Testing Competition in U.S. Offshore Oil and Gas Lease Auctions

Transcription

1 Testing Competition in U.S. Offshore Oil and Gas Lease Auctions Andres Aradillas-Lopez (Penn State) Phil Haile (Yale) Ken Hendricks (Wisconsin) Rob Porter (Northwestern) April 2018

2 Introduction Minerals Management Service (MMS, now BOEM) oil and gas auctions exploration/drilling rights, U.S. Outer Continental Shelf (OCS) offshore leasing program began in area wide leasing (AWL) in 1983; entire western and central gulf available each year, including deep water tracts. few alterations in lease terms and the auction mechanism. considerable revenue for the government

3 Introduction Minerals Management Service (MMS, now BOEM) oil and gas auctions exploration/drilling rights, U.S. Outer Continental Shelf (OCS) offshore leasing program began in area wide leasing (AWL) in 1983; entire western and central gulf available each year, including deep water tracts. few alterations in lease terms and the auction mechanism. considerable revenue for the government Recently Congress has requested CBO and GAO to study leasing program Concern over lack of drilling: are lease tenures too long? Concern over lack of competition: are auction revenues too low? Should royalty rates be higher?

4 Revenues Figure 1 plots sales revenue and royalty payments from Gulf of Mexico in two year increments; millions of 1982 dollars.

5 Questions How competitive are the auctions? Did behavior change during the AWL period? Tracts ( 9 sq. miles) often have adjacent tracts that are already under lease and, in some cases, drilled; Owners of these tracts (neighbors) may have incumbency advantages that deter non-neighbor firms; Benefits to neighbors from not competing: lower prices, information pooling Costs: not clear, joint bidding (except for Big 8) is legal; neighbors coordinate on production, incentive to coordinate on drilling. Figure 2 illustrates how fraction of tracts with neighbor leases has grown; dominate lease sales since mid 1970s.

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7 Objective Develop tests of competitive bidding in first-price, common values auctions where rejection suggests collusion. Nonparametric test of affi liation of participation and bidding decisions Test stochastic ordering of distribution of pivotal-expected values in number of neighbor firms Focus on neighborhood cartels Apply tests to sample of tracts with adjacent leases to determine whether owners of these leases (neighbors) bid competitively. Also apply the affi liation tests to sample of isolated tracts to determine whether the major firms bid competitively.

8 Contributions Tests provide a way of detecting collusive behavior in common value environments previous literature has focused on private value environments Important specification test: can competitive model be used to examine changes in information structure and policy? Government is considering proposals to change lease work requirements, tenure, and royalty rates Results inform lease and mechanism design

9 Previous work: Hendricks & Porter (1988): pre-1980 drainage auctions Bidding consistent with BNE of common value (CV) auction in which neighbors are better informed and collude. Hendricks, Pinkse & Porter (2003): pre-1980 wildcat auctions (mostly isolated tracts) Bidding consistent with BNE of symmetric CV auction Haile, Hendricks & Porter (2011): all auctions, number and value of bids, government share of rents, much lower in AWL period than in pre-awl period Compiani, Haile, Sant Anna (2018): pre-1983 neighbor lease auctions (mostly wildcat) Affi liated values, distribution of pseudo-pivotal-expected values ordered in number of neighbor firms

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11 Mechanism OCS lands divided into tracts, typically 5,760 acres (9 sq. miles) Lease sale: many tracts sold simultaneously in parallel, sealed bid, first-price auctions prior to 1983: restricted area, several hundred tracts, firms nominated tracts after 1983, all unleased tracts available for bid each year seismic testing and analysis of area; no drilling minimum bid: $15 - $25 per acre; gvt can reject high bid Lease terms: royalty rate: 1/6 on shallow, 1/8 on deep.(> 200 meters) 5 years on shallow, 8, 10 years on deep; automatic renewal if productive

12 Joint Bidding and Ownership Bidding consortia form prior to sale, often tract-specific. All firms were allowed to bid jointly prior to After 1975, Exxon, Gulf, Mobil, Shell, Chevron, Amoco, Texaco, and BP were banned from bidding jointly with each other. After the sale, firms often sign shared work or acquired interest agreements, typically with owners of adjacent leases. agreement specifies firms shares in costs and revenues and designates an operator agreements have to be approved by BOEM so can track ownership changes Major firms free to sign joint drilling and production agreements with each other common on deep-water tracts during AWL period potentially reduces future competition

13 Neighbor Tracts and Neighbor Firms Focus mainly on tracts in a sale with active neighbor leases Neighbor tracts tracts sharing an edge or a boundary point typically 8 neighbor tracts Neighbor firms (current) owners of at least one active neighbor lease ownership graph: two firms are connected if they are joint owners of a lease. number of neighbor firm groups is the number of components of the graph (including isolated firms).

14 Notation r t is the announced reserve price on auction t N t is number of neighbors (groups) on auction t NN t is number of non-neighbor bidders on tract t K t = N t + NN t, total number of bidders V t is value of lease t, same firms X it is private signal of firm i; X t = (S 1t,...S kt ) is signals of bidders Z t are publicly observable auction covariates.

15 Assumptions Information structure: Given any N t = n, Z t = z, (V, X t ) are affi liated F (V t, X t N t, Z t ) is exchangeable wrt bidder indices Entry model: All neighbor firms have signals Non-neighbors have to acquire signals Unique NN t for any (N t, Z t ) K t is common knowledge K t is stochastically increasing in N t. Exogenous variation: F (V t, X 1,.., X k K t = k, Z t = z) = F (V t, X 1,.., X k ) K t = k + 1, Z t = z)

16 Competitive Neighbors Null hypothesis: competitive participation & bidding. Key issue: bidders may not bid due to binding reserve price and/or (symmetric) bidding cost. Bidder i bids when x it x (z, n) where thresholds solve E [V t X it = x, Y it x ; N t = n, Z t = z] = r t Y it is the maximum signal among i s rivals note "winner s curse" correction Equilibrium bids B it = β (X it ; n), strictly increasing in X it

17 Collusive Neighbors A designated bidder model suggests what to expect under the alternative. Cartel selects one neighbor firm to bid in auction It may or may not pool information Neighbor firm participation and bids are negatively dependent Maximum neighbor bid does not vary with n. Phantom bidding could give positive dependence among cartel bids (e.g., phantom bid proportional to serious cartel bid) All of above true with unobserved heterogeneity, but uh likely strengthens positive dependence.

18 Nonparametric Affi liation Test: Participation Participation equation for tract t : P it = 1 {x it > x (Z t ; n t )} (X 1t,.., X kt ) are affi liated (P 1t,.., P kt ) are affi liated conditional on Z t. Test affi liation of neighbor firm participation. Do not observe NN t because they may not bid. Affi liation under symmetry implies that: ( nt ) 2 Pr (A t = l 1 Z t, n t ) Pr (A t = l + 1 Z t, n t ) l ( )( ) nt nt Pr (A t = l Z t, n t ) 2 (1) l 1 l + 1 where A t = n t i=1 P it, for l = 1,.., n t 1.

19 Example: N=2 For any p 1 {0, 1} 2 and p 2 {0, 1} 2, affi liation implies Pr{p 1 p 2 } Pr{p 1 p 2 } Pr{p 1 } Pr{p 2 } Inequality trivially satisfied if p 1 = (0, 0), p 1 = (1, 1)or p 1 = p 2. If p 1 = p 2 and p 1, p 2 {(1, 0), (0, 1)}, then Pr(0, 0) Pr(1, 1) Pr{1, 0) Pr(0, 1) or, equivalently, ( ) 2 1 ( ) 2 1 Pr{A = 0} Pr{A = 2} 0 2 Pr{A = 0} Pr{A = 2} 1 Pr{A = 1} 4 ( ) 2 2 Pr{A = 1} 1

20 The Test Statistic Density-weighted version of the affi liation inequality: Let Pr(A = k Z, n) = G k,n (Z ) and define τ k,n (Z ) as difference ( ) n 2 ( ) G 2 n 1 ( ) n 1 k k,n (Z ) G k 1 k + 1 k 1,n(Z )G k +1,n(Z ) weighted by the squared density fz 2,n(Z, n). Let Q k,n = E Z [max {τ k,n (Z ), 0} ω(z, k, n)], Q = 5 n 1 n=2 k =1 Q k,n where ω(z, k, L) is a positive weighting function. Affi liation of (P 1t,.., P nt ) conditional on Z t implies Q = 0, Q > 0 if affi liation is violated.

21 Constructing the Test Statistic Details worked out in Aradillas-Lopez (2014). We employ kernel-based nonparametric estimators. The estimators compute the sample analog of τ k,n (Z ), weighted by a kernel function of Z, aggregated over n. Under smoothness and regularity conditions, Q Q Q satisfies the asymptotic condition: where: Q = Q + 1 T (i) E [γ T (Z t )] = 0, (ii) T ( γ T (Z t ) + O p T 1/2 ɛ) t=1 γ T (Z t ) = 0 w.p.1 if the inequalities hold strictly

22 Test Statistic Let Σ 2 T = Var (γ T (Z t)). Our test statistic is ŝ T = T Q max{κ T, Σ T }. where κ T is a bandwidth sequence that converges to zero very slowly (at a logarithmic rate). Critical value is 1.96 for 2.5% significance level, for 5% level.

23 Nonparametric Affi liation Test: Bid Levels We construct an analogous test statistic for bid levels. Developed algorithm that generates affi liation inequalities for arbitrary number of bidders and discretization of bid space eliminate trivial inequalities, redundant inequalities due to symmetry and dependence Focus on three bins: {no bid, low bid, high bid} submitted bids are categorized as low vs. high, relative to the median bid for the sample under consideration. the number of event inequalities to be tested increases: 9 with 3 bidders, 18 with 4 bidders, etc. Test statistic examines frequency of these three actions for each neighbor.

24 Why Nonparametric Test Bidding is a tail event: most tracts in a sale do not receive any bids. More flexible patterns of correlation between bids. But mainly to deal with tracts that have no bids Do not observe nomination set in pre-awl or consideration set in AWL The affi liation inequalities continue to hold conditional on event of at least one neighbor bid. Note: sample consists of leases with N 2.

25 Stochastic Dominance Test Suppressing z, define w(x, y, n) = E [V X i = x, Y i = y; N = n] Assumptions on non-neighbor entry imply w is decreasing in n Best reply b i of neighbor i with signal x i satisfies w(x i, x i, n) = b i + G M B (b i b i, n) g M B (b i b i, n) M is the maximum rival bid. Given estimates of G M B and g M B for each n, infer w(x, x, n). Under null, FW n n+1 (w) < FW (w) for w > w(x, x, n + 1) Under alternative of collusion, FW n n+1 (w) FW (w). Note: sample consists of leases with N 1.

26 Estimation Standard approach: homogenize the bids and estimate G /g non-parametrically on residuals. But we do not observe zero bids of non-neighbors Alternative approach: based on suffi cient index assumption: F (V t, S t N t = n, Z t = z) = F (V t, S t n, λ n (Z )) For each n, estimate a random forest to predict binary outcome 1{M it > 0 N t, Z t } and continuous outcome M it N t, Z t. Choose a 1-D index λ n (z) based on these predictions. Use standard GPV kernals to estimate G M B (b b, n, λ n (z)) and g M B (b b, n, λ n (z)). Construct empirical distributions of pseudo-values for each n by taking random draws of z from its empirical distribution and, for each z, drawing S bids from Ĝ B ( n, λ n (z)).

27 Sub-Samples for Tests Challenge: sample selection due to unobserved heterogeneity most tracts do not receive bids, not well predicted by Z. Main sample: neighbor tracts with at least one bid. NN sub-sample: neighbor tracts bid by at least one non-neighbor NB sub-sample: neighbor tracts bid by at least one neighbor Isolated sub-sample: tracts with no active neighbor leases Three period/regions: Pre-AWL, AWL Shallow, AWL Deep

28 Data: Overview Sample period: 1954 to Complete bidding, lease ownership, lease location data Discounted revenue based on monthly oil and gas production through 2008 and forecasts of wellhead prices of oil and gas for leases that are still productive in 2008, forecast future output based on an econometric model of historical decay rates in lease production GDP deflator to 1982, 5% annual real discount rate. Costs: based on well drilling records and annual API surveys of drilling costs by location, depth and well type

29 Table: Summary Statistics Pre-AWL Shallow Deep No. of Leases Bid 3,693 9,062 7,389 No. of Bids High Bid No. of Leases Sold 3,271 8,602 7,192 Drill Rate Hit Rate Revenue Hit Cost Drilled *Dollar figures are in millions of 1982 dollars. Net Profit = Revenue - Cost - Bid - Royalty.

30 Table: Top 11 Firms: Leases Won Name Pre-AWL Shallow Deep Total Shell 349* 613* 1,401* 2,363 Getty/Arco/Conoco/Cities 630* 607* 604* 1,841 Chevron 389* 415* 560* 1,364 BP 264* * 1,233 Texaco * 602* 1,182 Exxon 303* * 1,174 Gulf/Hess 454* 332* 382 1,168 Amoco * 392* 1,022 Mobil 266* 300* Unocal Kerr-McGee *Firm is in top 7 for the period

31 Data: Covariates Use many covariates to minimize unobserved heterogeneity Sale characteristics: Real oil and gas prices (offshore Gulf first purchase price); year Tract characteristics: water depth; whether tract is re-offered Neighborhood characteristics, for varying neighborhood sizes: number of active neighbor leases neighbor tract bidding history neighbor lease exploration and production history as of the sale date for the tract in question

32 Participation Tests Table: Affi liation Test for All Neighbor Firms - Pre-AWL ŝ # teststat p value NB NN NBUNN 1, NB : Tracts Bid by Neighbor Firms NN : Tracts Bid by Non-neighbor Firms

33 Table: Affi liation Test for All Neighbor Firms - AWL Shallow ŝ # teststat p value NB 2, NN 4, NBUNN 6, Samples as indicated Table: Affi liation Test for All Neighbor Firms - AWL Deep ŝ # teststat p value NB 1, NN 1, NBUNN 2, Samples as indicated

34 Table: Affi liation Test for Top7 Firms - Isolated Tracts ŝ # teststat p value Pre AWL 1, AWL Shallow AWL Deep 2, Samples as indicated Tests are for Top 7 firm participation

35 Bid Level Tests Table: Affi liation Test for All Neighbor Firms - Pre-AWL t # teststat p value NB NN NBUNN 1, Samples as indicated

36 Table: Affi liation Test for All Neighbor Firms - AWL Shallow t # teststat p value NB 2, NN 4, NBUNN 6, Samples as indicated Table: Affi liation Test for All Neighbor Firms - AWL Deep t # teststat p value NB 1, NN 1, NBUNN 2, Samples as indicated

37 Table: Affi liation Test for Top7 Firms - Isolated Tracts t # teststat p value Pre AWL 1, AWL Shallow AWL Deep 2, Samples as indicated Tests are for Top 7 firm bid levels

38 Stochastic Dominance G M B, g M B estimated by Heckit model for each n

39 Stochastic Dominance: Deep

40 The End Neighbors appear to bid competitively pre-awl, but not under AWL, especially in deep water. Big 7 appear to bid competitively on isolated tracts pre-awl, less so for AWL Shallow, not for AWL Deep More work on tests: semi-parametric version of the affi liation test using index

41 Table: Statistics by Lease Type - Pre-AWL Isolated Neighbor No. of Leases Bid 1,723 1,970 No. of Bids High Bid No. of Leases Sold 1,575 1,696 Drill Rate Hit Rate Revenue Hit Cost Drilled Av. Net Profits *Dollar figures are in millions of 1982 dollars.

42 Table: Statistics by Lease Type - AWL Shallow Isolated Neighbor No. of Leases Bid 924 8,138 No. of Bids High Bid No. of Leases Sold 904 7,698 Drill Rate Hit Rate Revenue Hit Cost Drilled Av. Net Profits *Dollar figures are in millions of 1982 dollars.

43 Table: Statistics by Lease Type - AWL Deep Isolated Neighbor No. of Leases Bid 2,574 4,815 No. of Bids High Bid No. of Leases Sold 2,535 4,657 Drill Rate Hit Rate Revenue Hit Cost Drill Av. Net Profits *Dollar figures are in millions of 1982 dollars.

44 Pre-AWL Heckit Estimates

45 AWL-Deep Heckit Estimates