Financing Infrastructure in the Shadow of Expropriation: Acharya, Parlatore & Sundaresan (2025)

What this is. The core results, model equations, and propositions from this theory paper on optimal infrastructure financing under double moral hazard: enough to understand what the contract looks like and why, without reading all 50 pages. To replicate or extend it, read the full source at the original.

TL;DR

The paper develops a principal-agent model of public-private partnerships (PPPs) in which two moral hazard problems interact: a private-sector operator shirks if not given sufficient incentives, and a government expropriates project cash flows if its incentive to do so outweighs the cost. This double moral hazard simultaneously limits the feasibility (extensive margin) and scale (intensive margin) of infrastructure projects, explaining persistent infrastructure gaps globally. The second-best optimal financing contract combines (a) government guarantees to financiers against project failure to discipline the government’s expropriation incentive, (b) direct government coinvestment when the project return is sufficiently high, (c) development rights to private parties, and (d) tax subsidies. These features match institutional arrangements common in practice (TIFIA, UK Infrastructure Bank, Hong Kong MTR, etc.). India evidence shows 72% of stressed coal-power-plant failures are attributable to public moral hazard.

The paper relates to three literature strands. On government expropriation and growth, Myers (1997) establishes the debt overhang idea that motivated sovereign debt dynamics; here it applies to private infrastructure. On infrastructure financing specifically, Perotti (1995) shows credible privatization as a commitment device, and Martimort and Sand-Zantman (2006) characterize optimal delegated management contracts; neither features double moral hazard. On double moral hazard contracting under agency problems, Repullo and Suarez (1998) study venture capital, and Bolton and Dewatripont (2004) is a standard text reference. India evidence is consistent with Lewis and Bajari (2014) (contractor moral hazard in Minnesota highway procurement) and Gardner and Henry (2023) (government moral hazard limiting infrastructure investment globally).

Core results

Magnitudes and statements are as reported in propositions and tables; locators point to the source PDF.

#	Result	Locator	Magnitude
R1	Double moral hazard limits feasibility and scale jointly; neither moral hazard alone causes both problems	Props. 1-3, pp. 1382-1385; Table 1, p. 1385	Three feasibility thresholds: (i) if $(R-A_o) < \underline{\Gamma}$, project unfunded absent government MH; (ii) if $\underline{\Gamma} \leq (R-A_o) < \overline{\Gamma}$, project unfunded due to government MH; (iii) otherwise funded but at limited scale when $(R-A_o) < \Gamma^*$
R2	Optimal financing contract requires government guarantees $K_g > 0$ when $(R-A_o) \leq \Gamma^*$; guarantees decrease in project return net of operator agency rent	Prop. 4, pp. 1386-1387; Figure 4, p. 1388	$K_g^* = A_g + \underline{R} - (R-A_o)$ for $\overline{\Gamma} \leq (R-A_o) < \Gamma_R$; decreasing in $(R-A_o)$, positive throughout the region
R3	Pecking order: guarantees precede coinvestment; government coinvestment $I_g > 0$ only when project return is sufficiently high relative to moral hazard severity	Prop. 4, pp. 1386-1387; Figure 4, p. 1388	$I_g^* = 0$ for $(R-A_o) < \Gamma_I$; $I_g^* > 0$ and rising for $\Gamma_I \leq (R-A_o) < \Gamma^*$; maximal $I_g^* = \bar{K}_0$ at $\Gamma_R$
R4	Higher government benefit from expropriating ($C$) requires larger guarantees and a lower coupon; expropriation risk is worse in high-$C$ (developing-economy) settings	Prop. 5, p. 1389	$\partial K_g^*/\partial C > 0$ and $\partial R_f^*/\partial C \leq 0$; developing economies need higher guarantees but their fiscal limits make these harder to provide
R5	Government fiscal resources increase scale but not feasibility; resources available at the investment stage (date 0) weakly dominate those at the cash flow stage (date 1)	Props. 6-7, pp. 1389-1390	$\partial I^*/\partial \bar{K}_0 \geq \partial I^*/\partial \bar{K}_1 \geq 0$; feasibility thresholds $\overline{\Gamma}$ and $\underline{\Gamma}$ independent of $\bar{K}_0, \bar{K}_1$ (Prop. 6a: $\partial\overline{\Gamma}/\partial\bar{K}_0 = \partial\overline{\Gamma}/\partial\bar{K}_1 = 0$)
R6	Development rights and tax subsidies improve feasibility and scale; it is always optimal to set the tax-sharing rate $\tau = 1$ (fully share tax revenue with private sector)	Prop. 10-11, pp. 1395-1397	Development rights $D$ and externalities $X$ raise $\overline{\Gamma}$ and $I^*$; distribution of rights to operator vs. financiers depends on which constraint is binding
R7	India evidence confirms double moral hazard: 72% of stressed thermal power plant failures 2007-2011 due to public (government) moral hazard, 22% private, 6% unclassified	Table 5, p. 1400; Tables 2-3, p. 1398	34 stressed plants; 1,165 NHAI cases (40% of 2,912 highway disputes); government as petitioner in 123 of 139 arbitration-related cases

Overall (paper’s conclusion). The double moral hazard problem jointly limits the feasibility and scale of privately financed infrastructure, explaining global infrastructure gaps. The second-best optimal contract features government guarantees, coinvestment, development rights, and tax subsidies in a pecking order determined by project return relative to agency rents. These features are prevalent in practice. Third-party multilateral guarantees do not substitute for government guarantees because they do not discipline the government’s expropriation incentive; building fiscal capacity and governance is more effective for closing infrastructure gaps in developing economies.

Theory / model

The model has three players: (a) the government, (b) a private operator who builds and manages the project, and (c) private financiers. An infrastructure project has constant returns to scale up to a maximum scale $\bar{I}$; total investment $I = I_f + I_g$ where $I_f$ is from financiers and $I_g$ from the government. The per-unit payoff is $R > 1$ if successful and zero otherwise. The model runs over four stages (Section 1, p. 1375, Figure 2 p. 1376): an investment stage (financial contract set), a gestation stage (operator appointed, operational contract set), an operating stage (operator exerts effort), and a cash flow stage (payoffs distributed).

Operator’s moral hazard. Following Holmstrom and Tirole (1998), the operator exerts high or low effort (p. 1376). High effort yields success probability $p_h \in (0,1)$, low effort $p_l < p_h$. Let $\Delta p \equiv p_h - p_l$. Low effort gives the operator a private nonpecuniary benefit $BI$ as in Jensen and Meckling (1976). If the project succeeds the operator receives $R_o I$; if it fails the operator receives zero. The operator’s incentive compatibility constraint (ICO) is (p. 1379):

$$p_h R_o I \geq p_l R_o I + BI \tag{ICO}$$

Rearranging gives the familiar Holmstrom-Tirole (1998) condition: the operator requires an agency rent of at least

$$R_o \geq A_o \equiv \frac{B}{\Delta p}$$

Government’s moral hazard. The government can expropriate the operator’s cash flows by setting user fees below the contractual level (p. 1376). If the government expropriates, it receives a net benefit $CI$ (with $C > 0$ in economies with weak institutions, $C < 0$ otherwise). The government will agree not to expropriate $A_o$ if its incentive compatibility constraint (ICG) holds (p. 1380):

$$p_h R_g - (1-p_h)K_g \geq p_l (R - R_f + C) - (1-p_l)K_g \tag{ICG}$$

Rewriting:

$$K_g + R_g \geq A_g \equiv \frac{p_l(A_o + C)}{\Delta p}$$

where $A_g$ is the government’s agency rent. Since $C > -A_o$ and $p_l > 0$, Assumption 1c ($C > -B/\Delta p$) ensures $A_g > 0$: double moral hazard is present whenever $B > 0$.

Key structural insight (Section 2.2.3, p. 1383). The government agency rent $A_g$ is increasing in the operator agency rent $A_o$: the two moral hazards are intertwined. A higher $A_o$ means the operator needs more cash flows in the success state, leaving less for the government and sharpening the government’s expropriation incentive.

Payoff structure (Figure 3, p. 1378). If the project succeeds (probability $p$): financiers receive $R_f I$, operator $R_o I$, government $R_g I \equiv (R - R_f - R_o) I$. If it fails (probability $1-p$): financiers receive guarantee $K_g I$, operator zero, government $-K_g I$. The government has fiscal resources $\bar{K}_0$ at date 0 and $\bar{K}_1$ at date 1.

Method

This is a pure theory paper; the method is constrained optimization (mechanism design). The planner maximizes the net present value of the infrastructure project subject to incentive compatibility constraints for the operator (ICO) and the government (ICG), individual rationality constraints for the financiers (IRF) and the government (IRG), and no-default (fiscal) constraints on government guarantees (NDK) and promised returns (NDR). The full program is (Section 2.3, p. 1384):

$$\max_{I_g \in [0,\bar{K}_0],\, I_f \geq 0,\, K_g \geq 0,\, R_f \geq \underline{R}} (p_h R - r)(I_g + I_f)$$

subject to:

$$(1-p_h)K_g + p_h R_f \geq r \frac{I_f}{I_f + I_g} \tag{IRF}$$ $$(1-p_h)K_g + p_h R_f + r \frac{I_g}{I_f + I_g} \leq p_h(R - A_o) \tag{IRG}$$ $$K_g + (R - A_o - R_f) \geq A_g \tag{ICG}$$ $$K_g(I_f + I_g) \leq \bar{K}_1 + \bar{K}_0 - I_g \tag{NDK}$$ $$R_f(I_f + I_g) \leq (R - A_o)(I_f + I_g) + \bar{K}_1 + \bar{K}_0 - I_g \tag{NDR}$$ $$I_g + I_f \leq \bar{I} \tag{MS}$$

The total project scale is determined by the government guarantee via the NDK constraint:

$$I = \min\!\left\{ \frac{\bar{K}_1 + \bar{K}_0 - I_g}{K_g},\; \bar{I} \right\}$$

The paper proves eleven propositions by characterizing the optimal contract in three cases depending on which constraints bind (Appendix A.2, pp. 1406-1408): (Case 1) ICG binds and IRF is slack; (Case 2) ICG and IRF bind simultaneously; (Case 3) project is at maximal scale. The full optimal contract formulas are given in equations (A1)-(A4) (pp. 1408-1409).

Extensions in Section 3 solve the program with: (i) costly state verification microfounding $\underline{R}$ (Section 3.1); (ii) government’s limited commitment to financiers with default penalty $\Phi$ (Section 3.2, Prop. 8); (iii) random contract enforcement (probability $\delta$ of enforcement, Section 3.3); (iv) private-party coinvestment by the operator (Section 3.4, Prop. 9); and (v) development rights $D$ and government externalities $X$ (Section 3.5, Props. 10-11). The extension with limited commitment and externalities solves the program (Appendix B, p. 1410):

$$\max_{I_g \geq 0,\, I_f \geq 0,\, K_g \geq 0,\, R_f \geq \underline{R},\, D_f \in [0,D]} (p_h(R+X+D) - r)(I_g + I_f)$$

subject to modified IRF, IRG, ICG, NDK-LC, NDR-LC, and MS constraints, yielding a maximum feasible project scale:

$$\bar{I} = \frac{r\bar{K}_0 + (1-p_h)\min\!\{\Phi, \bar{K}_1\} + p_h\Phi}{(r - p_h D)} \tag{Eq. 1}$$

Empirical specifications

This paper has no econometric specifications. The empirical content is a structured case study of two Indian infrastructure sectors serving as illustration of double moral hazard.

Highway contracting in India (Section 4.1.1, pp. 1397-1399). Data from Mehta and Thomas (2022) on 2,912 highway contractual disputes 2007-2020 involving the National Highway Authority of India (NHAI). Tables 2-3 (p. 1398) classify disputes by project lifecycle phase and litigation driver. Key finding: 66% of disputes arise in the postaward/construction phase (consistent with operating-stage moral hazard); NHAI is the petitioner in 123 of 139 arbitration-related cases and the defendant in 75 of 90 payment-related cases, consistent with government moral hazard in payments.

Stressed thermal power plants in India (Section 4.1.2, pp. 1398-1400). Hand-collected data on 34 stressed coal-fueled power plants initiated 2007-2011, sourced from monthly Broad Status Reports of the Central Electricity Authority. Table 4 (p. 1399) shows 94% of failures occur in the postaward/construction phase. Table 5 (p. 1400) classifies each plant’s cause of failure into private moral hazard (22%) and public moral hazard (72%), with 6% unclassified. No regression or econometric test is performed; the classification is based on qualitative case-by-case analysis (Appendix B1).

Datasets used

Dataset	Role in paper	Wiki page
NHAI litigation data (Mehta and Thomas 2022)	Illustrative evidence of double moral hazard in highway contracting	No page yet
Central Electricity Authority Broad Status Reports (hand-collected)	34 stressed coal-fueled power plants; cause-of-failure classification	No page yet

The paper introduces the 34-plant hand-collected dataset for the Indian power sector stress episode (2007-2015). No quantitative empirical estimation is performed on either dataset.

When to read the full paper

Read the original if you are: designing or evaluating government guarantee programs for infrastructure; studying PPP contract design in settings with weak institutions; extending the model to stochastic government expropriation or multilateral guarantors; or seeking the formal proofs of Propositions 1-11 and the full characterization of the optimal contract (Appendix A-B, pp. 1404-1411).

Attribution and rights

Source: peer-reviewed, The Review of Financial Studies 38(5), 2025. Published by Oxford University Press on behalf of the Society for Financial Studies. All rights reserved; paywalled. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. Extract-only; no PDF hosted.

Acharya, Viral V., Cecilia Parlatore, and Suresh Sundaresan. “Financing Infrastructure in the Shadow of Expropriation.” The Review of Financial Studies 38, no. 5 (2025): 1368-1418. DOI: 10.1093/rfs/hhaf007. © The Author(s) 2025. Published by Oxford University Press.