[Federal Register Volume 64, Number 230 (Wednesday, December 1, 1999)]
[Rules and Regulations]
[Pages 67372-67416]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 99-30877]
[[Page 67371]]
_______________________________________________________________________
Part II
Federal Communications Commission
_______________________________________________________________________
47 CFR Parts 36, 54 and 69
Federal-State Joint Board on Universal Service; Forward-Looking
Mechanism for High Cost Support for Non-Rural LECs; Final Rules
Federal Register / Vol. 64, No. 230 / Wednesday, December 1, 1999 /
Rules and Regulations
[[Page 67372]]
FEDERAL COMMUNICATIONS COMMISSION
47 CFR Parts 36, 54, and 69
[CC Docket Nos. 96-45 and 97-160; FCC 99-304]
Federal-State Joint Board on Universal Service; Forward-Looking
Mechanism for High Cost Support for Non-Rural LECs
AGENCY: Federal Communications Commission.
ACTION: Final rule.
-----------------------------------------------------------------------
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
[[Page 67375]]
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
[[Page 67376]]
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
[[Page 67387]]
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
engineering and splicing costs) to approximate the cost of LEC
engineering. We find that, based on the record before us, the proposed
LEC engineering adjustment, as modified, is appropriate. We also find
that the level of the adjustment we proposed is reasonable. We note
that there is a general consensus among the commenters that the
proposed adjustment is necessary. We reject, however, the contentions
of those commenters that advocate that the level of the LEC adjustment
be based on company-specific data. As we explained, we find such data
to be unreliable. For similar reasons, we reject the LEC engineering
adjustment proposed by AT&T and MCI. As we explained, AT&T and MCI's
proposal is based on expert opinions which we find to be unsupported
and, therefore, unreliable. Accordingly, the level of the adjustment
that we proposed, which, as we explained in the Inputs Further Notice
represents the mid-point between the HAI default loading and the BCPM
default loading, is the most reasonable value on the record before us.
[[Page 67388]]
122. Sprint contends that we should calculate the loadings for LEC
engineering on a flat dollar basis rather than on a fixed percentage of
the labor and material costs of cable. We find persuasive Sprint's
contention that LEC engineering costs do not vary with the size of the
cable and therefore do not vary with the cost of the cable.
Accordingly, we find it reasonable to apply the loading for LEC
engineering in the manner that Sprint recommends.
123. We also find that the commenters are correct that the loading
for LEC engineering should not reflect any adjustment for buying power
because the buying power differential between non-rural and rural LECs
only relates to materials. We adjust our calculation accordingly.
Similarly, we also find it appropriate to include in the loading for
LEC engineering an allowance for LEC engineering associated with
splicing. We find that this is appropriate because the loading for LEC
engineering is based on BCPM and HAI default values for this loading
that are expressed as a percentage of cable costs inclusive of
engineering.
124. Splicing Adjustment. The third adjustment to the regression
equations that we proposed in the Inputs Further Notice was to account
for splicing costs, which also were not included in the RUS data. As we
explained, Gabel and Kennedy determined that the ratio of splicing
costs to copper cable costs (excluding splicing and LEC engineering
costs) is 9.4 percent for RUS companies in the NRRI Study. Similarly,
Gabel and Kennedy determined that the ratio of splicing costs to fiber
cable costs (excluding splicing and LEC engineering costs) is 4.7
percent. Thus, we tentatively concluded that we should adopt a loading
of 9.4 percent for splicing costs for 24-gauge aerial copper cable, 24-
gauge underground copper cable, and 24-gauge buried copper cable.
Correspondingly, we tentatively concluded that we should adopt a
loading of 4.7 percent for splicing costs for aerial fiber cable,
underground fiber cable, and buried fiber cable.
125. We affirm these tentative conclusions. We find that, based on
the record before us, the splicing cost adjustment is appropriate and
the levels of the adjustments proposed are reasonable. In reaching this
conclusion, we reject the claims of those commenters that advocate the
use of company-specific data to develop the splicing loadings. For the
reasons enumerated, we find such data unreliable.
126. We disagree with GTE's claim that, because the splicing factor
is based on the RUS data, it is flawed. This contention echoes GTE's
assertion that we should use company-specific data. As we explained,
however, we conclude that such data are not reliable. We also disagree
with GTE's contention that an analysis of the source contract data
shows that some splicing costs are invalid. GTE is mistaken. The RUS
cost data from which the regression equations in the NRRI Study and in
this Order are derived exclude splicing costs. Cable cost estimates
obtained by using this methodology and these data are net of LEC
engineering and splicing costs. We add to these cable cost estimates a
loading factor for splicing that Gabel and Kennedy developed separately
using the RUS data in the NRRI Study without using the regression
analysis. In the NRRI Study, Gabel and Kennedy determined the ratio of
splicing to cable costs by comparing the cost for splicing and the cost
for cable (exclusive of splicing and LEC engineering costs) reflected
in the contracts included in the RUS data base. Some of the splicing
costs reflected in this database are relatively high and some are
relatively low. None of these high or low values is likely to influence
significantly this ratio because it reflects a large number of
observations. Accordingly, we find it reasonable to apply the splicing
ratios developed in the NRRI Study to the cable cost estimates
developed separately in this Order by using the Huber methodology with
the RUS data.
127. We also disagree with AT&T and MCI's contention that, rather
than adopting the proposed splicing loadings or the incumbent LEC's
loading factors, we should adopt ``reasonable values for the costs of
cable placing, splicing, and engineering based on the expert opinions
submitted in this proceeding.'' As discussed, we find that these expert
opinions are unsupported, and therefore unreliable.
128. For the same reason, we also find unpersuasive AT&T and MCI's
claim that the loading of 9.4 percent for splicing copper cable is
excessive. AT&T and MCI estimates that splicing costs vary between 3.4
and 6.9 percent of cable investment in contrast to the proposed rate of
9.4 percent. We find that these estimates, which rely on assumptions
concerning the per-hour cost of labor, the number of hours required to
set up and close the splice, the number of splices per hour, and the
distance between splices, are unreliable. AT&T and MCI have provided no
evidence other than the unsupported opinions of their experts to
substantiate these data. In contrast, Bell Atlantic supports the use of
the 9.4 percent loading indicating, that this level is consistent with
its own data.
129. While Sprint agrees that a splicing loading is required in the
NRRI regression, Sprint recommends that a flat dollar ``per pair per
foot'' cost additive should be employed rather than the adjustment we
proposed. We disagree. We find that Sprint's flat dollar ``per pair per
foot'' cost additive ignores the differences in set-up costs among
different cable sizes. In contrast, the percent loading for splicing
costs we adopt herein implicitly recognizes such differences because
these loadings are applied to cable costs estimates (exclusive of
splicing and LEC engineering costs) derived from regression equations
that have an intercept term that provides a measure of the fixed cost
of cable. Accordingly, we conclude that the percent loading approach is
more reasonable.
130. Sprint also asserts that underground splicing costs are higher
due to the need to work in manholes. We agree. The dollar amounts
associated with the fixed percentage loadings adopted in this Order for
underground copper and fiber cable are generally larger than for aerial
and buried copper cable and fiber cable. The dollar amounts that we
adopt for splicing are generally larger for underground cable because
the costs that we develop from RUS data for underground cable net of
splicing and engineering costs are generally larger than the costs that
we develop for aerial and buried cable net of splicing and engineering
costs. As a result, when the fixed percentage is applied to these cable
costs, the dollar amount for splicing is generally larger for
underground cable than for aerial and buried cable.
131. We disagree with those commenters who argue that the splicing
costs do not vary with the cost of cable (net of splicing costs). We
find that cable costs increase as the size of the cable increases.
Splicing costs increase as the size of the cable increases because
larger cables require more splicing than small cables. Therefore,
splicing costs increase as the cost of the cable increases.
132. Finally, we disagree with SBC's claim that the 14 percent
splicing factor for fiber cable is more appropriate than the 4.7
percent we proposed. We find that the 14 percent factor SBC proposes is
unsupported. SBC asserts that this factor is based on an average cost
ratio from an analysis using various lengths of underground fiber
placement, including placing labor and comparing it to associated
splicing costs from
[[Page 67389]]
current cost dockets. However, SBC has not provided this analysis on
the record.
133. 26-Gauge Copper Cable. In the Inputs Further Notice, we
explained that, because the NRRI Study did not provide estimates for
26-gauge copper cable, we must either use another data source or find a
method to derive these estimates from those for 24-gauge copper cable.
To that end, we tentatively concluded that we should derive cost
estimates for 26-gauge cable by adjusting our estimates for 24-gauge
cable. We proposed to estimate these ratios using data on 26-gauge and
24-gauge cable costs submitted by Aliant and Sprint and the BCPM
default values for these costs. We noted, that while we would prefer to
develop these ratios based on data from more than these three sources,
we tentatively concluded that these were the best data available on the
record for this purpose.
134. We affirm our tentative conclusion to derive cost estimates
for 26-gauge cable by adjusting our estimates for 24-gauge cable. As we
explained in the Inputs Further Notice, we agree with the BCPM sponsors
that the cost of copper cable should not be estimated based solely on
the relative weight of the cable. Instead, we proposed to use the
ordinary least squares regression technique to estimate the ratio of
the cost of 26-gauge to 24-gauge cable for each plant type (i.e.,
aerial, underground, buried). We conclude that, based on the record
before us, this approach is reasonable.
135. Consistent with their position on estimating the costs of 24-
gauge cable, many commenters advocate that we use company-specific data
to estimate the costs of 26-gauge cable. As we explained, we have
determined that such data are not sufficiently reliable to employ in
the model. Accordingly, we reject the use of company-specific data to
estimate the costs of 26-gauge cable. We note that AT&T and MCI endorse
the derivation of cost estimates for 26-gauge cable from estimates for
24-gauge cable. Notwithstanding their support of the general approach
we proposed, AT&T and MCI oppose estimating the ratio of costs of 26-
gauge cable to 24-gauge cable using the cable costs submitted by Aliant
and Sprint and the BCPM default values. Instead, AT&T and MCI advocate
the use of the relative weight of copper to adjust the cost of the 24-
gauge copper. AT&T and MCI claim that this approach is the most logical
because 26-gauge copper costs are directly proportional to the weight
of the metallic copper in the cable. We reject AT&T and MCI's
recommended approach. We find that, because AT&T and MCI have provided
no evidence that the weight differential is approximately equal to the
price differential, there is insufficient evidence on the record
demonstrating the reasonableness of this approach.
136. Many of those commenters advocating the use of company-
specific data contend that there are flaws in the methodology adopted
herein to derive cost estimates for 26-gauge cable by adjusting our
estimates for 24-gauge cable. Bell Atlantic and GTE contend that our
methodology results in biased estimates due to statistical error. We
agree and modify our proposed methodology as explained.
137. As we explained in the Inputs Further Notice, in order to
derive the 26-gauge copper cable costs, we first estimated the cost for
24-gauge copper cable for each cable size from the RUS data using the
Huber methodology. More specifically, we obtained an estimate of the
expected or mean value of the cost for 24-gauge copper cable (for given
values of the independent variables in the regression equation). We
then obtained values for the ratio of 24-gauge copper cable to 26-gauge
copper cable for each cable size using ex parte data obtained from
Aliant and Sprint and BCPM default values for the costs and employing
ordinary least squares regression analysis. As a result, we obtained an
estimate of the expected value of the ratio of 24-gauge copper cable to
26-gauge copper cable (for given values of the independent variables in
the regression equation). Finally, we multiplied the reciprocal of this
ratio by the cost of 24-gauge copper cable obtained by using the Huber
methodology with RUS data to obtain the proposed 26-gauge copper cable
cost for each copper cable size. Bell Atlantic and GTE contend, and we
agree, that this is a biased estimate of the expected value of the cost
for 26-gauge copper cable because the expected value of the ratio of
two random variables, e.g., 26-gauge copper cable cost and 24-gauge
copper cable, does not equal the ratio of the expected value of the
first random variable to the expected value of the second random
variable. We note that the magnitude of the bias is larger as the
difference grows between the expected value of the ratio of 26-gauge
copper cable cost to 24-gauge copper cable cost and the ratio of the
expected value of 26-gauge copper cable cost to the expected value of
24-gauge copper cable cost.
138. Accordingly, we modify the methodology tentatively adopted in
the Inputs Further Notice to derive estimates of 26-gauge copper cable
costs from 24-gauge copper cable costs that are not biased. In addition
to estimating the expected value of the cost for 24-gauge copper cable
for each cable size using the RUS data, we also estimate the expected
value of the costs of 24-gauge and 26-gauge copper cable for each cable
size using the data submitted by Aliant and Sprint and the BCPM default
values, as well as data submitted by BellSouth, hereinafter identified
in the aggregate as ``the non-rural LEC data.'' We divide the estimate
of the expected value for 24-gauge copper cable cost derived from the
non-rural LEC data into the estimate of the expected value for 26-gauge
copper cable cost derived from these data for each cable size. The
result is a ratio of an estimate of the expected value for 26-gauge
copper cable cost to an estimate of the expected value for 24-gauge
cable cost for each cable size. Finally, we multiply this ratio by the
estimate of the expected value of the cost for 24-gauge copper cable
derived from the RUS data to obtain an estimate of the expected value
of the cost for 26-gauge copper cable for each cable size. We find that
this adjustment eliminates the bias identified by the commenters. We
conclude, therefore, that these estimates are reasonable and adopt them
as inputs for 26-gauge copper cable costs.
139. We note that, in adopting these modifications, we find that it
is reasonable to rely on the non-rural LEC data for calculating the
ratio of the cost for 24-gauge copper cable to that for 26-gauge copper
cable, but not for calculating the absolute cost for 24-gauge copper
cable and 26-gauge copper cable. As discussed, we find that the non-
rural LEC data are not a reliable measure of absolute costs.
Notwithstanding this finding, we conclude that it is reasonable to use
the non-rural LEC data to determine the relative value of the cost for
24-gauge copper cable to that for 26-gauge copper cable. We find that
it is reasonable to conclude that each LEC used the same methodology to
develop both 24-gauge and 26-gauge copper cable costs. Accordingly, any
bias in the costs for 24-gauge and 26-gauge copper cable that results
from using a given methodology is likely to be in the same direction
and of a similar magnitude. As a consequence, the estimate of the
expected value of the cost for 26-gauge copper cable for each cable
size and the estimate of the expected value of the cost for 24-gauge
copper cable obtained from non-rural LEC data are likely to be biased
by approximately the same factor. The ratios of the estimates of these
expected values are not likely to be affected significantly because the
bias in one estimate approximately cancels
[[Page 67390]]
the bias in the other estimate when the ratio is calculated.
140. GTE also contends that the proposed methodology systematically
reduces the amount of labor associated with placing cable. We conclude
that the adjustments made in response to GTE and Bell Atlantic's
criticisms discussed render this criticism irrelevant. We find that no
systematic bias will result because the ratio of the 24-gauge cost of
copper cable to the cost of 26-gauge copper cable represents the
installed cost of 26-gauge copper cable including all labor and
materials divided by the installed cost of 24-gauge copper cable
including all labor and materials. Moreover, this ratio is applied to
the installed cost of 24-gauge copper cable which includes all labor
and material costs.
141. BellSouth claims that neither the data used to develop the
ordinary least squares regression equation we employ in the Inputs
Further Notice to estimate the cost of 26-gauge copper cable or the
computations used to derive that equation have been provided. BellSouth
contends that, as a result, it is not possible to confirm or contradict
the discount value. We disagree. Contrary to BellSouth's assertion, the
data are available. As we explained, the regression equation uses ex
parte data submitted by Aliant and Sprint. These data are available
subject to the Commission's rules regarding the treatment of
confidential material. We also note that the BellSouth data we employ
in the adjusted methodology we adopt herein are publicly available.
Moreover, the BCPM data are publicly available.
4. Cable Fill Factors
142. We affirm our tentative conclusion that fill factors for
copper cable should be lower in the lowest density zones.
Significantly, those commenters addressing this issue agree that lower
density zones should utilize lower copper cable fill factor inputs. We
also reject, at the outset, certain assertions made by GTE and others,
challenging the overall approach we proposed and adopt herein for
determining the appropriate cable fill factors to use in the federal
mechanism and reject GTE's assertions that the model is flawed.
143. We disagree with GTE's assertion that the use of generalized
fill factors are not proper inputs for a cost model that seeks to
estimate the forward-looking costs of building a network. GTE claims
that the use of generalized fill factors disregards how actual
distribution plant is designed and that different levels of utilization
are observed in different parts of the local network. However, we find
that GTE's concerns are misplaced. Contrary to GTE's implication,
generalized fill factors are an administrative input and are not the
sole determinate of the effective fill factor. As we explained in the
Inputs Further Notice, the effective fill factor will vary with the
number of customer locations and the available discrete size of cable.
Thus, the effective fill factor will reflect how distribution plant is
designed and different levels of utilization that are observed in
different parts of the local network.
144. Similarly, we disagree with GTE's assertion that company-
specific information should be used to determine appropriate fill
factor inputs. We note that the final effective fill factors are the
result of the input of the administrative fill factors and company-
specific customer location data. We also disagree with the contention
that administrative fill factors must be company-specific. The
administrative fill factors are determined per engineering standards
and density zone conditions. These factors are independent of an
individual company's experience and measured effective fill factors.
The administrative fill factors would be the same for every efficient
competitive firm.
145. We reject GTE's contention that the model should be modified
to accept the number of pairs per location to determine the required
amount of distribution plant rather than using fill factors. GTE claims
that this is necessary because using fill factor inputs produces
anomalous results. GTE contends that the use of fill factors causes the
number of implicit lines per location to decrease as density increases,
in contrast to what occurs in reality. There are, according to GTE,
always more business customers in higher density zones; therefore, the
number of lines that must be provisioned per location should increase
as density increases.
146. We find that there is no need to modify the model to accept
pairs per location rather than fill factors, as GTE contends. The
number of implicit lines per location does not decrease in the model as
GTE claims. On the contrary, the number of implicit lines per location
increases as a function of the number of business lines. The model will
build to the level of business demand. With business demand increasing
as a function of density, the model generates a higher number of lines
per location as density increases. In sum, the anomaly that GTE
identifies does not exist. GTE's claim reflects a misunderstanding of
the model's operation.
147. Finally, we disagree with GTE's assertion that there is an
error in the way the model calculates density zones that prevents
correct application of zone-specific inputs. As GTE explains, after the
model has assigned customer locations to clusters, it constructs a
``convex hull'' around all locations in the cluster. The model then
calculates density as the lines in the cluster divided by the area
within the convex hull. GTE claims that the calculated densities will
be higher than those observed in the real world because the denominator
excludes all land not contained in the convex hull. While we agree with
GTE's description of how the model determines cluster density, we find
GTE's claim that this methodology is erroneous to be misplaced. In sum,
GTE argues that the model employs a restricted definition of area which
causes the model to use excessively high utilization factors. In other
words, the issue is whether the model should recognize all of the area
around a cluster. We conclude that it should not. If the land outside
the convex hull were included in the denominator, as GTE implies it
should, the denominator would recognize unoccupied areas where no
customers reside. As a result, the model would select density zone fill
factors that are lower than needed to service the customers in that
cluster. There would be a downward bias in the model fill factors.
Thus, there is not an error in the way the model calculates density
zones, as GTE contends. The model generates density values that
correspond to the way the population is dispersed. To do otherwise
would introduce a bias and distort the forward-looking cost estimates
generated by the model.
148. Distribution Fill Factors. We also affirm our tentative
conclusion that the fill factors selected for use in the federal
mechanism generally should reflect current demand and not reflect the
industry practice of building distribution plant to meet ultimate
demand. As we explained in the Inputs Further Notice, the fact that
industry may build distribution plant sufficient to meet demand for ten
or twenty years does not necessarily suggest that these costs should be
supported today by the federal universal service support mechanism.
149. We find unpersuasive GTE's assertion that the input values for
distribution fill factors should reflect ultimate demand. In concluding
that the fill factors should reflect current demand, we recognized that
correctly forecasting ultimate demand is a speculative exercise,
especially because of rapid technological advances in
[[Page 67391]]
telecommunications. For example, we note that ultimate demand decreases
substantially when computer modem users switch from dedicated lines
serving analog modems to digital subscriber lines where one pair of
copper wire provides the same function as a voice line and a separate
dedicated line. Given this uncertainty, we find that basing the fill
factors on current demand rather than ultimate demand is more
reasonable because it is less likely to result in excess capacity,
which would increase the model's cost estimates to levels higher than
an efficient firm's costs and could potentially result in excessive
universal service support payments.
150. Significantly, we note that, contrary to GTE's inference,
current demand as we define it includes an amount of excess capacity to
accommodate short-term growth. We find that GTE has not provided any
evidence that demonstrates that the level of excess capacity to
accommodate short-term growth is unreasonable. Rather, GTE claims that,
if distribution is not built to reflect ultimate demand there will be
delays in service and increased placement costs due to the need to
reinforce distribution plant in established neighborhoods on a regular
basis. GTE also contends that telephone companies do not design
distribution plant with the expectation that it will require
reinforcement because that is rarely the least-cost method of placing
plant. GTE also claims that, in a competitive environment, facilities-
based competitors would build plant to serve ultimate demand. We find,
however, that these unsupported claims do not demonstrate that
reflecting ultimate demand in the fill factors more closely represents
the behavior of an efficient firm and will not result in the modeling
of excess capacity. Finally, we find that we did not misinterpret the
meaning of building distribution plant to serve ``ultimate demand,'' as
GTE asserts. Rather, we refused to engage in the highly speculative
activity of defining ``ultimate demand.'' Moreover, we believe that
universal service support will be determined more accurately
considering current demand, and not ultimate demand. Although firms may
have installed excess capacity, it does not follow that the cost of
this choice should be supported by the universal service support
mechanism. As growth occurs, however, we anticipate that the
requirement for new capacity will be reflected in updates to the model.
151. Concomitantly, we adopt the proposed values for distribution
fill factors. As we explained in the Inputs Further Notice, the model
designs outside plant to meet current demand in the same manner as the
HAI model. Accordingly, it is appropriate to choose fill factors that
are set at less than 100 percent. We conclude that, based on the record
before us, the proposed values reflect the appropriate fill factors
needed to meet current demand.
152. There is divergence among the commenters with regard to the
adoption of the proposed values for the distribution fill factors.
Sprint does not object to the use of the proposed values, stating that
``they appear to reasonably represent realistic, forward-looking
practices.'' As noted, Ameritech contends that the copper distribution
and feeder fill factors are reasonable estimates to use if company-
specific or state-specific fill factors are not used. In contrast, SBC
disagrees with the HAI proponents' claim that the level of spare
capacity provided in the proposed values is sufficient to meet current
demand plus some amount of growth. SBC, however, offers no
controverting evidence demonstrating that the proposed values are
insufficient to meet current demand plus short-term growth. We find
that the lone fact that SBC disagrees is insufficient to controvert our
conclusion that the proposed values reflect the appropriate fill needed
to meet current demand. BellSouth contends that the proposed values
will significantly understate distribution cable requirements.
BellSouth submits instead projected fill factors for its distribution
copper, feeder copper, and fiber cables determined by BellSouth network
engineers. We find these estimates unsupported. Similarly, Bell
Atlantic contends that the proposed fill factors for feeder and
distribution are too high and recommends we adopt its proposed fill
factors. We find these recommended fill factors unsupported. We,
therefore, select the proposed values for distribution fill factors.
153. We also disagree with AT&T and MCI's contention that the
proposed values for the distribution fill factors are too low. AT&T and
MCI claim that distribution fill factors of 1.2 lines per household are
more than adequate in a forward-looking cost study. We disagree. We
find that 1.2 lines per household are inadequate because they simply
reflect the existing provision of telephone service and are less than
current demand as we define it herein. Moreover, AT&T and MCI's claim
is belied by their own assertions. AT&T and MCI contend that the
``proposed conservative fill factors will ensure sufficient plant
capacity to accommodate potentially unaccounted service needs in the
PNR data.'' AT&T and MCI also state that ``[t]he fill levels used in
HAI provides more than enough spare capacity for service work, churn,
and unforeseen spikes in demand. In sum, AT&T and MCI attest to the
reasonableness of not only use of the HAI default values for
distribution plant, but also the use of the average of the HAI and BCPM
default values for copper feeder.
154. We also disagree with AT&T and MCI's claim that higher factors
are appropriate because the model's sizing algorithm produces effective
fill factors that are lower than optimal values. As we explained in the
Inputs Further Notice, because cable and fiber are available only in
certain sizes, the effective fill factor may be lower than the
administrative fill factor adopted as an input. We find that AT&T and
MCI's claim ignores this fact.
155. Finally, we note that AT&T and MCI also claim that the factor
should be higher because universal service support does not include
residential second lines or multiple business lines. The Commission has
never acted on the recommendation in the First Recommended Decision, 61
FR 63778 (December 2, 1996, that only primary residential lines should
be supported. Moreover, we also note that AT&T and MCI's claim ignores
the sixth criterion, which requires that:
The Cost Study or model must estimate the cost of providing
service for all businesses and households * * * Such inclusion of
multi-line business services and multiple residential lines will
permit the cost study or model to reflect the economies of scale
associated with the provision of these services.
In sum, we find AT&T and MCI's claim in this regard unpersuasive.
156. Feeder Fill Factors. We also affirm our tentative conclusion
to adopt copper feeder fill factors that are the average of the HAI and
BCPM default values. The divergence among the commenters noted with
regard to the use of the average of the HAI and BCPM default values for
the distribution fill factors is reflected in the comments regarding
the proposed feeder fill factors. Sprint finds that use of the average
of the HAI and BCPM default values for feeder fill factors is
reasonable. Ameritech's conditional support was noted. In contrast,
BellSouth contends that the average of the HAI and BCPM default values
will significantly understate copper feeder cable requirements. As
noted, BellSouth advocates the use of projected fill factors for copper
feeder determined by BellSouth network engineers. Similarly, Bell
Atlantic contends that the feeder fill factors are too high. We reject
the
[[Page 67392]]
use of these fill projections for copper feeder for the reasons
enumerated. We also reject, for the reasons enumerated, AT&T and MCI's
contention that feeder fill factors based on the average of the HAI and
BCPM default values are too low.
157. Fiber Fill Factors. Finally, we affirm our tentative
conclusion that the input value for fiber fill in the federal mechanism
should be 100 percent. The majority of commenters addressing this
specific issue agree with our tentative conclusion. AT&T and MCI
contend that fiber feeder fill factors of 100 percent are appropriate
because the allocation of four fibers per integrated DLC site equates
to an actual fill of 50 percent, since a redundant transmit and a
redundant receive fiber are included in the four fibers per site. AT&T
and MCI explain that, because fiber capacity can easily be upgraded,
100 percent fill factors applied to four fibers per site are sufficient
to meet unexpected increases in demand, to accommodate customer churn,
and, to handle maintenance issues. Similarly, SBC asserts that fiber
fill factors of 100 percent can be obtained because they are not
currently subject to daily service order volatility and are more easily
administered. In contrast, BellSouth advocates that we employ projected
fills estimated by BellSouth engineers. As noted, these estimates are
unsupported and we reject them accordingly. In sum, we find that the
record demonstrates that it is appropriate to use 100 percent as the
input value for fiber fill in the federal mechanism.
5. Structure Costs
158. We affirm our tentative conclusions to use the regression
equation for aerial structure in the NRRI Study as a starting point for
the cost estimate for aerial structure; to use the regression equation
for underground structure in the Inputs Further Notice as a starting
point for the cost estimate for underground structure for density zones
1 and 2; and to use the regression equation for the cost of 24-gauge
buried copper cable and structure, as modified, to estimate the cost of
buried structure for density zones 1 and 2. Concomitantly, we affirm
our tentative conclusion to add to the estimates for aerial structure
the costs of anchors, guys, and other materials that support the poles.
As we explained in the Inputs Further Notice, the RUS data from which
this regression equation was derived do not include these costs. We
also adopt the following values we proposed in the Inputs Further
Notice for the distance between poles: 250 feet for density zones 1 and
2; 200 feet for zones 3 and 4; 175 feet for zones 5 and 6; and 150 feet
for zones 7, 8, and 9.
159. As noted, several commenters advocate that the input values we
adopt for structure costs reflect company-specific data. For the
reasons enumerated, we reject the use of the company-specific data we
have received to estimate the nationwide average input values for
structure costs to be used in the model.
160. Notwithstanding this conclusion, we find that it is
unnecessary to extrapolate cost estimates for underground and buried
structure for density zones 3 through 9 as we proposed. At the time of
the Inputs Further Notice, we believed the extrapolated data were the
best data available to us at the time for density zones 3 through 9
although we noted our preference to use data specific to those density
zones. Upon further examination, we find that cost data, which include
values for density zones 3 through 9, submitted by various state
commissions for use in this proceeding are more reliable than the
extrapolated data. Specifically, we reviewed structure cost data from
North Carolina, South Carolina, Indiana, Nebraska, New Mexico, Montana,
Minnesota, and Kentucky. These data reflect structure costs designed
for use in the HAI and BCPM models.
161. The structure costs submitted by the state commissions have
values for normal rock, soft rock, and hard rock for density zones 3
through 9. We adopt as the buried and underground structure cost input
values for these density zones weighted average structure costs
developed from these data based on the number of access lines for the
companies to which the state decisions regarding the submitted
structure costs apply. We find that these weighted averages represent
reasonable estimates for buried and underground structure costs in
normal, soft, and hard rock conditions for density zones 3 through 9.
162. Apart from the criticism of the extrapolation of structure
costs for density zones 3 through 9 from the estimates for density zone
2, the comments we have received regarding the values we proposed for
structure costs vary as to the type of structure the commenters address
and vary as to the position they take on the reasonableness of the
estimates. BellSouth states that the values we adopt for aerial
structures are ``fairly representative of BellSouth's values'' but
claims that, based on a comparison to its actual data, the values for
underground and buried structure are too low. Cincinnati Bell states
that the values we adopt for underground structure never vary from
Cincinnati Bell's actual costs by more than 15 percent. Sprint claims
that our proposed cost of poles are understated but the costs of anchor
and guys appear to be reasonable. SBC claims that its actual weighted
cost of a 40 foot pole is inconsistent with the loaded cost from the
NRRI Study. SBC asserts, however, that the NRRI-specified cost is more
closely aligned with SBC's anchor and guy costs. We find that, given
this divergence of positions, the support in the record for some of our
proposed values, and lack of back-up data to support the arguments
opposing our proposals, on balance, the structure cost estimates we
adopt for aerial, underground, and buried structure for density zones 1
and 2 are reasonable. Moreover, we find it is reasonable to use the
values we adopt for density zones 3 through 9. As we discussed, these
values reflect cost data for density zones 3 through 9 and have been
submitted to us by state commissions for use in this proceeding. These
values are more reliable than those derived through the extrapolation
of data reflecting density zones 1 and 2, and for the reasons
discussed, the company-specific data submitted on the record.
163. In reaching these conclusions, we note that AT&T and MCI
advocate that we adjust the regressions used to estimate structure
costs to reflect the buying power of large non-rural LECs. We find
that, because AT&T and MCI did not provide any data to support such a
determination, the record is insufficient to determine that such an
adjustment is necessary. We also reject AT&T and MCI's claim that the
costs of underground structure are excessive because they fail to
exclude manhole costs from the costs of underground distribution.
Contrary to AT&T and MCI's assertion, we find that manhole costs are
necessary to allow for splicing when the length of the distribution
cable exceeds minimum distance criteria adopted by the model.
164. Finally, we note, as described, that we have made adjustments
to certain of the regression equations in the Inputs Further Notice
from which we estimate structure costs in order to address certain of
the criticisms reflected in the comments and improve the regression
equations accordingly.
165. LEC Loading Adjustment. In the Inputs Further Notice, we
tentatively concluded that we should add a loading of ten percent to
the material and labor cost (net of LEC engineering) for aerial,
underground, and buried structure because the cost of LEC engineering
was not reflected in the data from which Gabel and Kennedy derived
their
[[Page 67393]]
estimates. We find that, based on the record before us, the LEC
engineering adjustment is appropriate and the proposed level of the
adjustment is reasonable. In reaching this conclusion, we reject at the
outset the position of those commenters advocating that the adjustment
be based on company-specific data. As we explained, we find such data
are not the most reliable data on the record.
166. As with the LEC adjustment proposed for cable costs discussed,
there is a general consensus on the record among the commenters that an
adjustment is necessary. We find, therefore, that an adjustment to
reflect the cost of LEC engineering is appropriate. Beyond the general
claim that we should adopt company-specific data, there is divergence
among the commenters regarding the appropriate level of this
adjustment. GTE claims that the adjustment should be greater than 10
percent based on a comparison to its data for buried plant. SBC agrees
that 10 percent is appropriate for aerial and buried structure but too
low for underground structure. SBC proposes a loading factor of 20
percent instead for underground structure. Based on our review of the
information, it is our judgement that the 10 percent adjustment is the
most reasonable value on the record before us to reflect the cost of
LEC engineering.
6. Plant Mix
167. As explained, although we tentatively chose to adopt
nationwide plant mix values, we presented and sought comment on an
alternative algorithm based on sheath miles reported in ARMIS to
develop plant mix values. Consistent with that alternative, GTE asserts
that company-specific plant mix should be used instead of nationwide
input values. Similarly, Sprint contends that company-specific or
state-specific plant mix values should be used. US West asserts that
the model should utilize study-area specific plant mix values that are
available in ARMIS as a starting point for plant mix inputs in the
model.
168. We find, however, as discussed, because companies do not
report aerial and buried route miles in ARMIS, that it is not possible
to develop plant mix factors directly from these data at this time.
Moreover, we note that the record does not reflect company-specific
plant mix values for all companies, nor has any commenter presented a
methodology that recognizes the fact that plant mix varies across
density zones and allocates it accordingly. In sum, we conclude that
neither company-specific nor ARMIS-derived data represent reasonable
alternatives to the use of nationwide inputs. We find, therefore, that
the use of nationwide inputs is the most reasonable approach in
developing plant mix values on the record before us.
169. US West claims that the plant mix algorithm we proposed places
too much plant in aerial. US West traces this flaw to several alleged
errors in the plant mix algorithm. US West claims that the algorithm
erroneously double weights the model plant mix. This is not an error as
US West claims. Because the model results used in US West's analysis
are based on the low aerial distribution input, we find that the double
weight should result in low levels of aerial construction rather than
high levels of aerial construction. US West also identifies several
formulaic errors. We find these errors attributable, however, to US
West's lack of understanding of how the proposed algorithm works. We
agree, however, with US West that the high aerial results do appear to
be a function of incorrectly weighting aerial plant. We find that this
problem is a function of treating the aerial plant mix factor as a
residual rather than directly estimating an aerial factor. Given this
flaw, we conclude that we should not adopt the plant mix algorithm on
which we sought comment.
170. As noted, we sought comment on alternatives to nationwide
plant mix input values. US West has proposed two algorithms. As
explained, we find that each of these has its own biases and,
therefore, that neither is a reasonable alternative to what we have
proposed. In brief, US West's first algorithm takes the geometric mean
of the national default and a structure ratio to determine the plant
mix factor. It defines the structure ratio for underground plant as the
ratio of ARMIS trench miles to model route miles; for buried and aerial
plant the structure ratio is defined as the relative sheath miles of
the structure type multiplied by the model route miles less the ARMIS
trench miles. We find that the final result of this algorithm places
too much underground structure because, for all but the lowest density
zone, the underground plant mix factor is significantly higher than the
ARMIS ratio. The second algorithm US West proposes starts with the
relative share of ARMIS sheath miles for all three structure types. It
then establishes two series of fractions that sum to one. In the first
series, the fractions increase as the density zone increases. This
series is applied to underground structure and thus places more
underground structure in the higher density zones. In the second
series, the fractions decrease as the density zones increase. This
series is applied to aerial structure, with the result that the
percentage of aerial cable declines as density increases. For buried
structure, the ARMIS ratio is used for all density zones. We find that
this algorithm is flawed because it does not recognize the difference
between sheath and route miles. As a consequence, the algorithm
produces a biased result. Specifically, it constructs too much
underground cable. We find that, until this problem is resolved,
relying directly on ARMIS information leads to unreasonable results.
171. Distribution Plant. We adopt the proposed input values for
distribution plant mix which. We conclude that these values for the
lowest to the highest density zones, which range from zero percent to
90 percent for underground plant; 60 to zero percent for buried plant;
and 40 to ten percent for aerial plant, are the most reasonable
estimates of distribution plant mix on the record before us.
172. There is divergence among the commenters with regard to the
appropriateness of the input values for the distribution plant mix
proposed in the Inputs Further Notice. SBC supports the proposed
distribution plant mix, noting that it ``closely aligns with the
embedded plant and future outside plant design.'' AT&T and MCI advocate
the use of the HAI default values for plant mix because, according to
AT&T and MCI, they more properly reflect the use of aerial and
underground cable than the proposed distribution plant mix inputs. AT&T
and MCI claim that the proposed inputs reflect too much underground and
too little aerial cable. As we explained in the Inputs Further Notice,
the model does not design outside plant that contains either riser
cable or block cable. Accordingly, use of the HAI default values, which
assume a high percentage of aerial plant in densely populated areas,
would be inconsistent with the model platform. AT&T and MCI ignore this
fact.
173. In the Inputs Further Notice, we stated that we disagreed with
HAI's assumption that there is very little underground distribution
plant and none in the six lowest density zones. In support of the HAI
values for underground distribution plant, AT&T and MCI proffer the
distribution plant mix values for BellSouth, notably the only company
to provide such data, showing that its underground distribution plant
mix value is very low. We find that, because we are not adopting a
company-specific algorithm, it is not necessary to address this issue.
As noted, we will not adopt an
[[Page 67394]]
alternative algorithm until the issue of underground structure
distances has been resolved. We adhere to employing a national value
because we find that, though it may not be exact for every company, it
will be reasonable for all companies.
174. Feeder Plant. We also adopt the proposed input values for
feeder plant mix. We conclude that these values for the lowest to the
highest density zones, which range from five percent to 95 percent for
underground plant; 50 to zero percent for buried plant; and 45 to five
percent for aerial plant, are the most reasonable estimates of
distribution plant mix on the record before us. GTE's and Sprint's
comments specifically address the specific issue of feeder plant mix
inputs. As noted, both carriers advocate the use of company-specific
data for plant mix. We reject the use of such data for feeder plant mix
for the reasons we enumerated.
175. Finally, we affirm our tentative conclusion that the plant mix
ratios should not vary between copper feeder and fiber feeder. In
reaching our tentative conclusion, we noted that, although the HAI
sponsors proposed plant mix values that vary between copper feeder and
fiber feeder, they have offered no convincing rationale for doing so.
We find such support still lacking. GTE claims that a distinction is
necessary because the existing plant mix indicates that the trend for
more out-of-sight construction has already resulted in differing copper
and fiber feeder plant mixes. In contrast, SBC contends that plant mix
ratios should not vary between copper feeder and fiber feeder because
existing structure is used whenever available for fiber and copper
placement so the mix ratio would not differ. We find neither of these
claims to be persuasive. Accordingly, we conclude that, given the
absence of controverting evidence, it is reasonable to assume that
plant mix ratios should not vary between copper feeder and fiber feeder
in the model.
D. Structure Sharing
176. We adopt the following structure sharing percentages that
represent what we find is a reasonable share of structure costs to be
incurred by the telephone company. For aerial structure, we assign 50
percent of structure cost in density zones 1-6 and 35 percent of the
costs in density zones 7-9 to the telephone company. For underground
and buried structure, we assign 100 percent of the cost in density
zones 1-2, 85 percent of the cost in density zone 3, 65 percent of the
cost in density zones 4-6, and 55 percent of the cost in density zones
7-9 to the telephone company. In doing so, we adopt the sharing
percentages we proposed in the Inputs Further Notice, except for buried
and underground structure sharing in density zones 1 and 2, as
explained.
177. Commenters continue to diverge sharply in their assessment of
structure sharing. As noted by US West, ``[s]ince forward-looking
sharing percentages for replacement of an entire network are not
readily observable, there is room for reasonable analysts to differ on
the precise values for those inputs.'' While commenters engage in
lengthy discourse on topics such as whether the model should assume a
``scorched node'' approach in developing structure sharing values,
little substantive evidence that can be verified has been added to the
debate. AT&T and MCI contend that the structure sharing percentages
proposed in the Inputs Further Notice assign too much of the cost to
the incumbent LEC and fail to reflect the greater potential for sharing
in a forward-looking cost model. In contrast, several commenters
contend that the proposed values assign too little cost to the
incumbent LEC and reflect unrealistic opportunities for sharing. In
support of this contention, some LEC commenters propose alternative
values that purport to reflect their existing structure sharing
percentages, but fail to substantiate those values. SBC, however,
claims that the structure sharing percentages we propose reflect its
current practice and concurs with the structure sharing values that we
adopt in this Order.
178. More than with other input values, our determination of
structure sharing percentages requires a degree of predictive
judgement. Even if we had accurate and verifiable data with respect to
the incumbent LECs' existing structure sharing percentages, we would
still need to decide whether or not those existing percentages were
appropriate starting points for determining the input values for the
forward-looking cost model. AT&T and MCI argue that past structure
sharing percentages should be disregarded in predicting future
structure sharing opportunities. Incumbent LEC commenters argue that
sharing in the future will be no more, and may be less, than current
practice.
179. In the Inputs Further Notice, we relied in part on the
deliberations of a state commission faced with making similar
predictive judgment relating to structure sharing. The Washington
Utilities and Transportation Commission, conducted an examination of
these issues and adopted sharing percentages similar to those we
proposed.
180. In developing the structure sharing percentages adopted in
this Order, we find the sharing percentages proposed by the incumbent
LECs to be, in some instances, overly conservative. While we do not
necessarily agree with AT&T and MCI as to the extent of available
structure sharing, we do agree that a forward-looking mechanism must
estimate the structure sharing opportunities available to a carrier
operating in the most-efficient manner. As discussed in more detail in
this Order, the forward-looking practice of a carrier does not
necessarily equate to the historical practice of the carrier. Given the
divergence of opinion on this issue, and of AT&T and MCI's contention
that further sharing opportunities will exist in the future, we have
made a reasonable predictive judgment, and also anticipate that this
issue will be revisited as part of the Commission's process to update
the model in a future proceeding.
181. In the 1997 Further Notice, 62 FR 42457 (August 7, 1997), the
Commission tentatively concluded that 100 percent of the cost of cable
buried with a plow should be assigned to the telephone company. In the
Inputs Further Notice, we sought comment on the possibility that some
opportunities for sharing existed for buried and underground structure
in the least dense areas and proposed assignment of 90 percent of the
cost in density zones 1-2 to the telephone company. Several commenters
contend that there are minimal opportunities for sharing of buried and
underground structure, particularly in lower density areas. In
addition, several commenters contend that, to the extent sharing is
included in the RUS data, it is inappropriate to count that sharing
again in the calculation of structure cost. While we agree that
structure sharing should not be double counted, we note that the RUS
data includes little or no sharing of underground or buried structure
in density zones 1-2. This does, however, support the contention of
commenters that there is, at most, minimal sharing of buried and
underground structure in these density zones. We therefore modify our
proposed input value in this instance and assign 100 percent of the
cost of buried and underground structure to the telephone company in
density zones 1-2.
182. We believe that the structure sharing percentages that we
adopt reflect a reasonable percentage of the structure costs that
should be assigned to the LEC. We note that our conclusion reflects the
general consensus among commenters that structure sharing varies by
structure type and density.
[[Page 67395]]
While disagreeing on the extent of sharing, the majority of commenters
agree that sharing occurs most frequently with aerial structure and in
higher density zones. The sharing values that we adopt reflect these
assumptions. SBC also concurs with our proposed structure sharing
values. In addition, as noted, the Washington Utilities and
Transportation Commission has adopted structure sharing values that are
similar to those that we adopt. We also note that the sharing values
that we adopt fall within the range of default values originally
proposed by the HAI and BCPM sponsors.
E. Serving Area Interfaces
183. We affirm our approach to derive the cost of an SAI on the
basis of the cost of its components and adopt a total cost of $21,708
for the 7200 pair indoor SAI. We find that there remains an absence of
contract data between the LECs and suppliers with regard to SAIs on the
record before us. Accordingly, we affirm, as discussed in more detail,
our tentative conclusions with respect to the following issues: (1) the
cost per pair for protector material; (2) the appropriate splicing rate
and corresponding labor rate; (3) the methodology employed in cross-
connecting in a SAI; and (4) the appropriate feederblock and
distribution installation rate.
184. Based on the record before us, we conclude that $4 per pair is
a reasonable estimate of the cost for protected material. As we
explained in the Inputs Further Notice, this estimate is based on an
analysis of ex parte submissions, which is the only evidence we have
available to evaluate the cost of SAI components. We also noted that
Sprint has agreed that $4 is a reasonable estimate of the cost. SBC and
AT&T and MCI concur with our tentative conclusion to adopt the $4 per
pair cost. In sum, the record fully supports our conclusion that $4 per
pair is a reasonable estimate of the cost for protector material.
185. We also conclude that the record demonstrates that a splicing
rate of 250 pairs is reasonable, and adopt it accordingly. As we
explained in the Inputs Further Notice, the HAI sponsors proposed a
splicing rate of 300 pairs per hour, while Sprint argued for a splicing
rate of 100 pairs per hour. We believed that HAI's proposed rate was a
reasonable splicing rate under optimal conditions, and therefore, we
tentatively concluded that Sprint's proposed rate was too low. We noted
that the HAI sponsors submitted a letter from AMP Corporation, a
leading manufacturer of wire connectors, in support of the HAI rate. We
recognized, however, that splicing under average conditions does not
always offer the same achievable level of productivity as suggested by
the HAI sponsors. For example, splicing is not typically accomplished
under controlled lighting or on a worktable. Having accounted for such
variables, we proposed a splicing rate of 250 pairs per hour.
186. AT&T and MCI, the proponents of the 300 pairs per hour rate,
support our tentative conclusion. Sprint takes issue with the splicing
rate we proposed. Sprint impugns the evidence, appearing in the form of
a letter from AMP Corporation on which we relied in part, to determine
a reasonable splicing rate. In sum, Sprint contends the letter
represents an ``unsupported claim of someone trying to sell
equipment.'' While Sprint is correct that the proponent is an equipment
manufacturer, neither Sprint nor any other commenter provided evidence
from any other equipment manufacturer to refute AMP.
187. Sprint also questions the fact that we did not utilize the
data available from the NRRI Study to determine the splicing rate.
Sprint maintains that an analysis of that data results in a splicing
rate of 58.8 pairs per hour, substantially less than the 300 pairs per
hour we recognized as a ceiling in our analysis. We based our proposed
splicing rate on an analysis of such rates as they relate specifically
to the installation of a complete and functional SAI. In contrast,
although the data to which Sprint refers is for modular splicing, it is
not clear, nor does Sprint claim, that such data specifically relates
to the installation of SAIs. In sum, the validity of this data as a
measure in the derivation of splicing rates for SAI installation is not
established on the record. Sprint's critique ignores this fact.
Accordingly, we reject the use of the data available from the NRRI
Study to determine the splicing rate.
188. We also conclude that the $60 per hour labor rate we proposed
for splicing is reasonable and adopt it accordingly. Those commenters
addressing this specific issue agree. As we explained in the Inputs
Further Notice, this rate, which equates with the prevalent labor rate
for mechanical apprentices, is well within the range of filings on the
record.
189. We also conclude that the model should assume that a
``jumper'' method will be used half the time and a ``punch down''
method will be used the remainder of the time to cross-connect an SAI.
A cross-connect is the physical wire in the SAI that connects the
feeder and distribution cable.
190. In the Inputs Further Notice, we tentatively concluded that
neither the jumper method nor the punch down method is used exclusively
in SAIs. We reached this tentative conclusion based on the conflicting
assertions of Sprint and the HAI sponsors. We noted that, Sprint
asserted that the ``jumper'' method generally will be employed to
cross-connect in a SAI. In contrast, the HAI sponsors claimed that the
``punch down'' method is generally used to cross-connect. We also noted
that, in buildings with high churn rates, such as commercial buildings,
carriers may be more likely to use the jumper method. On the other
hand, in residential buildings, where changes in service are less
likely, carriers may be more likely to use the less expensive punch
down method. Thus, we tentatively concluded that it appeared that both
methods are commonly used, and that neither is used substantially more
than the other.
191. Based on the record before us, we affirm our tentative
conclusion to assume that the ``jumper'' method and the ``punch down''
method will be used an equal portion of the time. SBC challenges this
conclusion, pointing out that it uses the ``jumper'' method in
applications involving hard lug or insulation displacement contact and
that it is currently replacing existing ``punch down'' interfaces. We
conclude that SBC's sole claim is not sufficient to demonstrate that
the ``jumper'' method is used substantially more than the ``punch
down'' method. We note also that Sprint contends that the cross-connect
proposed by AT&T and MCI is not an SAI, but a building entrance
terminal. We disagree. The design meets the SAI definition of providing
an interface between distribution and feeder facilities. In sum, we
find that the record demonstrates that it is reasonable for the model
to assume that a ``jumper'' method will be used half the time and a
``punch down'' method will be used the remainder of the time to cross-
connect an SAI.
192. We also adopt a feeder block and distribution installation
rate of 200 pairs per hour. As we explained in the Inputs Further
Notice, we derived this installation factor based on a comparison of
Sprint's proposed installation rate of 60 pairs per hour with HAI's
proposed 400 pair per hour rate. We concluded that, because neither
feeder block installation nor distribution block installation is a
complicated procedure, Sprint's rate of 60 pairs per hour is too low.
We also recognized that installation conditions are not always ideal.
As we explained, feeder block and distribution block installations are
not typically accomplished under
[[Page 67396]]
controlled lighting or on a worktable. We proposed a rate of 200 pairs
per hour to recognize these variables.
193. We note that our proposed feeder block and distribution block
rates are unchallenged. Significantly, SBC attests that this
installation rate aligns with time-in-motion studies performed in
cross-connect building applications. We conclude, therefore, that our
proposed rate is reasonable, and adopt input values based upon it
accordingly.
194. We also adopt the cost estimates for other size indoor and
outdoor SAIs tentatively adopted in the Inputs Further Notice. We
conclude that, based on the record before us, the derivation of the
costs of the other SAI sizes from the cost of the 7200 pair indoor SAI
is reasonable.
195. GTE takes issue with the derivation of the costs of the other
SAIs from the cost of the 7200 pair indoor SAI. First, GTE contends
that there is no need to extrapolate the costs of other SAIs because
the costs of individual SAI sizes and associated labor are readily
available. We disagree. We concluded that it was necessary to
extrapolate the costs of other SAI sizes from the cost of a 7200 pair
SAI because of the lack of component-by-component data for other SAI
sizes on the record. As noted, we find the record still lacks such
data. We also disagree with GTE's contention that SAI costs are not
subject to a linear relationship across all sizes as we determined. We
find GTE's contention, which relies on GTE's SAI estimates,
unpersuasive given the lack of substantiating data supporting these
estimates. In sum, the record demonstrates that the derivation of the
costs of the other SAIs from the cost of the 7200 pair indoor SAI is
reasonable.
196. US West contends that the costs of a SAI should be determined
by the actual cable sizes for the cables entering and leaving the SAI
rather than the number of cable pairs entering and leaving the
interface. We agree. The model has been revised to calculate the costs
of an SAI on the basis of actual cable sizes for the cables entering
and leaving the SAI.
197. US West raises an additional issue concerning the sizing of
SAIs. US West notes that some clusters created by the clustering module
exceed the default line limit of 1800 lines and gives as an example a
specific cluster containing 7,900 lines. The largest SAI can
accommodate only 7200 lines, counting both feeder side and distribution
side lines. Therefore, US West contends that, in situations such as
this, insufficient SAI plant is deployed by the model. We agree with
this analysis. There is no way to guarantee that the line limit of 1800
lines will not be exceeded for some clusters, even though modifications
have been made to the cluster algorithm to mitigate this possibility to
the greatest possible extent. Therefore, in the current version of the
model, we modify the input table for SAI costs so as to allow for
serving areas (clusters) in which the capacity of feeder cable plus
distribution cable meeting at the interface may exceed 7200. We do this
by allowing for line increments of 1800 up to a total line capacity of
28,800. The values in the input table assume that, whenever more than
7200 lines are required in an SAI, two or more standard SAIs are built,
one with full capacity of 7200 and the others with capacities equal to
1800, 3600, 5400 or 7200. The input values for each of the multiply-
placed SAIs are then summed.
198. A related issue is raised by US West with respect to drop
terminal capacity in the model. In previous versions of the model, drop
terminals were sized for residential housing units and small business
locations, with a maximum line capacity per drop location equal to 25
lines. For medium size and larger business locations with line demand
greater than 25 lines, no specific provision for additional drop
terminal capacity was provided, except in situations in which a single
business accounted for all of the lines in a single cluster. Again, we
agree with the US West analysis of this issue. Accordingly, we have
modified the input table for drop terminal costs by adding additional
line sizes equal to 50, 100, 200, 400, 600, 900, 1200, 1800, 2400,
3600, 5400, and 7200. At any location requiring a drop terminal with
capacity exceeding 25 lines, the model will assume that the location
will be served by an indoor SAI, and the cost of the corresponding
interface is equal to the corresponding value from the table for SAI
costs.
F. Digital Loop Carriers
199. We adopt an average of the contract data submitted on the
record, adjusted for cost changes over time, as the cost estimates for
DLCs. This decision is predicated on two conclusions. The first is our
determination that the contract data submitted to the Commission in
response to the 1997 Data Request, and in ex parte submissions
following the December 11, 1998, workshop, remains the most reliable
data on the record. Significantly, no additional information has been
proffered nor has any alternative method been proposed, on which to
base our estimate of DLC costs. The second is that we conclude that it
is reasonable to reduce both the fixed DLC cost and per-line DLC cost
reflected in this data by a factor of 2.6 percent per year in order to
capture changes in the cost of purchasing and installing DLCs over
time.
200. As we explained in the Inputs Further Notice, the contract
data submitted to the Commission in response to the 1997 Data Request,
and in ex parte submissions following the December 11, 1998, workshop,
is the most reliable data because, not only is it the only data on the
record, but it reflects the actual costs incurred in purchasing DLCs.
Moreover, although we would have preferred a larger sample, the
contract data is sufficiently representative of non-rural carriers
because it reflects the costs incurred by several of the largest non-
rural carriers, as well as two of the smallest non-rural carriers.
201. GTE, Bell Atlantic and Sprint support the use of the contract
data in estimating the cost of DLCs. Only AT&T and MCI and SBC
challenge the use of these data. SBC contends that the contract data is
not the most reliable data on DLC costs because labor costs associated
with testing, turn-up, and delivery of derived facilities are not
factored into the input values. We disagree. The data we identify as
``contract data'' include these costs. As we explained in the Inputs
Further Notice and noted, we sponsored a workshop on December 11, 1998,
to further develop the record on DLC costs in this proceeding. During
the workshop, we presented a template of the components of a typical
DLC to the attendees. The template provided the respondents the
opportunity to identify their contract costs with regard to each of the
components. In addition, we requested that the respondents identify,
and thereby include, other costs associated with DLC acquisition,
including labor costs associated with testing, turn-up, and delivery of
the DLC. Using this opportunity to submit DLC cost data, GTE and Aliant
included such costs in their submissions. Sprint submitted similar data
in a September 9, 1998 ex parte filing. These costs were identified and
added to the analysis of US West's and BellSouth's contract data. We
derived these costs from ex parte filings made by these carriers in
this proceeding.
202. AT&T and MCI allege that the contract data overstates the
actual costs of DLC equipment and therefore, should not be adopted.
AT&T and MCI instead advocate use of the HAI default values. AT&T and
MCI argue that the contract costs are not only unsupported by any
verifiable evidence but, more
[[Page 67397]]
importantly, are refuted by the contract information from which they
were derived. In support, AT&T and MCI submit an analysis of the DLC
cost submissions of Bell Atlantic, BellSouth, and Sprint. In each
instance, AT&T and MCI assert that these data demonstrate DLC costs
that are far below those proposed by the incumbent LECs and the
Commission and that are fully consistent with the HAI default values.
203. We disagree with AT&T and MCI's analysis. For example, AT&T
and MCI claim that information provided by Bell Atlantic shows that
total DLC common equipment costs for DLC systems capable of serving
672, 1344, and 2016 lines are similar to, and uniformly less than, the
corresponding HAI values. In reaching this conclusion, however, AT&T
and MCI omit the costs for line equipment. As Bell Atlantic points out,
the cost of digital line carrier equipment should include these costs,
and we agree.
204. Similarly, AT&T and MCI assert that certain of Sprint's costs
are significantly inflated and, once adjusted, are similar to and
uniformly less than the corresponding HAI values. We find, however,
these adjustments to be unsupported. AT&T and MCI reduce the supply
expenses associated with Sprint's DLC costs, more than 66 percent,
based on the experience of AT&T and MCI's engineering team members.
AT&T and MCI offer no evidence, however, other than the opinions of
their experts to substantiate this proposed adjustment.
205. AT&T and MCI also contend that Sprint applies excessive mark-
ups for sales tax. AT&T and MCI argue that, because Sprint operates its
own logistics company, there is no reason to apply sales tax to both
supply expense and materials. We find that AT&T and MCI offer no
support to demonstrate that this results in an excessive mark-up for
sales tax. We reach the same conclusion with regard to AT&T and MCI's
proposed reduction to Sprint's labor costs. AT&T and MCI contend that
Sprint's labor costs are inflated and propose reductions in such costs
through a reduction in the number of labor hours associated with DLC
installation. AT&T and MCI provide no support for such a reduction and,
therefore, we decline to reduce Sprint's labor costs.
206. Significantly, AT&T and MCI offer no evidence to controvert
our tentative conclusion that the HAI values they employ as a
comparative benchmark, and advocate that we adopt, are not more
reliable than the contract data. We rejected the use of the HAI and the
BCPM default values because they are based on the opinions of experts
without substantiating data. Similarly, we rejected data submitted by
the HAI sponsors following the December 11, 1998, workshop. We found
that data to be significantly lower than the contract data on the
record, and concluded that it would be inappropriate to use because it
also lacked support. AT&T and MCI have not provided any additional
evidence to substantiate the HAI data.
207. We also affirm our tentative conclusion that it is reasonable
to reduce both the fixed DLC costs and per-line DLC costs reflected in
the contract data in order to capture changes in the cost of purchasing
and installing DLCs. As we explained in the Inputs Further Notice, this
reduction recognizes the fact that the cost of purchasing and
installing a DLC diminishes over time because of improvements in the
methods and components used to produce DLCs, changes in both capital
and labor costs, and changes in the functionality requirements of DLCs.
The premise that overall DLC costs move downward over time is not
disputed on the record.
208. We also conclude that the 2.6 percent reduction we proposed in
both the fixed DLC costs and per-line DLC costs is appropriate. As we
explained in the Inputs Further Notice, this is a conservative
estimate, based on the change in cost of remote switches, which is a
reasonable proxy for changes in DLC cost. More importantly, a
comparison of data submitted on the record by Sprint for the years
1997, 1998, and 1999 demonstrates that an overall reduction of 2.6
percent is considerably less than Sprint's actual experience. An
analysis undertaken by staff produces an average reduction in DLC costs
for Sprint of 9.2 percent per year. We note that this estimate reflects
both material and labor costs.
209. Only SBC and GTE specifically address the 2.6 percent
reduction. SBC supports the 2.6 percent reduction in fixed and per-line
DLC costs as it applies to material costs only. In contrast, GTE
opposes the adjustment. GTE suggests that, as the inputs are adjusted
over time, the cost of current technology will be reflected in the
revised data. GTE is correct that the current cost of technology would
be reflected in revised data. The adjustment we proposed and adopt
updates cost to current cost. Implicit in SBC's comment is the premise
that labor costs will not decrease over time. Although this may be a
reasonable assumption, the 2.6 percent reduction we adopt is applied to
the overall cost of a DLC. As we explained, the 2.6 percent reduction
is a conservative estimate compared to the actual reductions we have
observed in the Sprint data. As a result, we conclude that increases in
labor will be offset by reductions in other factors in the cost of
DLCs.
210. Finally, as noted, we sought comment on the extent, if any, to
which we should increase our proposed estimates for DLCs to reflect
material handling and shipping costs because it was unclear whether the
DLC data submitted by other parties include these costs. On further
analysis, we note that material handling and shipping costs are
reflected in the proposed DLC estimates we adopt herein. Moreover, we
conclude that it is appropriate to include these costs in the cost
estimates for DLCs. We note that no comments were filed opposing the
inclusion of such costs.
IV. Switching and Interoffice Facilities
A. Switch Costs
211. Switch Cost Estimates. We adopt the fixed cost (in 1999
dollars) of a remote switch as $161,800 and the fixed cost (in 1999
dollars) of both host and stand-alone switches as $486,700. We adopt
the additional cost per line (in 1999 dollars) for remote, host, and
stand-alone switches as $87.
212. For the reasons set forth, we affirm our tentative conclusion
to use the publicly available data from LEC depreciation filings, and
to supplement the depreciation data with data from LEC reports to the
RUS. We also affirm our tentative conclusion that we should not rely on
the BCPM and HAI default values, because these values are largely based
on non-public information or opinions of their experts, without data
that enable us adequately to substantiate those opinions.
213. Switch Cost Data. The depreciation data contains for each
switch reported: The model designation of the switch; the year the
switch was first installed; and the lines of capacity and book-value
cost of purchasing and installing each switch at the time the
depreciation report was filed with the Commission. The RUS data
contains, for each switch reported: The switch type (i.e., host or
remote); the number of equipped lines; cost at installation; and year
of installation.
214. The sample that we use to estimate switch costs includes 1,085
observations. The sample contains 946 observations selected from the
depreciation data, which provide information on the costs of purchasing
and installing switches gathered from 20 states. All observations in
the depreciation data set are for switches with 1,000 lines or more. In
order to
[[Page 67398]]
better estimate the cost of small switches, we augmented the
depreciation data set by adding data from RUS. The RUS sample contains
139 observations which provide information from across the nation on
the costs of small switches purchased and installed by rural carriers.
Over 80 percent of the observations of switch costs in the RUS data set
measure the costs for switches with 1,000 lines of capacity or less.
The combined sample represents purchases of both host and remote
switches, with information on 490 host switches and 595 remote
switches, and covers switches installed between 1989 and 1996. This set
of data represents the most complete public information available to
the Commission on the costs of purchasing and installing new switches.
215. The depreciation data set proposed in the Inputs Further
Notice excluded 26 observations that had been deemed to be outliers by
the Bureau of Economic Analysis. Bell Atlantic criticizes the
Commission for excluding these outliers. The excluded observations were
not available in electronic form prior to the release of the Inputs
Further Notice. Subsequently, the Bureau obtained these outlying
observations from the Bureau of Economic Analysis and reinserted them
into the data set used to derive the input values we adopt herein. In
addition, several commenters recommend that the depreciation data set
also should include switches with fewer than 1,000 lines of capacity.
This information, however, is not available in electronic format and,
therefore, would be unduly burdensome to include.
216. In response to the 1997 Data Request, the Commission received
a second set of information pertaining to 1,486 switches. Upon
analysis, however, we have identified one or more problems with most of
the data submitted: missing switch costs; zero or negative installation
costs; zero or blank line counts; unidentifiable switches; or missing
or inconsistent Common Language Local Identification (CLLI) codes.
After excluding these corrupted observations, 302 observations
remained. The remaining observations represented switches purchased by
only four companies. We affirm our tentative conclusion that the data
set we use is superior to the data set obtained from the data request,
both in terms of the number of usable observations and the number of
companies represented in the data set.
217. Following the December 1, 1998, workshop, three companies
voluntarily submitted further data regarding the cost of purchasing and
installing switches: BellSouth provided data on switch investments for
its entire operating region; Sprint provided similar data for its
operations in Nevada, Missouri, and Kansas; and GTE provided switch
investment information for California. When consolidated, this
information forms a data set of approximately 300 observations
representing the costs of new switches. As AT&T has noted, however, the
information submitted contains some inconsistencies. Considering these
inconsistencies, the limited number of companies represented, and the
size of this voluntarily submitted data set, we conclude that the data
set we use is preferable.
218. BellSouth suggests that we merge either the information
received in response to the 1997 Data Request, the information from the
voluntary submissions, or both, with the data set we use. We reject
this suggestion because there are significant inconsistencies between
the different data sets. For example, in its voluntary submission, GTE
provides the amount of total investment for each of its California
switches at the time these switches were installed, but reports
associated line counts only for October 1998. This information is not
consistent with the data set used by the Commission, which contains
aggregate investment and line counts measured at the same point in
time. Second, our analysis of the information provided in both the
voluntary submissions and the data request reveals, based on simple
linear regression, inconsistencies between these two data sets and the
data set employed by the Commission. Our analysis reveals that both
alternative data sets contain information that is inconsistent with the
comments in this proceeding.
219. Adjustments to the Data. As discussed, in the Inputs Further
Notice, we proposed certain adjustments to the RUS data to account for
the cost of MDF and power equipment, which were omitted from the RUS
information. Specifically, we proposed increasing the cost of
purchasing and installing switches by $12 per line for MDF and $12,000,
$40,000, or $74,500, depending upon switch size, for power costs.
Commenters who address this issue agree that the RUS data must be
modified to account for the costs of MDF and power to make the RUS data
consistent with the depreciation data, which include these costs. Some
commenters who address these adjustments claim that we should use
different values for MDF and power costs, but provide little or no
information we can use to verify their suggested values. Sprint, for
example, claims our power costs are too low and provides a breakdown of
power costs, but does not supply any data to support their higher
proposed values for power costs. AT&T and MCI claim our proposed power
costs should be reduced because they are substantially higher than
those proposed by their experts.
220. We find that we need not attempt to resolve disagreement over
the reasonableness of our proposed values, in the absence of any
additional information, because we adopt an alternative methodology for
estimating MDF and power costs. We find that we should adjust the RUS
data for MDF and power equipment costs in a way that is more consistent
with the way in which these costs are estimated in the depreciation
data set. In the depreciation data, MDF and power equipment costs are
estimated as a percentage of the total cost of the switch, as are all
other components of the switch. Based on the estimates of Technology
Futures, Inc., we find that these costs were eight percent of total
cost. Because we are adjusting the RUS data so that they are comparable
with the depreciation data, we find it is appropriate to use a
comparable method to estimate the portion of total costs attributable
to MDF and power equipment. Accordingly, in order to account for the
cost of MDF and power equipment omitted from the RUS information, we
conclude that the cost of switches reported in the RUS data should be
increased by eight percent.
221. In the Inputs Further Notice, we tentatively concluded, based
on an estimate provided by Gabel and Kennedy, that $27,598 should be
added to the cost of each remote switch reported in the RUS data. SBC
recommends that remote termination costs should be added to remote
switch costs on a per-line basis, but provides no estimates of the per-
line cost of remote termination. Sprint provides remote termination
estimates of $22,636 for termination of remote switches with less than
641 lines and $46,332 for termination of remote switches with between
641 and 6,391 lines. Using Sprint's methodology, the average cost of
terminating a RUS remote switch on a RUS host switch is $29,840.
Sprint's estimate is consistent in magnitude with Gabel and Kennedy's
estimate. Therefore, because Sprint's tiered estimates captures
differences between remote termination costs associated with remote
switch size, we adopt Sprint's estimates.
222. Based upon Gabel and Kennedy recommendations, derived from
data
[[Page 67399]]
analysis undertaken by RUS, we conclude that the cost of switches
reported in the RUS data should be increased by eight percent in order
to account for the cost of LEC engineering. We conclude, however, that
this adjustment should not be added to the cost of power and MDF,
because these estimates already include the costs of LEC engineering.
223. Methodology. Consistent with our tentative conclusions in the
Inputs Further Notice, we employ regression analysis. In this Order, we
also adopt our tentative conclusion to use a linear function based on
examination of the data and statistical evidence.
224. Sprint recommends using a non-linear function, such as the
log-log function, to take into account the declining marginal cost of a
switch as the number of lines connected to it increases. We affirm our
tentative conclusion that the linear function we adopt provides a
better fit with the data than the log-log function. A discussion of the
effect of time and type of switch on switch cost is presented.
225. Based upon an analysis of the data and the record, we conclude
that the fixed cost (i.e., the base getting started cost of a switch,
excluding costs associated with connecting lines to the switch) of host
switches and remote switches differ, but that the per-line variable
cost (i.e., the costs associated with connecting additional lines to
the switch) of host and remote switches are approximately the same.
This is consistent with statistical evidence and the comments of
Sprint, BellSouth, and the HAI sponsors.
226. Accounting for Changes in Cost Over Time. We recognize that
the cost of purchasing and installing switching equipment changes over
time. Such changes result, for example, from improvements in the
methods used to produce switching equipment, changes in both capital
and labor costs, and changes in the functional requirements that
switches must meet for basic dial tone service. In order to capture
changes in the cost of purchasing and installing switching equipment
over time, we affirm our tentative conclusion in the Inputs Further
Notice to modify the data to adjust for the effects of inflation, and
explicitly incorporate variables in the regression analysis that
capture cost changes unique to the purchase and installation of digital
switches.
227. To the extent that the general level of prices in the economy
changes over time, the purchasing power of a dollar, in terms of the
volume of goods and services it can purchase, will change. In order to
account for such economy-wide inflationary effects, we multiply the
cost of purchasing and installing each switch in the data set by the
gross-domestic-product chain-type price index for 1997 and then divide
by the gross-domestic-product chain-type price index for the year in
which the switch was installed, thereby converting all costs to 1997
values.
228. In order to account for cost changes unique to switching
equipment, we enter time terms directly into the regression equation.
US West agrees that the costs of the equipment, such as switches and
multiplexers, used to provide telecommunications services are
declining, and that the per-unit cost of providing more services on
average is declining. Bell Atlantic and GTE, however, contend that the
cost of switches is not currently declining and therefore pricing
declines should not be expected to continue into the future. As
evidence, they cite their own fixed-cost contracts. As AT&T notes,
however, ``[i]f Bell Atlantic in fact agreed to switching contracts
that `effectively froze prices on switching equipment,' those prices
would reflect its idiosyncratic business judgement * * *'' GTE
expresses concern that, under certain specifications of time, the
regression equation produces investments for remote switch ``getting
started'' costs that are negative and that such specifications
overstate the decline in switch costs. As noted in the Inputs Further
Notice, the HAI sponsors also caution that the large percentage price
declines in switch prices seen in recent years may not continue. We
affirm our tentative conclusion that the reciprocal form of time in the
regression equation satisfies these concerns by yielding projections of
switch purchase and installation costs that are positive yet declining
over time.
229. Ameritech and GTE advocate the use of the Turner Price Index
to convert the embedded cost information contained in the depreciation
data to costs measured in current dollars. We note, however, that this
index and the data underlying it are not on the public record. We
prefer to rely on public data when available. Moreover, we affirm our
tentative conclusion that it is not necessary to rely on this index to
convert switch costs to current dollars. Rather, as described in the
preceding paragraph, we will account for cost changes over time
explicitly in the estimation process, rather than adopting a surrogate
such as the Turner Price Index.
230. Treatment of Switch Upgrades. The book-value costs recorded in
the depreciation data include both the cost of purchasing and
installing new equipment and the cost associated with installing and
purchasing subsequent upgrades to the equipment over time. Upgrades
costs will be a larger fraction of reported book-value costs in
instances where the book-value costs of purchasing and installing
switching equipment are reported well after the initial installation
date of the switch. We affirm our tentative conclusion that, in order
to estimate the costs associated with the purchase and installation of
new switches, and to exclude the costs associated with upgrading
switches, we should remove from the data set those switches installed
more than three years prior to the reporting of their associated book-
value costs. We believe that this restriction will eliminate switches
whose book values contain a significant amount of upgrade costs, and
recognizes that, when ordering new switches, carriers typically order
equipment designed to meet short-run demand.
231. Bell Atlantic criticizes the Commission for excluding a large
percentage of the observations from the initial depreciation data set.
As noted in the preceding paragraph, however, the observations that
have been excluded do not accurately represent the price of a new
switch.
232. We reject the suggestions of Ameritech, Bell Atlantic,
BellSouth, GTE, and Sprint that the costs associated with purchasing
and installing switching equipment upgrades should be included in our
cost estimates. The model platform we adopted is intended to use the
most cost-effective, forward-looking technology available at a
particular period in time. The installation costs of switches estimated
reflect the most cost-effective forward-looking technology for meeting
industry performance requirements. Switches, augmented by upgrades, may
provide carriers the ability to provide supported services, but do so
at greater costs. Therefore, such augmented switches do not constitute
cost-effective forward-looking technology. In addition, as industry
performance requirements change over time, so will the costs of
purchasing and installing new switches. The historical cost data
employed in this analysis reflect such changes over time, as do the
time-trended cost estimates.
233. Additional Variables. Several parties contend that additional
independent variables should be included in our regression equation.
Some of the recommended variables include minutes of use, calls,
digital line connections, vertical features, and regional, state, and
vendor-specific identifiers. For the purposes of this analysis, our
model specification is limited to include information that is in
[[Page 67400]]
both the RUS and depreciation data sets. Neither data set includes
information on minutes of use, calls, digital line connections,
vertical features, or differences between host and stand-alone
switches. State and regional identifiers are not included in the
regression because we only have depreciation data on switches from 20
states. Thus, we could not accurately estimate region-wide or state-
wide differences in the cost of switching. Our model specification also
does not include vendor-specific variables, because the model platform
does not distinguish between different vendors' switches.
234. Switch Cost Estimates. A number of commenters criticize the
switch cost estimates contained in the Inputs Further Notice and
suggest that they should be dismissed or substantially revised. For
example, Sprint suggests that we dismiss the results because the data
are collinear and the model is mis-specified. Bell Atlantic and
BellSouth suggest that the Commission underestimates the cost of
switches, while AT&T and MCI suggest that the Commission overestimates
the cost of switches. The Commission's estimates, however, are based
upon the most complete, publicly-available information on the costs of
purchasing and installing new switches and therefore represent the
Commission's best estimates of the cost of host and remote switches. We
have addressed the specific objections that have been raised by parties
with regard to the methodology, data set, or other aspects of the
approach we adopt to derive switch cost estimates, and for the reasons
given there, we reject those objections. We conclude that the remaining
evidence provided as grounds for dismissing or substantially revising
these estimates is largely anecdotal or unconfirmed and undocumented
and does not lead us to believe that our estimates should be altered.
We conclude, therefore, that the switch cost estimates we adopt are the
best estimates of forward-looking cost.
B. Use of the Local Exchange Routing Guide (LERG)
235. In the Inputs Further Notice, we tentatively concluded that
the Local Exchange Routing Guide (LERG) database should be used to
determine host-remote switch relationships in the federal high-cost
universal service support mechanism. We now affirm that conclusion. In
the 1997 Further Notice, the Commission requested ``engineering and
cost data to demonstrate the most cost-effective deployment of switches
in general and host-remote switching arrangements in particular.'' In
the Switching and Transport Public Notice, the Bureau concluded that
the model should permit individual switches to be identified as host,
remote, or stand-alone switches. The Bureau noted that, although stand-
alone switches are a standard component of networks in many areas,
current deployment patterns suggest that host-remote arrangements are
more cost-effective than stand-alone switches in certain cases. No
party has placed on the record in this proceeding an algorithm that
will determine whether a wire center should house a stand-alone, host,
or remote switch. We therefore affirm our conclusion to use the LERG to
determine host-remote switch relationships.
236. In the Platform Order, we concluded that the federal mechanism
should incorporate, with certain modifications, the HAI 5.0a switching
and interoffice facilities module. In its default mode, HAI assumes a
blended configuration of switch technologies, incorporating both hosts
and remotes, to develop switching cost curves. HAI also allows the user
the option of designating, in an input table, specific wire center
locations that house host, remote, and stand-alone switches. When the
host-remote option is selected, switching curves that correspond to
host, remote, and stand-alone switches are used to determine the
appropriate switching investment. The LERG database could be used as a
source to identify the host-remote switch relationships. In the
Platform Order, we stated that ``[i]n the inputs stage of this
proceeding we will weigh the benefits and costs of using the LERG
database to determine switch type and will consider alternative
approaches by which the selected model can incorporate the efficiencies
gained through the deployment of host-remote configurations.''
237. The majority of commenters throughout this proceeding have
supported the use of the LERG database as a means of determining the
deployment of host and remote switches. These commenters contend that
the use of the LERG to determine host-remote relationships will
incorporate the accumulated knowledge and efficiencies of many LECs and
engineering experts in deploying the existing switch configurations.
Sprint contends that there are many intangible variables that can not
be easily replicated in determining host-remote relationships.
Commenters also contend that an algorithm that realistically predicts
this deployment pattern is not feasible using publicly available data
and would be unnecessarily ``massive and complex.'' AT&T and MCI argue,
however, that use of the LERG to identify host-remote relationships may
reflect the use of embedded technology, pricing, and engineering
practices.
238. We conclude that the LERG database is the best source set
forth in this proceeding to determine host-remote switch relationships
in the federal high-cost universal service support mechanism. As noted,
no algorithm has been placed on the record to determine whether a wire
center should house a stand-alone, host, or remote switch. In addition,
many commenters contend that development of such an algorithm
independently would be difficult using publicly available data. While
GTE suggests that the best source of host-remote relationships would be
a file generated by each company, we note that no such information has
been submitted in this proceeding. In addition, GTE's proposal would
impose administrative burdens on carriers. We conclude that the use of
the LERG to identify the host-remote switch relationships is superior
to HAI's averaging methodology which may not, for example, accurately
reflect the fact that remote switches are more likely to be located in
rural rather than urban areas. We therefore conclude that use of the
LERG is the most feasible alternative currently available to
incorporate the efficiencies of host-remote relationships in the
federal high-cost universal service support mechanism.
C. Other Switching and Interoffice Transport Inputs
239. General. In the Inputs Further Notice, we proposed several
minor modifications to the switching inputs to reflect the fact that
the studies on which the Commission relied to develop switch costs
include all investments necessary to make a switch operational. These
investments include telephone company engineering and installation, the
main distribution frame (MDF), the protector frame (often included in
the MDF), and power costs. To avoid double counting these investments,
both as part of the switch and as separate input values, the commenters
agree that the MDF/Protector investment per line and power input values
should be set at zero. In addition, commenters agree that the Switch
Installation Multiplier should be set at 1.0. We agree that including
these investments both as part of the switch cost and as separate
investments would lead to double counting of these costs. We therefore
adopt these values.
240. Analog Line Offset. In the Inputs Further Notice, we
tentatively
[[Page 67401]]
concluded that the ``Analog Line Circuit Offset for Digital Lines''
input should be set at zero. We now affirm that conclusion. AT&T and
MCI contend that the switch investment in the model should be adjusted
downward to reflect the cost savings associated with terminating
digital, rather than analog, lines. AT&T and MCI assert that this cost
savings is due primarily to the elimination of a MDF and protector
frame termination. AT&T and MCI further contend that the model
produces, on average, 40 percent digital lines, while the data used to
determine switch costs reflect the use of only approximately 18 percent
digital lines. In contrast, GTE contends that the model may calculate
more analog lines than carriers have historically placed due to the use
of an 18,000 feet maximum copper loop length.
241. AT&T and MCI suggest that the analog line offset input should
reflect a $12 MDF and $18 switch port termination savings per line in
switch investment for terminating digital lines in the model. Several
commenters disagree and recommend setting the analog line offset to
zero. Sprint contends that the analog line offset is inherent in the
switching curve in the model, thus making this input unnecessary and,
therefore, justified only if the switch cost curve is based on 100
percent of analog line cost. Sprint argues that an unknown mixture of
analog and digital lines are taken into consideration in developing the
switch curve.
242. The record contains no basis on which to quantify savings
beyond those taken into consideration in developing the switch cost. We
also note that the depreciation data used to determine the switch costs
reflect the use of digital lines. The switch investment value will
therefore reflect savings associated with digital lines. AT&T and MCI's
proposed analog line offset per line is based on assumptions that are
neither supported by the record nor easily verified. For example, it is
not possible to determine from the depreciation data the percentage of
lines that are served by digital connections. It is therefore not
possible to verify AT&T and MCI's estimate of the digital line usage in
the ``historical'' data. In the absence of more explicit support of
AT&T and MCI's position, we conclude that the Analog Line Circuit
Offset for Digital Lines should be set at zero.
243. Switch Capacity Constraints. In the Inputs Further Notice, we
proposed to adopt the HAI default switch capacity constraint inputs as
proposed in the HAI 5.0a model documentation. We now adopt that
proposal. The forward-looking cost mechanism contains switch capacity
constraints based on the maximum line and traffic capabilities of the
switch. In their most recent filings on this issue, AT&T and MCI
recommend increasing the switch line and traffic capacity constraints
above the HAI input default values for those inputs. AT&T and MCI
contend that the default input values no longer reflect the use of the
most current technology. For example, AT&T and MCI recommend that the
maximum equipped line size per switch should be increased from 80,000
to 100,000 lines.
244. We conclude that the original HAI switch capacity constraint
default values are reasonable for use in the federal mechanism. We note
that Sprint, the only commenter to respond to this issue, supports this
conclusion. We also note that the HAI model documentation indicates
that the 80,000 line assumption was based on a conservative estimate
``recognizing that planners will not typically assume the full capacity
of the switch can be used.'' AT&T and MCI therefore originally
supported the 80,000 line limitation as the maximum equipped line size
value with the knowledge that the full capacity of the switch may be
higher.
245. Switch Port Administrative Fill. In the Inputs Further Notice,
we proposed a switch port administrative fill factor of 94 percent. We
now adopt that proposed value. The HAI model documentation defines the
switch port administrative fill as ``the percent of lines in a switch
that are assigned to subscribers compared to the total equipped lines
in a switch.'' HAI assigns a switch port administrative fill factor of
98 percent in its default input values. The BCPM default value for the
switch percent line fill is 88 percent.
246. Bell Atlantic contends that switches have significant
unassigned capacity due to the fact that equipment is installed at
intervals to handle growth. Sprint recommends an average fill factor of
80 percent. US West contends that its actual average fill factor is 78
percent. AT&T and MCI contend that the switching module currently
applies the fill factor input against the entire switch when it should
be applied only to the line port portion of the switch. AT&T and MCI
therefore contend that, either the formula should be modified, or the
input needs to be adjusted upward so that the overall switching
investment increase attributable to line fill will be the same as if
the formula were corrected.
247. We note that the switch port administrative fill factor of 94
percent has been adopted in several state universal service proceedings
and is supported by the Georgetown Consulting Group, a consultant of
BellSouth. We also note that this value falls within the range
established by the HAI and BCPM default input values. The BCPM model
documentation established a switch line fill default value of 88
percent that included ``allowances for growth over an engineering time
horizon of several years.'' Sprint has provided no substantiated
evidence to support its revised value of 80 percent. US West's average
fill factor of 78 percent is based on data that include switches with
unreasonably low fill factors. Regarding AT&T and MCI's contention that
the switching module currently applies the fill factor input against
the entire switch rather than the line port portion of the switch, we
note that this occurs only when the host-remote option is not utilized
in the switch module. As noted, we are using the host-remote option and
therefore no adjustment to the switch fill factor is required. We
therefore adopt a switch port administrative fill factor of 94 percent.
248. Trunking. In the Inputs Further Notice, we tentatively
concluded that the switch module should be modified to disable the
computation that reduces the end office investment by the difference in
the interoffice trunks and the 6:1 line to trunk ratio. In addition, we
tentatively adopted the proposed input value of $100.00 for the trunk
port investment. We now affirm these tentative conclusions and adopt
this approach.
249. The HAI switching and interoffice module developed switching
cost curves using the Northern Business Information (NBI) publication,
``U.S. Central Office Equipment Market: 1995 Database.'' These
investment figures were then reduced per line to remove trunk port
investment based on NBI's implicit line to trunk ratio of 6:1. The
actual number of trunks per wire center is calculated in the transport
calculation, and port investment for these trunks is then added back
into the switching investments.
250. Sprint notes that, under the HAI trunk investment approach,
raising the per-trunk investment leads to a decrease in the switch
investment per line, ``despite a reasonable and expected increase'' in
the investment per line. GTE also notes that the selection of the trunk
port input value creates a dilemma in that it is used to reduce the end
office investment, as noted, and to develop a tandem switch investment.
GTE and Sprint recommend that the switch module be modified by
disabling the computation that reduces the end office investment by the
difference in
[[Page 67402]]
the computed interoffice trunks and the 6:1 line to trunk ratio. MCI
agrees that the trunk port calculation should be deactivated in the
switching module.
251. In the Inputs Further Notice, we agreed with commenters that
the trunk port input creates inconsistencies in reducing the end office
investment. Consistent with the suggestions made by GTE and MCI, we
conclude that the switch module should be modified to disable the
computation that reduces the end office investment by the difference in
the computed interoffice trunks and the 6:1 line to trunk ratio.
Sprint, the only commenter to address this issue in response to the
Inputs Further Notice, agrees with our conclusion.
252. Because the trunk port input value is also used to determine
the tandem switch investment, we must determine the trunk port
investment. In the Inputs Further Notice, we proposed an input value
for trunk port investment per end of $100.00. SBC and Sprint contend
that this value should be higher--ranging from $150.00 to $200.00.
BellSouth has filed information on the record that supports our
proposed trunk port investment value. BellSouth notes that the four
states that have issued orders addressing the cost of the trunk port
for universal service have chosen estimates of the cost of the trunk
port that range from $62.73 to $110.77. We conclude that the record
supports the adoption of a trunk port investment per end of $100.00, as
supported by the HAI default values. As noted, this value is consistent
with the findings of several states and BellSouth. In addition, we note
that SBC and Sprint provide no data to support their higher proposed
trunk port investment value. We therefore adopt the HAI suggested input
value of $100.00 for the trunk port investment, per end.
V. Expenses
A. Plant-Specific Operations Expenses
253. Consistent with our tentative conclusions, we adopt input
values that reflect the average expenses that will be incurred by non-
rural carriers, rather than a set of company-specific maintenance
expense estimates. We adopt our proposed four-step methodology for
estimating expense-to-investment ratios using revised current-to-book
ratios and 1997 and 1998 ARMIS data. We clarify that the ARMIS
investment and expense balances used to calculate the expense-to-
investment ratios in steps three and four should be based on the
accounts for all non-rural ARMIS-filing companies. Although some rural
companies file ARMIS reports, the mechanism we adopt today will be
used, beginning January 1, 2000, to determine high-cost support only
for non-rural carriers. We find, therefore, that it is appropriate to
include only data from the non-rural ARMIS-filing companies in
calculating these expense-to-investment ratios.
254. Current Data. Parties commenting on whether we should update
our methodology using more current ARMIS data agree that we should use
the most currently available data. We obtained account-specific
current-to-book ratios for the related plant investment accounts, for
the years ending 1997 and 1998, from Ameritech, Bell Atlantic,
BellSouth, GTE, and SBC. Accordingly, we adopt input values using these
updated current-to-book ratios and 1997 and 1998 ARMIS data to
calculate the expense-to-investment ratios that we use to obtain plant-
specific operations expense estimates for use in the federal mechanism.
255. Nationwide Estimates. As discussed in this section, we adopt
nationwide average values for estimating plant-specific operations
expenses rather than company-specific values for several reasons. We
reject the explicit or implicit assumption of most LEC commenters that
the cost of maintaining incumbent LEC embedded plant is the best
predictor of the forward-looking cost of maintaining the network
investment predicted by the model. We find that, consistent with the
Universal Service Order's criteria, forward-looking expenses should
reflect the cost of maintaining the least-cost, most-efficient, and
reasonable technology being deployed today, not the cost of maintaining
the LECs' historic, embedded plant. We recognize that variability in
historic expenses among companies is due to a variety of factors and
does not simply reflect how efficient or inefficient a firm is in
providing the supported services. We reject arguments of the LECs,
however, that we should capture this variability by using company-
specific data in the model. We find that using company-specific data
for federal universal service support purposes would be
administratively unmanageable and inappropriate. Moreover, we find that
averages, rather than company-specific data, are better predictors of
the forward-looking costs that should be supported by the federal high-
cost mechanism. In addition, we find that using nationwide averages
will reward efficient companies and provide the proper incentives to
inefficient companies to become more efficient over time, and that this
reward system will drive the national average toward the cost that the
competitive firm could achieve. Accordingly, we affirm our tentative
conclusion that we should adopt nationwide average input values for
plant-specific operations expenses.
256. AT&T and MCI agree with our tentative conclusion that we
should adopt input values that reflect the average expenses incurred by
non-rural carriers, rather than company-specific expenses. They argue
that the universal service support mechanism should be based on the
costs that an efficient carrier could achieve, not on what any
individual carriers has achieved. In contrast, incumbent LEC commenters
argue that we should use company-specific values.
257. BellSouth, for example, contends that the approach suggested
by AT&T and MCI conflicts with the third criterion for a cost proxy
model, which states that ``[t]he study or model, however, must be based
upon an examination of the current cost of purchasing facilities and
equipment * * *.'' BellSouth argues that the ``only logical starting
point for estimating forward-looking expenses is the current actual
expenses of the ILECs.'' We agree that we should start with current
actual expenses, as we do, in estimating forward-looking maintenance
expenses. We do not agree with the inferences made by the incumbent LEC
commenters, however, that our input values should more closely match
their current maintenance expenses.
258. BellSouth's reliance on criterion three fails to quote the
first part of that criterion, which states:
Only long-run forward-looking economic cost may be included. The
long-run period must be a period long enough that all costs may be
treated as variable and avoidable. The costs must not be the
embedded cost of facilities, functions, or elements.
Thus, the model's forward-looking expense estimates should not reflect
the cost of maintaining the incumbent LEC's embedded plant. The
Universal Service Order's first criterion specifies that ``[t]he
technology assumed in the cost study or model must be the least-cost,
most efficient, and reasonable technology for providing the supported
services that is currently being deployed.'' As we explained in the
Inputs Further Notice, while the synthesis model uses existing
incumbent LEC wire center locations in designing outside plant, it does
not necessarily reflect existing incumbent LEC loop plant. Indeed, as
the Commission stated in the Platform Order, ``[e]xisting incumbent LEC
plant is not likely to reflect forward-looking technology or design
choices.'' Thus, for
[[Page 67403]]
example, the model may design outside plant with more fiber and DLCs
and less copper cable than has been deployed historically in an
incumbent LEC's network. We find that the forward-looking maintenance
expenses also should reflect changes in technology.
259. GTE argues that expense-to-investment ratios should not be
developed as national averages, because no national average can reflect
the composition of each company's market demographics and plant. GTE
argues further that costs vary by geographic area and that this
variability reflects operating difficulties due to terrain, remoteness,
cost of labor, and other relevant factors. GTE contends that ``[u]sing
national average operating expenses will either understate or overstate
the forward-looking costs of providing universal service for each
carrier, depending on the variability of each company to the average.''
GTE claims that the use of the national average penalizes efficient
companies that operate in high-cost areas.
260. Similarly, Sprint contends that the use of nationwide
estimated data does not accurately depict the realities of operating in
Sprint's service territories. Sprint claims that the national averages
are far below Sprint's actual costs, because the Commission's
methodology for estimating plant-specific expense inputs is heavily
weighted toward the Bell companies' urban operating territories.
According to Sprint, the Bell companies have a much higher access line
density than Sprint, and the expense data from such companies with a
higher density of customers will result in expense levels that are much
lower than the expense levels experienced by smaller carriers. AT&T and
MCI respond by showing that a particular small carrier, serving a lower
density area than Sprint, has plant-specific expenses that, on a per-
line basis, are less than half of Sprint's expenses. AT&T and MCI claim
that ``the most significant driver of cost differences between carriers
in the ARMIS study area data is efficiency.'' Like other LECs, SBC
argues that the costs for LECs vary dramatically, based on various
factors including size, operating territories, vendor contracts,
relationships with other utility providers and the willingness to
accept risk. SBC asserts that ``[t]hese differences are not in all
instances attributable to inefficient operations.''
261. We agree with SBC that not all variations in costs among
carriers are due to inefficiency. Although we believe that some cost
differences are attributable to efficiency, we are not convinced by
AT&T and MCI's example that Sprint is less efficient than the small
carrier they identify. Sprint could have higher maintenance costs
because it provides higher quality service. But we also are not
convinced by Sprint's argument that maintenance expenses necessarily
are inversely proportional to density. Sprint provides no evidence
linking higher maintenance costs with lower density zones, and we can
imagine situations where there are maintenance costs in densely
populated urban areas that are not faced by carriers in low density
areas. For example, busy streets may need to be closed and traffic re-
routed, or work may need to be performed at night and workers
compensated with overtime pay.
262. We cannot determine from the ARMIS data how much of the
differences among companies are attributable to inefficiency and how
much can be explained by regional differences or other factors.
BellSouth's consultant concedes that there is nothing in the ARMIS
expense account data that would enable the Commission to identify
significant regional differences. GTE concedes that it may be difficult
to analyze some data because companies have not been required to
maintain a sufficient level of detail in their publicly available
financial records. GTE's proposed solution for reflecting variations
among states is simply to use company-specific data. Indeed, none of
the LECs propose a specific alternative to using self-reported
information from companies. For example, SBC argues we should use
company-specific expenses provided pursuant to the Protective Order to
develop company-specific costs, because these are the costs that will
be incurred by the providers of universal service.
263. While reliance on company-specific data may be appropriate in
other contexts, we find that, for federal universal service support
purposes, it would be administratively unmanageable and inappropriate.
The incumbent LECs argue that virtually all model inputs should be
company-specific and reflect their individual costs, typically by state
or by study area. As parties in this proceeding have noted, selecting
inputs for use in the high-cost model is a complex process. Selecting
different values for each input for each of the fifty states, the
District of Columbia, and Puerto Rico, or for each of the 94 non-rural
study areas, would increase the Commission's administrative burden
significantly. Unless we simply accept the data the companies provide
us at face value, we would have to engage in a lengthy process of
verifying the reasonableness of each company's data. For example, in a
typical tariff investigation or state rate case, regulators examine
company data for one-time high or low costs, pro forma adjustments, and
other exceptions and direct carriers to adjust their rates accordingly.
Scrutinizing company-specific data to identify such anomalies and to
make the appropriate adjustments to the company-proposed input values
would be exceedingly time consuming and complicated given the number of
inputs to the model. We recognize that such anomalies invariably exist
in the ARMIS data, but we find that, by using averages, high and low
values will cancel each other out.
264. Where possible, we have tried to account for variations in
cost by objective means. As we stated in the Inputs Further Notice, we
believe that expenses vary by the type of plant installed. The model
takes this variance into account because, as investment in a particular
type of plant varies, the associated expense cost also varies. The
model reflects differences in structure costs by using different values
for the type of plant, the density zone, and soil conditions.
265. As discussed, we cannot determine from the ARMIS data how much
of the differences among companies are attributable to inefficiency and
how much can be explained by regional differences or other factors. To
the extent that some cost differences are attributable to inefficiency,
using nationwide averages will reward efficient companies and provide
the proper incentives to inefficient companies to become more efficient
over time. We find that it is reasonable to use nationwide input values
for maintenance expenses because they provide an objective measure of
forward-looking expenses. In addition, we find that using nationwide
averages in consistent with our forward-looking economic cost
methodology, which is designed to send the correct signals for entry,
investment, and innovation.
266. Bell Atlantic contends that using nationwide averages for
plant specific expenses, rather than ARMIS data disaggregated to the
study area level, defeats the purpose of a proxy model because it
averages high-cost states with low-cost states. Bell Atlantic argues
that we should use the most specific data inputs that are available,
whether region-wide, company specific, or study-area specific.
Conceding that data are not always available at fine levels of
disaggregation, Bell Atlantic contends there is no reason to throw out
data that more accurately identify the costs in
[[Page 67404]]
each area. Bell Atlantic argues that, even if the Commission does not
have current-to-book ratios for all of the ARMIS study areas, it could
use average current-to-book ratios and apply them to company-specific
ARMIS data.
267. Contrary to Bell Atlantic's contention, we do not find that
using nationwide average input values in the federal high-cost
mechanism is inconsistent with the purpose of using a cost model. In
addition to the administrative difficulties outlined, we find that
nationwide values are generally more appropriate than company-specific
input values for use in the federal high-cost model. In using the high-
cost model to estimate costs, we are trying to establish a national
benchmark for purposes of determining support amounts. The model
assumes, for example, that all customers will receive a certain quality
of service whether or not carriers actually are providing that quality
of service. Because differences in service quality can cause different
maintenance expense levels, by assuming a consistent nationwide quality
of service, we control for variations in company-specific maintenance
expenses due to variations in quality of service. Clearly, we are not
attempting to identify any particular company's cost of providing the
supported services. We are, as AT&T and MCI suggest, estimating the
costs an efficient provider would incur in providing the supported
services. We are not attempting to replicate past expenses, but to
predict what support amounts will be sufficient in the future. Because
high-cost support is portable, a competitive eligible
telecommunications carrier, rather than the incumbent LEC, may be the
recipient of the support. We find that using nationwide averages is a
better predictor of the forward-looking costs that should be supported
by the federal high-cost mechanism than any particular company's costs.
268. Estimating regional wage differences. We do not adjust our
nationwide input values for plant-specific operations expenses to
reflect regional wage differences. Most LEC commenters advocate the use
of company-specific data to reflect variations in wage rates. GTE, for
example, claims that regional wage rate differentials are reflected in
the company-specific data available from ARMIS. GTE complains that our
proposed input values suggest there is no difference in labor and
benefits costs between a company operating in Los Angeles and one
operating in Iowa. As discussed, the publicly available ARMIS expense
account data for plant-specific maintenance expenses do not provide
enough detail to permit us to verify significant regional differences
among study areas or companies based solely on labor rate variations.
For the reasons discussed, we find that we should not use company-
specific ARMIS data to estimate these expenses, but instead use input
values that reflect nationwide averages.
269. Although they would prefer that we use company-specific data,
some LEC commenters suggest that the wage differential indexes used by
the President's Pay Agent, on which we sought comment, would be an
appropriate method of disaggregating wage-related ARMIS expense data.
GTE, on the other hand, contends that these indexes are not relevant to
the telecommunications industry, because they are designed for a
specific labor sector, that is, federal employees. GTE claims that
there are numerous publicly available sources of labor statistics and
that, if we adopt an index factor, it should be specific to the
telecommunications industry.
270. We agree with GTE that, if we were to use an index to adjust
our input values for regional wage differences, it would be preferable
to use an index specific to the telecommunications industry. We looked
at other publicly available sources of labor statistics, however, and
were unable to find a data source that could be adapted easily for
making meaningful adjustments to the model input values for regional
wage differences. Specifically, we looked at U.S. Department of Labor,
Bureau of Labor Statistics (BLS) information on wage rate differentials
for communications workers comparing different regions of the country.
The Employment Cost Indexes calculated by BLS identify changes in
compensation costs for communications workers as compared to other
industry and occupational groups. In a number of the indexes,
communications is not broken out separately, but is included with other
service-producing industries: transportation, communication, and public
utilities; wholesale and retail trade; insurance, and real estate; and
service industries. In making regional comparisons, the Employment Cost
Indexes divide the nation into four regions: northeast, south, midwest,
and west. There also are separate indexes comparing metropolitan areas
to other areas.
271. We find that the regions used in the BLS data are too large to
make any significant improvement over our use of nationwide average
numbers. For example, Wyoming is in the same region as California, but
we have no reason to believe that wages in those two states are more
comparable than wages rates in California and Iowa. That is, there is
no simple way to use the BLS data to make the type of regional wage
adjustments suggested by GTE. We note that no party has suggested a
specific data source or methodology that would be useful in making such
adjustments. Accordingly, we decline to adopt a method for adjusting
our nationwide input values for plant-specific operations expenses to
reflect regional wage differences.
272. Methodology. As discussed in this section, we adopt our
proposed methodology for calculating expense-to-investment ratios to
estimate plant-specific operations expenses. We reject arguments of
some LEC commenters that this methodology inappropriately reduces these
expense estimates.
273. Several LEC commenters generally support our methodology for
calculating expense-to-investment ratios to estimate plant-specific
operations expenses, although, as discussed, only if we use company-
specific input values. For example, GTE agrees with our tentative
conclusion that input values for each plant-specific operations expense
account can be calculated as the ratio of booked expense to current
investment, but only if this calculation is performed on a company-
specific basis. BellSouth states that ``[t]he methodology proposed by
the Commission for plant-specific expenses is very similar to the
methodology employed by BellSouth.''
274. Other LEC commenters object to our use of current-to-book
ratios to convert historic account values to current cost. Although
their arguments differ somewhat, they essentially claim that the effect
of our methodology is to reduce forward-looking maintenance expenses
and that this is inappropriate because the input values are lower than
their current maintenance expenses. AT&T and MCI counter that, if there
is any problem with our maintenance expense ratios, it is that they
reflect the servicing of too much embedded plant, which has higher
maintenance costs, and too little forward-looking plant, which has
lower maintenance costs.
275. US West asserts that, while in theory it is correct to adjust
expense-to-investment ratios using current-to-book ratios, in practice
there is a problem because the current-to-book ratio is based on
reproduction costs and the model estimates replacement costs. US West
defines reproduction cost as the cost of reproducing the existing plant
using today's prices and replacement cost as the cost of replacing the
existing plant with equipment that harnesses new technologies and is
priced at
[[Page 67405]]
today's prices. US West claims that our methodology actually increases
the mismatch between historic and forward-looking investment levels
because the reproduction costs are not the same as the replacement
costs. We agree that reproduction costs are not the same as replacement
costs because the mix of equipment and technology will differ, but we
disagree with US West's characterization of this as a mismatch.
276. US West estimates that applying current-to-to book ratios to
existing investment would generate reproduction costs that are 141
percent higher than historic costs. US West claims that, in contrast,
forward-looking models generally show that the cost of replacing those
facilities would be slightly less than historic costs, if new
technologies were deployed. US West's claim that our methodology
results in a mismatch because of these cost differences, however, is
wrong. Rather, the differences between reproduction costs and
replacement costs merely show that the mix of technologies has changed.
The hypothetical example US West uses to illustrate its argument fails
to account for changes in technology. The following hypothetical
example illustrates how changes in the mix of technology will change
maintenance expenses. If historic investment on a company's books
consists of 100 miles of copper plant, at a cost of $10 per mile, and
10 miles of fiber plant, at a cost of $1 per mile, then the historic
cost is $1010. If current maintenance costs are $10 for the copper
plant and $0.10 for the fiber plant, the total maintenance expense is
$10.10. If the price of copper increases to $15 per mile and the price
of fiber decreases to 80 cents per mile, then the reproduction costs
would increase to $1508. If the forward-looking model designs a network
with 60 miles of copper and 50 miles of fiber, the resulting
replacement cost is $940. Using our methodology, we use the current-to-
book ratios of 1.5 ($15/$10) and .8 (80 cents divided by $1) to revalue
the copper and fiber investment, respectively, at current prices, and
the resulting maintenance expense for the forward-looking plant would
be $6.58 rather than $10.10. This does not result in a mismatch. In our
hypothetical example, the maintenance costs for fiber were
substantially less on a per-mile basis than they were for copper. Thus,
we would expect the forward-looking plant with considerably more fiber
and less copper to have lower maintenance costs than the current plant,
which has more copper. Because the mix of plant changes, the Commission
should not, as US West suggests, simply adjust book investment to
current dollars to derive maintenance expenses for the forward-looking
plant estimated by the model.
277. Sprint argues that we should simply divide the current year's
actual expense for each account by the average plant balance associated
with that expense. Sprint claims that, when this ratio is applied to
the investment calculated by the model, forward-looking expense
reductions occur in two ways: (1) the investment base is lower due to
the assumed economies of scale in reconstructing the forward-looking
network all at one time; and (2) greater use of fiber in the forward-
looking network reduces maintenance costs because less maintenance is
required of fiber than of the copper in embedded networks. Sprint
claims that reducing maintenance for a current-to-book ratio as well as
for technological factors constitutes a ``double-dip'' in maintenance
expense reduction.
278. Sprint's claim that our methodology constitutes a ``double
dip'' in reducing maintenance expenses is misleading because the effect
of using current-to-book ratios depends upon whether current costs have
risen or fallen relative to historic costs. Current-to-book ratios are
used to restate a company's historic investment account balances, which
reflect investment decisions made over many years, in present day
replacement costs. Thus, if current costs are higher than historic
costs for a particular investment account, the current-to-book ratio
will be greater than one, and the expense-to-investment ratio for that
account will decrease when the investment (the denominator in the
ratio) is adjusted to current replacement costs. Sprint calls this
double dipping because copper costs have risen and the model uses less
copper plant than that which is reflected on Sprint's books. If current
costs are lower than historic cost, however, the current-to-book ratio
will be less than one and the adjusted expense-to-investment ratio for
that account will increase when the investment (the denominator in the
ratio) is adjusted to current replacement costs. Fiber cable and
digital switching costs, for example, have fallen relative to historic
costs. Sprint essentially is arguing that our methodology is wrong
because it understates Sprint's historical costs. The input values we
select are not intended to replicate a particular company's historic
costs, for the reasons discussed.
279. SBC disputes our assumption that the model takes into account
variations in the type of plant installed because, as investment in a
particular type of plant varies, so do the associated expense costs.
SBC argues that expenses do not vary simply because investment varies.
Nonetheless, SBC believes that developing a ratio of expense to
investment and applying it to forward-looking investments is a
reasonable basis for identifying forward-looking plant specific
expenses. SBC complains that our methodology is inconsistent, however,
because it has defined two completely different sets of forward-looking
investments: one based on historical ARMIS investments adjusted to
current amounts; and another derived on a bottom-up basis employing the
cost model. Until we reconcile these ``inconsistencies,'' SBC
recommends that we use unadjusted historical investment amounts in
developing plant specific expense factors, because they are closer to
SBC's historical plant specific expenses.
280. Although they characterize the issue somewhat differently, US
West, Sprint, and SBC essentially argue that our methodology is wrong
because it understates their historical costs. AT&T and MCI counter
that a forward-looking network often will result in lower costs than an
embedded network and that the trend in the industry has been to develop
equipment and practices to minimize maintenance expense. AT&T and MCI
claim that, if there is any problem with our maintenance expense
ratios, it is that they reflect the servicing of too much embedded
plant, which has higher maintenance costs, and too little forward-
looking plant, which has lower maintenance costs. AT&T and MCI further
claim that, if our analysis had been based exclusively on financial
information that reflected equipment consistent with the most-efficient
forward-looking practices, the maintenance expenses would have been
lower.
281. None of the commenters provide a compelling reason why we
should not use current-to-book ratios to adjust historic investment to
current costs. SBC in fact suggests that the Commission consider using
the Telephone Plant Index (TPI) in future years to convert expense
estimates to current values. SBC appears to be confusing the effect of
measuring inputs in current dollars, which it recognizes is reasonable,
and the end result of the calculation, which includes the impact of
measuring all inputs in current dollars, changes in the mix of inputs,
the impact of least-cost optimal design used by the model, and the
model's engineering criteria. The relationship between maintenance
costs and investment in the Commission's
[[Page 67406]]
methodology is related to all of these factors.
282. Sprint also claims that our methodology understates
maintenance costs, because it assumes new plant and the average
maintenance rate will be higher than the rate in an asset's first year.
AT&T and MCI dispute Sprint's claim that maintenance costs per unit of
plant increase over time. Sprint provides an example which purports to
show that an asset with a ten year life, a ten percent maintenance fee
in the first year, and annual costs increasing annually at three
percent, would result in an average maintenance rate of 11.55 percent.
Sprint's example, however, does not consistently apply our methodology.
Sprint's example fails to apply the current-to-book ratio to the total
and average plant in service estimates used in the example. When the
current-to-book ratio is applied to the total and average plant in
service estimates, the resulting maintenance rate is ten percent for
all years.
283. BellSouth argues that the investment calculated by the model
is unrealistically low because sharing assigned to the telephone
company is unrealistically low and fill factors are unrealistically
high. BellSouth argues that, because it has shared in cost of
trenching, this does not mean the maintenance cost for buried cable
would be less, and in fact, the costs may be higher. BellSouth
apparently is confused about the Commission's methodology, because the
sharing percentages apply only to the costs of structure, not the costs
of the cable.
B. Common Support Services Expenses
284. Consistent with our tentative conclusions, we adopt input
values that estimate the average common support services expenses that
will be incurred by non-rural carriers on a per-line basis, rather than
a set of company-specific common support services expenses. We affirm
our tentative conclusion that input values for corporate operations,
customer service, and plant non-specific expenses should be estimated
on a nationwide basis, rather than a more disaggregated basis. As
noted, we find that for universal service purposes nationwide averages
are more appropriate than company-specific values. We conclude that we
should use Specification 1 of our proposed regression methodology to
estimate expenses for ARMIS accounts 6510 (Other Property, Plant, and
Equipment); 6530 (Network Operations); 6620 (Service Expense/Customer
Operations); and 6700 (Executive, Planning, General, and
Administrative). As discussed, we use an alternative methodology to
estimate expenses for ARMIS account 6610 (Marketing). We conclude that
we should use 1998 ARMIS data in both methodologies, and an estimate of
1998 Dial Equipment Minutes of Use (DEMs) in the regression equation,
to calculate these input values. We clarify that the ARMIS data we use
to calculate these estimates are based on ARMIS accounts for all non-
rural ARMIS-filing companies. We find that it is appropriate to include
only data from the non-rural ARMIS-filing companies in calculating the
expense per line for common support services expenses.
285. Current Data and Use of Productivity Factor. The input values
we adopt in this Order contains a summary of the per-line, per-month
input values for plant non-specific expenses, corporate operations
expenses, and customer services expenses, including regression results,
calculations, and certain adjustments made to the data based on the
methodologies described. Because we used 1996 ARMIS data in our
regression methodology to estimate our proposed input values for common
support services expenses, we proposed a method of converting those
estimates to 1999 values. Specifically, we proposed using a
productivity factor of 6.0 percent for the years 1997 and 1998 to
reduce the estimated input values. We further proposed adjusting the
expense data for those years with an inflation factor based on the
Gross Domestic Product Price Index (GDP-PI) in order to bring the input
values up to current expenditure levels.
286. AT&T and MCI claim that the 6.0 productivity factor is too
low, while most LEC commenters contend that it is too high. Sprint
argues that expenses should not be adjusted for a productivity or an
inflation factor and that we should use 1998 data. GTE argues that no
productivity adjustments are necessary, if we use current, company-
specific ARMIS data to develop input values. Although we generally
decline to adopt company-specific input values for common support
services expenses, we agree that using the most currently available
ARMIS data (1998) obviates the need to adjust our estimates for either
productivity gains or an inflation factor at this time. We believe,
however, that there should be an incentive for increased productive
efficiency among carriers receiving high-cost universal service
support. Accordingly, we believe that a reasonable productivity measure
or some other type of efficiency incentive to decrease costs associated
with common support services expenses should be incorporated into the
universal service high-cost support mechanism in the future. We intend
to address this issue in the proceeding on the future of the model.
287. The input values we adopt in this Order are estimates of the
portion of company-wide expenses that should be supported by the
federal high-cost mechanism. We derive the estimates using standard
economic analysis and forecasting methods. The analysis relies on
publicly available 1998 ARMIS expense data and the most current minutes
of use information from NECA. This data is organized by study area. The
estimate of 1998 DEMs is based on a calculated growth rate of 1997 to
1996 DEMs reported by NECA. As a result of deleting rural ARMIS-filing
companies and including company study area changes since 1996, pooling
of the 1998 data sets provides expense, minutes of use, and line count
data for 80 study areas. This is in comparison to the 91 study areas
resulting from pooling the 1996 data described in the Inputs Further
Notice.
288. Some parties object to our using data at the study area level,
because they claim that ARMIS-filing companies report data in two
distinct ways. Ameritech and US West argue that parent companies
generally assign a significant portion of plant non-specific and
customer operations expenses across their operating companies on the
basis of an allocation mechanism. As a result, they claim that a simple
regression on the study area observations will produce coefficients
that reflect a blend of two relationships: the cost-based relationship
and the allocation-based relationship, of which only the former is
appropriate to measure. They argue further that it is necessary to
model the allocation method explicitly, to net out the latter data, or
to aggregate the data to the parent company level. Although we
acknowledge that our accounting rules provide carriers with some
flexibility, we expect that the allocation mechanism used by the parent
company represents underlying cost differences among its study areas.
We find that it is reasonable to assume that the companies use
allocation mechanisms that are based on cost relationships to allocate
costs among their study areas. Accordingly, we find that it is
reasonable to use ARMIS data at the study area level in the regression
methodology.
289. Regression Methodology. As described in the Inputs Further
Notice, we adopt standard multi-variate regression analysis to
determine the portion of corporate operations expenses, customer
services expenses,
[[Page 67407]]
and plant non-specific expenses attributable to the services that
should be supported by the federal high-cost mechanism. We adopt an
equation (Specification 1) which estimates total expenses per line as a
function of the percentage of switched lines, the percentage of special
lines, and toll minutes per line. We use this regression methodology to
estimate the expenses attributable to universal service for the
following accounts:
Other Property, Plant, and Equipment (6510); Network Operations
(6530); Service Expense/Customer Operations (6620); and Executive,
Planning, General and Administrative (6700).
We adopt this specification, rather than an average of the two
specification estimates suggested in the Inputs Further Notice, to
separate the portion of expenses that could be estimated as
attributable to special access lines and toll usage, which are not
supported by the federal high-cost mechanism, from switched lines and
local usage. As explained, we use an adjusted weighted average of study
areas to estimate the support expense attributable to Account 6610,
Marketing.
290. Several parties contend that our regression analysis is
flawed. Sprint, for example, claims that we have exaggerated the
significance of our statistical findings beyond a level justified by
the regression result; and have made the often-committed error of
interpreting our regression results in a way that implies causality. US
West argues that, although there is a causal relationship between the
level of expenses and the variables we use in the regression, the
coefficient of determination or R2 is fairly low, which
implies that the causal relationship only explains a small portion of
the total costs. GTE claims that our regression is mis-specified
because it utilizes only the mix of output as explanatory variables,
and excludes important variables related to differences in input prices
and production functions. Because of this mis-specification and the
omitted variables, GTE also claims that our equations have a low
predictive ability, as measured by the R\2\s.
291. We disagree with commenters who claim that there is little
explanatory value in our regression analysis. In accounts 6620, 6700,
6530 the regressions explain a high degree of the variability in the
expense variables. Only account 6510 (Other Property, Plant, and
Equipment) has a low R\2\, which is not surprising given the reported
data in this account. Based on the 1998 ARMIS data, the resulting
regression coefficient for this expense category is negative due to the
numerous negative expenses reported by carriers in 1998. Because the
ARMIS reports represent actual 1998 expenses incurred by the non-rural
telecommunications companies within their various study areas, we find
that it is appropriate to include this negative expense in our
calculations. We note, however, that inclusion of this account in our
calculations represents less than one percent of the total expense
input for common support services expenses.
292. We believe that our regressions represent a cost-causative
relationship, and that common support services expenses are a function
of the number of total lines served, plus the volume of minutes.
Because in the long run, all costs are variable, we disagree with
commenters who suggest that our methodology is flawed because we do not
include an intercept term in our regression equation to represent fixed
or start-up costs. As discussed, the model is intended to estimate
long-run forward-looking cost over a time period long enough so that
all costs may be treated as variable and avoidable. Moreover, the
federal high-cost mechanism calculates support on a per-line basis,
which is distributed to eligible carriers based upon the number of
lines they serve. We would not provide support to carriers with no
lines. Nor would we vary support, which is portable, between an
incumbent and a competitive eligible telecommunications carrier, based
on differences in their fixed or start-up costs. We explicitly assume,
therefore, that if a company has zero lines and zero minutes, it should
have zero expenses. Thus, we have no constant or fixed cost in our
regressions. We also believe that these expenses are driven by the
number of channels, not the number of physical lines.
293. That is, our assumptions imply that expenses are a linear
function of lines and minutes. We next need to separate out the common
support services expenses related to special access lines and toll
minutes, because these services are not supported by the federal high-
cost mechanism. Therefore, we split the lines variable into switched
and special access lines, and we split the minutes variable into local
and toll minutes. In this modified equation, expenses are a function of
switched lines, plus special access lines, plus local minutes, plus
toll minutes. We believe that changes in local minutes, however, should
not cause changes in common support services expenses that are not
already reflected in the expenses associated with switched lines. We
find that it is reasonable to assume that local calls do not increase
these overheard costs in the same way that toll minutes do. For
example, in most jurisdictions local calls are a flat-rated service and
additional local calling requires no additional information on the
customer's bill. With toll calling, however, even subscribers that have
some kind of a calling plan receive detailed information about those
calls. It is reasonable to assume that adding an additional line on a
subscriber's bill for a toll call causes overhead costs that are not
caused by local calls. Moreover, toll calling outside a carrier's
serving area involves the costs associated with completing that call on
another carrier's network. As discussed, we tested our assumption that
local calls do not affect costs in the same way that toll calls do by
running the regressions to include local minutes. Based on theory and
our analysis, we decided to drop the local minutes variable, so that
expenses are a function of switched lines, plus special access lines,
plus toll minutes. Because we are calculating a per-line expense
estimate, we divide all the variables by the total number of lines to
derive our final equation: expenses divided by total lines equals the
percentage of switched lines, plus the percentage of special lines,
plus toll minutes divided by total lines.
294. US West claims that our regressions may not be based on
appropriate cost-causative relationships, because we count special
access lines by channels and not by physical pairs. The ARMIS data used
in the regressions count special lines as channels. That is, special
access lines are counted as DS0 equivalents: a DS1 has 24 channels, and
a DS3 has 672 channels. US West contends that it is far from clear how
this method of counting special access lines reflects how these
services cause expenses, because it is clear that DS1s and DS3s are not
priced as if they cause 24 and 672 times the amount of expenses as a
narrowband line.
295. The fact that DS1s and DS3s are priced differently in the
current marketplace does not imply that it is improper to count lines
as channels. US West's suggested alternative, counting special lines as
physical pairs, would assume that a residential customer with two lines
causes the same amount of overhead expenses as a special access
customer with one DS1 line. To the contrary, we find that it is
reasonable to assume that more overhead expenses are devoted to winning
and keeping the DS1 customer than the residential customer. Further, we
expect that more overhead expenses are related to customers using
higher capacity services than those using lower capacity
[[Page 67408]]
services. Accordingly, we find that it is reasonable to use channel
counts in our regression equations.
296. Some commenters also criticized our regression analysis on the
grounds that variables are highly correlated and that the predicted
coefficients are not stable. In particular, US West claims that the
confidence intervals and standard errors are large and that a dividing-
the-sample experiment leads to drastically different results. While
these commenters are correct that the correlation values are high for
the raw variables, the values are not high once the variables under
consideration are adjusted by dividing by total lines. We find that the
correlation values are all very reasonable. We note, in particular, the
-1 correlation between switched lines and special lines. The fact that
switched lines plus special lines equals one is the reason the
regression cannot be run with a separate constant. We note that our
parameterization has switched lines, special lines, and toll minutes as
explanatory variables. We have chosen not to include local minutes in
our regressions for theoretical reasons. So, the key correlation values
are the correlations of toll minutes with special lines and with
switched lines. We find that those values are reasonable.
297. Several commenters suggested that we use local minutes as an
explanatory variable. Despite our tentative conclusion that our
regressions should not include local minutes as a variable, in response
to these comments, we re-ran each of the regressions with local minutes
per line as an additional variable. In three of the four regressions,
the coefficient for local minutes was not significant at the five
percent level, and for account 6700, its sign was the opposite of what
was expected. The resulting difference in the estimated expenses
attributable to supported services was very small in magnitude as well.
If we used the local minutes variable in our parameterization, after
summing across all expense accounts, our per-line, per-month estimate
for a switched line would be approximately $0.01 more. Given our belief
that local minutes should not influence these expenses, the lack of
significance in the coefficients, and the overall lack of impact when
the variable was consistently included in the regressions, we conclude
that we should not include local DEMs per line in our specifications.
298. Except for the inclusion of local minutes as a variable, no
commenters have suggested a better parameterization or methodology for
using the ARMIS data to estimate expense inputs for these accounts.
Further, no commenters have suggested an alternative publicly available
data set to use for our estimation of expense input values. We
acknowledge that there is substantial variation in the underlying
expense data taken from the ARMIS reports. Common support services
expenses often contain charges unrelated to the specified relationships
in the regression equation. For example, there are many one-time
expenses and non-recurring charges associated with these accounts. We
have tried to limit the effect of this problem by making adjustments to
the expense data, as discussed. Given the data limitations and the
parameterization we have chosen, we find that the estimated
coefficients are the best estimate of the applicable expenses,
regardless of the resulting standard errors.
299. Removal of One-Time Expenses. In the Inputs Further Notice, we
discussed our efforts to adjust estimates of common support services
expenses to account for one-time and non-recurring expenses. We sought
comment on the need for information about and estimates of various
types of exogenous costs and common support service expenses that are
recovered through non-recurring charges and tariffs. These expenses
include specific one-time charges for the cost of mergers or
acquisitions and process re-engineering, and network and interexchange
carrier connection, disconnection, and re-connection (i.e., churn)
costs.
300. In the Inputs Further Notice, we tentatively concluded that we
should not use an analysis submitted by AT&T and MCI to estimate one-
time and non-recurring expenses for corporate and network operations
expenses. This analysis averaged five years (1993-1997) of data from
Security and Exchange Commission (SEC) 10-K and 10-Q filings for all
tier one companies to identify and calculate a percentage estimate of
corporate and network operations expenses classified as one-time and
non-recurring charges associated with these types of activities. Our
tentative conclusion not to rely on the AT&T and MCI analysis to make
these adjustments was based on the fact that we were using 1996 ARMIS
data to estimate the expense inputs. Because the SEC reports do not
indicate whether the one-time expenses were actually made solely during
a specific year indicated, we tentatively concluded that we could not
use the analysis' five year average or the actual 1996 SEC figures to
make adjustments to the 1996 ARMIS data. In the Inputs Further Notice,
we noted however that the AT&T and MCI analysis indicates that one-time
expenses for corporate and network operations can be significant. We
sought comment on how to identify and estimate one-time and non-
recurring expenses associated with these common support services.
301. AT&T and MCI disagree with our tentative decision to reject
their one-time cost estimates and argue that it is better to estimate
one-time costs through use of the SEC reports, although these reports
may imperfectly establish the precise date of the occurrence, than to
fail to exclude these costs at all. Although some LEC commenters may
agree that we should adjust our estimates to exclude one-time and non-
recurring expenses, they provide no data or methodology to accomplish
this, other than suggesting that we should get this information from
the companies. GTE claims that unless companies implement specific
tracking mechanisms, these data are not generally or easily identified
after the fact.
302. We now reconsider our tentative conclusion not to use the
analysis submitted by AT&T and MCI to adjust our network and corporate
operations expense estimates to account for one-time and non-recurring
expenses. We do so for a number of reasons. First, we received no
additional information on publicly available data sources or other
reasonable methods to estimate these one-time and non-recurring costs
at this time. Second, the problems associated with determining the
actual costs of 1996 one-time expenses based on the SEC reports are
obviated because we are using 1998 expense data to estimate the
forward-looking input values. We find that using the estimated average
of one-time costs over the five preceding years (1993-1997) to adjust
1998 data is a reasonable method to determine the impact of costs
related to mergers and acquisitions and work force restructuring.
Further, we believe any adjustments for one-time costs based on the
AT&T and MCI analysis may be biased downward after comparing the number
of companies involved in these types of activities in 1998 and 1999 to
those in 1993-1997. Accordingly, we adjust downward estimated expenses
in account 6530 (Network Operations) by 2.6 percent and in account 6700
(Executive, Planning, General, and Administrative) by 20 percent.
303. Removal of Non-Supported Expenses. In the Inputs Further
Notice, we also discussed our efforts to adjust marketing and other
customer service expenses to account for recurring expenses that are
not related to services supported by the federal high-cost mechanism.
The non-supported expenses we attempted to identify include vertical
features expenses,
[[Page 67409]]
billing and collection expenses not related to supported services,
operational support systems and other expenses associated with
providing unbundled network elements and wholesale services to
competitive local exchange carriers. We proposed adjustments to extract
non-supported service costs related to marketing, coin operations,
published directory, access billing, interexchange carrier office
operation, and service order processing. Specifically, we made
percentage reductions to the regression coefficient results for
specific expense accounts based on a time trend analysis of average
ARMIS 43-04 expense data for five years (1993-1997).
304. Some commenters argue that our proposed methodology removes
non-supported services twice because these expenses were already taken
out by the regression when expenses are subdivided among switched
lines, special lines, and toll minutes. Although we agree, as
discussed, that our methodology double counted the marketing expenses
associated with special access lines, we do not agree with the theory
that combining a percentage reduction with the regression methodology
invariably removes expenses twice. For example, vertical features
associated with switched lines such as call waiting are not supported,
but the expenses associated with call waiting are not removed using the
regression analysis. If we had the data to separately identify and
remove vertical features expenses from switched lines, we believe that
it would be appropriate to do so and to continue using the regression
analysis to separate the remaining expenses. Nonetheless, upon further
analysis, we find that we should not adopt our proposed method of
removing these non-supported recurring expenses. We find that this
method is not sufficient to adequately identify non-supported common
support service expenses due to differences in account classifications
from the ARMIS 43-03 and ARMIS 43-04 reports. Therefore, we do not
utilize the time trend analysis or take reductions for these non-
supported expenses in the input values at this time. We recognize that
this causes an overstatement of in our estimate of the expenses
attributable to supported services in account 6620 (Service Expense and
Customer Operations). Unlike the case with marketing, however, we do
not have an alternative source of information on which to base a
methodology for removing the non-supported expenses in this account. We
plan to seek comment on a verifiable and systematic method to identify
and remove these costs in the proceeding on the future of the model.
305. Marketing. As explained in the Inputs Further Notice, we made
an adjustment to the Account 6610 (Marketing) regression coefficient
based on an analysis made by Economics and Technology, Inc. (ETI). The
ETI analysis offered a method for disaggregating product management,
sales, and advertising expenses for basic (residential) telephone
service from total marketing costs. Based on information from the New
England Telephone Cost Study, ETI attributed an average of 95.6 percent
of company marketing costs to non-supported customers or activities,
such as vertical and new services. Relying on this analysis, we reduced
the input estimate to reflect 4.4 percent of marketing expenses
determined by the regression. In the Inputs Further Notice, we
tentatively concluded that this was the most accurate method on the
record for apportioning marketing expenses between supported and non-
supported services.
306. We agree with commenters that, in making this adjustment to
the post-regression analysis input estimate, we incorrectly estimated
marketing expenses because reductions were taken twice for special
access lines. We agree with the commenters that any adjustments to
exclude expenses based on the type of service should be made from total
relevant marketing expenses rather than the regression results.
Therefore, we do not use the regression methodology to estimate
marketing expenses. Instead, using the 1998 ARMIS data, we adjust the
total weighted average of relevant expenses for all study areas.
307. Commenters also point out that the adjustment figure of 4.4
percent based on the ETI Study as initially reported was determined
under the assumption that only expenses attributable to residential
local service would be supported. Further, the ETI estimate of costs
associated with the marketing of supported services was calculated by
taking a percentage of expenses only from Account 6611, Product
Management. Specifically, the ETI estimate did not include any relevant
expenses from Account 6613, Product Advertising. As noted in the Inputs
Further Notice, funding support for marketing is to be based on those
expenses associated with advertising. Section 214 of the Communications
Act requires eligible telecommunications carriers to advertise the
availability of residential local exchange and universal service
supported services. Moreover, we note that under the current high cost
loop support mechanism, carriers receive no support for marketing.
308. We received further documentation and an alternative analysis
from ETI which included an estimate for advertising expenditures. The
revised analysis included proportional allocations of advertising costs
based on the percentage of lines estimated for primary line residential
service and single-line business service. ETI also used line count
source material from the Preliminary Statistics of Common Carriers 1998
rather than relying on 1996 data used in its original analysis.
309. Based on the new information provided and the lack of any
reasonable alternative presented by the commenters, we calculate an
input estimate of supported advertising expenses using the ETI study
and 1998 ARMIS expenses. By adding a proportional allocation for multi-
line business advertising expenses to the ETI alternative analysis
(which only included an estimate representing primary line and single
line business advertising costs), we conclude that 34.4 percent of
Account 6613, Product Advertising, would be the most appropriate
expense amount for the advertising of universal service. Because the
additional data provided by ETI allowed for the calculation and
estimate of supported and non-supported advertising expenditures, we
did not allocate costs associated with product management or sales. As
previously mentioned, these marketing activities are not specifically
required for support under section 214 of the Communications Act and
currently receive no high cost loop support. Taking 34.84 percent of
total 1998 advertising expenses for the 80 non-rural high cost study
areas and dividing by total lines per month, the average per line per
month input value for advertising support is $0.09. This level of
advertising expenses represents 5.82 percent of total 1998 marketing
costs for non-rural carriers.
310. Local Number Portability. There is an additional input value
that we estimate separately from our consideration of other expense
input values. Specifically, the synthesis model has a user-adjustable
input for the per-line costs associated with local number portability
(LNP). In the Inputs Further Notice, we proposed a per-line monthly LNP
cost of $0.39, based on a weighted average of the LNP rates filed by
the LECs available at that time. AT&T and MCI point out that the
Commission suspended and investigated some of those rates, and that the
rates we approved are generally lower than the
[[Page 67410]]
rates we used to estimate our LNP input value. They argue that we
should use the line-weighted nationwide average of approved LNP rates,
which they estimate currently is $.032. GTE claims that there is no
justification for using the nationwide average LNP rate, as suggested
by AT&T and MCI, because the approved LNP rates provide the best
representation of each company's LNP costs. We agree with GTE and in
this instance depart from our general practice of using nationwide
input values in the federal universal service support mechanism.
Because the Commission has investigated and approved LNP rates for most
LECs, we find that it is appropriate to use the company-specific input
values listed. For those carriers that have not yet filed an LNP
tariff, we will use the line-weighted nationwide average of approved
LNP rates.
C. GSF Investment
311. We conclude that the model's preliminary estimates of GSF
investment should be reduced in the third step of the algorithm,
because we find that only a portion of GSF investment is related to the
cost of providing the services supported by the federal mechanism. In
response to certain comments, however, we modify our proposed
allocation factor, as discussed. Although we reject commenters'
arguments that the preliminary GSF investment should not be reduced at
all, we agree that we should not exclude facility-related maintenance
expenses in our proposed allocation factor. In addition, we modify our
method of calculating the denominator of our allocation factor so that
both the numerator and denominator are simple averages. Finally, we
clarify that the ARMIS TPIS used in the first step of the algorithm
excludes ARMIS GSF investment.
312. Reduction of Preliminary GSF Estimate. Several LEC commenters
argue that the preliminary GSF investment should not be reduced by an
allocator in the third step of the algorithm. SBC contends that the
factor we use to reduce our preliminary GSF investment estimates
substantially underestimates the GSF amounts related to the supported
services. SBC claims that the ratios used to estimate the preliminary
GSF investment already provides a reasonable basis for allocating GSF
to supported services, because the GSF ratio (derived from the ARMIS
accounts) is only applied to investment identified by the model as
associated with supported services. BellSouth also claims that the TPIS
calculated by the model is the investment necessary to provide the
supported services and that no further reductions in the preliminary
GSF investment estimate are appropriate. Sprint similarly claims that
by applying a book GSF ratio to the forward-looking plant necessary to
provide supported services, the modeled GSF plant also has been
converted to a forward-looking level necessary to provide the supported
services. Sprint contends that applying an additional allocator is not
necessary and has the effect of reducing GSF plant twice.
313. We disagree with SBC's contention that only a portion of GSF
is assigned to supported services in deriving our preliminary estimates
of GSF investment. To the contrary, the GSF ratio is applied to all
model investment, which includes the investment required to provide
both supported and non-supported services. As discussed, the model
estimates the cost of providing services for all businesses and
households within a geographic region, including the provision of
special access, private lines, and toll services. Because these
services are not supported by the federal high-cost mechanism, the
preliminary GSF investment estimate must be adjusted to reflect the
portion of GSF investment attributable to the supported services. Thus,
BellSouth's assertion that the TPIS calculated by the model is the
investment necessary to provide the supported services is wrong. For
the same reasons, we reject Sprint's argument that, by applying the
book GSF ratio, the modeled GSF plant has somehow been converted to a
forward-looking level necessary to provide the supported services. On
the contrary, the conversion estimates the amount of GSF investment
attributable to all services, supported and non-supported. The second
reduction is required to estimate the amount of GSF investment that
should be supported by the federal universal service support mechanism.
314. Allocation Factor. Assuming that we use an allocator to reduce
preliminary GSF investment, several commenters criticize the particular
allocator that we proposed in the Inputs Further Notice. For example,
GTE questions why we used only expenses for customer operations,
network operations, and corporate operations in the allocation
calculation and excluded plant-specific expenses. GTE argues that
plant-specific operations also use GSF investments and should be
counted in the calculation. SBC also argues that GSF investment
supports all aspects of a LEC's operations, and contends that it makes
no sense to exclude facility-related maintenance expenses in our
proposed allocation factor. We agree that expenses for plant-specific
operations expenses should be included in our calculation of the
nationwide allocation factor derived from the regression methodology.
Accordingly, the allocation factor we adopt to estimate GSF investment
includes plant-specific operations expenses.
315. GTE also contends that the forward-looking way to calculate a
GSF investment ratio is to convert all ARMIS investments to current
values using current-to-book ratios, before calculating an adjusted
ARMIS GSF to TPIS investment ratio. Although we concede there is some
logic to GTE's argument that we should convert ARMIS GSF investments to
current values by using current-to-book ratios, we note that this would
require a change in the model platform. As we explain, the model
platform uses a three-step algorithm to estimate GSF investment.
Although we can easily change the input value for the factor used in
step three, we could not adjust the ARMIS data by applying a current-
to-book factor without modifying the model platform. Proposals to
change the model platform are properly addressed in response to pending
petitions for reconsideration of the Platform Order or the proceeding
on the future of the model.
316. Finally, GTE claims that our estimation of the universal
service portion of the GSF investment is flawed because our regression
methodology uses a wrong specification and incorrectly excludes
expenses. GTE also claims that the calculation allocator itself is
flawed because the numerator is a simple average of expenses derived
from the regression results, but the denominator is a weighted average
of the total expenses developed from ARMIS data. GTE argues that the
type of average in the numerator and denominator should match. While we
do not agree that our regression methodology is flawed, we find that
GTE has pointed out an inconsistency in our GSF methodology.
Specifically, we agree that we should use the same type of average in
both the numerator and denominator of our allocation factor. As a
result, we use the simple average of total expenses in the denominator
of the allocation factor we adopt for estimating the portion of GSF
attributable to supported services.
317. Clarification. BellSouth claims that the algorithm used to
estimate GSF investment contains an error in consistency. BellSouth
suggests that in step one we should determine the ratio of ARMIS-based
GSF investment to the ARMIS-based TPIS less GSF investment. In step
two, this ratio is multiplied by
[[Page 67411]]
the TPIS investment determined by the model, which excludes GSF. We
clarify that the model calculates GSF investment as BellSouth suggests
it should. That is, the model uses ARMIS-based TPIS less GSF
investment. US West claims that in the second step of the algorithm the
synthesis model includes only fifty percent of the building investment
and no land investment. The synthesis model incorporates the HAI
switching and expense modules and calculates the investment related to
wire center buildings and land in the switching module. So, US West is
mistaken that fifty percent of the building and land investment is
eliminated, because this investment is added back in calculating
switching costs.
318. For the reasons stated, we adopt input values for GSF
investment that reflect the portion of GSF investment attributable to
the cost of providing the services supported by the federal mechanism.
Specifically, we calculate preliminary GSF investment on a study area
specific basis, using 1998 ARMIS data, and then multiply these
estimates by a nationwide allocation factor derived from the regression
methodology that we used to estimate the portion of common support
services expenses attributable to switched lines and local usage and
the portion of plant-specific operations expenses attributable to the
supported services. The allocation factor is the sum of plant specific
operations expenses, customer operations expenses, network operations
expenses, and corporate operations expenses attributable to the
supported services, divided by the sum of those expenses calculated on
a total regulated basis.
VI. Capital Costs
A. Depreciation
a. Method of Depreciation
319. For the reasons explained, we adopt a straight-line equal-
life-group method of depreciation. Further, we select curve shapes to
be used to distribute equal-life groups in each plant account.
320. Most commenters support our tentative conclusion to use the
straight-line equal-life-group method of depreciation. Ameritech
argues, however, that the Commission's adoption of a straight-line
depreciation method in other contexts need not limit us to that method
for use in this model, and that ``the method of depreciation for a
specific study area needs to be consistent with any study that underlie
[sic] the development of economic lives or net salvage.'' Although
Ameritech may correctly assert that there is no requirement that we
adopt a method of depreciation simply because it is the method
previously adopted by the Commission in another context, we believe
that the Commission's adoption, in other proceedings, of the straight-
line equal-life-group method reflects the well-considered conclusion
that this method of depreciation is best-suited to determining the
economic costs of providing local service. The straight-line equal-
life-group depreciation method is also consistent with our method of
developing economic lives and net salvage for the same plant accounts.
Because the Commission consistently uses a straight-line equal-life-
group depreciation method in all other Commission-proposed
depreciation, and in light of the general support received in favor of
straight-line equal-life-group depreciation, we conclude that straight-
line equal-life-group depreciation is appropriate for use in the high-
cost support mechanism.
321. In using the straight-line equal-life-group method of
depreciation in other contexts, the Commission has acknowledged that
the method necessarily requires the selection of a curve shape for the
distribution of the equal-life groups. The HAI model assumed a single
curve shape for all plant accounts. Because the curve shapes are not
easily averaged across all categories, however, we believe that use of
the single HAI curve shape will unduly distort the model input values.
We, therefore, determine that separate curve shapes should be adopted
for each plant account category. Actuaries have developed generic,
standardized curve shapes, called Gompertz-Makeham (GM) standard
curves, that describe generalized mortality patterns. GM standard curve
shapes are recognizable to many knowledgeable parties concerned with
depreciation methods and are normally more immediately meaningful to
them than nonstandard curve shapes, which are identified by the values
for three variables. For convenience purposes, GM standard curves are
often substituted for nonstandard curves. USTA has developed
nonstandard curve shapes for most plant accounts based on mortality
data provided by its members, using the same methodology approved in
other Commission proceedings. For the remaining plant accounts, the
Commission has developed composite curves, also nonstandard, utilizing
data from available depreciation studies. Because the GM standard
curves are recognizable and convenient to parties interpreting the data
inputs in the high-cost model, and because the standardized curves will
not vary significantly from the nonstandardized curves, we conclude
that GM standard curves will be more useful in the high-cost inputs
model than nonstandard curves. For each plant category, therefore, we
adopt the GM standard curve shape nearest that developed by USTA or the
Commission.
b. Depreciation Lives and Future Net Salvage Percentages
322. We adopt the tentative conclusion of the Inputs Further Notice
that we should use HAI's input values with respect to depreciation
lives and future net salvage percentages. As explained, we reject the
objections by some commenters that the HAI input values are not
appropriate for determining depreciation rates in a competitive
environment.
323. In estimating depreciation expenses, the model uses the
projected lives and future net salvage percentages for the asset
accounts in part 32 of the Commission's rules. Traditionally, the
projected lives and future net salvage values used in setting a
carrier's rates have been determined in a triennial review process
involving the state commission, the Commission, and the carrier. In
order to simplify this process, the Commission has prescribed ranges of
acceptable values for projected lives and future net salvage
percentages. The Commission's prescribed ranges reflect the weighted
average asset life for regulated telecommunications providers. These
ranges are treated as safe harbors, such that carriers that incorporate
values within the ranges into their depreciation filings will not be
challenged by the Commission. Carriers that submit life and salvage
values outside of the prescribed range must justify their submissions
with additional documentation and support. Commission-authorized
depreciation lives are not only estimates of the physical lives of
assets, but also reflect the impact of technological obsolescence and
forecasts of equipment replacement. We believe that this process of
combining statistical analysis of historical information with forecasts
of equipment replacement generates forward-looking projected lives that
are reasonable estimates of economic lives and, therefore, are
appropriate measures of depreciation.
324. We disagree with comments by incumbent LECs that the
Commission's prescribed ranges are not appropriate for determining
depreciation rates in a competitive environment. These parties argue
that rapid changes in technology and competition in local
[[Page 67412]]
telecommunications markets will diminish asset lives significantly
below the Commission's prescribed range by causing existing equipment
to become obsolete more quickly. We agree with GSA, AT&T and MCI that
there is no evidence to support the claim that increased competition or
advances in technology require the use of shorter depreciation lives in
the model than are currently prescribed by the Commission. The
Commission's prescribed lives are not based solely on the engineered
life of an asset, but also consider the impacts of technological change
and obsolescence. We note that the depreciation values we adopt are
generally at the lower end of the prescribed range. We also find
compelling the data presented in GSA's comments showing that, although
the average depreciation rate for an incumbent LEC's Total Plant in
Service is approximately seven percent, incumbent LECs are retiring
plant at a four percent rate. This difference has allowed depreciation
reserves to increase so that the depreciation reserve-ratio is
currently greater than 50 percent. We conclude that the existence of
this difference implies that the prescribed lives are shorter than the
engineered lives of these assets. In addition, this difference provides
a buffer against technological change and competitive risk for the
immediate future. We, therefore, conclude that the Commission's
prescribed ranges are appropriate to determine depreciation rates for
use in the federal high-cost mechanism.
325. We also decline to adopt the values for projected lives and
net salvage percentages submitted by several incumbent LEC commenters.
These commenters propose adoption of default values for projected lives
and salvage based LEC industry date surveys or on similar values
currently used by LECs for financial reporting purposes. The LEC
industry data survey's projected lives generally fall outside of the
Commission's prescribed ranges. This is significant because the values
that fall outside of the prescribed ranges represent accounts that
reflect the overwhelming majority of plant investment, thus potentially
triggering a dramatic distortion of the estimated cost of providing the
supported services. Moreover, these commenters assert that
technological advances and competition will have the effect of
displacing current technologies, but offer no specific evidence that
this displacement will occur at greater rates than the forward-looking
Commission-authorized depreciation lives take into account. The record
is particularly silent regarding the displacement of technologies
associated with the provision of services supported by the federal
high-cost mechanism. We do not believe that the LEC industry data
survey's projected lives have been adequately supported by the record
in this proceeding to justify their adoption.
326. We also agree with GSA's comments that the projected-life
values currently used by LECs for financial reporting purposes are
inappropriate for use in the model. In addition, the commenters
proposing these values have not explained why the values used for
financial reporting purposes would also reflect economic depreciation.
The depreciation values used in the LECs' financial reporting are
intended to protect investors by preferring a conservative
understatement of net assets, partially achieving this goal by erring
on the side of over-depreciation. These preferences are not compatible
with the accurate estimation of the cost of providing services that are
supported by the federal high-cost mechanism. We, therefore, decline to
adopt the projected life values used by LECs for financial reporting
purposes.
327. In the 1997 Further Notice, the Commission tentatively
concluded that it should adopt depreciation expenses that reflect a
weighted average of the rates authorized for carriers that are required
to submit their rates to us. The values submitted by the HAI sponsors
essentially reflect such a weighted average. The HAI values represent
the weighted average depreciation lives and net salvage percentages
from 76 study areas. According to the HAI sponsors, these depreciation
lives and salvage values reflect the experience of the incumbent LEC in
each of these study areas in retiring plant and its projected plans for
future retirements.
328. In the Inputs Further Notice, we tentatively concluded that
HAI's values represent the best forward-looking estimates of
depreciation lives and net salvage percentages. Generally, these values
fall within the ranges prescribed by the Commission for projected lives
and net salvage percentages. Although the HAI values for four account
categories fall outside of the Commission's prescribed ranges, these
values still reflect the weighted average of projected lives and net
salvage percentages that were approved by the Commission and,
therefore, are consistent with the approach proposed in the 1997
Further Notice. As noted, the fact that an approved value falls outside
of the prescribed range simply means that the carrier proposing the
value was required to provide additional justification to the
Commission for this value. We are satisfied that HAI calculated its
proposed rates using the proper underlying depreciation factors and
that HAI's documentation supports the selection of these values. We,
therefore, adopt HAI's values for estimating the depreciation lives and
net salvage percentages.
B. Cost of Capital
329. We now adopt the conclusions that we tentatively reached in
the Inputs Further Notice regarding the cost of capital. For the
reasons discussed, we do not find that any commenter has provided a
compelling argument for altering the current federal rate of return of
11.25 percent, absent the adoption of a different rate of return by the
Commission in a rate prescription order.
330. The cost of capital represents the annual percentage rate of
return that a company's debt-holders and equity holders require as
compensation for providing the debt and equity capital that a company
uses to finance its assets. In the Universal Service Order, the
Commission concluded that the current federal rate of return of 11.25
percent is a reasonable rate of return by which to determine forward-
looking costs.
331. GSA, AT&T and MCI comment that the cost of capital for
incumbent LECs is well below 11.25 percent. Bell Atlantic advocates a
cost of capital rate in the range of 12.75 to 13.15 percent. GTE and
USTA dispute the lower cost of capital asserted by AT&T and MCI and
GSA. All commenters addressing this issue agreed that, if a different
rate of return is adopted in a rate prescription order, that value
should be adopted in the model.
332. We find that the commenters proposing an adjustment to the
cost of capital have failed to make an adequate showing to justify
rates that differ from the current 11.25 percent federal rate of
return. We conclude, therefore, that the current rate is reasonable for
determining the cost of providing services supported by the federal
high-cost mechanism. If the Commission, in a rate prescription order,
adopts a different rate of return, we conclude the federal mechanism
should use the more recently determined rate of return.
C. Annual Charge Factors
333. We also now adopt our tentative conclusion in the Inputs
Further Notice to use HAI's annual charge factor methodology. As
explained, we find this appropriate because the synthesis model uses a
modified version of HAI's expense module.
[[Page 67413]]
334. Incumbent LECs develop cost factors, called ``annual charge
factors,'' to determine the dollar amount of recurring costs associated
with acquiring and using particular pieces of investment for a period
of one year. Incumbent LECs develop these annual charge factors for
each category of investment required. The annual charge factor is the
sum of depreciation, cost of capital, adjustments to include taxes on
equity, and maintenance costs.
335. To develop annual charge factors, the BCPM proponents proposed
a model with user-adjustable inputs to calculate the depreciation and
cost of capital rates for each account. The BCPM proponents stated that
this account-by-account process was designed to recognize that all of
the major accounts have, among other things, differing economic lives
and salvage values that lead to distinct capital costs. HAI's model is
also user adjustable and reflects the sum for the three inputs:
depreciation, cost of capital, and maintenance costs. In the Inputs
Further Notice, the Commission tentatively adopted HAI's annual charge
factor methodology, and invited comment on this tentative decision. GTE
argues that the annual charge factors should be company specific, in
order to make the cost calculations in the optimization phase and the
expense module comparable. We do not believe it would be appropriate to
adopt GTE's proposal of using company-specific annual charges, because
we are adopting nationwide averages for all other inputs, including
those that make up the annual charge. Adopting company-specific annual
charges would therefore result in likely inconsistencies between
various related inputs and in the model as a whole. AT&T and MCI
support the use of the HAI annual charge factor methodology.
336. Because the synthesis model uses HAI's expense module, with
modifications, we adopt HAI's annual charge factor methodology,
utilizing the capital cost and expense inputs adopted. We believe that
HAI's annual charge factor methodology is consistent with other inputs
used in the model adopted by the Commission, and is, therefore, easier
to implement and yields more reasonable results.
VII. Proposed Modification to Procedures for Distinguishing Rural
and Non-Rural Companies
337. Consistent with our tentative conclusion in the Inputs Further
Notice, we eliminate the annual filing requirements for carriers
serving fewer than 100,000 access lines that have self-certified as
rural, unless changes occur in their status as rural carriers. In
addition, we will require carriers serving study areas with more than
100,000 access lines to file rural self-certifications that are
consistent with the statutory interpretation discussed. Thereafter,
such carriers also will be required to file only in the event of a
change in their status.
338. As discussed, we interpret ``local exchange operating
company'' in section 153(37) of the Act to refer to the legal entity
that provides local exchange service. In addition, we interpret
``communities of more than 50,000'' in that section to refer to legally
incorporated localities, consolidated cities, and census-designated
places with populations of more than 50,000 according to Census Bureau
statistics.
339. With respect to our request for comment on whether we should
reconsider our use of section 153(37) to distinguish rural telephone
companies from non-rural companies, we conclude that we should not use
an alternative definition of rural telephone company to determine which
companies are subject to the rural or non-rural high-cost support
mechanisms.
340. Because of settled expectations in this ongoing proceeding,
the Commission will accept a carrier's current rural self-certification
for purposes of calculating support based on that status for calendar
year 2000. We will require carriers serving study areas with more than
100,000 access lines to certify their rural status by July 1, 2000, for
purposes of receiving support beginning January 1, 2001.
1. Annual Filing Requirement
341. Carriers serving study areas with fewer than 100,000 access
lines. We adopt the proposed change in the annual self-certification
requirement for rural carriers and will require that carriers serving
fewer than 100,000 access lines file a rural self-certification letter
only if their status has changed since their last filing. All
commenters addressing this issue urge the Commission to eliminate
annual filing requirements. We believe that this is a better approach
because the overwhelming majority of the companies that filed rural
certification letters qualified as rural telephone companies under the
50,000- or 100,000-line thresholds identified in the statute. Access
line counts can be verified easily with publicly available data.
Further, this relaxation in filing requirements will lessen the burden
on rural carriers. We estimate that this change will eliminate the
filing requirement for approximately 1,380 of the carriers that filed
in 1998, many of which are small businesses on which even limited
regulatory requirements may be unduly burdensome. We, therefore,
conclude that carriers serving study areas with fewer than 100,000
access lines that already have certified their rural status need not
re-certify for purposes of receiving support beginning January 1, 2000,
and need only file thereafter if their status changes. As explained, we
must determine the status for carriers serving study areas with more
than 100,000 access lines.
342. We believe, as GTE suggests, that carriers generally (although
not uniformly) have filed for rural status in this proceeding on a
study area basis. Indeed, the synthesis model that has been posted on
the Commission's Web site--allowing carriers to determine how the
Commission has been treating them throughout this proceeding--estimates
cost on a study area basis. Not all carriers, however, have uniformly
filed for rural status on a study area basis, as we noted in the Inputs
Further Notice, resulting in inconsistencies that must be resolved in
order to assure equitable treatment of all carriers. These
inconsistencies will be addressed.
343. Carriers serving study areas with more than 100,000 access
lines. For purposes of calculating high cost support using the model
for the year 2000, we will continue to treat carriers as rural if they
have previously self-certified as rural carriers. We will then require
rural carriers serving study areas with more than 100,000 access lines
to file certification letters by July 1, 2000, for their year 2001
status. Commenters that address the issue broadly support re-
certification requirements that require these carriers to re-certify
only if their status has changed, rather than require them to re-
certify each year. Finding that the relaxed re-certification
requirements will reduce administrative burdens for carriers subject to
rural certification and for the Commission, we conclude that certified
rural carriers with more than 100,000 access lines need only re-certify
their status if it changes. Therefore, in 2001 and subsequent years, a
carrier serving study areas with more than 100,000 access lines and
claiming rural status will be required to file only if its status
changes.
2. Statutory Terms
344. As noted in the Inputs Further Notice, carriers' line counts
are readily available to the Commission, but information about service
territories and communities served are not. As a result, the Commission
can easily determine whether a carrier satisfies criteria (B) or (C) of
the rural telephone company definition, because these criteria are
[[Page 67414]]
based on information that can be verified easily with publicly
available data--the number of access lines served by a carrier. In
contrast, criteria (A) and (D) require additional information and
analysis to verify a carrier's self-certification as a rural company.
Specifically, under criterion (A), a carrier is rural if its study area
does not include ``any incorporated place of 10,000 inhabitants or
more'' or ``any territory * * * in an urbanized area,'' based upon
Census Bureau statistics and definitions. Under criterion (D), a
carrier is rural if it had ``less than 15 percent of its access lines
in communities of more than 50,000 on the date of enactment of the
[1996 Act].''
345. We conclude that criterion (A), by referencing Census Bureau
sources, can be applied consistently without further interpretation by
the Commission. We will require, however, that carriers self-certifying
as rural telephone companies pursuant to criterion (A) include with
their self-certification letter a description of the study areas in
which they provide service and the basis for their assertion that they
meet the requirements of criterion (A).
346. In the Inputs Further Notice, we sought comment on the meaning
of the term ``local exchange operating entity.'' Commenters have
offered three different interpretations of this term. Many commenters
suggest that we should interpret the term as applying at the study area
level. Although in most cases an operating entity will provide service
to only one study area within a state, that is not always the case. As
a result, the study area approach could mean classifying a carrier at
an organizational level smaller than the actual legal entity
responsible for the provision of the local exchange services (e.g., a
``division'' of a company). In contrast, AT&T and MCI argue that the
term should mean the holding company within a state whose affiliates
provide the local exchange services. The third interpretation has been
proposed by RTC and Citizens Utilities, who argue that the most natural
understanding of ``local exchange operating entity'' is the legal
entity responsible for the provision of local exchange services,
regardless of whether that entity serves a single or multiple study
areas. We conclude that this interpretation is the most reasonable one.
347. We believe that it is most logical to classify the carrier at
the actual corporate level through which it offers its local exchange
services. As RTC and Citizens Utilities point out, it is that entity
that has legal responsibility for the provision of the local exchange
services. The holding company interpretation proposed by MCI and AT&T
seems to rest upon the concern that study area designations will be
manipulated and, as a result, carriers will inappropriately be eligible
for support as rural carriers, when they should not be. We do not
believe that the potential for manipulation of the federal universal
service support mechanism by rural carriers poses the threat that AT&T
and MCI suggest; to the contrary, the study area waiver process
provides the Commission with oversight over the creation, division, and
combination of study areas.
348. On the other hand, if a carrier should be operating within
multiple study areas, we see no basis for interpreting the term ``local
exchange operating entity'' in a manner that would ignore the legal
entity responsible for the provision of services by designating a
subunit of the legal entity as the local exchange operating entity for
a particular study area. Rather, it is more reasonable to have the term
local exchange operating entity be synonymous with the corporate entity
bearing legal responsibility for the services provided.
349. Although we adopt Citizen Utilities' interpretation of ``local
exchange operating entity,'' we reject its proposed interpretation of
criterion (C). Citizens Utilities proposes that a local exchange
carrier operating entity be considered a rural carrier for each of its
study areas, regardless of whether those study areas have fewer than
100,000 access lines, if any single study area in which it operates
contains fewer than 100,000 access lines. Under this interpretation,
which only Citizens Utilities supports, an incumbent LEC offering
service to a significant portion of a state, including major urban
areas, could be certified as a rural carrier for all study areas that
it serves within the state if it merely has one outlying study area
with less than 100,000 access lines. We find this interpretation to be
inconsistent with the statutory language that an entity is an rural
telephone company only ``to the extent'' that it serves a study area
with fewer than 100,000 lines. Essentially, Citizens Utilities'
interpretation would read that limiting language out of section
153(37). The effect of such a reading would be to permit some of the
largest LECs in the nation to claim rural status for all of their study
areas if they happen to serve a rural study area within in the state.
Such an interpretation would undermine not only the Commission's
universal service support mechanisms, but also the fundamental
procompetitive policies underlying the 1996 Act. We do not believe that
this could be what Congress intended when it specified that carriers
would be deemed rural telephone companies ``to the extent'' that they
satisfied the various criteria, including criterion (C) pertaining to
serving study areas with less than 100,000 access lines. Accordingly,
consistent with the language of the statutory provision, its purpose,
and its context in the Act, we adopt the interpretation that a LEC may
be properly considered a rural carrier with respect to those study
areas to which its operating company provides service to fewer than
100,000 access lines. In contrast, a LEC will be deemed a non-rural
carrier for study areas serving more than 100,000 access lines unless
it satisfies one of the other criteria under section 153(37).
350. We also sought comment in the Inputs Further Notice regarding
the proper interpretation of ``communities of more than 50,000.'' GTE
offers an interpretation of this phrase based on the definition of
``rural area'' in Sec. 54.5 of the Commission's rules. GTE calculates
its percentages of rural and non-rural lines by determining whether
each of its wire centers is associated with a metropolitan statistical
area (MSA). The lines in each wire center associated with an MSA are
considered to be urban, unless the wire center has rural pockets, as
defined by the most recent Goldsmith Modification. The approach
suggested by GTE in its comments has merit because it prevents rural
treatment of a suburban area adjacent to a census-designated place. At
this time, however, there is no information on the record to indicate
that this circumstance presents a serious problem in our determination
of a carrier's status as a rural or non-rural company. Other commenters
addressing the issue support the definition of ``communities of more
than 50,000'' by using Census Bureau statistics for legally
incorporated localities, consolidated cities, and census-designated
places, and some specifically reject the use of the Commission's
definition in Sec. 54.5 because of the added complication of its use.
351. Because GTE's approach is more complicated and difficult to
administer and because the consequences of the approach would reach
only a few, if any, rural carriers' study areas, we decline to adopt
GTE's interpretation of ``communities of more than 50,000.'' Instead,
we now adopt the use of Census Bureau statistics for legally
incorporated localities, consolidated, cities, and census-designated
places for identifying communities of more than 50,000, as Census
Bureau statistics are widely
[[Page 67415]]
available and may be consistently applied by the Commission. We further
require that, when a carrier files for rural certification under
criterion (D), it must include in its certifying letter a list of all
communities of more than 50,000 to which it provides service, the
population of those communities, the number of access lines serving
those communities, and the total number of access lines the carrier
serves.
3. Identification of Rural Telephone Companies
352. States apply the definition of rural telephone company in
determining whether a rural telephone company is entitled to an
exemption under section 251(f)(1) of the Act and in determining, under
section 214(e)(2) of the Act, whether to designate more than one
carrier as an eligible telecommunications carrier in an area served by
a rural telephone company. Although the Commission used the rural
telephone company definition to distinguish between rural and non-rural
carriers for purposes of calculating universal service support, there
is no statutory requirement that it do so. The Commission adopted the
Joint Board's recommendation to allow rural carriers to receive support
based on embedded costs for at least three years, because, as compared
to large LECs, rural carriers generally serve fewer subscribers, serve
more sparsely populated areas, and do not generally benefit as much
from economies of scale and scope. The Commission also noted that, for
many rural carriers, universal service support provides a large share
of the carriers' revenues, and thus, any sudden change in the support
mechanisms may disproportionately affect rural carriers' operations.
353. In the Inputs Further Notice, we sought comment on whether to
reconsider the means of distinguishing rural and non-rural carriers.
Commenters generally oppose any reconsideration of our decision to use
the definition of rural telephone company to distinguish between rural
and non-rural carriers for the purpose of evaluating universal service
support on the grounds that changing the definition at this time could
disrupt the settled expectations that they have developed. We agree
that we should not change our reliance on the statutory definition of
rural telephone company to distinguish between rural and non-rural
carriers for universal service purposes. Accordingly, we will leave in
place the Commission's decision to use the definition of rural
telephone company in section 153(37) of the Communications Act to
distinguish rural telephone companies from non-rural ones.
VIII. Appendices
354. Appendix A contains the input values adopted in this Order for
use in the synthesis model. Appendix B explains the methodology used
for estimating the input values for outside plant structure and cable
costs. Appendix C describes the methodology used for estimating the
input values for switching costs. Appendix D describes the methodology
used for estimating the input values for expenses, including: the
development of expense to investment ratios; the regression equations
used to estimate common support services expenses; the analysis used to
estimate marketing expenses; local number portability rates for
particular companies; and the formula used to calculate the general
support facilities allocation factor.
IX. Procedural Matters and Ordering Clause
A. Final Regulatory Flexibility Analysis
355. As required by the Regulatory Flexibility Act (RFA), an
Initial Regulatory Flexibility Analysis (IRFA) was incorporated in the
Inputs Further Notice. The Commission sought written public comment on
the proposals in the Inputs Further Notice, including comments on the
IRFA. The Final Regulatory Flexibility Analysis (FRFA) in this Order
conforms to the RFA, as amended.
356. Need for and Objectives of This Order. In the Universal
Service Order, the Commission adopted a plan for universal service
support for rural, insular, and high-cost areas to replace longstanding
federal subsidies to incumbent local telephone companies with explicit,
competitively neutral federal universal service mechanisms. In doing
so, the Commission adopted the recommendation of the Joint Board that
an eligible carrier's support should be based upon the forward-looking
economic cost of constructing and operating the networks facilities and
functions used to provide the services supported by the federal
universal service mechanism.
357. In the Universal Service Order, the Commission also determined
that rural and non-rural carriers will receive federal universal
service support determined by separate mechanisms until at least
January 1, 2001. The Commission stated that it would define rural
carriers as those carriers that meet the statutory definition of a
rural telephone company in section 153(37) of the Communications Act.
We have found that carriers self-certifying as rural have not always
applied section 153(37) uniformly. We clarify our interpretation of
section 153(37). We also address the possibility that our annual self-
certification requirements may be modified or eliminated in order to
reduce the reporting burden on filing entities.
358. Our plan to adopt a mechanism to estimate forward-looking
costs for larger, non-rural carriers has proceeded in two stages. On
October 28, 1998, the Commission completed the first stage of this
proceeding: the selection of the model platform. The platform
encompasses the aspects of the model that are essentially fixed,
primarily assumptions about the design of the network and network
engineering. In this Order, we complete the second stage of this
proceeding, by selecting input values for the cost model, such as the
cost of cables, switches and other network components, in addition to
various capital cost parameters.
359. Summary and Analysis of the Significant Issues Raised by
Public Comments in Response to the IRFA. No comments were received
specifically in response to the IRFA. We received several comments,
however, addressing concerns that may affect small entities. These
comments universally supported our proposal, adopted in this Order, to
reduce the burden of carriers self-certifying as rural by eliminating
the annual filing requirement.
360. Description and Estimate of the Number of Small Entities to
which the Order will Apply. The RFA generally defines ``small entity''
as having the same meaning as the term ``small business,'' ``small
organization,'' and ``small government jurisdiction.'' In addition, the
term ``small business'' has the same meaning as the term ``small
business concern'' under the Small Business Act, unless the Commission
has developed one or more definitions that are appropriate to its
activities. Under the Small Business Act, a ``small business concern''
is one that: (1) is independently owned and operated; (2) is not
dominant in its field of operation; and (3) meets any additional
criteria established by the SBA. The SBA has defined a small business
for Standard Industrial Classification (SIC) category 4813 (Telephone
Communications, Except Radiotelephone) to be small entities when they
have no more than 1,500 employees.
361. We have included small incumbent LECs in this present RFA
analysis. As noted, a ``small business'' under the RFA is one that,
inter alia, meets the pertinent small business size standard (e.g., a
telephone
[[Page 67416]]
communications business having 1,500 or fewer employees), and ``is not
dominant in its field of operation.'' The SBA's Office of Advocacy
contends that, for RFA purposes, small incumbent LECs are not dominant
in their field of operation because any such dominance is not
``national'' in scope. We have therefore included small incumbent LECs
in this RFA analysis, although we emphasize that this RFA action has no
effect on Commission analyses and determinations in other, non-RFA
contexts.
362. Local Exchange Carriers. Neither the Commission nor SBA has
developed a definition of small providers specifically directed toward
LECs. The closest applicable definition under SBA rules is for
telephone communications companies other than radiotelephone (wireless)
companies. The most reliable source of information regarding the number
of LECs nationwide of which we are aware appears to be the data that we
collect annually in connection with the Telecommunications Relay
Service (TRS). According to our most recent data, 1,410 companies
reported that they were engaged in the provision of local exchange
service as incumbents. Although it seems certain that some of these
carriers are not independently owned and operated, or have more than
1,500 employees, we are unable at this time to estimate with greater
precision the number of LECs that would qualify as small business
concerns under SBA's definition. Consequently, we estimate that there
are fewer than 1,410 small entity LECs that may be affected by this
Order. We also note that, with the exception of our clarification of
the definition of rural carrier under section 153(37) and the
modification of reporting requirements, the rules adopted by this Order
apply only to larger, non-rural LECs.
363. Description of Projected Reporting, Recordkeeping, and Other
Compliance Requirements. This Order imposes no new reporting,
recordkeeping, or other compliance requirements. As discussed, this
Order immediately eliminates the requirement that carriers serving
study areas with fewer than 100,000 access lines must annually file
letters certifying themselves as rural carriers in order to remain in
the rural carrier universal service support mechanism. Further, this
Order eliminates, after the July 1, 2000, filing deadline, the
requirement that rural carriers serving study areas with more than
100,000 access lines must file annual self-certification letters. All
rural carriers must, however, notify the Commission in the event of a
change in rural status.
364. The overall effect of this Order will be to reduce reporting,
recordkeeping, and other compliance requirements for small entities.
This benefit will apply to all carriers deemed rural under section
153(37), regardless of whether they are a small or large entity.
Carriers serving study areas with fewer than 100,000 access lines--
which are more likely to be small entities than those serving study
areas with more than 100,000 access lines--will be most immediately
benefited, as no further filings will be required of them unless and
until their rural status changes. The largest carriers will generally
be non-rural and not affected by this change in reporting. To the
extent that large and small entities are treated differently,
therefore, small entities will not carry a disproportionately high cost
of compliance.
365. Steps Taken to Minimize Significant Economic Impact on Small
Entities and Significant Alternatives Considered. As noted, with
respect to reporting requirements affecting small entities, we
eliminate the burden of an annual filing requirement for rural
carriers. For carriers serving study areas with fewer than 100,000
access lines, this change is effective immediately. Rural carriers
serving study areas with more than 100,000 access lines will be
required to file a self-certification letter by July 1, 2000, but will
not be required to refile additional annual certifications unless their
status changes. These changes have at their heart consideration of the
resources of small entities, and will reduce, if not eliminate, the
costs of compliance for small entities. The alternative to this
approach would have been to require additional unnecessary self-
certification letters from the vast majority of filing carriers, even
though the data supporting those self-certifications are easily
verified by publicly available documentation. The other changes to
Commission rules that we adopt in this Order affect only larger, non-
rural LECs, and should have no direct affect on small entities.
366. Report to Congress. The Commission will send a copy of this
Order, including this FRFA, in a report to be sent to Congress pursuant
to the Small Business Regulatory Enforcement Fairness Act of 1996. In
addition, the Commission will send a copy of this Order, including
FRFA, to the Chief Counsel for Advocacy of the Small Business
Administration. A copy of this Order and FRFA (or summaries thereof)
will also be published in the Federal Register.
B. Paperwork Reduction Act Analysis
367. The decision herein has been analyzed with respect to the
Paperwork Reduction Act of 1995, Pub. L. 104-13, and has been approved
in accordance with the provisions of that Act. On August 4, 1999, the
Office of Management and Budget approved the proposed requirements
contained in the Inputs Further Notice under OMB control number 3060-
0793.
C. Ordering Clauses
368. It is ordered, pursuant to sections 1, 4(i) and (j), 201-209,
218-222, 254, and 403 of the Communications Act, as amended, 47 U.S.C.
151, 154(i), 154(j), 201-209, 218-222, 254, and 403 that this Report
and Order is hereby adopted.
369. It is further ordered that the Commission's Office of Public
Affairs, Reference Operations Division, shall send a copy of this
Report and Order, including the Final Regulatory Flexibility Analysis,
to the Chief Counsel for Advocacy of the Small Business Administration.
List of Subjects
47 CFR Part 36
Reporting and recordkeeping requirements, Telephone.
47 CFR Part 54
Universal service.
47 CFR Part 69
Communications common carrier.
Federal Communications Commission.
Magalie Roman Salas,
Secretary.
[FR Doc. 99-30877 Filed 11-30-99; 8:45 am]
BILLING CODE 6712-01-P
