A User's Guide to the
World 011l Project
Demand Data Base*
by
Jacqueline Carson
August,
1978
MITEL78 - 016 WP
*This data base was assembled as part of an econometric study of world energy
demand. The work was funded by the National Science Foundation under Grants
SIA75- 00738 and AR78- 19044, and is part of a project to develop analytical
models of the world oil market. The data base now resides on the TROLLsys-
tem at the MIT Information Processing Service.
the membersof the World Oil Project for their
The author is indebted to
conmments
and suggestions.
TABLE OF CONTENTS
page
Chapter 1:
Introduction ..................
Chapter 2:
Industrial Sector Demand Data . . . . . . . . .
Table 2-1:
Chapter 3:
.
Residential Sector Data File Listing . . .
Transportation Sector Data File Listing.
Petroleum Product Demand Data .
Table 5-1:
. .
9
. 22
.
Transportation Sector Demand Data . . . . . .
Table 4-1:
Chapter 5:
Industrial Sector Data File Listing .
1
Residential Sector Demand Data . . . . . . . .
Table 3-1:
Chapter 4:
.
56
66
83
90
....
113
Petroleum Product Data File Listing . . . 129
Chapter 6:
Bibliography . . . . . . . . . . . . . . . . . 184
Chapter 7:
TROLL Data File Names . . . . . . . . . . . .
Chapter 8:
ConversionFactors
Chapter 9:
Purchasing Power Parities . . . . . . . . . .
218
Chapter 10:
Accessing the Data Base . . . . . . . . . .
237
..
191
. . . 208
.
CHAPTER ONE
Introduction
The User's Guide to the World Oil Project Demand Database* is designed
to serve several purposes.
One objective is to document the data used
in the World Oil Project demand analysis; to report the sources used,
and to describe
the transformations
standardized units.
required
to convert
the data
to
The guide should also be helpful as a reference for
finding out what information we have compiled, how to access this data, and
to understand some of the issues involved in energy consumption information.
This information will be presented with separate chapters for the different
sections of the database and for the different issues involved.
Most
of the information is also presented in tabular form for quick reference
to a complete
listing.
The data has been .compiled on TROLL,** a computer software package
which provides a comprehensive
environment for creating, estimating
and simulating economic models.
All of the data files cover years
1950-1974.
The data base can easily be made available to other groups.
A brief background of the origin of the database may help the user
to understand its structure and organization.
The data to be described
*This data base was assembled as part of an econometric study of
world energy demand, and is part of a project to develop analytical
models of the world oil market.
**For further information contact Information Processing Service at MIT.
-2-
here is the demanddata, used for econometric estimation
and analysis.
There was an interaction between the data collection and the modeling effort:
detailed questions concerning the oil market were to be investigated and
data avAilability had to be ascertained before precise research questions
could be formulated.
The result has been an emphasis on the following:
1) for the industrial
sector data: data on oil and other fuels consumed by industry amd prices of
energy, capital and labor;
2) for the residential sector: data on consumers'
expenditures on energy and other consumer items, prices and disposable income;
3) for the transportation
sector: data on the stock of vehicles, gasoline and
fuel consumption, prices, and taxes on fuels; and 4) data on oil
diesel
consumption by product, prices, and levels of economic activity for individual
countries and aggregate world regions.
The most important oil consuming countries
have the most extensive energy price, quantity, and expenditure data available
and therefore are included in the database in the most detail.
The data is organized by sector as described above, rather than by
country.
Since some data is common to more than one sector there may be some
duplication of information between sectors, and in order to locate all of the
data available for one country it may be necessary to look at several sectors.
At the end of this section, Tables 1-1 through
1-4 present summarized data
availability, listing the countries and variables fdr each sector.
Chapters Two through Five deal exclusively with the data.
Chapter Two
discusses the industrial data, consumption of fuels, prices of energy, capital
and labor, as summarized in Table 1-1.
and from where,
and from where,
will
will
How the information was obtained,
be described in Chapter
be described in Chapter
Two,
Two,
followed by a
followed by a
-3-
table which alphabetically lists all of the data files.
on data have the same format.
The other chapters
Chapter Three presents the residential data,
consumers expenditures and retail prices as summarized in Table 1-2.
Chapter Four gives the transportation data, stocks of vehicles and fuel
prices, summarized in Table 1-3.
Chapter Five contains the petroleum
product demand data, consumption of petroleum by product, fuel prices and
economic activity indicators, summarized in Table 1-4.
Chapter Six contains a bibliography of all the sources used.
The
bibliography is not an alphabetical listing of sources, but is numbered,
the numbers referring to a bibliography code number contained in the
comment section of each of the data files.
The bibliography code numbers
are also those in the tables in Chapters Two, Three, Four and Five.
The tables and indexes of Chapter Seven should help the user understand
the data file names and to locate specific variables.
The data file names
are actually mnemonics for the variables concerned, and these mnemonics
are listed alphabetically and defined.
There is also an index of terms
which refers to the data file names and indicates in which section a
particular variable can be found.
The next two chapters deal with some of the issues involved in
compiling this database.
Chapter Eight contains the fuel conversion
factors, defines the units used and discusses a few of the problems
encountered.
Chapter Nine deals with purchasing power parities, the
conversions used to convert currencies to US dollars, and contains tables
of the purchasing power parities used.
Chapter Ten provides information for those who may wish to access
the data on TROLL, or to otherwise obtain part of this data.
Some
-4-
information on TROLL data editing is included, with examples of data
vectors or files.
The following
tables, 1-1 through 1-4 summarize data availability
by sector and country.
These tables will give the reader an overall viewof
the scope of the database and will aid in directing the reader to specific
chapters .
-5-
1-1
Table
Industrial Sector
-
Chapter 2
countries:
Canada, France, Italy, Japan, Netherlands, Norway,
Sweden, United Kingdom, USA, West Germany
data:
industrial consumption of petroleum, coal, electricity, gas
industrial prices of petroleum, coal, electricity, gas
expenditure on fuels
price of capital
price of labor
gross value of output
value added
long term interest rate
asset price indices
producer durables
non-residential structures
wages
purchasing power parities
-6-
Table
Residential Sector
-
1-2
Chapter3
countries:
Belgium, Canada, France, Italy, Netherlands, Norway,
United Kingdon, USA, West Germany
data:
private consumption expenditure on: gas, electricity,
petroleum, coal, food, clothing, durables, transportation,
other, and indices for all items
retail prices of electricity, gas, liquid fuel, and coal
personal disposable income
population
temperature
purchasing power parities
-7-
Table 1-3
Transportation Sector
-
Chapter4
countries:
Austria, Belgium, Canada, Denmark, Finland, France,
Italy, Japan, Netherlands, Norway, Spain, Sweden,
Switzerland, Turkey, United Kingdom, USA, West Germany
data:
stock of vehicles, buses, goods vehicles, passenger cars
new registrations
traffic volume, buses, goods vehicles, passenger cars
consumption of motor gasoline, diesel fuel
percentage of motor gasoline and diesel fuel, used in
road vehicles
price of motor gasoline, diesel fuel
taxation of motor gasoline, diesel fuel
-8-
Table 1-4
Product Demand Data
-
Chapter 5
Group
I
countries:
Australia, Austria, Belgium, Canada, Denmark, Finland,
France, Greece, Ireland, Italy, Japan, Netherlands,
Norway, Spain, Sweden, Switzerland, Turkey, United
Kingdom, USA, West Germany
data:
total consumption of petroleum products: aviation gas,
crude, fuel oils, gasoline, jet fuel, kerosene, LPG
prices of some petroleum products
population
temperature
gdp and index
purchasing power parities for some countries
Group
II
countries:
Argentina, Brazil, India, Mexico, South Africa, Venezuela
data:
consumption of petroleum products: aviation gas and
motor fuel, fuel oil, jet fuel and kerosene, production
of feedstocks
prices of some petroleum products
population
gdp, index
temperature
-9
-
CHAPTER TWO
Industrial Sector Demand Data
Our industrial sector energy demand data was compiled for the World Oil
Project analysis of the industrial sector.
It contains historic time
series for ten major energy-consuming countries on value added, capital,
labor and energy prices, expenditure shares of manufacturing output, and
industrial consumption of the four major fuels.
Also included in this
section of the database are the components used for the construction of
the price indices of capital and labor, such as asset price indices,
depreciation rates, and gross fixed capital formation.
The components of
expenditures on capital, labor and energy are also included, as well as
nominal prices of fuels, gdp, and gdp deflators.*
This chapter describes the data in the industrial section, and
indicates where it was obtained and how indices and shares were constructed.
The reasons for choosing certain sources of information will be explained
and all data transformation will be described.
A few examples of the
TROLL data file names for the variables will be given.
At the end of this
chapter is an alphabetical listing of all of the data file names in the
industrial section.
*All of the data is stored on TROLL, a computer software package
which provides an environment for creating, estimatingand simulating
economic models. TROLL is at the Information Processing Service at MIT.
For more information see Chapter 10.
- 10 -
Countries
Ten countries are included in this section of the database:
Canada, France, Italy, Japan, the Netherlands, Norway, Sweden, the United
Kingdom, the United States and West Germany.
All of the variables to
be described here are stored for each of these countries.
Variables
Value Added
Data on value added at factor cost was obtained from the United
Nations' Growth of World Industry, or the UN Annual Yearbook, for eight of
For France and West Germany, however, value added
the ten countries.
was available only at producer cost.
For these two countries value added
tax, from the Statistical Office of the European Economic Community (SOEEC)
Tax Yearbook, was subtracted from value added at producer cost to obtain
factor cost.
Value added does not include energy expenditures.
These series
range from 1960-1973 and are expressed in units of millions of local
currency, nominal terms.
The data file name for value added is "country" VA LC,
and for value added tax is "country"_VAT_LC.
Expenditure on Labor
Expenditure on labor has been defined for our purposes to be wages
and salaries
as well as supplements to wages and salaries,
facturing sector.
paid to the manu-
Supplementsto wagesconsist of bonuses, profit
sharing,
stock options, sick pay, pension plans, holiday pay, health benefits, or other
- ll
fringe benefits.
-
As a share of total compensation supplements to wages
varies from country to country, but in all cases it is a substantial
component and therefore very important to include in total remuneration.
Because of complicated benefits packages, however, supplements to wages is
a difficult category for which to locate complete information.
For Canada, France, Italy, the Netherlands, Norway and West Germany,
wages and supplements to wages were obtained from the UN Growth of World
Industry, for the years 1960-1973 or 1974.
This publication lacked any
information on supplements for some of the other countries, or for some
of the years, so in a few cases this was determined by using
the percentage of total compensation which was supplemental, according
to the International Labor Organization's Statistical Yearbook.
The
percentage of total compensation which is accounted fr
by the manufacturing
sector was determined from UN National Accounts and ILO
tatistics for the
United Kingdom and Sweden.
Finally, for the US, Japan and France, national
statistical yearbooks such as Annuaire Statistique, Japan Economic Yearbook,
and the President's Economic Report were used to fill in the missing years
for this category.
The data files are stored on TROLL in millions of local currency, in
nominal terms.
The data is stored both as the components, wages, ad
plements, and as total compensation.
sup-
For example, for France there
is a data file for wages, FRAN WAGSLC;
for supplements, FRAN_WSUP_LC;
and total compensation, or expenditure on labor, FRAN_EXPLAB_LC.
The
expenditures on labor for all countries have also been combined in matrix
format, with data file name, EXPLAB LC.
Chapter Ten contains a complete
description 6f the format of TROLL matrices, their construction and how
to read them.)
- 12 -
Expenditures on Capital Services
.Expenditures on capital services was determined by subtracting labor
expenditures from value added.
expenditures on
capital services
_
value added
at factor cost
expenditures
on labor
(Figure 2-1)
The available data covers years 1960-1973.
for this variable is "country" EXPCAP_LC.
The TROLL data file name
All of the ten data files for
the ten countries have also been combined in matrix format, with data file
name EXPCAPLC,
Price of Labor
The price of labor is determined implicitly by dividing labor
expenditures by total manhours of employees.
price
hour
e
expenditure on labor
total manhours of employees
(Figure 2-2)
Total manhours of employees is not directly available, but can be
determined implicitly using the following formula and data
Growth of World Industry:
(next page)
from
the
- 13 -
*total manhours
manhours
operatives
of employees
number of employees
number of operatives
*
(Figure 2-3)
Operatives are all employees directly engaged in production, and employees
are all persons engaged, other than working proprietors and unpaid
workers.1
The price of labor was derived in this way for Canada, Italy,
Japan, Norway, Sweden, the US and West Germany for 1967.
For the remaining countries another method was employed, using the
average working hours from the International Labor Organization and the
number of employees from the UN:
total manhours
of employees
=
average
working hours
*
number of
employees
(Figure 2-4)
For the United Kingdom, this information was obtained from OECD Labor
Statistics.
Having constructed a price/hour for 1967
we then used a wage index,
1967=100, from the US Bureau of Labor Statistics
to convert our price/hour
for 1967 to a time series, 1960-1974, for every country except Norway.
The Bureau of Labor Statistics wage index includes both wages and
supplements.
The time series for Norway was calculated directly from the
data in the Growth of World Industry, using the equation in Figure 1-2.
Note that this index is not quality adjusted.
- 14 -
These components for the price of labor are all stored as data files
on TROLL except for the US Bureau of Labor Statistics index.
The TROLL
data file names are, for the number of operatives, "country"_NOP; for the
number of employees, "country" NEMP; for manhours of employees,
"country" MANHOP.
The price of labor data file, in local currency,
nominal terms, is stored as "country"_PL_LC.
There is an additional data
file for the price of labor, where the price has been deflated using the
1970 gdp deflator, and converted to US currency using the 1970 gdp purchasing
power parity (see Chapter Nine).
file is "country"_PL.
a matrix
and indexed
The file name for the constructed data
These individual country data files were combined in
to US 1970=1.
The matrix
file name
is PL.
Price of Capital Services
We compute a capital service price index separately for nonresidential structures (PNR) and producers durables (PD), and aggregate
these two series into a final price of capital services using a Divisia
index, where the investment shares of non-residential structures and
durables serve as the Divisia weights.
The computation of the price of
capital services of each component is based on Christensen and Jorgenson,
i.e., we assume that the investment price of an asset
q
is equal to the
present value of its future services evaluated at the service price
(which is the price we wish to ascertain).3
Then, disregarding
taxes, the asset price is related to the service price by:
qt=
P~
rldj
-t +l l+r
q ~ ¶(1-d)-t
Pj+t s
ls
(Figure 2-5)
P
We also assume that the
service from an asset declines geometrically over time.
j=t L
2
- 15 -
where d is the depreciation rate and
r
is the appropriate interest rate.
From this we can obtain the equations that relate the price index for
each type of capital service to the corresponding asset price index:
PNR(t)
= Rt)qNR(t-l
PD(t)
= R(t)qD(t-1
)
)
+ dNRqNR(t) -
+ dDqD(t) -
(qNR(t)-
(qD(t)-
qNR(t-1)
qD(t-)
)
)
(Figure 2-6)
Here R is a long-term government bond interest rate and qNR and qD
are tne asset price indices for non-residential structures and
4_
durables.
_.
The data required to compute the price of capital are time series on
depreciation rates, interest rates, asset price indices and investment
shares.
The interest rate used is the long-term government bond yield,
from International Financial Statistics, a publication of the IMF.
data covers years 1955-1974.
This
The depreciation rates were taken from
those in Christensen, et.al., or from life of capital figures in Denison. 5
Asset price indices for producer durables and non-residential
structures for Canada, France, Italy, the Netherlands, the United Kingdom
and the USA were obtained from Christensen, et.al.
for the remaining countries were derived
Asset price indices
implicitly using gross fixed
capital formation for producer durables and non-residential structures in
current and constant terms, as available in the UN or OECD National
Accounts, or the national statistical yearbooks.
The asset price indices
were converted to 1970 terms, relative to the US, using purchasing power parities.
-
16-
The investment shares of producer durables and non-residential
structures used to compute the Divisia index were derived from gross
fixed capital formation for those categories, obtained for all countries
from UN or OECDNational Accounts or national
For some years
completely
gross
fixed
disaggregated.
from the aggregate
historical
figures
capital
formation
statistical
yearbooks.
from these sources is not
In these cases the data points were derived
by assuming a certain
percentage based on the
ratios of the separate categories to the totals.
Our price index for
capital
services
must be viewed as approximate
for several reasons, one of which is that differences in corporate tax
structures are not taken into account.
The price of capital was computed
for each country and each year, and then combined into one data file for
each country, covering years 1955-1973, but because the price is approximate,
for some years the computed values are negative for some countries in some
years.
In these cases, Japan, Sweden, and West Germany in 1960, a new
value was calculated
1961 and using that
by averaging the observations for 1959, 1960 and
new value for
1960.
All of the price of capital data series were then indexed, relative
to US 1970=1.
The data series for each country were also combined in
matrix format, so there are both individual country data files for PK and
a matrix.
The TROLL file names for the individual data files are
"country"_PK, and for the matrix, simply PK.
Expenditure on Fuels
Industrial expenditure on fuels was determined using data on
consumption of fuels in tcals and price/tcal for the four major fuels.
- 17 -
expenditure
=
on fuels
quantity
*
price
consumed
(Figure 2-7)
The expenditure figures are in millions of local currency, expressed
in nominal or undeflated terms.
The data is stored both as individual
"country" EXPFUEL_LC,and as combined data files
country data files,
matrix format, EXPFUELLC.
in
Below is a discussion of the quantities and
prices used to compute the expenditures on fuel.
Fuel Quantities
Quantities of four fuels used in the industrial sector (which excludes
energy conversion), coal, gas, electricity, and petroleum
obtained from OECD publications.
were all
Two different publications were used,
Energy Balances of OECD Countries: 1960-1974 (Paris, 1976), which reports
the data in net tons of oil equivalent, and Energy Statistics, which
reports the data in metric tons or tcals.
The 1976 publication was used
for 1960-1974 because it contains recent, revised data.
were related to those
regressions.
These data series
in the earlier OECD publications via simple linear
These regressions were used to extrapolate the OECD 1960-1974
series back to 1955.
The units were then converted to net tcals.
(For
a definition of net tcals, see Chapter Eight.)
The US was treated differently from the other countries in that the
1976 publication showed a large amount of "crude oil and ngl" consumed by
industry.
Investigation of other publications and consultations with the
- 18 -
OECD and the International Studies division of the FEA have led us to
conclude that this category probably erroneously contains some petroleum
products used for petroleum feedstocks, non-petroleum hydrocarbons, and
refinery gas.
To keep our accounting consistent with other countries,
this category was not included in our industrial petroleum total for the
US.
are:
Some examples of TROLL data file names for industrial fuel consumption
"country"_FINDPRFO (for fuel oil), "country"_FINDGAS (for gas).
For
a complete list of the data file names see Table 2-2 at the end of this
chapter.
Fuel Prices
Industrial prices of heavy fuel oil, natural gas, coal and electicity
were obtained from SOEEC publications, the FEA, and the Edison Electric
Institute.
The prices were originally in the form of local currency/volumetric
unit or heat unit, and were converted to local currency/tcal (using the
conversion factors in Chapter 8).
The prices cover the years 1955-1974.
Examples of the TROLL data file names for prices are: FRANSINDPRFOLC
(for fuel oil), FRAN_SINDGAS_LC (for gas).
All of the data file names in
the industrial section are listed alphabetically in Table 2-2 at the end of
this chapter.
Price of Energy
An average "price of energy" was calculated from a translog price
aggregator.6
It is therefore not an explicit part of the database but
rather a product of one of the econometric models.
GDP deflators and
purchasing power parities are part of the database, and are used to deflate
-19
and convert the fuel prices to a standard unit, 1970 US dollars.
For a
discussion of purchasing power parities, see Chapter Nine.
Net Value of Output
Net value of output is defined here as value addedat factor cost
plus
expenditure
on fuel.
This was calculated on TROLL with our matrices
for value added and expenditure on fuel.
There is therefore no individual
country data file for net value of output but only a matrix containing
all of the countries, with file name NVOLC.
Expenditure Shares
Expenditures on fuel, labor and capital as a fraction of net value of
output were calculated.
share of
abor
=
expenditure on labor
net value of output
(Figure 2-8)
There are no individual
country data files
for the shares, but as
with net value of output, this data is stored in matrix format, containing
all of the countries.
The data file
names for the shares are SHARECAP,
SHARE ENG, and SHARE LAB.
Fuel Shares
The share of each fuel 'as a fraction of total industrial fuel consumption was calculated for oil, gas, coal and electricity consumed in the
industrial sector.
- 20 -
industrial
share of coal
coal consumption
-
total industrial fuels consumption
(Figure 2-9)
The data series on fuel shares are stored both as individial country data
files covering years 1955-1974, and in a ntrix
containing all years, all
countries, named SH FINDELEC, SH FINDGAS, etc.
Deflators
A gdp price deflator was collected for every country.
from Christensen, et al., or from OECD National Accounts.
cover years 1950-1974, with 1970 = 100.
They were obtained
All gdp deflators
These data files are stored with
data file name, "country" IPRIC_GDP.
Purchasing Power Parities
Purchasing power parities were used to convert different currencies into
US dollars.
Chapter Nine contains a discussion of why prchasing
were used and how they were derived.
power parities
Tables of PPP's used are also included.
Tables
Examples of data file names which are also mnemonics for the
have been included in the above text.
ariables
The following table, Table 2-1, is an
alphabetical listing of all of the data file names in this section of the
database.
Note that all of the industrial data files are stored in the section
or archive "ind".
This means that all data file names are preceded by the letters
"ind"'. For an index of terms and data file names and an explanation of TROLL
naming
conventions see Chapter Seven.
Included in the following table Is information on the range of years
for each file, the units, the definition, and a bibliography code number
which refers to the bibliography in Chapter Six.
- 21 -
References
1.
The Growth of World Industry, Vol. I, General Industrial Statistics,
1975, Publishing Service, United Nations, New York, NY.
2.
Christensen, L.R., and D.W. Jorgenson, "The Measurement of U.S.
Real Capital Input, 1929-1967," Review of Income and Wealth,
Series
15, 1969,
pp. 293-320.
3.
Denny, M., and M. Fuss, "The Use of Approximation Analysis to Test
for Separability and the Existence of Consistent Aggregates,"
Working Paper No. 7506, Institute for the Quantitative Analysis of
Social and Economic Policy, University of Toronto, August 1975.
4.
Diewert, W.E., "An Application of the Shepard Duality Theorem:
A Generalized Leontief Production Function," Journal of Politcal
Economy,
5.
Vol.
79, No. 3, May 1971.
The Brookings Institution,
Denison, E.F., Why Growth Rates Differ,
Washington, 1967.
6. Pindyck, R.S.,
"Interfuel
Substitution
and the Industrial
Demandfor
An International Comparison," Working Paper No. MIT EL 77-026WP,
Energy:
MIT Energy Laboratory, August, 1977.
- 22 -
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- 56 -
CHAPTER THREE
Residential Sector Demand Data
The residential sector demand data was constructed for econometric
analysis of residential energy demand.1
Annual data needed for the model
were historical times series covering years 1955 or 1960 through 1974, of
residential consumers' expenditures on petroleum and its substitutes, coal,
electricity and gas, as well as other consumption categories such as food,
clothing, and durables.
Other variables needed for this analysis were price
deflators for these consumption categories, prices of fuels, disposable
income, temperature data and population.
Purchasing power parities
were
also needed to convert different currencies into one comparable monetary
unit.*
This section will describe these variables and explain how and from
where they were obtained, why certain sources were chosen, and includes
examples of the computer data file names.
Following the discussion is a
table which alphabetically lists all of the data file names, a definition of
each, the units, and a bibliography number which refers to the bibliography
in Chapter Six.
This section of the World Oil Project data base in an updated version
of the data which was previously documented in "A User's Guide to the MIT
World Energy Demand Data Base," May 1976.
Some data has been changed from
the first version, all variables have had recent years' observations added, and
*All of the data is stored on TROLL, a computer software package which
provides an environment for creating, estimating and simulating economic
models. TROLL is at the Information Processing Service at MIT. See
Chapter Ten for more information.
- 57 -
in a few cases a data series was totally replaced.
It is important to
any readers who are familiar with the model to remember that this is the
"raw" data base which was used by the modelers.
This data was often
transformed to different units or aggregated for the purpose of analysis.
Where appropriate, the type of conversion performed will be very briefly
described in order to help the reader understand the transition from
data base to data for analysis.
Countries
The countries included in this section are:
Belgium, Canada, France,
Italy, the Netherlands, Norway, the United Kingdom, the United States and
West Germany.
All of the variables described below have been collected
and stored on TROLL for each of these nine countries.
Variables
Private Consumption Expenditure
Private Consumption Expenditure is defined as the value of final
expenditure by households and private non-profit institutions on current
goods and services or purchases of goods, less the sales of second-hand
goods and excluding land and buildings.
Households, as a consumer group,
include all individuals who are normal residents and groups with less than
two full-time employees.2
It was necessary to find sources with a
consistent disaggregation of not only the usual categories of consumption,
clothing, food, housing, etc., but that also included expenditures on
individual fuel products.
The four fuels concerned are liquid fuel or
distillate fuel oil, solid fuel or coal, gas, and electricity.
- 58 -
Private Consumption Expenditure on Fuels
One of the main objectives in the data collection effort was to
obtain a breakdown of private consumption expenditure on fuels.
The Statistical Office of the European Economic Community (SOEEC)
National Accounts includes expenditures with a fuel breakdown for years
1960-1970 or 1972, for EEC countries.
currency
This data, in millions of local
in nominal or undeflated terms, was used.
Additional sources were needed to update these categories because
the SOEEC has discontinued this disaggregation in recent years.
For this
reason a different approach was used to arrive at this expenditure
category for the remaining years.
Expenditures for the four fuels in
recent years were determined by multiplying the quantity consumed by the
price.
Data for the residential consumption of the various fuels was
taken from the OECD Energy Statistics on magnetic tape for these recent
years' observations.
The 'residential' category here refers only to
private dwellings and private non-profit institutions; it is a subset of
the domestic category which also includes agriculture, public utilities,
commerce, returns to refineries, miners coal and "all other."
An implicit
private consumption expenditure on fuels was calculated for EEC countries
using retail prices of the four fuels and these OECD quantities.
expenditure
on gas
_
retail
gas price
(Figure 3-1)
*
quantity
consumed
- 59 -
An expenditure figure in local currency, nominal units was obtained in
this way, and then converted to millions of local currency units.
In a few cases the OECD tape, Energy Statistics, did not disaggregate
the domestic sector, and residential sector consumption data was
unavailable.
Where this occurred the historical ratio
of residential
consumption, as implied in the SOEEC, to "domestic" consumption in OECD
was applied to the domestic consumption data from the OECD for recent
years, to obtain a residential consumption approximation.
For the United
Kingdom the same methodology was used for 1972-1974, but quantity data
was taken from the United Kingdom Digest of Energy Statistics.
The data sources used for the other countries, the USA, Canada, and
Norway, to obtain private consumption expenditure on fuels for 1960-1970 or
1972 as well as the update through 1974 were the U.S.-Survey of Current
Business, the Economic Report of the President, Norway's national statistical
yearbooks, Detailed Supply and Demand Statistics of Canada, and Canada's
Energy Databank.
All of these data series are in millions of local
currency units in nominal terms, i.e., no adjustments made to account
for inflation.
Examples of the data file names follow.
private consumption expenditure on:
gas
"country"_NEXGAS_LC
liquid fuel
"country"_NEXFLIQ_LC
(Figure 3-2)
- 60 -
Private Consumption Expenditure on Food
For most countries, expenditures on food was obtained from SOEEC,
OECD, or UN National Accounts. For the United Kingdom, the UK Annual
Abstracts of Statistics was used for 1955-1972, and UN and OECD National
Accounts were used for 1972-1974. All series were collected as available
in both current and constant terms, in millions of local currency.
Each
constant or real currency series has a base year of 1970, and this was
used to compute an implicit price index.
In some cases a price index,
base year 1970=100, was obtained from OECD National Accounts.
The TROLL
data file name for this category is:
nominal units:
"country" NEXF_LC
price index:
"country"_Y70 NEXF
(Figure 3-3)
Private Consumption Expenditure on Clothing
For most countries,
expenditures on clothing
SOEEC,OECD,or UN National Accounts.
UK Annual Abstract
of Statistics
data was obtained from
For the United Kingdom, the
was used for
1955-1972 and OECDNational
Accounts was used for 1972-1974. All series were collected
as available
in both current and constant terms, in millions of local currency.
Each
constant or real currency series has a base year of 1970, and this was
used to compute an implicit
price index.
In some cases a price index, base
year 1970=100, was obtained from OECDNational Accounts.
the data file
nameformat, see Figure 3-3.
For an example of
- 61 -
Private Consumption Expenditure on Durables
Expenditures on durables for most countries
SOEEC, OECD, or UN National Accounts.
For the United Kingdom, the
UK Annual Abstract of Statistics was used for
Accounts was used for 1972-1974.
was obtained from
1975-1972 and OECO National
All series were collected as available
in both current and constant terms,
Each
in millions of local currency.
constant or real currency series has a base year of 1970, and this was
used to compute an implicit price index .
In some cases a price index, base
year 1970=100, was obtained from OECD National Accounts.
For an example
of the data file name format, see Figure 3-3.
Private Consumption Expenditure on Transportation and Communication
Expenditures on transportation and communication for most countries
was obtained from SOEEC, OECD, or UN National Accounts.
For the United
Kingdom, the UK Annual Abstract of Statistics was used for 1955-1972 and
OEOD National Accounts was used for 1972-1974.
All series were collected
as available in both current and constant terms, in millions of local
currency.
Each constant
or real currency series has a base year of 1970,
and this was used to compute an implicit price index.
In some cases a
price index, base year 1970=100, was obtained from OECD National Accounts.
For an example of the data file name format, see Figure 3-3.
Private Consumption Ependiture, Total
Data for most countries' total private consumption expenditure was
obtained from OECD National Accounts.
All series were collected in
millions of local currency, current terms.
A price index, base year 1970=100,
- 62 -
was also collected.
Figure
For an example of the data file name format, see
3-3.
Private Consumption Expenditure, All Other
Data for this category was constructed by subtracting from the total
the expenditures for food, clothing, durables, transportation and
communication and the four fuels.
Also, a price index, using base year
1970=100, for the "all other" category from the OECD National Accounts
was collected.
The data file is "country"_Y70 NEXO.
No price index was collected, or implicit index computed for the
categories representing the fuels, as these categories received different
treatment in the econometric analysis.
An average price of energy was
derived from the fuel prices, estimated with the translog aggregator.1
The fuel price indices obtained in this way are not stored as data files.
For use by the translog aggregator, the data was deflated to 1970
terms using the total consumption expenditure index, and converted to
US dollars using purchasing power parities.
Fuel Prices
Retail
prices of home heating oil (#2 fuel oil), electricity, coal,
and gas were needed for this analysis.
The original price series and all
updates were obtained from SOEEC publications Studien and Erhabungen and
Energy Statistics, from the IEA and FEA, The Basic Petroleum Data Book,
the Edison Electric Institute, the UK Digest of Energy Statistics, the
Economic Report of the President, and Norway's national statistical
yearbooks.
- 63 -
The price series range from 1955 or 1960 through 1974 and all are
in local currency/tcal, current prices.
in "natural"
units, such as tons,
These prices are usually published
barrels,
or liters
and were
tcals, or Tera Calories, which is a heat measurement.
defines tcals and lists the conversion factors.
converted
to
Chapter Eight
The SOEEC figures were
published in "eurs"/natural unit, as quarterly observations.
"Eurs" is
an international monetary unit, and these were converted to local currency
using the SOEEC exchange rates.
annual figure.
Quarterly data was averaged to obtain an
For use in econometric modeling
these data files were
deflated to 1970 values using the total private consumption expenditure
index, 1970=100, and converted to 1970 US dollars/tcal with purchasing power
parities.
For a discussion of purchasing power parities, see Chapter Nine.
Although the consumption expenditure categories are called liquid fuel and
solid fuel, these categories include mainly fuel oil and coal respectively.
The retail prices for fuel oil and coal therefore are assumed to correspond
to these categories.
Examples of the data file names follow.
retail price:
fuel oil
"country"
gas
SRETPRFO
LC
"country" SRETGASLC
(Figure 3-4)
Some changes that were made in the original data will be of interest
to users who have obtained the first edition of the User's Guide and a
copy of the data base prior to January 1977.
Errors were detected and the
following steps were taken to correct the data. (Next page)
- 64 -
Data File
Correction
France retail gas price
* 10
Italy retail gas price
* 10
Netherlands retail gas price
* 10
Norway retail gas price
replaced
Japan retail gas price
deleted
(Figure 3-5)
Personal Disposable Income
Personal disposable income, with data file name "country"_NDI_LC, was
obtained from certain editions of OECD National Accounts.
This is a
subset of national disposable income, which does not include corporate
enterprises
and government.
disaggregate this item.
Not all issues of the OECD publication
Data is in millions of local currency and covers
from 1950 to 1955 through 1974.
Population
Population data was taken from several editions of the UN
Yearbook.
countries.
Demographic
Data is in millions and covers years 1950-1974 for all
This is filed on TROLL as "country"_POP.
Temperature
Temperature data is an average temperature of the five coldest
months, taken from monthly data published in the U.S. Weather Bureau's
Monthly Climatic Data for the World.
For a small country the average of
- 65 -
the capital city was collected, for large or geographically and climatically
diverse countries, data for the largest cities was collected.
in degrees Fahrenheit and covers from 1955 to 1975.
Data is
Data file names are
"country" TEMP, or "country" "city" TEMP.
Data Tables
Examples of data file names, which are also mnemonics for the
The following table,
variables, have been included in the above text.
Table 3-1, is an alphabetical listing of all of the data file names in
Note that all the residential data files are stored in
this sector.
TROLL in the section "TWOTIME".
This means that all data file names are
preceded by the word "TWOTIME".
For an index of terms and data file
names
see Chapter Seven.
Included in the following table is information
years for each file, the units,
the definition,
on the range of
and a bibliography code
number which refers to the bibliography in Chapter Six.
References
1.
Pindyck,
R.S., "International Comparisons of the Residential Demand
for Energy," Working Paper #MIT EL 76-023WP, MIT Energy Lab,
Cambridge,
MA.
.
2.
.
.
.
.
.
.
. .
. ...
...
....
....
....
Yearbook of National Accounts Statistics, Department of Economic
and Social Affairs, Statistical Office of the UN, New York, NY.
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- 83 -
CHAPTER FOUR
Transportation Sector Demand Data
The Transportation sector data has been compiled for the purpose of
estimation and analysis of petroleum consumption by the transportation
sector.
The focus of this section is actually on passenger vehicles and
consumption of diesel fuel and gasoline.
Air, rail and water transport
vehicles and the associated fuels are not included.
All of the data to be
described here has been collected from public sources and stored as time
series on the computer system, TROLL*.
The data generally covers years
1960-1973 as annual observations for fifteen major energy consuming
countries.
The variables contained in this section are: vehicles in use,
new registrations of vehicles, traffic volume, consumption of motor gasoline
and diesel fuel, taxation of those fuels, the quantity of each of those
fuels consumed by road vehicles, an index of private consumption expenditure on personal transportation equipment, an auto depreciation rate, and
a price index for autos.
The sources of the information and the character-
istics of the data will be briefly discussed in this chapter, and followed
by a table which alphabetically lists all of the computer data file names
along with the units used, a brief definition of the variable, and a source
code number which refers to the bibliography in Chapter Six.
*TROLL is a computer software package which provides a comprehensive
environment for economic models. TROLL operates at the Information
Processing Service at MIT. For more inormation see Chapter Ten.
- 84 -
Countries
The countries included in this section are: Austria, Belgium, Canada,
Denmark, Finland, France, Italy, the Netherlands, Norway, Spain, Sweden,
Switzerland, the United Kingdom, the United States, and West Germany.
For
Austria, Denmark, Finland, and Spain, however, complete information was not
available.
The variables described below are always available for the
remaining eleven and sometimes available for Austria, Denmark, Finland,
and Spain.
Variables
Vehicles in Use
Data on vehicles in use, or the stock of vehicles for passenger cars,
buses and goods vehicles (commercial use) was collected.
The data represents
the number of each type of vehicle registered as of December 31 of the
given year.
The sources used were the UN Statistical Yearbook, World
Road Statistics, The Motor Industry in Great Britain, Canada Yearbook, and
Highway Statistics.
The range of years available varies from country to
country but most often the data is available from 1950 through 1975.
Often
several sources had to be used for one country to obtain a complete time
series.
Examples of the data file names used on the computer system follow.
"country"_SPC
stock of passenger cars
"country"_SB
stock of buses
"country" SGV
stock of goods vehicles
(Figure
4-1)
- 85 -
New Registrations
The number of new registrations of vehicles for the same categories,
passenger cars, buses and goods vehicles, was also collected.
The data
indicates the number of vehicles registered for the first time in the
given country.
The sources used were: World Road Statistics, The Motor
Industry in Great Britain, World Motor Vehicle Data, and Highway Statistics.
Several sources were often used for one country in order to obtain a
complete time series.
An example of the data file name used to store
the data on TROLL follows.
"country" NRPC
new registrations of passenger vehicles
(Figure 4-2)
Traffic Volume
A means of measuring actual traffic volume in addition to number of
vehicles was required.
World Road Statistics contains such a measure of
traffic volume, as million vehicle kilometers, (the number of kilometers
travelled
per
million
vehicles). The unit of measure for the
United States is miles per million vehicles.
available for the years 1962-1975.
The data was generally
An example of the TROLL data file name
appears below.
"country" TVPC
traffic volume of passenger vehicles
(Figure 4-3)
- 86 -
Consumption of Motor Gasoline and Diesel Fuel
Final internal consumption of motor gasoline and diesel fuel was
obtained from OECD Energy Statistics 1960-1973.
The information is
published as thousands of metric tons but the figures were converted to
tcals for incorporation in the data base (See Chapter Eight for a definition of tcals and a table of conversion factors used). The data is stored
under these file names:
"country"_FPRMG
final consumption of motor gas
"country"_FPRDS
final consumption of diesel fuel
(Figure 4-4)
Consumption of these two fuels for road transport purposes was also
found in OECD Energy Statistics, 1960-1973.
The data was converted to
tcals and is stored as:
"country" VCMG
vehicular consumption of motor gas
"country"_VCDL
vehicular consumption of diesel fuel
(Figure 4-5)
Taxation of Motor Gasoline and Diesel Fuel
Taxation of motor gasoline and diesel fuel was found in World Road
Statistics.
price.
The data is expressed
The data
is
not always
as a percentage
available
of the total
retail
for each of our countries but
- 87 -
the range of years is generally 1962-1975.
The data file names are:
"country"_TMG
taxation of motor gas
"country"_TDL
taxation of diesel fuel
(Figure
·
4-6)
Index of Private Consumption Expenditure on Personal Transportation Equipment
An index of private
xpenditure on personal transportation equipment
was created using the ratio of expenditure in current purchasers value
(local currency) to expenditures in constant purchasers values at 1970.
The
information is from the UN National Accounts Yearbook, where personal
transportation equipment is found under transpcrtation and communication.
The data
file name is:
"country"_INEXPTE_Y70
index of private consumption expenditure
on personal transportation equipment
(Figure 4-7)
Consumption of Motor Gasoline Per Distance Unit
Total consumption of motor gasoline per million vehicle kilometers
was calculated using final consumption of motor gasoline and the measure
of traffic volume.
The measure of traffic volume in the US was converted
from miles to million vehicle kilometers for this calculation, so the units
of this variable are always tcals/million vehicle kilometers.
This measure
- 88 -
is actually the inverse of fuel efficiency.
The TROLL data file name is:
motor gasoline consumed/million
vehicle kilometers
"country"_CPMK
(Figure 4-8)
Depreciation Rate
A depreciation rate for autos was derived from the data on stock
of passenger cars and new registrations of passenger cars.
The de-
preciation rate is measured as the fraction of autos registered December
31 of the previous year not registered December 31 of the current year.
Depreciation+ =
(SPC+ 1
- SPC+ + NRPC)/SPC+1
(Figure 4-9)
The data file name for this variable is:
"country"_DEPRATE
(Figure 4-10)
Price Index for Passenger Vehicles
A relative price index of cars was constructed using the index of
private consumption expenditure on personal transportation equipment,
1970=100, and dividing by the gdp price index, 1970=100.
This ratio was
- 89 -
then scaled to 1970=100 and stored as:
relative price of cars, 1970=100
"country"_RPCARS_Y
(Figure 4-11)
A Note About Data Reliability
Most of the sources used are annual publications and sometimes
a minor change in definition or reporting occurred from year to year.
This means that a particular time series as reported is not actually a
continual time series.
This
employed for this sector.
occurs
quite frequently in the sources
In addition, we detected a number of transposed
years and other printing errors.
Where
we
detected
these errors we corrected the numbers, and in some cases "smoothed" a
few years.
Where this was done is noted in the "comments" section of
the data file.
Tables
The following table, table 4-1, is an alphabetical listing of all of
the data files in the transportation section of the database.
The user
should note that all of this data is in the transportation "archive" which
means that all data file names are preceded by the character string "trans".
The table that follows includes a brief description of the variable, the
range of years included, the units, and a bibliography code number that
refers to the bibliography in Chapter Six.
An explanation of TROLL data
file naming conventions can be found in Chapter Seven along with an
index of terms.
Chapter Ten contains information on accessing TROLL,
and obtaining the database.
- 90 -
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- 113 -
CHAPTER FIVE
Petroleum Product Demand Data
The product demand data of the World Oil Project data base is the result
of an attempt to account for all petroleum consumption in the nonCommunist countries and to provide information for an aalysis
consumption.*
of petroleum
Unlike the other sections of the data base which contain
fuel consumption by sector (residential, industrial, etc.), this section
concerns total oil consumption by product.
All petroTeum products consumed
are included for years 1950-1973 or 1974, in addition to prices of fuels,
economic activity indicators, temperature, and purchasing power parities.**
Where this data was found and how it was transformed for inclusion in the
data base will be described.
The countries included will be enumerated
and the world aggregate regions used will be defined.
Following this is a
table which alphabetically lists all of the data file names used on the
computer, including a brief variable definition, the units, the range of years,
and a bibliography number which refers to the bibliography in Chapter Six.
The product demand data is the most straightforward of all the sections
of the data base, since it often appears virtually unchanged from the form
in which it was published.
Very little transformation or manipulation
*The petroleum product demand data was constructed for the purposes
of estimation and ahalysis ad also as data inputs for a simulation model.
**All of the data is stored on TROLL, a computer software package which
provides a comprehensive environment for creating, estimating, and simulatinq
economic models. TROLL operates at the Information Processing Service at MIT.
For more information see Chapter Ten.
- 114 of data was required to standardize the information.
Quantity data was
converted to tcals and prices are expressed in terms of local currency/tcal.
Chapters Eight and Nine define tcals and explain the conversion factors
used to standardize units.
Countries and World Regions
The Product Demand Data accounts for total world oil consumption
excluding the Sino-Soviet area.
The following OECD countries are
accounted for in the most detail:
Austria
France
Sweden
Australia
Italy
United Kingdom
Belgium
Japan
United States
Canada
Netherlands
West Germany
Denmark
Norway
Turkey
Finland
Spain
The non-OECD countries which are included are:
Argentina
Mexico
Brazil
South Africa
India
Venezuela
(Figure 5-1)
The remainder of the non-Communist world is dealt with as aggregated
regions, according to the UN groupings, with both totals for the regions
and as net regions, that is, net of all the countries listed individually
above:
(next page)
- 115 -
North America
Western Europe
Carribean America
Asia
Other America
Oceania
(Figure 5-2)
Some of these net regions are also aggregated in another way, as two
regions, oil exporting countries (OEXP), and net less developed countries
(NLDC).
will
Different data files exist for all of these aggregations, which
be described
below:
NLDC
OEXP
The Middle East
India
(net Turkey)
Net Africa
Nigeria
Net Asia
Algeria
Net Oceania
Libya
Net Americas
Indonesia
Mexi co
Venezuela
(Figure 5-3)
- 116 -
Variables
Demand by Product
Total demand for petroleum was disaggregated into demand by product.
For the OECD countries which are included, the following categories were
taken from the OECD Energy Statistics 1950-1973, which was chosen for
its inclusion of all products for the greatest time period.
Consumption Category
Final
Internal
Product
Consumption:
In Data File
aviation gas
FPRAG
diesel
FPRDS
oil
fuel oil
FPRFO
jet fuel
FPRJF
kerosene
FPRKS
LPG
FPRLG
motor gas
FPRMG
Industrial Sector Consumption:
fuel oil
FINDPRFO
Domestic Sector Consumption:
fuel oil
FOPRFO
Transformation Sector
Consumption:
LPG
TRANPRLG
fuel oil
TRANPRFO
crude
TRANPCRU
Other:
oil
feedstock
refinery
gas
fuel oil
(Table 5-1)
tail
FFDSK
FREFTG
FOTHPRFO
Name as:
II
- 117 -
Note that fuel oil (light and heavy) is included as four consumption
categories, industrial, residential, transformation and all "other"
sector consumption, because of the varied uses of this product.
The
category 'final internal consumption of fuel oil' is actually a redundant
category because it constitutes industrial, residential and all "other"
consumption.
It is not used to compute total demand.
To obtain a demand
total which excludes bunkers and refinery losses, all of the other
categories must be included.
Japan is the only country which reports
crude oil consumption by the transformation sector, but otherwise all of
the countries'
totals
are obtained
in the same way.
All of this OECD data was published in units of thousand metric
The data was converted to tcals, using the conversion factors in
tons.
Chapter Eight.
The total obtained by adding all of the categories still
excludes refinery losses and bunkers.
To obtain a total in volumetric
terms, convert tcals to millions of barrels per day (MBD), add 9% for
refinery. losses and 1% for bunkers.
The 9% refinery loss is assumed
because of the conversion from tcals to a volumetric unit.
See Chapter
Eight for a discussion of refinery loss.
When the industrial and residential models were incorporated in the
simulation model, (See Chapters Two and Three)
the data files for industrial
and residential consumption had to correspond to the data files used for
those models.
Different sources and different methods were used for the product
demand data, and consequently the 'industrial' and 'domestic' fuel oil categories
do not correspond to those used for the industrial and residential models.
For those countries involved, the data files for residential and industrial
consumption were used and the "other" fuel oil category was adjusted to account
for discrepancies between different classifications.
(Figure 5-4, next page.)
- 118 -
product demand data,
industrial demand data,
remainder"
industrial
industrial
fuel oil
fuel oil
("country"_SECOND_FINDPRO)
("country"
fuel oil
INDFINDFPET)
*This data file in product demand archive
**This data file from industrial archive and used by simulation model
***Added to "other product" category for use by the model
product demand data,
residential data,
domestic
residential
fuel oil
consumption
("country" SECONDFOPRFO)
fuel oil
"remainder"
=
fuel oil
consumption
("country"_TWOTIME_FRESPRFO)
*This data file in product demand archive
**This data-file from residential data archive, used by simulation model
***Added to "other product" category for use by the model
(Figure 5-4)
The table below (next page) shows how the data files, as organized
on TROLL, were aggregated for use in the World Oil Project simulation model,
and also demonstrates how the files should be manipulated to obtain a
petroleum consumption total for an OECD country.
- 119
UNITS:
-
tcals
TROLL Archive
Data File
product demand
diesel
product demand
motor gas
fuel
industrial demand
industrial
residential demand
residential
product demand
aviation gas
product demand
jet fuel
product demand
kerosene
product demand
feedstocks
product demand
"other"
product demand
refinery gas
product demand
transformation
fuel
fuel
fuel oil
fuel oil
product demand
transformation
LPG
product demand
transformation
crude
product demand
(industrial and domestic
fuel net the residential
and industrial archive
fuels)
"remainder"
Total
fuel oil
tcals
tcals x 685/365/1,000,000 = MBD
MBD + 10% = final total
(Table 5-2)
- 120 -
The UN publication World Energy Supplies 1950-1974 was used for the
"- ~
rest of the non-Communist world.
individual countries:
and Venezuela.
This UN data was used for the following
Argentina, Brazil, India, South Africa, South Korea,
The rest of the world, dealt with as total regions,
consists of the UN aggregations net of the individual country data to
avoid double counting.
The UN data was chosen because of the range of
years available, but consumption of feedstocks is not available.
World Energy Supplies breaks petroleum product consumption into four
categories:
Products
In Data File Name as:
aviation and motor gas
FPRMG
kerosene and jet fuel
FPRKS
distillate and residual fuel oil
FPRFO
LPG
FPRLG
(Figure 5-5)
The regions listed in the 'countries' section are defined in more detail
in Table 5-3 below. (Next page)
.
~~~~ ~.
.~~~~~~~~~~~~~~~~.
.
,
- 121 Table 5-3
UN Aggregation
In
Data
File
UN Region
Africa:
North America:
Central America:
Other America:
Middle East:
Far East:
Western Europe:
Countries
Includes every country from South
Africa to the Suez Canal, including
Egypt
Includes Canada, U.S.A., Bermuda,
Greenland, St. Pierre, S. Miguelan
Includes Costa Rica, E Salvador,
Guatemala, Honduras, Nicaragua,
Bahamas, Barbados, Columbia, Cuba,
Dominica, Haiti, Jamaica, Mexico,
Panama, Puerto Rico, Trinidad,
U.S. Virgin Islands, Venezuela
Name as:
AFR
NAMR
CAMR
Includes Argentina, Bolivia, Brazil,
Chile, Ecuador, Guyana, Paraguay,
Peru, Surinam, Uruguay
OAMR
Bahrain, Cyprus, Iran, Iraq, Israel,
Jordan, Kuwait, Lebanon, Saudi Arabia,
DM Yemen, Syria, Turkey
MIDE
Includes Afghanistan, Bangladesh,
Burma, Cambodia, Hong Kong, India,
Indonesia, Japan, South Korea, Laos,
Macau, West Malaysia, Nepal, Pakistan,
Phillippines, Singapore, Thailand,
South Vietnam
FARE
Includes Austria, Belgium, Denmark,
Finland, France, West Germany, Iceland,
Ireland, Italy, Luxembourg, Netherlands,
Norway, Portugal, Sweden, Switzerland,
United Kingdom
WEUR
(continued)
- 122 -
Table 5-3, continued
Other Europe:
Oceania:
Includes Gibraltar, Greece, Malta,
Spain, Yugoslavia
OEUR
Includes Australia, American Samoa,
Fiji, Polynesia, Guam, Samoa,
New Zealand, Pacific Islands
OCEN
There is a data file for each of these regions for each of the four
categories of consumption.
There is also a net region data file for the
four categories net of the UN data for the individual countries.
The
construction of these net regions is shown below with the sources of the
data included.
Table 5-4
Data
Region
Definition
File
Name
Net Africa:
UN Africa, net UN South Africa
EAFR
Net North America:
UN North America, net UN U.S.A.
and Canada
ENAMR
UN Central America, net UN Mexico
and Venezuela
ECAMR
UN Other America, net UN Brazil
and Argentina
EOAMR
Net Central America:
Net Other America:
(continued)
- 123 -
Table 5-4, continued
Net Far East:
UN Far East, net UN Japan
EFARE
Net Middle East:
UN Middle East, net UN Turkey
EMIDE
Net Other Europe:
UN Other Europe, net UN Spain
EOEUR
Net Western Europe:
UN Western Europe, net UN Austria,
Belgium, Denmark, Finland, France,
Iceland, Ireland, Italy, Luxembourg,
Netherlands, Norway, Portugal, Sweden,
Switzerland, United Kingdom and
West Germany
Net Oceania:
UN Oceania, net UN Australia
EWEUR
It can be seen that wherever possible OECD data was used.
EOCEN
The UN data
for consumption is not the optimal source for consumption data, lacking
feedstocks, and does not compare favorably with other International
sources of consumption data, such as the tureau of
ines.
Because of this problem, an alternative set of remaining world
regions from a different source, the Bureau of Mines, was constructed.
From the International Petroleum Annual, two regions of oil consumption
were constructed, oil exporting countries (OEXP) and net less developed
countries (NLDC).
(Figure 5-6, next page.)
- 124 -
Bureau of Mines Data, Alternative Aggregation
OEXP:
Middle East (excluding Turkey), Algeria,
Indonesia, Libya, Mexico, Nigeria, Venezuela
NLDC:
India, Net Africa (net OEXP countries and
South Africa), Net Asia (net Japan),
Net Oceania (net Australia), Net Americas
(net Canada, U.S., Mexico, Venezuela)
(Figure 5-6)
Economic Activity Indicators and Deflators
For OECD countries OECD National Accounts was used.
GDP in current
and constant local currency was collected and stored on TROLL in the
product demand section.
The data file mnemonic name is "country"_GDP_LC
for the current currency series and "country" GDPLCY58,
the constant series.
Y63, Y70, for
The constant series has a base year which is
reflected in the file name.
The base year indicates that the information
is reported in terms of the currency of that year, therefore non-inflated
currency.
An implicit price index was then constructed, using the current
and constant series.
An implicit price index, base year 1970=100, was
computed for all the OECD countries.
There are also data files in
current and constant units for total private consumption expenditure,
NEX1_LC or NEX1_LCYNN,
"country"_Y70NEXI.
in addition to a constructed price index
There are also data files for disposable income, in
- 125 nominal currency, data file name "country"_NDI_LC.
For the remaining countries, Argentina, Brazil, India, Mexico,
South Africa and Venezuela, UN National Accounts was used for the same
data series, gdp and private consumption expenditure in current and
constant units.
Population
Population data for all countries was collected from the UN
Demographic Yearbook.
The unit of measurement is millions and the range
of years is 1950-1974.
Temperature
Temperature data for 1950-1974 was collected for all countries from
the American Meteorological Association's Monthly Climatic Data for the
World.
The temperature is the average of the five coldest months
measured in degrees Fahrenheit.
Data was collected for the capital city,
or in cases of large or climatically diverse countries, for the major
population centers.
Prices of Petroleum Products
For most of the data series the range of the observations usually
covers years 1950-1973 or 1974, but prices are the exception.
To obtain
a consistent set of average annual prices for a variety of retail and
wholesale products has not been possible.
The price information for
developed countries is more available and reliable than the information
for developing countries.
There are also problems posed by prices that
- 126 -
are quoted for a country that has large fluctuations in its currency
value.
This can be compounded by prices being quoted in U.S. dollars,
and not local currency.
In some cases we have compiled and stored as
data files on TROLL two different price
same country.
series for the same product,
This is because it was found that two different sources
quoted contradictory prices and the validity of one over the other could
not be established.
Retail and wholesale prices of all petroleum products and their
substitutes, such as coal, natural gas and electricity, were collected.
For European countries, Canada and the U.S., Japan and Australia, the IEA,
the FEA, the Edison Electric Institute, and Canada's Energy Databank were used.
The Basic Petroleum Data Book also contained price information, as did
the President's Economic Report and the United Kingdom Digest of Energy
Statistics.
Price information for developing countries seems to be characterized
mainly by its scarcity.
In all cases we were looking for a complete
range of prices in addition to reliability of source.
finally used for developing countries are:
Some of the sources
The International Petroleum
Annual for gasoline, diesel oil, fuel oil and Bunker "C", and Americas en
Cifras, and Exxon.
National statistical yearbooks were useful for some
countries, Annuario Estadistico for Mexico, Statistiches Bundesamt
yielded prices for Germany as well as other countries, and Norwegian
yearbooks.
The FEA also provided us with some unpublished fuel prices
and fuel price indices.
All price units were converted to tcals.
currency/tcal.
Most are stored in local
Some data files are in US dollars/tcal, as the method of
- 127 -
original conversion from local currency to U.S. dollars is not known.
Although the units in the TROLL data files are usually local currency,
these are converted to 1970 U.S. dollars for analysis.
This is done with
the use of purchasing power parities and price deflators.
power parities are not available, exchange rates are used.
When purchasing
Although
the PPP's are a constant 1970 value, they are stored as time series,
1950-1974, for convenient use with the other time series.
For a discussion
of purchasing power parities, see Chapter Nine.
Some examples of data file names for fuel prices follow:
retail price
electricity
"country".SRETELEC LC
gas
"country" SRETGAS_LC
(Figure 5-7)
If the data is not in local currency, the namedoes not contain
If there is more than one file
' 1'
or
' 2'
for the same price,
the file
'_LC'.
name is
at the end.
Example:
BRAZ_SRETELEC 1
BRAZ SRETELEC 2
(Figure 5-8)
The wholesale or industrial
prices have the same name format, except
the 'SRET' part of the name is replaced by 'SWH' for wholesale and
'SIND' for industrial.
An index is indicated with 'IWH' or 'IRET'.
- 128 -
Tables
Examples of data file names which are also mnemonics for the variables
have been included in the above text.
The following table, Table 5-5,
is an alphabetical listing of all of the data file names in this section of
the database.
Note that all of the
the section or archive "second".
preceded be the letters "second".
product
data files are stored in
This means that all data file names are
For an index of terms and data file names
and an explanation of TROLL data file naming conventions see Chapter Seven.
Included in the following table is information on the range of years
for each file, the units, the definition, and a bibliography code number
that refers to the bibliography in Chapter Six.
- 129 -
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- 184 -
CHAPTER SIX
Bibliography
Table 6-1 is a bibliography
compilation of the data base.
of all of the sources
used
in the
These references are not listed alpha-
betically, but each has a number which corresponds to the number in
both the tables in Chapters Two, Three, Four and Five, and in the
'comments' section of each TROLL data file.
- 185 -
Table
6-1
Bibliography and Code Numbers
1.
World Currency Charts, 7th Edition, San Francisco, American International
Investment Corporation, Dewey Library, HG 3863.A512.
2.
Petroleum Facts and Figures, Washington D.C., American Petroleum
Institute, 1971, Dewey Library, HD9563.A512.
3.
Yearbook of the Commonwealth of Australia,Canberra, Australia,
Commonwealth Bureau of Census and Statistics, Dewey Library,
HA3001 .A938.
4.
Annuaire Statistique de la Belgique, Bruxelles, Institut National de
Statistique, Ministere des Affaires Economiques, Dewey Library,
HA1391.A25, Lamont Library, Documents.
5.
National Accounts Income and Expenditure, Historical Revision, Canada
Statistics, 1926-1971, Dewey Library HClll.A42.
_
6.
Statistics Canada, Catalogue, Ottawa, Canada, Dewey Library, ZHA741.A3.
7.
Energy Statistics Yearbook, E.E.C., Brussels, Statistical Office of the
European Economic Community (SOEEC), Dewey Library, QC73.5797.
8.
Basic Petroleum Data Book, Petroleum Industry Statistics, American
Petroleum Institute, Washington D.C.
9.
Annuaire Statistique de la France, Paris, Institut National de la
Statistique, Ministere de l'economie et des finances, insee, Dewey,
HA1213AY.
10.
Les Collections de L'INSEE, Les Comptes des Entreprises par Secteur,
Institut National de la Statistique et des Etudes, Dewey Library,
HA1213,A43, Series C.
11.
Monthly Abstract of Statistics, India, Central Statistical Organization,
Delhi Manager of Publications, Dewey Library, Periodicals.
12.
Statistical Abstract of the Indian Union, New Delhi, Central Statistical Organization, Cabinet Secretariat, Gov't of India, Dept. of State,
Dewey Library, HA1712,A34.
13.
International Financial Statistics, International Monetary Fund, Dewey
Library, Periodicals.
- 186 -
14.
Annuario Statistico Italiano, Rome, Instituto Centrale di Statistica,
Dewey Library, HA1360.A2.
16. Annual Report
on National
Income Statistics,
Japan, Economic Planning
Agency, Government of Japan, Dewey Library, HC465.I5.A3.
17.
Energy of Japan, Tokyo, Institute of Energy Economics, Dewey Library.
18.
Japan Economic Yearbook, The Oriental Economist.
19.
Japan Statistical Yearbook, Bureau of Statistics, Office of the Prime
Minister, Dewey Library, HA1832.J35.
20. The Petroleum Industry in Japan, Tokyo, Japanese National Committee
of the World Petroleum Congress, Tokyo, Dewey Library, HD9576.J32.
21.
World Petroleum Congress, An Annual Review of International Oil
Operations, Dewey, HD9560.4W927.
22.
Encyclopedia of Chemical Technology, Kirk and other eds., John Wiley and
Sons, New York, Science Library, TP9.E56.
23.
Chemistry of Coal Utilization, Lowry Homer, Ed., National Research
Council, John Wiley and Sons, Inc., New York, Science Library,
TP953.N276.
24.
De produktstructuur, Netherlands Central Bureau of Statistics, Dewey
Library, HC325.A3.
25.
Statistical Yearbook of the Netherlands, Netherlands Central Bureau of
Statistics, Den Hague, Staatsuitgeverij, Annul.
26.
OECD Economic Surveys, Paris, OECD, Dewey, HC325.08.
27.
Electrisitet Statistikk, Norway, Oslo, Norway, Lamont Library.
28.
Historical Statistics, Norway, Oslo, Norway, Lamont Library.
29.
Industristatistikk Annual, Norway Offisielle Statistikk, Oslo,
Norway, Lamont Library.
30.
National Accounts, Oslo, Norway, Lamont Library.
31.
Statistisk Arbok, Norway, Central Bureau of Statistics, Oslo, Norway,
Dewey Library.
32.
Organization for Economic Cooperation and Development, National
Account Statistics, OECD, Statistics of Energy (publication and magnetic
tape).
- 187 -
33.
Organization for Economic Cooperation and Development, Historical
Statistics, Main Economic Indicators, Dewey Library, HC59.068.
34.
OECD Economic Surveys:
HC325.068.
35.
Organization for Economic Cooperation and Development, National Accounts
Statistics, Dewey Library, HC79.I5.068.
36.
Statistich Bundesamt,Wiesbaden, Preise Lohn Wirtsehaftsrechnunger,
Preise und Preisindizesim Ausland, 1973, Harvard University Econ.,
8075.2009.5/
37.
Energy Statistics, Brussels, OECD, 1974, Dewey Library.
38.
Energy Statistics, L'Evolution des Prix des Charbon Dans les Pays de
la Communaute, SOEEC, 1973, Dewey Library.
39.
SOEEC, L'Evolution des Prix du Fuel-Oil dans les Pays de la Communaute,
SOEEC, Dewey Library.
40.
SOEEC, National Accounts, Luxembourg, SOEEC, Dewey Library.
41.
SOEEC, Statistiche Studien und Eihabungen, SOEEC, Dewey Library.
42.
Industri Sveriges Officiella Statistik, Sweden, Lamont Library.
43.
Statistisk Arbok for Sverige, Statistika Centraobyran, Stockholm, Sweden.
44.
Annual Abstract of UK Statistics, United Kingdom Central Statistical
Office, 1968.
45.
The British Economy: Key Statistics, London and Cambridge Economic
Service, Cambridge, Dewey Library 1137.L847.
46.
Bureau of Mines, Coal Statistics.
47.
Digest of Energy Statistics, United Kingdom, 1974, Dewey Library,
Great Britain, Department of Trade and Industry.
49.
UNIPEDE:
Netherlands, Paris, OECD, Dewey Library,
Statistics, Dewey Library, HD9685.Al.I61.
50. Annual Bulletin
of Coal Statistics
for Europe, UnitedNations,
Economic Commission for Europe, Dewey UN Collection and HD9555.A1.U571.
51.
Annual Bulletin of Gas Statistics for Europe, United Nations,
Economic Commission for Europe, Dewey Library, UN Collection and
HD9685.E52.U58.
52.
Annual Bulletin of Gas Statistics for Europe, United Nations,
Economic Commission for Eruope, Dewey Library, UN Collection and
TP732.U57.
- 188 -
53.
Quarterly Bulletin of Coal Statistics for Europe, United Nations,
Economic Commission for Europe, Dewey Library, Periodicals.
54.
Growth of World Industry, United Nations, Dewey Library, HA40.I6.U5852.
55.
Indexes to the International Standard Industrial Classification of All
Economic Activities, New York, UN Statistical Papers, 1971, Dewey
Library, UN Collection.
56.
United Nations Standard Country Code, New York, UN Statistical Papers,
Kewey Library, UN Collection.
57.
VN Yearbook of National Accounts Statistics, New York, United Nations,
Dewey Library, HC79.I5.U57.
58.
Annual Survey of Manufacturers, Washington, US Bureau of the Census,
Dewey Library, HD9724.A211.
59.
US Bureau of Labor Statistics Bulletin, US Bureau of Labor Statistics,
Dewey Library, HD8051,A61.
60.
Historical Statistics of the US, US Department of Commerce, Dewey
Library, HA202.A385.
61.
Petroleum Facts and Figures, Washington, D.C., American Petroleum
Institute, Dewey, HD9563.A512.
62.
National Income and Product Accounts, The National Income and Product
Accounts of the US, 1929-1965, US Department of Commerce, Office of
Business Economics.
63.
US Statistical Abstract, US Department of Commerce, Dewey Library
Reference.
64.
Statistical Yearbook of the Electric Utility Industry, Edison Electric
Institute, New York.
65.
Survey of Current Business No. 7, 1971, US Bureau of Economic Analysis,
Washington, D.C., Dewey Library, HCllO.I5.A2.
66.
Monthly Climatic Data for the World, USA Weather Bureau and the World
Meteorological Organization.
67.
Anuario Estadistico de Espana, Madrid, Instituto Nacional de
Estadistica, Dewey Library, HA1543.A2, Lamont Library.
68.
La Contabalidad Nacional, Spain, Lamont Library.
69.. Statistisk Arbok, Danmarks Statistisk, Dewey Library, HC351.A.
79.
Zccicmic RPeportof the President, US Government, Washington, D.C.
- 189 -
80.
Canada Yearbook, Ottawa, Minister of Industry, Trade, and Commerce,
Dewey Library, HA744.c212.
86.
Statistical Abstract of Latin America, eds. K. Ruddle and D. Oberman,
Latin American Center, UCLA.
89.
GNP; Growth Rates and Trend Data, US Agency for International
Development, Statistics and Reports Divison, Office of Financial
Management, Dewey Library.
92.
Demographic Yearbook of the United States, Dewey Library, HAS383 U.25.
98.
Statistics of Energy, Magnetic Tape, OECD.
99.
Energy Databank: Canada 1960-1973, 1975 Version, University of
Toronto, Institute for Policy Analysis.
100,
Real Product, Real Factor Input
101. and Productivity,
103.
Basic
Statistics
110.
Gross National
International
Christensen, et. al.
of
the Community, SOEEC, Paris.
Product,
Growth
Rates and Trend Data, Agency for
Development.
123.
Energy Balances of OECDCountries,
207.
World Energy Supplies,
1950-1974,
1960-1974,
UN Collection
Paris,
1976.
ST/ESA/STAT/ser.
J/19,
New York, 1976.
211.
Oil Statistics: Supply and Disposal, OECD, 1975, Dewey, HD9560.4, .068 (ref.).
214.
Comite Professionnel du Petrole, Bulletin Mensuel, 20 Septembre, 1976.
223.
Federal Energy Administration.
224.
International Petroleum Annual, US Bureau of Mines, United States
Department of the Interior,
225.
Annuario Estadistico,
250.
Handbook of Labor Statistics,
266.
Monthly Climatic
1966-to date.
Commison Nacional
de Seguros, Mexico.
US Department of Labor.
Data for the World, US Environ. Data Service,
US Weather Bureau.
270.
Americas en Cifras,
Secretaria
General de la Organizacion
de los
Estados Americanos, Washington, D.C.
272.
Statistical Yearbook of the Netherlands, Netherlands Central Bureau
of Statistics, Dewey Library, HA1381.A3, 1974, The Hague, 1975.
- 190 -
279.
Economic Report of the President, US Government, Washington, D.C.
282.
Japan Economic Yearbook, The Oriental Economist 76/77, Dewey Library,
HC461 J35e.
292.
Eurostat: Tax Statistics, EEC, Paris, Dewey, HJ2599.5
300.
U.N. Statistical Yearbook 1974, HA12.5 .U57 (Dewey), United Nations,
1976.
310.
World Road Statistics, 1968, 1972, 1976, HE333.I61, 1976, (Eng.),
International Road Federation, Washington, D.C.
1972 Issue is at D.O.T. Kendall Sq., 8th Floor Library.
1968 Issue is at I.R.F. Headquarters in Washington.
320.
The Motor Industry in Great Britain, HD9710.G71.M919 (Dewey),
Society of Motor Manufacturers and Traders LTD, Statistical Dept.
321.
Canada Yearbook, HA744.C212, 1970, 1971, (Dewey), Canada Dominion
Bureau of Statistics.
330.
World Motor Vehicle Data, HD9710.A39, (Dewey), Automobile Manufacturer's
Association, 1966, Detroit.
331.
Highway Statistics, HE355.A3.A47, Federal Highway Administration, D.O.T.
.J797.
- 191 -
CHAPTER SEVEN
TROLL Data File Names
Each variable for each country has a TROLL* data file name under
which it is stored on the computer system.
The file names consist of
mnemonics or abbreviations for the sector,
the country and the variable.
A brief description of the TROLL naming conventions will help the user
understand the construction of the data file names.
The TROLL data file name can haveseveral
characters separated by an underscore.
segments, or strings of
The successive segments will here
be referred to as 'first level,' 'second level,' etc.
Examples
Format of Data File Name:
Data File Name:
xxxxxxxx_ xxxxxxxx
xxxxxxxx
first
second
third
level
level
level
IND_ BELG_ FPRMG
(Figure
7-1)
*The data base is stored on TROLL, a software package which provides
a cormonrhnsive environment for creating, estimating, and simulating
-C.nr
r in ra-is
. F;or further information, contact the Information
Processinc,
Service at MIT.
- 192 -
The
irst level of the name is known as the "archive name."
The
archive names in the World Oil Project data base indicate the sector; for
example, 'IND' indicates industrial data.
The second level is an abbreviation
for a country or region and indicates the country or region of that data
file.
The third and fourth levels of the name indicate the variable.
There are many variables and the abbreviations or mnemonics employed are
quite complex.
Table 7-1 alphabetically lists all of the archive names, or character
strings that are in the first level, and which sector each represents.
Table 7-2 alphabetically lists the second level character strings,
abbreviations for all the countries and regions.
Table 7-3 alphabatically
lists the mnemonics for the variables.
Table 7-4 is an index of all of the variables in the data base.
This index refers to the third and fourth level
or the mnemonic for the variable.
of the data file name,
The index will also refer to the
archive, or sector, in which the variable can be found.
- 193 -
Table
7-1
First Level TROLL Names - Archive Names
ind
industrial sector data
second
product demand data
trans
transportation sector data
twotime
residential sector data
- 194 -
Table
7-2
Second Level TROLL Names - Country or Regions
Abbreviation
Defin i tion
afr
African continent, UN classification
arg
Argentina
ausl
Australia
aust
Austria
belg
Belgium
braz
Brazil
camr
Central American countries, UN classification
can
Canada
den
Denmark
eafr
Africa, excluding South Africa
fare
Far East, UN classification
fin
Finland
fran
France
indi
India
ital
Italy
jap
Japan
mex
Mexico
mide
Middle Eastern countries, UN classification
namr
North America, UN classification
reth
the Netherlands
nor
Norway
195 -
(Table 7-2, continued)
Abbreviation
Definition
oamr
Other America, UN classification
ocen
Oceanic countries, UN classification
oeur
Other European countries, UN classification
safr
South Africa
skor
South Korea
span
Spain
swed
Sweden
swit
Switzerland
turk
Turkey
uk
United Kingdom
usa
USA
ven
Venezuela
weur
Western Europe, UN classification
wger
West Germany
-
- 196 -
Table 7-3
Third and Fourth Level TROLL Names - Variables
Mnemonic
Definition
a
apparel
allfuels
all fuels consumed by industrial sector
b
beverages
c
communication
cap
capital
cbkb
lignite briquettes
ccok
coke oven coke
cdl
consumption of diesel fuel (transportation sector)
cgo
gas oven coke
cmg
consumption of motor gasol ine (transportation sector)
colb
brown
colh
hard
con
construction
cpat
patent
d
durables
dm
durables and maintenance
dnr
depreciation rate, non-residential structures
dpd
depreciation rate, producer durables
elec
electricity
eng
energy
er
education and recreation
expcap
expenditure on capital
expfuel
expenditure on fuels (industrial sector)
explab
expenditure on labor
coal
coal
fuel
- 197 (Table 7-3, continued)
Mnemonic
Definition
f
food
f* * **
total consumption of * * * * (fuel type)
(example: fpcru = total
consumption
of crude oil)
fb
food and alcoholic
find* * * *
* * * * (fuel type) consumed by industry
fliq
liquid
fres* * * *
* **
fdsk
feedstocks
* * *
fo
veverages
fuel
* (fuel type) consumed by residential sector
* * * * (fuel type) consumed by "other" sector
foth* * * *
* * * * (fuel type) consumed by domestic sector
fpet
petroleum products, all
fref* * * *
* * * * (fuel type) consumed by refineries
fsol
solid
fuel
all fuels
f* * * 2
same as f* * * *, but from a different source
gas
gas
gman
town, or manufactured gas
gnat
natural gas
gvo, ngvo
gross value of output
gdp
gross domestic product
gnp
gross national product
i**
* *
fuel
index of * * * *
iaspric
asset price index
inex
index of private consumption expenditure
ind
industrial
inr
gross fixed capital formation, non-residential
structures
ipd
gross fixed capital formation, producer durables
- 198
(Table 7-3, continued)
-
Mnemonic
Definition
iret
retail price index
iwh
wholesale price index
lab
labor
lc
local currency
m
medical
man
manufacturing
manhop
manhours worked by operatives
mars
Marseille
me
gross fixed capital formation, machinery and
equipment
ndi
net private disposable income
nemp
number of employees
nex
private consumption expenditure
nexl
total private consumption expeintures
ngnp
gross national product
nop
number of operatives
nr
non-residential structures
nrb
new registrations, buses
nrgv
new registrations, goods vehicles
nrpc
new registrations, passenger cars
o
other
oc
other construction
oeq
other machinery and equipment
p* * *
productionof * * ** (fud type)
pcdlrv
percentage of diesel fuel used in road vehicles
pcmgrb
percentage of gasoline used in road vehicles
pcru
crude oil
pd
producer durables
199 -
(Table 7-3, continued)
Mnemoni c
Definition
-
pdl
price of diesel oil (transportation sector)
pet
all petroleum products
pk
price of capital
pl
price of labor
pmg
price of motor gas (transportation sector)
pop
population
PPPf
ppp~f
purchasing power parity: Fisher ideal
_g
German
n
native
pr
petroleum product
prag
aviation
prbc
heavy
prds
diesel
pref
refinery
prfo
fuel oil
prjf
jet fuel
prks
kerosene
prl g
LPG
prmg
motor gas
prmp
premium motor fuel
prmr
regular motor fuel
r
government bond yield
ros
Rosario
sb
stock of buses
sh
* * **
fuel
fuel oil
fuel
fuel
share of * * * *
sind* * * *
industrialprice of * * * * (fuel type)
spc
stock of passenger vehicles
sret* * * *
swh* * * *
wholesale price of * * * * (fuel type)
- 200 -
(Table 7-3, continued)
Mnemonic
Definition
tc
transportation and communication
tdl
taxation,
temp
temperature
tmg
taxation, motor gasoline
tran* * * *
* * * * (fuel type) consumed by transporation sector
traneq
transportation equipement, gross fixed
capital formation
tvb
traffic volume, buses
tvgv
traffic volume, goods vehicles
tvpc
traffic volume, passenger cars
u_pppnr
purchasing power parity, non-residential structures
u_ppppd
purchasing power parity, producer durables
va
value added
vat
value added tax
vcdl
vehicular consumption of diesel fuel
vcmg
vehicular consumption of motor gas
wags
wages and salaries
wsup
wages and supplements
xeurs
monetary conversion, eurs
xrate
exchange rate
Y_
base year * * (example: y70 = base year 1970)
diesel
fuel
- 201 -
Table 7-4
Index of Terms
-
-
mnemonic
found in archive:
Apparel
private consumption
expenditure on
a
twotime
nexa
twotime
Brown coal
colb
consumption of, see Consumption
Buses
new registrations
traffic volume
tvb
sb
trans
trans
trans
cap
ind
nr,inr
oeq,ipd
ind
Chemical feedstocks
production of
fdsk
pfdsk
second
second
Communication
private consumption expenditure on
c
nexc
twotime
twotime
Construction
con
ind
Consumption
expenditure, private, on
apparel
nex
twotime
nexa
nexeng
nexfsol
nexd
nexelec
nexf
twotime
twotime
twotime
vehicles
nrb
in use (stock)
Capital
see Price of
gross fixed capital formation
non-residential structures
producer durables
all energy
coal
durables
electricity
food
fuel oil
gas
nexfl iq
medical
other
transportation
nexm
total
nexgas
and conmmnunication
nexo
nextc
nexl
ind
twoti me
twoti me
twotime
twotime
twoti me,
twoti me
twoti me
twotinme
twotime
- 202 -
(Table 7-4, continued)
-
mnemonic
found in archive:
Consumption
of fuel * * * *
f
Consumption
by industrial sector, of fuel* * * *
find* * * *
ind
Consumption
by residential second, of fuel****
fres* * * *
twotime
Consumption
total petroleum
fprtot
second
Consumption
by transformation sector
of fuel * * * .*
tran* * * *
second
* * *
Consumption
by transportation sector,
of fuel * *
second
trans
Crude oil
pcru
pcru
second
Depreciation rate
non-residential structures
producer durables
dnr
dpd
ind
consumption of, see Consumption
percentage consumed by road vehicles
price of, see Price
pcdlrv
trans
tax on
tcl
trans
elec
twotime, ind
nexelec
twotime
nemp
nop
ind
ind
Diesel oil
Electricity
consumption of, see Consumption
prices of, see Prices
private consumption expenditure on
Employees
number in industry
number of operatives
I
-
ind
- 203 -
(Table 7-4, continued)
-
mnemonic
found in archive:
Energy
private consumption expenditure on
eng
nexeng
twotime
twotime
Exchange rates
also see Purchasing Power Parities
xrate
twotime,second
Expenditure
see Consumption, expenditure, private
on Capital
expcap
on (industrial) fuels
on labor
expfue
Feedstocks, chemical
Final internal
consumption
ind
explab
ind
fdsk
second
second
of fuel
Food and beverages
Fuel oil,
ind
1
total
light
heavy
f
twotime
prfo
prfo
prbc
twotime,second
twotime,second
second,ind
gas
gnat
twotime,ind
nrgv
sgv
trans
trans
trans
r
incI
Fuels
see Consumption, and individual
products
Gas
natural
manufactured'
Goods Vehicles
new registrations
traffic volume
vehicles (stock of)
Government bond yield
tvgv
d ern
Gross domestic product
gdp,
in( A
gnp
-
-
-
.a I -W..
-
I
- 204
-
(Table 7-4, continued)
found in archive:
mnemonic
-
Gross Fixed capital formation
non-residential structures
producer durables
nr,inr
oeq,ipd
ind
ind
Hard coal
colh
twotime,second,
ind
prbc
second,ind
Hours worked
by operatives
manhop
ind
Income,personal disposable
ndi
second,twotime
Index of ****
i ****
ind,twotime,
second
inexl
iaspric
twotime
iaspricnr
ind
iaspric_pd
ind
i ret****
iwh****
second
second
Heavy
fuel oil
Hourly wage rate
see Price of labor
of total private consumption
expenditure
asset price
non-residential structures
producer durables
of retail price of ****
of wholesale price of ****
ind
Industrial data
all in one archive
all industrial data preceded
ind
ind
si nd****
second,ind
findprbc
allfuel
ind
ind
by; ind
Industrial price of ****
Industrial sector, consumption of
heavy fuel
all fuels
-
-
-
I
- 205 -
(Table 7-4, continued)
mnemonic
found in archive:
Interest rate
government bond yield
r
ind
Jet fuel
prjf
second
prks
second
Labor
compensation of labor
expenditure on labor
number of employees in industry
supplements to wages
wags
explab
nemp
wsup
ind
ind
ind
ind
Light fuel oil
prfo
second,ind,
twotime
Liquid
fliq
twotime
Liquified petroleum gas
consumption of, see Consumption
prl g
second
Local currency
lc
second,ind,
twotime
Machinery and Equipment
gross fixed capital formation
me
me
ind
ind
Manhours
of operatives
manhop
ind
gman
twotime
consumption of, see Consumption
Kerosene
consumption of, see Consumption
Fuel
consumption of, see Consumption
LPG
see Liquified petroleum gas
Manufacture, or town gas
consumption of, see Consumption
- 206 -'
(Table 7-4, continued)
mnemonic
I
J
i
found in archive:
prmg
second
prmp
prmr
second
second
Net Personal disposable income
ndi
second,twotime
Number of employees, industry
nemp
ind
Operatives
number of
manhours worked by
nop
ind
manhop
ind
Other
private consumption expenditure on
o
twotime
nexo
twotime
Other construction
oc
ind
Passenger cars
new registrations
traffic volume
vehicles in use (stock of)
nrpc
tvpc
spc
trans
trans
trans
Petroleum Products
total consumption of, excluding
refinery loss and bunkers
fprtot
second
Population
pop
second
Premium motor fuel
consumption of, see consumption
prices of, see Prices
prmp
second
Motor
fuel
consumption of, see Consumption
premium
regular
prices of, see Prices
also, see individual products
see Consumption
- 207 -
(Table 7-4, continued)
mnemonic
found in archive:
temp
twotime,second
tvb
tvgv
tvpc
trans
trans
trans
Transformation sector
consumption of ****
tran****
second
Transportation
private consumption expenditure on
next
twotime
trans
trans
Units
local currency
lc
second, twotime
Value
va
ind
ind
Value added tax
vat
ind
Vehicles in use (stock of)
buses
goods vehicles
passenger cars
sb
sgv
spc
trans
trans
trans
ynn
second,ind,
twotime
Temperature
Traffic volume
bus
goods vehicles
passenger cars
Transportation data
all transportation sector data
preceded
by "trans'"
Added
Wages
total compensation, see Expenditure on
Labor
hours, see Prices of Labor
Year
base year nn of data
- 208 -
CHAPTER EIGHT
Conversion Factors
All of the quantity and price data is expressed in terms of tcals,
which is a measure of heat content.
In this section tcals will be defined.
Some issues which are important when using heat measurements and when
defining total consumption will be discussed.
The tables which appear
at the end of this chapter show how tcals relate to BTU's, metric
tons, and barrels, in addition to other relevant conversion factors.
Tcals
A tcal, or 109 kcals, is a precise, scientific measure of heat energy.
One kcal, or kilogram calorie is the amount of heat required to raise the
temperature of one kilogram of water at 14.5 degrees Celsius, one degree Celsius.
There are however, net tcals and gross tcals for the purpose of measuring
heat content of fuels.
The difference between net and gross values is
the amount of heat lost as steam during combustion.
For coal and oil,
there is approximately a 5% difference between net and gross measures
of heat value, for gas
approximately 9%, and no difference in electricity.
It is usually the European convention to use net tcals and the American
convention to use gross tcals.
The World Oil Project data is expressed
as gross tcals, except for the industrial data, which is in net tcals.
- 209 -
Refinery Loss:
Heat and Volumetric Measures
As part of the larger effort to analyze the world oil market, a
means of calculating all of the non-communist world oil consumption was
required.
While using the demand by product information in tcals, a total
in millions of barrels/day (crude equivalent) was desired.
Switching from
a heat measure to a volumetric measure, however, poses some problems and
certain factors must be accounted for.
When product consumption is added by heat content rather than by
volume, a 4% loss in the apparent energy content results.
While industry
sources may display balance sheets using volumetric data which indicates
a 5% refinery
loss,
is the 5% volumetric
the true loss in conversion
loss plus an additional
from crude
4% loss.
This
oil
to products
is because
the average barrel of product has about 4% less energy than a barrel of
crude.
This can be illustrated with a few examples.
A misleading error
occurs when consumption categories are added volumetrically and deemed
equivalent to that volume of crude oil, as in Figure 8-1.
1 barrel gasoline
+
1 barrel distillate
=
2 barrels crude
(Figure 8-1)
A barrel of gasoline in terms of energy content, however, is not
equivalent to a barrel of crude oil, as Table 8-1 shows.
(Next page)
- 210 -
Table
8-1
Heat Content in Tcal/bbli
Product
crude oil
.00146
distillate oil
.00147
residual
.00159
oil
kerosene
.00143
gasoline
.00132
diesel oil
.00146
Adding by energy content, it can be seen that the method used in
Figure 8-1 leads to inaccuracies.
1 barrel
+
gasoline
1 barrel distillate
=
.00132
tcals
=
.00147
tcals
.00279
tcals
crude
=
1.91 barrels of crude
(Figure 8-2)
This shows that when product demand is added by heat content rather than
by volume, the result is underestimation.
By using a 9% refinery loss
when converting from tcals to barrels, the resultant equivalent demand for
crude is correct.
- 211 -
Defining Consumption
There are several definitions of total demand involving different
accounting methods.
The consumption data on the data base, from OECD
and UN sources, categorizes by end-use
and defines consumption as
"total inland consumption."
consumption
=
production + imports -exports - bunkers + stock changes
(Figure 8-3)
This formulation excludes bunkers from maritime vessels. 2
To arrive at a
"total consumption" figure from the information in the data base, something
must be added for bunkers.
The category "stock changes" is often used to
account for statistical differences, although some more recent publications
now use a separate category for this.
Demand can also be defined as "apparent inland consumption."
consumption
=
production
+
imports
-
exports
-
bunkers
(Figure 8-4)
Changes in inventory are not accounted for in this formulation.
is used, input/output tables do not balance.
When this
With all'definitions of
demand there is room for error; fuel in transit is not explicitly
accounted for and may appear in the statistical difference, or stock
change category, or may not be accounted for at all.
These differences
- 212 -
in accounting are significant and must be compensated for in order to
compare different demand figures.
Tables
Following are tables of the conversion factors used to convert
different
units to tcals, in addition to other relevant conversion
factors.
There are variations in the actual values of fuels between
countries and different grades or qualities of fuels and products,
but the conversion factors used here are all average values.
References
1.
The Energy Index, Environment Information Center, Energy Reference
Department, New York, NY, 1973.
2.
Darmstadter,Joel, Energy in the World Economy, Johns Hopkins Press,
Baltimore,
1971.
- 213 -
Table 8-2
Conversion Factors, Gross Values
From
tons
to
tcal
To
Convert
cubic
meters
to
tcal
KWH
to
tcal
liters
to
tcal
gallons
to
tcal
barrel
to
tcal
-
Multiply by
Crude oil
.0107
*
*
Fuel oil,
.0107
*
*
residual
.16residual .0106
*
*
*
.32x10
.00146
*
4
.00892x10
3
.00147
.00851xlO
3
.00159
.00875x10 - 3
.00143
.008259x10 3
.00132
.00892xlO0 3
.00146
1 ight
Kerosene
.0112
*
*
.325x10
Motor gas
.0112
*
*
.315xlO
Diesel oil
.0110
*
*
.32x10
gas .0108
*
*
.315xlO - 4
.008254x10- 3
.00127
Jet fuel
.0112
*
*
.325x10- 4
03
.00875xlO
.00143
Naphtha
.0134
*
*
*
LNG
.0124
*
*
*
Aviation
Natural
gas
*.009xO
.~~~~~~~
3
4
4
** **
*** *
* Not applicable, or not used in WOP data base.
,
- 214 -
(Table 8-2, continued)
From
To
tons
cubic
KWH
Convert
to
tcal
meters
to
tcal
to
tcal
liters
gallons
to
tcal
barrels
to
tcal
to
tcal
*
Multiply by
*
Manufactured
.0042xlO
3
*
*
*
*
*
*
gas
Coal
.007
Coke
.0047
Electricity
*
*
*
*
.859x10
6
*
* Not applicable, or not used in WOPdata base.
**
*
- 215 -
Table 8-3
Volumetric and Calorific Conversion Factors
1012 calories
1 Tcal
1 Kcal
1 cal
1 BTU
3.968 BTU
-
.003968 BTU
252 calories
-
:
1 KWH
:
ton
calories
-
:
1 therm
1 therm
1 metric
103
:
-5
10 BTU
2.52 x 10
tcals
3412 BTU
2204
1 bs.
1000 kg.
1 barrel
42 US gallons
159 liters
1 US gallon
.833 imperial gallons
3.785 liters
1 imperial gallon
1.2 US gallons
1 liter
.2642 US gallons
1 kiloliter
6.29 barrels
Table 8-4
Conversion Fac;ors
Used ill [.le oil
a .. e
.ilctare cntnjed n thc vlumes publishedjointly hy c AmericanSocietyor Testing
uil olticiacnnvcrin, :.ntl -'i. tlb
Compiled y "Ice l'ornilcm lcnnnt.
MeusurcmentTables".
Materialsand thc nstilutc of Pcirolcum.undertihetitle "AblM/111i'etIolcusim
Linear and square mensures
I inch
Ioz
= 16.387 cubic centimetrcs
=0.0254 mctrc
=28.35 grammes
I pint
I foot
I lb
-0.453592 kilogrammc
-0.009 cwt
=0.5683 litre
=0.333 yard
- 12 inches
I litre
=0.305 m
-I 000 cubic centimetres
=61.024 cubic inches
-1.7597 pints
=0.26117 American gallon
=0.21997 Imperial gallon
-0.035314 cubic foot
I yard
-3 feet
=?6 inches
-0.914 m
I metrc (m)
= 1.094 yards
=3.281 feet
e39.37 inches
I kg
=2.20462 lb
-0.01 quintal
I cwt
=112 lb
-50.802 kg
I hectolitre
I metric ton
-0.98421 long ton
- 100 litres
I American gallon
=231 cubic inches
-3.7854 litres
-0.83268 Imperial gallon
-0.133681 cubic foot
-0.0238095 American barrel
=0.0037854 cubic metre
I Imperial gallon
-277.42 cubic inches
-0.001 km
I kilometre (kin)
I 000 mctres
-0.621 mile
I statute mile
=1 760 yards
-1.609 km
I nautical mile
=4.5461
-6080 feet
-1.15152 statute miles
I square foot
-0.093 square metro
1 square yard
=9 square feet
-0.836 square metre
1 square metre
- 1.196 square yards
-
Weights
Cubic measures
I cubic inch
10.764 square feet
I ace
-0.405 hectare
-4 840 square yards
I hectare (ha)
-0.01 square km
-2.471 acres
I square km
=0.386 square mile
-100 ha
I square mile
-2.590 square km
-640 acres
-259 ha
-1.10231
-2000 lb
itres
"Theseconversionsare basedon theassumption
thatallweightsare eights min
air. vhich is the
correctbasisfor computingbulk commercial
quantitiesof peuolcum.
=0.160544 cubic foot
-1.20094 American gallons
=0.028594 American barrel
=0.0045461 cubic metre
I cubic foot
=28.317 litres
-7.4805 American gallons
-6.2288 Imperial gallons
=0.17811 American barrel
-=0.028317cubic metre
I American barrel
Power and hcat uits
I hp (horsepower)
-550 foot pounds per second
-0.746 kilowatt
- 1.014 PS (or Cheval Vapeur)
1 PS (Pfcrdestacrke)
-9 702 cubic inchcs
-0.986 hp
-0.736 k
=158.99 litres
-42 American gallons
-34.9726 Imperial gallons
=5.6146 cubic feet
=0.15899 cubic metre
I cubic metre
-35.315 cubic feet
-1 000 litres
-264.17 American gallons
-219.97 Imperial gallons
-6.2898 American barrels
-1000 litres
-6.2898 American barrels
I gross ton (shipping)
Source:
of
-100 cubic feet or 2.83 cubic nimetres
permanently cnclosed space
I kW (kilowatt)
-I 000 watts
-1.340 hp
-1.359 PS or CV
-737 foot pounds per second
I foot pound per second
-0.00136 kilowatt
-0.00182 hp
-0.00184 PS or CV
I therm
-100000 Btu (British thermal units)
-25 200 kilocalories
=25.2 themlics
=29.3 kilowatt hours
I 000 kilocalorics (large calories)
=3 968 Bizu
-1.163 kilowatt hours
Liquefied methane
-1 thermic
I ton of liquefied mcthane
=approximately 16 barrels
-approximately
or CV (Clicval Vapcu,,)
-542 foot pounds per second
I kilolitre
Specific gravity: volume per ton
Barrels per
Spec.
Degrees
gravity
long ton
met. ton
API
7.09
6.98
0.904
25
7.02
7.13
0.898
26
7.18
7.06
0.893
27
7.10
7.22
0.887
28
7.15
7.27
0.882
29
7.19
7.31
0.876
30
7.24
7.36
0.871
31
7.28
7.40
0.865
32
7.33
7.45
0.860
33
7.37
7.49
0.855
34
7.42
7.54
0.850
35
7.46
7.58
36
0.845
7.51
7.63
0.40
37
7.55
7.67
0.835
3S
/.0
7.72
0.,30
39
7.64
7.76
0.8::5
40
0.820
7.69
7.81
41
7.73
7.85
0.816
42
"Approx figures 60'F
short tons
-2 204.6 lb
I English or ous ton
-1.01605 metric tons*
-1.12 short tons
I short ton
=0.892857 long ton'
=0.907185 metric ton*
I kilowatt hour
50 000 cubic feet (I 400
cubic mnetrcs)of natural gas, depending on mliethanei
content
Specific gravity ranges
SPwcC.
grnavity
0. 80-0.97
Crude oils
Aviation gasolines 0. 70-0.78
Motor gasolincs
0. 71-0.79
0. 78-0.84
Kcrosines
0.z;2-0.90
Gas oils
0. 82-0.92
Dicscl oils
Lut)ricating oils
0.1;5-O.9S
0. 92-0.99
Fucl oils
Asphaltic bitumens 1.00-1.10
Petroleum Economist (June 1975)
=3 411 Btu
= 1.340hp hours
=859.6 kilocalories
Colorific value of fuels
Iarrels per
metric on
8.0-6.6
9.1-8.2
9.0-".;
,Roughgross values in Btu per lb
Crude oils
Gasolines
Kcrosines
.
18 300-19 500
20 500
19 800
8.2-7.6
,Bfnzolc
7.8-7.1
Ethyl alcohol
Gas oils
Fucl oils (Bunkcr)
I 1 .)
19 20
18 300
Coal (lituminous)
LNG
10 200-14 600
22 300
7.8-6.9
7.5-6.7
6.9-6.5
6.4-5.8
18 100
- 217
-
References for Tables
The Energy Index, Environment Information Center, Energy Reference
Department,
New York,
NY, 1973.
Petroleum Economist, June 1975.
Petroleum Facts and Figures, American Petroleum Institute.
- 218 -
CHAPTER NINE
Purchasing Power Parities
.....
9.1
.
~ -
.
..
Methodological Problems in the Use of Purchasing Power Parities
There are three conceivable ways to generate international price and
expenditure comparisons:
(i)
(ii)
(iii)
at official exchange rates
at purchasing power parities
at "free market" exchange rates.
A competent discussion of the merits and demerits of various methodologies
can be found in Samuelson1 and in Chenery and Syrquin.2
Method (i),
conversion at official exchange rates, will receive no further attention
here since such rates are a priori inadmissable (due to distortions,
rigidities and controls) to deflate nominal to real values in an economically
meaningful way.
Method (iii) may prove to be of some value.
"Free market" rates between
individual countries may loosely be had for a large number of countries
(i.e., those with minimal tariff distortions and exchange controls) by
selecting pairs of countries with substantial trade balances and a timeperiod thought to qualitatively reflect "equilibrium" forces.
Alternatively,
estimates of the "effective rate of protection" can be found for those
countries
with
substantial
distortions and used to deflate to nominal
values
- 219 3
(see Schydlowsky and Syrquin ).
Such estimates may be found in Balassa
and Associates4 and in Balassa.5
But there is little formal theoretical
defense for such procedures and they, too, will receive no further
discussion.
The remaining option is to deflate nominal values by estimates of the
relative purchasing power of the various national currencies.
There are
three ways such comparisons can be made:
(i)
Implicitly:
one can take a nominal national currency
estimate of national product and base currency (such as US$) estimate
of the same national product and produce an implicit purchasing power
deflator.
Such a procedure is followed in Lluch and Powell6 and Lluch
and Williams7 .
(ii)
Explicitly, making binary comparisons.
A formula of the
form
EPAQA/zPBQA-
EPAQB/EPBQB
(Figure
with P
PPPA
=
PPP
9-1)
Q, A, and B representing price, quantity, country A and country B
respectively, gives the A and B weighted (Laspeyre and Paasche) price
index numbers, PPPA and PPPs
and Kloek and
Theil
As Samuelson1 points out, theoretically,
show empirically, there are good reasons to
prefer a Fisher "ideal" geometric mean of these two numbers as a
single index of relative purchasing power.
These binary comparisons, in turn, can be used to make multilateral
purchasing power comparisons.
While the use of a single "bridge"
country to make these comparisons guarantees a transitive international
ordering, such an ordering is not invariant with respect to changes in
the choice of "bridge" country.
It can also be argued tat,
in the case
- 220 -
of incomplete data, the binary comparison of "included" prices neglects
valuable information on "excluded" prices that could be utilized in
multilateral price level comparison.
Kravis, et.al.,9 discusses
these issues in Chapter 5.
(iii)
Explicitly, making multilateral comparisons.
Within a
regression model, a variance components strcture is used to estimate
the purchasing power parity for a single category of expenditure, as a
function of all other international price ratios.
Such a formulation
is both transitive and base-country invariant in the orderings
produced.
9
Kravis, et.al.,
have implemented such an approach.
After we have obtained base-year parities, we are faced with the problem
of constructing intertemporal conversion indices to deflate our time series
data.
To do this, note that such an exchange rate between countries A and B
equals the relative prices, in the national currency, of some given market
basket of goods, i.e.,
XAB
=
PA/PBfor some base year
(Figure
b
PA(b)/PB(b)
9-2)
and that
PA(b+t)
PA(b+t)/PB(b)
=
PA(b)
XAB.
(Figure 9-3)
Given a base-year purchasing power parity, and time series of the consumer
price indices, we can easily construct the implicit ratio
of relative
intertemporal purchasing powers in terms of the base-year numeraire.
- 221 -
9.2
Availability of Data
In all the following,
estimated PPP ratio.
listed.
B denotes
a bilateral
PPP ratio,
M a multilaterally
Countries for which the comparisons have been made are
A * denotes that both home and foreign country weighted (i.e.,
Laspeyne and Paasche) price indices have been constructed; otherwise, assume
that only the weights of the country (or author's country) of issue are
used.
"DET" indicates PPP ratios
by detailed category of consumer
expenditure are available; "INV" indicates a ratio for the gross investment
deflator has also been constructed.
1.
Implicit ratios
(N.C.U.
=
(_N.C.U. GNP )
$ GNP
national currency unit)
Table
(i)
Lluch and Powell6
Year = variable,
Thailand
generally
S. Korea
in
early '60's
(ii)
9-1
Greece
Panama
U.K.
W. Germany
Phillipines
Taiwan
Ecuador
Chile
Jamaica
S. Africa
Australia
Sweden
U.S.A.
Thailand
Greece
U.K.
W. Germany
S. Africa
Australia
Sweden
U.S.A.
Ireland
Puerto Rico
Italy
Israel
Lluch and Williams7
Year of comparison
occasionally varies
from above study
S. Korea
Taiwan
Ireland
Italy
Jamaica
Israel
- 222 -
2.
Explicitly calculated PPP series.
Table 9-2
(i)
Gilbert, et.al.,
0
B
Year
(ii)
= 1950,
DET,
INV
U.S *
France *
Denmark *
U.K. *
Norway *
Belgium *
Netherlands *
Germany *
U.S.
Japan
Italy *
Watanabe & Komiya, 1
B
Year
(iii)
=
1952,
DET, INV
Economic
Communityty, 12
Communi
Statistical Office of the European Economic
B
Year = 1955-61
Germany *
France *
Italy *
Netherlands*
(iv)
Belgium *
Saarland *
Statistical Office of the EuropeanEconomic
13
Community,1
B
Germany *
France *
Belgium *
Luxembourg *
Netherlands *
(v)
Economic Commision for Latin America,1 4
B
Year = 1960, 62, DET, INV
All 18 Latin American countries*
(vi)
Salazar-Carrillo,
5
B
Year = 1968
Argentina
Bolivia
Brazil
Chile
Co 1ombi a
Ecuador
Mexico
Paraguay
Peru
Uruguay
Venezue 1a
- 223 -
(Table 9-2, continued)
(vii)
Kravis, et.al.,9
B, M
Year = 1970, DET, INV
Colombia *
France *
Germany *
Hungary *
India
Italy
Japan
Kenya
*
*
*
*
UK *
US *
B
Year = 1967, DET, INV
Hungary *
India *
Japan *
(viii)
Kenya *
UK *
US *
Statistiches Bundesamt,16
B
Year
=
1949-73, DET
Belgium*
Denmark *
Finland *
France *
UK *
Italy *
Israel *
Poland
Greece
Yugoslavia
Spain
Czechoslovakia
Turkey
Hungary
Ethiopia
Ghana
Cameroun
Mauretania
Niger
Senegal
South Africa
Tunisia
Chad
Uganda
Argentina
Bolivia
Brazil
Chile
Costa Rica
Dominican Rep.
Guatema 1a
Colombia
Portugal
Mexico
Luxembourg *
Netherlands *
Norway *
Austria *
Sweden *
Switzerland *
USSR *
Kenya *
Rhodesia *
Tanzania
Canada *
US *
Panama
Paraguay
Peru
Uruguay
Venezuela
India
Australia *
New Zealand
- 224 -
9.3
Calculation of 1970 Base-Year Purchasing
Power Parities for Detailed
Expenditure Classes
We are currently using Fisher ideal values from the Statistiches Bundesamt
and much of Kravis' data.
For overall purchasing power parities, we used all
expenditures without rent, since rent figures are of dubious reliability.
For
detailed expenditure losses, two methods were used, depending on the availability
of detailed expenditure indices.
A.
Detailed Expenditure Class Indices Available
The final result we want is a 1970 base-year parity with the U.S. as
the base country.
(All U.S. purchasing power parities =
e.g., for England we want Pd.'70/$'70.
by definition),
The data available (see Table 9-3)
from the Statistiches Bundesamt is in the form of a purchasing power parity
with Germany as base-country and with a base-year which varies for different
countries and is usually not 1970.
The first step in arriving at a U.S.-based
purchasing power parity will be to transform the Germany-based PPP's to 1970
base-year.
An example, using Norway, which has a June 1960 PPP (see table),
follows:
DM '70/Pd. '70
=
PM June'60/Pd. June'60 x DM'60/DM June'60
Pd.-'60/Pd.June'60
x
PdM'70/PdM'60
Pd.'70/Pd.'60
(Figure 9-4)
The two expressions on the right can be obtained from
indices for the
expenditure
transportationand
classes we used--apparel,durables,
food,
communication, and other.
To obtain the precise index value for a month and year, e.g. June '60, we
simply extrapolated using the midyear point of the preceding year and the midyear
- 225 -
point of the succeeding year as base points.
1960 is presumed
to be an average
That is, an index given for
for the 12 months.
Since
one has to pick a
single point in closest approximation, this index number is presumed to be
that of June, 1960 exactly.
Thus, the index for August, 1960 would be
extrapolated 2/12ths of the way between the 1960 and the 1961 index.
Once the German-based 1970 PPP is obtained, the U.S. based PPP is simply
Pd.'70 (or any currency)/$70
(DM'70/$'70)/(DM'70/Pd.'70)
(Figure 9-5)
Since DM'70/$'70 = 3.32, this reduces to
(US-based lg7OPPP)-1 97 0 LC'70
(US-based
197PPP)-
$7
0
3
DM'2O
.3 2 /-rLC'70
(German-based
PPP)
(Figure 9-6)
B.
General Expenditure Index Available Only
In cases where indices for detailed expenditure classes were not
available, the following formula was used as the next best approximation
(ants
is used):
DM'70
Pd'70 (17
PPP for apparel)
DM'70(apparel)/DM'72(apparel)
DM'72
Pd'70(general/Pd'72(general) x Pd'72
1970 PPP
for apparel'
(Figure 9-7)
All four terms in the middle expression above are expenditure indices.
Note that all the indices for Germany were available, whereas England is used
as an example of a country for which the index for apparel was not available.
-226-
0H-
0
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- 227
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co
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C\ O~~~Y
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co
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o,,:,
oo
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0)
LO¢.
LO
tO
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0
6CC
CO cS
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0
4
Ln
to
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(A
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ecoS
(D4.JC
- 4_)C_
O_.....
,,>co]
·to
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,( a 4)
07
()~*
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=
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4( ,
- 228 -
Table 9-5
PPP = Rent
Country
Date
Unit
G
N
F
Belgium
July 1953
100 Bfr
4.89
4.89
4.89
France
Oct./Nov. '58
100 Ffr
1.29
1.59
1.43
Italy
April '52
10,000 Lr
102.52
102.52
102.52
Netherlands
Nov.
1 Hfl
1.52
1.54
1.53
United States
March '53
1 vs. $
1.17
1.17
1.17
Spain
April
100 Ptas
'60
'53
14.43
- 229 -
9.4
Computing the Relative Price in US$ of a Good in Another Country
Note
_tatwe_bave_available:
PPP defined
1,
where
currency)
basket
of all goods
in LC;
is the price of the same basket
of all goods
in US$.
PLC is the price of a market
P$
(LC = local
as PLC/P$
PPP can be interpreted
as
"the price
of 1 U.S. dollar's
worth
of all goods
in terms of LC."
2.
PPPi defined
as PLC/Pi$
where PiLC is the price of good i in LC;
P$
is the price of good i in US$.
PPPi can be interpreted as "the price of one U.S. dollar's worth of good in
in terms of LC."
Thus, to compute the relative price in US$ of good
i
in another country,
and PPP.:
venPPP.
gives the LC price of one US$ worth of i
PPP1
1/PPP
*
gives the overall purchasing power in US$ of
X units
PP
Conclusion:
*
/PPP
pPPPi/PPP
of LC,
so
gives the overall purchasing power in US$ of
the LC necessary to buy one US$ worth of i.
gives the relative price in US$ of good
US price = 1 $ US.
Foreign price = PPPi/PPP $ US.
i.
- 230 -
To compute the relative price in US$ of good
given a price series for
i
/PPP
in another country,
in LC(PiLC) and another price series for
in $ (in the US) (P ius), recall
X *
i
i
that
gives you
the real value
in US$ of X LC,
PLc/PPP
= real price in US$ of good
i
abroad;
PUS
= real
i
in US.
price
in US$ of good
so
Finally, note that all of the above computations have assumed PPP's and
price series are in a single constant year's currency units.
1.
To convert PiLC in nominal units to PiLC in constant year units,
(a)
normalize
PLC to 1 in desired
base year
t*.
Call this PLC(t*).
(b)
let PLC(t*)
= PiLC in constant year t* LC units.
Then P LC(t*)
2.
To convert
=
PiLc/PLC(t*)
a PPP (or PPPi)
from base year
*
(a)
set PPP(t2 ) = PPP(t)
to base
(l)
P~t~
LC(tl) / i(t3
PLC(t2)
b)
t
i$(t2
(tl)
Pi$(tl(t
(b)2set
PP(
i
II xFPiLC(tl)
LC(t2T Pi
set PP~i~i2)
PPP
=
t2 ,
TABL- -- 7
)1: TIlL MU'LTlI. ATLRAL ('ONiPARlSO'.S
,,LrS
sirllw ary
ui!:!at:a! rAnie14.3. Purhasing-Pcwer
Line nutner
!
I Contunpzim. lP
"! ti,'t
i tu 3J
I to 33
! o
6
7to 12
13 to 14
15to 17
Oils & fts
.8 to 20)
Fruits & vegetables
21 to 26
Coffee, tea. cocoa
27 to 29
30 to 33
Spices & sweets. suga
4 to 37
lBeverages
'83to 39
Tobacco
40( to
I
Clothing & tootwear
41) to 47
Clothing
Footw*.r
48to 51
Gross rent, uel
52o
4; .
Gross rents
52 to 53
54 to 57
F:uel & nower
i to i71
Housc turnihmnin.S. orera ion
Furniture. appliancs
58 tO 66
Suppliei & oper.ation
6? to 71
MNledical
ca:e
'2 :o 7
Transport & comir;u::i.a vons 79 to 91
Equipment
79 to 80
Operation cots
81 to 84
Purchased transport
85 to 39
n to 91
(Commll:lica.tion
92 to 103
Recreation & ducation
RP.ecreation
92 to 98
Education
99 to 103
Other expenditure
104 to 109
Personil care
104 to 106
Mcise!lanneous crvices
1q)7to 109
Capital formation
111 to 148
Construction
111 to 124
Residential
I11 to 112
Nonresidential blds.
113 to 120
Other construction
121 to 124
Producers' durables
125 to 146
Transport equipment
125 to 131
Nonelect:ical machinery
132 to 140
Electrical machinery
141 to 144
Other diur:ables
14.5to 146
(Goenment
149 to 153
Compenation
149 to 152
Commodities
153
C~si i:re.,tc prroilct
I to 153
-
72
3
4
S
6
7
8
9
10
11
12
13
ood, t'e'crage, ib..ou
Food
Bread & cereals
Meat
Frish
Milk, chcee, eggs
-
14
IS
16
!7
-
-
_l)
21
22
23
24
25
26
27
23
29
33
14
35
36
37
38
39
40
41
42
43
44
15
a6
47
4,
__.
,ote: Line numbers reer
-
:.S.
. U'r,.ie,
I.Diar, t970
(;CrColominainy,
bia Franc:e
I .R.
(peo)) (ftrar:J I
.rr
8.3
9.5
9.8
9.3
10.1
12.6
10.7
14.9
8.5
12.1
8.7
11.6
4.1
1l1.1
12.2
7.3
10.6
10.8
7.8
9.0
13.7
5.5
3.0
11.5
37.5
9.9
5.4
2.8
5.5
12.4
2.9
7.5
11.1
6.8
8.1
4.6
4.3
4.1
4.8
22.8
21.9
25.9
20.5
17.7
6.4
4.9
8.0
3.0
4 64
4.I.
5.24
5.33
5.33
6.18
3.92
6.15
4.94
7.85
6.24
3.25
4.00n
b.69
6.98
5.19
3.86
3.16
7.71
4.91
5.78
3.63
2.3'
7.25
6.23
8.62
5.66
4.52
5.39
4.92
6.59
3.80
5.84
3.27
4.51
3.96
4.71
3.77
3.19
5.04
4.86
5.17
4.43
, 1U.1India
'0:r.
3.32
7.3
-3.96
3.78
3.95
3.43
2.9S
4.61
3.94
9.70
4.36
2.50
4.41
21.2
11.3
28.0
15.1
17.3
30.0
.8.5
95.5
24.9
23.3
4
3.n)
2.86
2.43
4.87
3.21
3.77
2.40
1.53
. 5
4.8-4
4.23
6.01
3.68
9X
<'
3.31
3.36
3.37
2.7
3.85
2.37
S.)3
2.43
2.60
2.31
2.32
3.81
4.67
5.44
3.50
3.22
4.21
4.3S
3.Sa
4.63
4.48
3.11
3.38
3.16
3.14
23.9
25.0
!8.3
).5
24
6) 3
3.13
2.24
4.41
1.43
622.
645.
523.
677.
691.
3.20
5.73
-
4.07
4.26
_
3.43
6.55
4.55
13.19
~.RO
3.48
3.57
3.26
2.19
t.32
s5.1
5.23
1.83
1.63
1.64
21.5
23.3
!..3
1.4
'6.4
46.2
21.5
16.9
9.5
6.4
9.7
7.2
15.1
12.6
15.5
20.8
15.4
16.9
18.1
11.6
29.9
29.2
32.8
21.7
29.1
13.7
6.8
21.2
16.1
Italyv Japan
(rupe)
I
619.
649.
571.
1657.
786.
3.52'
8.76
3.34
2.32
-
1.71
0.61
4.71
6.35
3.44
667.
577.
591.
404.
240.
251.
4.64
3b7.
290.
853.
493.
55i
395
I-~l.
69.
643.
852.
'58.
2.31
3.74
1.91
2.74
1.69
1.69
1.85
1.54
453.
591.
1.15
0.67
2.45
2. 1
0.309
0.4 23
0.307
0.286
0.315
0.37 3
3.43
3.
-
378.
0.232
4.03
4.06
381.
(,. 34
8.40
.10.
414.
4_0.
377.
437.
'63.
293.
595.
739.
5 30.
453.
679.
' .
532.
472.
483.
0.301
3.34
474.
523.
595.
7.31
8.23
326.
283.
361.
0,333
0.5 16
0.6t4)
5.!3 1
iSi.
0.334
0.3b0
0.22?
5.41
2.46
289.1
257.
414.
4.68
4.92
3
O.331
0.277
0.457
258.
308.
191.
67.
240.
404.
4.7h
0.342
5.87
3
0.407
0.254
01.133
0.439
490.
127.
200.
195.
249.
1A.
169.
255.
150.
286.
249.
254.
328.
196.
329.
382.
291.
327.
351.
215.
166.
254.
244.
U.S.
(pound) I ul!:ir)
( I312
I .')')
".369
4.29
4.19
'6.59
5.10
2.60
7.92
7.96
430.
II.K.
316.
-
_)t 9
233.
339.
343.
238.
Kenya
~.i4
10.39
3.87
4.-22
3.49
2.53
0.519
5.46
0.288
0.220
1.00
1.00
1.00
1.00'
1.00
1.00
1.00
1.0'0
I .00
t1.03
1.00
1.00
1.00
1.00
1.00
1.00
l.00
1.01)
00
1.00
· 1.00
;;! .9'3
.,.U!
.OO
0.397
0.399
1.0)
0.236
0.2 36
1.51
3.06
4.91
0.2~1
0.269
0.28.6
2.76
5.39
4.06
0.311
0.24.
0.192
3.23
3.45
7. 7
.30')
0.257
0.395
7.19
7.16
7 78
0.572
7.30
0.350
0.252
0.356
, 07
1.1)
1I .00
.00
1.01)
!1.00
.U0
1.00
1.00
1.0
1.00
1.00
1.01)
1.00
1.00
11.00
.Ort)
11.00
.GO
1.00
1.00
1.00
1.00
.
0314
41.79
I"
3.1
0.285
( 300)
3.74
').36I
! ."0
: .0
I1.00
o the Appendix Table 14.4 and show the de ti:ed categories that are inudedc in tact, afrceation.
Source:
Kravis, et.al .
-
. -
.
- - :,.
-
-
,
_*4__
'.
TABLE9-7, continued
TIlE RESULTS OF TMI: COMPARISONS
Appendix Tab!e 14.2. Continued
Colombia
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
II- ".~O
1:
1.500
U ,; irmal hudtl'lg%
11.600
1 .701
I I.,O
12.[00
'rns-n:,;v J,m. Ulility lines
12.200
Other construction
12.300
13.000
Land mprovernent
14.110
Locomotives
Other
14.120
14.200
Pissenger cars
14.300
Trucks. huses. trailers
14.400
Aircr aft
sh ,1'i.b ir*s
14.50t
14.O00
Uthcr r.nqprt
15.100
Enrines and turbines
15.210
ractors
15.220 Other agricultural machinery
15.300
Office machinery
15.400 Metalworking machinery
15.500
Construct:oti. niining
15.600
Special industrial
15.700 General tnduitrial
15.30.:t
:'rv~e t.lu.~trfl
1o.100
iec;riCal transmnli.on
16.200
C(,imurcatlon
equipment
16.300
Other electrical
16.400
Liistrurnents
17.100
Furniture. fixtures
17.200
Other durahle oods'
ld.000 [ncruase in stocks
19.000
Lxports less imports
20.100
1.lue-cillar. uki1led
20.2;0
Llue-.1r.
<ikled
20.20
',, e-cotlar
t52
20.300
Professional
153
2 .000
Government expenditure
commodities
on
France
0.23
0.54
0.81
0.27
0.s$
0.11
0': I
1.7 5
0. 79.
2.06.
0.10
1.46
1.02
1.57
0.14
0.07
0.09
O.IS
0.00
0.03
0.69
1.14
0.I5
0.14
0.20
0.01
0.07
0.35
0.S9
0.53
0.73
0.97
0 04
0.44
0.29
O.S
.
0.29
0.29
0.24 .
1.7S
1.63
0.64
0.1
0.8o
0.16
O).4 5
0.41
0.29
0 i1.)
1.53
0. 16
2.33
I .~6
2.98
0.22
0.07
0.02
0.42
0.23
0.3S
1.00
1.16
0.56
')
I
O.91
.0.62
0.18
0.76
0.43
0.72
2.88
0.20
0.1S
0..6
1. $
t) ! ,
Italy
Japan
0.11
0.34
0.07
0.63
0.24
0.43
0.68
0.24
0.17
1 33
I .'8,
0.09
.52
2.51
1.08
1.10
O.0S
0.11
0.05
0.9
0.03
0.85
0.82
0.07
0.52
0.07
0.07
1.16
1.16
0.21
o 5
0.10
0.22
.1j71
0.17
O.1S
0.08
0.02
0.11
0.07
0.23
0.17
2.32
3.07
0.07
0.11
0.01
1.5a
1.98
O.05
0.54
0.21
0.29
0.06
0.27
0.03
0.I5
0.10
0.20
0.33
0.19
0.76
!.04
0.66
1.49
.17
.)1
0.90
0.20
0.7S
0.70
0.70
2.23
1.82
0.30
0.85
2.90
2.50
0.82
. I2
,4.
0 36
0.07
0.62
1.40
0.14
3.83
2.92
0.71
0.C04
U.o)
0.27
0.75
0.06
'1.5A
0.$o
0.10
O.Oo
0.02
0.47
2.59
0.67
1.5S
0.13
1.74
0.48
0.50
0.0')
0.75
0.30
0.33
0.27
0.32
0.66
I.S6
0.09
1.31
0.53
2.19
1.72
1.63
). I
i.0o
1.I
0.31
0.58
0.74I
0.40
4.36
1.26
1.37
2.70
0.o6
1.56
0.17
0.23
0.79
0.60
0.66
0.97
0.76
0.20
0.38
india
1.00
0.21
0.51
0.00
0.05
0.07
0.14
0.U12
Hungary
0.09
0.93
0.78
0.98
0.32
0.47
Germany,
.R.
).48
0.20
!.25
0.26
0.40
0.59
0.33
0.64
0.68
U.K.
U.S.
0).3S
0.68
0.36
0.07
0. 0 )
1.13
0.8S
1.36
0.4S
0.27
0.48
0.99
0.33
0.67
0.35
0.07
Kenya
0. I 8
O.410
0. ) 2
0 39
1.07
1.46
0.S4
0.29
0.24
0.02
0.28
0.21
0.02
0.13
0.71
0.92
0.30
0.03
0.68
0.07
0.20
1.14
0.26
0.64
0.40
0 04
0.05
0.21
0.08
0.04
0.13
0.01
0.10
0.23
0.33
0.18
0.:.
0. 5S
0.25
0.67
0.37
0.34
0.32
0.41
0.35
0.3s
0.11
0.49
0.46
0.99
0.94
0.20
0.40
0.29
0.11
0.14
0.19
0.52
2.32
1.73
.29
0.30
1.17
0.51
0.8S
2.36
0.06
0.73
0.95
0.20
0.19
0.21
0.05
0.74
0.79
2.49
0.31
1.97
OIS.
0.03
0.33
0
0.41
r.35
0.15
0.38
97
2.2,
1.64
1.25
0.57
0.60
1.15
0.37
0.39
2.91
0.90
2.77
2.0
3.3
6.0
5.8
3.3
2.8
2.2
2.7
3.4
7.0
Note: The net exprenditure of residents abroad, line
10 in the bnary tables, has been consolidated with othcr services (line 109) in the
multilatcral comparisons. Thus, there are 152 categories in this table. See page 70.
Appendix Table 14.3. Purchasing-Power Parities per U.S. Dollar. Nine Countries, and International Prices, 1970
France
(franc)
Getmany, !
unF.R.
gary
D.markl Iforint)
India
Itrpee)
Italy
(lir.)
12.0
15.4
11.4
22.2
16.4
7.1
8.7
4.3
8.9
4.80
8.32
2.65
8.65
6.53
4.30
4.97
4.42t
5.02
5.20
6.56
2.14
4.43
3.60
3.67
4.16
3.66
3.84
49.7
19.9
S.1
22.3
R.2
IS.9
40.4
13.0
45.7
4.50
3.07
:.56
8.58
3.05
10.96
2.48
4.01
2 II
539.
1.042.
321.
759.
896.
4S6.
6St.
542.t
696.
18.0
6.31
;.01
30.5
6.55
8 I.
4
9.3
13.4
4.92
-.05
4.53
4.99
17.4
8.1
14.3
17.4
15.6
8.07
3.17
3.36
5.54
6.16
4.39 r
4.28
1.06
7.93
2.77
5.06
S-35
474.
I 1.7
10.0
23.2
8.5
62.4
12.4
15.4
31.2
26.2
410.1?
438.
299.
421;
375.
348;
261.
Coloebia
(peso)
I
2
3
4
01.101
Rice
01.102
Meal, other cereals
01.103
Bread.rolls
01.104 Bficuits.
cakes
5
01.105
Cereal preparations
6
01.106
Maaroni. spaghetti, related foods
7
01.111
Fresh eel, veil
8
01.112
Fresh limb, mutton
9
01.113
1-reih pork
1 0 (1!.! 14
-re, I rtil:rv
II
01.115 uin r resm mreat
12 O.1 16
Frozen. salted mc.st
13 01.122
Iresh. frozen fish
14 01.122
Canned fish
IS 01.131
lesh milk
16 01.1 32
ktill
products
17 01.1 33
t-gg. e. products
I
23
24
.2 Il,1 2
01.15- 3
01.161
/,)tl'cr fre'.lt rruits
r¢% vec:etihjes
1-ruit other than fresh
.!.3
.I.5
22.6
7.1
28.4
5.79
3.18
4.77
2.37
3.30
3.17
A I) I
-, I75
,.,
.4 00
7 II
.-
',.
1
2.92
2.6')
8.70
2.1-'
5.18
7.5
4Q.2
676.
648.
749.
526.
643.
706.
3 4.3
2.42
6.00
o!2.
Japan
IvenI
Kenya
(shilfin!)
440.
311.
178.
314.
352.t
352.
597.
377.t
448.
4.16
4.L6
2.50
1.06
8.06
11.8S
3.41
3.30
6.01
.1 I').
I ,~.?
.; A
4
270.
299.
1,022.
37 3.
I'0 ..
J37.
. .1'
J.19t
6.21
2.79
12.64
4.29
S.17
5.64
.1 4 5i
U.K.
(pound)
17'e1r~ivonail
prtes
0.$90
0.317
O.S3
0.243
0.240
0.384
0 241
0.247
0.347
.39 I
0.324
1.00
1 39
0.55
1.21
0.90
1.05
1.10
0 93
I 60
I
1.1'
0.3-10
1.3I
0.387
0.490
0.81
1.71
0.37S 1.00
0.186 1.0 I
1.14
0.376
I ¢ )
.).:!7
I .:-,
2.'i1I
14.77
1.7')
3.59
i'.
53
~"'
,0.7
0.370
0.21IS
0.2''1
3i
!
..4
-
-
lAj
RESULTS o
TABLE9-7, continued
Till: NIULTILATERALCONIPARIS:.
49
Appendix Table 1.3. Cuntinued
Colorbia
(peso)
25
26
27
28
01.162
01.170
01.191
01.192
Vegetables other than fresh
Pot.itoes. manioc. other tubers
Coffee
Tea
29
30
01.103
0I.1S0
CJcoa
Siir
31
32
33
34
35
36
01.201
01.202
01.203
01.310
01.321
01.322
J.iil. s>r'p. honey
Chocolate. ice cream
S.dIt. spices, s.auces
Nonalcoholic beverages
Spirits
Wine, cider
37
01.323 Beer
38
39
40
41
42
01.410
01.420
02.11 0
02.121
02.122
;
"W"
-
Cigarettes
Other tobacco
Cl,,thM, naterials
'Men's .hotnins
Women's clothing
02.123
Boys'.girls' clothint
02.131
02.132
Men's, bo)s' underwear
Women's, $irls' undlerwear
4',
02.150
02. 60
Other clothin
Clothing rental. repair
48
49'
02.211
02.212
Men's footwear
Women's footwear
50
02.213
(hildren's fotwear
51
52
53
54
SS
S6
57
58
59
60
61
62
63
64
65
66
67
02.2.0
03.110
03.120
03.210
03.220
03.230
03.240
04.110
04.120
04.200
04.310
04.320
04.330
04.340
04.350
04.360
04.400
04.510
l-ootwear repairs
Gross rents
Indoor repair, upkeep
Electricity
Gas
Liquid fuels
Other fuels, ice
F:urniture. fixtures
Floor coverin_
Hrue,.ihold rexr-I,. etc.
,:fr .:raitors. etc.
Washling a'Plances
Cooking ap11iances
lle:ttinz appliances
Cleaning appliances
Other household appliances
Household utensils
Nondurable household goods
·. .520
Domestic serviaces
,
4.530
lloi.elhntJ
73
t'ou'eho i t n, iting repairs
Drugs. medic:sl preparations
Medical supplies
74
05.200
Therapeutic equipment
75
76
77
78
79
05.310
05.320
05.330
05.410
06.110
Oo.120
0o.210
06.220
06.230
06.240
06.310
Physicians' services
Dentists' ervieeServices. nurses. other personnel
Hospitals. etc.
Personal cars
Other ertonal ransport
Lires. ukes. accessories
Repatr charces
Gasoline ol. etc.
P'arking, toils, etc.
Local transport
81
82
83
84
85
86
87
88
89
,1
94
9S
06.321 IRail transport
06.322
06.32 3
06.330
06.410
, 3.420
Bus transport
Air transport
Miscelicaeous traisport
I',;tal omini:unic.i:in
clephoocv.tcie.t aph
07.110
Radio. IV phtnotraph.etc.
07.120
07.130
Major durable recreation equip.
Other recreatirn equipment
07.210
P'ublic ntert.iinment
96
07.230 ,ther
97
07.310
98
99
100
07.320
07.411
07.4 2
-
re.i-t,il. ultur.il eents
., c¢,, I.1 itan-S
Sttionery
U,,,
Te..hcrs. 1it. nd
Tleacher. clleze
27.8
6.7
I 1.1
16.6
11.9
10.2
14.3
8.91
2.01
8.67
5.72
5.40
4.77
4.75
6.79
2.88
10.88
7.53
4.98
4 54
3.31
13.9
9.45
5.88
5.4
9.4
5.99
3.00
4.51
2.87
10.3
3.98
2.48
S0.0
1.93
2.03
8.3
3.6
7.4
2.96
4.46
2.47
2.23
4.97
2.14
13.2
10.1
17.2
.39
5.32
9.81
5.24
3.02
5.00
12.9
12.7
4.84
6.16
4.23
4.98
13.3
10.0
h.2
7.7
9.13
5.14
5.17
3.44
3.94
4.56
5.18
3.19
6.9
5.84
2.90
7.3
o.4
10.6
13.2
4.47
5.00
3.02
4.02
3 0
2.55
2.42
2.49
7.3
. 7.76
9.76
5.61
4.24
5.3t
I0.5
S.8S
4.67
4.38
2.17
10.3t
6.22
3.91
11.2
6.66
5.69
Z5S.9
4.82
2.h2
64.2
9.14
5.22
16.2
36.6
65.1
6.29
10.78
7.57
4.38
5.52
4.79
45.1
7.8
7.90
4.03
4.86
2.70
10.4
1.0
5.7
18.6
18.5
9.3
10.4
3.4
4.5
0.
5.45
5.55
5.90
5.5
2.76
3.50
2.77
7.13
14.78
0.59
3.65
3.66
4.14
3.66
3.35
2.27
1.99
3.65
5.34
0.70
I.S
0.40
0.27
38.2
35.9
17.9
4.5
17.1
4.4t
2.7
3.7
4.0
8.8
6.3
7.1
2.6
24.3
23.8
24.2
6.8
5.')
10.2
10.1
1.3
6.4
6.26
6.54
5.30
7.51
11.81
3.06
3.91
3.22
5.23
6.19
4.52
4.15
5.47
10.4S
6.04
o.16
2.98
3.24
3..92
4.49
4.29
4.32
3.S3
4.15
4.29
8.02
2.43
2.95
2.62
2.63
4.42
2.79
2. 22
3.13
4.81
3.37
4.12
1.68
3.35
3. i .
3.62
2.54
7.57
6.1
10.8
-
ric
04.600
0.110
05.120
SO
-
-
Germany,
France
I.R.
(franc) (.m:nark)
13.40
Hungpry
lorint)
29.4
11 .4
109.9
45.9
45.3
-
-
31 7
17.9
29.0
14.7
24.4
26.1
15.3
17.2
8.3
17.1
S7.7
21.4
26.8
22.7
26.8
24.7
1.4
-
9.0
22.9
19.5
11.7
16.9
8.6
.1
31.3
15.9
7.91
3.34
41.9
-
6.9
19.1
23.7
25.0
53.3
29.S
11.9
43.7
32.8
24.4
23.s
28.3
9.3
-
I 3.9
10.4
11.5
21.5
3.2
2.0
1.4
1.9
3.9
62.1
-
241
14.6
18.1
28.6
5.4
9.0
11.9
1S.0
20.1t
-
25.3
12.8
7.3
37.9
26.3
2S.2
4.8
!.5
44.6
3.8
5.3
Ildia
Irupec)
Italy
(lira)
6.48
2.95
10.46
2.38
7.50
7 37
S.2u
6.10
2.23
5.41
9.95
18.49
9.97
8.10
2.63
7.27
2.95
3.30
2.46
1.78
S.53
6.73
1.19
3.52
3.40
2.68
3.2:
.1.38
0.87
7.39
722
12.97
2.89
3.19
0.49
2,39
9.t95
7.50
6.02
4.99
7.50
4.09
3.98
979.
394.
1,553.
957.
1.144.
3.;
n.
5 30.
1.137.
444.
S17.
396.
304.
408.
676.
559.
747.
505.
859.
461.
611.
769.
453.
254.
566.
484.
488.
I.1.
292.
.228.
761.
1,359.
644.
739.
469.
706.
744.
417.
591.
436.
1,099.
897.
769.
369.
Kenya
Jpain
{(hflvyen) ling)
InterU.K. ntional
(pound) pricer
SOS.
283.
709.
236.
312.t
508.
367.
512.
159.
328.
266.
292.
332.
211.
157.t
410.
243.
235.
268.
264.
255.
191.
12.t
195.
202..
177.
12.
261.
184.
303.
1,039.
414.
192.
239.
313.
323.
59
285.
21I.
566.
427.
312.
212.
5.13
2.02
7.82
2.54
8.70
i.44
3.37
18.05
5.22
12.59
6.51
13.21
8.21
5.14
4.59
7.71
4.6-4
9.46
3.09
8.85
4.39
4.14
3.02
2.99
1.73
1.89
4.01
4.94
3.59
9.34
11.33
17.14
I.8u
3.71
8.71
't.i
,.11
i.10.31
12.14T
11.1'
14.35?
13.26t
13.71
3.59
0.348
0.201
0.369
0.159)
0.293
(. 291
0.1 )3
0.349
0.343
0.345
0.418
0.S96
0.571
0.638
0.658
0.380
0.313
0.441
0.299
0.432
0.350
0.245
0.320
0.237
0.242
0.195
0.220
0.264
0.348
0.338
0.323
0.413
0.2:)2
.0.530
0.) 2
t
0..33!
0.435
0.357
0.814
0.823
0.643
0.376
2.20
0.69
2.50
0.39
1.49
I .)2
13
1.77
0.93
1.18
1.i2
0.70
1.27 .
1.12
1.12
2.09
1.13
1.46
1.15
1.36
1.47
1.02
0.65
1.05
1.01
0.89
. 36
0.84
'0.78
1.29
1.69
1.61
0.91
1.07
0.73
1.34
I.i
1.60
1.14
2.13
2.02
1.59
1.05S
4.39
549.
266.
6.66
0.278
1.30
0.24
1.10
0.61bit
2.44
2.32
1.06
625.
526.
396.
152.
432.
136.
21.
178.'
140.
194.
69.
0.67
3.72
1.70
3.55
4.25
1.90
0.185
0.2 42
0.138
0.1i2
0.211
0.17!
0 17
0.3
. .;1
0.79
0.86
0.63
0.23
0.04
424.
492.
50.
35.
I.53
2.86
0.178
0.273
0.60
1.17
31.
34.
363.
.o7.
279.
347.
590.
273.
58.
117.
147.
237.
96.
257.
192.
198.
198.
2(:6.
362.
153.
0.40
1.00
10.57
10.01
9.77 .
1.36
7.31
2.04
1.97
3.15
2.66
11.98
2.26
4.96
3.1n
0I.51
11.57
7.2 3
2.0.
2.5
0.113
0.092.
0.520
0 08
.216
0.164
0.587
0.298
0.223
0.283
0.233
0.117
0.720
0130
U 17
0.560
0.444
0.408
0.130
1
0.25
0.22
1.63
I. t
1.13
0.95
1.82'
0.71
O.45
0.69
0.87
1.46
0.36
1) 7
0.74
7.12t
5.63
2.34
1.0!
61.
43.
652.
626.
436.
487.
1,642.
231.
333.
255.
343.
619.
340.
519.
372.
91.
621.
653.
459.
380o.
1.55
0.17
429.
267.
0.45
0.22
1.93
6.3.1
7.'6
1.10
7.S7
1.341
1.42
2.19
2.10
7.22
1.61
1.97
1.o9
7.12
' :,6
'
(.2
625.
505.
5. 1q.
...
;.
92.
I .1.
I1.30
0.65
,
I
i
, O
i'.t .
-
hl
1.28
:;
I 3.74
).70,
3S .'
l
.
TABLE9-7, continued
TIE RESULI.S Of: TIE COMPARISONS
250
'
Appendix Table I 4.3.
ontinued
I!
112
113
114
!IS
116
117
118
119
120
10.100
10.200
11.100
11.200
11.300
11.400
i I.500
11.600
11.700
11.800
Frducatinal fIJcilities
upples
E-Jual.nia
Other euL.tl.In expenditures
13Bjrler. eauty shops
Tlet :rties
goods
Othilr ersonlcare
cafes
Rer.urnt,.
H- fels. e:c.
Other 'ervices
I- and :-dweilin4 buildings
MultuIcd-.ellingbuildings
litels. etc.
Indutrial buildings
Cormr.ercul baildings
Office bili ns
Edu.cational buildings
HoFiltai lbuildings
Az.ricultural buildings
Other buildirics
121
122
123
124
125
126
127
128
129
130
12.100
12.200
12.300
13.000
14.1 o
14.120
14.200
14.300
14.400
I .,:0
Roz. h:hways
utility lines
Tr:nmssion.
Other construction
Lind iproement
Lnmotives
Other
Passenger cars
Trucks. eutes. trailers
Aircraft
.
hi
3.31
14.600
Uir.r tr;:mrort
132
15.100
15.210
15.2 20
Ehr.gnes 3arl turbines
07.420
101
102 07.431
103
07.432
08.100
104
105 08.210
08.220
106
08.310
107
08.320
108
S.400
109
I
13
134
135
136
137
138
139
laO
I
! '°
I&1
142
143
144
145
146
147
148
149
ISO
151
152
-
.1 I
i 7,J
4.8
.
21.8
Special industrial
General industrial
Service ;n.!u,:ral
-
[.lec:rlJl tranemission
Coimmunicatiun eqluipment
Other electrical
Other durablte goods
Increase n stocks
Exports less imports
Ulue.collar. unskilled
Bl'.:e -c,
re
20.300
%:h fte-collar
i'r. fe¢sional
z I.UUUI
~o~¥Ilerntne
ll d
CxpxnulalI,
-
on;11
18.3
16.7
14.0
18.6
2.3
3.0
3.0
6.4
1.99
3.41
0.89
1.32
4.95
4.50t
5.61
4.57t
3.35
h
35.:.,01- I6.o+
' 32.1.4,
..
-
3.59
9.13
390
3.68
2.74
5.66
3.38
2.57
-
3.60
5.46
3.60
4.23
4.24
-
1.A
6.61
5.26
S.55
3.23
3.12
30.7
23.0
45.1.
29.0
36.5
34.3
3.41
4.89
I 1.lt
24.9
Instruments
Furniture. ixtures
-
29.9
36.5
32.3
23.7
17.0
10.7
10.6
11.7
62.2
31.6'
69.5
27.8
31.1*
2.03
5.38
S.S7
3.04
7.89
13.9
32.1
5.02
2.70
3.53
1.33
S.52
4.09
3.15
3.79
3.60
2.06t
4.35
2.53'
4.89
44.0
24.1
1.1
23.2
3.4
11.7
'8.9
19.7
16.8
0.4
17.0
15.7
16.8
24.5
19.6
12.4
14.1
11.7
21.8
21.4
2'.10
.4'
19.5
32.3
1.71
3.0
4.04
2.11
1.39
2.44
1.65
4.92
.9 I
6
tforllt
2.05
3.76
4.'95
2..0
2.47
2.95
3.54
1.83
3.30
8.93
4.8St
4.65
irunpce} tIhra)
.D.ni.rkl
403
4.93t
3S.6
Construrion. mining
-
.
23.1
Trictors
1S.4,00
15.500
15.600
15.700
15..30(
16.100
16.200
16.300
16.400
17.100
17.200
13.000
19.000
20.100
_20.w10
20.224)
15.1
9.4
10.5S
8.1
4.6
4.5
3.9
3.8
6.4
4.3
2.8
2.8
3.6t
5.6t
4.4t
.. 3
5.5
4.3
4.4
16.58
13.6t
35.0
31.6
Other arculcural machinery
Office inachinery
,:kinzmachinery
'.:etJl-
I .30)
5.65
3.94
5.39
'2.97
5.34
8.39
3.93
3.72
3.61
5.85
3.93
1.97
4.04
2.47
3.45
6.45
3.341
6.00
4.10t
9.3
10.7
12.7
27.8
6.80
-
2.3.
3.66
2.96
2.89
3.71
4.67
3.98
3.66
2.75
-
4.00
4.03
4.63
Itly
Indi.
1: R. ,-ary
France
(tfranc)
bin
(rs¢)
2.4.9
2.39
3.56
3.16
37.3
22.1
15.3
27.1
25.6
23.9
51.2
23.4
30.0
4.4
-
5.0'
5.3
5.9
21.2
0
690.
507.
595.
280.
694.
692.
520.
451.
'1.76
3o14
1.68
1.60
1.70
2.31
1.64t
1.55
1.72
1.34
2.51t
1.99+
506.
302.
229.
413.
257.
327.
460.
333.t
542.
1.61
1.631.51
1.10
11.74+
6.80
S.81
901
S.1'
3
3.4 6 "
244.
4019.
83.
309.
986.
739.t
73.
4aIR.'r
51.
661.
4.+C
ne:-
Kenya
!
J;pin
t(hil-
U.K.
Iyen)
hi.
Itun-
:,ny.
Coluom-.
prict
(pjUnltd)
7.09
5.81
.4.97
2.3,
12. 31
3.76
2.56
4.01
14
3.27
3.61
4.36
4.48
3.59
2.59
2.63
3.19
2.38
4 044
35.
322.
331.
! l5
307.
194.
130.
267.
1 .
256.
247.
290.
435.
426.
284.
206.
181.
765.
13.
nflit,'
3.95
291.
7.05
154.
8.96
188.
3.59
149.
6.84
549+
9.46
334.+
S.15
374.
6.99
390.6.761374.7 - . - 5.4
267.
0.417
0.154
0.330
0.133
0.351
0.279
0. 37
0.2Si
0. _7
0.1 38
0.199
0.399
0.379
0.339'
0.253
0.306
0.242
0.245
0 373
13
0.95
1.33
0.53
I 2
IJ
1,0
).!7
,
,
0).91
0.
O.h)
1. )6
0,73
0.72
0.94
0.74
1.19)
I.q
0.249
0.209
0.324
0.336
0.300
0.560t
0.670
0.77
0.78
0.70
0.61
2.35,
1.72
1.59
1.61
1.46
0.92
0569t
0.553
.335
U
0.Sa;
4:I !~~~~~~~
4.57.'~~~~~~~~
62},.
28
557.
6.81
2,38 .'
0.32 2
·f%;J
J
5.14 6.77
6.31
3.32
J 20,
5.83
7.53
7.63
i I.
'563.
670.
327.
881.
521.
4 2.
523.
9.0UK
401.
.08t
5.7ft
591.
272.
.35 I.
3.38
6.30
577.
568.
.195.
314.'
9.68t
736.
3.45
7.50
0.34
0 37
.3d
1.21
2.4S
606.
625.
346.
345.
391.
492.
472.
v
I.5
22.U
-1. -~
301.-
303.:
410.
303.
217.
792.
"02.'`
4.48
9.92
0.23
-
4.19
1.19
0.540
0.21
O.164
1.12
0.3 10 r
7.14
0.48
13
1418
1 .() i
4.26
1.73
1.34
1.5 3
1.12p1
1. 3
1.43
5.44
11.93
5.5 1
326.
360.
142.
0.357
0.366
0.314
0. 3 3 t
5.91
6.S65
leas
1.35
1.38
1.3
1.11
0.43 I
1 1.09
4.89 t
7.28
370.t
IS.
0.3~8
7.54
47.+
124.
243.'
254.
9.56
9.08
6.64
50.
. .
I
1.00
1."2
0.199
0.442'I
0.381
0.417
1.65
1.3
1.50
0.23
0.153
0.1. 35
0.201
0.274
0.309
-
0.51
0.33
1.06
comm)oudities
of residents abroad, line 110 in e inary tables. has been consolidated with other services tine 109) in the
Note: The net ex'ndtuure
multilateral comparisons. Thus there are 15 2 cateories n this table. See plage 70.
een estimated by applying the doutble-weighted CPD
tcre
rnssine hae
tThe PPPs or these items where price and quantity data
n;methodand are considered less reliable than direct esuinates. he corresponding quantity estunates in Table 14.5 share the same
weakncss.
Appendix Table 14.4. Quantities per Capita with U.S.=I 00, 1970
Colombia
01.101
J
-)
.3
6
2
' ,.
.; I.:i
3
Lr.,.'. . '.-.,;-
I .I 4
01.105
01.106
432.0
Rice
t ,I.If,
.43.9
2. .7
s , :; ..,.: c-e.re.ti,
. i:kcs
i. -t
Cereal pr- r itmns
sphiettil.
M\acaroni.
related (()ds
3.8
12.3
146.8
France
,6.2
'.1.5
-i3 5.4
81.0
3.5
216.7
Germany,
F.R.
62.4
9.3
306.8
55.9
'8.1
188.6
Hun
gary
India
69.2
1.7 2 7.2
129.
3 0.6
21.9
113.7
II.S
~1
-I
0.7
0.2
1.6
2.1
Italy
Japan
I . I
2.. 4o . 5
1 .3.
25.4
39.6
1.8
9)80.2
54.3
108..1
I tS. 4
6.2
287.4
Kenya
U.K.
U.S.
1.
.I;,q
7'
'.24..?
0.6
I 31.4
1.2
0.7
56.5
43.1
111.6
4 3.
1'. I
1!:.)
100.1
I )OO.
Iso0.
- 235 -
References
1.
Samuelson, "Analytical Notes on International Real Income Measures,"
Economic Journal, September 1974.
2.
Chenery and Syrquin, Patterns of Development, 1950-1970, 1975.
3.
Schydlowsky and Syrquin, "The Estimation of CES Production Functions and
Neutral
Efficiency
Levels Using Effective Rates of Protection as
Price Deflators,"
Review of Economics and Statistics, February 1972.
4.
Balassa
and Associates, The Structure of Protection in Developing Countries,
1971.
5.
Balassa, "Tarrif
Journal
Protection in Industrial
of Political
Countries; An Evaluation,"
Economy, December 1965.
6.
Lluch and Powell, "International Comparisons of Expenditure Patterns,"
European Economic Review, 1975.
7.
Lluch and Williams, "Cross Country Demand and Savings Patterns: An
Application of the Extended Linear Expenditure System," Review of
Economics and Statistics, August, 1975.
8.
Kloek and Theii, "International Comparisons of
Consumer," Econometrics, July 1965.
9.
Kravis, et al., A System of International Comparisons of Gross Product
and Purchasing Power, 1975.
10.
Prices and Quantities
Gilbert and Associates, Comparative National Products and Price Levels, 1958.
Gilbert and Kravis, An International Comparison of National Products
and the Purchasing Power of Currencies, 1954.
11.
Watanabe and Komiya, "Findings from Price Comparison; Principally Japan
vs. the United States," Weltiwirtschaftliches Archiv, October 1958.
12.
Statistical Office of the European Economic Community, "Nota Statistica,"
General
Statistics,
Nos. 3, 7, 8, 1962.
13.
Statistical Office of the European Economic Community, Statistiche Studien
und Erhebungen, No. 3, 1972, p. 63.
14.
ECLA, "A Measurement of Price Levels and the Purchasing Power of Currencies
in Latin America,
1960-62," Economic Bulletin
for
Latin
America,
October 1963.
ECLA, "The Measurement of Latin American Real Income in U.S. Dollars."
Economic Bulletin for Latin America, No. 2, 1968.
- 236 -
15.
Salazar-Carrillo, "Price, Purchasing Power and Real Product Comparisons
in Latin America," Review of Income and Wealth, March, 1973.
16.
Statistiches Bundesamt, "Internationaler ergleich der Preise fur die
Lebenshaltung," Preise Lohne Wirtschaftsrechsnungen, Reihe 10,
various years.
- 237 -
CHAPTER TEN
Accessing the Data Base
10.1
Access
to TROLL
As stated elsewhere in this document, all of the data is stored at the
Information Processing Service at MIT
on TROLL, a software package which
provides a comprehensive environment for creating, estimating and simulating
economic models.
Any profit or non-profit organization can obtain a
computer account at IPS and access it via Telenet, which allows the user to
make a local phone call to connect with IPS.
TROLL, contact Charlie Byron at IPS (address:
253-8421).
For more information on
39-485 MIT; telephone:
Special arrangements must be made with the World Oil Project
at the Energy Lab at
IT to obtain the privilege to 'read' the project's
account at IPS.
10.2
Accessing on Tape
Most users in the past have obtained copies of the data base on
magnetic tape.
involved
There is a charge for the computer time and other costs
in making
the
tape.
The tape can be formatted to be compatible
with TROLL,or put on the tape in a general or fortran
type of format.
-
- 23
The specifications of a TROLL format type of tape are:
9 track
density = 1600 BPI
fixed block record format
logical
record
length
= 80
block size = 20,000
The specifications of a standard format type of tape are:
9 track
density = 1600 BPI
fixed block record format
= 80
logical record length
block size = 1600
For information on obtaining a copy of the tape, contact Jackie Carson at the
MIT Energy Laboratory, Building E40-132, telephone 253-1443.
10.3 TROLL
Two
Data Files
types of files are utilized
the one-dimensional data file.
TROLL format.
In includes the file name, comment section, and the data.
TROLL COM!tAND: prtdata
SECOND ARC, FPRM1
AT&
QUANTITY OF
TCALS
1950
1954
19 8
1962
1966
1970
1974
-
FROM
the data
arg_fprmg;
DATE RVISED:
1950
is "not available".
TO
3/14/77
1974
4MOTOR
AND AVIATIOtN GASOLINE
207
CONV
18312.
18692.8
22635.2
32054 .4
39648.
RO{
MHMET
TOt{S
17001.6
19 R 91.
2
19409.6
30329.6
39715.2
48070.4
44968.
470/40.
TROLL CO't)IAND:
Most common is
Below is a copy of that kind of data file, in
'NA' is used to fill a year when
ANNUAL
for this database.
.
(Figure
10-1)
BY:
*11200
17494.4
19230.4
22601.6
3236 q .
40376.
49795.2
17505.6
22097.6
27563.2
37654.4
44329.6
55059.2
- 239 -
Data is sometimes stored in a matrix format which is created from data
files with a special command.
A matrix has two dimensions, rows and columns,
the rows being the years, the columns headed by countries.
The countries
for the columns are always ordered alphabetically, from left to right.
(See next page, Figure 10-2).
Matrices can have accompanying label files
which are created by the user to label the rows and columns, but these
have not been used in Figure 10-2.
Both data files and matrices are used in the data base, the majority
of data stored as the single dimension data file.
Matrices are easy to
identify from data files, because in the list of data file names they
are the only names not preceded by a country or region, i.e., "arg_fprmg"
(arg for Argentina) for final consumption of motor gas and "pl" for
price of labor, for all countries in the industrial section.
The print commands are included here just to show the different
This is not an exhaustive list of data storage
types of data files used.
possibilities on TROLL.
text.
No other commands have been explained in this
For complete information on data manipulation and data editing on
TROLL, consult the TROLL Primer or manual.
240 -
Figure
T OLL C('i'IA';'):
pftnt
.8
(
10-2
);
PL
RO'J
¢,Cl'JNN 1
I
$
2
1
';A
!1&
4A
ttA
NA
YA
'IA
';A
4A
A
N.
4A
NA
IqA
NA
NA
N1 A
NA
N.4A
7
NA
It
NA
9
NiA
A
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0.350638
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