Are Price Caps on Russian Oil Exports Effective?

Cardoso, Salant, and Daubanes (2025) ask whether price caps on Russian oil exports reduce Russia’s profits once it can evade sanctions by expanding a “shadow fleet.” They use a calibrated dynamic simulation model based on global oil market data and observed export patterns. They find that sanctions reduce Russia’s profits by over 20%, but tighter caps can paradoxically increase long-run profits by raising global oil prices and accelerating fleet expansion. For example, a service ban initially lowers profits but raises them within one quarter. Overall, a $60 cap reduces profits slightly more than stricter policies while avoiding large price spikes.

Why This Article Was Selected for The Policy Scientist

This article addresses a policy domain of substantial global importance: the effectiveness of economic sanctions as tools of statecraft in constraining revenue flows from strategic commodities. Oil market intervention, especially price caps, operate at the intersection of geopolitics, energy security, and macroeconomic stability, making their design consequential not only for Russia but for global price formation and future sanction regimes. The topic is particularly timely given ongoing adjustments to sanctions and discussions of extending similar mechanisms to other countries. Cardoso, Salant, and Daubanes contribute to a growing literature in this area, advancing prior work by explicitly modeling evasion dynamics through shadow fleet expansion.

The analysis relies on calibrated simulation rather than causal inference or experimental design, which limits empirical validation of the results. While the data inputs appear grounded in credible sources, the findings depend heavily on structural assumptions. Generalizability is plausible to other sanctioned oil exporters, though institutional differences may matter. Future research incorporating quasi-experimental or causal designs would strengthen inference.

Full Citation and Link to Article

Cardoso, D. S., Salant, S. W., & Daubanes, J. X. (forthcoming). The dynamics of evasion: The price cap on Russian oil exports and the amassing of the shadow fleet. American Economic Journal: Economic Policy. https://www.aeaweb.org/articles?id=10.1257/pol.20250007 

Central Research Question

The article investigates whether Western-imposed price caps on Russian oil exports effectively reduce Russia’s oil profits once endogenous evasion is taken into account. Specifically, Cardoso, Salant, and Daubanes (forthcoming) examine how Russia’s ability to expand a “shadow fleet” of tankers—operating outside Western services—alters the short- and long-run effectiveness of sanctions. The central question is not simply whether sanctions reduce revenues, but how dynamic adjustments in evasion capacity and global oil prices interact to determine the present value of Russia’s profits. The analysis further explores whether tightening sanctions—either by lowering the cap or imposing a full service ban—necessarily strengthens their intended effect, or whether such policies can generate counterintuitive outcomes through market feedback mechanisms.

Previous Literature

The study builds on a rapidly emerging literature examining the economic effects of sanctions on Russian oil exports following the invasion of Ukraine. Early contributions, including work by Johnson, Rachel, and Wolfram, emphasize the potential for price caps to reduce Russian revenues while stabilizing global oil markets. Other studies, such as Wachtmeister, Gars, and Spiro, analyze static effects of caps under assumptions of limited or no evasion, generally concluding that tighter caps reduce profits. Turner and Sappington introduce strategic interaction through a Cournot framework, while empirical contributions using transaction-level data document discounts on Russian oil and shifts in trade flows toward non-coalition countries.

However, much of this literature treats evasion capacity—particularly the shadow fleet—as fixed or exogenous. Cardoso, Salant, and Daubanes extend this body of work by modeling the dynamic expansion of evasion capacity as an endogenous investment decision. In doing so, they shift the analytical focus from static comparative statics to intertemporal optimization, emphasizing how policy-induced incentives shape long-run outcomes. Their contribution lies in demonstrating that the effectiveness of sanctions depends critically on how quickly and extensively the sanctioned country can adapt its export infrastructure.

Data

The analysis relies on a calibrated simulation model rather than direct econometric estimation. The authors assemble parameter values from multiple empirical sources to approximate real-world conditions in global oil markets. These include International Energy Agency data on pre-invasion export volumes and global supply, World Bank and industry estimates of oil demand elasticity, and observed price differentials between Russian and benchmark crude (e.g., Brent–Urals spreads). Initial conditions—such as Russia’s export volumes to coalition and non-coalition countries—are anchored in 2021 data, prior to the imposition of sanctions.

The model incorporates estimates of production costs, investment costs associated with expanding the shadow fleet, and a fixed per-barrel discount reflecting the higher costs of evasion (including longer shipping routes and insurance premia). Calibration targets include observed growth in shadow fleet capacity during the first two years of sanctions. While the dataset is not a conventional panel or micro-level dataset, it synthesizes credible macro-level inputs to construct a structurally coherent representation of the oil market.

Methods

The authors develop a dynamic equilibrium model in which Russia maximizes the present value of its oil export profits over a finite horizon. In each period, exports occur through two channels: capped sales using Western services and uncapped sales via the shadow fleet, subject to capacity constraints. The model incorporates endogenous investment in fleet capacity, with convex adjustment costs, allowing Russia to expand its evasion capabilities over time.

Global oil prices are determined by an inverse demand function, with Russia treated as a price taker. The model assumes inelastic global demand and fixed non-Russian supply in the baseline specification, isolating the role of Russian behavior and sanction design. The authors solve the model numerically and simulate outcomes under alternative policy scenarios, including different cap levels, a full service ban, and varying enforcement intensity.

The methodological approach is structural and simulation-based rather than econometric. It emphasizes theoretical consistency and calibration to observed data rather than causal identification. While this allows for detailed exploration of dynamic mechanisms, it also implies that results depend on maintained assumptions about functional forms and parameter values.

Findings/Size Effects

The simulations yield several substantive and, in some cases, counterintuitive findings. First, sanctions—whether implemented as a price cap or a service ban—substantially reduce the present value of Russia’s oil profits relative to a no-policy baseline, with reductions exceeding approximately 20–25%. This confirms that sanctions are broadly effective in constraining revenue.

However, the model shows that tighter sanctions do not necessarily produce larger reductions in profits. Lowering the price cap accelerates the expansion of the shadow fleet, enabling Russia to shift more exports outside the capped regime. At the same time, reduced supply through capped channels raises global oil prices. These effects can offset or even outweigh the intended impact of stricter sanctions.

For example, a full service ban initially reduces quarterly profits more than a $60 price cap (e.g., roughly $5.5 billion versus $8.9 billion in the first quarter). Yet within one quarter, profits under the ban exceed those under the cap due to faster fleet expansion and higher world prices. Over the full simulation horizon, the present value of profits under the $60 cap is slightly lower—by roughly 2%—than under the service ban.

Similarly, lowering the cap below $60 can increase long-run profits by inducing faster evasion and higher prices. The model identifies a cap level near $69 as minimizing Russia’s discounted profits under baseline assumptions. These results highlight the importance of dynamic responses and market equilibrium effects in shaping policy outcomes.

The analysis also shows that policies targeting the shadow fleet directly—such as increasing the cost of expansion or reducing fleet capacity—can have complex effects depending on timing and market conditions. In some scenarios, reductions in fleet capacity raise global prices sufficiently to increase Russia’s overall profits.

Conclusion

The article demonstrates that the effectiveness of sanctions on Russian oil exports depends critically on dynamic adaptation and market responses. While price caps and related measures reduce profits in aggregate, their marginal effectiveness is shaped by the interaction between evasion capacity and global price adjustments. Policies that appear more stringent in a static sense may generate weaker—or even counterproductive—outcomes when these dynamics are considered.

The study contributes to the literature by endogenizing evasion and emphasizing intertemporal trade-offs, offering a more nuanced framework for evaluating sanctions. At the same time, the reliance on calibrated simulation limits causal inference, as results are sensitive to assumptions about demand elasticity, cost structures, and enforcement. Future research incorporating quasi-experimental variation or natural experiments could strengthen empirical validation.

Despite these limitations, the findings have broader implications for sanction design in commodity markets. They suggest that policymakers must account for substitution, adaptation, and price feedback effects when evaluating the likely impact of economic restrictions.