E-book analyse of Usng Bd and Market outcome Data

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1 A Comparson of Ex Ante versus Ex Post Vertcal Market Power: Evdence from the Electrcty Supply Industry * by Joshua S. Gans Frank A. Wolak Melbourne Busness School Department of Economcs 200 Lecester Street Stanford Unversty Carlton VIC 3053 Stanford, CA Australa USA e-mal: [email protected] e-mal: [email protected] Frst Draft: July 10, 2007 Ths paper provdes a prospectve and retrospectve quanttatve assessment of the mpact of a passve vertcal ntegraton between a large electrcty retaler and a large electrcty generator n the Australan Natonal Electrcty Market (NEM). We adapt a standard model of fxed-prce forward contractng behavor by an electrcty retaler before and after the acquston of a share of a baseload electrcty generaton plant to determne the lkely change n ts contractng behavor. Usng bd and market outcome data durng the three years leadng up to the acquston, we estmate the change n bddng behavor of the generaton unt owner from the change n ts fxed-prce forward contract oblgatons brought about by the acquston. Ths change n bddng behavor s used to compute a prospectve change n each half-hourly wholesale prce durng the pre-acquston perod. Because ths acquston was allowed to take place, we also use market-clearng prces of wholesale electrcty n the four states of Australa n NEM at that tme and the prce of the margnal nput fuel durng the pre-acquston and postacquston tme perods to compute a varety of treatment effects estmates of the mpact of ths acquston. We fnd farly close agreement between the prospectve and retrospectve quanttatve mpact of the acquston on wholesale prces. In both methodologes fnd a sgnfcant ncrease n wholesale electrcty prces assocated wth the acquston, whch emphaszes the mportance of takng nto account the extreme susceptblty of short-term wholesale electrcty markets to the exercse of unlateral market n any competton analyss n ths ndustry. * We thank Darryl Bggar, Stephen Kng, Smon Uthmeyer and Fleur Gbbons for helpful dscussons and comments on early versons of ths analyss. We also thank Tomas Rodrquez for outstandng research assstance. Both authors were engaged and dd early analyss (n 2003) on ths ssue on behalf of the ACCC. The vews represented here are the result of contnued research ndependently carred out by the authors and should not be construed as beng held or supported by the ACCC. The latest verson of ths paper s avalable at and

2 1 Introducton Re-structurng of vertcally-ntegrated electrcty supply ndustres has typcally nvolved the horzontal separaton of the monopolst s generaton and retalng assets, vertcal separaton of transmsson from generaton and retalng, and the requrement that each retaler offer an open-access tarff to charge compettors for the use of ts local dstrbuton network. The goal of the vertcal separaton process s to provde all partcpants n the potentally compettve segments of the ndustry generaton and retalng wth equal access to the bottleneck portons of the ndustry the transmsson and dstrbuton networks. Horzontal separaton n the generaton and retalng segments was ntended to create the ntal condtons necessary for vgorous competton. Several countres also separated retalng from generaton because of a belef that vertcal ntegraton dd not nvolve any potental gans n productve effcency and separaton was preferable to ntegraton to avod potental ant-compettve concerns. In several countres, ths vertcal separaton has been challenged by ndustry ncumbents. In the UK, a number of large generaton companes servng the England and Wales market ntegrated downstream nto electrcty retalng. In New Zealand, ntegraton between generaton and retalng has left the ndustry vrtually fully ntegrated wth fve man partcpant generator-retalers. A smlar structure has been n place snce the start of the market n Span, wth Endessa and Iberdrola, the two largest generaton unt owners also beng the largest retalers. These ndustry structures rase the queston of whether n the absence of a regulated retal prce vertcal ntegraton enhances the ablty of the combned entty to exercse unlateral market power.

3 Ths paper provdes both an ex ante and ex post quanttatve assessment of the mpact of vertcal ntegraton on market outcomes n Australan Natonal Electrcty Market (NEM). Untl Aprl 2004, there was very lttle vertcal ntegraton n the NEM, although a few retalers owned peakng generaton plants and some generators have retal arms amed at large ndustral users. In 2003, the largest energy retaler n the state of Vctora, Australa Gas Lght Company, known as AGL, proposed to acqure a stake (as part of a consortum) n the largest base-load generator n the Vctora, Loy Yang A (LYA). Concerned about potental ant-compettve harm as well as the dea that ths mght be a frst step n a wave of vertcal acqustons, the competton authorty, the Australan Competton and Consumer Commsson (ACCC), challenged the acquston. The mergng partes successfully overcame ths challenge and effectve Aprl 1, 2004 control of LYA was transferred to the consortum n whch AGL held a 35 percent stake. A pre-condton to the acquston was that AGL would gve Court enforceable undertakngs that t would not be nvolved n the day-to-day bddng and contract tradng of LYA wth representaton only at the Board of Drectors level. That s, the acquston would be a passve one. To partes unfamlar wth the susceptblty of wholesale electrcty markets to the exercse of unlateral market power, ths acquston would not seem to rase compettve concerns. 1 If the broad geographc market of the eastern Australan states of New South Wales, Queensland, South Australa and Vctora was accepted, both the electrcty generaton and retalng sectors were not at levels of concentraton that would 1 The evdence on vertcal ntegraton wth state-level regulaton of retal electrcty prces n the Unted States has nvolved complete mergers wth actve control. See Mansur (2003) and Bushnell, Mansur and Sarava (2005) for a descrpton of these results that suggest vertcal ntegraton can have pro-compettve mpacts. It s mportant emphasze the role of explct state-level retal prce regulaton, somethng that dd not exst n Vctora at the tme of the acquston, n delverng these results. 2

4 normally rase concerns under typcal merger gudelnes. 2 In addton, the potental antcompettve consequences of vertcal ntegraton are controversal at the best of tmes, let alone a partal ownershp n a moderately concentrated market. Add to that the passveness of the acquston, and the usual mechansms by whch ant-compettve harm could arse namely, rasng rvals costs and foreclosure may not be avalable to the acqurer. 3 These reasons alone may ndcate that such an acquston should not command sgnfcant regulatory attenton. However, n ths paper, we provde a novel theoretcal argument that t s the very passveness of the acquston along wth the partcular characterstcs of wholesale electrcty markets that could make ths type of acquston a sgnfcant competton concern. Usng bd and market outcome data over the pre-acquston tme perod from January 1, 2000 to June 30, 2003 and a model of expected proft-maxmzng bddng behavor wth fxed-prce forward contracts from Wolak (2000 and 2003a), we provde prospectve assessment of the lkely magntude of the ncrease n wholesale electrcty prces that would result from ths acquston. We then use pre-acquston versus postacquston market outcome data to perform several retrospectve treatment effects analyses of the mpact of the acquston on wholesale electrcty prces n the Australan NEM. These results are smlar n magntude to the ex ante predctons from our analyss based on the pre-acquston bd and market outcome data, suggestng that the ACCC s concerns wth the vertcal combnaton were justfed. 2 Gans (2007) provdes a modfcaton of concentraton measures to take nto account potental ssues assocated wth vertcal ntegraton. Wth ether a broad or narrow geographc market defnton, such concentraton would not have ncreased as AGL would have remaned a net buyer n the market postacquston. 3 A number of legal commentators rase concerns about the noton that vertcal ntegraton s a compettve concern even n hghly concentrated markets. However, a recent lterature, has suggested that concentraton s a factor n allowng ntegrated companes to engage n foreclosure actvtes. See Rey and Trole (2007) for a recent survey and de Fontenay and Gans (2005) for a general model n an olgopolstc envronment. 3

5 The remander of the paper proceeds as follows. Secton 2 develops a smple model that demonstrates that a passve vertcal acquston provdes the acqurng electrcty retaler wth a non-contractual natural hedge aganst fluctuatons n the wholesale prce of electrcty. Ths causes the retaler to reduce ts demand for fxed-prce forward contracts for electrcty whch n turn reduces the total volume of such contracts held by generators. The reduced fxed-prce forward contract oblgatons of generaton unt owners, ncreases ther ncentve to exercse unlateral market power n the shortterm wholesale electrcty market, whch s rases the equlbrum spot market prces feedng back nto hgher wholesale prces overall. Secton 3 descrbes the procedure we use to quantfy the extent to whch LYA and other generaton unt owners are able to rase short-term wholesale electrcty prces because of the reduced forward contract oblgatons that result from the acquston. We apply the mplct functon theorem to the frst-order condtons from expected proftmaxmzng bddng behavor for a pre-specfed quantty of fxed-prce forward contract oblgatons to derve an expresson for the change n the generaton unt owner s bds nto the short-term market as a result of change n ts daly fxed-prce forward contract oblgatons. Usng an estmate of the lkely reducton n LYA s fxed-prce forward contract oblgaton derved from the model n Secton 2, we compute the change n ts expected proft-maxmzng the bd curve for each half-hour perod of our pre-acquston perod. The ntersecton between ths counterfactual bd curve and the actual resdual demand curve faced by LYA gves an estmate of the counterfactual post-acquston prce 4

6 Secton 4 presents the results of ths calculaton for each half hour of our sample perod from January 1, 2000 to June 30, We fnd a prospectve prce ncrease between 15% and 20%, dependng on the half-hour perod of the day, for all years n our sample as a result of the change n the unlateral expected proft-maxmzng bddng behavor of LYA because of our predcted reducton n ts fxed-prce forward contract oblgatons as result of the acquston. These estmated percent prce ncreases from the acquston justfy the concerns rased by the ACCC. Secton 5 attempts to determne whether the prce ncreases from the acquston mpled by the prospectve analyss reported n Secton 4 actually occurred usng halfhourly market-clearng wholesale electrcty prces from the states of Vctora, New South Wales, Queensland and South Australa for the perod Aprl 1, 2003 to Aprl 1, Treatment effects estmaton procedures are used to determne whether wholesale electrcty prces n Vctora and throughout the NEM and were sgnfcantly hgher followng the acquston. We employ a number of dfferent controls for the underlyng tme trend n wholesale electrcty prces that would have occurred n the absence of the acquston. For all of these controls we fnd evdence consstent wth the acquston leadng to a statstcally sgnfcant ncrease n wholesale electrcty prces on the order of the magntudes found n the prospectve analyss reported n Secton 4. Secton 6 dscusses the mplcatons of these results for desrablty of allowng sgnfcant vertcal ntegraton between electrcty generaton and retalng. We argue that n an electrcty supply ndustry wthout explct retal prce regulaton, as s the case n the NEM, 4 vertcal ntegraton can ncrease the opportuntes for generaton unt owners 4 From January 13, 2002 onwards all retal customers n Vctora had Full Retal Competton (FRC), meanng that they could choose ther retal electrcty suppler from a number of compettors. There s 5

7 to rase wholesale electrcty prces and ultmately retal electrcty prces. Ths paper provdes both a theoretcal ratonale for polcy concern regardng passve vertcal acqustons n electrcty markets and ex ante and ex post emprcal verfcaton of the sgnfcance of ths concern. Although there are a number of caveats assocated our analyss and these conclusons, we hope that our paper wll cause anttrust and competton authortes to turn a more skeptcal eye to vertcal combnatons between generaton and retalng n electrcty supply ndustres where retal prces are not subject to explct cost-of-servce regulaton. 2 Passve Vertcal Acqustons and Forward Contractng by Electrcty Retalers To study the mpact of a passve vertcal acquston on the contractng behavor of the acqurng electrcty retaler, we adapt a model of the nteracton of forward and spot market outcomes n electrcty from Powell (1993). A key feature of ths model s that electrcty retalers are rsk averse. The magntude of short-term electrcty prce volatlty n the NEM documented n Wolak (1999) and the level of prce bd cap on the wholesale market ($AU 10,000/MWh snce Aprl 2002 and $AU 5,000/MWh before that), combned wth the fact that vrtually all electrcty n Australa s sold to fnal consumers at retal prces that do not vary wth half-hourly wholesale prces all mply that some degree of rsk averson by electrcty retalers seemed justfed. Wth average wholesale prces of $AU 30/MWh durng our pre-acquston sample perod, even a small fracton of the half-hour perods durng the year wth wholesale prces close to $AU requrement for retalers to set prces below a maxmum retal tarff, but the regulator recognzes the need for allow headroom above the cost-of-servce prce for retal competton to develop. By June 30, 2005, t s estmated that approxmately 45% of Vctora customers had swtched retalers. 6

8 10,000/MWh wll quckly bankrupt a retaler sellng at fxed retal prce (set to recover ths average wholesale prce) that does not have vrtually all of ts fnal demand covered wth fxed-prce forward contracts. We desgnate retalers wth ndex w and generators wth ndex j (where w and j stand for retalers and generators other than w and j respectvely). The model focuses on the operaton of the market forward and spot markets n a gven half-hourly tme perod,. We assume that n ths tme perod, the market demand for electrcty, QD s known wth certanty and that consumers pay a regulated prce, P. The short-term market or spot prce of electrcty at tme s p. It s determned by the ndependent system operator (ISO) equatng the demand for electrcty wth the supply as defned by the bd curves submtted by generators for that tme perod. Whle the actual settled spot prce wll depend upon transmsson constrants and lne losses, we gnore features of actual wholesale electrcty markets because they do not mpact the general conclusons we draw from our analyss. In addton, ncorporatng these aspects of wholesale electrcty markets consderably complcates our modelng effort. For smlar reasons, we assume that generator j has a constant margnal cost, C j, and fxed capacty, k j, whle retaler w has no producton costs (besdes wholesale energy costs) or capacty lmts, just an nelastc demand, q w. Followng Powell (1993), the only source of uncertanty n ths model s the realzed short-term market prce, p. We assume there s a random shock, ε, to the spot prce wth compact support (because of the bd prce floor and cap n the NEM), expected 7

9 value of 0 and varance of σ 2. 5 The expected spot prce s Ep [ ]. The forward prce of electrcty s f. We assume: (1) that retalers are rsk averse (wth mean-varance utlty 6 ) whle generators are rsk neutral; 7 and (2) that forward markets are suffcently lqud so that n any perod, f = Ep [ ]. The ratonale for ths equalty s that f there was a forward market premum, energy traders would fnd t advantageous to sell electrcty n the forward market and buy t back n the short-term market, and f there was a forward dscount they would fnd t advantageous to buy electrcty n the forward market and sell n the short-term market. These attempts by traders to arbtrage temporal prce dfferences cause the forward prce to equal the expected spot prce. We adopt the followng standard tmng for analyzng the jont contract and spot market equlbrum: 1. Gven f, retalers and generators choose ther contract quanttes, QC w and QC j. 2. Gven QC j, generators chooses ther spot market strategy (.e., based on ther resdual demand). Ths s the tmelne of Newbery (1998), Green (1999) and Powell (1993) for the case where generators are unable to collude n the contract or spot markets. The resdual demand of a generator j, DR ( p ), s the dfference between the market demand QD and j the aggregate wllngness to supply curve of all generaton unt owners durng perod 5 We could also assume that retal demand s stochastc. Ths wll add notaton but does not fundamentally alter our results. 6 1 If proft s π w, the retaler s utlty functon takes the form E[ π ] λvar[ π ] where λ s a coeffcent w 2 w of absolute rsk averson. Ths may dffer from retaler to retaler but for notatonal smplcty, as we only focus on a sngle retaler below, the subscrpt s omtted. 7 All we need to assume s that generators are less rsk averse than retalers. Ths s very lkely to be an emprcally vald assumpton as forward contract prema n electrcty are rarely negatve (Powell, 1993). 8

10 besdes frm j, SO ( p ) whch s computed usng the half-hourly bd supply curves of all j other generatons unt owners besdes frm j. Mathematcally, DR ( p ) = QD SO ( p ). j j 2.1 Stand-alone Generators We begn by consderng the stuaton where generator j s a stand-alone entty and s not owned by any retaler. Generaton unt owner j s varable profts (excludng fxed costs) are: π j = ( DR j( p ) QC j)( p C j ) + ( f C j ) QC j j's Spot Profts j's Contract Profts (1) The frst term n (1) s the varable profts from short-term market partcpaton and the second term s the varable profts from long-term contract sales. Because the frst term n (1) depends on QC j and s multpled by the short-term wholesale prce, p, as dscussed n Wolak (2000 and 2003a), the expected proft-maxmzng bd of the generaton unt owner wll depend on QC j. In ths next secton, we wll use ths assumpton to compute how generaton unt owner j s expected proft-maxmzng bd curve wll change as a result of havng less fxed-prce forward contract oblgatons. As dscussed n Wolak (2000 and 2003a), f a generaton unt owner has more fxed-prce forward contract oblgatons, t wll bd to set lower short-term prces. We assume that the retaler recognzes that ts forward contractng decson mpacts both the mean and varance of the dstrbuton of short-term prces. Specfcally, < 0 and E[ p ] QCw 0 2 σ QC w < ; ncreases n the retaler s forward contract quantty reduces the mean and the varance of short-term prces We are now n a poston to determne a retaler s demand for fxed-prce forward contracts. Retaler w s objectve functon s: 9

11 ( ) 2 2 σ U = ( P E[ p ]) q + ( E[ p ] f ) QC λ q QC (2) 1 w w w 2 w w where q w s the retaler s demand for wholesale electrcty to serve ts retal customers n perod. The frst-order condtons for retal w s optmal forward contract choce s: 2 Uw E[ p ] σ Ep f ( qw QCw ) λ( qw QCw ) σ 2 λ( qw QCw ) QCw QCw QCw = [ ] + = 0(3) Thus, the retaler s demand for contracts comes from: (a) any dscount assocated wth contractng (the frst term); (b) ts desre to mtgate the spot market power of generators (the second term); (c) ts averson to rsk (the thrd term); and (d) ts desre to reduce short-term prce volatlty (the last term). Of course, ths frst motve wll not be present, n equlbrum, between the forward market and short-term market because of the actons of traders descrbed above. Thus, (3) can be re-wrtten: E[ p ] σ ( QC λσ 2 λ QC ) f Ep [ ] = + ( q QC ) ( q QC ) (4) w w w w w w = 0 > 0 Imposng the forward/spot market arbtrage condton, f = Ep [ ] mples that, expected utlty maxmzng retalers wll be fully hedged (.e., QCw = qw ). Ths outcome s consstent wth the publcly-stated hedgng strateges of the retalers n the NEM at the tme of proposed acquston. 2.2 Post-Acquston Consder a stuaton where a sngle retaler, w, purchases a share, α, of generator j. Because ths acquston s passve, generator j s behavor n terms of bddng and contractng wll not be controlled by retaler w s preferences. However, retaler w s behavor wll change. Specfcally, ts varable proft functon becomes: 10

12 π w + απ j (( ) ) = ( P p ) q + ( p f ) QC + α DR ( p ) QC ( p C ) + ( f C ) QC w w j j j j j (5) These profts have varance of: 2 2 Var[ π + απ ] = ( q QC + αqc ) σ w j w w j ( qw QCw αqcj) αcov[ p, DRj ( p )( p Cj )] α Var[ DRj ( p )( p Cj )] (6) Solvng the optmal forward contractng problem for the retaler can become far more complex under these condtons. Consequently, we adopt here a smplfyng assumpton, namely, that DR j s not a random varable from the retaler s perspectve and so s ndependent, n partcular, of p. Ths assumpton states that generator j s dspatched market load n every perod s known or does not vary. We beleve ths s a reasonable smplfyng assumpton to make n the present context. LYA s a baseload generator wth a capacty factor over our sample perod n excess of 0.90 and for all but one year of our sample ts annual capacty factor s above Therefore, LYA s dspatched output for a gven half-hour perod of the day s lkely to be predctable wth a hgh degree of precson, so that the wholesale prce rsk component of the overall proft rsk faced by AGL n a half-hour perod should swamp the quantty rsk assocated wth LYA s output durng that half-hour perod. Appendx A presents a comparson of the half-hourly coeffcent of varaton of the Vctora prce and LYA s half-hourly output for each year from January 1, 2000 to June 30, We fnd that the coeffcent of varaton of the half-hourly prce s always several tmes larger than ths same varable for LYA s half-hourly quantty, and n many half-hours for each of the years, the prce rsk (as measured by the coeffcent of varaton) s more than ten tmes 8 The capacty factor of a generaton faclty s defned as the total amount of megawatt hours (MWh) produced over certan tme perod dvded by the nameplate capacty of the generaton unt tmes the number of hours n that tme perod. Capacty factors are typcally reported on an annual bass. 11

13 larger than the quantty rsk. Therefore, to smplfy our theoretcal analyss we assume the quantty rsk borne by the retaler havng a stake n a generator s zero and although we recognze that n realty ths quantty rsk s nonzero and t may margnally mpact the retaler s demand for fxed-prce forward contracts. Gven ths, retaler w s demand for contracts s now the soluton to: max ( P E[ p ]) q + ( E[ p ] f ) QC QCw w w 1 2 (( DR j ( p ) QC j )( E[ p ] C j ) ( f C j ) QC j ) qw QCw ( DRj ( p ) QCj ) + α + ( ) 2 2 λ α σ (7) Note that, by the envelope theorem, there the mpact on j s profts through the spot prce effect does not factor n w s contractng choce. The frst-order condton s: f E[ p] = 0 E[ p 2 ] 2 1 σ ( QC λσ λ ( q ( ( ) )) )( ( ( ) )) w 2 w QCw α DRj p QCj QC q w w QCw α DRj p QCj = + Notce that there are two mpacts of the acquston on retaler w s demand for contracts. Frst, because some of those contracts are held by generator j, payments on those contracts are partally returned. However, gven the compettve contract market, ths has no net effect on the quantty chosen by retaler w. On the other hand, retaler w s now partally hedged; reducng the varance of ther profts. The ownershp stake s a perfect substtute for explct contracts. Indeed, from (8), t can be seen that: ( ( ) ) QC = q α DR p QC (9) w w j j Retaler w s fully hedged but does not necessarly use contracts to acheve ths. Instead, f generator j s not fully hedged (.e., f DR ( p ) > QC ), the demand for contracts wll decrease. j j (8) 12

14 Note, however, that the level of hedgng by other retalers wll be unchanged. They wll contnue to be fully hedged. Thus, the total amount of hedge contracts n the ndustry wll fall by: ( DR ( p ) QC ) Δ α (10) j j Ths s the level of the natural hedge that retaler j acheves from the acquston. Ths llustrates the frst-order behavoral mpact of the acquston; that s, passve acqustons change the behavor of the acqurers even f they have no explct ablty to mpact the behavor of the acqured frm or other frms. However, the ant-compettve effect comes from what ths change means for wholesale prces. In partcular, f the reducton n the quantty of hedge contracts sold by a generaton unt owner caused by the reducton n the retaler s demand for contracts leads the generaton unt owner to bd to set hgher prces, ths should result n hgher prces after the acquston. In the next secton we descrbe our methodology for calculatng prospectve changes n wholesale prces based on counterfactual changes n the quantty of fxed-prce forward contracts sold by baseload generaton unt owners. 2.3 Relatonshp to the Lterature It s worthwhle at ths pont to relate the above model to the lterature on vertcal ntegraton. That lterature, wthout excepton (to our knowledge), consders such ntegraton as actve. In so dong, the ant-compettve harm from vertcal ntegraton comes from the potental softenng of downstream prce competton or barganng effects that lead to foreclosure (Hart and Trole, 1990; Rey and Trole, 2007; de Fontenay and Gans, 2005). Here, however, the man mpact of ntegraton s to facltate a reducton n the effectveness of forward contract markets n constranng generator market power n 13

15 the short-term wholesale market. Ths arses because of ts passve nature rather than a change n the behavor of the acqured frm towards the acqurer s rvals. O Bren and Salop (2000) do llustrate how passve acqustons can lead to antcompettve effects n horzontal mergers. Bascally, whle a (passve) acquston does not alter the prcng behavor of the acqured frm t does alter the acqurer s ncentves to prce aggressvely as they now nternalze the effect of ths on the profts they accrue from ther subsdary. Here, we model passve acquston n much the same way; however, the nternalzaton effect s not present as the acqurer s n a dfferent vertcal segment to the acqured frm. Instead, the acqurer receves somethng valuable from the acquston a natural hedge and ths causes t to substtute away from explct contractng wth generators. In electrcty, that change has an mpact on prces. As such, the model here s really one specfc to the electrcty ndustry and to ndustres where forward contracts play a role n constranng market power. Nonetheless, t does renforce the O Bren and Salop nsght that passvty does not provde an unequvocal defense aganst accusatons of potental ant-compettve harm. 2.4 Calculatng the Natural Hedge To begn, t should be noted that LYA s regstered capacty s 2000 MW, however, ther average half-hourly capacty utlzaton has hstorcally on the order of 1900 MW. In addton, n publc dsclosures the future owners of LYA clamed that they would am to hedge 75 percent of ths half-hourly output. Ths suggests that the level of the natural hedge AGL would gan would be α x 0.25 x 1900 MW = α475 MW or MW at an α = 0.35 ownershp stake. 14

16 In actualty, however, ths type of back of the envelope calculaton msses an mportant effect: that f the natural hedge causes AGL to reduce ts contractng wth LYA (as s possble), ths wll rase LYA s uncontracted poston whch, n turn, rases AGL s natural hedge. It s relatvely straghtforward to resolvng the crcularty. Suppose that f AGL s natural hedge s NH, t wll plan to reduce ts contract cover wth LYA by γnh (where 0< γ < 1). Now suppose that LYA s uncontracted capacty s currently 475 MW (consstent wth a 75% contracted strategy). Then the natural hedge s the mplct soluton to the equaton: 0.35 (475 + γnh) = NH or NH = /(1-0.35γ) Notce that ths NH ranges from approxmately 166 MW when γ = 0 to 256 MW when γ = 1. The lower bound corresponds to a stuaton where LYA s contract poston remans unchanged post-acquston whereas the upper bound corresponds to a stuaton where t bears the full mpact of any reducton n demand from AGL. The realty s lkely to be some ntermedate value. Gven the range of possble ntended and lkely levels of the natural hedge, we use the above calculatons as an ndcator of the order of magntude of potental reductons n contract postons of generators post acquston. Frst, even f AGL were to reduce ts contract cover by NH, t s not certan that ths reducton would translate fully nto the reducton n contract postons held by baseload generators. Speculators and generators n other states may choose to unwnd ther contract holdngs or retalers may choose to expand ther postons. However, these are extremely rsky strateges for these market partcpants to pursue gven the prce effects of the acquston that we estmate. Further 15

17 evdence of the perceved rsk of ths strategy s that generaton unt owners from neghborng states are reluctant to sell fxed-prce forward contracts that clear aganst prces n states where they do not own generaton capacty. Second, t s not clear what the dstrbuton of reduced contract holdngs wll be across generators. It s unlkely that the entre reducton would be wth LYA although some proportonate reducton across baseload generators s more plausble. Nonetheless, we argue that an analyss focusng on a sngle generator s ndcatve of the potental effects that may arse as a result of the acquston studed here and may underestmate the magntude of the prce ncrease for the reasons dscussed n Secton 4. 3 Ex Ante Estmaton the Spot Prce Impact of Acquston The model n the prevous secton demonstrates that a passve vertcal acquston of a share of a baseload generaton faclty s lkely to cause the acqurng retaler to reduce ts demand for fxed-prce forward contracts by between 166 MW to 256 MW. In ths secton, we use the model of expected proft-maxmzng bddng behavor of a suppler n a wholesale electrcty market wth fxed-prce forward contract oblgatons developed n Wolak (2000 and 2003a) to derve an estmate of the change n a suppler s daly bddng behavor as a result of a reducton n ts fxed-prce forward contract oblgatons. For each day durng the perod January 1, 2000 to June 30, 2003 we compute 48 counterfactual half-hourly bds curves for LYA assocated wth a lower level of fxedprce forward contract oblgatons for that day. We then compute the counterfactual halfhourly market prce and quantty of electrcty sold by LYA for ths reduced level of 16

18 fxed-prce forward contract oblgatons by ntersectng the actual half-hourly resdual demand curve faced by LYA wth ts half-hourly counterfactual bd curve. The ex ante predcted mpact of the acquston s the percent dfference between the annual mean of the counterfactual half-hourly prce and the annual mean of actual half-hourly prce. We frst descrbe how the assumpton of expected proft-maxmzng bddng behavor for a gven level of fxed-prce forward contract oblgatons s used to derve the change n bddng behavor that results from a change the suppler s vector of daly fxedprce forward contract oblgatons. We then descrbe how to compute the counterfactual market outcome mpled by the change n bddng behavor mpled by suppler s lower level of fxed-prce forward contract oblgatons. Fnally, we present a comparson of the mean counterfactual and mean actual prces for each half-hour of the day for the perod January 1, 2000 to June 30, Computng Counterfactual Bds from Expected Proft-Maxmzng Bddng Behavor Each day generaton unt owners n the Natonal Electrcty Market (NEM) of Australa submt ther wllngness to supply electrcty from the generaton unts they own for all 48 half-hour perods of the followng day to the Natonal Electrcty Market Management Company (NEMMCO). These bd functons are weakly ncreasng step functons gvng a suppler s wllngness to provde electrcty from ts generaton unts n that half-hour as a functon of the market prce. The NEM market rules requre that the system operator choose whch unts operate and how much they operate by mnmzng the as-bd costs of meetng demand at each locaton n the transmsson network takng nto account the capacty of the natonal transmsson network and the mpact of 17

19 transmsson losses n movng the electrcty from where t s produced to the where t s ultmately consumed. These generaton unt-level bd functons and dspatch levels are publcly avalable. 9 The NEM chooses the least as-bd cost of meetng the demand for electrcty at all locatons n the transmsson network and sets prces for the four states of southeastern Australa n the NEM--Vctora (VIC), New South Wales (NSW), Queensland (QLD) and South Australa (SA)--that account for transmsson losses. 10 If there are no constrants n the transmsson network, these prces dffer only because of the transmsson losses assocated wth movng power between the four states. For example, f electrcty s flowng from Vctora to the South Australa and there are no transmsson constrants between these two regons, the prce n Vctora wll be less than the prce n South Australa because all energy produced n Vctora s beng pad the same prce, but the amount flowng from Vctora to South Australa ncurs transmsson losses before t arrves n the South Australa. Transmsson losses across regons are typcally less than 5 percent, meanng that prce dfferences between regons less than 5% are very lkely to reflect only transmsson losses. More substantal prce dfferences across regons are caused by constrants n the transmsson network whch prevent generaton unts wth lower prced bds from beng 9 All bd and market outcome data s avalable the day after the market operates at 10 Transmsson losses account for the fact that a suppler njectng 1 MWh of energy at ts locaton n the transmsson network wll result n less than 1 MWh of energy arrvng at the locaton n the transmsson network where t s consumed. As a general rule, the greater the dstance between the locaton the energy s njected and the pont at whch t s consumed, the greater are the transmsson losses. Another factor mpactng the magntude of transmsson losses s whether a generaton unt s located n a generaton-rch or generaton-poor regon of the transmsson network. Those unts located n more generaton-rch regons experence greater transmsson losses. The NEM accounts for these transmsson losses by nflatng the bd prces of supplers located far away from the pont where the energy s beng consumed and those located n generaton rch areas to reflect the fact that 1 MWh of energy njected by these unts wll result n less than 1 MWh delvered at the pont ths energy s wthdrawn. 18

20 dspatched because of ther locaton and cause unts wth hgher bd prces to be dspatched because of ther locaton. Ths occurs when there s nsuffcent transmsson capacty across the states of Australa to transfer all of the lower-prced energy from one state nto another state. For example, even f after controllng for the mpact of transmsson losses, an addtonal 100 MWh of energy from Vctora would be cheaper to use n South Australa than a 100 MWh of addtonal energy produced from unts wthn South Australa, NEMMCO may not be able to use all of ths 100 MWh from Vctora f the amount of avalable transmsson capacty between these two regons s less than 100 MW. Under these crcumstances, NEMMCO wll have to reduce the prce n Vctora to lmt the amount of output taken from generaton unts there and ncrease the prce n South Australa to ncrease amount of energy suppled from generaton unts n South Australa. Although the mpact of these transmsson constrants can be ncorporated nto our model of expected proft-maxmzng bddng behavor, n order to take a conservatve approach to assessng mpact of the acquston on market prces, n computng the counterfactual prces we assume that bds from all generaton unts n all four Australan states compete wth bds from LYA regardless of the amount of unused capacty n the transmsson network. We now dscuss our model of expected proft-maxmzng behavor n a bd-based wholesale electrcty market that forms the bass for computng our counterfactual bds for LYA. In the NEM, each tradng day s composed of 48 half-hour long load perods. The tradng day begns wth the half-hour from 4:00 am to 4:30 am and ends wth the half-hour from 3:30 am to 4:00 am followng day. The day before the start of the tradng day, supplers submt up to ten bd prces and up to 480 half-hourly quantty ncrements 19

21 for each generaton unt they own. An electrcty generaton plant s typcally composed of multple generaton unts or gensets. For example, Loy Yang s a 2000 MW faclty composed of four generaton unts each wth 500 MW of capacty. Consequently, LYA submts bd curves of ths form for each half-hour of the day for each of ts four generaton unts. Ths mples that LYA sets up to 40 bd prces and 1920 half-hourly quantty ncrements each tradng day. We requre the followng notaton to present our methodology for computng the change n LYA s bddng behavor as a result of change n ts forward contract oblgatons: QD : Total market demand n load perod, ( = 1,..., 48) SO (p): Amount of energy all other frms besdes LYA are wllng to supply to the market n load perod at prce p DR (p) = QD SO (p): Resdual demand faced by LYA n load perod (specfyng the demand faced by LYA at prce p) QC : Contract quantty for load perod for LYA PC : Quantty-weghted average (over all hedge contracts sgned for that load perod and day) contract prce for load perod for LYA. π (p): Varable profts of LYA at prce p, n load perod SL j (p,θ): Bd functon of genset j owned LYA for load perod gvng the amount t s wllng to supply from ths unt as a functon of the prce p and vector of daly bd parameters Θ Q j = total amount produced from unt j durng load perod Qj = ( Q1j, Q2 j,..., Q48j) = vector of daly outputs from genset j C j = the varable cost of producng output from genset j J SL( p, Θ ) = SL (, ) j 1 j p Θ = total quantty bd n by LYA at prce p = durng load perod. Let ε equal the shock to LYA s resdual demand functon n load perod ( = 1,..., 48). Re-wrte ths resdual demand functon n load perod accountng for ths demand shock as DR (p,ε ). The resdual demand shock reflects uncertanty n both total system demand and the offers of all other supplers at the tme LYA submts ts bds to the wholesale 20

22 market the day before the tradng day. Collect these 48 half-hourly demand shocks nto the vector ε = ( ε1, ε2,..., ε48). Θ= (,...,,,...,,,...,,...,,..., ) as the vector Defne p11 pjk q1,11 q1, JK q2,11 q2, JK q48,11 q48, JK of daly bd prces and quanttes submtted by LYA. There are K ncrements for each of the J gensets owned by LYA. The NEM rules requre a sngle bd prce, p jk, to be set for each of the k = 1,..., K bd ncrements for each of the j = 1,..., J gensets owned by LYA for the entre day. However, the quantty, q jk made avalable to produce electrcty n load perod from each of the k = 1,..., K bd ncrements for the j = 1,..., J gensets owned by LYA can vary across the = 1,..., 48 load perods throughout the day. The NEM rules specfy the value of K as 10, so the dmenson of Θ s 10J J. LYA owns four gensets so J = The market clearng prce p for load perod s determned by solvng for the smallest prce such that the equaton SL ( p, Θ ) = DR ( p, ε ) holds, whch we denote by p ( ε, Θ ). Ths prce depends on both realzaton of ε and the value of Θ, because t s determned by the ntersecton of the realzed resdual demand curve and the bd curve of LYA for load perod. Note that, SL ( p( ε, Θ), Θ ) = Q, meanng that the amount j j produced by genset j n load perod s equal to the quantty bd by genset j n load perod at the market prce durng perod. In terms of ths notaton, the realzed varable proft for LYA gven the vector of bd parameters for the day, Θ, and the vector of half-hourly forward contract quanttes for the day QC = ( QC1, QC2,..., QC48) s: 11 Fgure 4.1 of Wolak (2003a) provdes a graphcal llustraton of how the constrants on prce and quantty bds requred by the NEM rules mpact the ablty of supplers to alter ther half-hourly supply curves across the tradng day. 21

23 48 J Π( Θ, ε) = DR( p( ε, Θ)) p( ε, Θ) ( p( ε, Θ) PC) QC C jslj( p( ε, Θ), Θ) = 1 j= 1 (11) To economze on notaton n what follows, we abbrevate p ( ε, Θ ) as p, even though t depends on both ε and Θ. LYA s best-reply bddng strategy s the vector, Θ, that maxmzes the expected value of Π(Θ,ε) (taken wth respect to the jont densty of ε) subject to the constrants that all bd quantty ncrements, q jk, must be greater than or equal to zero for all load perods,, gensets, j, and bd ncrements, k, and that for each genset the sum of bd quantty ncrements durng each load perod s less than the capacty, CAP j, of genset j. All daly prce ncrements must be greater than $9, /MWh and less than $5,000/MWh before Aprl 1, 2002 and less than $10,000/MWH after Aprl 1, 2002, where all dollar magntudes are n Australan dollars ($AU). All of these constrants can be wrtten as a lnear combnaton of the elements of Θ. In terms of the above notaton, LYA s expected proft-maxmzng bddng strategy can be wrtten as: [ ε ] max Θ Eε Πd( Θ, ) subject to bu RΘ bl (12) where E ε [.] s the expectaton wth respect to the jont dstrbuton of ε. The frst-order condtons for ths optmzaton problem are: [ (, ε )] E Π Θ ε d Θ = R λ R μ (13) RΘ b l, bu RΘ (14) f ( RΘ b) > 0, then μ = 0 and ( RΘ b ) < 0, then λ = 0 (15) l k k u k k 22

24 where (X) k s the k th element of the vector X and μ k and λ k are the k th elements of the vectors of Kuhn-Tucker multplers, μ and λ. If all of the nequalty constrants assocated wth an element of Θ, say p jk, are slack, then the frst-order condton reduces to: E ε [ Π( Θ, ε )] p jk = 0 (16) For out sample perod, all of the daly bd prces assocated wth LYA s gensets over the sample perod le n the nteror of the nterval (-9,999.99,5,000) before Aprl 1, 2002 and the nterval ( ,10,000) after Aprl 1, 2002, whch mples that all bd prces satsfy the frst-order condtons gven n (16) for all days, gensets, j, and bd ncrements, k. For ths reason, we can use the frst-order condtons for daly expected proft maxmzaton wth respect to LYA s choce of the vector of daly bd prces to compute the change n ts bd prces as a result of a change n ts vector of daly forward contract quanttes, QC. LYA operates 4 unts durng sample perod and each of them has bd 10 ncrements, whch mples 40 frst-order condtons hold as equaltes for each day of our sample. These frst-order condtons mply that the daly bd prce vector s a functon of the vector of daly forward contract quanttes, QC. Usng the mplct functon theorem, we can then compute the matrx of partal dervatves of the vector of daly bd prces wth respect to QC for each day. Multplyng ths matrx by the change n QC that results from the acquston yelds our predcted change n the vector of daly bd prces. It s not possble to use the frst-order condtons wth respect to the bd quantty ncrements to compute the change n the vector of daly quantty bds that results from the acquston because for a number of load perods throughout the day for vrtually all days on our sample several bd quantty ncrements are zero and/or the sum of the ten bd 23

25 quantty ncrements for that load perod s equal to the capacty of the genset. As noted n Wolak (2004), when ether of these condtons holds for a bd quantty ncrement, the best that can one can do s nfer the sgn of the partal dervatve of the daly expected proft functon wth respect to ths bd quantty ncrement. Thus, we are unable to apply the mplct functon theorem to derve the expected proft-maxmzng bd quantty response to a change n the value of QC for LYA. Therefore, we compute LYA s 48 counterfactual daly bd curves that result from a lower value of QC under the assumpton that all of ts half-hourly bd quantty ncrements for the day do not change. Only the vector of daly bd prces are allowed to change as a result of the hypotheszed change n QC. Although ths assumpton s necesstated by the fact that the Kuhn-Tucker multplers n (15) are not observed, we do not beleve t sgnfcantly mpacts our counterfactual market prce results. Because LYA has very low varable costs relatve to the vast majorty of half-hourly market-clearng prces, t operates close to capacty most hours of the year as noted earler. Consequently, the half-hourly bd quantty choces for each of the four unts owned by LYA are often corner solutons wth nonzero Kuhn- Tucker multplers, so that small changes n the daly bd prces for these unts are unlkely to cause these corner solutons n the bd quanttes to become nteror solutons, although the values of the assocated Kuhn-Tucker multplers are lkely to change. For these reasons, we beleve that for many of the bd ncrements n a gven load perod, the expected proft-maxmzng bd quantty response to a change n QC would be small. It s also mportant to note that a change n a bd quantty ncrement for load perod only mpacts the suppler s expected profts n that load perod, but a change n a bd prce mpacts expected the suppler s expected profts n all 48 load perods of the day. 24

26 Consequently, we would expect any bd prce ncrement to be must more senstve to changes n QC that any bd quantty ncrement. We hope to examne the senstvty of our counterfactual prcng results to the assumpton of fxed bd quantty ncrements n future work. To make the dependence of QC explct, we re-wrte the (40x1) vector frst-order condtons for daly expected proft-maxmzaton wth respect to Frm LYA s choce of the daly bd prce ncrements as: E ε Π( Θ, QC, ε ) = 0 θ, (17) where the vector θ s composed of the 40 bd prce ncrements for the day (4 gensets tmes 10 bd ncrements per genset) for the gensets owned by LYA and Θ s vector of bd prce and quantty ncrements for the day. In terms of prevously defned notaton: θ = (,..., )'. The varable QC s the value of the vector of half-hourly forward p11 p JK contract oblgatons for that day. Equaton (17) and the second-order condtons for expected proft-maxmzng bddng behavor mply that the optmal bd prce ncrement for genset s and ncrement t for that day can be wrtten as p * ( QC ) for all 40 daly bd prce ncrements. Because the st NEM rules requre bd prces to be fxed for the entre day, each bd prce potentally depends on all 48 elements of QC. Let θ * ( QC) denote the entre (40x1) vector of these optmal daly bd prces. We can wrte equaton (17) n terms of ths new notaton as: * Π( θ ( QC), QC, ε) E ε = 0 θ. (18) 25

27 Applyng the mplct functon theorem to (18), we can compute the 40x48 matrx of partal dervatves of the elements of θ * ( QC) wth respect to the elements of QC as: 1 * 2 * 2 * θ ( QC) Π( θ ( QC), QC, ε) Π( θ ( QC), QC, ε) = Eε Eε QC θθ θ QC, (19) where we have swtched the order of ntegraton and dfferentaton n computng the matrces of partal dervatves of (18) wth respect to θ and QC, respectvely. The matrx E ε 2 * Π( θ ( QC ), QC, ε) θθ has (m, n) element 2 * Π( θ ( QC ), QC, ε) Eε θm θn for m = 1,..., 40 and n = 1,..., 40. The matrx E ε 2 * Π( θ ( QC ), QC, ε) θ QC has (r, s) element 2 * Π( θ ( QC ), QC, ε) Eε θr QCs for r = 1,..., 40 and s = 1,..., 48. Ths mples that θ * ( QC ) QC s (40 x 48) matrx. Our goal s to obtan a consstent estmate of θ * ( QC ) QC for each day durng our sample perod. To do ths we compute the estmates of the two matrces n equaton (19) usng averages of the sample analogues of these magntudes for B realzatons from the dstrbuton of ε. A sngle element of the vector * Π( θ ( QC), QC, ε( b)) θ for resdual demand uncertanty realzaton ε(b) for the prce moment restrcton for ncrement t of genset s s: DR ( p( ε( b), Θ), ε( b)) p( ε( b), Θ) p ( ε ( b), Θ ) SLj ( p ( ε ( b), Θ), Θ) ( ) (20) J 48 Π( Θ, QC, ε ( b)) DR( p( ε( b), ), ε( b)) QC C + Θ j p pst = j= 1 pst = 1 J SLj ( p ( ε ( b), Θ), Θ) C j pst j= 1 Note that we are mplctly assumng that the realzed proft functon s dfferentable n the LYP s bd prces and the market-clearng prces. Followng the descrpton of our procedure for computng the post-acquston market-clearng prces, we descrbe how we smooth the realzed proft functon to obtan a dfferentable realzed proft functon. 26

28 Let A(b) equal the (40x40) matrx that s the sample analogue of the frst term n equaton (19) for resdual demand uncertanty realzaton ε(b). The matrx A(b) has 2 ΠΘ (, QC, ε ( b)) representatve element, where : p p 2 ΠΘ (, QC, ε ( b)) p p st rv st rv 2 DR ( p ( ε ( b), Θ ), ε ( b)) + DR ( p ( ε ( b), Θ), ε ( b)) p ( ε ( b), Θ) 48 2 p p j p J = 2 j rv = 1 j 2 C prv pst = 1 pst j= 1 J SL ( p ( ε ( b), Θ), Θ) SL ( p ( ε ( b), Θ), Θ) C j ( ) ( p p ) p j DR ( p( ε( b), Θ), ε( b)) p( ε( b), Θ ) + DR( p( ε( b), Θ), ε) QC 2 J p + ( ( ε ( ), ), ) C j p pst prv j= 1 J ( ) 2 SLj ( p ( ε ( b), Θ), Θ) p C j p. st p p j= 1 J SL ( p ( ε ( b), Θ), Θ) SL p b Θ Θ C j ( ) ( p p ) j= 1 2 j j st rv rv Let C(b) equal the (40x48) matrx that s sample analogue of second term of equaton (19) for resdual demand uncertanty realzaton ε(b). The matrx C(b) has representatve element: (21) 2 ΠΘ (, QC, ε ( b)) ph = p QC p st h st (22) We take B draws from the dstrbuton of resdual demand uncertanty for each day n our sample accordng to the followng algorthm. One draw s the actual resdual demand realzaton for all 48 half-hours of that day and the remanng draws are the 48 halfhourly resdual demand realzatons for B 1 days from the prevous, current, and followng month wth a daly peak demand closest to the actual peak demand for that day. For the frst month of the sample, we use the frst three months of sample and for the last month of the sample, we use the last three months of the sample. We expermented wth other algorthms for constructng the dstrbuton of daly resdual demand curves faced 27

29 by LYA and found that our results dd not apprecably change as long as the value of B was chosen to be suffcently large. The sample analogues for the matrces n (19) are computed as: B B 1 1 (, ) = B ( ) and (, ) = B ( ) b= 1 b= 1 (23) SM A B A b SM C B C b where SM(X,B) s the sample mean of X(b) for B draws from the dstrbuton of resdual demand uncertanty. Let P( θ, QC, B) equal the estmate of θ * ( QC ) QC for B draws: 1 [ ] [ ] P( θ, QC, B) = SM ( A, B) SM ( C, B) (24) Because of the exstence of the bd prce floor of and the bd prce cap of 5000 or 10,000, all of the realzatons of A(b) and C(b) are bounded random matrces. Therefore, under sutable regularty condtons, as B, both SM(A,B) and SM(C,B) tend to ther populaton values gven n equaton (19) by an approprate law of large θ numbers. In ths sense, we obtan a consstent estmate of * ( QC ). Multplyng P( θ, QC, B) by our hypotheszed change n the vector of daly forward contract quanttes, ΔQC, yelds the our estmated change n the value of the vector of daly bd prces, Δθ: [ ( θ,, )] The counterfactual daly bd prce vector then becomes: QC Δ θ = P QC B Δ QC (25) c θ = θ +Δ θ (26) We then compute Θ c, the counterfactual daly bd prce and quantty vector by replacng θ wth θ c n Θ, wth no change n the bd quantty ncrements. The counterfactual market prce for load perod that results from a ΔQC change n QC s the prce at ntersecton of 28

30 the actual resdual demand realzaton for that load perod wth the counterfactual bd curve for that load perod. Mathematcally, ths counterfactual prce s equal to the smallest value of p that solves: c whch we denote p ( ε, Θ ). c SL ( p, Θ ) = DR ( p, ε ), (27) As noted above, ths procedure requres the realzed proft functon for LYP to be dfferentable n the bd prces and market-clearng prces. We accomplsh by usng the flexble smoothng procedure descrbed n Wolak (2003a and 2004) to construct a h dfferentable approxmaton to Π( Θ, C, ε ). Let Π ( Θ, C, ε ) equal the dfferentable verson of LYA s daly varable proft functon ndexed by the smoothng parameter h. h When h = 0, there s no approxmaton because, Π ( Θ, C, ε ) =Π( Θ, C, ε ). Usng ths smooth, dfferentable approxmaton to Π( Θ, C, ε ), the order of ntegraton and dfferentaton can be swtched n the frst-order condtons for expected proftmaxmzng bddng behavor to produce the equalty: h E (,, ) h ε Π Θ C ε Π ( Θ, C, ε ) = Eε Θ Θ h= 0 h= 0 (28) h Ths smooth, dfferental verson of Π ( Θ, C, ε ) takes the followng form. A dfferentable resdual demand functon facng LYA that allows the resdual demand uncertanty to mpact both the market demand and the bd curves of other supplers s: h h DR ( p, ε ) = Q ( ε ) SO ( p, ε ) (29) where the smoothed aggregate bd supply functon of all other market partcpants besdes LYA n load perod s equal to: 29

31 N 10 h ε = nkφ nk n= 1 k= 1 ( ) (30) SO ( p, ) qo ( p po )/ h qo nk s the k th bd ncrement of genset n n load perod and po nk s bd prce for ncrement k of genset n, where N s the total number of gensets n the market excludng those owned by LYA. Because the bd curves of other market partcpants change daly, the values of qo nk and po nk change on a daly bass. Φ(t) s the standard normal cumulatve dstrbuton functon and h s the smoothng parameter. Ths parameterzaton h of SO ( p ) smoothes the corners on the step-functon bd curves of all other market partcpants create a supply functon that s dfferentable n p for all postve values of h. Ths smoothng procedure results n the followng expresson for dervatve of LYA s resdual demand functon wth respect to the market prce n load perod : h ddr ( p, ε ) = dp N 10 1 h n= 1 k= 1 where φ(t) s the standard normal densty functon. qonkϕ (( p ponk )/ h) (31) Ths same procedure s followed to make SL ( p, Θ ) dfferentable wth respect to both the market prce, p, and Θ, the prce and quantty bd parameters that make up LYA s wllngness-to-supply functon. Defne SL h ( p, Θ ) as: whch mples: j j ( ) h 10 j Θ = jkφ jk k = 1 SL ( p, ) q ( p p )/ h (32) ( ) J 10 h Θ = jkφ jk j= 1 k= 1 SL ( p, ) q ( p p )/ h (33) where t s understood that q jk and p jk change on a daly bass. Ths defnton of SLj ( p, Θ ) yelds the followng partal dervatves: 30

32 SL q j jk (( p pjk )/ h) =Φ (34) SL (( jk )/ ) 10 j 1 = h qjkϕ p p h (35) p k = 1 SL p j jk ( jk ) 1 = q ϕ ( p p )/ h h jk (36) 2 SLj 1 = 2 qjkϕ ( p pjk )/ h ( p pjk )/ h h p p jk ( )( ) (37) Appendx B computes expressons for the all of the remanng partal dervatves that enter (21) and (22) n terms of the smoothed resdual demand curves and LYA bd curves. Provng consstency of the smoothed proft-functon verson of ths procedure for estmatng θ * ( QC ) QC s only slghtly complcated by the fact that we must let h 0 as B. Because we are only concerned wth consstent estmaton of the two elements of equaton (19), by requrng h to tend to zero suffcently slow such that h 2 B as B s suffcent for the smoothed verson of SM(A,B) and SM(C,B) tend to ther populaton values gven n equaton (19), so that we obtan a consstent estmate of θ * ( QC ) QC. 4 Emprcal Results Ths secton frst descrbes the detals of the mplementaton of our methodology for computng counterfactual, post-acquston wholesale prces for each half-hour over our sample perod. We dscuss the lkely senstvty of our results to certan modelng assumptons that smplfy our analyss. Fnally, we present our emprcal results that 31

33 show szeable and statstcally sgnfcant dfferences between the annual half-hourly mean of actual prces and the counterfactual prces for vrtually all half-hour perods of the day for all the years n our sample. 4.1 Implementng the Emprcal Methodology For each day of our sample perod from January 1, 2000 to June 30, 2003 we use the half-hourly bd, generaton unt-level producton, forward contract quanttes, and market-clearng prce data to compute the smoothed values of A(b) and C(b) for a fxed value of h. We end our sample on June 30, 2003 because ths s the approxmate date when publc dscussons of the acquston began. To guard aganst our results beng mpacted by changes n bddng behavor due to the ongong analyss of the acquston, we stopped our sample at ths date. In order to be as conservatve as possble n estmatng the ablty of LYA to rase wholesale electrcty prces, we assume that LYP competes n the largest geographc market possble, the entre Natonal Electrcty Market (NEM) composed of Queensland, New South Wales, Vctora and South Australa. Ths means that we nclude the bd prces and quantty ncrement bds of all generaton unts (besdes those owned by LYA) n these four states n resdual demand curve faced by LYA. Ths assumpton s equvalent to assumng nfnte transmsson capacty between the four states. The varable cost of each genset owned by LYA s one pece of nformaton not avalable from the NEMMECO web-ste necessary to compute A(b) and C(b). However, there appears to be general agreement between LYA and other partes that ths varable cost s approxmately $AU 4/MWh. These generaton unts burn brown coal that can be surface mned usng an automated process. Fgure 1 contans photographs of ths 32

34 producton process for LYA. The power plant s constructed near the coal depost and a mechancal devce to dg the coal and transport t to the power plant s constructed. Fgure 1(a) s a pcture of the LYA ste whch ncludes a surface mne and nearby generaton unts. Fgure 1(b) s a pcture of the mnng machne that dgs up the brown coal and puts t on a conveyer belt connected to the generaton unts. Fgure 1(c) s a pcture of the coal on the conveyer belt. Once constructed, ths process runs wth lttle human nterventon and at an extremely low varable cost relatve to the average prce of electrcty over our sample perod of $AU 30/MWh. Our analyss uses a value of $AU 4/MWh for C j all four gensets owned by LYA. Our results were not senstve to plausble changes n the value of ths varable cost fgure. Our algorthm for drawng resdual demand curves for each day n our sample s based on the logc that the peak demand for the day s major determnant of the extent of unlateral market power a suppler expects to exercse durng that day. The NEM rule that a suppler s bd prces must reman fxed for all 48 load perods of the day provdes further justfcaton for our focus on peak demand n selectng resdual demand draws from days from the same and neghborng months wth close to the same daly peak demand as the day under consderaton. As dscussed earler, to compute draws from the dstrbuton of resdual demand uncertanty, we selected values of QD, the market demand for load perod, qo nk, the bd quantty ncrement for load perod and genset n and bd ncrement k, and po nk, the bd prce for genset n and bd ncrement k for all 48 load perods from the same day, rather than sample ndependently from load perods wthn or across days. Ths was done to preserve the wthn-day correlaton among bd parameters of the LYA s compettors that s lkely to exst. We expermented wth other 33

35 resdual demand draws selecton algorthms such as average daly demand or a weghed sum of mnmum and maxmum demand and found lttle dfference n our results for a gven value of h as long as the number of draws was large enough. Our analyss uses B = 25 and h = 1. We found that these values for the number of draws and the smoothng parameter best balanced our need for computatonal effcency (because we compute counterfactual prces for every day n our sample) and precson n our estmate of the two matrces n (19). Smaller values of h requre larger values of B, but larger values of B sgnfcantly ncrease the tme needed to compute our estmate of θ * ( QC ) QC for each day. Because all bd prces le between and 5000 (or 10,000) and the average prce of electrcty durng our sample s $AU 30/MWh, a value of h = 1, roughly 3.3% of the sample average market prce, does not mply a sgnfcant amount of smoothng of the step functon supply curves. A fnal ssue wth mplementng our procedure for computng counterfactual prces s the selecton of the vector ΔQC. The analyss n Secton 2 uses a sngle load perod-level model, but we are dealng wth a daly market wth 48 half-hourly perods. Because we expect the slope of the resdual demand curve faced by LYA to become more elastc n off-peak perods of the day relatve to peak perods of the day, we mght expect that elements of ΔQC for the off-peak perods of the day to be less than the elements durng the peak perods of the day. However, gven ts extremely low varable cost, LYA operates as a baseload unt and market partcpants expect t to run close to capacty durng all hours of the day, so t may reduce t forward contract oblgatons durng off- 34

36 peak hours to take advantage of opportuntes to exercse unlateral market power n these load perods as well. 12 Rather than attempt to fne tune our hypotheszed change n the level of fxedprce forward contracts held by LYP on a half-hourly bass, we assume a very smple structure for ΔQC. There are very lkely to be ΔQC vectors that yeld hgher daly average prce ncreases, but to avod beng subject to the crtcsm that our results are drven by the choce of ΔQC, we take an unsophstcated approach to selectng t. Let 48 ι = (1,1,...,1)' R be a 48x1 vector of 1 s and Δqc a scalar. We assume QC ι( qc) Δ = Δ. Ths mples a reducton n LYA s forward contract oblgatons of Δqc for all half-hours of the day. All of our emprcal results use the value of Δqc = 200 MW. We expermented wth values of Δqc between 150 and 250 and obtaned qualtatvely smlar results wth larger (n absolute value) values of Δqc assocated wth larger average prce ncreases. To compute the counterfactual prces from that result from the acquston we assume that all of the reducton n the demand for fxed-prce forward contracts by AGL as result of the acquston comes from those sold by LYA. Although we do not beleve ths outcome s lkely, ths allocaton of the AGL s reducton n ts forward contract demand was done to smplfy our analyss. Table 1 lsts all of the Vctora generaton unts that exsted as of June There s approxmately 5,400 MW of brown coal capacty n Vctora spread among four szeable generaton unt owners. We would expect the reducton n AGL s demand for fxed-prce forward contracts to come from all of these supplers. 12 Wolak (2003b) found sgnfcant opportuntes for supplers to exercse unlateral market power n the Calforna electrcty market durng off-peak hours of the day for the perod June to September

37 Our assumpton that the entre reducton n AGL s demand for fxed-prce forward contracts results n same quantty of reduced sales of forward contracts by LYA may bas our results aganst a fndng of sgnfcant prce ncreases as result of the acquston, because ths approach assumes no change n the bddng behavor of the other large brown coal supplers n Vctora. If the some of the reducton n the demand for fxed-prce forward contracts by AGL comes from the remanng baseload supplers n Vctora, followng the logc of Secton 3, the expected proft-maxmzng prce bds of these supplers would change and the prce bds of LYA would change less than f all of reducton the AGL s demand for fxed-prce forward contracts demand came from forward contracts sold by LYA. It s unclear whch of these two effects domnate n terms of resultng n hgher counterfactual prces. Resolvng ths queston amounts to determnng whether reducng the amount of electrcty a sngle generaton unt owner wth a 20 percent market share s wllng to supply at a pre-specfed prce (by allocatng the entre reducton n forward contract demand to that suppler) yelds a hgher counterfactual prce than allocatng ths same forward contract demand reducton to several supplers wth a total market share of 60 percent. If the sngle suppler n the frst case reduces ts wllngness to supply by 10 percent, ths mples a total reducton n supply to the market at ths prce of 2 percent. If the supplers wth a total market share of 60 percent each reduce ther supply by 4 percent, then the total reducton n supply to the market at ths prce s 2.4 percent. Therefore, despte the fact that the reducton n the amount suppled by each producer s much less than 10 percent, because ths reducton s appled to a larger fracton of total supply, the resultng counterfactual prce s hgher. 36

38 Our analyss can be modfed to address any allocaton of these fxed-prce forward contract oblgatons among the large baseload supplers. Consder the case that ths forward contract reducton s allocated equally to Loy Yang, Hazelwood and Yallourn, the three largest supplers n Vctora. Let SL (p,θ L ), SH (p,θ H ), and SY (p,θ Y ) equal, respectvely, the bd supply curves of Loy Yang (L), Hazelwood (H), and Yallourn (Y) for load perod, where Θ K s the daly bd prce and bd quantty ncrement vector for suppler K = L, H and Y. For each of these supplers we could compute the matrx of partal dervatves of ther daly bd prce vector wth respect to ther daly forward contract quantty vector and then apply the hypotheszed vector of fxed-prce forward contract quantty reductons for that suppler to compute the change n each suppler s daly bd prce vector. We would then compute the counterfactual post acquston market prces as the smaller prce that solves: Lc Hc Yc SL ( p, Θ ) + SH ( p, Θ ) + SY ( p, Θ ) = DRB ( p, ε ) (38) where Θ Kc s the counterfactual daly bd prce and bd quantty ncrement vector for suppler K =L, H and Y that results from that suppler s hypotheszed reducton n daly forward contract oblgatons and DRB ( p, ε ) s the realzed resdual demand curve faced by these three baseload supplers. Computng ths counterfactual prce requres three tmes the computaton effort of our approach and t s unclear s the resultng counterfactual prce s hgher or lower. Ths s a topc we plan to explore n future work. 4.2 Emprcal Results from Ex Ante Methodology Let p d equal the actual prce for load perod of day d and c p d the counterfactual prce for load perod of day d. To report our results, we compute 37

39 p D 1 D d = 1 c 1 c = p and p = p (39) d D D d = 1 d where D s the total number of days n each year of our sample. For 2000, 2001 and 2002 t s the total number of days n the year and for 2003 t s total number of days from January 1, 2003 to June 30, We also compute the standard devaton of each halfhourly prce for each year, D 1/2 1 2 = D d and d = 1 σ ( p ) ( p p ) D 1/2 c 1 c c 2 = D d (40) d = 1 σ ( p ) ( p p ) Fgures 2(a) plots the values of p for = 1,..., 48 for the year 2000 and percentage dfference between c p and p, whch s equal to for = 1,..., 48. Fgure 2(b) c plots the values of σ ( p ) for =1,...,48 for year 2000 and the rato σ( p )/ σ ( p) for = 1,..., 48. These results show a persstent mean prce ncrease between 10 and 25 percent for the majorty of half-hour perods (and even hgher n several other half-hour perods) as a result of the acquston and an ncrease n half-hourly prce volatlty n most halfhour perods. Ths prce volatlty ncrease s much hgher durng some half-hour perods. Fgure 3(a) and 3(b) through 5(a) and 5(b) present ths same nformaton for 2001, 2002 and These fgures tell a smlar story. Sgnfcant prce ncreases n the 10 to 25 percent range durng most half-hours wth hgher values durng some hours are predcted by the acquston. There s also an ncrease n prce volatlty n most half-hour perods, and ths ncrease n prce volatlty s substantal durng some half-hour perods of each year. 38

40 The frst secton of Table 2 presents values of c p and p the asymptotc t-statstc for the null hypothess that the expected value of X = p p s equal to zero for the c d d d year 2000 for all 48 half-hour perods. Ths asymptotc t-statstc s equal to: Z D D = X D σ ( X ) where X = D X d and σ ( X) = D ( Xd X) (41) d = 1 d = 1 1/2 where D s the total number of days n that year of the sample. The statstc, Z s asymptotcally dstrbuted as a N(0,1) random varable under the null hypothess. For all 48 load perods n 2000, we fnd that the null hypothess that E[X d ] = 0, the expected prce dfference s zero, s rejected at conventonal levels of sgnfcance and s greater than zero, whch provdes statstcally sgnfcant evdence that the mean counterfactual prce s hgher than the mean actual prce for all load perods. The remanng three sectons of Tables 2 present the same three numbers for each load perod for 2001, 2002 and For all years and the vast majorty of load perods wthn each year, the mean dfference between the counterfactual half-hourly prce and the actual half-hourly prce s statstcally dfferent from zero and postve. 5 Ex Post Analyss of the Acquston Because the acquston was effectve Aprl 1, 2004 and we have market outcome data before and after ths date we can also perform an ex post analyss of the acquston usng a treatment effects approach. We estmate two dstnct treatment effects assocated wth the acquston. The frst reles on the fact that the hghest varable cost generaton unt operatng durng most hours of the year n the NEM s a natural-gas fred generaton unt so that by usng the daly prce of natural gas as our control prce, we are account for 39

41 the mpact of nput cost dfferences over tme n the prce of wholesale electrcty n Vctora. The second approach reles on the fact that Vctora has the lowest average wholesale electrcty prce over our sample perod and as a consequence frequently exports electrcty to the other four states. Durng a number half-hours of the year not all of the low-prced electrcty avalable from Vctora producers can be transferred to New South Wales because the nterconnecton between Vctora and New South Wales s congested and the prce n Vctora s determned only by competton among generaton unt owners n that state. The second analyss asks f durng load perods when congeston between Vctora to New South Wales s lkely, Vctora prces are hgher relatve New South Wales prces after the acquston. The sample perod for our treatment effects analyss s one year before the acquston occurred and one year after the acquston occurred. Increasng the sze of the sample perod ncreases the rsk that any treatment effect we fnd may be due to other factors besdes the acquston. Reducng the sze of the sample perod reduces the precson of any treatment effect we mght measure. We settled on a year-long pretreatment perod and post-treatment perod to capture the mpact of the acquston on a full year of half-hourly electrcty prces. The data used n the frst treatment effects analyss s the half-hourly wholesale electrcty prce n Vctora n $AU/MWh and the daly natural gas prce from the Vctora natural gas wholesale market n $AU per Ggajoule (GJ). A ggajoule s equal to mllon Brtsh Thermal Unts (MMBTUs), the standard unt of measure for natural gas sold n the Unted States. Daly natural gas prces were obtaned from the 40

42 Vencorp web-ste. 13 Vencorp manages the Vctoran wholesale natural gas market and sets spot prces on a daly bass. Fgure 6 plots the daly natural gas prce n Vctora from Aprl 1, 2003 to Aprl 1, 2005 n $AU/GJ and the daly average Vctora wholesale electrcty prce n $AU/MWh. Fgure 7 repeats ths plot for the daly average prce of electrcty n New South Wales. Note the dramatcally larger scale for the left axs measurng wholesale electrcty prces for the New South Wales graph versus the Vctora graph. Durng our sample perod, the maxmum daly average prce n New South Wales s more than $AU 1200/MWh and the maxmum daly average prce n Vctora s approxmately $AU 325/MWh. Fgure 8 plots an estmate of the aggregate margnal cost curve as of June 2003 for electrcty suppled to the Vctora market that labels the dfferent technologes used to produce electrcty. We enter the hydroelectrcty capacty at a varable cost of $AU 0/MWh to account for the fact that the ncurred cost of producng electrcty from a hydroelectrc unt s zero. However, we recognze that these unts wll operate based on the opportunty cost of the water they have behnd ther turbne. The next collecton of generaton facltes n the margnal cost curve are the brown coal unts. The 2000 MW LYA plant s n ths step of curve. The next step of the margnal cost curve accounts for the nterconnecton capacty from New South Wales. We estmate the varable cost of ths step at $AU 15/MWh, whch s the varable cost of the black coal unts producng electrcty n New South Wales. The fnal ncreasng porton of the aggregate margnal cost curve s composed of natural gas-fred generaton unts wth dfferent thermal effcences. The dea behnd our 13 Daly natural gas prce data for Vctoran wholesale market can be downloaded from the Vencorp webste at the lnk 41

43 treatment effects analyss s to use natural gas prces as our control varable for nput prce dfferences over tme. Our two mantaned assumptons are that these natural gas prce are not be mpacted by the acquston and they adequately control for nput cost dfferences n the prce of wholesale electrcty over our sample perod. It our understandng that the prce of brown coal n Vctora and the prce of black coal n New South Wales dd not notceably change over our sample perod. If LYA and other baseload brown coal unts n Vctora exercse more unlateral market power n the NEM as a result a reduced level of fxed-prce forward contract oblgatons, then we would expect the hgher margnal cost natural gas-fred generaton unts (those lower thermal effcences n convertng natural gas to electrcty) n ths aggregate margnal cost curve to be settng the market prce more frequently and ths wll result n a hgher wholesale electrcty prces. Usng the prce of natural gas as our control varable accounts for the fact that some of the prce changes post-acquston could be due to changes n natural gas prces and not changes n the thermal effcency of the hghest varable cost unt operatng. Our treatment effects analyss uses a dfference-n-dfference regresson framework separately for each half-hour of the day. Specfcally, defne the followng varables: y jd = the natural logarthm of the prce n market j durng load perod of day d Post_Acq d = an ndcator varable that equals 1 f load perod of day d s after Aprl 1, 2004 and zero otherwse Vc jd = an ndcator varable that equals 1 f the observaton for load perod of day d s from the Vctora wholesale electrcty market and zero otherwse Vc jd *Post_Act d = an ndcator varable that equals 1 f load perod of day d s after Aprl 1, 2004 and the observaton s from the Vctora wholesale electrcty market. 42

44 The subscrpt j takes on two values, one for the Vctora wholesale electrcty market and the other for the Vctora wholesale natural gas market. We run the followng regresson for each half-hour of the day for our sample perod: 100* y = α + β PostAcq + β Vc + β Vc * PostAcq + η (42) jd 1 d 2 jd 3 jd d jd Pre-multplyng our dependent varable by 100 converts all of our coeffcents to approxmate percent changes. Specfcally, β 3 now becomes the estmated approxmate percent change n Vctoran wholesale electrcty prces that result from the acquston. Rather than present a table wth the results of the 48 half-hourly regressons, we plot the estmated values of our percent treatment effects from the acquston, β 3, for each of the 48 half-hour perods, along wth the pontwse upper and lower 95% confdence bound on ths estmated half-hourly treatment effect. Ths plot s gven n Fgure 9 and shows that for all half-hours of the day estmated treatment effect of the acquston (usng the prce natural gas n Vctora as the control varable ) s between 15 and 30 percent and statstcally dfferent from zero. Moreover, the pontwse confdence bounds on these half-hourly estmates contan the vast majorty of the ex ante half-hourly estmated percent prce ncreases from the acquston gven Fgures 2(a) to 5(a) for 2000 to To assess the extent to whch these prce ncreases from the acquston also occurred n New South Wales, the other major populaton center n Australa, we repeated ths treatment effects analyss substtutng the logarthm of the half-hourly wholesale electrcty prce n New South Wales for the one n Vctora as the dependent varable n our 48 regressons. Fgure 10 plots these results usng the same format as 43

45 Fgure 9. They are consstent wth the vew that substantal wholesale electrcty prce ncreases assocated wth the acquston also occurred n New South Wales. To provde further evdence that the acquston enhanced the ablty of LYA and other baseload Vctora supplers to exercse unlateral market power we performed a treatment effects analyss that attempts to measure the dfferental mpact of the acquston on prces n Vctora versus New South Wales. As noted earler when there s congeston from Vctora nto New South Wales prces n New South Wales are hgher than those n Vctora n order to attract suffcent supply from New South Wales to make up for the low-cost supply from Vctora that cannot be transferred to New South Wales because of transmsson constrants. To understand ths mechansm for exercsng unlateral market power by Vctora generaton unt owners, consder the followng example. Suppose that supplers n Vctora are wllng to provde 1000 MW beyond demand n Vctora to New South Wales at a prce of $AU 25/MWh before acquston and that the prce n New South Wales s $AU 35/MWh because the transmsson lnk between these states s only 500 MW. If supplers n Vctora face less competton after the acquston, they may be able to rase the prce n Vctora to just below $AU 35/MWh whch would stll prevent supplers n New South Wales from sellng n Vctora but now Vctora generaton unt owners would receve a substantally hgher prce for ther sales n Vctora durng congested perods. Consequently, one measure of the ncreased extent of unlateral market power exercsed after the acquston s the extent that prces durng congested perods between Vctora and New South Wales are hgher n Vctora relatve to New South Wales. 44

46 Durng these congested perods, supplers n Vctora do not face competton from supplers n New South Wales untl ther bd prces exceed the hghest bd prce accepted n New South Wales. Therefore, f supplers n Vctora are able to exercse more unlateral market power durng congested perods n Vctora after the acquston, then we would expect the rato between of the prce n Vctora to the prce n New South Wales durng congested perods to be hgher after the acquston. Ths logc suggests that the approprate dependent varable for our analyss s the logarthm of the prce n Vctora dvded by the prce n New South Wales. As shown n Fgure 8, there s more than 1500 MW of transmsson capacty between Vctora and New South Wales. There s relatvely a small nterconnecton between South Australa and Vctora and slghtly larger nterconnecton between Queensland and New South Wales. Because South Australa and Queensland are not electrcally connected to one another except through Vctora and New South Wales, we use the logarthm of the rato of the prces n these two states as the control varable for the rato of prces n Vctora and New South Wales durng congested perods before and after the acquston. We are hard pressed to thnk of a reason why the pattern of the prce rato between South Australa and Queensland would be mpacted by the acquston. To summarze, the two dependent varables for our model are now: (1) the logarthm of the rato of the wholesale electrcty prce n Vctora dvded by the wholesale electrcty prce n New South Wales and (2) the control varable s the logarthm of the prce rato of the wholesale electrcty prce n South Australa dvded by the wholesale electrcty prce n Queensland. We run the regresson gven n (42) for each half-hour perod n our sample when there s lkely to be congeston between 45

47 Vctora and New South Wales. Our sample selecton rule to determne a half-hour wth congeston s f the prce n Vctora s less than the prce n New South Wales. Ths s a conservatve measure of the lkelhood that congeston between these two markets actually exsts and s lkely to capture hours when there no congeston and the prce dfference between the states s due to transmsson losses only. If we requre the prce n New South Wales to be more than 5 percent larger than the prce n Vctora (a clear ndcaton of congeston and not smply the result of transmsson losses), we estmate smlar treatment effects n sgn and magntude. The treatment effect coeffcent for these prce dfference or congeston regresson measures the percent ncrease n the rato of the prce n Vctora relatve to the prce n New South Wales after the acquston controllng for all other factors mpactng ths prce rato pre- and post-acquston wth the rato of prces n South Australa to Queensland. Fgure 11 plots the estmated values of β 3 and the pontwse 95% percent upper and lower confdence bounds on the coeffcent estmate. For the vast majorty of half-hour perods we fnd an estmated treatment effect that s postve and for many of these half-hour perods t s statstcally dfferent from zero, partcularly durng the hghprced perods of the day. Fgure 12 plots the estmated values of β 3 and the pontwse 95% percent upper and lower confdence bounds on the coeffcent estmate for our model estmated over all half-hours durng the sample perod of Aprl 1, 2003 to Aprl 1, As expected the magntude of these treatment effects s smaller because these regressons ncludes half-hours the prce n Vctora exceeds the prce n New South Wales, but the vast majorty of these half-hourly treatment effects are postve and statstcally dfferent from zero. 46

48 These treatment effects are consstent wth the vew that when the Vctora market s solated from New South Wales because of congeston, the extent of unlateral market power exercsed by supplers n the Vctora market s hgher post-acquston. The mplcaton of the natural gas prce treatment effects results for Vctora and New South Wales s that wholesale electrcty prces n these two states are hgher post-acquston, whch s consstent wth greater exercse of unlateral market power n the NEM postacquston. To estmate an overall average natural gas prce treatment effect and an overall congeston prce treatment effect, we estmated equaton (42) pooled over all half-hour perods, wth fxed effects for each half-hour of the day, but a sngle treatment effect coeffcent. For the natural gas prce model ths overall percent treatment effect coeffcent estmate s wth an estmated standard error (0.53), ndcatng that average prces across all load perods are 20 percent hgher post-acquston. For the congeston prce treatment effect for the sample restrcted to half-hours when the prce n Vctora s less than the prce n New South Wales, the overall treatment effect s 8.48 wth an estmated standard error of (0.76). Ths mples that durng perods wth congeston the rato of prces n Vctora dvded by prces n New South Wales s 8.47 percent hgher. The overall congeston prce treatment effect for all half-hours from Aprl 1, 2003 to Aprl 1, 2005 s 7.25 wth an estmated standard error of (0.50). These overall results further confrm the exstence of large and statstcally sgnfcant ncreases n the overall prce n the NEM and relatve prce n Vctora relatve to New South Wales durng congested perods as a result of the acquston. 47

49 6 Conclusons, Caveats and Drectons for Future Research The role of econometrc analyss n predctng the compettve mpact of mergers has had mxed success n legal and regulatory domans. Where t has been appled, the pre-domnant purpose has been to assst courts n questons of market defnton rather than drectly evaluatng the potental mpact of an acquston on prces. Moreover, wth regard to vertcal acqustons, quanttatve assessments have been vrtually non-exstent. In ths paper, we have demonstrated that, n structured markets, such as electrcty, the wealth of data as well as the precse 'rules of the game' allow us to dentfy both ex ante and ex post the compettve consequences of changes n ownershp. Here we examned an acquston that, usng only qualtatve assessments, would not have rased concerns. It nvolved a downstream frm acqurng a () partal and () passve stake n an upstream frm that tself was part of a segment that was not hghly concentrated. However, a careful formal analyss of how the ncentves of all frms n the ndustry ndcated the possblty that the acquston may materally and sgnfcantly rase wholesale prces. We have taken the opportunty to use the prospectve analyss motvated by economc theory to evaluate compettve assessments both before and after that fact for the AGL-LYA acquston n Australa n Sgnfcantly, the ex post analyss ndcates that ths acquston dd, n fact, lead to a rse n wholesale prces n the NEM; provdng emprcal verfcaton for our theory that such acqustons should be examned closely by competton authortes. In addton, we demonstrated that the evaluaton of ths theory ex ante, predcted the prce ncreases observed ex post. Consequently, we see 48

50 ths as a valdaton for the use of econometrc technques tghtly lnked to theory n the prospectve compettve assessment of mergers n data rch ndustres. 49

51 References Bushnell, J., E.T. Mansur and C. Sarava (2005), "Vertcal Arrangements, Market Structure, and Competton: An Analyss of Restructured U.S. Electrcty Markets" Unversty of Calforna Energy Insttute, Center for the Study of Energy Markets, Workng Paper number 126, February. de Fontenay, C.C. and J.S. Gans (2005), Vertcal Integraton n the Presence of Upstream Competton, RAND Journal of Economcs 36(3), pp Gans, J.S. (2007), Concentraton-based Merger Tests and Vertcal Market Structure, Journal of Law and Economcs, forthcomng. Green, R. (1999), The Electrcty Contract Market n England and Wales, Journal of Industral Economcs, 47 (1), pp Hart, O. and J. Trole (1990), Vertcal Integraton and Market Foreclosure, Brookngs Papers on Economc Actvty, Mcroeconomcs, Mansur, E.T. (2003), Vertcal Integraton n Restructured Electrcty Markets: Measurng Market Effcency and Frm Conduct, CSEM Workng Paper, No.117, Berkeley. Newbery, D.M. (1998), Competton, Contracts and Entry n the Electrcty Spot Market, Rand Journal of Economcs, 29, pp O Bren, D.P. and S. Salop (2000), Compettve Effects of Partal Ownershp: Fnancal Interest and Corporate Control, Anttrust Law Journal. Powell, A. (1993), Tradng Forward n an Imperfect Market: The Case of Electrcty n Brtan, Economc Journal, 103, pp Rey, P. and J. Trole (2007), A Prmer on Foreclosure, Handbook of Industral Organzaton, Vol.III, North Holland: Amsterdam (forthcomng). Sarava, C. (2003), Speculatve Tradng and Market Performance: The Effect of Arbtrageurs on Effcency and Market Power n the New York Electrcty Market, CSEM Workng Paper, No.121, Berkeley. Wolak, F.A. (2000), An Emprcal Analyss of the Impact of Hedge Contracts on Bddng Behavor n a Compettve Electrcty Market, Internatonal Economc Journal, 14 (2), pp Wolak, F.A. (2003a) Identfcaton and Estmaton of Cost Functons Usng Observed Bd Data: An Applcaton to Electrcty Markets, n M. Dewatrpont, L.P. Hansen, and S.J. Turnovsky, eds., Advances n Economc and Econometrcs: Theory and Applcatons, Eght World Congress, Volume II. New York: Cambrdge Unversty Press, pp Wolak, F.A. (2003b), Measurng Unlateral Market Power n Wholesale Electrcty Markets: The Calforna Market, , Amercan Economc Revew, pp

52 Wolak, F.A. (2004), Quantfyng the Supply-Sde Benefts from Forward Contractng n Wholesale Electrcty Markets, avalable from 51

53 Appendx A Ths appendx emprcally documents the substantally larger prce rsk versus quantty rsk faced by AGL assocated wth t acqurng a passve ownershp share n LYA. For each half-hour perod and each year from January 1, 2000 to June 30, 2003, we compute CV(p ), the coeffcent of varaton for the market prce n Vctora n load perod as: σ ( p ) CV ( p ) =. p We also compute the half-hourly mean and standard devaton of LYA s output as: Q D D = D Qd and σ ( Q) = D ( Qd Q ) d = 1 d = 1 1/2 where Q J = Q s the total producton from all J = 4 unts owned by LYA durng j = 1 jd load perod of day d. Defne the coeffcent of varaton for LYA s half-hourly output as: σ ( Q ) CV ( Q ) = Q and the rato of these two coeffcents of varaton as: Rato = CV ( p ) / CV ( Q ). The frst secton of Table B-1 contans the values of CV(p ), CV(Q ), and Rato for each half-hour perod of For all half-perods durng 2000 the rato of the coeffcent of varaton for the half-hourly prce s more than 5 tmes the value of the coeffcent of varaton for LYA s half-hourly output. For a sgnfcant fracton of half-hours of the day, ths rato s more than 10 and n several half-hours t s over 50. These same qualtatve conclusons hold for the remanng years of the sample, although the 52

54 mnmum daly average value of Rato s smaller than t s n For the vast majorty of half-hour perods n each year, the quantty rsk faced by LYA s much smaller than the prce rsk, where s measured by the half-hourly coeffcent of varaton for LYA s output and the Vctora market prce, respectvely. These calculatons provde emprcal support for our smplfyng assumpton that AGL faces zero quantty rsk assocated wth ts share n LYA. 53

55 CV of Prce n Vctora CV of Loy Yang Quantty Rato CV of Prce n Vctora CV of Loy Yang Quantty Rato CV of Prce n Vctora CV of Loy Yang Quantty Rato CV of Prce n Vctora CV of Loy Yang Quantty Rato Half Hour Perod

56 Appendx B Ths appendx computes all of the partal dervatves n equaton (21) and (22)usng the smoothed resdual demand curves and bd supply curves of LYA. To derve the rght-hand sde of equaton (22), apply the mplct functon theorem to the equaton used to determne the market-clearng prce. Ths yelds the expresson: h h p (, )) ( (, ), ) ε Θ SL p ε Θ Θ p = jk h h p jk ddr ( p( ε, Θ), ε) dsl( p( ε, Θ), Θ) dp dp (43) where the dervatve of the resdual demand curve wth respect to prce used n ths expresson s gven n equaton (31) and the other partal dervatves are gven n (34). Ths expresson quantfes, respectvely, how the market-clearng prce changes n response to changes n the LYA s daly bd prces. The remanng partal dervatves n equaton (21) not defned n the text are lsted below. The partal dervatve of (43) wth respect to bd prce ncrement v of unt r s: p pjk prv p p SL p Θ Θ DR p SL p p SL p 2 SLj ( p ( ε, Θ), Θ) p DR( p( ε, Θ), ε) SL( p( ε, Θ), Θ) j ( ( ε, ), ) ( ) ( ) r( ( ε, Θ), Θ) p( ε, Θ) pjk p p prv p prv = 2 pjk prv DR( p( ε, Θ), ε) SL( p( ε, Θ), Θ) p p (44) The partal dervatve of (43) wth respect to p jk s equal to: 55

57 2 p( ε, Θ ) 2 = p jk 2 2 SLj ( p ( ε, Θ), Θ) SLj ( p ) p DR( p( ε, Θ), ε) SL( p( ε, Θ), Θ) + 2 pjk pjk p p jk p p SLj ( p ( ε, Θ), Θ) DR ( ) ( ) SLj ( p (, ), ) p SL p p ε Θ Θ 2 2 p jk p p pjk p p jk (45) 2 DR( p( ε, Θ), ε) SL( p( ε, Θ), Θ) p p The partal dervatve of SLj ( p ( ε, Θ), Θ) p wth respect to p s equal to SL ( p ( ε, Θ), Θ ) = qjkϕ (( p pjk )/ h)( ( p pjk )/ h) (46) 2 10 j 1 2 h p k = 1 The partal dervatve of SLj ( p ( ε, Θ), Θ) p jk wth respect to any other bd prce ncrement besdes p jk s zero. The dervatve wth respect to p jk s: 2 SLj ( p ( ε, Θ), Θ ) 1 2 = 2 h jkϕ jk jk p jk (( )/ )(( )/ ) q p p h p p h (47) 56

58 Fgure 1(a): Surface Brown Coal Mne and Electrcty Generaton Unts Fgure 1(b): Mnng Shovel and Transport Faclty Fgure 1(c):Transportaton

59 Prce ($/MWh) Percent Change Relatve to Actual Prce Half Hour perod Actual % Varaton Fgure 2(a): Actual Half-Hourly Prce and Counterfactual Percent Prce Change for Standard Devaton Half Hour perod Rato of Counterfactual to Actual Standard Devaton of Prce Actual Rato Fgure 2(b): Standard Devaton of Half-Hourly Prce and Relatve Standard Devaton of Counterfactual Prce for 2000

60 Prce ($/MWh) Half Hour perod Actual % Varaton Fgure 3(a): Actual Half-Hourly Prce and Counterfactual Percent Prce Change for Percent Change Relatve to Actual Prce Standard Devaton Half Hour perod Rato of Counterfactual to Actual Standard Devaton of Prce Actual Rato Fgure 3(b): Standard Devaton of Half-Hourly Prce and Relatve Standard Devaton of Counterfactual Prce for 2001

61 Prce ($/MWh) Percent Change Relatve to Actual Prce Half Hour perod Actual % Varaton Fgure 4(a): Actual Half-Hourly Prce and Counterfactual Percent Prce Change for Standard Devaton Rato of Counterfactual to Actual Standard Devaton of Prce Half Hour perod 0 Actual Rato Fgure 4(b): Standard Devaton of Half-Hourly Prce and Relatve Standard Devaton of Counterfactual Prce for 2002

62 Prce ($/MWh) Percent Change Relatve to Actual Prce Half Hour perod Actual % Varaton Fgure 5(a): Actual Half-Hourly Prce and Counterfactual Percent Prce Change for Standard Devaton Half Hour perod Rato of Counterfactual to Actual Standard Devaton of Prce Actual Rato Fgure 5(b): Standard Devaton of Half-Hourly Prce and Relatve Standard Devaton of Counterfactual Prce for 2003

63 $AU/MWh $AU/GJ Day of Sample Average Daly Electrcty Prce n Vctora Gas Prce Fgure 6: Daly Natural Gas Prces n Vctora and Daly Average Wholesale Electrcty Prces n Vctora (Aprl 1, 2003 to Aprl 1, 2005) $AU/MWh $AU/GJ Day of Sample Average Daly Electrcty Prce n NSW Gas Prce Fgure 7: Daly Natural Gas Prces n Vctora and Daly Average Wholesale Electrcty Prces n New South Wales (Aprl 1, 2003 to Aprl 1, 2005)

64 50 45 Hydro Coal NSW-VIC Interconnect Gas Varable Cost $AU/MWh Capacty (MW) Fgure 8: Aggregate Margnal Cost Curve for State of Vctora n June Gas Prce Treatment Effect Half Hour Perod Fgure 9: Natural Gas Prce Treatment Effect for Vctora Electrcty Prces (Percent Prce n Increase from Acquston)

65 40 35 Gas Prce Treatment Effect Half Hour Perod Fgure 10: Natural Gas Prce Treatment Effect for New South Wales Electrcty Prces (Percent Prce Increase from Acquston) Congeston Treatment Effect Half Hour Perod Fgure 11: Congeston Prce Treatment Effect for Rato of Prce n Vctora to Prce n New South Wales for P VIC /P NSW < 1 Sample (Percent Increase n Prce Rato from Acquston)

66 Congeston Treatment Effect Half Hour Perod Fgure 12: Congeston Prce Treatment Effect for Rato of Prce n Vctora to Prce n New South Wales for Full Sample (Percent Increase n Prce Rato from Acquston)

67 Table 1: Ownershp and Generaton Capacty n Vctora Entty (Owner) Generaton plant Capacty (MW) Baseload SECV Anglesea, steam coal 150 Loy Yang Power Loy Yang A, steam coal 2000 Edson Msson Loy Yang B, steam coal 1000 Chna Lght & Power Yallourn, steam coal 1450 Natonal Power Hazelwood, steam coal 1600 Energybrx Morewell, steam coal 170 Interconnect Snowy Interconnect Vc mport capacty 1900 Intermedate Duke Energy Barnsdale, gas 86 Erarang Hume, hydro 50 TXU Ecogen AES Yarra, gas 500 Peak Southern Hydro Varous, hydro 498 TXU Ecogen AES Jeeralng, gas 466 Contact Energy Valley Power, gas 300 AGL Somerton, gas 150

68 Table 2: Actual Half-Hourly Prce, Counterfactual Half-Hourly Prce and Z-Statstc for Test of Mean Dfference Actual Prce ($AU/M Wh) Counterfactual Prce ($AU/M Wh) Z Actual Prce ($AU/M Wh) Counterfactual Prce ($AU/M Wh) Z Actual Prce ($AU/M Wh) Counterfactual Prce ($AU/M Wh) Z Actual Prce ($AU/M Wh) Counterfactual Prce ($AU/M Wh) Z Half Hour Perod