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

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

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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

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Platform Order that certain types of roads and road segments should be

excluded because they are unlikely to be associated with customer

locations. In addition, we note that PNR's reliance on the Census

Bureau's TIGER files ensures a degree of reliability and availability

for review of much of the data underlying PNR's road surrogate

algorithm, in compliance with criterion eight of the Universal Service

Order. The PNR road surrogate algorithm is also generally supported by

commenters addressing this issue. While AT&T and MCI advocate the use

of actual geocode data points, AT&T and MCI endorse the PNR road

surrogate algorithm to identify surrogate locations in the absence of

actual geocode data. We therefore affirm our tentative conclusion in

the Inputs Further Notice and adopt the PNR road surrogate algorithm

and data set to determine customer locations for use in the model

beginning on January 1, 2000.

3. Methodology for Estimating the Number of Customer Locations

15. In addition to selecting a source of customer data, we also

must select a methodology for estimating the number of customer

locations within the geographic region that will be used in developing

the customer location data. In addition, we must determine how demand

for service at each customer location should be estimated and how

customer locations should be allocated to each wire center. In the

Inputs Further Notice, we tentatively concluded that PNR's methodology

for estimating the number of customer locations based on households

should be used for developing the customer location data. In addition,

we also tentatively concluded that we should use PNR's methodology for

estimating the demand for service at each location, and for allocating

customer locations to wire centers. We now affirm these tentative

conclusions.

16. In the Universal Service Order, the Commission concluded that a

``model must estimate the cost of providing service for all businesses

and households within a geographic region.'' The Commission has sought

comment on the appropriate method for defining ``households,'' or

residential locations, for the purpose of calculating the forward-

looking cost of providing supported services. Interested parties have

proposed alternative methods to comply with this requirement.

17. AT&T, MCI, and Ameritech support the methodology devised by

PNR, which is based upon the number of households in each Census Block,

while BellSouth, GTE, SBC, USTA, and US West propose that we use a

methodology based upon the number of housing units in each Census

Block. A household is an occupied residence, while housing units

include all residences, whether occupied or not.

18. In the Inputs Further Notice, we tentatively adopted the use of

the PNR National Access Line Model, as proposed by AT&T and MCI, to

estimate the number of customer locations within Census Blocks and wire

centers. The PNR National Access Line Model uses a variety of

information sources, including: survey information; the LERG; Business

Location Research (BLR) wire center boundaries; Dun & Bradstreet's

business database; Metromail's residential database; Claritas's

demographic database; and U.S. Census Bureau estimates. PNR's model

uses these sources in a series of steps to estimate the number of

residential and business locations, and the number of access lines

demanded at each location. The model makes these estimates for each

Census Block, and for each wire center in the United States. In

addition, each customer location is associated with a particular wire

center. We conclude that PNR's process for estimating the number of

customer locations should be used for developing the customer location

data. We also conclude that we should use PNR's methodology for

estimating the demand for service at each location, and for allocating

customer locations to wire centers. We believe that the PNR methodology

is a reasonable method for determining the number of customer locations

to be served in calculating the cost of providing supported services.

19. PNR's process for estimating the number of customer locations

results in an estimate of residential locations that is greater than or

equal to the Census Bureau's estimate of households, by Census Block

Group, and its estimate is disaggregated to the Census Block level.

PNR's estimate of demand for both residential and business lines in

each study area will also be greater than or equal to the number of

access lines in the Automated Reporting and Management Information

System (ARMIS) for that study area.

20. The BCPM model relied on many of the same data sources as those

used in PNR's National Access Line Model. For example, BCPM 3.1 used

wire center data obtained from BLR and business line data obtained from

PNR. In estimating the number of residential locations, however, the

BCPM model used Census Bureau data that include household and housing

unit counts from the 1990 Census, updated based upon 1995 Census Bureau

statistics regarding household growth by county. In addition, rather

than attempting to estimate demand by location at the Block level, the

BCPM model builds two lines to every residential location and at least

six lines to every business.

21. A number of commenters contend that the total cost estimated by

the model should include the cost of providing service to all possible

customer locations, even if some locations currently do not receive

service. Some commenters further contend that, if total cost is based

on a smaller number of locations, support will not be sufficient to

enable carriers to meet their carrier-of-last-resort obligations. These

commenters argue that basing the estimate of residential locations on

households instead of housing units will underestimate the cost of

building a network that can provide universal service. They therefore

assert that residential locations should be based on the number of

housing units--whether occupied or unoccupied. These commenters contend

that only this approach reflects the obligation to provide service to

any residence that may request it in the future.

22. Some commenters also contend that the PNR National Access Line

Model has not been made adequately available for review. As noted, the

National Access Line Model is a multi-step process used to develop

customer location counts and demand and associate those customer

locations with Census Blocks and wire centers. As a result, PNR

contends that the National Access Line Model cannot be provided in a

single, uniform format. The HAI sponsors have provided a description of

the National Access Line Model process in the HAI model documentation.

PNR has made the National Access Line Model process available for

review through on-site examination and has provided more detailed

explanation of the National Access Line Model upon request from

interested parties. PNR notes that several parties have taken advantage

of this opportunity. PNR also notes that the National Access Line Model

computer code is available for review on-site. PNR also has filed with

the Commission the complete output of the National Access Line Model

process. In addition, Bell Atlantic and Sprint argue that the National

Access Line Model produces line counts that vary significantly from

actual line counts.

23. In adopting the PNR approach for developing customer location

counts, we note that the synthesis model currently calculates the

average cost per line by dividing the total cost of serving customer

locations by the current number of lines. Because the current

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number of lines is used in this average cost calculation, we agree with

AT&T and MCI that the total cost should be determined by using the

current number of customer locations. As AT&T and MCI note, ``the key

issue is the consistency of the numerator and denominator'' in the

average cost calculation. According to AT&T and MCI, other proposed

approaches result in inconsistency because they use the highest

possible cost in the numerator and divide by the lowest possible number

of lines in the denominator, and therefore result in larger than

necessary support levels. AT&T and MCI also assert that, in order to be

consistent, housing units must be used in the determination of total

lines if they are used in the determination of total costs. MCI points

out that ``[i]f used consistently in this manner, building to housing

units as GTE proposes is unlikely to make any difference in cost per

line.'' Although SBC advocates the use of housing units, it agrees that

the number of lines resulting from this approach should also be used in

the denominator of any cost per line calculation to prevent the

distortion noted by AT&T and MCI. We agree with AT&T and MCI that, as

long as there is consistency in the development of total lines and

total cost, it makes little difference whether households or housing

units are used in determining cost per line. For the reasons discussed,

we believe that PNR's methodology based on households is less complex

and more consistent with a forward-looking methodology than housing

units.

24. To the extent that the PNR methodology includes the cost of

providing service to all currently served households, we conclude that

this is consistent with a forward-looking cost model, which is designed

to estimate the cost of serving current demand. As noted by AT&T and

MCI, adopting housing units as the standard would inflate the cost per

line by using the highest possible numerator (all occupied and

unoccupied housing units) and dividing by the lowest possible

denominator (the number of customers with telephones).

25. If we were to calculate the cost of a network that would serve

all potential customers, it would not be consistent to calculate the

cost per line by using current demand. In other words, it would not be

consistent to estimate the cost per line by dividing the total cost of

serving all potential customers by the number of lines currently

served. The level and source of future demand, however, is uncertain.

Future demand might include not only demand from currently unoccupied

housing units, but also demand from new housing units, or potential

increases in demand from currently subscribing households. We also

recognize that population or demographic changes may cause future

demand levels in some areas to decline. Given the uncertainty of future

demand, we noted in the Inputs Further Notice that we are concerned

that including such a highly speculative cost of future demand may not

reflect forward-looking cost and may perpetuate a system of implicit

support. Ameritech and AT&T and MCI also note that adopting the

proposed conservative fill factors will ensure sufficient plant to deal

with any customer churn created as a result of temporarily vacant

households.

26. In addition, we do not believe that including the cost of

providing service to all housing units would necessarily promote

universal service to unserved customers. We note that there is no

guarantee that carriers would use any support derived from the cost of

serving all housing units to provide service to these customers. Many

states permit carriers to charge substantial line extension or

construction fees for connecting customers in remote areas to their

network. If that fee is unaffordable to a particular customer, raising

the carrier's support level by including the costs of serving that

customer in the model's calculations would have no effect on whether

the customer actually receives service. In fact, as long as the

customer remains unserved, the carrier would receive a windfall. We

recognize that providing service to currently unserved customers in

such circumstances is an important universal service goal and the

Commission is addressing this issue more directly in another

proceeding.

27. We also find that interested parties have been given a

reasonable opportunity to review and understand the National Access

Line Model process for developing customer counts. The HAI sponsors

have documented the process by which the National Access Line Model

derives customer location counts and PNR has made itself available to

respond to inquiries from interested parties. The National Access Line

Model is a commercially licensed product developed by PNR, and we do

not find it unreasonable for PNR to place some restriction on its

distribution to the public. In addition, we agree that the National

Access Line Model is more correctly characterized as a process

consisting of several steps, and therefore we find no practical

alternative to on-site review. Even if it were possible for PNR to turn

the National Access Line Model over to the public in a single format,

we believe that this would be of limited utility without a detailed

explanation of the entire process. We therefore conclude that PNR has

made reasonable efforts to ensure that interested parties understand

the underlying process by which the National Access Line Model develops

customer counts and has made that process reasonably available to

interested parties. In addition, unlike the case with PNR's geocode

data points, PNR's road surrogate customer location points are

available for review and comparison by interested parties.

28. In response to Bell Atlantic and Sprint's concern regarding the

line counts generated by the National Access Line Model, we note that

the line count data proposed in the Inputs Further Notice had been

trued up by PNR to 1996 ARMIS line counts. We subsequently have

modified those data to reflect the most currently available ARMIS data.

Accordingly, the input values that we adopt in this Order will true up

the line counts generated by the National Access Line Model to 1998

ARMIS line counts. While the Commission has requested line count data

from the non-rural LECs, no party has suggested, and we have not been

able to discern, any feasible way of associating such data with wire

centers used in the model. The Commission intends to continue to review

this issue in addressing future refinements to the forward-looking cost

model.

29. In the Inputs Further Notice, we also noted that the accuracy

of wire center boundaries is important in estimating the number of

customer locations. PNR currently uses BLR wire center information to

estimate wire center boundaries. As noted, the BCPM model also uses BLR

wire center boundaries, as does Stopwatch Maps in its road surrogate

customer location files. A few commenters support the use of BLR wire

center boundaries, noting widespread use by the model proponents.

Others advocate the use of actual wire center boundaries. These

commenters acknowledge, however, that this information is generally

considered confidential and may not be released publicly by the

incumbent LEC. We conclude that the BLR wire center boundaries are the

best available data that are open to inspection and that they provide a

reasonably reliable estimation of wire center boundaries. We note that

both the BCPM and HAI proponents have utilized the BLR wire center data

in their respective models. While use of actual wire center boundaries

may be preferable, we agree that such information is currently

unavailable or proprietary. We therefore approve the

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use of the BLR wire center boundaries in the current customer location

data set.

III. Outside Plant Input Values

A. Introduction

30. In this section, we consider inputs to the model related to

outside plant. The Universal Service Order's first criterion specifies

that ``[t]he technology assumed in the cost study or model must be the

least-cost, most efficient, and reasonable technology for providing the

supported services that is currently being deployed.'' Thus, while the

model uses existing incumbent LEC wire center locations in designing

outside plant, it does not necessarily reflect existing incumbent LEC

loop plant. Indeed, as the Commission stated in the Platform Order,

``[e]xisting incumbent LEC plant is not likely to reflect forward-

looking technology or design choices.'' The Universal Service Order's

third criterion specifies that ``[o]nly long-run forward-looking costs

may be included.'' We select input values consistent with these

criteria.

31. As the Commission noted in the Platform Order, outside plant,

or loop plant, constitutes the largest portion of total network

investment, particularly in rural areas. Outside plant investment

includes the copper cables in the distribution plant and the copper and

optical fiber cables in the feeder plant that connect the customers'

premises to the central office. Cable costs include the material costs

of the cable, as well as the costs of installing the cable.

32. Outside plant consists of a mix of aerial, underground, and

buried cable. Aerial cable is strung between poles above ground.

Underground cable is placed underground within conduits for added

support and protection. Buried cable is placed underground but without

any conduit. A significant portion of outside plant investment consists

of the poles, trenches, conduits, and other structure that support or

house the copper and fiber cables. In some cases, electric utilities,

cable companies, and other telecommunications providers share structure

with the LEC and, therefore, only a portion of the costs associated

with that structure are borne by the LEC. Outside plant investment also

includes the cost of the SAIs and DLCs that connect the feeder and

distribution plant.

B. Engineering Assumptions and Optimizing Routines

33. As noted in the Inputs Further Notice, the model determines

outside plant investment based on certain cost minimization and

engineering considerations that have associated input values. In the

Inputs Further Notice, we recognized that it was necessary to examine

certain input values related to the engineering assumptions and

optimization routines in the model that affect outside plant costs.

Specifically, we tentatively concluded that: (1) The optimization

routine in the model should be fully activated; (2) the model should

not use T-1 feeder technology; and (3) the model should use rectilinear

distances and a ``road factor'' of one.

1. Optimization

34. When running the model, the user has the option of optimizing

distribution plant routing via a minimum spanning tree algorithm

discussed in the model documentation. The algorithm functions by first

calculating distribution routing using an engineering rule of thumb and

then comparing the cost with the spanning tree result, choosing the

routing that minimizes annualized cost. The user has the option of not

using the distribution optimization feature, thereby saving a

significant amount of computation time, but reporting network costs

that may be significantly higher than with the optimization. The user

also has the option of using the optimization feature only in the

lowest density zones.

35. In reaching our tentative conclusion that the model should be

run with the optimization routine fully activated in all density zones,

we recognized that using full optimization can substantially increase

the model's run time. We noted that a preliminary analysis of

comparison runs with full optimization versus runs with no optimization

indicated that, for clusters with line density greater than 500, the

rule of thumb algorithm results in the same or lower cost for nearly

all clusters. Accordingly, we sought comment on whether an acceptable

compromise to full optimization would be to set the optimization factor

at ``-p500,'' as described in the model documentation.

36. We adopt our tentative conclusion that the model should be run

with the optimization routine fully activated in all density zones when

the model is used to calculate the forward-looking cost of providing

the services supported by the federal mechanism. The first of the ten

criteria pronounced by the Commission to ensure consistency in

calculations of federal universal support specifies that ``[t]he

technology assumed in the cost study or model must be the least-cost,

most efficient, and reasonable technology for providing the supported

services that is currently being deployed.'' As we explained in the

Inputs Further Notice, running the model with the optimization routine

fully activated complies with this requirement. In contrast, running

the model with the optimization routine disabled may result in costs

that are significantly higher than with full optimization. The majority

of commenters that address the optimization issue support the use of

full optimization. GTE opposes any implementation of optimization.

37. We agree with AT&T and MCI and GTE that it is inappropriate to

deviate from full optimization merely to minimize computer run time.

While the rule of thumb algorithm generally results in costs that are

approximately the same as the spanning tree algorithm for dense

clusters, for some dense clusters the spanning tree algorithm will

result in lower costs. For this reason, we believe that any choice in

maximum density clusters in which the minimum spanning tree algorithm

is not applied may result in an arbitrary overestimate of costs for

some clusters. Accordingly, running the model with full optimization is

consistent with ensuring that the model uses the least-cost, most

efficient, and reasonable distribution plant routings for providing the

supported services.

38. As explained, the model seeks to minimize costs by selecting

the lower of the cost estimates from the spanning tree algorithm and

the rule of thumb algorithm. Both GTE and US West challenge the

selection of the routing that minimizes annualized cost on the basis of

a comparison between an engineering rule of thumb and the spanning tree

result. US West claims that use of the rule of thumb approach is

inappropriate because combining it with the spanning tree analytical

approach to determine the amount of needed plant biases the results

downward and will produce inappropriately low results.

39. We find that US West's concerns are misplaced. Contrary to US

West's characterization, the rule of thumb used in the model is not an

averaging methodology. Instead, it is a methodology that determines a

sufficient amount of investment to serve each customer in every cluster

using a standardized approach to network design. This approach connects

every populated microgrid cell to the SAI using routes which are placed

along the vertical and horizontal boundaries of the microgrid cells

constructed in the distribution algorithm. The rule-of-thumb algorithm

is somewhat similar in

[[Page 67377]]

its functioning to the so-called ``pinetree'' methodology proposed by

both the early HAI and BCPM models for building feeder plant. Thus, the

rule of thumb provides an independent calculation of sufficient outside

plant for each cluster. The minimum spanning tree algorithm connects

drop terminal points to the SAI using a more sophisticated algorithm in

which routes are not restricted to following the vertical and

horizontal boundaries of microgrid cells. The algorithm ``chooses'' a

path independently of the set route structure defined by the rule-of-

thumb, but still connects all drop terminals to the SAI. Since both the

rule of thumb algorithm and the spanning tree algorithm use currently

available technologies and generate investments that are sufficient to

provide supported services, an approach which selects the minimum cost

based on an evaluation of both of the algorithms is fully consistent

with cost minimization principles.

40. We also disagree with GTE's assertion that the optimization

routine should be disabled because it disproportionately affects lower

density areas where universal service is needed most. The task of the

model is to estimate the cost of the least-cost, most-efficient network

that is sufficient to provide the supported services. Moreover, we note

that the model does not determine the level of high-cost support

amounts. We have taken steps in our companion order to ensure that

sufficient support is provided for rural and high-cost areas.

41. We also reject GTE's claim that the optimization routine does

not work as intended. GTE bases this contention on the observation that

in some instances when the optimization factor is increased from -p100

to -p200 (i.e. going from density zones less than or equal to 100 lines

per square mile to density zones less than or equal to 200 lines per

square mile), both loop investment and universal service requirements

increase. This, according to GTE, would not happen if the optimization

worked properly.

42. We disagree. Optimizing the distribution plant is not

synonymous with optimizing the entire network. Because the model's

optimization routine optimizes distribution and feeder sequentially,

and the starting point for the optimization of feeder plant is the

distribution plant routing chosen, there are occasions when the optimal

feeder plant will be more costly than it would be if distribution plant

and feeder plant had been optimized simultaneously. In some cases, the

lower distribution investment produced by the optimization routine may

be offset by higher feeder investment, resulting in higher total

outside plant costs than produced by the rule of thumb algorithm.

Contrary to GTE's assertion, this phenomenon does not demonstrate that

the optimization works improperly. To the contrary, it demonstrates

that optimization occurs properly within the constraints of the model's

design.

43. Moreover, we conclude that such rare occurrences do not

outweigh the benefits of the optimization routine. The magnitude of the

difference between the network cost produced by the optimization

routine in these instances and the rule of thumb algorithm is de

minimis. Furthermore, altering the model to optimize distribution

investment and feeder investment simultaneously would greatly add to

the complexity of the model.

2. T-1 Technology

44. A user of the model also has the option of using T-1 on copper

technology as an alternative to analog copper feeder or fiber feeder in

certain circumstances. T-1 is a technology that allows digital signals

to be transmitted on two pairs of copper wires at 1.544 Megabits per

second (Mbps). If the T-1 option is enabled, the optimizing routines in

the model will choose the least cost feeder technology among three

options: analog copper; T-1 on copper; and fiber. For serving clusters

with loop distances below the maximum copper loop length, the model

could choose among all three options; between 18,000 feet and the fiber

crossover point, which earlier versions of the model set at 24,000

feet, the model could choose between fiber and T-1, and above the fiber

crossover point, the model would always use fiber. In the HAI model, T-

1 technology is used to serve very small outlier clusters in locations

where the copper distribution cable would exceed 18,000 feet.

45. In the Inputs Further Notice, we tentatively concluded that the

T-1 option in the model should not be used at this time. We noted that

the only input values for T-1 costs on the record were the HAI default

values and tentatively found that, because the model and HAI model use

T-1 differently, it would be inappropriate to use the T-1 technology in

the model based on these input values. We also noted that the BCPM

sponsors and other LECs maintained that T-1 was not a forward-looking

technology and therefore should not be used in the model. Other sources

indicated that advanced technologies, such as HDSL, could be used to

transmit information at T-1 or higher rates. We sought comment on this

issue. We also sought comment on the extent to which HDSL technology

presently is being used to provide T-1 service.

46. We conclude that the T-1 option should not be employed in the

current version of the model. We agree with those commenters addressing

this issue that traditional T-1 using repeaters at 6000 foot intervals

is not a forward-looking technology. While HDSL and other DSL variants

are forward-looking technologies, we do not at this time have

sufficient information to determine appropriate input values for these

technologies for use in the model. We conclude, therefore, that use of

T-1 in the optimization routine as an alternative to analog copper or

digital fiber feeder for certain loops under 24,000 feet is not

appropriate at this time. Accordingly, the model will be run for

universal service purposes with the T-1 option disabled.

3. Distance Calculations and Road Factor

47. In the distribution and feeder computations within the model,

costs for cable and structure are computed by multiplying the route

distances by the cost per foot of the cable or the structure facility,

which depends on capacity and terrain factors. Distances between any

two points in the network are computed using either of two distance

functions. The model allows a separate road factor for each distance

function, and every distance measurement made in the model is

multiplied by the designated factor. Road factors could be computed by

comparing average distances between geographic points along actual

roads with distances computed using either of the two distance

functions. Given sufficient data, these factors could be computed at

highly disaggregated levels, such as the state, county, or individual

wire center.

48. In the Inputs Further Notice, we tentatively concluded that the

model should use rectilinear distance in calculating outside plant

distances, rather than airline distance, because rectilinear distance

more accurately reflects the routing of telephone plant along roads and

other rights of way. We also tentatively concluded that the road factor

in the model, which reflects the ratio between route distance and road

distance, should be set equal to one. In addition, we asked whether we

should use airline miles with wire center specific road factors as an

alternative to rectilinear distance.

49. We reaffirm our tentative conclusion that the model should use

rectilinear distance rather than airline distance in calculating

outside plant

[[Page 67378]]

distances. As we noted in the Inputs Further Notice, research suggests

that, on average, rectilinear distance closely approximates road

distances. We agree with SBC that the calculation of outside plant

distances should reflect the closest approximation to actual route

conditions and road distance. We also conclude that it would be

inappropriate to use airline distance in the model without

simultaneously developing a process for determining accurate road

factors (which would be uniformly greater than or equal to 1 in this

case). While the use of geographically disaggregated road factors may

merit further investigation, we note that the absence of such a data

set on the record at this time precludes our ability to adopt that

approach. We therefore conclude that the model should use a rectilinear

distance metric with a road factor of one.

C. Cable and Structure Costs

1. Nationwide Values

50. As discussed in this section, we adopt nationwide average

values for estimating cable and structure costs in the model rather

than company-specific values. In reaching this conclusion, we reject

the explicit or implicit assumption of most LEC commenters that

company-specific values, which reflect the costs of their embedded

plant, are the best predictor of the forward-looking cost of

constructing the network investment predicted by the model. We find

that, consistent with the Universal Services Order's third criterion,

the forward-looking cost of constructing a plant should reflect costs

that an efficient carrier would incur, not the embedded cost of the

facilities, functions, or elements of a carrier. We recognize that

variability in historic costs among companies is due to a variety of

factors and does not simply reflect how efficient or inefficient a firm

is in providing the supported services. We reject arguments of the

LECs, however, that we should capture this variability by using

company-specific data rather than nationwide average values in the

model. We find that using company-specific data for federal universal

service support purposes would be administratively unmanageable and

inappropriate. Moreover, we find that averages, rather than company-

specific data, are better predictors of the forward-looking costs that

should be supported by the federal high-cost mechanism. Furthermore, we

note that we are not attempting to identify any particular company's

cost of providing the supported services. We are estimating the costs

that an efficient provider would incur in providing the supported

services.

51. AT&T and MCI agree that nationwide input values generally

should be used for the input values in the model. AT&T and MCI concur

with our tentative conclusion that the use of nationwide values is more

consistent with the forward-looking nature of the high-cost model

because it mitigates the rewards to less efficient companies.

Additionally, AT&T and MCI maintain that developing separate inputs

values on a state-specific, study-area specific, or holding company-

specific basis is not practicable. As AT&T and MCI contend, doing so

would be costly and administratively burdensome.

52. While reliance on company-specific data may be appropriate in

other contexts, we find that for federal universal service support

purposes it would be administratively unmanageable and inappropriate.

The incumbent LECs argue that virtually all model inputs should be

company-specific and reflect their individual costs, typically by state

or by study area. For example, GTE claims that the costs that an

efficient carrier incurs to provide basic service vary among states and

even among geographic areas within a state. GTE asserts that the only

way for the model to generate accurate estimates, i.e., estimates that

reflect these differences, is to use company-specific inputs rather

than nationwide input values. As parties in this proceeding have noted,

however, selecting inputs for use in the high-cost model is a complex

process. Selecting different values for each input for each of the

fifty states, the District of Columbia, and Puerto Rico, or for each of

the 94 non-rural study areas, would increase the Commission's

administrative burden significantly. Unless we simply accept the data

the companies provide us at face value, we would have to engage in a

lengthy process of verifying the reasonableness of each company's data.

For example, in a typical tariff investigation or state rate case,

regulators examine company data for one time high or low costs, pro

forma adjustments, and other exceptions and direct carriers to adjust

their rates accordingly. Scrutinizing company-specific data to identify

such anomalies and to make the appropriate adjustments to the company-

proposed input values to ensure that they are reasonable would be

exceedingly time consuming and complicated given the number of inputs

to the model.

53. Where possible, we have tried to account for variations in

costs by objective means. As explained, the model reflects differences

in structure costs by using different values for the type of plant, the

density zone, and geological conditions. As discussed, we sought

comment in the Inputs Further Notice on alternatives to nationwide

plant mix values, but the algorithms on the record produce biased

results. We continue to believe that varying plant mix by state, study

area, or region of the country may more accurately reflect variations

in forward-looking costs and intend to seek further comment on this

issue in the future of the model proceeding.

2. Preliminary Cable Cost Issues

54. Use of 24-gauge and 26-gauge Copper. In the Inputs Further

Notice, we tentatively concluded that the model should use both 24-

gauge and 26-gauge copper in all available pair-sizes. We based our

tentative conclusion on a preliminary analysis of the results of the

structure and cable cost survey, in which it appeared that a

significant amount of 24-gauge copper cable in larger pair sizes

currently is being deployed. We also noted that, while HAI default

values assume that all copper cable below 400 pairs in size is 24-gauge

and all copper cable of 400 pairs and larger is 26-gauge, the BCPM

default values include separate costs for 24-and 26-gauge copper of all

sizes.

55. We conclude that the model should use both 24-gauge and 26-

gauge copper in all available pair sizes. No commenter refuted our

observation that a significant amount of 24-gauge copper cable in

larger pair sizes currently is being deployed. Those commenters

addressing this issue concur with our tentative conclusion. SBC

confirms our analysis of the survey data and notes that it deploys 24-

gauge cable in sizes from 25 to 2400 pairs. GTE explains, and we agree,

that the model should use both 24-gauge and 26-gauge copper in all

available pair sizes in order to stay within transmission guidelines

when modeling 18 kilofoot loops.

56. Distinguishing Feeder and Distribution Cable Costs. In the

Inputs Further Notice, we reaffirmed the Commission's tentative

conclusion in the 1997 Further Notice that the same input values should

be used for copper cable whether it is used in feeder or in

distribution plant. We adopt this tentative conclusion. Those

commenters addressing this issue agree with our tentative conclusion.

GTE contends that it is both unnecessary and inappropriate to have

different costs for feeder and distribution cable material. GTE

explains that, although quantities of material and labor related to

cable size may differ between feeder and distribution, the unit costs

for each

[[Page 67379]]

remain the same. Similarly, Sprint agrees that the material cost of

cable is the same whether it is used for distribution or feeder. In

sum, we find that the record demonstrates that it is appropriate to use

the same input values for copper cable whether it is used in feeder or

in distribution plant.

57. Distinguishing Underground, Buried, and Aerial Installation

Costs. In the Inputs Further Notice, we also tentatively concluded that

we should adopt separate input values for the cost of aerial,

underground, and buried cable. We reached this tentative conclusion on

the basis of our analysis of cable cost data supplied to us in response

to data requests and through ex parte presentations. We found

considerable differences in the per foot cost of cable, depending upon

whether the cable was strung on poles, pulled through conduit, or

buried.

58. We conclude that separate input values for the cost of aerial,

underground, and buried cable should be adopted. Those commenters

addressing this issue confirm our analysis of the data, i.e., that

there are differences, some significant, in placement costs for aerial,

underground, and buried cable. GTE explains that, from a material

perspective, the cable may have different protective sheathing,

depending on construction applications. GTE adds that labor costs also

differ depending on the type of placement. Both SBC and Sprint identify

the cost of labor as varying significantly depending upon the type of

placement. Based upon a review of the record in this proceeding, we

conclude that separate input values for the cost of aerial,

underground, and buried cable are, therefore, warranted.

59. Deployment of Digital Lines. We also conclude that two inputs,

``pct__DS1'' and ``pct__1sa'', should be modified to provide more

accurate deployment of digital lines in the distribution plant. The

model can deploy a portion of distribution plant on digital DS1

circuits by specifying these two user adjustable inputs. The input

``pct__DS1'' determines the percentage of switched business traffic

carried on DS1 circuits, and the input ``pct__1sa'' determines the

percentage of special access lines carried on DS1 circuits. Previously,

we used default values for the inputs ``pct__DS1'' and ``pct__1sa.'' We

now adopt more accurate values for these inputs using 1998 line count

data, following the methodology described.

60. Initially the model determines the number of special access

lines from a ``LineCount'' table in the database ``hcpm.mdb,'' which

provides for each wire center the number of residential lines, business

lines, special access lines, public lines, and single business lines.

The Commission required incumbent LECs to provide line counts for

business switched and non-switched access lines on a voice equivalent

basis and on a facilities basis. Upon receipt of those filings, we

determined industry totals for each of the line count items requested.

By applying the model's engineering conventions to the totals, the

model determines the percentage of switched and non-switched lines

provided as DS1-type service. Thus, using the channel and facility

counts submitted in response to the 1999 Data Request, it is possible

to determine the ``pct__DS1'' input value using the following formula:

(1-pct__DS1)*channels + pct__DS1*channels/12 = facilities. A similar

calculation is performed to solve for the ``pct__1sa'' input value. For

both switched business and special access lines, the number of digital

lines is then determined by multiplying the respective line count by

the input value ``pct__DS1'' or ``pct__1sa.'' Since 24 communications

channels can be carried by two pairs of copper wires, the number of

copper cables required to carry digital traffic is computed by dividing

the number of digital channels by 12. These percentages are used to

adjust the wire center cable requirements by reducing the facilities

needed to serve multi-line business and special access customers.

3. Cost Per Foot of Cable

61. We affirm our tentative conclusion that we should use, with

certain modifications, the estimates in the NRRI Study for the per-foot

cost of aerial, underground, and buried 24-gauge copper cable and for

the per-foot cost of aerial, underground, and buried fiber cable. We

conclude that, on balance, these estimates, as modified in the Inputs

Further Notice, and further adjusted herein, are the most reasonable

estimates of the per-foot cost of aerial, underground, and buried 24-

gauge copper cable and fiber cable on the record before us. In reaching

this conclusion, we reject, for the reasons enumerated, the arguments

of those commenters who contend that we should use company-specific

data to develop the inputs for the per-foot cost of cable to be used in

the model.

62. Company-specific data. As we discussed, we have determined to

use nationwide average input values for estimating outside plant costs.

In reaching this conclusion, we determined that the use of company-

specific inputs was inappropriate because of the difficulty in

verifying the reasonableness of each company's data, among other

reasons. We have examined cable cost and structure cost data received

from a number of non-rural LECs, as well as AT&T, in response to the

structure and cable cost survey and through a series of ex parte

filings. In addition, we have examined additional company-specific data

submitted by certain parties with their comments. We conclude that

these data are not sufficiently reliable to use to estimate the

nationwide input values for cable costs or structure costs to be used

in the model.

63. We conclude that the cable cost and structure cost data

received in response to the structure and cable cost survey, in the ex

parte filings, and in the comments are not verifiable. We find that

with regard to the survey data, notwithstanding our request, most

respondents did not trace the costs submitted in response to the survey

from dollar amounts set forth in contracts by providing copies of these

contracts and all of the interim calculations for a single project or a

randomly selected central office. With regard to the ex parte data and

data submitted with the comments, we find that, because most

respondents did not document in sufficient detail the methodology,

calculations, assumptions, and other data used to develop the costs

they submitted, nor did they submit contracts or invoices setting forth

in detail the cable and structure costs they incurred, these data

cannot be substantiated. Moreover, we note that the structure and cable

costs reported in the survey by some parties differ significantly from

those reported by the same parties in the ex parte filings. These

differences are not explained, and render those sets of data

unreliable.

64. We find this lack of back-up information particularly

unsettling given the magnitude of certain of the costs reported. We

agree with AT&T and MCI that the cable installation costs submitted by

the incumbent LECs appear to be high. We also agree with AT&T and MCI

that this is because the loading factors employed in calculating these

costs appear to be overstated. Because of the lack of back-up

information to explain these loading costs, however, there is no

evidence on the record to controvert our initial assessment.

Accordingly, the level of these costs remains suspect.

65. Moreover, we find additional deficiencies beyond the critical

lack of substantiating data, impugning the reliability of the LEC

survey data and the ex parte data we have received. As discussed, the

task of the model is to

[[Page 67380]]

calculate forward-looking costs of constructing a wireline local

telephone network. To that end, the survey directed respondents to

submit cable and structure costs for growth projects for which

expenditures were at least $50,000. We believed that such projects

would best reflect the costs that a LEC would incur today to install

cable if it were to construct a local telephone network using current

technology. In contrast, absent from the data would be costs associated

with maintenance or projects of smaller scale which do not represent

the costs of installing cable during such construction using current

technology. Thus, the data would capture the economies of scale enjoyed

on large projects which, should result in lower cable costs on a per-

foot basis. Notwithstanding the survey directions, several of the

respondents submitted data representing projects that were not growth

projects or projects for which expenditures were less than the $50,000

minimum we established.

66. Conversely, some respondents included costs that should have

been excluded under the definitions employed in the survey. For

example, some respondents included costs for terminating structures,

such as cross-connect boxes, in the cable costs they reported.

Similarly, some respondents reported underground structure costs on a

``per duct foot'' basis contrary to the instructions set forth in the

survey directing that such costs be reported on a ``per foot'' basis.

We find that these inconsistencies render the use of the survey data

inappropriate.

67. In sum, we find that certain of the concerns we identified with

regard to using company-specific data, rather than nationwide average

inputs for model inputs, have been borne out in our review of the cable

cost and structure cost data we have reviewed. Specifically, we find

that we are unable to verify the reasonableness of such data.

Accordingly, we find that we are unable to use the company-specific

data we have received for the estimation of cable cost and structure

cost inputs for the model.

68. In reaching this conclusion, we reject the contention that the

inability to link the costs submitted in response to the cable and

structure cost survey to contracts is irrelevant because the survey

request was not intended to create such a trail. This claim ignores the

fact that the reasonableness of the survey data was placed into

question by the presence of data received on the record that was

inconsistent with the survey data. For this reason, as GTE attests, we

attempted to create such a trail by requesting contracts and other

supporting data in an effort to verify the reasonableness of the

company-specific data received in response to the survey as well as in

ex parte filings.

69. Methodology. As we explained in the Inputs Further Notice, our

tentative decision to rely on the NRRI Study was predicated on our

inability to substantiate the default input values for cable costs and

structure costs provided by the HAI and BCPM sponsors. For that reason,

we tentatively concluded, in the absence of more reliable evidence of

cable and structure costs for non-rural LECs, to use estimates in Gabel

and Kennedy's analysis of RUS data, subject to certain modifications,

to estimate cable and structure costs for non-rural LECs. As we

explained, Gabel and Kennedy first developed a data base of raw data

from contracts for construction related to the extension of service

into new areas, and reconstruction of existing exchanges, by rural-LECs

financed by the RUS. Gabel and Kennedy then performed regression

analyses, using data from the HAI model on line counts and rock, soil,

and water conditions for the geographic region in which each company in

the database operates to estimate cable and structure costs. Regression

analysis is a standard method used to study the dependence of one

variable, the dependent variable, on one or more other variables, the

explanatory variables. It is used to predict or forecast the mean value

of the dependent variable on the basis of known or expected values of

the explanatory variables.

70. Those commenters advocating the use of company-specific data

provide a litany of alleged weaknesses and flaws in the NRRI Study, and

the modifications we proposed, to discredit its use to estimate the

input values for cable costs and structure costs. In sum, they argue

that the overall approach we proposed is unsuitable for estimating the

cable and structure costs of non-rural LECs and generally leads to

estimates which understate actual forward-looking costs. We find the

contentions in support of this claim unpersuasive. Significantly, we

note that these commenters provide no evidence that substantiates the

reasonableness of the company-specific cable costs and structure costs

submitted on the record to permit their use as an alternative in the

estimation of cable and structure cost inputs to be used in the model.

71. For similar reasons, we reject AT&T and MCI's recommendation

that we rely on the RUS data to develop cost estimates for the material

cost of cable and then adopt ``reasonable'' values for the costs of

cable placing, splicing, and engineering based on the expert opinions

submitted by AT&T and MCI in this proceeding. We find that the expert

opinions on which AT&T and MCI's proposed methodology relies lack

additional support that would permit us to substantiate those opinions.

Moreover, we reject AT&T and MCI's contentions, often analogous to

those raised by the non-rural LECs, that the approach we proposed to

estimate cable and structure costs is flawed in certain respects.

72. We reject the contentions of the commenters, either express or

implied, that it is inappropriate to employ the NRRI Study because the

RUS data set on which it relies is not a sufficiently reliable data

source for structure and cable costs. We find that the RUS data set is

a reasonably reliable source of absolute cable costs and structure

costs, and more reliable and verifiable than the company-specific data

we have reviewed. As explained in the NRRI Study, and noted, the RUS

data reflect contract costs for construction related to the extension

into new areas, and reconstruction of existing exchanges, by rural LECs

financed by the RUS. Thus, the RUS data reflect actual costs derived

from contracts between LECs and vendors. These costs are not estimates,

but actual costs. Nor do they reflect only the opinions of outside

plant engineers. In sum, we conclude that these are verifiable data.

73. We also note that the RUS data reflect the costs from 171

contracts covering 57 companies operating in 27 states adjusted to 1997

dollars. These companies operate in areas that have different terrain,

weather, and density characteristics. This fact makes the RUS data

sample suitable for econometric analysis. Moreover, we find that,

because the costs are for construction that must abide by the

engineering standards established by the RUS, these data are

consistent. We note also that the imposition of consistent engineering

requirements mitigate the impact of any inefficiencies or inferior

technologies that may otherwise be reflected in the data.

74. Finally, as noted, the RUS data reflect costs for additions to

existing plant or new construction. The use of such costs is consistent

with the objective of the model to identify the cost today of building

an entire network using current technology.

75. In reaching our conclusion to use the NRRI Study and thus the

underlying RUS data, we have considered and rejected the contentions of

the commenters that the RUS data set is flawed thereby rendering use of

the NRRI Study inappropriate. GTE claims

[[Page 67381]]

that because certain high-cost observations were removed from the RUS

data, the NRRI Study's results are unrepresentative of rural companies'

costs, and are even less representative of non-rural companies' costs.

We disagree. Gabel and Kennedy omitted data reflecting certain

contracts from the RUS data they used to develop cost estimates because

estimates produced using the data were inconsistent with the values of

such estimates suggested by a priori reasoning or evidence. For

example, they excluded certain observations from the buried copper and

structure regression analysis because buried copper cable and structure

estimates obtained from this analysis would otherwise be higher in low

density areas than in higher density areas. Such a result is contrary

to the information contained in the more than 1000 observations

reflected in the data from which Gabel and Kennedy developed their

buried copper cable and structure regression equation. Thus, removing

the observations does not render the remaining data set less

representative of rural companies' costs or, as adjusted, the estimates

of the costs of non-rural companies. Moreover, we note that the

evidence supplied on the record in this proceeding demonstrates that

structure costs increase as population density increases. Thus, we find

that the RUS data set is not flawed as GTE contends. We conclude that

the removal of certain high cost observations was reasonable.

76. We also disagree with GTE's and Bell Atlantic's assertion that

the NRRI Study is flawed because the RUS company contracts do not

reflect actual unit costs for work performed, but rather the total cost

for a project. Both commenters claim that this alleged failure results

in unexplained variations in the RUS data which undermine the validity

of the estimates produced. Contrary to GTE's and Bell Atlantic's

contention, the contracts from which Gabel and Kennedy developed their

data base for developing structure and cable costs do set forth per

unit costs for materials and per unit costs for specific labor tasks.

77. We also disagree with AT&T and MCI's claim that the RUS data

are defective because they consist of primarily small cables. AT&T and

MCI claim that 74 percent of the RUS data are for cables of 50 pairs or

less, and 95 percent are for cable sizes of 200 pairs or less. As a

result, AT&T and MCI contend that the RUS data are inaccurate,

especially for cable sizes above 200 pairs. We disagree with AT&T and

MCI's analysis. We note that, for the buried copper cable and structure

regression equations we proposed and adopt, approximately 39 percent of

the observations are for cable sizes of 50 pairs or less, and

approximately 76 percent are for 200 pairs or less. For the underground

copper cable regression equation we proposed and adopt, approximately

10 percent of the observations are for cable sizes of 50 pairs or less,

and approximately 33 percent are for 200 pairs or less. For the aerial

copper cable regression equation we proposed and adopt, approximately

40 percent of the observations are for cable sizes of 50 pairs or less,

and approximately 76 percent are for 200 pairs or less. Thus, the

proportion of the observations reflected in the copper cable cost

estimates we adopt are significantly greater for relatively large

cables than what AT&T and MCI contend.

78. Finally, we reject the contention that it is inappropriate to

use the NRRI Study because the RUS data base is not designed for the

purpose of developing input values for the model. In the NRRI Study,

Gabel and Kennedy explain that they began developing the data base as

an outgrowth of the Commission's January 1997 workshop on cost proxy

models when it became apparent that costs used as inputs in such models

should be able to be validated by regulatory commissions. For this

reason, they prepared data that is in the public domain to provide

independent estimates of structure and cable costs.

79. We also find unpersuasive the contention that there are

econometric flaws in the NRRI Study which render it unsuitable for

developing input values. We disagree with the contentions of several

commenters that the structure cost and cable cost regression equations

that we develop from the RUS data are flawed because they are based on

a relatively small number of observations. As a general rule of thumb,

in order to obtain reliable estimates for the intercept and the slope

coefficients in a regression equation, the number of observations on

which the regression is based should be at least 10 times the number of

independent variables in the regression equation. Ameritech claims that

the sample size used to estimate the costs of buried placement is too

small because it contains only 26 observations in density zone one.

Ameritech's criticism ignores the fact that we use a single regression

equation to estimate buried copper cable and structure costs for

density zones one and two based on 1,131 observations (1,105 in zone

two and 26 in zone one). There are four independent variables in the

buried copper cable and structure regression equation, i.e., the

variables that indicate the size of the cable, presence of a high water

table, combined rock and soil type, and density zone. This suggests

that approximately 40 observations are needed to obtain reliable

estimates for the parameters in this regression equation. The total

number of observations used to estimate this regression equation,

1,131, readily exceeds the number suggested for estimating reliably

this regression equation. The number of observations for density zone

one alone, 26, provides 65 percent of the suggested number of

observations. Similarly, AT&T and MCI claim that the sample size for

underground cable is too small because it contains only 80

observations. There is one independent variable in the adopted

underground copper cable equation, i.e., the variable that indicates

the size of the cable. Based on the rule of thumb noted, 10

observations are needed to reliably estimate this regression equation.

The number of observations used to estimate the adopted underground

copper cable regression equation, 81, is more than eight times this

suggested number. Moreover, we note that Ameritech does not provide any

evidence that suggest that a sample that has 26 observations in density

zone 1 produces biased estimates of buried structure and cable costs

for density zone one. Similarly AT&T and MCI do not provide any

evidence to support their allegation that a sample size of 80

observations produces biased estimates of underground copper cable

costs. Finally, we note that GTE contends that the regression results

for aerial structure are undermined because the sample size for poles

is based only on 19 observations. While a sample of this size fails to

satisfy the general rule of thumb we noted, we find that the estimates

produced are reasonable. As we pointed out in the Inputs Further

Notice, the average material price reported in the NRRI Study for a 40-

foot, class four pole is $213.94. This is close to our calculations of

the unweighted average material cost for a 40-foot, class four pole,

$213.97, and the weighted average material cost, by line count,

$228.22, based on data submitted in response to the 1997 Data Request.

Moreover, we note that GTE does not provide any evidence that suggests

that a sample size of 19 poles for developing aerial structure costs

produces biased estimates as GTE seems to allege.

80. We also disagree with GTE's contention that the NRRI Study

contains three methodological errors that make its results unreliable.

First, GTE asserts

[[Page 67382]]

that the most serious of these flaws is that the NRRI Study improperly

averages ordinal or categorical data, i.e., qualitative values, for the

costs of placing structure in different types of soil. Contrary to

GTE's claim, the independent variables that indicate soil type, rock

hardness, and the presence of a high water table used in the regression

equations for aerial and underground structure and buried structure and

cable costs in the NRRI Study and proposed in the Inputs Further Notice

do not reflect an incorrect averaging of ordinal data. The variables

for soil, rock, and water indicate the average soil, rock, and water

conditions in the service areas of RUS companies. They are based on

averages of data obtained from the HAI database for the Census Block

Groups in which the RUS companies operate. In general, the magnitude of

the t-statistics for the coefficients of the independent variables for

soil, rock, and water in the structure regression equations indicate

that these variables have a statistically significant impact on

structure costs. The magnitude of the F-statistic indicates that the

independent variables in the structure regression equations, including

those that indicate water, rock, and soil type, jointly provide a

statistically significant explanation of the variation in structure

costs. These statistical findings justify use of these variables in the

structure regression equations. We also note that HAI uses as cardinal

values, i.e., quantitative, not ordinal values, the soil and rock data

from which the averages reflected in the rock and soil variables in the

NRRI Study are calculated. For example, HAI uses a multiplier of

between 1 and 4 to calculate the increase in placement cost

attributable to the soil condition. Moreover, and more importantly, we

note that no commenter has demonstrated the degree of, or even the

direction of, any bias in the cost estimates derived in the NRRI Study

or in the regression equations proposed in the Inputs Further Notice as

a result of the use of soil, water, and rock variables based on

averages of HAI data.

81. GTE also claims that the NRRI Study is flawed because it relies

on the HAI model's values relating to soil type which GTE claims were

``made up.'' GTE contends that this renders the variable relating to

soil type judgmental and biased. We find GTE's concern misplaced. As

explained, the econometric analyses of the data demonstrate a

statistically significant relationship between the geological variables

developed from the HAI data and the structure costs. Finally, we

disagree with GTE's claim that the NRRI Study is flawed because of a

mismatch in the geographic coverage of the RUS data and the HAI model

variables. GTE does not provide any evidence showing that the alleged

mismatch introduces an upward or downward bias on the cost estimates

obtained from the regression equations. Moreover, and more importantly,

the t-statistics for the coefficients of the variables that measure

rock and soil type generally indicate that these geological variables

provide a statistically significant explanation of variations in RUS

companies' structure costs.

82. We also reject the claims that the derivation of the equations

for 24-gauge buried copper cable, buried structure, and buried fiber

cable from the NRRI Study regression equations for 24-gauge buried

copper cable and structure and buried fiber cable and structure,

respectively, is inappropriate. As we explained in the Inputs Further

Notice, we modified the regression equations in the NRRI Study for 24-

gauge buried copper cable and structure and buried fiber cable and

structure, as modified by the Huber methodology described, to estimate

the cost of 24-gauge buried copper cable, buried structure and buried

fiber cable because the regression equations for buried copper cable

and structure and buried fiber cable and structure provide estimates

for labor and material costs for both buried cable and structure

combined. In layman's terms, we split the modified 24-gauge buried

copper cable and structure regression equation into two separate

equations, one for 24-gauge buried copper cable and one for buried

structure costs. We also split the modified buried fiber cable and

structure regression equation to obtain an equation for buried fiber

cable. We did this because the model requires a separate input for

labor and material costs for cable and a separate input for labor and

material costs for structure. In contrast, the RUS data and buried

cable and structure regression equations developed from these data,

reflect labor and material costs for buried cable and structure

combined.

83. Significantly, the criticisms of our development of the 24-

gauge buried copper cable equation, buried structure equation and

buried fiber cable equation in this manner ignore the fact that

reliable, alternative data for buried cable costs and buried structure

costs is not available on the record. Given that the model requires a

separate input reflecting labor and material costs for both copper and

fiber cable and a separate input reflecting labor and material costs

for structure, and that the only reliable data on the record does not

separate such costs between cable and structure, we find it necessary

to split the regression equation.

84. Contrary to the assertions of the commenters, either express or

implied, the steps we took to derive these equations were not

arbitrary. We used a single buried structure equation to estimate the

cost for buried structure without distinguishing between the equation

for buried copper structure and the equation for buried fiber structure

because the model does not distinguish between buried copper structure

costs and buried fiber structure costs. We find that this is reasonable

because the intercept and the coefficients for the variables that

primarily explain the variation in structure costs, i.e., the variables

that indicate density zone, the combined soil and rock type, and the

presence of a high water table, in the combined regression equation for

buried fiber cable and structure are not statistically different from

the intercept and the coefficients for these variables in the combined

regression equation for 24-gauge buried copper cable and structure. We

also find that it is reasonable to develop a separate structure

equation from the regression equation for the combined cost of 24-gauge

buried copper cable and structure rather than from the regression

equation for the combined cost of buried fiber cable and structure

because the water and soil and rock type indicator variables in the

regression equation for the combined cost of 24-gauge buried copper

cable and structure are statistically significant. In contrast, these

variables are not statistically significant in the buried fiber cable

and structure regression equation. In addition, we note that the number

of observations used to estimate the 24-gauge buried copper cable and

structure regression equation, 1,131, exceeds the number of

observations used to estimate the buried fiber cable and structure

regression equation, 707 observations.

85. We note that we included in the separate buried cable equations

the variable for cable size and its coefficient reflected in the

combined cable and structure regression equations. We find that this is

reasonable because the cable size variable and its coefficient explain

the variation in cable costs. We also note that we excluded from the

separate buried cable equations the independent variables in the

combined cable and structure regression equations that indicate density

zone, the presence of a high water table, and the soil and rock type.

We find that this is reasonable because these variables and their

coefficients explain primarily the variation in buried structure costs.

[[Page 67383]]

Conversely, we excluded from the separate buried structure equation the

variable for cable size and its coefficient reflected in the combined

24-gauge buried copper cable and structure regression equation because

this variable and its coefficient explain the variation in cable costs.

86. We also included in the separate structure equation the

variables and the coefficients for the variables that indicate density

zone, the combined soil and rock type, and the presence of a high water

table in the combined regression equation for 24-gauge buried copper

cable and structure. Again, we find this is reasonable because these

independent variables and coefficients primarily explain the variation

in structure costs.

87. Finally, because the estimated intercepts in the regression

equations for the cost of buried cable and structure reflect the fixed

cost for both buried cable and structure in density zone one, we

included in the separate equations for buried cable an intercept

reflecting the fixed cost of cable. Similarly, we included in the

equation for buried structure an intercept reflecting the fixed cost of

structure in density zone one. Specifically, we allocated an estimate

of the portion of the combined fixed cable and structure costs that

represents the fixed copper cable costs reflected in the intercept in

the 24-gauge buried copper cable and structure cost regression equation

to the intercept in the equation for 24-gauge buried copper cable.

Correspondingly, we allocated an estimate of the portion of fixed cable

and structure cost that represents the fixed costs of buried structure

reflected in the intercept in the buried 24-gauge copper cable and

structure cost regression equation to the intercept in the equation for

structure costs. We also allocated to the intercept in the separate

buried fiber cable equation the remaining portion of the fixed costs

reflected in the intercept in the combined buried fiber cable and

structure regression equation after subtracting from the value of this

intercept the estimate for fixed structure costs in density zone 1 in

the separate buried structure equation. The sum of the particular

values that we adopt for the fixed cable cost in the separate 24-gauge

copper cable equation, $.46, and the fixed structure cost in density

zone 1 in the separate structure equation, $.70, equals the 24 gauge

buried copper cable and structure fixed costs reflected in the

intercept in the combined copper cable and structure regression

equation of $1.16. The sum of the particular values that we adopt for

the fixed cable cost in density zone 1 in the separate fiber cable

equation, $.47, and the fixed structure cost in the separate structure

equation of $.70 equals the buried fiber cable and structure fixed

costs reflected in the intercept in the combined fiber cable and

structure regression equation, $1.17. We find that these values are

reasonable. We note that $.46 lies between AT&T and MCI's estimate of

the fixed cost for a 24-gauge buried copper cable of $.12 and the HAI

default value for the installed cost of a 6-pair 24-gauge buried copper

cable of $.63. Moreover, we note that we could have used relatively

higher or lower values for the fixed structure and cable costs in the

separate structure and cable equations. However, we note that the sum

of the fixed costs reflected in the buried structure cost estimates

(excluding LEC engineering costs) developed from the separate buried

structure equation and the fixed costs reflected in the buried cable

cost estimates (excluding LEC engineering and splicing costs) developed

from the separate buried copper or fiber cable equation is not affected

by the relative values that we use for the fixed cost in these separate

equations.

88. Finally, we note that GTE contends that the proposed equations

for buried cable and buried structure are questionable because the

buried structure costs would not vary with the presence of water. We

have modified the regression equation for buried copper cable and

structure by adding the variable that indicates the presence of a high

water table. We obtain structure cost estimates used as input values by

setting the coefficient for the water indicator variable equal to zero.

These structure cost estimates, therefore, assume that a high water

table is not present. The model adjusts these estimates to reflect the

impact on these costs of a high water table. GTE also claims that the

proposed equations are questionable because the costs for buried

structure derived from the buried structure equation would not vary

with cable size. We reject this contention. GTE has not provided any

evidence that demonstrates that buried structure costs vary with cable

size. To the contrary, GTE states that it cannot produce such evidence

because it is not able to separate actual costs of buried structure

from total costs of buried plant.

89. In sum, we find that the regression equations we proposed and

tentatively adopted in the Inputs Further Notice are an appropriate

starting point for estimating cable costs and structure costs for non-

rural LECs for purposes of developing inputs for the model,

particularly given the absence of more reliable cable and structure

cost data from any other source. We find, however, that certain

commenters' criticisms of the regression equations we proposed have

merit. We make the following adjustments to improve the regression

equations consistent with those criticisms.

90. First, we remove the independent variable that indicates

whether two or more cables are placed at the same location from the

regression equations for 24-gauge aerial copper cable, 24-gauge buried

copper cable and structure, aerial fiber cable, and buried fiber cable

and structure. As a result, the regression equations we adopt do not

have this variable as an independent variable. We do not include this

independent variable in any of the cable and structure equations

because the model does not use a different cable cost if the outside

plant portion of the network it builds requires more than one cable.

91. We also remove from the regression equation for 24-gauge

underground copper cable the variable that is the mathematical square

of the number of copper cable pairs. We remove this variable because

its use results in negative values for the largest cable sizes, as some

parties point out. We note that none of the other proposed cable and

structure regression equations had this variable as an independent

variable.

92. We add the variable that indicates the presence of a high water

table to the regression equations for buried copper cable and structure

and underground structure costs. With this change, all of the

regression equations for structure costs adopted in this Order have

this variable as an independent variable. We include this variable in

the structure equations because the model applies a cost multiplier to

all structure costs when the water table depth is less than the

critical water depth. To develop structure cost inputs, we set the

value of the water indicator variable equal to zero in the structure

regression equations, thereby developing structure costs that assume

that there is no water in the geographic area where the structure is

installed. The multiplier in the model then adjusts these costs to

reflect the impact on these costs of a high water table when it

determines that the water table depth is less than the critical water

depth.

93. We reduce the value of the intercept to $.46 from $.80 in the

equation proposed in the Inputs Further Notice for calculating the

labor and material costs for buried copper cable (excluding structure,

LEC engineering, and splicing costs). We now estimate the buried 24-

gauge copper cable and structure regression equation after

[[Page 67384]]

removing the multi-cable variable and adding the water indicator

variable. The value of the intercept in this regression equation of

$1.16 is less than the intercept in the proposed regression equation of

$1.51. As we did in the Inputs Further Notice, we derive the buried

copper cable equation from the regression equation for 24-gauge buried

copper cable and structure costs. The value of the intercept in the

buried copper cable and structure regression equation represents the

fixed cost for both buried copper cable and buried copper cable

structure in density zone 1. We assume, as we did in the Inputs Further

Notice, that $.70 is the fixed cost for buried copper cable structure

in density zone 1. Accordingly, the fixed labor and material cost for

buried copper cable is $1.16 minus $.70, or $.46.

94. We also reduce the value of the intercept to $.47 from $.60 in

the equation proposed in the Inputs Further Notice for calculating the

labor and material costs for buried fiber cable (excluding structure,

LEC engineering, and splicing costs). We now estimate the buried fiber

cable and structure regression equation after removing the multi-cable

variable. The value of the intercept in this regression equation,

$1.17, is greater than the value of the intercept in the proposed

regression equation, $1.14. As we did in the Inputs Further Notice, we

derive the buried fiber cable equation from the regression equation for

buried fiber cable and structure costs. The value of the intercept in

the buried fiber cable and structure regression equation represents the

fixed cost for both buried fiber cable and buried fiber cable structure

in density zone 1. We assume that $.70 is the fixed cost for buried

fiber cable structure in density zone 1. Accordingly, the fixed labor

and material cost for buried fiber cable in density zone 1 is $1.17

minus $.70 or $.47

95. Huber Adjustment. In the Inputs Further Notice, we tentatively

concluded that one substantive change should be made to Gabel and

Kennedy's analysis. As we explained, we tentatively concluded that the

regression equations in the NRRI Study should be modified using the

Huber regression technique to mitigate the influence of outliers in the

RUS data. Statistical outliers are values that are much higher or lower

than other data in the data set. The Huber algorithm uses a standard

statistical criterion to determine the most extreme outliers and

exclude those outliers. Thereafter, the Huber algorithm iteratively

performs a regression, then for each observation calculates an

observation weight based on the absolute value of the observation

residual. Finally, the algorithm performs a weighted least squares

regression using the calculated weights. This process is repeated until

the values of the weights effectively stop changing.

96. We affirm our tentative conclusion to modify the regression

equations in the NRRI Study using the Huber methodology to develop

input values for cable and structure costs. The cable and structure

cost inputs used in the model should reflect values that are typical

for cable and structure for a number of different density and terrain

conditions. If they do not reflect values that are typical, the model

may substantially overestimate or underestimate the cost of building a

local telephone network. As discussed, application of the Huber

methodology minimizes this risk, thereby producing estimates that are

consistent with the goal of developing cable and structure cost inputs

that reflect values that are typical for cable and structure for

different density and terrain conditions.

97. The commenters attest to the fact that there are significant

variances in the RUS structure and cable cost data. We find that the

presence of these outliers warrants the use of the Huber methodology.

By relying on the Huber methodology to identify and to exclude or give

less than full weight to these data outliers in the regressions, we

decrease the likelihood that the cost estimates produced reflect

measurement error or data anomalies that may represent unusual

circumstances that do not reflect the typical case. We note that we are

not readily able to ascertain the specific circumstances that may

explain why some data points are outliers relative to more clustered

data points because of the multivariate nature of the database. Such

occurrences are expected when dealing with such a database. Not only

are there many observations, but these observations reflect the

circumstances surrounding the construction work of many different

contractors done for a large number of companies on different projects

over a number of years. We also note that the task of identifying

structure cost outliers without using a statistical approach such as

Huber is especially difficult because these costs are a function of

different geological conditions and population densities. Given that it

is not feasible, as a practical matter, to determine why particular

data points are outliers and our objective is to develop typical cable

and structure costs, we conclude that use of the Huber methodology is

appropriate.

98. We find the comments opposing application of the Huber

methodology unpersuasive. In the first instance, we reject the

assertions of the commenters, either express or implied, that the

application of robust regression analysis is not the preferred method

of dealing with outliers in a regression. There is no preferred method.

The use of robust regression techniques is a matter of judgement for

the estimator. As we explained, the goal of our analysis is to estimate

values that are typical for cable and structure costs for different

density and terrain conditions. We determined that we should mitigate

the effects of outliers occurring in the data to ensure that the

estimates we produce reflect typical costs. Noting that such outliers

have an undue influence on ordinary least squares regression estimates

because the residual associated with each outlier is squared in

calculating the regression, we determined, in our expert opinion, to

employ the Huber methodology to diminish the destabilizing effects of

these outliers. Thus, while it can be argued that we could have

produced a different estimate, the commenters have not established that

application of the Huber methodology produces an unreasonable estimate.

99. Bell Atlantic and GTE assert that the probability distribution

of the error term must be symmetric about its mean and have fatter

tails than in the normal distribution in order to use the Huber

methodology. We disagree. The Huber methodology in effect fits a line

or a plane to a set of data. The algebraic expression of this line or

plane explains or predicts the effects on a dependent variable, e.g.,

24-gauge aerial copper cable cost, of changes in independent variables,

e.g., aerial copper cable size. It does this by assigning zero or less

than full weight to observations that have extremely high or extremely

low values. The assignment of weights to observations depends on the

values of the observations. It does not depend on the probability of

observing these values. The error term to which Bell Atlantic and GTE

refer is the difference between the predicted or estimated values of

the dependent variable and the observed values of the dependent

variable. Given that the error term is the difference between the

predicted and observed values of the dependent variable, and that the

assignment of weights by the Huber methodology does not depend on the

probability of observing particular values of this variable, this

assignment of weights does not depend on the probability of observing

particular values of the error

[[Page 67385]]

term. It, therefore, does not depend on whether the probability

distribution of the error term is symmetric about its mean and has

fatter tails than in the normal distribution.

100. Bell Atlantic also argues that the Huber methodology should

not be used unless there is evidence that outliers in the RUS data are

erroneous. We disagree. We believe that use of the Huber methodology

with RUS data ensures that cost estimates reflect typical costs

regardless of whether there is evidence that outliers in the RUS data

are erroneous. The RUS data, as Bell Atlantic and other parties point

out, have a number of high values and low values. These outliers may

reflect unusual circumstances that are unlikely to occur in the future.

The Huber methodology dampens the effects of anomalistically high or

low values that may reflect unusual circumstances. Notwithstanding the

dispersion in the RUS data, we believe that there are relatively few

errors in these data. As we explained, the RUS data are derived from

contracts. Gabel and Kennedy determined that the values reflected in

the RUS data are within one percent of the values set forth on the

contracts. There are likely to be few errors in the contracts

themselves because these are binding agreements that involve

substantial sums of money between RUS companies and contractors. These

parties have an obvious interest in ensuring that these values are

correctly reflected in these contracts. While we believe that errors in

these contracts are likely to be infrequent, outlier observations in

the RUS data may reflect large errors. The Huber methodology dampens

the effects of outlier observations that may reflect large errors.

101. We find that the estimates produced by applying the Huber

methodology are reasonable. The estimates resulting from application of

the Huber methodology reflect most of the information represented in

nearly all of the cable and structure cost observations in the RUS

data. Approximately 80 percent of the cable and structure observations

are assigned a weight of at least 80 percent in each structure and

regression equation that we adopt. This large majority comprises

closely clustered observations that clearly represent typical costs.

Conversely, approximately 20 percent of the cable and structure

observations are assigned a weight of less than .8 in each of these

regression equations. This small minority comprises observations that

have extremely high and extremely low values that do not represent

typical costs. We also note that because the Huber methodology treats

symmetrically observations that have high or low values, it excludes or

assigns less than full weight to data outliers without regard to

whether these are high or low cost observations.

102. Buying Power Adjustment. In the Inputs Further Notice, we

tentatively concluded that we should make three adjustments to the

regression equations in the NRRI Study, as modified by the Huber

methodology described, to estimate the cost of 24-gauge aerial copper

cable, 24-gauge underground copper cable, and 24-gauge buried copper

cable. We further tentatively concluded that these adjustments should

be made in the estimation of the cost of aerial fiber cable, buried

fiber cable, and underground fiber cable. The first of these

adjustments was to adjust the equation to reflect the superior buying

power that non-rural LECs may have in comparison to the LECs

represented in the RUS data. We noted that Gabel and Kennedy determined

that Bell Atlantic's material costs for aerial copper cable are

approximately 15.2 percent less than these costs for the RUS companies

based on data entered into the record in a proceeding before the Maine

Public Utilities Commission (the ``Maine Commission). Similarly, Gabel

and Kennedy determined that Bell Atlantic's material costs for aerial

fiber cable are approximately 33.8 percent less than these costs for

the RUS companies. We also noted that Gabel and Kennedy determined that

Bell Atlantic's material costs for underground copper cable are

approximately 16.3 percent less than these costs for the RUS companies

and 27.8 percent less for underground fiber cable. We tentatively

concluded that these figures represent reasonable estimates of the

difference in the material costs that non-rural LECs pay in comparison

to those that the RUS companies pay for cable. Accordingly, to reflect

this degree of buying power in the copper cable cost estimates that we

derived for non-rural LECs, we proposed to reduce the regression

coefficient for the number of copper pairs by 15.2 percent for aerial

copper cable, and 16.3 percent for 24-gauge underground copper cable.

103. We also proposed to reduce the regression coefficient for the

number of fiber strands by 33.8 percent for aerial fiber cable and 27.8

percent for underground fiber cable. As we explained, this coefficient

measures the incremental or additional cost associated with one

additional copper pair or fiber strand, as applicable, and therefore,

largely reflects the material cost of the cable. Because the NRRI Study

did not include a recommendation for such an adjustment for buried

copper cable or buried fiber, we tentatively concluded we should reduce

the coefficient by 15.2 percent for buried copper cable and 27.8

percent for buried fiber cable. We explained that the level of these

adjustments reflect the lower of the reductions used for aerial and

underground copper cable and aerial and underground fiber cable,

respectively.

104. We adopt the tentative conclusion in the Inputs Further Notice

and select buying power adjustments of 15.2 percent, 16.3 percent and

15.2 percent for 24-gauge aerial copper cable, 24-gauge underground

copper cable, and 24-gauge buried copper cable, respectively.

Correspondingly, we adopt buying power adjustments of 33.8 percent,

27.8 percent, and 27.8 percent for aerial fiber cable, underground

fiber cable, and buried fiber cable, respectively. We find that, based

on the record before us, the buying power adjustment is appropriate and

the levels of the adjustments we proposed for the categories of copper

and fiber cable we identified are reasonable.

105. As we explained in the Inputs Further Notice, the buying power

adjustment is intended to reflect the difference in the materials

prices that non-rural LECs pay in comparison to those that the RUS

companies pay. Because non-rural LECs pay less for cable, a downward

adjustment to the estimates developed from data reflecting the costs of

rural-LECs is necessary to derive estimates representative of cable

costs for non-rural LECs. The commenters generally concede that such

differences exist. There is, however, disagreement among the commenters

that an adjustment is necessary in this instance to reflect this

difference.

106. Those commenters advocating the use of company-specific data

oppose the buying power adjustment as unnecessary. GTE and Sprint

contend that the use of a more representative data set, i.e., company-

specific data, would account for any differences in buying power. As we

explained, however, the RUS data are the most reliable data on the

record before us for estimating cable and structure costs. Because

there is a difference in the material costs that non-rural LECs pay in

comparison to those that the RUS companies pay, a downward adjustment

to the RUS cable estimates is necessary to obtain representative cable

cost estimates for non-rural LECs.

107. We note that AT&T and MCI support the proposed adjustment for

aerial and underground copper and fiber cable. AT&T and MCI oppose,

however,

[[Page 67386]]

the use of the lower of the reductions adopted for aerial and

underground cable categories, for the buried cable category. Although

AT&T and MCI agree that an adjustment is appropriate for buried cable,

they contend that the buying power adjustment should be set at the

higher figures of 16.3 percent for buried copper cable and 33.8 percent

for buried fiber cable, or at the very least, at the average of the

higher and lower values for aerial and underground cable. We disagree.

We find that AT&T and MCI offer no support to demonstrate why the

higher values should be used. As explained, the levels of the

adjustments we proposed and adopt are the most conservative based on

the available record evidence.

108. Apart from opposing the buying power adjustment on the ground

that as a general matter the adjustment is unnecessary, those opposing

the adjustment take issue with the adjustment on methodological

grounds. GTE contends that the adjustment cannot properly convert RUS

data into costs for non-rural carriers because the RUS data do not

reflect the cost structure of rural carriers. As we explained, the

assertion that the RUS data does not reflect the cost structure of

rural carriers is without merit. GTE also contends that the application

of the adjustment factors to the coefficients in the regression

equations is contrary to the fundamentals of sound economic analysis.

The solution GTE recommends is that additional observations for non-

rural companies be added to the data set. This solution echoes GTE's

assertion that company-specific data should be used. Reliable

observations for non-rural LECs are not available, however, as

explained.

109. GTE also identifies what it considers flaws in the development

of the buying power adjustment. GTE argues that because the adjustment

to the RUS data was developed using only one larger company's data

(Bell Atlantic's) reflecting costs for a single year, the adjustment is

not proper. We disagree for several reasons. First, we note that

although we specifically requested comment on this adjustment and its

derivation in the Inputs Further Notice, GTE and other parties

challenging the use of Bell Atlantic's data have not provided any

alternative data for measuring the level of market power, despite their

general agreement that such market power exists. These parties failed

to submit comparable verifiable data to show that the buying power

adjustment we proposed was inaccurate. Under these circumstances, we

cannot give credence to the unsupported claims that the Bell Atlantic

data is not representative.

110. Equally important, we have reason to conclude that the

adjustment we adopt is a conservative one. The buying power adjustment

we proposed and adopt is based upon a submission by Bell Atlantic to

the Maine Commission in a proceeding to establish permanent unbundled

network element (UNE) rates. In that context, it was in Bell Atlantic's

interests to submit the highest possible cost data in order to ensure

that the UNE rates would give it ample compensation. But in the context

of the adjustment we consider here for buying power, a relatively

higher cost translates into a reduced adjustment because the greater

the LEC costs, the less the differential between LEC and rural carrier

costs. Therefore, given the source of this data, we conclude that it is

likely to produce a conservative buying power adjustment, not an

excessive one. Nevertheless, in the proceeding on the future of the

model, we intend to seek further comment on the development of an

appropriate buying power adjustment to reflect the forward-looking

costs of the competitive efficient firm. In sum, we find that GTE's

criticisms are not persuasive, and that the adjustment is a reasonable

one, supported by the record.

111. GTE also asserts a litany of other concerns that, according to

GTE, render the buying power adjustment invalid. We find these concerns

unpersuasive. GTE claims that the adjustment is suspect because some

RUS observations used in the determination of material costs are not

used in the regression. We disagree. As discussed, we apply the Huber

methodology to RUS cable costs that reflect both labor and material

costs. The observations in the RUS database to which the Huber

methodology assigns zero or less than full weight are those with the

highest and the lowest values. As described, a statistical analysis

demonstrates that this assignment of weights to these observations has

little impact on the level of material costs reflected in the cable

cost estimates derived by using this methodology. Therefore, material

cost averages based on all of the RUS data are not likely to vary

significantly from material cost averages based on a subset of these

data.

112. Specifically, with one exception, the value of the regression

coefficient for the variable representing the size of the cable in the

cable cost regression equations derived by using the Huber methodology

lies inside the 95 percent confidence interval surrounding the value of

this coefficient in these regression equations in the NRRI Study

obtained by using ordinary least squares. The coefficient for the

variable that represents cable size represents the additional cost for

an additional pair of cable and therefore represents cable material

costs. The values of the coefficient for the cable size variable

obtained by using Huber and ordinary least squares are based on a

sample of RUS companies' cable costs drawn from a larger population of

such costs. The values of the coefficient obtained from this sample by

using the Huber methodology and ordinary least squares are estimates of

the true values of this coefficient theoretically obtained from the

population of cable costs by using these techniques. Generally

speaking, a 95 percent confidence interval associated with a

coefficient estimate contains, with a probability of 95 percent, the

true value of the coefficient. The fact that the value of the cable

size coefficient obtained by using the Huber methodology lies within an

interval that contains with 95 percent certainty the true value of the

ordinary least squares cable size coefficient supports the conclusion

that the Huber methodology does not by its weighting methodology have a

statistically significant impact on the level of the material costs

reflected in the cable cost estimates derived by using this

methodology.

113. GTE also claims that some RUS observations appear to be from

rescinded contracts or contracts excluded from the NRRI Study per-foot

cable cost calculation. However, GTE offers no evidence that this is

the case. Finally, GTE claims that some RUS observations are for

technologies that may not be appropriate for a forward-looking cost

model. On the contrary, loading coils were excluded from the RUS data

base. Thus, we find that the RUS data do not reflect any non-forward-

looking technologies.

114. GTE and Sprint each attempt to impugn the validity of the

buying power adjustment, claiming that there may be an incongruity

between the data submitted to the Maine Commission by Bell Atlantic and

the RUS data. We find this claim unpersuasive. Both GTE and Sprint

assert that it is unknown whether the underlying data include such

items as sales tax or shipping costs and, if so, whether the level of

these items is comparable between Maine and the states included in the

RUS data. Significantly, neither claim that such an incongruity exists

in fact, nor do they provide viable alternatives for the calculation of

the adjustment. We note that the RUS data reflect the same categories

of costs as those reflected in the Bell Atlantic data. More

importantly, this data reflects the best

[[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

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estimates. We find that, based on the record before us, the LEC

engineering adjustment is appropri

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