## 0.063 percent, respectively, according to the preferred specification presented in column [7]. ## Source details **Canonical URL:** [0.063 percent, respectively, according to the preferred specification presented in column [7].](https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024021-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2024/english/wpiea2024021-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2024/english/wpiea2024021-print-pdf.pdf.json) --- ### Key empirical findings - A 10 percent increase in real GDP per capita in origin countries is associated with an average increase of 7.8 percent in bilateral trade flows. - A 10 percent increase in real GDP per capita in destination countries is associated with an average increase of 6.3 percent in bilateral trade flows. - The elasticity of bilateral trade flows with respect to geographic distance is, on average, -0.150 percent in the baseline specification; a 10 percent increase in geographic distance lowers bilateral trade flows by more than 1.5 percent on average. - Geographical contiguity increases trade; cultural similarities and historical ties (common official language, common religion, colonial relations) have significant positive effects on bilateral trade flows. - Population contributes positively to trade; the elasticity with respect to population in destination country is almost five times greater than the coefficient on population in origin country. - Membership in international trade organizations increases trade flows: - The impact of WTO membership is significantly greater than that of GATT. - Presence of FTA increases bilateral trade flows, with magnitude almost twice as large as WTO membership for the full sample and even larger for developing countries. - Geopolitical distance (gravity PPML baseline) is estimated at 0.025*** in a primary specification, declines to 0.015*** when democracy controls are added in column [6], and to 0.011 (statistically insignificant) when interaction terms are included in column [7]. - Democracy variables generally show positive effects; examples: - Democracy, origin: 0.000, 0.000, 0.001, 0.002*, 0.010***, 0.004***, 0.003**. - Democracy, destination: 0.002**, 0.001, 0.001, 0.001, 0.003*, 0.003***. - Interaction terms Geopolitics*Democracy (origin and destination) are often positive and sometimes significant; examples: - Geopolitics*Democracy, origin: 0.000, 0.001, 0.001, 0.006***, 0.002***, 0.001*, 0.006***, 0.003***. - Geopolitics*Democracy, destination: 0.001, 0.001, 0.001, 0.002***, 0.001***, 0.001*. ### Gravity model estimates (selected coefficients and notes) - Real GDP per capita, origin: reported coefficients include 0.092***, 0.091***, 0.094***, 0.093***, 0.089***, 0.078***, 0.078***, 0.125***, 0.061***, 0.070***, 0.065***, 0.085***, 0.092***, 0.086***. - Real GDP per capita, destination: reported coefficients include 0.059***, 0.059***, 0.075***, 0.073***, 0.066***, 0.063***, 0.063***, 0.084***, 0.053***, 0.078***, 0.049***, 0.072***, 0.096***, 0.065***. - Distance: coefficients reported include -0.184***, -0.164***, -0.164***, -0.154***, -0.162***, -0.149***, -0.150***, -0.140***, -0.147***, -0.142***. - Geographical contiguity: coefficients include 0.038**, 0.039**, 0.036**, 0.037**, 0.002, 0.002, 0.030, 0.113, 0.057***. - Common language: coefficients include 0.092***, 0.093***, 0.091***, 0.097***, 0.095***, 0.094***, 0.079***, 0.060***, 0.099***. - Common religion: coefficients include 0.030***, 0.029***, 0.025***, 0.027***, 0.067***, 0.066***, 0.045***, 0.009, 0.043**. - Colonial history: coefficients include 0.129***, 0.130***, 0.135***, 0.125***, 0.145***, 0.145***, 0.091*, 0.234***, 0.040. - Population, origin: coefficients include 0.022***, 0.028***, 0.027***, 0.067***, 0.069***, 0.077***, 0.033*, 0.016, 0.014, 0.040**, 0.020, 0.018. - Population, destination: coefficients include 0.095***, 0.106***, 0.113***, 0.122***, 0.125***, 0.136***, 0.110***, 0.074***, 0.153***, 0.115***, 0.082***, 0.160***. - GATT, origin: coefficients include 0.013*, 0.023***, 0.006, 0.006, 0.007, 0.008, 0.140***, 0.009, 0.009, 0.142***, 0.010. - WTO, origin and destination: examples include WTO, origin coefficients 0.027***, 0.017***; WTO, destination coefficients 0.027***, 0.024***, 0.023**, 0.023**, 0.028**, 0.024**. - FTA: coefficients include 0.069***, 0.075***, 0.086***, 0.087***, 0.090***, 0.101***, 0.015, 0.130***, 0.103***, 0.018, 0.132***. - Model notes: - Dependent variable: bilateral trade flows. - Standard errors clustered at the country-pair level. - *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. - Number of observations reported in various specifications include 744,900; 709,859; 629,848; 123,265; 98,569; 27,751; 70,818. - Fixed effects vary across specifications (Country FE, Country-pair FE, Country-time FE reported as Yes/No). - R-squared examples: 0.66, 0.67, 0.69, 0.68, 0.62, 0.33, 0.24, 0.61, 0.32, 0.24. ### Heterogeneity by income group and sample trimming - Truncating the sample at the 5th and 95th percentiles turns the coefficient on geopolitical distance negative for the whole sample (column [9]). - Subsample by income groups: - Advanced economies: coefficient on geopolitical distance is positive (column [10]). - Developing countries: coefficient on geopolitical distance is negative and statistically insignificant (column [11]). - Democracy effects: - Democracy in origin and destination countries are positive and statistically significant; democracy in origin matters more for trade than democracy in destination. - Magnitude of democracy effects is considerably greater in advanced economies than in developing countries. - Interaction terms 퐺푒표_ijt * 퐷푒푚_it and 퐺푒표_ijt * 퐷푒푚_jt: - Democracy in origin and destination countries helps moderate the potential consequences of geopolitics for bilateral trade. - Interaction-term magnitudes are substantially larger in advanced economies than in developing countries. - Results are robust to estimating specifications with country-pair fixed effects. ### 2SLS-IV estimations and endogeneity control (selected IV results) - Instruments: child mortality for democracy; lagged geopolitics variable for geopolitical distance. - IV results (Table 3) confirm: - Geopolitical distance has a positive impact on bilateral trade flows in advanced economies and a negative impact in developing countries; example coefficients include 0.154***, 0.171***, 0.127***, 0.186***, -0.782***, -1.144***. - Real GDP per capita, origin and destination show much larger coefficients in IV specifications; examples: - Real GDP per capita, origin: 0.691***, 0.709***, 0.447***, 0.423***, 0.613***, 0.619***. - Real GDP per capita, destination: 0.648***, 0.612***, 0.764***, 0.738***, 0.475***, 0.481***. - Distance in IV specifications shows large negative coefficients: -1.327***, -1.326***, -1.437***, -1.430***, -1.106***, -1.111***. - Democracy coefficients in IV are positive and significant in advanced economies; examples: - Democracy, origin: 0.185***, 0.206***. - Democracy, destination: 0.142***, 0.107***. - Interaction terms in IV are positive and significant: Geopolitics*Democracy, origin 0.001***, 0.004***, 0.002***; Geopolitics*Democracy, destination 0.001***, 0.001***, 0.002***. - IV estimations indicate magnitudes larger than OLS/PPML counterparts and confirm that democracy stabilizes trade against geopolitical tensions; this stabilizing impact is marginally larger in advanced economies than in developing countries. - IV model notes: - Number of observations in IV tables: 79,190; 23,421; 55,769 across columns. - Country FE: Yes; Country-pair FE: No; Country-time FE: Yes. - R-squared examples in IV: 0.46, 0.70, 0.71, 0.25, 0.28. ### Policy implications and conclusions - Geopolitical distance (measured by UN voting similarity) has contradictory effects on trade depending on economic development: positive in advanced economies, negative and statistically insignificant in developing countries. - The economic magnitude of the geopolitical effect is smaller than that of income and geographic distance and diminishes when democracy is controlled for and when outliers are removed. - Democracy has a robust positive effect on international trade and a moderating influence on the negative effects of geopolitical distance; democracy in the origin country matters more for trade than democracy in the destination country. - Policy message: pursue appropriate policies to increase openness while reducing the socioeconomic burden of globalization; avoid nationalist and protectionist policies that could reduce global economic resilience and increase inequality. - The paper emphasizes the evolving nature of global value chains and that trade linkages and supply chains do not remain constant over time. *Source: wpiea2024021-print-pdf* ### 0.063 percent, respectively, according to the preferred specification presented in column [7]. ### wpiea2024021-print-pdf - 0.063 percent, respectively, according to the preferred specification presented in column [7]. ### Key empirical findings - A 10 percent increase in real GDP per capita in origin countries is associated with an average increase of 7.8 percent in bilateral trade flows. - A 10 percent increase in real GDP per capita in destination countries is associated with an average increase of 6.3 percent in bilateral trade flows. - The elasticity of bilateral trade flows with respect to geographic distance is, on average, -0.150 percent in the baseline specification; a 10 percent increase in geographic distance lowers bilateral trade flows by more than 1.5 percent on average. - Geographical contiguity increases trade; cultural similarities and historical ties (common official language, common religion, colonial relations) have significant positive effects on bilateral trade flows. - Population contributes positively to trade; the elasticity with respect to population in destination country is almost five times greater than the coefficient on population in origin country. - Membership in international trade organizations increases trade flows: - The impact of WTO membership is significantly greater than that of GATT. - Presence of FTA increases bilateral trade flows, with magnitude almost twice as large as WTO membership for the full sample and even larger for developing countries. ### Gravity model estimates (selected coefficients and notes) - Real GDP per capita, origin: reported coefficients include 0.092***, 0.091***, 0.094***, 0.093***, 0.089***, 0.078***, 0.078***, 0.125***, 0.061***, 0.070***, 0.065***, 0.085***, 0.092***, 0.086*** (standard errors in brackets). - Real GDP per capita, destination: reported coefficients include 0.059***, 0.059***, 0.075***, 0.073***, 0.066***, 0.063***, 0.063***, 0.084***, 0.053***, 0.078***, 0.049***, 0.072***, 0.096***, 0.065***. - Distance: coefficients reported include -0.184***, -0.164***, -0.164***, -0.154***, -0.162***, -0.149***, -0.150***, -0.140***, -0.147***, -0.142***. - Geographical contiguity: coefficients include 0.038**, 0.039**, 0.036**, 0.037**, 0.002, 0.002, 0.030, 0.113, 0.057***. - Common language: coefficients include 0.092***, 0.093***, 0.091***, 0.097***, 0.095***, 0.094***, 0.079***, 0.060***, 0.099***. - Common religion: coefficients include 0.030***, 0.029***, 0.025***, 0.027***, 0.067***, 0.066***, 0.045***, 0.009, 0.043**. - Colonial history: coefficients include 0.129***, 0.130***, 0.135***, 0.125***, 0.145***, 0.145***, 0.091*, 0.234***, 0.040. - Population, origin: coefficients include 0.022***, 0.028***, 0.027***, 0.067***, 0.069***, 0.077***, 0.033*, 0.016, 0.014, 0.040**, 0.020, 0.018. - Population, destination: coefficients include 0.095***, 0.106***, 0.113***, 0.122***, 0.125***, 0.136***, 0.110***, 0.074***, 0.153***, 0.115***, 0.082***, 0.160***. - GATT, origin: coefficients include 0.013*, 0.023***, 0.006, 0.006, 0.007, 0.008, 0.140***, 0.009, 0.009, 0.142***, 0.010. - WTO, origin and destination: examples include WTO, origin coefficients 0.027***, 0.017***; WTO, destination coefficients 0.027***, 0.024***, 0.023**, 0.023**, 0.028**, 0.024**. - FTA: coefficients include 0.069***, 0.075***, 0.086***, 0.087***, 0.090***, 0.101***, 0.015, 0.130***, 0.103***, 0.018, 0.132***. - Geopolitical distance (gravity PPML baseline): coefficient 0.025*** in a primary specification, declines to 0.015*** when democracy controls are added in column [6], and to 0.011 (statistically insignificant) when interaction terms are included in column [7]. - Democracy variables: Democracy, origin and destination show positive effects in several specifications (examples include Democracy, origin reported as 0.000, 0.000, 0.001, 0.002*, 0.010***, 0.004***, 0.003**; Democracy, destination examples include 0.002**, 0.001, 0.001, 0.001, 0.003*, 0.003***). - Interaction terms Geopolitics*Democracy (origin and destination) reported positive and sometimes significant (examples include Geopolitics*Democracy, origin rows showing 0.000, 0.001, 0.001, 0.006***, 0.002***, 0.001*, 0.006***, 0.003***; Geopolitics*Democracy, destination examples include 0.001, 0.001, 0.001, 0.002***, 0.001***, 0.001*). - Model notes: - Dependent variable: bilateral trade flows. - Standard errors clustered at the country-pair level. - *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. - Number of observations reported in various specifications (examples include 744,900; 709,859; 629,848; 123,265; 98,569; 27,751; 70,818; 98,569; 27,751; 70,818). - Fixed effects vary across specifications (Country FE, Country-pair FE, Country-time FE reported as Yes/No in different columns). - R-squared examples: 0.66, 0.67, 0.69, 0.68, 0.62, 0.33, 0.24, 0.61, 0.32, 0.24 in different columns. ### Heterogeneity by income group and sample trimming - Truncating the sample at the 5th and 95th percentiles turns the coefficient on geopolitical distance negative for the whole sample (column [9]). - Subsample by income groups reveals: - Advanced economies: coefficient on geopolitical distance is positive (column [10]). - Developing countries: coefficient on geopolitical distance is negative and statistically insignificant (column [11]). - Democracy effects: - Democracy in origin and destination countries are positive and statistically significant; democracy in origin matters more for trade than democracy in destination. - Magnitude of democracy effects is considerably greater in advanced economies than in developing countries. - Interaction terms 퐺푒표_ijt * 퐷푒푚_it and 퐺푒표_ijt * 퐷푒푚_jt: - Democracy in origin and destination countries helps moderate the potential consequences of geopolitics for bilateral trade. - Interaction-term magnitudes are substantially larger in advanced economies than in developing countries. - Results are robust to estimating specifications with country-pair fixed effects. ### 2SLS-IV estimations and endogeneity control (selected IV results) - Instruments: child mortality for democracy; lagged geopolitics variable for geopolitical distance. - IV results (Table 3) confirm: - Geopolitical distance has a positive impact on bilateral trade flows in advanced economies and a negative impact in developing countries (examples: Geopolitical distance coefficients 0.154***, 0.171***, 0.127***, 0.186***, -0.782***, -1.144*** in different IV columns). - Real GDP per capita, origin and destination show much larger coefficients in IV specifications (examples: Real GDP per capita, origin 0.691***, 0.709***, 0.447***, 0.423***, 0.613***, 0.619***; Real GDP per capita, destination 0.648***, 0.612***, 0.764***, 0.738***, 0.475***, 0.481***). - Distance in IV specifications shows large negative coefficients (examples: -1.327***, -1.326***, -1.437***, -1.430***, -1.106***, -1.111***). - Democracy coefficients in IV are positive and significant in advanced economies (examples: Democracy, origin 0.185***, 0.206***; Democracy, destination 0.142***, 0.107***). - Interaction terms in IV are positive and significant (Geopolitics*Democracy, origin 0.001***, 0.004***, 0.002***; Geopolitics*Democracy, destination 0.001***, 0.001***, 0.002***). - IV estimations indicate magnitudes larger than OLS/PPML counterparts and confirm that democracy stabilizes trade against geopolitical tensions; this stabilizing impact is marginally larger in advanced economies than in developing countries. - IV model notes: - Number of observations in IV tables: 79,190; 23,421; 55,769 across columns. - Country FE: Yes; Country-pair FE: No; Country-time FE: Yes. - R-squared examples in IV: 0.46, 0.70, 0.71, 0.25, 0.28. ### Policy implications and conclusions - Geopolitical distance (measured by UN voting similarity) has contradictory effects on trade depending on economic development: positive in advanced economies, negative and statistically insignificant in developing countries. - The economic magnitude of the geopolitical effect is smaller than that of income and geographic distance and diminishes when democracy is controlled for and when outliers are removed. - Democracy has a robust positive effect on international trade and a moderating influence on the negative effects of geopolitical distance; democracy in the origin country matters more for trade than democracy in the destination country. - Policy message: pursue appropriate policies to increase openness while reducing the socioeconomic burden of globalization; avoid nationalist and protectionist policies that could reduce global economic resilience and increase inequality. - The paper emphasizes the evolving nature of global value chains and that trade linkages and supply chains do not remain constant over time. *Source: wpiea2024021-print-pdf* ### REFERENCES ### REFERENCES ### Foundational gravity and trade theory - Tinbergen, J. (1962). “Shaping the World Economy: Suggestions for an International Economic Policy,” The International Executive, Vol. 5, pp. 27–30. - Anderson, J. (1979). “A Theoretical Foundation for the Gravity Equation,” American Economic Review, Vol. 69, pp. 106-116. - Bergstrand, J. (1985). “The Gravity Equation in International Trade: Some Microeconomic Foundations and Empirical Evidence,” Review of Economics and Statistics, Vol. 67, pp. 474–481. - Anderson, J., and E. van Wincoop (2003). “Gravity with Gravitas: A Solution to the Border Puzzle,” American Economic Review, Vol. 93, pp. 170–192. - Baldwin, R., and D. Taglioni (2006). “Gravity for Dummies and Dummies for Gravity Equations,” NBER Working Paper No. 12516. - Santos Silva, J., and S. Tenreyro (2006). “The Log of Gravity,” Review of Economics and Statistics, Vol. 88, pp. 641–658. - Chaney, T. (2008). “Distorted Gravity: The Intensive and Extensive Margins of International Trade,” American Economic Review, Vol. 98, pp. 1707-1721. - Yotov, Y., R. Piermartini, J.-A. Monteiro, and M. Larch (2017). An Advanced Guide to Trade Policy Analysis: The Structural Gravity Model (New York: United Nations and World Trade Organization). - Egger, P. (2000). “A Note on the Proper Econometric Specification of the Gravity Equation,” Economic Letters, Vol. 66, pp. 25–31. - Deardorff, A. (1198). “Determinants of Bilateral Trade: Does Gravity Work in a Neoclassical World?” in The Regionalization of the World Economy, edited by J. Frankel (Chicago: University of Chicago Press). - Olivero, M., and Y. Yotov (2012). “Dynamic Gravity: Endogenous Country size and Asset Accumulation,” Canadian Journal of Economics, Vol. 45, pp. 64–92. - Okawa, Y., and E. van Wincoop (2012). "Gravity in International Finance," Journal of International Economics, Vol. 87, pp. 205–215. - Conte, M., P. Cotteriaz, and T. Mayer (2022). “The CEPII Gravity Database,” CEPII Working Paper No. 2022-05. - Mayer, T., and S. Zignago (2011). “Notes on CEPII’s Distance Measures: The GeoDist Database,” CEPII Working Paper No. 2011-25. ### Geopolitics, geoeconomic fragmentation, and political determinants of cross-border flows - Aiyar, S., J. Chen, C. Ebeke, R. Garcia-Saltos, T. Gudmundsson, A. Ilyina, A. Kangur, T. Kunaratskul, S. Rodriguez, M. Ruta, T. Schulze, G. Soderberg, and J. Trevino (2023). “Geoeconomic Fragmentation and the Future of Multilateralism,” IMF Staff Discussion Note No. 2023/1. - IMF (2023). “Geoeconomic Fragmentation and Foreign Direct Investment,” World Economic Outlook, Chapter 4, Spring. - Cevik, S. (2023). “Long Live Globalization: Geopolitical Shocks and International Trade,” IMF Working Paper No. 23/225. - Caldara, D., and M. Iacoviello (2022). “Measuring Geopolitical Risk,” American Economic Review, Vol. 112, pp.1194–1225. - Aiyar, S., D. Malacrinom, and A. Presbitero (2023). “Investing in Friends: The Role of Geopolitical Alignment in FDI Flows,” CEPR Discussion Paper No. 18434. - Damioli, G., and W. Gregori (2023). “Diplomatic Relations and Cross-Border Investments in the European Union,” European Journal of Political Economy, 102261. - Desbordes, R. (2010). “Global and Diplomatic Political Risks and Foreign Direct Investment,” Economics & Politics, Vol. 22, pp.92–125. - Desbordes, R., and V. Vicard (2009). “Foreign Direct Investment and Bilateral Investment Treaties: An International Political Perspective,” Journal of Comparative Economics, Vol. 37, pp. 372–386. - Knill, A., B.-S. Lee, and N. Mauck (2012). “Bilateral Political Relations and Sovereign Wealth Fund Investment,” Journal of Corporate Finance, Vol. 18, pp. 108–123. - Daviс, C., A. Fuchs, and K. Johnson (2019). “State Control and the Effects of Foreign Relations on Bilateral Trade,” Journal of Conflict Resolution, Vol. 63, pp. 405–438. - Bertrand, O., M.-A. Betschinger, A. Settles (2016). “The Relevance of Political Affinity for the Initial Acquisition Premium in Cross-Border Acquisitions,” Strategic Management Journal, Vol. 37, pp. 2071–2091. - Li, J., K. Meyer, H. Zhange, and Y. Ding (2018). “Diplomatic and Corporate Networks: Bridges to Foreign Locations,” Journal of International Business Studies, Vol. 49, pp. 659–683. - Lugo, S., and M. Montone (2022). “Friend or Foe? Bilateral Political Relations and the Portfolio Allocation of Foreign Institutional Investors,” Available at SSRN: https://ssrn.com/abstract=4278880. - Gupta, N., and X. Yu (2007). “Does Money Follow the Flag?” Available at SSRN: https://ssrn.com/abstract=1316364. - Vreeland, J., and A. Dreher (2014). The Political Economy of the United Nations Security Council: Money and Influence (New York: Cambridge University Press). - Bailey, M., A. Strezhnev, and E. Voeten (2017). "Estimating Dynamic State Preferences from United Nations Voting Data," Journal of Conflict Resolution, Vol. 61, pp. 430–456. - Voeten, E. (2013). “Data and Analyses of Voting in the United Nations General Assembly.” In Routledge Handbook of International Organization, edited by Bob Reinalda (New York Routledge). - Bowen, R., J. Broz, and B. Rosendorff (2023). “A Theory of Trade Policy Transitions,” NBER Working Paper No. 31662. - Boungou, W., F. Osei-Tutu and A. Zongo (2023). “Democracy and Intra-Africa Trade,” Comparative Economic Studies. - Morrow, J., R. Siverson, and T. Tabares (1998). “The Political Determinants of International Trade: The Major Powers, 1907-90,” American Political Science Review, Vol. 92, pp. 649–661. - Grofman, B., and M. Gray (2000). “Geopolitical Influences on Trade Openness in Thirty-One Long-Term Democracies, 1960-1995,” Working Paper (Irvine, CA: University of California). ### Democracy, political regime characteristics, and trade/financial openness - Mansfield, E., H. Milner, and B. Rosendorff (2000). “Free to Trade: Democracies, Autocracies, and International Trade,” American Political Science Review, Vol. 94, pp. 305–321. - Quinn, D. (2001). “Democracy and International Financial Liberalization,” Working Paper (Washington, DC: Georgetown University). - Decker, J., and J. Lim (2009). “Democracy and Trade: An Empirical Study,” Economics of Governance, Vol. 10, pp. 165–186. - Eichengreen, B., and D. Leblang (2008). “Democracy and Globalization,” Economics & Politics, Vol. 20, pp. 289–334. - Kubota, K., and H. Milner (2005). “Why the Move to Free Trade? Democracy and Trade Policy in the Developing Countries,” International Organization, Vol. 59, pp. 107–143. - Yu, M. (2010). “Trade, Democracy, and the Gravity Equation,” Journal of Development Economics, Vol. 91, pp. 289–300. - Yue, J., and S. Zhou (2018). “Democracy’s Comparative Advantage: Evidence from Aggregated Trade Data, 1962-2010,” World Development, Vol. 111, pp. 27–40. - Fidrumc, J. (2003). “Economic Reform, Democracy and Growth During Post-Communist Transition,” European Journal of Political Economy, Vol. 19, pp. 583–604. - Marshall, M., and T. Gurr (2021). “Polity 5: Political Regime Characteristics and Transitions, 1800-2018,” (Vienna, VA: Center for Systemic Peace). ### Globalization, deglobalization, and historical perspectives - Irwin, D. (2020). “Trade Policy in American Economic History,” Annual Review of Economics, Vol. 12, pp. 23–44. - Osterhammel, J., and N. Petersson (2005). Globalization: A Short History (Princeton, NJ: Princeton University Press). - Zeihan, P. (2022). The End of the World Is Just the Beginning: Mapping the Collapse of Globalization (New York: Harper Business). - Goldberg, P., and T. Reed (2023). “Is the Global Economy Deglobalizing? And If So, Why? And What Is Next?” NBER Working Paper No. 31115. - Heimberger, P. (2022). “Does Economic Globalization Promote Economic Growth? A Meta-Analysis,” The World Economy, Vol. 45, pp. 1690–1712. - Hammar, O., and D. Waldenstrom (2020). “Global Earnings Inequality, 1970-2018,” Economic Journal, Vol. 130, pp. 2526–2545. - Alfaro, L., and D. Chor (2023). “Global Supply Chains: The Looming “Great Reallocation”,” NBER Working Paper No. 31661. - Cevik, S. (2022). “Going Viral: A Gravity Model of Infectious Diseases and Tourism Flows,” Open Economies Review, Vol. 33, pp. 141–156. - Gil-Pareja, S., R. Llorca-Vivero, and J. Martínez-Serrano (2007). “The Effect of EMU on Tourism,” Review of International Economics, Vol. 15, pp. 302–312. - Glick, R., and A. Rose (2002). “Does a Currency Union Affect Trade? The Time‐Series Evidence,” European Economic Review, Vol. 46, pp. 1125–1151. - Rose, A., and E. van Wincoop (2001). “National Money as a Barrier to International Trade: The Real Case for Currency Union,” American Economic Review, Vol. 91, pp. 386–390. - Santana-Gallego, M., F. Ledesma-Rodríguez, and J. Pérez-Rodríguez (2010). “Exchange Rate Regimes and Tourism,” Tourism Economics, Vol. 16, pp. 25–43. - Gil-Pareja, S., R. Llorca-Vivero, and J. Martínez-Serrano (2007). “The Effect of EMU on Tourism,” Review of International Economics, Vol. 15, pp. 302–312. ### Capital flows, FDI, and financial determinants - Portes, R., and H. Rey (2005). “The Determinants of Cross-Border Equity Flows,” Journal of International Economics, Vol. 65, pp. 269–296. - Head, K., and J. Ries (2008). “FDI as an Outcome of the Market for Corporate Control: Theory and Evidence,” Journal of International Economics, Vol. 74, pp. 2–20. - Kempf, E., M. Luo, L. Schafer, and M. Tsoutsoura (2021). “Political Ideology and International Capital Allocation,” NBER Working Paper No. 29280. - Knill, A., B.-S. Lee, and N. Mauck (2012). “Bilateral Political Relations and Sovereign Wealth Fund Investment,” Journal of Corporate Finance, Vol. 18, pp. 108–123. - Portes, R., and H. Rey (2005). “The Determinants of Cross-Border Equity Flows,” Journal of International Economics, Vol. 65, pp. 269–296. - Desbordes, R. (2010). “Global and Diplomatic Political Risks and Foreign Direct Investment,” Economics & Politics, Vol. 22, pp.92–125. - Baier, S., and J. Bergstrand (2007). “Do Free Trade Agreements Really Increase Members’ Trade,” Journal of International Economics, Vol. 71, pp. 72–95. - Head, K., and J. Ries (2008). “FDI as an Outcome of the Market for Corporate Control: Theory and Evidence,” Journal of International Economics, Vol. 74, pp. 2–20. ### Methodological tools, databases, and empirical approaches - CEPII GeoDist and CEPII Gravity Database references: - Mayer, T., and S. Zignago (2011). “Notes on CEPII’s Distance Measures: The GeoDist Database,” CEPII Working Paper No. 2011-25. - Conte, M., P. Cotteriaz, and T. Mayer (2022). “The CEPII Gravity Database,” CEPII Working Paper No. 2022-05. - Caldara, D., and M. Iacoviello (2022). “Measuring Geopolitical Risk,” American Economic Review, Vol. 112, pp.1194–1225. - Bailey, M., A. Strezhnev, and E. Voeten (2017). "Estimating Dynamic State Preferences from United Nations Voting Data," Journal of Conflict Resolution, Vol. 61, pp. 430–456. - Santos Silva, J., and S. Tenreyro (2006). “The Log of Gravity,” Review of Economics and Statistics, Vol. 88, pp. 641–658. - Okawa, Y., and E. van Wincoop (2012). "Gravity in International Finance," Journal of International Economics, Vol. 87, pp. 205–215. - Yotov, Y., R. Piermartini, J.-A. Monteiro, and M. Larch (2017). An Advanced Guide to Trade Policy Analysis: The Structural Gravity Model (New York: United Nations and World Trade Organization). *References listed in the source PDF.* --- _Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024021-print-pdf.pdf_