Abstract:
Keywords: Fragmentation; Geoeconomics; Trade; Financial Flows
JEL Classification: F14; F36; F50; F60
Over the past decade, the trajectory of global economic integration has come under intense scrutiny due to heightened geopolitical tensions, increasing emphasis on national security, and a proliferation of policies explicitly aimed at reshaping global supply chains. While traditional indicators, such as the ratio of global trade to GDP, have suggested resilience, closer scrutiny of bilateral trade and financial flows reveals emerging patterns of fragmentation aligned with geopolitical considerations (Aiyar et al. (2023a), Gopinath et al. (2025)). Rising geopolitical tensions--exemplified notably by Russia's invasion of Ukraine in 2022, intensified trade disputes between the United States and China, and ongoing shifts towards protectionism--have triggered substantial reallocations in both trade and financial linkages. Concurrently, policymakers have increasingly utilized economic policy interventions, such as tariffs, subsidies, and export controls, to strategically reshape economic relationships, directly influencing fragmentation patterns. These developments have renewed interest in understanding the precise dynamics of geopolitical fragmentation and its broader economic consequences, particularly the role of deliberate economic policy choices.
A rapidly expanding literature quantifies geopolitical fragmentation by identifying alignment blocs based on countries' voting behaviors in international institutions, particularly the United Nations General Assembly (UNGA). The seminal contribution by Bailey et al. (2017) introduced a spatial voting model to estimate countries' ideal points on a geopolitical spectrum, leading to the widely-adopted Ideal Point Distance (IPD). This metric has since been integral to analyses exploring the economic impacts of political alignment, documenting negative associations between geopolitical distance and cross-border trade, foreign direct investment (FDI), and financial asset flows (Aiyar et al. (2023a,b); Blanga-Gubbay and Rubínová (2023); Catalan et al. (2024)). Building on the Ideal Point Distance (IPD), we develop a new measure—seg—that captures each country's relative geopolitical alignment between the United States and China. This normalized score provides a continuous, interpretable indicator of alignment and allows us to track recent shifts in global alliances. Recent studies also emphasize how escalating U.S.-China tensions and the geopolitical fallout from Russia's invasion of Ukraine have exacerbated fragmentation trends (Jakubik and Ruta (2023); Campos et al. (2024); Qiu et al. (2023)). Yet, despite these insights, significant uncertainty remains regarding how sensitive conclusions about fragmentation are to methodological choices in constructing IPD measures.
Recent contributions have proposed alternative measures of geopolitical fragmentation (e.g., Fernández-Villaverde et al. (2024)) and quantified the heterogeneous effects of fragmentation on trade using foreign policy alignment data (Hakobyan et al. (2024)), highlighting the need for systematic, alignment-based measures like those we develop here.
In this paper, we systematically address this uncertainty by examining how methodological variations in IPD specifications affect the measurement and interpretation of geoeconomic fragmentation. Specifically, we revisit critical methodological choices, including the selected historical sample period and the inclusion of specific vote categories (general vs. economic votes), building upon the spatial voting framework developed by Bailey et al. (2017). Our analysis demonstrates that seemingly minor methodological variations significantly influence the interpretation and magnitude of geopolitical realignments, with substantial implications for policy interpretation and academic research.
Employing bilateral trade data from UN Comtrade (2001-2023) and bilateral financial flows from the IMF's Coordinated Portfolio Investment Survey (CPIS, 2015-2023), we document substantial heterogeneity in fragmentation outcomes contingent on IPD specification. We find robust evidence of increased trade fragmentation following Russia's 2022 invasion of Ukraine, consistent with Gopinath et al. (2025). However, the estimated impact varies notably depending on the IPD measure: IPDs capturing recent geopolitical shifts yield significantly higher fragmentation effects, highlighting how acute geopolitical events, like wars or major diplomatic disputes, substantially reshape trade patterns. Conversely, IPDs based exclusively on economic votes produce more moderate fragmentation effects, suggesting that broader political tensions have stronger repercussions on trade than purely economic disagreements.
In contrast, financial fragmentation is generally weaker and more heterogeneous across IPD specifications. This result suggests that global financial linkages exhibit greater resilience to geopolitical shocks or are mediated through third-party financial centers, reflecting the complexity and indirect nature of financial market responses to geopolitical uncertainty. These insights emphasize that while geopolitical tensions directly disrupt trade flows, financial markets may respond in subtler, more context-specific ways, reflecting underlying differences in trade versus financial integration structures.
Using detailed policy intervention data from the Global Trade Alert (GTA), we demonstrate that economic policy interventions explicitly reflect strategic motives aligned with geopolitical fragmentation. Policies targeting sectors crucial for national security, strategic autonomy, and resilience--such as critical minerals, advanced technology, and digital infrastructure--are particularly prevalent and strongly correlated with geopolitical distance. This strategic targeting of policy interventions substantially amplifies fragmentation trends, as governments actively reshape economic linkages according to geopolitical priorities, suggesting that economic policy plays a central role in driving observed fragmentation patterns.
We deepen our analysis by decomposing bilateral trade flows across technology classifications (high-tech, medium-tech, and low-tech goods) using product-level data from Gaulier and Zignago (2010). Our findings indicate substantial fragmentation effects across all technology classes, though disruptions are particularly pronounced in medium-tech and low-tech sectors. Medium-tech goods, which include petroleum and industrial products, face significant disruption due to geopolitical tensions affecting energy and resource security. Low-tech goods, easier to substitute and relocate, experience fragmentation as countries seek alternative suppliers aligned with their geopolitical blocs. In contrast, high-tech goods display relatively smaller disruptions, possibly due to concentrated global production networks and significant barriers to rapid restructuring.
Our findings carry significant implications for research methodology and policy formulation. Methodologically, our results underscore the critical importance of transparency and careful sensitivity analyses when constructing geopolitical distance measures. From a policy perspective, understanding the nuanced dimensions of geopolitical fragmentation can enable governments and international institutions to craft more targeted, effective strategies to enhance economic resilience, manage risk, and achieve strategic autonomy. In particular, our proposed seg metric provides a concise and interpretable measure of each country's relative alignment with the U.S. versus China, offering a valuable tool to monitor geopolitical realignments. Recent literature has highlighted the close correlation between economic interdependence and geopolitical alignment. Kleinman et al.(2024), for example, find robust empirical evidence that increased economic ties correlate strongly with greater political alignment among countries. While their analysis focuses primarily on economic relationships driving geopolitical outcomes, our paper examines the reverse direction--investigating how geopolitical distances, measured through IPDs, influence patterns of trade and financial fragmentation.
The remainder of the paper is structured as follows: Section 2 details the methodologies underlying the IPD calculations and alternative specifications. Section 3 empirically assesses fragmentation patterns in trade and financial flows, including a technology-based trade classification. Section 4 evaluates fragmentation in economic policy interventions in greater depth, and Section 5 concludes.
We measure geopolitical alignment using the methodology developed by Bailey et al. (2017), based on roll-call voting data from the United Nations General Assembly (UNGA). The key idea is to translate voting behavior into numerical indicators reflecting countries' underlying foreign policy positions, known as ideal points. Each country is assumed to occupy a specific position along a single ideological dimension.
Votes at the UNGA are categorized into three possible outcomes for each participating country: approval (yes), opposition (no), or neutrality (abstain). The model identifies two latent thresholds or cut-points for each vote, which delineate the ranges of ideal points corresponding to these distinct voting decisions. For instance, countries with ideal points close to those ofWestern-aligned nations may vote similarly to the United States on many issues, while countries with different ideological preferences might oppose or abstain.
Additionally, each resolution is characterized by a discrimination parameter, reflecting how effectively it separates countries along the geopolitical alignment dimension. Votes with higher discrimination parameters are particularly informative about ideological differences and thus receive greater weight in the ideal point estimation. Conversely, less informative votes, which fail to differentiate clearly between countries, exert minimal influence on the alignment estimates.
Ideal points are estimated using Bayesian Markov Chain Monte Carlo (MCMC) techniques, providing posterior distributions that capture uncertainty about each country's alignment position. The posterior mean is employed as the definitive measure of a country's ideal point for each year. Geopolitical distances between two countries—Ideal Point Distances (IPDs)—are then calculated as the absolute differences between their respective ideal points. For example, a substantial IPD indicates significant divergence in foreign policy preferences.
Our primary data source is the comprehensive UNGA roll-call voting dataset compiled by Voeten (2021), spanning from the first session in 1946 to session 78 in 2023. This dataset includes all votes cast within the General Assembly and classifies resolutions into thematic categories such as colonialism, disarmament, human rights, Middle East issues, nuclear weapons, and economic development. Each resolution is tagged with specific keywords and metadata, allowing us to isolate alignment by thematic areas. Thus, we can compute IPDs based on all available resolutions or restrict the analysis to specific thematic categories, enhancing the flexibility and specificity of our alignment measures.
This methodology offers several advantages compared to simpler indices, such as basic agreement percentages. First, by explicitly estimating vote-specific thresholds, the model can distinguish between changes arising from shifting country positions and those resulting from variations in the resolution agenda. This allows a clearer interpretation of alignment shifts. Second, the inclusion of vote-level discrimination parameters ensures only significant votes substantially influence alignment measures. This prevents consensus or near-unanimous votes—which do not effectively differentiate countries—from skewing alignment estimates.
Building on the ideal point estimates described in the previous section, we develop several measures of geopolitical distance to assess trade fragmentation. Our approach proceeds in three steps. First, we estimate time-varying IPDs across countries under three different assumptions about the scope and time window of the voting data. Second, we transform these IPDs into normalized alignment scores that capture countries' relative positioning between the United States and China. Third, we construct discrete bloc segmentations based on the cross-sectional distribution of alignment scores at selected points in time, obtaining four different country classifications.
In the first stage, we construct three alternative IPD series, each reflecting a different methodological choice regarding the scope and time window of the voting data:
Figure 1 presents our three estimated IPD measures over time for a selected group of country pairs. While the relative distance between countries remains similar across all IPD measures, the choice of vote subset significantly influences the calculation of geopolitical distance. Additionally, the degree of stationarity varies across measures, affecting their sensitivity to short-term geopolitical developments, as evidenced by the economic IPDs.
Figure 1: IPD measures for selected country pairs.
After estimating IPDs using the strategies described above, we refine these measures in a second stage by transforming them into normalized alignment scores. These scores reflect each country's relative positioning between the United States and China, providing a more intuitive view of global geopolitical dynamics. By normalizing the bilateral IPDs, we can assess how closely countries align with either the U.S. or China over time, facilitating the analysis of shifts in strategic orientation.
We transform each bilateral IPD series into a normalized alignment index, denoted as seg$$(s)$$, defined as:
| $$\displaystyle seg(s)=\frac{IPD(s,China)-IPD(s,U.S.)}{IPD(s,U.S.)+IPD(s,China)},$$ |
The seg index ranges from -1 to +1, with values near -1 indicating stronger alignment with China, values near +1 indicating stronger alignment with the United States, and values close to zero indicating relative neutrality. This transformation produces three corresponding time series of seg measures, each aligned with a different IPD estimation.
Figure 2 illustrates alignments differ across these measures. In each panel, the vertical axis shows the baseline seg based on full-vote IPDs estimated through 2021, while the horizontal axis shows seg based on one of the alternative IPD definitions: in (a), geopolitical alignment is obtained using the first alternative, the 2023 IPD values; in (b), geopolitical alignment is obtained using economic votes only; and in (c), geopolitical alignment using only post-1990 votes. Each point represents a country. Points above the 45-degree line indicate countries that are relatively more China-aligned in the alternative measure; points below the line are relatively more U.S.-aligned.
Figure 2: Comparison of IPD measures.
Subfigure (a) shows that from 2021 to 2023, countries shifted closer to the U.S., albeit from a strong-China starting point. This trend is especially pronounced for Advanced Foreign Economies (AFEs) (light blue dots).3 This pattern suggests that recent geopolitical developments—such as Russia's invasion of Ukraine and escalating U.S.--China tensions--may have played a key role in reshaping alliances. The fact that the shift is more pronounced among AFEs, which are historically aligned with the U.S., may indicate a tightening of alliances within the Western bloc, particularly in response to economic and security concerns.
In subfigure (b) we focus on economic votes. In this case, if a country falls below the 45-degree line, it is more aligned with the U.S. than when using the full vote sample. The clustering along the 45-degree line suggests that geopolitical alignment is relatively stable across voting subsets. However, we observe a concentration around -0.5 on the x-axis, indicating that some countries appear more neutral when votes on human rights, colonialism, among others, are excluded.
Subfigure (c) shows that the choice of the estimation sample period (i.e., starting in 1946 or 1990) does not introduce substantial differences in geopolitical alignment, suggesting that IPD estimates are relatively robust to the estimation sample window.
Figure 3 translates the IPD shifts from Figure 2(a) into a geographic visualization, showing how countries realigned between 2021 and 2023. The map reveals a notable pattern of nations moving closer to the U.S. despite initially being more China-aligned. This geographic perspective highlights regional trends not apparent in scatter plots, such as the coherent shift among European and Oceanic countries toward the U.S., while responses vary across Africa, South America, and Southeast Asia. Though the map does not capture the full magnitude of these shifts, it effectively illustrates how recent geopolitical events—Russia's invasion of Ukraine, U.S.-China tensions, and changing economic relationships—have influenced international alignments. This visualization provides geographical context to the numerical data, showing the spatial distribution of realignment patterns in response to evolving great power dynamics.
Figure 3: Evolution of segmented IPDs from 2021 to 2023
In the third stage, we use the segmented alignment scores to construct discrete country blocs. Our Baseline bloc classification is based on the 2021 distribution of seg derived from the 1946-2923, all votes IPDs. We construct three alternative categorizations: (i) based on the 2023 distribution from the same IPD estimation, (ii) based on the 2021 distribution of the economic-votes IPDs, and (iii) based on the 2021 distribution of the post-1990 IPDs. Thus, while we estimate three underlying IPD (and seg) time series, we generate four alternative bloc classifications depending on the year and vote subset used. The formal definition of bloc membership—classifying countries as U.S.-aligned, Chinaaligned, or nonaligned—is detailed in the next section.
In this section, we follow the methodology outlined by Gopinath et al. (2025) to examine whether trade and financial flows are fragmenting along geopolitical lines and whether these findings are sensitive to the specific IPD measure used. By analyzing variations in the Ideal Point Distance (IPD) specifications, we assess the robustness of observed fragmentation patterns, particularly considering recent geopolitical shifts.
First, we construct geopolitical alignment blocs based on countries' seg$$(s)$$ scores derived from the estimated IPDs. Countries are classified into three groups: a U.S.-aligned bloc, comprising those in the top quartile of alignment with the United States; a China-aligned bloc, comprising those in the top quartile of alignment with China; and a nonaligned bloc, comprising all remaining countries. We apply this classification separately under each of the four IPD specifications introduced in the previous section: the baseline measure based on 2021 IPDs, the updated measure based on 2023 IPDs, the economic vote IPDs from 2021, and the post-1990 IPDs from 2021.
Comparing bloc assignments under each alternative IPD specification to the baseline classification, we find that 48% of countries change blocs when using the 2023 IPD, 31% change blocs with the economic vote IPD, and only 2% change blocs when using post-1990 IPDs. The higher reclassification rate under the 2023 IPD suggests that countries are reacting to immediate political and economic pressures rather than maintaining longstanding alignments. In contrast, the relative stability of bloc classifications under the economic vote and post-1990 measures indicates that economic relationships and post–Cold War alignments may be more resistant to short-term geopolitical disruptions.
Second, we define three dummy variables based on geopolitical bloc membership. Specifically, $$\textit{Between Bloc}_{sd}$$ equals 1 if countries $$s$$ and $$d$$ belong to different blocs, whereas $$\textit{Within}$$ $$\textit{Bloc}_{sd}$$ equals 1 if both countries belong to the same bloc. Lastly, $$\textit{Nonaligned}_{sd}$$ equals 1 if at least one country in the pair belongs to the nonaligned bloc. These dummy variables may vary for the same country pair $$sd$$, depending on the IPD specification and the resulting classification into blocs.
We estimate the following gravity equation.
| $$\begin{equation*}Y_{sdt}=\beta_1 Between Bloc_{sd} \times Post_t + \beta_2 Nonaligned_{sd} \times Post_t + \delta_{sd} + \tau_{st} + \phi_{dt} + \epsilon_{sdt},\end{equation*}$$ | (1) |
where $$Y_{sdt}$$ is the value of total trade of goods between the country $$s$$ and the country $$d$$ or the change in the share of portfolio assets held by the reporting country $$s$$ in the counterpart country $$d$$ between year $$t$$ and $$t-1$$. $$Post$$ is an indicator equal to 1 after Russia's invasion of Ukraine (years 2022-2023). $$\delta_{sd}$$, $$\tau_{st}$$, and $$\phi_{dt}$$ are country-pair, source × time and destination × time fixed effects, included in all specifications.
For trade, we estimate the gravity model using Poisson pseudo-maximum likelihood (PPML), using annual data for the period 2001-2023, from UN Comtrade. For portfolio holdings, we estimate the gravity model with OLS using semi-annual data for the period 2015s1-2023s2. Financial data contains bilateral data on countries' holdings of cross-border portfolio investment (equity or debt) securities, excluding Foreign Direct Investment (FDI), from the IMF's Coordinated Portfolio Investment Survey (CPIS).
Table 1 panel (I) shows the estimation results for trade under alternative IPD specifications.4 The first column presents the results using the baseline IPD measure to construct the blocs, showing evidence of geopolitical fragmentation in trade flows. The estimated coefficient indicates that in the post-invasion period, trade flows between countries in different geopolitical blocs are 11.8% lower, on average, compared to trade flows between countries within the same bloc.5 This result is statistically significant at the 1% level. In contrast, trade flows between countrypairs where at least one country is nonaligned are not significantly different from trade flows within the same bloc. These findings align with those of Gopinath et al. (2025), despite differences in the underlying trade data.6
Table 1: Regression Results (I) Trade
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Between Bloc × Post | -0.125*** | -0.276*** | -0.094** | -0.128*** |
| Std. Error | (0.040) | (0.058) | (0.038) | (0.041) |
| Nonaligned × Post | -0.044 | -0.066 | -0.017 | -0.041 |
| Std. Error | (0.070) | (0.057) | (0.053) | (0.068) |
| Observations | 389,747 | 389,761 | 387,589 | 389,747 |
Table 1: Regression Results (II) Portfolio Holdings
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Between Bloc × Post | -0.026* | -0.014 | -0.000 | -0.026* |
| Std. Error | (0.016) | (0.016) | (0.012) | (0.016) |
| Nonaligned × Post | -0.021 | -0.016 | -0.023 | -0.021 |
| Std. Error | (0.022) | (0.020) | (0.022) | (0.021) |
| Observations | 231,450 | 231,971 | 231,578 | 231,450 |
Note: Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$. For trade, we estimate the gravity model using Poisson pseudo-maximum likelihood (PPML), using annual data for the period 2001-2023, from UN Comtrade. Standard errors are clustered at the country-pair level. Coefficient interpretation is the following: $$\left(e^{\text{coefficient}}-1\right)\times100$$. For portfolio holdings, we estimate the gravity model with OLS using semi-annual data for the period 2015s1-2023s2. Financial data contains bilateral data on countries' holdings of cross-border portfolio investment (equity or debt) securities, excluding Foreign Direct Investment (FDI), from the IMF's Coordinated Portfolio Investment Survey (CPIS). $$Post$$ is a dummy that captures the post invasion of Ukraine period and takes the value 1 for the years 2022 and 2023. Each column shows the results using a different IPD measure to construct the country blocs. (1) uses the Baseline IPD measure, which is the 2021 values of IPDs estimated using UNGA voting data from 1946-2023 across all vote categories; (2) uses the 2023 values of IPDs estimated using UNGA voting data from 1946-2023 across all vote categories; (3) uses the 2021 values of Economics IPD, which narrows the focus to economic votes while maintaining historical coverage from 1971 onwards; (4) uses 2021 values of IPD estimated using a subsample of votes after the Cold War (1990–2023) while maintaining all vote categories.
This effect strengthens with the 2023 IPD (-24.1%), reflecting recent geopolitical shifts, but weakens with economic votes (-9%), suggesting that trade alignment follows broader political ties more than economic cooperation. The post-1990 IPD yields similar results to the baseline (-12%), indicating that Cold War-era voting does not significantly affect fragmentation estimates. In all specifications, the coefficient for the interaction term $$Nonaligned \times Post$$ remains small and statistically insignificant, reinforcing the idea that trade among nonaligned countries is less affected by geopolitical tensions.
Overall, we find that the specification of IPDs plays a crucial role in shaping conclusions about the degree of trade fragmentation, as the magnitude of the observed effects varies depending on the choice of IPD measure. The 2023 IPD best captures recent geopolitical disruptions, making it ideal for analyzing short-term realignments. The economic votes IPD provides a clearer picture of trade relations driven by economic dependencies rather than broad political alliances. The post-1990 IPD offers a historically consistent view of geopolitical fragmentation, minimizing distortions from Cold War-era dynamics. The findings confirm that trade fragmentation is increasing, but its intensity depends on the chosen IPD measure.
Panel (II) in Table 1 provides some evidence of financial fragmentation, with the share of portfolio holdings between countries in opposing blocs declining by 0.3 percentage points post-invasion in baseline and post-1990 IPDs. However, results are weaker and less robust across specifications, particularly in 2023 and economic votes IPDs. Meanwhile, the coefficients for nonaligned countries remain small and insignificant across all specifications, reinforcing the idea that financial flows among nonaligned countries were less affected by geopolitical tensions. Overall, while some specifications suggest portfolio fragmentation along geopolitical lines, these effects are highly sensitive to the choice of IPD measure, contrasting with the more robust and consistent fragmentation observed in trade flows.
The presence of offshore financial centers poses challenges for accurately identifying underlying geopolitical exposures, as such centers may obscure true investor-recipient relationships. To address this issue, in the Appendix we re-estimate our results excluding prominent financial hubs, following the methodology proposed by Coppola et al. (2021), who specifically examine the role of these centers in global portfolio allocations.7
In this section, we explore two alternative specifications of gravity equations to evaluate whether trade flows fragment along geopolitical lines.
While our previous analysis used normalized alignment measures (seg$$(s)$$) to classify countries into distinct blocs, here we employ bilateral IPD measures directly to assess fragmentation without imposing explicit bloc boundaries. We use lagged IPD to mitigate simultaneity concerns, assuming geopolitical alignment evolves gradually and is predetermined relative to trade and financial flows. The interaction term with $$Post$$, captures whether trade flows are affected by changes in geopolitical distance in the post-invasion period. Fixed effects are the same as in 1.
| $$\displaystyle Y_{sdt}=\beta$$ IPD$$\displaystyle _{sdt-1} \times$$ Post$$\displaystyle _t + \delta_{sd} + \tau_{st} + \phi_{dt} + \epsilon_{sdt},$$ | (2) |
Table 2 presents the estimation results of equation (2), using the IPDs estimated using all votes from 1946 to 2023, and those restricted to economic votes. We omit the results for IPD estimated vith all votes since the 1990s, as they are nearly identical to the baseline specification. Columns 1 and 2 report the coefficients for the interaction term $$IPD \times Post$$, while columns 3 and 4 include the direct effect of geopolitical distance $$IPD$$ without interaction with the post-invasion period.8
Panel (I) of Table 2 presents the estimation results for total bilateral trade. In columns 1 and 2, we find strong evidence of trade fragmentation along geopolitical lines in the post-invasion period. A one-unit increase in geopolitical distance is associated with an approximately 8% decline in trade flows, on average. These results are statistically significant at the 1% level and remain consistent across the baseline and economic IPD specifications.
Table 2: Regression Results with IPD (I) Trade
| Description | IPD all votes (IPD × Post) (1) | Economic IPD (IPD × Post) (2) | IPD all votes (IPD) (3) | Economic IPD (IPD) (4) |
|---|---|---|---|---|
| $$\beta$$ Coefficient | -0.087*** | -0.080*** | -0.068** | 0.021* |
| Std. Error | (0.023) | (0.024) | (0.027) | (0.012) |
| Observations | 374,365 | 371,609 | 374,365 | 371,609 |
Table 2: Regression Results with IPD (II) Portfolio holdings
| Description | IPD all votes (IPD × Post) (1) | Economic IPD (IPD × Post) (2) | IPD all votes (IPD) (3) | Economic IPD (IPD) (4) |
|---|---|---|---|---|
| $$\beta$$ Coefficient | -0.075** | -0.058* | 0.051 | 0.068 |
| Std. Error | (0.038) | (0.037) | (0.076) | (0.062) |
| Observations | 115,142 | 114,129 | 115,142 | 114,129 |
Note: Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$. For trade, we estimate the gravity model using Poisson pseudo-maximum likelihood (PPML), using annual data for the period 2001-2023, from UN Comtrade. Standard errors are clustered at the country-pair level. Coefficient interpretation is the following: $$\left(e^{\text{coefficient}}-1\right)\times100$$. For portfolio holdings, we estimate the gravity model with OLS using semi-annual data for the period 2015s1-2023s2. Financial data contains bilateral data on countries' holdings of cross-border portfolio investment (equity or debt) securities, excluding Foreign Direct Investment (FDI), from the IMF's Coordinated Portfolio Investment Survey (CPIS). Columns (1) and (3) use the IPD estimated with all votes from 1946 to 2023 as explanatory variable. Columns (2) and (4) use the Economic IPD, estimated using only economic votes from 1971 to 2023.
Columns 3 and 4 assess the direct effect of geopolitical distance on trade flows, independent of the post-invasion period. The estimated coefficient remains statistically significant under the baseline IPD measure, with a 7% decline in trade flows. However, the economic IPD (column 4) yields a smaller but positive coefficient (2%), suggesting that trade relationships based on economic alignment may be more resilient to geopolitical fragmentation, particularly in periods prior to 2022. The contrasting coefficients between $$IPD \times Post$$ (column 2) and direct IPD effects (column 4) reveal an important temporal pattern in how economic voting alignment relates to trade flows. While countries with different economic voting patterns show reduced trade after 2022 (negative $$IPD \times Post$$ coefficient), they actually maintained stronger trade relationships in the pre-invasion period (positive IPD coefficient). This suggests that economic voting differences did not disrupt trade until recent geopolitical tensions transformed how such alignment matters for economic relationships.
Part (II) of Table 2 provides evidence of financial fragmentation, though the effects vary across specifications. In columns 1 and 2, the interaction term $$IPD \times Post$$ is negative and statistically significant (-0.075 and -0.058, respectively), indicating that in the post-invasion period, greater geopolitical distance is associated with a reduction in portfolio holdings between country pairs. However, in columns 3 and 4, where we estimate the direct effect of IPD independently of the post-invasion period, the coefficients are statistically insignificant. This suggests that while geopolitical distance has played a greater role in shaping portfolio allocations in recent years, its influence was more limited before 2022. Unlike trade, where fragmentation effects appear more persistent, portfolio holdings seem more reactive to recent geopolitical shocks rather than long-standing geopolitical alignments.
In this section, we examine a new definition of country blocs based on the distribution of the geopolitical distance of the country pair $$IPD(s,d)$$, instead of working on the segment space. Rather than assigning a central role to the U.S. and China in defining global geopolitical alignments, this approach classifies country pairs solely based on their relative geopolitical proximity. We define a bloc of aligned country pairs, which includes country pairs in the lower quartile of the IPD distribution in a given year, a bloc of distant country pairs, which includes country pairs in the top quartile of the IPD distribution in a given year, and a set of nonaligned country pairs, comprising the remaining economies. When comparing bloc classifications under different IPD specifications to the baseline IPD, we find that 25% of country-pairs change blocs when using the 2023 IPD, 30% change blocs with the economic votes IPD, and only 1.6% change blocs when using data since the 1990s.
This alternative bloc definition provides a more general perspective on geopolitical alignment, removing the emphasis on specific anchor countries like the U.S. and China. Using these blocs, we estimate the following gravity equation for different IPD specifications:
| $$\begin{equation*}Y_{sdt}=\beta_1\end{equation*}$$ Distant$$\begin{equation*}_{sd} \times\end{equation*}$$ Post$$\begin{equation*}_t + \beta_2\end{equation*}$$ Nonaligned$$\begin{equation*}_{sd} \times\end{equation*}$$ Post$$\begin{equation*}_t + \delta_{sd} + \tau_{st} + \phi_{dt} + \epsilon_{sdt},\end{equation*}$$ | (3) |
Table 3: Regression Results (I) Trade
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) |
|---|---|---|---|
| Distant x Post | -0.048 | -0.090** | -0.113** |
| Std. Error | (0.037) | (0.036) | (0.055) |
| Nonaligned x Post | -0.010 | 0.037 | 0.009 |
| Std. Error | (0.032) | (0.032) | (0.025) |
| Observations | 387,698 | 387,559 | 383,468 |
Table 3: Regression Results (II) Portfolio holdings
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) |
|---|---|---|---|
| Distant x Post | -0.010 | -0.010 | -0.019 |
| Std. Error | (0.014) | (0.014) | (0.014) |
| Nonaligned x Post | -0.010 | -0.001 | -0.023** |
| Std. Error | (0.010) | (0.010) | (0.010) |
| Observations | 230,675 | 227,154 | 230,675 |
Note: Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$. For trade, we estimate the gravity model using Poisson pseudo-maximum likelihood (PPML), using annual data for the period 2001-2023, from UN Comtrade. Standard errors are clustered at the country-pair level. Coefficient interpretation is the following: $$\left(e^{\text{coefficient}}-1\right)\times100$$. For portfolio holdings, we estimate the gravity model with OLS using semi-annual data for the period 2015s1-2023s2. Financial data contains bilateral data on countries' holdings of cross-border portfolio investment (equity or debt) securities, excluding Foreign Direct Investment (FDI), from the IMF's Coordinated Portfolio Investment Survey (CPIS). $$Post$$ is a dummy that captures the post invasion of Ukraine period and takes the value 1 for the years 2022 and 2023. Each column shows the results using a different IPD measure to construct the country blocs. (1) uses the Baseline IPD measure, which is the 2021 values of IPDs estimated using UNGA voting data from 1946-2023 across all vote categories; (2) uses the 2023 values of IPDs estimated using UNGA voting data from 1946-2023 across all vote categories; (3) uses the 2021 values of Economics IPD, which narrows the focus to economic votes while maintaining historical coverage from 1971 onwards.
where $$\textit{ Distant}_{sd}$$ is a dummy variable that takes the value 1 if the country pair $$sd$$ belongs to the bloc distant and $$\textit{Nonaligned}_{sd}$$ is a dummy variable that takes the value 1 if the country pair $$sd$$ belongs to the bloc nonaligned. Table 3 shows the estimation results of equation (3) for trade and changes in the portfolio holding's share.
The first panel shows the results for trade. The coefficient for the interaction term $$\textit{ Distant}_{sd}$$ $$\times \textit{Post}_t$$ is negative and statistically significant in columns 2 and 3, suggesting that trade flows between distant country pairs decline significantly in the post-invasion period when constructing the blocs with the latest data or when restricting to economic votes. Using the economic IPD measure (column 3), trade flows between distant pairs are estimated to be around 11% lower, on average, compared to aligned pairs, underscoring the heightened impact of geopolitical distance on trade fragmentation. In contrast, the baseline specification (column 1) shows a smaller and statistically insignificant coefficient, highlighting the sensitivity of results to the choice of IPD measure and blocs definition. These findings reinforce the idea that geopolitical tensions disproportionately disrupt trade relationships between distant countries.
The coefficients for the interaction term $$\textit{ Nonaligned}_{sd} \times \textit{Post}_t$$ are small and statistically insignificant across all specifications, indicating that trade flows between nonaligned pairs remain stable in the post-invasion period. This result aligns with earlier findings using U.S.- and China-centric bloc definitions, where trade relationships involving nonaligned countries were less affected by geopolitical tensions.
Panel (II) of Table 3 shows the results for the change in the share of portfolio holdings. Unlike trade, the effects of geopolitical fragmentation on financial flows appear more muted. The coefficients for the interaction term $$\textit{Distant}_{sd} \times \textit{Post}_t$$ are small and statistically insignificant across all specifications, suggesting that geopolitical distance has not led to a significant reallocation of portfolio holdings in the post-invasion period when we consider geopolitical distance in a global perspective instead of focusing on US-China blocs. Similarly, the coefficients for the interaction term $$\textit{Nonaligned}{sd} \times \textit{Post}_t$$ are also small and mostly insignificant, with the exception of the economic IPD specification (column 3), where the coefficient is negative and indicates that, when considering economic alignment, the share of portfolio holdings between nonaligned country pairs decline by approximately 2.3% in the post-invasion period. Taken together, these results indicate that financial flows, while responsive to geopolitical tensions, remain substantially less sensitive compared to trade flows, reflecting the broader economic and financial considerations that drive portfolio decisions.
However, the weaker evidence for financial fragmentation may also partially reflect the unique and dominant role of the United States in global financial markets. As illustrated in Figure 2, the IPD measures consistently place the U.S. in the far-right tail of the distribution, signaling its great geopolitical distance from many international counterparts. When we exclude the U.S. from the analysis in equation (3), the effects of geopolitical distance on portfolio holdings become clearer and more significant. Specifically, under the economic IPD specification, portfolio investments between distant country pairs decline significantly by about 2.5% following recent geopolitical shocks.9 This finding suggests that the United States' central role in global finance may mask underlying fragmentation trends, offsetting or moderating the impact of geopolitical tensions on international portfolio allocations.
Thus far, our analysis of trade has focused solely on aggregate bilateral trade flows. While the findings above suggest increasing fragmentation in international trade, recent geopolitical events--particularly Russia's invasion of Ukraine and heightened U.S.-China tensions--highlight that the reallocation of trade flows may vary considerably across sectors. Increasingly, governments are adopting strategic trade policies aimed explicitly at reducing dependencies, enhancing supply chain resilience, and reinforcing alliances through "decoupling," "de-risking," and "friendshoring." To investigate how these contemporary geopolitical dynamics affect different segments of global trade, we decompose bilateral trade flows into high-tech, medium-tech, and low-tech manufacturing classes, examining each sector's evolving patterns and responses to geopolitical pressures.
For this analysis, we utilize BACI, a rich dataset from the Centre d'Etudes Prospectives et d'Informations Internationales (CEPII) that reconciles discrepancies in national trade statistics to construct bilateral trade flows at the country pair-year-product level. BACI spans from 1996-2023 and has coverage of more than 200 countries and 5,000 product codes. To ensure symmetry with our previous analysis using UN Comtrade data, we restrict the data to the period 2001-2023. To construct technology classes, we first re-classify the products in BACI from the HS-6 level to ISIC Revision 3. From here, we make judgment-based designations of goods as high-tech, medium-tech, low-tech, or other. Our categorizations for each technology class can be found in the Appendix, section 7.2.
We begin by re-estimating equation (1) using the segmented IPDs interacted with our $$Post$$ indicator. We run separate gravity equations for each technology class. Table 4 presents the estimation results of geopolitical distance on tech-based trade flows using our segmented distribution. As in our previous estimations for trade, we rerun our gravity equations with several different IPD measures. Table 4 shows the estimation results for each technology class. Using the baseline IPD measure to construct the blocs, we see a negative coefficient for each tech-class, suggesting that in the post-invasion period, trade flows between countries of different geopolitical blocs was lower on average compared to trade flows between countries within the same bloc for each respective tech-class. The effects vary in magnitude, with the decrease in trade flows being the largest for low-tech goods (15.2%) and the smallest for high-tech (9.3%). Our results are significant at the 1% level for high and low-tech goods and the the 5% level for medium-tech goods. Across nearly all specifications and technology classes, the coefficients for $$\textit{ Nonaligned}_{sd} \times \textit{Post}_t$$ are statistically insignificant with the exception of specification (2) for low-tech goods. A possible explanation for this, is that nonaligned countries may have experienced shifts in trade patterns for low-tech goods due to increased uncertainty, disruption of supply chains involving sanctioned or geopolitically distant economies, or opportunistic redirection of exports toward politically neutral markets. As such, we would expect these shifts in trade flows to be more pronounced for low-tech goods as they typically have fewer barriers to shifting suppliers or markets compared to medium- or high-tech goods even among nonaligned countries. Moreover, low-tech goods trade may be more affected by geopolitical distance than high-tech trade primarily due to their higher elasticity of substitution. Unlike high-tech products, which typically require specialized inputs, advanced infrastructure, and stable long-term supplier relationships, low-tech goods are relatively standardized and production processes less capital-intensive. Consequently, when geopolitical tensions rise, it is substantially easier--and less costly--for countries to rapidly shift sourcing or redirect exports of low-tech goods away from geopolitically distant markets, amplifying their sensitivity compared to high-tech goods.
Consistent with our results from Table 1, the effect strengthens with the 2023 IPDs for all tech classes, particularly for medium-tech goods. There is a notable increase in the magnitude for trade of medium-tech goods when we consider 2023 IPDs. The particularly pronounced fragmentation observed in medium-tech goods when employing the updated 2023 IPD measure may reflect significant recent disruptions to trade in petroleum products and related commodities due to sanctions on Russia. Additionally, weaker and less significant fragmentation effects identified with economic-vote-based IPDs reinforce the interpretation that geopolitical rather than purely economic alignments primarily drive these observed trade disruptions.
Table 4: Regression Results (I) High-tech Trade
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Between Bloc × Post | -0.098*** | -0.209*** | -0.075** | -0.098*** |
| Std. Error | (0.037) | (0.056) | (0.035) | (0.038) |
| Nonaligned × Post | -0.133* | -0.076* | 0.041 | -0.141 |
| Std. Error | (0.078) | (0.045) | (0.058) | (0.074) |
| Observations | 291,816 | 291,702 | 290,284 | 291,831 |
Table 4: Regression Results (II) Medium-tech Trade
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Between Bloc × Post | -0.135** | -0.330*** | -0.074 | -0.134** |
| Std. Error | (0.055) | (0.069) | (0.052) | (0.055) |
| Nonaligned × Post | -0.060 | -0.116 | -0.026 | -0.045 |
| Std. Error | (0.100) | (0.101) | (0.085) | (0.098) |
| Observations | 255,976 | 255,815 | 254,711 | 255,995 |
Table 4: Regression Results (III) Low-tech Trade
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Between Bloc × Post | -0.165*** | -0.213*** | -0.151*** | -0.176*** |
| Std. Error | (0.033) | (0.039) | (0.0289) | (0.034) |
| Nonaligned × Post | -0.076 | -0.151*** | -0.066 | -0.072 |
| Std. Error | (0.051) | (0.041) | (0.054) | (0.050) |
| Observations | 288,982 | 288,913 | 287,500 | 289,006 |
Note: Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$. For each tech-class, we estimate the gravity model using Poisson pseudo-maximum likelihood (PPML), using annual data for the period 2001-2023, from CEPII. Standard errors are clustered at the country-pair level. Coefficient interpretation is the following: $$\left(e^{\text{coefficient}}-1\right)\times100$$. $$Post$$ is a dummy that captures the post invasion of Ukraine period and takes the value 1 for the years 2022 and 2023. Each column shows the results using a different IPD measure to construct the country blocs. (1) uses the Baseline IPD measure, which is the 2021 values of IPDs estimated using UNGA voting data from 1946-2023 across all vote categories; (2) uses the 2023 values of IPDs estimated using UNGA voting data from 1946-2023 across all vote categories; (3) uses the 2021 values of Economics IPD, which narrows the focus to economic votes while maintaining historical coverage from 1971 onwards; (4) uses 2021 values of IPD estimated using a subsample of votes after the Cold War (1990–2023) while maintaining all vote categories.
In the Appendix, we re-estimate equations (2) and (3) separately for each technology class, and find results consistent with those presented in Tables 2 and 3. These additional estimations further highlight the heterogeneity across technology classes. Although trade flows decrease among geopolitically distant country pairs for all technology categories, the magnitude of this decline differs significantly. This variation aligns intuitively with the distribution and substitutability of trade across sectors. For instance, high-tech trade, dominated by complex products such as computers, electrical equipment, machinery, and chemicals, is highly concentrated within a few key countries like the United States, China, Taiwan, and major Euro-area economies.10 Consequently, rebalancing trade flows for these goods in response to geopolitical shocks, such as Russia's invasion of Ukraine, may be economically challenging or practically infeasible. Conversely, production of low-tech goods, like textiles, can be reshored or decoupled more easily, leading to more pronounced trade reallocation in this category.
Given the evidence from previous sections that trade and financial flows have fragmented along geopolitical lines, it is important to examine whether deliberate economic policies may further incentivize or exacerbate this fragmentation. Indeed, geopolitical fragmentation increasingly manifests not only as shifts in economic flows but also through targeted policy interventions explicitly aimed at reshaping cross-border economic interactions. These include tariffs, subsidies, export controls, and regulatory measures intended either to protect domestic interests or strategically limit foreign commercial activities, reflecting underlying geopolitical motives or strategic considerations. Such policies could directly reinforce fragmentation patterns observed in global trade and financial markets. To explore this policy-driven dimension of geopolitical fragmentation, we empirically analyze detailed records from the Global Trade Alert (GTA)--specifically the Newly Implemented Policy Outcomes (NIPO) database--which systematically documents economic policy interventions adopted globally.11 This section evaluates the extent to which economic policy interventions align with geopolitical blocs defined by various IPD specifications, thus complementing and extending our earlier analysis of trade and financial fragmentation.
In this section, we utilize the GTA NIPO database, which provides detailed records of economic policy interventions ("acts") implemented globally since 2017. The GTA NIPO systematically classifies interventions as either distortive or liberalizing. Distortive interventions explicitly discriminate against foreign commercial interests, either by restricting market access or by providing preferential subsidies to domestic firms. Conversely, liberalizing policies are characterized by non-discriminatory interventions that enhance market access.
Our analysis specifically focuses on interventions where the implementing jurisdiction and affected jurisdictions differ, restricting attention exclusively to individual countries. Consequently, we exclude interventions enacted by supranational entities such as the European Union, leaving their inclusion as an avenue for future research.
Given that most interventions simultaneously affect multiple jurisdictions, we first transform the data into a dyadic format. Specifically, if a single policy intervention affects multiple countries, we record it separately for each affected country, whereby each restriction is counted once per impacted jurisdiction. In doing so, we assign each intervention to geopolitical blocs based on the geopolitical distance of the involved country pair, using both the baseline and alternative IPD measures. This dyadic structure enables a more detailed examination of policy dynamics across geopolitical lines.
For the remainder of the paper, we focus on dyadic relationships defined by the baseline IPD and the bloc classification into aligned, nonaligned, or distant, as outlined in Section 4.1.1. Results obtained using alternative IPD measures are presented in the appendix, while results for bloc classification within the US-China spectrum are available upon request.
The GTA database also classifies a subset of interventions as "NIPO interventions," explicitly reflecting strategic economic or geopolitical motivations. Specifically, a NIPO intervention is associated with at least one of six predefined strategic motives: (i) National Security, covering policies aimed at safeguarding national security interests, such as export controls on sensitive technologies; (ii) Resilience and Security of Supply, referring to measures ensuring stable domestic access to essential non-food products and raw materials, such as critical minerals; (iii) Strategic Competitiveness, involving actions that promote domestic innovation and competitiveness in strategically vital sectors; (iv) Climate Change Mitigation, capturing interventions explicitly targeting reductions in carbon emissions and facilitating transitions toward renewable energy; (v) Geopolitical Concerns, which includes measures directly addressing threats posed by particular countries or geopolitical blocs, notably economic sanctions (e.g., sanctions imposed after Russia's invasion of Ukraine); and (vi) Digital Transformation, encompassing policies designed to support the adoption and expansion of digital technologies and infrastructure. By analyzing NIPO interventions separately from general distortions, we obtain deeper insights into the explicitly strategic or geopolitical intentions underlying economic policy actions.
We first examine total distortive announcements by geopolitical blocs, using our baseline IPD measure. We observe a substantial increase in distortive announcements after Russia's invasion of Ukraine in 2022, particularly evident in interactions between countries belonging to distant and nonaligned geopolitical blocs. Notably, the sharp rise in discriminatory interventions post-2022 suggests that geopolitical fragmentation is increasingly reflected through targeted economic policies.
Analyzing net distortive announcements (distortive minus liberalizing interventions) provides additional clarity. The net measure exhibits a pronounced rise after 2022, emphasizing intensified fragmentation primarily driven by distortive measures outweighing liberalizing initiatives. The divergence between aligned and nonaligned/distant net discriminatory announcements suggests significant geopolitical realignments in economic policy.
Figure 4: Distortive and net trade restriction announcements by bloc
The suggestive evidence on geoeconomic fragmentation in economic policies holds regardless of the IPD measure used to classify countries into blocs. However, the relative importance of trade restrictions between distant countries increases significantly, especially in recent years, when we allocate countries into blocs using the 2023 IPD measure, using all votes. In contrast, when we focus on the economic votes, we observe a wider difference between the number of trade restrictions between non-aligned and distant countries, with the non-aligned ranking first for all measures.12
Figure 5 highlights how geopolitical blocs differ significantly in terms of policy interventions when we specifically focus on measures explicitly driven by strategic NIPO motives. Compared to the broader set of distortive interventions, these strategically motivated actions show even clearer distinctions across blocs, with notably more pronounced differences in both total distortive (panel a) and net distortive interventions (panel b). This suggests that geopolitical considerations play a particularly important role in shaping policy actions when strategic economic or security-related motives—such as national security, resilience, or geopolitical concerns—are explicitly involved.
Figure 5: Distortive and net NIPO trade restriction announcements by bloc
The detailed classification by motive thus provides deeper insight into how geopolitical factors increasingly influence national economic policies, reinforcing global fragmentation dynamics.
Figure 6 illustrates the evolution of distortive interventions by motive and geopolitical bloc from 2020 to 2023. The figure highlights a substantial increase in distortive interventions, primarily driven by policies motivated by national security, resilience and security of supply, and geopolitical concerns, particularly in 2023. However, the trends vary across geopolitical blocs. Distant blocs exhibit a sharp rise in interventions, predominantly justified by national security and resilience motives, reflecting growing concerns over strategic autonomy and resource security. Nonaligned blocs also show significant increases, but these are primarily driven by geopolitical concerns and resilience-related policies, suggesting a more nuanced positioning in the geopolitical landscape. In contrast, aligned blocs display a more moderate increase in distortive interventions, likely reflecting more stable policy coordination within the bloc rather than a reactive escalation of economic measures.
The sectoral analysis presented in Figure 7 provides deeper insights into the strategic nature of economic policy interventions. Distortive interventions are heavily concentrated in key sectors, particularly critical minerals, dual-use products, advanced technology, and industrial raw materials, with a marked increase following 2022. However, the extent of intervention varies across geopolitical blocs. Distant and nonaligned blocs have intensified interventions in these sectors, likely as part of broader efforts to secure technological leadership and essential resources. In contrast, aligned blocs have also increased interventions, but at a relatively more moderate pace, potentially reflecting a different approach to industrial policy rather than direct strategic competition. These trends highlight the sector-specific nature of economic policy fragmentation and suggest that interventions are not only a response to geopolitical tensions but also part of broader economic security strategies.
Figure 6: Distortive interventions by motives and bloc
Overall, the clear strategic differentiation across geopolitical blocs, driven by both motive and sector-specific interventions, underscores the deep strategic and geopolitical dimensions shaping economic policy fragmentation. These dynamics have critical implications for the future structure of global markets and international economic cooperation.
Figure 7: Discriminatory interventions by bloc and sector
Finally, in figure 8 we analyze the distribution among types of distortive interventions. In the GTA NIPO database, each intervention is classified into one of the following categories. First, Horizontal implies that the policy applies broadly across all sectors within a country. Second, R&D Related refers to interventions that target research, innovation, or R&D activities. Third, Infrastructure, Transport, Cargo or Logistics refers to policies related to industrial and transport infrastructure, cargo handling, and logistics. Fourth, Support Electrical Energy includes industrial policies related to electricity generation and supply. Fifth, Recycling Service involves policies related to recycling activities. Sixth, FDI Screening Mechanism denotes procedures assessing, investigating, authorizing, conditioning, prohibiting, or reversing inward or outward FDI. Finally, Sanctions includes trade-related sanctions imposed in security or foreign-policy contexts. We omit Recycling Service and FDI Screening Mechanism from the analysis, as no distortive interventions were identified in these categories in the period under analysis.
The analysis reveals significant heterogeneity across intervention types and geopolitical alignments. Notably, sanctions have surged dramatically after 2022, predominantly among distant and nonaligned blocs, illustrating their increased reliance on economic measures explicitly aimed at isolating geopolitical rivals. Interventions related to infrastructure, transport, cargo, or logistics and support for electrical energy also show notable increases, again largely concentrated among distant and nonaligned blocs, highlighting strategic attempts to control critical infrastructure and energy resources.
Figure 8: Distortive interventions by type and bloc
Horizontal and R&D-related restrictions display differing patterns: horizontal restrictions (broad policy measures not sector-specific) significantly increased in aligned and nonaligned blocs, indicating broader-based policy actions aimed at reshoring or reinforcing intra-bloc cooperation. In contrast, R&D-related restrictions spiked sharply among distant blocs, reflecting intensified strategic competition in innovation and technological advancement.
These patterns emphasize that geopolitical blocs strategically choose intervention types aligning closely with their broader economic and security objectives. The pronounced use of sanctions and targeted interventions related to infrastructure, R&D, and energy among distant blocs indicates increasingly explicit geopolitical contention. These strategic intervention patterns further deepen global economic fragmentation, reflecting a policy environment shaped by intensified geopolitical rivalry.
Table 5 presents the estimation results of equation (3), analyzing how geopolitical fragmentation—captured by alternative measures of geopolitical distance (IPDs)—influences the frequency of distortive economic policy interventions. We use Poisson pseudo- maximum likelihood (PPML) estimations, which are well-suited for count-data structures. This regression analysis complements the descriptive evidence presented in previous sections, providing a robust quantitative assessment of the extent of economic policy fragmentation associated with geopolitical alignment.
The baseline IPD measure (column 1) yields a positive but statistically insignificant coefficient for Between Bloc $$\times$$ Post (0.029), indicating limited evidence of increased distortive interventions between geopolitically distant blocs following Russia's invasion of Ukraine in 2022 under this specification. In contrast, alternative IPD measures that better capture recent geopolitical realignments yield stronger and statistically significant results. Specifically, when employing the 2023 IPD (column 2) or the subsample IPD (column 4), the estimated coefficients increase markedly to approximately 32.7% and 30%, respectively. These results imply that after the Russian invasion of Ukraine, geopolitically distant country pairs implemented, on average, roughly 30–33% more distortive policy interventions against each other compared to aligned country pairs, reflecting substantial policy-driven fragmentation. Conversely, the IPD based exclusively on economic votes (column 3) yields no statistically significant results, suggesting that observed policy fragmentation is driven more strongly by broader geopolitical tensions rather than purely economic alignment. Further robustness checks with alternative fixed-effect structures are provided in Table 16 of the appendix.1314
Table 5: Regression Results: Distortive Interventions
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Total Distortive Interventions: Distant Bloc $$\times$$ Post | 0.029, (0.030) | 0.283***, (0.049) | 0.025, (0.025) | 0.262***, (0.047) |
| Total Distortive Interventions: Nonaligned $$\times$$ Post | -0.028, (0.023) | 0.163***, (0.046) | -0.017, (0.023) | 0.070, (0.046) |
| Total Distortive Interventions: Pseudo R2 | 0.607 | 0.435 | 0.606 | 0.434 |
| Total Distortive Interventions: Observations | 15,665 | 15,665 | 15,606 | 15,606 |
Note: Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$. Result estimations of equation (3) using Poisson pseudo-maximum likelihood (PPML), using annual data for the period 2017-2023, from GTA NIPO. Standard errors are clustered at the country-pair level. Coefficient interpretation is the following: $$\left(e^{\text{coefficient}}-1\right)\times100$$. $$Post$$ is a dummy that captures the post invasion of Ukraine period and takes the value 1 for the years 2022 and 2023. Each column shows the results using a different IPD measure to construct the country blocs. (1) uses the Baseline IPD measure, which is the 2021 values of IPDs estimated using UNGA voting data from 1946-2023 across all vote categories; (2) uses the 2023 values of IPDs estimated using UNGA voting data from 1946-2023 across all vote categories; (3) uses the 2021 values of Economics IPD, which narrows the focus to economic votes while maintaining historical coverage from 1971 onwards; (4) uses 2021 values of IPD estimated using a subsample of votes after the Cold War (1990–2023) while maintaining all vote categories.
Overall, these findings strongly support the interpretation that deliberate policy measures, such as tariffs and sanctions, contribute directly to reinforcing geopolitical fragmentation observed in trade and financial markets. The analysis underscores the importance of methodological transparency, as conclusions regarding economic policy fragmentation are substantially influenced by how geopolitical distances are measured.
Our study highlights significant methodological sensitivities in measuring geoeconomic fragmentation and underscores distinct economic impacts across trade flows, financial portfolios, and economic policy interventions. Trade relationships display robust and consistent fragmentation along geopolitical lines, particularly evident in strategic technology sectors and policy interventions driven by national security and geopolitical concerns. Medium-tech and low-tech trade sectors exhibit especially pronounced responses. In contrast, financial portfolios appear comparatively resilient, with weaker and context-sensitive fragmentation effects, suggesting financial markets may mitigate some impacts of geopolitical tensions through market-based mechanisms.
These results emphasize the critical importance of methodological transparency in constructing geopolitical distance measures, as seemingly minor methodological decisions materially influence conclusions. For immediate policy concerns, IPD measures incorporating recent geopolitical events (such as the 2023 IPD) are most informative. For structural and long-term analyses, economic-vote IPDs or post-Cold War measures provide additional stability and insight. Policymakers should therefore carefully consider methodological choices to accurately assess risks and design strategic responses that balance economic integration, security, and resilience in an increasingly fragmented world economy.
In this section we perform several robustness checks to our analysis of the degree of fragmentation in financial markets.
International financial centers (IFC) can blur the geopolitical distances between recipient and ultimate investor countries, as shown by the literature.15 In our portfolio holdings analysis, we do not include well-known financial centers, such as Bermuda, the British Virgin Islands, the Cayman Islands, and Hong Kong SAR, as source countries. In this section, we present a robustness check where we further exclude other countries frequently identified as international financial centers, including Ireland, Luxembourg, the Netherlands, and Singapore.
Tables 6, 7 and 8 show the estimation results for equations (1), (2) and (3), respectively, when we exclude IFC from the sample. The tables show that the overall pattern of results remains consistent, with only minor changes in magnitude and significance. This pattern suggests that financial centers play a role in channeling investments across geopolitical blocs, but their exclusion does not fundamentally alter the observed fragmentation trends in portfolio holdings.
Table 6: Regression Results of equation 1 excluding IFC
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Portfolio Holdings: Between Bloc × Post | -0.018 | -0.012 | 0.001 | -0.018 |
| Portfolio Holdings: Std. Error | (0.016) | (0.012) | (0.012) | (0.016) |
| Portfolio Holdings: Nonaligned × Post | -0.014 | -0.004 | -0.017 | -0.014 |
| Portfolio Holdings: Std. Error | (0.020) | (0.019) | (0.021) | (0.020) |
| Portfolio Holdings: Observations | 214,165 | 214,577 | 214,184 | 214,165 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
Table 7: Regression Results equation (2) excluding IFC
| Description | IPD all votes: IPD × Post (1) | Economic IPD: IPD × Post (2) | IPD all votes: IPD (3) | Economic IPD: IPD (4) |
|---|---|---|---|---|
| Portfolio holdings: $$\beta$$ Coefficient | -0.071* | -0.063* | -0.057 | 0.037 |
| Portfolio holdings: Std. Error | (0.037) | (0.037) | (0.063) | (0.037) |
| Portfolio holdings: Observations | 106,503 | 105,544 | 106,503 | 105,544 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
Table 8: Regression Results equation (3) excluding IFC
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) |
|---|---|---|---|
| Portfolio holdings: Distant x Post | -0.011 | -0.011 | -0.014 |
| Portfolio holdings: Std. Error | (0.014) | (0.014) | (0.014) |
| Portfolio holdings: Nonaligned x Post | -0.006 | -0.001 | -0.017** |
| Portfolio holdings: Std. Error | (0.007) | (0.008) | (0.008) |
| Portfolio holdings: Observations | 213,286 | 209,899 | 213,286 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
In this section we show the important role played by the United States in international financial markets when considering general blocs of geopolitical distance, defined by the country-pair measures. The IPD measures position the U.S. in the right tail of the distribution, indicating that it is distant from most of its international counterparts, as discussed in our analysis of Figure 2. When we exclude the U.S. from the estimation regressions in equation (3) for portfolio holdings, the effect of geopolitical distance becomes negative and statistically significant at the 1% level under the economic IPD specification. Specifically, portfolio holdings between distant country pairs decline by 2.5% in the post-invasion period. We obtain similar results if we include country-pair and time fixed effects instead of country-pair, source and destination by time fixed effects.
Table 9: Regression Results equation (3) excluding the United States
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) |
|---|---|---|---|
| Portfolio holdings: Distant x Post Coefficient | -0.014 | -0.009 | -0.025*** |
| Portfolio holdings: Std. Error | (0.010) | (0.010) | (0.010) |
| Portfolio holdings: Nonaligned x Post Coefficient | -0.009 | 0.002 | -0.021** |
| Portfolio holdings: Std. Error | (0.010) | (0.009) | (0.010) |
| Portfolio holdings: Observations | 226,246 | 222,751 | 226,246 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
In this section we explore the use of two alternative definitions for the dependent variable to study the degree of fragmentation in financial markets: portfolio share and financial flows (in billions of USD).
Our sensitivity analysis further confirms the nuanced impact of geopolitical fragmentation on financial integration. Using alternative measures such as portfolio shares and financial flows, we find that financial fragmentation effects remain sensitive to IPD specifications. While portfolio share fragmentation appears weak and mostly insignificant, financial flows measured in dollar terms show a clear negative and statistically significant response in most specifications, indicating a decline in financial flows between geopolitically distant country pairs following the Russian invasion of Ukraine. The magnitude and significance of these results vary considerably across IPD definitions, highlighting a stronger and more consistent effect when employing recent geopolitical distance metrics, notably the 2023 IPD. This pattern reinforces the conclusion from our main analysis: geopolitical fragmentation has more pronounced and robust effects on trade and economic policies, whereas financial integration shows weaker and more context-sensitive responses.
Table 10: Regression Results: Portfolio Share and Financial Flows (I) Portfolio Share
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Between Bloc $$\times$$ Post | 0.001, (0.075) | -0.028, (0.099) | -0.139*, (0.084) | 0.002, (0.075) |
| Nonaligned $$\times$$ Post | -0.182, (0.214) | -0.132, (0.117) | -0.114, (0.196) | -0.187, (0.213) |
| Observations | 119,859 | 120,071 | 119,934 | 119,859 |
Table 10: Regression Results: Portfolio Share and Financial Flows (II) Financial Flows
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Between Bloc $$\times$$ Post | -1.807***, (0.632) | -2.244***, (0.714) | -0.652, (0.531) | -1.807***, (0.632) |
| Nonaligned $$\times$$ Post | -2.722***, (0.723) | -2.522***, (0.786) | -0.199, (0.808) | -2.724***, (0.723) |
| Observations | 234,212 | 234,746 | 234,343 | 234,212 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
Table 11: Regression Results: Portfolio Share and Financial Flows (I) Portfolio Share
| Description | IPD all votes: IPD × Post (1) | Economic IPD: IPD × Post (2) | IPD all votes: IPD (3) | Economic IPD: IPD (4) |
|---|---|---|---|---|
| $$\beta$$ Coefficient | -0.050, (0.033) | -0.051, (0.037) | -0.111, (0.093) | -0.039, (0.045) |
| Observations | 58,355 | 58,204 | 58,355 | 58,204 |
Table 11: Regression Results: Portfolio Share and Financial Flows (II) Financial Flows
| Description | IPD all votes: IPD × Post (1) | Economic IPD: IPD × Post (2) | IPD all votes: IPD (3) | Economic IPD: IPD (4) |
|---|---|---|---|---|
| $$\beta$$ Coefficient | -1.152***, (0.342) | -1.049***, (0.318) | 0.268, (0.649) | 1.554**, (0.614) |
| Observations | 115,142 | 114,129 | 115,142 | 114,129 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
Table 12: Regression Results: Portfolio Share and Financial Flows (I) Portfolio Share
| Description | Baseline and Economic IPD: Baseline IPD (complete, 2021) (1) | Baseline and Economic IPD: IPD all (complete, 2023) (2) | Baseline and Economic IPD: IPD economic (complete, 2021) (3) | Baseline and Economic IPD: IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Distant Bloc $$\times$$ Post | -0.066, (0.083) | -0.062, (0.075) | -0.187**, (0.081) | -0.057, (0.084) |
| Nonaligned $$\times$$ Post | 0.033, (0.064) | 0.068, (0.057) | 0.036, (0.058) | 0.028, (0.064) |
| Observations | 233,475 | 229,879 | 233,475 | 233,475 |
Table 12: Regression Results: Portfolio Share and Financial Flows (II) Financial Flows
| Description | Baseline and Economic IPD: Baseline IPD (complete, 2021) (1) | Baseline and Economic IPD: IPD all (complete, 2023) (2) | Baseline and Economic IPD: IPD economic (complete, 2021) (3) | Baseline and Economic IPD: IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Distant Bloc $$\times$$ Post | -1.392*, (0.838) | -1.853***, (0.613) | -1.929***, (0.638) | -1.306**, (0.840) |
| Nonaligned $$\times$$ Post | -0.128, (0.258) | 0.156, (0.225) | 0.251, (0.308) | -0.159, (0.251) |
| Observations | 233,475 | 229,879 | 233,475 | 233,475 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
In this section we conduct robustness checks for our gravity equations of geopolitical distance on trade by technology classification. Similarly to Table 3, in Table 13, we generalize our analysis to the full distribution of $$IPD(s,d)$$ instead of only working on the segmented distribution. Our results indicate that when using the distribution of geopolitical distance, trade flows between distant countries for all goods classes declined significantly in the post-invasion period when we use the latest IPD values and declined for low and high-tech goods when only economic votes are used. Additionally, we observe small and statistically insignificant relationships for nonaligned countries in the post-invasion period. These results are consistent with Table 3 as well our larger finding that results are sensitive to the choice of IPD measure and bloc definition.
In Table 14, we replicate our estimation results from equation (2) with our trade by technology class. Across all specifications and technology classifications, we find negative and statistically significant relationships between geopolitical distance and trade flows both in the post-invasion period and over the full sample period. Consistent with Table 2, the effect is slightly weaker when using Economic IPDs to generate blocs. The diminishing effect of geopolitical distance on trade as technology class increases is also evident in our results.
Table 13: Regression Results (I) High-tech Trade
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Distant × Post Coefficient | -0.0261 | -0.095*** | -0.156** | -0.050 |
| Std. Error | (0.038) | (0.036) | (0.066) | (0.042) |
| Nonaligned × Post Coefficient | 0.012 | 0.016 | 0.011 | 0.017 |
| Std. Error | (0.024) | (0.031) | (0.025) | (0.024) |
| Observations | 270,157 | 261,452 | 267,739 | 270157 |
Table 13: Regression Results (II) Medium-tech Trade
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Distant × Post Coefficient | -0.098* | -0.182*** | -0.031 | -0.086 |
| Std. Error | (0.055) | (0.062) | (0.071) | (0.058) |
| Nonaligned × Post Coefficient | -0.083* | -0.089* | -0.007 | -0.0864** |
| Std. Error | (0.040) | (0.046) | (0.037) | (0.037) |
| Observations | 232,540 | 225,038 | 230,788 | 232,540 |
Table 13: Regression Results (III) Low-tech Trade
| Description | Baseline IPD (complete, 2021) (1) | IPD all (complete, 2023) (2) | IPD economic (complete, 2021) (3) | IPD all (subsample, 2021) (4) |
|---|---|---|---|---|
| Distant × Post Coefficient | -0.0827** | -0.109*** | -0.121*** | -0.119*** |
| Std. Error | (0.032) | (0.034) | (0.039) | (0.033) |
| Nonaligned × Post Coefficient | -0.010 | 0.020 | -0.010 | -0.016 |
| Std. Error | (0.021) | (0.026) | (0.022) | (0.021) |
| Observations | 267,022 | 259,244 | 264,616 | 267,022 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
Table 14: Regression Results with IPD (I) High-tech Trade
| Description | IPD all votes: IPD × Post (1) | Economic IPD: IPD × Post (2) | IPD all votes: IPD (3) | Economic IPD: IPD (4) |
|---|---|---|---|---|
| $$\beta$$ Coefficient | -0.078*** | -0.072*** | -0.088*** | -0.075*** |
| Std. Error | (0.020) | (0.019) | (0.027) | (0.024) |
| Observations | 292,042 | 292,042 | 290,161 | 292,042 |
Table 14: Regression Results with IPD (II) Medium-tech Trade
| Description | IPD all votes: IPD × Post (1) | Economic IPD: IPD × Post (2) | IPD all votes: IPD (3) | Economic IPD: IPD (4) |
|---|---|---|---|---|
| $$\beta$$ Coefficient | -0.106*** | -0.098*** | -0.123*** | -0.106*** |
| Std. Error | (0.027) | (0.025) | (0.034) | (0.031) |
| Observations | 256,506 | 256,506 | 256,506 | 256,506 |
Table 14: Regression Results with IPD (III) Low-tech Trade
| Description | IPD all votes: IPD × Post (1) | Economic IPD: IPD × Post (2) | IPD all votes: IPD (3) | Economic IPD: IPD (4) |
|---|---|---|---|---|
| $$\beta$$ Coefficient | -0.082*** | -0.076*** | -0.090*** | -0.075*** |
| Std. Error | (0.014) | (0.013) | (0.021) | (0.020) |
| Observations | 288,251 | 288,251 | 288,251 | 288,251 |
Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$
Table 15: ISIC Revision-3 Technology Classifications (High-tech)
| Technology Classifications | |
|---|---|
| 30, 33 | Computer, electronic and optical products |
| 31-32 | Electrical equipment |
| 24 | Chemicals and pharmaceuticals |
| 29 | Machinery and equipment |
| 34-35 | Transport equipment |
Table 15: ISIC Revision-3 Technology Classifications (Low-tech)
| Technology Classifications | |
|---|---|
| 15-16 | Food, beverages, tobacco |
| 17-19 | Textiles, apparel, leather |
| 20-22 | Wood products |
| 36 | Furniture & Other Manufacturing |
| 26 | Non-metallic mineral products |
Table 15: ISIC Revision-3 Technology Classifications (Medium-tech)
| Technology Classifications | |
|---|---|
| 27-28 | Basic metals and metal products |
| 25 | Rubber and plastics |
| 23 | Petroleum products |
For completeness, in this section we present the distribution of distortive interventions by motive and bloc, under alternative IPDs. Figure 9 shows the results that classify countries into aligned, nonaligned, and distant using the 2023 IPD with all votes, while 10 uses only economic votes. The objective is to examine whether the distribution of economic policy interventions across strategic motives varies when using different IPD definitions.
Reclassifying country blocs with alternative IPD metrics does not fundamentally alter the conclusions from our main analysis, but highlights important nuances. Using the 2023 IPD, distortive interventions related to national security, resilience, and geopolitical concerns become more prominent among distant blocs, reflecting recent geopolitical realignments. Conversely, when employing the economic-votes IPD, interventions driven explicitly by economic motives such as resilience and strategic competitiveness gain relative importance. These findings reinforce that methodological choices in defining geopolitical blocs affect not just the observed magnitude but also the strategic composition of economic policy fragmentation.
Figure 9: Country blocs defined using the 2023 IPD with all votes.
Figure 10: Country blocs defined using the 2021 Economic IPD.
Figure 11 shows the sectoral results that classify countries using the IPD 2023 with all votes, while 12 uses only economic votes. This analysis further confirms our main conclusion regarding the strategic targeting of economic policy interventions, though differences emerge when varying IPD definitions. The sectoral distribution of policy interventions remains broadly consistent across IPD specifications, with critical minerals, dual-use products, advanced technology, and industrial raw materials consistently targeted by geopolitically distant and nonaligned blocs. However, notable differences arise depending on the IPD measure chosen: using the 2023 IPD, the concentration of interventions in advanced technologies and critical sectors is particularly pronounced among distant blocs, highlighting recent shifts toward intensified strategic competition. In contrast, when using the economic-votes IPD, the sectoral focus appears less sharply differentiated between blocs, suggesting that economic-voting-based alignment captures broader and less polarizing sectoral interventions. Overall, the core findings regarding sectoral targeting persist, though their intensity and polarization vary with IPD methodological choices.
Figure 11: Country blocs defined using the 2023 IPD with all votes.
Figure 12: Country blocs defined using the 2021 Economic IPD.
In table 16 we show the sensitivity of the results of the estimation of equation (3) for distortive interventions to the inclusion of different sets of fixed effects. Removing source-year and destination-year fixed effects and adding simple time fixed effects (columns 5-8) further amplifies fragmentation effects. Under these specifications, the largest observed fragmentation effects occur with the 2023 IPD specification (approximately 39.8%), suggesting an even greater increase in targeted distortive interventions post-invasion. Additionally, the Nonaligned $$\times$$ Post coefficients also become significantly positive (16.1%), indicating that nonaligned countries also intensified their policy interventions.
Table 16: Regression Results: Distortive Interventions (I) Total Distortive Interventions
| Description | Baseline IPD (complete, 2021) | IPD all (complete, 2023) | IPD economic (complete, 2021) | IPD all (subsample, 2021) | Baseline IPD (complete, 2021) | IPD all (complete, 2023) | IPD economic (complete, 2021) | IPD all (subsample, 2021) |
|---|---|---|---|---|---|---|---|---|
| Distant Bloc $$\times$$ Post | 0.029, (0.030) | 0.283***, (0.049) | 0.025, (0.025) | 0.262***, (0.047) | 0.073**, (0.033) | 0.335***, (0.052) | 0.021, (0.030) | 0.293***, (0.049) |
| Nonaligned $$\times$$ Post | -0.028, (0.023) | 0.163***, (0.046) | -0.017, (0.023) | 0.070, (0.046) | 0.006, (0.024) | 0.149***, (0.048) | -0.038*, (0.022) | 0.164***, (0.045) |
| Pseudo R2 | 0.607 | 0.435 | 0.606 | 0.434 | 0.607 | 0.435 | 0.607 | 0.435 |
| Observations | 15,665 | 15,665 | 15,606 | 15,606 | 15,604 | 15,604 | 15,665 | 15,665 |
| Country-Pair FE | Y | Y | Y | Y | Y | Y | Y | Y |
| Time FE | N | N | N | N | Y | Y | Y | Y |
| Source $$\times$$ Year FE | Y | Y | Y | Y | N | N | N | N |
| Destination $$\times$$ Year FE | Y | Y | Y | Y | N | N | N | N |
Notes: Significance thresholds: *** $$p < 0.01$$, ** $$p < 0.05$$, * $$p < 0.1$$. Result estimations of equation (3) using Poisson pseudo-maximum likelihood (PPML), using annual data for the period 2017-2023, from GTA NIPO. Standard errors are clustered at the country-pair level. Coefficient interpretation is the following: $$\left(e^{\text{coefficient}}-1\right)\times100$$.
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