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Understand the complexities of regulating natural monopolies in the presence of information asymmetry. Learn how to balance costs, demand, and market power to ensure efficient output levels and minimize deadweight loss.
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Economie Publique IIFebruary-May 2010 Lecture 3 Regulating Monopolies under information asymmetry Prof. A. Estache
How did the seminar on procurement relate to what we are studying in class? • The seminar was about deciding: • Who should provide public services • How to organize the provisions of these public services • How to do so at the lowest possible costs for taxpayers and users • Accounting for the facts that: • Even when you have natural monopolies you can try to introduce competition (an operator may be forced to BID for the right to enjoy the monopoly!) • Governments are not always very good at delivery things • Governments face significant information gaps on both the supply (cost, quality) and the demand side when they design these markets to procure the public services they need to deliver
Now back to theory… a brief reminder • What are we worried about in this class? • How best to deliver services that are essential to society • Accounting for the fact that the cost structures of these services make it difficult to rely on the simplest forms of competition… • Existence of deadweight loss (DWL) • Rent earned by a monopoly (over and above normal profit) as a waste for society • Size of that DWL • Big or small? • Need to do something about it or not? • Distribution of DWL • Taxpayers vs. users
To address these concerns we need to remember that … • …means that: • Most of what we do in applied regulation build on 3 aspects: • k, reflecting the costs which we take as given! • driven by technology, various dimensions of quality and effort levels • demand side (ε) • driven by preferences but also budget constraints which drive the ability to pay of the users • how the monopoly plays with P and y to maximize profits given these costs and the demand elasticity!!
Remember also that now we know how prices relate to market power in an industry • Since • And since MR=MC…we can derive: • (P-MC)/P= -1/ε = the Lerner Index of market power • The monopoly’s profit margin is high when high (demand elasticity) ε and low in the opposite case! • => Key variable to focus on to know about potential troubles in terms of DWL is ε (DEMAND!!!) • More variables when you start accounting for the information asymmetry problems…
…so… Regulating a Natural Monopoly boils down to understanding that: • A natural monopoly cannot be forced to rely on marginal cost pricing alone • Doing so makes the firm lose money, so it exits, destroys both the market and any gains-to-trade. • How far the monopoly will go distancing itself from MC pricing depends on ε • If close to |1|: Huge markup => huge DWL • If much higher than |1|: Small markup => small DWL • So challenge is to pick regulatory schemes to induce the natural monopolist to produce the efficient output level without exiting. • But nothing to say about the cost side of the business..and yet, we know it matters as much as the demand side!
So how to come up with fair regulation of pricing by a Natural Monopoly??? Dollars Unregulated monopoly "Fair rate of return" production Which allows cost recovery Number of Households Served • Set this return • on assets? • *Set an average price • generating this return • * Allow for a more • complex pricing • Structure • Simply set a • maximum price • (price cap)? • Give the operator a • Subsidy/transfer A $60 C $29 LRATC F $15 MC B MR D 50,000 100,000 85,000 7
In other words, what should optimal regulation theory focus on ? The key relevant factors are: • The specific regulatory objectives • The economic and financial costs of paying for subsidies • Hmmm… what’s the difference btw econ and financial costs?? • Range of policy instruments available • Bargaining power of regulators • Information needs and asymmetry • “Purity” of the regulator • Regulator’s long terms commitment ability
Now… • Let’s become a bit more formal about all this discussion of optimal regulation… • That’s when the Armstrong-Sappington paper kicks in…
Remember the notation of the key variables • v(n), the aggregate consumer surplus associated with the consumption of the “n” products made available to society by producers • ηis the elasticity of demand • S, the total welfare surplus of the consumers accounting for the fact that they are hurt by taxes if taxes are needed to finance the monopoly • π(p),the operators’ profit with a price vector associated with the range of products provided of p • R,the total rent associated with any monopoly power the operator may have on the sector, accounting for the fact that the government may subsidize its activities • p=(p1, p2, ….., pn)the set of prices associated with these n products • T any transfer paid by consumers (taxpayers) to operators as part of the price paid for the services • Λ is the cost of raising funds from the taxpayers (=social cost of public funds) • Λ≥ 0 because taxes distort production and consumption activities => create DWL • If Λ = 0, no tax driven distortions! • If Λ > 0, MC pricing becomes much more complex because added costs due to added distortions in the system! • W, the total welfare of society, given the preferences of consumers, the profit of monopolists and the costs and benefits to taxpayers of the regulatory instruments in place • α,the weight given by the regulator to R (it is =1 if the regulator only cares about efficiency and there NO distributional preferences; if = 0, the gvt cares as much about consumers as about producers
How does one set up a model to identify the optimal regulation? • Consider • S = v(p) - (1+Λ)T and R = π(p) +T • a non negativity constraint with respect to the rent R: R≥0 • Note: π(p) may be negative but must be recovered by T • =>W=S + αR= v(p) – (1+Λ)T + α (π(p) +T) • Main question: Do the designers of the optimal regulatory regime have full or partial access to the information needed to design this regime? • Main assumptionin benchmark: the regulator knows the two functions v(n) and π(p) perfectly => relatively simple to design optimal regulation
First, we have full information but we need to consider 2 cases • We need to distinguish between 2 cases: • The government can make or get a transfer • That is (1): a subsidy to the firm • That is (2): a transfer can be negative, so a subsidy may become a tax! • The government cannot afford a transfer
Case 1: Transfer are feasible but costly (Λ>0) • This is the most general case • We start from W=S + αR= v(p) – (1+Λ)T + α (π(p) +T) • Since α ≤1 and Λ≥0, it is optimal to extract all firm profit and use it to reduce the tax burden since society is worse off when a T is needed to support the operator • This happens if dW/dT = -(1+ Λ) + α <0 • Want T as small as possible but need to accept the constraint that Rent may not be negative • R=0 at minimum since R = π(p) + T , R= 0 π(p) = -T • Now replace T by - π(p) into W • So total welfare with prices p is W = v(p) – (1+Λ)T + α (π(p) +T) = v(p) + (1+Λ) π(p) Note: • The α plays not role here at the optimum • $1 of lower tax makes the taxpayer better off by $(1+Λ)
So what is W when transfers are feasible? Total welfare at optimum with prices p is W = v(p) + (1+Λ) π(p) Note: The α plays not role here because you don’t force a corner solution! $1 of lower tax makes the taxpayer better off by $(1+Λ)
What happens if there is no cost to raise public funds? • If Λ=0(as often assumed in basic micro textbooks) Still need to find p that maximizes W=v+π = total surplus • Under full information when transfers are possible, no rents are left to the firm and … • marginal cost pricing is the optimal regulatory rule accounting for the fact that the firm will still break even thanks to the transfers/subsidies • this is the full information outcome we always worked with in standard microeconomics !
What happens if there is a cost to raise public funds? • If Λ>0 , then optimal prices are above MC (on average) • We get into the markup story (to allow the firm to pay for taxes) such as the Lerner pricing we discussed earlier • In the single product case with π(p)=(p-c)*(q(c), optimal price derived from • dW/dp = v’(p) + (1+Λ) π’(p)=0 • dW/dp = -q(p) + ((1+Λ)*(q(p))+ ((p-c)*q’(p))=0 • (p-c)p = (Λ/(1+Λ)* (1/η) • => at optimum, we chose p to maximize this expression where c is MC and η is the elasticity of demand • We see that • Price-cost margin is higher when Λ is higher and η lower • You’ll see later that this is like Ramsey-Boiteux pricing • but here Λ is not the shadow price of the firm’s budget constraint but the MC of raising gvt revenue and then distributing this revenue to the firms to cover its costs
Case 2: perfect information BUT unfeasible transfers (1) • In this case, no possibility of transfers • (no taxes or subsidies) • => the operator must be financially autonomous • But if increasing returns to scale, MC pricing leads to financial losses • => need to add a constraint to the previous social welfare function: max v(p) + π(p) s.t. π(p) ≥ 0 (and here Λ and α now play no role) • So denote λ ≥ 0, the Lagrange multiplier associated with the profit constraint, then choose p to maximize v(p) + π(p)+ (1+ λ ) π(p) => the 2 problems take the same form, the only difference is that in the former case Λ is exogenous, while here λ is endogenously chosen to make the operator break even
Perfect information and unfeasible transfers (2) max W=v(p) + π(p) s.t. π(p) ≥ 0 => Set dW/dp = v’(p) + (1+λ) π’(p)=0 dW/dp = -q(p) + ((1+ λ)*(q(p))+ ((p-c)*q’(p))=0 • At optimum: Chose p so as to maximize (p-c)p = (λ /(1+ λ )* (1/η) Where c is MC and η is the elasticity of demand
By the way: why Ramsey-Boiteux? • Ramsey (1927) looked at how to max consumer surplus while relying on proportional taxes to raise a target level of revenue • Boiteux (1956) looked at how to max consumer surplus while marking prices up above marginal cost to recover fixed costs
$ True ATC Claimed ATC MC MR D Q Now …how do we deal with Asymmetric Information! A profit motive exists for a natural monopoly to mislead a regulator over ATC!!!! PMP PATCP QMP QATCP Fig 12.3
What the rest of this class boils down to: • Set up the regulation problem as an agency problem (principal vs agent) • Find a regulatory mechanism • that takes into account the social costs • adverse selection • and moral hazard • subject to the participation constraint of the firm and • subject to the budget constraint of the government • End up balancing the costs associated with adverse selection and moral hazard • Ultimately…it is all about taking regulatory action to reduce information asymmetries!
More generally, under imperfect information we will need too be able to fill in this matrix
What’ s special about the modern theory of regulation? • There a clear concern for pushing the monopolist provider to perform in terms of: • Delivering the services • Not very different from early tradition of regulation focusing on trying to push for more quantity to meet demand • …but more importantly to do so at so at the lowest possible cost and hence price! • So the big deal is to tell the operator: I don’t believe your costs…I will tell you what I am willing to believe and allow you to recover! • This is when price cap regimes start to replace cost-plus regime or old fashion rate of return regulation • But what is the difference between of cost-plus or rate of return regulation and a price cap
A first look at price caps vs cost-plus regulation • So prices for network industry services can be set according to: • Cost-based estimates for industry • Prices revealed through competition for the market • NOTE: • The same rules are used to reset prices since regulated prices are not set forever! • Regulated prices need to be adjusted over time to take into account changes in costs due to: • E.g., changes in wages or exchange rates • Technological or efficiency gains in industry
1st Methods of Price AdjustmentRate of Return Regulation • Under rate of return regulation, prices are adjusted to permit investors to achieve a specified rate of return on investments • Provides security to investors, thereby also lowering the cost of capital • Firms have weak incentives to increase efficiency since they do not benefit from lowering costs • Because returns are based on value of capital investments, firms may have incentive to over-invest in capital • Examples: traditional form of regulation for public and private utilities around the world
2nd Methods of Price Adjustment: Price Cap Regulation • Under a price cap, maximum prices for a basket of services are fixed for 3-5 years with a formula (RPI-X) reflecting future inflation and expected efficiency gains • Firms have strong incentives to improve efficiency, since they retain the benefits of lower than expected costs • Risks for investors are higher than under rate of return, resulting in a higher cost of capital • Difficult to make correct predictions about future conditions in between price reviews • examples: started by Littlechild in the UK and now copied everywhere
Rate of Return vs. Price Cap? • Pure Rate of Return • frequent discretionary reviews • current prices based on previous year’s costs • relatively low risk for investors • Pure Price Cap • infrequent mandatory reviews • future prices based on cost projections • relatively higher risk for investors • …In practice, as we will see later in the course, rate of return and price cap schemes can be similar • Regulators effectively decide on an allowable rate of return at time of price cap review • If reviews are frequent (e.g., interim price reviews in UK utilities), they can approximate rate of return
A 3rd complementary methods of price adjustment: Yardstick” Regulation • Under yardstick regulation, regulators compare firms’ performance with that of similar firms to arrive at a cost standard • Comparison may be with similar firms in different geographical regions (e.g., UK) or with a model “efficient” firm (e.g., Chile) • Can reduce subjectivity of evaluations by regulators • Can be difficult to identify valid comparators and can be complex to implement • example: electricity in Holland, Water in the UK, port terminals in Argentina
So how does incentive theory fit into all this? • Basically, it tells you when it is a good idea to replace cost-plus (or rate of return) regulation with price cap regulation and vice versa…accounting for the type of information asymmetry that you (the regulator) faces • The built in assumption is that price caps will usually be better when you want a monopoly to deliver at the lowest possible cost and to push the monopoly to do so for the long run
Starting point for incentive based regulation • Recognize that regulatory has less than perfect information about: • Cost reduction opportunities of operator • Behavior of operator • Demand for the regulated services • => strategic advantage to the regulated firm!...very different from the full information story! • Advantage even stronger if regulated firm can capture the regulator! • The only good news for the regulator is that regulation is a repeated game… • so regulator can learn • Operator needs to be careful not to build a bad reputation!
Assume first our regulator is a benevolent regulator (no private agenda) • For a benevolent regulator: the optimization is: • Find a regulatory mechanism that takes the social cost of adverse selection (and moral hazard) into account subject to this participation constraint • Balance costs of reducing costs, with risks of pushing too hard • The costs vs the risks is what drives the choice between fixed prices and flexible price contracts • Fixed price is price cap • Flexible price is cost-plus or rate of return
What if the main information problem is adverse selection? • A firm’s costs may be high or low based on: • inherent attributes of its technical production opportunities, • exogenous input cost changes over time (summer vs winter) or space (rural vs urban) • This is not much, but it is at least some information the regulator can use • His knowledge takes the form of a set of probabilities assigned to the various possible costs => end up working with a lower and an upper bound within which the regulator knows the true costs lies…and the name of the game is to try to close the gap between the upper and the lower bound as much as possible • But regulator faces a participation constraint for the firm • If the firms finds regulation is too harsh, it pulls out! • The operator knows it and hence has an incentive to act strategically
What if the main information problem is moral hazard? • A firm’s costs also depends on the level of managerial efforts to lower the costs! • Poor efforts = X-inefficiency! • Problem is that regulator cannot observe effort directly • => even if regulator can observe costs ex-post, it cannot simply reimburse costs (cost-plus regulation) assuming that the firm really tried its best • …but again…if the regulator hits too hard…the risk that the participation constraint becomes binding! • => firm again has an incentive to act strategically!
Price cap (or fixed prices) again • Set a fixed price EX-ANTE that the regulated firm will be allowed to charge • What’s special about it? • Prices are not influenced by managerial effort even if costs are • => any additional profit from cost reducing efforts goes to operator • => high powered incentives to max effort! • => solve moral hazard risk • BUT: full cost of adverse selection: if need a high cost firm, will have to accept high price and for low cost firms, high unshared rent…
What if regulator awards a cost-plus contract instead? • The operator is compensated for all the costs of production that it incurs • Some assumptions built in.. • Firm will reveal whether it is a high or low cost firm • =>There is no rent as excess profit • => No adverse selection problem • BUT moral hazard problem and associated costs are fully realized!
How are these 2 regulatory options usually modeled? • The firm has a revenue requirement, RR, to cover its costs C • RR can be based : • on a pre-set fixed component a, as well as • acomponent b contingent on a firm’s realized costs (b is the realized cost sharing parameter) • => RR = a + (1-b) C • For a price cap: RR = a since b=1 and a=C*, the regulators’ assessment of efficient costs • For cost of service: RR = C since a=b=0 • For a hybrid or performance based mechanism: • O<b<1 and 0<a<C*
How’s the jargon on this?This is about rent extraction vs. incentives…
So how does one pick between cost-plus and price cap? (1) • If moral hazard (effort) is the main problem and adverse selection is not a big deal • Price cap • If adverse selection is the main problem and moral hazard is not a big deal • Cost plus • But in real life…probably optimum is often in the middle: • Profit sharing mechanisms or sliding scale regulatory mechanisms • Price allowed is partially responsive to realized costs and partially fixed ex-ante
So how does one pick between cost-plus and price cap? (2) • Ideally, come up with a menu of contracts • Make it profitable for a firms with low cost to choose a high powered incentive scheme (price cap) and a high cost firm, a low powered incentive (cost plus) • If the low cost firm behaves as a high cost one, it lose the rent • If the market only has high cost firms, then participation constraint is not binding for these firms • => contracts with different a’s and b’s!!!
Back to the formal model under asymmetric information • The benevolent regulator’s objective is the same as before • Max W=S + αR • Assume also: • α≤1 => we assume that the firms’s profits are less valued than consumer welfare • Λ=0 => no social cost of public funds
Let’s focus first on regulation under adverse selection • We don’t know the type of the firm (low or high cost?) • Different ways of modeling this: • Baron-Myerson (1982) • Lewis-Sappington (1989) • Laffont-Tirole (1986) • Lewis-Sappington (1988) • Armstrong-Sappington (2008)
Baron-Myerson (1982) • Exogenous marginal cost c Є {cL, c H} unobserved by the regulator • The probability of the firm being a low cost is Ø • The fixed cost Fof the operator are common knowledge • Consumer demand Q(p) and consumer surplus v(p) are also common knowledge • Focus on a trade off between allocative efficiency and rent minimization
Lewis-Sappington (1989) • Focuses on different sources of adverse selection • Exogenous marginal and fixed costs unobserved by regulators • Cost function • C(Q) = cL Q + F L orC(Q) = cH Q + F H • Negative relation between marginal and fixed costs: cL <cL F H >F H • Consumer demand is common knowledge
Laffont-Tirole (1986) • Again different sources of adverse selection but also leaves room for moral hazard • Endogenous marginal costs, observed by regulator • Regulator does not know trade-off between fixed and marginal costs • Fixed cost (or effort) are not observable • Low cost firms can achieve marginal cost c by incurring a high fixed cost FL(c) • High cost firms can achieve marginal cost c by incurring a high fixed cost FH(c) >FL(c) • The probability of the firm being a low cost is Ø • Consumer demand is common knowledge • Again trade-off between efficiency (productive) and rent minimization
Lewis-Sappington (1988) • Cost function C(Q) is common knowledge • Consumer demand is exogenous but unobserved by regulator • Demand is QL(p) or QH(p) > QL(p) • The probability of low demand is Ø
A unified treatment of regulation under adverse selection (Armstrong-Sappington (2008) (1) • Firm’s private information is binary • State i=L or H (low or high) • Exogenous probability of state L is Ø • Firms profits in state i with price p is πi(p) • Contract for firm i is price pi and Transfer Ti • Firm’s equilibrium rent in state i is Ri =πi(p) + Ti • Define difference Δπ(p) in firm’s profits in state H compared to stage L, given (regulated) price: Δπ(p) ≡ πH(p) - πL(p) In the case of Baron-Myerson, =(p-cH )q(p) – (p-cL )q(p) = (cL – cH) q(p)
A unified treatment of regulation under adverse selection (Armstrong-Sappington (2008) (2) • Consumer surplus in state i with price p is vi(p) • Net consumer surplus is vi(pi)-Ti • With social cost of public funds, this would become as before vi(pi) + (1+Λ) Ti • The welfare in state i is thus W = [vi(pi) - Ti ]+αRi , i= L or H • A better expression is W = wi(pi) – (1-α)Ri where wi (p)= vi(p) – πi (p) • The expected welfare is W=ØWL + (1-Ø)WH • With 2 participation constraints:Ri, Ri ≥0 • If full info: p maximizes W and R=0
Adding asymmetry adds incentive constraints to the participation constraints!! • We need 2 incentive compatibility constraints (ICC): 1. Low cost firms must not have the incentive to pretend they are high costs • RL =πL(pL) + Ti ≥Ri =πH(pH) + TH = RH - Δπ(pH) 2. High cost firms must not have the incentive to pretend they are Low costs • RH≥RL + Δπ(pL) • Adding these inequalities implies Δπ(pH) ≥ Δπ(pL) and so it must be the case that pH ≥ pL • => full information outcome is only possible if the ICC are met • Problem is that with the B-M or L-T assumptions and models…this is NEVER possible! • => marginal cost pricing won’t do it! • Low price firms will have high profits, the others, low ones
