Assessing the effectiveness of economic sanctions

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Abstract

The strength of sanctions can significantly impact the outcome of a dispute. The effectiveness of economic sanctions will be explored within the context of the conflict between Organization of Petroleum Exporting Countries (OPEC) and US shale oil producers in 2014. The outcome was not what OPEC anticipated, perhaps because OPEC misperceived the opponent’s preferences. Sensitivity to sanctions is a major component of a decision maker’s preferences when a dispute, or a negotiation, is modeled within the Graph Model for Conflict Resolution (GMCR). This study uses Inverse GMCR to determine what preference rankings would be required for the conflict to end as OPEC wished. The difference between the original preference ranking and the required rankings reflects the miscalculation of the strength of the economic “squeeze” that OPEC imposed when it flooded the market with oil to reduce the price. OPEC expected this sanction to be strong enough to damage, and perhaps destroy, the shale industry, but shale producers were able to withstand it. The graph model analysis suggests why this conflict ended as it did, and provides guidelines for understanding whether sanctions can be effective in forcing a particular outcome on a dispute.

Introduction

Economic sanctions are an essential foreign policy tool of Western countries; they are the withdrawal of customary trade and financial relations to alter the strategic decisions that threaten the sanctioner’s interests. Economic sanctions are often used when diplomacy fails; they are viewed as a low-cost, low-risk course of action compared to aggressive measures such as warfare. In 1990, economic sanctions were imposed on Somalia, Liberia, and Yugoslavia (SCR 2013) to attempt to force desirable behavior from the viewpoint of the sanctioning nation; they have grown to be popular and demonstrated to be effective in some situations. These sanctions come in the form of capital restraints, import and export restrictions, arms embargoes, and travel bans. The United States has used economic sanctions more than any other country; one famous instance was the financial sanctions on countries and organizations supporting terrorists after the 9/11 attacks in 2001: President George W. Bush signed an executive order on September 23, 2001 to freeze the assets of individuals and entities suspected of supporting terrorism. Moreover, President Bush gave the Treasury Department the authority to mark financial institutions as “primary money laundering concerns”. As long as there is a reasonable suspicion, the Treasury can target entities without the need for evidence; these measures increased the risk for the financial institutions engaged in suspicious activities. Violating the sanctions can lead to significant financial and reputational damage. In 2014, the bank BNP Paribas was found guilty of lending billions of dollars to blacklisted entities. The bank paid a fine of almost 9 billion dollars and lost the right to convert any currency into US dollars for a year (SCR 2013).

An important conflict in which sanctions were utilized was the 2014 dispute between the Organization of Petroleum Exporting Countries (OPEC) and shale oil producers in the US. In an attempt to establish dominance over the petroleum market, OPEC flooded the market with oil to decrease the oil price and thereby put pressure on shale oil producers. But the major shale oil producers were not seriously harmed, so the question arises as to why the OPEC sanction did not work as OPEC expected. Accordingly, the objective of this research is to carry out a formal strategic analysis of the situation using the Graph Model for Conflict Resolution (GMCR), to understand what actually happened. Since the production of shale oil was already underway for more than a decade, this conflict is not modeled using an entry deterrence game, but rather strategically using GMCR. Moreover, unlike many other situations in which economic sanctions were utilized, they were strategically successful in this case, as the current paper explains.

In the coming sections, the impact of the economic sanctions levied by OPEC on the US shale oil producers is assessed. After a description of the historical background to this dispute, the GMCR methodology is summarized in Sect. 3, and inverse GMCR is explained in Sect. 4. Subsequently, the OPEC-shale conflict is formally modeled and analyzed in Sects. 5 and 6. Conclusions are drawn in the final section.

Section snippets

Background

Since World War Two, the US has been worried about its energy dependency. In 1973, the price of oil skyrocketed from $19 a barrel up to $50, due to the manipulation of supply and demand by the Arab members of OPEC. In turn, this increased the US concern over the dependency on exported oil, which led to the rise of US shale oil production.

In 2014, oil prices dropped as OPEC and the US shale oil producers battled to gain market share. World oil demand increased between 2010 and 2014, both OPEC

GMCR

In GMCR, the DMs control the movement of a dispute from one state to another, in hopes of improving their situation. GMCR consists of two main stages, Modeling, which is the problem structuring phase, and Analysis, as shown in Fig. 4.

Stage 1: Modeling—In this stage, the user will identify the conflict’s parameters.

  • (a)

    Point of time of the conflict: choosing a point of time is crucial as most disputes are dynamic and the parameters may change as the dispute develops.

  • (b)

    Decision makers: DMs who can

Inverse GMCR

Inverse GMCR (Kinsara et al. 2015a) can be very useful in negotiations. It allows an analyst to find the preferences that will make a particular state an equilibrium. This will enable the analyst to understand what specific actions the DMs must take to reach their desired equilibrium, and whether there is a barrier. Inverse GMCR does not need the preference rankings to be defined for all DMs; its main requirements are as follows:

  • (a)

    Decision makers (DM)

  • (b)

    Options for each DM.

  • (c)

    Infeasible states.

  • (d)

Modeling of the OPEC-shale conflict

The first quarter of 2014 is chosen as the point of time for the model: when OPEC made a decision that shaped the outcome. The two DMs are OPEC and shale oil producers, which are referred to as DM1 and DM2, respectively. It was recognized and built into the model that DM1 was the market leader and price setter, who possessed greater capability, whereas DM2 was a “smaller” player. In this model, DM1 attempts use strategy to avoid any cooperative solution. DM1 has two options: one is to

Analysis and findings

The information gathered in the modeling phase is used as an input for the GMCR + decision support system (Kinsara et al. 2015b). Inverse GMCR, also embedded in GMCR + software, is used to identify the strength of the sanction squeeze by determining which of the two preference rankings was chosen, and, therefore, to recognize whether the sanction was tolerable or intolerable. In the real-world conflict, DM1 squeezed and oil prices crashed, but DM2 did not accommodate, which is state 3. At this

Conclusions

International conflicts are tough to predict. Often one cannot easily identify the most impactful action to alter opponent’s preferences. Added to this, it is often not possible to accurately calculate the effect of a sanction on a given entity promptly, making it even harder for one to choose an action to alter a dispute. Modeling the OPEC-shale conflict using GMCR provided a convenient and accurate analysis of the conflict which mimics the outcome of the real-world dispute. Inverse GMCR was

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