See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/253388811 The Economics of Public-Private Partnerships1 Article · January 2008 CITATIONS READS 3 137 2 authors: Elisabetta Iossa David Martimort 59 PUBLICATIONS 942 CITATIONS 165 PUBLICATIONS 5,312 CITATIONS University of Rome Tor Vergata SEE PROFILE Ecole d'économie de Paris SEE PROFILE All content following this page was uploaded by Elisabetta Iossa on 22 January 2014. The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. The Economics of Public-Private Partnerships1 Elisabetta Iossa2 and David Martimort3 March 21, 2008 DO NOT QUOTE: PRELIMINARY Abstract In this paper we provide a unified theoretical framework to discuss incentive issues in Public Private Partnerships (PPPs) and to identify the circumstances in which the main characteristics of PPPs are suitable to provide adequate incentives for private contractors in infrastructure and public service provision. We also extensively describe the empirical evidence on PPPs and use our insights to derive clear policy implications. 1 For useful comments or discussions, we with to thank Jérôme Pouyet, John Bennett, Jean-Etienne de Bettignies, Thomas Ross and Zoe Moss and seminar participants at E.S.N.I.E. (Ajaccio), at the Congrès de l’ Association Française d’Economie (Paris-Sorbonne) and at the UBC P3 Project (Vancouver). 2 Brunel University and University of Rome Tor Vergata, CEDI and CMPO. 3 Toulouse School of Economics and EHESS. 1 1 Introduction Under a public-private partnership (PPP), a local authority or a central-government agency enters a long-term contractual arrangement with a private supplier for the delivery of services. The supplier takes responsibility for investing in the capital assets required, financing the investment and then managing the facilities. PPPs are being used across Europe, Canada, the US and a number of developing countries as part of a general trend seeing an increasing involvement of the private sector in the provision of public services, in the form of privatization, deregulation, outsourcing and in general downsizing of government. In 2004-5, 206 PPP contracts were signed worldwide involving USD 52 billion in investments (PWC, 2005). PPPs have traditionally been employed for example for transport, energy and water but their use has recently been extended to IT services, accommodation, leisure facilities, prisons, military training, waste management, schools and hospitals. In Europe the PPP approach was pioneered by the Private Finance Initiative (PFI), which was launched in 1992 in the UK (Grout, 1997). As of December 2006, 794 PFI projects had been signed for a capital value of £ 55 billion (CBI, 2007). PPPs have also been in operation for more than 10 years in Portugal where investment through PPPs equalled about 20 percent of total public investment during 1999-2003 (Valila Kozluk and Mehrotra, 2005). Other European countries have also invested in PPPs, especially Ireland, Greece, the Netherlands and Spain (EIB, 2004). In the US, PPPs are most common for projects involving highway and road transportation, rail, and water supply and wastewater treatment (CBO, 2007). The cumulative project costs of PPPs funded or completed by October 2006 totaled about $48 billion out of nominal capital spending on infrastructure by the federal government and states and localities of $1.6 trillion between 1985 and 2004 (averaging $80 billion annually).1 Whilst PPPs have not accounted for a significant share of public infrastructure spending in the US so far, they are being actively encouraged by federal departments (DOT 2007). PPPs are also being encouraged in Canada: in November 2007 the Canadian federal government announced a plan to promote use of PPPs and created a national fund for PPP 1 In the US, a number of PPPs were also developed in the 70’s for inner-city infrastructure (see Rosenau, 2000). 2 investments of 1.26 CD. In developing countries, PPP agreements have grown steadily since the 1990s. According to the World Bank’s Private Participation in Infrastructure (PPI) database, 2750 infrastructure projects involving private and public investment for capital value of USD 786 billion have been implemented in 1990-2003 (in 2002 constant dollars). Around 1000 projects and 47% of the investment took place in Latin American and the Caribbean (LAC) countries, where Chile and Mexico were pioneers in the use of PPPs (IMF, 2004). In Central and European Countries many PPP projects were conceived in the second half of the 1990s. The PPI list 217 projects in the region by 2003, with 64 projects for building and operating new facilities amounting to 64 amounting to an investment of EUR 22.6b. Despite this growth, evidence on PPP performance remains mixed. On the one hand, PFI projects in the UK seem to be delivering cost saving compared to traditional procurement (Arthur Andersen and LSE, 2000). Improvements in time and cost of delivery have also been achieved; the HM Treasury (2003) reports that 76% of PPP projects have been completed on time, compared to 30% of traditionally procured projects. On the other hand, in France PPPs have resulted in higher water prices than traditional procurement (Saussier, 2006). PPP seems also unsuitable for fast-moving sectors; performance failures have been widespread in PPPs for specialized IT in the UK. Existing evidence also suggests that renegotiation of contract terms has played a pervasive role in PPP arrangements worldwide. In LAC countries numerous instances have been recorded where governments have failed to honour contract terms and projects have been abandoned (Guash 2004). Adverse institutional conditions have also mattered. High transaction costs, and unrealistic demand expectations, made PPPs in Central and Eastern Europe less successful than in other countries (Brench, Beckers, Torsten and von Hirschhausen, 2005). In this paper we provide a unified theoretical framework to discuss incentive issues in PPPs and to identify the circumstances in which the main characteristics of PPP are suitable to provide adequate incentives for private contractors in infrastructure and public service provision. We also extensively describe the empirical evidence on PPPs and use our insights to derive clear policy implications. 3 For our purpose we characterize PPPs by three main features: (i) bundling, (ii) risk transfer, (iii) long-term contract. (i) Bundling. A PPP typically involves the bundling of the design, building, finance, and operation of the project, which are contracted out to a consortium of private firms. The consortium includes a construction company and a facility-management company and it is responsible for all aspects of services. The DBFO model (‘Design’, ‘Build’ ‘Finance’ and ‘Operate’), the BOT model (‘Build’, ‘Operate’ and ‘Transfer’) or the BOO (‘Build’, ‘Own’ and ‘Operate’) albeit with differences all account for bundling of building and operation. (ii) Risk transfer. Compared to traditional procurement, the PPP contract involves a greater transfer of risk and responsibility to the contractor. A system of output specifications is used: the government specifies the service it wants and the basic standards, but it leaves the consortium with control rights and responsibility over how to deliver the service and meet the pre-specified standards. So design, construction and operational risk are generally substantially transferred to the private sector party. (iii) Long term contract. A PPP contract is a long-term contract lasting typically 25-35 years. The payments to the private-sector party for the use of the facility is made either by the government (as in the case of PFI projects) or by the general public as users of the facility (as in more standard concession contracts). We present a simple model of procurement in a moral hazard context. Moral hazard is key to investigate two issues that are pervasive in the economics of PPPs. The first one is related to the existing agency costs borne by governments when delegating to the private sector the task of providing a service for society. The second one concerns the issue of risk-sharing between this government and the delegatee. A key point of the analysis is to discuss the nature of these agency costs in a multitask environment where the agent not only manages existing assets necessary to provide the service but also may design, build and finance these assets. Consistently with what is observed in practice, our model features altogether aspects of the optimal contracting literature (the verifiability of the operating costs and the need to share operating risk between the public-sector party and the firm) but also dimensions of the property rights literature. We present this basic model in Section 2. 4 In Section 3 we use the basic model to study the conditions under which bundling of project phases (in particular building and operation) into a single contract is optimal. An important distinction that we draw is between positive and negative externalities across different stages of production. We use the term ‘positive externality’ (resp. ‘negative externality’) when a building innovation is associated with reduced (resp. increased) cost at the management stage. Bundling induces the contractors to look at the longterm performance of the asset (so called ‘whole life asset management’) and this affects its incentives to invest in asset quality. We shall however show that bundling improves the incentives of the contractor when the externality across stages is positive but it has a negative or no effect when the externality is negative. Provided there is an incentive problem, our results hold regardless of the contractual framework used and of the precision of the information held by the government. An interesting feature of optimal contracting which we emphasize is that bundling goes one to one with higher power incentives: when bundling is optimal also more risk transfer is optimal. This provides the rationale for both bundling and risk transfer to characterize PPP arrangements and it explains the greater risk premium that is typically observed in PPP contracts compared to traditional procurement. Furthermore, we show that private ownership during the contract dominates public ownership and the gain from bundling with private ownership is greater for generic facilities, such as leisure centres, accommodations and public housing, than for specific facilities, such as prisons, hospitals and school which have limited use outside the public sector. Once equipped with the rationale for bundling and risk transfer in PPP agreements, we develop our basic insights in more elaborated environments which have been viewed as particularly interesting both in the public debate and within recent academic research. The first steps consist in considering dynamic environments where long-term contracting is subject to many hazards. We do this in Section 4. We start by considering the case of countries with strong governance where the risk of unilateral changes of contract terms by governments are minimal. With full commitment we show that incentives are optimized under a long-term contract that entails increasing power of incentives over time. The benefit of bundling is maximized through cost plus contracts in early periods and fixed price contracts in later periods. 5 Long-term contracts however suffer from major uncertainty over the realizations of future demand and cost levels. When estimates turn out to have been optimistic, renegotiation of contract terms may occur, partially nullifying the incentive power of the initial contract. We then extend our analysis of the dynamics of PPPs by considering the distortions that are needed to prevent cost-overruns. We show there that incentive constraints should be tilted towards being low-powered although this cannot undo the benefits of bundling. Long-term contract also suffer from uncertainty over the of future evolutions of users’ needs which make them unsuitable in circumstances where users needs evolve fast and the output specifications set up in the initial contract become quickly obsolete. We discuss this cost of PPP contracts in Section 5. We argue that for fast-moving sectors the benefit of bundling needs to be weighed against the cost of contract rigidity, which may be severe enough to make PPPs unsuitable in circumstances where users’ needs are likely to evolve fast. In Section 6 we move to the case of countries with weak governance where the risk of unilateral changes of contract terms by governments is significant. We then relax the assumption of full commitment and discuss the importance of institutional quality, focusing on the negative effect on incentives of political/regulatory risk. We show that there incentive constraints should be tilted towards being low-powered which reduces the benefits of bundling. In Section 7 we deal with the issue of risk transfer in more depth by distinguishing between cost and demand risk. We then analyze some of the factors that affect the optimal allocation of these risks between the private and public partner and derive their implications for the use of users charges and the choice of contract length. We also discuss the case of financially-free standing project where users’ fees represent all of the contractor’s revenue. This allows us to discuss one other characteristics of many PPP arrangements, namely the use of private finance.[Incomplete] Section 9 summarizes some of our conclusions and discusses the scope for future research. 6 2 The Basic Model We consider the following public procurement context: A welfare maximizer government G (thereafter the principal) relies on a private firm F (the agent) to provide a public service for society. Examples of such delegation include of course transportation, water insanitation, waste disposal, etc. Such settings are characterized by the fact that providing the service can only be done if an infrastructure of a sufficiently good quality has been first designed and built. Clearly, this points out that the analysis of this delegation of services towards the private sector must be modelled in a multi-task context.2 The main feature of a PPP is the bundling of various phases of contracting. Typically the design (D), building (B), finance (F) and operation (O) of the project (this is the so-called “DBFO model") are contracted out to a consortium of private firms (hereafter “the agent.") The consortium includes at least a construction company and a facility-management company and it is responsible for all aspects of services. Variations of the DBFO contract can include Design-Build-Operate (DBO), Build-Operate-Transfer (BOT), Build-OwnOperate-Transfer (BOOT), Build-Lease-Operate-Transfer (BLOT), etc. By exerting a quality-improving effort or, in an alternative interpretation of our model that will be sometimes used thereafter, making some investment in the quality of the infrastructure, the agent improves the quality of public service. The yields a normalized social benefit which is worth B = b0 + ba where the marginal benefit of the agent’s effort is positive (b > 0) and b0 denotes some base level for the benefits of the service. Providing the service costs to the firm an amount C = θ0 − e − δa + ε. ε is a random variable which is assumed to be normally distributed with variance σ 2ε and zero mean. It captures the amount of operational risk incorporated into the activities. θ0 is the innate cost of the service (linked to the technology used) and e is the agent’s effort in cost-reducing activities. 2 Homstrom and Milgrom (1991). 7 Two alternative scenarios will be particularly analyzed in the sequel. The case δ > 0 corresponds to a positive externality where improving the quality of the infrastructure also reduces the operational costs. For example, the design of a prison with better sightlines for staff that improve security (i.e., social benefit) may yield the positive externality that the required number of security guards is reduced. The case δ < 0 corresponds to a negative externality where improving the quality of infrastructure increases operational costs. For example, an innovative design of a hospital, using recently-developed materials, may lead to improved lighting and air quality, and therefore better clinical outcomes, but have the negative externality of increased maintenance costs. Quality-enhancing and operating efforts have monetary costs for the agent. These costs are respectively given by the quadratic disutility functions ϕ(a) = a2 2 and ψ(e) = e2 . 2 Note that the overall cost of efforts is separable so that there is neither costs nor benefits of splitting the building and the management tasks on the agent’s cost function side. Delegation of services to the private sector takes place in a moral hazard environment so that both a and e are non-verifiable. Only the operating cost C is observable and can be used ex ante at the time of contracting between G and F . Consistently with many examples of PPP projects, the social value of the project is hardly contractible and no related statistics even a rough one can be used to conditions payments to the agent as a function of this social value. The risk-neutral government G is supposed to maximize expected social welfare, defined as the social benefit of the service net of its costs and of the payment made to F . The firm F maximizes also his expected utility and is risk averse. This captures the fact that a PPP project might represent a large share of the firm’s activities; we assume that this firm is not fully diversified and has constant risk-aversion given by r > 0. • Benchmark: For future references, it is worth describing the first-best levels of effort aF B and eF B that would be achieved had efforts been contractible. The first-best contractual outcome has of course the risk-averse agent being fully insured by the risk-neutral government with a cost-plus contract. Given that the public authority can run a competitive auction to attract potential service providers, we assume that it has all bargaining power and chooses a fee for the service provider that makes him just indifferent between producing the service or getting his outside option normalized at zero. Moreover, that 8 contract also forces the agent to choose the first-best efforts which solve: ¡ FB F B¢ a2 e2 a ,e = arg max b0 − θ0 + (b + δ)a + e − − = (b + δ, 1). (a,e) 2 2 (1) At the optimum, the quality-enhancing effort aF B trades off the marginal social value of that effort, including its impact on operating costs (δ) and on the social value of the service (b), with its marginal cost (a). The operating cost-reducing effort eF B trades off the marginal benefit of lowering those operating costs (1) with its marginal monetary disutility (e). • Timing: We consider the following timing of our contracting game, depicted by means of a time line. Builder chooses a non-verifiable Bundling or Unbundling ? C = θ0 − e − δa + ε realized ? 6 Contract t(c) with the operator ? 6 Operator chooses e non-verifiable 6 Social value b0 + ba Figure 1: Timing of the game of organizational choice and contracting. 3 Bundling or Unbundling? Pure Agency Considerations In the analysis below, we want to provide a rationale for relying on a PPP rather than adopting a more traditional procurement model where G buys first the infrastructure from a given builder and then selects an operator. Therefore we investigate whether the two tasks of respectively designing-building the assets and operating them should be bundled and performed by the same contractor (a consortium) or instead they should be unbundled and undertaken by two separate firms (a builder and an operator). 9 3.1 Unbundling Under traditional contracting, G approaches first a builder and then a distinct operator who receives a cost-reimbursement rule t(c) net of its cost. Given the CARA-normal distribution environment under scrutiny, we may follow Holmström and Milgrom (1991) and restrict the analysis to the case of linear rules of the form t(c) = α − βc. The case β = 0 corresponds to a cost-plus contract with no incentives in cost reduction, whereas β = 1 holds for a fixed-price. To simplify the presentation, we rule out the possibility that the builder obtains an incentive payment that would depend on the realized cost C. Instead, the builder receives a fixed payment. This contractual limitations may be justified when G has a limited ability to commit to future rewards for the builder and cannot delay payment for the delivery of the infrastructure. There is also the possibility of a collusion between G and the operator to exaggerate the contribution of the operator to cost-reducing activities and underestimate that of the builder.3 Since he receives only a fixed payment that cannot reward him for the quality enhancing effort he may put into the design of the project, the builder does not exert any such effort: au = 0. (2) Turning now to the operator who is willing to maximize the certainty equivalent of his expected utility given the builder’s own effort, his incentives constraint can be written as: e = arg max α − β (θ0 − ẽ) − ẽ ẽ2 rσ 2 β 2 − = β. 2 2 (3) An increase in the power of the incentive scheme (β) raises cost-reducing effort, but as more operational risk is transferred to F the risk premium rσ2 β 2 2 increases. Assuming that G has all the bargaining power ex ante with both the builder and the operator, he can extract all their rent and just leave them indifferent between providing the service and getting their outside opportunities normalized at zero. In particular, this means that the fee α is set to also to cover the risk-premium that must be paid to have the risk-averse 3 We briefly discuss how the results can be extended when this assumption is relaxed in Section 3.3 below. 10 operator bearing some operational risk as requested for incentive reasons. Finally, G just maximizes social welfare’s problem taking into account the incentive constraints (2) and (3) and the total benefit and cost of effort, including the risk premium. This yields the following expression of G’s problem: max b0 − θ0 + e − e (1 + rσ 2 ) 2 e. 2 Immediate optimization yields the second-best value of the operating effort as: eSB u = 1 < 1. 1 + rσ 2 (4) Because providing incentives requires the agent to bear more risk and this is socially costly, the second-best effort is less than its first-best level. Finally, social welfare under unbundling can be written as : WuSB = b0 − θ0 + 3.2 1 . 2(1 + rσ 2 ) (5) Bundling With this organization of form, both the building and the operational phases are in the hands of a consortium. The consortium’s expected overall payoff is maximized when the effort levels are jointly chosen to solve: (e, a) = arg max (ẽ,ã) α − β (θ0 − ẽ − δã) − ã2 ẽ2 − . 2 2 Taking into account the additional non-negativity constraint a ≥ 0 yields the following incentive constraints e = β and a = ( βδ if δ > 0 0 if δ < 0. (6) Let us analyze two cases in turn depending on the sign of the externality. • Negative Externality: When δ < 0, the consortium never chooses to perform a quality-enhancing effort because it receives no direct reward for doing so and the impact is to increase his own operating cost. This replicates exactly the same solution as in the case of unbundling. 11 Result 1 With a negative externality (δ < 0), bundling and unbundling yields the same welfare. WbSB = WuSB . There is no infrastructure quality-enhancing effort and a less than optimal cost-reducing effort. SB FB = aSB = eSB . aSB b u = 0 and eb u < e • Positive externality When δ > 0, a consortium internalizes somewhat the impact of building an infrastructure because it reduces its operating costs. Raising the power of incentives by using a contract looking more as a fixed-price also raises incentives on infrastructure quality-enhancing; an objective which cannot be directly achieved by the public authority since that quality is hardly contractible. Aggregating the two relevant incentive constraints in (6) by eliminating the slope of the incentive scheme β yields the following expression of G’s problem: max (a,e) b0 − θ0 + (b + δ)a + e − a2 (1 + rσ 2 ) 2 − e 2 2 subject to a = δe. (7) Optimizing yields the effort levels = eSB b 1 + δ(b + δ) and aSB = δeSB b b 1 + δ 2 + rσ 2 (8) and the expression of the expected welfare as: WbSB = b0 − θ0 + (1 + (b + δ)δ)2 . 2(1 + δ 2 + rσ 2 ) Result 2 With a positive externality (δ > 0), bundling strictly dominates unbundling WbSB > WuSB . The welfare gain from bundling increases with the magnitude of the externality δ. ∂ (W SB − WuSB ) > 0. ∂δ b 12 (9) There is a positive infrastructure quality-enhancing effort and an increase in cost-reducing effort.4 SB aSB > aSB > eSB b u = 0 and eb u . When the externality is positive, bundling induces the agent to internalize the effect of his quality-enhancing investment a on the fraction of cost that he bears in the operational stage. This unambiguously raises welfare and the stronger the positive externality, the greater the benefit of bundling. To see why consider the following thought experiment: Take the incentive scheme offered to the operator under unbundling, and suppose it is now given to the consortium. The incremental welfare gain from doing this is (b + δ)aSB u − 2 (aSB u ) 2 > 0 since now the SB consortium exerts a quality-enhancing effort aSB u = δeu . Bundling shifts more operational risk to F and brings the additional benefit of increasing its incentives to invest in asset quality. For this reason, moving from traditional procurement to PPP changes cost-reimbursement rules. Bundling and fixed-price contracts go hands in hands under PPP whereas unbundling and cost-plus contracts are more likely under traditional procurement. This is in lines with existing evidence that PPP projects are characterized by a greater degree of risk transfer to the private sector parties and thus a greater risk-premium than traditional procurement. Result 3 PPP projects are associated with higher powered incentives and more operational risk being transferred to the private sector: SB = eSB > β SB for δ > 0. β SB b b u = eu 4 The exact expressions for those efforts are b(1 + rσ 2 ) + δrσ 2 (b + δ) δ 2 + δ SB ¡ ¢ ¢ = eSB > eSB = aSB u + δ¡ u . b 2 > 0 and eb 2 1 + rσ + δ 1 + rσ 2 + δ 2 (1 + rσ 2 ) may be greater than e∗ if b is large enough. Note that eSB b 13 3.3 Robustness Check: Complete Contracting So far, we have ruled out the possibility that, under unbundling, the builder receives also an incentive payment that would better track the realized investment. Of course, the kind of contracts that can be signed with a builder and thus the best organizational form that arises depend on the set of contractible variables available to G. Let us envision the consequences of allowing more complete contracts between G and F . 3.3.1 Costs Incentives A first obvious candidate incentive scheme for a builder links his payment to the realized level of operating costs since these costs also reflect the quality of the infrastructure. For simplicity, let assume again that contracts are linear and of the form tB (C) = αB − β B C. Unbundling: First note that such a payment gives a positive incentive to the builder for exerting effort a. The builder’s incentive constraint is indeed given by: a = β B δ. Clearly, there always exists a payment that implements the same effort pair under unbundling than under bundling. If the builder is risk-averse (with supposedly the same degree of risk-aversion as the operator) such payment has also a social cost rσ2 β 2B 2 = rσ 2 a2 2δ2 which is the risk-premium needed to induce the builder’s participation. Clearly, this premium increases quickly when the positive externality is small enough, i.e., when the noisy observable do not track so easily the builder’s effort. Under unbundling, the optimal quality-enhancing effort is easily obtained as trading off the efficiency gain of more effort against the risk-premium and one finds: aSBC u (b + δ)δ 2 . = 2 δ + rσ 2 (10) This yields the following expression of the expected welfare with complete contracts and unbundling: WuSBC = WuSB δ 2 (b + δ)2 . + 2(b2 + rσ 2 ) (11) It is easily seen that the gain from writing complete contracts compared with the setting of Section 3 is of order δ 2 which is rather small for a weak externality. 14 Bundling: When bundling is chosen, a single incentive scheme must incentivize both dimensions of effort. The outcome is the same as in Section 3.2. The welfare is now of order δ when δ is small as it can be seen on (19) so that: Result 4 Assume that there is a small positive externality. Bundling strictly dominates unbundling in the more general context where complete contracts contingent on operating costs can be signed with both the builder and the operator. The intuition is straightforward. By bundling tasks in a context where only operating costs can be contracted upon, G can reduce the incentive power of the builder’s costreimbursement rule, reducing thereby the risk premium needed to induce his participation. Compared to unbundling, bundling makes it more valuable to move towards a fixedprice cost-reimbursement rule (β increases) and raises both types of efforts a and e if the externality is positive. At the optimum, G optimally trades off incentives with insurance. However, because now part of the incentives to invest in quality-enhancing effort is given through lower operating cost ex post, there is less need to have the consortium bear so much risk. 3.3.2 Quality Incentives Let us now suppose that an index q of the quality of the infrastructure is available and can be written as: q = a + ε0 where ε0 is a random variable which is assumed to be normally distributed with variance σ 2ε and zero mean. For simplicity we keep the same noise on q and the operating costs. This assumption is particularly relevant on the case where that index q is in fact an earlier realization of operating costs in a context where the investment consists of complementary and renewed assets. Now the builder’s incentive scheme links his reward to the realized level of q. For simplicity, we assume again that contracts are linear and of the form tB (q) = −αB + β B q. Unbundling: The effort levels and welfare are clearly the same as in (10) and (11). 15 Bundling: The consortium’s incentive constraint can be written as: (a, e) = arg max α + β B ã − β(θ0 − ẽ) − (ã,ẽ) ã2 ẽ2 rσ 2 β 2B rσ 2 β 2 − − − + βδa. 2 2 2 2 This leads to the following incentive constraints: a = β B + βδ, and e = β. (12) By making the payment to F contingent also on the quality index, G provides F with stronger incentives to invest in asset quality. However, as F is risk-averse such contingent payment raises the risk premium to be paid to F by rσ 2 β 2B . 2 Taking into account that G has all bargaining power in designing the consortium’s contract and use accordingly the fixed-fee to extract all its ex ante surplus, G’s optimization problem becomes: max b0 − θ0 + e + (b + δ)a − (a,e) a2 rσ 2 (1 + rσ 2 )e2 − (a − δe)2 − . 2 2 2 Optimization leads to the following effort levels: = aSB aSB b u + δrσ 2 (b + rσ 2 (b + rσ 2 (b + δ)) δrσ 2 (1 + rσ 2 (1 + δ 2 + bδ) SB SB , and e = e + b u (1 + rσ 2 )D (1 + rσ 2 )D (13) where D = (1 + rσ 2 )2 + rδ 2 σ 2 . It is immediate to check that SB > aSB > eSB aSB b u and eb u if and only if δ > 0. Finally, Result 5. Result 5 Bundling strictly dominates unbundling in the more general context where complete contracts on both operating costs and a quality index are allowed. Even if better information over asset quality eases the agency problem under unbundling, bundling remains the preferred option whenever the auditing of infrastructure quality is imperfect. 3.4 Residual Value and Ownership Taken in tandem Results 1 and 2 just tell that bundling at worst weakly dominates unbundling. With a positive externality, the internalization of the externality that occurs 16 under bundling raises welfare as it eases the moral hazard problem. With a negative externality and under unbundling, investment a is already at a minimum, the builder having no incentives to invest; thus the internalization of the negative externality that occurs under bundling does not improve things. If we were taking only the agency route as a justification of PPPs in practice, the results of this simple model would be a little bit too weak: PPP should always be preferred. The open question to which we will answer below is to find conditions under which unbundling, viewed as a more traditional form of public procurement, may lead to a strictly greater payoff.5 To do so we will now identify PPP as an organizational form where there is bundling of design and operation phases but also private ownership of the assets during the contract. Traditional contracting corresponds instead to the case where G buys an asset built (and thus initially owned) by the private sector and operates it through a second firm be it private or public. Let us thus turn to the issue of knowing who should own the infrastructure. Ownership matters to the extent that assets have some residual value for the owner at the end of the contract. Implicitly ownership entitles the owner with the market value of these assets. Enjoying this residual value might provide incentives to invest in asset quality and be a substitute to more complete contracts. Of course, that residual value will depend on how specific assets are. Indeed, facilities for the provision of public services are distinguished into two categories: (i) generic facilities, such as leisure centers, office accommodation, general IT systems and land use; and (ii) specific facilities, such as hospitals, prisons and schools. In the case of generic facilities, there is demand from users other than the government, so that the public and private residual value do not differ significantly. To model these issues, let sa, with s > 0, denote the value of the asset at the end of the contract when the assets will be used by the government for public-service provision, and let γsa, with γ < 1, denote the corresponding value of the assets for the private sector. Consistently with much of the incomplete contracts literature,6 the residual value of these assets cannot be specified ex ante in a contract although it is ex-post observable and can be bargained at that stage. γ captures the degree of asset specificity, with γ being higher 5 Actually, one can show that in the context of Section ?? unbundling dominates for a negative externality. 6 Hart (1995). 17 the less specific is the facility. Since γ < 1 it is always optimal that the facility be owned by G at the end of the contract. That the asset returns to G at the end of the contract is indeed one of the main features that distinguishes PPP from privatization. As a benchmark, note that the first-best level of a now solves aF B = s + b + δ. 3.4.1 Public Ownership Suppose that G owns the asset throughout the contract. Since a is not contractible and since no sale of the facility occurs once the contract expires, there is no way for giving any incentives to the firm. Whether bundling or unbundling is chosen, efforts and welfare with public ownership remains the same as before in both cases. Result 6 Public ownership has no impact on incentives. Whether bundling strictly dominates depends on the sign of the externality as in Section 3. 3.4.2 Private Ownership Suppose assets are privately owned. At the end of the contract, efficiency requires to transfer ownership to G. Assuming that ex post, the price p∗ at which ownership is transferred results from Nash bargaining with equal bargaining power between G and F : p∗ = arg max(sa − p)(p − γsa) = p This yields to the private owner a net benefit (1−γ) sa 2 (1 + γ) sa. 2 which is increasing in a and boosts his incentives to enhance the quality of the assets if he is a builder.7 Note that the owner’s incentives to invest is greater when the asset is less specific. Indeed, asset specificity decreases the status quo payoff if ownership is not transferred to the public sector at the end of the contract. This exacerbates the hold-up problem that occurs through ex post bargaining and dampens the private owner’s incentives. 7 It should be clear that under unbundling ownership by the builder is preferable to ownership by the operator since the operator has no control on quality-enhancing effort. 18 • Private ownership and unbundling: G can extract all the owner’s surplus through an ex ante fee because he has all bargaining power ex ante. With unbundling and ownership by the builder, the builder’s incentive constraint can be written as: apr u = (1 − γ) s. 2 (14) Of course, the operator’s effort and optimal incentive scheme remain the same as in Section 3.1: SB epr u = eu . (15) Finally, this leads to the following expression of social welfare: Wupr = WuSB + (b + s + δ)(1 − γ)s (1 − γ)2 s2 − . 2 8 (16) • Private ownership and bundling: Ownership has still some value with bundling. The consortium’s expected payoff is maximized for effort levels that solve: (1 − γ) ã2 ẽ2 sã + α − β (θ0 − ẽ − δã) − − . 2 2 2 (e, a) = arg max (ẽ,ã) This yields the following incentive constraints: e = β and a = βδ + (1 − γ) s. 2 (17) where we assume that s is large enough to insure a positive quality-enhancing effort even with a negative externality. Now G extracts all ex ante surplus from the consortium by raising the fixed-fee α by an amount which covers the extra net benefit that the owner can withdraw from his investment. Finally, aggregating the two incentive constraints in (17) yields the following expression of G’s maximization problem: max (e,a) b0 − θ0 + (b + s + δ)a + e − a2 (1 + rσ 2 ) 2 − e 2 2 (1 − γ) s. (18) 2 Using (18) to express G’s objective function under unbundling and private ownership subject to a = δe + before optimization with respect to e as: Wbpr (e) = Wupr (e) ¶ µ δ 2 e2 1+γ s δe − + b+δ+ 2 2 19 (19) where Wupr (e) = b0 − θ0 + sapr u − 2 (apr (1 + rσ 2 ) 2 u ) +e− e. 2 2 Comparing public ownership with private ownership, we immediately obtain: Result 7 Private ownership always dominates public ownership. The gain from private ownership is non-increasing in the level of asset specificity. Comparing now both organizational forms, we get: Result 8 PPPs, i.e., Private ownership and bundling, strictly dominates traditional contracting, i.e., private ownership and unbundling, if and only if the externality between the design and the operation phase is positive: Wbpr > Wupr if and only if δ > 0. Efforts are greater under bundling if and only if the externality is positive. pr pr pr apr b > au = 0 and eb > eu if and only if δ > 0. Compared to the case of public ownership, with a negative externality now bundling leads to strictly lower efforts than unbundling. This is because ownership of the asset gives the builder positive incentives to invest in asset quality. These incentives are then depressed if the builder is induced to internalize the negative externality that asset quality exerts on operational cost. Giving ownership of the infrastructure to the builder reduces the hold-up problem and boosts quality-enhancing effort a (whatever the sign of the externality).8 The builder, when an owner appropriates part of the surplus from enhancing quality of the infrastructure and the more so the greater the asset specificity (i.e. higher is γ). Since the value of improving quality is not risky, there is no risk premium associated to private ownership and private ownership is always optimal. Then private ownership is more beneficial 8 However, results may change if a has a negative impact on the market value of the asset, though it still increases the value of the asset when used for public purposes. This is likely to occur for facilities for which the design is very specific to the delivery of the public service. See Rajan and Zingales (1998). 20 for generic facilities (where γ is high), such as leisure centers and housing, than for specific facilities such as hospitals, prisons or schools. However, since the contractor never fully internalizes the positive effect on social benefit b, underinvestment in quality always arises. When a higher building quality raises operational cost, bundling is suboptimal as internalization of the externality would depress investment further. 3.5 Related Literature and Applications Literature : Our model has merged two strands of the literature on PPPs which have both emphasized the multitask nature of the procurement problem when building and managing assets are at stake. Hart (2003) built on Hart, Shleifer and Vishny (1997) provided a model where the sole source of incentives is ownership. A builder can perform two kinds of investment (productive and improductive) which may both reduce operating costs, although only the productive investment raises also the benefit of providing the service. Under traditional procurement, the builder cannot internalize the impact of his effort neither on benefits nor on costs and, as a result, implements too little of the productive investment but the right amount of the unproductive one. Under PPP, the builder internalizes partly the impact of his productive investment whereas he also exerts too much of the unproductive one. Bennett and Iossa (2006) studied the desirability of bundling project phases and of giving ownership to the investor. In their model innovations are non-contractible ex ante but verifiable ex post. Ownership of the asset then gives control right to the owner to decide whether to implement quality enhancing or cost-reducing innovation proposed by the investor. It is shown that the holdup problem is less severe under PPP, compared with traditional procurement, when there is a positive externality between the building and managing stages. With a negative externality the opposite can hold. Further public ownership acts as a commitment for the government to renegotiate and share with the investor the surplus from the implementation of the innovation. Private ownership is however optimal for generic facilities with high residual value. Martimort and Pouyet (2007) built a model where both the quality of the infrastructure and operating costs are contractible. Agency costs are lower under a PPP when there is a positive externality between building and managing assets compared with traditional 21 procurement. Granting ownership is an imperfect way of aligning incentives but, to a large extent, the important issue is not who owns the asset but instead whether tasks are bundled or not. That insight is developed in various extensions of their basic model allowing for risk-sharing as a motive for forming consortia, or political economy. In this respect, a common theme of their model and ours is that PPP comes with higher powered incentives which are prone to collusion and capture of public officials. When those institutional costs are taken into account, relying on PPP becomes less attractive. An alternative, complete-contract, approach to PFI was taken by Bentz, Grout and Halonen (2001). They showed that the government will wish to buy services (as in PFI) rather than facilities (as in TP) if the building and service delivery costs are low. Applications: Our results suggest that PPPs are likely to deliver efficiency gains when a whole-life cost approach to the project has the potential to yield significant cost savings and when risk is effectively transferred to the private-sector operator. Transfer of design, construction and operating risk to the contractor provides incentives for within cost delivery of the infrastructure and in general provision of the service. A report commissioned by the Treasury Taskforce (Arthur Andersen and LSE, 2000) estimated saving on a sample of PFI projects equal to 17%, compared to traditional procurement.9 Significant cost savings were realized in the prison sector. The National Audit Office (2003a) reported that innovative design solutions helped to reduce the level of staffing needed to ensure security and this resulted in an overall cost reduction by approximately 30%. 80% of a prison’s running costs are indeed staff costs. When instead a higher asset quality increases social benefit but has a negative impact on whole-life cost, the scope for PPP is reduced if not eliminated. Evidence of negative externalities is more difficult to find, however, a report by the Audit Commission (see PPP Focus, Education 2, 2004) noted that the quality of many early PFI school buildings was disappointing. Schools had few windows, poor acoustic and air quality, compared to traditionally procured schools. School quality has a direct positive impact on pupil behavior and educational achievement and a higher number of windows which provide daylight is more costly to maintain because of the risk of school vandalism. Local Education Au9 However, Pollock and Vickers (2000) question the Andersen report and argue that once outliers are excluded from the calculations the average saving is 6 per cent. 22 thorities now anticipate this problem and include more detailed output specifications in the contract. As a result the quality of school buildings has improved. Our results also shed some lights on the current approach to facility ownership. Under PPP, ownership of the infrastructure during the contract period belongs to the consortium, but the ownership once the contract expires varies depending on the circumstances. Assets tend to revert to the public sector either when there is no practical alternative use for them or when the asset is needed to provide a continuing service after contract end (for example, schools, prisons and hospitals). For generic facilities with an alternative use outside the public sector and no clear long-term public sector need, ownership is retained by the private sector. We have focused on the benefits of bundling that may come from inducing the contractor to take a long-term approach to the project and follow a whole-life costing approach. However, bundling also bring other effects, not discussed above. First, PPP projects are characterized by a longer procurement process and by higher cost of bidding than traditional procurement. Albeit with differences between sectors, it has been estimated that PPP tendering periods last an average of 34 months (NAO, 2007) and that procurement costs can reach 5-10% of the capital cost of a project (Yescombe, 2007). These transaction costs are also to a large extent independent of the size of a project, which suffices to make PPP unsuitable for low capital value projects. The HM Treasury (2006) currently considers PFI projects for less than £20m as poor value for money. Second, bundling of different phases of the project increases project complexity and limits participation of small construction companies that do not have the necessary financial resources to sustain the costs and risks of bidding for PPP contracts. Albeit with differences across sectors, in the UK there is an average of 4 bidders per PPP contract. This is problematic as collusion among bidders is certainly more likely if the number of participants is small. Furthermore, whilst our focus in the basic model has been on efficiency considerations rather than on distributional ones, distributional considerations also matter in the economics of PPPs. With a low number of participants, the contractor will be able to secure a rent for himself and the cost of provision of public services will be increased by the increased recourse to distortive taxation. 23 In our basic model we have talked about only two tasks: building and operation. In practice, the realization of a project comprises a wider variety of tasks. Services in the operational stage for example include ‘soft’ facility-management services (e.g. cleaning, catering, security) and ‘hard’ facility-management services (e.g. routine and/or life-cycle maintenance of buildings and equipment). The arguments set up in this section apply to hard services where asset quality matters but not to soft services where asset quality plays a limited role. Whether to include soft-facility-management services in PPP contracts should follow other considerations. On the one hand, including soft-services has the advantage of creating a single point of responsibility within the private sector in charge of final service provision. unbundling helps to employ shorter-term contracts for soft services and benefit from the competitive pressure that more frequent tenders guarantee. Separate tendering for soft services also favors the participation of small firms. There are no uniform experiences across countries regarding service unbundling and the HM Treasury (2006) currently advises against their inclusion. 4 Contractual Performances Over Time Section 3.4 was interesting to describe the basic agency problem that occurs at the inception of PPP projects. Those projects are typically long term projects which might cover twenty to thirty years. 4.1 The Trade-Off Between Investment and Maintenance Over a long lasting project where the quality of durable assets and infrastructures may significantly depreciate, an important issue concerns the extent to which contractors are willing to invest to improve the stock of existing infrastructure in the long-run or whether they prefer to choose management strategies that maintain costs low in the short-run. To analyze the trade-off between investment and maintenance, let us consider a twicerepeated and slightly modified version of our basic procurement model. To focus on the operator’s incentives to invest, we will assume that the firm inheritates of a basic stock of infrastructure to run off public service on G’s behalf at date t = 1. Improving this stock requires some extra investment which costs a2 2 24 today but this pays off tomorrow in terms of lowering operating costs by an amount a. Another strategy would be to avoid incurring any initial investment and then cutting operating costs with more maintenance. Costs in each period are respectively given by: C1 = θ0 − e1 + a2 + ε1 2 and C2 = θ0 − e2 − a + ε2 where the operating cost uncertainty εi (i = 1, 2) is N (0, σ 2 ) and ei is maintenance effort undertaken at date i.10 Note that investing increases accounting costs in the short-run but, because of a positive externality between design and operation, reduces the long-run cost of the service.11 Implicit in our formulation is the fact that the cost of investment is not observable to G meaning that it is (at least partly) aggregated with other costs, noticeably the first-period operating costs, in the firm’s book.12 Finally, and consistently with Section 3, we assume that the stock of new investment has a social value b0 + ba with b > 0. In practice, this simply means that there is a difference between the social and the private returns on investment. Assuming that investment is verifiable, its first-best level satisfies: aF B = 1 + b whereas a = 1 would be privately optimal. For simplicity, there is no discounting. • Non-Verifiable Investment: Let us turn now to the case where the investment a is non-verifiable and must be induced by G through designing adequate incentives. Denote ti (ci ) = αi − β i ci the cost-reimbursement rule used at date i. Let us consider the case where G can commit himself to such a two-period contract. Still assuming a quadratic disutility of maintenance effort in each period, the firm chooses its whole array of actions (a∗ , e∗1 , e∗2 ) to maximize its long-run expected payoff: ! Ã 2 2 2 X a2 (1 + rσ )e i − β 1 + β 2 a. (a∗ , e∗1 , e∗2 ) = arg max αi − β i (θ0 − ei ) − (a,e1 ,e2 ) 2 2 i=1 10 In full generality, we could allow uncertainty on operating costs to be time-dependent. In particular, we might give particular attention to the case σ 2 < σ 1 which means that uncertainty on operating costs may decrease over time (due for instance to learning by doing and better assessments of performances). 11 With respect to Section 3.4, the investment a has no impact on the social value of the assets which remains fixed and equal to b0 . 12 In this respect, our formulation differs from that in Section 2 where the cost of the quality enhancing effort was off the book. 25 This leads to the following incentive constraints: e1 = β 1 , e2 = β 2 , and β 2 = β 1 a. (20) Taking into account that G has all bargaining power in designing the firm’s contract and use accordingly the intertemporal fixed-fee α1 + α2 to extract all ex ante surplus from the firm, and aggregating the two incentive constraints (20) into a single one (21), G’s optimization problem becomes: max 2(b0 − θ0 ) + (a,e1 ,e2 ) ( 2 X i=1 (1 + rσ 2 )e2i ei − 2 ) + (1 + b)a − subject to e2 = ae1 . a2 . 2 (21) A first interesting benchmark is obtained when G offers the stationary contract with SB slope β SB and an investment u . This contract induces a stationary effort e1 = e2 = β u level which is privately optimal but not socially so. There is too little investment with such contract. More generally, raising the investment calls for modifications of the intertemporal pattern of incentives. Result 9 Assuming full commitment to a long-term cost-reimbursement rule; the optimal long-term contract entails higher powered incentives towards the end of the contract than at the beginning and an inefficient investment: SB SB SB < aF B . eSB 1 < eu < e2 , and a The intuition behind this proposition is straightforward. To boost the firm’s incentives to undertake a non-verifiable investment, G must let F bear less of the costs and enjoy most of the benefits associated to that investment. This is best achieved by offering costplus contracts in the earlier periods and fixed-price contracts in the sequel. Still, this is not enough to align the private incentives to invest with the socially optimal ones and underinvestment follows. 26 Remark 1: Learning about operating costs over time could be modelled by allowing the noise on the firm’s maintenance effort to diminish over time. This effect also goes towards having higher powered incentives in later periods of the relationship which boosts incentives to invest. Similarly, in the case of a growing demand, having growing operating costs in the second-period may also call for greater returns on maintenance as time goes on. This non-stationary contract also requires higher powered incentives in later stages which boost investment. Remark 2: If is straightforward to extend the framework above to the case where the firm would enjoy some residual value when owning the asset during the life of the contract. Using the notations of Section 3.4, this would amount to introduce a residual value worth γsa with s 6= 0 (whereas our analysis above has supposed s = 0). Of course, there is still not enough investment because of the hold-up problem ex post. However, private ownership still boosts incentives to invest and is thus complementary to a shift of secondperiod contracts towards fixed-prices. Remark 3: Consider the case where the effect of a new investment depreciates over time. The power of the incentive scheme must decrease over time to optimally trade off incentives and risk insurance. As investments have been made earlier in the past, the firm will rely more on maintenance to keep operational costs low. 4.2 Cost Overruns Contracting takes thus place under major uncertainty on the realization of future costs and demand considerations. In infrastructure projects, contractors are often overly optimistic in estimating future costs, as empirically shown by Flyvbjerg, Skamris Holm and Buhl, (2002). Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, the authors found with overwhelming statistical significance that the cost estimates used to decide whether such projects should be built are highly and systematically misleading. Following cost overuns, long-term contracting may be subject to significant renegotiation in those environments. Firms may obtain a tariff increase or an increase in the number of cost components passed through tariffs, a reduction in their payment to the public sector 27 and delays and reduction in investments. To model those issue, we will simplify the modelling of the previous section, neglect the investment issue or the building stage of contracting and instead focus on the hazard coming from uncertainty on future costs. Costs can now be written as: C = θ̃0 − e + ε where we suppose now that, ex ante, the base cost level θ̃0 is random and may be either high, θ0 = θ̄ with probability 1 − ν or low, θ0 = θ with probability ν (denote ∆θ = θ̄ − θ > 0). Suppose that contracting takes place ex ante before the firm gets any information on the base cost level. However, ex post, this level is privately observed by the firm so that there is ex post asymmetric information. In full generality, an incentive mechanism must induce ex post revelation of the firm’s private information on the cost level. From the Revelation Principle13 a revelation mechanism in such context consists of a pair of contracts {(α(θ̂0 ), β(θ̂0 ))}θ̂0 ∈{θ,θ̄} stipulating a fixed fee α(θ̂0 ) and a share β(θ̂0 ) of the cost borne by the firm as a function of its report θ̂0 on its innate base cost level. Since, for any slope of the incentive scheme β(θ̂0 ), the firm always choose an effort level given by e = β(θ̂0 ), we may define the certainty equivalent of a firm knowing ex post θ0 as: U (θ0 ) = max α(θ̂0 ) − β(θ̂0 )θ0 + θ̂0 (1 − rσ 2 )β 2 (θ̂0 ) . 2 (22) We may follow Laffont and Martimort (2002, Chapter 2) and take the profile {(U(θ̂0 ), β(θ̂0 ))}θ̂0 ∈{θ,θ̄} as the primitives of our problem. This approach has then the merit of showing how ex post asymmetric information affects efficiency even when taking into account the moral hazard problem. Inducing information revelation ex post requires that the following truthtelling constraint be satisfied:14 U(θ) ≥ U(θ̄) + ∆θβ(θ̄). (23) Contracting taking place ex ante, the following ex ante participation constraint must 13 See Laffont and Martimort (2002) for instance. We only write there the relevant upward incentive constraints where the low cost firm wants to exaggerate its innate cost. 14 28 be satisfied: νu(U(θ)) + (1 − ν)u(U (θ̄)) ≥ 0, where the firm’s Von Neuman-Morgenstern utility can be written as u(x) = exp(−rx)) because it has constant risk-aversion. (24) 1 (1 r − Given the social benefit b0 of the project, the government’s problem can now be written as: (P ea ) : max b0 + Eθ̃0 (U(·),b(·)) Ã ! (1 + rσ 2 )β 2 (θ̃0 ) −θ̃0 + β(θ̃0 ) − − U(θ̃0 ) 2 subject to (23) and (24). Clearly, the optimal solution consisting in offering U ∗ (θ̃0 ) = 0 and e∗ (θ̃0 ) = eSB u that would be offered had θ̃0 being contractible ex ante can no longer be implemented because in front of such mechanism, a firm which knows that its base cost level will be efficient has strong incentives to exaggerate those costs. Costs overruns is then an equilibrium phenomenon. To avoid cost overruns, the truthtelling constraint (23) must be binding at the optimum. This requires to create some risk in terms of the certainty equivalent that the firm may get ex post once knowing its innate cost. This increases the risk-premium that society has to borne to induce participation and requires to make the firm’s payoff less sensitive to its knowledge of innate costs. This is obtained by distorting downward e(θ̄), i.e., by choosing for an inefficient firm a contract that looks more like a cost-plus one inducing low powered incentives but insensitive to strategic cost overruns. Of course, reducing the powered of incentives on cost management to avoid strategic cost overruns makes it less valuable to bundle construction and management in an extended multi-task version of the model. This does not mean at all that bundling is no longer optimal. Indeed, the issue of cost overruns also occurs with the more traditional mode of contracting and would shift the power of incentives in cost management exactly in the same direction. We can summarize the analysis as: Result 10 With ex ante uncertainty and ex post asymmetric information on the realization of future costs, strategic cost overruns arise. The optimal menu of incentive contracts 29 calls for less powered incentives to the less efficient firm and incomplete insurance on cost uncertainty:15 SB U SB (θ) > 0 > U SB (θ̄) and eSB (θ) = eSB (θ̄). u > e Remark 4: The optimal contract sketched above is not renegotiation-proof. Indeed, to induce revelation information by the most efficient firm, this contract requires that an inefficient one runs a loss. This creates an incentive for the least efficient firm to stop the ongoing project if costs are too high. Anticipating this outcome, the government may not be able to refrain from instilling more subsidy to ensure that even the worst firms will break even; another instance of the soft budget constraint fallacy. Such renegotiation is thus akin to assuming that the firm is protected by a pair of interim participation constraints ensuring it breaks for each state: U(θ̃) ≥ ∀θ̃. (25) Such constraints harden the trade-off between incentive and participation constraints. It can be easily seen that the firm obtains now a positive expected payoff and the corresponding distortion of his incentive contracts are exacerbated leading to an effort choice given now by: SB e 4.3 1 (θ̄) = 1 + rσ 2 µ 1− ¶ ν ∆θ . 1−ν Related literature and applications Literature : The seminal paper on intertemporal effort allocation in the presence of incentive problems is Lambert (1984), who showed that when the project has time separable, mutually independent returns each subperiod, a risk-averse agent will smooth 15 Observing that both (23) and (24) are binding yields: ¢ ¢ 1 ¡ 1 ¡ U SB (θ) = ∆bSB (θ̄)+ ln 1 − ν + νexp(−r∆θbSB (θ̄)) and U SB (θ̄) = ln 1 − ν + νexp(−r∆θbSB (θ̄)) ; r r and in terms of effort levels eSB (θ) = bSB (θ) = eSB u 1 and eSB (θ̄) = bSB (θ̄) = 1 + rσ 2 30 ¢! ¡ ν∆θ 1 − exp(−r∆θeSB (θ̄)) < eSB 1− u . 1 − ν + νexp(−r∆θeSB (θ̄)) Ã his effort choice to reduce variance in his consumption. This is in contrast to the first-best, where each period’s effort is independent of previous periods’ output. Laffont and Tirole (1993, Chapter 8) proposed an adverse selection model with repeated auctions of incentive contracts which shares many features of our model, most noticeably the shift towards higher powered incentives over time. An incumbent firm invests in period 1 but, because of contract renewal, may lose the benefits of its investment if it is not granted the new contract for date 2. They particularly focused on the necessary bias towards the incumbent as an incentive tool to secure investment and show that this bias matters all the more that investment is not easily transferable. Higher effort over time is also found by Ray (2007) who studied the value of interim performance evaluation and their effect on the intertemporal effort allocation. He built a two-period model in which both periods’ efforts contribute only to the single final outcome and first period effort is useless unless second-period effort occurs. Performance evaluation increases efficiency by providing the option to end projects with low early returns and the agent to work harder in later stages because of the risk of termination. This result holds under a variety of scenarios: when the worker has unknown ability, when the outside options vary with output, and in an agency context with a risk-neutral principal and a risk-averse agent. In non-agency settings different insights are obtained. In line with the career concerns literature, Lewis (1986) shows that reputational concerns lead firms to choose higher effort in earlier stages of their procurement contract in order to send favorable signals to the principal regarding their productivity and avoid that project be terminated too soon. Applications: Empirical evidence on effort allocation in long-term projects shows that effort rises over time. Projects within firms often run beyond deadlines and most resources are increased towards the final stages (see Marshall and Meckling 1962 and Mansfield et al. 1995). Actual costs often significantly exceed cost estimates used to decide whether public projects should be built. As shown by Flyvbjerg et al. (2002) using a sample of 258 transportation infrastructure projects worth US$90 billion, cost overruns arise irrespective of the project type, geographical region and historical period. 31 PPPs are not immune from cost overruns, though no clear evidence exists as to whether cost overurns under PPPs are more or less likely than under traditional procurement. In the UK, with traditionally procured contracts, in 73 per cent of central government’s construction projects the price to the public sector had exceeded the contractors’ tender price and the project ran over budget; actual costs were between 2 and 14 per cent of estimates. The equivalent figure with PFI was 22 per cent although that was due to the private companies bearing the cost increase rather than the cost increase not occurring (NAO, 2003). Examples of cost overruns under PPP also include the disastrous case of Metronet, the private tube contractor for London Underground, whose cost overruns lead it to bankruptcy. Whilst risk allocation in PPPs generally provides for the contractor to bear a significant part of the construction and operational risk, the actual risk allocation may differ from what originally planned. Government are providers of last resort and contractors are aware that Authorities cannot afford prolonged service disruption. The re-tendering of a PPP contact is a long and costly process; also, as the case of London Underground points out, a market for secondary contracts may not always exist. In fact very few PPP contacts have been pre-maturely terminated. The Channel Tunnel Raillink is one example of the government bailing out the PPP contractor. More generally, empirical evidence supports that risk allocation in practice often departs from what laid out in theory (see e.g. Lobina and Hall, 2003). As stressed by The World Bank, whether PPPs “perform better than full provision by state-owned enterprises depends in particular on whether performance risk is effectively shifted from taxpayers to the private shareholders of the company that enters into a concession-type arrangement” (World Bank, 2002: 23-24). In LAC countries, renegotiation of concession contracts is a well known phenomenon. Using a data set of nearly 1000 concessions awarded from 1989 to 2000 in telecommunications, energy, transport and water, Guash, Laffont and Straub (2003) show that the probability of firm-led renegotiation is positively related to the characteristic of the concession contract among other things. Firm led renegotiation on average tended to favor the contractor. 32 5 Uncertainty, Flexibility and the Costs of PPPs PPP agreements are output based in the sense that the public-sector party specifies basic capacity and quality standards (such as heating and lighting levels, quality of cleaning and availability of rooms) but the private-sector party assumes responsibility over how to meet the output specified. PPP agreements also develop along a long-time horizon, typically 25-30 years. Both these features imply that the provisions set in the initial contract are likely to become obsolete during the life of the contract. The need for flexibility and adaptation of the contractual relationship is then far greater than in a more traditional types of procurement where provisions are input based and contracts are short term. When the factors that affect the suitability of the initial contractual clauses are anticipated, they can be regulated by the initial contract (e.g. changes in capacity). Other possible changes, however, may be unexpected and hard to specify in advance. Changes in society preferences, such as desirable standards for educational, clinical and prison services, are typically hard to anticipate. Contract flexibility is then key for PPP agreements in fast-moving sectors like the health sector and the IT sector where preferences and/or technology change fast. When the original output specifications become obsolete, the contractual agreement can be modified by the mutual consent of the parties. Flexibility may then be achievable through well designed "change-mechanism clauses" that regulate the possibility of renegotiation of contract terms. However, contract renegotiation typically occurs in a bilateral ‘lock-in’ situation rather than in the multilateral competitive one as under original contract drafting and awarding. The risk is twofold: the contractor can exploit its now strong bargaining position or the government can expropriate the contractor of its past investment. Thus both when renegotiation occurs and new contract terms are drafted and when the contract is rigid and no change occurs, PPP might deal inefficiently with uncertainty on future demand. This induces a cost of PPPs that we now study. For simplicity we focus on the case where the contract fails to adapt to uncertainty on future demand and renegotiation does not occur. Let us come back on the basic model but assume that the inelastic demand for the 33 services can be written as: C = θ0 − γe − δa + ε where γ is a positive random variable with mean 1, Eγ (γ) = 1 and we assume a positive externality δ > 0. Had γ being common knowledge at the time of contracting, our previous result would go through and bundling design and operation would dominate strictly unbundling. Suppose now that the bundling contract (viewed as a PPP) is offered before γ is realized and cannot be made contingent on that parameter, assuming it is not verifiable at the time of contracting. In other words, G ties his hands with such a contract and loses any flexibility. The realized expected welfare is still given by (19). In particular, when δ is small, we know that WbSB − WuSB is of order δ. Alternatively consider unbundling tasks. Contracting with the operator might be delayed up to the point where γ becomes verifiable. Of course there is no quality-enhancing investment but the operator’s incentive scheme can be tailored to the particular realization of γ. This captures the value of information that comes with unbundling. The optimal effort depends now on γ. Easy computations yield: eR u (γ) = γ 2 < 1. 1 + rσ γ2 (26) The expected welfare becomes: WuR = b0 − θ0 + Eγ µ γ4 2(γ 2 + rσ 2 ) ¶ > WuSB (27) where the last inequality follows from Jensen inequality and WuR differs from WuSB by a term of order zero in δ. This leads us to state: Result 11 Unbundling dominates bundling for small positive externality in the case of uncertainty. The point is that unbundling tasks allows to enjoy the value of information on γ and that it is not possible under bundling. This points at the cost of PPPs in very uncertain environments. A reinterpretation of our framework also suggests that long-term contracts 34 are unsuitable in uncertain environments. Consider the framework of Section 4.1 but now let period-2 cost be given by C2 = θ0 − γe2 − δa + ε2 Assume that γ is realized during period 1 and that a two-period contract covering both periods 1 and 2 cannot be made contingent on the realized γ.It is easy to show that with a low externality (δ low) a one-period contract becomes preferable to a contract covering both periods. This is because when the externality is small, the loss from not internalizing period 2 cost at the time of choosing asset quality a is small and the one-period contract allows to enjoy the value of information on γ whilst the two-period contract cannot. Literature: Bajari and Tadelis (2001) discussed the choice of the procurement con- tract, focusing on fixed price versus cost plus, so as to reduce the cost of design renegotiation. When the firm has private information on the cost of the new design, they show that cost plus contracts are cheaper to renegotiate than fixed price contracts. In this respect, sectors where changes in demand are highly expected may be better procured through cost-plus contracts in spite of fixed-price contracts being preferable for inducing the agent’s cost reducing effort. When put together our results emphasize that the long-term nature of PPP contracts favours incentives for cost reducing effort but has a cost in terms of reduced flexibility. In the economics literature on procurement contracts, this tradeoff was recently examined by Ellman (2006) though his focus was on investment by the government rather than by the firm. He showed that a longer contract length helps to protect the contractor from his investment being expropriated by the government but it reduces the incentives of the government to discover new service innovations since changes are costly to renegotiate. Applications : Our results point out at the unsuitability of PPP for fast-moving sectors. This is in line with empirical evidence. Several recent reports on PPP contracting highlight the cost of changes in user needs that — in the presence of rigid contracts have sometimes triggered very costly renegotiation (see e.g. HM Treasury 2006). In the UK it was reported that changes occurred during negotiations with the contractors for 33% of Central Government Departments PFI projects signed between 2004 and 2006. 35 The changes amounted to a value of over £4m per project per year equivalent to about 17% of the value of the project (NAO, 2007). Illustrative is also the case of specialized IT provision where the appropriate use of the facility involves continuous adaptation. Following performance failure and costly contract renegotiation, the HM Treasury in the UK now recommends against the use of PPPs for IT projects (see HM Treasury 2006). Examples of PPP failure in IT include the £400m Libra project to provide IT systems for magistrates’ courts. Renegotiation by the government of concession contracts in Latin American and Caribbean Countries is also widespread. Considering a compiled data set of more than 1,000 concessions granted during 1985—2000, Guash (2004) showed that 30 percent of the concessions were renegotiated and in 26 percent of the cases, the government initiated the renegotiation. 6 The Role of the Institutional Framework: Regulatory and Political Risks The non-stationary path of incentives described in Result 9 is of course highly dependent of G’s ability to commit to increase subsidies in the second period to reward F ’s initial investment. Assume now that such commitment power is absent and that renegotiation takes place at date 2 with G still having all bargaining power at that stage and extracting, through an adequate fee, all surplus that F could withdraw from renegotiation. When date 2 comes along, F ’s investment a0 is sunk and the second period cost reimbursement rule is renegotiated to reach the optimal trade-off between maintenance effort and insurance that would arise in a static context, i.e., conditionally on the investment level a0 which was previously sunk. This yields the following expression of the second period maintenance effort and slope of the renegotiated incentive scheme: β 02 = e02 = eSB u = 1 . 1 + rσ 2 Under limited commitment, G can still adjust the second-period fixed-fee to extract all surplus of the firm given his expectation over the investment level a0 at this date and, of course, expectations are correct in equilibrium. 36 Anticipating the slope of date 2 incentive scheme, and knowing also the slope of the first-period incentive scheme, F chooses his investment so that eSB u = e1 a. (28) Taking all those facts into account, G’s problem in the first period can be written as: max 2(b0 − θ0 ) + (a,e1 ) 1 (1 + rσ 2 ) 2 a2 + e − e + (1 + b )a − 1 0 1 2(1 + rσ 2 ) 2 2 subject to (28). Clearly, with an opportunistic principal, welfare is lower than with full commitment. Moreover, the second-period contract is lowered powered than under full commitment because the second-period incarnation of G does not take into account the impact of the SB contract he offers on the firm’s incentives to invest at date 1. Since e02 = eSB u < e2 , (28) implies that the firm enjoys less of the benefits of investment. To maintain investment it must be that the firm is even more reimbursed for its first-period costs which moves first-period incentives also towards cost-plus contracts. Result 12 With an opportunistic principal, investment is lower and cost-reimbursement rules are even more tilted towards cost-plus contracts in both periods than under full commitment: e01 < eSB 1 , e02 < eSB and a0 < aSB . 2 Assume now that renegotiation takes place at date 2 only with probability p. This might model settings where the identity of the government may change between dates 1 and 2 with some probability due to elections or where exogenous events occur that induce the current government to renege. In some cases, PPP contract clauses sought to insure the private operator against aggregate risks, but episodes have occurred where governments have reneged on these clauses when a severe macroeconomic crisis occurred. The assumption of limited commitment fits well settings with weak enforcement power which may characterize developing countries. In our setting, when date 2 comes along, F ’s investment a0 is sunk and with probability p the second period cost reimbursement rule is renegotiated to reach the optimal trade-off 37 between maintenance effort and insurance conditionally on the investment level a0 . This yields the following expression of the firm’s incentive constraint which mixes (21) and (28): peSB u + (1 − p)e2 = e1 a. (29) The effort levels in this model with political risk are intermediary between the full commitment and the case of an opportunistic principal viewed above. Result 13 An increase in regulatory risk (i.e., p greater) lowers incentives for investment in asset quality and induces more low powered incentives. Literature : The term “ratchet effect" refers to the possibility that an agent with a high performance today will tomorrow face a demanding incentive scheme. Laffont and Tirole (1993) formalized this effect in a two-period principal-agent model with limited commitment and adverse selection. They showed that the ratchet effect leads to much pooling in the first period as the agent becomes reluctant to convey favorable information early in the relationship. In our model the emphasis is on moral hazard and we show that the ratchet effect induces the agent to invest less in early periods which, in the context of PPP contracts, partially nullifies the benefits of bundling. Aubert and Laffont (2002) analyzed the mechanism through which a government can affect future contracting by distorting regulatory requirements to take into account possible political changes and subsequent contract renegotiation. Assuming that the current contract binds all future governments, imperfect commitment yields two main distortions. First, the initial government will delay the payment of the rent to the second period, thereby free-riding on the cost of producing a higher quantity and leaving higher rents. Second, the degree of information revelation in the first period is strategically determined to affect the beliefs of the new government. A number of political motives have been proposed to explain the interests of the publicsector party itself in reneging PPP contracts. The government may increase its chances to be re-elected by expanding spending or by promoting investment in public works that create jobs and boost economic activity (Guasch, 2004). By reneging, the government may also circumvent the opposition’s scrutiny and reap the political benefits resulting 38 from higher present spending, e.g. a higher probability of being re-elected (Engel, Fisher and Galetovic, 2006). Applications: Institutional quality plays a critical role in the provision of public services by the private sector; Hammami, Ruhashyankiko and Yehoue (2006) indeed find that private participation (in the form of PPP, privatization or traditional procurement) is more prevalent in countries with less corruption and with an effective rule of law. For PPP contracts the benefit of whole-life management cannot be realized in the absence of strong governance and minimal risk of unilateral changes of contract terms by the government. Governments’ failure to honor the terms of concession contracts is a pervasive phenomenon. In Latin America and Caribbean Countries, it is common for a new administration to decide not to honor tariffs increase stated in the concession contract granted by previous administrations. Examples include the Limeira water concession in Brazil which was denied a tariffs adjustment provided by a contract signed by a previous administration. There are also cases where legislation was passed to nullify contractual clauses. The Buenos Aires water concession indexed local-currency denominated tariffs to the US dollar to protect the contractor against currency risk. However, after a devaluation of the local currency, Congress passed an economic emergence law that nullified these guarantees (Lobina and Hall, 2003). Using a sample of 307 water and transport projects in 5 Latin American countries between 1989 and 2000, Guash Laffont and Straub (2006) found that 79% of the total government-led renegotiations occurred after the first election that took place during the life of the project. In many cases the central or local government during a re-election campaign decided in a unilateral fashion to cut tariffs or not to honor agreed tariff increases to secure popular support. Political risk has also played a crucial role in Central and Easter Europe. As reported by Brench, Beckers, Heinrich, and von Hirschhausen, (2005), a major obstacle to the PPP policy in Hungary was the frequent change in political attitudes towards PPPs and user tolls. Since 1990 each change in government resulted in a different attitude and a different institutional framework for PPPs. The impact of regulatory risk in PPPs is significant as it discourages potential investors 39 and raises the cost of capital and the risk premium (bigger tariffs, or smaller transfer price) paid for a PPP contracts. Guasch and Spiller (1999) estimate that the cost of regulatory risk ranges from 2 to 6 percentage points to be added to the cost of capital depending on country and sector. An increase of 5 percentage points in the cost of capital to account for the regulatory risk leads to a reduction of the offered transfer fee or sale price of about 35% or equivalently it requires a compensatory increase in tariffs of about 20%. Regulatory risk also discourages investors; in the £16 billion London Underground project of 2002-03 a high level of political controversy made lenders nervous, with the result that 85% of the debt had to be guaranteed by the public sector at a fairly late stage in the procurement process. Guash Laffont and Straub (2006) show that the role of an experienced and independent regulator (or in general the quality of bureaucracy) is especially important in contexts characterized by weak governance and high likelihood of political expropriation. In LAC countries, regulatory agencies were rarely given training and instruments adequate to their mandate and even lacked political support from the government. The empirical study by Hammami (2006) also provided evidence to support the importance of institutional quality as a larger number of PPP projects are found in countries with less corruption and effective rule of law. To improve governance, a number of countries have created dedicated PPP units centre of expertise - to manage the contract with the private contractor.16 Different approaches have been taken with regard to the governance of these units as some of them have been set up within the public sector (e.g. Central PPP Policy Unit in the Department of Finance 1 in Ireland or the Unita’ Tecnica della Finanza di Progetto in Italy), others outside (Partnership UK in the UK which is a joint venture between the public and private sector with a majority stake held by the private sector). 16 Bennett and Iossa (2006b) use an incomplete-contract approach to compare contract management by a public-sector agency with delegation of contract management to a PPP that is a joint venture between private and public sector agents. They show that delegation may be desirable to curb innovations that reduce the cost of provision but also reduce social benefit. 40 7 Demand Risk A critical aspect of any PPP contract is the allocation of demand risk between the government and the contractor as it is not at all uncommon that lower-than-expected revenues are realized from the provision of the service. The means through which demand risk is allocated is the payment mechanism. Broadly speaking there are three main payment mechanisms, depending on whether the payment is based on (i) user charges, (ii) usage, or on (iii) availability.17 In a payment mechanism solely based on user charges, the contractor receives its revenues directly through charges on the end users of the infrastructure facility and bears all demand risk. Instead, in a payment mechanism based on usage, the government collects user charges and then makes unitary payments to the contractor. The allocation of demand risk depends on the relationship between the payment and the actual usage level. In a payment mechanism based on availability, the government rewards the contractor for making the service available but the payment is independent of the service actual usage; the government retains all demand risk. In many schemes the payment to the contractor comprises a combination of the above payment schemes. So far we have implicitly focused on conventional PPPs, under which the public sector pays the private-sector party for the service that it will provide using the infrastructure. Providers of PPP hospitals, schools and prisons receive their funding in this manner. PPP arrangements however are often characterized by the private sector financing a substantial part, or all of, the project (the “F" in the DBFO model). With financially free-standing projects, the private provider then recoups its initial investment through charges to final users. Here, the public sector involvement is limited to facilitating the project and the PPP is very similar to a concession contract. In this section we briefly study the case of financially free standing projects. To see the factors that affect the optimal allocation of demand risk and the choice of the payment mechanism, assume that the inelastic demand for the service is given by D = a + η, where the random variable η is normally distributed, i.e., η ∼ N (0, σ 2η ), and a is the 17 For a more in depth discussion see Iossa, Spagnolo and Vellez (2007). 41 quality-enhancing effort which increases demand. We denote by P the consumers willingness per unit of the service. By means of a fixedfee (for instance a toll in the case of highways), the firm may extract all the consumer’s surplus which is now worth: B = P (a + η). To simplify the modelling, we assume away any incentive problem on the cost side. Consistently with the PFI practices, we consider a setting where there are no direct subsidies from the government to the firm. The firm must cover its initial investment I from the revenues it withdraws from charging user fees over the length T of the contract. After date T , the PPP comes back under public ownership and the access toll is set at zero. To complete the modelling, assume that uncertainty shocks on the level of demand are drawn independently at each date whereas the cost of effort is sunk and borne once for all at date 0. With these notations in mind, we may rewrite the firm’s discounted stream of certainty-equivalent payoffs when choosing effort a as: a2 rσ 2η − (1 − exp(−ρT ))2 P 2 (1 − exp(−ρT ))P a − 2 2 where ρ is the interest rate in the economy. This immediately leads to the following moral hazard constraint: a = (1 − exp(−ρT ))P. (30) Clearly, the longer the duration of the contract, the higher the firm’s effort since its benefits accrues longer. Also, undertaking the investment is optimal when: a2 rσ 2η (1 − exp(−ρT ))P a − − (1 − exp(−ρT ))2 P 2 ≥ I. 2 2 (31) The social welfare maximizing government is concerned by the social value of the project over its whole life. This gives us the following expression of the government’s problem: (P pf i ) : max P a − (a,T ) a2 rσ 2η − (1 − exp(−ρT ))2 P 2 − I 2 2 42 subject to (30) and (31). The second-best effort level that is obtained when the investment constraint (31) is slack is easily obtained as: a∗ = P = P (1 − exp(−ρT )). 1 + rσ 2η From which, we derive the optimal length of the franchise as: ¸ ∙ 1 1 ∗ T = ln 1 + 2 . ρ rσ η However, with financially free-standing projects the length of the contract is chosen so as to guarantee that the stream of expected revenues coming from user charges is sufficient to cover the firm’s investment as well as the risk premium. When for the pair (a∗ , T ∗ ), (31) does not hold, the length of the contract has to be modified to ensure the firm’s break even. Assuming 1 > rσ 2η , this is obtained with a new duration of the contract given by: (1 − rσ 2η )(1 − exp(−ρT SB ))2 P 2 = I From which, we immediately derive the following implications: Result 14 Franchise lengths are shorter in more uncertain environments, when consumers’ willingness to pay is greater, investment is lower. Literature: Our framework is related to Engel, Fischer and Galetovic (2001) who study optimal contract length in concession contracts, but in their paper there is no moral hazard. Engel, Fischer and Galetovic (2006) study the rationale for private finance in PPPs.18 They showed that private finance cannot be a means to save on distortionary taxation. Any additional $1 invested by the contractor saves society distortionary taxes but the concessionaire must be compensated for the additional investment through a longer contract term and this costs society future distortionary taxes equal to the initial tax saving. Further, when there is substantial exogenous demand risk the optimal contract is characterized by a minimum revenue guarantee and a cap on the firm’s revenues. 18 See also the informal discussion in De Bettignies and Ross (2004). 43 Applications: Our results suggest that when demand is affected by the contractor’s effort, transferring demand risk to the contractor helps incentives. In practice, with financially free-standing PPP projects, the payment mechanism is based on user charges and demand revenue risk lies with the contractor who is then residual claimant for demand changes. With conventional PPP projects, such as hospitals, schools and prisons, the contractor’s effort has little impact on demand levels as government policies determine most of demand changes. The payment mechanism is then based on usage with the government bearing demand risk. In our model, it is immediate that if D is independent of a then it is suboptimal to transfer demand risk to the contractor. The private finance aspect of PPPs has allowed the public sector to finance the construction of infrastructure “off the balance sheet” and to accelerate delivery of projects (see IPPR, 2004). The accounting treatment of this stream of payments can vary and it can often make the government budget look healthier than what it is, thereby undervaluing the cost of PPP financed infrastructure. This not only biases decisions in favour of PPPs as opposed to more traditional procurement arrangements but it can make PPPs a means to unduly transfer costs from current to future generations.19 There is no economic justification for PPPs being promoted for allowing investment off the balance sheet and in order to ensure homogeneity across member states and limit accounting tricks made to comply with the rules of the Stability and Growth Pact, the Eurostat has recently made a decision (news release 18/2004) on the accounting of PPPs, which has the power to clarify and make the process of accounting true PPPs more transparent. However, the temptation to adopt PPPs as a tool to window dress budget deficits has not been fully removed.20 19 See Maskin and Tirole for a study on optimal public accounting rules when the official’s choice among projects is biased by ideology or social ties or because of pandering to special interests. 20 According to the Eurostat’s decision assets involved in a PPP should be classified as non-government assets, and therefore recorded off balance sheet for government, if the private partner bears the construction risk and at least one of either the availability risk or the demand risk. Otherwise, the assets should be classified as government assets. 44 8 Direct Public Finance or Bank Finance? To be written. Intuition, Arrow-Lind suggests that spreading risks among many taxpayers decreases the costs of public finance. But this also introduces a free-riding problem among taxpayers in monitoring projects. Bank finance introduces an extra layer of agency problem but also provides efficient monitoring. 9 Conclusions PPPs have many potential advantages. They provide incentives to the private contractor to take into account the long-term project costs, from building to maintenance to operation, and, through appropriate risk transfer, they improve the likelihood of projects being on time and on budget. Satisfaction of consumer’s needs and high service quality can also be ensured through appropriate demand-risk allocation. The advantages from using PPPs are then greater when the following conditions hold: • A whole-life costing approach to the project is likely to result in significant cost saving; that is, when better quality of the infrastructure can significantly reduce cost at the operational stage (e.g. maintenance cost). • There is scope for innovative solutions to public service delivery. Thus, bringing in the expertise of the private sector has the potential to deliver innovative solutions. • It is either possible to verify the residual value of the facility once the contract expires or the facility has market value in the sense that it has alternative use outside the public sector. • Project risks can be effectively transferred to the private sector in the sense that the threat of project termination is credible and can be used to discipline the contractor. • The legal and political institutions in place ensure contract enforceability and commitment to the contract by the public sector. • The public sector has the expertise (or it can rely on a competitive market for expert knowledge) to be involved in complex contract drafting. 45 • Project risks are manageable and the demand for the service can be forecasted. • The needs of users of the service evolve slowly over time. 46 References [1] Arthur Andersen and Enterprise LSE (2000), Value for Money Drivers in the Private Finance Initiative. 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