Chapter 3 Infrastructure Privatization and the Marginal Cost of Public Funds It is commonplace to talk of a wave of infrastructure privatization, spreading out across the world from the initial stones cast by Maggie Thatcher. What is a little less appreciated is that some countries have been drenched by the wave, while others have stayed entirely dry. Exploring an efficiency-based motivation for privatization, the paper offers a possible explanation for why the wave has hit some countries and not others. The theory is tested using data from the World Bank’s Private Participation in Infrastructure (PPI) database.1 The PPI database deals with private investment in electricity, water, telecommunications, railroads, toll-roads, and ports, in developing countries. It records money paid to the government for purchase or use of government assets, and money invested by private firms in expanding or modernizing these facilities. The annual sum of money invested around the world rose from US$0.074 billion in 1984, to a peak just before the financial crises in 1997 of US$115 billion, and has more recently declined to a still considerable annual level of US$48 billion in 2002. Infrastructure privatization is a global phenomenon. The PPI database lists 135 developing countries that have had at least one PPI transaction.2 1

http://rru.worldbank.org/PPI/ Among countries included in the World Bank’s World Development Indicators CDROM (World Bank 2001), the only countries not to have privatized at least one infrastructure firm during 1984-2002 were American Samoa, Antigua and Barbuda, Bahrain, Bhutan, Ethiopia, Iraq, Isle of Man, Democratic Republic of Korea, Republic of Korea, Liberia, Libya, Marshall Islands, Mayotte, Federated States of Micronesia, Palau, Puerto 2

69

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But this figure masks tremendous concentration of transactions. Ranked by the number of privatization transactions, the top ten countries account for 60% of transactions.3 Ranked by the amount invested, the top ten countries account for 67% of all PPI in developing countries.4 Brazil alone accounts for almost 20% of total funds invested between 1984 and 2002. In trying to understand why some countries have undertaken considerable infrastructure privatization, while others have not, a useful starting point is the existing literature on general privatization. Bortolotti, Fantini & Siniscalco (2003) have recently examined panel data for privatization around the world, highlighting four groups of determinants of privatization: political ideology, fiscal pressures on a liquidity constrained government, legal origin and stock market liquidity. They find privatization is more likely in wealthy democracies with right wing governments, high debt, liquid stock markets and a legal system that better protects shareholders. They do not consider the possibility that governments privatize depending on whether or not privatization enhances welfare. They start from the premise that privatization enhances efficiency. So if governments do not privatize, it must be for reasons other than efficiency. In fact, theory is ambiguous about whether privatization enhances welfare. Laffont & Tirole (1991) highlight the theoretical ambiguity, listing a number of arguments which suggest privatization could either improve, worsen or make no difference to total welfare. Empirically, however, there is evidence of the superior performance of private firms in competitive industries. Megginson & Netter (2001) survey empirical evidence from 38 studies of privatization. It seems that privatization provides stronger incentives to improve productivity and lower costs, which in a competitive industry translates into higher welfare. So for purposes of Bortolotti et al. (2003), who address privatization across all industries, it is reasonable to suppose that privatization always enhances efficiency. But in natural monopoly infrastructure markets the empirical welfare effect of privatization is less clear. Studies that permit large samples for econometric analysis involve comparison of pre- and post-privatization enterprise performance as measured by accounting data.5 Among these studies, only D’Souza & Megginson (2000) address an infrastructure industry – telecommunications – finding operational and financial improvements in Rico, St. Vincent and the Grenadines, Suriname, Syria, and Turkmenistan. 3 The top ten are: China, Russian Federation, Brazil, Argentina, India, Mexico, Chile, Thailand, Malaysia, and Colombia. 4 In descending order of total investment, the countries are: Brazil, Argentina, China, Mexico, Malaysia, Chile, Philippines, India, Indonesia and Thailand. 5 Table 4, Megginson & Netter (2001)

71 firm performance. For skeptics of infrastructure privatization, however, such performance improvements could be accompanied by welfare-reducing exploitation of market power. Another group of studies compares post-privatization changes with a comparison group of firms or a counterfactual.6 Many of these studies address telecommunications. When the effects of telecommunications competition are distinguished from those of privatization, there is not unanimity that privatization makes a difference to welfare. In any case, telecommunications is arguably becoming a normal competitive industry, without implications for the effect of privatizing a natural monopoly in other infrastructure industries. There remain some careful case studies, notably Galal, Jones, Tandon & Vogelsang (1994) and Newbery & Pollitt (1997), that find net welfare gains in other infrastructure privatizations.7 But again, the determined skeptic could argue that a handful of case studies hardly proves the thesis that privatization invariably improves welfare. Even if all empirical studies of infrastructure privatization showed unambiguous improvements in welfare, this would not prove that all governments should privatize all their infrastructure services. Such evidence would be entirely consistent with a theory of privatization in which governments are motivated by efficiency, and privatize only when they expect to increase welfare. It thus seems warranted to enquire more closely into the determinants of infrastructure privatization, focusing on an efficiency motivation. An important recent contribution in this domain is the paper of Auriol & Picard (2002). They find that whether privatization improves welfare depends on the shadow cost of public funds and on the extent to which the privatization transaction transfers expected profit of the privatized firm to the government. If the transaction fully transfers expected profits through a franchise fee, then privatization improves welfare for high values of the shadow cost of public funds and reduces welfare for low values of the shadow cost of public funds. When the government does not fully capture expected profit, privatization improves welfare for intermediate values of the shadow cost of public funds, but public ownership is preferable for high or low values. The paper thus offers a potential explanation for privatization. Governments privatize when the shadow cost of public funds changes, moving from a situation in which public ownership is optimal to one where privatization is optimal. An important qualification on the paper of Auriol & Picard (2002) is 6

Table 3, Megginson & Netter (2001) A further paper, Chisari, Estache & Romero (1999), was not considered by Megginson & Netter (2001). The study uses a computable general equilibrium model of Argentina, to find net welfare gains from privatization. 7

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that it supposes that privatization involves a shift from a regulated public monopoly to an unregulated private monopoly. The trade-off in their model is between information rents of a regulated public firm, and market power rents of an unregulated private firm. They argue that it is difficult to establish credible regulatory agencies in developing countries, so that privatization is effectively a move to laissez-faire, including the deregulation of prices. This view of infrastructure privatization in developing countries seems too extreme. It is very rare, if not impossible, to find an infrastructure privatization that is not subject to regulatory control. Governments are highly conscious of the political impact of increases in prices of water, electricity, telecommunications, and rail and road tolls. This political sensitivity makes investors wary of infrastructure privatization. They know that once investments are sunk, governments have short term political incentives to renege on price commitments, lowering regulated prices and profitability. In Latin America, where there has been the most progress in privatizing retail distribution systems that interact directly with consumers, some of the regulatory agencies are among the most sophisticated in the world. The African country leading in terms of the number of deals and total investment, South Africa, has similarly established effective regulatory agencies. In Asia, infrastructure privatization has more frequently involved wholesale purchases, such as independent power plants.8 Here a typical model is an auction of the right to sell power to the government purchaser, with the winner being the firm that promises the lowest price. The final terms and conditions are written into a binding contract, frequently subject to international arbitration in the event of a dispute. In transactions such as these, the absence or weakness of an explicit regulatory agency does not mean that the private firm has freedom to set price and quality. It seems more appropriate to model privatized firms as being subject to regulation. But if both public and private firms are regulated, an efficiencybased theory of privatization must find an alternative explanation for differences in performance. An answer can be found in the literature on soft budget constraints. When social surplus exceeds profit, it can be optimal for a benevolent 8

In East Asia and the Pacific, 58% of PPI transactions have been greenfield developments, of which electricity deals have amounted to 46%. The most popular business model for these transactions has been the independent power plant: see Gray & Schuster (1998). Among non-greenfield projects, toll-roads have been the most frequent PPI transactions (38% of non-greenfield projects). Both of these types of transactions typically involve a regulatory framework implemented in terms of an explicit contract with performance incentives, and with little scope for intervention of specialist regulatory agencies. Source: PPI database.

73 government to support a loss-making firm, to avoid closure of the firm. When the possibility of such bail-outs exists, the firm’s budget constraint is ‘soft’. The manager knows that if the firm makes a loss the government will provide a subsidy. Segal (1998) illustrates how this possibility can create incentives for a firm’s manager to increase costs, in order to obtain such support. He further shows how this problem is likely to be more extreme in monopoly industries than competitive industries. For purposes of this paper, Schmidt (1996) provides the most relevant analysis of the soft budget constraint affecting a regulated firm. Schmidt argues that the government is better informed about the firm’s costs and profits if it owns and directly controls the firm than if the firm is privatized. He considers the right to detailed cost and profit information to be a residual right of control, which cannot be contractually allocated. To make the point clear for modelling purposes, Schmidt supposes that when a firm is in public ownership the government has perfect information about the firm’s costs, but knows only the potential distribution of the firm’s costs when it is privatized. Schmidt further supposes that the manager receives a higher benefit from a higher level of output, and can make a personally costly investment to reduce costs. Accordingly, the government wishes to cut back production when costs are high, in order to punish the under-performing manager. But if realized costs are high, in public ownership with perfect information about costs, the government has an ex post incentive to set prices at the efficient level, where marginal benefit equals the inflated marginal cost. Thus, with public ownership it is not credible for the government to threaten to cut back production to punish the manager. Accordingly the manager has an incentive to under-invest in cost reduction. In contrast, with asymmetric information and private ownership, the optimal regulatory scheme involves cutting production below the ex post efficient level when high costs are realized. Thus, privatization acts as a commitment device by the government not to acquire detailed cost information, and provides a stronger incentive to the firm’s manager to invest in cost reduction. To summarize, Schmidt (1996) provides a theory suggesting that public regulated firms have higher costs than private regulated firms. The theory of soft budget constraints can be criticized on the grounds that governments bail out private firms as well as public firms. This is especially likely to occur for essential public services, which are judged to be too socially important to be allowed to fail. Arguably, however, bail-outs are less likely to occur with private firms. The argument of Schmidt (1996) suggests that the severity of the problem could be related to the degree to which government information about the firm’s costs differs under public and private ownership. Even if the theory of soft budget constraints is not

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the explanation, privatization appears empirically effective in reducing costs, notwithstanding the unclear evidence concerning total welfare. The model presented in section 2 uses the demand and technology assumptions and the notation of Auriol & Picard (2002), with modifications to embrace the setting of Schmidt (1996). In particular, the inefficiency of public ownership arises from the soft budget constraint problem, while the inefficiency of private ownership is the ex post inefficient production level that arises with a regulated firm under information asymmetry. Section 3 explores theoretically how the relative efficiency of the two ownership regimes differs for different values of the shadow cost of public funds, and suggests that a decrease in the shadow cost of public funds may trigger privatization. Section 4 tests this theory using the PPI database for developing countries, applying the methodology of Bortolotti et al. (2003). Section 5 concludes.

3.1

The Model

It is assumed that the government privatizes if privatization increases expected welfare. The game’s timing is: 1. The government constructs an infrastructure facility that has fixed cost sufficiently large that no other firm can survive in the market. The fixed cost is sunk and thus plays no further role. 2. Nature reveals to all parties the values of all parameters except the value of β, the firm’s type. 3. The government decides whether or not to privatize. If the firm is privatized, a private investor pays a lump sum transaction fee, F , to the government to purchase the regulated firm; and the firm is transferred to the investor. 4. The actual value of β is realized. If the firm is public the value of β is revealed to the firm and the government. If the firm is private, the value of β is revealed to the firm only. 5. The government announces the regulatory mechanism, fixing the quantity, Q, and the transfer, t, to the regulated firm as a function of the firm’s type, using the actual type for a public firm, and the reported type for a private firm.9 9

When regulating the private firm under information asymmetry, the ‘Revelation Principle’ ensures that there is no loss of generality in restricting the government to use ‘truthful

3.1. THE MODEL

75

6. Production occurs. Decisions are made in periods 3, 5 and 6. In period 6, inverse demand is given by P (Q) = a − bQ (3.1) where P is the price, Q is the quantity, and a > 0 and b > 0 are parameters. The implied gross consumer surplus is: b S(Q) = aQ − Q2 . 2

(3.2)

The firm uses linear pricing, giving firm revenue: R(Q) = P (Q)Q

(3.3)

Whether the firm is public or private it incurs fixed cost K in period 6. The firm’s marginal cost is dependent on the firm’s type, β > 0, which ¯ with probability g(β), where g(·) has a is drawn from the support [β, β] cumulative density function G(β). Expectations over β are denoted by E[·] = R β¯ (·)g(β)dβ. β If the firm is private, its cost function is given by: C = βQ + K

(3.4)

Two technical assumptions ensure no corner solutions, and no ‘bunching’ in the period 5 regulatory decision under asymmetric information. a ≥ β¯ +

¯ G(β) ¯ g(β)

G(β) is non decreasing g(β)

(3.5) (3.6)

When the firm is publicly owned, the firm incurs costs in period 6 equal to: C = (β + x(λ))Q + K

(3.7)

The addition of the marginal cost penalty, x(λ), due to public ownership should be thought of as a reduced form representation of Schmidt’s (1996) analysis. That is, although x is exogenous here, there is an endogenous direct’ regulatory mechanisms. Mechanisms are ‘direct’ when the firm tells the government its type. Mechanisms are ‘truthful’ when the firm has no incentive to lie about its type. See Laffont & Martimort (2002).

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explanation for why costs are higher under public ownership than private ownership. The shadow cost of public funds, λ > 0, measures the deadweight loss induced when taxation revenue is increased marginally. A dollar of tax revenue has a welfare cost of (1 + λ) dollars. It is assumed that x is a decreasing function of λ. Segal (1998) observes that as the cost of public funds becomes very high, bail-outs of underperforming public firms become prohibitively expensive. Knowing that subsidies will not be forthcoming, managers do not have an incentive to underinvest. Thus x tends to zero as the cost of public funds tends to infinity:10 x > 0;

dx lim < 0; x=0 λ→∞ dλ

(3.8)

A technical assumption ensures no corner solutions under public ownership: a ≥ β¯ + x(0)

(3.9)

In each period the firm seeks to maximize profit, which in period 6 is given by its information rent: π = R(Q) − C(Q; β) + t

(3.10)

where t is the net transfer received from the government. In period 5, the government chooses a regulatory scheme to maximize welfare. The control variables are quantity, Q, and the transfer, t. The objective function is net social welfare, being gross consumer surplus less the cost of production and the social cost of any transfer to the firm: W = S(Q) − C(Q; β) − λt

(3.11)

Transfers may be positive (subsidy) or negative (taxation) in either public ownership or privatized settings.11 10

The important feature is that for high (low) values of λ the marginal cost penalty of public ownership is smaller (greater) than the information rent under regulated private monopoly. The results are qualitatively unchanged if, for example, the cost penalty is modelled as interacting multiplicatively with β, C = K + (1 + x(λ))βQ, or if x is modelled as a constant. In the latter case an additional assumption is required that x not be too large (x < E[G/g]), to ensure that a public firm is more efficient than a private firm for at least some values of λ. This is necessary to explain why public firms are observed in the first place. 11 The possibility of positive or negative transfers to a regulated firm is a staple of the theory of regulation under asymmetric information. It might, however, seem at odds with

3.1. THE MODEL

77

If the firm is public, the government maximizes welfare subject to the firm’s participation constraint, π ≥ 0. Since the government has full information, the public firm can be constrained to have zero rents in period 6. If the firm is private, the government regulates under asymmetric information and incentive compatibility constraints must be added to the government’s period 5 problem. The regulated private firm earns information rents which are strictly positive in expectation. In period 3, the private investor is assumed to be risk neutral and so pays a transaction fee equal to the information rents expected in period 6: F = E[π].12 The transaction fee is sunk in subsequent periods and so affects neither the firm’s participation constraint nor the government’s objective function in period 5. In making the privatization decision at period 3, the government compares expected welfare under privatization and continued public ownership. If the government privatizes, it must take account of the transaction fee, F . Total welfare is reduced by F when the investor pays the fee, but is increased by (1 + λ)F when the government spends the fee on public services. The net effect, λF , is added to the period 6 measure of ‘ownership welfare’ to give a measure of period 4 ‘total welfare’ under privatization: ˜T = W ˜ + λE[π] W

(3.12)

The government’s assessment at time 3 of expected ‘total welfare’ under continued public ownership is simply: WT = W

(3.13)

The difference in the government’s objective functions in periods 3 and 5 arises because of the game’s timing: F is sunk at time 5. Higher welfare the real world that governments would subsidize a privatized firm. It could be argued that the hardening of a privatized firm’s budget constraint occurs because it is politically unacceptable to transfer public funds to a private firm that would be loss-making without the transfer. As the Schmidt (1996) paper demonstrates, ‘hardening’ of the budget constraint can occur without such a political economy explanation. Perhaps more significantly, the different forms of private participation create various opportunities to make transfers to the private firm. For example, a lease of infrastructure facilities leaves the government responsible for large fixed cost investments, while making the private operator responsible for the operating costs. And of course tax concessions are not unknown in infrastructure privatizations. 12 As the game is structured, F is necessarily positive. There is no change to the results of the paper if the firm incurs an additional fixed cost in period 4. In this case, F is equal to the expected period 6 information rents less the period 4 fixed cost, and could be positive or negative. This change embraces the possibility of a ‘negative concession’, where an infrastructure firm is franchised to the bidder who demands the lowest subsidy.

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could be achieved if the timing of the game were different, so that there were consistent objectives at the time of privatization and the time of regulation. In particular, it would seem possible for the government to write its regulatory decision into the privatization contract at time 3. With no asymmetry of information at time 3, the privatization contract would yield first-best regulation in expected terms. Since the privatized firm does not suffer from the soft budget constraint problem, privatization would always be preferable to public ownership, and the model could not explain why public firms are observed. But it seems unrealistic to deny asymmetric information in regulatory decisions concerning long-lived infrastructure. Initial privatization contracts may indeed contain regulatory provisions but, empirically, government regulatory decisions evolve subsequently in conditions of asymmetric information. Further, with the amended timing, the firm would have zero expected profit, so the investor would pay a zero transaction fee. This would be inconsistent with frequently observed positive transaction fees. The timing presented at the beginning of this section seems more closely to represent the observed timing of privatization and regulatory decisions.

3.2

A Theory of Infrastructure Privatization

Privatization can be viewed as a public finance instrument. Privatization is efficient if the marginal welfare cost of privatization revenue is less than the marginal welfare cost of other sources of funds, λ. That is, privatization may increase total welfare (period 3) even if it reduces ownership welfare (period 5), provided that it raises sufficient government revenue.13 It is thus interesting to explore the determinants of ownership welfare and government revenue, before investigating the determinants of total welfare. The privatization decision is made on the basis of total welfare.

3.2.1

Comparing Public and Private Ownership

The first step is to compare welfare expected in period 5 under private and public ownership. When the firm is publicly owned the government observes β and chooses 13

The tension between ownership efficiency and government revenue is often observed within teams responsible for organizing privatization transactions. ‘Reformers’ would like to increase efficiency; government budget officers and advisers working for a transactionbased premium would happily sell an unregulated private monopoly if it would raise greater revenue.

3.2. A THEORY OF INFRASTRUCTURE PRIVATIZATION

79

Q and t to solve: max W subject to π ≥ 0

(3.14)

Solving this problem, expected welfare is given by: i h  1+λ V2 −K W (λ) = E (1 + λ) 1 + 2λ 2b

(3.15)

where V is defined by: V =a−β−x

(3.16)

When the firm is privatized, the government cannot observe the firm’s marginal cost. Incentive compatibility constraints are added to problem (3.14). The resulting expected welfare is given by:14 i h  ˜2 ˜ (λ) = E (1 + λ) 1 + λ V − K W 1 + 2λ 2b

(3.17)

where V˜ is defined by: V˜ = a − β −

λ G(β) 1 + λ g(β)

(3.18)

The increase in welfare from a change of ownership can thus be defined as: ∆W =

(1 + λ)2 E[V˜ 2 − V 2 ] 2b(1 + 2λ)

(3.19)

The relative efficiency of public and private ownership depends on the shadow cost of public funds. Proposition 1 summarizes the circumstances under which private ownership yields higher period 5 expected welfare. The proofs of all propositions are in the Appendices. Proposition 1 There exists a critical value of the shadow cost of public funds, λ1 > 0 such that ∆W > 0 if and only if λ < λ1 . For low values of λ private ownership yields higher expected welfare than public ownership, while the situation is reversed for high values of λ. The welfare cost of information asymmetry under private ownership increases with λ. For sufficiently high values of λ this effect outweighs the marginal cost disadvantage of public ownership. 14

The methodology for deriving this result is well known. See, e.g., Laffont & Martimort (2002).

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Comparing (3.16) with (3.18), the critical value λ1 satisfies: G λ1 E[ ] (3.20) x= 1 + λ1 g The critical value is independent of the demand parameters, a and b, and the fixed cost, K, but increases as β decreases.15 Thus, shifts in demand do not alter the balance between public and private ownership, but technological progress that lowers marginal cost, β, does. In particular as β falls, it becomes more likely that the actual shadow cost of public funds is less than the critical value, and that private ownership is preferable to public ownership. A re-interpretation of (3.20) also reveals something of the magnitude of the soft budget constraint problem. For example, if β is uniformly distributed β−β and λ = 31 then private ownership is preferable if x ≥ 14 2 . Thus, if β can vary by ±10% around its mean value, private ownership is preferable if the soft budget constraint inflates average marginal cost by 2.5% or more.

3.2.2

The Change in Government Revenue

The next step is to discover when privatization increases government revenue expected at time 3. The expected government revenue raised by a public firm is the negative of the transfer to the firm:16 λ (1 + λ) 2 V − K] (3.21) b (1 + 2λ)2 The government revenue generated by a privately owned firm is the transaction fee F = E[π], less the expected transfer to the firm, which in this case includes an element of information rent: t = π −R(·)+C(·). The information rents are netted out, yielding expected government revenue:17 # " λ 1 G λ(1 + λ) ˜ − C(·) ˜ − K] = E V˜ 2 + V˜ − K (3.22) T˜ = E[R(·) b(1 + 2λ)2 (1 + 2λ) b g T = E[−t] = E[R(·) − C(·)] = E[

Comparison of equations (3.21) and (3.22) yields the expected increase in government revenue resulting from privatization: " ( )# λ 1 + λ G ∆T ≡ T˜ − T = E (V˜ 2 − V 2 ) + V˜ (3.23) b(1 + 2λ) 1 + 2λ g 15

Totally differentiating (3.20) yields



dλ1 dβ = dx dλ < 0.

.  λ 1 dx − 1+λ E[ d(G/g) ] E[G/g]− dβ (1+λ)2 dλ ≤

0 since by assumption d(G/g) ≥ 0 and dβ 1+λ a−ω 16 Public firm revenue and cost can be calculated using Q = 1+2λ with ω = β + x. b 17 ˜ = 1+λ a−˜ω with ω Private firm revenue and cost can be calculated using Q ˜ =β+ 1+2λ b λ G 1+λ g .

3.2. A THEORY OF INFRASTRUCTURE PRIVATIZATION

81

Using this result, Proposition 2 summarizes the conditions under which privatization increases expected government revenue. Proposition 2 There exists a critical value λ2 > λ1 such that ∆T > 0 if and only if λ < λ2 . Privatization may not always reduce the government’s budget deficit. For high values of the shadow cost of public funds (λ > λ2 ) privatization increases the deficit, while for low values (λ < λ2 ) privatization reduces the deficit. An implication of Proposition 2 is that whenever private ownership is more efficient than public ownership (λ < λ1 ), privatization increases expected government revenue.

3.2.3

The Privatization Decision

The privatization decision in period 3 is based on a comparison of expected welfare under public ownership or after privatization, taking account of the expected transaction fee. Using expressions (3.12) and (3.13), the increase in total welfare from privatization expected in period 3 is: ∆WT = ∆W + λE[π] =

1 λ ˜G (1 + λ)2 E[ (V˜ 2 − V 2 ) + V ] b(1 + 2λ) 2 (1 + λ) g

(3.24)

Privatization increases total welfare if λ ≤ λ1 , since it increases ownership welfare and government revenue. Privatization decreases total welfare if λ ≥ λ2 , since it lowers ownership welfare and government revenue. The interesting ˆ 2 , where privatization increases government revenue case is when λ1 ≤ λ ≤ λ but reduces ownership welfare. The conditions under which the government decides to privatize are summarized in Proposition 3. Proposition 3 There exists a critical value λ3 in (λ1 , λ2 ) such that ∆WT > 0 if and only if λ < λ3 . The government decides to privatize only if the shadow cost of public funds is sufficiently small (λ < λ3 ). Since λ3 < λ2 , expected government revenue increases whenever it is efficient to privatize. Proposition 3 yields a possible explanation for privatization. Privatization may be triggered if the shadow cost of public funds falls, shifting from greater than λ3 to less than λ3 . Not all falls in λ trigger privatization, only those that move beyond the critical value. Nevertheless, the critical value is determined by demand and technology parameters, so a reduction in a country’s shadow cost of public funds may trigger privatization of some industries but not others.

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A government may also decide to privatize when λ stays unchanged, but shifts in demand or technology increase the critical value λ3 . It can be shown that λ3 increases in response to an increase in a or a decrease in β.18 Privatization becomes more likely as a country becomes richer (higher a) or adopts new technology (lower β). The theory helps to understand the difficulty for empirical studies to demonstrate that privatization improves total welfare,19 while there has been stronger evidence of improvements in indicators of firm performance. The switching point between the two ownership regimes occurs when expected welfare under each regime is equal. The firm’s marginal cost should fall after privatization because the public firm’s cost disadvantage is eliminated. Profitability should rise after privatization because a regulated private firm earns information rents that a public firm does not. The theory may also help to understand difficulties with public acceptance of privatization. At time 5, a marginal privatization occurring when λ = λ3 reduces ownership efficiency: λ > λ1 ⇒ ∆W < 0. If the public does not take account of the additional public services purchased with the (sunk) transaction fee, privatization may be perceived as lowering welfare.

3.3

Empirical Determinants of Privatization

The theory predicts that reductions in the marginal cost of public funds can induce privatization. To examine the relevance of this prediction, the empirical determinants of infrastructure privatization are examined using probit regressions with panel data from the PPI database. The data cover 155 developing countries as defined by the World Bank (2001), for the years 1984-1998. The variables used are described in Appendices A.4 and A.5. The results are presented in Table 3.1. The dependent variable, P rivit , is set equal to one if country i privatizes at least one infrastructure firm in year t, and zero otherwise. The transactions include management contracts, leases, concessions and divestitures. Greenfield projects (i.e., entirely new infrastructure facilities) are excluded since they do not represent a transition of a facility from public to private operation. The mean value of P rivit indicates that infrastructure privatizations occurred in 8.8% of the country-year observations, from 1984 to 1998. Forty From the proof of Proposition 3, the critical value λ3 satisfies E[a − β]x − 12 x2 − dλ3 3 = 0. Totally differentiating this expression yields dλ da > 0 and dβ < 0. Data availability, and assessing the counterfactual, are probably the greatest difficulties in assessing welfare changes after privatization. 18

λ3 G 2 1 2 2 ( 1+λ3 ) E[( g ) ] 19

3.3. EMPIRICAL DETERMINANTS OF PRIVATIZATION

83

percent of the countries (62 countries) did not privatize any infrastructure during 1984-1998. An ideal empirical examination of the theory would compare data on changes in the cost of public funds with the incidence of privatization. Unfortunately very few estimates of the marginal cost of public funds in developing countries have been made, certainly nothing like the necessary panel data corresponding to the PPI database. Moreover, there seems to be no suitable proxy for the level of the shadow cost of public funds. The shadow cost of public funds is the result of a complex general equilibrium interaction between multiple tax instruments, and cannot be simply computed from widely observed macroeconomic indicators such as summary taxation statistics.20 But the variable of interest here is the effect of shifts in the shadow cost of public funds. For this we do, arguably, have two potential proxies. The first possibly proxy for a decrease in λ is a decrease in public debt. When a government’s debt level increases, for example because foreign denominated debt increases with a currency devaluation, the government must meet increased repayments by borrowing more, possibly at a higher interest rate, or by increasing taxation, or a combination of the two measures. Although the linkage deserves a thorough theoretical and empirical examination, it seems probable that a decrease in the level of public debt leads to a decrease in the marginal cost of public funds.21 Since it takes time for 20

Some authors have suggested that λ is inversely related to the level of development. This is unclear on several grounds. First, for a given level of revenue, a rich country with a more efficient tax system will have a lower cost of public funds than a poor country. But for a given tax system, λ is an increasing function of revenue, and rich countries tax a higher proportion of GDP than poor countries. Second, some poor countries may have some highly inefficient individual tax instruments, with high marginal costs of public funds (MCF). But the existence of great inefficiency in one sector often means that increasing an existing tax in another sector will help to restore relative prices, reducing the inefficiency in the first sector, and resulting in a low MCF for the second tax instrument. Inefficient tax systems may thus exhibit wider variance of MCFs of individual tax instruments, but have similar revenue-weighted average MCFs to those of more efficient systems. Third, empirical MCF estimates for developing countries do not seem very different from those in rich countries (see Chapter 2 and Devarajan, Suthiwart-Narueput & Thierfelder (2001)). Finally, some think of λ as the opportunity cost of public funds, the marginal benefit of public spending that must be foregone in order to finance a new project. If all governments had the same concave welfare function, the marginal benefits of expenditure would be greater in countries with low levels of expenditure, that is, poor countries. But inefficiency in public expenditure in poor countries is likely to reduce such marginal benefits significantly. For example, Reinikka & Svensson (2002) present evidence from Kenya, where only 20% of public education funds reached the intended destination. 21 This suggestion should not be taken to imply that countries with higher debt necessarily have higher values of λ. The level of λ is determined by a country’s tax system, but for a given tax system an increase in debt is likely to increase the government’s revenue

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an decrease in debt to have an effect on the tax system and the shadow cost of public funds, and it takes time for privatization transactions to be implemented, lagged values of changes in public debt are used. The second possible proxy for a shift in the shadow cost of public funds is the introduction of a VAT. VATs have been widely introduced around the world in recent decades, usually replacing less efficient systems of cascading sales taxes (see Ebrill, Keen, Bodin & Summers (2002)). The introduction of a VAT is thus likely to reduce the shadow cost of public funds. Again, because it takes time for a VAT to be settled into place,22 and because privatization transactions take time, lagged values of the year of introduction of a VAT are used. Column (1) of Table 3.1 considers the effects of lagged values of these two explanatory variables on the probability of privatization. Column (1) is the outcome of an iterative variable selection process. Initially, values of the change in debt, lagged from two to six years prior to the privatization, were regressed against ‘Priv’, giving significant results only for ∆Debtt−5 . A regression with values for the introduction of a VAT gave significant results for all lags from two to six years prior to privatization. Combining ∆Debtt−5 with the lagged VAT values, the only significant coefficients were those used in the regression of Column (1). Column (1) provides some support for the hypothesis that a reduction in the cost of public funds may induce infrastructure privatization. The five to six year lag can be understood noting that it takes time for shocks to the cost of public funds to filter through to implementation of privatization transactions. The existence of this lag suggests that the introduction of a VAT is not simply an indicator of a reforming government. While reforming governments might desire both privatization and VATs, there is no particular reason to suppose that they would on average wait for five to six years after the introduction of a VAT to implement their privatization program. To test the robustness of the results in Column (1) a range of additional control variables were added to the regression. Variables that were not significant at the 10% level were not included in the regression reported in Column (2). These additional variables are GNP per capita, the growth rate of GDP, an index of the extent of regulatory intervention in the economy as a whole, and an index measuring respect for the rule of law. For the latter two measures, higher scores reflect better governance. The first point to note is that the inclusion of these additional variables requirement, and thus to increase λ. 22 For example, in the first year or so many businesses entitled to VAT refunds do not claim them, so initial VAT revenues may be higher than in subsequent years.

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Table 3.1: Probit Equations for the Probability of Privatization (1) (2) (3) Constant -1.32 -1.97 -2.30 (0.10) (0.15) (0.25) ∆Debtt−5 -0.69* (0.32) VATt−5 0.64* 0.72 (0.27) (0.23) VATt−6 0.88 1.10 1.20 (0.26) (0.24) (0.35) GNP per capita 0.22 0.34 (0.04) (0.08) Growth 0.03 0.04 (0.01) (0.02) Regulation 0.72 1.16 (0.19) (0.39) Rule of Law -0.50 -1.52 (0.19) (0.36) Capitalisation 1.82 (0.39) Turnover 0.76 (0.19) No. Obs. 1397 1917 634 No. Countries 125 120 62 Log likelihood -519 -606 -293 Standard errors in parentheses. All coefficients are significant at the 1% level, except those marked with an asterisk, which are significant at 5%.

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renders the coefficient on ∆Debtt−5 statistically insignificant. A possible explanation for why changes in debt explain privatization less well then the introduction of a VAT is that debt levels fluctuate, so that a reduction in debt is not a guarantee of lower λ in the future. In contrast, there have been very few instances of the repeal of a VAT, so a VAT’s effect on λ is likely to be permanent.23 Turning attention to the newly included variables, the results suggest that richer countries and faster growing countries are more likely to privatize infrastructure.24 These results could be accommodated in terms of the theoretical model. As noted in the previous section, if richer countries have higher a, privatization is more likely. Growth could be correlated with adoption of new technologies, lowering β and rendering privatization more likely. But too much should not be made of these interpretations. The assumed demand function is not well adapted for analysis of income effects, and it is not clear that new technology is diffused more quickly to faster growing countries. Moreover there are probably some transmission mechanisms that are not readily embraced by the theoretical model. For example, a country’s wealth and growth may affect risk-averse investors’ demand for privatization transactions. The theoretical model addresses only the supply of privatizations. Considerations of investor demand for privatizations led to the inclusion of measures of country risk. Column (2) suggests that infrastructure privatization is more likely in countries with more market-friendly regulation (e.g., fewer price controls, adequate bank supervision, and lower perceptions of excessive regulation in foreign trade and business development). Surprisingly the results also suggest that privatization is more likely in countries with less respect for the rule of law (the index aggregates measures such as perceptions of the incidence of crime, the effectiveness and predictability of the judiciary, and the enforceability of contracts). It is difficult to explain this latter result. A number of other variables were tested for Column (2), but found not to be statistically significant. A surprising result was that the level of debt, measured as a proportion of GDP was not a significant determinant of privatization. Bortolotti et al. (2003) suggest that general privatizations are more likely with higher levels of debt. But when attention is confined to infrastructure privatizations the level of debt is not a significant determinant, indeed Column (1) suggests that privatization is more likely after a decrease 23

In April 2001, 123 countries had VATs. Only five countries have ever removed a VAT (Belize, Grenada, Ghana, Malta and Vietnam), and the last three subsequently reintroduced their VATs: Ebrill et al. (2002). 24 It is interesting that Bortolotti et al. (2003) do not find growth to be a significant determinant of general privatizations.

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in debt. This difference of outcomes confirms that infrastructure monopolies cannot be analyzed in the same way as competitive industries. Privatization is often seen as being ideologically driven, an analysis that is supported by Bortolotti et al. (2003). But using measures of government ideology (whether a government was left-wing, right-wing, centrist or nondemocratic) we found no significant role for ideology in the decision to privatize infrastructure. The hypothesis that privatization is associated with new governments, able to use their political capital to push through unpopular reforms, was tested using a dummy variable for an election year, as well as one- and twoyear lags of the dummy. No significant effects were found. Additional variables measuring other aspects of country risk and governance were also tested. These included: ‘Voice and Accountability’, an index of various aspects of the political process, civil liberties and political rights; ‘Political Stability and Absence of Violence’, an index measuring perceptions of the likelihood that the government will be overthrown by violence; ‘Government Effectiveness’ a measure of the quality of public service provision, the competence of civil servants and the credibility of governments’ commitments to policies; and ‘Control of Corruption’, measuring perceptions of corruption. These data were drawn from Kaufmann, Kraay & Mastruzzi (2004), for the year 1996. No significant effects were found on the probability of privatization. In Column (3) two additional variables suggested by Bortolotti et al. (2003) are included. These were not included in the testing for Column (2) because their inclusion significantly reduces the sample size. The variables are measures of stock market liquidity drawn from Beck, Demirgüç-Kunt & Levine (1999). Stock market liquidity reduces constraints on raising large amounts of capital necessary for infrastructure investment, and also favours the participation of local investors, which may enhance the sustainability of the privatisation. The variables used are the ratio of stock market capitalisation to GDP and the ratio of stock market total value traded to total market capitalisation. These two variables were found to have the expected positive effects on the likelihood of privatization. Their inclusion also renders the coefficient on VATt−5 statistically insignificant. Nevertheless VATt−6 remains statistically significant. That is, controlling for the country’s wealth, growth rate, aspects of country risk and stock market liquidity, the results suggest that the introduction of a VAT significantly increases the probability of infrastructure privatization six years later.

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Conclusion

The theoretical section of the paper suggests that a reduction in the shadow cost of public funds may induce infrastructure privatization. When the cost of raising public funds through taxation is high, the government prefers to keep the firm in the public sector as a relatively cheap (in welfare terms) source of revenue. When the cost of raising public funds through taxation is low, the information rents earned by a privatized firm are cheap in terms of welfare cost, and the government prefers to privatize to take advantage of the private sector’s stronger incentives to lower operating costs. The empirical section lends support to this hypothesis, finding that the introduction of a VAT (a proxy for a reduction in the shadow cost of public funds) significantly increases the probability of infrastructure privatization. The empirical support for the theory suggests that infrastructure privatization is not always welfare enhancing and that governments are more likely to privatize when conditions occur under which privatization does increase welfare.25 Even if welfare maximization were not the government’s motivation, it seems likely that privatization would be politically easier to implement when it enhanced welfare (at least in theory permitting losers to be compensated) than when it reduces overall welfare. One possible motivation for privatization that was not examined in the empirical section is donor conditionality. The literature on the effectiveness of aid conditionality suggests that international institutions and bilateral donors are unlikely to be successful in pushing for privatization when the government does not want to privatize. Ghosh Banerjee & Rondinelli (2003) support this conclusion in the case of general privatizations, although they do not separately consider infrastructure privatizations. Nevertheless, foreign aid has presumably low welfare cost for a recipient government,26 so additional aid could lower the government’s marginal cost of public funds, and thereby induce privatization. The causation envisaged is via the cost of public funds, rather than via donor conditionality. Among the assumptions used to obtain the theoretical results, one in particular could be examined in more detail. The model supposes that the government extracts the full expected value of the firm from a risk neutral 25

Another interpretation of the empirical evidence arises if the theory is simply accepted as correct. The data then support the proposition that the introduction of VATs has lowered the marginal cost of public funds in many developing countries. 26 But even grant funds have a welfare cost, because administering them consumes considerable amounts of bureaucrats’ time. Given the extreme shortage of skilled civil servants in many developing countries, this administration time represents reform opportunities forgone.

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investor. Under this assumption, the model of Auriol & Picard (2002) suggests that privatization improves welfare for high values of the shadow cost of public funds (the opposite result from this paper). But Auriol & Picard (2002) go further and allow for the government to extract varying proportions of the firm’s expected value. This change introduces a range of low values of λ for which privatization enhances welfare. Thus, the proportion of value captured by the government plays an important role in a model very similar to that of the present paper. This idea receives further support in the empirical results, where factors which could affect investors’ perceptions of risk were found to play a role in the likelihood of privatization. Other extensions could also be envisaged. The paper has stressed the role of uncertainty over marginal cost, but for infrastructure firms uncertainty over the size of post-privatization capital investments is probably at least as important. Foreign ownership of the privatized firm means that some of the welfare benefits of privatization leak out of the country when the transaction fee is less than the realized profit. Empirically, it would be interesting to test the theory with a direct measure of the shadow cost of public funds. In this, as in other fields of public policy assessment, there is a need for more information about the shadow cost of public funds in different countries, and how it shifts across time.

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Appendix A.1

Proof of Proposition 1

Private ownership yields higher welfare if ∆W > 0, or equivalently, if V˜ 2 − V 2 > 0. When λ = 0, V˜ 2 − V 2 > 0. When λ → ∞, V˜ 2 − V 2 → E[(a − β − G(β) 2 ) ]−E[(a−β)2 ] 0 if and only if λ < λ1 .

A.2

Proof of Proposition 2

(1+λ) ˜ 2 Define X ≡ (1+2λ) (V − V 2 ) + Gg V˜ . Then ∆T > 0 ⇔ E[X(λ)] > 0. For λ ≤ λ1 , V˜ 2 − V 2 ≥ 0 ⇒ X(λ) > 0. As λ → ∞, X → − 12 ( Gg )2 < 0. By continuity, there exists an odd number of values of λ such that E[X(λ)] = 0. Denote one such value λ2 . Define Y ≡ (1 + 2λ)(1 + λ)E[X]. Then, E[X] > 0 ⇔ Y > 0. Now, Y = 


2  E (1+λ)2 (a−β)2 −V 2 − 2(a−β)λ−(1+2λ) (1+λ) Gg −2λ2 Gg . Define Z as expression Y with the variable V replaced by the constant a−β −x(λ2 ), that is, by V evaluated at λ2 . Z is quadratic in λ, implying there are at most two values of λ > 0 for which Z = 0. Since there is an odd number of roots, λ2 is the unique value for which Z = 0. Since V (λ) is increasing in λ, if λ < λ2 then Y > Z; if λ > λ2 then Y < Z. Since Z > 0 if and only if λ < λ2 it follows that Y > 0 if and only if λ < λ2 ; and thus ∆T > 0 if and only if λ < λ2 .

A.3

Proof of Proposition 3

λ G˜ Define φ ≡ 21 (V˜ 2 − V 2 ) + 1+λ V . Then ∆WT > 0 ⇔ E[φ] > 0. When g λ ≤ λ1 , (V˜ 2 − V 2 ) ≥ 0, so φ > 0. Using the proof of Proposition 2, when (1+λ) ˜ 2 (V − V 2 ) + Gg V˜ ≤ 0. It follows that when λ ≥ λ2 , φ < 0. λ ≥ λ2 , X = (1+2λ)

91

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λ 2 G 2 ) ( g ) . Thus, Expanding the expression, φ = (a − β)x − 12 x2 − 12 ( 1+λ dφ dx λ G 2 = (a − β − x) dλ − (1+λ)3 ( g ) < 0. Since φ is monotone decreasing, there dλ is a unique value λ3 in (λ1 , λ2 ) for which φ = 0.

A.4

Variable Descriptions

Privit

Debtit

∆Debtit VATit GNP per capitait Growthit Rightit

Leftit Centerit Election Yearit Capitalisationit Turnoverit Accountabilityi Stabilityi Effectivenessi

Regulationi Rule of Lawi Corruptioni

Dummy variable equal to 1 if country i privatizes at least one infrastructure firm in year t, 0 otherwise. All transactions except greenfield investments are considered. Data for 1984-1998 Public debt divided by GDP at current market prices in US dollars. Data for 1980-1998 to allow for lags corresponding to the first privatization observations The change in Debtit between years t − 1 and t, positive for an increase in debt Dummy variable equal to 1 if a VAT was introduced in year t 1,000 current USD, Atlas method, 1984-1998 GDP growth, annual percent, 1984-1998 Dummy variable equal to 1 if the government is classified as right-wing in the Politics database, 0 otherwise. Governments are classified as right, left, center, or non-democratic. 1984-1998. As for ‘Right’. As for ‘Right’. Dummy variable equal to 1 if an election took place in year t. Ratio of stock market capitalisation to GDP Ratio of stock market total value traded to total market capitalisation Index of aspects of civil liberties and political rights. Index of political stability and absence of violence Index of government effectiveness, including quality of service provision, quality of the bureaucracy and the credibility of governments’ commitments Index measuring market-unfriendly policies such as price controls and perceptions of excessive regulation Index combining the incidence of crime, predictability of the judiciary and the enforceability of contracts Index of perceptions of corruption

A.5. DESCRIPTIVE STATISTICS AND SOURCES

A.5

93

Descriptive Statistics and Sources

Variable Mean S.D. N Source Privit 0.088 0.286 2945 PPI Debtit 0.586 0.655 2189 WDI ∆Debtit 0.024 0.209 2054 WDI VATit 0.024 0.154 2945 VAT GNP per capitait 1.661 1.873 2266 WDI Growthit 2.598 6.656 2441 WDI Rightit 0.172 0.377 2610 Politics Leftit 0.315 0.464 2610 Politics Centerit 0.041 0.198 2610 Politics Election Yearit 0.140 0.347 2610 Politics Capitalisationit 0.059 0.167 711 Finance Turnoverit 0.282 0.443 662 Finance Accountabilityi -0.276 0.811 2850 Governance Stabilityi -0.235 0.836 2451 Governance Effectivenessi -0.390 0.538 2736 Governance Regulationi -0.313 0.726 2774 Governance Rule of Lawi -0.364 0.632 2185 Governance Corruptioni -0.390 0.587 2185 Governance Sources: PPI–PPI database; WDI–World Bank (2001); VAT–Ebrill et al. (2002); Politics–Beck, Clarke, Groff, Keefer & Walsh (2001); Finance–Beck et al. (1999); Governance–Kaufmann et al. (2004). Indices in the Governance data set are constructed with mean 0 and standard deviation 1. Higher values correspond to improvements in governance. The means and standard deviations reported here differ from 0 and 1 because only those countries for which PPI data were available were selected. The Governance data are for 1996.

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