Measuring Market Power on the German Electricity Market
in Theory and Practice
– Critical Notes on the LE Study –
by
Prof. Dr. Axel Ockenfels
University of Cologne, Germany
Report for RWE Aktiengesellschaft
(Translation from German)
3 July 2007
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
2
Content
1. Executive Summary and Introduction
3
2. Market power potential
5
2.1. Traditional indicators
5
2.1.1. Capacity
6
2.1.2. Generation
7
2.2. New indicators
2.3. Conclusion
3. Exercise of market power
8
11
11
3.1. Problems with the quality of the data
14
3.2. Emissions trading
17
3.3. International electricity trading
18
3.4. Scarcity prices
19
3.5. Conceptual problems
24
3.5.1. Definition and use of the Lerner Index
24
3.5.2. Platts prices
25
3.5.3. Averaging the marginal cost curve
26
3.5.4. Weighting market power indicators
28
3.6. Conclusion
4. Fixed costs
29
30
4.1. Dynamics
31
4.2. Averaging
34
5. Regression analyses
35
5.1. Measurement errors
35
5.2. Lack of robustness
37
5.2.1. Explanation content
37
5.2.2. Scarcity variables
38
5.2.3. The sign of the correlation
40
6. Withholding
41
7. Oligopolistic competition
42
8. Concluding remarks
45
9. Bibliography
46
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
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1. Executive Summary and Introduction
The Directorate General for Competition of the European Commission commissioned the
consultancy firm “London Economics” (LE) to carry out a detailed study into the performance
and structure of selected European wholesale markets. The reason for this was the results of
the Sector Inquiry (2007), which suggested that market concentration in some European countries is high and that major customers were expressing doubt as to the performance of a number of markets.
LE, in conjunction with Global Energy Decisions, presented a study entitled “Structure and
Performance of Six European Wholesale Electricity Markets in 2003, 2004 and 2005” in February 2007. In this document, I look critically at the scientific substance of those parts of the
study that examine the German wholesale electricity market.
The LE study suffers from methodological and empirical failings. Marginal costs, power station availabilities and market power are erroneously defined and/or measured in a distorted
fashion. The results arrived at are analysed and interpreted without theoretical fundament on
the mechanisms in (imperfect) competitive markets and without appropriate consideration of
the particular dynamics at work on electricity markets and of international electricity trading.
As a consequence, it systematically overstates the issue of market power.
In particular, the following holds:
•
As the LE study concedes, traditional indicators for measuring market power potentials such as the HHI are methodologically and empirically problematic, albeit they
might serve as a benchmark for international market structure comparisons. With respect to the HHI, Germany is one of the least concentrated electricity markets.
•
When international electricity trading is included, and on the basis of market power
indicators specific to the electricity market, the LE study reveals that there is largely
unproblematic to non-existent market power potential in Germany.1
•
In assessing the exercise of market power, errors are made in the measurement and/or
definition of marginal costs. To this are added conceptual deficiencies in calculation of
the price-cost mark-ups, such as the inadequate consideration given to international
electricity trading, the lack of consideration paid to the need for the long-term turnaround of investment costs, or the improper averaging of the marginal cost curve.
1
Only for one of the two indicators used and one of the suppliers investigated is a value arrived at for one of the
two benchmark scenarios examined with international electricity trading, which is slightly greater than the
threshold used by LE for problematic market power potential.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
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These deficiencies result in a systematic overestimation of mark-ups, which in addition are further artificially raised by being weighted according to load.
•
The LE study paints a theoretically and empirically false picture of the incentives for
investment in new capacity. Both the price-estimators in the LE study in the case of
perfect competition and the actual prices on the EEX 2003 and 2004 are below the
long-term average-cost level. It was not until 2005 that prices for a number of cost
scenarios were raised by the emissions trading policy above the long-term requisite
level.
•
The regressions as presented reflect the problems in the measurement of market power.
They suggest that the supplier with the greatest market power potential in Germany
uses this to reduce deviations from the perfect competition price in a statistically and
economically significant manner.
•
The LE study focuses on the fictitious, unattainable benchmark of an atomistic, ‘perfect’ competitive market. It does not analyse what results should prevail in the case of
‘workable’ competition.
All in all, the LE study cannot provide a robust basis for decision-making in relation to competition policy or regulatory measures.
In my report, I follow the overall structure laid down in the LE study. In Section 2, I start with
a number of observations as to the LE analysis of market power potential on the German electricity market. In Sections 3 and 7 of my report, I deal with a series of questions aimed at verifying the scientific content of the LE analysis of the exercise of market power, a central part
of the LE study. In Sections 4 to 6, finally, I examine and assess the complementary analyses
in the LE study on the profit situation of electricity suppliers, on the relation between market
power potential and exercise, and on capacity withholding.2
2
It should be noted at this point that the LE study withholds information (partly out of considerations of confidentiality), does not always use or define terms consistently and fails to make a theoretically self-contained presentation, so that individual misunderstandings cannot be ruled out.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
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2. Market power potential
2.1 Traditional indicators
In Chapter 6.2 of the LE study, traditional measures of concentration are first calculated.
There does exist doubt as to whether these indicators are suited to electricity markets (see for
instance Twomey et al. 2006, Stoft 2002, Energy Sector Inquiry 2007, Swider et al. 2007 and
the literature cited therein). Market power potential in the electricity market is dependent on
capacity reserves and available transfer capacities, and these can vary greatly over time. Even
‘small’ suppliers can become pivotal when capacity reserves are scarce and hence possess
relevant market power potential, whereas, by contrast, at times when a major part of capacity
is not being used, even ‘large’ suppliers can find it more difficult, if not impossible, to exercise market power. Accordingly, by contrast with the case in most other industries, market
power potential can vary greatly in the course of a day, week or over months and years on the
basis of the dynamics of supply and demand. Traditional measures of concentration are static
in nature and therefore have difficulties in taking into account the dynamics of market power
potential on electricity markets. The picture is similar for the influence of demand elasticity
and electricity market design (bidding in price-quantity combinations). From an empirical
standpoint, too, traditional measures of concentration such as the Herfindahl-HirschmanIndex (HHI) or market-share indicators (CR(n)) have proved not to be very meaningful.
The LE study concedes these problems in part:
“… it should be noted that a number of studies have shown that HHI and concentration measures can be
sensitive to the assumptions, and conclude that they are of little use in studying market concentration
(market power) in electricity markets.” (page 42)
“However, care and caution are required in the use and interpretation of these traditional structural
measures with respect to structural phenomenon in electricity markets. These caveats stem mostly from
the non-storability of electricity, and the real-time dynamic nature of supply and demand on the grid,
thus creating competitive conditions in electricity markets that are very transient – changing hour-byhour, day-to-day, season-to-season, etc. Therefore, the problem is that an electricity market may be very
competitive at certain times of the year/day and potentially very uncompetitive at other times. Traditional concentration measures have generally been found to be unable to reflect this transience in electricity markets, on average.” (page 54)
“Previous research has highlighted the problematic nature of using measures such as the HHI as they
both exhibit very little variation and have been found to be largely inappropriate for such analysis in the
electricity sector.” (page 346)
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
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Furthermore, the values calculated in the LE study are liable to stark variation not only from
hour to hour but also according to market definition (Swider et al. 2007). Finally, the threshold values used in assessing market power potential have not been developed on the fundament of theoretical or empirical analyses of electricity markets, but have rather at best proved
their worth in the case of ‘traditional’ industries and markets. Given the uncertain scientific
terrain, therefore, traditional measures of concentration and their interpretation on the basis of
traditional threshold values are generally to be treated with care.
2.1.1 Capacity
In the case of Germany, the benchmark calculation of the HHI on the basis of AIC (‘available
installed capacity’) arrives at an average value of 1,914, just over the 1,800 threshold value
for a highly concentrated market.3 Calculations based on other variables arrive at similar HHI
values. If electricity trading with other markets is taken account of, the LE study comes to the
conclusion that, on the basis of AIC, the average HHI lies between 1,160 and 2,603. For the
high value, the presumption is made that, for every hour, the entire interconnector capacity is
allotted to the largest supplier (it is import capacity that is relevant to supply), whereas, for the
low value, an ‘atomistic’ supplier structure is assumed, so that the additional capacity does
not affect suppliers’ market shares.
That means that international electricity trading can have a strong influence on the indicators
and, hence, on the assessment of the extent of concentration in Germany. An analysis of the
new indicators, which are specially designed for electricity markets (see Section 2.2), strongly
supports this conjecture. Hence, isolated consideration of national markets in calculating market power potential does not go far enough.
Independently from that, traditional indicators based on ‘national’ market definitions at times
serve as a benchmark for international market-structure comparisons. Indeed, the LE study
describes the use of traditional indicators on the basis of the scientifically critical assessment
as follows:
3
I restrict myself here to the reference analyses with respect to AIC and electricity generation. The other scenarios calculated (see for instance Tables 6.5 and 6.10) essentially serve as a robustness check; inclusion would not
affect any of the ultimate conclusions. Nor do I at this juncture deal with the presented CR(n) analysis of individual market shares. The HHI can be interpreted as an aggregation of the market shares; an additional, detailed
discussion of market shares would likewise in no way affect any of the ultimate conclusions of this report.
Prof. Dr. Axel Ockenfels
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“Nevertheless, they do provide a useful provisional assessment of concentration in a context that can be
summarily benchmarked against other markets and measures.” (page 54)
If one carries out such a structural comparison of national electricity markets, then Germany
scores well. In comparison with the other six countries in the LE study, Germany comes out
as the second least-concentrated market, after England (Table 10.1). For Europe, Newberry
graphically presented capacity shares of the largest producers together with the HHI for 13
countries based on the Energy Sector Inquiry (2007; see for instance Table 54) in a 2006
presentation. In Newberry’s compilation, Germany is shown as the second or third leastconcentrated electricity market, depending on the relevant indicator. In an older comparative
study, Schmalensee and Golub (1984) find that, with regard to 170 regionally demarcated US
electricity markets, 35-60% of the regions show HHI values of over 1,800. In a more recent
investigation, Cardell et al. (1997) find that 90% of the 112 regions examined in North America had HHI values of more than 2,500 (see also the LE study, page 42). Thus, Germany
comes out amongst the top least-concentrated markets in Europe and – at least based on the
more recent US investigation – beyond.
2.1.2 Generation
The LE study also assesses the influence of international electricity trading on the HHI on the
basis of actual generation. In doing so, international electricity trading is aggregated and balanced off, and is accordingly then imputed to total generation in Germany. This process
means that the positive effect of cross-border electricity trading is not accounted for; Germany’s exports have the effect of disciplining market power and subduing the price of electricity in the (cross-border) electricity market. Moreover, as a result of aggregating electricity
trades, the effects that imports have on disciplining market power are also left out of consideration. Ultimately, the fact that Germany is modelled as an isolated market with ‘exogenous’
international electricity trading means that the German electricity market’s integration in international competition has a negative effect on the assessment of concentration on the basis
of electricity generation. (See Section 3.3 for a further example of inadequate inclusion of
international electricity market competition in the LE study.)
Likewise it is questionable whether electricity generation can at all act as a measure for market power potential on electricity markets. First, for the exercise of market power it is not total
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
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electricity generation that is decisive but only the unhedged capacity on the spot market. Electricity that is already sold in forward markets is not relevant since, by its very nature, a
movement in the spot market price for electricity cannot raise the profit on electricity that has
already been sold. A supplier that has sold 100% of its generation on forward markets has no
interest in high spot prices. A supplier that has sold more than 100% of its generation prefers
low spot prices because it has to buy electricity in. And, for a supplier that has already sold,
say, 80% of its generation in forward markets, only the remaining 20% can constitute a relevant impetus for market power on the electricity market. (It might also be mentioned that the
last of these suppliers can also benefit from low market prices if it is thereby able to buy electricity back profitably from the spot market or when it has to cover for unexpected outages.)
Since the major suppliers typically sell the predominant share of their production via forward
markets,4 total electricity generation is no suitable measure for market power potential.
Second, as a matter of principle it is questionable whether one should measure market power
potential with the aid of market behaviour (viz. electricity generation). By way of illustration,
observe that in the case of given capacity, the relevant indicators in the LE study evidence that
a supplier has less market power potential the more market power that supplier actually exercises (and the more generation capacity it withholds).
2.2 New indicators
In Chapter 6.2, the LE study calculates new indicators aimed at suppliers’ pivotalness, which
have been developed for suppliers on electricity markets. The first of these is the Pivotal Supplier Index (PSI), which shows for every given hour whether a given supplier is ‘pivotal’, i.e.
necessary to meet demand. The average over all hours might be interpreted as the probability
that the supplier is pivotal. The second of these is the Residual Supplier Index (RSI), which
shows for every given hour the extent to which the entire production capacity exceeds the
capacity of a given supplier relative to load. The RSI might thus be interpreted as a ‘continuous’ measure of the PSI, and can likewise be aggregated over all hours.5
Whilst such indicators can better cope with the specific electricity market dynamics than traditional indicators, here too it is first necessary to identify the relevant market. The results can
4
For instance, in its quarterly information for Q1, RWE states it has already sold 95% of annual production, and
for the year 2008 already over 80% (http://www.rwe.com/generator.aspx/investor-relations/konferenz--streamings/conference-call-15-mai-2007/7-uhr/property=Data/id=464378/chartpraesentation-pdf.pdf).
5
Precise definitions as used in the LE study can be found there on pages 74 et seq.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
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significantly depend on the definition of product and market geography. Of particular importance here is the selected geographic market demarcation.
If interconnector capacity is left out of account in the benchmark analysis (see Tables 6.15
and 6.21 in the LE study), both pivotalness indicators come to the conclusion that the threshold values for significant market potential are partly exceeded.6 The inclusion of interconnector capacity and electricity trading (Tables 6.24, 6.30, 6.28 and 6.34) in the LE study’s calculations, however, leads to a massive subduing effect in the benchmark analyses, to the point
that the indicators can (nearly) identify no further pivotalness, and the market potential of the
suppliers surveyed as a whole has to be classified as unproblematic. For instance, of the eight
benchmark PSI indicators calculated (4 surveyed suppliers * 2 calculation methods) for the
years 2003-2005, seven result in a value of zero per cent (meaning that, in the period examined, these suppliers were never pivotal) and one in a value of 0.1% (the threshold value for
problematic market power potential under the LE study is 200 times greater). Only in one of
the benchmark analyses that is calculated does one supplier slightly exceed the threshold
value according to one of the two indicators examined (RSI): instead of the permitted 5% of
hours with an RSI < 110%, the supplier arrives at 6.2%.7
A direct implication of the significant influence of international trade on market power potential is that an economically appropriate definition of the relevant market has to take account of
international electricity trading; this is particularly applicable to the German electricity market,
which, as a consequence of its central position, is relatively heavily involved in cross-border
electricity trading. Failure to take account of the price-disciplining effects of cross-border
electricity trading is contrary to the definition and understanding of the indicators in the scientific literature (see for instance Twomey et al. 2006 and the literature cited there). It is also in
contradiction with the observations that prices and price movements on the electricity ex6
Here I limit myself to the benchmark analyses. The other scenarios calculated (Scenarios 1 and 2) are used
essentially as a robustness check; inclusion affects none of the final conclusions in this report.
7
If it is desired also to include consideration of the alternative scenarios, the market power potential of the same
supplier also qualifies as problematic in Scenario 2 (Table 6.26). In none of the other three alternative scenarios
do the indicators lie over the threshold value. On page 308, the LE study states that the procedure for calculating
the indicators is favourable to the subduing effect of foreign countries: “it is important to bear in mind the particular circumstances that have brought this result about, the apportionment of significant amounts of interconnector capacity to the companies at the same time as the load is being reduced, on average, due to the real life
position of Germany as a net exporter of electricity. Nevertheless, this scenario does point towards the potential
impact interconnectors could have on the German electricity market.” The fact that the inclusion of foreign countries actually has a significant influence on the assessment of market power potential is also confirmed in the
analysis by the Energy Sector Inquiry (2007) or by Peek (2005), who writes (translation from German): “Taking
account of foreign countries, capacity shortages would only arise in the case of withholding of the entire capacity
of the two largest companies – E.ON and RWE – and then only in peak demand periods, in order to be able to
meet demand in those periods (approx. 75-80 GW). The capacities of an individual company are not necessary to
meet demand, even in peak demand periods. In low demand periods, demand (approx. 30-35 GW) could also be
met without the power station output of the four largest electricity suppliers.”
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changes in Germany, France and Austria are at times virtually identical despite major differences in cost structure, which indicates that the market is to a large extent integrated. The
large number of, inter alia, foreign suppliers on the EEX spot market (Energy Sector Inquiry
2007), currently around 56%, is also evidence of this. Finally, simulations carried out by
Ehlers and Erdmann (2007) for the period 2005-2006 suggest that the price-setting leeway for
suppliers was actually very narrow.8
The LE study nonetheless essentially considers the German electricity market as being the
relevant market. For example, this is made clear in the conclusions, where the pricedisciplinary role of cross-border trade is only given qualified recognition:
“The broad conclusion in relation to this section of the report is that the German electricity market is not
structurally conducive to competitive market outcomes. Results of the RSI analysis show that in at least
one year all four of the largest four companies in Germany are in breach of the indicative threshold and
are indispensable to meeting demand in the market in a significant number of hours. In particular two of
the companies have substantial degrees of market power in a large number of hours and these results are
consistent across a number of alternative scenarios. However, once one accounts for the potential impact of Germany’s interconnectors by using one of two assumptions made in relation to the apportionment of capacity, one can see that the market power of each of the four largest companies in Germany is
substantially diminished.” (page 314)
In the overall conclusions at the end of the LE study, even this qualification regarding indicators specific to the electricity market falls out of consideration:
“The electricity-specific measures of market structure in general confirmed the qualitative conclusions
of the HHI and CR(2) for Germany. However, there is more contrast between the two types of indicator
with Germany than in some other countries. The RSI and PSI pointed more towards possible poor market structure. In general, the largest two companies’ RSIs failed the proposed screening test with
RSI<110% in greater than 5% of hours. Similar results were found for the PSI in Germany, with the PSI
finding a single company was pivotal in between 49.8% of hours. This percentage of hours of pivotalness is well in excess of any screen for possible market power problems. Thus the electricity specific
market structure measures point towards a market structure that is likely to exhibit non competitive outcomes.” (page 822)9
8
According to the study, price-setting leeway was at best high in July 2006 (which is not covered by the LE
study), the stated reasons for which are high temperatures, environmental conditions and capacity shortage.
9
The regressions in Chapter 6.8 also ignore the influence of foreign countries.
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2.3 Conclusion
The indicators calculated for market power potential by the LE study come to very different
results depending on how the indicator and market are defined. In particular, calculation of
the HHI values in taking consideration of international electricity trading allows a broad scope
for conclusions and interpretations. By international comparison, with regard to the HHI,
Germany is one of the least-concentrated electricity markets.
Traditional indicators are judged critically as regards their significance for electricity markets
from both a theoretical and an empirical standpoint. Of greater significance are new indicators
specific to the electricity market. They demonstrate that consideration of cross-border trade
significantly affects the assessment of market power potential in Germany. The indicators
calculated suggest that market power potential is reduced as a result of international electricity
trading to a point that it practically plays no role. Only one German supplier, for only one of
the two indicators investigated, in only one of the benchmark scenarios lies just over the
threshold value applied by LE. (Following the regressions in Chapter 6.9 of the LE study, it
cannot be assumed that this supplier deploys its market power potential in order to attain
higher prices; see Section 5 of this report.) An economically appropriate definition of the
relevant market must take account of the subduing effect of international electricity trading on
the pivotalness of individual suppliers and, thus, on market power potential.
3. Exercise of market power
The central part of the LE study is its analysis of the exercise of market power. Using cost and
demand data, an estimate is made of the price that would establish itself under perfect competition. Under perfect competition, no supplier can affect the market price to his benefit. Thus,
one also speaks of all suppliers being “price takers”; they have to accept the price as given.
The price estimator for perfect competition results from the marginal costs of the marginal
power station.
When estimating prices under perfect competition, two things should be kept in mind:
•
Perfect competition is a simple, idealised fiction. It implies an infinite number of suppliers that can each supply only a marginal quantity of electricity.10 This is obviously
true of no electricity market. (In other industries, too, perfect competition is generally
unattainable.)
10
Otherwise, there would exist price-setting leeway, especially in the case of capacity shortages (Section 7).
Prof. Dr. Axel Ockenfels
•
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Perfect competition does not imply a perfect market. In particular, perfect supplier
competition does not rule out there being demand-driven, technological or politically
induced imperfections, complexities and uncertainties in the electricity market, which
can lead to inefficiency or undesirable or unexpected price effects.
The analysis of a perfect competition market can be useful on two grounds:
•
Deviations from what can be expected under perfect competition conditions can – with
precise empirical analysis – constitute an indication for the exercise of market power.
This is why the LE study examines perfect competition as a benchmark case.
•
By the same token, conversely, phenomena that would also be expected under perfect
competition cannot be used as an indicator of market power. (This section shows that
the central data analyses of the LE study do not contradict perfect competition.)
Whilst the method followed by the LE study is therefore valid in principle, estimation of the
prices under perfect competition and interpretation of the results are fraught with problems.
The central element in measuring the exercise of market power is the marginal costs for electricity generation, since these determine the prices under perfect competition. Marginal costs
are by definition those costs that are incurred by a supplier in the production of an additional
output unit.11 Marginal costs can consequently be avoided by not producing an additional output unit. This implies that suppliers under perfect competition bid marginal costs at the electricity exchange and, hence, that the market price under perfect competition is equal to the
marginal costs of the most expensive power station deployed with respect to the marginal
costs (the so-called “marginal power station”).12
Marginal costs are also opportunity costs. Opportunity costs arise when production resources
are not applied to the use with the greatest possible value. Taking account of opportunity costs
is nothing unusual and is a normal procedure on all competitively organised markets. On electricity markets, opportunities arise in a series of interdependent markets. Take, for example, a
hydro power station that is fed from a small dam. If it produces electricity for the current electricity wholesale market, the production capacity for tomorrow’s market (the opportunity) is
11
At this juncture, I would already point out that the LE study (at least implicitly) fallaciously defines the costs
of the last production unit as the marginal cost, and thereby underestimates the true marginal costs in scarcity
situations (see Section 3.4).
12
For a more in-depth presentation, see for instance Stoft (2002) or Ockenfels (2007).
Prof. Dr. Axel Ockenfels
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less. If the supplier expects higher prices tomorrow, the consequence is that he will produce
no electricity today, not even if today’s price is greater than the current variable costs of production. The supplier will rather demand at least a price that corresponds to his opportunity
cost, which is dependent on future prices. Moreover, electricity that is sold in electricity spot
trading on the EEX cannot, for instance, be sold on reserve markets or in neighbouring wholesale markets. The prices resulting on these competing markets are therefore of direct relevance for price demands and bid decisions on the electricity exchange.
The next section illustrates problems with data quality in the measurement of marginal costs,
Sections 3.2-3.3 reveal various further empirical distortions, and Section 3.5 describes a series
of underlying conceptual problems that arise in the LE study in measuring marginal costs,
estimating prices under perfect competition and the comparison with market prices.
In addition, it should already be pointed out at this juncture that an interpretation of the price
estimators under perfect competition is not trivial even in the case of a precise measurement
of the marginal costs. Perfect competition is a fiction. Yet, what deviations from the price
estimator under perfect competition are tolerable? Whereas, in the market power potential
analysis, the LE study still defines threshold values for unobjectionable market power potential, no tolerance limits are stated in the analysis of the exercise of market power. For an illustration of the problem, observe that for about 10 or more equally large suppliers (HHI index
of less than 1,000), market power potential is regarded as unobjectionable. However, deviations from prices that arise under perfect competition are nevertheless also to be expected in
markets with 50 or more equally large suppliers. I revert to this problem in Section 7.
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3.1 Problems with the quality of the data
The LE study has an extensive dataset at its disposal, which was provided by suppliers in the
form of answers to questionnaires. On this count, the data quality in comparison with other
studies is assumed to be relative good. Nonetheless, there are evidently multiple problems in
measuring marginal costs.
Figure 3.1 is based on graph 6.19 (page 332) in the LE study and illustrates the problem. For
2005, it shows the distribution of the price mark-ups over the estimated marginal costs for
Germany. With perfect competition and precise measurement of marginal costs, all observations should be nil: there should be neither price mark-ups nor mark-downs from the calculated marginal costs. However, according to the LE study, there are in fact a large number of
deviations. The relevant question is to what degree the deviations reflect the exercise of market power, and to what degree they are determined by methodological and empirical inadequacies.
Perfect
Price mark-downs:
measurement errors! competition
Price mark-ups:
what is the cause?
Figure 3.1: Deviations of the EEX price from the LE price estimator
Prof. Dr. Axel Ockenfels
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Downward deviations would, where the marginal costs are accurately calculated, imply that
suppliers are supplying electricity at less than marginal costs. This (typically)13 is economic
nonsense. Hence, where the LE study arrives at negative deviations in price from the modelled marginal costs, this means that LE has made errors in measuring or defining marginal
costs. Such errors can for instance come about by inadequate treatment of indivisibilities,
start-up costs, must-run capacities, coupled production and various categories of opportunity
costs. The LE study concedes that such problems exist (pages 322 and 324).
It nevertheless seems improbable that, in measuring marginal costs, downward errors have
been made but not upward ones,14 for the sources of error mentioned above can lead not only
to overestimates but also to underestimates of marginal costs. Below, further potential sources
of error are put forward:
•
In the survey, the costs asked for were not unambiguously defined. For instance, with
respect to CO2 and fuel costs, LE could have both procurement and also dispatchrelevant costs to hand. In the former case, the opportunities would not be correctly
represented. Since the opportunity costs typically rose in the period 2003-2005, this
results in a systematic underestimation of the marginal costs.
•
Pumping processes at pump storage plants were not taken into consideration in the
survey. Whether and how this was modelled in the LE study is unclear.
•
It is unclear in relation to pump and reservoir storage how far restrictions of, say, hydrological and environmental nature are taken account of.
•
The requisite capacities needed for the storage of balancing electricity are significantly
underestimated. Whereas LE assumes a required balancing output amounting to 5% of
the calculated load (Appendix 1, page 11), the actual values are 8.6% of the maximum
and 18.3% of the minimum load in Germany (see for instance VDN 2005, and
13
In any event, it could be argued that suppliers accept prices at less than marginal cost in order to deter competitors. This argument is not adduced in the LE study. Even so, it is not economically convincing since there is
no evidence that prices on average are less than marginal costs. In this report, this possibility is therefore not
further pursued.
14
The corresponding graphics in the LE study for 2003 and 2004 show relatively more cases with price markups than the graph for 2005. Observe also that erroneous downward deviations can only amount to a maximum
of –1, whereas upward deviations are unlimited. Finally, it should be pointed out that the LE study also had considerable difficulties in calculating the actual costs realised; see for example Table 6.37 and the explanations on
pages 321-322.
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Measuring Market Power in Theory and Practice
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Richmann and Loske 2006).15 In addition, there are the reserve stocks due to stochastic wind energy input.
•
Furthermore, there are power station units that predominantly supply balancing electricity. Restriction of storage to 20% of the installed capacity, as is assumed in the LE
study, cannot reflect this. Hence, the modelled input of these power stations does not
correspond to reality. Other units have to be deployed ‘suboptimally’ because they
have to supply a minimum quantity for the spot market in order to qualify for balancing electricity offerings. Finally, it is unclear how LE takes account of reserve stocks
for exceptional outages.
•
Start-up costs appear only to be taken account of by LE in the sense of a higher fuel
consumption. This method neglects impacts relevant to marginal costs as a result of
greater technical utilisation and asset depreciation and also neglects the risk of possible outages (see also Schwarz and Lang 2006).
•
A further, conceptual problem in ascertaining marginal costs is the treatment of uncertainty. The relevant marginal costs for measuring the exercise of market power on the
day-ahead (spot) market are the marginal and opportunity costs presumed by the suppliers at the time of preparing the bid (on the previous day). Because LE does not have
data on suppliers’ presumptions, it is to be assumed that LE has used an ex ante costminimal supply decision under perfect competition conditions or some other auxiliary
constructions as a basis for the price estimations.16 As a result, real uncertainties as to
the true development on the next day are neglected, such as with regard to the opportunity costs induced in other markets (balancing electricity market, foreign wholesale
markets, future electricity prices in the case of storage power stations, CO2 price dynamics), load, wind energy generation and load capacities (restrictions as a result of
outside influences) by own power stations and those located in the market (in so far as
they influence the cost-minimal supply), interconnector capacity, etc. These uncertainties can drive a wedge between EEX prices based on expectations and actually incurred marginal costs. Additionally, suppliers are unable to react to false expectations
with infinite speed. Each deviation from a model of perfect foresight or infinitely
quick adjustment therefore leads to an underestimate of the actual marginal costs.
15
The description of the price effects of stocks of balancing electricity in the appendix to the LE study is unclear;
it raises doubt as to whether the corresponding modelling mechanisms correctly depict the German electricity
market.
16
For instance, in the survey, only factual outages and only revision plans, which were known 6 months ahead of
time, were asked for.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
17
Overall, in view of Figure 3.1 together with the open questions and potential error sources, it
cannot be assumed that the dataset for the LE study allows a precise estimation of marginal
costs. Unfortunately, virtually no sensitivity analyses or other assessments of error variances
are carried out, so that a judgement of the robustness of the estimations is not possible. However, Figure 3.1 suggests that the errors might be considerable (see also Swider et al. 2007).
3.2 Emissions trading
Opportunity costs are partly neglected in the LE study in connection with emissions trading.
In Tables 1.2, 6.39 and 6.42 and, for instance, in Table 10.2, the LE study shows the Lerner
Index (LI) and price-cost mark-ups (PCMU) without opportunity costs arising as a consequence of emissions trading being taken into account in calculating the marginal costs. LE
writes:
“It is possible that recent prices in the power exchanges studied are not reflective of the opportunity cost
of carbon, since companies received their emissions trading allowances free. In this case, the mark-ups
presented above would be higher. Whether companies’ should price in carbon fully is another question,
which we did not address.” (page 22)
“Bearing in mind that companies in fact did not pay for their initial carbon emissions rights under the
ETS, it is an interesting test to calculate the expected LI value in 2005 for which the cost of carbon in
this year is ignored.” (page 324)
“Of considerable further interest is the breakdown of the power exchange price into the constituent
components; cost, mark-up and the cost of CO2 emissions since the introduction of the ETS. We cannot
fully interpret whether companies have passed on the full cost of carbon or whether they have ‘raised’
margins in response to carbon. It is perhaps that companies do not pass on the full cost since they have
received allowances for free. We further do not take a stance on what should have been done. We have
included the full cost of carbon in our comparisons, as this is the maximum amount a competitive market would have passed on the cost of carbon. Thus we take the most conservative approach.” (page 814)
It almost appears that, with the parallel presentation of cost mark-ups with and without consideration of the certificate costs, the LE study tries to suggest that, because of the free allocation of a major share of the certificates, anything between small and full ‘pricing in’ would be
conceivable. It appears that LE thus does not to want to take a clear stance in this question
(see also page 824 of the LE study). The question whether and how much is priced in is, however, irrelevant. Of sole relevance for the simulation analysis is what can be expected under
perfect competition. Certificate costs are (among economists) without question a component
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
18
of marginal costs.17 Under perfect competition, the costs for tradable certificates are fully
priced in – regardless of what is assumed about the actual practice or what effect is desired.
On this ground, it is not understandable that ‘cost mark-ups’ are shown that neglect these opportunity costs. The way the results are presented may give a misleading impression of the
scope of market power exercise.
3.3 International electricity trading
The analysis of market power potential has shown that the view of isolated competition on the
German electricity market is inappropriate, since the import and export of electricity have
significant effects on market power potential. In the analysis of the exercise of market power,
the LE study nevertheless implicitly assumes that the national markets are ‘independent’
competitive markets: exports are merely taken account of in the load; imports are disregarded.
This method results in a systematic underestimation of marginal costs and an overestimation
of the exercise of market power – especially in peak load times.
Specifically, the LE study assumes that, in spite of cross-border electricity trading, prices can
be explained by only taking into account national electricity generation and the costs thereof.
This would imply that the attainable ‘national’ prices are independent from international
competition. However, imports, when price-setting, do drive a wedge between the marginal
costs of domestic production and the domestic price – even under perfect competition. The
wedge tends to be greater the scarcer (and more inelastic) the supply. Failure to take account
of imports thus results in an underestimation of the price estimator and, hence to an overestimation of the exercise of market power, especially in peak load times.
In considering exports, the assumption that prices can only be explained by the costs of national electricity generation leads to an inherent inconsistency. In the case of international
price differences, foreign prices can pose opportunity costs for the domestic electricity supply.
No domestic supplier is prepared to supply at a price that is less than the price he can get
abroad. With price differences and the possibility of cross-border electricity trading, therefore,
the prices attainable abroad have to be taken account of as opportunity costs (as appropriate,
17
See for example Pindyck and Rubinfeld (2005), Varian (2003) or other propaedeutic text books on economic
science for the context of opportunity costs and marginal costs. For the application to emissions trading, see for
example Varming et al. (Riso National Laboratory, 2000, page 89), Woerdman (University of Groningen, 2001,
page 3), Harrison and Radov (NERA, commissioned by the EU, 2002, page 23), Brandt (University of Southern
Denmark, 2002, pages 17-18), Matthes et al. (Ökoinstitut, DIW and Ecofys, 2003, page 30), Martinez and Neuhoff (University of Cambridge, 2004), Neuhoff (University of Cambridge, 2005, page 5), Sijm et al. (ECN, 2005,
page 33) Bode (HWWA 2006, page 3), as well as Schwarz and Lang (2006) and von Hirschhausen (2007).
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
19
factoring in the transport costs). Neglecting the opportunity costs again results in a systematic
overestimation of the exercise of market power.
3.4 Scarcity prices
The LE analysis fails to take account of the need for the long-term coverage of investment
costs. Although the LE study is aware of the issue, it eventually ignores it in the chapters on
measuring the exercise of market power.18
If prices were at all times at the level that the LE study price estimators ‘allow’, the unavoidable long-term consequence would be a breakdown of the market, since in that case (at least)
the marginal power stations would be unable to cover their investment costs. As a result, in
the long term, the market would not offer adequate capacities. Hence, even under perfect competition, the marginal costs must at times be greater than the costs of the last-produced unit
during peak demand periods.19 The price estimators, as calculated in the LE study, nonetheless do not admit of such scarcity prices (a more precise definition of scarcity prices is provided below). As a result, the long-term average price estimators are systematically less than
the prices that should be observed under perfect competition. Therefore, price-cost mark-ups
in the LE study could not without further analysis be interpreted as the exercise of market
power, even if all variable production costs and opportunity costs were correctly determined.
A market emits price signals for the build-up of capacity when capacity becomes scarce.
When there are sufficient capacity reserves on the market, marginal cost prices are typically
below average cost prices with regard to the marginal power station since, in calculating the
costs of an additional unit, the fixed cost components are ignored.
18
In Chapter 6.8 of the LE study, investment incentives are dealt with. However, the analysis is neither theoretically nor empirically convincing, as Section 4 of this report demonstrates. In Chapter 6.9 of the LE study, it
appears that it is implicitly conceded by the introduction of explanatory variables to reflect capacity scarcity in
the regression analyses, that distortions are possible in the price estimators because of the fact that long-term
marginal costs are not taken account of.
19
See for example Stoft (2002), Kahn (2002) or Joskow (2006).
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
Price
[€/MWh]
p*
20
Supply (MC)
Costs of the last unit
Inframarginal
returns
Quantity
Figure 3.2: No scarcity
Figure 3.2 shows a situation with sufficient capacity reserves. It is assumed that there is perfect competition and hence the supply curve is the same as the marginal cost curve. The figure
illustrates that so-called ‘inframarginal returns’ (the orange-coloured area) arise, which can be
used to cover fixed costs, since the income for the power stations deployed (apart from the
marginal power station) generally exceed the relevant marginal costs. Even if on average
coverage of fixed costs over all power stations were to be possible, a state such as is depicted
in Figure 3.2 cannot be stable in the long term. The reason is that the marginal power station
cannot realise any revenue over and above its marginal costs, so that the fixed costs of (at
least) the marginal power station cannot be covered.20 None of the power stations to the right
of the marginal power station can bring in any revenue at all. The problem is aggravated by
the fact that, often, peak load power stations not only are able to bring in little or no inframarginal return but also run for few hours a year, so that the average costs typically by far exceed
the marginal costs in relation to actual production. As long as no capacity shortage arises,
such power stations are unable to cover their investment costs under perfect competition. No
one is prepared to invest in the necessary peak load power stations. Capacity is therefore reduced until capacity scarcity is reached, as illustrated in Figure 3.3.
20
In the long-term equilibrium under perfect competition, all power stations can just cover their fixed costs (see
for example Joskow 2006); for simplicity’s sake, at this juncture I concentrate on the marginal power stations,
which are unable to bring in any inframarginal return. In so doing, I leave aside volatilities and stochastics in the
electricity market, which have no bearing on my argument here.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
Price
[€/MWh]
21
Supply (MC)
p*
Costs of the last unit
Quantity
Figure 3.3: Scarcity situation
Figure 3.3 shows what happens when capacity becomes scarce. In the example, capacity is
fully exhausted; no further capacity is available. The market clearing price is greater than the
costs for the last unit and is determined by demand. This has to be so because, if the price
were the same as the costs of the last unit, the resultant demand could not be met. The price
p* determined by demand is called the scarcity price. It rations the scarce capacity so that
demand and supply on the electricity exchange can be brought into balance.
Figure 3.3 illustrates that, in such scarcity situations, even the marginal power station can attain returns in excess of the costs of the last unit. In a long-term equilibrium, the peak load
power stations have to be able to fully recover their investment costs with the aid of scarcity
prices.
Scarcity prices, which are far in excess of the costs of the last unit, also arise under perfect
competition and with free market entry. In Figure 3.3, it can straightaway be assumed that all
suppliers bid marginal costs, since at the capacity limit the marginal costs are undetermined,
and hence prices above the costs of the last unit are consistent with marginal cost price-setting
(see Stoft 2002).
A dynamic price mechanism with scarcity prices at the capacity limits that are steered by demand is not unusual in principle. The marginal costs in the cases of airline tickets, hotel
rooms, rental cars and theatre tickets are very low as long as no capacity shortages arise, and
are typically lower than average costs. Here, too, prices fluctuate greatly. On Friday after-
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
22
noons, the days before bank holidays and during trade fairs, airline ticket prices rise fairly
sharply. At such times, they are driven not only by variable costs but by the customers’ willingness to pay. This enables suppliers to cover their investment costs.
It is worthy of note that the LE study takes no account of the fact that prices under perfect
competition can also exceed the costs of the last unit, even though the issue is recognised:
“As has previously been noted one of the primary difficulties in conducting a study on competition in
electricity markets is the difficulty surrounding calculating a marginal cost for the system. The hourly
marginal cost used throughout this report is the result of a cost minimising commitment and despatch
simulation, using GED’s MARKETSYM software, to serve the load in each market, in each hour. This
approach allows for the returned generators’ data and dynamic constraints on the system to be modelled
and for a marginal cost to be estimated as a result of the optimal commitment and despatch of the system. However, due to the presence of relatively high fixed costs in the electricity markets, in general,
using the system lambda as the marginal cost on the system may lead to the use of an artificially low
system marginal cost in the calculation of the market outcome measures, as the system lambda is likely
to be insufficient allow for contributions to the relatively high fixed costs. In order to avoid this problem
a marginal average cost approach has been adopted.” (pages 91, 92)
The last statement in the quotation above might well suggest that the problem is solved – yet,
this is not the case, as becomes evident when one reads further:
“Under this approach the cost minimising commitment and despatch to serve load is simulated. An associated generation cost is similarly returned for each unit on an hourly basis. Start costs are computed
by the model and in hours where they are apparent for units they are removed. Furthermore, a number
of units are excluded from setting the marginal average cost based on prior specification and modelling
results. … For the remaining stations, modelled generation cost (less start costs) of each unit is divided
by the non-zero generation of the unit to arrive at the average cost that the generator must cover in that
hour. The station(s) with the highest average costs in an hour are identified as marginal; their average
costs are the Marginal Average Costs.” (page 92)
The “marginal average costs” as thus defined reflect an average of the variable production
costs and perhaps opportunity costs; fixed costs, investment costs and demand-induced scarcity prices have apparently no bearing in the calculation. This is made all the clearer upon
reading the corresponding section in Appendix II to the LE study where, once again, the relevance of the issue is emphasised:
“In industries with relatively high fixed costs, and relatively low barriers to entry and exit, an “industry
marginal cost” cannot generally be identified with a market price. The reason is that the marginal unit
cannot cover its fixed costs, and if it could have avoided participating in the money-losing transaction it
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
23
would have. This is precisely the obstacle to using the simple System Lambda from a PROSYM run as
a benchmark price measure. The “investment decision” to participate in an hour’s market is simply the
cost to commit (or the cost to decommit), and it is generally easy for a generator to leave (or enter) this
market; as a result one does not expect to see prices equal to the short-run system marginal cost. This
has not prevented the use of the PROSYM systems from being used to support price forecasts. The
usual approach is to identify the markups to bids that are necessary to allow different types of units (in
particular low-capacity-factor peaking stations, and the high-cost peaking segments of mid-merit and
base-loaded generation) to cover all of the costs of commitment and dispatch, and make a reasonable
contribution to fixed costs. In the usual practice, this approach may be understood as a monotonic transformation of the underlying industry incremental cost curve, with the low-cost elements of segments of
the cost curve, corresponding to base-loaded generation, marked up little if at all, but with markups increasing as one moves up the curve, so that units or segments that run very rarely are able to recover
their non-incremental costs in the small number of hours that they actually run. Since the resulting bidaugmented stack is a monotonic transformation of the system incremental costs, and since electricity is
modelled in the short run as having inelastic demand, the resulting dispatch is efficient. Indeed, a PROSYM dispatch that includes carefully-calibrated bid markups will be identical to a pure cost-based
markup, albeit with simulated system lambdas that can be used to forecast prices.” (pages 1-2)
Although the method described in the quotation for taking account of fixed costs could in
principle allow coverage of fixed costs, it would nonetheless be inconsistent with the price
movements that ought to be observed on electricity markets under perfect competition. This
criticism, however, is ultimately unimportant since the LE study in any case takes no account
of scarcity prices or the need to recover investment costs, as it goes on to concede:
“A drawback of this approach, however, is that it requires that the analyst decide what a “reasonable
contribution” is, and to otherwise introduce values into the model that are not directly derived from
more-or-less objective data. This makes the conventional price-forecasting approach unsuitable for use
in regulatory and investigative contexts, where the objective is to identify problems in industrial structure and conduct. The MAC approach avoids this drawback by eliminating the subjective bid-markup
step, and instead simply identifying the costs that would need to be covered by market prices in an efficient dispatch, which may then be compared to the wholesale prices for electricity that were actually
observed.”
Not taking into consideration the need to recover investment costs in the long term and the
incorrect definition of marginal costs in the case of capacity scarcity when calculating the
price estimators are problematic failings. The consequence is that, in scarcity situations, the
LE-model may produce massively exaggerated market power measures – even when prices
have not yet reached the level necessary to induce investment. In this regard, see also Section 4.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
24
3.5 Conceptual problems
3.5.1 Definition and use of the Lerner Index
The LE study calculates two indicators for the exercise of market power. The first one is
called the “Lerner Index” (LI). The LI is unequivocally defined in economics text books as a
standard measure for the exercise of market power, and the theoretical underpinning is well
understood for simple competition models. However, the LE study appears to define the LI
incorrectly. The LI is correctly defined as a gap between the price (bid) of a supplier and his
(actual) marginal costs, stated as a ratio of the price (bid) (see Twomey et al. 2006 or Pindyck
and Rubinfeld 2005). By contrast, the LE study defines the LI (implicitly for the supplier of
the marginal power station) as the gap between price and fictitious marginal costs (relative to
the price) that would have come about under perfect competition.21 If the marginal power station does actually have higher marginal costs than the marginal costs that would have arisen
under perfect competition according to the LE study’s calculation, the LI calculated in the LE
study overstates the correctly defined LI.
Aside from that, there is also a fundamental problem with the LI as an indicator for the exercise of market power in electricity markets: sensitivity to changes in marginal costs. A decrease in marginal costs as a consequence of supply reduction resulting from (economic) capacity withholding (when demand is not inelastic, bids above marginal costs can in case of a
rising price lead to a fall in demand and, simultaneously, to lower marginal costs) would be
economically harmless, but would nonetheless drive the LI upwards. A rise in marginal costs
as a consequence of (physical) capacity withholding, on the other hand, subdues the effect on
the market power indicator. Stoft, in his text book on electricity markets (2002, Chapters 4-5),
describes this and other problems with the LI and comes to the conclusion that the LI produces unreliable results and is therefore unsuited to electricity markets. The LE study does not
discuss such problems in relation to the meaningfulness of LI values.
21
The LI is based on the “system marginal cost” (page 317), whose definition appears not always to be used
consistently. The context nonetheless suggests that the marginal cost estimators under perfect competition were
used. The calculation of the LI used by LE is only correct if the actual marginal costs and the marginal costs
under perfect competition are identical. For the exercise of market power, neither is this theoretically to be expected nor does it accord with the evidence put forward in the LE study (e.g. in Chapters 6.5 and 6.10).
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
25
3.5.2 Platts prices
Alongside EEX prices, the LE study also uses “Platts assessment prices” to calculate market
power indices. LE justifies these analyses as follows:
“This price series provides a base and peak price for electricity on a daily basis on weekdays and a base
price for electricity on weekends. As this price is constant for all hours of base and peak in the relevant
days, this price may be a more appropriate representation of the price of electricity contracted forward
(over periods greater than a day) in Germany, a quantity considerably greater than that traded on a day
ahead basis.”
The approach and its justification are problematic on methodological grounds. For one, Platts
prices are not market (clearing) prices but are determined on the basis of OTC quotations and
OTC deals. Hence, they can only depict a part of market dealings. Above all, the precise determination of the prices is not transparent.
Moreover, the idea of conducting market power analyses with the aid of forward dealings is
also fundamentally flawed, since electricity prices on all markets that predate electricity spot
market trading are oriented towards the (anticipated) EEX prices. The reason is that traders
can procure electricity alternatively in either market. The outcome of trading is that the future
market price for electricity that is supplied at moment t has to be equal to the anticipated spot
price at moment t. If there is a difference between the two prices, then arbitrage possibilities
arise. Because purchasers and sellers of electricity are always able to buy and sell at the spot
market, market clearing prices on futures markets and anticipated exchange prices may not
systematically deviate from one another.22 Therefore, as long as there are sufficient arbitrage
possibilities, no market power can be exercised on forward markets.
Methodologically, it is additionally problematic that, in the LE analysis, ‘average’ Platts
prices are linked to hourly marginal costs. The Platts prices used are in each case kept constant over several hours, but the marginal costs (and the EEX prices) over those hours are
typically highly volatile. Platts prices, which are unable to trace this volatility, necessarily
drive a wedge between marginal costs and prices – even with perfect competition conditions –
and therefore distort the results.
Since calculation of the Platts prices is not entirely transparent, it cannot be stated with certainty in which direction the distortion goes. However, if, say, exceptionally high hourly price
22
Systematic deviations are only conceivable where, for instance, electricity sellers are more risk-averse than
buyers.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
26
and marginal cost peaks, as they can arise under perfect competition, are not adequately reflected by Platts prices, the exercise of market power can end up being systematically overestimated.
In sum, the LE study offers no theoretical justification for using Platts prices, does not derive
any hypotheses for the change of outcome when using Platts prices and does not interpret the
empirical results attained. A self-evident explanation for why the indices determined on the
basis of Platts prices identify deviations from perfect competition is that, by using Platts
prices, marginal costs and prices are disconnected from one another.
3.5.3 Averaging the marginal cost curve
The price under perfect competition is in each hour the result of market supply and demand
conditions, whereby the supply is determined by marginal costs and power station availabilities, which vary from hour to hour, day to day and week to week. However, for each month,
the LE study takes a constant average marginal cost curve as the basis for its price estimations.
This approach neglects the core characteristics of electricity markets: the high volatility and
stochastics (also) on the supply side, together with a non-linear marginal cost curve that gets
steeper along with capacity utilisation. As a particular consequence, the averaging – similar to
the use of Platts assessment prices – results in price estimators being disconnected from the
actually realised marginal costs. This must automatically lead to deviations of prices and marginal costs – even under perfect competition.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
Price
[€/MWh]
27
Demand
Average price
(actual price)
Price at average supply
(price estimate)
Supply
Quantity
Figure 3.4: Averaging in relation to availabilities
LE does not justify this approach, but possibly implicitly assumes that downward and upward
errors ought to cancel each other out. The assumption is nevertheless false, as Figure 3.4
shows. Owing to the characteristic convex form of the marginal cost curve, the use of an average marginal cost curve in case of volatile availabilities leads to systematic underestimation
of the prices under perfect competition.23
Assume that competition is perfect and the marginal cost curve (= supply under perfect competition) moves in approximately equal measure between the far left and far right marginal
cost curves (for a given demand) in Figure 3.4. The mid line then shows the average marginal
cost curve. Owing to the convex characteristic of the marginal cost curve, the price estimator
in Figure 3.4 in the case of the average curve is smaller than the actual average price, which is
shown in the illustration simply as the mid-point between the highest and lowest realised
prices. If volatility is ignored by averaging, there will therefore be an underestimation of the
prices that would arise under perfect competition.
For off-peak load times, the corresponding error in estimate is smaller. It is easy to see with
the help of Figure 3.4 that the difference between price estimators in the case of the average
marginal cost curve and the actual average price gets smaller when the mid-demand moves to
the left. In other words, the errors induced by averaging tends to rise with the load.
23
That the marginal cost curve is actually convex and, as a result, the illustration above is correctly depicted, can
be seen for example in graph 6.2 in the LE study on page 260. The Energy Sector Inquiry (2007) estimates that
the hourly total capacity demonstrates a standard deviation (relative to the average) of 6.9%.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
28
3.5.4 Weighting market power indicators
The LE study calculates market power exercise indicators that are weighted over time by way
of load. The reasons stated are:
“… one must consider the implications of this on the formulation of outcome measures in the electricity
sector, where data is available hourly and there is substantial variation in price and cost, driven by demand, within any one particular day. Failure to account for demand conditions leads one to a conclusion
on the outcome measures that may not be correct by placing equal weight on the calculated measures
for say the peak hour and the lowest demand hour in a particular day. A negative outcome measure in
off-peak hours is a very different proposition to that in peak hours as firms may willingly utilise loss
making generation capacity in off-peak hours for a number of reasons, including; to avoid turning units
off and thus not having to pay large start-up costs, to ensure units are on to meet demand in subsequent
hours, or the units may already be on to meet other need such as contract positions, industrial processes
or reserve commitments. In peak hours, negative outcome measures are not considered to be a likely
outcome and thus merit further attention if they are a systematic occurrence. Therefore, simple averages
should be replace by load weighted averages of both the price and cost in order to correctly assess the
outcomes produced by the underlying market. This approach is adopted in the remainder of this chapter.” (page 324)
If the aim is an accurate measurement of the price-cost mark-up, there is a priori no need to
weight the data according to load. Ultimately, the justification for weighting according to load
appears to boil down to the marginal costs in the case of lower load not having been correctly
determined. No supplier would supply at prices less than marginal costs. Yet, in the case of
low load the LE study identifies prices that are lower than the computed marginal costs,
which once again points to errors in the measurement or definition of the marginal costs (see
Section 3.1 of this report). By weighting the price-cost mark-up according to load, these errors
in the assessment of market power exercise can be extenuated or suppressed.
This approach is nonetheless not unproblematic since a whole series of conceptual errors that
result in a systematic overestimation of the exercise of market power are only or particularly
pronounced in peak load times. To this belong the inadequate consideration of international
electricity trading (that tendentially creates bigger errors in peak load times), the failure to
take account of scarcity prices in times of scarce capacity (which can lead to massive errors in
peak load times) and the monthly averaging of the marginal cost curve (which leads to particularly large errors in peak load times). That means that, as a result of the selected weighting
procedure, errors that close the price-cost gap are systematically played down, and at the same
time systematic errors that enlarge the price-cost gap are inflated.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
29
The regressions in Chapter 6.9 of the LE study, which do not weight the market power indicators according to load, appear to confirm that significant errors have been made in measuring
market power, particularly in the case of high load. Variables that are correlated to capacity
scarcity contribute an economically and statistically significant explanation for the calculated
price-cost mark-ups, which ought not necessarily to be expected in the case of a precise
measurement of marginal costs.
It could at least have been expected that the LE study would have informed readers of the
results in the case of a straight-line, ‘natural’ averaging. This is not the case.
3.6 Conclusion
The measurement and/or definition of the hypothetical marginal costs under perfect competition and the corresponding price-cost mark-ups in the LE study are flawed. This is for example evident as a result of the large number of purportedly negative price mark-ups, which
ought not to occur with accurate measurement of marginal costs and modelling (e.g. in relation to start-up costs).
Nonetheless, even if the LE study had succeeded in exactly determining the relevant marginal
costs for each and every power station for the dispatch decision in the spot market, a series of
conceptual errors result in an uncoupling of price estimators from actual marginal costs,
which leads to a systematic overestimation of market power. Included here are the incomplete
account that is taken of international electricity trading and competition, the failure to take
account of scarcity prices in peak load times and the improper averaging over time of the
marginal costs curve. These errors are systematically inflated as a result of the weighting of
market power indicators according to load. In short, the problems in the measurements, definitions and methods can lead to massive error in the estimates.
It might be mentioned at this juncture that simulation studies of this sort have as a matter of
principle to battle with measurement problems owing to the particular dynamics of the electricity market. Smeers (2005) and the works cited therein give a summary of typical problems
encountered. Simulation studies as in the LE study are, especially at peak load times, sensitive
with regard to the assumed marginal costs, opportunity costs, availabilities, outside influences,
modelling of electricity trading, dynamic power station deployment planning, uncertainty,
network restrictions, scarcity pricing, dynamic storage reserves, etc. It thus should not come
as a surprise that a comparison of various simulation analyses that have hitherto been carried
out gives neither robust results nor always unequivocal conclusions. For the controversial
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
30
discussions on the Californian market, which indeed has been analysed more than any other,
see Smeers (2005) and also Harvey and Hogan (2002) and the literature quoted therein.24 For
Germany, there are three studies that deal with the more recent evolution in prices, using the
simulation method: Schwarz and Lang (2006), von Hirschhausen et al. (2007) and the LE
study. In particular, all three studies look into the considerable price rises in 2005, which were
and still are the source of much of the discussion on German electricity market prices. The
three studies nevertheless came to diametrically opposing results as regards the causes of
these price rises.25
Whereas von Hirschhausen et al. (2007, page 43, translation from German) conclude from
their analysis that “tendentially, the (absolute and relative) price mark-ups appear to rise between the beginning of 2005 and 2006”, Schwarz and Lang (2007, page 21) conclude “… that
only fundamental factors were responsible for the sharp price rise in 2005”, whilst the LE
study reveals a significant drop in the 2005 mark-up by 31%. The spectrum of diagnoses for
the 2005 price rises based on the same methodological approach therefore ranges from ‘tendentially market-power-driven’ (Hirschhausen et al.) via ‘no influence of market power’
(Schwarz and Lang) through to ‘cushioning of the price increase by a significant decrease of
market power’ (LE study).
4. Fixed costs
In Chapter 6.8 of the LE study, for the first time fixed and investment costs are brought up for
discussion. The approach is motivated as follows:
“For a large part of the time it is legitimate to consider, although it may be somewhat of a simplification,
that if a unit is generating and is not setting the price on the system then this and all other unit, apart
from that one setting the price, is operating with costs below the system marginal cost or the price.
These units and thus the companies that own them will earn rents or contributions to fixed costs associated with running their plants, which are more efficient than the plants at the margin. Given that this
takes place in the real world and is sufficient to ensure continued investment in the electricity market, it
is important to consider whether the results of the GED system modelling are consistent with the sustainability of the market, thus allowing for companies to still contribute to fixed costs.
In order to test this, the €/MWh cost of generation returned on a unit by unit basis by all of the companies in the study, calculated as the product of fuel cost by heat rate of the units (including warm weather
24
Following an overview of various studies, Harvey and Hogan (2002) come to the conclusion that robust conclusions from such simulation studies are not possible; the errors are greater than the estimated effect.
25
In other respects, too, the three studies arrive at differing results. Critical assessments of these and a fourth
study (Müsgens 2006), which deals with older price developments in Germany with the aid of the simulation
method, can be found partly in Swider et al. (2007) and Ockenfels (2007).
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
31
derations and the full cost of carbon in 2005), is subtracted from the hourly system marginal cost produced by the GED model, which is equivalent to the market price in a perfectly competitive market, and
then this hourly figure is multiplied by the hourly optimal unit despatch of each unit, again from the
GED modelling of the market.” (page 339)
The description is misleading. Because the simulation model does not capture long-term marginal costs or scarcity prices, price estimators are unable to generate sufficiently high profits
in order to ensure efficient investment incentives in the long term. This already theoretically
answers the question of whether the estimated prices guarantee a sustainable electricity market: they do not.
Why, then, does the LE study empirically arrive at a different conclusion? The reasons lie in a
misleading modelling of the investment incentives. For one, the LE analysis does not offer a
long-term analysis but merely a momentary snapshot (distorted by the emissions trading policy). For another, it improperly calculates average coverage amounts for the entire capacity
and thereby obscures a core problem for long-term efficiency in electricity markets.
4.1 Dynamics
In the short term, prices in the electricity generation sector are typically above or below average costs because of investment cycles, volatile and stochastic demand (weather, economic
growth, etc.), high fuel and certificate price volatility, volatile and stochastic capacity availabilities, long adjustment and construction times etc. For instance, if the marginal costs of the
marginal power station rise because of a rise in gas price or CO2 certificate price, this can lead
to short-term high profits for nuclear and hydro stations, which may also result in higher average profits of all power stations. If they fall, corresponding profit downturns can result. As a
consequence, in every given year, profits can be strongly above-average or below-average –
even where there is perfect competition. This applies particularly in times where marginal
costs are highly volatile as a result of high variance in fuel and certificate prices.
In the long term, cycles can result – a high level of investment (with high total investment
costs) gives rise to low prices, and a low level of investment (with low total investment costs)
give rise to high prices.26 In Germany, for instance, there were years after deregulation, in
which prices were below levels necessary to cover investment costs; sooner or later, years
26
Prior to deregulation, the relationship was reversed: high investment costs implied high (regulated) prices and
low investment costs low prices.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
32
with above-average revenues have to crop up. High revenues indicate the need for new investment. This also applies under perfect competition.
The high profits partly shown in Table 6.45 in the LE study have to be construed within this
both short-term and long-term dynamic context. An assessment of the ‘profit situation under
perfect competition’ on the basis of a momentary snapshot typically cannot be interpreted as
an indicator for the sustainability of the market.
If, for instance, one looks in greater detail at Table 6.45 in the LE study, it is noticeable that
the profits over the periods under consideration grew strongly. By far the biggest profits were
computed for 2005, when they grew by nearly 30% in comparison with 2004. This profit
growth presumably has to do with the emissions trading and the rise in fuel costs, both of
which typically cause the marginal costs of the marginal power station (and thus the price
estimators) to increase for given capacities. Since there is only one spot market price for electricity, many electricity-producing power stations to the left of the marginal power station are
able to profit from the rises in electricity prices, especially if they do not emit CO2. Since the
bulk of the certificates are issued free of charge, the price rises due to emissions trading bear
no relation to a similar increase in full costs or average costs. Thus, even under perfect competition, the profits must temporarily become larger.
Finally, the emissions trading policy can also mean that, for a certain time, marginal cost
prices climb above average cost prices without there being any capacity scarcity. In the medium term, the profits will be reduced again – until capacities are adjusted by the construction
of new power stations. Uncertainties as to the detailed mechanisms of emissions trading including the allocation of allowances together with long planning and construction times hinder(ed) earlier, anticipatory adjustments of the market.
2005’s high profits due to emission trading are therefore mainly politically induced and temporary in nature. It is accordingly economically impermissible to use such profits as an indicator for the long-term sustainability of electricity market competition on the basis of the price
estimators in the LE study. The following rough calculation, based on the LE study’s approach, illustrates that the final conclusions drawn by the LE study with respect to fixed cost
contributions depend on the price rise and the revenues that can be attributed to emissions
trading policy.
When I add together all the fixed cost contributions in Table 6.45, I arrive at the sum of
KEUR 20,612,750.20 for the years 2003-2005. Divided by 3, I get an average of KEUR
6,870,916.73 p.a. The average annual contribution per installed MW of capacity according to
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
33
the LE study is KEUR 76.942 p.a. per MW. That means that the average installed capacity is
KEUR 6,870,916.73 p.a. / KEUR 76.942 p.a. per MW = 89,299.95 MW.27
If now instead of taking 2003 to 2005 as a basis, one disregards 2005, the following sum results: according to Table 6.45, the sum of the fixed cost contributions for the two years is
KEUR 11,928,701.60. The annual average is KEUR 11,928,701.60/2 years = KEUR
5,964,350.80 p.a. Dividing this figure by the average installed capacity (which is taken here as
not significantly deviating from the average installed capacity for all three years) gives KEUR
5,964,350.80 p.a. / 89,299.95 MW = KEUR 66.790 p.a. per MW. This amount is somewhat
less than the minimum amount calculated by the LE study that could give rise to investment
inducement in CCGT. That means that, discounting 2005 – with its exceptional politically
induced marginal cost movements (see for example graph 6.13 on page 316 of the LE study) –
the positive final conclusion in the LE study regarding the sustainability of the modelled electricity market no longer holds true.28
The next sub-section demonstrates that, in a direct comparison with the requisite prices for
sustainable investment incentive, the LE price estimators are also inadequate. Both observations seem to reconcile the theoretical finding with the empirical facts.
27
In Table 6.49 on page 389, the value of 89,373 is stated. If I take this value, the ‘under-coverage’ in 2003 and
2004 becomes greater.
28
Moreover, the LE study implicitly assumes that all sales are at spot market prices. This is not so. If the average
prices attained lie under the average spot market price, the fixed cost contributions are overestimated. Finally, it
is at least unclear what the profitability is of that capacity that is not taken into account by the LE study.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
34
4.2 Averaging
The approach followed by the LE study distorts the picture as a result of improper averaging
over all power stations. The fixed cost contributions typically occur in the form of inframarginal returns. The investment problem therefore arises especially for peak load power stations, which are running for only relatively few hours a year and are not or hardly able to
bring in any inframarginal returns (see Figure 3.2). In the few hours with scarce capacity, in
which the marginal costs are able to rise above the costs of the last unit, they have to cover
their investment costs, otherwise there would be no investment in them. No economically
rational supplier invests in a (peak load) power station if that power station does not bring in
sufficiently high profits in terms of expectation.
The relevant question therefore is not whether in the short run average total profits exceed
average total costs, but whether in the long run electricity prices create the requisite incentives
for investment in an efficient mix of power stations. The LE study does not calculate prices
that are needed to cover full costs in the long run. According to Gatzen et al. (2005, page 6,
translation from German) these prices are “depending on the level of fuel prices between
about €27 and €35 per MWh for a base supply and between €42 and €56/MWh for a peak
supply.” Because of a rise in construction costs, more recent calculations arrive at higher estimations. See for example CERA (2007; graph 15 on page 18), which, for base supplies, arrives at “levelized costs of electricity generation” of over €55 per MWh (CCGT).
The LE study estimates the average prices under perfect competition to be €19.46 (2003),
€24.27 (2004) and €28.17 (2005) (see Table 6.36 on page 315 of the LE study). The prices
allowed by the LE study under perfect competition are therefore far below the prices that
would yield sustainable investment. Only in the most favourable fuel scenario and only in
2005 is the ‘full cost price’ for a base supply (but not for a peak supply) at a level just under
the LE price estimator.
If one looks at the actual average price attained on the EEX (see Table 6.36 on page 315 of
the LE study), then, once again, the long-term price level for full-cost coverage is above the
spot market prices realised on the EEX up to the end of 2004. It was not until the price markups, which are politically induced by emissions trading in 2005, that the price rose to a level
allowing investment for a certain spectrum of cost scenarios.
These observations remind one of more recent empirical scientific investigations from the
USA (under the heading ‘missing money’); these suggest competition on the electricity markets alone is not capable of covering full costs of an efficient power station mix. Joskow
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
35
(2006) writes in this context that a “failure of organized wholesale power markets to provide
adequate incentives to stimulate investment in new generating capacity to balance supply and
demand efficiently” has meanwhile been acknowledge as a general fact in the USA. All in all,
therefore, what seems to crystallise out of these initial empirical studies on the long-term development of electricity markets is that electricity prices in competitive markets can be too
low to be able to justify investment in ‘suitable’ capacities. As far as I am aware, there is no
comparable investigation for Europe. CERA (2007, page 17) writes, however: “As both the
need for new capacity and the cost of building that capacity increase, a critical issue in years
to come will be whether wholesale prices can give the right price signal and trigger investments.”
The LE study makes no mention of the problems and methods raised in this literature. Although it mentions that the results in this respect need to be interpreted with caution (e.g. page
24), it nevertheless goes on to paint a theoretically and empirically erroneous picture of the
capability of its own price estimators to ensure sustainable generation capacity.
5. Regression analyses
The regression analyses in Chapter 6.9 of the LE study try to establish a relationship between
market power potential and the exercise of market power.
5.1 Measurement errors
The regression analyses are based on the foregoing market power measurements, so that the
same empirical and methodological problems are reflected as have been discussed in Sections
2 and 3 of my report. These include for example the lack of consideration given to international electricity competition and the erroneous measurement of marginal costs. The result of
these shortcomings is that the regressions would also ‘reveal’ a relationship between market
power potential and the exercise of market power if perfect competition were indeed to be a
fact and no market power were to be exercised.
This is demonstrated in Figure 5.1, which is taken from Chapter 6.9 (page 355) of the LE
study. The graph shows, for supplier 0436-S-DE, the correlation between the PCMU (pricecost mark-up) arrived at by the LE study and the measured RSI (based on the assumption of a
competitively isolated German electricity market). Similar graphs are given for all the suppliers surveyed.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
36
Figure 5.1: Correlation between RSI and PCMU
The graph also shows the result of a simple regression (“Fitted values”). The gradient of the
regression line confirms that PCMU and RSI are negatively correlated. The LE study interprets this as the establishment of the expected correlation between market power potential and
the exercise of market power. However, an alternative hypothesis for the negative correlation
is that both the RSI and the measured PCMU are correlated with capacity scarcity. The RSI is
a pivotalness indicator and, with a given installed capacity, pivotalness is directly correlated
with capacity scarcity. The reason for the correlation between measured PCMU and capacity
scarcity lies in the averaging of the supply function (Section 3.5.3), the neglect of scarcity
prices (Section 3.4) and the failure to take proper consideration of price effects of imports and
exports (Section 3.3). These methodological problems result in overestimations of the pricecost gap when capacity is scarce. Capacity scarcity therefore results not only in low RSI but
also in higher measured PCMU values. The negative correlation could therefore be attributable to measuring problems.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
37
5.2 Lack of robustness
A series of observations in Chapter 6.9 confirm the hypothesis that the regressions are incapable of creating a robust image and it is predominantly measuring errors that determine the
image.
5.2.1 Explanation content
Figure 5.2: Regressions
One gains an initial indication when looking at the four graphs showing the observed and estimated relation between RSI and LI in the first part of Chapter 6.9 of the LE study. Whereas,
by definition, the Lerner Index can only have a value between zero and one, the legend on the
vertical axis of each of the graphs depicting the LI starts at –250 and ends with zero. (Figure
5.2, taken from the LE study, is an example.) An LI of zero would be expected under perfect
competition. An LI of less than zero would imply, if the definition and measurements were
accurate, that the price is at a level less than marginal costs. Therefore, looking at the distribution of the observations in the graphs already suggests that there are significant measurement
errors or definition problems in the analysis of the exercise of market power.
One gains a second indication when one looks more precisely at the explanation content of the
regressions. In its analyses in Chapter 6.9, the LE study restricts itself almost exclusively to
the sign of the effect and its statistical (but not the economic) significance. For instance, in
connection with the above graph it is concluded as follows from the corresponding regression:
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
38
“The result of the first simple regression of the hourly Lerner Index on the RSI values reflecting the indispensability of company 0436-S-DE to serving the previously defined market demand, estimate that
the coefficient on the RSI variable is statistically significant and that it is of the expected negative sign,
indicating that market power is positively correlated with margins.” (page 350)
However, at least from an economic policy point of view, the relevant question is not whether
there is a statistical connection, but rather to what extent that connection is meaningful for the
performance of the market. The reason is that, even where there are small deviations from the
fiction of perfect competition, a connection is to be expected (see Section 7) – at least when
the variables are accurately measured.
In fact, the economic significance of the results is not unproblematic. Inserting the average
value for the RSI in the estimated regression equation into the regression for supplier 0436-SDE (1.14; page 293 of the LE study) gives a (slightly) negative average LI. That means that,
whilst the correlation between the measured values of RSI and LI is statistically significantly
negative, so is the average level of LI.29 The small R square furthermore points to the fact that
the RSI only contributes to explaining the LI to a negligible extent; the proportion of variance
in exercise of market power explained by market power potential is less than 4%.30 Nor is the
explanatory power of the regression improved when the data is verified for heteroscedasticity
or serial correlation (pages 352 and 353).
5.2.2 Scarcity variables
Following the description of the simple regressions, the LE study augments them with further
explanatory variables. Unfortunately, the hypotheses to be tested are not formulated, the departure points for the regressions are not derived from economic models and the selection of
variables is in general not further justified (with the exception of one scarcity variable; see
below). In the following, I shall not go into all the specifications but simply state my position
in relation to the two most important extrapolations in the second and third parts of Chapter
6.9.
In the second part (Chapter 6.9.2) a variable that measures capacity scarcity on the market is
included. The LE study cites as follows:
29
For the other suppliers, on average, positive values are obtained for LI.
The situation is similar for the corresponding regressions for the other suppliers; regressions with the PCMU
lead in this regard to better results.
30
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
39
“To further test the specification of the model and the findings of the simple regressions presented previously in this section, a measure of scarcity is included in the company specific regressions as an explanatory variable in the model. The rationale for this was that a certain amount of mark up in the electricity market might be properly (from an economic standpoint) be reflective merely of the scarcity rents
in the market and the economic value of capacity, and the tradeoffs between capacity cost and thermal
efficiency.” (page 374)
At this point, the LE study appears to concede that, in the long term, its own price estimators
for a performing market fall short of the mark. Therefore a scarcity variable is incorporated,
which is supposed to capture the requisite scarcity returns. However, the result of the regression is hardly intuitive since the sign preceding the scarcity variable is other than expected.
There are two possible causes for this. First, the RSI already measures scarcity on the market
and, second, it appears not to be unlikely that scarcity bears a profoundly non-linear correlation to mark-ups on the costs of the last unit (see Section 3.4 or Stoft 2002), so that the proposed regressions capture scarcity returns inadequately, if at all.
The LE study reacts to the unsatisfactory results with the addition of a whole series of further
variables, such as dummy variables for the year and for the time of year and days of the week.
These dummy variables may well subdue the problem of the non-linear influences of capacity
scarcity. The results are (a) a stark increase in the explanatory power of the regression and (b)
a stark reduction in the explanatory content of the RSI for price mark-ups. For the supplier
that is first surveyed (0436-S-DE) for instance, the R square rises from 0.16 to 0.24, whilst, at
the same time, the regression coefficient for market power potential, RSI, drops from –5.93 to
only –0.84 (pages 375-376).31 This result is consistent with the hypothesis that the measurements of market power potential and market power exercise are tainted with considerable errors, which are especially manifested in scarcity situations. Accordingly, the price-cost markups shown in Chapter 6.5 of the LE study cannot be traced back to the exercise of market
power, or at least not in the magnitude shown.
31
Qualitatively similar results, albeit weaker with respect to regression coefficients, are also observed in the case
of the other suppliers.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
40
5.2.3 The sign of the correlation
The third part of the regression analysis (Chapter 6.9.3) mainly deals with the question of how
market power potential and exercise interact when considering several suppliers simultaneously. The question is important, because the same price results from the interaction of all
market players. At this juncture there is once again no theoretical analysis and no derivation
of empirical hypotheses. One hypothesis from the oligopoly theory is that large suppliers
(with greater market power potential) exercise more market power than small suppliers. The
reason is that large suppliers possess a larger inframarginal quantity and hence profit more
from the price effects of capacity withholding (see for example Ausubel and Cramton 2002).
A further reason is that larger suppliers are at any point of time typically more often (with
binary measurement) or ‘more strongly’ (with continuous measurement) pivotal.
According to Chapter 6.3.3 of the LE study, supplier 1338-S-DE possesses by far the greatest
market power potential (see for example Table 6.15, page 292, or 6.21, page 301). If one does
not neglect the effects of international electricity trading in disciplining market power, supplier 1338-S-DE is in addition the sole supplier for which, in certain circumstances, any market power potential at all can be identified (Section 2). Therefore, if one assumes that the
measurements accurately reflect market power potential, it should be expected that supplier
1338-S-DE possesses the greatest incentive for exercising market power and therefore has the
greatest influence to raise prices.
However, the regressions show a diametrically different picture. Already in part 2 of Chapter
6.9, in which capacity scarcity but not supplier interaction effects are controlled for, both relevant regressions show that, for supplier 1338-S-DE, market power potential and market power
exercise are negatively correlated. This observation endures in all regressions in part 3 of the
chapter. There, it is not only statistically but also economically significant: as the theory suggests, it is indeed so that no other supplier has such a large influence (measured using the absolute amount of the regression coefficients) on the price-cost gap – except that the influence
goes in the ‘wrong’ direction.
Two possible conclusions suggest themselves: first, the observation that the large supplier
deploys its market power potential in order to reduce prices is so robust that, in spite of the
errors in the measurement of market power, it is identified through the regressions as economically and statistically significant. Second, the errors in the measurement of market power
are so great that no faith can be placed in the regression analyses even if they show robust,
economically and statistically significant results.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
41
Both conclusions are inconsistent with the picture that the LE study draws. It is true, also in
the summary to Chapter 6.11, it does concede the existence of general problems of interpretation:
“Whether Germany in fact is concentrated or highly concentrated, price cost margins (LI and PCMU)
were significantly related to market structure via the regressions. This latter finding could indicate that
market power use or market imperfections exist/have existed. Of course, alternatively, it is always possible that the regression models as specified are unable to distinguish between this explanation and
32
some alternative unknown, but more benign, rationale.” (page 398)
However, in the overall conclusions at the end of the study, reference is made merely to the
statistical significance of the RSI variables. The economic significance of the observations
and the issue with the wrong sign of the effect are not alluded to:
“The RSI is a significant explanatory variable for the margins estimated in Germany. The inclusion of
additional variables such as scarcity did not change this conclusion, nor did the inclusion of more than
one RSI variable. Statistical significance was in general robust to a number of changes in the assumptions, including changing specifications, dummy variables for peak and off peak, and violations of the
classical linear regression assumptions.” (page 823)
6. Withholding
At the end of the analysis for Germany, in Chapter 6.10, the LE study presents a series of tables on the suppliers’ capacity withholding, in which the estimation of capacity deployed under perfect competition is compared with the capacity actually deployed. All the problems in
data quality and the conceptual errors put forward for calculation of the price estimator also
directly distort the picture relative to estimated capacity withholding.
32
In addition, in connection with the wrong sign issue, it speculates on the possibility that important aspects of
the measurement of market power were left out of account: “In the subsequent regression analysis presented in
this Section and in a number of alternative specifications estimated as part of this study, the result presented here
in relation to the estimated coefficient on the RSI variable of company 1338-S-DE remains robust in the presence of scarcity. This result is not in keeping with our ex-ante expectations on the expected sign of the estimated
coefficient of the RSI variable. Scarcity appears to explain the expected behaviour of the market but the estimated coefficient on the RSI variable is counter-intuitive. Further analysis of this result was not undertaken as
part of this study as it is potentially brought about by a host of different factors; company strategy, specifics of
long-term contracts (the company is a net purchaser of a substantial amount of electricity on an hourly basis), as
well as a number of other factors for which detailed data was not collected as part of this study. Nevertheless,
this result does not diminish the relevance of the results found throughout this report on the relationship between
structure and outcome measures in the European electricity market, it merely represents an interesting case that
merits further work as part of future DG Competition studies.” (page 380).
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
42
The LE study does not comment on or interpret its estimates; nor does it draw any conclusions therefrom. Likewise, the problems in the measurement of capacity withholding identified in the literature and the methods discussed there for demonstrating capacity withholding
are also not alluded to. Since Twomey et al. (2006), Stoft (2002) and Harvey et al. (2005) and
the literature they cite all do so and because the LE study leaves this chapter without comment,33 I shall dispense with taking any further position at this juncture.
7. Oligopolistic competition
If all the methodological and empirical problems mentioned in Section 3 of this report were to
be solved, deviations from the marginal cost estimator could be interpreted as evidence for the
exercise of market power. However, in and of itself, such a diagnosis would not be of especial
interest, for an electricity market with perfect competition might be a useful, but unattainable
fiction. Only in an atomistic electricity market could prices be expected as under perfect competition. The relevant question for economic policy therefore is not whether actual prices deviate from the price estimator but rather what deviation from perfect competition is still tolerable and at what point intervention ought to be considered.
In order to answer these questions, it is first necessary to gain a theoretical and empirical understanding of the short- and long-term effects of oligopolistic competition:34 Through what
strategies is market power exercised? What determinants influence the extent to which market
power is exercised? What price movements are to be expected when there is oligopolistic
competition? What medium- and long-term effects does the exercise of market power have on
capacity and generation? How does market power interact with the technological, demanddriven and policy-induced complexities of the electricity market? What market structure is
desirable in view of the uncertainties, indivisibilities and high fixed costs? When can a market
structure be said to be ‘workable’?
The benchmark model in the LE study is perfect competition. However, this model does not
offer any answers to the above questions. In the following, I will indicate how, in the absence
33
That the data as presented might be problematic, even in the eyes of the LE, could perhaps be inferred from
the fact that the chapter is headed up “Withholding” whereas the corresponding tables in the chapter are headed
up “Potential Withholding“. Even the general summary in Chapter 6.11 contains no reference to Chapter 6.10.
34
The term “oligopoly” is understood by the general public differently than by economists. Oligopolistic market
power as defined in economics exists even where there is only marginal ability to influence prices. In all electricity markets (and in most other markets), market power exists under this definition, since an electricity market
without market power would necessarily have to consist of an infinite number of suppliers, each with negligible
capacity. In this sense, an electricity market without market power is an unattainable fiction. An oligopolistic
electricity market that disciplines market power to such an extent that it does not create significant problems is
nonetheless a worthy goal.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
43
of a model that incorporates oligopolistic competition, erroneous conclusions might be drawn
when interpreting the results of the LE study, even if all the measurements and estimates were
to be accurate.
In the LE study, the analysis of oligopolistic competition is limited to a simple, hypothetical
example in which there are two suppliers with constant variable costs, which differ only
slightly from one another. In its analysis, the LE study comes to the result that – as long as
none of the suppliers is pivotal – Bertrand competition and marginal cost prices are to be anticipated. Interested readers wanting further details are referred to pages 54-56 of the LE study
– and at the same time are told that the LE study’s analysis is not right in that, even in the case
of homogenous goods and constant variable costs, one cannot necessarily expect cost prices
as the LE study appears to suggest.35 Moreover, in the long term, the equilibrium prices determined by the LE study cannot describe a stable situation since they do not allow suppliers
to cover fixed costs. Capacity reduction would result. Sooner or later prices above the cost of
the last unit are necessary for both workable and perfect competition (Section 3.4).
However, when capacity becomes scarce, suppliers can sooner or later become pivotal and
drive the price up even before the capacity limit is reached, as the LE study describes it in the
context of the example. This would be inconsistent with perfect competition. Yet, how problematic are (a) pivotalness and (b) prices above marginal costs under imperfect competition?
(a) Pivotalness must also occur when all suppliers behave as under perfect competition (and at
all times bid exactly marginal costs). The dynamic in the above example illustrates this. A
situation in which the suppliers acting as under perfect competition do not arrive at the capacity limits cannot be stable in the long term. The reason is that, in competitive markets, capacity scarcities are necessary to create scarcity prices, which are in turn necessary for covering
investment costs. However, if the suppliers are ‘sufficiently’ near to the capacity limit, they
are pivotal – regardless of the number and size of the individual suppliers. It follows from this
that pivotalness is not inconsistent with competitive behavior, and therefore also may not per
se be interpreted as a sign of unsatisfactory competition. It is far more the case that a market
in which capacity never becomes scarce cannot be a perfect competition market.36
35
The example ignores a central strategic option that suppliers have on electricity markets: suppliers offer supply
functions. For instance, both suppliers can offer D/2 (-epsilon) units at variable costs and all further units at a
high price p. The market price is then p and none of the suppliers would have any incentive to deviate from its
strategy. It should moreover be pointed out that, when demand is inelastic as is supposed in the LE example,
even Bertrand competition can lead to high prices in the game-theoretical equilibrium.
36
At least in the absence of capacity markets. In a perfect market with atomistic suppliers, the exercise of market
power is ruled out by definition.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
44
(b) Under perfect competition, scarcity prices at the capacity limit are determined by the willingness of the customers to pay (e.g. demand response abilities). A supplier who can exercise
market power near to the capacity limits can likewise be eventually disciplined by customers’
willingness to pay. In both cases, the resulting prices typically reflect scarcity. In both cases,
moreover, the prices are above the costs of the last unit.
However, because oligopolistic price mark-ups are above marginal costs, they are principally
inferior to perfect competition prices from an efficiency viewpoint. Yet the answer to the central question of how great the problem of oligopolistic price-setting is cannot be provided just
from an analysis of the short-term marginal cost mark-ups. In a case of inelastic demand and
unhindered market entry, scenarios can be constructed in which cost mark-ups entail no negative price effects or efficiency problems. The lack of demand elasticity cannot give rise to
undesirable quantity effects, and market entry can ensure that the average price does not rise
above the long-term average costs of production. The reason for this is that the exercise of
market power in the long run has a self-correcting effect because no supplier can be excluded
from the price rises induced by market power. If market entry is possible, higher prices and
higher profits attract new suppliers, who expand capacity and ensure that the long-term average price cannot systematically be higher than average costs – even if the short-term exercise
of market power influences prices.
The arguments that pivotalness is not necessarily inconsistent with workable competition and
that mark-ups on marginal costs do not necessarily imply significant losses of efficiency or
redistributions in favour of the suppliers should not deceive one into (fallaciously) concluding
that pivotalness and mark-ups on marginal costs are generally harmless. But they do show that,
as a basis for policy-oriented decision-making, the actual effects of pivotalness and cost markups have to be evaluated. For that, factors such as market entry, demand elasticity, long-term
marginal cost prices and other aspects of dynamic competition would have to be evaluated.37
For the question whether competition is perfect in the short term, these factors are irrelevant.
For the assessment of whether competition is workable in the long term, they are essential.
The LE analysis examines perfection.
37
E.g., typically, a price-cost mark-up of, say, 15% has to be interpreted differently where it is the result of a
starkly regressive trend (as the LE study shows for Germany) from where it is the result of a starkly progressive
trend (as is shown for England).
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
45
8. Concluding remarks
The evidence presented in the LE study is methodologically and empirically tainted with error,
and the interpretations and conclusions presented suffer from insufficient economic foundation. This leads to an overestimation of the market power problem.
When account is taken of international electricity trading, the LE analysis suggests unproblematic to non-existent market power potential. The price-cost mark-ups that are revealed are
systematically inflated owing to a series of errors in measurement, definition and procedure.
No robust conclusions can be drawn.
Particular in regards to its short- and long-term dynamics, the electricity market is complex,
volatile and often only poorly understood. Many prominent proposals for direct market intervention, that would supposedly result in a fall in prices, are based on an inadequate comprehension of the competition mechanisms of electricity markets and are thus counterproductive
(see, for instance, Stoft 2002 and Ockenfels 2007 and the literature cited therein for numerous
examples). At the same time, however, there are a series of uncontroversial suggestions from
economists that are apt to structurally strengthen the electricity market and competition and,
hence, give it a robust foundation. These include bolstering the integration of the European
electricity markets, greater involvement of electricity demand on the spot market, creating
sufficient incentives for investment in generation capacities and market entry and a consistent
and efficient climate and emissions trading policy that avoids mistakes in incentive systems
and market design. Such measures will give rise to positive price and efficiency effects.
Prof. Dr. Axel Ockenfels
Measuring Market Power in Theory and Practice
46
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