Download Optimal Natural Gas Extraction Under the Conditions of

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Optimal Natural Gas Extraction
Under the Conditions of Potential hold-up Costs.
Guych Nuriyev
School of Management and Economics
Queen’s University Belfast
[email protected]
Introduction:
Thesis statement
Ex ante, one would expect the optimal extraction of natural gas to follow a pattern that can be
explained by the analogy between the gas field and the question of when to cut a growing
tree. Based on this analogy one can state that owners of natural gas allocate extraction over
time according to the rule that the percentage change in value of the resource should equal the
alternative market investment rate. Then one should observe a relationship between prices of
natural gas and its extraction. If no anticipation is assumed, then I would expect extraction
rate to move simply in the same direction and in proportion with the price. If producers are
assumed to be able to fully predict future prices, then extraction would slow down before an
anticipated price hike and increase when the price has risen. However, considering the prices
of natural gas and its production in Central Asia and Russia we do not observe such a simple
relationship. In my work I argue that the above paradox can be explained by taking into
account the features of the market under consideration relating to hold-ups and lack of
property rights, both of which may be endogenously determined by internal and external
factors, both of which will be more fully explained below.
Justify the analogy:
In a small country case where it is reasonable to assume that the world price is unaffected be
the country’s natural gas output, I argue that it is possible to draw an analogy between the
reserves of natural gas and a growing tree because like the value of a growing tree, the value
of natural gas reserves is increasing over time. Under the assumption of fixed expected prices
the reasons for this increase are the following.
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First, due to technological progress the costs of resource extraction are constantly going
down. This cost reduction, in turn, increases the value of proven reserves that are
economically feasible to extract (more precisely, it increases the value of the stream of profits
from extraction of those reserves).
Second, extraction costs reduction also may switch some of the explored (known) reserves
from the category of economically non-feasible to extract into the reserves that are profitable
to exploit. This increases the total value of all reserves in a given country.
Third, exploration works might increase the stock of known reserves, which also increases
the total value of all reserves. Assuming that unexplored areas of a country decreases over
time the possibility of increasing the value of reserves through new discoveries diminishes. In
my opinion this feature resembles the diminishing rate of growth of a tree.
The reasons above relate to three types of costs (T1, T2 and T3 respectively). Because these
costs are a function of price, it makes the output to depend on price very much. And that in
turn connects the output function (Q = f(P), T1(P), T2(P), T3(P)) with the model of a growing
tree.
Why consider natural gas?
I will mainly focus on natural gas because first, unlike in the supply of oil, there is no cartel
organization among the suppliers of natural gas. This eliminated the OPEC problem.
However, natural gas industry has its own interesting problems to consider: (a) the decisions
of individual suppliers (resource rich countries) are investment and extraction decisions based
on appropriable quasi-rents; (b) because of the special features of natural gas transportation,
this industry would tend to have more the hold-up situations that oil industry and hold-up
potentials would have a greater impact. Second, most of the models in the literature of natural
resource extraction refer to oil, while the coverage of natural gas riches is not as wide. Third,
the country for which I have the most information (Turkmenistan) is rich in mainly natural
gas rather than oil.
Explanation of the paradox:
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To begin to explain the seeming paradox that natural gas prices have no apparent effect on
natural gas extraction rates we consider the situation in the Central Asian region after the
break-up of Soviet Union. The situation can be briefly described as follows:
• Maintaining stability has been a concern for the countries of Central Asia (e.g. wars in
Afghanistan and Tajikistan).
• Economic relations within CIS were generally disrupted, leading to a dramatic
increase in transaction costs in intra-regional trade between the various republics.
• Foreign investments were discouraged because of the unsettled political climate and
inadequate legal systems, etc.
• In many cases natural gas exporting countries interrupted supplies because of nonpayment of bills. In addition, considerable arrears related to natural gas had accumulated.
• Main natural gas transit countries (Ukraine, Kazakhstan, Uzbekistan and Russia) have
systematically increased there transit fees, thus increasingly obstructing trade and leading
to political frictions (e.g. between Russia and Ukraine), which in turn adds to instability.
• Russia considers Turkmenistan as a competitor on the European market. Thus it has
been reluctant to permit Turkmenistan to export natural gas to Europe through Russia.
Natural gas pipelines are a very important aspect of the industry, because there are no
alternative ways to deliver the gas. One of the main gas pipelines in the region is called
“Central Asia – Center”. This pipeline starts in Turkmenistan and connects Central Asian gas
producing countries with Russia, running through Uzbekistan and Kazakhstan. There are also
smaller pipelines going from Turkmenistan to Iran, from Uzbekistan to neighboring
Tajikistan, Kyrgyzstan and Kazakhstan and there is a branch from “Central Asia – Center”
that delivers Turkmen gas to Caucasus. This relative underdevelopment of natural gas
transportation system in Central Asia leaves the suppliers with few export options and
enables Russia to influence prices and production volumes of natural gas in the region.
Russia has the comparative advantage, because it already has the existing pipelines from
Central Asia to Russia and from Russia to Europe (although through Ukraine). Armed with
the potential to influence the natural gas industry in Central Asia, Russia would be expected
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to raise the transit prices to the point where suppliers in Central Asia are on the margin of
building alternative pipelines.
In my opinion, the peculiar situation in natural gas industry of Central Asia, with Russia
being the main player, is the reason why the relationship between prices and extraction rates
cannot be easily observed.
Set out the problem
For this paper the problem is to determine the extraction rates of the various republics in
response to natural gas demand price changes. However, the task is complicated by the
necessity to understand how such factors as hold-up potential, country instability and one
supplier dominance would influence the comparison of returns from investments in the
natural gas industry and other industries. Thus the issue is transformed into the questions of
what is the optimal amount of investments and extraction of natural gas given the economic
and political conditions faced by the countries.
The issues of influence of the natural gas industry and extraction patterns on overall economy
of the country (such as Dutch disease or staple trap model) are purposefully excluded from
the analysis, since they relate to a different topic of the use of revenues from resource
extraction.
I’m going to address the problem by constructing a model that would describe the decision
making by a small resource rich country that cannot influence the world prices of the
resource. As mentioned above the model will be based on the issue of when to cut a growing
tree, or the questions of duration of investments. I am going to employ two versions of the
model, one describing decisions under no market imperfections and second considering
oligopoly with a dominant competitor. The model will be further discussed in the “Model
review” part of this document.
Literature review
The literature contains many papers that model optimal use of natural resources. The history
of such papers starts from paper by Hotelling (1931). This paper concluded that prices of
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depletable resources should constantly grow at the rate of interest. Hoteling also considered
the effects of monopoly, cumulative production and uncertainty on the rate of resource
depletion. Fifty years later Devarajan and Fisher (1981) discussed Hotelling’s work in their
paper. However, some of the questions for Hotelling’s model are still unanswered. Trying to
explain why in reality resource prices do not behave in a way Hotelling’s model predicts,
Gaitan, Tol and Yetkiner (2004) revisited the model by introducing endogenous discount rate.
The result was that depending on the production function assumed, the prices do not have to
constantly rise at the rate of interest.
Of course many papers are not directly based on Hotelling’s work. Posner (1972) in his
article considers the question of how rapidly to deplete gas reserves and suggests an analogy
with discovering a gold deposit. The author discusses the influence of price, extraction costs
and discount rate on the extraction pattern. Later Hoel (1978) analyses resource extraction
under uncertainty with respect to size of reserve stock and future extraction costs.
The paper by Krautkraemer and Toman (2003) does a good job of summarizing the evolution
of theories of optimal resource provision since Hotelling’s article. The paper also covers the
effects of market distortions, uncertainty and “backstop” technology.
Most of the models in the literature look at oil reserves, rather than reserves of natural gas.
For example, the model by Gao, Hartley and Sickles (2004) considers optimal dynamic oil
production for a large field. Interestingly, the model includes relevant engineering variables.
The approach used is a simulation.
A paper that stands out among the others is Black and LaFrance (1998), because it questions
a necessity of any economic model of resource extraction by comparing the geo-engineering
rule of maximum recovery with an economic model of oil production. In the result the
economic model outperformed the geo-engineering rule.
As one can notice most of the models in the literature are simulation or purely econometrical
models. They often do not provide an easily followed intuition of the obtained results. The
approach in our work differs from other studies in the theoretical model that we build the
analysis on. Papers by Klein (1988, 1996) to some degree help us to build our theoretical
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framework. These papers provide insights into the nature of hold-ups. And due to high capital
intensity and features of transportation the industry we look at – natural gas industry – has a
high probability for hold-ups to occur.
Model review
First-best solution:
The first version of the model will describe the basic conditions for optimal extraction of
natural gas with the assumption of no market imperfections. The model is based on
maximizing net present value of the resource taking into account the future expected prices
and interest rates. The optimal extraction resulting from this model would be the 1st-best
solution. I expect the following data to be required for description of the 1st-best solution:
extraction of natural gas, prices of natural gas, investments into the industry, rate of growth of
the natural gas industry and discount rate (interest rate).
Assumptions:
• A small country under consideration has a gas field and it cannot influence world
oil/gas prices.
• It knows the future expected prices.
• The investments in oil/gas sector are such that initial fixed costs are very high, but
variable costs are relatively low.
• Storing gas in tanks is more difficult than storing oil, thus, the producers often prefer
to use or sell all of the extracted gas.
• Options for gas exports are often limited because of the necessity to have a gas-pipe
connecting the exporter and importer (whereas oil can be transported in tankers).
In this part of the model, where we consider the first-best solution, we assume that the
country has many gas-pipes, thus, many options for gas exports. In the following analysis,
taking into account the changing prices of energy sources, I’ll try to make an analogy
between a gas field and an aging bottle of wine or a growing forest. In this part of the model
I’ll write about the optimal duration of the investments. Then, I’ll illustrate the net stream of
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outflows and revenues on a time line, and present the connections between these two parts of
the model.
From 1990 to 2005 wellhead price of natural gas in the USA increased approximately 3.5
times (I don’t have the price data on other countries now, but I expect the same trend in the
world prices) (source: EconStats); from 1992 to 2005 the export price of natural gas in
Turkmenistan increased 4.5 times, from 12.9 USD/ThCM (thousand cubic meters) to 58
USD/ThCM. Based on this change in prices we can make an analogy between a gas field and
an aging bottle of wine or a growing tree, the value of which increase over time. The owner
of the wine and a tree would ask when to sell the wine or cut the tree. The same applies to
extraction of natural gas. This question is the question of duration. In analyzing optimal
duration, we could employ the graph that is commonly used in theory on investment decision.
Figure 1
This graph illustrates relationship between the value of investments and its duration. In this
part of our model the amount of investment is treated as given. The initial investments are
represented by the y-intercept of S-curve, amount of investments would be the positive value
of I0. S curve represents the value (not discounted) of investments depending on its duration.
Curves V, V’, V’’, etc. each represent the set of values that if discounted to time t0 would
each be equal to respective value V0, V’0, V’’0, etc. For example, values corresponding to
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points W1, W2 and W2, when discounted to time t0, would all be equal to the value V’’0.
Alternatively: V = Vert, (where e is mathematical constant, approximately 2.71, and r is the
discount rate [interest rate]). Based on the definition of the V-curves, it can be said that lower
the discount rate flatter would the V-curves be. V-curves represent the market’s investment
opportunities or discount curves. For example, a point W1 can be transformed to the point
W3 on the market.
What would be the optimal duration of the investments in this model? The optimal duration
of investment would be the duration that allows the S-curve, the value curve of the
investment, to reach for the highest reachable V-curve. In point A S-curve touches the highest
reachable discount curve. Although point B corresponds to the maximum undiscounted value,
it is located further in time; thus when discounted it corresponds to a lower value at time t0.
Alternative answer to the duration question would be to continue investing while the
investment value grows at a faster speed than the market rate of return, and discontinue
investing when the growth rate is no greater than market rate of return. When looking at the
graph, this answer would be to prolong the duration when S-curve is steeper than V-curves
until vise versa.
Next we consider the net stream of outflows and inflows on a time line. Naturally, capital
outflow will take place at the initial stages of the gas field development (because of
exploration costs, building infrastructure, drilling wells, etc.). The initial stage of
development is from 0 till T1. Once production of gas starts, the country will have capital
inflow in the form of gas sales revenue. It is worth noticing that gas production from a field
has three distinguishable stages. At the first stage gas production increases rapidly, due to
high pressure of the gas in the deposit. The first stage is relatively short (3-5 years), from T1
till T2. At the second stage the production stabilizes on a particular level, this period is often
called plateau, from T2 till T3. During the second half of the plateau, to keep the extraction of
natural gas at the same level, producers pump water into the deposit. Extraction wells are
placed in circles on a gas field, and the deposit diminishes towards its center. The water is
pumped into the deposit from the wells of larger circles, which become outside the deposit as
it depletes. At the third stage the production gradually decreases, from T3 to T4. The second
and third stages are each relatively long (10-15 years). Let’s call the function illustrated
below as DRF (discounted revenue function).
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Figure 2
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Maximizing the NPV is equal to maximizing the discounted revenue. Intuitively we can see
that to maximize discounted revenue we are to maximize the area of DRF. By changing
investments and shifting the extraction between periods of time, it is possible to influence the
function. This could make DRF taller and thinner or shorter and wider, mainly affecting the
second stage of gas production. Shifting the extraction between the periods of time could
change the DRF only to some extent. At some point, the maximum capacity of the
infrastructure on the gas field would limit further increase in the extraction in the particular
period. Shifting the extraction between the periods of time would only change the shape of
the part of DRF that is to the right from T1, because the outflows of investments for creation
of the infrastructure don’t change. Increasing the initial investments would increase the area
of DRF below the horizontal time line; it will also increase the maximum extraction capacity
of the gas field.
The description above refers to one particular price of the resource. If the future expected
price of natural gas changes, the investments into exploration and production would also
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change. This would change the extraction and possibly value of known reserves. Thus,
reserves can be considered to be a function of the price. Hence, for different future expected
prices the stream of money outflows and inflows would look as follows.
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Figure 3
The figure1 and figures 2 & 3 are two approaches to the same question of duration of the
asset of gas field, because choosing the extraction pattern implies choosing the duration of
the field. Thus, the extraction pattern that maximizes the area of the DRF brings the same
duration that is suggested by the analysis of figure 1.
On the following graph we show the short-run behaviour model for a small price-taker
country.
Figure 4
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In figure 4 SC line represents the sunk costs, or fixed costs, of natural gas industry. The S
curve is the supply curve of the small exporter country. As known from the basic economic
theory, the supply curve also is the marginal costs curve. The country’s market share is
assumed to be so low that it cannot influence the market prices, i.e. the country is a pricetaker. Because the country is a price-taker, each additional unit of the resource sold is sold at
the same price P*. This means that marginal revenue is constant for all levels of exports.
Hence, the MR line, which represents marginal revenue, is horizontal. Under such conditions
the exporter will choose to supply Q* amount of natural gas, because that is the volume for
which the country’s marginal revenue equals marginal costs. Otherwise, if the country
chooses to supply less than Q* then marginal revenue from increase in exports is higher than
marginal costs of increasing production. Thus, with increase in exports the revenue would
grow by a larger amount than the cost would. This means that in such situation it would be
profitable for the country to increase exports. And vice versa, if the country exports an
amount larger than Q* it would be profitable to decrease the exports, because in this case the
revenue would decrease by a lesser amount than costs would.
Now let’s have another look at the supply curve. The supply curve is upward sloping (or
marginal cost is increasing) due to the law of diminishing returns of basic economics. Since
variable costs are relatively low in natural gas industry and marginal costs are not affected by
fixed costs, the supply curve for this industry is not steep.
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As we can see form above, there are different angles to look at the question of optimal
resource use. In the next part of model review, where we consider the second-best solution,
we shall build up on the part of the benchmark model that relates to figure 4.
Second-best solution:
The analysis above considers the case of perfect competition. However, in reality there are
many factors that affect the decisions regarding the extraction patterns. The second version of
the model for optimal extraction and investments would incorporate the risk premium,
proximity of the markets, potential for hold-ups, property rights and instability measures of
the countries. Proximity of the markets, this factor is important, because gas has to be
delivered to the market via gas-pipelines, whereas there are more options for transportation of
oil. Although this last argument can be contradicted by an argument of SWAP agreements
between exporters of natural gas, I expect the sales of gas to depend the factor of market
proximity; this factor can be captured by categorizing geographical position of the countries
and by existence of gas-pipes. The hold-up potential factor arises if the markets are remote.
Again, the importance of this factor is increased because of transportation of gas requires
large investments, from which it is difficult, if not impossible, to extract back the used
capital. And the hold-up potential, in turn, brings up the issue of property rights. The hold-up
potential gives a degree of power to one party over another. Thus, questioning the extent of
property rights of the latter party for the resource. This model is expected to deliver results
that would more accurately describe the actual situations in the natural gas industry.
The market features resemble an oligopoly market in this part of the analysis. The economic
literature contains multiple models of duopoly and oligopoly: Cournot model, Bertrand
model, Edgeworth model, Stackelberg model and others. In my opinion, the variety of
approaches indicates an inherent difficulty of developing theoretical framework for
oligopoly. In addition, one of the weak points of Cournot model is its assumption about the
costs, because at any price higher than the long-run marginal cost a permanent oligopoly
would not exist. However, the legal restrictions to enter the natural gas market and the
property rights over this resource allow dismissing the entry of new firms into the market. My
analysis is based on Bertrand and Stackelberg equilibria. For the remainder of this argument I
use the example of Russia and Turkmenistan; this region will be considered more closely
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than others in my analysis. The similarity with the Bertrand equilibrium is that Russia and
Turkmenistan set the prices of their natural gas for the importing countries. The difference
from Bertrand equilibrium is that Russia can influence the production of natural gas in
Turkmenistan by charging transit fee for transporting Turkmen gas through Russian territory.
In case if the price received by Turkmenistan included the transit fees, then Russia directly
influences the prices and production of Turkmenistan. Currently the price of Turkmen gas
reflected in the contracts doesn’t include transit fees; in this case, by changing transit fees
Russia changes the price paid by the importer. Thus, Russia indirectly influences the
production of natural gas in Turkmenistan through altering the consumers’ opportunity costs
of buying the gas from other suppliers. This difference from the Bertrand equilibrium shows
that Russia plays a role of oligopoly leader, which makes this model a combination of
Bertrand and Stackelberg equilibria. Uncertainty also plays an important role. In addition to
the uncertainty about the volume of reserves, which is common everywhere, there is
uncertainty with respect to the transit fees set by Russia, hold-up potential and property
rights. The uncertainty influences the discount rate and the extraction volumes of
Turkmenistan. According to Hotelling’s model, the uncertainty about the reserves decreases
the extraction rate compared to the extraction rate with no market imperfection. And the
uncertainty with respect to hold-ups and property rights tends to increase the extraction rate
compared to the case of no uncertainty. This is true, because the hold-ups and hold-ups
potential decrease the degree of property rights over the resource, which in turn makes the
owner to shift extraction from future to the present.
The game takes place between the exporter of natural gas and the country, through which the
gas is delivered to the importer. The steps of the game can be briefly described as follows.
There are two suppliers of natural gas, countries A (Russia) and B (Turkmenistan), and
consumer is country C (Europe and Ukraine). Delivery to consumer C can only be made
through country A. The demand of consumer C is large. Producers are maximizing their
streams of revenues based on expectations about prices and production costs. Cost functions
are assumed to be similar for the countries A and B. Supplier A makes the first move and sets
the price of its natural gas and transit fee. Then country B replies with its price and
production level. The following graph illustrates the process.
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Russia’s supply of natural gas is represented by the Sr curve. Turkmenistan’s supply of gas,
represented by the curve St, is higher due to higher costs, which include hold-up costs. The
line labelled SC is the sunk costs (fixed costs) line, which applies to both Russia and
Turkmenistan. The demand for gas of country C (Europe and Ukraine) is represented by the
D curve. Because Russia makes the first move, D is the demand that it faces. Thus, MR curve
is the marginal curve for Russia. Making the first move, Russia will sell Qr amount of natural
gas for the price of Pr. The demand that Turkmenistan faces is the difference between the
initial demand D and Russian choice of supply Qr; it is represented by Dt curve. Thus, MRt is
Turkmenistan’s marginal revenue curve. Turkmenistan will choose to sell Qt amount of
natural gas at the price of Pt. The results predicted by this part of the model are supported by
the actual exports and prices of Russia and Turkmenistan, the price of Turkmen natural gas
are on average four times lower than the price of Russian gas.
Figure 5
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If Turkmen gas didn’t have to go through Russia, then Turkmenistan would have the profits,
represented by the area PtACB. However, by varying the transit fee and other hold-up costs,
Russia can decrease Turkmenistan’s profits. It is in Russia’s interests to leave Turkmenistan
at the point of breaking even. Thus, Russia can extract the quasi-rents equal to
Turkmenistan’s profit, leaving it with the revenue represented by the area OBCQt. Moreover,
after Turkmenistan has made its investments Russia can extract quasi-rents even in excess of
the profit received by Turkmenistan. Maximum one time rip off by Russia can be as large as
the area of the rectangular OPtAQt. However, since this rip off can be done only once, it is in
Russia’s interests to compare the immediate benefit with the discounted value of future
revenues from transit fees. If the discounted future revenues from transit fees are greater than
possible immediate benefit, then Russia will not choose to rip off. Because the future
revenues are taken into account, Turkmenistan’s total natural gas reserves in the ground also
influence the probability of hold-ups taking place. In addition, the last period problem also
appears in the relationship between Russia and Turkmenistan, because when Turkmenistan’s
total gas reserves decrease significantly, the probability of rip off occurring becomes higher.
The choice made by the small supplier that maximizes the revenue stream can be generally
illustrated with the following Lagrange maximization problem.
Max R (P, X, I) + ȝ (ʌ1 U(Y1, C1) + ʌ2 U(Y2, C2) + ʌ3 U(Y3, C3))
R – revenue from sales of natural gas, P – price of gas, X – exports, I – investments, ȝ –
Lagrange multiplier, ʌ – probability belief of three different hold-up costs to take place, U –
utility/welfare function, Y – the country’s income (GDP), C – the transit fee (hold-up costs).
The costs of extraction of natural gas are excluded because of two reasons. Firstly, the data
on extraction costs is difficult to obtain. Secondly, comparing to the price of natural gas, the
extraction costs per 1000 cubic meters is small.
The relationship between the revenue and the price of gas, as well as between the revenue
and the extraction is, obviously, positive. The relationship between the revenue and the
investments is locally positive around the actual investments amount. When the amount of
the investments is too little or too much, the relationship is negative.
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During the decision-making the producer faces the uncertainty about the actual hold-up costs
imposed (quantified as transit fees). There are three scenarios regarding the hold-up costs. C1
> C2 > C3. C1 corresponds to the exporter’s reservation return after the investments into the
industry have been made r1. In case if the exporter faces the hold-up costs higher than C1, the
exporter prefers to invest large amounts into construction of new gas pipeline, connecting it
with another market. C2 corresponds to the exporter’s reservation return before the
investments into the industry have been made r2. C3 corresponds to the return than the
exporter would get in case of a competitive market model. Hence, r1 < r2 < r3.
Based on own beliefs and knowledge about Russia, Turkmenistan assigns the values of ʌ1,
ʌ2, ʌ3 and calculates the expected values of C1, C2, C3. Turkmenistan’s decision about the
investments into the gas industry is influenced by the weighted average of C1, C2 and C3,
with the probabilities being the weights. Once Russian decision of the transit cost is revealed,
Turkmenistan cannot change its decision on exports, because of the contracts signed. For
some values of average expected transit costs Turkmenistan’s decision on the investments
and exports can be less preferable for Russia than in the case if Turkmenistan could foresee
the true transit costs. It may happen if Turkmenistan assigns a too high probability of facing
high hold-up costs. To some degree, such situation is similar to Akerlof’s model of lemons
and peaches. In this case Russia might engage in some kind of signalling about the transit
costs or engage in a long-term commitment about the transit costs.
However, what is actually best choice of hold-up costs for Russia? In the short-run it is
optimal for Russia to extract all of the hold-up quasi-rents. It is possible to do so, because of
Turkmenistan’s contractual commitments to deliver natural gas to the importers, and because
of the desire to cover at least the fixed costs of investments, in case if the hold-up costs are
not too high. On the other hand, in the long-run it is not optimal for Russia to instantly rip off
Turkmenistan if it expects to profitably cooperate with the country in the future. Same as in
the game called prisoner’s dilemma, if the parties play the game many times, it is not the best
strategy to rip off the immediate benefits. Thus, the strategy employed by Russia (strategy
being the choice of higher or lower hold-up costs) would depend on its expectations about the
length of time period of natural gas deals with Turkmenistan. This, in turn, depends on the
total amount of resources in Turkmenistan and political stability in the country. It is
reasonable to suggest that if the country is politically unstable, Russia could use the rip off
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strategy and hope for future successful negotiations with the new government of
Turkmenistan. In general, in the long-run optimal strategy for Russia would be acting as a
Stackelberg leader. However, there is no certainty that Russia would behave rationally.
Regarding the uncertainty, mentioned above, and the property rights the supplier has belief
system, which assigns probabilities to the three scenarios. This belief system connects the
hold-up costs uncertainty with the property rights issue in the following way: higher the
supplier depends on the transit country, higher the hold-ups potential, which in turn decreases
the extent of property rights of the supplier and increases the probability of facing high holdup costs (transit fees). Here ʌ shows the probability belief of facing high hold-up costs, thus
higher the value of ʌ higher the hold-up potential and the influence of the transit country on
the property rights of the supplier. Facing the uncertainty about the property rights the
supplier would try to increase extraction rate, increasing the revenue and lowering the
duration of the gas field. However, this could be achieved only in the short-run, because in
this case the supplier would be reluctant to make large investments into the natural gas
industry. Thus, in the long-run the relationship between the probability belief and the
extraction and revenue can be illustrated as follows.
Figure 6
9
In addition, to the implication of the figure above, there is another explanation for shifting the
extraction towards the earlier periods. If the probability beliefs of the hold-ups increase,
affecting the property rights, the discount factor would also increase, making the producer to
extract more in the earlier periods.
Data analysis
Empirical analysis includes 69 countries. 22 of these countries are the large net exporters of
natural gas (Algeria, Argentina, Australia, Bolivia, Brunei, Canada, China, Denmark,
Indonesia, Kazakhstan, Libya, Malaysia, Netherlands, Nigeria, Norway, Oman, Qatar,
+++,-,
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Russia, Trinidad and Tobago, Turkmenistan, UAE and Uzbekistan). The time period is 1985
– 2006. Below is the summary of the data used.
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------year |
1518
1995.5
6.346379
1985
2006
price |
1518
119.2712
49.01637
75.0854
270.6722
net_exp |
1461
25.5859
31344.63
-102294
201241
gr_cap_form |
409
46759.26
103502.9
447.9078
1065474
gdp_n |
1412
402223.8
1179203
919.0645
1.32e+07
gdp_pc |
1412
11173.48
12529.11
154.7989
89563.63
yw_m_n |
476
2058.906
2418.423
17.0673
13201.82
yw_m_pc |
476
16756.62
11359.22
276.585
44155
oil_pr |
1483
24.14886
12.07743
11.66
66.92
inv_imp |
792
101052.1
223678.4
7.890413
2188700
h_imp |
1004
114.6675
59.52712
31
240
i_h_imp |
792
9060362
9297357
520.7673
7.88e+07
mkt_rem |
484
1.365702
.7963044
1
4
pri |
474
49.45359
11.72874
24
80
hold_up |
474
69.94093
51.89439
28
240
inv_h |
409
151493.5
421086.7
2519.809
3258691
x_i |
1518
.3188406
.4661807
0
1
invest |
409
1614.065
2666.471
35.99727
21702.43
cntry_n |
1518
35
19.92306
1
69
price – Price of natural gas in Europe (USD/1000 cubic meters).
net_exp – Net exports of natural gas (million cubic meters).
gr_cap_form – Gross fixed capital formation (million USD).
oil_pr – Average price of oil for Europe (USD/bbl).
gdp_n – Nominal GDP (million USD).
gdp_pc – GDP per capita (USD).
yw_m_n – Weighted average of nominal GDP of importers, using share of exports as weights
(billion USD).
yw_m_pc – Weighted average of GDP per capita of importers, using share of exports as
weights (USD).
oil_pr – Average cost of total crude oil imports for Europe, Nothern America, and Asia and
Oceania.
inv_imp – Weighted average of investments of exporters by importing country, using share of
exports as weights (million USD).
h_imp – Weighted average of hold-up potential index of exporters by importing country,
using share of exports as weights.
i_h_imp – multiplication: inv_imp * h_imp.
+++,-,
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mkt_rem – Index of accessibility of natural gas markets (from 1 to 4).
pri – political risk index (from 0 to 100).
hold_up – Hold-up potential index (hold_up = mkt_rem * pri).
inv_h – multiplication: invest * hold_up
x_i – dummy variable for exporter vs importer
invest – Share of natural gas investments in total gross fixed capital formation for the
country: gr_cap_form * (price * production of natural gas / GDP)
cntry_n – Country.
In this data analysis we consider the responsiveness of exports to prices of natural gas. We
use a simultaneous equations model with two equations: demand equation and supply
equation. In our model price of natural gas is endogenous variable. In the demand equation
we use the sample of 47 importers of natural gas and in supply equation we use 22 exporters.
However, according to our research questions, we are more interested in the supply equation.
Before continuing with data analysis, we first present our data description. Since for natural
gas, unlike oil, there is no single world price, we decided to use the European prices, or
Norwegian prices to be precise. The data source for the price of natural gas is Statistics
Norway. The data on exports of natural gas was provided by Economic and Social
Development Service (ESDS).
We used the gross fixed capital formation figures by the World Bank (WB) to calculate the
proxy for investments into natural gas industry. The calculation of the proxy is as follows:
multiply local price of natural gas by production of natural gas, which gives us the value of
natural gas produced in the country. Then we divide the value of natural gas produced by
GDP, which gives us an approximation of share of natural gas industry in GDP of the
country. We do this only for the years 2000 – 2006, because for earlier years it is difficult to
obtain local prices of natural gas. Then we multiply the gross fixed capital formation by the
share of natural gas industry in GDP, this gives us a proxy for investments into natural gas
industry.
The data on political risk index was obtained from Business Environment Risk Intelligence
(BERI). The political risk index by BERI varies from 0 to 100, where 0 being a prohibitive
risk. In order to have the risk increasing with the value of the index, we transformed the
+++,-,
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political risk index by BERI by subtracting it from 100. This gives us a new political risk
index that also varies from 0 to 100, but this time 100 shows a prohibitive risk.
The data on average price of oil for Europe, Northern America, and Asia and Oceania was
obtained from ESDS.
WB was the source of data on GDP of the countries in our sample. Since we use the sample
of importing countries in our demand equation and exporting countries in our supply
equation, we cannot use the variables that refer only to importers as instrumental variables for
price in the supply equation. In the demand equation the exogenous variables are oil price and
GDP. One of these variables – GDP, which influences the demand for natural gas – we
cannot use right away as in instrumental variable for price in the supply equation. The reason
is that this variable refers particularly to the importers of natural gas and is not present in the
observations referring to the exporters. Thus, we have to create a GDP variable that would
influence demand for natural gas and refer to exporters, thus it could be used as an
instrumental variable in the supply equation. We created a variable showing weighted
average of GDP of importers sorted by exporting country and called it yw_m_n and
yw_m_pc (for nominal GDP and GDP per capita, respectively). We created this variable in
the following way. For each exporter we have a list of importers and share of exports they
purchase, this was obtained from the EIA (Energy Information Administration). Then we
multiply the share of exports for each importer by its GDP, and sum up the resulting figures.
This gave us weighted average of GDP of importers for each exporter, with share of exports
purchased by the importers from the particular exporter used as weights.
Market remoteness index was created by us, and it reflects accessibility of natural gas
markets for the exporting countries. The index varies from 1 to 4. If exporter supplies the
resource to its direct neighbours we assign the index value of 1 (Canada, Denmark,
Netherlands, USA). We also assign the index value of 1 if country supplies most of its
exports by ocean in the form of liquefied natural gas (Indonesia, Malaysia, Qatar). If the
resource is supplied through a single transit country, which has little influence over the transit
volumes, we assign the index value of 2 (Algeria after establishment of the EU in 1993,
Russia before collapse of the USSR in 1991). If the exports have to go through a transit
country, which can influence the transit volumes to some degree, we assign the index value
+++,-,
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0 YƵĞƐƚ !
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of 3 (Algeria before establishment of the EU, Russia after collapse of the USSR,
Turkmenistan before collapse of the USSR). If the supplies of natural gas have to go through
a transit country, which can considerably influence the transit volumes, we assign the index
value of 4 (Turkmenistan after collapse of the USSR).
In the supply equation the exogenous variable are investments, hold-up and investments *
hold-up. All of these variables refer only to exporters and cannot be directly applied to
importers. Thus, they cannot be used straight away as instrumental variables for price in the
demand equation. Using a similar procedure to the one used to create weighted average of
GDP of importers for each exporter, we constructed the variables weighted average of
investments and weighted average of hold-up potential of exporters for each importer. These
newly constructed variables are then used as instrumental variables in the demand equation.
Next, having finished data description, we continue with data analysis. In our opinion there
are two components that may contribute to potential hold-ups: external and internal factors.
The external factors are incorporated into our econometric model in the form of market
remoteness variable. It is easy to see that further the exporting country is from its importers
more the potential is for third party to influence the exports. The internal factors are added
into the model as political risk index. We expect the political risk to increase the potential for
hold-ups for the following reasons. Political risk increases uncertainty about the future for the
management of the country, or owner of the natural resource. Thus, it may shift the extraction
and exports pattern from future towards the present, making the country “want” to export
more now rather than save some of the resource for future. This, in turn, makes the country
more vulnerable for external influence from, for example, transit countries.
For empirical analysis we use simultaneous equations model in order to account for both the
supply and demand side of the mutual influence between prices and exports. The method of
estimation is two stage least squares, random effects. The reason for using the random effects
model, rather than the fixed effects model which seems to be applied more often in panel data
estimations, is due to theoretical considerations. It is well known that a random effects model
is recommended when possible omitted variables can vary across entities as well as across
time. In our opinion, possible omitted variables such as budget deficits, external debt, central
government debt, etc. influence the willingness to increase exports of the resource and shift
resource-use pattern towards the present. All of such possible omitted variables are expected
+++,-,
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to vary across entities, as well as across time. Hence, we chose to employ random effects
model. We have the following form of econometric model (p-values in parentheses).
Demand:
R-sq 0.5866
Q = -1705 – 61.103 price + 135.27 oil_pr - 0.012 gdp_n
(0.358)
(0.578)
(0.000)
Supply:
R-sq 0.6834
Q = -2416 + 42.685 price + 6.197 invest + 175.41 hold_up – 0.013 inv_h
(0.014)
(0.000)
(0.000)
(0.083)
Although, we are more interested in the supply side of the model, let’s first look at the
demand estimations. The model shows that both price of natural gas and price of oil are
statistically insignificant. This confirms that demand for energy resources is inelastic.
However, we see that income is statistically significant and has the expected sign.
Now let’s consider the supply side of the model. The regression above shows that market
remoteness, property rights issues and hold-up potential have a significant influence on the
decisions of extraction and sales of natural gas. It complies with our expectations, because
uncertainty about the future revenues, due to lack of property rights, presses the owners to
shift extraction pattern from future towards present. The prices of natural gas also gain
statistical importance with the expected sign when the above mentioned factors are accounted
for. Investments are also statistically significant with the expected sign. The multiplication
variable of investments and hold-up is also significant with an expected sign. We expected
this variable to be negative, because we expect the relationship between investments and
hold-up also to be negative. If the owners of the resource face a higher hold-up potential, they
will be more reluctant to invest.
We also did similar empirical tests using only the suppliers of natural gas, which is a usual
practice in estimating demand and supply equation. As one would expect, the results relating
to the supply equation are similar to the one above. However, the demand equation has a
much lower goodness of fit and the story with the significant variable is different from the
previous result. Overall, we think that the estimation of the demand equation above that
includes only the importers of natural gas are more reliable. Below are the regression results
with only the suppliers of natural gas.
+++,-,
,./-
01
YƵĞƐƚ !
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Demand:
R-sq 0.0056
Q = 65430 – 1431 price + 5694 oil_pr – 5.168 gdp_n
(0.041)
(0.032)
(0.358)
Supply:
R-sq 0.6662
Q = -1707 + 37.824 price + 9.980 invest + 167.34 hold_up – 0.037 inv_h
(0.025)
(0.000)
(0.000)
(0.000)
Conclusion
When the markets are remote, the possibility of hold-ups emerges. This possibility gives rise
to some form of beliefs of the exporter regarding the hold-ups occurring and creates some
uncertainty with respect to the property rights. Thus, the discount factor for the exporter
increases, shifting the extraction of natural gas towards the earlier periods. Hence, in the
short-run overproduction takes place compared to the case of no hold-up potential. The extent
of the influence of the hold-up potential depends on the type of the exporter’s probability
beliefs; if the government is risk averse the extent of the influence increases.
+++,-,
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Klein (1988), “Vertical integration as organizational ownership: the Fisher body – General
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pp. 199-213.
+++,-,
,./-
0#