## Introduction — Currency Usage for Cross-Border Payments

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---

### Overview
- Cross-border payments enable international exchange of goods and services, settlement of cross-border financial contracts, and channeling of international aid.
- Currency configuration for cross-border payments is dominated by the U.S. dollar and the euro. As of end 2021:
  - The U.S. dollar accounted for about 40 percent of cross-border Swift flows.
  - The Chinese renminbi (RMB) had a share rising to about 2.5 percent.
- A few other currencies—British pound, Japanese yen, Australian dollar, Hong Kong dollar, and Canadian dollar—have shares of more than 1 percent.

### Currency roles and recent drivers of change
- Money functions: medium of exchange, unit of account, and store of value; the U.S. dollar often plays a vehicle currency role in trade invoicing, asset pricing, and central bank reserves.
- Historical IMF system transitions: sterling dominance under the gold standard → coequal sterling and U.S. dollar inter-war → U.S. dollar dominance institutionalized under Bretton Woods and persisting after 1973.
- Recent technological and geopolitical changes may reduce switching costs and weaken incumbent advantages; digitalization (including crypto-assets and CBDCs) could substantially reduce switching costs and increase currency competition.

### Payment infrastructures and alternative platforms
- International payments mainly conducted via financial intermediaries and cross-border financial infrastructures.
- Swift: leading global messaging platform with membership of more than 11,000 institutions in over 200 countries and territories.
- Alternative infrastructures: China, India, and Russia have developed CIPS, UPI and SPFS; as of January 2023 their participating bank numbers and market shares remain limited.
- Crypto-asset-based providers emerged for remittances, but recent crypto valuation declines and failures highlight policy needs.

### Data and datasets used
- Primary microdata: Swift monthly data (Watch solution) back to 2010, aggregated at country level and anonymous. Each observation contains Swift message code, ordering/sending and beneficiary/receiving countries, currency of the transaction, and its correspondent U.S. dollar value.
- Two constructed Swift datasets:
  - Transfer dataset: Swift messages MT 103 (single customer credit transfers; includes MT 103, MT 103+, and MT 103R) and MT 202 (general financial institutions transfer).
  - Trade dataset: Swift messages MT 400 (documentary collections) and MT 700 (standby letters of credit).
- Other country-level databases:
  - IMF World Economic Outlook (GDP, inflation, monetary aggregates).
  - Basel AML Index (Basel Institute on Governance) — assesses risk of money laundering or terrorist financing; higher value indicates a lower risk of ML/TF.
  - Financial Development Index (IMF) — higher value indicates a better developed financial system.
  - AREAER (IMF) — tracks currency legal tender status and exchange rate regimes.
  - National University of Singapore Credit Research Initiative (probability of default) — higher value is worse.
  - S&P Global Ratings sovereign ratings — higher value indicates a worse sovereign rating.
- Pairwise data and controls:
  - Bilateral shares of FDI stock (CDIS) and portfolio investment stock (CPIS).
  - Political proximity: UN voting similarity index (Voeten, Strezhnev, Bailey, 2009) — higher values indicate closer political proximity.
  - Trade data: IMF Direction of Trade Statistics (exports measured FOB, imports include insurance; values denominated in U.S. dollars).
  - Geographic and cultural indicators: CEPII GeoDist (distance between capitals in kilometers, border contiguity, common official language, former colonial relationship, former common country).

### Empirical approach
- Dependent variable: share of Swift sent (received) flows in currency c over total flows sent (received) from sender country s to receiver country r in year t.
- Main regression specification (lagged share included to capture inertia) relates currency share to:
  - Lagged share, political proximity, geographic proximity, sender and receiver country controls (including legal tender status, AML compliance, financial development), trade and financial linkages, and fixed effects (currency and year).
- Model includes interaction effects for each currency with relevant variables in pooled specifications.

### Key empirical findings
- Inertia, legal tender status, and political proximity are major determinants of currency usage for cross-border payments.
- Inertia:
  - Estimated inertia coefficients range between 0.71 and 0.92 across currencies.
  - Inertia is highly statistically significant for all currencies and is a major explanatory factor.
- Legal tender status:
  - Use of a currency that is legal tender in sender or receiver country could increase its market share by up to 29 percentage points in some regressions; combined sender-plus-receiver legal tender effect could increase market share by up to 30 percentage points.
  - In pooled regressions with currency interactions, legal tender status increases market share between 0.8 and 12.3 percentage points depending on the currency.
  - The legal tender effect is relatively more important for the USD than for other currencies.
- Political proximity and geographic/cultural factors:
  - Geographic contiguity, common official language, and former colonial relationships generally have positive but relatively small effects on currency usage; greater geographic distance tends to have a negative effect.
  - Political proximity shows a positive and statistically significant effect for the JPY, RMB and other currencies.
  - A one percent increase in political proximity could increase the market share of the RMB and other currencies by about 1.8 and 3 percentage points, respectively.
  - Political proximity has a negative effect for USD and EUR in some specifications.
- Anti-money laundering compliance and financial development:
  - Effects vary by currency: negative effect on usage for USD and EUR, positive effect for other currencies.
- Trade ties and financial linkages:
  - Trade ties or financial linkages do not appear to have a major impact on currency choices in the main results.

### Hypothetical simulations and scenarios
- Simulation method: recursively estimating one-year ahead OLS best prediction over a 20-year horizon (2020 to 2040); only the simulated variables are changed in each scenario.
- RMB legal tender adoption scenario:
  - Assumption: a 3 percent probability that the RMB becomes legal tender in Asian and African countries every year.
  - Results: a relatively small but sustained increase in legal tender status in Africa and Asia could lead the RMB to reach market share levels above 20 percent in some of these countries.
  - Effect limited for China because a very large fraction of China’s cross-border payments is with advanced economies (U.S., Germany, Japan) where only a small fraction of payments is in RMB.
  - Even substantial adoption in small countries would not substantially change the RMB’s global share in cross-border payments.
- Political proximity scenarios:
  - Political convergence scenario: political proximity increases randomly across countries by 5 percent each year.
  - Political fragmentation scenario: political proximity decreases randomly across countries by 5 percent each year.
  - Results: effects are larger for "other currencies" than for RMB, driven by larger estimated parameters for those currencies; effects on China remain limited.

### Inertia and macroeconomic implications
- Core finding: Currencies in global payments exhibit a high degree of inertia.
- Implications:
  - Large inertia reaffirms limitations of exchange rate movements to buffer domestic economies from macroeconomic shocks.
  - Consistent with the dominant pricing paradigm, large exchange rate movements may be required for rebalancing external positions when trade flows are mainly dominated by a third-country currency.
  - Supportive macroeconomic policies may be justified when large exchange rate fluctuations carry adverse effects.

### Digital money, crypto, and potential IMS transformations
- Short- and medium-term outlook: large inertia implies a drastic change in the distribution of world currencies or their digital successors—the e-Dollar or the e-Euro—would be unlikely.
- Possible near-term scenario:
  - Smooth transition from the U.S. dollar or the euro to a digital version without drastic market-share changes.
  - Slow increase of usage of other competing digital currencies regionally or among political allies.
- Potential rapid-change sources and uncertainties:
  - Abrupt geo-political shifts could accelerate IMS transformation.
  - Broader introduction of crypto assets as legal tender could weaken fiat money and significantly impact the IMS.
  - Decisive central bank efforts to digitalize public money or introduce CBDCs could reduce switching costs and facilitate faster currency configuration changes.
  - Further geo-political tensions could accelerate fragmentation of the payment system and give rise to new digital currency blocs.
- Note: Many key model variables are slowly moving or stationary; rapid changes in legal tender status, political distance, or financial development are not expected in the near future.

### Conclusion and outlook
- Using granular Swift cross-border payments data, the study empirically estimates drivers of cross-region variations in currency usage, focusing on legal tender status and geo-political distance.
- Main conclusions:
  - High inertia in currency usage implies limited near-term shifts in the distribution of global currencies or their digital successors.
  - Legal tender status, geographic distance, and political distance are significant determinants of cross-country variation in currency usage, with notable exceptions for the U.S. dollar.
  - Profound transformations of the IMS have typically been slow historically; however, future major geo-political events or rapid digitalization could drastically accelerate transformation toward multipolarity.

### Annex I — Selected statistics for main variables
- Basel AML Score: Count 1,280; Mean 5.6; St. Dev. 1.2; Min 1.8; P25 4.8; P50 5.6; P75 6.5; Max 8.6. (Higher value indicates a higher risk of money laundering and terrorist financing)
- Distance between Capitals (km): Count 50,176; Mean 8,472; St. Dev. 4,705; Min 0.9; P25 4,768; P50 8,081; P75 12,005; Max 19,951.
- Financial Development Index: Count 1,920; Mean 0.3; St. Dev. 0.2; Min 0; P25 0.1; P50 0.2; P75 0.4; Max 1. (Higher value indicates a better developed financial system)
- Political Proximity: Count 407,616; Mean 0.8; St. Dev. 0.2; Min 0; P25 0.7; P50 0.9; P75 0.9; Max 1. (Higher value indicates greater political alignment)
- Prob of Default: Count 1,186; Mean 1.0%; St. Dev. 1.1%; Min 0%; P25 0.4%; P50 0.7%; P75 1.1%; Max 13.7%. (Higher value is worse)
- Sov. Rating: Count 1,496; Mean 13.8; St. Dev. 8.4; Min 1; P25 8; P50 14; P75 19; Max 33. (Higher value is a worse sovereign rating)
- Border Contiguity: Count 50,176; Mean 0; St. Dev. 0.1; Min 0; P25 0; P50 0; P75 0; Max 1. (Most countries do not share a border)
- Common Official Language: Count 50,176; Mean 0.2; St. Dev. 0.4; Min 0; P25 0; P50 0; P75 0; Max 1. (Most countries do not share common official language)
- Former Colonial Relationship: Count 50,176; Mean 0; St. Dev. 0.1; Min 0; P25 0; P50 0; P75 0; Max 1. (Most country relationships are not one of colonizer-colony)
- Former Single Country: Count 50,176; Mean 0; St. Dev. 0.1; Min 0; P25 0; P50 0; P75 0; Max 1. (Most countries were not previously part of the same country)

*Source: Introduction, "Currency Usage for Cross-Border Payments", Working Paper No. WP/2023/072 — wpiea2023072-print-pdf.*

### Introduction ...........................................................................................................

### Introduction

### Major sections (with page references)
- Introduction ......................................................................................................................................................... 3
- Data Sources ....................................................................................................................................................... 5
- Swift Data ....................................................................................................................................................... 5
- Country-Level Data ........................................................................................................................................ 7
- Data by Pairs of Countries ............................................................................................................................. 7
- Patterns of Currency Usage ............................................................................................................................... 9
- Empirical Analysis ............................................................................................................................................ 12
- Hypothetical Simulations ................................................................................................................................. 18
- Conclusion ......................................................................................................................................................... 21
- Annex I. Statistics for Main Variables ............................................................................................................. 23
- References ......................................................................................................................................................... 24

### Figures listed
- 1. Swift Messages MT 103 .................................................................................................................................... 6
- 2. Swift Messages MT 700 .................................................................................................................................... 6
- 3. Share of World Currencies Over the Years (Swift Messages MT 103 and MT 202) ......................................... 9
- 4. Number of Currencies Used for Cross-Border Payments ................................................................................. 9
- 5. Distribution of the Concentration of Currency Usage Across Countries (HHI Index) ...................................... 11
- 6. Cross Country Distribution of Swift Payments ................................................................................................ 11
- 7. Dynamic Projection of Market Share Growth for RMB, Changes in Legal Tender Status .............................. 19
- 8. Dynamic Projection of Market Share Growth for OTH and RMB, Changes in Political Proximity ................... 20

### Tables listed
- 1. Main Variables and Data Bases Used in Empirical Model ................................................................................ 8
- 2. Regression Results, Currency by Currency, Transfer-related Messages (MT 103 and MT 202) ................... 13
- 3. Regression Results, Pooled Panel of Currencies, Transfers (Messages MT 103 and MT 202) ..................... 15
- 4. Regression Results, Currency by Currency, Trade-related Messages (MT 400 and MT 700) ....................... 16
- 5. Regression Results, Pooled Panel of Currencies, Trade-related Messages (Messages MT 400 and MT 700) ............................................................................................................................................................................ 17

*Source: wpiea2023072-print-pdf - Introduction (IMF Working Paper), pages and listings as provided in the source content.*

### Introduction

### Introduction

### Overview
- Cross-border payments enable international exchange of goods and services, settlement of cross-border financial contracts, and channeling of international aid, forming the backbone of globalization in trade and finance.
- The currency configuration for cross-border payments is dominated by the U.S. dollar and the euro. As of end 2021:
  - The U.S. dollar accounted for about 40 percent of cross-border Swift flows.
  - The Chinese renminbi (RMB) had a share rising to about 2.5 percent.
- A few other currencies—British pound, Japanese yen, Australian dollar, Hong Kong dollar, and Canadian dollar—have shares of more than 1 percent.

### Currency roles and recent drivers of change
- Money functions as medium of exchange, unit of account, and store of value. The U.S. dollar often plays a vehicle currency role in trade invoicing, asset pricing, and central bank reserves.
- Dominant currency paradigms highlight complementarity between unit of account and store of value functions and raise questions about exchange rate flexibility for external adjustment.
- Historically, the IMS transitioned from sterling dominance under the gold standard, to coequal sterling and U.S. dollar inter-war, to U.S. dollar dominance institutionalized under Bretton Woods and persisting after 1973.
- Recent technological and geopolitical changes may accelerate IMS change by reducing switching costs and weakening incumbent advantages. Digitalization (including crypto-assets and CBDCs) could substantially reduce switching costs and increase currency competition.

### Payment infrastructures and alternative platforms
- International payments are mainly conducted via financial intermediaries and cross-border financial infrastructures.
- Swift is the leading global messaging platform with membership of more than 11,000 institutions in over 200 countries and territories.
- China, India, and Russia have developed alternative cross-border infrastructures, such as CIPS, UPI and SPFS; as of January 2023 their participating bank numbers and market shares remain limited.
- Crypto-asset-based alternative payment providers have emerged, particularly for remittances, but recent crypto valuation declines and failures highlight the need for effective policies.

### Data and datasets used
- Primary microdata: Swift monthly data (Watch solution) going back to 2010, aggregated at country level and anonymous. Each observation contains Swift message code, ordering/sending and beneficiary/receiving countries, currency of the transaction, and its correspondent U.S. dollar value.
- Two constructed Swift datasets:
  - Transfer dataset: Swift messages MT 103 (single customer credit transfers; includes MT 103, MT 103+, and MT 103R) and MT 202 (general financial institutions transfer).
  - Trade dataset: Swift messages MT 400 (documentary collections) and MT 700 (standby letters of credit).
- Other country-level databases:
  - IMF World Economic Outlook (GDP, inflation, monetary aggregates).
  - Basel AML Index (Basel Institute on Governance) — assesses risk of money laundering or terrorist financing; higher value indicates a lower risk of ML/TF.
  - Financial Development Index (IMF) — higher value indicates a better developed financial system.
  - AREAER (IMF) — tracks currency legal tender status and exchange rate regimes.
  - National University of Singapore Credit Research Initiative (probability of default) — higher value is worse.
  - S&P Global Ratings sovereign ratings — higher value indicates a worse sovereign rating.
- Pairwise data and controls:
  - Bilateral shares of FDI stock (CDIS) and portfolio investment stock (CPIS).
  - Political proximity: UN voting similarity index (Voeten, Strezhnev, Bailey, 2009) — higher values indicate closer political proximity.
  - Trade data: IMF Direction of Trade Statistics (exports measured FOB, imports include insurance; values denominated in U.S. dollars).
  - Geographic and cultural indicators: CEPII GeoDist (distance between capitals in kilometers, border contiguity, common official language, former colonial relationship, former common country).

### Empirical approach
- Dependent variable: share of Swift sent (received) flows in currency c over total flows sent (received) from sender country s to receiver country r in year t.
- Main regression specification (lagged share included to capture inertia) relates currency share to:
  - Lagged share, political proximity, geographic proximity, sender and receiver country controls (including legal tender status, AML compliance, financial development), trade and financial linkages, and fixed effects (currency and year).
- The model includes interaction effects for each currency with relevant variables in pooled specifications.

### Key empirical findings
- Inertia, legal tender status, and political proximity are major determinants of currency usage for cross-border payments.
- Inertia:
  - Estimated inertia coefficients range between 0.71 and 0.92 across currencies.
  - Inertia is highly statistically significant for all currencies and is a major explanatory factor.
- Legal tender status:
  - Use of a currency that is legal tender in sender or receiver country could increase its market share by up to 29 percentage points in some regressions; combined sender-plus-receiver legal tender effect could increase market share by up to 30 percentage points.
  - In pooled regressions with currency interactions, legal tender status increases market share between 0.8 and 12.3 percentage points depending on the currency.
  - The legal tender effect is relatively more important for the USD than for other currencies.
- Political proximity and geographic/cultural factors:
  - Geographic contiguity, common official language, and former colonial relationships generally have positive but relatively small effects on currency usage; greater geographic distance tends to have a negative effect.
  - Political proximity shows a positive and statistically significant effect for the JPY, RMB and other currencies.
  - A one percent increase in political proximity could increase the market share of the RMB and other currencies by about 1.8 and 3 percentage points, respectively.
  - Political proximity has a negative effect for USD and EUR in some specifications, possibly reflecting USD/EUR use in low-income or emerging countries not politically aligned with advanced economies.
- Anti-money laundering compliance and financial development:
  - Effects vary by currency: negative effect on usage for USD and EUR, positive effect for other currencies, possibly reflecting that countries with weaker financial development/integrity frameworks use dominant reserve currencies to connect with international markets.
- Trade ties and financial linkages:
  - Trade ties or financial linkages do not appear to have a major impact on currency choices in the main results.

### Hypothetical simulations and scenarios
- Simulations performed by recursively estimating one-year ahead OLS best prediction over a 20-year horizon (2020 to 2040); only the simulated variables are changed in each scenario.
- RMB legal tender adoption scenario:
  - Assumed a 3 percent probability that the RMB becomes legal tender in Asian and African countries every year.
  - Results: a relatively small but sustained increase in legal tender status in Africa and Asia could lead the RMB to reach market share levels above 20 percent in some of these countries.
  - The effect is limited for China because a very large fraction of China’s cross-border payments is with advanced economies (U.S., Germany, Japan) where only a small fraction of payments is in RMB.
  - Even substantial adoption in small countries would not substantially change the RMB’s global share in cross-border payments.
- Political proximity scenarios:
  - Political convergence scenario: political proximity increases randomly across countries by 5 percent each year.
  - Political fragmentation scenario: political proximity decreases randomly across countries by 5 percent each year.
  - Results: effects are larger for "other currencies" than for RMB, driven by larger estimated parameters for those currencies; effects on China remain limited for reasons noted above.

### Policy-relevant implications and overall conclusions
- The substantial inertia of the IMS (large network effects and switching costs) and the concentration of payment volumes through large advanced economies imply relatively slow change in the global currency landscape for cross-border payments.
- Relatively minor but sustained changes in political distance or legal tender status could elevate the share of alternative currencies in selected countries, but aggregate global effects are limited because major payment volumes are concentrated in large economies unlikely to change key variables.
- New technologies and rapid geopolitical shifts could accelerate transformation, but impacts remain highly uncertain:
  - Broader introduction of crypto-assets as legal tender could weaken the role of fiat money and significantly impact the IMS.
  - Decisive central bank efforts to digitalize public money or introduce CBDCs could reduce switching costs and inertia and facilitate faster currency configuration changes.
  - Abrupt geopolitical evolution could accelerate fragmentation of payment systems and give rise to new currency blocs.

*Source: Introduction, "Currency Usage for Cross-Border Payments", IMF Working Paper (content unit: wpiea2023072-print-pdf - Introduction).*

### conclusions apply to the scenario where changes in political fragmentation or political proximity are considered,

### Currency Usage for Cross-Border Payments

### Dynamic projections and scenario assumptions
- Simulated market share observations represent the RMB for cross-border payments in a given country and year.
- Assumptions used in simulations:
  - There is a 3 percent probability that the RMB becomes legal tender in Asian and African countries every year (except for India and Japan).
  - India and Japan are assumed not to adopt the RMB as legal tender because of their large size.
  - There is a random increase in political proximity of 5 percent for all countries in every year in relevant political-proximity scenarios.
- Caveat: These simple forecasting simulations provide only a first-order empirical approximation as they do not consider which currencies are losing market share when the currency of interest increases its market share.

### Inertia in currency use and macroeconomic implications
- Core finding: Currencies in global payments exhibit a high degree of inertia.
- Implications:
  - Large inertia reaffirms the potential limitations of exchange rate movements to buffer domestic economies from macroeconomic shocks.
  - Consistent with the dominant pricing paradigm, large exchange rate movements may be required for rebalancing external positions when trade flows are mainly dominated by a third-country currency.
  - The use of supportive macroeconomic policies may be justified when large exchange rate fluctuations carry adverse effects.

### Effects of geography, politics, and legal tender status
- Legal tender status, geographic distance, and political distance play an important role in determining variation in currency usage across countries.
- Differences across major currencies:
  - Geographic and geopolitical distance would preclude the acceptance of a currency other than the U.S. dollar, but this does not apply to the U.S. dollar.
  - Geographic contiguity, having a common official language, or having a prior colonial relationship may have a positive effect on currency usage, but the effect is typically small.
  - Trade linkages appear to matter for emerging currencies, while less so for established currencies.

### Digital money, crypto, and potential IMS transformations
- The large inertia effects suggest that in the short and medium term a drastic change in the distribution of world currencies or their digital successors—the e-Dollar or the e-Euro—would be unlikely.
- Possible near-term scenario:
  - A smooth transition from the U.S. dollar or the euro to a digital version of these currencies, without changing drastically their market shares.
  - A slow increase of usage of other competing digital currencies, which could experience higher usage with closer regional and trading partners, or with political allies.
- Sources of potential rapid change and uncertainty:
  - A much more abrupt geo-political shift could accelerate the transformation of the International Monetary System (IMS).
  - A broader introduction of crypto assets as legal tender could weaken the role of fiat money and significantly impact the IMS.
  - A decisive effort of central banks to digitalize public money, or introduce CBDCs, could facilitate rapid currency configuration changes by reducing the cost of switching, though the impact remains highly uncertain.
  - Further geo-political tensions could accelerate fragmentation of the payment system and give rise to new digital currency blocs in a multipolar IMS.
- Note: Many key model variables are slowly moving or stationary over the sample period; rapid changes in legal tender status, political distance, or financial development are not expected in the near future due to inertia and political/regulatory barriers.

### Conclusion and outlook
- Using granular Swift cross-border payments data, the study empirically estimates drivers of cross-region variations in currency usage, focusing on legal tender status and geo-political distance.
- Main conclusions:
  - High inertia in currency usage implies limited near-term shifts in the distribution of global currencies or their digital successors.
  - Legal tender status, geographic distance, and political distance are significant determinants of cross-country variation in currency usage, with notable exceptions for the U.S. dollar.
  - Profound transformations of the IMS have typically been slow historically; however, future major geo-political events or rapid digitalization could drastically accelerate transformation toward multipolarity.

### Annex I — Selected statistics for main variables
- Basel AML Score: Count 1,280; Mean 5.6; St. Dev. 1.2; Min 1.8; P25 4.8; P50 5.6; P75 6.5; Max 8.6. (Higher value indicates a higher risk of money laundering and terrorist financing)
- Distance between Capitals (km): Count 50,176; Mean 8,472; St. Dev. 4,705; Min 0.9; P25 4,768; P50 8,081; P75 12,005; Max 19,951.
- Financial Development Index: Count 1,920; Mean 0.3; St. Dev. 0.2; Min 0; P25 0.1; P50 0.2; P75 0.4; Max 1. (Higher value indicates a better developed financial system)
- Political Proximity: Count 407,616; Mean 0.8; St. Dev. 0.2; Min 0; P25 0.7; P50 0.9; P75 0.9; Max 1. (Higher value indicates greater political alignment)
- Prob of Default: Count 1,186; Mean 1.0%; St. Dev. 1.1%; Min 0%; P25 0.4%; P50 0.7%; P75 1.1%; Max 13.7%. (Higher value is worse)
- Sov. Rating: Count 1,496; Mean 13.8; St. Dev. 8.4; Min 1; P25 8; P50 14; P75 19; Max 33. (Higher value is a worse sovereign rating)
- Border Contiguity: Count 50,176; Mean 0; St. Dev. 0.1; Min 0; P25 0; P50 0; P75 0; Max 1. (Most countries do not share a border)
- Common Official Language: Count 50,176; Mean 0.2; St. Dev. 0.4; Min 0; P25 0; P50 0; P75 0; Max 1. (Most countries do not share common official language)
- Former Colonial Relationship: Count 50,176; Mean 0; St. Dev. 0.1; Min 0; P25 0; P50 0; P75 0; Max 1. (Most country relationships are not one of colonizer-colony)
- Former Single Country: Count 50,176; Mean 0; St. Dev. 0.1; Min 0; P25 0; P50 0; P75 0; Max 1. (Most countries were not previously part of the same country)

*Working Paper No. WP/2023/072 — Currency Usage for Cross-Border Payments*

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_Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023072-print-pdf.pdf_