches. A time term was also added in conjunction with the line
    variable, in order to measure cost changes unique to line additions
    on switches.

[^654]: ^?^ US West *Inputs Further Notice* comments at 64-65.

[^655]: ^?^ *See* Bell Atlantic *Inputs Further Notice* comments at 20,
    21; GTE *Inputs Further Notice Reply* comments at 32.

[^656]: ^?^ AT&T/MCI *Inputs Further Notice Reply* comments at 35, n.54.

[^657]: ^?^ GTE Dec. 18 *ex parte* at 4.

[^658]: ^?^ *See Inputs Further Notice* at para. 168. *See also* AT&T
    Jan. 7 *ex parte* at 4.

[^659]: ^?^ Although the log specification of time proposed in the
    December 1, 1998, workshop yields similar results, it produces
    investments for host switch \"getting started\" costs that become
    negative in 2000 and consequently overstates pricing declines.

[^660]: ^?^ *See* Ameritech Dec. 16, 1998 comments at 5; GTE Dec. 18,
    1998 *ex parte* at 4. The Turner Price Index is an index designed to
    measure the changing cost of telecommunications plant published
    semi-annually by AUS consultants.

[^661]: ^?^ *Inputs Further Notice* at para. 170.

[^662]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 12.

[^663]: ^?^ Ameritech Dec. 16, 1998 comments at 4-5; GTE Dec. 18, 1998
    *ex parte* at 4-5; Sprint Dec. 22, 1998 *ex parte* at 5-7; GTE
    *Inputs Further Notice* comments at 68; Bell Atlantic *Inputs
    Further Notice* comments, Affidavit of Harold Ware and Christian
    Michael Dippon at 9-13; Bell Atlantic *Inputs Further Notice*
    comments at 8-13; BellSouth *Inputs Further Notice* comments at B-15
    and B-16; Sprint *Inputs Further Notice* comments at 47 and 48.

[^664]: ^?^ GTE Dec. 18, 1998 *ex parte* at 5; Sprint Dec. 22, 1998 *ex
    parte* at 13; Ameritech Dec. 16, 1998 comments at 6; Bell Atlantic
    *Inputs Further Notice* comments, Affidavit of Harold Ware and
    Christian Michael Dippon at 17 and 18.

[^665]: ^?^ Moreover, even if the model platform were changed, we do not
    believe that it would be appropriate to use vendor-specific input
    values for switch costs. The model is intended to estimate the
    least-cost, most-efficient technology being deployed, not the
    technology available from a particular vendor.

[^666]: ^?^ In Appendix C, we discuss the issues of multicollinearity
    and mis-specification identified by Sprint in its comments.

[^667]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 36; Bell
    Atlantic *Inputs Further Notice* comments at 10-11; Sprint *Inputs
    Further Notice* comments at 46; BellSouth *Inputs Further Notice*
    comments at B-15.

[^668]: ^?^ *Inputs Further Notice* at para. 174. The LERG is a database
    of switching information maintained by Telecordia Technologies
    (formerly Bellcore) that includes the existing host-remote
    relationships. The HAI proponents have placed on the record the
    portion of the LERG that identifies the host-remote relationships.
    Letter from Chris Frentrup, MCI Worldcom, to Magalie Roman Salas,
    FCC, dated September 14, 1998 (MCI Sept. 14 *ex parte*).

[^669]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18560-61, para. 122.

[^670]: ^?^ *Switching and Transport Public Notice* at 2. Switches can
    be designated as host, remote, or stand-alone switches. Both a host
    and a stand-alone switch can provide a full complement of switching
    services without relying on another switch. A remote switch relies
    on a host switch to supply a complete array of switching functions
    and to interconnect with other switches.

[^671]: ^?^ *Switching and Transport Public Notice* at 2-3.

[^672]: ^?^ *Platform Order*, 13 FCC Rcd at 21355, para. 76.

[^673]: ^?^ *Platform Order*, 13 FCC Rcd at 21354-55, para. 75.

[^674]: ^?^ HAI Feb. 3, 1998 submission, Model Description at 58.

[^675]: ^?^ *Platform Order*, 13 FCC Rcd at 21355, para. 76.

[^676]: ^?^ *See, e.g.*, BellSouth *Inputs Further Notice* reply
    comments at 17; Sprint *Inputs Further Notice* comments at 48. *See
    also* Aliant *Switching and Transport Public Notice* comments at 2;
    Bell Atlantic *Switching and Transport Public Notice* reply comments
    at 2.

[^677]: ^?^ Bell Atlantic *Switching and Transport Public Notice* reply
    comments, Attachment 1 at 2; BellSouth et al. *Switching and
    Transport Public Notice* reply comments, Attachment 1 at 2-3.

[^678]: ^?^ Sprint *Inputs Further Notice* comments at 48.

[^679]: ^?^ *See, e.g.*, AT&T/MCI *Switching and Transport Public
    Notice* comments at 6; BellSouth et al. *Switching and Transport
    Public Notice* reply comments, Attachment 1 at 2.

[^680]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 44-45. Although
    AT&T and MCI oppose the use of the LERG, they have taken steps to
    ensure that the LERG database is compatible with use in the
    switching module of the synthesis model. *See* MCI Sept. 14 *ex
    parte*; Letter from Richard N. Clarke, AT&T, to Magalie Roman Salas,
    FCC, dated September 16, 1998 (AT&T Sept. 16 *ex parte*).

[^681]: ^?^ *See, e.g.*, Ameritech *Switching and Transport Public
    Notice* comments at 3; AT&T/MCI *Switching and Transport Public
    Notice* comments at 6; BellSouth et al. *Switching and Transport
    Public Notice* comments Attachment 1 at 1-2; GTE *Switching and
    Transport Public Notice* at 11-12.

[^682]: ^?^ GTE *Inputs Further Notice* comments at 69.

[^683]: ^?^ *Inputs Further Notice* at para. 178.

[^684]: ^?^ AT&T Jan. 7 *ex parte*; Sprint Dec. 22 *ex parte* at 9.

[^685]: ^?^ AT&T *Inputs Further Notice* comments at 40; GTE Dec. 18 *ex
    parte* at 5-6; Sprint *Inputs Further Notice* comments at 49.

[^686]: ^?^ *See, e.g.*, AT&T *Inputs Further Notice* comments at 40;
    GTE Dec. 18 *ex parte* at 6; Sprint *Inputs Further Notice* comments
    at 49.

[^687]: ^?^ *Inputs Further Notice* at para. 179.

[^688]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 41-42. AT&T/MCI
    contend that the cost of terminating digital lines is significantly
    less expensive than terminating analog lines.

[^689]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 41.

[^690]: ^?^ GTE *Inputs Further Notice* comments at 66.

[^691]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 42.

[^692]: ^?^ BellSouth *Inputs Further Notice* comments at 16; GTE
    *Inputs Further Notice* comments at 66-67; Sprint *Inputs Further
    Notice* comments at 49.

[^693]: ^?^ Sprint *Inputs Further Notice* comments at 49.

[^694]: ^?^ Sprint Dec. 22 *ex parte* at 12.

[^695]: ^?^ HAI Feb. 3, 1998 submission, App. B at 38-39.

[^696]: ^?^ AT&T Jan. 7 *ex parte*. The HAI proponents included the
    updated switch capacity constraints in a table attached to the Jan.
    7 *ex parte*.

[^697]: ^?^ AT&T Jan. 7 *ex parte*.

[^698]: ^?^ AT&T Jan. 7 *ex parte*.

[^699]: ^?^ Sprint *Inputs Further Notice* comments at 49.

[^700]: ^?^ *See* HAI Dec. 11 submission, Model Inputs at 80.

[^701]: ^?^ In addition, we note that a decision to adopt the revised
    HAI values for maximum equipped lines per switch would have only a
    minimal impact on the overall forward-looking cost estimation
    because fewer than 2 percent of wire centers have more than 80,000
    lines. A review of the data indicates that, of the 12,506 wire
    centers served by non-rural LECs, only 189 (1.5 percent) have more
    than 80,000 lines and 57 (0.5 percent) have more than 100,000 lines.
    *See* HAI Feb. 3, 1998 model submission.

[^702]: ^?^ *Inputs Further Notice* at para. 184.

[^703]: ^?^ HAI Dec. 11, 1997 submission, Inputs Portfolio at 80.

[^704]: ^?^ HAI Dec. 11, 1997 submission, Inputs Portfolio at 80.

[^705]: ^?^ BCPM April 30, 1998 submission, Switch Model Inputs at
    20-21. BCPM defines Switch Percent Line Fill as the ratio between
    the number of working lines on the switch and the total number of
    lines for which the switch is engineered.

[^706]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 8-9.

[^707]: ^?^ Sprint *Inputs Further Notice* comments at 50.

[^708]: ^?^ *See* Letter from Pete Sywenki, Sprint, to Magalie Roman
    Salas, FCC, dated Jan. 8, 1999 (attachment includes US West switch
    data) (Sprint Jan. 8 *ex parte*).

[^709]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 43.

[^710]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 43.

[^711]: ^?^ BellSouth *Inputs Public Notice* reply comments at Exhibit
    2-13; Commonwealth of Kentucky, *An Inquiry Into Universal Service
    and Funding Fees*, Administrative Case No. 360, App. F at 13;
    Louisiana Public Service Commission, *State Forward-Looking Cost
    Studies for Federal Universal Service Support* (May 19, 1998)
    (Louisiana Cost Study).

[^712]: ^?^ BCPM April 30, 1998 submission, Switch Model Inputs at
    20-21.

[^713]: ^?^ For example, switches with installed lines of 65,001,
    48,818, 11,520, 12,288, 74,039, 12,800, and 36,897 were listed as
    having, 1,1, 2, 10, 10, 21, and 26 working lines, respectively, or
    collectively, an average fill factor of .027 percent. *See* Sprint
    Jan. 8 *ex parte*. Our analysis of the US West data indicated that,
    after eliminating the observations with unreasonably low fill
    factors, the majority of US West switches had fill factors ranging
    from 88 percent to 98 percent.

[^714]: ^?^ *Inputs Further Notice* at para. 187.

[^715]: ^?^ HAI Dec. 11, 1997 submission, Model Description at 52.

[^716]: ^?^ HAI Dec. 11, 1997 submission, Model Description at 53.

[^717]: ^?^ Sprint Dec. 22 *ex parte* at 10.

[^718]: ^?^ GTE Dec. 18 *ex parte* at 6.

[^719]: ^?^ GTE Dec. 18 *ex parte* at 6; Sprint *Inputs Further Notice*
    comments at 50.

[^720]: ^?^ Letter from Chris Frentrup, MCI Worldcom, to Magalie Roman
    Salas, FCC, dated Feb. 9, 1999 (MCI Worldcom Feb. 9 *ex parte*) at
    24.

[^721]: ^?^ *Inputs Further Notice* at para. 190.

[^722]: ^?^ Sprint *Inputs Further Notice* comments at 50.

[^723]: ^?^ HAI defines this input as the \"per trunk equivalent
    investment in switch trunk port at each end of a trunk.\" HAI Dec.
    11, 1997 submission, Appendix B (HM 5.0 Inputs, Assumptions, and
    Default Values) at 46.

[^724]: ^?^ *Inputs Further Notice* at para. 191.

[^725]: ^?^ SBC *Inputs Further Notice* comments at 14; Sprint *Inputs
    Further Notice* comments at 50.

[^726]: ^?^ Letter from William W. Jordan, BellSouth, to Magalie Roman
    Salas, FCC, dated August 7, 1998, Attachment to Question 1 at 5, 9,
    13, 17 (dated July 15, 1998) (BellSouth Aug. 7 *ex parte*).

[^727]: ^?^ BellSouth Aug. 7 *ex parte*, Attachment to Question 1 at 5,
    9, 13, 17. The four states are Kentucky, Louisiana, North Carolina,
    and South Carolina.

[^728]: ^?^ BellSouth Aug. 7 *ex parte*, Attachment to Question 1 at 5,
    9, 13, 17.

[^729]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8915, para. 250,
    criterion 7; *see also* 47 U.S.C. § 254 (k).

[^730]: ^?^ *See Universal Service Order*, 12 FCC Rcd at 8913, para.
    250, criteria 1, 3; *see also infra* para. 351.

[^731]: ^?^ Plant specific operations expenses (that are not associated
    with GSF) include the cost of maintaining telecommunications plant
    and equipment. These network related expenses are not considered to
    be \"joint and common costs.\" In ARMIS accounts, plant-specific
    operations expenses include GSF expenses.

[^732]: ^?^ Plant non-specific expenses include the costs of
    engineering, network operations, and power expenses.

[^733]: ^?^ Corporate operations expenses include the costs of
    administration, human resources, legal, and accounting expenses.

[^734]: ^?^ Customer services expenses include the costs of marketing,
    billing, and directory listing expenses.

[^735]: ^?^ *Platform Order*, 13 FCC Rcd at 21357, para. 81.

[^736]: ^?^ *Platform Order*, 13 FCC Rcd at 21360, para. 87.

[^737]: ^?^ *Inputs Further Notice* at para. 204.

[^738]: ^?^ *Inputs Further Notice* at para. 213.

[^739]: ^?^ *Inputs Further Notice* at paras. 198, 214.

[^740]: ^?^ *Inputs Further Notice* at paras. 210-11.

[^741]: ^?^ Plant-specific operations expenses correspond to the
    following ARMIS 43-03 report accounts:

    6110 - Network Support Expense

    6120 - General Support Expense

    6210 - COE Switch

    6212 - COE Digital Electronic Switch only

    6220 - Operator Systems

    6230 - COE Transmission

    6231 - Radio Systems

    6232 - COE Circuit - DDS

    6232 - COE Circuit - Other than DDS

    6310 - Information Origination/Termination

    6311 - Station Apparatus (only)

    6341 - Large PBX

    6351 - Public Telephone

    6362 - Other Terminal Equipment

    6411 - Poles

    6421.1 - Aerial Cable - Metallic (Copper)

    6421.2 - Aerial Cable - Fiber

    6422.1 - Underground Cable - Metallic (Copper)

    6422.2 - Underground Cable - Fiber

    6423.1 - Buried Cable - Metallic (Copper)

    6423.2 - Buried Cable - Fiber

    6441 - Conduit Systems

[^742]: ^?^ *Inputs Further Notice* at paras. 205-208.

[^743]: ^?^ *Inputs Further Notice* at para. 205. For each account or
    sub-account, a current-to-book ratio is developed by first revaluing
    each type of equipment at its current replacement cost. The sum of
    these current costs is then divided by the total, embedded cost
    account balance. The resulting current-to-book ratio will be greater
    than one if current costs are rising relative to the historic costs
    and less than one if current costs are declining. The
    current-to-book ratios submitted by Ameritech, Bell Atlantic,
    BellSouth, GTE, and SBC are proprietary information subject to
    provisions in the *Protective Order* and therefore are not
    reproduced here. Although we would prefer to have data from more
    companies, the other ARMIS-filing carriers informed us that they
    either no longer maintain this type of information, or never used
    current-to-book ratios for accounting purposes.

[^744]: ^?^ *Inputs Further Notice* at para. 206. For each study area of
    the five holding companies that provided current-to-book ratios, we
    obtained year-end 1995 and 1996 investment balances from ARMIS for
    the plant accounts consistent with the aforementioned plant-specific
    expense accounts. Study area-specific current-to-book ratios for the
    two periods were multiplied by the 1995 and 1996 ARMIS investments
    in each account to derive the forward-looking, \"current,\" year-end
    1995 and 1996 investment levels by account and by study area. The
    ARMIS and current investments were then summed separately, by year
    and by account, for all study areas of the five holding companies.
    The resulting total current investment (by year and by account for
    the sum of all study areas) was then divided by the total ARMIS
    investment (by year and by account for the sum of all study areas)
    producing two sets of composite current-to-book ratios (year end
    1995 and 1996).

[^745]: ^?^ *Inputs Further Notice* at para. 207. To calculate the
    expense-to-investment ratios for the plant-specific operations
    expense accounts, we obtained total, year-end 1995 and 1996
    investment account balances from the ARMIS 43-03 reports for all
    ARMIS-filing companies. To make these embedded account balances
    forward-looking, we next multiplied each investment account balance
    for each year by the current-to-book ratios for the same year
    developed earlier. The resulting year-end 1995 and year-end 1996
    \"current\" account balances were then averaged by adding the two
    years together and dividing by two.

[^746]: ^?^ *Inputs Further Notice* at para. 208. From the 1996 ARMIS
    43-03 report, we obtained the 1996 balances for each plant-specific
    operations expense account for all ARMIS-filing companies. The
    expense account balances were divided by their respective average
    \"current\" investment to obtain expense-to-investment ratios.

[^747]: ^?^ *Inputs Further Notice* at para. 198.

[^748]: ^?^ *See* State Members\' Report on the Use of Cost Proxy
    Models, March 26, 1997, at 22.

[^749]: ^?^ *Inputs Further Notice* at para. 198.

[^750]: ^?^ *Inputs Further Notice* at para. 198.

[^751]: ^?^ *Inputs Further Notice* at para. 199.

[^752]: ^?^ *Inputs Further Notice* at para. 199.

[^753]: ^?^ *Inputs Further Notice* at para. 199.

[^754]: ^?^ *Inputs Further Notice* at para. 200. These indexes are used
    to calculate locality pay differentials for federal employees. *See
    Report on Locality-based Comparability Payments for the General
    Schedule,* Annual Report of the President\'s Pay Agent, Appendix II,
    1995.

[^755]: ^?^ *Inputs Further Notice* at para. 201.

[^756]: ^?^ *Inputs Further Notice* at para. 201.

[^757]: ^?^ *Inputs Further Notice* at para. 201.

[^758]: ^?^ *Inputs Further Notice* at para. 209.

[^759]: ^?^ *Inputs Further Notice* at para. 209.

[^760]: ^?^ Our proposed expense-to-investment ratios were based on
    ARMIS data for 91 study areas. The input values we adopt herein are
    based on ARMIS data for 80 non-rural study areas. We note that there
    generally is little or no difference between the expense ratios
    calculated using total ARMIS expense and investment accounts and
    non-rural ARMIS expense and investment. Where there are differences,
    the ratios based on non-rural data are higher for all categories
    except network support and general support.

[^761]: ^?^ *See, e.g.,* GTE *Inputs Further Notice* comments at 76;
    Sprint *Inputs Further Notice* comments at 59.

[^762]: ^?^ Due to the manner in which SBC develops current-to-book
    ratios for each year (average beginning and end-of-year current
    investment divided by average beginning and end-of-year embedded
    investment) year-end 1998 current-to-book ratios are not available
    for SBC. Therefore, we applied year-end 1997 current-to-book ratios
    to both SBC\'s year-end 1997 and year-end 1998 investment in
    developing 1998 expense-to-investment ratios.

[^763]: ^?^ *See* Appendix D at D-4.

[^764]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 45.

[^765]: ^?^ *See, e.g.,* Bell Atlantic *Inputs Further Notice* comments
    at 20-21; BellSouth *Inputs Further Notice* comments at B-16, B-18;
    GTE *Inputs Further Notice* comments at 75-76.

[^766]: ^?^ *See* BellSouth *Inputs Further Notice* reply comments at 17
    (citing *Universal Service Order*, 12 FCC Rcd at 8913, para. 250,
    criterion three).

[^767]: ^?^ BellSouth *Inputs Further Notice* reply comments at 17-18.

[^768]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250
    (criterion three).

[^769]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250.

[^770]: ^?^ *Inputs Further Notice* at para. 50.

[^771]: ^?^ *Platform Order*, 12 FCC Rcd at 21350, para. 66. \"Instead,
    incumbent LECs\' existing plant will tend to reflect choices made at
    a time when different technology options existed or when the
    relative cost of equipment to labor may have been different than it
    is today.\" *Id.*

[^772]: ^?^ GTE *Inputs Further Notice* comments at 76.

[^773]: ^?^ GTE *Inputs Further Notice* comments at 73.

[^774]: ^?^ GTE *Inputs Further Notice* comments at 72.

[^775]: ^?^ GTE *Inputs Further Notice* comments at 73.

[^776]: ^?^ Sprint *Inputs Further Notice* comments at 51.

[^777]: ^?^ Sprint *Inputs Further Notice* comments at 51.

[^778]: ^?^ Sprint *Inputs Further Notice* comments at 51-52.

[^779]: ^?^ AT&T/MCI *Inputs Further Notice* reply comments at 38 n.58.

[^780]: ^?^ AT&T/MCI *Inputs Further Notice* reply comments at 38 n.58.

[^781]: ^?^ SBC *Inputs Further Notice* comments at 4.

[^782]: ^?^ SBC *Inputs Further Notice* comments at 4.

[^783]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment A at
    A-13. (comments of Georgetown Consulting Group, Inc.).

[^784]: ^?^ GTE *Inputs Further Notice* comments at 73.

[^785]: ^?^ GTE *Inputs Further Notice* comments at 73.

[^786]: ^?^ In its reply comments, Sprint argues that inputs should vary
    by company size and region, but does not provide a specific
    methodology for doing so. *See* Sprint *Inputs Further Notice* reply
    comments at 3-4.

[^787]: ^?^ SBC *Inputs Further Notice* comments at 14-15.

[^788]: ^?^ *See, e.g.,* Bell Atlantic *Inputs Further Notice* comments
    at 20-21; BellSouth *Inputs Further Notice* comments, Attachment B
    at B-16, B-18; GTE *Inputs Further Notice* comments at 75-76.

[^789]: ^?^ *See, e.g.,* AT&T/MCI *Inputs Further Notice* reply comments
    at 3-7.

[^790]: ^?^ There are 94 non-rural study areas. As noted above, the
    expense-to-investment ratios were calculated using ARMIS data for 80
    non-rural study areas. There are more non-rural study areas than
    there are non-rural study areas for which we have ARMIS data because
    some non-rural companies do not file ARMIS data (Roseville, North
    State, and Contel of Minnesota) and some ARMIS-filing companies file
    consolidated data for combined study areas (Puerto Rico, some GTE
    companies). *See supra* note 756.

[^791]: ^?^ As discussed below, when the Commission has had the
    opportunity to scrutinize carriers\' company-specific costs, as with
    the local number portability tariffs, we use company-specific input
    values in the model. *See infra* at para. 408.

[^792]: ^?^ *Inputs Further Notice* at para. 199.

[^793]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 20.

[^794]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 20.

[^795]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 20.

[^796]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 20.

[^797]: ^?^ In contrast, if we were determining the rates a carrier
    could charge for a particular service, the quality of service the
    carrier actually was providing could be a relevant factor.

[^798]: ^?^ *See supra* para. 349; AT&T/MCI *Inputs Further Notice*
    comments at 45.

[^799]: ^?^ As noted above, the Commission has not considered what type
    of input values, company-specific or nationwide, nor what specific
    input values, would be appropriate for any other purposes and
    caution parties from making any claims in other proceedings based
    upon the input values we adopt in this Order. *See supra* para. 32.

[^800]: ^?^ *See, e.g.,* Bell Atlantic *Inputs Further Notice* comments
    at 20; GTE *Inputs Further Notice* comments at 74-75; Sprint *Inputs
    Further Notice* comments at 54.

[^801]: ^?^ GTE *Inputs Further Notice* comments at 74-75.

[^802]: ^?^ GTE *Inputs Further Notice* comments at 74-75.

[^803]: ^?^ *See supra* para. 355.

[^804]: ^?^ *See supra* para. 356.

[^805]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 21; Sprint
    *Inputs Further Notice* comments at 54.

[^806]: ^?^ GTE *Inputs Further Notice* comments at 75.

[^807]: ^?^ GTE *Inputs Further Notice* comments at 75.

[^808]: ^?^ *See* Bureau of Labor Statistics, Employment Cost Trends,
    Employment Cost Index, June 1999, at
    http://www.bls.gov/news.releases/eci.toc.htm. In particular, we
    looked at the following tables: Table 4, Compensation (not
    seasonally adjusted), Employment Cost Index for total compensation,
    private industry workers, by bargaining status, region and area;
    Table 5, Wages and Salaries (not seasonally adjusted), Employment
    Cost Index for wages and salaries only, civilian, and state and
    local government workers, by industry and occupational group; and
    Table 7, Wages and Salaries (not seasonally adjusted) Employment
    Cost Index for wages and salaries only, private industry workers, by
    bargaining status, regional and area.

[^809]: ^?^ GTE *Inputs Further Notice* comments at 72, 75-76.

[^810]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment B at
    B-16.

[^811]: ^?^ *See* SBC *Inputs Further Notice* comments at 14-18; Sprint
    *Inputs Further Notice* comments at 55-59; US West *Inputs Further
    Notice* comments at 21-26.

[^812]: ^?^ AT&T/MCI *Inputs Further Notice* reply comments at 38.

[^813]: ^?^ US West *Inputs Further Notice* comments at 23-24.

[^814]: ^?^ US West *Inputs Further Notice* comments at 23-24.

[^815]: ^?^ US West *Inputs Further Notice* comments at 23-24.

[^816]: ^?^ US West *Inputs Further Notice* comments at 24-25. US West
    indicates that it used the Telephone Plant Index (TPI) to derive the
    141 percent figure. US West implies, therefore, that the TPI is a
    reproduction cost index. This raises questions with respect to how a
    reproduction index deals with old technology that cannot be
    purchased today at any price. Without detailed knowledge about the
    TPI, we cannot say whether it reflects only reproduction costs or
    may also reflect replacement costs when new technology has replaced
    old technology.

[^817]: ^?^ The values used in this example are hypothetical and do not
    represent actual input values.

[^818]: ^?^ Our hypothetical example reflects US West's contention that
    reproduction costs are significantly higher than replacement costs
    and that replacement costs are only slightly lower than historic
    costs.

[^819]: ^?^ To revalue the copper investment, we multiply \$1000 by 1.5
    (=\$1500); then to calculate the expense-to-investment ratio, we
    divide current maintenance expenses for copper by the adjusted
    copper investment (\$10/\$1500 = .0067). Similarly, to revalue the
    fiber investment, we multiply \$10 by .8(=\$8); then to calculate
    the expense-to-investment ratio, we divide current maintenance
    expenses for fiber by the adjusted fiber investment (\$.10/8=.0125).
    Finally, we apply these adjusted expense-to-investment ratios to the
    forward-looking plant to derive the forward-looking maintenance
    expenses: \$900 x .0067 (\$6.03) + \$40 x .0125(.50) = \$6.58.

[^820]: ^?^ Sprint *Inputs Further Notice* comments at 55.

[^821]: ^?^ Sprint *Inputs Further Notice* comments at 55.

[^822]: ^?^ Sprint *Inputs Further Notice* comments at 55.

[^823]: ^?^ For example, if a pole cost \$200 to install in 1980, and
    \$400 today, the current-to-book ratio is \$400/\$200 = 2.0. If the
    maintenance expense associated with the pole is \$20, the
    expense-to-investment ratio on the books is \$20/\$200 = .10; and
    the expense-to-investment ratio adjusted by the current-to-book
    ratio is \$20/\$400 = .05.

[^824]: ^?^ *See supra* para. 351.

[^825]: ^?^ SBC *Inputs Further Notice* comments at 15.

[^826]: ^?^ SBC *Inputs Further Notice* comments at 15.

[^827]: ^?^ SBC *Inputs Further Notice* comments at 15.

[^828]: ^?^ SBC *Inputs Further Notice* comments at 16.

[^829]: ^?^ SBC *Inputs Further Notice* comments at 16-17, Attachment A
    (comparing Southwestern Bell/Texas costs of 5.96 percent of related
    investments to the Commission\'s proposed 3.08 percent of related
    investment).

[^830]: ^?^ AT&T/MCI *Inputs Further Notice* reply comments at 38.

[^831]: ^?^ AT&T/MCI *Inputs Further Notice* reply comments at 38.

[^832]: ^?^ AT&T/MCI *Inputs Further Notice* reply comments at 38.

[^833]: ^?^ SBC *Inputs Further Notice* comments at 15.

[^834]: ^?^ Sprint *Inputs Further Notice* comments at 55.

[^835]: ^?^ AT&T/MCI *Inputs Further Notice* reply comments at 38.

[^836]: ^?^ Sprint *Inputs Further Notice* comments at 55-57, Attachment
    10a.

[^837]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment B at
    B-19.

[^838]: ^?^ BellSouth *Inputs Further Notice* comments, Attachment B at
    B-16.

[^839]: ^?^ Corporate operations and customer service expenses include
    the following ARMIS accounts and their subaccounts:

    6610 - Marketing Total

    6611 - Product Management

    6612 - Sales

    6613 - Product Advertising

    6620 - Service Expense Total

    6621 - Call Completion (Operator Service Expense)

    6622 - Number Services (Directory Publishing Expense)

    6623 - Customer Services

    6710 - Executive and Planning Total

    6711 - Executive

    6712 - Planning

    6720 - General and Administrative

    6721 - Accounting and Finance

    6722 - External Relations

    6723 - Human Resources

    6724 - Information Management

    6725 - Legal

    6726 - Procurement

    6727 - Research and Development

    6728 - Other General and Administrative

[^840]: ^?^ Plant non-specific expenses include the following ARMIS
    expense accounts:

    6510 - Other Property Plant and Equipment Expense

    6530 - Network Operations

[^841]: ^?^ *Inputs Further Notice* at para. 213.

[^842]: ^?^ Data was taken from 1996 ARMIS 43-01, Subject to Separations
    (Column F) for Accounts 6610, 6620, 6710 and 6720. Data was taken
    from 1996 ARMIS 43-03, Subject to Separations (Column M) for
    Accounts 6510 and 6530. Line counts were taken from 1996 ARMIS
    43-08, Table III, Total Switched Lines (Column DJ) and Total Access
    Lines (Column DM).

[^843]: ^?^ Dial Equipment Minutes of Use (DEMs) for 1996 were taken
    from NECA and are available on the Commission\'s Web site at
    http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-State_Link/neca.html.

[^844]: ^?^ *Inputs Further Notice* at para. 217.

[^845]: ^?^ *See Inputs Further Notice* at para. 218-19. Specification 1
    used the following regression equation: Expense/Total Lines = β~1~
    (Switched Lines/Total Lines)+ β~2~ (Special Lines/Total Lines)+ β~3~
    (Toll Minutes/Total Lines). Specification 2 used the following
    equation: Expense/Total Lines = β~1~ (Switched Lines/Total Lines)+
    β~2~ (Special Lines/Total Lines)+ β~3~ (State Toll Minutes/Total
    Lines)+ β~4~ (Interstate Toll Minutes/Total Lines).

[^846]: ^?^ *See Inputs Further Notice* at para. 214.

[^847]: ^?^ *Inputs Further Notice* at para. 215.

[^848]: ^?^ *Inputs Further Notice* at para. 215.

[^849]: ^?^ *Inputs Further Notice* at para. 215.

[^850]: ^?^ *Inputs Further Notice* at para. 223.

[^851]: ^?^ *Inputs Further Notice* at para. 224.

[^852]: ^?^ *Inputs Further Notice* at para. 225.

[^853]: ^?^ *Inputs Further Notice* at para. 220-222.

[^854]: ^?^ *Inputs Further Notice* at para. 221.

[^855]: ^?^ *Inputs Further Notice* at para. 222.

[^856]: ^?^ *Inputs Further Notice* at para. 226.

[^857]: ^?^ *Inputs Further Notice* at para. 226.

[^858]: ^?^ *Inputs Further Notice* at para. 226.

[^859]: ^?^ [Aggregate ARMIS Accounts]{.underline} [Expense Input
    Values]{.underline}

    6510 Other Property, Plant, and Equipment \$ (0.05)

    6530 Network Operations 1.48

    6610 Marketing 0.09

    6620 Service Expense/Customer Operations 3.62

    6700 Executive, Planning, General, and Administrative
    [2.18]{.underline}

    Total Common Support Services Expenses Per Line, Per Month \$ 7.32

    Rather than using the \$7.32 directly as an input value, the model
    uses this amount, annualized and adjusted for uncollectibles, or
    \$92.46316, which appears in cell C33 of the per line tab of the
    wire center expense module.

[^860]: ^?^ *See supra* para. 348.

[^861]: ^?^ Specifically, we adopt estimates using results solely from
    the Specification 1 regression equation: Expense/Total Lines = β1
    (Switched Lines/Total Lines) + β2 (Special Lines/Total Lines) + β3
    (Toll Minutes/Total Lines) rather than an average of results from
    two model specifications, as proposed. *See Inputs Further Notice*
    at para. 218.

[^862]: ^?^ *See infra* paras. 403-407.

[^863]: ^?^ As noted above, although some rural companies file ARMIS
    reports, the mechanism we adopt today will be used, beginning
    January 1, 2000, to determine high-cost support for non-rural
    carriers*. See supra* para. 346.

[^864]: ^?^ *See* Appendix D at D-5.

[^865]: ^?^ *Inputs Further Notice* at para. 226

[^866]: ^?^ *Inputs Further Notice* at para. 226

[^867]: ^?^ *Inputs Further Notice* at para. 226

[^868]: ^?^ *See* AT&T/MCI *Inputs Further Notice* comments at 46-47.

[^869]: ^?^ *See e.g.,* Aliant *Inputs Further Notice* comments at 2-3;
    Bell Atlantic *Inputs Further Notice* comments at 22; BellSouth
    *Inputs Further Notice* comments at B-21-B-23; USTA *Inputs Further
    Notice* comments at 2.

[^870]: ^?^ Sprint *Inputs Further Notice* comments at 60, 68.

[^871]: ^?^ GTE *Inputs Further Notice* comments at 88.

[^872]: ^?^ Data were taken from 1998 ARMIS 43-03, Total Regulated
    (Column I) for Accounts 6610, 6620, 6710, 6720, 6510, and 6530. Line
    counts were taken from 1998 ARMIS 43-08, Table III, Total Switched
    Lines (Column DJ) and Total Access Lines (Column DM).

[^873]: ^?^ Dial Equipment Minutes of Use (DEMS) for 1996 and 1997 were
    taken from NECA, available on the Commission\'s web site at
    http://www.fcc.gov/Bureaus/Common_Carrier/Reports/FCC-State_Link/neca.html.
    Estimated 1998 DEMs were calculated by multiplying the number of
    1997 DEMs for each study area by the ratio of 1997 DEMs to 1996 DEMs
    for that study area. Actual 1998 DEMs classified by local,
    interstate and intrastate toll minutes needed for use as variables
    in the regression analysis are not currently available from NECA.

[^874]: ^?^ *See* Appendix D at D-1.

[^875]: ^?^ *Inputs Further Notice* at para. 217.

[^876]: ^?^ *See* Ameritech *Inputs Further Notice* comments at 28; US
    West *Inputs Further Notice* comments, Attachment A at 27.

[^877]: ^?^ *See* Ameritech *Inputs Further Notice* comments at 28; US
    West *Inputs Further Notice* comments at Attachment A, 27.

[^878]: ^?^ To the extent a particular company believes that its ARMIS
    filings do not represent cost differences among its study areas, we
    would be interested in receiving more detailed information.

[^879]: ^?^ Standard multi-variate regression analysis uses ordinary
    least squares with more than one variable.

[^880]: ^?^ Expense/Total Lines = β1 (Switched Lines/Total Lines) + β2
    (Special Lines/Total Lines) + β3 (Toll Minutes/Total Lines).

[^881]: ^?^ *See* US West *Inputs Further Notice* comments, Attachment A
    at 22 (claiming it is inappropriate to average the two
    specifications).

[^882]: ^?^ *See, e.g.,* Ameritech *Inputs Further Notice* comments at
    25-28; GTE *Inputs Further Notice* comments at 79-82; Sprint *Inputs
    Further Notice* comments at 61-65; US West *Inputs Further Notice*
    comments at 53-57, Attachment A at 20-27.

[^883]: ^?^ Sprint *Inputs Further Notice* comments at 61.

[^884]: ^?^ US West *Inputs Further Notice* comments at 55.

[^885]: ^?^ GTE *Inputs Further Notice* comments at 81.

[^886]: ^?^ GTE *Inputs Further Notice* comments at 81.

[^887]: ^?^ According to our calculations using the 1998 data, the R^2^s
    for the four regressions are:

    Account: 6620 6700 6510 6530

    R^2^: 0.96 0.92 0.20 0.95

    We note that the commenters\' analysis was based on the 1996 ARMIS
    data.

[^888]: ^?^ As we discuss below, we no longer use the regression for the
    6610 account.

[^889]: ^?^ We calculate an expense input value of -\$0.05 for Account
    6510 (Other Property, Plant, and Equipment) and a total expense
    input value of \$7.32 for total common support services expenses,
    per line, per month.

[^890]: ^?^ *See, e.g.,* Sprint *Inputs Further Notice* comments at
    62-64 & n.15.

[^891]: ^?^ *See supra* para. 351.

[^892]: ^?^ Expenses = β~1~ Lines + β~2~ DEMS + ε.

[^893]: ^?^ Expenses = β~1~ Switched Lines + β~2~ Special Lines + β~3~
    Local DEMS + β~4~ Toll DEMs + ε.

[^894]: ^?^ Expenses = β~1~ Switched Lines + β~2~ Special Lines + β~3~
    Toll DEMs + ε.

[^895]: ^?^ Expenses/Total Lines = β~1~ (Switched Lines/Total Lines) +
    β~2~ (Special Lines/Total Lines) + β~3~ (Toll DEMs/Total Lines) +
    ε\'.

[^896]: ^?^ US West *Inputs Further Notice* comments at Attachment A,
    21. US West also claims that our regression analysis estimates a
    common support per minute of access of \$0.02, which does not
    include any of the capital or maintenance costs associated with the
    switching investment used to provide access. Because the traffic
    sensitive common costs associated with access services alone exceeds
    the current access charge rate of approximately \$.01 to \$.02 per
    minute, US West claims that are analysis shows that access charges
    are priced below costs. US West *Inputs Further Notice* comments at
    56-57. The coefficient for toll is an estimate of the increase in
    expenses due to an increase in 1000 toll minutes. Summing across all
    accounts and dividing by 1000, according to our calculations an
    estimate of the expense cost per toll minute is equal to \$
    0.0006331807.

[^897]: ^?^ US West *Inputs Further Notice* comments, Attachment A at
    21.

[^898]: ^?^ We note that we also count switched business lines as
    channels in our regression equations.

[^899]: ^?^ *See, e.g.,* Ameritech *Inputs Further Notice* comments at
    27-28; GTE *Inputs Further Notice* comments at 79-80; US West
    *Inputs Further Notice* comments, Attachment A at 21-22.

[^900]: ^?^ US West *Inputs Further Notice* comments at 53-57,
    Attachment A at 20-27.

[^901]: ^?^ The correlation matrix for the variables under consideration
    is:

    switched special toll local

    switched 1.00 ‑1.00 0.54 0.06

    special ‑1.00 1.00 ‑0.54 ‑0.06

    toll 0.54 ‑0.54 1.00 ‑0.13

    local 0.06 ‑0.06 ‑0.13 1.00

[^902]: ^?^ *See, e.g.*, Ameritech *Inputs Further Notice* comments at
    25-28; GTE *Inputs Further Notice* comments at 79-82; Sprint *Inputs
    Further Notice* comments at 61-65; US West *Inputs Further Notice*
    comments at 53-57, Attachment A at 20-27.

[^903]: ^?^ *See* Appendix D at D-6.

[^904]: ^?^ The table below shows the cost per switched line without
    local minutes in the equation (nloc), with local minutes in the
    equation and an average number of local minutes for each line
    (wloc), and the difference between the two in dollars.

    nloc wloc diff

    lm6620 3.39 3.62 ‑0.24\*

    lm6700 2.47 2.18 0.30

    lm6510 ‑0.05 ‑0.05 0.00

    lm6530 1.41 1.48 ‑0.07

    We note that the 6620 account is the one regression where local
    minutes variable is significant. In the other cases it is not.

[^905]: ^?^ *Inputs Further Notice* at paras. 220-225.

[^906]: ^?^ *Inputs Further Notice* at para. 221.

[^907]: ^?^ *Inputs Further Notice* at para. 221.

[^908]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 45-46.

[^909]: ^?^ SBC does not believe one-time and non-recurring costs are
    significant, but agrees that they should be excluded to the extent
    they are significant. SBC suggests we could either base our inputs
    on company data that does not include these costs or base the inputs
    on data from years where it is known that no one-time or
    non-recurring activities occurred. SBC *Inputs Further Notice*
    comments at 20. Sprint suggests that current information with
    respect to one-time corporate operations expenses should be supplied
    by the companies on an annual basis. Sprint *Inputs Further Notice*
    comments at 65. GTE, on the other hand, agrees with our tentative
    conclusion and argues that we should not attempt to adjust our input
    values for one-time, non-recurring, and non-supported costs. GTE
    argues that, if we do so, we should also adjust our estimates to
    account for certain cost increases due to regulatory requirements,
    and other factors. If any adjustments are made, GTE claims that
    company-specific cost adjustments would have to be requested from
    each company annually. GTE *Inputs Further Notice* comments at 82.

[^910]: ^?^ GTE *Inputs Further Notice* comments at 82.

[^911]: ^?^ The following companies have either filed notice with the
    Commission or have indicated in the press that they were or are
    actively engaged in merger discussions and activity: Bell Atlantic,
    GTE, US WEST, Ameritech, SBC, Frontier, Puerto Rico Telephone,
    Cincinnati Bell, Aliant Communications, and Sprint.

[^912]: ^?^ *Inputs Further Notice* at para. 221, 223-225.

[^913]: ^?^ *See infra* paras. 403-407.

[^914]: ^?^ *See Inputs Further Notice* at para. 225.

[^915]: ^?^ *See, e.g.,* GTE *Inputs Further Notice* comments at 84;
    Sprint *Inputs Further Notice* comments at 67.

[^916]: ^?^ Account 6610 Marketing consists of three sub-accounts: 6611
    Product Management, 6612 Sales, and 6613 Advertising.

[^917]: ^?^ *Inputs Further Notice* at para. 224.

[^918]: ^?^ *Inputs Further Notice* at para. 225.

[^919]: ^?^ *See, e.g.,* Sprint *Inputs Further Notice* comments at
    65-66 (arguing direct reduction of total company marketing expenses
    for only ETI factor is an acceptable method); Ameritech *Inputs
    Further Notice* comments at 29; US West *Inputs Further Notice*
    comments, Attachment at 27-28.

[^920]: ^?^ *See,, e.g.,* Ameritech *Inputs Further Notice* comments at
    29; GTE *Inputs Further Notice* comments at 83; US West *Inputs
    Further Notice* comments, Attachment at 28.

[^921]: ^?^ 47 U.S.C. § 214(e)(1)(B).

[^922]: ^?^ *See, e.g.,* NECA, Universal Service Fund 1999 Submission of
    1998 Study Results, Oct.1, 1999 at Tab 2. The data collection
    instructions identify the accounts that are included in calculating
    high-cost loop support. Accounts 6610 (Total Marketing),
    6611(Product Management), 6612(Sales), and 6613(Advertising) do not
    appear in the list of accounts included in calculating high-cost
    loop support.

[^923]: ^?^ *See* Susan Baldwin, An Alternative Analysis of Marketing
    Expenses Related to Calculation of USF Support. This paper
    supplements the earlier ETI study: Susan M. Baldwin, Lee L. Selwyn,
    Economics and Technology, Inc. *Converging on a Cost Proxy Model
    Primary Line Basic Residential Service,* August 1996.

[^924]: ^?^ *See* Appendix D at D-7 for analysis. For further
    information regarding formulas and calculations, see the spreadsheet
    posted on the Commission\'s Web site.

[^925]: ^?^ Although the statute requires advertising of the supported
    services, as noted above, we do not find that this requires
    advertising of secondary lines to consumers already receiving the
    supported services.

[^926]: ^?^ *See Inputs Further Notice* at Appendix A, A-31.

[^927]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 47.

[^928]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 47.

[^929]: ^?^ GTE *Inputs Further Notice* reply comments at 32.

[^930]: ^?^ *See* Appendix D at D-8.

[^931]: ^?^ In the synthesis model, ARMIS data for each non-rural study
    area are contained in the \"1996 Actuals\" tab of the expense
    modules.

[^932]: ^?^ As calculated by the model, TPIS excludes GSF investment.

[^933]: ^?^ The synthesis model platform incorporates HAI\'s expense and
    GSF module. *See Platform Order*, 13 FCC Rcd at 21361, para. 91.

[^934]: ^?^ *Inputs Further Notice* at 211.

[^935]: ^?^ The HAI model used the following two factors to reduce the
    preliminary GSF investment estimates: (1) one minus the Total
    Operations General Support Allocator (Total Operations Allocator) or
    (2) the Office Worker General Support Allocator (Office Worker
    Allocator). Each of these allocators is a fraction. The Total
    Operations Allocator is the ratio of the sum of customer operations
    expenses and corporate operations expenses to total operating
    expenses. The Office Worker Allocator is the ratio of the sum of
    corporate operations expenses and network operations expenses to the
    sum of customer operations expenses, corporate operations expenses
    and network operations expenses. The Total Operations Allocator is
    applied to the Motor Vehicles, Garage Work Equipment, and Other Work
    Equipment accounts. The Office Worker Allocator is applied to the
    Furniture, Office Equipment, Buildings and General Purpose Computer
    accounts. *See* HAI Dec. 11, 1997 submission.

[^936]: ^?^ The proposed ratio was the sum of customer operations
    expenses, network operations expenses, and corporate operations
    expenses attributable to the supported services, to the sum of those
    expenses calculated on a total regulated basis.

[^937]: ^?^ *See, e.g.,* BellSouth *Inputs Further Notice* comments at
    Attachment B, B-21; SBC *Inputs Further Notice* comments at 17;
    Sprint *Inputs Further Notice* comments at 59-60. US West also
    claims generally that our multi-step process results in a
    significant reduction in costs \"assumed to be recoverable.\" US
    West *Inputs Further Notice* comments at 47.

[^938]: ^?^ SBC *Inputs Further Notice* comments at 17.

[^939]: ^?^ SBC *Inputs Further Notice* comments at 17.

[^940]: ^?^ BellSouth *Inputs Further Notice* comments at Attachment B,
    B-21.

[^941]: ^?^ Sprint *Inputs Further Notice* comments at 59-60. Sprint
    also claims that we used a mathematically incorrect method to
    compute the GSF ratio by including ARMIS GSF investment in the
    denominator and then applying that to TPIS investment as calculated
    by the model, which does not include GSF investment. We clarify
    below, that the ARMIS GSF investment used in the denominator also
    excludes GSF investment, and we thus calculate the ratio as Sprint
    suggests: ARMIS GSF plant divided by ARMIS TPIS less ARMIS GSF
    plant. *See infra* para. 417.

[^942]: ^?^ *See SBC Inputs Further Notice* comments at 17.

[^943]: ^?^ *See supra* paras. 49, 391.

[^944]: ^?^ GTE *Inputs Further Notice* comments at 77. Although GTE
    agrees that we should not base a reduction to the preliminary GSF
    investment on the same factors used in the HAI model, GTE claims our
    proposed methodology has several problems.

[^945]: ^?^ SBC *Inputs Further Notice* comments at 18.

[^946]: ^?^ Due to equations embedded in the HAI expense module, the
    total operations general support allocator is set equal to one minus
    the office worker general support allocator. That is, because one
    factor is one minus the other in the HAI expense module, to use the
    same allocation factor for all GSF investment, we must enter one
    minus the factor in some instances. *See* Appendix D at D-9.

[^947]: ^?^ GTE *Inputs Further Notice* comments at 77.

[^948]: ^?^ *See supra* para. 409

[^949]: ^?^ We also do not at this time consider Bell Atlantic\'s
    suggestion that we develop GSF investments on some other basis, such
    as an activity based approach, rather than as a ratio of investment.
    *See* Bell Atlantic *Inputs Further Notice* comments at 21. Such an
    approach also would require changes to the model platform.

[^950]: ^?^ GTE *Inputs Further Notice* comments at 77-78.

[^951]: ^?^ GTE *Inputs Further Notice* comments at 78.

[^952]: ^?^ GTE *Inputs Further Notice* comments at 78.

[^953]: ^?^ Specifically, the GSF allocator is the ratio of universal
    service expenses to total company expenses. Universal service
    expenses are determined by the following: switched lines to total
    lines times loop maintenance plus switched lines to total lines
    times circuit maintenance plus local DEMs to total DEMs times switch
    maintenance plus \$7.32, which is the per-line, per month amount for
    the common support services expenses attributed to the supported
    services, as discussed above. *See supra* note 855. Total company
    expenses are the sum of loop maintenance, circuit, switch
    maintenance, and the total corporate overhead. This allocator is
    .6769.

[^954]: ^?^ BellSouth *Inputs Further Notice* comments at Attachment B,
    B-20.

[^955]: ^?^ This can be verified by examining the formulas in the \" 96
    Actuals\" tab of the expense modules.

[^956]: ^?^ US West *Inputs Further Notice* comments at 48.

[^957]: ^?^ To the extent that not all of the land investment is
    included in the synthesis model logic, such a change would require a
    change to the model platform.

[^958]: ^?^ *See* Appendix D at D-9.

[^959]: ^?^ *1997 Further Notice,* 12 FCC Rcd at 18570, para. 149. The
    projection life of an asset is the asset\'s expected service life at
    installation, reflecting not only the physical life of the
    equipment, but also the obsolescence associated with the replacement
    of older equipment with equipment that uses new technologies and
    forecasts of future replacements. The adjusted projection life of an
    asset is its projection life adjusted by its future net salvage
    value. Future net salvage is the percentage of the asset\'s value
    that the owner expects to obtain when selling the asset at the end
    of its useful life. *Id.*

[^960]: ^?^ Depreciation charges are computed in this manner for the
    first year. In subsequent years, depreciation charges are computed
    using reserve.

[^961]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913-14, para. 250
    (criterion 5).

[^962]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18571, para. 152.

[^963]: ^?^ *Id.*

[^964]: ^?^ *See Inputs Public Notice*.

[^965]: ^?^ *Inputs Further Notice* at para. 231.

[^966]: ^?^ 47 C.F.R. § 32.2000(g). The equal-life-group procedure
    subdivides all units of a plant account installed in a particular
    year (a \"vintage\") into groups in which all units are expected to
    have the same life span. Each group is depreciated using the
    straight-line method, which spreads depreciation costs equally over
    the life of the group. *See* *Amendment of Part 31 (Uniform System
    of Accounts for Class A and B Companies) so as to Permit Depreciable
    Property to be Placed in Groups Comprised of Units with Expected
    Equal Life for Depreciation Under the Straight‑Line Method*, Report
    and Order, 83 FCC2d 267 (1980), recon., 87 FCC2d 916 (1981),
    supplemental opinion, 87 FCC2d 1112 (1981) \[*Straight-Line
    Equal-Life-Group Report and Order*\]. Thus, the annual depreciation
    of a single vintage of a plant account equals the sum of the
    depreciation amounts of all surviving life-groups from that vintage.
    For a discussion of the equal-life-group method of depreciation, see
    Bryan Clopton, \"Equal Life Group Depreciation Rates\", in National
    Ass\'n of Regulatory Utility Commissioners, Public Utility
    Depreciation Practices at 165-186 (August 1996) \[Public Utility
    Depreciation Practices\].

[^967]: ^?^ A curve shape is the result of actuarial analysis which
    determines the probable frequency of plant mortality for a
    particular plant account from the time the plant vintage is placed
    in service to the end of life of the final surviving plant of that
    vintage. In the equal-life-group methodology, curve shapes are used
    to determine the number of units of a plant account in each
    equal-life group. *See generally* Public Utility Depreciation
    Practices at 111-129. The adopted curve shapes for each plant
    account are shown in the table attached in Appendix A at A-30.

[^968]: ^?^ See AT&T/MCI *Inputs Further Notice* comments at 47-48;
    BellSouth *Inputs Further Notice* comments, Appendix B at B-26; GTE
    *Inputs Further Notice* comments at 85; Sprint *Inputs Further
    Notice* comments at 75; AT&T/MCI *Inputs Further Notice* reply
    comments at 41.

[^969]: ^?^ Ameritech *Inputs Further Notice* comments at 30.

[^970]: ^?^ We note, furthermore, in response to the comments of
    AT&T/MCI, that we intend to follow our standard practice of
    accounting for the impact of deferred taxes. *See, e.g.,* 47 C.F.R.
    § 65.830(a)(1).

[^971]: ^?^ *See, e.g.,* *Straight-Line Equal-Life-Group Report and
    Order*.

[^972]: ^?^ Letter from Chris Frentrup, MCI WorldCom, to Magalie Roman
    Salas, FCC, dated July 16, 1999 (AT&T/MCI July 15 *ex parte*).

[^973]: ^?^ The variables describing a nonstandard curve shape are not
    usually meaningful in and of themselves. There are an infinite
    number of curves that the variables could describe and the variables
    themselves offer no insight into the shape of the curve until they
    have been used to actually plot the curve they describe. Until that
    has been done the depreciation consequences of a particular set of
    variables are unknown. The GM standard curves are a set of thirteen
    generalized curves that may stand in place of the infinite number of
    possible nonstandard curves. Because of the small, finite number of
    GM standard curves, a person familiar with depreciation practices
    will recognize the depreciation consequences of a particular
    identified GM standard curve. For a detailed discussion of GM
    standard curves, formerly known as Bell standard curves, see
    American Telegraph & Telephone, Engineering Economy 345-65 (1977).

[^974]: ^?^ *See* Public Utility Depreciation Practices at 120-26.

[^975]: ^?^ See Public Utility Depreciation Practices at 123-25 for a
    discussion regarding the method for matching generalized curves to
    observed life table values. The adopted curve shapes for each plant
    account category are shown in the table attached in Appendix A at
    A-30..

[^976]: ^?^ *See* 47 C.F.R. § 32.2000(j)

[^977]: ^?^ *See* 47 C.F.R. § 32.2000(g)(iii).

[^978]: ^?^ The Commission has proposed streamlining the depreciation
    prescription process by, *inter alia*, expanding the prescribed
    range for the digital switching plant account and eliminating
    salvage from the depreciation process. *See 1998 Biennial Regulatory
    Review \-- Review of Depreciation Requirements for Incumbent Local
    Exchange Carriers*, Notice of Proposed Rulemaking, CC Docket No.
    98-137, 13 FCC Rcd 20542 (1998).

[^979]: ^?^ Ameritech *Inputs Further Notice* comments at 31-32; Bell
    Atlantic *Inputs Further Notice* comments at 7, 23-24, and
    Attachment B, at 7-10; BellSouth *Inputs Further Notice* comments,
    Appendix B at B--23-B--26; Cincinnati Bell Telephone *Inputs Further
    Notice* comments at 5; GTE *Inputs Further Notice* comments at
    85-86; SBC *Inputs Further Notice* comments at 21-23; Sprint *Inputs
    Further Notice* comments at 76-79; *see also* Aliant June 1, 1998
    comments at 3-4; Ameritech June 1, 1998 comments at 4; BCPM June 1,
    1998 comments at 11-13; GTE June 1, 1998 comments at 15-16;
    Southwestern June 1, 1998 comments at 9-10.

[^980]: ^?^ Ameritech *Inputs Further Notice* comments at 31-32; Bell
    Atlantic *Inputs Further Notice* comments at 7, 23-24, and
    Attachment B, at 7-10; BellSouth *Inputs Further Notice* comments,
    Appendix B at B--24-B--26; GTE *Inputs Further Notice* comments at
    85-86; Sprint *Inputs Further Notice* comments at 76-79; *see also*
    BCPM June 1, 1998 comments at 12; SBC June 1, 1998 comments at 17;
    GTE June 1, 1998 comments at 16; Ameritech June 1, 1998 comments at
    4.

[^981]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 47-48; GSA
    *Inputs Further Notice* comments at 5-6, Attachment 1; *see also*
    HAI June 1, 1998 comments at 13.

[^982]: ^?^ GSA *Inputs Further Notice* comments at 5-6, Attachment 1.

[^983]: ^?^ *Id.*

[^984]: ^?^ *See*, *e.g.*, Ameritech *Inputs Further Notice* comments at
    31-32 (recommending adoption of values endorsed by Technology
    Futures, Inc.).

[^985]: ^?^ *See*, *e.g.*, Bell Atlantic *Inputs Further Notice*
    comments at 24; GTE *Inputs Further Notice* comments at 86; SBC
    *Inputs Further Notice* comments at 22-23.

[^986]: ^?^ The eight categories in which BCPM\'s values fall outside
    required ranges for projected lives were: Digital Circuit Equipment;
    Digital Switching; Aerial Cable-Metallic; Aerial Cable-Non-Metallic;
    Underground Cable-Metallic; Underground Cable-Non-Metallic; Buried
    Cable-Metallic; and Buried Cable-Non-Metallic. The two categories in
    which BCPM\'s values fall outside required ranges for net salvage
    percentage were Digital Circuit Equipment and Poles.

[^987]: ^?^ GSA *Inputs Further Notice* reply comments at 5.

[^988]: ^?^ *1997 Further Notice*, 12 FCC Rcd at 18571, para. 152.

[^989]: ^?^ HAI June 1, 1998 comments at 10.

[^990]: ^?^ *Id.*

[^991]: ^?^ The proposed values for these inputs are listed in Appendix
    A.

[^992]: ^?^ HAI\'s lives and salvage values fall within the
    Commission\'s prescribed ranges with the exception of values for
    four accounts: Digital Circuit Equipment; Garage Work Equipment;
    Operator Systems; and Poles.

[^993]: ^?^ Rate of return is the percentage which a telephone carrier
    is authorized to earn on its rate base. For example, if the rate of
    return is 11.25% and the rate base is \$1 million, the carrier is
    authorized to earn \$112,500.

[^994]: ^?^ *See Local Exchange Carriers\' Rates, Terms, and Conditions
    for Expanded Interconnection Through Physical Collocation for
    Special Access and Switched Transport*, Second Report and Order, CC
    Docket No. 93-316212, FCC Rcd 18370, 18765 (1997).

[^995]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8913, para. 250.

[^996]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 50-51 (arguing
    that forward-looking cost of capital is approximately 8.5-9.0
    percent, but endorsing HAI value of 10.01 percent); GSA *Inputs
    Further Notice* comments at 6-7 (noting that GSA had recommended 9.5
    percent in rate of return proceeding); AT&T/MCI *Inputs Further
    Notice* reply comments at 41 (arguing that true forward-looking cost
    of capital is 8.64 percent); *see also* HAI June 1, 1998 comments at
    13.

[^997]: ^?^ Bell Atlantic *Inputs Further Notice* comments at 23.

[^998]: ^?^ USTA *Inputs Further Notice* reply comments at 3-4; GTE
    *Inputs Further Notice* reply comments at 34-35; *see also* BCPM
    Dec. 11 submission (advocating an 11.36 percent cost of capital).

[^999]: ^?^ *See, e.g.,* Ameritech *Inputs Further Notice* comments at
    33-34; AT&T/MCI *Inputs Further Notice* comments at 50-51 (but
    advocating adoption of different rate for model if rate prescription
    proceeding will not be concluded prior to January 1, 2000
    implementation of model); GSA *Inputs Further Notice* comments at
    6-7; USTA reply comments at 3.

[^1000]: ^?^ BCPM Dec. 11 submission at 80.

[^1001]: ^?^ *Id.* BCPM\'s model includes all of the methodologies that
    are in practice today, including: Deferred taxes; Mid-year,
    Beginning Year, and End Year placing conventions; Gompertz-Makeham
    Survival Curves; Future Net Salvage Values; Equal Life Group
    Methods; and others. The model also incorporates separate Cost of
    Debt and Equity rates, along with the Debt to Equity ratio. *Id.*

[^1002]: ^?^ HAI Dec. 11 submission at 41.

[^1003]: ^?^ *Inputs Further Notice* at para. 242.

[^1004]: ^?^ GTE *Inputs Further Notice* comments at 87.

[^1005]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 51.

[^1006]: ^?^ The expense module contains the expense values including
    plant-specific maintenance ratios and the algorithms that determine
    monthly cost per-line, given the results of all other modules.

[^1007]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8927, para. 273.

[^1008]: ^?^ *See* 47 U.S.C. § 153(37); *Universal Service Order*, 12
    FCC Rcd at 8944, para. 310.

[^1009]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8943-44, para.
    310.

[^1010]: ^?^ Self-Certification as a Rural Telephone Company, *Public
    Notice*, CC Docket No. 96-45, DA 97-1748 (rel. Sept. 23, 1997)
    (*Self-Certification Public Notice*).

[^1011]: ^?^ *See* 47 U.S.C. § 153(37).

[^1012]: ^?^ Commission Acknowledges Receipt of Letters Self-Certifying
    LECs as Rural Telephone Companies, *Public Notice*, CC Docket No.
    96-45, DA 98-1205 (rel. June 22, 1998).

[^1013]: ^?^ *Inputs Further Notice* at para. 255.

[^1014]: ^?^ *Common Carrier Bureau Extends Rural Carrier
    Recertification Filing Deadline*, Public Notice, CC Docket No.
    96-45, DA 99-1948 (rel. September 27, 1999).

[^1015]: ^?^ *Inputs Further Notice* at para. 246.

[^1016]: ^?^ *Id.* The National Exchange Carrier Association, Inc.
    (NECA) has requested that the Commission eliminate the annual rural
    certification process. NECA states that the majority of carriers
    that meet the rural definition are small LECs with limited
    resources, whose status is not likely to change. Letter from Richard
    A. Askoff, NECA to Irene Flannery, FCC, dated April 9, 1999.

[^1017]: ^?^ *Inputs Further Notice* at paras. 251-53.

[^1018]: ^?^ *Id.* at para. 254.

[^1019]: ^?^ ALLTEL *Further Inputs Notice* comments at 2; Alaska
    Telephone Association *Further Inputs Notice* comments at 2;
    Bentleyville Telephone Company *Further Inputs Notice* comments at
    1; CenturyTel *Further Inputs Notice* comments at 7; Citizens
    Utilities *Further Inputs Notice* comments at 6; GTE *Further Inputs
    Notice* comments at 91; GVNW Consulting (GVNW) *Further Inputs
    Notice* comments at 2; Matanuska Telephone Association (MTA)
    *Further Inputs Notice* comments at 1; NECA *Further Inputs Notice*
    comments at 2; Rural Telephone Coalition (RTC) *Further Inputs
    Notice* comments at 8; Skyline Telephone Membership Corporation
    *Further Inputs Notice* comments at 1, 3; South Slope Cooperative
    Telephone Company *Further Inputs Notice* comments at 1-2; TXU
    Communications Telephone Company (TXU) *Further Inputs Notice*
    comments at 6; USTA *Further Inputs Notice* comments at 6; Virgin
    Islands Telephone Corporation (Vitelco) *Further Inputs Notice*
    comments at 7; Yukon Telephone Company *Further Inputs Notice*
    comments at 1.

[^1020]: ^?^ The model also estimates cost on a wire center basis. Also,
    we note that PRTC and Anchorage Telephone Utility previously had
    been excluded from the non-rural model runs because of the
    unavailability of data for Puerto Rico and Alaska, but those
    companies have participated in the proceeding on the presumption
    that were non-rural. The formerly unavailable data is now available,
    and has been incorporated into the model posted on the Commission\'s
    web site. *See* Letter from Charles A. White, PNR, to Magalie Roman
    Salas, FCC, dated July 29, 1999 (PNR July 29 *ex parte*).

[^1021]: ^?^ *See, e.g.,* ALLTEL *Inputs Further Notice* comments at 2;
    CenturyTel *Inputs Further Notice* comments at 7; GVNW *Inputs
    Further Notice Comments* at 2; MTA *Inputs Further Notice Comments*
    at 1; NECA *Inputs Further Notice* comments at 2; TXU *Inputs
    Further Notice* comments at 6; USTA *Inputs Further Notice* comments
    at 6; TXU *Inputs Further Notice* reply comments at 4.

[^1022]: ^?^ 47 U.S.C. § 153(37)(B), (C).

[^1023]: ^?^ Most carriers asserting rural status under criteria (A) or
    (D) also claim rural status under the access line thresholds in
    criteria (B) or (C). In those cases, the Commission does not need
    additional information to verify the carrier\'s rural status.

[^1024]: ^?^ 47 U.S.C. § 153(37)(A).

[^1025]: ^?^ 47 U.S.C. § 153(37)(D).

[^1026]: ^?^ CenturyTel *Further Inputs Notice* comments at 3-4;
    Commonwealth Telephone Company (Commonwealth) *Further Inputs
    Notice* comments at 4-5; GTE *Further Inputs Notice* comments at
    92-93; USTA *Further Inputs Notice* comments at 7; USTA *Further
    Inputs Notice* reply comments at 4-5; Vitelco *Further Inputs
    Notice* comments at 8; Vitelco *Further Inputs Notice* reply
    comments at 1-4.

[^1027]: ^?^ AT&T/MCI *Inputs Further Notice* comments at 42.

[^1028]: ^?^ Citizens Utilities *Further Inputs Notice* comments at 3-5;
    RTC *Further Inputs Notice* comments at 9-11; RTC *Further Inputs
    Notice* reply comments at 2; TXU *Further Inputs Notice* reply
    comments.

[^1029]: ^?^ Citizens Utilities *Further Inputs Notice* comments at 3-5;
    RTC *Further Inputs Notice* comments at 9-11; RTC *Further Inputs
    Notice* reply comments at 2; TXU *Further Inputs Notice* reply
    comments.

[^1030]: ^?^ *See* AT&T/MCI *Inputs Further Notice* comments at 42.

[^1031]: ^?^ Study areas have been \"frozen\" since November 15, 1984,
    except where a waiver has been obtained. 47 C.F.R. § 36 (App.)
    (defining \"study area\").

[^1032]: ^?^ We further note that it appears that some carriers with
    multiple study areas within a state will have a separate corporate
    entity for each study area. As a result, for these carriers there
    would be little practical difference between the first
    interpretation and the one that we adopt.

[^1033]: ^?^ Citizens Utilities *Further Inputs Notice* comments at 6.

[^1034]: ^?^ For example, if a carrier with more than one study area
    could claim that it was rural because one of its study areas served
    less than 100,000 lines, it could, under Citizen Utilities\'
    definition of criterion (C), also claim that it was exempt from the
    obligations of 251(c) throughout its service territory.

[^1035]: ^?^ GTE *Further Inputs Notice* comments at 94-96. Section 54.5
    provides the following definition of rural area:

    A \"rural area\" is a non-metropolitan county or county equivalent,
    as defined in the Office of Management and Budget\'s (OMB) Revised
    Standards for Defining Metropolitan Areas in the 1990s and
    identifiable from the most recent Metropolitan Statistical Area
    (MSA) list released by OMB, or any contiguous non-urban Census Tract
    or Block Numbered Area within an MSA-listed metropolitan county
    identified in the most recent Goldsmith Modification published by
    the Office of Rural Health Policy of the U.S. Department of Health
    and Human Services.

    47 C.F.R. § 54.5.

[^1036]: ^?^ *See* 47 C.F.R. § 54.5.

[^1037]: ^?^ CenturyTel *Further Inputs Notice* comments at 7; Citizens
    Utilities *Further Inputs Notice* comments at 7-8; Commonwealth
    *Further Inputs Notice* comments at 5.

[^1038]: ^?^ Citizens Utilities *Further Inputs Notice* comments at 8;
    Commonwealth *Further Inputs Notice* comments at 5.

[^1039]: ^?^ 47 U.S.C. §§ 214(e)(2), 251(f)(1).

[^1040]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8936, para. 294.

[^1041]: ^?^ *Universal Service Order*, 12 FCC Rcd at 8936, para. 294.

[^1042]: ^?^ CenturyTel *Further Inputs Notice* comments at 6;
    Commonwealth *Further Inputs Notice* comments at 2; Citizens
    Utilities *Further Inputs Notice* comments at 3; GTE *Further Inputs
    Notice* comments at 96-98; RTC *Further Inputs Notice* comments at
    15; RTC *Further Inputs Notice* reply comments at 2; TXU *Further
    Inputs Notice* reply comments at 4; USTA *Further Inputs Notice*
    comments at 7.

[^1043]: ^?^ *See* 5 U.S.C. § 603. The RFA, *see* 5 U.S.C. § 601 *et
    seq*., has been amended by the Contract with America Advancement Act
    of 1996, Pub. L. No. 104-121, 110 Stat. 847 (1996) (CWAAA). Title II
    of the CWAAA is the Small Business Regulatory Enforcement Fairness
    Act of 1996 (SBREFA).

[^1044]: ^?^ *Inputs Further Notice* at paras. 257-271.

[^1045]: ^?^ *See* 5 U.S.C. § 604.

[^1046]: ^?^ *Universal Service Order*, 12 FCC Rcd. 8776.

[^1047]: ^?^ *Id.* at 8927, para. 273.

[^1048]: ^?^ *See* 47 U.S.C. § 153(37); *Universal Service Order*, 12
    FCC Rcd at 8944, para. 310.

[^1049]: ^?^ *See Inputs Further Notice* at para. 249.

[^1050]: ^?^ *See* section IX, above.

[^1051]: ^?^ 5 U.S.C. § 601(6).

[^1052]: ^?^ 5 U.S.C. § 601(3) (incorporating by reference the
    definition of \"small business concern\" in 5 U.S.C. § 632).
    Pursuant to 5 U.S.C. § 601(3), the statutory definition of a small
    business applies \"unless an agency after consultation with the
    Office of Advocacy of the Small Business Administration and after
    opportunity for public comment, establishes one or more definitions
    of such term which are appropriate to the activities of the agency
    and publishes such definition in the Federal Register.\"

[^1053]: ^?^ 15 U.S.C. § 632. *See, e.g., Brown Transport Truckload,
    Inc. v. Southern Wipers, Inc.*, 176 B.R. 82 (N.D. Ga. 1994).

[^1054]: ^?^ 13 C.F.R. § 121.201.

[^1055]: ^?^ 5 U.S.C. § 601(3).

[^1056]: ^?^ Letter *from* Jere W. Glover, Chief Counsel for Advocacy,
    SBA, *to* William E. Kennard, Chairman, FCC (May 27, 1999). The
    Small Business Act contains a definition of \"small business
    concern,\" which the RFA incorporates into its own definition of
    \"small business.\" *See* 15 U.S.C. § 632(a) (Small Business Act); 5
    U.S.C. § 601(3) (RFA). SBA regulations interpret \"small business
    concern\" to include the concept of dominance on a national basis.
    13 C.F.R. § 121.102(b). Since 1996, out of an abundance of caution,
    the Commission has included small incumbent LECs in its regulatory
    flexibility analyses. *Implementation of the Local Competition
    Provisions of the Telecommunications Act of 1996*, CC Docket, 96-98,
    First Report and Order, 11 FCC Rcd 15499, 16144‑45 (1996).

[^1057]: ^?^ *See* 47 C.F.R. § 64.601 *et seq*.

[^1058]: ^?^ FCC, *Carrier Locator: Interstate Service Providers*, at
    Figure 1 (Jan. 1999).

[^1059]: ^?^ *See* para. 447, *supra*.

[^1060]: ^?^ *See* para. 447, *supra.*

[^1061]: ^?^ *See* 5 U.S.C. § 801(a)(1)(A).

[^1062]: ^?^ *See* 5 U.S.C. § 604(b).