# Federal-State Joint Board on Universal Service; Forward-Looking Mechanism for High Cost Support for Non-Rural LECs

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URL: https://www.frixlaw.com/law-library/documents/fr%3A99-30877

## Record

- **Collection:** Federal Register
- **Document type:** Rule
- **Published:** December 1, 1999
- **Citation:** 64 FR 67372

## Text

SUMMARY: This document concerning the Federal-State Joint Boar on
Universal Service and Forward-Looking Mechanism for High Cost Support
for Non-Rural LECs completes the selection of a model to estimate
forward-looking cost by selecting input values for the synthesis model
the Commission previously adopted.

DATES: Effective December 1, 1999.

FOR FURTHER INFROMATION CONTACT: Richard Smith, Attorney, Common
Carrier Bureau, Accounting Policy Division, (202) 418-7400.

SUPPLEMENTARY INFORMATION: This is a summary of the Commission's Tenth
Report and Order in CC Docket Nos. 96-45 and 97-160 released on
November 2, 1999. The full text of this document is available for
public inspection during regular business hours in the FCC Reference
Center, Room CY-A257, 445 Twelfth Street, S.W., Washington, D.C. 20554.
The full text of this document is also available on the Internet:
www.fcc.gov/ccb/universal__service.

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,
62 FR 32862 (June 17, 1997), 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 in the final rule document published elsewhere in
this issue of the Federal Register.

II. Determining Customer Locations

A. Customer Location Data

1. Geocode Data
3. While we affirm our conclusion in the Platform Order, 63 FR
63993 (November 18, 1998), 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 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.
4. 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.
5. In the Universal Service Order, 62 FR 32862 (June 17, 1997), 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,
63 FR 42753 (August 11, 1998). 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.
6. Consistent with our tentative conclusion in the Inputs Further
Notice, 64 FR 31780 (June 14, 1999), 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

[[Page 67373]]

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
7. 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.
8. 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.
9. 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.
10. 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.''
11. 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.
12. 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.
13. 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, 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.
14. 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

[[Page 67374]]

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
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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.
22. Some commenters also contend that the PNR National Access Line
Model has not been made adequately available for review. As noted, 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.
23. 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

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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,
we believe that PNR's methodology based on households is less complex
and more consistent with a forward-looking methodology than housing
units.
24. 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).
25. 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.
26. 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.
27. 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.
28. 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.
29. 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, 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

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use of the BLR wire center boundaries in the current customer location
data set.

III. Outside Plant Input Values

A. Introduction

30. 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.
31. 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.
32. 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

33. 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
34. 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.
35. 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.
36. 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.
37. 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.
38. As explained, 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.
39. 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

[[Page 67377]]

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.
40. 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.
41. 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.
42. 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.
43. 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
44. 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.
45. 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.
46. 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
47. 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.
48. 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.
49. We reaffirm our tentative conclusion that the model should use
rectilinear distance rather than airline distance in calculating
outside plant

[[Page 67378]]

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. Nationwide Values
50. 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.
51. 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.
52. 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.
53. Where possible, we have tried to account for variations in
costs by objective means. As explained, the model reflects differences
in structure costs by using different values for the type of plant, the
density zone, and geological conditions. As discussed, 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.
2. Preliminary Cable Cost Issues
54. 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.
55. 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.
56. 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

[[Page 67379]]

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.
57. 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.
58. 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.
59. 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.
60. 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.
3. Cost Per Foot of Cable
61. We affirm our tentative conclusion that we should use, with
certain modifications, 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, 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.
62. Company-specific data. As we discussed, 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. 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.
63. 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.
64. 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.
65. 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, the
task of the model is to

[[Page 67380]]

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.
66. 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.
67. 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.
68. 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.
69. 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.
70. 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. 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.
71. 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, 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.
72. 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, 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.
73. 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.
74. Finally, as noted, 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.
75. 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

[[Page 67381]]

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, 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.
76. 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.
77. 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.
78. 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.
79. 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, 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, 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.
80. We also disagree with GTE's contention that the NRRI Study
contains three methodological errors that make its results unreliable.
First, GTE asserts

[[Page 67382]]

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.
81. 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, 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.
82. 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, 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.
83. 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.
84. 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.
85. 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.

[[Page 67383]]

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.
86. 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.
87. 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.
88. 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. 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.
89. 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.
90. 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.
91. 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.
92. 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.
93. 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

[[Page 67384]]

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.
94. 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
95. 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.
96. 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, 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.
97. 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.
98. 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, 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.
99. 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

[[Page 67385]]

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.
100. 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.
101. We find that the estimates produced by applying the Huber
methodology are reasonable. 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.
102. 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, 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.
103. 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.
104. 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.
105. 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.
106. 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, 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.
107. 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,

[[Page 67386]]

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, the levels of the
adjustments we proposed and adopt are the most conservative based on
the available record evidence.
108. 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, 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.
109. 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.
110. 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.
111. 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, 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, 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.
112. 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.
113. 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.
114. 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

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available evidence on the record on which to base the buying power
adjustment.
115. 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.
116. 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.
117. 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.
118. 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.
119. 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.
120. 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.
121. 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

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Source: Frix Law Library, https://www.frixlaw.com/law-library/documents/fr%3A99-30877. Public record. Not legal advice.