Selected Oil Price Benchmarks and Sustainable Revenue Profile of OPEC Member Countries: A Symmetric and Asymmetric Analyses

Abstract

This paper examines the impact of different oil price benchmarks on the revenue profile of OPEC countries from 1990 to 2024. While there are prior studies on this subject, most of these studies adopted a symmetrical approach, overlooking the asymmetric effects of price shocks on government revenues. Additionally, prior research often aggregates oil price benchmarks, simplifying the complex dynamics influencing OPEC revenues. This study fills these gaps by evaluating both symmetrical and asymmetrical responses of the revenue profiles of OPEC countries to major global oil price benchmarks—NYMEX WTI, ICE Brent, DME Oman, and the OPEC reference basket. The study employs linear and nonlinear panel (ARDL) models on annual data from 1990 to 2024. The linear ARDL results indicate that government revenues respond positively to ICE Brent, NYMEX WTI and OPEC spot prices but negatively to DME Oman prices. The nonlinear ARDL model reveals asymmetric responses: revenue is more sensitive to negative shocks in ICE Brent and OPEC prices, while DME Oman price increases reduce revenues. Notably, NYMEX WTI fluctuations have minimal impact. The main conclusion of the paper is that OPEC’s fiscal stability is highly vulnerable to asymmetric, long-run oil price shocks. The study recommends policymakers adopt benchmark-specific fiscal hedging strategies, such as put options, and strategically diversify export markets to mitigate risk.

1. Introduction

Crude oil is the most traded commodity in the world, and despite the numerous attempts and resources directed towards the search for alternative sources of energy, the world is still mainly dependent on crude oil as the primary source of energy [1]. The importance of crude oil was made evident during the oil supply shock of the 1970s, when a geopolitical crisis in the Middle East led to an embargo and disruption of the global oil supply [2]. The ripple effects on the global energy price and its multiplier effects on global economic indicators, especially the consumer price index, drove home the importance of crude oil [3]. For members of the Organization of the Petroleum Exporting Countries (OPEC), oil revenues constitute a significant portion of government budgets with major implications for economic stability [4]. However, despite its importance, the price of crude oil is highly volatile and susceptible to geopolitical events across the globe [5], as evidenced in the spike in global oil price due to the Russia/Ukraine crisis and the resultant Western sanctions on Russia in 2022 [6].

These oil price fluctuations have a significant impact on the economy of both the oil-exporting and importing countries [7]. Research has shown that the revenue of the majority of the oil-exporting countries is generated mainly from oil. Despite attempts at economic diversification in these countries, the major portion of revenue is still derived from oil and hence the significance of the oil price to these countries [7]. Despite the role played by OPEC in trying to stabilize the supply and price of crude oil, the revenues of most oil-producing countries, especially those whose economies have not successfully diversified, are still vulnerable to external shock ]. This is due to its aforementioned market volatility and susceptibility to geopolitical tensions, supply–demand imbalances, and macroeconomic uncertainties [2]. However, the responsiveness of these revenues to oil price shocks and broader economic uncertainties remains a critical issue.

Although extensive studies such as [1,2,7,8,9,10] have been conducted on the impact of oil price fluctuations on macroeconomic variables like economic growth (GDP), inflation, exchange rate and fiscal stability of oil-exporting countries, this study contributes uniquely to the existing literature in some ways. First, the existing literature tends to measure the effect of oil price shocks symmetrically, failing to account for the asymmetric effects of positive and negative oil price fluctuations on government revenue of oil-exporting countries. Although few studies, such as [10], have employed a nonlinear model to assess the asymmetric impacts, these are relatively scarce, and none, to the best of researchers’ knowledge, has done so concerning the revenue of OPEC member countries. Also, unlike other studies that used a single aggregated oil price data as a measure of global oil price and, by so doing, oversimplified the complex dynamics that affect OPEC member revenues, this study is distinguished by disaggregating the oil price into the three major global oil benchmarks, which are West Texas Intermediate (WTI), ICE Brent, and DME Oman, alongside the OPEC spot price. This approach enables the assessment of the relative influence of each benchmark on the revenue profile of OPEC members, reflecting differences in each country’s exposures, vulnerability and contractual dependencies to specific oil pricing benchmarks.

Further, this concern is particularly significant in the context of the United Nations Sustainable Development Goals (SDGs). Among these, SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) stand out as especially relevant for OPEC member states. SDG 7 emphasizes the need for universal access to affordable, reliable, sustainable, and modern energy. Yet, many OPEC countries, whose fiscal revenues are heavily dependent on oil exports, face challenges achieving this goal because of the global transition away from fossil fuels. Similarly, SDG 13 calls for urgent action to combat climate change and its impacts, recognizing that climate change is a global challenge requiring immediate and coordinated responses from governments. These two goals are closely linked: reducing emissions requires a transition from fossil fuels to low-carbon energy sources such as wind, solar, and other renewables, alongside improved efficiency and reduced consumption [11].

Climate crises are primarily driven by fossil fuels, which account for over 90% of global carbon emissions across extraction, processing, and combustion activities (IISD, 2024) [11]. As major oil and gas producers, OPEC member countries are substantial contributors to these emissions, and thus central players in addressing climate change. Scholars note that achieving net-zero carbon emissions by mid-century will require a managed transition away from coal, oil, and gas [12]. However, this transition poses a profound dilemma for OPEC states. On one hand, it is necessary to safeguard the planet and align with SDGs 7 and 13. On the other hand, it threatens the very foundation of their revenue sustainability, as these nations derive significant shares of their revenue from oil and gas exports.

Given the above context, this study therefore seeks to investigate the dynamics of oil price fluctuations and revenue stability in OPEC member countries. By analyzing historical data of the major global oil price benchmarks such as NYMEX WTI, ICE Brent and DMI Oman along OPEC reference basket spot price, the research aims to determine the degree of responsiveness and identify factors that enhance or weaken fiscal resilience. The findings could inform better policy formulation to mitigate adverse effects on OPEC economies in an increasingly uncertain global energy landscape.

The rest of the work is divided into four sections. The Section 2 deals with the review of related literature, the third deals with the methodology, the Section 4 presents the analysis and discussion of findings, and the last part presents the conclusion with relevant policy, research and social recommendations.

2. Stylized Facts on Oil Price Fluctuation, Economic Uncertainty and the Elasticity of Revenue Profile of OPEC Member Countries

Historical Movement of Oil Prices

Oil-exporting countries, especially those whose economy is solely reliant on revenue from oil export, have a critical interest in the stability of the oil price [13]. However, history has shown that the price of oil is highly volatile and susceptible to geopolitical events, conflicts, global economic conditions and supply disruption [14]. The history of oil price regulations can be traced to the geopolitical conflicts in the 1970s between five (5) Arab nations and Israel [15]. The roles played by Western countries in those conflicts led to OPEC placing an embargo on some selected countries, which resulted in a decline in global oil production and supply by 7.5 percent [16]. This resulted in a spike in global oil prices to 12.5 U.S dollars per barrel, which is more than double the price before the embargo. Another noticeable surge in the global price of oil was during the Iranian revolution in 1978, which resulted in a shortage of global oil supply by 4.5 percent and a spike in global oil price to 21 U.S dollars per barrel [16]. There was no other substantial hike in global oil price until 1990 when the invasion of Kuwait by Iran led to the disruption of global oil supply by 8.5 percent, leading to a corresponding increase in oil price [16]. What followed was a steady and consistent growth in global oil price until the 2007/2008 global financial crisis, which led to a crash in global oil price from 100 U.S dollars per barrel to 35 U.S dollars per barrel. The period after the financial crisis witnessed a highly fluctuating global oil price, starting with its rebound from the crisis to 110 U.S dollars in 2013, to about a 50 percent crash in price in 2014 [17]. There was a relatively stable average global oil price at 53 U.S dollars between 2015 and 2019 [8]. However, the global pandemic and the associated travel restrictions led to a decline in world demand for oil and a subsequent fall in the price [16]. OPEC has played a pivotal role in returning the global oil price to equilibrium after each spike or decline [18]. This role sometimes requires collaboration with other non-OPEC oil-producing nations [18]. For example, during the 2020 global Pandemic, OPEC members agreed to reduce production in reaction to the decrease in oil price due to declining demand. However, Russia declined such a request, which led to a reciprocal flooding of supply by Saudi Arabia until Russia agreed to collaborate with OPEC [18]. This scenario perfectly demonstrates the influence OPEC wield on the determination of the production, distribution and global price of oil.

Much research has been carried out on oil price fluctuation, and it is almost a consensus that these fluctuations are mainly due to changes in the global demand and supply interactions. It has been observed that global oil price movements mirrored global economic performance [9], that is, rising during economic booms due to increased industrial demand and falling during economic downturns [9]. As the most traded global commodity, it serves as an indicator of global economic expectation, and the impact of price fluctuation varies between countries [19]. Usually, a spike in oil price benefits oil-exporting countries and increases the energy burden of oil-importing countries and vice versa [19]. As already mentioned, the global oil price is susceptible to geopolitical events, especially those concerning the oil-exporting countries. That is why [20] argued that the current crisis in the Middle East and the ongoing tension between the U.S and Iran should be an indicator to stakeholders in the oil market to be prepared to be rocked by further uncertainties in the global oil market in terms of supply and consequent price fluctuations.

The increase in its demand and liquidity, coupled with the advancement in the global information transmission technology, has resulted in the integration of the global oil market [21]. That is, the global oil market appears to be converging as one, and the different types of oil from different regions of the world appear to be moving together in theory [22]. However, in practice, different oil-producing regions, organizations or benchmarks play crucial and influential role in the determination of oil prices than others [22]. The role and how influential those organizations or benchmarks are largely depend on the power game played and the production capacity of the concerned organizations or benchmark [22]. Between the 1970s and mid-1980s, the global oil market was ravaged by uncertainties, price differences, and arbitrage activities, which made investment in the sector highly risky and unattractive [21]. To protect the oil market stakeholders, three major benchmarks besides the activities of OPEC+ were set up from the mid-1980s as a guide in the determination of global oil prices [21]. This international oil standard includes European Brent, West Texas Intermediate (WTI), and Middle East Dubai/Oman. These benchmarks operate on three exchange platforms, which are Intercontinental Exchange (ICE) for Brent, New York Mercantile Exchange (NYMEX) for WTI and Dubai Mercantile Exchange (DME) for Oman [22]. Together, these organizations provide the global benchmark for crude oil pricing, thereby facilitating transparency in the oil market [23]. They represent the major oil-producing nations from North America (U.S., Canada and Mexico), South America (Colombia, Ecuador and Venezuela), Europe (Norway, Russia and UK), Africa (Algeria, Angola, Libya and Nigeria), and the Middle East (Iran, Iraq, Kuwait, Oman, Qatar, Saudi Arabia, Abu Dhabi and Dubai) and account for at least 80 percent of the world oil production at the first quarter of 2025 [21].

As its core objective, OPEC operates as a form of trade union or a powerful cartel that manipulates the supply-side dynamics to influence oil prices [4]. It currently has 12 members comprising Algeria, Congo, Equatorial Guinea, Gabon, Iran, Iraq, Kuwait, Liberia, Nigeria, Saudi Arabia, United Arab Emirates and Venezuela [11]. The membership can increase or decrease as some oil-exporting countries join or withdraw their membership. OPEC has control over 40 percent of the global oil supply and over 80 percent of global proven crude oil reserves [13]. This makes it the most powerful player in the production, distribution and determination of global oil price, and this has been demonstrated. For instance, between January 1999 and January 2000, OPEC decided to reduce their production quota, and the non-OPEC oil-producing countries responded by massively increasing their production to meet global demand. However, their interventions fell largely insufficient without cooperation from OPEC, which resulted in a global oil price spike [14]. Likewise, OPEC in 2014 acted in favor of oil importers and increased their production quota, leading to a crash in the price of global oil price [14]. Various studies, including [24,25,26], have revealed that OPEC has controlling power over the behavior of global oil prices through the manipulation of its production level. These finding was also echoed by the United State Energy Information Administration, who stated that OPEC operate on the belief that crude oil production plays important role in the determination of oil price and as such regulate the production level of their members [27]. In fact, the behavior of OPEC also affects the pricing of the other three major players as revealed by the [23] who employed co-integration and vector error correction model (VECM) to examine the effect of OPEC strategies on other major crude oil prices and discovered that not only did OPEC affect the other major global spot price but that their influence is becoming stronger diminishing the benchmark set by Brent, WTI and Oman [27]. OPEC’s role extends beyond mere production cuts; it also shapes market sentiment through forward guidance, as seen in its 2022–2025 output reductions to counter recessionary demand fears.

Brent crude oil is another important player in the determination of global crude oil prices [28]. It has major influences on the energy market, trade policies and oil strategies relating to crude oil sourced from the North Sea, Africa, and the Mediterranean [28]. The crude oil sources controlled by Brent are generally considered to be desirable for being processed into gas and diesel due to their light and sweet features [28]. And just like other global oil pricing benchmarks, it is susceptible to geopolitical events around the world [23]. It was established in the 1980s to serve as a benchmark for setting a standard global oil spot reference price and, in the process, facilitating transparency and liquidity in the global oil market [23]. Speculators, investors, arbitrageurs and financial analysts rely on Brent’s benchmark to forecast the future price or occurrence in the oil market [23]. The historical evolution of Brent crude pricing is reflective of the volatile nature of the oil market. This is reflected in its peaked pricing of 147.50 U.S dollars per barrel during the 2007/2008 financial crisis due to supply disruption and speculative trading [28]. This pricing is also greatly affected by the actions of OPEC. Aside from OPEC, Brent operation is also influenced by other competitive benchmarks like WTI and DME Oman [28]. Historically, Brent oil pricing has always aligned with that of WTI. However, for the past decade, their pricing has diverged. Brent is now traded at a premium due to WTI logistical bottlenecks and the superior quality of Brent crude oil [28].

WTI is the primary benchmark for U.S. crude and reflects the dynamics of a landlocked market centered on Cushing, Oklahoma [29]. It is traded on the New York Mercantile Exchange [30]. It is generally regarded as the world’s most liquid oil, which offers direct contact to U.S light sweet crude [30]. Its pricing, like the other benchmarks, is influenced by OPEC behavior, supply–demand dynamics and other geopolitical or macroeconomic events [29]. Its futures contracts are physically deliverable, linking prices directly to storage capacity limits, a vulnerability exposed in 2020 when contract holders with no intent to take delivery overwhelmed infrastructure, triggering negative prices [29]. WTI’s financial personality, dominated by hedge funds and banks, often decouples it from fundamental supply–demand shifts, though it remains critical for pricing America-based crude [30]. WTI historically trade at a discount to Brent due to Brent’s water accessibility and broader global adoption.

DME Oman is the crude oil benchmark launched in 2007 by the Dubai Mercantile Exchange to serve as the pricing benchmark for Asian crude oil markets [31]. The DME Oman contract emerged from a joint venture between Dubai Holding, Oman Investment Authority, and CME Group, with Oman’s Ministry of Oil and Gas adopting it as the official pricing mechanism for its crude exports in 2007 [31]. Over time, other regional producers, including Saudi Aramco, Kuwait Petroleum Corporation, and Bahrain Petroleum Company, incorporated the DME Oman price into their export formulas, solidifying its benchmark status [31]. Trading volumes grew steadily, peaking at 744 million barrels in 2010, while the introduction of linked contracts in 2010 (e.g., swaps and options) expanded their adoption [32]. The exchange’s migration to CME Globex in 2009 improved accessibility, integrating it with global benchmarks like WTI and Brent. It is the world’s largest physically delivered crude oil futures contract, with over 3 billion barrels delivered through its exchange mechanism and 21 billion barrels traded since inception [32]. The contract is traded on CME Globex and cleared via CME ClearPort, providing a regulated and transparent platform for price discovery and risk management in the East of Suez market. It was established to address the lack of a reliable pricing model for Middle Eastern crude oil exports to Asia [23]. The contract’s physical settlement mechanism ensures alignment with real-world supply dynamics, distinguishing it from financially settled benchmarks. It was rebranded to the Gulf Mercantile Exchange (GME) in 2024 to further demonstrate its mission to expand its role as the main crude oil pricing benchmark in Asia. The DME Oman benchmark exerts significant influence over Middle Eastern crude pricing, as it is explicitly tied to the Official Selling Prices (OSPs) of Oman, Dubai, and other GCC producers [23]. Its role in China’s crude imports, particularly for the Shanghai International Energy Exchange’s sour crude contract, further enhances its regional and global relevance.

Although these benchmarks provide pricing benchmarks for oil in different regions across the globe, their price tend to mirror each other with an average spread of less than 10 U.S dollars between them [23]. This is because they are all susceptible to the same geopolitical tensions, which might disrupt the supply of global crude oil. And as aforementioned, they are all affected by the production and stabilization intervention of OPEC.

As shown in Figure 1, which is a graph made from the data drawn from OPEC statistical database 2024, the historical price movements of the major global oil benchmarks closely mirror one another, reflecting shared exposure to geopolitical shocks and macroeconomic cycles. A detailed susceptibility of their movement to these shocks is discussed below:

Early 1990s (1990–1994): The global oil price was relatively low and stable in the early 1990s until the Gulf War-driven price spike in 1991 due to supply disruptions.

Mid to Late 1990s (1995–1999): The mid-1990s started with a decline and later recovered until the Asian financial crisis of 1997 led to a decline in oil prices due to a demand crash.

Early 2000s (2000–2008): Disruption in supply due to crises and geopolitical instability in the Middle East, especially the 2003 Iraq War, resulted in price increases, which peaked in 2008.

2009–2014: The aftermath of the 2007/2008 global financial crisis initially resulted in oil prices decline which later recovered due to conflicts, particularly in Libya and political unrest in other oil-producing countries. The price rise peaked between 2012 and 2014.

2015–2016: There was a significant drop in the global oil price as a result of oversupply due to competitive production surges between OPEC and non-OPEC members like Russia and the United States’ Shale revolution.

2017–2020: Prices began to recover but then fell again in 2020 due to the COVID-19 pandemic, whose preventive measures led to a drastic crash in global oil demand.

Post-2021: Prices surged again due to recovering demand and geopolitical tensions related to conflicts and production cuts. In 2022, prices exceeded USD 100 per barrel in response to Russia’s invasion of Ukraine and the Western sanctions imposed on Russian oil.

3. Empirical Review

Many studies have been carried out on the impacts of the relationship between government revenue, oil price fluctuation, and economic policy uncertainties, with varied results. The majority of the existing literature is of the agreement that oil price fluctuation has a significant relationship with government revenue and other macroeconomic variables of both the oil-exporting and oil-importing countries. However, the magnitude and direction of these relationships are dependent on the nature of the economy or the source of oil price fluctuation. Ref. [21] in their investigation of oil price shocks and their macroeconomic effects concluded that oil price fluctuation resulting the demand side is likely to have an improving or positive effects on revenue and other macroeconomic indicators while those oil price fluctuations that are driven by the supply-side shock might likely have a destabilizing effects on revenue and other macroeconomic variables. These findings are backed by the work of [8], who discovered while investigating the nexus between oil price shocks, market returns and revenues in developed economies such as; China, Japan, USA, France, and Germany oil price fluctuations resulting from disruptions of supply have an adverse impact on revenue and financial market returns.

The impact of oil price fluctuation is exacerbated when applied in the context of oil-producing countries. This is because majority of these countries are dependent on oil revenue for their fiscal budget. Their dependency on crude oil for their fiscal stability has resulted in them being more susceptible to the impact of oil price fluctuations. This has been asserted by many scholar who, however, opined that the level of vulnerability of oil-exporting countries to oil price volatility is dependent on their level of economic diversification, fiscal and economic policy soundness. The work of [12,33] corroborated these facts. While examining the peculiar structure of government revenue of OPEC member countries due to their dependency on the global oil price, ref. [33] argued that, although crude oil equips the fiscal capacity of oil-producing countries in financing capital-intensive projects, their economy is vulnerable to external shock as a result of the volatility of the oil price and non-diversification of their revenue sources. This was echoed by [12], who demonstrated that gross domestic product and government revenue in oil-producing countries have a strong relationship with oil prices. They also argued that the long-term fiscal stability of oil-exporting countries and their resilient oil price fluctuations are dependent oil the level of their economic diversification.

Oil-producing countries do not have the same vulnerabilities to oil price fluctuations. Some countries have been taking proactive steps to create a buffer between their economies and the adverse effects of oil price fluctuations. Ref. [1] argued that even as OPEC countries struggled to cope with the frequent fluctuations in oil price, some countries like Saudi Arabia, the United Arab Emirates and Qatar have shown promise of having higher resilience in dealing with oil price and revenue fluctuation resulting from economic policy uncertainty. The result from their analysis shows that Saudi Arabia and the United Arab Emirates have more diversified economies as they are relatively more balanced between their oil export revenue and non-oil export revenue compared to other OPEC countries, of which Iraq has the least diversification. They argued that besides diversification, these countries have created a Sovereign Wealth Fund for investment and an enabling tourist attraction to facilitate an additional revenue-generating channel. While other oil-producing countries like Nigeria, Gabon and the like are still struggling with their diversification efforts due to the combination of either inadequate infrastructure, mismanagement, weak fiscal and economic policies or corruption. In Nigeria, ref. [34] discovered that oil price fluctuations negatively and significantly affect government revenue in both the short and long run. They argued that this was a result of weak macroeconomic policies and non-diversification of revenue sources. This was corroborated by [35] who further highlighted Nigeria’s over-reliance on oil exports, which leaves the economy vulnerable to external shocks. They advocate for diversification, improved governance, and reduced import dependency to enhance macroeconomic resilience. Likewise, the review of Indonesia’s economy by [36] revealed a similar over-reliance of their fiscal stability on the oil price. He concluded that the majority of their government revenue is generated from crude oil exports, and as such, oil price fluctuation has an asymmetric impact on the country’s revenue. In Iran, ref. [37] noted that oil accounts for 60% of government revenue and 90% of export earnings, making the economy highly sensitive to global oil price movements.

The responsiveness of the revenue of OPEC member countries might not always be negative. Oil price fluctuations might be an increase in oil price or a decrease in oil price. For oil-exporting countries, an increase in oil price should equate to an increase in government revenue, depending on their oil production capacity. The measurement of the responsiveness of government revenue in OPEC member countries to a decrease or an increase in oil price requires the adoption of a technique capable of decomposing oil price fluctuations into positive and negative partial sums. This is demonstrated in the work of [10], who employed a nonlinear ARDL estimation technique to assess the responsiveness of government revenue and expenditure in OPEC members to oil price fluctuations. His findings reveal an asymmetric long-term response of revenue and expenditure to oil price fluctuation. He also references the stabilizing role of diversification of government revenue in mitigating the adverse effects of oil price fluctuations.

It is therefore very necessary for oil-exporting countries to diversify their revenue sources. As aforementioned, history has shown that oil prices are very volatile to geopolitical tensions, conflicts or crises. The current tension between Iran and the United States, coupled with the ongoing Middle East crisis, is an indication of potential future disruption of oil supply and oil price fluctuations. This has made the regulating roles played by OPEC all the more important to oil-dependent countries. OPEC always attempt to stabilize global oil prices by regulating supply through the regulations of production quotas of its members. The fact that OPEC, on average, controls over 40 percent of the world’s global oil and over 80 percent of global oil reserves ensures that their action has an impact on all the major global oil pricing benchmarks [38]. This is corroborated in the work of [20,28]. Ref. [28] also examined OPEC’s cartel behavior, showing that production adjustments are strategically used to stabilize prices, reinforcing the organization’s role in managing oil market volatility. Their finding was later echoed in the work of [20] which investigated the nexus between oil prices and economic growth in OPEC countries and discovered that oil production has an impact on oil and economic growth.

Theoretically, Gelb in 1988 and later Auty in 1993 used the term ‘resource curse’ theory to explain the observed paradox where the endowment of natural resources proved inadequate in facilitating economic success [39]. It has been observed that many countries in Africa, South America and the Middle East, which are blessed with oil and other natural resources, continue to suffer low per capita income, and are home to some of the poorest people in the world. These countries exhibit lower development outcomes than some other countries with lower natural resource endowment. The theory posits that countries blessed with natural resources like oil and minerals often experience poorer economic growth, suffer greater political instability and higher levels of corruption than countries with lower resource endowment. Many researchers have attributed this phenomenon to mismanagement, weak institutions and a lack of diversification prevalent in natural resources-rich countries, resulting in economic distortions [39]. It is important to note that the proponents of resource curse theory are not arguing that the resource-rich countries are better off without their resources [39]. Rather, they are attempting to explain the reasons why these countries experience a lower rate of economic growth and development [40]. Refs. [41,42] pointed to preceding concepts such as Dutch disease, where export of natural resources results in currency appreciation at the detriment of other industries and rentier-state dynamics, where governments are over reliant on receipts from their natural resources exports at the expense of other channels like taxation, thereby reducing accountability [40]. Some OPEC members have put measures in place to mitigate against the challenges of the resource curse theory. Countries like Saudi Arabia and the United Arab Emirates have created sovereign wealth funds and invested in green energy to diversify their revenue source.

4. Methodology

4.1. Data

To evaluate the symmetric and asymmetric response of the revenue profile of OPEC member countries to oil price fluctuations, annual panel data between 1990 and 2024 were utilized. This data range was selected because of the availability of data for OPEC member countries and the timing of the research. The research scope spanned the entire current membership of OPEC, which includes Algeria, Congo, Equatorial Guinea, Gabon, Iran, Iraq, Kuwait, Liberia, Nigeria, Saudi Arabia, the United Arab Emirates and Venezuela.

The dependent variable of this research work is government revenue, which is defined as the total funds generated by the government of a country from various sources, excluding grants, to finance public expenditures, services and infrastructures. The explanatory variable is the oil price. Oil price is decomposed into the OPEC reference basket spot price, ICE Brent spot price, NYMEX WTI spot price and DME Oman spot price. The data for oil prices was generated from the OPEC Annual Statistical Bulletin. While that of government revenue was obtained from the International Monetary Fund data depository. The control variable is the real effective exchange rate, which is generated from World Development Indicators.

4.2. Empirical Techniques

To evaluate the symmetric and asymmetric elasticity of the revenue profile of OPEC member countries to oil price fluctuation between the periods of 1990 and 2024, we employed both the linear panel Autoregressive distributed lag and the nonlinear panel Autoregressive distributed lag. The adoption of the panel estimation technique enabled us to simultaneously evaluate the responsiveness of the revenue profile of OPEC member countries to oil price fluctuations and also control the unobserved heterogeneity of the 12 OPEC countries. The linear panel Autoregressive distributed lag estimation technique enabled us to evaluate the symmetric response of revenue profile to our explanatory variables, while also enabling the respective response of revenue profile to our explanatory variables due to the fiscal policy lag and structural economic shift in the respective OPEC member countries. Panel ARDL also provides measures for country-specific heterogeneity, which allows for fixed or random effects, accounting for differences in fiscal policies, diversification levels, and oil dependency across OPEC member countries. The adoption of the nonlinear panel ARDL enabled us to decompose the oil price benchmarks into positive and negative partial sums to test asymmetry. It also enabled us to capture the difference in fiscal policy response or fiscal conservatism in the respective OPEC member countries. For instance, some OPEC member countries’ budgetary or fiscal structure might enable them to cut spending faster in response to the oil price drop than others. First, we present our static Panel model as obtained from the work of [43] as:

$${\mathsf{\gamma }}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_{\mathrm{i}}+\mathsf{\beta }{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}$$

where ${\mathsf{\gamma }}_{\mathrm{i}\mathrm{t}}$ denote the dependent variable and ${\mathrm{X}}_{\mathrm{i}\mathrm{t}}$ represents a vector of explanatory variables. Subscripts i and t index represent the cross-sectional (countries) and time series dimension, respectively. The composite error term, ${\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}$ can be decomposed into country-specific effects and the remainder disturbance term. Hereafter, i will be referring to them as individual OPEC member countries. To account for the individual country-specific effects, we decomposed the error terms to form the general linear ARDL model, which was obtained and modified from the work of [44] and presented as:

$${\mathsf{\gamma }}_{\mathrm{i}\mathrm{t}}={\mathsf{\alpha }}_{\mathrm{i}}+\sum _{\mathrm{l}=1}^{\mathrm{p}}{\mathsf{\beta }}_{\mathrm{i}\mathrm{l}}{{\mathsf{\gamma }}_{\mathrm{i},\mathrm{t}}}_{\mathrm{i},\mathrm{t}-1}+\sum _{\mathrm{k}=0}^{\mathrm{q}}{\mathsf{\gamma }}_{\mathrm{i}\mathrm{l}}{\mathrm{X}}_{\mathrm{i},\mathrm{t}-1}+{\in }_{\mathrm{i}\mathrm{t}}$$

where

${\mathsf{\gamma }}_{\mathrm{i}\mathrm{t}}$ = Dependent variable for unit i and t.

${\mathrm{X}}_{\mathrm{i},\mathrm{t}}$ = independent variables

${\mathsf{\alpha }}_{\mathrm{i}}$ = Unit-specific fixed effects (capturing unobserved heterogeneity

${\mathsf{\beta }}_{\mathrm{i}\mathrm{l}}$ = Autoregressive coefficients for lagged dependent variable

${\mathrm{y}}_{\mathrm{i}\mathrm{t}}$ = Distributed lag coefficient or current and lagged independent variables ${\mathrm{X}}_{\mathrm{i},\mathrm{t}}$

${\in }_{\mathrm{i}\mathrm{t}}$ = Error terms

We can therefore incorporate our variables into the above model to present our explicit model representations as:

$$\begin{gathered} {GOVR}_{it}={\pi }_{ip}+\sum _{i=1}^k{\delta }_{ip}∆{GOVR}_{it-i}+\sum _{i=1}^{k1}{\delta }_{1p}∆{NWTI}_{it-i}+\sum _{i=1}^{k2}{\delta }_{2p}∆{ICEB}_{it-i}+\sum _{i=1}^{k3}{\delta }_{3p}∆{DMEO}_{it-i} \\ +\sum _{i=1}^{k4}{\delta }_{4p}∆{OPEC}_{it-i}+\sum _{i=1}^{k8}{\gamma }_{1p}∆{EXCR}_{it-i}+{\theta }_{ip}{GOVR}_{it-1}+{\theta }_{2p}{NWTI}_{it-1}+{\theta }_{3p}{ICEB}_{it-1} \\ +{\theta }_{4p}{DMEO}_{it-1}+{\theta }_{5p}{OPEC}_{it-1}+{\vartheta }_{1p}{EXCR}_{it-1}+{\epsilon }_{it} \end{gathered}$$

where GOVR is government revenue, NWTI is NYMEX West Texas Intermediate spot oil price, ICEB is ICE Brent spot price, DMEO is DME Oman spot price, OPEC is OPEC spot price, EXCR is the real effective exchange rate, $∆$ represents the short-run dynamics, $\mathsf{\delta }$ represents the long-run equilibrium, and the error correction term (ECT), that is, the coefficient of ${\mathsf{\delta }}_1 \mathrm{o}\mathrm{n} {\mathrm{T}\mathrm{V}\mathrm{R}}_{\mathrm{i},\mathrm{t}-1}$ indicates the speed of adjustment to equilibrium. Other parameters remain the same as defined above.

The nonlinear Panel ARDL Model enabled us to measure the asymmetric response of the revenue profile of OPEC member countries to oil price fluctuation and economic uncertainty. That is, it accounted for how the positive and negative changes in our explanatory variables affect the revenue profile of OPEC member countries differently. To achieve this, we used the Partial Sum Decomposition (PDS) to split variables into positive and negative components. The model for decomposing the explanatory variables to give the cumulative positive and negative changes is presented as:

$$X^{+}= \sum _{k=1}^{t}max(∆X_{k,}0), X^{-} = \sum _{k=1}^{t}min(∆X_{k,}0)$$

Therefore, our nonlinear Panel ARDL Model, as obtained from the work of (Şanlı et al., 2023) [44] and modified to suit this research, will be presented as:

$$\begin{gathered} {GOVR}_{it}={\pi }_{ip}+\sum _{i=1}^k{\delta }_{ip}∆{GOVR}_{it-i}+\sum _{i=1}^{k1}{\delta }_{1p}^{+}∆{NWTI}_{it-1}^{+}+\sum _{i=1}^{k1}{\delta }_{1p}^{-}∆{NWTI}_{it-1}^{-}+\sum _{i=1}^{k2}{\delta }_{2p}^{+}∆{ICEB}_{it-1}^{+} \\ +\sum _{i=1}^{k2}{\delta }_{2p}^{-}∆{ICEB}_{it-1}^{-}+\sum _{i=1}^{k3}{\delta }_{3p}^{+}∆{DMEO}_{it-1}^{+}+\sum _{i=1}^{k3}{\delta }_{3p}^{-}∆{DMEO}_{it-1}^{-}+\sum _{i=1}^{k4}{\delta }_{4p}^{+}∆{OPEC}_{it-1}^{+} \\ +\sum _{i=1}^{k4}{\delta }_{4p}^{-}∆{OPEC}_{it-1}^{-}+\sum _{i=1}^{k5}{\gamma }_{1p}^{+}∆{EXCR}_{it-1}^{+}+\sum _{i=1}^{k5}{\gamma }_{1p}^{-}∆{EXCR}_{it-1}^{-}+{\theta }_{ip}{GOVR}_{it-1}+{\theta }_{2p}^{+}{NWTI}_{it-1}^{+} \\ +{\theta }_{2p}^{-}{NWTI}_{it-1}^{-}+{\theta }_{3p}^{+}{ICEB}_{it-1}^{+}+{\theta }_{3p}^{-}{ICEB}_{it-1}^{-}+{\theta }_{4p}^{+}{DMEO}_{it-1}^{+}+{\theta }_{4p}^{-}{DMEO}_{it-1}^{-} \\ +{\theta }_{5p}^{+}{OPEC}_{it-1}^{+}+{\theta }_{5p}^{-}{OPEC}_{it-1}^{-}+{\sigma }_{1p}^{+}{EXCR}_{it-1}^{+}+{\sigma }_{1p}^{-}{EXCR}_{it-1}^{-}+{\epsilon }_{it} \end{gathered}$$

where ${\mathsf{\gamma }}_{\mathrm{k}}^{+}$, ${\mathsf{\gamma }}_{\mathrm{k}}^{-}$ are the short-run asymmetric responses and ${\mathsf{\delta }}_2^{+}$, ${\mathsf{\delta }}_2^{-}$ are the long-run asymmetric responses. All other terms are as defined in the linear model above.

5. Results

A descriptive statistics test was carried out to ascertain the properties of the panel datasets. This includes the measure of central tendency and dispersion. The results of the linear panel and the nonlinear panel are displayed in

Table 1. Panel Linear and Nonlinear Descriptive Statistics.

Linear Descriptive Statistics
Variables	Mean	Std. Dev.	Coefficient of Variation	Minimum	Maximum
GOVR	30.05	16.44	0.55	4.84	85.78
NWTI	51.01	28.37	0.56	14.36	100.00
ICEB	52.76	31.96	0.61	12.71	111.62
DMEO	50.97	31.51	0.62	12.06	109.35
OPEC	51.72	31.99	0.62	12.28	109.45
EXCR	226.40	451.61	1.99	0.27	2002.41
Nonlinear Descriptive Statistics
Variables	Mean	Std. Dev.	Coefficient of Variation	Minimum	Maximum
GOVR	30.25	16.04	0.53	4.84	85.78
NWTI_NEG	−55.19	50.66	0.93	−149.32	−2.91
NWTI_POS	82.53	69.02	0.84	0.00	206.94
ICEB_NEG	−58.57	55.36	0.95	−162.11	−3.55
ICEB_POS	88.58	76.13	0.95	0.00	225.82
DMEO_NEG	−54.34	52.98	0.97	−152.25	−3.75
DMEO_POS	85.31	73.64	0.86	0.00	217.34
OPEC_NEG	−57.59	55.74	0.97	−166.74	−3.64
OPEC_POS	87.92	77.06	0.97	0.00	235.11
EXCR_NEG	−125.23	199.53	1.59	−1131.99	0.00
EXCR_POS	85.90	155.54	1.81	0.00	831.17

below. The linear descriptive statistics indicate that the average of the dependent variable (GOVR) is 30.5, with a standard deviation of 6.44. This indicates that the GOVR data has a normal distribution and that it is not overly spread around the mean.

The minimum and maximum values for GOVR range from −40.64 to 85.78, highlighting significant disparities in revenue performance among OPEC countries, with some nations experiencing negative revenues in certain periods.

The oil price benchmarks, NYMEX WTI (NWTI), ICE Brent (ICEB), DME Oman (DMEO), and OPEC spot price (OPEC), exhibit similar mean values, hovering around 51 to 53, with standard deviations ranging from 28 to 32, indicating substantial fluctuations in oil prices over time. The CVs for these benchmarks are relatively close, ranging between 0.56 and 0.62, confirming that oil prices are highly volatile, with deviations from the mean exceeding 50%. The minimum and maximum values for these benchmarks show wide ranges, with prices starting as low as 12.06 (DMEO) and reaching as high as 111.62 (ICEB), underscoring the extreme variations in global oil markets.

Additionally, the variable EXCR, which represents the exchange rate, has a much higher mean (226.39) and standard deviation (451.61) compared to the other variables. However, its CV is notably low at 1.99, suggesting that despite its large absolute values, its relative variability is minimal. The range for EXCR is extensive, from 0.26 to 2002.41, indicating potential outliers or extreme values in certain observations. Overall, these statistics highlight the dynamic nature of oil prices and government revenues in OPEC countries, with significant variations that may influence the elasticity analysis.

Likewise, the nonlinear descriptive statistics indicate that all the variables are normally distributed. They are not overly spread around the mean, evidenced by the coefficient of variation, which is all less than unity (1) except for the negative and positive partial sum of the exchange rate. The unstable nature of the exchange rate of some of the OPEC member countries, especially those from Africa, can explain the behavior of this data.

The linear panel correlation matrix in

Table 2. Linear Panel Correlation Matrix.

	GOVR	ICEB	NWTI	OPEC	DMEO	EXCR
GOVR	1	0.10	0.11	0.09	0.10	0.15
ICEB	0.10	1	0.99	0.99	0.99	−0.05
NWTI	0.11	0.99	1	0.98	0.99	−0.05
OPEC	0.09	0.99	0.99	1	0.99	−0.05
DMEO	0.10	0.99	0.99	0.99	1	−0.04
EXCR	0.15	−0.05	−0.05	−0.05	−0.04	1

below reveals key relationships between government revenue (GOVR) and the oil price benchmarks, along with other variables in the study. GOVR exhibits positive correlations with the oil price benchmarks, ICE Brent (ICEB), NYMEX WTI (NWTI), OPEC spot price (OPEC), and DME Oman (DMEO), with coefficients ranging from 0.09 to 0.11. This suggests that higher oil prices are generally associated with increased government revenue in OPEC countries. GOVR also shows a positive correlation with EXCR (0.15).

The nonlinear correlational matrix is presented in

Table 3. Nonlinear Panel Correlational Matrix.

Variables	GOVR	NWTI_NEG	NWTI_POS	ICEB_NEG	ICEB_POS	DMEO_NEG	DMEO_POS	OPEC_NEG	OPEC_POS	EXCR_NEG	EXCR_POS
GOVR	1	0.13	−0.05	0.13	−0.06	0.14	−0.06	0.13	−0.06	0.07	−0.46
NWTI_NEG	0.1	1	−0.94	0.99	−0.94	0.99	−0.94	0.99	−0.94	0.26	−0.38
NWTI_POS	−0.05	−0.94	1	−0.93	0.99	−0.92	0.99	−0.92	0.99	−0.30	0.37
ICEB_NEG	0.13	0.99	−0.92	1	−0.93	0.99	−0.93	0.99	−0.94	0.26	−0.38
ICEB_POS	−0.06	−0.94	0.99	−0.93	1	−0.93	0.99	−0.93	0.99	−0.30	0.36
DMEO_NEG	0.14	0.99	−0.92	0.99	−0.93	1	−0.93	0.99	−0.93	0.25	−0.38
DMEO_POS	−0.06	−0.94	0.99	−0.93	0.99	−0.93	1	−0.93	0.99	−0.30	0.37
OPEC_NEG	0.13	0.99	−0.92	0.999	−0.93	0.99	−0.93	1	−0.94	0.25	−0.38
OPEC_POS	−0.06	−0.94	0.99	−0.94	0.99	−0.93	0.99	−0.93	1	−0.30	0.37
EXCR_NEG	0.07	0.26	−0.30	0.26	−0.30	0.25	−0.30	0.25	−0.30	1	−0.27
EXCR_POS	−0.46	−0.38	0.36	−0.38	0.37	−0.38	0.36	−0.38	0.37	−0.27	1

below:

According to the nonlinear panel correlation, the result above, evidence of positive and negative linear association is shown in the reported test. It is worth noting that the outcome variable (GOVR) shares a positive correlation with most of the positive and negative partial sums of the global oil price benchmarks. This suggests that an increase in the prices of these benchmarks will result in to increase in government revenue. Ordinarily, the strong negative correlation between GOVR and EXCR-pos might have point to a potential inverse causal relationship between the two variables; however, EXCR-pos is a control variable in our model.

The result of both the linear and nonlinear cross-sectional dependency tests in

Table 4. Linear and Nonlinear Cross-Dependency Test.

Linear Cross-Dependency Test
Variables	Breusch-Pagan LM	Pesaran Scaled LM	Bias-Corrected Scaled LM	Pesaran CD
GOVR	323.84 (0.00)	22.44 (0.00)	22.27 (0.00)	11.54 (0.00)	There is cross-sectional dependency
NWTI	2310.00 (0.00)	323.84 (0.00)	195.32 (0.00)	195.14 (0.00)	There is cross-sectional dependency
ICEB	2310.00 (0.00)	323.84 (0.00)	195.32 (0.00)	195.14 (0.00)	There is cross-sectional dependency
DMEO	2310.00 (0.00)	323.84 (0.00)	195.32 (0.00)	195.14 (0.00)	There is cross-sectional dependency
OPEC	2310.00 (0.00)	323.84 (0.00)	195.32 (0.00)	195.14 (0.00)	There is cross-sectional dependency
EXCR	350.07 (0.00)	28.13 (0.00)	27.97 (0.00)	1.91 (0.06)	There is cross-sectional dependency
Nonlinear Cross-Dependency Test
NWTI_NEG	2244.00 (0.00)	189.57 (0.00)	189.39 (0.00)	47.37 (0.00)	There is cross-sectional dependency
NWTI_POS	2244.00 (0.00)	189.57 (0.00)	189.39 (0.00)	47.37 (0.00)	There is cross-sectional dependency
ICEB_NEG	2244.00 (0.00)	189.57 (0.00)	189.39 (0.00)	47.37 (0.00)	There is cross-sectional dependency
ICEB_POS	2244.00 (0.00)	189.57 (0.00)	189.39 (0.00)	47.37 (0.00)	There is cross-sectional dependency
DMEO_NEG	2244.00 (0.00)	189.57 (0.00)	189.39 (0.00)	47.37 (0.00)	There is cross-sectional dependency
DMEO_POS	2244.00 (0.00)	189.57 (0.00)	189.39 (0.00)	47.37 (0.00)	There is cross-sectional dependency
OPEC_NEG	2244.00 (0.00)	189.57 (0.00)	189.39 (0.00)	47.37 (0.00)	There is cross-sectional dependency
OPEC_POS	2244.00 (0.00)	189.57 (0.00)	189.39 (0.00)	47.37 (0.00)	There is cross-sectional dependency
EXCR_NEG	1287.80 (0.00)	117.54 (0.00)	117.38 (0.00)	35.56 (0.00)	There is cross-sectional dependency
EXCR_POS	1153.01 (0.00)	104.70 (0.00)	104.53 (0.00)	33.29 (0.00)	There is cross-sectional dependency

below. indicates the existence of cross-sectional dependency among all the variables. This necessitated the adoption of the Hadri-Z test for the stationarity test.

Table 5. Panel Linear and Nonlinear Unit Root Test.

Linear Unit Root Test
Variables	Statistic	Prob	Inference
GOVR	1.77	0.04	1(0)
NWTI	9.01	0.00	1(0)
ICEB	9.26	0.00	1(0)
DMEO	6.92	0.00	1(0)
OPEC	6.13	0.00	1(0)
EXCR	8.17	0.00	1(0)
Nonlinear Unit Root Test
NWTI_NEG	10.63	0.00	1(0)
NWTI_POS	11.57	0.00	1(0)
ICEB_NEG	10.40	0.00	1(0)
ICEB_POS	11.51	0.00	1(0)
DMEO_NEG	10.27	0.00	1(0)
DMEO_POS	11.48	0.00	1(0)
OPEC_NEG	10.40	0.00	1(0)
OPEC_POS	11.56	0.00	1(0)
EXCR_NEG	9.68	0.00	1(0)
EXCR_POS	9.11	0.00	1(0)

presents the linear and nonlinear form of the panel unit root test.

The result of the unit root test reveals the absence of a unit root in the variables and the negative and positive partial sums of the explanatory variables. The result also indicated that all the variables are stationary at 1(0) confirming the absence of unit roots. This justified the adoption of both the linear and nonlinear ARDL estimation techniques, as it is capable of handling variables with this kind of integration.

Lag Selection Criteria

A lag selection criteria test was carried out following the Akaike Information Criteria in a form that reported the graph of the top 20 models as shown in Figure 2 below:

Model 59040 is the optimum model with the least information criteria and the highest log likelihood ratio. The lag length for the optimal model is (1,1,1,1,0,1,1,0,1,1). This means that the optimal lag is the first lag of the dependent variable with all the other variables at first lag except for ICEB-NEG and DMEO-NEG whose optimality exist at zero lag.

The linear panel ARDL results in

Table 6. Linear Panel ARLD Estimate.

	Short Run				Long Run
Variables	Coefficient	Std. Error	t-Statistic	Prob.	Coefficient	Std. Error	t-Statistic	Prob.
NWTI	0.04	0.17	0.22	0.83	1.00	0.48	2.10	0.04
ICEB	−0.77	0.68	1.12	0.2627	5.64	1.98	2.84	0.005
DMEO	1.073578	0.72	1.49	0.14	−9.92	2.11	4.70	0.00
OPEC	−0.19	0.69	0.28	0.78	3.32	0.93	3.57	0.0004
EXCR	41.70	33.18	1.26	0.21	0.02	0.003	6.40	0.00
ECM	−0.15	0.08	1.95	0.05

below reveal significant relationships between OPEC member countries’ government revenues and various oil price benchmarks, with distinct long-run and short-run dynamics.

The error correction, which is the first lag of the residual entered with a right sign (it has a negative coefficient of −0.15, and it is statistically significant with p-value = 0.05). This is evidence of a certain return to long-run equilibrium after every short-run disequilibrium triggered by a change in any of the influencing variables. With a negative significant coefficient of 15% (speed of adjustment), it shows that every short-run deviation is restored at the speed of 15% per annum, indicating a slow convergence to the long-run equilibrium. This means that it takes approximately 7 years for full equilibrium to be restored. This result falls within a predictable threshold given that it is less than unity (1). All other joint statistics are in shape and support the validity of the chosen estimation technique.

The result indicates that in the short run, the revenue profiles of OPEC member countries do not respond significantly to fluctuations in the global oil pricing benchmarks. This is reflected in the statistically non-significant coefficient of these benchmarks, NWTI, ICEB, DMEO and OPEC, which all have a p-value greater than 0.05. This signifies that their government’s revenue is more resilient to oil price changes in the short run.

In the long run, ICE Brent, NYMEX WTI benchmarks and the OPEC spot price have a positive and significant relationship with the government revenue of OPEC member countries (coefficients = 5.641, 0.999, and 3.323, p = 0.0048, 0.04 and 0.0004, respectively). This implies that a unit increase in these variables will result in a respective 5.64, 1.00, and 3.32 increase in their government revenue. However, the DMEO benchmark has a negative relationship with government revenue, indicating that an increase in its price will lead to a decrease in government revenue of OPEC member countries. This is possible due to its role as a pricing reference for OPEC’s Asian customers, where higher DMEO prices could result in to crash in demand and a reduction in the production quotas.

The disparity between long-run and short-run effects can be attributed to OPEC’s fiscal and operational strategies. The strong negative response to DMEO prices in the long run may reflect structural dependencies, where sustained high prices in this benchmark could lead to demand destruction or substitution effects, particularly in Asia, a key market for OPEC. Meanwhile, the positive effects of ICEB and OPEC prices align with OPEC’s ability to leverage Brent-linked pricing and its mechanisms to stabilize revenues. The muted short-run responses suggest that OPEC members may use financial buffers or production adjustments to mitigate immediate revenue shocks, while long-run effects capture the underlying fiscal reliance on oil exports. The exchange rate’s minor role underscores the dominance of dollar-denominated oil contracts, which insulate revenues from currency fluctuations to some extent. Overall, these findings highlight the complex interplay between global oil benchmarks and OPEC’s revenue dynamics, with long-run effects dominating due to the structural nature of oil-dependent fiscal systems.

The results from the nonlinear ARDL analysis in

Table 7. Nonlinear Panel ARDL Result.

	Short Run				Long Run
Variables	Coeff.	Std. Error	t-Statistic	Prob	Coeff.	Std. Error	t-Statistic	Prob.
NWTI-POS	−1.23	0.62	−1.99	0.05	1.08	0.69	1.57	0.12
NWTI-NEG	0.66	0.27	2.46	0.01	−0.44	0.23	1.91	0.06
ICEB-POS	−0.80	0.62	1.29	0.1977	2.54	0.86	2.95	0.004
ICEB-NEG	1.81	0.96	1.89	0.06	−4.77	2.14	2.22	0.03
DMEO-POS	0.75	0.36	2.10	0.04	−2.79	0.83	3.38	0.001
DMEO-NEG	0.20	1.04	0.19	0.85	2.06	1.20	1.72	0.09
OPEC-POS	−0.69	0.73	0.94	0.35	1.96	0.67	2.94	0.004
OPEC-NEG	−0.37	0.37	1.01	0.31	0.63	0.33	1.93	0.06
EXCR-POS	70.19	55.57	1.26	0.21	−0.04	0.02	2.53	0.01
EXCR-NEG	2931.12	2860.14	1.02	0.31	0.01	0.02	0.45	0.65
ECM	−0.54	0.10	5.19	0.00

below reveal both symmetric and asymmetric responses of government revenue (GOVR) in OPEC member countries to fluctuations in oil prices. Before discussing the short-run and long-run results, the most relevant joint short-run statistic, which is the error statistic which is the error correction term, is discussed. The error correction, which is the first lag of the residual entered with a right sign (it has a negative coefficient of −0.54, and it is statistically significant with p-value = 0.00). This is evidence of a certain return to long-run equilibrium after every short-run disequilibrium triggered by a change in any of the influencing variables. With a significantly negative significant coefficient of 54% (speed of adjustment), it shows that every short-run deviation is restored at the speed of 54% per annum. By implication, it takes approximately 2 years for full equilibrium to be restored. This result is not only supportive of a long-term convergence, but it falls within a predictable threshold given that it is less than unity (1). All other joint statistics are in shape and support the validity of the chosen estimation technique.

In the short run, only the positive fluctuation in the DMEO benchmark and the negative fluctuation in the NWTI benchmark have a significant relationship with the government revenue of OPEC Member countries. A positive shock in the DMEO benchmark leads to a 0.75 increase in government revenue of OPEC member countries (coefficient = 0.746, p = 0.0370), while a negative shock in the NWTI benchmark leads to a 1.23 decrease in government revenue of OPEC member countries. (coefficient = −1.233, p = 0.0477).

The long-run dynamics further highlight the asymmetries. In the long run, the positive and negative shocks of oil price benchmarks (DMEO, ICEB, NWTI, OPEC) exhibit asymmetric effects on government revenue. A positive shock in prices of DMEO benchmarks will result in a 2.79 decrease in government revenue. Whereas negative shock shows a non-significant relationship with government revenue (coefficient = 2.063, p = 0.0874). This signifies that the revenue profiles of OPEC member countries are more vulnerable to increases in the DMEO benchmarks. In the same manner, a positive fluctuation in ICEB will lead to a 2.54 increase in government revenue, while a negative shock will lead to a 4.77 decrease in government revenue of OPEC member countries. This implies that the government revenue is proportionately more sensitive to the downward movements in the ICEB prices. The result also revealed that the OPEC spot price has an asymmetric effect on government revenue; a negative fluctuation will result in a 1.96 increase in government revenue of OPEC member countries. This is more than the effect of the positive shock, whose unit increase will lead to a 0.63 increase in government revenue, signifying that government revenue is less sensitive to an OPEC spot price increase than to a decrease.

Both the positive and negative fluctuations in NWTI benchmarks exert a non-significant influence on the government revenue of OPEC member countries. The non-significant influence of this benchmark on the government revenue of OPEC member countries could be explained by the market segmentation or market relevance theory. This theory states that a commodity might be so segmented from a particular market or geographical location in such a way that a particular benchmark in such a market or region will have no significant effect on it. NYMEX WTI is a U.S.-centric benchmark, and according to the OPEC statistical bulletin, the crude oil export of OPEC member countries to the American region is minor relative to other continents. Between 2019 and 2024, OPEC member countries exported an annual average of less than 5% of their total oil export to this region. Specifically, they exported less than 3% of their total export was to America region in 2019 (that is 1183 out of 21,159 volume exported), less than 5% in 2020 (811 out of 18,481 exported), less than 5% in 2021 (890 out of 18,577 volume exported), and approximately 5% in 2022 (1027 out of 20,304 volume exported), and exactly 5.2% in 2023 (1027 of the 19,707 volume exported). These statistics provide reasons as to why the responsiveness of revenue profiles of OPEC member countries is not significant.

The asymmetric responses can be justified by OPEC’s pricing power and fiscal dependency on oil. Revenue is more sensitive to price drops (e.g., ICEB_NEG) due to rigid expenditure commitments, while price increases may not translate proportionally into higher revenue due to production adjustments or hedging. The stronger reaction to DMEO_POS and ICEB_NEG suggests that benchmark pricing differentials and market speculation influence OPEC revenues. The nonlinear ARDL result highlights the structural vulnerabilities and asymmetries in OPEC revenue profiles, driven by external price shocks and exchange rate movements.

Robustness Check

The validity and robustness of the panel ARDL estimates are confirmed by the estimation of selected panel model such as the fully modified panel least squares, pooled least squares, fixed effect and random effect estimators as reported in

Table 8. FMOLS and Panel Least Square Results.

Panel Fully Modified Lest Squares (FMOLS)
Variable	Coefficient	Std. Error	t-Statistic	Prob.
NWTI_NEG	−0.88	0.06	−14.79	0.00
NWTI_POS	0.15	0.05	2.66	0.01
ICEB_NEG	3.21	0.06	53.58	0.00
ICEB_POS	−0.42	0.07	−6.02	0.00
DMEO_NEG	−2.12	0.07	−32.13	0.00
DMEO_POS	0.27	0.05	5.01	0.00
OPEC_NEG	−0.34	0.07	−4.65	0.00
OPEC_POS	0.60	0.05	12.28	0.00
EXCR_NEG	−0.01	0.04	−0.20	0.84
EXCR_POS	−0.14	0.04	−3.34	0.00
R2			0.49
Adjusted R2			0.42
DW Stat			1.567 approx. 2
Panel Least Squares
Variable	Coefficient	Std. Error	t-Statistic	Prob.
NWTI_NEG	−0.13	0.71	−0.192	0.85
NWTI_POS	0.39	0.26	1.51	0.13
ICEB_NEG	1.41	2.27	0.62	0.54
ICEB_POS	0.06	0.97	0.06	0.95
DMEO_NEG	−0.91	1.44	−0.63	0.53
DMEO_POS	−0.70	1.00	−0.70	0.48
OPEC_NEG	−0.27	0.82	−0.34	0.74
OPEC_POS	0.38	0.40	0.95	0.34
EXCR_NEG	−0.00	0.00	−0.48	0.63
EXCR_POS	−0.05	0.01	−9.94	0.00
R2	0.26		F-Stat	12.98
Adjusted R2	0.24		Prob.	0.00
DW Stat	2.01 approx 2
Panel Least Square (Fixed Effect)
Variable	Coefficient	Std. Error	t-Statistic	Prob.
NWTI_NEG	−0.00	0.01	−0.12	0.90
NWTI_POS	0.01	0.00	1.43	0.15
ICEB_NEG	0.01	0.04	0.30	0.77
ICEB_POS	0.01	0.02	0.30	0.77
DMEO_NEG	0.00	0.03	0.11	0.91
DMEO_POS	−0.02	0.02	−0.92	0.36
OPEC_NEG	−0.01	0.01	−0.57	0.57
OPEC_POS	0.0	0.01	1.11	0.27
EXCR_NEG	9.72	0.00	0.84	0.40
EXCR_POS	−0.00	0.00	−10.58	0.00
R2	0.81		F-Stat	75.98
Adjusted R2	0.80		Prob.	0.00
DW Stat	2.11 approx 2
Cross-Section Random Effects
Variable	Coefficient	Std. Error	t-Statistic	Prob.
NWTI_NEG	−0.00	0.013	−0.12	0.91
NWTI_POS	0.01	0.00	1.43	0.15
ICEB_NEG	0.01	0.04	0.29	0.77
ICEB_POS	0.01	0.02	0.30	0.77
DMEO_NEG	0.00	0.03	0.12	0.91
DMEO_POS	−0.02	0.02	−0.92	0.36
OPEC_NEG	−0.01	0.01	−0.56	0.57
OPEC_POS	0.01	0.01	1.12	0.26
EXCR_NEG	8.	0.00	0.77	0.44
EXCR_POS	−0.00	0.00	−10.83	0.00
R2	0.42		F-Stat	26
Adjusted R2	0.40		Prob.	0.00
DW Stat	1.81 approx 2

below.

The goodness of fit of the panel model varied across estimators, with the fixed effect model showing the highest explained variation of 81% and the pooled least squares showing the least of 26%. The fully modified least squares show an explained variation of 49% and the random effect reports 42%. All the models report that the overall panel regression is statistically significant, as evidenced by the F-stat and the associated probability values. Suspicion of autocorrelation is erased by the Durbin Watson statistics, which in all cases are approximately equal to 2.

The elasticity of government revenue to the studied oil price benchmarks in the static models of pooled least squares, fixed effect and random effect was found to be non-significant. Significant values of almost all the explanatory variables are found in the fully modified panel least squares. This confirms the need for dynamic models in the evaluation of the oil price and revenue nexus.

Evidently, the static models prove to be inappropriate for the investigation of the dynamics of oil price and government revenue. The static models ignore the time dynamics of variables and do not account for lagged and differenced effects over time. Static models treat time points as independent, focusing on cross-sectional comparisons without considerations for changes over time [45]. FMOLS, which shares dynamic properties with the panel linear and nonlinear ARDL used in this study, offers strong supportive evidence. Based on the FMOLS result presented in Table 8 below, all the parameters exert similar influences as those of the Nonlinear ARDL on the revenue profile of OPEC member countries in terms of magnitude, with only a few deviations in the direction of influence. The results of the FMOLS support the correlation of the proof of evidence with the power of tests.

Country-specific cointegrating graph of the revenue and oil price relation is shown in Appendix A. This further illustrates the dynamic properties of the investigated relationship and support the long-run impact of oil price movement on the revenue profile of OPEC member countries.

Discussion of findings

Based on the empirical findings above, the global oil benchmarks and fiscal revenues of OPEC member countries have a complex relationship. The key findings, such as the contradictory effect of increasing DME Oman price and strong asymmetries in the increment in the ICE Brent and OPEC basket price, are discussed below:

The most contradictory result from our findings is the negative long-run correlation between DME Oman price changes and government revenue. Ordinarily, it would be assumed that an increase in oil prices would have a net positive impact on oil-exporting countries. The negative value of DME Oman positive shock (−2.79), however, indicates a more complicated dynamic based on market structure and regional geopolitics. This contradictory finding might be explained in line with economic and geographical rationales.

Economically, DME Oman is the main pricing benchmark of oil exports to Asia, the largest and fastest-growing market of OPEC oil. Oil imports by countries like China, India, Japan, South Korea and other Asian countries are priced using the DME Oman benchmark. A prolonged increase in this benchmark might render OPEC crude oil more expensive to its most significant consumers. This might result in what is termed ‘demand destruction where these Asian countries, especially the emerging ones, might be compelled to reduce their oil demand by drawing from their strategic petroleum reserves to mitigate costs, seek more affordable suppliers from non-OPEC producers, reduce consumption through efficiency measures and intensify investments in alternative energy sources such as solar, wind and nuclear. Additionally, the main price stabilization mechanism used by OPEC is the adjustment of production quotas. DME Oman price spike may be an indicator of an overheated market, and OPEC might increase production to stabilize prices and retain market share. And should the price increase be a result of supply fluctuation (e.g., geopolitical tension in the Strait of Hormuz), additional production by the other members may not completely compensate for the revenue lost due to the decreased supply, resulting in a net reduction in revenue to the organization. On the other hand, the model indicates that a fall in prices of DME Oman has no serious negative effect on the revenue, perhaps due to lower prices attracting enough demand in Asia to keep or even grow the total revenue in the form of increased volumes, a classic price-elastic response.

Geographically, the DME Oman contradictory findings can be explained in line with the Asian premium and strategic positioning rationale. Over the past decades, Asian consumers have been complaining about an Asian Premium, in which they pay a higher price for Middle Eastern crude compared to European or American buyers. This premium will be exacerbated by an increase in the DME Oman benchmark, creating political friction and prompting Asian countries to diversify their supply sources away from OPEC to other non-OPEC producers. Additionally, the members of OPEC, especially the Gulf Cooperation Council (GCC), are interested in a long-term and stable relationship with Asian consumers. Persistent increment in the DME Oman benchmarks might be perceived as opportunistic, jeopardizing these strategic partnerships. Therefore, what appears as a revenue loss in a purely quantitative model might be a strategic choice to preserve long-term market access and political alliances, which are crucial for sustained fiscal stability. This finding is in line with the work of [10,33,35].

The findings also revealed that a negative in ICE Brent is more detrimental to government revenue than the beneficial impact of a positive shock. This can be justified through fiscal rigidity and expenditure commitments. OPEC governments, particularly those with high proportions of the public sector and social welfare programs, cannot easily trim their spending when revenue drops. Budgets are usually pegged to optimistic oil price forecasts. A crash in prices leaves these countries in severe fiscal deficits, which have to either rely on drawing down sovereign wealth funds or accumulate debt. The impact is immediate and severe, thus the large, significant coefficient for negative shocks. Also, during a price spike, the revenue boom from such a price increment is usually moderated. The revenue boom might be used to replenish the sovereign fund rather than being expended. Moreover, certain nations might have hedging mechanisms which limit their profit above a specific price. Also, OPEC+ can jointly decide to reduce output to avoid overheating the market and thus voluntarily forego some potential revenue to achieve stability. This creates a scenario where the upside revenue potential is managed and capped, while the downside risk is fully exposed.

The findings revealed that revenue is more sensitive to a decrease in the OPEC basket price (coefficient of −0.96) than to a positive shock (coefficient of 0.63). This can be justified in line with OPEC’s core dilemma. The major objective of OPEC is the stabilization of the global oil market. A decrease in the OPEC reference basket will present an existential threat to its members, prompting a strong and unified response through coordinated production cuts. This collective action to defend the price floor of global oil has a clear and significant positive effect on mitigating revenue erosion, captured by the strong response to negative shocks. Likewise, a significant increase in the OPEC reference basket price will test the unity of the organization. OPEC member countries have demonstrated what is termed ‘prisoner’s dilemma’ in the past. Their overreliance on oil revenue tempts individual members to cheat on their production quotas to maximize revenue during a price spike. Additionally, an excessive increase in the OPEC reference basket usually attracts increased production from non-OPEC producers and accelerates the global transition to renewable energy. Therefore, OPEC often acts to moderate price increases to maintain market share and long-term demand. This moderating influence means that revenue does not increase as dramatically during price upswings, leading to a smaller, less sensitive coefficient for positive shocks. The OPEC basket, therefore, acts as a stabilizer: it powerfully cushions against falls but deliberately dampens excessive gains. These findings echoed the discovery of [7,10,23,34,44].

The comparison of our findings with existing literature revealed that in some cases, our results corroborate the established findings of previous studies, while in some cases, a more nuanced analysis reveals some critical distinctions. For instance, our finding that a positive increase in DME Oman results in decrease in government revenue of OPEC member countries corresponds to the work of [34], who discovered that Nigerian government revenue has a negative and significant relationship with global oil prices in both the short and long run. However, although it is also in line with the work of [14,37] in terms of magnitude, as they all found government revenue of oil-exporting countries to be a significant function of global oil price, it contradicts in terms of direction of influence. They posited that the direction of responsiveness of the revenue profiles of oil-exporting countries to oil price fluctuations is dependent on the economic status of each country. Countries with a diversified economy are likely to be more resilient to oil price shocks.

Likewise, the findings that ICE Brent negative shocks have a larger impact than positive shocks align with the works of [10] who also documented the asymmetric revenue responses to OPEC states. However, our disaggregated approach revealed that this asymmetry is not consistent across all benchmarks, a nuance not captured by studies using aggregated oil prices such as [1,7,15]. The non-significant influence of NYMEX WTI benchmark on the revenue profile of OPEC member countries aligned with the market segmentation hypothesis in the work of [19] but contradicts the assumptions of a globally integrated market. Similarly, refs. [5,6] also asymmetrically investigated the responsiveness of government revenue of OPEC countries to oil price shocks and discovered a significant relation between them. This is consistent with our finding of an increased in revenue when there is a positive shock in the OPEC reference basket.

The findings of this study highlight the fact that the revenue profiles of OPEC member countries are caught in a trilemma of maximizing short-term income, maintaining long-term market share, and managing geopolitical relationships.

6. Conclusions

The study examined the symmetrical and asymmetrical responsiveness of the revenue profiles of OPEC member countries to oil price fluctuations, providing critical insights into the fiscal stability of these oil-dependent economies. Despite the efforts for alternative and environmentally friendly sources of energy, crude oil is still the global main source of energy, hence its importance to both the exporting and importing countries. OPEC was established in 1960 to stabilize the oil market through coordinated production policy among its members. However, the revenue of its members is still susceptible to external shocks, geopolitical tensions, as well as demand–supply imbalances, as exemplified in the historical events such as the 1970s oil embargo, the 2008 financial crisis, COVID-19, and the Russia–Ukraine conflict. These fluctuations necessitated the understanding of the impact of both the positive and negative volatility of global oil prices on the government revenue of OPEC member countries.

Despite the interventions by OPEC, the revenue profile of their member countries shows varied levels of sensitivity to various oil price benchmarks such as NYMEX WTI, ICE Brent, DME Oman, and the OPEC reference basket. To investigate these dynamics, this study employed both linear and nonlinear panel ARDL models to capture both symmetrical and asymmetrical effects.

The linear panel ARDL results revealed that, in the short run, the revenue profile of OPEC member countries is inelastic to fluctuations in these oil price benchmarks. However, in the long run, government revenues of OPEC countries are positively and significantly responsive to changes in ICE Brent, NYMEX WTI, and OPEC spot prices, and have a negative relationship with prices of DME Oman. This implies that the rise in the prices of ICE Brent, NYMEX WTI and OPEC will boost revenues, but the increase in the prices of DME Oman can potentially diminish revenues because of the possible demand crash in the Asian markets.

In contrast, the nonlinear panel ARDL model revealed asymmetric effects, where government revenues respond differently to positive and negative oil price shocks. Notably, negative shocks in ICE Brent prices had a more pronounced adverse impact on revenues than positive shocks, highlighting fiscal vulnerabilities during price declines. Similarly, positive shocks in DME Oman prices reduced revenues in the long run, while negative shocks had no significant effect, suggesting an asymmetric dependency on this benchmark. The OPEC spot price also exhibited asymmetry, with revenue being more sensitive to price decreases than increases. Interestingly, NYMEX WTI fluctuations had no significant impact, likely due to the minimal export volumes of OPEC members to the U.S. market, reinforcing the market segmentation theory.

This study demonstrates that the revenue profile of OPEC member countries is more vulnerable to oil price fluctuations in the long run than in the short run. The asymmetrical effects accentuate the increased sensitivity to price declines, emphasizing the need for robust fiscal policies, diversification strategies, and risk-hedging mechanisms to mitigate revenue instability. The findings also underscore the varying influence of different oil benchmarks, with DME Oman posing unique challenges due to its regional demand dynamics.

Drawing inferences from the empirical findings, this study proposed some recommendations for policymakers in OPEC nations, which include benchmark-specific fiscal hedging. That is, given the detrimental impact of a decrease in some of the benchmarks, the decision-makers in these countries should adopt targeted hedging strategies. This might involve put options linked specifically to various benchmarks to mitigate damaging revenue shortfalls during a market downturn. Additionally, in order to prevent or reduce vulnerability to specific benchmarks, these countries should adopt strategic diversification of export markets. This should reduce revenue vulnerability to a demand crash as a result of an increase in a specific benchmark price. The slow speed of adjustment to equilibrium accentuates the importance of robust sovereign wealth funds and sound fiscal structures. Therefore, decision-makers should pursue the policy of strengthening fiscal buffers and institutional frameworks. This will mandate automatic transfers of funds to the sovereign wealth during the oil boom that will mitigate the revenue fall during a price decrease. And most importantly and as recommended by previous studies, these countries should intensify their diversification effort to reduce their fiscal dependency on oil revenue.

Although this research work offers valuable insight and contributes to existing knowledge, its findings should be interpreted in the context of some limitations that provide opportunities for future research work. The model might suffer from omitted variable bias as some critical factors, such as institutional quality and technological advancements in renewable energy, which might impact fiscal resilience, were not included in the model due to data and scope constraints. Also, the model treats global oil benchmarks as exogenous. However, the collective production decisions of OPEC members are a primary factor influencing these very benchmarks. This interdependence suggests that the relationship is more simultaneous than a one-way causal link from prices to revenue. And while the Panel ARDL model controls for some unobserved heterogeneity, it might be incapable of explaining country-specific endogeneity. The specific fiscal policies, distinct subsidy regimes, and different political economies in each OPEC member country may affect the revenue collection as well as the exposure of the country to the shocks in oil prices, which may cause bias in the pooled estimates. Furthermore, whilst this study treats benchmarks as exogenous, OPEC’s production decision themselves influence global prices, introducing potential simultaneity bias that future research could address though instrumental variable approaches.

This study further demonstrates that the revenue profile of OPEC member countries is more vulnerable to oil price fluctuations in the long run than in the short run. The asymmetrical effects accentuate the increased sensitivity to price declines, emphasizing the need for robust fiscal policies, diversification strategies, and risk-hedging mechanisms to mitigate revenue instability. The findings also underscore the varying influence of different oil benchmarks, with DME Oman posing unique challenges due to its regional demand dynamics. Policymakers in OPEC nations should prioritize structural reforms to reduce oil dependency while leveraging OPEC’s collective mechanisms to stabilize revenues in an increasingly uncertain global energy landscape. This research also indicates that, while SDGs 7 and 13 adoption is crucial for reducing carbon emissions, it also presents an opportunity for OPEC nations to diversify their economies. These fiscal strategies not only enhance revenue stability but also create fiscal space for investments in renewable energy infrastructure, aligning with SDG 7 (affordable and clean energy) and SDG 13 (climate action). As the world moves towards a low-carbon future, the OPEC must adopt sustainable energy or confront future socio-economic, geopolitical, and environmental challenges [46].