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                            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

     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
 
     1.   In the Telecommunications Act of 1996 (1996 Act), 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.  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.    

     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. 
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.  
        
                    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; dial equipment minutes (DEM) weighting; and the
Long-Term Support (LTS) program.  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.  After receiving the recommendations of
the Joint Board on November 7, 1996, 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.   
      
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.  The 1997 Further Notice
divided questions related to the cost models into "platform design" issues and "input value"
issues.  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.

     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.

     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.  The Bureau also issued data requests
designed to acquire information that could be useful in determining the final input values, and
conducted a series of public workshops designed to elicit further comment from interested parties
in selecting final input values.  Finally, the Bureau conducted numerous ex parte meetings with
interested parties throughout this proceeding.
     
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.  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);  (2) the HAI Model, Version 5.0a (HAI); and (3) the Hybrid Cost Proxy
Model, Version 2.5 (HCPM).  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; (2) the road surrogate method to
determine the location of non-geocoded customer locations; and (3) the use of the local
exchange routing guide (LERG) to identify the existing host-remote switch relationships.

     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.  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.  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.  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.  

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.  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.  For the most important inputs, we provided a detailed description of the
methodology that was used to arrive at the proposed values.  

     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.

          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.  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.

     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.  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.  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.

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.  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.   

     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.  After identifying efficient serving areas, the
model designs outside plant to the customer locations.  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.

     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.  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.  In addition, the Commission adopted the HAI platform module for
calculating expenses and capital costs, such as depreciation. 

     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.  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.  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.  All changes to the model
platform are posted on the Commission's Web site.

     2.   Validation

     21.       In the Universal Service Order, the Commission concluded that high-cost support
should be based on forward-looking costs.  Since that time, the Commission has continued to
work to adopt a cost model that is reasonably accurate and verifiable.  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."  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. 

     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.  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.  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.  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. 

     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. 
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."  As noted below, existing LEC plant in a
particular area may not reflect forward-looking technology or design choices.  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."  Rather, the model "must be based upon an examination of the
current cost of purchasing facilities and equipment."

     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.  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.  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.  

     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. 
Each of the models under consideration in the Platform Order provided a methodology for
determining customer locations.  The Bureau sought comment on these proposals and solicited
alternative proposals from interested parties for locating customers.

     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.  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."  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." 

     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.  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.  

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.  

     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.  We noted that
commenters generally support the use of accurate geocode data in the federal mechanism where
available.  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."  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.  

     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."  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.  PNR took initial steps to comply with this requirement in
December 1998 by making available the "BIN" files derived from the geocoded points to
interested parties pursuant to the Protective Order.  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.  In addition, PNR acknowledges that its
geocode database relies on third-party data that PNR is not permitted to disclose.
 
     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.  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.  

     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.  

     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."  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.  In the Inputs Further Notice, we tentatively adopted the
PNR road surrogate algorithm to determine customer locations.

     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.  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.  On
February 22, 1999, PNR filed a more detailed description of its road surrogate algorithm. 
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.

     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.  For each Census Block, PNR determines how many
customers and which roads are located within the Census Block.  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.  For
example, PNR excludes highway access ramps, alleys, and ferry crossings.  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.   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."

     44.       Stopwatch Maps has compiled road surrogate customer location files for six states
suitable for use in the federal mechanism.  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.  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.  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.  AT&T and MCI therefore suggest a downward adjustment to produce
more accurate outside plant cost estimates.  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. 
GTE argues that there is no evidence to conclude, therefore, that a uniform dispersion of
customers is likely to overstate outside plant costs.  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.

     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.  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.  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."  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.  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.  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.  The PNR road surrogate algorithm is also generally supported by commenters
addressing this issue.  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.  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.  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.  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."  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.  Interested parties have proposed alternative methods to comply
with this requirement.  

     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.  A household is an occupied residence, while housing units include all
residences, whether occupied or not.  

     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.  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.  The model makes these
estimates for each Census Block, and for each wire center in the United States.  In addition,
each customer location is associated with a particular wire center.  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. 
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.  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.  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.  They therefore assert that residential locations
should be based on the number of housing units -- whether occupied or unoccupied.  These
commenters contend that only this approach reflects the obligation to provide service to any
residence that may request it in the future.  

     55.       Some commenters also contend that the PNR National Access Line Model has not
been made adequately available for review.  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.  As a result, PNR contends that the
National Access Line Model cannot be provided in a single, uniform format.  The HAI sponsors
have provided a description of the National Access Line Model process in the HAI model
documentation.  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.  PNR also notes that the National Access Line Model computer
code is available for review on-site.  PNR also has filed with the Commission the complete
output of the National Access Line Model process.  In addition, Bell Atlantic and Sprint argue
that the National Access Line Model produces line counts that vary significantly from actual line
counts.

     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.  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.  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."  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.  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).  

     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.  
     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. 

     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, 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.  PNR currently uses
BLR wire center information to estimate wire center boundaries.  As noted above, the BCPM
model also uses BLR wire center boundaries, as does Stopwatch Maps in its road surrogate
customer location files.  A few commenters support the use of BLR wire center boundaries,
noting widespread use by the model proponents.  Others advocate the use of actual wire center
boundaries.  These commenters acknowledge, however, that this information is generally
considered confidential and may not be released publicly by the incumbent LEC.  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."  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.  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."  The Universal Service Order's third criterion specifies that "[o]nly long-run forward-
looking costs may be included."  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.  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.
  
     65.  Outside plant consists of a mix of aerial, underground, and buried cable.  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.  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.  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.

          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.  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.  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.  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.  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.

     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."  As we explained in the Inputs Further Notice, running the model
with the optimization routine fully activated complies with this requirement.  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.  GTE opposes any implementation of optimization.

     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.  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.  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.   
          
     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.  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.  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.   

     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.  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.  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.
     
     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.  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.  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. 
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.  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.  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.  Other
sources indicated that advanced technologies, such as HDSL, could be used to transmit
information at T-1 or higher rates.  We sought comment on this issue.  We also sought
comment on the extent to which HDSL technology presently is being used to provide T-1
service.
     
     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.  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.  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.  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.  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.  In addition, we
asked whether we should use airline miles with wire center specific road factors as an alternative
to rectilinear distance. 
     
     82.  We reaffirm our tentative conclusion that the model should use rectilinear distance
rather than airline distance in calculating outside plant distances.  As we noted in the Inputs
Further Notice, research suggests that, on average, rectilinear distance closely approximates road
distances.  We agree with SBC that the calculation of outside plant distances should reflect the
closest approximation to actual route conditions and road distance.  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.  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.

      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.  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.  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.  The Commission tentatively concluded
that cable material and installation costs should be separately identified by both density zone and
terrain type.  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.

     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.  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.  The
Commission noted that "recent installations of outside structure may more closely meet forward-
looking design criteria than do historical installations."  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.  

      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.  On December 11, 1998, the Bureau held a public workshop designed to elicit
comment on the input values for materials costs.  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.  Ten companies eventually responded to the survey.
     
     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.  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.  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.  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.  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.  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.  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.  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.
     
     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.  SBC confirms our analysis of the survey data and
notes that it deploys 24-gauge cable in sizes from 25 to 2400 pairs.  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.  
                
     96.  Distinguishing Feeder and Distribution Cable Costs.  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. 
We adopt this tentative conclusion.  Those commenters addressing this issue agree with our
tentative conclusion.  GTE contends that it is both unnecessary and inappropriate to have
different costs for feeder and distribution cable material.  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.  Similarly, Sprint agrees that the material cost of cable is the
same whether it is used for distribution or feeder.  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.  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.  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.  GTE explains that, from a material perspective, the cable may
have different protective sheathing, depending on construction applications.  GTE adds that
labor costs also differ depending on the type of placement.  Both SBC and Sprint identify the
cost of labor as varying significantly depending upon the type of placement.  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.  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. 
The Commission required incumbent LECs to provide line counts for business switched and non-
switched access lines on a voice equivalent basis and on a facilities basis.  Upon receipt of
those filings, we determined industry totals for each of the line count items requested.  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.  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.  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.  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.

     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.  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.  

     103. The HAI sponsors supported using the publicly available RUS data in the NRRI
Study to estimate cable costs and structure costs.  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.  Sprint pointed out that the RUS data only reflect information from the two
lowest density zones.  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.  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. 

     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.  

          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.   

     106. Company-specific data.  As we discussed above, we have determined to use
nationwide average input values for estimating outside plant costs.  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.
  
     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.  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.  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.  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.  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. 

     113. Methodology.  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.  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.   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.  

     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.  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.  

     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.  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. 
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.  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.  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. 
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.  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.

     121. We also disagree with AT&T and MCI's claim that the RUS data are defective
because they consist of primarily small cables.  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.  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.

     123. We also find unpersuasive the contention that there are econometric flaws in the
NRRI Study which render it unsuitable for developing input values.  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.  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.  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. 
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.  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.  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.  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.  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."  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.  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.  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.  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.  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.  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.  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.  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.  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 lies between AT&T and MCI's estimate of the
fixed cost for a 24-gauge buried copper cable of $.12 and the HAI default value for the installed
cost of a 6-pair 24-gauge buried copper cable of $.63.  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.
      
     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.  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.  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.

     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.  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.  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.  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.  In the Inputs Further Notice, we tentatively concluded that
one substantive change should be made to Gabel and Kennedy's analysis.  As we explained, we
tentatively concluded that the regression equations in the NRRI Study should be modified using
the Huber regression technique to mitigate the influence of outliers in the RUS data. 
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.

     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.  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.  

     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.  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.  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.  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. 
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.  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.  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.  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.  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). 
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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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, 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.  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.  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 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.  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.   We disagree.  As discussed above, we apply the Huber
methodology to RUS cable costs that reflect both labor and material costs.  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.  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.  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.

     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. 
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.  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.  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.  Bell Atlantic
makes a similar claim.  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.  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.  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.  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.  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.  We are estimating the
costs that an efficient provider would incur to provide the supported services.  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.  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.  As we noted, the BCM2 default values include a loading of five
percent for engineering.  In contrast, the HAI sponsors claimed that engineering constitutes
approximately 15 percent of the cost of installing outside plant cables.  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.   

     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.  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.  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.   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.  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.  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.  

     168. Splicing Adjustment.  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.  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.  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.  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.  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.

     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.  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.  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.  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."  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.  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.   

     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.  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.  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.

     175. We disagree with those commenters who argue that the splicing costs do not vary
with the cost of cable (net of splicing costs).  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.  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.  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. 
To that end, we tentatively concluded that we should derive cost estimates for 26-gauge cable by
adjusting our estimates for 24-gauge cable.  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.   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.  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.  As we explained above, we have determined that such data are not sufficiently reliable to
employ in the model.  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.  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.  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.  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.  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, 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.  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.  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.  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.  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. 

     187.      Variance Among Density Zones.  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.  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.  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.  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. 

     188.      Distribution Fill Factors.  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.  In contrast, the BCPM default fill factors for distribution cable are set at
100 percent for all density zones.  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.  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. 
 
     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.  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.

     190.       We tentatively concluded 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.  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.
          
     191.       Feeder Fill Factors.  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.  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.  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.

     192.      Fiber Fill Factors.  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. 
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. 
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. 

     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.  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.  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.  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.  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.  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.  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.  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.  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.  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.  

     200. We find unpersuasive GTE's assertion that the input values for distribution fill
factors should reflect ultimate demand.  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.  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.  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.  GTE
also claims that, in a competitive environment, facilities-based competitors would build plant to
serve ultimate demand.  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.  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.

     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.  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." 
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.  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.  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.  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.  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.  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.  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."  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.  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.  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.  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.  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.  

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

     207. Feeder Fill Factors.  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.  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.  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.  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.  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.  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.  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.  In contrast, BellSouth advocates that we employ projected fills estimated by
BellSouth engineers.  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.  We explained that aerial structure consists of
telephone poles and associated hardware such as anchors and guys.   Buried structure consists of
trenches.  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.  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.  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.  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.  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.  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.  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.  We
tentatively concluded that these are reasonable estimates for the cost of anchors, guys, and other
pole-related items.
     
     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.  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.  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.  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.  

     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.  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.

     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.  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.  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.  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.  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.

     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,  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.  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.  Cincinnati Bell states that the values we adopt for underground structure
never vary from Cincinnati Bell's actual costs by more than 15 percent.  Sprint claims that our
proposed cost of poles are understated but the costs of anchor and guys appear to be
reasonable.  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.  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.  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.  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. 
     
     225. LEC Loading Adjustment.  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.  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.  SBC
agrees that 10 percent is appropriate for aerial and buried structure but too low for underground
structure.  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.          
     
     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.  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.  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.  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.  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.  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, 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. 
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.  We sought comment on a
modified version of this algorithm as an alternative to nationwide plant mix values.
          
          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.  Similarly,
Sprint contends that company-specific or state-specific plant mix values should be used.  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.    

     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.  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.  We find these errors attributable, however, to US West's lack of
understanding of how the proposed algorithm works.  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.  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.  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."  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.   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. 
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.  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.  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.  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.  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.  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.  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.
     
     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.  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.  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. 
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."  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.  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.  In contrast, several commenters contend that the proposed values
assign too little cost to the incumbent LEC and reflect unrealistic opportunities for sharing.  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.    

     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.  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.  The
Washington Utilities and Transportation Commission, conducted an examination of these issues
and adopted sharing percentages similar to those we proposed.  

     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.  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. 
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.  Several
commenters contend that there are minimal opportunities for sharing of buried and underground
structure, particularly in lower density areas.  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.  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.  This does, however, support the contention of commenters that
there is, at most, minimal sharing of buried and underground structure in these density zones. 
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.  The sharing values that we adopt reflect these assumptions.  SBC also concurs with our
proposed structure sharing values.  In addition, as noted above, the Washington Utilities and
Transportation Commission has adopted structure sharing values that are similar to those that we
adopt.  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.  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.  In addition, Sprint submitted
cost estimates for a 7200 pair indoor SAI.  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.  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.  The Bureau also conducted a
workshop on December 11, 1998, to discuss the posted preliminary inputs.  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. 
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. 
     
     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.  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.  
     
     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.  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.  We also noted that Sprint has agreed that $4
is a reasonable estimate of the cost.   SBC and AT&T and MCI concur with our tentative
conclusion to adopt the $4 per pair cost.  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.  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.  We noted that the HAI sponsors submitted a letter from AMP Corporation, a leading
manufacturer of wire connectors, in support of the HAI rate.  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.  Sprint takes issue with the splicing rate we proposed.  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.  In sum, Sprint contends the letter represents an
"unsupported claim of someone trying to sell equipment."  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.  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. 
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.  
          
     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.  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. 
In contrast, the HAI sponsors claimed that the "punch down" method is generally used to cross-
connect.  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.
     
     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. 
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.  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.  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.  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.  
     
     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.  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.  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.  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. 
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.  We find GTE's contention, which relies on GTE's SAI estimates, unpersuasive
given the lack of substantiating data supporting these estimates.  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.  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.  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.  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.  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.  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.  These contract data included data submitted to the Commission in
response to the 1997 Data Request, and in ex parte submissions following the December 11,
1998 workshop we sponsored, to estimate the costs of DLCs in the model.  We found these
data to be the most reliable proffered at that time.  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.  Additionally, we rejected data submitted by the HAI sponsors
following the workshop.  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. 
     
     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.  We noted that, throughout this proceeding, the Commission had repeatedly
requested cost data on DLCs, largely to no avail.  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.
      
     272.      In the Inputs Further Notice, we also recognized that the cost of purchasing and
installing a DLC changes over time.  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.  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.  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.  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.  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. 
     
     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.  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.  Only AT&T and MCI and SBC challenge the use of these data.  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.  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.  AT&T and MCI instead advocate use of the
HAI default values.   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.  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. 
     
     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.  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.  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.  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. 
AT&T and MCI provide no support for such a reduction and, therefore, we decline to reduce
Sprint's labor costs.
     
     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.  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. 
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.  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.  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.  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.  GTE suggests that, as the inputs are adjusted
over time, the cost of current technology will be reflected in the revised data.  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.  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."  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.  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.  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.
     
     288.      Both HAI and BCPM sponsors have provided default input values for estimating
the forward-looking economic cost of switching and interoffice network.  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.   
     
     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.  In addition, we tentatively adopted the Local Exchange Routing Guide
(LERG) database to identify host-remote switch relationships.  

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, and the Bureau of Economic
Analysis (BEA) of the U.S. Department of Commerce.  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).  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.  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. 

     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.  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.  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.  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.  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).  

     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.  

     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.

     2.   Discussion
     
     296.      Switch Cost Estimates.  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. 
     
     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.  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.  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. 
     
     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.  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.
     
     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.  When consolidated, this information forms a data set of
approximately 300 observations representing the costs of new switches.  As AT&T has noted,
however, the information submitted contains some inconsistencies.  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.  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.  Our analysis
reveals that both alternative data sets contain information that is inconsistent with the comments
in this proceeding.
     
     304. Adjustments to the Data.  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.  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.  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.  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.  AT&T and MCI claim our proposed power costs should be reduced because they are
substantially higher than those proposed by their experts.

     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.   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.  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.  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.  Using Sprint's methodology, the average cost of terminating a RUS
remote switch on a RUS host switch is $29,840.   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.    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.  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.  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 and the comments of
Sprint, BellSouth, and the HAI sponsors.  
     
     311.      Accounting for Changes in Cost Over Time.  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 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.  
     
     313.      In order to account for cost changes unique to switching equipment, we enter time
terms directly into the regression equation.  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.  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.  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 . . ."  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.  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.  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.
     
     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.  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. 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.  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.   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.  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.  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.   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.
     
     319.      Switch Cost Estimates.  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.  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. 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.  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."  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.  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.  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.  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. 
In its default mode, HAI assumes a blended configuration of switch technologies, incorporating
both hosts and remotes, to develop switching cost curves.  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."
     
     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. 
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.  Sprint contends that there are many intangible
variables that can not be easily replicated in determining host-remote relationships. 
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." 
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.  
     
     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.  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.  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.  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.  These
investments include telephone company engineering and installation, the main distribution frame
(MDF), the protector frame (often included in the MDF), and power costs.  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.  In addition, commenters agree that the Switch Installation Multiplier should be set
at 1.0.  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.  In the Inputs Further Notice, we tentatively concluded that
the "Analog Line Circuit Offset for Digital Lines" input should be set at zero.  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.  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.  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.    

     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.  Several commenters disagree and recommend setting the analog line offset to
zero.  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.  Sprint argues that an unknown mixture of analog and digital
lines are taken into consideration in developing the switch curve.   
     
     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.  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.  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.  AT&T and
MCI contend that the default input values no longer reflect the use of the most current
technology.  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.  
     
     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.  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."  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.  
     
     330.      Switch Port Administrative Fill.  In the Inputs Further Notice, we proposed a
switch port administrative fill factor of 94 percent.  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."  HAI
assigns a switch port administrative fill factor of 98 percent in its default input values.  The
BCPM default value for the switch percent line fill is 88 percent.  
     
     331.      Bell Atlantic contends that switches have significant unassigned capacity due to the
fact that equipment is installed at intervals to handle growth.  Sprint recommends an average fill
factor of 80 percent.  US West contends that its actual average fill factor is 78 percent. 
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.  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.    
     
     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.  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."  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.  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.  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.  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."  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.  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.  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.  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.  MCI agrees that
the trunk port calculation should be deactivated in the switching module. 
     
     336.      In the Inputs Further Notice, we agreed with commenters that the trunk port input
creates inconsistencies in reducing the end office investment.  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.
     
     337.      Because the trunk port input value is also used to determine the tandem switch
investment, we must determine the trunk port investment.  In the Inputs Further Notice, we
proposed an input value for trunk port investment per end of $100.00.  SBC and Sprint contend
that this value should be higher -- ranging from $150.00 to $200.00.  BellSouth has filed
information on the record that supports our proposed trunk port investment value.  BellSouth
notes that the four states that have issued orders addressing the cost of the trunk port for
universal service have chosen estimates of the cost of the trunk port that range from $62.73 to
$110.77.  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."  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.
     
     339.      GSF costs include the investment and expenses related to vehicles, land, buildings, and general purpose computers.  Other expenses
include:  plant-specific operations expenses, plant non-specific expenses, corporate operations expenses, and customer services expenses.  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.  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.  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, and that the input values for common support services expenses should be estimated on a per-line basis.  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.  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.
     
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.  In
the Inputs Further Notice, we proposed a methodology for estimating expense-to-investment ratios consisting of four steps.  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.  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.  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.  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.  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.  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.  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.  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.
     
     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.   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.  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.  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.
     
     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.  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.  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. 
     
     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.  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.
     
     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.
     
     347.      Current Data.  Parties commenting on whether we should update our methodology using more current ARMIS data agree that we should
use the most currently available data.  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.  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.
     
     348.      Nationwide Estimates.  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.  In contrast, incumbent LEC commenters argue that we should use company-specific values.
     
     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 . . .."  BellSouth
argues that the "only logical starting point for estimating forward-looking expenses is the current actual expenses of the ILECs."  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.
     
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."  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.  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."  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.  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.  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." 
GTE claims that the use of the national average penalizes efficient companies that operate in high-cost areas.  
     
     353.      Similarly, Sprint contends that the use of nationwide estimated data does not accurately depict the realities of operating in Sprint's service
territories.  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.  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.  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.  AT&T and MCI claim that "the most significant driver of cost differences
between carriers in the ARMIS study area data is efficiency."  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.  SBC asserts that "[t]hese
differences are not in all instances attributable to inefficient operations."
     
     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.  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.  GTE's proposed solution for reflecting variations among states is
simply to use company-specific data.  Indeed, none of the LECs propose a specific alternative to using self-reported information from companies.  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.  
     
     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.  As parties in this proceeding have noted, 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 would be exceedingly time
consuming and complicated given the number of inputs to the model.  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.  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.   Bell Atlantic argues that we should use the most specific
data inputs that are available, whether region-wide, company specific, or study-area specific.   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.  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.
     
     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.  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, 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.

     361.      Estimating regional wage differences.   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.  GTE, for example, claims that
regional wage rate differentials are reflected in the company-specific data available from ARMIS.  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.  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.  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.

     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.  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.  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.

     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.  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.  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.  BellSouth states that "[t]he methodology proposed by the Commission for plant-specific expenses is very similar to the
methodology employed by BellSouth."
     
     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.  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.
     
     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.  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.  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.  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.  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.  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.  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.  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.  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.  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.
     
     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.  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.
     
     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.  SBC argues that expenses do not vary simply because investment varies.  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.  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. 
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.
     
     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.  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.  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. 
     
     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. 
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.  AT&T and MCI dispute Sprint's claim that maintenance costs per unit of plant increase over time.  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.  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.  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.  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.  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.
     
     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.  In the regression methodology, we used publicly available 1996 ARMIS expense data and minutes of use
information from NECA, by study area, to estimate the portion of these company-wide expenses that should be supported by the federal high-cost
mechanism.  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).  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.  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.  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.  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).  
     
     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.  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.  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.  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.  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.  We sought comment on how to identify, estimate, and remove these one-time non-recurring
expenses.
     
     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.  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).  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.
     
     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.  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.  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).  As
discussed below, we use an alternative methodology to estimate expenses for ARMIS account 6610 (Marketing).  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.
     
     383.      Current Data and Use of Productivity Factor.  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.  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.  Specifically, we proposed using a productivity factor of 6.0 percent for the years 1997 and 1998 to reduce the estimated input
values.  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.
     
     384.      AT&T and MCI claim that the 6.0 productivity factor is too low, while most LEC commenters contend that it is too high.  Sprint
argues that expenses should not be adjusted for a productivity or an inflation factor and that we should use 1998 data.  GTE argues that no productivity
adjustments are necessary, if we use current, company-specific ARMIS data to develop input values.  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.  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.  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.  This is in comparison to the 91 study
areas resulting from pooling the 1996 data described in the Inputs Further Notice.  

     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.  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.  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.  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.  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.  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.  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.  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.  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.  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 R2 is fairly low, which implies that the causal relationship only explains a small portion of the total costs.  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.  Because of this mis-specification and the omitted variables, GTE also claims that our equations have a low predictive ability, as
measured by the R2s.

     389.      We disagree with commenters who claim that there is little explanatory value in our regression analysis.  In accounts 6620, 6700, 6530
the regressions explain a high degree of the variability in the expense variables.  Only account 6510 (Other Property, Plant, and Equipment) has a low R2, 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.

     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.  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.  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.  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.  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.  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.

     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.  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. 
     
     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.

     394.      Some commenters also criticized our regression analysis on the grounds that variables are highly correlated and that the predicted
coefficients are not stable.  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.  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.  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.  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.  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.  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.  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.  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.  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.  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. 
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.  GTE claims that unless companies implement
specific tracking mechanisms, these data are not generally or easily identified after the fact.

     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.  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.  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.  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,coin operations, published directory, access billing, interexchange carrier
office operation, and service order processing.  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.  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.  As explained in the Inputs Further Notice, we made an adjustment to the Account 6610 (Marketing) regression coefficient
based on an analysis made by Economics and Technology, Inc. (ETI).  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.  
     
     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.  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.  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.  Moreover, we note that under the current high cost loop support mechanism, carriers receive no
support for marketing.
     
     406.      We received further documentation and an alternative analysis from ETI which included an estimate for advertising expenditures.  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.  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. 
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.  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.  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.  They argue that we should use the line-weighted nationwide average of approved LNP rates, which they estimate currently is
$.032.  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.  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.  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.  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.  Third, the model reduces the preliminary GSF investment estimates for each account by multiplying these
estimates by one of two factors. 
     
     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.  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.  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.
     
     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.  Several LEC commenters argue that the preliminary GSF investment should not be reduced by
an allocator in the third step of the algorithm.  SBC contends that the factor we use to reduce our preliminary GSF investment estimates substantially
underestimates the GSF amounts related to the supported services.  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.  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.  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.   
     
     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.  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.  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.  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.  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.  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.  

     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.  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.  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.  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.  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.  GTE argues that the type of average in the numerator and denominator should match.  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.
     
     417.      Clarification.  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.  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.  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.  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.

     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.  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.  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.  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.  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.  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.  The May 4 Public Notice requested further information on these issues.    

     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.  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.
     
     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.

     423. Most commenters support our tentative conclusion to use the straight-line equal-life-group method of depreciation.  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." 
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. 

     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.  The HAI model assumed a single curve shape for all plant
accounts.  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.  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.  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.

     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.  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.  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.  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.  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.  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.  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.  This difference has allowed depreciation reserves to increase so that the
depreciation reserve-ratio is currently greater than 50 percent.  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 or on similar values currently used by
LECs for financial reporting purposes.  The LEC industry data survey's projected lives generally fall outside of the Commission's prescribed ranges.  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.  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.  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.  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.  

     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.  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, 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 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.  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.

     434.      GSA, AT&T and MCI comment that the cost of capital for incumbent LECs is well below 11.25 percent.  Bell Atlantic advocates a
cost of capital rate in the range of 12.75 to 13.15 percent.  GTE and USTA dispute the lower cost of capital asserted by AT&T and MCI and GSA.  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.

     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.  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.  HAI's model is also user adjustable and reflects
the sum for the three inputs:  depreciation, cost of capital, and maintenance costs.  In the Inputs Further Notice, the Commission tentatively adopted HAI's
annual charge factor methodology, and invited comment on this tentative decision.  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.  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.
     
     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.  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.  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.  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.  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.  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.  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.  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.  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.

     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.  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.  We
also sought comment on certain terms relevant to the definition of a rural telephone company in section 153(37) of the Act.  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.

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.  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.  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.  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.  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.  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, 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.  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.  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]."

     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.  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.  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.  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.  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.  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. 

     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.

     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 study area in which it operates contains fewer than 100,000 access lines.  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.  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.  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.  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, and some specifically reject the use of the Commission's definition in section 54.5
because of the added complication of its use.  

     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.  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.  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.  

     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.  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), an Initial Regulatory Flexibility Analysis (IRFA) was incorporated in the Inputs
Further Notice.  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.

     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.  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.  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.  We have found that carriers self-certifying as rural have
not always applied section 153(37) uniformly.  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, 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."  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.  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.  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.
     
     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." 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. 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.  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).  According to our most recent data, 1,410 companies reported that they were engaged in the provision of local
exchange service as incumbents.  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.  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.  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.   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.

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.