**AMERICAN STATISTICAL ASSOCIATION**

**COMMITTEE ON ENERGY STATISTICS**

**THURSDAY,**

**APRIL 20, 1995**

The Committee met in the Columbia Room at the Holiday Inn Capitol, 550 C
Street, S.W., Washington, D.C., at 9:00 a.m., Timothy D. Mount, Chair,
presiding.

[PRESENT]{.underline}:

TIMOTHY D. MOUNT, Chair

DAVID R. BELLHOUSE

CHARLES W. BISCHOFF

BRENDA G. COX

FAYE DUCHIN

JOHN D. GRACE

PHILIP HANSER

CALVIN KENT

GRETA M. LJUNG

JAMES L. O\'BRIEN

DANIEL A. RELLES

BRADLEY O. SKARPNESS

G. CAMPBELL WATKINS

**AGENDA**

Introductions by Committee Chair . . . . . . . . . 3

Opening Remarks by Administrator . . . . . . . . . 6

Summary of EIA Follow-up on Comments From

Previous Meetings, Yvonne Bishop . . . . . . . . 28

Effects of Structural Changes in Industry

1\. Electricity Issues

Impact on EIA\'s Data Collection Activities . . 31

Noel Balthasar, Presenter

Phil Hanser, Discussant

Computer Modeling and Restructuring of the

Electric Power Industry . . . . . . . . . . . . 41

Art Holland, Presenter

Phil Hanser & Campbell Watkins, Discussants

2\. Natural Gas Issues

Constructing Price Index when Reliable Data are Sparse and Type of Data
are changing . . 84

Bill Robinson, Presenter

Campbell Watkins, Discussant

How the Transportation Module of National

Energy Modeling System Operates . . . . . . 108 David Chien, Presenter

Charles Bischoff, Discussant

Reconciliation of Forecasts from AEQ and

STEO . . . . . . . . . . . . . . . . . . . . 143 Art Andersen, Presenter

Greta Ljung, Discussant

P-R-O-C-E-E-D-I-N-G-S

9:19 a.m.

CHAIRMAN MOUNT: I\'m Tim Mount from Cornell University. I am chairman of
this Committee. I apologize for starting late. I have a number of
announcements to make. This meeting is being held under the provision of
the Federal Advisory Committee Act. This is an American Statistical
Association, not an EIA Committee. It provides advice to the EIA. The
meeting is open to the public. Public comments are welcome. Time will be
set aside for comments at the end of each session. Written comments are
welcome and can be sent to either the American Statistical Association
or EIA. Non-EIA attendees should sign the register if they wish to
receive a copy of the meeting highlights.

Anybody making comments are requested to speak into a microphone, and
that includes members of a committee. The microphones can be moved so
that the recorder can get the information clearly. Members need to speak
loudly if they are making comments.

There are a couple of announcements for the Committee. The lunch meeting
has been moved from the Apollo Room to the Louis Room, which is just
around the corner. In addition, the dinner this evening for those who
wish to attend at Le Rivage at 6:30. Tina Jones over there would like
people who want to attend that dinner to let her know by lunchtime.
There is also going to be a breakfast meeting tomorrow morning in the
Apollo Room at 8:00 a.m.

To get the meeting started, I think we should all introduce ourselves
starting on my right.

MR. HAKES: Jay Hakes, Energy Information Administration.

MS. BISHOP: Yvonne Bishop, Energy Information Administration.

MS. MILLER: Renee Miller, EIA.

MR. BISCHOFF: Chuck Bischoff, State University of New York at
Binghamton.

MR. SKARPNESS: Bradley Skarpness, Battelle.

MR. BELLHOUSE: David Bellhouse, University of Western Ontario, Canada.

MS. COX: Brenda Cox, Mathematica Policy Research.

MR. ROBINSON: Bill Robinson, EIA.

MR. GRACE: John Grace, Troika Energy.

MS. DUCHIN: Faye Duchin, NYU.

MR. RELLES: I\'m Dan Relles from Rand.

MS. LJUNG: Greta Ljung, MIT.

MR. KENT: Cal Kent, Marshall University.

MR. HANSER: Phil Hanser, Putnam, Hayes, and Bartlett.

MR. WATKINS: Campbell Watkins, Charles River Associates.

MR. HOLLAND: Art Holland, EIA.

MR. BALTHASAR: Noel Balthasar, EIA.

CHAIRMAN MOUNT: And now we go back into the public section. Can you just
give your name and your affiliation, please, starting in the front.

(Public introductions not picked up on tape.)

CHAIRMAN MOUNT: Thank you. And I would like to welcome our two new
Committee members, Faye Duchin, who already introduced herself, and Cal
Kent, former administrator here. Now, I would like to hand the
microphone over to Jay Hakes, and we will find out what is going on
here. I certainly hope that EIA is being treated better than the State
University of New York.

MR. HAKES: I\'m not certain what our baseline is on that, so I will have
to confer with you more. I would like to add my welcome to the new
members. New members are bringing to this organization a diversity of
talent. I think this is a case where the whole is greater than the sum
of its parts because of the different expertise that are represented by
the members. But I was particularly intrigued in Faye Duchin\'s resume
with the forthcoming Journal article called \"The Choice of Technology
and Associated Change and Prices in the U.S. Economy.\" She has also
done a lot of work on Indonesia, and EIA right now has a lot of interest
in both technology and emerging nations.

Cal Kent was the administrator of EIA, who started many of the good
things we are doing today. He was particularly a strong force in the
development of the National Energy Modeling System, and a person who was
helpful to me when I was trying to learn a little bit about this job in
the early days. It is good to see Cal here.

And David Bellhouse, who is with us, has worked extensively in the
sampling area, and we always feel blessed when we have sampling experts,
as we do with several members, because we try to do well in that area
and we need the collaborative work.

There is another member named Harry Martz, who is a technical staff
member at the Los Alamos National Laboratory, who is not with us today I
understand.

I have a couple of things I would like to talk about. I usually do say a
few words about the National Energy Modeling System, and I would like to
do so again today. We do publish the results annually in a publication
called [The Annual Energy Outlook]{.underline}. I think it is one of our
flagship publications and it comes out every January. I don\'t want to
try to cover everything, but I think there are two emphases in this that
I would like to bring to your attention.

We are coming down in our price projections for most fuels from what
they have been in the past. This has been a somewhat regular pattern. As
you remember, there has been much criticism of the models in the early
1980\'s for projecting prices and fuels that never came to pass. And the
market system and technology and other things have brought about lower
prices than foreseen.

Incidentally, several EIA members have written an article that is being
peer reviewed for publication in a non-EIA publication that evaluates
our forecasting over the years. We figured we would do it to ourselves
before someone else did it to us.

Basically what we find in the early years, we weren\'t coming very
close, but since those early years, things have tightened up a lot. This
reflects basically a feeling that the OPEC countries will try to
maintain market share, will be able to find the investment dollars to
supply the growth and demand coming from the emerging nations. So we
have gone from \$29.00 to \$24.00 a barrel in real dollars for the year
2010. And that is reflected across most of the other fuels.

In gas, the decline is less, but there still is somewhat of a drop in
gas prices. Basically, the red line is the old forecast from 1994, and
the black line is the forecast from the 1995 edition.

In coal, you can see a big drop in price. The whole coal industry has
been, I think, a surprise to a lot of people. They are moving from an
old style of drilling called Ruman Piller to what is called long-wall
mining, where they are able to get longer cavities in which they can
work. That combined with improved equipment, drills, et cetera, has led
to tremendous productivity gains in the coal industry. I have been
trying to share some of this information with the Federal Reserve Board
so that they would see that their fears of inflation at least are not
being driven by increased inflationary fears in the energy area.

Now the impact of this, as you might suspect, can also be seen in
electricity prices. This decline in price projections is based mainly on
a drop in fuel prices. Coal, obviously, is the major fuel for
electricity. It does not attempt to reflect anticipation of deregulation
of the electric industry and what impacts that might have. We might feel
next year that it is time to do that, yes, but we do not try to
anticipate future policy in our models. If it is clear at that point
that the policy is coming, then we might try to do that. That will be a
little bit tricky. It could conceivably cause us to lower projections
again.

Now basically another thing that we\'ve done, and I would just sort of
throw one thing out and hope that you look at the document, if you
haven\'t looked at it yet. And that is that we have added a third set of
scenarios to the AEO. We always have variations in the world price of
oil and variations in economic growth, but this year we are trying to
look at variations in the rate of technology penetration. It makes a big
difference in natural gas how much improvement there is in seismology.
And it makes a lot of difference in the electric industry if there are
technological improvements in turbines. So we have tried to look at
different rates of technology penetration and see what those do, and I
would just draw that to your attention.

Here is a set of scenarios in residential energy consumption based on
the rate of technology penetration. As you see, what is kind of
considered a worst case scenario, which is just the maintenance of 1993
technology, to be honest we had to include in that the future appliance
efficiency standards. So even here, this one is a little bit untypical.
But even if you assume the worst case, that the consumer, whenever they
are making the choice, are buying either at the efficiency level of 1993
or they are buying what is mandated by law because there is some code
that says we can\'t build it as inefficiently as 1993, you get that
scenario which is the black line at the top.

We do believe in our reference case that consumers would choose in some
cases more efficient technology than they bought in 1993 or that was
required by law. And then in the blue line, which is somewhat misnamed,
we assume that they buy the most efficient that is available on the
market. We do not assume that there is a lot of new stuff coming onto
the market that we don\'t foresee. So in one sense, it is very
over-optimistic, and in another sense it is somewhat pessimistic. But at
least it shows the policy makers another dimension of this modeling that
they might want to be taking a look at, and people are free to make
other assumptions about the rate of technology penetration and play with
the model in that sense. But I think it is an important way to look at
the model because it is one of the uncertainties that is out there. And
it is an uncertainty that policy might be geared to want to change. So
analytically, it is there for them to look at.

We will have a presentation on the agenda this afternoon on the
transportation sector demand in the modeling system, and it is going to
focus on vehicle miles traveled in the forecasting model. One little
NEMS item. We do have these now NEMS brochures that you can carry around
in your pocket. I brought enough with me, certainly, for the committee.
If there are leftovers, we can get them to others. In fact, we do have
them in large quantities back at the office.

But what it does is it enables you to always be able to project into the
future, no matter where you are. And actually, a number of policy makers
in the department now do carry these around when they travel because it
is handy, at least for the basic projections, to have them available in
this form. So I commend this to you and hope that you will take
advantage of it.

MR. HANSER: Have you thought of developing one for a filofax? That way
you can just sort of keep it in your daytimer record.

MR. HAKES: We have also the energy information card. A number of people
in the Department of Energy have found a little plastic pocket that fits
into a notebook that is exactly the size of this card. So they just plug
that card in the notebook and have this wherever they go.

I will mention while we are on pocket items that if you want to reach us
on the Internet, please get one of these cards. This is a prototype
card. It is going to be slightly smaller when it is in final version. It
is credit card size and it has a picture of our Home Page on one side
and how to reach us on the Internet on the other side. We also have
these here for you.

Another area, just moving on and sort of touching some of the high
points, EIA is doing a lot right now in customer surveys and performance
measurement. We have basically approached it several ways. We had one
project in the office that was everybody who called us over a three-day
period sort of became a census of customers. I think we actually did the
whole census. We went back and called back the people who had called us
\-- not the person that they had called, but someone else \-- and did a
confidential survey asking them to evaluate the service they received
from EIA and to evaluate products that they were familiar with, and we
asked a number of questions.

It was very interesting because we asked about the quality of the data.
Of course, we are very proud of the quality of the data and so we
thought we would get a very high rating. It wasn\'t quite as high as we
wanted and the reason was because a number of people said they didn\'t
know. I mean the general public calls in and they say, well, I assume it
must be good data or you wouldn\'t be giving it out, but they didn\'t
know our methodologies and quality control. So there wasn\'t anyone who
said we had bad data or bad quality. It was just that some of them
didn\'t know.

There was a desire to have more timely data on the part of many
customers. A lot of people gave EIA very high marks for being responsive
to customers, for providing good data, for getting back to them quickly,
and for providing a good range of products that they liked. So of all
the consumer surveys that I\'ve seen, at least at the Department of
Energy, I think EIA has had the highest grades.

And we have tried, I think, to not only do a survey like that but get
back to customers and sit down and spend some time with them and see
what they are interested in. We have recently finished a number of in
depth interviews with reference librarians at the major universities in
the country to try to serve the academic community better.

On performance measures, we\'ve had a performance measures committee. I
think we will be the first agency in the Department of Energy to have a
performance measure system. Basically, we have developed a suite of 12
performance measures that are very balanced. In other words, if you had
a measure on timeliness and you didn\'t have a measure on quality, you
would probably make your system worse than it is now. If you had one on
quality but didn\'t measure timeliness, the system might slow down. So
we try to have a suite of measures.

We\'ve done a lot of input/output analysis, for those of you who are
interested in this area or would like to help us and advise us in this
area. We would welcome you to take a look at that. But we do feel quite
good about this, and Secretary O\'Leary feels that EIA is ahead of
everybody else in the Department, and we are doing a briefing for her
and her senior staff in a couple of weeks. And as she said, she hoped
the rest of the Department would learn from our example.

Electronic data dissemination is my third topic. I guess we\'re looking
at a variety of approaches. And I think it is starting to fall into
place what is a system that is going to be helpful to the academic
community, to the policy-making community, and to the business
community, and that is to continue most of the hard copy publications we
do because there is a certain utility that they have that electronic
does not have. The energy card is certainly a commitment that we want
people to not have to carry computers with them everywhere they go.

But we\'ve found that there are a lot of ways within EIA that the
different parts of our organization can communicate better with each
other through a local area network and be able to access each other\'s
data in a user-friendly manner. And once we can do that ourselves, it is
a very short step to get a server and get it out there on the Internet.
And there is a certain sense of excitement, I think, among many of our
employees about this expanded audience for their data and sort of being
a leader in this coming area of technology. We have people signing up
for courses taught by our own employees on how to develop products for
the Home Page. We are trying to have no acronyms. We have very clear
titles and nice icons that you can work with. I just think it is going
to provide tremendous value. It really provides the ability to move data
around the world. And this also, once we get to this point, I think it
will not be that hard to create what I have called, and I mentioned at
breakfast this morning, a virtual federal statistical agency where the
different agencies get together and develop a Home Page where you can
get federal statistics without having to know all the different bureaus
and offices of the United States Government. The statistical agency
heads have discussed this now at about three meetings and are excited
about it. And I feel that it is quite confident to do \-- we are quite
capable of doing this.

There has been a lot of trepidation saying this will be very expensive
or how can we do this and isn\'t this way off in the future. My own view
is that if people like myself who sort of grew up in a non-computer age
sort of get out of the way, almost all of the statistical agencies have
employees within them who know this stuff backwards and forwards. They
know what needs to be done. If you get a group of them together that
represents different points of views, they can figure out how to do this
and do it pretty quickly and do it quite inexpensively.

Our short-term energy model now runs on Microsoft access. It costs about
\$300.00 at Egghead Software. We did it with a developers kit so there
are no licensing restrictions on it. So you know have something
developed in a very common, low-priced software that is out there for
the world to use and yet it is very powerful. So we urge you to get our
energy Home Page credit card. We are not charging yet for this stuff.
Some of the university libraries now have \-- are starting to charge. I
think Carnegie-Mellon and Michigan are working on that. But so far it is
free out there and it is, I think, very valuable stuff.

I would also say that for people in the policy-making community, I
don\'t think the Internet will be that useful because the staff people
do not have the time to wait for the Internet to operate. So we have
made a decision to move to a CD-ROM package as well. We have not totally
nailed down how often it would be published, but we are kind of leaning
now that we might have to do this every month. And it might be a CD-ROM
that would contain most of what we publish all in one CD-ROM, and it
would sort of pattern the Monthly Review so that if you had the most
current CD-ROM, you would be confident that you were working off the
most recent data-set that we had.

The policy-making community is very interested that they have the most
up-to-date data. Because energy is a contentious issue, people want
their data to always stand up in a debate. So if it is the latest data,
then they won\'t be embarrassed in the debate.

So if we do that \-- and none of these things are far off. Again,
because people, I think, are so excited about it, we have teams of
employees that are working quite feverishly on all of these things. It
has been fun because it has been one of those things in the work
schedule that everything is happening ahead of when it was scheduled to
happen. So we are kind of enjoying that right now.

The other issue that it was suggested I talk about is budget issues and
also talks around that the Department of Energy might be abolished.
There are a number of people who have called for the abolition of the
Department of Energy. There is only one group that I am aware of that
has called for the dissolution of the Energy Information Administration
and has said that the private industry could handle it, and that is the
CATO Institute, which is a very libertarian organization that also
believes in the legalization of heroin and cocaine.

They, I think, have not studied this issue in any great depth. If you
talk to the major private vendors in the energy statistics business, as
I have done over the last month or so, they will all volunteer that they
could not do their business without the EIA, and it is impossible for a
non-governmental body to do the type of work that EIA does.

One is I think EIA or some governmental body could get to the material.
I mean, we ultimately have the subpoena power and the ability to require
statistical reporting, which I think the Government would not be willing
to cede to the private sector. We also have a commitment to the
comprehensiveness of the data. We are not just going to collect data
where we can get the biggest price for it. Because we want to present a
comprehensive ability to analyze the energy system, we collect all parts
of the system.

We also can, I think, even under our current statues give pretty good
assurances on confidentiality. We have been able to maintain it although
it has come under threat from time to time. But I do expect that new
legislation will be passed in the next year or so that will give us
better confidentiality coverage, and that will, I think, strengthen our
position.

There was one member of our Appropriations Committee who probed me and
others on whether EIA should charge for its equipment and compete in the
market. And the Department of Energy\'s Assistant Secretary for Policy
was nice enough to testify right after me that if she had to pay for EIA
data that she would have to double her budget. And you do find that a
lot of our offices at DOE can do what they do because as soon as they
get an assignment or someone else\'s analysis project, they come right
to our office to get the data or the modeling that will be required to
complete it.

I would have to say \-- I am not an economist, but it would seem to me
that one would want to analyze the transaction costs if Government
agencies were charging each other for data and whether that would start
to get a little silly and in cases the transaction costs would be almost
more than the cost of the data at some point. It is also a little hard
to know how you price information. I guess it is true of all
intellectual property, but the cost for us generally comes in the
collection of the data and the building of the models. The actual
dissemination of information is a relatively cheap part of the process.
Under the federal laws that exist now, the federal agencies are only
allowed to charge for the cost of dissemination and not for the cost of
collecting the data.

Now the Appropriations Committee has before it our budget request for
fiscal year 1996, and I guess it is probably not appropriate for me to
speculate on what the Appropriations Committee is going to do.
Certainly, the talk has been, particularly in the House, that all
agencies will be taking sharp cuts. I think we do live in an age where
there are expectations of high levels of service and also expectations
that resources can be reduced and still provide these services. Up to a
point, I think there is truth to this, and I think that EIA as an agency
can do this up to a point.

In the information business, the computer, obviously, has had a
revolutionary effect. Some offices in government have concluded that
they can save money by not buying new computers. My own feeling is that
we can save money by buying better computers and making sure people are
on the platforms to be able to do the kind of work that they need to do
in a quick, fast, and efficient way. And I think that over time this
will reduce costs, but I think it has to be done gradually. I think if
someone comes in with a meat ax, they are going to kill off valuable
work. I think if they allow us to make investments in getting off of
legacy systems \-- we haven\'t made final decisions about the future of
the mainframe, but I think certainly there is a scenario in which you
would reduce mainframe usage, as we have done with the National Energy
Modeling System, and move to other types of environments that are lower
maintenance, where the upgrades are free, and people who come to work
for you can pretty well know how to operate them before they get here
and they all communicate with each other.

So there are lots of opportunities there for us, and I think EIA is a
leader in trying to seize those opportunities. And so we think that we
are going to come out of this in good shape, but there are no
guarantees. I mean I cannot sit here and predict today what the
Appropriations Committee is going to do. The Department is going through
an alignment process to try to demonstrate to the Congress and to the
public that the Department can do more with less or maybe in some cases
do less with less, and we don\'t know the final outcome of that. The
Secretary will announce at least some parts of it on May 3rd. I have
been meeting almost every day with them and it is still not totally
clear what will be proposed.

But I think that within the Department and elsewhere, EIA has a strong
level of respect for what it has done in terms of the intelligent use of
technology and in terms of the development of output measures and the
emphasis that we\'ve put on customer satisfaction. I think this is all
very helpful to us at this point in time.

I don\'t know, is it appropriate for me to just open up the floor for a
second to take some questions? I have to apologize, last time I was able
to stay through almost all the sessions. My schedule has gotten too
conflicted to be able to do that at this time. In fact, I am going to be
going to the airport in a little bit. Larry Pettis, who is the Deputy
Administrator, is right now at an alignment meeting back at the
Department, and when that finishes he will be joining us and I think
will be here for a good part of the proceedings. But if there are
questions at this point, I would be glad to answer them before I have to
run off.

MR. HANSER: If you don\'t have Internet access, do you get a copy of
NEMS?

MR. HAKES: Well, what are the options? We have developed PC versions now
of NEMS that are still in the testing phase, and I think we could get
that for any committee members before they left who would like it. How
many people would like to take one of those home with them? Okay. It
looks like we need about five or six. Mary Hutzler, who is the head of
the NEMS system is back there next to Doug Hale. So that is available to
you.

Now there are modules that also can run on a PC \-- expanded modules. So
it sort of depends on what you want. You may talk to Mary about that.

MR. HANSER: Does it include documentation?

MR. HAKES: Will that include documentation? Yes. Obviously, this is
another one of those things

as I was talking with Cal earlier. It is something that we\'ve always
thought would be nice to do and you never quite knew the pace at which
you could do it. And it has been moving along in a fairly nice fashion.
We demonstrated it for the NEMS conference back in February and it
seemed to be well-received. It is still work in progress. I mean it is
the test version and we are always welcoming feedback. Any other
questions or comments? Thank you very much. I am going to leave these
AEO brochures, the Home Page credit cards, and the energy information
cards up here at the front. Those are available in fairly good
quantities. So if you need to take one back for a colleague or something
like that, that is fine too. And we can maybe get some more over here
tomorrow if we run out. Thank you very much.

CHAIRMAN MOUNT: Thank you very much, Jay. We certainly appreciate your
comments and I am very pleased to see that you are still trying to keep
free access to public data. This is obviously very important to most of
us on this committee. Next, we turn to Yvonne Bishop, who is going to
give us a summary of the follow-up from the last meeting.

MS. BISHOP: I think I\'ll just briefly run through the various
presentations from last time and say what we\'ve done or haven\'t done
about it.

Mike Giriffey spoke last time about petroleum price data. The Committee
urged him to publish the results from the motor gasoline price survey.
They are currently disseminating it through EPUP. It will be on the Home
Page and one of the other systems, but the paper publication version is
waiting for redesign of the weekly petroleum status report which hasn\'t
taken place yet.

Electronic data dissemination, I think Jay took away most of the words
except that I think I can tell you some other things that we are doing
in terms of the documentation of what is being put out. We take your
comments very much to heart about wanting to know what it is. We have a
group who are looking into end-use sector definitions. We will be
hearing more about that today. Renee sent you a preliminary report. This
group will come up with a document. If it doesn\'t reconcile the
differences between supply and consumption, it will at least explain why
they differ. And the Petroleum Marketing Monthly plans to include more
explanatory notes in their electronic system.

Data requirements for NEMS, the Committee suggested consumer expenditure
survey, but we did not find what we needed there. However, Scott Sitzer
tells me that he is delighted that the Committee suggested looking at
the Atlas Project and they are in touch and working with the staff there
and think that that will be a useful cooperation.

Another thing was when I went to some of the NEMS presentations, it
seemed that every time a person asked a question, the NEMS modelers say
we haven\'t got the data. So we are busily trying to fill in some of
those gaps.

Emissions of greenhouse gases. Now Arthur Rypinski tells me that indeed
the methodology is flawed. However, he has done some more mathematics
and he believes that the error is from not taking into account that the
methane used up some CO~2~. The error is closer to 4.5 percent, not the
30 percent that you claimed, and I will be happy to give it to you.

And then sensitivity allowances to Ted Herman \-- data needs. Doug tells
me that this paper has been revised to incorporate committee comments
and it will shortly be available.

CHAIRMAN MOUNT: We\'re running a little bit behind time, but we are now
moving on to one of the formal areas of discussion. I think this is a
good example of EIA trying to anticipate problems in the future looking
initially at the implications of changing structure in the electric
utility industry. The presenter is Noel Balthasar from the Office of
Coal, Nuclear, Electric, and Alternative Fuels.

MR. BALTHASAR: Good morning, everybody. My name is Noel Balthasar. I am
the Acting Chief of the Generating and Fuels Branch here in EIA. I am
going to talk this morning about the electric power industry
restructuring and the impacts on our data collections, particularly the
electric ones.

I do want to start off just as a beginning as to what the current status
of the industry is. And the viewgraph that is up there or should be up
there in a minute basically shows the composition as of 1993. If you
look at the highest bar there, which is non-utility generators, you can
see there are about 2,100 non-utility generators. Everything to the left
of that bar are the utilities, broken up into the different types. And
they would end up being about on the order of 3,000 or so utilities,
plants in particular.

The investor-owned, which is the 254 number, probably represents the
large majority of the generation and sales of electricity in the United
States, maybe being on the order of about 75 to 80 percent.

The little question mark on the right pretty much stands for the
marketers that are out there today. There are 100 licensed marketers of
electricity, and about 10 of those are active at the moment. But that is
a very important and growing part of the industry.

Now I would like to turn to a particular area in the next viewgraph and
talk about electric industry generation, since that is something that we
specifically collect data about in the branch that I am in. If you look
on the left side for the electric utilities and compare that at about
3,000 billion kilowatt hours generation in the order of 1993 with the
non-utilities on the right side at a little over 300 billion kilowatt
hours, you see that basically they are representing now around 10 or 11
percent of generation, and you can also see that it has been growing
very rapidly recently. There was a period of time when we were
collecting that data in the 1970\'s up to about 1979, and then we
stopped collecting it because we thought it was doing just the opposite.
We thought it was a declining part of the industry, and lo and behold
when we stopped collecting, it turned around the other way. Mainly the
impact there was from PRPA, which basically started opening up the
non-utility generators and allowed that industry to grow. Now I would
like to talk a little bit about what \-- and I know everybody has got
different ideas of this \-- but I am just going to talk about the
restructuring issues. I started to think about this the other day. You
could compare it with natural gas. You could compare it with the
telephone industry. And I wondered if someday we were going to see those
commercials coming on TV that are sort of like you see now with MCI and
AT&T. Because basically they would be marketing power across the United
States and they would be going to every segment of the society, in
particular the retail side.

The number one bullet there is basically about that issue, the
open-wheeling or open- transmission access; the wholesale selling of
electricity and the retail buying of that and how it is going to work.
There is a notice of proposed rule making that FERC came out with, I
think it was about the end of March or beginning of April, that
basically said, yes, we are opening it up. Right now I think there are
very few utilities who have provided open access, maybe less than 20,
I\'m sure. That open access is going to really radically change the
industry. Even though it has been anticipated, it could change the way
we collect the data in particular and be effectively what everybody has
thought might happen.

The utilities have a big problem with that in general because they are
worried about their investments being stranded. Well what about all this
money that we spent figuring that we were going to be able to utilize
these facilities for 10 or 15 or 20 or 30 years? In the proposed rule
making, FERC has essentially dealt with that issue and sort of said,
well, if we can verify or validate those, you will be able to get your
stranded investment costs. That takes one big worry out of the ball
game.

In terms of customer choices, you basically have two options: cost-based
regulation or just plain open competition. And there is quite a bit of
difference between those obviously. Therefore, the whole marketing of
electric power and the basis for it would change a lot of the things we
do in EIA.

I mentioned the power marketers. I did send out in the initial mail-out
of the material an article about Consolidated Edison, who has been in
the market now for a number of months with the idea that that is going
to become a very big part of the market. And they are, in effect, the
number one power marketer in the United States right now.

Going on to the next viewgraph and continuing in some of the issues,
obviously you\'ve probably been reading about the Federal Power
Marketing Administrations. There is something in the paper almost every
day about those. Are they going to be privatized or are they not? That
is a big issue. They are generally the low-cost hydro-, a lot out in the
west in particular, based utilities, and they will have a big effect on
what the market and the price of power looks like in the future.

When it comes to the independent power producers, I also sent out some
articles about the fact that many of them have had contracts with the
utilities and now all of a sudden the contracts are coming to an end.
And the utilities are saying, wait a minute, I don\'t think we want to
enter into a long-term contract with you anymore. We probably just want
to do a short-term contract at best or maybe no contract. And that has
been a big issue to the independent power producers.

How that works out and how those contracts are renewed over time will
have a lot to do with our rates and the rates of the big industrial
companies that are trying in general in a lot of places to get this
cheaper power.

In the retail area, the decreasing industrial rates is going to have an
impact in increasing retail rates potentially. And generally, in terms
of our collections, we are going to be dealing with a different set of
companies. There could be basically an unbundling. Right now generation
and transmission in the notice of proposed rule making that FERC put out
are essentially wide open. In those two areas, this idea of the GenCo
and the TransCo is probably coming to a reality very soon, and
potentially even on the distribution side. Many of you may know or have
read about like Fairfax, Virginia, a city close by here, that basically
wanted to get away from Virginia Power and essentially all they would
own is the meters, but they would be the distributer and they would
collect the money.

I want to go now a little \-- after talking about some of those issues
and things going on \-- to talk about what we are doing and what some of
our thinking is. We, in particular in this area, it is not the only area
that there are changes going on. But I would like to say generally we
are working with the Federal Energy Regulatory Commission, FERC, and
also the North American Electric Reliability Council, NERC, in trying to
work out as best we can where we are going in our data collections. In
particular, to give you an example, the NERC we are working with now in
terms of our Form-860, which is the basis, for instance, for our annual
capacity figures, to work on a joint effort where they would sort of be
a middle man in terms of collecting data from the utilities and
forwarding it to us in an efficient electronic manner and essentially
reduce the burden down on both sides. And that has so far been a very
successful effort and it is also being done in some other areas. So
there is a lot going on in terms of not only ourselves but other
portions of the industry in the restructuring. I specifically would
like, though, to spend a couple of minutes talking about generation and,
therefore, the consumption and stocks data that we collect as a
particular example. The viewgraph that is up there now shows the North
American Electric control areas. These areas basically are the ones, and
there are roughly 150 of them, that control the flow of electricity.
They are the ones that have the meters for metering the generation and
also the transmission of electricity around the United States. We have
started talking to the control areas, some of which are utilities or
groups of utilities, in trying to get a way of collecting data
immediately from them rather than the utilities or the non-utilities.
And the reason for this is that one of our basic thrusts is to collect
more frequent non-utility data and to think of the industry from a
generation viewpoint as just one group, not two separate groups and deal
with them differently as we are today.

The next viewgraph kind of shows our current status there. Currently, we
have a monthly census of electric generation, consumption, and stocks
from those roughly 3,000 plants on the Form EIA-759. We have an annual
census of the non-utilities, about 2,100 facilities, on the 867. There
is a Federal Register notice that is up here that is about to go out
that basically proposes that we collect monthly, non-utility data along
with the utility data, and we do it in one of two ways. Either we get it
from the control areas, and we basically have talked to a few already
and are continuing that effort to try and collect it from less entities
and collect it quicker, more electronically, and get the level of detail
that we could use, or just estimate whatever we can from that say
sample, maybe. That is a very open area at the moment, and there are a
lot of questions in the Federal Register notice that would give us
answers as to exactly how would we work out that collection in terms of
our current data.

Option 2 is a more straightforward approach to it, and that is just to
sample both utilities and non-utilities using the 759. We currently do
not collect even any stocks data from the non-utilities. We collect
fuels consumption and generation.

Finally, getting down to what is really the issues related to ASA, we
obviously would have problems with estimation of generation and/or
consumption and stocks. And we have essentially put together a paper
related to option 2 or the sampling plan for the 759 and would like your
comments sometime in maybe the next few months. We also, when we get
comments back from the Federal Register notice and put a plan together
as to how we might do the collection from the control areas, we will put
together a paper and also forward it to you, and we would like your
comments at that time.

I think there are many other issues that are going to come up along the
way. We are in the midst of collecting or putting together our clearance
package on the electric forms which expire at the end of this year.
Anyone who has ideas are welcome to comment relative to the Federal
Register notice on either the whole package or this particular issue.
Thank you.

CHAIRMAN MOUNT: Thank you, Noel. So the discussant is Phil Hanser. Oh,
we are going to have the second paper before the discussant, is that
right? We were going to. So it is Al Holland, the construction engineer
from the Office of Integrated Analysis and Forecasting.

MR. HOLLAND: Good morning. I am Art Holland. And for the next ten
minutes, I will be describing the modeling efforts by the Office of
Integrated Analysis and Forecasting at EIA to simulate the effects of
the restructuring of the electric power industry.

I have been asked to review the questions before I started, so here they
are. The questions for the panelists are, \"What is your opinion of the
use of the levelized cost of a combustion turbine plant or gas-fired
combined cycle plant as the fixed or capacity component of the long-run
marginal cost calculation?\" \"Should the value of capacity in the
long-run marginal cost calculation be a function of the capacity
utilization in the region?\" In other words, if there is a situation
where you have over-capacity, does that lower the market value of that
capacity? \"Would it be better to use a foresight mechanism in the model
to identify the next technology choice that the model will build in a
given region and use the cost characteristics of that technology in the
long-run marginal cost calculation?\" \"What is your opinion of limiting
the recovery of potential additional revenues due to competition by
price or revenue caps?\" \"How will electricity prices be set in a
competitive market, and this from a mathematical specification point of
view?\" In other words, how should it be modeled?

The role of the Office of Integrated Analysis and Forecasting in
modeling competition in the electric power industry is four-fold. First,
we are trying to determine the magnitude and direction of wholesale and
retail price effects. Second, the magnitude and direction of changes in
revenues to electric utilities. In other words, what are the transition
costs? Third, the changes in capacity expansion plans that result from
two things; first, generating plants that have to be replaced because
they become too expensive to operate; and second, as a result of the
effects of increased business risk or higher overall cost of capital.
The fourth thing we are trying to look at is what are the environmental
impacts of restructure.

There have been two efforts to date. The first is ongoing and the second
has been completed. It was by customer request. In the first effort, the
focus was on the endogenous or calculated within the framework of the
model calculation of market-based rates. And the purpose was to analyze
four things, the four that I mentioned previously. There is an error on
this slide. Number 3 should not be the changes in capacity expansion
plans that result from simply the chances in capacity plans that I
mentioned earlier.

The second effort, which has been completed, was focused on the
emissions consequences of restructuring and the purpose was to do just
that, number 4 from our overall efforts. In this effort, we made
exogenous or user-input assumptions that were changed in order to model
the scenarios that the customers wanted us to look at.

In effort 1, as I mentioned, the focus was on the calculation of
market-based rates and revenues. We did this by using a long-run
marginal cost algorithm to calculate the generation stage of production.
Now there are two components of the long-run marginal cost. First is the
fixed or capacity component. To simulate this, we used a levelized cost
of a combustion turbine. Let me explain what I mean by levelized. If you
take the total capacity cost of a power generation project for the life
of the project, what amount would you pay each year if those costs were
paid in equal installments? In other words, if you turned all of those
costs into a level or fixed annuity over the life of the project.

The second component, the variable or energy component, is a little
harder to explain. That is the weighted marginal cost of energy for each
dispatched time period. Let me try to explain that. Each slice of time
in the model that is dispatched, and these slices of time are a
combination of the hour of the day, the day of the week, and the season
of the year, for which there is economic dispatch, we took the marginal
or the highest cost planned, the highest operating cost planned, for
each of those slices that we use for dispatch. We weighted that marginal
cost by the sales volume for each slice in the model and summed them.
And what we ended up with was an annualized marginal operating cost.
These costs are allocated to the customer classes. There are four
customer classes that we calculate average prices for. We compare these
allocated market-based rates to those costs that were allocated based on
the embedded traditional cost of service rate-making method and then an
average market price for each customer class is calculated by dividing
the market revenues by sales.

There were also two exogenous assumptions that are being made in this
effort. First is we\'ve cut general administrative costs by 20 percent.
This is to simulate the effects of streamlining and consolidation that
we anticipate in the industry. Also, we\'ve increased the cost of equity
by 2 percentage points which results in an overall weighted average cost
of capital increase of about 1 percentage point. Again, this is to
simulate or to try to get at the increased risk that we anticipate in a
more competitive market.

These are our preliminary results. The two bars show the embedded costs,
which are based on the traditional rate of return regulation method, and
the second bar for each region, the regions are over here on the map,
are the potential market revenues that have been calculated using the
long-run marginal costs.

You will note that some regions had higher potential market revenues,
which are based on the long-run marginal costs, than embedded costs, and
some had lower potential market revenues than the embedded costs. Not
all of these results are what we would anticipate or expect. For
example, Region 6, which is New York, and Region 13, which is
California, are showing higher potential market revenues than embedded
costs. This is not what we would expect at this point, and we are
looking at and digging into why this is happening.

There are several possibilities. One is that there could be an
aggregation problem. That is, we are combining winners or utilities that
are going to benefit from competition, with losers or utilities that may
be hurt by competition. And we are also looking at the marginal costs
that are coming out of the model. There are some mathematical problems
that may be present now. We also need to spend some time looking at fuel
contracts and how we are treating those in this market-based approach,
and also how we are treating the purchased power contracts.

The second effort focused on the emissions consequences of
restructuring. Here, we made changes in 8 exogenous assumptions, and I
will review these quickly with you. First, we changed the elasticities
of demand so that there was an increased demand response to changes in
electricity prices. Second, we had higher coal plant capacity factors.
This means that coal plants will be used more. This is the assumption
implicit in this change in inputs. We had higher interregional
transmission capacity. That means that more energy could be transmitted,
for example, from the Pacific Northwest or Region 11 into California.
Lower capacity reserve margins. The idea here is that reserve capacity
or idle capacity would have a higher opportunity cost in a competitive
market. We also raised the cost of equity, as I previously mentioned.
Earlier nuclear capacity retirement. Now this was kind of a quick and
dirty way of getting at what some people feel may happen. There are
nuclear plants that may not be able to compete because of high operating
costs and may have to be shut down. So this is a way that we are trying
to get at the effects of that lost capacity. There were no renewable
technologies set aside. That assumption was changed. The assumption
there is that renewable technology will not be as effective in competing
under a competitive market. The big driver in this effort was the
dramatic reduction in electricity prices. This did not use the
methodology that I mentioned previously. This was an exogenous
adjustment to the price of electricity to drive the results that we
wanted.

And these are the results. Let me take just a moment to review the
scenarios that you see in the legend. AEO95 is the annual energy outlook
that we just published. The DEFSUP scenario is like the annual energy
outlook scenario, but we\'ve used the demand models with the increased
elasticities of demand to give us another baseline with those new demand
modules. WIPOUT means with phaseout. In other words, we made the
dramatic change in electricity prices that you see, but that change or
that adjustment was phased out over the life of the forecast or over the
forecast horizon so that the price drifted back up to the reference
case. NOPOUT was a no phase-out scenario where we made a dramatic
adjustment to electricity prices and then held that adjustment steady
through the forecast.

The results in demands were what we would anticipate. The top line is
the NOPOUT or no phase-out scenario. And you can see the demands
adjusted upward and stayed there. The with phase-out demands drifted
back to the reference case levels.

Again, as anticipated, utility steam coal generating capacity was higher
where we had the adjustment to prices and the higher demands. And this
was a result of higher demands and the loss of the nuclear capacity that
I mentioned earlier. With the phase out of those price adjustments, we
had not quite as high a steam/coal generating capacity at the end of the
forecast.

And finally, the point of the effort was to try to determine what
impacts these adjustments would have from a modeling perspective, and
you can see in the no phase-out case, carbon emissions were, indeed,
higher throughout most of the forecast.

If you put both of these modeling efforts together, there are some
observations that can be made. Again, I should review the scenarios with
you. AEO95B is the annual energy outlook just published. RSTRC2 is
restructuring 2. That is the first effort where the long-run marginal
cost was used to determine market clearing prices and NOPOUT or no
phase-out was the case that I just mentioned where exogenous changes
were made to the model to drive the changes. The use of the long-run
marginal cost as a market clearing price did not result in the dramatic
price declines that the exogenous or emissions case created. And the use
of the revenue caps meant that prices were lower in some regions even
though potential market revenues were higher. Region 11, as you recall,
which is the Pacific Northwest, had higher potential market revenues in
the first case, but because of the use of revenue caps, those higher
potential market revenues were not achievable, so that the price
declined as a result of the other assumptions that were made.

Again, some of the things that we are looking at in particular are the
treatment of purchase power contracts and fuel contracts and we are
constantly reviewing the mathematical specifications that we\'ve made to
try to tighten up the algorithms that we\'ve used here.

Thank you for the opportunity to speak with you this morning. If you
could put the questions back up, I\'ve been asked to leave those up
there. Thank you very much.

CHAIRMAN MOUNT: Thanks a lot, Art. So now we move to our two
discussants. Who is going to go first? Phil Hanser.

MR. HANSER: I\'m just going to go through the questions to start with
because \-- can you hear me? Is this okay? I think that the full
implications of what deregulation is going to look like haven\'t quite
surfaced. I don\'t know quite how to describe that, but if you think
about deregulation of this industry \-- of the electric utility industry
\-- it seems to me that you are going to have a series of changes so
dramatic that even talking about utilities in the way that we\'ve talked
about them in the past just doesn\'t make much sense.

For example, talking about what is the cost of capacity. If you truly
have a deregulated market, the cost of capacity is zero because what is
created on the market is kilowatt hours and not kilowatts. Now you may
create capacity tickets which allow somebody to enter the electricity
marketplace, and so there may be some value to capacity in that sense,
but it is only as a kind of entry fee so you can play the game. What
gets created are kilowatt hours on an hourly basis. So calculating the
cost of capacity is sort of an irrelevant thing to be computing. What
you really want to know is what is the cost of the marginal plant at
each hour, because that, in fact, will be setting the price of
electricity for each hour. Now that is the second sort of exercise that
you went through.

So in a truly deregulated market, some of these questions in some sense
strike me as being sort of irrelevant in that sense. So you have to ask
yourself the question, which I was told you are supposed to do every
time someone gives you a data problem, what question are you trying to
answer with the data that you are collecting and with the modeling that
you are trying to produce. And if what you are trying to do is forecast
from the existing integrated utility system as to what things will look
like in the future, that is one question. But if you really do believe
that the markets are going to become deregulated, then you need a very
different set of models and way of thinking about what you are doing
than the current structure of your models. Okay?

For example, you raised the question about price caps. Well in a truly
deregulated world, price caps will only be able to be applied to the
regulated entities, but the only entities that will continue to be
regulated in that fully deregulated world will probably be something
like a transmission company, which is simply providing wire services at
high voltages and maybe some low level wire service, which will be
providing, again, the ability to get electricity to the final retail
customers. And those costs, as a portion of the overall cost of the
system, probably represent at the most maybe 40 percent, and that is
largely because of the nature of regulation nowadays. Kilowatt hour
costs will probably be largely passed through even to eventually retail
customers. So when you talk about price caps, you have to define what
the regulatory environment is and how that is going to actually operate.

So in answer to your first two questions, I see nothing wrong with using
a combined cycle plant as a proxy for capacity costs, but it seems to me
that the fundamental question is what is the marginal energy cost that
you are going to end up using. What is the plant that at the margin is
going to set the hour by hour cost?

As far as the next technology choice, I guess it seems to me that that
is a very tricky question because, in fact, if you want consistency, it
is entirely dependent on what your fuel forecasts are for coal and oil
and gas and whatever else. Because nobody will make a choice about
generating electricity which isn\'t, in fact, dependent on what the
forecast is for prices of the various fuels that are going to be used.
So I raise the question, tell me what the fuel forecast is, and then I
can have some basis for thinking about what the next technology choice
is going to be. I don\'t think that you can do that in the absence of a
fuel forecast. So I think for consistency purposes relative to the EIA,
if you are going to do that, you want to think about that as the basis
for your choice.

As far as how will electricity prices be set in a competitive market \--
the way prices are always set in competitive markets; the laws of supply
and demand. I hate to say it, but it is quite that straightforward. Tell
me what the characteristics are of the long-run marginal cost curve for
producing a kilowatt hour and give me a prediction of what the likely
demand will be for wholesale and retail customers, and that is the basis
for predicting what the prices will be.

So that sort of \-- I don\'t mean to be flippant about it, and I
apologize if I seem that way. That is an attitude I picked up when I was
15 or something. But I think you have to be very careful. You have to be
\-- you have to think about quite fully what those markets will look
like. And then, on that basis, develop what your models are going to be.
Let me give you another example. Later on in your presentation you said
you have four customer classes. And I guess my question would be why is
there not just two customer classes? Customers who take delivery at high
voltage and basically everybody else who has got energy being passed
through some distribution system. The distinctions between industrial
and commercial and so on essentially go away in deregulated markets, and
you are left with customers who take electricity basically at different
voltages and who incur different levels of cost of having that power
transmitted to them.

The easiest one to deal with is simply one in which there is a group of
customers which probably takes electricity at 69 kilovolts and above and
a group of customers who take it at 69 kilovolts and below. Those 69 kV
folks and below nominally would be called retail customers and the folks
above would be called wholesale customers, for lack of a better term.

You\'ve got an exogenous assumption down here about the cost of equity
having been increased by 2 percentage points, and I guess the question
is who is it that has their equity increased by 2 percentage points. Is
it the generating company? Because if you have regulated transmission
and distribution, their risk characteristics are no different than the
current risk characteristics of an integrated utility, and therefore,
should probably face no higher increase in equity. So if you are going
to raise equity, the equity costs really have to be a burden primarily
of the generating companies who are deregulating.

I think this chart of yours that shows the embedded cost versus
potential market revenues is the most peculiar chart I have ever seen in
some ways. Just let me explain why. My estimate of the stranded costs
for electric utilities in the ones I have seen is something around 150
billion dollars. That number is so large that I can\'t believe that
every bar that says embedded cost is not significantly higher than the
one that says potential market revenues. Even in the best of all
possible roles, I think you are looking for large numbers of utilities
very, very substantial write-downs of their assets unless somebody comes
in to rescue them. But the way, the FERC note as I read it basically
effects about 6 percent of electricity sales and essentially doesn\'t
deal with stranded investment, although it says it does. It says it
basically passes the issue of stranded investment on to the state
regulators. Well, since none of the state regulators want to deal with
it, God knows what that is going to look like.

Finally, let me just spend one minute on your assumptions under emission
consequences of restructuring. You\'ve got higher coal plant capacity
factors as one of your assumptions, and it wasn\'t clear to me whether
that was meant to be a direct result of your early nuclear capacity
retirement and as a result in some sense they fall naturally one from
the other, or did you have higher coal plant capacity factors and then
also get early nuclear capacity retirement? The reason I raise that is
you don\'t want to get double-counted. You don\'t want to be in the
position where in some sense you just say, well, they are going to be
exogenously going up and then simultaneously you\'ve made an assumption
which forces them to go up. Do you follow the problem?

By the way, you can also get a fairly good estimate of earlier nuclear
capacity retirement from some work that was done that appears in the
Energy Journal about three years ago on nuclear decommissioning. There
is somebody who regularly calculates what the levelized cost for
kilowatt hour is when you include decommissioning costs, and it is
roughly about 50 percent of nuclear capacity that is out there.
Sometimes as high as 20 percent, depending on who estimates it. That was
a special issue of the Energy Journal. And you can take a look at that
and get a fairly decent basis for which plants will be uneconomical at
that point.

By the way, whether or not they are uneconomical frankly depends on how
quickly public utility commissions are going to let electric utilities
depreciate those assets. Some commissions are letting their utilities
depreciate them at ridiculously high rates. But that is another story we
can talk about around economists rather than statisticians. Thank you.

CHAIRMAN MOUNT: Discussant Number 2, Campbell Watkins.

MR. WATKINS: I feel a bit like one of these lawyers who when you follow
the proceeding lawyer ticking off all of your questions you were going
to ask. Let me make some comments mainly on Art Holland\'s paper first
of all, and then I will address one or two things that came up in the
presentation as well.

First thing, on your effort 1, there seems to be a supposition there, if
I am right, that the long-run marginal cost is always rising. That need
not be the case and in fact is not the case recently with gas-fired
combine cycle plans. And if you have a declining long-run marginal cost
curve, then you really have, of course, very severely problems of
stranded investment, and Phil has just referred to that, and also the
natural monopoly market starts to emerge again.

It wasn\'t clear to me that the calculation when you talk about the
customer class contribution, whether that related to the system peak or
whether it was an assumption of a co-instant peak. It also wasn\'t clear
to me why the gas combustion turbine was chosen as the marginal
supplier. Basically, that serves peak requirements, and Philip\'s
comment about your interest really in short-run marginal cost, and in
that case it will be short-run marginal peaking cost. You can levelize
it, yes. But if it is for peaking, I am not sure the levelization
process means too much there.

In the case of the system base load, I think you are looking at a gas
combine cycle, and I think in effort 2 that comes out of that.

I think I now understand what you were doing with variable marginal
cost. I think first of all I thought you were making an average of
marginal costs, but in fact what you are doing is taking the marginal
cost literally of the most expensive unit in the particular slice of
time and then averaging that. So I think I understand what you are doing
there.

Footnote 3 on page 5 actually is very important. That is because you are
really defining short-run marginal cost. And as Philip has pointed out,
in a deregulated market, the short-run marginal cost is the governing
factor. The question then arises, is that \-- where does long-run
marginal cost fit in. Well, in very broad terms, that acts as a ceiling
to short-run marginal cost. That is how you plug it into your
deregulated market modeling effort.

There isn\'t any discussion in your paper of the way that one might call
the cost allocation method is dealt with at a distribution level between
customer classes. That, in itself, will be affected by market
deregulation. It may be something you\'ve looked at already, but it
didn\'t really appear in the paper.

You have the assumption that revenue will always be lost rather than
gained. Just from a matter of principle, given the vintage aspect of the
regulated market, there is nothing odd about there being a revenue gain
per se. Because if you\'ve got fully depreciated assets and your
marginal cost is, in fact, rising, then you would get more money from
pricing everything at marginal cost. You recall in the regulated market
\-- I mean the price of a vintage related scheme, there is no
relationship to a market price. It could be above or below.

The debt ratio and the increase in the price of equity, that bothered me
a bit. Going back to just technical economics and Modigliani-Miller
hypothesis, there is no reason per se why the cost of capital would go
up in the way you suppose unless you shift in risk class. If that is the
case, yes. But I think you need to bring that out in the paper.

I think particularly in effort 1 and the relationship with effort 2,
much more attention needs to be paid to the role of coal and what has
been going on there. My understanding is on coal plans that what we\'ve
seen going on is investment in maintenance, which is extending the life
of coal. This is one of the reasons why you think with the great
advantage that gas has over coal in terms of new plants, why is coal
doing so well. The reason it is doing so well is in fact the coal plants
on a short-run marginal cost basis are much more attractive than new gas
plants and more attractive than existing gas plants. So the ability of
the coal plants to stay in there is related to this question of
extending their life and the way that rehabilitative maintenance
expenditures can do that, and the fact, of course, is the siting
problem. The \"not in my backyard\" issue makes it attractive to try to
sustain the life of existing plants rather than invest in new plants. I
think that whole aspect, which also muddies the whole question of
marginal cost and how they get at marginal cost, is something you need
to consider.

Fuel price volatility \-- I think Phil touched on this. It is one of the
other aspects that would affect your choice of generation.

A few comments on effort 2. It wasn\'t clear what your demand elasticity
was. Is that short-run or long-run? Do you need also to have modeling
efforts that would take account of possible asymmetrical price
responses? Lower capacity reserve margins is also related to the
facilitation of regional transfers. I would suggest on the deregulation,
particularly when in adjacent regions, you don\'t have co-instant peaks
so that it releases capacity to be transported across regional
boundaries. You talk about the capacity required to replace nuclear
plants, and that may well, of course, not be gas-fired turbines, because
they have been used for peaking purposes. It is the relationship between
your effort 2 and your effort 1 analysis \-- I wasn\'t clear on how
consistent that was.

Availability of spare and stored capacity to affect prices. If you have
a deregulated market, clearly yes. The spare capacity, just as it has
with the natural gas industry, is going to affect your prices. You can
use in a way or look at what happened with gas here with the gas bubble.
Actually, it was more like a sausage that prevailed during regulation of
the gas industry and the effect that had on prices. I had a question on
how the higher steam coal capacity arises. Is it through new plant
additions, in which case again I raise the question of the consistency
between your effort 2 and effort 1 analysis? Or is it more utilization
of stored capacity? Or is it the factor I mentioned about the extension
of the coal plant life?

Let me finish by making a few comments on one or two things that were
raised in the earlier presentation. Long-term contracts for independent
power \-- one would expect to see that severely compressed, just as has
happened in the gas industry. In the gas industry, 20 to 25 contracts
were the norm until deregulation. Now they are the rarity.

I think I\'ve covered most of the other aspects I wanted to talk about.
I wasn\'t clear when you mentioned stock data what you were referring to
there. Was that coal stocks for plants? I see. So maybe I can just
finish up with the questions. I\'ve answered some of them or given my
opinion on some of them.

The levelized cost procedure \-- yes, annuitizing costs is fair enough,
but you have to distinguish between the peaking costs and the base load
costs.

Question 2, yes, it will be affected by over-capacity. The foresight
mechanism is really quite difficult to get at because of the difficulty
of appraising and even getting at what really is the long-run marginal
cost among all the various alternatives. This price cap issue I would
treat as primarily political. It would have no place in a deregulated
market. You have to have, as Phil has mentioned, a regulated sector left
there somehow as treatment of the stranded investments. And electricity
prices being set in the competitive market \-- well, if you have the
supply side modeled correctly and the demand side modeled correctly,
then the price will be derived, of course, from the intersections we
talk about.

I think that overall the analysis or the paper might be assisted by a
more distinct distinction between what you are assuming on the
generation, transmission, and distribution. You assume the transmission
is going to be still regulated in some measure because of natural
monopoly elements, as long as there is freedom of access. With
generation, I think that is easily to treat or simulate what the
competitive market might yield you. You hadn\'t mentioned much about
what you are assuming on distribution and the role of by-pass and all
those messy issues.

So those three elements, as I see it, will be treated separately in the
models, and I am not clear from your paper how that is actually dealt
with. Thank you.

MR. HANSER: I wanted to raise just one issue on the data which I forgot
to which was I would argue that the compliance by electric utilities, in
terms of the EIA forms and the quality with which the data is supplied,
is frankly a function of regulation in the sense that if I am a
regulated entity, I can pass the cost of filling out the forms and
things onto my customers. I would argue that I would expect to see a
decline in the quality of data that will be collected by EIA from
whatever the generating entities are out there and what they will be
called. Because it seems to me that it is going to be hard to
demonstrate to them the value of performing that activity and complying
with it.

So I think that you may want to think about building in some kind of
data quality control procedure fairly quickly before the deregulation
really sets in. I know that data quality is a problem in competitive
markets now. Not with just energy data but lots of other things that are
collected. So I think it is something that will have to be faced by EIA
that is probably not a severe issue currently with regulated electric
utilities.

CHAIRMAN MOUNT: So we now open this up for discussion among the panel,
and I would like to take the chairman\'s prerogative and make a few
comments of my own. I really appreciate the two presenters today. This
is a very difficult subject and a very important subject to deal with.
And as a modeler myself, the idea of having to sort of make predictions
about what is going to happen at the moment, the only way one can do
that is to make very big assumptions.

I want to second Phil Hanser\'s comment about the importance of data. It
is going to be increasingly difficult to get data, particularly if sort
of bilateral trading is allowed. Cost data or price data is just going
to be harder to get and it is very important to try to figure out how to
do this well.

But I also want to address an issue that was mentioned about stranded
costs, and this is really advice for the Department of Energy more than
EIA. I just want to quote something that I downloaded on Internet from
the Federal Energy Regulatory Commission. Utilities would be allowed the
opportunity to recover stranded costs. Now the bottom line is we have a
system that allows utilities to recover stranded costs. That is what the
regulation is all about. The trouble is at the moment there are
important groups of people who don\'t like that system. There are
independent power producers that think that they can do a better job and
have demonstrated it, and there are large industrial customers that are
tired of paying high prices. And the real problem is that the federal
agencies cannot really pass these issues down to the states, much as
they would like to, because it, to a large extent, revolves around
nuclear power. So in New York, we are seriously considering retiring
nuclear power plants because their operating costs are too high. We do
not know what is going to happen to those plants. Nobody wants to pay
for them. That is the whole issue. We really need a federal policy of
how to deal with these costs.

So open to the discussion. The usual thing, put your card up if you want
to comment. Everybody is quiet. This is a most unusual situation. Cal?

MR. KENT: I want to, I guess, be the third person then to talk about the
importance of the stranded cost investment issue here. Because I think
that is going to be driving the industry. If not in the short-run, I
think it is going to be driving the industry in the long-run. Those
costs are not going to go away. And if you look at the natural gas
experience, you will find that FERC is not capable of considering them
or using them for reasons already pointed out. You are not going to get
them passed down effectively to the state regulatory bodies. I don\'t
think they could, even if they had the inclination to do it. In the long
run, you\'ve got that one politically viable group called the customers,
the captured customers, who really in the short-run are going to have no
alternative. They are not going to want to be in a competitive market in
which they cannot participate. So that is going to create political
pressures to at least maintain some form of regulation, which I think is
going to require that there is going to be some form of recapture of
these stranded investments allowed. Otherwise, you are going to have
total chaos in what used to be the regulated end of the market.

So I think all of that has to be factored in when you are making these
calculations. And I think that is going to put a break to a degree on
the decline in energy prices, which is why I think that the AEO
projections there are probably, if maybe not for the right reasons, at
least the right price path that we are going to follow.

MR. WATKINS: I want to make another comment on stranded cost. I really
have difficulty with that issue as you raise it, Calvin. The fact of the
matter is if you are going to have open access on transmission lines and
the cheaper producers, therefore, would have access to customers, I
think the stranded investments are going to be just that. Once the
market takes over, it will just grind them out.

MS. DUCHIN: I wanted to comment about two things about the environmental
question which hasn\'t been picked up and about technology.

With respect to the environment, what you showed us were comparisons in
carbon emissions, and under the new scenarios, the carbon emissions
increase for various reasons that we can understand. But the question
that I ask, to come back to an earlier observation \-- the question I
ask myself is, what is the motivation for carrying out the computation?
And presumably, it is to get some anticipation of what a certain form of
deregulation might mean. After all, deregulation is not that well
defined that we know exactly what it means. It takes a lot of different
possible forms, but I guess this isn\'t the time and place to discuss
that.

But just under this scenario, presumably the motivation for the
experiment is to see what it might mean from a lot of different points
of view, one of which is the environmental point of view. And I just
want to point out that carbon emissions is important, but only one
aspect of the environmental question. And if really one wants to look at
that question, one wants to see trade-offs. And aside from sulphur,
which of course is also important, the other fundamental environmental
issue has to do with nuclear waste or nuclear materials and their fate
under the different scenarios, since there are different things
happening to nuclear plants and nuclear production under the different
scenarios. That is one thing.

The other thing about technology, I interpret one of your questions to
be asking whether the technological assumptions might be endogenous.
Presumably the alternative is for them to be exogenous, or maybe for
there not even to be any alternative assumptions about technology. That
is the third question. Whereas the second or perhaps the first one looks
at the prospect of using a given technology as the basis for the average
cost of capacity. And I assume that those two questions are related
because if one has the capability and the interest to look at
alternative technologies in response to the third point, then presumably
in the costing out one of the important issues is to cost out the
alternative technologies.

And I just want to suggest, although I understand it is a matter of
modeling philosophy and a matter of a lot of considerations, that these
phenomena don\'t have to be endogenous in order for them to be useful in
looking at scenarios. And as a first pass it might be useful to make
some exogenous assumptions about alternative technologies and to cost
them out. I know that a data user like me is very interested in that
question mainly because I think it matters in evaluating different
scenarios what technologies are used and what the costs are that are
associated with the capacity \-- the putting in place of capacity of
different scenarios.

MR. RELLES: Just to say a few words about data quality. At Rand we do a
fair amount of work for the military, and I think in recent years there
has been a notice of a death and a degradation in quality of data
supplied by various low-level types. For example, if I am fixing
aircraft engines, my goal is to get aircraft engines fixed, not to
fulfill a lot of data requirements, even though in theory the generals
have control over my future. And the notion that under deregulation EIA
loses or government agencies lose the power to require compliance with
data entry, I think is a very serious one. I don\'t know to what degree
EIA is right now evaluating its data quality, but what will happen, I
believe, is you will have several years where the data get a little bit
worse and yet it takes a long time for the flags to rise.

One suggestion I would like to make, perhaps because I have heard all of
these economists speaking this morning, is the notion that EIA is going
to have to start paying for some data and that it might consider, as
this deregulation begins, trying some experiments where in some places
they do pay the marginal costs of collecting the data and try to see
what they can find out about its effects on quality.

CHAIRMAN MOUNT: Well, I noticed that Yvonne left the room when you said
that you wanted EIA to pay for data. So if we get it free, we ought to
at least argue that they should get it free.

MR. RELLES: Well, I would argue that we shouldn\'t get it free either,
but that is for a different paper that I am supposed to discuss later.
MR. HOLLAND: I feel compelled to respond to some of the comments on the
modeling efforts. First, I wanted to make it clear, if I didn\'t, that
the point of the presentation was to display method and not results. We
are not happy with the results yet either. Hopefully next year we will
be able to talk about that in a little more detail, or hopefully sooner.

There is a lot of discussion also about the market value of capacity.
The idea there is that capacity \-- I think we are doing the right
things in the first effort, possibly not for the reasons that I
mentioned. The market value of capacity in our thinking is its
contribution to the lower probability of blackouts. The price of the
energy \-- I agree with both the panelists \-- should be more a function
of the cost of the fuels and the variable costs of generation and
transmission and distribution. So I think we are on the same wavelength
there except for that idea that capacity does have a market value as
long as you are not in a situation of overcapacity. That is our current
thinking, but again, we are trying to be open minded about that.

Finally, I wanted to make a comment about the idea of having two
customer classes. Maybe I can give you a little insight into our
thinking if I express it as not so much the prices that the customer
classes see as the prices that the customer classes pay in our
calculation of those average prices for each class.

The residential and commercial classes have different load
characteristics \-- load shape characteristics. And for that reason, I
believe that it is still worthwhile to try to make some determination as
to differences in what they will actually pay over time versus \-- as I
said, if you think in terms of what they see, they may be very similar,
but not necessarily what they end up paying. Thank you.

MR. BALTHASAR: I guess I would just like to say that we realize about
data quality being an issue in this and also just the fact that it
wouldn\'t be as easy to collect data in general also. And we are trying
to address that. We are thinking of doing some like case studies. We
have already really taken a long, hard look at the non-utility data,
which we already judge is not as good quality as the utility data, and
that is a big issue. You basically try and deal with them as one group.
So we are certainly aware of it, and we are working on the problem and
hopefully will be successful.

CHAIRMAN MOUNT: So anybody got any last minute thoughts. It looks as
though we are on schedule and we can have a break now. No \-- sorry, I
missed it. Sorry. I knew it was too good to be true. Phil?

MR. HANSER: Well, that was certainly an inducement to speak. I guess
that one issue I would like to raise, which is kind of a tricky one, is
I worry that we spend all of our time on the stranded investment issue
and not enough time thinking about what do we want to do to make these
markets function appropriately. And it is amazing to me that in all of
our modeling, we spend all of this time worrying about where the dollars
are going to go around stranded investment and then find ourselves sort
of not spending enough time thinking about how the markets are going to
operate.

So, for example, you use the highest marginal cost plant as the basis
for doing so. But I can think about Dutch auction systems, for example,
that produce different results than that and that produces a different
characteristic in terms of how market participants behave.

So I think one of the things you may want to think about is that there
are lots of different alternatives in terms of how those markets will
operate, and I think that is important in terms of what those prices
will in the end be.

Let me also go back to this point about capacity. Nobody trades capacity
on the market. That is the real issue. So if there is nobody trading
capacity on the market, then the way that you firm up power is going to
change very dramatically in terms of the way it is done compared to now.

For example, in a commodities market, other commodities markets, you
utilize future and forward contracts as a means of taking non-firm
contracts and making them firm. That is a possibility also in this
market. But again, that would require almost the most minimal amount of
capacity to firm it up. You will not see reserved margins of 22 or 26
percent or anything like that. You will see more reserved margins that
are more like 8 or 11 percent or whatever is the intrinsic variability
of a load.

Given also the ability to more easily transfer loads across areas, that
will also reduce the need for capacity as we think about it now. So it
is because the basic commodity that is traded in the market is no longer
going to be capacity, per se, but rather kilowatt hours. I think it
really changes dramatically how you think about it.

MR. WATKINS: A quick comment on capacity. Maybe the capacity issue is
more in terms of transmission and not generation. There, you will have
that issue. And also, I didn\'t\' mention but I could, mechanisms for
capacity release also will emerge.

CHAIRMAN MOUNT: So, I thank the speakers again. This is a tough subject
and I am glad that we have an opportunity to speak about it. I am also
glad that we had the comments from Phil Hanser at the end there. Oh,
sorry. I\'ve got to look over here.

MS. BISHOP: I just wondered whether any

\-- those members of the committee who are involved in this and so on,
would be willing to look at those sums and plans when they are ready.

CHAIRMAN MOUNT: I\'m sure they would. Nods down there. So I think that
separating the stranded cost issue from the future structure of the
market for electricity is very important indeed. So that is a very good
point. So we are going to have \-- I keep on looking around. I have
missed \-- so, we are going to have a break for 15 minutes.

(Whereupon, at 11:09 a.m. off the record until 11:30 a.m.)

CHAIRMAN MOUNT: Could you take your seats, please? I have one
announcement. Jerry Coffey from OMB brought a new report about the
reduction of paperwork that he gave to Yvonne Bishop. He only brought
one copy, hint hint, and if any people on the committee would like to
see a copy, talk to Yvonne.

MS. BISHOP: It\'s the conference report on the Paperwork Reduction Act,
and we have to go through all sorts of shenanigans of a 10 percent
reduction this year, 10 percent reduction next year.

CHAIRMAN MOUNT: And I made another faux pas as chair. In addition to
having poor peripheral vision, I forgot to introduce a new member of the
committee because I think he wasn\'t there when I turned up at
breakfast. David Bellhouse is a new member.

So we are now turning to the next subject, which is natural gas under
the general heading of structural changes in the industry. Do we have a
presenter? Yes, down there. Bill Robinson from the Office of Statistical
Standards.

MR. ROBINSON: I work for the EIA Office of Statistical Standards. Today,
I will be speaking on some preliminary ideas that are being considered
within the agency for dealing with sets of price data where the reliable
data that is available is sparse. That is to say that there is only a
limited amount of data available. And furthermore, where the set of data
for which there are reliable data values tends to be changing from year
to year. That is, it is not \-- the set of data for which we have good
values is not the same from one year to the next.

So we are looking at ways for making useful inferences from this type of
very imperfect data sets. In particular, one of the areas of interest is
natural gas transportation rates. In circumstances where the gas is
being transported by a pipeline for some other owner, that is the gas is
not owned by the pipeline, this type of data is becoming increasingly
important because of changes that are taking place in the natural gas
industry.

A few years ago, it was generally the case that when a pipeline
transported gas, they owned the gas that they were transporting. They
would purchase it from a particular source, transport it, and then
resell it someplace else. This type of bundled service of sales and
transportation, this bundled service is rapidly vanishing. The trend now
is for the end users to make deals with intermediate marketers for
direct sales of natural gas and for the pipelines for them to receive a
transportation fee for transporting the gas that someone else owns.

Now one possible source of data on transportation rates is FERC Form 2.
One of the problems that we\'ve encountered in the past with the data
from FERC Form 2 is that FERC does not stringently enforce submission
requirements. In some cases, rates are not submitted. In many other
cases, the data is difficult to use because it is not clear what service
is being \-- what is the precise service that the data refers to and
whether it is consistent from one year to the next.

At one level, we would ideally like to have enough information about
transportation rates that we could calculate exactly how much does it
cost to get from any particular source to any particular end use
destination that we might be interested in. But if it is not possible to
have that kind of complete information, it is worthwhile to consider
ways of making useful inferences from a limited amount of data that is
available. For many purposes, you don\'t need extreme detailed
information about individual pipelines. You may just be interested in
how certain aggregates of transportation rates are moving over fairly
broad aggregates, and you can attempt to make inferences from limited
amounts of information.

I should point out the fact that we are considering ways of making
inferences from a sparse data set doesn\'t mean that we\'ve given up
hope that FERC will improve their submission requirements in the future.
We remain hopeful for that. But we are still considering ways of
utilizing a limited data set.

One option we are considering is constructing a price index that shows
for broad aggregates of transportation rates how the rates have changed
over time. One commonly used price index is the well-known Laspeyres
index. The Laspeyres index is the approach that is used for the consumer
price index in the United States. It is simply a very simple measure of
how much a bundle of commodities has changed in price compared to some
base years. So, for example, the subscript out here refers to a specific
commodity. So the numerator here shows the total expenditures for some
bundle of commodities. We are looking at the bundle of commodities that
existed in the base year. The numerator shows how much that bundle of
commodities cost in the base year. The numerator shows how much that
bundle of commodities cost in year Y, and the denominator shows how much
that bundle of commodities cost in the base year. If you look at the
ratio of those two, you get an indication of how much price has changed
overall. It is customary to express it as a percentage. So you multiply
it by 100.

So to give a simple example, if 1980 were the base year and all prices
had doubled since the base year, then the consumer price index would be
200, because all prices have doubled.

Another interpretation of the spare index is that you can view it as a
weighted average of price increases compared to the base year. So this
ratio here, Pi^y^/Pi^b^, shows how for one particular commodity what was
the proportionate increase in the price of that commodity compared to
the base year. Each of these weights here is a fraction. Specifically,
that fraction is the proportion of expenditures for good i in the base
year. So by taking this weighted average, you get a measure of how price
has changed since the base year. Again, you express that as a
percentage. Now there is an alternative approach called chain indexing,
and this is the approach that they use in France for their consumer
price index. What they do is instead of keeping the bundle of
commodities or the quantities fixed at some base year, for each year
they construct a simple index that shows what the proportionate increase
in prices was from one year to the next and they chain them together to
get a measure of the change in price over several years. So, for
example, in this case, this little value R^y^ shows what the
proportionate change in prices was from year Y to year Y+1. Now if we,
therefore, want to use that to get a measure of the proportionate change
in prices over a longer period of time, we can just multiply several
values together. So, for example, if in the base year prices went up by
10 percent, then this factor would be 1.1. If in the following year
prices went up by another 10 percent from year Y^b^+1 to Y^b^+2, then it
would go up by another \-- you would have a factor of 1.1 again.
Multiply the two things together and that says that over a 2-year period
prices went up by a factor of 1.21 or prices went up by 21 percent. So
that is how chain indexing works.

An advantage of using chain indexing, particularly for the application
that we are talking about here, is that it allows for the bundle of
commodities that are being considered for these price changes to change
from one year to the next. When we are talking about this bundle of
transportation services, this could occur for a couple of reasons. One
reason is that new services may become available from one year to the
next or another reason is that there may be reliable data available for
different sets of transportation services from one year to the next. So,
therefore, this allows for us to get a measure of changes in the prices
for this bundle of services from one year to the next even though the
bundle of services that are available may be changing.

Now an important issue here is to what extent should the various types
of transportation services be lumped together as a single commodity when
we perform this kind of calculation. At one extreme, you could say well
let\'s just consider all transportation services as a single commodity.
We might measure it according to the number of MCF miles of gas
transported and look at the average price of that and just see how that
average price has changed from one year to the next. There are obvious
problems with that because from one year to the next you may have data
available for different sets of transportation services. Suppose one
year you had data available for relatively inexpensive transportation
services and a later year you may have data available for more expensive
transportation services. Then your measured value of transportation
rates would show a price increase even though there may have, in
reality, been no change in the price of a particular type of
transportation service. So this aggregating things to an extreme in that
way is not a good approach.

There is an opposite extreme where you could just treat each piece of
transportation data by each company as a separate commodity for purposes
of this calculation. The problem you get into there is that when a new
pipeline is added and if the costs that are associated with that service
happen to be different from services for similar transportation services
that already exist, ideally you would want the calculation to show that
the cost of services has changed over the period in which that new
pipeline was added. So if you just treat it as a new commodity, you
would be able to measure how the price of that service changed from that
point on, but you wouldn\'t catch how the cost of that service changed
over the period in which the new pipeline was added. So if you are
adding a new pipeline and the service that is provided is similar to
something that has already existed, then you would want to aggregate it
in with those similar services that are already in existence.

Now we are talking here about getting measures of what is happening to
transportation rates in an aggregate sense, but we would naturally be
interested in certain sub-aggregates. We would probably be interested in
focusing on subsets of transportation rates where the transportation
service in question is used to transport gas within a particular region
or perhaps looking at a pair of regions for those services that are used
to transport gas from one region to the next. We would also be
interested in looking at sub-indices that make a distinction between
firm and interruptable transportation services.

Some questions for the committee then are can you refer us to any
related literature that deals with the problem of constructing price
indices in circumstances where the data is sparse and is not consistent
from one year to the next? Is there any alternative techniques you would
have us consider for dealing with this problem? And are there any
particular recommendations you might make, in particular is there some
way of modifying the techniques described here to deal with biases that
might occur in the calculations that I have described?

MR. WATKINS: It seems the first two topics that we\'ve had to deal with
are certainly difficult ones, and this is another one. How to get at
some kind of representative index that would provide useful information
on what seems to be happening to the cost of transporting natural gas.

I found in reading Bill Robinson\'s notes that I think it would help if
you maybe spell out the purpose of the index in a bit more detail. For
example, is it mainly intended to measure shifts in consumer prices and
where are those consumers located? Are you talking about city gate or
are you talking about the burner tip? Or is it to measure the cost of
transportation per se? Or even, and I shall mention this in a moment, to
measure the cost of, for example, materials used in transportation.

I have some ideas to put forward to you, but they are also conditional
on the exact nature of the data, and I am not familiar with some of the
actual data you are dealing with. To the extent any weighted average of
available transportation rates involve vintage pricing, which it does
under the regulatory regimes we have now, that can really mask
variations in the actual cost of moving gas, particularly where you have
weighting problems that shift weights in ways that will either detract
from or add to those pipelines where the assets are more fully
depreciated under cost of service regulation. You could get some awkward
results.

I think your reference to cost indices could be widened in that one of
the things that would be of interest, and I guess it goes back to the
question actually that Jay Hakes was raising earlier about productivity,
and that is indices of the cost of materials used in pipeline
construction. I know that basically goes beyond a bit what you have
talked about in your paper, but I think it is another important element
in the whole picture.

You make allusion to some kind of national index. That, unless it was
very carefully constructed, could be quite misleading. If you are
calculating it on some kind of regional weighted share basis, obviously
the shifts in the shares in the regions could produce shifts in the
index that may have nothing to do with the inherent costs of gas
transportation.

I think here where you are dealing with weighted shares or shares that
would produce a weighted index that one technique that I don\'t see
mentioned in your paper that you should look at is the use of a divisia
index where you would rate the growth rates in pipeline tariffs, for
example by expenditures or volumes. I am certain Chuck Bischoff could
give you a 10-hour lecture on all the theoretical niceties of the
divisia indices in a lot of the work that has been done with demand
analysis.

I think in the question of regional indices, I would suggest you really
direct your attention initially to the major trunk lines. And I
certainly agree with the suggestion in your paper that you look at the
regional stuff first because first of all that is what composes the data
set in a way. But if you can get some good regional indices calculated,
that would be the first step. And to do that, I suggest you look at the
major trunk lines.

You mentioned establishing good statistical relationships between
end-use prices, well-head prices, and transportation rates as being a
worthy objective. It may be a worthy objective, but you may find it
difficult to estimate the relationships statistically because a lot of
them are linear.

You make mention of the Pash index later on in the paper. I think that
triggered for me a general comment which was that I think you could
expand your discussion of the virtues and deficiencies of the various
indices. You did ask for some references on index number questions. I
will give you a couple and I will give you the references maybe
afterwards or give them to Renee. One is an article in the Journal of
the Raw Statistical Society in 1981 on the theory and practice of chain
price index numbers, and the other one is an entry in the Palgrave
Dictionary on Indices by Irwin Dewiert of UBC. It is a very difficult
article, I might add, but at least it covers a lot of the essential
properties of indices and also the various axioms that govern them.

You mentioned MCF miles as defining a commodity. I raise a question
about that because there is a substantial difference between
transporting a large volume a small distance and a small volume a large
distance. Both may have the same number of MCF miles, but in fact the
commodities are quite different.

And in terms of commodities, and this is certainly one of the more
difficult problems that you mentioned, is the variety of services that
are provided and also the services provided with deregulation have
expanded very greatly. If I just look at one major trunk line, it offers
at least 8 tariffs, varying from firm service to temporary winter
service to peaking service to interruptable summer service and storable
services. So you have a great variety of services that can be provided,
and the question is and what you are trying to grapple with is how do
you get a handle of some index of overall transportation costs given
this mixed bag. If you had good information on each type of service over
time, then you can do the individual indices and also maybe aggregate in
some way that would make some sense.

Another response which I suggest you look at, however, is to really
focus on the key elements, and of that there are two. One is the demand
charge, which is a charge for the use of capacity of a pipeline, and the
other is the commodity charge or the charge for the amount of gas that
you move through the line. Now if you have a time series on these
charges for a given pipeline \-- and that is why I don\'t know actually
the exact nature of the data that you have

\-- that sort of index for those two elements at least would give you a
good indication of what is happening to pipeline costs or costs of
transporting gas that doesn\'t get obscured by either shifts in the
regulatory regime or shifts in load factors which become part of the
eventual cost paid for the gas. But if you want to really focus on gas
costs, you probably want to normalize, for example, for changes in load
factors. If you are able to get a time series for demand charges, you
don\'t have to bother with that problem because it is the demand charge
that you are interested in \-- the charge of capacity.

The problem of incorporating new pipelines in the index. This is also
complicated by the regulatory regimes. It depends on whether the tariffs
are defined on a rolled-in basis or whether it is on an incremental
basis. Maybe with further deregulation that sort of problem will
disappear, but it is certainly a problem at the moment. In other words,
the actual nature of the regulatory regime can determine how those
tariffs are, of course, formulated and you can get widely different
results. That is why the focus on say one pipeline under one
jurisdiction at least could provide you with some consistency if, in
fact, the regulatory regime is consistent.

Your appendix shows the technique for dealing with missing values, but I
think if I understand your manipulations correctly in the appendix, you
actually do not use the information. You do have some commodities in
1993 in the example you cite. And it seems to me that you could do some
fairly simple manipulation there to at least use that information in
constructing the in-between year index. I don\'t think you use it in
your current formulation.

So let me summarize. I am making kind of four main points. First of all
that you concentrate on the major trunk lines. Secondly, you have to be
very careful in aggregating across your regional data or your trunk line
data, and you should look at other techniques of indices perhaps to do
that. Thirdly, I suggest you see if you can focus on demand and
commodity charges with separate indices for each. And fourthly, I
didn\'t mention this in my prior remarks, is that your chain index
shifts in bundles of commodities. Because those commodities are so
different, I am very uneasy about your chain indices with shifting
weights. Again, because you can have shifts in the nature of the market
being served in effect obscuring what you want to get at, namely what is
happening to the transportation of gas from individual service. Thank
you.

CHAIRMAN MOUNT: So we can open the discussion up to the panel.

MS. DUCHIN: I don\'t know if you are familiar with it or not, but your
problem seems very similar from a practical point of view to the
construction of the consumer price indices and the wholesale prices
indices at the Bureau of Labor Statistics with changing market baskets,
quality change, the need to construct regional price indices and
national price index. So you might find it useful to take a look at some
more detailed work that they have done.

CHAIRMAN MOUNT: Brenda?

MS. COX: Although I\'m sure you have looked at the consumer price index,
there is a new article out by Rick Valliant and Silvia Liever. It is in
[Business Survey Methods]{.underline}, which just was published. I am
one of the editors. That is the reason I know about it. It is really
nice. I read it and I felt that I understood the construction of the
consumer price indices, and that is a pretty powerful remark to make. So
it is very good. There are copies floating around EIA. I would recommend
that.

Then there are a couple of articles that have appeared by Rick in the
Journal of Business and Economic Statistics. One is about three years
ago, I would say, and then maybe about five or six years ago. So you
might want to look for those. He mentions many of the same points
you\'ve talked about in his article like the problem that new
commodities give. And frankly, I didn\'t feel that happy with what
happens with new commodities because you wind up comparing the new
commodity to something that really isn\'t the same thing and talking
about price increases. He uses an example in the book of Dove ice cream
bars and comparing it to chocolate bars. Well, they aren\'t the same
thing. He also talks about the impact that missing data has for seasonal
items and things like that. I don\'t know if that is a problem for the
price index you are creating, but I thought in terms of laying out the
issues and what they were currently doing that it was really marvelous.

CHAIRMAN MOUNT: Faye again.

MS. DUCHIN: A price index, of course, always has its shortcomings in
what it is trying to do. As to the difference between a Dove bar and
another item of consumption that is more or less similar, it is a moot
point. I think that one of the business issues \-- I think there were
two big issues in their construction, and these are both sort of soft
issues, not ones that can be resolved through mathematical formulas. One
of them is how to group things and how to decide how much difference is
not that much or so much that you have to call it different. But I think
the bottom line is that you can\'t expect to deal perfectly well with
quality change and that an index number that is considered important
should always be accompanied by qualitative statements that gives the
user some indication of what lies behind it in terms of these changes.

CHAIRMAN MOUNT: I have one question. I was interested to find out
whether there was some bias in the reporting. Do certain types of
customers not fill out their forms? I don\'t know whether you would like
to reply to the discussion.

MR. ROBINSON: I haven\'t investigated whether there is a bias in that
sense, but I would expect it is possible that those firms that are most
likely to make special deals that are inconsistent with what the market
price is are the most likely to not fill out data values. So there is a
potential bias, but I can\'t really think of any way of dealing with
that unless you have some suggestions.

CHAIRMAN MOUNT: So if there are no other comments from the panel, this
is the time when the public has an opportunity to make comments about
the whole morning session if there is anybody in the back there. Before
that, Bill, do you want to respond?

MR. ROBINSON: One of the comments dealt with the problems of using MCF
miles as a measure of the quantity of the service provided. I would
agree. I would expect when you classify things together as a particular
commodity that one of the things that you would do is look for services
that are similar in the sense that they go from a similar source to a
similar destination that is in close proximity. So you are talking about
when you classify services as the same commodity, you are talking about
the same distance traveled. If you were talking about a totally
different service that was a separate point and a separate destination,
you would probably classify that as a separate commodity. So I think if
you construct the system and classify things as commodities properly,
then you are not going to have the kind of problem that was cited.

Certainly I agree that \-- in my paper I mentioned making a distinction
between firm and interruptable services, and in reality we would
probably want to make a much greater disaggregation than that looking at
the various components of the services and making a distinction between
the reservation fee and the usage fee and to get separate measures for
each of those. That is all I have to say.

CHAIRMAN MOUNT: Sorry to cut you off before. So now we can go to the
back of the room. A shy group. No volunteers. It looks like we get lunch
early, and I remind you members of the committee who have not signed up
for the dinner tonight, see one of us \-- maybe see me at lunch. We are
going to convene again here at 2:00 to talk about transportation and
reconciling forecasts.

(Whereupon, the meeting was adjourned at 12:07 p.m. for lunch to
reconvene at 2:06 p.m. this same day.)

A-F-T-E-R-N-O-O-N S-E-S-S-I-O-N

2:06 p.m.

CHAIRMAN MOUNT: Could everybody take their seats, please, so we can get
started. So we are going to start this afternoon\'s session looking at
the transportation module of NEMS, and the presenter is going to be
David Chien from the Office of Integrated Analysis and Forecasting.

MR. CHIEN: Thank you very much \-- I would first like to thank Andy
Callus for inviting me to give this talk, and I would also like to thank
Art Andersen and Barry Cohen for assisting me on the graphics. So thank
you very much for the opportunity to discuss the transportation model
with you today. I will have to figure out some way to pay those guys
back later.

The talk that I am going to give today is with regard to the VMT
equation in the transportation module of NEMS. The importance of the
NEMS transportation VMT equation I have listed in three different
categories. It is important because it has policy implications for oil
use and energy security relative to transportation energy use. It is
also important because in our findings and in several other findings,
the fuel price has a small marginal effect on VMT. This is a similar
result to studies that David Greene did in 1992 and 1994, Gately in
1990, and Mayo-Mathis in 1988.

Some of the reasons for why that may be the case, the following things
have been mentioned. Fuel cost is a very small percentage of total
travel costs. Another is that vehicle efficiency improvements offset
fuel price increases so that there are no long-run adjustment factors
over time.

Another interesting discovery that was made was by David Greene in his
1992 report in which he took a look at the data and divided it into two
parts. He did an estimation between 1966 and 1977 in which he found a
price elasticity of -.27, and he used data between 1978 and 1989 and
found a price elasticity of -.06.

One of the important factors, I think, with respect to our VMT equation
that makes it different from some of the other equations out there is
that we have included two demographic enhancements to the model. These
factors are the age distribution of the driving population and female to
male VMT per capita assumptions.

This is the generalized difference equation which I am sure most people
here are familiar with. The data that we used was between 1970 and 1990
to do the estimation using FHWA VMT numbers. The difference equation
here accounts for zero correlation of the first order. The input
variables that I have listed there are vehicle miles \-- well actually
the dependent variable is VMTPC, which is vehicle miles traveled per
capita, and then that is a function of the fuel cost of driving per
mile, which is basically the fuel price divided by the fuel efficiency
and that is the on-road fleet fuel efficiency \-- disposable personal
income per capita and the ratio per capita for female VMT to male VMT.
And this is to try to capture the increasing female participation in the
labor force which was first modeled by David Greene in 1987.

First we applied the linear specification and then we found that the
Durbin & Watson wasn\'t acceptable. So we applied the Cochrane & Orcutt
procedure for first order serial correlation, which is not exactly
astounding in that Greene in 1992, Gately and Mayo-Mathis all applied
the same types of procedures with first order serial correlation.

You can see from the equation, taking a look at Table 2, that the
coefficients have the correct signs so that you would expect that as the
cost of driving goes up, VMT goes down per capita. As income goes up,
VMT per capita increases. And the variable PrFem, which as I mentioned
before is the ratio of female VMT to male VMT \-- so that all things
being the same, if female VMT increases per capita over time and you
multiply that by the same population, you will get \-- it raises the
equation for the VMT equation. And I will go through its net effects.

A couple of things I want to mention \--

MR. RELLES: Could I have a quick question? Are we talking about \-- at
what level are these data collected and what are the periods of years
and is it city-level data or is it national data?

MR. CHIEN: This is national data that comes from the National Personal
Transportation Survey.

MR. RELLES: These are totals for the nation by year?

MR. CHIEN: Yes.

MR. RELLES: Okay. Thank you.

MR. CHIEN: I will go over the distributions for these demographic trends
in more detail.

MR. WATKINS: What period is Table 2 estimated over? What is the
estimation period?

MR. CHIEN: 1970 to 1990 using FHWA VMT numbers.

MR. WATKINS: That is yearly data?

MR. CHIEN: Yes. We also took a look at the variable that we call P\>60,
which is a tri-state account for the ratio of the driving population for
those that are 60 and above divided by those that are less than 60 years
old to try to capture some type of movement in the age distribution over
time. And when we did it for the linear specification, we found that it
was marginally significant. But after we applied the Cochrane & Orcutt
procedure, it becomes insignificant.

A couple of other things that I would like to mention about the
specification. One is that we did try quite a few specifications, log
linear as well. And one of the reasons that we didn\'t use that is
because it has the assumption of a constant elasticity and according to
the Greene study of 1992 that I mentioned earlier, he had found that
depending upon which time period you used, the more recent time period
will give you a much lower elasticity for fuel price. So that tells you
that the elasticity may be changing over time and that is one of the
reasons why we did not use a constant elasticity function.

VMT and T-1 is also not an explanatory variable for the following
reasons. One is that it was found insignificant, and in other studies
such as Jones 1994 and Greene 1992, they found that when you did include
the lag variable, income became an insignificant variable.

Now on this next slide, I want to describe to you the demographic
adjustment factor at the bottom of this table. What we do is we take
VMTPC, which is VMT per capita times the driving age population times
this demographic adjustment factor called DAF, and that is how we get
our revised VMT forecast. Now you can see from the \-- I hope everyone
can see this \--at the bottom there, you can see the specification for
DAF. What we are basically doing there is PVMT60 corresponds to the
ratio of vehicle miles traveled per capita for those \>60 divided by
those \<60. And P\>60 refers to the two respective populations. What we
are basically doing here is we are weighting the VMT per capita by their
populations, and that is how the index works. So you can see that \--

MR. WATKINS: For population distribution?

MR. CHIEN: Yes. And what you can see is that if PVMT60 equals 1, then
DAF becomes 1. And then the demographic effect is basically zero.

Now this gets into the two assumptions that we had to make for the
demographic variables over the forecast. Let\'s first look at the PrFem
line, which is the line that is kind of yellowish. You can see that this
is using NPTS data. We had basically four points because the survey is
taken every seven years. You can see that it is declining. What this
basically tells you is that the VMT is declining for those that are \>60
relative to those that are \<60, but it does not say that per capita
driving is less for those that are \>60. Because as the data shows, as
well as our forecast, both are increasing over time.

So what we basically did was the last point that we had was 1990, and
that was approximately .52 or 52 percent and we asymptoted that to 50
percent throughout the forecast.

Now the next thing that I want to talk about is the PrFem line. That is
the blue line. You can see that that is rising over time so that the
female VMT per capita is slowly approaching the male VMT per capita. Now
what we did is we asymptoted that to 80 percent by 20/30. The reason why
we didn\'t make it 100 percent is for the following two reasons. One is
that if we were to say that VMT per capita for females was going to be
the same as male, then we would be saying that there would be no
homemakers. I mean, that is possibly one explanation.

The other would be that \--

MR. WATKINS: Or that they are all homemakers.

MR. CHIEN: Well, if they are all homemakers, they would have to be
driving. I mean I am making the assumption that a homemaker drives less
than a working individual. I don\'t want to say that homemakers aren\'t
workers. Considering all the driving that homemakers have to do for
their children taking them to soccer games, I definitely don\'t want to
belittle that at all. If anything, I think they deserve immense credit
for that.

MS. DUCHIN: But you are assuming that the homemakers are all women?

MR. CHIEN: Excuse me?

MS. DUCHIN: You are assuming that homemakers are all women?

MR. CHIEN: That is true too. We will just have to add another
demographic factor. The other factor is \-- and this is kind of an
interesting topic \-- it has to do with when both male and female are in
the car, who is doing the driving. Now I know that we each have our own
little sample of one, so I will leave that for you to decide if that is
a valid factor or not.

CHAIRMAN MOUNT: One drives and one directs.

MR. CHIEN: That\'s right. The next graph is kind of interesting. This
comes from NPTS data, at least the 1990 data does. Over the forecast
period, that came from the forecast. The two points I want to make here
are that all VMT is rising for all age groups, which I don\'t think
should be that surprising to most people. The other is that VMT declines
with rising age. I don\'t think that one is probably necessarily that
astounding either, but I just think that is kind of interesting. I am
sure everyone right now is looking at which category they belong in and
seeing if it corresponds with their form of reality.

So I think with this graph, this brings us to one of the reasons why we
have this demographic adjustment factor in there. So you can see that if
people drive less as they age and this graph showing that distribution
of the driving age population, you can see that the darker bars
represent 1990 and how the graph is skewed to the left. There is that
group that we call the babyboomers, and according to census projections
by 2010, you can see that the graph becomes skewed to the right. So if
we are saying that the babyboomers as they get older and they represent
a very large portion of the population and if they drive less, then this
is going to have some kind of negative effect upon VMT over time.

This graph indicates the effects of both of the demographic factors that
I have mentioned. The line in the center is basically the line that is
our revised VMT forecast. You can see that the inclusion of DAF \-- the
very top line is without the demographic adjustment factor and the
middle line is where it declines towards with the demographic factor.
Similarly, with the variable PrFem, that raises the VMT. So that the
bottom line with the asterisks refers to the ratio of VMT per capita for
a female to male if we held that constant at the 1990 rate. Given our
assumption, that raises the revised forecast for VMT.

The next graphic tries to break out by VMT component the contributions
of each towards the total VMT over the forecast period. As you can see,
one of the variables I didn\'t discuss, because the discussion so far
has mainly been on VMT per capita which eventually gets multiplied by
the driving age population which is TPOP16. So you can see by looking at
this graphic that the largest contributing factor is the rising driving
age population over the forecast, and the second largest is PrFem, which
as we have discovered, as I mentioned before, over the last decade or so
with more women entering the work force, this has had a large factor on
the rising VMT per capita. And then income. The cost of driving per mile
and the demographic adjustment factor, which is a -2 percent.

These are the questions that I posed to the ASA committee members and
the discussant. Are there any quantitative methods for incorporating
anticipated changes in demography? This refers to the demographic
adjustment factor in which the data over the forecast period has not \--
we haven\'t seen that huge movement of the babyboomers yet to the older
portions of the population or the distribution of the population. So we
know through census data that it is going to occur. It hasn\'t occurred
yet. And then the question is how do we try to incorporate something
like this in the model. It is difficult to do that econometrically
because it hasn\'t occurred yet. So we applied a demographic adjustment
factor as opposed to an econometric adjustment factor. And the question
is are there any other quantitative techniques to handle this kind of
situation.

The second question is how should rising VMT trends be evaluated as the
forecast horizon moves out, especially given that there may be some kind
of saturation effect with these demographic trends for VMT per capita.
And are there any other factors that the committee feels that we should
consider in relation to this.

The third is kind of a subset of the second question because what we
have decided to at least look at were certain portions of the
distributions such as the 60+ driving population. We noticed that the
70+ is a very large portion of the distribution, and somehow or another
we would like to try to incorporate that in our model. The other is that
in VMT per capita, the 30- to 40-year-old range has the highest VMT per
capita. So we wanted to include some more specifics on that distribution
itself over time. So those are the three questions that I have. Thank
you.

CHAIRMAN MOUNT: And our discussant is Charles Bischoff.

MR. BISCHOFF: My kid has been kidding me that I am showing my employment
by wearing a blazer and asking for a blackboard. Nevertheless, the
questions that David has put up on the blackboard today are not the
questions he told me he was going to ask yesterday. So I will try to
answer them in addition to a few questions that he did ask yesterday and
questions that I am going to ask.

Now we have this VMT equation, and I had a question about the variable
P/60. Let me understand. P/60 is the population \>60 divided by the
population \<60, not the total population, right?

MR. CHIEN: Correct.

MR. BISCHOFF: Okay. Well, let me start with the female driving
adjustment because it illustrates a point that I will make later on when
I discuss the demographic adjustment. Now let us say we have two VMT
equations, one for males and one for females. The male equation is a
function of income and cost. The female equation is a function of income
and cost and also the ratio that David uses \-- the ratio of VMTPC
female to male. Now let\'s call this

\-- now let\'s suppose we want to take a weighted average of the two. So
we will say \-- it is a weighted average of the G function and the H
function. Now since cost and income are in both functions, we can fit
the equation econometrically and let the coefficients take care of the
weights. But you notice the ratio should be weighted by the ratio of
females to total population. Actually, this is probably not going to
make much difference because the ratio of female to total population has
probably been constant during the sample period and probably will be
constant during the forecast period. But this illustrates the way one
should do it when one is making adjustments of this sort.

Now let\'s move on to the question of the demographic adjustment factor.
If you multiply out all of the parentheses in the demographic adjustment
factor, you get \-- now you notice POP greater than 50 \-- this ratio is
related to the population under 60, and if it is properly done, it will
be the ratio of the population under 60. But you notice here we have
PVMT divided by the ratio of POP over 60 divided by POP under 60. Now we
always like to do things thinking about theoretical extremes. Suppose we
had a population that was entirely over 60, and this could example if,
for example, fertility declined so much that there was nobody under 60.
In this case, this ratio would go to infinity. Just like here the
appropriate denominator is not population under 60, it is total
population. It is okay for this to be a ratio of the two VMT\'s because
they will never go to zero, but the weighted average should be a ratio
of the extra group to the population under 60. And since we are talking
about the say 70\'s and 30\'s, the fact is one could make up a
demographic adjustment factor for every group, and it would be like this
with the appropriate adjustment of the P factor. I wonder about this
because I look at David\'s chart with total VMT per capita by age
cohort. If you wanted to have a demographic adjustment factor for every
factor, you would just have to do it exactly the way this chart was made
up. He didn\'t say when this chart was made up, but if you want to know
the adjustment for VMT in 2010 with the adjustment factor for every
group, then however you do it just add up all those groups for 2010 and
you\'ve got it.

Now the real problem with going to 2010 for the various groups is the
various asymptotic assumptions. I am afraid I don\'t have any better way
to make asymptotic assumptions than the way David has already done it. I
could say well you ought to look at the VMT trend for each age group and
say fit a Box-Jenkins. That would be the pure statisticians way to do
it. But although I have fit plenty of Box-Jenkins in my life, I think
you are just as well off thinking about, when you are going really into
the deep distant future, saying what asymptotic result I want to have
and let\'s just make a reasonable path to it. So I think you can do that
for every age group, not just for over 60, but over 70, 30 to 40, and
all of those various different ways.

Okay. I want to make one more comment though about the price elasticity.
David Greene, I guess, was said to have found very different price
elasticities for two different periods, 1966 to 1977 and 1978 to 1989.
And David Chien told us that that was the reason they used a linear
specification instead of a log linear one, because they wanted to allow
the price elasticity to vary. Well, of course, I can\'t tell from the
data we have here how much the price elasticity does vary over the
sample, but I would like to ask him whether with this linear
specification he did any tests of the stability of coefficients, any
Chow tests, for example, if you will, for whether or not the linear
specification allows the elasticity to vary enough, and if it doesn\'t,
then he needs another specification that will account for the data
within his model \-- that allows for Greene\'s results.

Basically, to try to answer the questions that were asked of me. Are
there any other quantitative methods? Well, as I mentioned, you could
use Box-Jenkins for the trends, but I think in this case introspection
is as good as any. How should rising trends be evaluated? I guess that
is the same question. Maybe I misunderstood the two questions.

Are there any other factors that should be considered? Well, I don\'t
know. It seems to me that certainly when you are doing automobile
transportation, you ought to take some account of other modes of
transportation and their costs. These may change in the next 20 years.
And certainly there is a question of congestion. We\'ve got automobile
transportation rising in all classes. It seems to me that you ought to
at least think about some possibility of congestion and congestion costs
that is going to happen. I don\'t see a lot of new highways being built
or a lot of new roads or anything like that.

Okay, the third question was is there any guidance you can provide on
breaking out the 60+ driving population. Well, I have already, I think,
discussed what I have to say on that.

But I want to spend one minute discussing a topic that I thought I was
going to be asked but that was cancelled from the agenda at the last
minute, and that has to do with the alternative fuel vehicle equation.
Those of you who are familiar with the transportation sector know that
there was a survey taken in Southern California sometime before 1991 by
Bunch and his colleagues in which they were asked about alternative fuel
vehicles and they surveyed 962 people. I had thought that the question
that was being asked of me was whether they could generalize this to the
nation, but then I found in an administration supplement to the energy
outlook coefficients summarizing consumer evaluation of vehicle
attributes were derived from a stated preference survey conducted in
California and are assumed to be representative of the United States.

Well, I think this is a big mistake. The data were analyzed in a
multinomial logit framework and there were basically four things that
determined alternative vehicle use. There was the cost. There was the
driving range. There was the emissions factor. And there was the
availability of the fuel. And basically at least the first three of
these, I think, are likely to be drastically different in Southern
California than in the rest of the country and therefore likely to
effect the coefficients.

The coefficient on cost is going to be a function of income because
income isn\'t in the equation. And certainly Southern California income
I think is a little different than the rest of the country. Driving
distance \-- I get the impression that there are different driving
distances in California than in the rest of the country. Certainly
Southern Californians are likely to be more sensitive to emissions than
other people in the rest of the country.

Now you could try to fix up the coefficient, and at the very least I
would try to fix up the coefficients on the basis of back of the
envelope calculations. But certainly I think there is a question as to
whether if it is not too expensive whether such a survey can be done in
the nation as a whole. Thank you.

CHAIRMAN MOUNT: Thanks a lot. So we open up the discussion for the
committee here.

MR. RELLES: Actually, I have more a question than a discussion. I recall
last time there was some discussion about wanting to see a module of
NEMS, and I presume this is showing us a module of NEMS, and I
appreciate seeing the details of these calculations. And from what I
know, it looks like you\'ve done a reasonable job. I guess the question
I have is when Jay Hakes at the beginning says things about prices of
fuels out in the year 2010 or quantity consumed, how does this interact
with other things in NEMS? And I guess as a user of NEMS, I would see
your description and I would say, okay, I believe that with a range of
plus or minus something, and I guess I would want to know whether that
impacts a number that is being derived out of NEMS that I have no idea
how it relates back to this quantity. So is there any way for me to
understand how your VMT calculation is going to impact other things that
I may care about more directly?

MR. CHIEN: Okay. Are you talking about, for example, feedback effects?

MR. RELLES: Yes.

MR. CHIEN: Okay. Basically, I am not sure how much \-- if I am going
over something that one of the other presenters had discussed, I am
sorry. I wasn\'t here for that. So I will try and give you a rough idea
of how it operates.

Basically, we try to calculate demand given a price for each fuel type.
That gets sent back to the supply side and they take that quantity and
put it into their supply curve and come back with a price. Then they
send us back another price and we keep going back and forth like that
through somewhere around 10 iterations per year.

MR. RELLES: So is there any way for me to have an intuition about what
an uncertainty of 25 percent in your prediction might mean on the price
that comes out of NEMS or on the use of some other

\-- for gasoline versus some other fuel? Or is there documentation that
I can look at that will help me understand that?

MR. CHIEN: Yes, there is. There was some work that Andy Kydes had done
on uncertainty with respect to the model. Whether it was a combination
of the inputs that are assumed over the forecast or whether it is the
specifications of the equations, that was separated out in a study that
was done. And afterwards, I will give you the citation for that and a
telephone number for Andy. You can check that out.

One other thing I wanted to mention. Something else came to my mind.
There are macroeconomic feedback that goes on through the model as well.
So it is not like these are stand alone runs that we are talking about
with the AEO. So, for example, if you apply tax, it is going to have an
effect on the economy and then it is going to spread through all of the
other macroeconomic variables. So through this iterative process, you
get a lot of the feedback effects. I might add that with as large a
model as we have, it is a very complex thing to not only do but also to
understand what is going on because the models are so detailed.

MR. WATKINS: Several comments and a couple of questions. First of all,
on the actual vehicle miles traveled data, could you comment a bit
further when I am through on just how that is constructed? Because the
quality of those data is very important.

My second point is that I wondered did you try estimating the equation
on the basis of households rather than per capita? I say that because
the household, in a way, is a more natural unit of consumption than the
per capita basis. I might also refer you to some work that is ongoing at
MIT. There are two professors there, Dick Schmalsey and Tom Stoerker,
who have done a lot of work on cross section data. As I recall, the
important aspect of that work, and it is a variable that does not appear
in your model, is the number of drivers per household, a very critical
variable. And I suggest it would be worth your while to get in touch
with them and have a look at that work.

This question of the price effects, as I understand the equation, you
basically don\'t have an e there at all except implicitly, is that
right?

MR. CHIEN: No. It is in there. It is the cost of driving per mile which
is the fuel price divided by the fleet fuel economy.

MR. WATKINS: Yes. Okay. The variable that is important that is implicit
in your model is the stock of vehicles and the change in the nature of
that stock, not only just through efficiency improvements but also the
nature of the stock itself, the kinds of vehicles. And that, again, is
linked up with the household impact and the number of drivers per
household. Those are variables \-- I wouldn\'t call them omitted
variables, but they are disguised variables that are influencing your
parameters.

On the question of price elasticities, the use of the linear form
ostensibly gives you elasticities that vary, but it is purely a
mathematical artifact. The reason they vary is just purely from the
specification. It isn\'t a behavioral aspect. Any more, for that matter,
is the say double log formulation a constant elasticity. If you want to
suggest that there is a shift in elasticities, and I think that is
probably the case because if you think back to the 1970\'s and what I
would call the big stock effect, downsizing of vehicles and the use of
different materials than now, that is an effect that is not really
reversible. So the issue raised this morning of asymmetrical responses
is relevant here as well. And it isn\'t sufficient to my mind to just
simply say well we\'ll use a linear form because that varies the
elasticities, when that form is purely a function of the mathematics and
not of the market.

MR. CHIEN: Do you want me to respond?

CHAIRMAN MOUNT: I think, let\'s leave them to the end. Otherwise, we are
going to be here and miss the next section.

MR. SKARPNESS: When I was thinking of this problem as basically fuel
cost and disposable personal income and getting back to what Charles
said about if you look at the total number of miles of available roads
\-- you know, there is not as much construction going on now as there
has been in the 1950\'s, 1960\'s, and 1970\'s, yet the population keeps
increasing. And then the ISTEA and clean air regulations come into place
here \-- you know, congestion management and those factors. And they are
not even part of this whole equation here. So is there a way possibly of
factoring some of those implications into this vehicle miles traveled.
Because those congestions and so forth are going to play an important
role down the road along with alternative fuel vehicles and types of
vehicles that are available.

MR. BISCHOFF: I forgot one whole portion of my comments. I thought it
was pretty short. The vehicle miles is gotten by the VMTPC multiplied by
the population and multiplied by the demographic adjustment factor. But
the demographic adjustment factor must be normalized. And as far as I
can see, the appropriate normalization is by the average demographic
adjustment factor during the sample. Otherwise, if you had a value of
the demographic adjustment factor that was not equal to 1 but not equal
to the average of the sample, you would not get out what you put in. So
it should be the ratio of the 1 minus by all that stuff over something,
and I think it should be the average DAF in the sample. It won\'t make
much difference in your projection because they only make 2 percent, but
it might make a 10 percent difference in what your demographic
adjustment factor comes out. And especially if you start putting in lots
of demographic adjustment factors, it could make a big difference.

CHAIRMAN MOUNT: I would like to ask one question. It seems to me that
for a subject that is as important as this, there must be a richer
source of data to do the estimation. Because I think, to follow up on
Charles Bischoff\'s comments, that there are going to be major changes
in behavior in certain regions of the country, California being one and
Washington to Boston being another area because of environmental
concerns, and it would be very interesting to be able to understand the
different behavior in those regions. And I would have thought it would
be very cheap, in a way, to supplement something like the consumer
expenditure survey by taking a diary of driving habits. This is done for
food expenditures and it seems to me a lot easier to do it for driving
habits. So if you want to reply to the comments.

MR. CHIEN: The first question had to do with the VMT data and the
quality of it. That is definitely a problem. There was actually a study,
I am not sure how long ago it was done, by someone in Oakridge that
actually analyzed FHWA data. And for those of us who are on the highway
and you happen to see some car with a little orange light at the top and
it is off the side of the road and there is some guy here who is making
little check marks and he looks like he is asleep, those kinds of
factors are very difficult. The FHWA data comes from state data, and
there is the problem because each state does it differently. They use
different methodologies for collecting the data. It is a major problem.
And when you try to reconcile, for example, FHWA data with, for people
who are familiar, TIUS data, which is the Truck Inventory and Use Survey
done by Commerce, that is probably one of the top issues as far as VMT
estimation as far as a problem in the transportation sector. Trying to
reconcile those two numbers is a very difficult process. You have to
choose one or the other and they are significantly different.

I mean there is not too much else I can say other than that. I wish that
the collection procedures could be a little more consistent, but that is
something that we just have to accept.

The other question that was posed had to do with household data. We
didn\'t use households because the structure of the households has
changed significantly over time. It is a very \-- it is not

\-- how can I describe this. You either do it by looking at the driver
population or you do it by looking at the household. I think that the
data is a little bit better per driver based on NPTS than looking at the
household. Now the household data is going to be much more disaggregated
and it is going to be a little more difficult to work with. I am not
sure that it is necessarily any better. There are lots of faults with
both attacks that you take.

So, for example, you could say that our model is maybe misspecified
because it doesn\'t have infrastructure in it. However, as we all know,
this is an aggregate model. Implicitly that is in there maybe through
the alpha, making that kind of assumption. That would be a very
difficult thing also to project what the infrastructure is going to be
like in the future because that is very dependent upon funding and other
factors. I just think that it is a very much more detailed analysis,
maybe sometimes looking at point to point type movements, which are
usually done on more regional type models rather than on a larger
aggregate scale, usually through simulations such as what seems to be
popular with the University of California crowd.

The price effect \-- I would like to mention that the price effect is
small and the cost of driving per mile, as I mentioned before, is the
fuel price divided by the fleet MPG. And you had mentioned about the
effects of MPG and the nature and kinds of vehicles. Well, the fleet MPG
in the NEMS transportation model is a stock model and it has six EPA
size classes for cars and six for light trucks. And we keep track of the
stocks of the vehicles. So that is reflected in the fleet MPG number
that goes into the cost of driving per mile.

The other thing that was mentioned was the infrastructure and
congestion. Congestion is, to a certain degree, captured in the model
because when we use fleet MPG, what we are basically doing is we are
taking the new car MPG and then multiplying it by this factor called a
degradation factor. Now a degradation factor, and what I am basically
talking about is this gap, has to do with what EPA tells you you are
going to get and what you actually get on the road. And there are lots
of factors that go into that degradation factor. The ratio of city to
highway driving, average highway speeds, and there are other things such
as weather. And one of those factors is congestion. And that is included
in the fleet MPG number. So that when you take the new MPG and come up
with the actual MPG that is used on the road, that factors into the
fleet MPG projection.

One other thing that I wanted to mention about looking at consumer
expenditures. I think that that was a very good suggestion. One of the
problems is that RTEX no longer has the diaries. They estimate rather
than actually using the diaries that they used to use for RTEX. And
right now that is actually something that we are actively \-- the EIA
deciding whether we are going to go back to that or not. We don\'t know
if the funds are there or not. So it is

\-- I mean I would prefer that they go back to the diaries, but that
does cost money and this isn\'t exactly a free-spending type period in
the Government.

The last thing I would like to mention had to do with the alternative
fuel vehicle equation. I do realize the transferability problem. That is
a major problem. The difficulty is that there is not much else out
there. I do not like to sit down and make up coefficients. I know there
are quite a few people who do that. I am not one of those. And it gives
you some relative idea. I mean, I do realize that \-- not to offend
anyone who is from Southern California, but they are maybe a little
different than maybe the national population. I am not saying in which
way. Often people have called me on the carpet for the emissions
variable as an example in that Southern Californians actually value the
emissions coming out of the vehicle, whereas I am not so sure if people
in the rest of the nation do. Usually, that is put in the category of
like exornalities. So that is a problem. The only thing I can say is
that from having worked with the equation and the model that that
variable, although it is significant, the coefficient actually does not
effectively affect the forecast very much at all. Just based on some
playing around with the model and sensitivity analysis. But right now,
actually I believe there is an effort to collect national data, and I
certainly welcome that with open arms. Then again, the problem is
funding and who is going to do it. This is not a cost-less procedure.
But I certainly would encourage someone to do it and hopefully the
funding will come through for such a project. Thank you very much, and I
appreciate the opportunity to speak for you. Thank you.

CHAIRMAN MOUNT: Thank you. We can break now. And since we are running a
bit late, I think we should reconvene in 10 minutes rather than 15
minutes.

(Whereupon, at 3:01 p.m. off the record until 3:13 p.m.)

CHAIRMAN MOUNT: Shall we try to get started. I have got some
announcements for the committee. I want to encourage you to look in your
package for the list of future topics. This is something that we will
try to talk about at breakfast tomorrow, and if necessary at the lunch
meeting as well. In addition, those of you who plan to go to dinner with
the committee, I think we should meet in the lobby of the hotel at about
6:15 and walk down there together.

MS. BISHOP: Would you specify which lobby?

CHAIRMAN MOUNT: Oh, which lobby. You give me a choice? The one where you
check in lobby. How about that one. The new one. The renovated lobby.
And the topic now is reconciliation of forecasts, Art Andersen, Office
of Integrated analysis and forecasting.

MR. ANDERSEN: Good afternoon. What I am describing today is a work in
progress. We hope that the fruits of this effort will be reflected in
substantial measure in the next AEO forecast which will come out next
January. That is point number one.

Point number two is here I am talking about it, but the exercise
involves a substantial group of people in EIA, and it is assisted by Dr.
George Lady, who is a professor at Temple. He and I are trying to be
catalysts in what is a cooperative effort within the organization to
develop better communication between outputs of the STEO, the short-term
forecast, and outputs of the Annual Energy Outlook.

Now I have distributed copies of the slides here. We will walk through
these things fairly quickly. The problem is there are differences in
various numbers produced in the STEO versus the AEO, where there are
common years, about two years, of overlap in the forecast. And people
have been concerned about these differences in part because the question
is which is the official forecast. And perhaps more important,
especially in the AEO review process, concern that somebody is doing
something wrong, either in STEO or in the AEO, and maybe it reflects
some errors in the way we are pursuing our activities.

There are a number of people who are not particularly happy that this
exercise took on formal dimension. The basic proposition is the two
models try to deal with two different kinds of problems. STEO tries to
look at short-term issues affected by things like weather and where you
are in the business cycle. The AEO tries to look at long-term
developments in the economy and in an equilibrium environment and where
would you expect the models to be the same, at least in a point estimate
context.

So several people as we started this exercise said you are wasting your
time. There are reasons to have differences and you don\'t need to look
at it.

Well, partly because I observed the review process of the AEO and I
characterized it at one point as a process filled with agony largely
because of people reviewing and saying, hey, this is different from the
STEO, I offered to try to get in the middle and see what we could
determine.

So the first point was, well, what are the differences. So we looked at
\-- and George is more than a catalyst in this environment \-- we looked
at a whole variety of common estimates to see where there were
differences, and we came up with 55 where there were some differences.
Some of them were kind of interesting like the rest of the world
petroleum production you had a big difference and some substantial
difference in renewable electric power in the two models, in natural gas
production, a difference of 11 percent, and so on.

So we tried then to understand what are the sources of this difference,
and some of them are pretty easy to understand. Like one presentation
left out China and a few other countries with regards to world oil
consumption and production and the other didn\'t. One has a better
representation of renewable data as such relative to the other. And then
there are interesting things that sort of relate to the way in which the
State Energy Data System puts aggregations together versus the way the
residential and other survey data systems put data together. For
instance, in the SEDS data system, we have no representation of
electricity consumption in transportation. AMTRAK doesn\'t show up in
SEDS at this point. But we had specific data on AMTRAK in NEMS in the
transportation sector and so there is an example of some data issue that
give rise to differences.

We undertook to look at a standard approach to energy intensity
calculations using the RECS system and SEDS and using the SEDS
presentation on commercial versus the survey data on commercial. And you
find differences. In the case of RECS versus SEDS for residential, the
differences seem to be fairly systematic through time. It relates to
certain characteristics in RECS wherein vacation homes are not captured
and vacant residences are not captured.

There is a paper out that Renee prepared which I think has been
distributed and it is worth looking at for a better understanding of
some underlying issues of data and how it relates to activity
representation within the EIA data sets.

Then in comparing SEDS with commercial numbers versus CBECS, you find
some differences. They are not systematic. We are not sure that we can
track why there should be these variations in the particular years, but
the point is if you use one data set versus another and try to look at
energy intensities, you may come up with some different numbers.

Beyond that, there are questions about how we handle certain kinds of
modeling. In NEMS, we assume that demand equals supply with no
adjustment. In fact, in the underlying data there is always a
discrepancy, and in the STEO environment they recognize the discrepancy
and in fact estimate it. We plan, in the next AEO, to in fact include a
discrepancy estimate in NEMS. But that was one thing.

We found that the economic drivers used for the industrial sector within
STEO versus AEO are different. One relies on monthly and quarterly data
to drive the industrial sector. The other relies on annual data. We
tried to create and we did create an estimate of what would the annual
data look like if it were made quarterly and then drive the STEO
industrial model in that context to see what kind of differences could
come about, and there were some differences. But we have concluded that
there are too many uncertainties associated with trying to create
quarterly data from annual data. So we are not going to change this
particular circumstance.

Oil and gas production within NEMS versus STEO, you have a situation
where the decline rate appears to be larger in NEMS as opposed to STEO.
It may well be due to the fact that STEO is refreshed with each quarter
by Dallas field office estimates of what is going on with regard to real
oil and gas recovery, and indeed lots of technology has changed in
recent times and these changes may not be well reflected in the NEMS
estimation of decline characteristics in oil and gas production. It
affects our estimate of imports in the AEO versus STEO, obviously, to
the extent that there are differences in oil and gas production.

And then you have another example. The graph overestimates how much of a
difference partly because I\'ve done something that Yvonne doesn\'t
really like us to do. We don\'t have a zero-based graph here partly
because there isn\'t a weather adjustment in the STEO number here. So to
the extent our weather adjustment was put in, it would probably be a
little bit less. But the point is the two models in the electricity
sector, residential and commercial, tend to walk away from each other as
you step out, with the STEO consistently showing higher rates of growth
in electricity.

This has led to very detailed discussions about how does electricity
demand get driven in the context of the AEO versus STIFS. And indeed,
you find a different specification of the variable with regards to
energy intensity associated with new customers. And in the NEMS
environment, the energy intensities are consistently less than in the
STEO environment. That is one thing. And then the other thing is that
NEMS models a variety of end uses which we think at this stage of the
game tends to be too static relative to what is really going on in the
world, and we haven\'t figured out how to better represent in NEMS some
new uses. George going from 1 to 3 PC\'s on in his house and other
people beginning to take on the use of hot tubs and the like.

So, I\'ve already eluded to the fact that we\'ve done more than pick up
rocks and see what the worms are like underneath them. We\'ve tried to
begin to initiate a process where a variety of changes will, in fact, be
achieved. The housekeeping changes, to the extent we have found them, I
think are fairly straightforward although it is surprising how difficult
some of these issues can end up being. For example, hydroelectricity, I
learned just today \-- how we characterize hydro output in NEMS versus
STEO, there are some differences, small differences. We think it relates
to how pump storage is handled. It is a problem that can be addressed,
but it truly is lifting up a rock and finding a worm there.

So there are a lot of data type issues or transmission losses in the
system. Do the models use the same data series for that, and it turns
out no. We are dealing with that. So those kinds of things are underway,
and there are other kinds of things that are in process that relate to,
in the NEMS environment, a variety of issues with regards to
intensities. Trying to account for the fact that homes are getting
larger through time may contribute to a higher rate of growth in the
NEMS environment. On the other hand, in the steel environment, the fact
that the last data year indicates some slowing in the rate of growth and
then how to weight that relative to the full-time series that STEO uses.
Those kinds of considerations are being worked on and we should have a
product next go around that has fewer circumstances where people say,
oh, I am really worried about those differences.

But after we\'ve done all this, there still will be differences because
of the inherent difference in the nature of the modeling exercises. So
then we will have the question of how big a difference is too big and
what should we do in a representation sense to depict or rationalize
these differences.

Then the last point on this is we have learned, and in Renee\'s paper I
think you find this developed in detail, that there are problems
associated with activity data in the EIA data bases and those problems
are not static. In fact, those problems are evolving through time.
Competition in the electric utility industry has led to reclassification
of customers such that reporting on a rate structure basis may not well
indicate what is going on in an activity context. This is true also with
regards to natural gas. So there are problems associated with the
activity characteristics reported in the EIA data series that have
probably become more intense as competition, first in the natural gas
area and then more recently now in the electric utility area, which pose
difficulties especially for NEMS, which is a model that is very much
activity oriented in the way it attempts to develop and present its
results. That is the overview, and I don\'t know we

\-- have we got a discussant?

CHAIRMAN MOUNT: We certainly have.

MR. ANDERSEN: Okay.

CHAIRMAN MOUNT: The discussant is Greta Ljung.

MS. LJUNG: What to do about the differences between the forecast from
the two different systems, I am voting for option number 3, to accept
the differences but explain their basis. And I think that the
presentation here gave a very good explanation for just leaving things
as they are. Obviously, overriding the NEMS results with results from
STIFS would not be acceptable because that would sort of create a break
in the forecasts.

Benchmarking NEMS to STIFS for common years \-- I know that this is
something that has already been done in the recently published
forecasts. The disadvantage of this, of course, is if when the
historical series differ, it doesn\'t really make sense to try to make
the forecasts connect if there are differences between the series.

Looking at that particular example here, of course one sees that there
would be a very big break if one just accepted the short-term forecast
on STIFS and then continued with NEMS for the remaining years.

One question I had about this particular series, it seems like it is
called an unbenchmarked forecast, but the two forecasts do originate at
the same spot, it seems, and they really shouldn\'t be doing that in
this particular case because what we are looking at is the SEDS series,
which supposedly is used for a short-term forecast, while NEMS would use
this survey data.

MR. ANDERSEN: Actually, survey data and SEDS data are used in
conjunction in the NEMS environment. The survey data gives more
refinement to the activity characteristics that underlie the SEDS data.
So in some sense, the SEDS informs the NEMS model as well.

MS. LJUNG: All right, sir. So then it does make sense. I felt in this
particular case the survey series was quite different from the SEDS
series. That was one of the graphs that Renee had included in here paper
there.

Now it seems, just looking at the graphs, that if one looks at the
long-term trend, it seems like the short-term forecasts are a little too
optimistic while the NEMS seem to be a little bit on the low side. There
has been quite a bit of discussion in the literature about a combination
of forecasts and it would seem in this particular instance that
combining the two forecasts, sort of just by simple averaging, that has
been shown to work quite well. That could sort of be something one might
consider, but that, of course, would not really help the NEMS forecast.
Then one would have the trouble of connecting the NEMS forecast maybe to
the drawings. So it is probably not an option, but it would seem in this
particular instance it would help some.

Now what I would have wanted to see, perhaps, in the presentation are
some graphs on what happens to the NEMS forecast after benchmarking.
Sort of a comparison between unbenchmarked and benchmarked NEMS
forecasts, just to see how sensitive the long-term forecasts are to the
start-up values.

Also, a comparison of the NEMS for different lead times \-- no, not lead
times but time origins. I think those are useful. I noticed this morning
that the administrator, Dr. Hakes, had some graphs that showed
differences in forecasts made in 1994 and 1995, and I felt that they
were fairly substantial and I was just wondering whether they are sort
of explanations of sort of some kind of structural change that has taken
place because they was a quite big discrepancy I felt between the two
long-term forecasts. Is that a property of the NEMS model?

And of course comparing the NEMS forecast data that you have published
earlier, I think that would be useful prior to 1994 on the three
different models.

Now how big a difference is too big, I think looking at the percentage
difference like you did, I think that is a good idea. Also, I understand
you are keeping track of the forecast errors quite carefully for a
short-term system, and I think looking at the distribution of the
forecast errors will give an idea of how much noise or how accurate
those forecasts are comparing the observed differences between the two
techniques. That kind of distribution might give some insights. Perhaps
constructing probability limits short-term using some technique. If you
use the time series model, it would be fairly easy to attach probability
limits and one could compare and see how those two forecasts fall in
those limits. That would give an idea.

Of course, if the historical data differed, then there is no particular
reason why these forecasts would be too similar. I mean that is sort of
an assumption. The historical series would have to agree. Otherwise, one
can\'t really expect that the forecast would be really similar.

Now I do have concerns about the NEMS model and the size of the model. I
think the size of the model makes the possibility of misspecification
quite high, and I think that will influence the forecasts. Because the
model is being launched at a time when I think there has been a trend
toward the use of smaller models for forecasting, and I think this is a
trend that, at least in part, originated with the publication of Box &
Jenkins work in 1970. In that book, they introduce a fairly simple class
of time series models and they discuss model building procedures. They
proposed a three-stage procedure of specification, fit parameter
estimation, and a diagnostic model checking, and finally forecasting
after one was convinced that the model did provide adequate
representation of the properties of the series.

Now this particular approach to time series modeling and forecasting has
become very popular, surprisingly popular. And I think one reason for
the popularity is that it has been shown that these models will give
good predictions. There have been sort of studies done with predictions
from big econometric models and it has been shown that the time series
forecasts in many cases are just as good and sometimes better than a
forecast generated using bigger models.

Now I think one reason why a univariat model would do fairly well \-- I
mean, it looks very simple, but the past observations that one has in
the model serve as proxies for factors that are not included in the
model. So the model actually does more than one would think sort of just
looking at it. And also, of course, with time series analysis, one has
the option of doing multivariat modeling and vector limit modeling.
There are now software capabilities available for that. Often, though,
in many cases looking at several series jointly doesn\'t really improve
the forecasting all that much.

Now, of course, I am not recommending that you replace the NEMS system
by vector limit models or anything like that, but the point I am trying
to make is that bigger is not necessarily better, at least in terms of
forecasting. So I guess my recommendation would be that what I would
like to see happen is that you would continue to scale down the model
and work on the reduced form versions of the models and try to develop
those \-- downsize basically.

Now I know that you for the short-term system have done some time series
modeling and been able to obtain some improvements last year. Kundra
presented a very nice paper where he did time series modeling as well as
the SNAPS model and combined the forecast and was able to get quite an
improvement in the forecasting performance there. So I think work along
these lines would be very valuable.

CHAIRMAN MOUNT: So any comments from the committee? Yes, Dan.

MR. RELLES: In every statistical investigation, uncertainty sort of has
two components. Model uncertainty and error uncertainty. What you have
here is a model uncertainty problem, which is certainly not a new one
and I think one that we have experience with, namely mostly we duck it.
If you look in the scientific literature, people are reporting one model
and they are reporting standard errors that go along with that model.
And that seems to be the peer review standards that are out there. But
you can\'t duck it because you\'ve got these numbers just sitting there.
And I guess what to do \-- I think forcing consistency would be awfully
expensive. For starters, you are going to have to merge your STIFS team
with your NEMS team and you can\'t make a change to one without sort of
considering the impact on the other. And I think sort of forcing
consistency puts you in a sort of much higher standard than what we now
call science. And if you\'ve got enough money, I would say do it. But my
guess is that you don\'t have enough money to do that, and you can\'t
reorganize EIA around it.

The other alternatives are \-- what you are looking at, I think \-- it
sounds like when you look at these things and you can\'t tell the
difference, there is no obvious preference of one for the other given
all the uncertainties in there. But when you do see an uncertainty that
looks a little too big, you tend to \-- the teams tend to interact and
to try to improve each of the models. Try to understand where they are
different and where they can be made better. I think it is perfectly
acceptable policy to simply say where there is model uncertainty, we
will flag it, and when the model uncertainty gets to be \-- where the
differences between models gets to be so large that you just don\'t
believe either of them that you will look into and change them. I think
that would be perfectly acceptable policy for me. So I guess I feel it
is healthy to have alternative models of the same thing so that you can
check each one of them and thereby improve both. But I don\'t think the
inconsistency is something that has to force you into achieving a
consistent number basically because of the cost of doing that.

MR. WATKINS: Ostensibly, because the models are quite different and have
different data bases, one should not be concerned about the fact that
they do yield different results. Having said that, however, particularly
as regards the definition of the sectors and the definition of the
exogenous variables, one should at least strive to keep those as
consistent as possible. I think one\'s concern is where I would or where
you would see what I would call a systematic bias. How to detect that
would be to repeat these kind of two-year experiments over different
intervals and then see if there is a tendency for one or the other to
consistently under or over estimate. If you treat the NEMS as a kind of
long-run model, you would expect to see the short-run model be either
side of it unless there is a systematic bias. And if there is a revealed
systematic bias, that would to my mind trigger the need for you to look
at what is going on.

Finally, I would like to support what Greta was saying about the issue
of what percentages to use in that if you have a confidence band for one
or other of the models, then you can use a criterion such as say two
standard deviations to trigger again whether you get concerned about the
magnitudes of difference.

CHAIRMAN MOUNT: Jim?

MR. O\'BRIEN: I\'m somewhat uneasy with the idea of accepting
differences without examining the implications. And I have not heard any
reference to what used to be regarded as a fairly common sense way of
judging how big a difference is too big. And that is what actions are
taken as a result of feeding these data into either policy makers
informal subjective models or into formalized models where you can
measure the sensitivity.

In fact, to take a specific instance from the data here where I believe
60 percent of the differences, 33 out of 55, were below 5 percent, the
absolute differences. It could well be that the models that use these
will not change the action specified for differences that are that
small. On the other hand, the 8 percent, which are above 20 percent
difference, seem to me to be much more likely to change the difference.
Some sort of simply examples in the past would be in the allocation of
population to city blocks. That error rate was sometimes as high as 8
percent of geographic errors. But it turned out that over 90 percent of
them were the next block in either direction and there were very few
instances where that seriously affected the amount of money that a town
would get. And the most egregious kind of error would be one where you
put them into an adjoining state, particularly if that gave that state
an additional representative at the expense of the contributing state.
That is a rare instance and it is one that is very difficult to predict
because you don\'t know until you have all the data as to how big a
transfer it would take to make the difference of a representative.

But it seems to me that as a kind of managerial principle almost more
than a statistical principle is that statistically significant
differences may not be practically significant. The difference in height
between Californians and Texans, say, is very probably statistically
different, except that maybe the Texans heads are bigger, but it would
not have any great practical significance in general. So I guess my
suggestion would be that I would be very cautious about agreeing to
accept differences regardless of their size. But one practical measure
would be to look at them and see whether this is really an error of the
next block, that is of 1 or 2 percent, or whether it is an error of an
adjoining state, particularly \-- and you can look at historical data if
this would suggest that there would be a difference in the allocation of
political power or something which would result in the enactment of
legislation or other things that would have actual effects on the
population.

MR. KENT: I don\'t think I may have very much new to offer, but I would
like to get an update from Art on the problems that you listed here on
page 6. Have you already taken care of those? Have you reconciled
whether NEMS does or does not include the production from the China and
the former USSR? Have these things already been done?

MR. ANDERSEN: Either been done or are in process, yes. When you said 6
\-- oh, yes. Those are characterized as being fairly simple and easy to
reconcile. And that is in process. The things that get to be more
puzzling are things where there seem to be differences, for instance, in
the rate of growth in electric demand in the residential sector or in
the commercial sector. And that has been more of a puzzle and we have
been looking behind each of the models to see what might be there that
warrants some reexamination and we found a few things. I think there
have been some comments about the usefulness of looking at this and the
other point of don\'t become obsessed by the notion of making everything
the same, and I think that that is certainly where we are, I believe, at
this stage of the game. It wasn\'t clear to me how much would come of
this when I started out except that it seemed to be a real problem from
the standpoint of the organization being able to interact in a
constructive way. So I think what has come out of this is that we\'ve
come to a better understanding of what differences we are talking about.
We have gotten to a point where we can examine or try to identify
differences in a more systematic way rather than in an idiosyncratic
way, which is the way it was going on before, and from that examination
identify things that seem to be worth thinking about from the standpoint
of how one models. For instance, on the short-term side, there is a
problem if you have standards going into effect in a year or two or even
standards that have already gone into effect as happened in 1994. How do
you handle that in the context of the short-term forecast where you have
just one observation since the standards went into effect? So we are
trying to think about that in a more systematic situation for the
short-term model.

In the long-term model, of course there is a stock turnover component in
the model, so it is built in there. From that, we\'ve concluded that
probably STIFS has some higher rate of demand growth that won\'t be
realized that we want to try to adjust that model for. But on the other
hand, you known when we talk about SEDS data versus the survey data, the
survey and SEDS data are integral components with regards to the NEMS
modeling exercise. And the SEDS data, as far as history is concerned,
NEMS reconciles to SEDS history as does the STIFS model.

The question that we were looking at is once you move away from history,
what inherent behavioral characteristics are in the model that seem to
be worth worrying about. And as I say, these growth rates in electricity
and decline rates in production, are notably different which raise
questions about are the two models capturing something different that we
need to worry about. And I think the story in NEMS is we probably don\'t
capture new uses as well as we need to, and we haven\'t figured that out
as I mentioned. And STIFS will not, or at this point doesn\'t, capture
the near-term policy changes that are going into effect with regards to
the efficiency standards.

But I think the real benefit of this \-- there will be certain things
that will be cleaned up that just were sloppier than they needed to be,
and there will be a more systematic review of why and to what extent do
we believe the differences and are willing to accept those differences
in each of the subsequent reporting cycles. So I hope that the whole
organization feels the way George and I have come to feel about this
exercise that we understand the problem better and there is now an
opportunity to sort out that which is a problem potentially and that
which should be just accepted as part of the way we are doing business.
And to the extent that we identify things that seem to be worth worrying
about, we can develop priorities in terms of how to explore the problem
and also identify who the players should be in that context.

MR. KENT: Can I make just two more comments? Number one is to build on
some things that were said by Dan and Jim and that is as you look at
this, I think you need a two-criteria test before you start to worry.
The first thing is is there a significant difference, and secondly, does
it really matter in terms of the feedback effects. There may be a very
large difference, but if it is insignificant in terms of getting the
forecast, I wouldn\'t worry about it. So I think that it appears that
you are already doing that, but I think that with your scarce resources,
those are the things you need to concentrate on. First of all, is there
a large gap between the two, and secondly, will it really make any
difference in your major statistic.

The other thing that I might mention as a possibility is at the end of
the AEO, we always or you all always have a discussion of differences
between your forecasts and other forecasts for the comparable period. I
wonder if you would want to say anything in there so that you\'ve got it
on record of the differences between AEO and the short-term forecast, or
how you might want to get this out so that if somebody calls up and says
I am confused that you can get it to them in a format so they will know
what the source of the differences are.

MR. ANDERSEN: Well, in the last AEO, we did have a page on differences.
That page represents a lot more improvisation than will be necessary in
the subsequent ones. We will probably always have a page that addresses
that issue.

MS. DUCHIN: It\'s near the end of the day, so I hope you permit me to
make a more general observation. It is related to the point that Dr.
O\'Brien brought up. I think it is clear that it matters in evaluating
the differences how important they are in terms of subsequent decision
making processes, but it is also clear that that evaluation is pretty
subjective. Of course, one could attempt to develop index numbers for
making that evaluation by assigning some number and weighting the
relative importance of the different differences, but I would like to
suggest that that may not be the best way to go. And that is what makes
me think of this more general observation.

My own feelings about it come out of interdisciplinary work that we as
economist have been doing with ecologists. And in interdisciplinary
work, one of the biggest problems is having different vocabularies,
different mindsets, and different everything and so you\'ve got to try
to find some way to communicate. And after several years, I think that
there is one very powerful notion that has come up in this work which is
we call it two-layered modeling where the first layer is qualitative,
descriptive, and process oriented and describes in as systematic way as
we know how to the other partner what our understanding is of the
process. And the second layer is a quantification of it. And the idea is
if you move just to the model, a model is such a simplification of what
we know to be the reality that for someone who is coming from another
mindset to have just the model, if they have any independent spirit,
they are not going to accept it because they know that all of these
other things matter and it is not there.

So what I want to suggest \-- what I think comes out of that that for me
is a very powerful more general observation is that qualitative,
descriptive, process-oriented work is legitimate and that I think that
in most cases, especially in a situation where things are changing \--
and when we talked in the last presentation about vehicle miles and the
fact that elasticities are changing because our values and behaviors are
\-- especially in a situation where things are changing, it seems to me
not only legitimate but indispensable to start by trying to scope out
what is happening in a more qualitative way and not to have to be
ashamed that that is not rigorous. Because I think that is a step that
shouldn\'t be skipped in getting on to the modeling and the quantitative
part.

MR. O\'BRIEN: I have a one sentence comment, not on that point. My
experience over several years, let\'s say 30 years of comparing the
results from at least two or more alternative ways of making an estimate
and reconciling the differences in estimates has been one of the most
fruitful ways of improving both estimating processes.

MR. ANDERSEN: I think that that will, in fact, be what will be one of
the benefits of this. Because as I said, to this point, it has been a
continuing problem within EIA. In fact, there was an ASA meeting back in
1983 on this, and I read the minutes associated with that. But I think
the problem has been there and the way in which it has been dealt with
has been unsystematic. And I think we now may be to a point where we can
think and address this thing in a more systematic way so that it is not
\--it doesn\'t come up in a review process like, hey, somebody is
blowing a whistle and I am so tired now I need to get this job done, why
is he doing that, which is sort of the environment that we have had the
thing operate in in the past. So we are very hopeful of trying to
systematize this review.

I was quite impressed \-- we had a NEMS conference in February and one
of the speakers there made the comment, which I related to when I
listened to it. And that is, he said I have a short-term model and I
have a long-term model. And if there are big differences, my boss has to
understand why those differences exist and that I understand why they
exist and that they make sense from the standpoint of how we think about
using these tools. And in the same way, I think we here need to have
that in hand.

CHAIRMAN MOUNT: I think that is a very good point to end this part of
the discussion. We should open it up to the public now. But it seems to
me that this topic is very important to discuss and in some sense
difficult to discuss. But when the discussion occurs, the likelihood is
that both models will be improved by it. So let\'s see if there is
anybody in the public who wants to comment, and this is on the whole of
the afternoon session. Silence from the public. I think then we can
finish the public meeting now. I would like the committee to stay around
for a few minutes because we have some scheduling issues that need to be
discussed for future meetings. So thank you all for attending, and we
meet tomorrow at 9:00 for the very controversial competition of graphs.
And breakfast is at 8:00 for the Committee.

(Whereupon, the meeting was concluded at 4:05 p.m.)