The pursuit of de-dollarization by BRICS, including Brazil, Russia, India, Chin and South Africa, though indirect, represents a development of growing global relevance in the evolving international monetary order. This study explores the stability of BRICS currencies within this broader context, emphasizing the bloc’s recent symbolic initiative to issue a common banknote. It advances optimal currency area (OCA) theory by integrating macroeconomic and institutional dimensions to assess the viability of a new currency framework.
This paper introduces the Currency Clear Condition Index (CCCI), which combines BRICS national currencies and sanction data using the Morris method. A panel vector autoregressive model is then applied to assess the effects of real gross domestic product (GDP), political stability (PS) and logistical shocks on the CCCI, drawing on data from 2010 to 2023.
Results show that Russia has exhibited the highest CCCI values since 2013, reflecting elevated exchange rate volatility and persistent sanctions. Impulse response analysis reveals that a 1% increase in the Logistics Performance Index raises the CCCI by nearly 2% for about 2.5 years, while a 1% improvement in PS reduces it by approximately 2.5% for 1.5 years. A 1% increase in GDP lowers the index by around 0.5% over 2.5 years. These findings indicate that BRICS de-dollarization depends more on macroeconomic strength and institutional stability than on logistical or symbolic actions.
The analysis is confined to the 2010–2023 period and may not fully capture post-2023 developments. Future studies could refine the CCCI and evaluate its applicability to other regional blocs pursuing currency cooperation.
The analysis is limited to the 2010–2023 period and may not capture emerging dynamics post-2023. Future research can refine the CCCI and examine its applicability to other regional blocs.
The study broadens OCA theory by incorporating political and logistical asymmetries and introduces the CCCI as a novel metric for assessing currency-clearing feasibility under sanctions and financial fragmentation.
1. Introduction
The BRICS countries–Brazil, Russia, India, China and South Africa–have increasingly become central players in global economic and geopolitical discussions. The first BRICS summit, held in Russia in 2009, marked the beginning of a significant intergovernmental platform aimed at promoting foreign investment, economic collaboration and multilateral governance. South Africa joined the bloc in 2010, and by January 2024, Iran, Egypt, Ethiopia and the United Arab Emirates (UAE) joined as new members. Today, BRICS nations collectively account for nearly 30% of the world's landmass and approximately 45% of the global population, representing a substantial share of the global economy . According to the United Nations (2024), China's share of the world's gross domestic product (GDP) was 17.6%, while South Africa's was 0.4%. India, Brazil and Russia accounted for 3.5%, 2.2% and 2.0%, respectively (United Nations Statistics Division – National Accounts, 2024). This indicates that approximately 25% of the world's GDP–and more than 80% of the total GDP of BRICS members–comes from the bloc's original members: China, India, Brazil, Russia and South Africa. Furthermore, around 35% of the world's population resides in China and India, making these two countries the most demographically dominant members of BRICS.
At the 16th BRICS Summit in Kazan, Russia unveiled a symbolic BRICS banknote - a gesture that reignited debates over the dominance of the US dollar and the bloc's de-dollarization agenda (Siddiqui, 2024). Far from being merely a commemorative artifact, the banknote was widely interpreted as a move toward the creation of a “political currency” - one that symbolizes monetary sovereignty, challenges Western-centric financial architectures and reflects BRICS’ broader aspirations to reshape global economic governance.
Over time, the BRICS bloc has broadened its focus from economic growth and investment to deeper geopolitical and financial collaboration. One of its primary objectives is to reduce reliance on the US dollar and create a more diversified, multipolar global financial system. This shift is especially relevant in the context of rising global tensions and economic uncertainties. In February 2023, Brazil authorized its first renminbi clearing bank, established by the People's Bank of China – a pivotal step in BRICS’ pursuit of de-dollarization (Mayer, 2024). Additionally, China is expanding the global influence of its currency, the yuan, with recent agreements in Argentina and Brazil aiming to reduce reliance on the US dollar. This shift reflects China's strategic efforts to offer alternatives to the dollar in Latin America (Bouchard, 2023, September 27).
However, the possibility of introducing a shared currency for BRICS has been met with significant debate. While discussions on alternatives to the US dollar have gained traction, the idea of a unified currency faces numerous challenges due to the diverse political, economic and logistical realities of BRICS member countries. In 2024, President-elect Donald Trump issued a warning to BRICS nations about the potential risks of creating a new currency, threatening tariffs on exports from any country that challenge the US dollar's dominance (Burrows & Braml, 2025). Despite this, the question of whether BRICS could establish a common currency remains crucial, especially as the bloc seeks to assert its economic influence on the global stage. Putin has emphasized that a BRICS currency would serve as a strategic alternative to the US dollar, particularly in the context of increasing sanctions pressure on the bloc (Liu & Papa, 2022). As such, the BRICS currency can be viewed as a “political money - a tool” not only aimed at facilitating international trade within the BRICS bloc but also designed to circumvent US-led financial sanctions. The idea behind this currency is rooted in the geopolitical reality that the US dollar has long been a key lever of US foreign policy, especially in exerting economic pressure through sanctions (Kroes, 2023). By creating an alternative to the dollar, BRICS aims to diminish the bloc's vulnerability to US sanctions, which have been weaponized to target countries like Russia and Iran (Mosharrafa, 2024).
The potential BRICS currency could enable member states to bypass the US financial system and engage in direct trade using their own currencies or a new common currency, thereby reducing the impact of sanctions (Bastanifar, Khan, & Koch, 2025). For instance, Russia's experience with sanctions since 2014, which targeted its financial, energy and defense sectors, has pushed it to seek alternatives that reduce dependency on the dollar (Oxenstierna & Olsson, 2015). This has been further demonstrated by Russia's push for using the Russian ruble or local currencies for trade with BRICS nations and other partners like China (Kondratov, 2021; Vinokurov, Ahunbaev, & Zaboev, 2022)
The theory of optimal currency area (OCA), first proposed by Robert Mundell in 1961, provides a framework for understanding the potential benefits and challenges of currency integration. According to OCA theory, countries within a certain region could benefit from sharing a currency if they meet specific criteria, including economic alignment, political cooperation and the capacity to absorb external economic shocks (Mundell, 1961). For BRICS, the concept of an OCA offers a potential pathway to reduce reliance on the US dollar. However, given the disparities in economic development, political systems and financial integration across BRICS countries, the feasibility of implementing a unified currency is complex. This study builds on the theory of OCA by exploring how economic, political and logistical shocks affect the de-dollarization process within the BRICS bloc. Following Haralambides, Bastanifar, Khan and Shahryari (2024), the concept of distance is expanded beyond physical and economic factors to also encompass shocks that affect macroeconomic variables. While their work primarily treats distance as a shock influencing economies, this study adopts a broader approach by incorporating logistical, economic and political shocks. These shocks–arising from trade uncertainties, geopolitical tensions and real GDP disruptions–play a critical role in shaping the capacity of BRICS countries to reduce their dependence on the US dollar. To evaluate these dynamics, the shocks are applied to the Currency Clear Condition Index (CCCI) using a panel vector autoregression () framework and data from 2000 to 2022, in order to assess how the CCCI of Brazil, Russia, India, China and South Africa responds to fluctuations in a basket of currencies.
Existing scholarship on monetary integration has been dominated by the OCA framework (Mundell, 1961; McKinnon, 1963; Kenen, 1969) and its application to the Eurozone (Bayoumi & Eichengreen, 1997; De Grauwe, 2018). Parallel work on currency power and de-dollarization highlights the entrenched role of the US dollar in global finance (Eichengreen, 2011; Cohen, 2015) and the political challenges of reserve currency transitions. More recent studies explore BRICS institutions such as the New Development Bank (NDB) (Armijo & Roberts, 2014; Stuenkel, 2020; Khan, Bastanifar, & Mohammadi, 2025) and analyze the bloc's cautious moves toward financial autonomy (Liu & Papa, 2022; Mosharrafa, 2024). Yet, this body of work has three important limitations. First, much of it is largely theoretical or rhetorical, focusing on aspirations rather than measurable conditions for integration. Second, although some studies consider BRICS monetary cooperation (e.g. Bastanifar et al., 2025), they rarely examine how sanctions, political instability and logistical disruptions shape de-dollarization outcomes. Third, existing OCA-based approaches do not explicitly account for the costs of currency clearing under such shocks.
To evaluate this dynamic, we apply a PVAR model to examine how shocks to real GDP, political stability (PS) and logistical performance impact the stability of BRICS currencies in relation to the US dollar. Our analysis, using data from 2010 to 2023, shows that Russia consistently exhibits the highest CCCI, particularly since 2013, which reflects the effects of sanctions and political instability on its currency. Impulse response functions (IRF) further reveal that shocks to logistics, as measured by the logistics performance index (LPI), increase the CCCI, suggesting higher currency clearing costs. Conversely, shocks to PS and GDP result in a decrease in the CCCI, indicating that stronger political and economic stability may support the de-dollarization process.
The findings of this study provide important insights into the feasibility of BRICS currency integration. They suggest that while external logistical factors, such as trade disruptions, do not directly support de-dollarization, improvements in political and economic stability are key drivers in reducing dependence on the US dollar. This highlights the importance of BRICS' internal cohesion and alignment on economic and political issues for achieving its goals of currency integration and de-dollarization. This paper addresses this gap by introducing the CCCI as a new metric for assessing clearing costs under sanctions and exchange-rate pressures and by applying a PVAR framework to evaluate the effects of PS, GDP and logistical shocks. In doing so, the study expands the OCA framework into the asymmetric distance optimal currency area (ADOCA), explicitly integrating political and logistical uncertainties into the analysis. The value of this approach lies in providing a systematic and quantitative assessment of BRICS de-dollarization efforts, moving beyond debates over symbolism toward an empirical evaluation of currency integration feasibility.
This study aims to address three key research questions that explore the dynamics of BRICS' potential for de-dollarization and currency integration. First, why is the concept of an OCA more relevant than physical proximity when considering BRICS as a potential currency bloc? This question investigates whether the economic, political and financial integration of BRICS countries can create the conditions necessary for a shared currency, even though these countries are geographically dispersed. It examines how economic alignment, rather than simple geographic proximity, may foster the benefits associated with a common currency. Second, how can a BRICS currency function as a clearing mechanism for intra-BRICS transactions? This question delves into the practical implications of introducing a BRICS currency, focusing on how such a currency could streamline financial exchanges and reduce reliance on the US dollar for cross-border transactions. It explores the potential benefits of a shared currency in simplifying trade and financial operations within the BRICS bloc and promoting greater economic integration. Third, how do economic, political and logistical shocks impact the process of de-dollarization within BRICS countries? This question examines how external shocks–such as trade disruptions, political instability and global financial crises–affect BRICS' efforts to reduce their dependence on the US dollar. This study explores the challenges and opportunities these shocks create in terms of shifting to alternative currencies and advancing the de-dollarization agenda within the bloc.
By addressing these questions, this paper expands on the theory of OCA and explores the potential for a BRICS currency to serve as a new international monetary instrument. This research contributes to understanding how economic, political and financial integration might shape the future of global finance, particularly in the context of de-dollarization.
2. BRICS motivations towards de-dollarization
The BRICS bloc has become increasingly motivated to reduce its reliance on the US dollar, with a focus on strengthening economic cooperation among member nations and exploring alternatives to the dollar in global finance. However, rather than pursuing the creation of a new currency to rival the US dollar or the euro, the BRICS countries are more interested in leveraging their political influence to address sanctions and restructure their financial interactions, thereby minimizing dependence on Western-dominated systems (Brown, 2023). President Vladimir Putin has made it clear that any new BRICS currency will not completely displace the role of the US dollar in global markets. As Putin emphasized, the goal is not to challenge the dollar's dominance directly, but to establish a financial system that allows for trade and payments among BRICS members without complete dependence on the West (Patel, 2023). This approach reflects Russia's cautious stance, recognizing that while reducing reliance on the dollar is important, an outright push to create a new global currency would likely not succeed in the current global order. Instead, Russia sees the BRICS currency as a tool for political leverage, designed to facilitate clearing and settlement of transactions within the bloc, especially during politically sensitive periods.
By positioning BRICS as a bloc with an alternative financial system, Russia is testing the limits of USA influence, but this remains a strategic move–rather than a total break from the dollar. This geopolitical maneuver reflects broader standoffs between Russia and the West, where BRICS serves as a counterweight to US sanctions and financial dominance (Asthana, 2025, March 16). Thus, the BRICS bloc's currency ambitions are increasingly political rather than economic in nature. The idea of creating a BRICS currency is not about competing with the US dollar or the euro as a global reserve currency, but rather about establishing a “political money” system–one that serves as a tool for trade and clearing in politically charged conditions. This would allow BRICS members to navigate transactions and trade without the direct interference of the US financial system, especially when dealing with sanctions or geopolitical pressures. Such a system would not be a safe haven for banking reserves like the US dollar or the euro, but rather a politically motivated mechanism for intra-BRICS trade and financial settlements, particularly in circumstances where the global political landscape is volatile and sensitive to US influence (Smith, 2021).
In a broader geopolitical context, this strategy allows BRICS countries, especially Russia, to strengthen their economic resilience while reducing their dependency on the US dollar. However, this approach faces significant hurdles, as the US dollar's dominance in global markets remains entrenched due to its liquidity, stability and universal acceptance. While BRICS countries are working to diversify their financial interactions, these efforts are not geared toward creating a new global currency, but rather toward creating an alternative that functions as a political tool in the face of rising tensions with Western powers (Davies, 2022).
The NDB and the contingent reserve arrangement are key institutional efforts that align with these political motivations, offering alternatives to Western-controlled financial institutions like the International Monetary Fund and World Bank (Rodriguez, 2023). These mechanisms, however, are not primarily focused on currency competition but on ensuring the BRICS nations' economic autonomy and providing a buffer against external pressures. Technological advancements, such as Central Bank Digital Currencies (CBDCs), are also part of this strategy to reduce reliance on the US dollar. Recent studies highlight that digital currencies like the e-CNY and the proposed digital ruble may facilitate cross-border settlement outside US-dominated payment systems (Auer, Cornelli, & Frost, 2020; Bank for International Settlements [BIS], 2021) thereby contributing to “digital de-dollarization”. Nevertheless, the core aim remains political leverage, with BRICS currencies–digital or otherwise–acting as tools for clearing and trade rather than as global reserve currencies (Nguyen, 2023).
3. Theoretical framework
The concept of an OCA was first introduced by Mundell (1961), though some argue its origins can be traced to Lerner's works in 1944 and 1947 (Scitovsky, 1984). The OCA theory seeks to address how a group of countries, each located in different regions, can adopt a unified currency while abandoning their national currencies (Andrade & Duarte, 2015). At its core, the theory suggests that regions where geographic and economic conditions enhance efficiency should consider adopting a common currency (Mundell, 1961).
Expanding on Mundell's work, scholars such as McKinnon (1963), Kenen (1969), Corden (1972) and Mundell himself (1975) emphasize that the optimality of a currency area hinges on the members sharing similar monetary needs (Tornielli, 2018). The monetary approach to exchange rates posits that a country's monetary policy directly determines the value of its exchange rate. Consequently, variations and fluctuations in exchange rates reflect responses to differing monetary requirements . This implies that greater diversity and instability in national currencies hinder economic convergence. Conversely, countries with aligned currency values and lower exchange rate volatility are more likely to form a successful OCA. Furthermore, proponents of monetary unions argue that aligning monetary policies across member nations can mitigate exchange rate instability, thereby enhancing economic integration (David, 2009). As such, exchange rates play a pivotal role in analyzing the viability of an OCA (Bayoumi & Eichengreen, 1996).
However, this theory has faced significant criticism. Studies such as those by Bofinger (1994), Bayoumi and Eichengreen (1997) and Mongelli (2008) argue that the OCA framework is overly biased toward smaller currencies and focuses narrowly on asymmetric real shocks. These critiques highlight the need for broader considerations. For instance, the COVID-19 pandemic underscored the importance of tailoring monetary instruments to individual country-specific shocks. Uniform monetary policy responses, such as adjustments in interest rates or quantitative easing, may not address the diverse vulnerabilities of different nations. Recognizing this, Bastanifar (2024) emphasizes that categorizing countries based on their varying susceptibility to shocks and adopting a strategic view of distance are critical in managing such disruptions.
Building on this, the integration of AD theory with the principles of OCA, first introduced by Haralambides et al. (2024), presents a novel framework–ADOCA. Unlike traditional OCA theory, which primarily emphasizes geographical proximity and trade flows, ADOCA centers on the idea that economic and political uncertainties–rather than just physical distance–are crucial factors in shaping the dynamics of currency integration. This expanded framework suggests that trade through economic corridors (Khan et al., 2024; Khan et al., 2023) can help stabilize regional currencies, facilitating the formation of an OCA. The key concept of ADOCA is that trade routes, especially those influenced by geopolitical and economic uncertainties, can either reduce or increase exchange rate volatility among the countries involved. If a trade route helps reduce exchange rate volatility–particularly amid political and economic shocks–the region may qualify as an ADOCA. Conversely, if such volatility increases due to these uncertainties, the region may not meet the ADOCA criteria and will resemble a more traditional OCA. The authors further develop the ADOCA model by incorporating various sources of uncertainty in an integrated currency framework, using econometric methods instead of the dynamic stochastic general equilibrium model applied in the original version of ADOCA.
This model emphasizes the stabilizing role that strategic trade routes can play in supporting optimal currency regions, particularly when political, economic and logistical shocks are taken into account. A key example of this potential is the cooperation between BRICS nations, which has shown promise for enhancing economic integration. Among the BRICS countries, differing economic structures and policies continue to present challenges, but their potential for greater currency integration remains significant. If trade through such corridors reduces exchange rate volatility among BRICS currencies, they may begin to meet the criteria for an ADOCA. Conversely, if volatility remains high, these regions would more likely function as traditional OCAs. Thus, the ADOCA version of the OCA theory emphasizes the critical role of political, economic and logistical shocks in determining the success of currency integration within regions. By focusing on the uncertainty that arises from these factors–rather than merely the physical distance between countries–ADOCA provides a more comprehensive understanding of how economic corridors can play a key role in creating a stable, integrated currency region.
4. Data and model
This section first presents the data required to analyze the research questions, including exchange rates, sanction intensity, the BRICS CCCI, PS, real GDP (constant 2015 US$) and the LPI. It then introduces the panel vector autoregression (PVAR) model used for the analysis.
4.1 Data
Table 1 outlines the variables employed in the model. The study utilizes panel data spanning the period from 2010 to 2023, covering Brazil, China, India, Russia and South Africa.
Factors influencing exchange rates
| Factors | Sources |
|---|---|
| Exchange rates | https://data.worldbank.org/indicator/PA.NUS.FCRF |
| Sanction intensity | https://www.globalsanctionsdatabase.com/ |
| BRICS currency clear condition index | Authors |
| Political stability | https://data.worldbank.org/indicator/PV.PER.RNK |
| Real GDP (Constant, 2015 US dollar) | https://data.worldbank.org/indicator/NY.GDP.MKTP.KD |
| Logistic performance index | https://lpi.worldbank.org/ |
| Factors | Sources |
|---|---|
| Exchange rates | |
| Sanction intensity | |
| BRICS currency clear condition index | Authors |
| Political stability | |
| Real GDP (Constant, 2015 US dollar) | |
| Logistic performance index |
Figure 1 illustrates fluctuations in the exchange rates of the BRICS nations against the US dollar. The variable is measured as the value of each national currency per US dollar (source: https://data.oecd.org/conversion/exchange-rates.htm). For example, in 2023, 82.6 Indian rupees were required to purchase 1 US dollar. The data show that from 2010 to 2023, the US dollar consistently appreciated relative to all five national currencies. This indicates that Brazil, China, South Africa, Russia and India each maintained weaker currencies compared to the US dollar throughout the period.
The vertical axis of the line graph is labeled “exchange rate (L C U per U S dollar, period average)” and ranges from 0 to 90 in increments of 10. The horizontal axis displays years from 2008 to 2024 in increments of 2 years. There are five colored lines on the graph. The legend at the bottom identifies the lines as “Brazil” (light blue), “China” (orange), “India” (green), “Russia Federation” (dark blue), and “South Africa” (brown). The line for “Brazil” starts at (2010, 1.76), increases slightly, passes through (2017, 3.19), and ends at (2023, 4.99). The line for “China” starts at (2020, 6.77), stays almost constant, passes through (2016, 6.64), and ends at (2023, 7.08). The line for “India” starts at (2020, 45.73), increases to (2016, 67.2), drops slightly to (2021, 73.92), and ends at (2023, 82.6). The line for “Russia Federation” starts at (2020, 30.37), increases to (2016, 67.06), drops to (2017, 58.34), and ends with fluctuations at (2023, 85.16). The line for “South Africa” starts at (2020, 7.32), increases gradually to (2016, 14.71), passes through (2020, 16.46), and rises to end at (2023, 18.45).Trend of the exchange rate of the five countries against the US dollar. Source: Authors’ calculations
The vertical axis of the line graph is labeled “exchange rate (L C U per U S dollar, period average)” and ranges from 0 to 90 in increments of 10. The horizontal axis displays years from 2008 to 2024 in increments of 2 years. There are five colored lines on the graph. The legend at the bottom identifies the lines as “Brazil” (light blue), “China” (orange), “India” (green), “Russia Federation” (dark blue), and “South Africa” (brown). The line for “Brazil” starts at (2010, 1.76), increases slightly, passes through (2017, 3.19), and ends at (2023, 4.99). The line for “China” starts at (2020, 6.77), stays almost constant, passes through (2016, 6.64), and ends at (2023, 7.08). The line for “India” starts at (2020, 45.73), increases to (2016, 67.2), drops slightly to (2021, 73.92), and ends at (2023, 82.6). The line for “Russia Federation” starts at (2020, 30.37), increases to (2016, 67.06), drops to (2017, 58.34), and ends with fluctuations at (2023, 85.16). The line for “South Africa” starts at (2020, 7.32), increases gradually to (2016, 14.71), passes through (2020, 16.46), and rises to end at (2023, 18.45).Trend of the exchange rate of the five countries against the US dollar. Source: Authors’ calculations
Table 2 presents the minimum (Min), maximum (Max), average (AVE) and standard deviation (SD) of national currencies per US$ between 2010 and 2023. The data show that Brazil recorded the lowest minimum exchange rate (1.67 per US$), while Russia exhibited the weakest currency overall, with the highest maximum value against the dollar. Brazil also maintained the lowest average exchange rate, whereas China demonstrated the lowest SD, indicating more effective management of exchange rate volatility compared to the other BRICS countries.
Min, Max, Ave and SD of the exchange rate of the five countries against the US dollar
| Country | Min | Max | AVE | SD |
|---|---|---|---|---|
| Brazil | 1.67 | 5.39 | 3.44 | 1.34 |
| China (People's Republic of) | 6.14 | 7.08 | 6.59 | 0.29 |
| India | 45.73 | 82.60 | 65.00 | 11.10 |
| Russia | 29.38 | 85.16 | 55.28 | 19.09 |
| South Africa | 7.26 | 18.45 | 12.70 | 3.55 |
| Country | Min | Max | AVE | SD |
|---|---|---|---|---|
| Brazil | 1.67 | 5.39 | 3.44 | 1.34 |
| China (People's Republic of) | 6.14 | 7.08 | 6.59 | 0.29 |
| India | 45.73 | 82.60 | 65.00 | 11.10 |
| Russia | 29.38 | 85.16 | 55.28 | 19.09 |
| South Africa | 7.26 | 18.45 | 12.70 | 3.55 |
Figure 2 illustrates the trend of sanction intensity imposed on BRICS countries between 2010 and 2023. According to the Global Sanctions Database (version 4), sanctions are classified into two categories: state sanctions and subject sanctions. Subject sanctions–including those targeting finance, trade and other sectors–are incorporated as weighted components within state sanctions (source: https://www.globalsanctionsdatabase.com/). The figure shows a clear increase in sanction intensity across BRICS members during this period. Details of the methodology used to measure sanction intensity are provided in Appendices 1–5.
The horizontal axis is marked with years ranging from “2010” through “2023” in one-year increments. The vertical axis is labeled “Sanction intensity” and ranges from 0 to 250 in increments of 50 units. The graph shows five lines. A legend below the graph identifies the five lines as “Brazil”, “China”, “India”, “Russian Federation”, and “South Africa”. The “Brazil” line remains at 0 from 2010 to 2023. The “China” line stays near 0 from 2010 to 2016, then increases gradually, passing through (2017, 8.74), (2019, 13.11), (2021, 27.97), then steeply increases to end at (2023, 39.34). The “India” line stays at 0 from 2010 to 2023. The “Russian Federation” line stays at 0 from 2010 to 2013, rises sharply to (2014, 50.00), remains near 50 from 2015 through 2018, increases slightly to (2021, 67.31), then rises steeply to (2022, 163.46), and continues upward to end at (2023, 220.28). The “South Africa” line remains close to 0 from 2010 to 2023, increasing slightly to (2023, 6.99). Note: All numerical data values are approximated.Trend of sanction intensity imposed on BRICS. Source: Authors’ calculations
The horizontal axis is marked with years ranging from “2010” through “2023” in one-year increments. The vertical axis is labeled “Sanction intensity” and ranges from 0 to 250 in increments of 50 units. The graph shows five lines. A legend below the graph identifies the five lines as “Brazil”, “China”, “India”, “Russian Federation”, and “South Africa”. The “Brazil” line remains at 0 from 2010 to 2023. The “China” line stays near 0 from 2010 to 2016, then increases gradually, passing through (2017, 8.74), (2019, 13.11), (2021, 27.97), then steeply increases to end at (2023, 39.34). The “India” line stays at 0 from 2010 to 2023. The “Russian Federation” line stays at 0 from 2010 to 2013, rises sharply to (2014, 50.00), remains near 50 from 2015 through 2018, increases slightly to (2021, 67.31), then rises steeply to (2022, 163.46), and continues upward to end at (2023, 220.28). The “South Africa” line remains close to 0 from 2010 to 2023, increasing slightly to (2023, 6.99). Note: All numerical data values are approximated.Trend of sanction intensity imposed on BRICS. Source: Authors’ calculations
To capture exchange rate conditions under political pressure, the BRICS CCCI is constructed using the Morris method. This approach, widely applied in the literature for composite index construction, has recently been employed in studies on economic resilience (Bastanifar & Shirkhani, 2025) and de-dollarization (Bastanifar et al., 2025).
Equation (1) shows the Morris imbalance index.
In this formula, Y represents the normalized score of the index, denotes the i-th variable in the time series and and are the minimum and maximum observed values of that variable. In this study, the BRICS CCCI ranges between zero and one (Ghaffary Fard, AbuNoori, & Nazari, 2022). To construct the index, the geometric mean of normalized exchange rates and sanction intensity is applied. The geometric mean is widely used in composite economic indices, such as the Human Development Index, because it is better suited than the arithmetic mean for capturing trends and growth rates across multiple domains (Spizman & Weinstein, 2008). As illustrated in Figure 3, a higher CCCI score reflects greater costs associated with clearing international transactions.
The vertical axis of the line graph is labeled “Currency Clear Condition Index (0 equals low to 1 equals high)” and ranges from 0.00 to 1.20 in increments of 0.20. The horizontal axis displays years from 2010 to 2023 in increments of one year. There are five colored lines on the graph. The legend at the bottom identifies the lines as “Brazil” (blue), “China” (orange), “India” (gray), “Russian Federation” (yellow), and “South Africa” (dark blue). There are two labeled rectangles: “sanctions in 2014” is placed over the year 2014, and “Russia-Ukraine conflict in 2022” is placed above the year 2022. The line for “Brazil” starts at (2010, 0), stays constant at 0 till 2020, and slightly increases to end at (2023, 0.01). The line for “China” starts at (2010, 0.03), stays constant at 0.03 till 2016, and slightly increases to end at (2023, 0.010). The line for “India” starts at (2010, 0), stays constant at 0, and ends at (2023, 0). The line for “Russian Federation” starts at ( 2010, 0.04), reaches (2013, 0.00), and increases sharply to (2016, 0.43), and moves further upward to end at (2023, 1). The line for “South Africa” starts at (2010, 0), stays constant at 0 till 2018, and slightly increases to end at (2023, 0.006). The line for “Russian Federation” remains mostly above the other lines.Trend of BRICS CCCI (0 = low to 1 = high). Source: Authors’ calculations
The vertical axis of the line graph is labeled “Currency Clear Condition Index (0 equals low to 1 equals high)” and ranges from 0.00 to 1.20 in increments of 0.20. The horizontal axis displays years from 2010 to 2023 in increments of one year. There are five colored lines on the graph. The legend at the bottom identifies the lines as “Brazil” (blue), “China” (orange), “India” (gray), “Russian Federation” (yellow), and “South Africa” (dark blue). There are two labeled rectangles: “sanctions in 2014” is placed over the year 2014, and “Russia-Ukraine conflict in 2022” is placed above the year 2022. The line for “Brazil” starts at (2010, 0), stays constant at 0 till 2020, and slightly increases to end at (2023, 0.01). The line for “China” starts at (2010, 0.03), stays constant at 0.03 till 2016, and slightly increases to end at (2023, 0.010). The line for “India” starts at (2010, 0), stays constant at 0, and ends at (2023, 0). The line for “Russian Federation” starts at ( 2010, 0.04), reaches (2013, 0.00), and increases sharply to (2016, 0.43), and moves further upward to end at (2023, 1). The line for “South Africa” starts at (2010, 0), stays constant at 0 till 2018, and slightly increases to end at (2023, 0.006). The line for “Russian Federation” remains mostly above the other lines.Trend of BRICS CCCI (0 = low to 1 = high). Source: Authors’ calculations
In order to calculate the CCCI, as detailed in Appendix 6, we first compute the geometric mean of sanction intensity and exchange rate (Ex) with the same weight for each country during the period from 2000 to 2023 (. Then, the minimum ( and maximum () score are then identified. The minimum value () is set to zero for countries that experienced no sanction, while the maximum () is 136.253, observed for Russia in 2023 when the geometric mean of sanction intensity (218) and the exchange rate (85.16 rubles per US dollar) reached this level.
For years with zero sanction intensity, , is defined as zero, whereas for non-zero sanction intensity, would be a score between zero and unity. For example, in 2022, Russia's exchange rate was 64.48 rubles per US dollar and sanction intensity was 162. The geometric mean of these two variables equaled 105.33 (). According to Equation (1), with equals 136.253 and = 0, yielded a normalized index value of = 0.773. Rounded to two decimal places, this equals 0.77.
To assess the robustness of the index, the Pearson Correlation Coefficient (PCC) between the CCCI and sanction intensity was calculated. The results indicate PCC values of 100%, 99%, 95% and 81% for Brazil, China, Russia and South Africa, respectively. Since India did not experience sanctions during the study period, the Pearson correlation is not applicable to this country.
These findings suggest that as sanctions intensify, the CCCI–serves as a proxy for the cost of international clearing–also increases. This rising cost is largely the result of political restrictions on the use of the US dollar, which strengthens the motivation for de-dollarization. In other words, a higher CCCI score reflects greater incentives for BRICS members to bypass the US dollar in trade, driven primarily by political rather than purely economic factors.
Figure 4 illustrates fluctuations in PS across the five BRICS countries over the study period. The data show a notable improvement in India, where the index increased from 11.37% in 2010 to a peak of 24.06% in 2022, before slightly declining to 21.33% in 2023. In contrast, Brazil, China, Russia and South Africa experienced downward trends in PS, reflecting growing political uncertainty in recent years.
The horizontal axis is marked with years and ranges from “2010” through “2023” in one-year increments. The vertical axis is labeled “Political Stability (0 equals low to 100 equals high)” and ranges from 0.00 to 60.00 in increments of 10.00 units. The graph shows five lines. A legend below the graph identifies the five lines as “Brazil”, “China”, “India”, “Russia”, and “South Africa”. The “Brazil” line begins at (2010, 46.24), peaks at (2012, 46.38) and (2014, 42.84), and ends at (2023, 28.23). The “China” line begins at (2010, 25.53), peaks at (2014, 28.79) and (2017, 38.58), and ends at (2023, 24.82). The “India” line begins at (2010, 11.35), peaks at (2017, 18.30) and (2020, 18.3), and ends at (2023, 21.28). The “Russia” line begins at (2010, 18.58), peaks at (2013, 22.13) and (2018, 27.23), and ends at (2023, 13.19). The “South Africa” line begins at (2010, 44.96), peaks at (2013, 45.11) and (2018, 38.3), and ends at approximately (2023, 20.99). Two vertical reference lines highlight “sanctions in 2014” and “Russia-Ukraine conflict in 2022”. Note: All numerical data values are approximated.Trend of the PS for five countries (percentile Rank). Source: Authors’ calculations
The horizontal axis is marked with years and ranges from “2010” through “2023” in one-year increments. The vertical axis is labeled “Political Stability (0 equals low to 100 equals high)” and ranges from 0.00 to 60.00 in increments of 10.00 units. The graph shows five lines. A legend below the graph identifies the five lines as “Brazil”, “China”, “India”, “Russia”, and “South Africa”. The “Brazil” line begins at (2010, 46.24), peaks at (2012, 46.38) and (2014, 42.84), and ends at (2023, 28.23). The “China” line begins at (2010, 25.53), peaks at (2014, 28.79) and (2017, 38.58), and ends at (2023, 24.82). The “India” line begins at (2010, 11.35), peaks at (2017, 18.30) and (2020, 18.3), and ends at (2023, 21.28). The “Russia” line begins at (2010, 18.58), peaks at (2013, 22.13) and (2018, 27.23), and ends at (2023, 13.19). The “South Africa” line begins at (2010, 44.96), peaks at (2013, 45.11) and (2018, 38.3), and ends at approximately (2023, 20.99). Two vertical reference lines highlight “sanctions in 2014” and “Russia-Ukraine conflict in 2022”. Note: All numerical data values are approximated.Trend of the PS for five countries (percentile Rank). Source: Authors’ calculations
Real GDP dynamics are plotted in Figure 5, which shows the trajectory of constant-2015-USD GDP for each BRICS economy over 2010–2023.
The vertical axis of the line graph is labeled “Real G D P (Constant 2015 U S Dollar)” and ranges from 0 to 2 E plus 13. The horizontal axis displays years from 2010 to 2023 in increments of one year. There are five colored lines on the graph. The legend at the bottom identifies the lines as “Brazil” (red), “China” (purple), “India” (green), “Russian Federation” (gray), and “South Africa” (yellow). There are two labeled rectangles: “sanctions in 2014” is placed over the year 2014, and “Russia-Ukraine conflict in 2022” is placed above the year 2022. The line for “Brazil” starts at (2010, 1.7032 E plus 12), is relatively flat, and ends with a small increase at (2023, 1.9548 E plus 12). The line for “China” starts at (2010, 7.5541 E plus 12), and continuously increases with a positive slope, with a small fluctuation between 2019 and 2021, and ends at (2023, 1.7276 E Plus 13). The line for “India” starts at (2010, 1.5359 E plus 12), increases constantly, and ends at (2023, 3.216 E plus 12). The line for “Russian Federation” starts at (2010, 1.2507 E plus 12), increases constantly, and ends at (2023, 1.5245 E plus 12). The line for “South Africa” starts at (2010, 3.1165 E plus 11), increases constantly, and ends at (2023, 3.6331 E plus 11).Trend of the real GDP (Constant, 2015 US$). Source: Authors’ calculations
The vertical axis of the line graph is labeled “Real G D P (Constant 2015 U S Dollar)” and ranges from 0 to 2 E plus 13. The horizontal axis displays years from 2010 to 2023 in increments of one year. There are five colored lines on the graph. The legend at the bottom identifies the lines as “Brazil” (red), “China” (purple), “India” (green), “Russian Federation” (gray), and “South Africa” (yellow). There are two labeled rectangles: “sanctions in 2014” is placed over the year 2014, and “Russia-Ukraine conflict in 2022” is placed above the year 2022. The line for “Brazil” starts at (2010, 1.7032 E plus 12), is relatively flat, and ends with a small increase at (2023, 1.9548 E plus 12). The line for “China” starts at (2010, 7.5541 E plus 12), and continuously increases with a positive slope, with a small fluctuation between 2019 and 2021, and ends at (2023, 1.7276 E Plus 13). The line for “India” starts at (2010, 1.5359 E plus 12), increases constantly, and ends at (2023, 3.216 E plus 12). The line for “Russian Federation” starts at (2010, 1.2507 E plus 12), increases constantly, and ends at (2023, 1.5245 E plus 12). The line for “South Africa” starts at (2010, 3.1165 E plus 11), increases constantly, and ends at (2023, 3.6331 E plus 11).Trend of the real GDP (Constant, 2015 US$). Source: Authors’ calculations
Figure 6 shows the share of BRICS economies in global real GDP (constant 2015 US$). In 2010, BRICS collectively accounted for 19% of world GDP, rising to 26% in 2023. This increase is driven primarily by China's rapid economic growth, underscoring its central role in shaping the bloc's overall contribution to the global economy.
The horizontal axis is labeled with years and ranges from “2010” through “2023” in one-year increments. The vertical axis is labeled “Real G D P (Constant 2015 U S Dollar) of BRICS from the World” and ranges from .000 to 0.300 in increments of .050 units. Each year has a vertical stacked bar composed of five segments. A legend at the bottom identifies the segments as “Brazil”, “China”, “India”, “Russia”, and “South Africa”. The data for the bars from left to right is as follows: 2010: Brazil: 0.02; China: 0.12; India: 0.02; Russia: 0.02; South Africa: 0.01. 2011: Brazil: 0.02; China: 0.13; India: 0.02; Russia: 0.02; South Africa: 0.01. 2012: Brazil: 0.02; China: 0.13; India: 0.02; Russia: 0.02; South Africa: 0.01. 2013: Brazil: 0.02; China: 0.14; India: 0.02; Russia: 0.02; South Africa: 0.01. 2014: Brazil: 0.02; China: 0.15; India: 0.03; Russia: 0.02; South Africa: 0.01. 2015: Brazil: 0.02; China: 0.15; India: 0.03; Russia: 0.02; South Africa: 0.01. 2016: Brazil: 0.02; China: 0.15; India: 0.03; Russia: 0.02; South Africa: 0.01. 2017: Brazil: 0.02; China: 0.16; India: 0.03; Russia: 0.02; South Africa: 0.01. 2018: Brazil: 0.02; China: 0.16; India: 0.03; Russia: 0.02; South Africa: 0.01. 2019: Brazil: 0.02; China: 0.17; India: 0.03; Russia: 0.02; South Africa: 0.01. 2020: Brazil: 0.02; China: 0.18; India: 0.03; Russia: 0.02; South Africa: 0.01. 2021: Brazil: 0.02; China: 0.18; India: 0.03; Russia: 0.02; South Africa: 0.01. 2022: Brazil: 0.02; China: 0.18; India: 0.03; Russia: 0.02; South Africa: 0.01. 2023: Brazil: 0.02; China: 0.19; India: 0.03; Russia: 0.02; South Africa: 0.01. Note: All numerical data values are approximated.Real GDP (Constant, 2015 US$) of BRICS from the world. Source: Authors’ calculations
The horizontal axis is labeled with years and ranges from “2010” through “2023” in one-year increments. The vertical axis is labeled “Real G D P (Constant 2015 U S Dollar) of BRICS from the World” and ranges from .000 to 0.300 in increments of .050 units. Each year has a vertical stacked bar composed of five segments. A legend at the bottom identifies the segments as “Brazil”, “China”, “India”, “Russia”, and “South Africa”. The data for the bars from left to right is as follows: 2010: Brazil: 0.02; China: 0.12; India: 0.02; Russia: 0.02; South Africa: 0.01. 2011: Brazil: 0.02; China: 0.13; India: 0.02; Russia: 0.02; South Africa: 0.01. 2012: Brazil: 0.02; China: 0.13; India: 0.02; Russia: 0.02; South Africa: 0.01. 2013: Brazil: 0.02; China: 0.14; India: 0.02; Russia: 0.02; South Africa: 0.01. 2014: Brazil: 0.02; China: 0.15; India: 0.03; Russia: 0.02; South Africa: 0.01. 2015: Brazil: 0.02; China: 0.15; India: 0.03; Russia: 0.02; South Africa: 0.01. 2016: Brazil: 0.02; China: 0.15; India: 0.03; Russia: 0.02; South Africa: 0.01. 2017: Brazil: 0.02; China: 0.16; India: 0.03; Russia: 0.02; South Africa: 0.01. 2018: Brazil: 0.02; China: 0.16; India: 0.03; Russia: 0.02; South Africa: 0.01. 2019: Brazil: 0.02; China: 0.17; India: 0.03; Russia: 0.02; South Africa: 0.01. 2020: Brazil: 0.02; China: 0.18; India: 0.03; Russia: 0.02; South Africa: 0.01. 2021: Brazil: 0.02; China: 0.18; India: 0.03; Russia: 0.02; South Africa: 0.01. 2022: Brazil: 0.02; China: 0.18; India: 0.03; Russia: 0.02; South Africa: 0.01. 2023: Brazil: 0.02; China: 0.19; India: 0.03; Russia: 0.02; South Africa: 0.01. Note: All numerical data values are approximated.Real GDP (Constant, 2015 US$) of BRICS from the world. Source: Authors’ calculations
Figure 7 presents the trend of the LPI for the BRICS countries. While South Africa recorded the highest scores in certain years, China maintained the highest average LPI across the entire period. In contrast, Russia consistently ranked at the lowest among BRICS members in terms of logistics performance.
The vertical axis of the line graph is labeled “Logistic Performance Index (1 equals low to 5 equals high)” and ranges from 0 to 4 in increments of 0.5. The horizontal axis displays years from 2010 to 2023 in increments of one year. There are five colored lines on the graph. The legend at the bottom identifies the lines as “Brazil” (light blue), “China” (orange), “India” (gray), “Russia” (yellow), and “South Africa” (dark blue). A labeled rectangle “sanctions in 2014” is placed above the year 2014, and another labeled rectangle “Russia-Ukraine conflict in 2022” is placed above the year 2022. The line for “Brazil” starts at (2010, 3.2), moves with a wavy nature, and ends at (2023, 3.3). The line for “China” starts at (2010, 3.49), increases slightly upward, and ends at (2023, 3.8). The line for “India” starts at (2010, 3.12), moves with a wavy nature, and ends at (2023, 3.41). The line for “Russia” starts at (2010, 2.61), moves with a wavy nature, and ends at (2023, 2.61). The line for “South Africa” starts at (2010, 3.46), moves with a wavy nature, and ends at (2023, 3.71).The trend of logistic performance index: quality of trade and transport-related infrastructure (1 = low to 5 = high). Source: Authors’ calculations
The vertical axis of the line graph is labeled “Logistic Performance Index (1 equals low to 5 equals high)” and ranges from 0 to 4 in increments of 0.5. The horizontal axis displays years from 2010 to 2023 in increments of one year. There are five colored lines on the graph. The legend at the bottom identifies the lines as “Brazil” (light blue), “China” (orange), “India” (gray), “Russia” (yellow), and “South Africa” (dark blue). A labeled rectangle “sanctions in 2014” is placed above the year 2014, and another labeled rectangle “Russia-Ukraine conflict in 2022” is placed above the year 2022. The line for “Brazil” starts at (2010, 3.2), moves with a wavy nature, and ends at (2023, 3.3). The line for “China” starts at (2010, 3.49), increases slightly upward, and ends at (2023, 3.8). The line for “India” starts at (2010, 3.12), moves with a wavy nature, and ends at (2023, 3.41). The line for “Russia” starts at (2010, 2.61), moves with a wavy nature, and ends at (2023, 2.61). The line for “South Africa” starts at (2010, 3.46), moves with a wavy nature, and ends at (2023, 3.71).The trend of logistic performance index: quality of trade and transport-related infrastructure (1 = low to 5 = high). Source: Authors’ calculations
The data utilized in the survey for all variables are presented in Appendix 6.
4.2 Model specification
The PVAR model is grounded in dynamic equilibrium theory and is employed to analyze interactions among endogenous variables in panel data. A key limitation of the traditional VAR model is that as the number of variables increases, the number of parameters to be estimated grows exponentially. Consequently, effective parameter estimation can only be achieved when sufficient observable sample values are available. Panel data provide a larger set of observations, which has led scholars to develop the PVAR model, combining the strengths of both panel data and the VAR framework to more effectively address research needs (Yang, An, Chen, & Yang, 2023).
Settings and Classifications of the PVAR Model. Set as the M*1 vector of M interdependent endogenous variables of individual in period , is the N*1 vector of N exogenous variables of individual in period . And, set as the M*1 vector of M unobservable individual fixed effects of individual in period , which is called the individual intercept vector. and is the short-term influence coefficient of phase lag vector and on vector , which is called regression coefficient. They are M*M matrix and M*N matrix, respectively. The PVAR model can be set as
This study employs a PVAR model to analyze the impact of variables, PS, LPI and GDP shocks on the BRICS CCCI. IRF are used as a key criterion to measure the magnitude of these shocks and the duration of their effects.
5. Results
5.1 Stationarity results
Building on the theoretical foundation of the ADOCA framework, this section translates the conceptual relationships into an empirical setting. Full coefficient estimates, standard errors and t-statistics are reported in Appendix 7, Table A12. The PVAR model is employed to capture how asymmetric shocks in PS, logistics performance (LPI), and economic growth (GDP) dynamically affect the CCCI, which serves as a quantitative measure of currency integration feasibility under institutional and macroeconomic heterogeneity. This empirical design allows us to operationalize the theoretical constructs of the ADOCA model and test their implications for BRICS currency-clearing dynamics.
To examine stationarity, the Augmented Dickey-Fuller test was employed. As shown in Table 3, all variables were stationary after first differencing.
5.2 Optimal lags results
The Schwarz Information Criterion was applied to determine the optimal lag length of the model. Based on the results presented in Table 4, the optimal lag length was identified as two.
Lag-length test results
| Lag | LogL | (Likelihood Ratio) | (Akaike’s Final Prediction Error Criterion) | (Akaike information Criterion) | (Schwarz Criterion) | (Hannan-Quinn Criterion) |
|---|---|---|---|---|---|---|
| 0 | −1712.22 | Not Available | 7.66 | 68.64 | 68.80 | 68.70 |
| 1 | −1682.46 | 53.57 | 4.43 | 68.09 | 68.86 | 68.38 |
| 2 | −1645.86 | 60.01 | 1.97 | 67.27 | 68.65* | 67.79 |
| 3 | −1621.51 | 36.05 | 1.46 | 66.94 | 68.92 | 67.69 |
| Lag | LogL | (Likelihood Ratio) | (Akaike’s Final Prediction Error Criterion) | (Akaike information Criterion) | (Schwarz Criterion) | (Hannan-Quinn Criterion) |
|---|---|---|---|---|---|---|
| 0 | −1712.22 | Not Available | 7.66 | 68.64 | 68.80 | 68.70 |
| 1 | −1682.46 | 53.57 | 4.43 | 68.09 | 68.86 | 68.38 |
| 2 | −1645.86 | 60.01 | 1.97 | 67.27 | 68.65* | 67.79 |
| 3 | −1621.51 | 36.05 | 1.46 | 66.94 | 68.92 | 67.69 |
5.3 Impulse response functions (IRF)
Consistent with the ADOCA framework, the following empirical results illustrate how political, institutional and logistical asymmetries shape the dynamics of currency-clearing feasibility among BRICS economies.
Figures 8–10 present the IRF of the BRICS CCCI to shocks in PS, the LPI and GDP, respectively. Confidence intervals for the IRF were computed using Monte Carlo simulations at the 95% confidence level. The results indicate that an LPI shock leads to an increase in the CCCI, whereas shocks to PS and GDP reduce the index. Specifically, a 1% increase in the LPI raises the CCCI by nearly 2%, with the effect persisting for approximately two and a half years. By contrast, a 1% improvement in PS reduces the CCCI by around 2.5% for one and a half years, while a 1% increase in GDP decreases the index by about 0.5% over a period of two and a half years.
The horizontal axis is marked with numbers ranging from “1” through “10” in one-unit increments. The vertical axis ranges from negative 6 to 6 in increments of 2 units. The graph shows one solid line and two dashed lines. The solid line begins at (1, 0.00), drops to (2, negative 2.34), rises to (3, 1.63), returns near (4, negative 0.28), and remains close to zero from periods 5 through 10 with small fluctuations between negative 0.2 and 0.2. The upper red dashed line begins at (1, 0.00), rises sharply to (3, 4.75), declines to (5, 1.83), and gradually approaches (10, 0.98). The lower red dashed line begins at (1, 0.00), drops steeply to (2, negative 5.67), rises to (3, negative 1.37), and gradually approaches (10, negative 0.96). Note: All numerical data values are approximated.Response of CCCI to PS. Source: Authors
The horizontal axis is marked with numbers ranging from “1” through “10” in one-unit increments. The vertical axis ranges from negative 6 to 6 in increments of 2 units. The graph shows one solid line and two dashed lines. The solid line begins at (1, 0.00), drops to (2, negative 2.34), rises to (3, 1.63), returns near (4, negative 0.28), and remains close to zero from periods 5 through 10 with small fluctuations between negative 0.2 and 0.2. The upper red dashed line begins at (1, 0.00), rises sharply to (3, 4.75), declines to (5, 1.83), and gradually approaches (10, 0.98). The lower red dashed line begins at (1, 0.00), drops steeply to (2, negative 5.67), rises to (3, negative 1.37), and gradually approaches (10, negative 0.96). Note: All numerical data values are approximated.Response of CCCI to PS. Source: Authors
The horizontal axis is marked with numbers and ranges from “1” through “10” in one-unit increments. The vertical axis ranges from negative 6 to 6 in increments of 2 units. The graph shows one solid line and two dashed lines. The solid line begins at (1, 0), increases to (3, 1.72), drops to (5, negative 1.17), rises to (6, 1.17), falls again to (8, negative 0.27), and finally ends at (10, 0.627). The lower dashed line follows a similar trend to the solid line. It begins at (1, 0), drops to (2, negative 2.14), drops further to (5, negative 3.87), rises to (6, negative 1.07), drops to (8, negative 1.88), and rises to end at (10, negative 0.93). The upper dashed line follows a similar trend to the solid line. It begins at (1, 0), increases to (2, 5.19), drops to (5, 1.49), rises to (6, 3.42), drops to (8, 1.27), and rises to end at (10, 2.20). Note: All numerical data values are approximated.Response of CCCI to LPI. Source: Authors
The horizontal axis is marked with numbers and ranges from “1” through “10” in one-unit increments. The vertical axis ranges from negative 6 to 6 in increments of 2 units. The graph shows one solid line and two dashed lines. The solid line begins at (1, 0), increases to (3, 1.72), drops to (5, negative 1.17), rises to (6, 1.17), falls again to (8, negative 0.27), and finally ends at (10, 0.627). The lower dashed line follows a similar trend to the solid line. It begins at (1, 0), drops to (2, negative 2.14), drops further to (5, negative 3.87), rises to (6, negative 1.07), drops to (8, negative 1.88), and rises to end at (10, negative 0.93). The upper dashed line follows a similar trend to the solid line. It begins at (1, 0), increases to (2, 5.19), drops to (5, 1.49), rises to (6, 3.42), drops to (8, 1.27), and rises to end at (10, 2.20). Note: All numerical data values are approximated.Response of CCCI to LPI. Source: Authors
The horizontal axis is marked with numbers and ranges from “1” through “10” in one-unit increments. The vertical axis ranges from negative 6 to 6 in increments of 2 units. The graph shows one solid line and two dashed lines. The solid blue line begins at (1, 0.00), decreases slightly to (2, negative 0.26), rises to (3, 0.46), returns near (4, negative 0.48), and remains close to zero from periods 5 through 10 with small fluctuations between negative 0.1 and 0.2. The upper red dashed line begins at (1, 0.00), rises to (3, 1.84), declines to (5, 1.1), and gradually approaches (10, 0.94). The lower red dashed line begins at (1, 0.00), drops to (2, negative 2.14), rises to (4, negative 1.55), and gradually approaches (10, negative 0.93). Note: All numerical data values are approximated.Response of CCCI to GDP. Source: Authors
The horizontal axis is marked with numbers and ranges from “1” through “10” in one-unit increments. The vertical axis ranges from negative 6 to 6 in increments of 2 units. The graph shows one solid line and two dashed lines. The solid blue line begins at (1, 0.00), decreases slightly to (2, negative 0.26), rises to (3, 0.46), returns near (4, negative 0.48), and remains close to zero from periods 5 through 10 with small fluctuations between negative 0.1 and 0.2. The upper red dashed line begins at (1, 0.00), rises to (3, 1.84), declines to (5, 1.1), and gradually approaches (10, 0.94). The lower red dashed line begins at (1, 0.00), drops to (2, negative 2.14), rises to (4, negative 1.55), and gradually approaches (10, negative 0.93). Note: All numerical data values are approximated.Response of CCCI to GDP. Source: Authors
From a policy perspective, the magnitude of changes in the CCCI provides useful insights into the cost and feasibility of cross-border currency clearing. A rise in the CCCI implies higher transaction and settlement costs. This outcome is inconsistent with the de-dollarization objectives. In contrast, a decline in the CCCI signals improved clearing efficiency, reduced frictions and stronger institutional coordination within BRICS economies.
5.4 Variance decomposition
Table 5 presents the results of the variance decomposition for the CCCI in BRICS. The table shows that PS and the LPI have a significant impact on the variance of the CCCI. In contrast, GDP contributes only a small portion to the variance decomposition of the CCCI.
Variance decomposition results
| Period | Standard Error (SE) | PS | LPI | GDP | CCCI |
|---|---|---|---|---|---|
| 1 | 5.16 | 1.28 | 0.21 | 0.89 | 97.59 |
| 2 | 5.34 | 3.44 | 0.45 | 0.76 | 95.32 |
| 3 | 5.50 | 5.33 | 2.03 | 0.79 | 91.83 |
| 4 | 5.56 | 5.31 | 2.50 | 0.79 | 91.37 |
| 5 | 5.58 | 5.24 | 3.81 | 0.96 | 89.97 |
| 6 | 5.60 | 5.20 | 4.42 | 0.97 | 89.39 |
| 7 | 5.61 | 5.22 | 4.49 | 1.03 | 89.25 |
| 8 | 5.62 | 5.22 | 4.66 | 1.03 | 89.07 |
| 9 | 5.62 | 5.22 | 4.71 | 1.06 | 88.99 |
| 10 | 5.62 | 5.22 | 4.75 | 1.06 | 88.95 |
| Period | Standard Error (SE) | PS | LPI | GDP | CCCI |
|---|---|---|---|---|---|
| 1 | 5.16 | 1.28 | 0.21 | 0.89 | 97.59 |
| 2 | 5.34 | 3.44 | 0.45 | 0.76 | 95.32 |
| 3 | 5.50 | 5.33 | 2.03 | 0.79 | 91.83 |
| 4 | 5.56 | 5.31 | 2.50 | 0.79 | 91.37 |
| 5 | 5.58 | 5.24 | 3.81 | 0.96 | 89.97 |
| 6 | 5.60 | 5.20 | 4.42 | 0.97 | 89.39 |
| 7 | 5.61 | 5.22 | 4.49 | 1.03 | 89.25 |
| 8 | 5.62 | 5.22 | 4.66 | 1.03 | 89.07 |
| 9 | 5.62 | 5.22 | 4.71 | 1.06 | 88.99 |
| 10 | 5.62 | 5.22 | 4.75 | 1.06 | 88.95 |
5.5 Model stability test
To assess dynamic stability, we examine the inverse roots of the AR characteristic polynomial (Figure 11). All roots lie inside the unit circle, confirming the stability of the estimated PVAR. The results show that all inverse roots are located within the unit circle, showing that the estimated PVAR model satisfies the stability condition and is correctly specified.
The horizontal axis of the scatter plot ranges from negative 1.5 to 1.5 in increments of 0.5 units. The vertical axis ranges from negative 1.5 to 1.5 in increments of 0.5 units. Horizontal and vertical lines are drawn, and they intersect at the origin at (0, 0). A faint circle centered at the origin with radius 1.0 is drawn inside the plotting area. Eight points appear within the circle. The coordinates of the points are (0, 0.71), (negative 0.21, 0.55), (negative 0.68, 0.15), (negative 0.69, negative 0.12), (negative 0.23, negative 0.53), (0, negative 0.7), (0.25, 0), and (0.94, 0). Note: All numerical data values are approximated.Unit circle diagram for model stability. Source: Authors
The horizontal axis of the scatter plot ranges from negative 1.5 to 1.5 in increments of 0.5 units. The vertical axis ranges from negative 1.5 to 1.5 in increments of 0.5 units. Horizontal and vertical lines are drawn, and they intersect at the origin at (0, 0). A faint circle centered at the origin with radius 1.0 is drawn inside the plotting area. Eight points appear within the circle. The coordinates of the points are (0, 0.71), (negative 0.21, 0.55), (negative 0.68, 0.15), (negative 0.69, negative 0.12), (negative 0.23, negative 0.53), (0, negative 0.7), (0.25, 0), and (0.94, 0). Note: All numerical data values are approximated.Unit circle diagram for model stability. Source: Authors
5.6 Model stability under the adjusted weighting index
To further examine the robustness of the CCCI, the component weights were modified to reflect structural differences in exchange-rate regimes. Brazil, India and South Africa–operating under floating or managed-float systems–experience higher exchange-rate volatility and stronger inflation pass-through than China and Russia, which maintain more managed or sanction-driven regimes that limit fluctuations. Accordingly, the exchange-rate component in the CCCI was double-weighted for these three countries. The index was recalculated and the PVAR model was re-estimated; stability diagnostics confirmed that all characteristic roots remained within the unit circle (Figure 12), demonstrating that the model remained dynamically stable and that the empirical findings are robust across alternative weighting specifications.
The diagram shows a square plotting area. The horizontal axis ranges from negative 1.5 to 1.5 in increments of 0.5 units. The vertical axis ranges from negative 1.5 to 1.5 in increments of 0.5 units. A faint circle centered at the origin with radius 1.0 is drawn inside the plotting area. Gridlines extend vertically and horizontally through the origin. Eight blue points appear within the circle. The coordinates of the points are as follows: (negative 0.67, 0.15), (negative 0.66, negative 0.14), (negative 0.21, 0.56), (0.21, negative 0.54), (0.27, 0), (0.01, negative 0.7), and (0.96, 0.01). Note: All numerical data values are approximated.Unit circle diagram for model stability. Source: Authors
The diagram shows a square plotting area. The horizontal axis ranges from negative 1.5 to 1.5 in increments of 0.5 units. The vertical axis ranges from negative 1.5 to 1.5 in increments of 0.5 units. A faint circle centered at the origin with radius 1.0 is drawn inside the plotting area. Gridlines extend vertically and horizontally through the origin. Eight blue points appear within the circle. The coordinates of the points are as follows: (negative 0.67, 0.15), (negative 0.66, negative 0.14), (negative 0.21, 0.56), (0.21, negative 0.54), (0.27, 0), (0.01, negative 0.7), and (0.96, 0.01). Note: All numerical data values are approximated.Unit circle diagram for model stability. Source: Authors
6. Discussion
In addressing the first research question of this paper, we argue that traditional concepts of the OCA theory are not entirely applicable when considering the establishment of a new currency, particularly in the context of the BRICS nations. While distance and economic integration are commonly emphasized in OCA theory, the paper highlights that in the current global environment, factors such as economic, political and logistical uncertainties should take precedence. These uncertainties–including trade disruptions, geopolitical tensions and logistical challenges–are often more influential in determining the feasibility of a new currency than traditional geographic proximity or economic convergence. As a result, an alternative theoretical framework is needed to better explain the motivations behind creating an integrated currency within the BRICS bloc, one that is not solely based on geographical considerations but also accounts for these complex and evolving factors.
For the second research question, we examined the various functions of money–such as serving as a medium of exchange, a store of value and an instrument for deferred payment–and demonstrated how sanctions create political motivations that drive countries to seek alternatives for currency clearing. Sanctions impose significant costs on the economies of BRICS nations, and in response, these countries are more likely to pursue the development of a new currency as a means of mitigating the economic impact of such political pressures. This currency would not serve as a direct alternative to global reserve currencies like the euro or the US dollar, but rather as an instrument to bypass the political constraints imposed by other nations, particularly those related to economic sanctions.
Through our analysis of the BRICS bloc, we identified two distinct groups of countries regarding their potential for de-dollarization. The first group, consisting of Brazil, South Africa and China, demonstrates a high potential for de-dollarization, primarily due to their relative economic strength and stability. On the other hand, Russia and India, although part of the BRICS bloc, show less enthusiasm or inclination toward de-dollarization. This difference can be attributed to the value and stability of their currencies, with the first group possessing stronger and less volatile currencies compared to the latter. Furthermore, the political and economic conditions of the countries in these two groups also contribute to the differing levels of motivation for pursuing a de-dollarized economy.
Looking specifically at sanctions, the data from the Global Sanction Database (version 4) reveals that Russia's sanctions escalated significantly starting in 2014, with a particularly sharp increase in 2022. This increase in sanctions came from the United States, the EU, Japan, the UK, Canada, France, Germany and Italy. The intensity of these sanctions clearly indicates that Russia needs to explore ways to reduce the economic costs imposed by these restrictions, and a BRICS currency could play a vital role in this context by facilitating internal trade and providing an alternative to the US dollar. Russia and Iran's challenges with the Society for Worldwide Interbank Financial Telecommunication (SWIFT) system have already spurred efforts to find alternative payment mechanisms, and this geopolitical drive to bypass USA and European sanctions may encourage the development of new international monetary systems that are less dependent on the US dollar.
Following Russia, China has also faced significant sanctions, particularly since 2016 when it was included in the IMF's Special Drawing Rights basket. These sanctions limit China's ability to use domestic devaluation policies to boost exports, instead forcing it to deepen its financial markets in line with IMF guidelines, which perpetuate dollarization. Despite China's significant push for initiatives like the Belt and Road Initiative, its de-dollarization efforts are complicated by the IMF's concerns regarding the international use of the Yuan. Among all the BRICS members studied in this paper, India stands out as the only country that did not experience sanctions between 2010 and 2023.
The data presented in Figure 3 illustrates the dramatic increase in Russia's CCCI post-2014, signaling Russia's heightened motivation to reduce the costs associated with sanctions. Although Russia is not ranked among the top initial BRICS countries in terms of population and GDP, its national currency is considered the most vulnerable among China, India, Brazil and South Africa. It suffers BRICS aims to achieve de-dollarization.
Despite India's lack of sanction experience during this period, Pearson correlation analysis between CCCI and sanction intensity demonstrates a robust relationship for South Africa and Brazil, with correlations ranging from 81% to 100%. This suggests that the CCCI serves as a reliable indicator of political motivations, particularly the desire to create a new currency to circumvent the political tensions associated with sanction intensity, especially those imposed by the USA
Addressing the third research question, the paper highlights the growing role of the BRICS economies in the global economic landscape. As depicted in Figures 5 and 6, the share of BRICS economies in global GDP has increased from 19% in 2010 to 26% in 2023. This increase is largely due to China's remarkable economic growth. In 2010, China accounted for 62% of BRICS' real GDP and 11.6% of global GDP. By 2023, this share had increased to 71% of BRICS' real GDP and 18.4% of global GDP. Following China, India, Brazil and Russia play pivotal roles in the BRICS bloc, though their contributions are significantly smaller, especially when compared to China's dominance. South Africa, in contrast, contributes less than 1% to the BRICS economy and can be considered negligible in this regard.
In terms of PS, as shown in Figure 4, India has experienced a steady improvement in its PS index, while Brazil and South Africa have seen declines. The PS indices for China and Russia have been more volatile, reflecting the political challenges faced by these countries in recent years. Regarding logistics, China has made significant strides in infrastructure development to enhance trade efficiency, whereas Russia ranks among the lowest in terms of logistical performance. The variance decomposition and IRF further demonstrate that logistics performance and PS are crucial factors influencing the volatility of de-dollarization within BRICS. PS and GDP have a positive impact on reducing the gap between BRICS' national currencies, while improvements in logistic infrastructure appear to widen the gap. These results suggest that efforts to enhance PS and economic strength within BRICS will be essential for reducing dependence on the US dollar, while improvements in logistics could either support or hinder de-dollarization efforts depending on the broader context.
Since logistical coordination and institutional stability matter, BRICS need a new mechanism of settlement payment based on bilateral clearing agreements. This mechanism can be applied by local currencies of the members. It opens a new method of payment not reliant on the US dollar and the SWIFT. The new mechanism decreases the impact of currency volatility on BRICS members' economies and enhances trust and transparency among the countries.
With the rapid expansion of CBDCs, BRICS requires a multi-central bank digital currency (CBDC) platform to lead digital settlement payments. Such a platform should be able to connect to China's digital RMB, e-CNY and digital currency electronic payment (DCEP). A multi-CBDC system could facilitate cross-border payments within the bloc, linking existing initiatives such as China's e-CNY and DCEP with similar projects from other member states. Importantly, global experiments such as the BIS-led Project mBridge, which successfully piloted cross-border CBDC transactions among China, Hong Kong, Thailand and the UAE, demonstrate the feasibility of such platforms and provide a model that BRICS could adapt (Bank for International Settlements, 2022). This would enhance payment efficiency, lower transaction costs and reduce exposure to sanctions or financial fragmentation. Ultimately, such a platform could serve as the foundation for a more integrated BRICS financial architecture, complementing national currencies while laying the groundwork for broader monetary cooperation.
7. Conclusion
In conclusion, while the BRICS countries could theoretically establish a new currency union, such a move may not be optimal unless the PS within the group improves. The increasing range of sanctions–such as financial and travel sanctions on Russia and China–could hinder the expansion of trade routes and increase the opportunity cost of using national currencies and clearing systems. Thus, a regional logistical clearing mechanism would be vital for supporting de-dollarization and minimizing the impact of political pressures on BRICS economies.
It is recommended that BRICS members incorporate the CCCI into their macroeconomic and international policy frameworks. These policies should aim to improve the CCCI by both strengthening national currencies and mitigating the impact of external sanctions. Additionally, such policies should promote international trade–particularly bilateral trade–and establish new financial settlement mechanisms, such as clearing and settlement systems in China, to encourage private sector participation in secure and profitable trade among BRICS members. These mechanisms can be further enhanced through the integration of digital technologies. Moreover, the expansion of financial systems, including fintech initiatives and development banks that support international trade, should be prioritized rather than taken for granted.
In order to have a new stable currency union, the gap in the index should be decreased, especially between Russia and other BRICS members. This requires the following unique international relationships that lead to decreasing or bypassing sanctions. BRICS also needs a new payment mechanism focusing on bilateral clearing systems and digital currencies.
Limitations: This study focuses on the five leading BRICS countries–Brazil, Russia, India, China and South Africa–because of the lack of reliable data for other BRICS members. This paper explores the economic, political and logistical uncertainties surrounding de-dollarization within these nations. Future research could explore the role of environmental uncertainties, such as climate change and the growing demand for green products, in shaping the motivations for de-dollarization among BRICS nations. This study assumes an exogenous mechanism of weights for sanction intensity and exchange rate. Future studies can calculate the CCCI with different endogenous mechanism weights for politics and exchange rate. Additionally, the role of the private sector in enhancing PS, macroeconomic and logistic strategies should be considered.
The authors gratefully acknowledge the academic environment provided by Paragon International University, Phnom Penh, Cambodia. The institution’s commitment to research excellence and continuous encouragement has been instrumental in the successful completion of this work.
The supplementary material for this article can be found online

