Mapping Extent of Spillover Channels in Monetary Space: Study of Multidimensional Spatial Effects of US Dollar Liquidity
Abstract
:1. Introduction
2. The Theoretical Framework of Monetary Space
2.1. Political (System) Subspace
2.2. Linguistic (Culture) Subspace
2.3. Economic (Trade) Subspace
2.4. War (Conflict) Subspace
3. Empirical Analysis
3.1. Empirical Background
3.1.1. General Economic Indicators: GDP, CPI, and BBL
3.1.2. Conduction Mechanism: Spatial Matrix and Its Abstraction
3.1.3. Conduction Means: SLX, SDM, and GNSM
3.2. Data and Empirical Results
3.2.1. Data Description
3.2.2. Hypotheses
3.2.3. Econometric Approach
3.2.4. Empirical Results
3.2.5. Empirical Conclusions
4. Research Findings and Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Trade Subspace | |||||||||
GDP | CPI | BBL | |||||||
(1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | |
Explanatory variables | |||||||||
log_USDL | 0.189 ** | 0.008 *** | 0.014 ** | −0.008 | 0.000 | 0.000 | −0.057 | 0.000 | 0.000 |
(0.002) | (0.001) | (0.001) | (0.348) | (0.536) | (0.536) | (0.533) | (0.910) | (0.943) | |
Wlog (X) | 165.2 | 6.730 | 25.27 * | 71.65 ** | 9.390 ** | 9.391 *** | 11.81 | 0.201 * | 0.208 * |
(0.359) | (0.372) | (0.061) | (0.002) | (0.000) | (0.000) | (0.258) | (0.034) | (0.038) | |
Control variable | |||||||||
log (ENDR) | 0.015 | 0.000 | 0.000 | ||||||
(0.667) | (0.160) | (0.175) | |||||||
log (EXPD) | 0.280 * | 0.003 ** | 0.003 ** | ||||||
(0.024) | (0.007) | (0.007) | |||||||
log (LCPI) | 0.229 ** | 0.003 *** | 0.003 *** | ||||||
(0.009) | (0.000) | (0.000) | |||||||
label11 | −0.041 | −0.002 | −0.002 | ||||||
(0.968) | (0.854) | (0.860) | |||||||
label12 | 0.096 | −0.001 | −0.001 | ||||||
(0.942) | (0.908) | (0.916) | |||||||
log (USDL): label 11 | 0.004 | 0.000 | 0.000 | ||||||
(0.961) | (0.850) | (0.856) | |||||||
log (USDL): label 12 | −0.015 | 0.000 | 0.000 | ||||||
(0.899) | (0.969) | (0.976) | |||||||
log (FRES) | 0.167 * | 0.009 *** | 0.017 *** | −0.067 | −0.001 * | −0.001 * | |||
(0.015) | (0.001) | (0.001) | (0.258) | (0.066) | (0.071) | ||||
lag_log_FRES | 76.53 | 6.674 | 1.871 | ||||||
(0.550) | (0.214) | (0.843) | |||||||
LRAT | 0.004 | −0.001 | 0.000 | 0.003 * | 0.000 ** | 0.000 ** | |||
(0.865) | (0.561) | (0.871) | (0.046) | (0.007) | (0.007) | ||||
lagLRAT | 5.744 | 0.857 | 0.018 | −34.88 *** | −2.541 *** | −2.541 *** | |||
(0.736) | (0.230) | (0.989) | (0.000) | (0.000) | (0.000) | ||||
LRAT2 | 0.000 | 0.000 | 0.000 | ||||||
(0.487) | (0.858) | (0.819) | |||||||
PEXP | −0.0035 ** | −0.002 *** | −0.004 *** | 0.004 * | 0.000 | 0.000 | |||
(0.000) | (0.000) | (0.000) | (0.050) | (0.953) | (0.954) | ||||
lagPEXP | −38.31 * | −1.587 * | −3.475 ** | −10.18 * | −1.005 ** | −1.005 ** | |||
(0.022) | (0.023) | (0.003) | (0.012) | (0.002) | (0.002) | ||||
Political Subspace | |||||||||
GDP | CPI | BBL | |||||||
(1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | |
Explanatory variables | |||||||||
log_USDL | −2.698 *** | −2.980 *** | −2.877 *** | −0.145 * | −0.144 * | −0.140 * | −0.002 | −0.039 | −0.030 |
(0.000) | (0.000) | (0.000) | (0.035) | (0.027) | (0.031) | (0.984) | (0.817) | (0.872) | |
Wlog (X) | 0.031 *** | 0.034 *** | 0.033 *** | 0.001 * | 0.001 * | 0.001 * | −0.001 | −0.001 | −0.002 * |
(0.000) | (0.000) | (0.000) | (0.055) | (0.045) | (0.055) | (0.057) | (0.104) | (0.058) | |
Control variable | |||||||||
log (ENDR) | 0.003 | 0.028 | 0.024 | ||||||
(0.941) | (0.656) | (0.729) | |||||||
log (EXPD) | 0.203 * | 0.379 | 0.362 * | ||||||
(0.066) | (0.044) | (0.087) | |||||||
log (LCPI) | 0.138 | 0.212 | 0.175 | ||||||
(0.115) | (0.156) | (0.2925) | |||||||
label11 | 0.043 | 0.036 | 0.067 | ||||||
(0.966) | (0.983) | (0.972) | |||||||
label12 | 0.271 | 0.458 | 0.538 | ||||||
(0.836) | (0.838) | (0.829) | |||||||
log (USDL): label 11 | −0.004 | −0.001 | −0.004 | ||||||
(0.969) | (0.994) | (0.980) | |||||||
log (USDL): label 12 | −0.029 | −0.049 | −0.056 | ||||||
(0.805) | (0.890) | (0.804) | |||||||
log (FRES) | 2.979 *** | 3.376 *** | 3.528 *** | −0.015 | −0.061 * | −0.047 * | |||
(0.000) | (0.000) | (0.000) | (0.795) | (0.529) | (0.667) | ||||
Conflict Subspace | |||||||||
GDP | CPI | BBL | |||||||
(1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | |
Explanatory variables | |||||||||
log_USDL | −0.491 *** | −0.498 *** | −0.521 *** | −0.031 * | −0.028 *** | −0.026 *** | −0.104 | −0.106 | 0.092 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.239) | (0.212) | (0.206) | |
Wlog (X) | −0.019 | −0.01 | 0.133 | 0.017 | 0.018 * | 0.018 *** | 0.004 * | 0.004 * | −0.001 |
(0.692) | (0.818) | (0.104) | (0.124) | (0.073) | (0.000) | (0.034) | (0.025) | (0.775) | |
Control variable | |||||||||
log (ENDR) | 0.051 | 0.053 | −0.091 ** | ||||||
(0.197) | (0.161) | (0.006) | |||||||
log (EXPD) | 0.268 * | 0.269 * | −0.104 * | ||||||
(0.008) | (0.013) | (0.096) | |||||||
log (LCPI) | 0.211 ** | 0.231 ** | 0.006 | ||||||
(0.008) | (0.004) | (0.933) | |||||||
label11 | 0.051 | 0.055 | 0.297 | ||||||
(0.959) | (0.954) | (0.713) | |||||||
label12 | 0.212 | 0.217 | 0.349 | ||||||
(0.871) | (0.864) | (0.746) | |||||||
log (USDL): label 11 | −0.005 | −0.005 | −0.033 | ||||||
(0.960) | (0.956) | (0.656) | |||||||
log (USDL): label 12 | −0.027 | −0.028 | −0.04 | ||||||
(0.820) | (0.808) | (0.684) | |||||||
log (FRES) | −0.052 | −0.052 | −0.107 * | −0.058 | −0.064 | −0.099 * | |||
(0.377) | (0.351) | (0.039) | (0.289) | (0.232) | (0.035) | ||||
LRAT | −0.015 | −0.020 | −0.029 | 0.004 * | 0.003 * | 0.003 * | |||
(0.501) | (0.823) | (0.136) | (0.014) | (0.047) | (0.042) | ||||
lagLRAT | 0.012 | 0.012 | 0.057 ** | −0.002 * | −0.002 | −0.002 *** | |||
(0.251) | (0.218) | (0.000) | (0.040) | (0.057) | (0.000) | ||||
LRAT2 | 0.000 | 0.001 | 0.002 | ||||||
(0.921) | (0.858) | (0.607) | |||||||
PEXP | −0.014 * | −0.013 * | −0.009 | 0.010 *** | 0.010 *** | 0.010 *** | |||
Geographic Subspace | |||||||||
GDP | CPI | BBL | |||||||
(1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | |
Explanatory variables | |||||||||
log_USDL | 0.378 *** | 0.380 *** | 0.400 *** | −0.035 *** | −0.032 *** | −0.044 *** | −0.084 | −0.076 | −0.073 |
(0.026) | (0.024) | (0.019) | (0.007) | (0.007) | (0.006) | (0.077) | (0.073) | (0.073) | |
Wlog (X) | −0.285 *** | −0.257 *** | −0.199 *** | 0.027 * | 0.020 | 0.021 * | −0.012 | −0.016 * | −0.016 * |
(0.044) | (0.041) | (0.039) | (0.011) | (0.011) | (0.010) | (0.008) | (0.007) | (0.007) | |
Control variable | |||||||||
log (ENDR) | 0.036 | 0.049 | 0.047 | ||||||
(0.032) | (0.031) | (0.031) | |||||||
log (EXPD) | 0.256 * | 0.306 ** | 0.301 ** | ||||||
(0.100) | (0.095) | (0.096) | |||||||
log (LCPI) | 0.294 *** | 0.309 *** | 0.309 *** | ||||||
(0.069) | (0.043) | (0.067) | |||||||
label11 | 0.172 | 0.197 | 0.198 | ||||||
(0.867) | (0.828) | (0.824) | |||||||
label12 | 0.455 | 0.498 | 0.504 | ||||||
(1.131) | (1.080) | (1.075) | |||||||
log (USDL): label 11 | −0.015 | −0.017 | −0.017 | ||||||
(0.080) | (0.076) | (0.076) | |||||||
log (USDL): label 12 | −0.048 | −0.052 | −0.053 | ||||||
(0.102) | (0.098) | (0.097) | |||||||
log (FRES) | 0.203 *** | 0.197 *** | 0.204 *** | −0.090 * | −0.118 * | −0.115 * | |||
(0.030) | (0.028) | (0.023) | (0.045) | (0.043) | (0.043) | ||||
lag_log_FRES | 0.285 *** | 0.282 *** | 0.211 *** | ||||||
(0.034) | (0.031) | (0.030) | |||||||
LRAT | 0.013 | 0.030 | 0.042 ** | 0.005 ** | 0.006 *** | 0.004 ** | |||
(0.018) | (0.017) | (0.015) | (0.002) | (0.002) | (0.002) | ||||
lagLRAT | 0.004 | 0.003 | 0.005 | 0.003 | 0.003 | 0.004 * | |||
(0.008) | (0.007) | (0.007) | (0.002) | (0.002) | (0.002) | ||||
LRAT2 | 0.000 | −0.001 * | −0.001 ** | ||||||
(0.000) | (0.000) | (0.000) | |||||||
PEXP | −0.054 *** | −0.054 *** | −0.051 *** | 0.009 ** | 0.009 *** | 0.011 *** | |||
Languag Subspace | |||||||||
GDP | CPI | BBL | |||||||
(1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | (1) SLX | (2) SDM | (3) GNSM | |
Explanatory variables | |||||||||
log_USDL | 0.381 *** | 0.381 *** | 0.415 *** | −0.023 *** | −0.024 *** | −0.023 *** | −0.095 | −0.111 | −0.077 |
(0.029) | (0.026) | (0.027) | (0.006) | (0.006) | (0.006) | (0.075) | (0.073) | (0.071) | |
Wlog (X) | −0.012 * | −0.090 * | −0.048 * | −0.014 * | −0.014 * | −0.014 * | 0.003 *** | −0.003 *** | 0.002 *** |
(0.042) | (0.038) | (0.019) | (0.005) | (0.005) | (0.005) | (0.001) | (0.001) | (0.001) | |
Control variable | |||||||||
log (ENDR) | 0.018 | 0.001 | −0.029 | ||||||
(0.031) | (0.031) | (0.030) | |||||||
log (EXPD) | 0.320 ** | 0.373 *** | 0.227 * | ||||||
(0.099) | (0.096) | (0.089) | |||||||
log (LCPI) | 0.304 *** | 0.299 *** | 0.248 *** | ||||||
(0.068) | (0.067) | (0.060) | |||||||
label11 | 0.240 | 0.213 | 0.487 | ||||||
(0.843) | (0.819) | (0.776) | |||||||
label12 | 0.497 | 0.450 | 0.492 | ||||||
(1.100) | (1.070) | (1.015) | |||||||
log (USDL): label 11 | −0.022 | −0.018 | −0.047 | ||||||
(0.077) | (0.075) | (0.073) | |||||||
log (USDL): label 12 | −0.054 | −0.049 | −0.057 | ||||||
(0.100) | (0.097) | (0.094) | |||||||
log (FRES) | 0.121 ** | 0.134 *** | 0.160 *** | −0.088 * | −0.120 ** | −0.066 * | |||
(0.036) | (0.033) | (0.036) | (0.044) | (0.043) | (0.041) | ||||
lag_log_FRES | 0.026 | −0.008 *** | −0.029 | ||||||
(0.040) | (0.036) | (0.023) | |||||||
LRAT | −0.051 | −0.051 * | −0.002 ** | 0.011 *** | 0.010 *** | 0.011 *** | |||
(0.021) | (0.019) | (0.002) | (0.002) | (0.002) | (0.002) | ||||
lagLRAT | 0.003 | −0.003 | −0.005 | −0.006 ** | −0.006 ** | −0.006 ** | |||
(0.008) | (0.007) | (0.006) | (0.002) | (0.002) | (0.002) | ||||
LRAT2 | 0.001 | 0.001 * | 0.000 | ||||||
(0.000) | (0.000) | (0.000) | |||||||
PEXP | −0.027 *** | −0.019 * | −0.024 *** | 0.007 ** | 0.007 * | 0.007 ** | |||
(0.007) | (0.006) | (0.006) | (0.002) | (0.002) | (0.002) | ||||
lag PEXP | 0.010 | 0.008 | 0.003 | 0.000 | 0.000 | 0.000 |
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Linguistic (Culture) Subspace | |
input | Field Explanation |
family_id | Language family number (lowest level of language classification) |
legend | Language group number (top level linguistic classification) |
countries | Name of country |
process | |
1. Count the number of different language families within each country. | |
2. The minor language families are grouped together according to the legends label and incorporated into the highest level of classification to create a quantitative distribution of the languages spoken in a country according to the highest level of classification. | |
3. Calculate the linguistic distance between countries by means of vector cosines. | |
output | |
matrix | 34 × 4 language submatrix |
Note: Data source: Built-in database for the R package Glottospace (Norder et al., 2022) Web link: Index of /src/contrib/Archive/glottospace (accessed on 27 April 2024) | |
War (Conflict) Subspace | |
input | Field Explanation |
state_name | Name of country |
left_censor | Left Delete |
right_censor | Right Delete |
defense | Number of defenses |
neutrality | Number of neutrality |
nonaggression | Number of non-violations |
entente | Number of contractual truces |
process | |
1. Create a vector of responses to the above response conflicts for each country according to the country labels | |
2. For each vector, the cosine distance is used to obtain the conflict distance between countries | |
output | |
matrix | 34 × 34 War Matrix |
Note: Data source: The Correlates of War Project, “Militarized Interstate Disputes (v5.0)” Web Link: https://www.correlatesofwar.org/data-sets/MIDs (accessed on 27 April 2024) | |
Political (System) Subspace | |
input | Field Explanation |
Voice and Accountability | “Voice and accountability” reflects the extent to which a country’s citizens can participate in choosing their government, as well as freedom of speech, association, and media. |
Political Stability and Absence of Violence/Terrorism | Political stability and absence of violence/terrorism measure people’s perceptions of the likelihood of political instability and/or politically motivated violence (including terrorism). |
Government Effectiveness | “Government effectiveness” measures public perceptions of the quality of public services, the quality of the civil service and its independence from political pressure, the quality of policy formulation and implementation, and the credibility of the government’s commitment to those policies. |
Regulatory Quality | The quality of regulation reflects the government’s ability to develop and implement sound policies and regulations that allow and facilitate private sector development. |
Rule of Law | The rule of law reflects the perception of the degree of trust in and compliance with the rules of society, particularly the quality of contract enforcement, property rights, police and courts, and the potential for crime and violence. |
Control of Corruption | Controlling Corruption captures public perceptions of the extent to which public power is exercised for private gain, including small and large forms of corruption and the “capture “of the state by elites and private interests. |
Country Name | Name of country |
Notes | All of the above scores obeyed a normal distribution with a range of −2.5 to 2.5. |
process | |
1. Create vectors for each country according to country labels that reflect the above politics | |
2. Calculated for each vector using the cosine distance to obtain the political distance between countries | |
output | |
matrix | 34 × 34 political matrix |
Note: Data source: World Bank’s Global Governance Index (WGI) database Web link: https://info.Worldbank.org/governance/wgi https://datatopics.worldbank.org/world-development-indicators/ (accessed on 27 April 2024) | |
Economic (Trade) Subspace | |
input | Field Explanation |
Reporter | Country name (here is the trade A, B is China) |
Export | Number of Export |
Import | Number of Import |
Re-Export | Re-export refers to the national trades from foreign imports of finished products, without processing and manufacturing and exported for sale abroad, which consist of two parts, namely, from the country’s free trade zone or customs bonded warehouse re-export and nationalized goods re-export. |
Re-Import | Re-importation refers to the trade process in which a domestic manufacturer or trader sells domestic goods abroad and imports them into the country unprocessed. |
process | |
1 Create vectors for each country according to the country labels that reflect the above responses to economics and trade. | |
2 Calculate each vector using the cosine distance to obtain the trade distance between countries. | |
output | |
matrix | 34 × 34 Trade Matrix |
(a) | |
Variable Name | Field Explanation |
USDL | US dollar-denominated bonds: A stronger dollar can depreciate other countries’ currencies in relative terms, raising foreign exchange market pressures on emerging market currencies. Since they cannot borrow in their own currencies, a stronger dollar would affect their access to short-term borrowing, thus widening their foreign exchange gap. At the same time, as capital leaves emerging markets, dollar creditors are likely to be reluctant to want to lend to borrowers with weaker currencies. Sudden foreign exchange outflows from emerging markets mean higher default risks, stalled investment and development projects, and further complications for immediate interest repayment. |
ENDR | Exchange rate against the US: The exchange rate of a country’s currency against the US, an increase in that value, implies a depreciation of the national currency against the US dollar, a decrease in that value. In international trade using the invoice system, an appreciation of the dollar implies an implicit depreciation of the bilateral dollar exchange rate of other countries. On the one hand, this does not reduce the volume of export trade to the dominant economy denominated in dollars, but it increases the cost of imports in non-dollar currencies for both the dominant and non-dominant economies, so exports from all non-dominant economies will contract. On the other hand, the cost of non-dominant imports denominated in domestic currency will rise while the cost of imports for the dominant economy falls. Thus, for the non-dominant economies, an appreciation against the US exchange rate increases inflation by reducing the volume of their exports, thereby reducing economic growth. In addition, an appreciation against the US exchange rate implies capital flight from the non-dominant country. The risk of inflation due to lower export volumes can accelerate financial asset outflows by making investors averse to financial assets denominated in the currencies of non-dominant countries. However, a weaker dollar does not necessarily increase capital from non-dominant countries, as other factors, such as politics in non-dominant countries, may result in higher transaction costs. The exchange rate against the US, expressed as the daily closing price of each country against the US dollar, is used as a control variable to keep asset price bubbles unaffected by changes in the exchange rate against the US |
EXPD | Imports: The current value of import trade of each country, measured in billions of dollars, is the control variable of the model. Changes in the exchange rate due to changes in the import value of each country can affect asset price bubbles, and controlling for the constant import value of each country can avoid biased and inconsistent estimation results due to omitted variables. |
(b) | |
Variable Name | Field Explanation |
USDL | US dollar-denominated bonds: same questions and explanations as above |
FRES | Foreign exchange reserve amount: same question and explanation as above |
LRAT | Benchmark interest rate: the benchmark interest rate for each country is expressed as a unit percentage and is taken as the nominal interest rate value, which is obtained from the estimated conversion for countries without benchmark interest rates and is used as a control variable in the model. |
PEXP | Imports to GDP ratio: the ratio of countries ‘current import trade to countries’ current nominal GDP, in percent, as a control variable. |
Trade Matrix | War Matrix | |||||
CPI | GDP | BBL | CPI | CDP | BBL | |
SLX | −* | + | − | − | − | −* |
SDM | −* | + | −* | −* | − | −* |
GNSM | −* | +* | −* | −* | + | + |
Political Matrix | Language Matrix | |||||
CPI | GDP | BBL | CPI | CDP | BBL | |
SLX | −* | +* | +* | −* | −* | − |
SDM | −* | +* | +* | −* | −* | −* |
GNSM | −* | +* | +* | −* | −* | − |
Geographic Matrix | ||||||
CPI | GDP | BBL | ||||
SLX | −* | −* | −* | |||
SDM | + | −* | −* | |||
GNSM | + | −* | −* |
Direct Effect | |||||||||
Sub | GDP | CPI | BBL | ||||||
Space | SLX | SDM | GNSM | SLX | SDM | GNSM | SLX | SDM | GNSM |
Geo | +* | +* | +* | −* | −* | −* | − | − | − |
Tra | +* | +* | +* | − | − | ||||
War | +* | +* | +* | −* | −* | −* | − | − | + |
Lan | +* | +* | +* | −* | −* | −* | − | − | − |
Pol | −* | −* | −* | −* | −* | −* | − | − | − |
Direct Effect | |||||||||
Sub | GDP | CPI | BBL | ||||||
Space | SLX | SDM | GNSM | SLX | SDM | GNSM | SLX | SDM | GNSM |
Geo | −* | −* | −* | +* | + | +* | − | −* | −* |
Tra | + | + | +* | +* | +* | +* | + | +* | +* |
War | − | − | + | + | +* | +* | +* | +* | − |
Lan | −* | −* | −* | −* | −* | −* | +* | +* | +* |
Pol | +* | +* | +* | +* | +* | +* | − | − | −* |
Hypothesis | Conclusion |
---|---|
Hypothesis 1: Risk sensitivity | The more sensitive the indicator is to risk, the larger the absolute value of the coefficient of the regression equation, i.e., the more significant the dollar externality |
Hypothesis 2: Model Complexity | The higher the spatial identification of the model, the larger the absolute value of the coefficient of the regression equation, i.e., the more significant the dollar externality |
Hypothesis 3: Spatial matrix composition | The more abstract the matrix, the larger the absolute value of the coefficient of the regression equation and the more significant the dollar externality |
Index | Effect | *** | ** | * | Base | Ratio 1 |
---|---|---|---|---|---|---|
GDP | Direct | 13 | 2 | 0 | 15 | 95.56% |
Indirect | 6 | 0 | 4 | 15 | 48.89% | |
CPI | Direct | 8 | 1 | 3 | 15 | 64.44% |
Indirect | 3 | 1 | 9 | 15 | 44.44% | |
BBL | Direct | 0 | 0 | 0 | 15 | 0.00% |
Indirect | 3 | 0 | 7 | 15 | 35.56% |
Index | Effect | *** | ** | * | Base | Ratio 1 | Ratio 2 |
---|---|---|---|---|---|---|---|
SLX | Direct | 6 | 2 | 1 | 15 | 51.11% | 43.33% |
Indirect | 3 | 1 | 5 | 15 | 35.56% | ||
SDM | Direct | 8 | 0 | 1 | 15 | 55.56% | 48.89% |
Indirect | 4 | 0 | 7 | 15 | 42.22% | ||
GNSM | Direct | 7 | 1 | 1 | 15 | 53.33% | 52.22% |
Indirect | 5 | 0 | 8 | 15 | 51.11% |
Index | Effect | *** | ** | * | Base | Ratio 1 | Ratio 2 |
---|---|---|---|---|---|---|---|
Geography | Direct | 6 | 0 | 0 | 9 | 66.67% | 57.41% |
Indirect | 3 | 0 | 4 | 9 | 48.15% | ||
Language | Direct | 5 | 1 | 0 | 9 | 62.96% | 59.26% |
Indirect | 3 | 0 | 6 | 9 | 55.56% | ||
Politics | Direct | 3 | 0 | 3 | 9 | 44.44% | 46.30% |
Indirect | 3 | 0 | 4 | 9 | 48.15% | ||
Conflict | Direct | 6 | 0 | 0 | 9 | 66.67% | 44.44% |
Indirect | 1 | 0 | 3 | 9 | 22.22% | ||
Trade | Direct | 1 | 2 | 0 | 9 | 25.93% | 33.33% |
Indirect | 2 | 1 | 3 | 9 | 40.74% |
Case1 | Possible reasons |
Result Significant positive direct spillover effect on GDP | ① The positive effect of lower trade costs between neighboring countries-appreciation of dollar-denominated bonds leads to lower import costs, saving foreign exchange, and increasing domestic output. |
Details This occurs in 12 of the 15 equations involving direct effects of GDP, covering all forms of spatial equations in the four major subspaces of geography, trade, and language of war (SLX, SDM, GNSM). | ② Increased trade between neighboring countries can improve tariff preferences, customer quotas, and transportation costs, which in turn can increase a country’s GDP; in addition, reduced import costs in dollar-dominated trade areas can significantly reduce raw material and energy costs, thus promoting circular domestic economic output. |
Case2 | Possible reasons |
Result Significant positive direct spillover effect on CPI | ① Appreciation of dollar-denominated bonds makes other countries’ currencies implicitly depreciate. ② Higher dollar-denominated import costs for countries using non-dollar currencies lead to export contraction. The appreciation of dollar-denominated bonds will lead to the relative depreciation of other countries’ currencies, which will increase the cost of imports, thus pushing up the consumer price level and causing the CPI to rise; at the same time, the increase in the cost of dollar-denominated imports from countries that are mainly processing |
Details This occurs in 10 of the 15 equations involving the indirect effects of CPI, covering almost all forms of spatial equations in the four major subspaces of geography, trade, war, and politics (SLX\SDMGNSM). | ③The appreciation of dollar-denominated bonds will leadto the relative depreciation of other countries’ currencies.which will increase the cost of imports, thus pushing up theconsumer price level and causing the CPl to rise, at thesame time, the increase in the cost of dollar-denominatedimports from countries that are mainly processing materials and using non-dollar currencies will cause their exports to contract, thus pushing up their domestic price levels; finally, the decrease in the cost of imports from countries that use the dollar as their main currency will lead to economic growth due to the decrease in the cost of imports from dollar-dominated trading areas, which will stimulate the price level to rise. |
Case3 | Possible reasons |
Result Direct effects and indirect spillover effects are not significant | ① The impact of US dollar-denominated bonds on a country’s GDP takes a long transmission path, and there is no way to consider the specifics. ② Appreciation of dollar-denominated bonds leads to capital flight from non-dollar major trading area countries but is panicky, unpredictable, and difficult to quantify. |
Details Out of a total of 45 equations (90 coefficients), 26 insignificant traits emerged; in particular, the direct effect of the BBL model was insignificant for almost all of its coefficients, although they responded to spillover effects in the same direction | ③ The impact of US dollar-denominated bonds on a country’s GDP needs to pass through a long transmission path, and this process is full of uncertainties, so it is difficult to consider the specific situation; while the appreciation of US dollar-denominated bonds may lead to capital outflows from non-US dollar major trading area countries, but the validity and empirical evidence are relatively weak due to its panic and unpredictability; in addition, the investment environment of non-US dollar major trading area countries is affected by political and war factors, and it is difficult to measure accurately, so CPI and other covariates are not easy to estimate significantly. |
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Lu, C.; Liu, L.; Yu, F.; Li, J.; Zheng, G. Mapping Extent of Spillover Channels in Monetary Space: Study of Multidimensional Spatial Effects of US Dollar Liquidity. Int. J. Financial Stud. 2025, 13, 72. https://doi.org/10.3390/ijfs13020072
Lu C, Liu L, Yu F, Li J, Zheng G. Mapping Extent of Spillover Channels in Monetary Space: Study of Multidimensional Spatial Effects of US Dollar Liquidity. International Journal of Financial Studies. 2025; 13(2):72. https://doi.org/10.3390/ijfs13020072
Chicago/Turabian StyleLu, Changrong, Lian Liu, Fandi Yu, Jiaxiang Li, and Guanghong Zheng. 2025. "Mapping Extent of Spillover Channels in Monetary Space: Study of Multidimensional Spatial Effects of US Dollar Liquidity" International Journal of Financial Studies 13, no. 2: 72. https://doi.org/10.3390/ijfs13020072
APA StyleLu, C., Liu, L., Yu, F., Li, J., & Zheng, G. (2025). Mapping Extent of Spillover Channels in Monetary Space: Study of Multidimensional Spatial Effects of US Dollar Liquidity. International Journal of Financial Studies, 13(2), 72. https://doi.org/10.3390/ijfs13020072