Analytical Studies Branch Research Paper Series Accounting for Natural Capital in Productivity of the Mining and Oil and Gas SectorAnalytical Studies Branch Research Paper Series
Accounting for Natural Capital in Productivity of the Mining and Oil and Gas Sector
Information identified as archived is provided for reference, research or recordkeeping purposes. It is not subject to the Government of Canada Web Standards and has not been altered or updated since it was archived. Please "contact us" to request a format other than those available.
This paper presents a growth accounting
framework in which subsoil mineral and energy resources are recognized as
natural capital input into the production process. It is the first study of its
kind in Canada. Firstly, the income attributable to subsoil resources, or
resource rent, is estimated as a surplus value after all extraction costs and
normal returns on produced capital have been accounted for. The value of a
resource reserve is then estimated as the present value of the future resource
rents generated from the efficient extraction of the reserve. Lastly, with
extraction as the observed service flows of natural capital, multifactor
productivity (MFP) growth and the other sources of economic growth can be
reassessed by updating the income shares of all inputs, and then, by estimating
the contribution to growth coming from changes in the value of natural capital
input.
This framework is then applied to the
Canadian oil and gas extraction sector. The empirical results show that, in
Canada, adding subsoil resources into production as natural capital reduces the
negative MFP growth over the study period. Overall, by including subsoil
resources, MFP declines by 1.5% per year over the 1981-to-2009 period, compared
to a 2.2% decline without including these resources. During the same period,
the real value-added growth in this industry was 2.3% per year, of which about
0.3 percentage points or 15% comes from natural capital.
This paper presents a growth accounting
framework in which subsoil mineral and energy resources are recognized as
natural capital input into production; as such, income attributable to natural
capital and value of subsoil resource reserves are estimated, and multifactor
productivity (MFP) growth and the sources of economic growth are reassessed. It
is the first study of its kind in Canada.
In the paper, income attributable to natural
capital, or the resource rent, is first estimated. The resource rent is defined
as a surplus value after all extraction costs and normal returns on produced
capital have been accounted for. For the calculation of the resource rent, a
rate of return on produced capital needs to be used to estimate the value of
services derived from natural capital in the production process. This paper
uses the long-term average of the internal rate of return on produced capital
in the non-mining business industries as a whole to calculate the normal
returns on produced capital in a mining industry. This is derived from the
internal rate of return taken from the Canadian Productivity Accounts. By doing
so, the growth accounting framework used herein remains consistent with the
remainder of the MFP estimates in other industries, in that it makes use of the
internal rates of return throughout to assess the cost of capital and uses what
might be called an endogenous approach based on available data on rates of
return. In this system, the surplus profits are zero in all business
industries.
The measured resource rents can then be
used for the estimation of resource reserve values using an income approach.
Specifically, the value of a resource reserve is calculated as the sum of the
present value of expected future resource rents generated from extracting the
reserve. A discount rate needs to be chosen for this purpose. This paper adopts
Hotelling’s rule in this regard. Hotelling’s rule predicts that, along the
efficient (optimal) extraction path, the shadow price of a resource reserve
grows at the rate of nominal interest rate on a numeraire asset. Using
Hotelling’s rule, the value of a resource reserve is simply the product of the
present resource rent, and the reserve life calculated using the present
extraction amount.
The physical extraction of a resource
reserve is used as the natural capital input in the extraction of this
resource. The asset-level natural capital inputs are then aggregated into an
industry-level measure. Given the resource rent and natural capital input, the
industry-level MFP growth and the sources of real value-added growth can then
be estimated. The impact of adding natural capital into the production process
on the MFP growth would be positive (negative) if the natural capital input
grows at a slower (faster) pace than produced capital. Also, the impact of
these changes becomes larger (smaller) when the income share of resource rent
is higher (lower).
This growth accounting framework is applied
to the Canadian oil and gas extraction industry. The empirical results show
that, in Canada, adding subsoil resources into production as natural capital
reduces the negative MFP growth over the study period. Overall, by including
subsoil resources, MFP declines by 1.5% per year over the 1981 to 2009 period,
compared to a 2.2% decline without including these resources. During the same
period, the real value-added growth in this industry was 2.3% per year, of
which about 0.3 percentage points or 15% comes from natural capital.
1 Introduction
This paper has two objectives. The first is
to estimate the resource rent generated through the extraction of subsoil
mineral and energy resources, as well as the associated monetary value of
resource reservesNote 1 in Canadian mining industries
what is referred to here as the value of natural capital. The second
is to treat subsoil resources themselves as a factor input in resource
extractions. This is done by estimating the flow of services derived from
natural capital input, and adding it to the value of labour and produced
capital inputs in the standard multifactor productivity (MFP) estimating
equation. This produces a measure of MFP growth that is more complete, and
provides an estimate of the significance of subsoil resources as a source of
economic and productivity growth in the Canadian mineral and energy resource
sector.
Subsoil mineral and energy resources are
treated as non-produced and non-financial assets in the System of National
Accounts (SNA). To be consistent, exploration and development expenditures are
capitalized as produced capital assets in SNA. Therefore, the value of subsoil
resources as non-produced assets reflects only the value of resource scarcity.
The present Canadian Productivity Accounts (CPA)
calculate MFP growth as the difference between the growth in output and a
weighted average growth of all inputs
one of which is the capital derived from investments in fixed
assets. The fixed assets included in the accounts for the mining, oil and gas
industries include investments in machinery and equipment, structures, and
engineering assets such as mine shafts, as well as exploration and development
expenditures. Natural capital
the value of the resources
is not included.
This paper offers a way in which this can be
done and provides estimates of MFP growth when the cost of using natural
capital is included. Specifically, the resource rent of subsoil assets is
calculated as a surplus value after all extraction costs and normal returns on
produced capital have been accounted for. The value of a resource reserve is
then set equal to the sum of the present value of expected future resource rent
flows generated from extracting the resource over its reserve life.
This treatment is akin to recognizing that
the value of all the produced capital employed in the mineral industries is not
equal to the cost of investments. Normally, it is assumed that well-functioning
markets will bring the cost of capital and its value into equilibrium
the present value of the stream of earnings that are produced by it.
But, on occasion, this will not occur because of the scarcity of assets or
imperfections in markets. When that occurs, capital in excess of that derived
from the costs of investment is employed in the industry. And that is regarded
as the case, particularly in the resource sector where endowments cannot be
changed by human activity
or at least not in the short run.
Two important parameters are required for
valuing subsoil resources. One is the rate of return on produced capital that
will be used for calculating the resource rent, and the other is the nominal
discount rate that will be used for the net present value (NPV) of a resource
reserve. The System of Environmental-Economic Accounting (SEEA) (United Nations
et al. 2014, page 145) recommends that the rate of return on produced capital
and the discount rate should be equal and suggests using an economy-wide
interest rate, derived from returns on government bonds, as the rate of return
that should be used on produced capital, as well as the nominal discount rate.
This is akin to choosing an arbitrary exogenous rate of return for estimating
the value of produced capital services in the MFP estimation process
a practice that Statistics Canada does not follow in its
productivity accounts for two reasons. The rate of return that is required is
the rate that the capital markets would require to cover the cost of capital.
Using a government bond rate involves understating the cost of business-sector
capital, since it involves greater risk. Secondly, its use generates estimates
of surplus that are earned above requirements of capital markets that are
difficult to interpret. This method leaves values of surplus across non-resource
industries that, to be consistent with the approach adopted here, should also
be incorporated into the Multifactor Productivity Program.
This paper uses an assumption that is in
accord with the practice used in the CPA. The CPA calculate the internal rate
of return on produced capital from the estimates of surplus and produced
capital stock at an industry level. This paper assumes that, over a long term,
on average, produced capital earns the same rate of return in both the mining
industry and non-mining business industries as a whole.Note 2 The internal rate of return on produced capital for the non-mining
business industries as a whole can then be used in calculating the cost of
capital services for produced capital in the mining industry. In turn, the
resource rent in a mining industry can be calculated as the residual of the
surplus, estimated from the SNA, minus the produced capital services used in
this industry. This approach is consistent with that followed in the CPA, and
profit remains zero for all industries except those using natural capital.
Once the resource rent as the surplus is
derived, an estimate of the value of natural capital that is the source of this
surplus is derived from calculating the NPV of these surpluses. This is
calculated using the estimates of resource reserves to estimate the years of
remaining life at present extraction rates, and then calculating the NPV of the
surplus. The crucial parameter that is required for this analysis is the
discount rate.
This paper adopts Hotelling’s rule as the
principle in the calculation of the NPV of subsoil resource reserves. Hotelling’s
rule defines the optimal extraction path of non-renewable natural resources,
and predicts that the net price (unit resource rent) of a non-renewable natural
resource is expected to increase at the rate of nominal interest that would be
earned by an appropriate asset.Note 3 Under Hotelling’s rule, the real discount rate becomes zero and the
corresponding NPV of a subsoil resource reserve would reflect its value to a
society, if the source reserve is efficiently extracted.
Alternate choices for the discount rate
have been suggested. For example, the SEEA (United Nations et al. 2014) assumes
that the unit resource rent is expected to increase at the rate of general
inflation. Under this assumption, the real discount rate used would equal the
real rate of interest. In this case, the value of a resource reserve would be
much smaller than that calculated using Hotelling’s rule. This paper also
provides an estimate of the value of natural capital that makes use of this
assumption for the purposes of comparison.
The rest of the paper is organized as
follows. Section 2 develops a framework for accounting for subsoil resources in
production and wealth accumulation. Section 3 presents the empirical results
for the Canadian mining industries, and Section 4 concludes.
2 Framework for accounting for subsoil resources
To isolate their contribution in
production, subsoil resources are treated as a distinct factor of production in
the same manner as labour and produced capital. Kendrick (1976) recommended
that capital measures include machinery and equipment, structures, land,
inventories and natural-resource capital. Following the recommendation, a
Hicksian neutral production function of subsoil resource extraction can be
written as
where the output (
) is value-added based and a
function of labour input (
), produced capital input (
), and natural capital input
(
), are augmented by
productivity (
). For the production
function to be well-defined, it is assumed that the marginal products of each
factor are increasing (
,
,
) at a decreasing rate (
,
,
), and that all cross-marginal products are increasing (
,
,
,
).
Equation (1) can be applied for the
extraction of single or multiple subsoil resources. Logarithmically
differentiating (1) yields
where
,
and
denote the elasticities of output with respect
to labour, produced capital and natural capital, respectively. These
elasticities are not observable, but can be derived by imposing the
optimization conditions such that, for each factor of input, the value of its
marginal products and its user costs are the same. Under the assumption of
perfect competition, and given output price (
) and factor input prices (
), the output elasticities
can be measured as
Income and expenditure in extraction can be
equated under the assumption of constant returns to scale, i.e.,
where the labour cost is equal to the hours
worked (
) multiplied by the nominal
wage rate
(
); the cost of produced
capital is equal to its nominal stock value (
) multiplied by the unit user
cost of produced capital (
); and the user cost of
natural capital is equal to its nominal stock value (
) multiplied by the resource
rent parameter (
). Equations (3) and (4) show
that the output elasticities in (2) can be replaced with the corresponding
factor shares (
,
, and
) in the total value-added,
i.e.,
To use Equation (5) for growth accounting,
the growth of natural capital input and the resource rent associated with the
use of natural capital need to be estimated.
2.1 Measuring
resource rent
In this paper, the resource rent of natural
capital is derived by using a residual value method.Note 4 From Equation (4), the resource rent (
) generated from extracting a
subsoil resource is calculated residually as
The data required for calculating the
resource rent, generated from single subsoil resource extraction, comprise the
corresponding gross operating surplus (
) calculated as nominal
value-added net of labour cost, nominal value of produced capital stock, and
the unit user cost of produced capital.
The unit user cost of produced capital,
which is equal to the sum of a rate of return on and a rate of depreciation of
produced capital, needs to be exogenous to the mining industries in order to
calculate the resource rent residually. There is no consensus in the literature
on the choice of the exogenous rate of return on produced capital.Note 5 The borrowing cost is
one proposal. The borrowing cost in financial markets
generally reflects the compensation to lenders for the provision of funds and
the risk of loans not being returned. For example, a risk-free rate (the
internal reference rate between banks), plus a risk premium of 1.5%, is used as
the exogenous rate of return on produced capital, in the Dutch national
accounts, for the calculation of resource rent in mining (Veldhuizen et al.
2012). Another example is the
approach proposed in a cross-country study by Brandt, Schreyer and Zipperer
(2013) for the Organisation for Economic Co-operation and Development, in which
average extraction costs across countries are used to derive exogenously the
resource rent of natural capital. Baldwin and Gu (2007)
used a weighted average of the actual long-term debt costs, and the equity rate
of return earned in Canada, for the purpose of examining how this approach
compares to the endogenous estimate, when deriving capital services and MFP
growth in Canada. They find that the two are relatively similar for Canada.
There are several
issues related to the use of an exogenous rate of return on produced capital
based on financial market information. First, using a
flat exogenous rate of return will lead to high volatility in the measured
resource rent, and, sometimes, negative resource rent that may not accord with
long-run expectations, which are relevant for the derivation of the concept of
the user cost of capital. Second, deriving a variable rate from financial
market data that corresponds with longer-run expectations is difficult, because
short-run financial market fluctuations may not necessarily reflect long-run
expectations. Third, a rate of return obtained from financial markets is
usually an after-tax measure, and needs to be converted into a before-tax
measure; otherwise the resource rent would be overstated. Finally, it should be
noted that, for our purposes, consistency is required between the estimates of
the mining sector and other industries. Industry revenues and costs may not be
equal elsewhere when an exogenous rate of return is used, and a “profit
residual” may be generated across industries other than mining. While the
“profit residual” is interpreted as the resource rent in a mining industry, it
is more difficult to classify the reason or reasons for the residual elsewhere,
other than short-run deviations from market clearing, and, therefore, leads to
unnecessary white noise in interpreting the estimates for users.
To overcome these issues, this paper describes
an alternate way of splitting the operating surpluses into returns on produced
capital and returns on natural capital (resource rent) than those suggested by
the SEEA. Specifically, the
internal rates of return on produced capital are adjusted such that produced
capital in a mining industry earns the same rate of return as in the non-mining
business sector on average over a long period.
2.1.1 Resource rent at the commodity level
The industry level at which MFP growth is
estimated is more aggregated than the level of commodity data produced in the
Environment Accounts at Statistics Canada, and each mining industry at this
level involves multiple resources that are estimated separately in the Canadian
System of Environmental and Resource Accounts. While the latter involve more
detailed data at the commodity level, they are not at the moment fully
reconciled to the industry accounts that make up the basis for the MFP
estimates. To calculate the resource rent at the industry level used in the
CPA, the gross operating surplus and the nominal value of produced capital
stock at the commodity level are benchmarked to those at the industry level.
After the benchmarking, the internal rates
of return on produced capital for the mining industries at the commodity level
and the corresponding adjusted rates are then calculated. At the commodity
level, data for produced capital by asset type and associated tax parameters
are not readily available. Therefore, the internal rates of return on produced
capital are calculated before tax and depreciation and have no asset details.
Specifically, the gross internal rate of return on produced capital for commodity
and industry
is defined as
Resource rent at the commodity level in a
mining industry is calculated asNote 6
where
is the sample average of the gross internal
rate of return on produced capital for the non-mining business sector, and
is that for the extraction of commodity
in industry
.
2.1.2 Resource rent at the industry level
At the industry level, more data are
available; therefore the internal rate of return on produced capital after tax
can be estimated. According to the user cost formula for produced capital
developed in Christensen and Jorgenson (1969), the internal rate of return on
produced capital in an industry (
) can be
estimated as
The asset-specific
variables used in (9) include the user cost of produced capital (
), produced capital stock (
), asset price (
), depreciation rate (
), capital gains (
), the present value of
depreciation deductions for tax purposes on a dollar’s investment (
), and the rate of the investment
tax credit (
). Other variables are the
effective rate of property taxes (
) and the corporate income tax
rate (
). We then use Equation (9) to calculate the sample
averages of the internal rate of return on produced capital for the non-mining
business sector (
) and a mining industry (
) as
These sample averages can sensibly be
related to expectations over the same period. It is usually expected that
because
includes returns on both produced and natural
capital. If this is the case, one can assume that produced capital earns the
same rate of return on average over the sample period in these mining
industries as in the non-mining business sector.Note 7 The
internal rates of return on produced capital in the mining industries with
are then adjusted by the ratio of the two
sample averages. However, it can be the case that the actual data gives
in the extraction of some subsoil resources.
When this happens, the resource rent in these industries will be zero. For the
industries with
, the adjustment
is made asNote 8
For a mining industry with
, the adjustment made by Equation (10) does
not change the pattern over time of the internal rate of return on produced
capital (
), but ensures that the
sample averages of the adjusted rate of return on produced capital in the
mining industry is the same as in the non-mining business sector, i.e.,
In addition, the internal rates of return
of produced capital derived from Equation (10) are external to the mining industry
of interest since it uses information of other industries. However, it uses information
from the national accounts only.
The
resource rents in a mining industry with
are then
residually calculated by subtracting the returns on produced capital calculated
using the adjusted rates of return on produced capital, i.e.,
2.1.3 Resource rent benchmarking
Because
of data limitations at the commodity level, the resource rent estimate at the
industry level is in general more reliable when the commodity-level resource
rents are all positive. In this case, the commodity-level resource rent is
benchmarked using the industry-level resource rent as the control total, i.e.,
However,
when the industry-level resource rent is zero or very small, it is recalculated
as the sum of the resource rents at commodity-level,Note 9 i.e.,
The resource rent generated from extracting
a subsoil resource is taken here as the user cost or capital service of this
natural capital asset. It is what the rental market for the assets would have
to extract for the use of the natural capital if its use was rented out over
the course of the year.Note 10
2.1.4 Resource rent decomposition
Let
be the physical extraction of a subsoil asset,
and
be the unit user cost of the natural capital
or the net price of the resource extracted at a point of time; we then have
Exploration and development expenditures
have been capitalized as produced capital in the SNA, implying that their
returns have then been deducted in the calculation of the resource rent. As a
result, the unit resource rent (
) reflects purely the value
of a subsoil resource arising from its scarcity and the quality of deposit.Note 11
Similarly to the user cost of produced
capital, the resource rent can also be split into the depletion cost and
returns on natural capital. Let
,
and
denote the shadow price of,
the depletion rate of, and the rate of returns on natural capital,
respectively. The resource rent or the user cost of natural capital can then be
written as
2.2 Valuing subsoil resource reserves
As there are often no readily available
market prices for subsoil resource reserves,Note 12 the NPV of the flow of natural resource rents is used here.Note 13 The NPV method values a resource reserve from an ex-ante perspective. It converts the expected future streams of
resource rents into the present value of a resource reserve. Let
be the expected future nominal rate of return
on a numeraire asset that is used for discounting future income flows,
be the expected future growth rate of the unit
resource rent, and
be the reserve life of a subsoil resource at a
point of time. The
of the reserve of a subsoil
resource becomes
For notational simplicity, we replace the
period-specific discount rates and growth rates of the unit resource rent in (16)
with their annual averages over the reserve life, which yields
where
Hotelling’s ruleNote 14 suggests that the socially and economically optimal time path of a
non-renewable resource extraction is one along which the resource price, net of
all extraction costs (unit resource rent), is expected to grow at the rate of
return on investment (discount rate). That is
To understand the proposition, we assume
that the representative agent chooses an extraction path to maximize the NPV of
a resource reserve. The optimization can be written as
The Lagrangian function for this problem
can be written as
The first order conditions can be derived
by taking the derivative of Equation (21), with respect to the physical
extraction in each time, i.e.,
It is required that
for Equation (22) to hold. Otherwise, the
current extraction is not optimal because the marginal profit of extraction (
) and the marginal value of
holding (
) are not equal to each
other. This is Hotelling’s rule. Substituting
into Equations (22), (21) and
(17) gives
Therefore, along the optimal extraction
path, the shadow price of a resource reserve is equal to the unit resource rent,
and both are expected to grow at the rate of nominal interest rate of a
numeraire asset. The NPV of a resource reserve can then be calculated as the
current resource rent, multiplied by the number of periods of extraction at
current level.
Hotelling’s rule also implies that the rate
of return on natural capital is zero. This can be seen using Equation (15), when
the shadow price of a resource reserve (
) is equal to the unit
resource rent (
). So the benefits today
(resource rents) fully reflect the cost of future loss (depletion costs).
In the above formulation, Hotelling’s rule
was used to define the optimal extraction path of non-renewable natural
resources to give the conceptual and theoretical framework for understanding
and analyzing the depletion of non-renewable natural resources.
In support of the use to which the rule is
being used here, Miller and Upton (1985) found that, for a sample of U.S. oil and
gas extraction companies, estimates of reserve values, when calculated using
Hotelling’s rule, account for a significant portion of their market values. Miller
and Upton (1985) also compared the accuracy of using Hotelling’s rule, as
opposed to two widely cited and publicly available alternatives
the Securities and Exchange Commission and Herold appraisals
reported that Hotelling’s rule performed better in the valuation of the
resource reserves values. This supports the use to which Hotelling’s rule is
being used here. It suggests that expectations are being formed to determine
the values being estimated here, using something approximating Hotelling’s
rule.
It is, however, the case that Livernois
(2009) reports that empirical studies that examine the actual price trajectory
find imperfect evidence that the actual trajectory of resource prices follows
Hotelling’s rule. But the question is not whether the trajectory follows
Hotelling’s rule exactly, but whether the expected values using an
approximation to this rule accord with values being created in markets, which
is the criterion that accords with the spirit of measurement within the SNA and
the Multifactor Productivity Program.
Kronenberg (2008) discussed factors that
may lead to deviations of outcomes, in the real world, from those obtained applying
Hotelling’s rule. One category of these factors relates to the assumptions made
for deriving Hotelling’s rule, such as perfect competition, zero extraction
cost, no technical progress, fixed stock of reserves, and constant market
conditions. These assumptions can be relaxed. And in this paper, we do so by
calculating the value of a resource by updating information continuously on the
extraction cost, reserve stock, and market conditions, implying that the
corresponding optimal extraction path of a resource reserve changes over time.
The other category of these factors is institutional, such as uncertain
property rights and the strategic interaction between suppliers and consumers. Although
these institutional factors may lead to a market failure, such that the actual
extraction path is not socially optimal, valuing a resource reserve along its
optimal path of extraction gives the value that can be achieved from efficient
extraction of a resource reserve.Note 15
2.3 Industry-level
measures
To this point, measures on the quantity and
price for each natural capital asset have been derived. The industry-level
quantity and price measures are then aggregated from those for each asset using
the Fisher formula. For the natural capital stock in a mining industry, its
quantity and price indexes are calculated as
In the case of mining, the physical
extractions are the service flows provided by the natural capital. The
industry-level quantity and price indexes of natural capital service (input)
can then be estimated as
The discrete approximation of the growth
accounting formula can be derived from (2) as
MFP growth can then be estimated
residually. It is noteworthy that the growth accounting of (26) does not take
into account the impact of changes in natural capital quality, so the derived
MFP growth, at this point, refers only to the (natural capital)
quality-unadjusted measure.Note 16 Also,
the impact of adding natural capital, as an input into production on MFP growth,
relies on the relative growth of produced and natural capital. It raises MFP
growth when the natural capital growth is lower than that for produced capital
and vice versa.
3 Empirical results for Canadian oil and gas extraction
In this section, the growth accounting
framework developed in the previous section is applied for the Canadian oil and
gas mining industry as an experimental analysis. The commodity-level
(asset-level) data on the gross operating surplus and nominal produced capital
stock for the mineral sector is compiled by the Environment Accounts and
Statistics Division of Statistics Canada based on various data sources.Note 17 These data are benchmarked to the industry-level data first and
then the benchmarked data are used for the calculation of the resource rents at
the commodity level. The quantity measures of the stock, depletion and addition
of each subsoil resource reserve are obtained from CANSIM tables 153-0012 to
153-0015. Combined with the estimates of resource rents, these data are used
for the calculation of reserve value at the commodity level and the quantity
and price indexes of natural capital stock and natural capital input at the
industry level. The industry-level data of value-added, labour compensation,
labour and produced capital inputs come from the KLEMS (capital, labour, energy,
materials and services) database used in the CPA, and the industry-level
geometric-based nominal produced capital stock data come from CANSIM table
031-0002.Note 18 The gross operating surplus and the nominal capital stock data at
both industry and commodity levels are used for estimating the resource rents
at both commodity and industry levels. A zero real discount rate is used
throughout our experimental assessment. Given that the natural capital input is
measured by the amount of physical extraction, the choice of the discount rate
has no impact on the measurement of MFP growth. However, the measured value of
natural capital stock is much larger under Hotelling’s rule (zero real discount
rate) than that with the discount rate being at 4%.Note 19 This discount rate is currently used in the Canadian System of Environmental
and Resource Accounts (CSERA) and as well as in many other national statistical
agencies.
Oil and gas extraction involves the
extraction of natural gas, crude oil and crude bitumen. Natural gas liquids are
included in the asset category of natural gas.Note 20 The estimates on the volume of reserve and extraction for each type
of resources are presented first. The estimates of the nominal value of reserve
and the resource rent of the extraction are presented next. The volume
estimates of reserves are then aggregated across different types of resources
to derive total natural capital stock, while the extractions are aggregated to
derive the flow of services for the natural capital (or natural capital input),
using weights based on resource rents. Finally, the contribution of the natural
capital to output and its effect on MFP estimates are presented.
3.1 Resource
reserve and extraction
The established
reserve of oil and gas in Canada has experienced a large compositional shift
towards crude bitumen. As shown in Chart 1, from 1981 to 2009, the established
reserve trended down slightly for both natural gas and crude oil. It dropped by
about 25% for both natural gas and crude oil over the whole sample period. At
the same time, the established reserve of crude bitumen increased dramatically,
especially during the periods from 1997 to 1999 and after 2005. It increased by
more than 12 times, or about 9.6% per year on average.
Description for chart 1
The title of the graph is "Chart 1 Trend in established oil and gas reserves, 1981 to 2009."
This is a line chart.
There are in total 29 categories in the horizontal axis. The primary vertical axis starts at 0 and ends at 120 with ticks every 20 points. The secondary vertical axis starts at 0 and ends at 1,400 with ticks every 200 points.
There are 3 series in this graph.
The units of the horizontal axis are years from 1981 to 2009.
The title of series 1 is "Natural gas (left scale)."
The vertical axis is "index (1981 = 100)."
The minimum value is 67.26 occurring in 2003.
The maximum value is 102.83 occurring in 1982.
The title of series 2 is "Crude oil (left scale)."
The vertical axis is "index (1981 = 100)."
The minimum value is 63.63 occurring in 1996.
The maximum value is 100.00 occurring in 1981.
The title of series 3 is "Crude bitumen (right scale)."
The vertical axis is "index (1981 = 100)."
The minimum value is 95.51 occurring in 1983.
The maximum value is 1,323.08 occurring in 2008.
Data table for chart 1 Table Summary
This table displays the results of Chart 1 Trend in established oil and gas reserves Natural gas (left scale), Crude oil (left scale) and Crude bitumen (right scale), calculated using index (1981 = 100) units of measure (appearing as column headers).
Natural gas (left scale)
Crude oil (left scale)
Crude bitumen (right scale)
index (1981 = 100)
index (1981 = 100)
index (1981 = 100)
1981
100.00
100.00
100.00
1982
102.83
94.30
97.11
1983
101.65
95.72
95.51
1984
101.21
93.78
101.17
1985
100.01
95.49
105.66
1986
97.91
93.57
176.74
1987
95.08
91.04
176.15
1988
94.45
89.30
174.31
1989
95.59
85.50
166.83
1990
95.99
79.40
161.23
1991
95.21
74.28
154.37
1992
93.66
71.32
148.37
1993
90.49
70.33
140.80
1994
88.70
65.78
173.85
1995
89.19
66.80
176.62
1996
83.24
63.63
203.32
1997
77.90
64.29
188.92
1998
75.17
81.36
411.08
1999
73.73
77.62
581.88
2000
75.59
80.61
572.31
2001
72.55
77.88
563.08
2002
71.16
73.22
566.15
2003
67.26
71.27
529.23
2004
68.33
72.94
510.77
2005
70.52
90.88
498.46
2006
71.79
86.08
1027.69
2007
69.46
87.19
1076.92
2008
75.47
83.21
1323.08
2009
76.44
75.20
1297.23
Source: Statistics Canada, authors' calculations based on data from CANSIM tables 153-0013 to 153-0015.
Unlike the pattern of the established
reserve over time, the extraction of all three oil and gas resources has
increased, although at quite different paces (Chart 2). From 1981 to 2009, extraction
grew by about 2.4% per year for natural gas, by 0.1% per year for crude oil,
and by 8.4% per year for crude bitumen.
Description for chart 2
The title of the graph is "Chart 2 Trend in extraction of oil and gas reserves, 1981 to 2009."
This is a line chart.
There are in total 29 categories in the horizontal axis. The primary vertical axis starts at 0 and ends at 250 with ticks every 50 points. The secondary vertical axis starts at 0 and ends at 1,200 with ticks every 200 points.
There are 3 series in this graph.
The units of the horizontal axis are years from 1981 to 2009.
The title of series 1 is "Natural gas (left scale)."
The vertical axis is "index (1981 = 100)."
The minimum value is 90.45 occurring in 1982.
The maximum value is 228.54 occurring in 2004.
The title of series 2 is "Crude oil (left scale)."
The vertical axis is "index (1981 = 100)."
The minimum value is 97.31 occurring in 1982.
The maximum value is 124.48 occurring in 2003.
The title of series 3 is "Crude bitumen (right scale)."
The vertical axis is "index (1981 = 100)."
The minimum value is 100.00 occurring in 1981.
The maximum value is 966.29 occurring in 2009.
Data table for chart 2 Table Summary
This table displays the results of Chart 2 Trend in extraction of oil and gas reserves Natural gas (left scale), Crude oil (left scale) and Crude bitumen (right scale), calculated using index (1981 = 100) units of measure (appearing as column headers).
Natural gas (left scale)
Crude oil (left scale)
Crude bitumen (right scale)
index (1981 = 100)
index (1981 = 100)
index (1981 = 100)
1981
100.00
100.00
100.00
1982
90.45
97.31
105.62
1983
95.86
101.19
194.38
1984
99.93
110.00
130.34
1985
108.10
106.57
173.03
1986
102.63
101.04
212.36
1987
104.43
104.78
225.84
1988
130.85
108.51
240.45
1989
132.34
103.28
260.67
1990
140.19
101.64
255.06
1991
128.50
100.75
253.93
1992
158.97
103.73
267.42
1993
180.65
108.06
276.40
1994
180.23
112.39
269.66
1995
190.19
115.07
316.85
1996
198.45
117.61
315.73
1997
201.70
118.96
369.66
1998
207.54
119.85
426.97
1999
216.97
113.73
404.49
2000
221.82
118.66
438.20
2001
228.26
118.21
471.91
2002
224.06
123.13
539.33
2003
221.39
124.48
629.21
2004
228.54
120.15
707.87
2005
220.12
116.12
643.35
2006
220.59
114.93
738.28
2007
218.68
118.81
865.17
2008
210.27
114.18
853.93
2009
194.50
104.03
966.29
Source: Statistics Canada, authors' calculations based on data from CANSIM tables 153-0012, 153-0014, and 153-0015.
3.2 Resource rent and reserve value
Chart
3 presents the estimated value of oil and gas reserves and the resource rent
from the extraction of oil and gas from 1981 to 2009. As shown, the patterns of
the reserve value and resource rent, over time, are quite close to each other.
Both stayed low and stagnant before 1999, and then grew rapidly thereafter. The
annual resource rent declined by 2.5% per year over the 1981-to-1999 period and
by 17.7% per year over the 1999-to-2009 period. The corresponding growth rates
for the reserve value were -4.2% and 22.9% per year for the two periods,
respectively.
Description for chart 3
The title of the graph is "Chart 3 Oil and gas resource rent and reserve value, 1981 to 2009."
This is a line chart.
There are in total 29 categories in the horizontal axis. The primary vertical axis starts at 0 and ends at 35 with ticks every 5 points. The secondary vertical axis starts at 0 and ends at 700 with ticks every 100 points.
There are 2 series in this graph.
The units of the horizontal axis are years from 1981 to 2009.
The title of series 1 is "Resource rent (left scale)."
The vertical axis is "billions of dollars."
The minimum value is 1.02 occurring in 1992.
The maximum value is 31.27 occurring in 2007.
The title of series 2 is "Reserve value (right scale)."
The vertical axis is "billions of dollars."
The minimum value is 13.85 occurring in 1992.
The maximum value is 614.45 occurring in 2008.
Data table for chart 3 Table Summary
This table displays the results of Chart 3 Oil and gas resource rent and reserve value Resource rent (left scale) and Reserve value (right scale), calculated using billions of dollars units of measure (appearing as column headers).
Resource rent (left scale)
Reserve value (right scale)
billions of dollars
billions of dollars
1981
6.34
139.53
1982
6.87
157.05
1983
7.06
138.72
1984
6.80
130.18
1985
5.15
93.35
1986
3.38
70.79
1987
1.69
31.89
1988
1.73
28.03
1989
1.96
31.83
1990
1.79
27.71
1991
1.46
23.54
1992
1.02
13.85
1993
1.37
16.91
1994
1.81
22.54
1995
2.51
29.78
1996
3.00
34.12
1997
2.81
29.90
1998
2.48
30.90
1999
4.06
63.91
2000
6.80
99.16
2001
8.04
99.39
2002
9.02
126.70
2003
12.33
143.16
2004
18.80
216.37
2005
24.78
300.74
2006
29.00
547.31
2007
31.27
545.45
2008
26.10
614.45
2009
20.61
504.26
Source: Statistics Canada, authors' calculations based on data from the KLEMS database and the environment accounts.
3.3 Natural capital stock and natural capital
input
The
natural capital input in this industry trended up steadily without major
interruptions (Chart 4). It grew by 2.4% per year on average from 1981 to
2009. At the same time, the pattern of the natural capital stock, over time, is
quite different from that of the natural capital input. The natural capital
stock trended down gradually and dropped by about 17% before 1997, reflecting
the down-trending movements in natural gas and crude oil reserves. After 1997,
the natural capital stock exhibited a pattern, over time, similar to that of
crude bitumen. It increased largely from 1997 to 1999 and after 2005, and
decreased moderately from 2000 to 2005.
Description for chart 4
The title of the graph is "Chart 4 Trend in oil and gas natural capital stock and natural capital input, 1981 to 2009."
This is a line chart.
There are in total 29 categories in the horizontal axis. The vertical axis starts at 60 and ends at 220 with ticks every 20 points.
There are 2 series in this graph.
The vertical axis is "index (1981 = 100)."
The units of the horizontal axis are years from 1981 to 2009.
The title of series 1 is "Natural capital stock."
The minimum value is 82.83 occurring in 1997.
The maximum value is 190.29 occurring in 2008.
The title of series 2 is "Natural capital input."
The minimum value is 94.18 occurring in 1982.
The maximum value is 205.22 occurring in 2007.
Data table for chart 4 Table Summary
This table displays the results of Chart 4 Trend in oil and gas natural capital stock and natural capital input Natural capital stock and Natural capital input (appearing as column headers).
Natural capital stock
Natural capital input
1981
100.00
100.00
1982
100.26
94.18
1983
99.76
103.27
1984
99.38
106.50
1985
99.54
110.35
1986
102.15
106.46
1987
99.55
109.59
1988
98.53
124.74
1989
97.71
123.54
1990
95.47
125.67
1991
92.77
120.19
1992
90.42
135.82
1993
87.57
148.20
1994
87.75
149.58
1995
88.58
157.96
1996
87.33
162.64
1997
82.83
168.17
1998
104.94
174.60
1999
119.05
176.02
2000
119.84
182.37
2001
116.30
187.50
2002
114.74
190.52
2003
108.29
194.71
2004
107.79
201.25
2005
111.78
192.05
2006
160.31
197.08
2007
163.87
205.22
2008
190.29
198.67
2009
186.14
195.71
Source: Statistics Canada, authors' calculations based on data from the KLEMS database and the environment accounts.
3.4 Multifactor productivity growth
In
the growth accounting framework, adding natural capital has no impact on either
output (value-added) growth or the contribution of labour input. However, the
income share and, hence, the contribution of produced capital input will be
reduced; as a result, MFP growth would be impacted if the produced capital
input and the natural capital input grew at different paces.
As
shown in Chart 5, MFP growth in oil and gas extraction was positive before 1993,
and became largely negative after 1993. Note that adding natural capital in the
growth accounting framework has little impact on the pattern of MFP growth over
time. After adjusting for natural capital, annual MFP growth increases from 1.8%
to 2.0% before 1993, and from -5.1% to -4.0%, after 1993.
Overall,
by including subsoil resources, MFP declines by 1.5% per year over the
1981-to-2009 period, compared to a 2.2% decline without including these
resources.
Description for chart 5
The title of the graph is "Chart 5 Alternative measures of multifactor productivity, oil and gas extraction industry, 1981 to 2009."
This is a line chart.
There are in total 29 categories in the horizontal axis. The vertical axis starts at 40 and ends at 140 with ticks every 10 points.
There are 2 series in this graph.
The vertical axis is "index (1981 = 100)."
The units of the horizontal axis are years from 1981 to 2009.
The title of series 1 is "Standard."
The minimum value is 53.02 occurring in 2009.
The maximum value is 123.46 occurring in 1993.
The title of series 2 is "Adjusted for natural capital."
The minimum value is 65.49 occurring in 2009.
The maximum value is 126.54 occurring in 1993.
Data table for chart 5 Table Summary
This table displays the results of Chart 5 Alternative measures of multifactor productivity Standard and Adjusted for natural capital (appearing as column headers).
Standard
Adjusted for natural capital
1981
100.00
100.00
1982
93.98
99.26
1983
96.80
100.79
1984
96.35
100.92
1985
97.02
102.25
1986
90.27
95.91
1987
94.48
99.79
1988
100.84
105.00
1989
97.17
101.12
1990
98.94
102.62
1991
103.14
107.53
1992
115.95
119.49
1993
123.46
126.54
1994
120.86
124.63
1995
117.75
121.56
1996
112.24
116.17
1997
108.10
112.70
1998
108.48
113.37
1999
105.66
110.94
2000
99.23
105.02
2001
89.15
95.42
2002
89.84
96.98
2003
85.40
93.17
2004
78.66
86.93
2005
69.44
80.38
2006
65.06
77.09
2007
61.49
73.49
2008
55.48
68.22
2009
53.02
65.49
Source: Statistics Canada, authors' calculations based on data from the KLEMS database and the environment accounts.
3.5 Natural capital contribution to value-added
growth
The
contribution of the natural capital input to the industry value-added growth is
moderate in oil and gas extraction. From 1981 to 2009, the log growth of
value-added in oil and gas extraction was about 2.3% per year, of which about
0.3 percentage points per year or 15% came from the growth in the natural
capital input (Table 1).
Table 1
Source of value-added growth, and multifactor productivity growth, oil and gas extraction industry, selected periods, 1981 to 2009 Table summary
This table displays the results of Source of value-added growth Period, 1981 to 2000, 2000 to 2008 and 1981 to 2009, calculated using percent and percentage points units of measure (appearing as column headers).
Period
1981 to 2000
2000 to 2008
1981 to 2009
percent
Value-added growth (log), annual average
3.22
0.39
2.31
percentage points
Contribution
Labour input
0.08
0.84
0.32
Produced capital input
2.45
4.64
3.16
Natural capital input
0.43
0.16
0.34
Multifactor productiivty
0.26
-5.25
-1.51
percent
Multifactor productivity growth (log), annual average before adding natural capital
-0.04
-6.96
-2.27
Source: Statistics Canada, authors' calculations based on data from the KLEMS database and the environment accounts.
4 Conclusion
To
recognize subsoil energy and mineral resources as a capital input into the
production process, this paper presents a growth accounting framework that
allows the derivation of measures on natural capital stock and natural capital
input in the mining industries and provides a better understanding of contribution
of natural capital to economic growth and the impact of adding natural capital
on productivity measurement.
The
empirical results suggest a significant contribution of natural capital to the
real value-added economic growth in the Canadian oil and gas extraction.
However, the impact of adding natural capital in the growth accounting on the
measured MFP growth changes over time. It is small before 1993 and becomes
larger thereafter.
5 Appendix
Appendix Table 1
Sensitivity of natural capital value to real discount rate, oil and gas extraction industry, average, 1981 to 2009 Table summary
This table displays the results of Sensitivity of natural capital value to real discount rate Value at 0% discount divided by
value at 4% discount, calculated using ratio units of measure (appearing as column headers).
Value at 0% discount divided by
value at 4% discount
ratio
Total
1.49
Natural gas
1.38
Crude oil
1.21
Crude bitumen
1.80
Source: Statistics Canada, authors' calculations based on data from the KLEMS database and the environment accounts.
Appendix Table 2
Input cost shares and input growth, oil and gas extraction industry, selected periods, 1981 to 2009 Table summary
This table displays the results of Input cost shares and input growth Period, 1981 to 2000, 2000 to 2008 and 1981 to 2009, calculated using percent units of measure (appearing as column headers).
Period
1981 to 2000
2000 to 2008
1981 to 2009
percent
Annual average cost share
Labour
12.80
9.60
11.80
Produced capital
70.80
64.90
68.50
Natural capital
16.50
25.50
19.70
Average annual input growth (log)
Labour
1.87
9.17
4.22
Produced capital
3.57
7.17
4.73
Natural capital
3.16
0.78
2.40
Source: Statistics Canada, authors' calculations based data from the KLEMS database and environment accounts.
References
Baldwin, J. R., and W. Gu. 2007. Multifactor Productivity in Canada: An Evaluation of Alternative Methods of Estimating Capital Services. The Canadian Productivity Review, no. 9, Statistics Canada Catalogue no. 15-206-X. Ottawa: Statistics Canada.
Brandt, N., P. Schreyer, and V. Zipperer. 2013. Productivity Measurement with Natural Capital. Economics Department, Organisation for Economic Co-operation and Development, Working Paper no. 1092. Paris: OECD.
Christensen, L.R., and D.W. Jorgenson. 1969. “The measurement of U.S. real capital input, 1929–1967.” Review of Income and Wealth 15: 293–320.
European Commission, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations, and World Bank. 2009. System of National Accounts 2008. New York: United Nations.
Hotelling, H. 1931. “The economics of exhaustible resources.” Journal of Political Economy, 39 (2): 131–175.
Kendrick, J.W. 1976. The Formation and Stocks of Total Capital. New York: National Bureau of Economic Research.
Kronenberg, T. 2008. “Should we worry about the failure of the Hotelling rule?” Journal of Economic Surveys 22 (4): 774–793.
Livernois, J. 2009. “On the empirical significance of the Hotelling rule.” Review of Environmental Economics and Policy 3 (1): 22–41.
Miller, M.H., and C.W. Upton. 1985. “A test of the Hotelling valuation principle.” Journal of Political Economy 93 (1): 1–25.
Solow, R.M. 1974. “The economics of resources or the resources of economics.” The American Economic Review 64 (2): 1–14.
Statistics Canada. 2006. Concepts, Sources and Methods of the Canadian System of Environmental and Resource Accounts. Environment Accounts and Statistics Division, System of National Accounts. Statistics Canada Catalogue no. 16-505-G. Ottawa: Statistics Canada.
United Nations, European Commission, Food and Agriculture Organization of the United Nations, International Monetary Fund, Organisation for Economic Co-operation and Development, and World Bank. 2014. System of Environmental-Economic Accounting 2012: Central Framework. New York: United Nations.
Veldhuizen, E., M. de Haan, M. Tanriseven, and M. van Rooijen-Hoesten. 2012. The Dutch Growth Accounts: Measuring Productivity With Non-Zero Profits. Paper presented at the 32nd General Conference of the International Association for Research in Income and Wealth, Boston, August 5–11.
About Analytical Studies
The
Analytical Studies Branch Research Paper Series provides for the circulation,
on a pre-publication basis, of research conducted by Analytical Studies Branch
staff, visiting fellows, and academic associates. The Analytical Studies Branch
Research Paper Series is intended to stimulate discussion on a variety of
topics, including labour, business firm dynamics, pensions, agriculture,
mortality, language, immigration, and statistical computing and simulation.
Readers of the series are encouraged to contact the authors with their comments
and suggestions.
Papers in
the series are distributed to research institutes, and specialty libraries.
These papers can be accessed for free at www.statcan.gc.ca.
Acknowledgements
The authors would like to thank John
Baldwin, Wulong Gu, Michael Wright of Statistics Canada; Pierre-Alain Pionnier
of the OECD; Michael Smedes of the Australian Bureau of Statistics; Vernon Topp
of the Australian Productivity Commission; Erik Veldhuizen of Statistics Netherlands;
and Carl Obst of the London Group for their valuable comments and suggestions. Thanks
also to participants of the 2013 CANSEE
(Canadian Society for Ecological Economics) conference at York University,
Toronto; and 2014 NAPW (North American Productivity Workshop) VIII Conference
at Ottawa/Gatineau, for helpful discussions. Any errors are those of the
authors.
More information
ISSN: 1205-9153
Note of appreciation
Canada owes the success of its statistical system to a long-standing partnership between Statistics Canada, the citizens of Canada, its businesses, governments and other institutions. Accurate and timely statistical information could not be produced without their continued co-operation and goodwill.
Standards of service to the public
Statistics Canada is committed to serving its clients in a prompt, reliable and courteous manner. To this end, the Agency has developed standards of service which its employees observe in serving its clients.
Copyright
Published by authority of the Minister responsible for Statistics Canada.