This study aims to analyze how the Russia–Ukraine conflict affected Ecuador’s exports to Russia and Ukraine, assessing whether observed trade contractions reflect conflict-associated geopolitical shocks or broader changes in global competitiveness. This paper focuses on asymmetric effects, structural breaks and mechanisms of trade reorientation in a small open economy.
The analysis uses annual export data from the International Trade Centre for the period 2017–2024. It combines descriptive trend analysis, pre- and postconflict contrasts, nonparametric tests, structural break diagnostics (Chow test), elasticity analysis and econometric models, including semi-log intervention models and fixed-effects panel specifications. A k-means clustering analysis is applied to identify heterogeneous patterns in product-level exposure and resilience.
The results reveal a strongly asymmetric geopolitical shock. Exports to Ukraine collapsed by more than 80%, accompanied by a statistically significant structural break and persistent disruption of bilateral trade. In contrast, exports to Russia declined moderately without evidence of a structural rupture. At the same time, Ecuador’s exports to the rest of the world continued to expand, indicating that bilateral contractions are driven by a localized geopolitical shock rather than a generalized loss of competitiveness. Product-level evidence shows selective resilience and trade diversion toward alternative markets.
This study provides novel empirical evidence on how major geopolitical conflicts reshape bilateral trade relationships in small open economies, with direct relevance for trade policy, risk management and export diversification strategies.
Introduction
International trade has long been a key driver of economic growth and global integration. The expansion of cross-border flows of goods, services and capital has promoted productive specialization, technological diffusion and welfare gains across countries [World Trade Organization (WTO), 2023]. At the same time, deeper interdependence has increased exposure to systemic risks arising from exogenous shocks such as financial crises, pandemics, geopolitical tensions and armed conflicts, all of which can disrupt global value chains and trade patterns (Baldwin and Freeman, 2022; Evenett, 2023). In recent decades, events such as the 2008 global financial crisis, the COVID-19 pandemic and renewed geopolitical tensions have revealed the fragility of international logistics, trade finance and supply networks, contributing to a reconfiguration of globalization marked by supply chain reorganization, regionalization and a growing emphasis on resilience (Evenett and Baldwin, 2022; Gereffi, 2020; Antràs, 2021; Freund et al., 2022; Bown, 2023).
Within this context of heightened global uncertainty, Latin America faces persistent challenges in integrating into an increasingly fragmented international system. According to the Economic Commission for Latin America and the Caribbean (ECLAC, 2023), the region’s external trade continues to show structural weaknesses, including high concentration in primary commodities and limited diversification of export destinations. These vulnerabilities are compounded by dependence on imported energy, technology and fertilizers, as well as by exposure to international price volatility (Rosales and Kuwayama, 2022). The region has also absorbed successive shocks in a short period, including the COVID-19 pandemic, the global energy crisis and the outbreak of the Russia–Ukraine war, all of which have affected logistics costs, access to inputs and terms of trade (Araya et al., 2023; Díaz-Bonilla, 2023).
The Russia–Ukraine conflict, which began in February 2022, constitutes one of the most disruptive geopolitical events for the global economy in recent years. Its effects extend beyond the military sphere, affecting energy, food and financial stability through sanctions, logistical disruptions and interruptions in Black Sea trade routes. Because Russia and Ukraine play important roles in global energy, fertilizer and agricultural markets, the conflict has had cascading effects on countries exposed to these trade networks (International Food Policy Research Institute [IFPRI], 2023; Mottaleb et al., 2022; Steinbach, 2023).
At the regional level, the effects of the conflict on Latin America have been heterogeneous. While some countries benefited from higher international prices for key commodities, others experienced increased import costs and deteriorating trade balances (Cárdenas and Hernández, 2022). In this context, small open economies are especially sensitive to disruptions in global trade structures. Ecuador represents a relevant case, given its dependence on primary exports, such as bananas, shrimp, flowers, cocoa and oil, combined with reliance on imported agricultural and industrial inputs from multiple markets, including Russia and Ukraine [Banco Central del Ecuador (BCE), 2024].
Before the outbreak of the conflict, Russia was a strategic destination for Ecuadorian exports of bananas and flowers, while trade relations with Ukraine were smaller but were gradually expanding. After the onset of hostilities, however, bilateral trade flows experienced abrupt and asymmetric changes in both volume and value. Logistical disruptions, financial restrictions, exchange-rate volatility and higher transport costs altered the competitiveness of Ecuadorian products and affected trade conditions with both countries (García-Herrero and Tan, 2023). Despite the relevance of these developments, empirical evidence on how the Russia–Ukraine conflict affected Ecuador’s bilateral trade with Russia and Ukraine remains limited.
This article analyzes the effects associated with the Russia–Ukraine conflict on Ecuador’s exports to Russia and Ukraine, taking into account broader transformations in the international trade environment. Using foreign trade data from the International Trade Centre (ITC), a joint agency of the United Nations Conference on Trade and Development (UNCTAD) and the World Trade Organization (WTO), the study examines changes in the structure, composition and dynamics of bilateral trade flows before and after the onset of the conflict.
This study contributes to the international trade literature in three ways. First, it provides empirical evidence on how a major geopolitical shock is associated with changes in bilateral trade relationships in a small open economy, distinguishing among structural collapse, partial disruption and relative resilience. Second, by contrasting exports to Russia and Ukraine with Ecuador’s aggregate export performance to the rest of the world, the analysis identifies a localized geopolitical shock within broader global trade dynamics. Third, the product-level approach reveals heterogeneous adjustment patterns, including selective resilience and evidence consistent with trade reorientation, with implications for trade policy under increasing global fragmentation (Tajaddini, 2023; Bown, 2023).
Literature review
Contemporary international trade theory increasingly recognizes that economies are highly exposed to external shocks that affect relative costs, global supply chains and final markets. Growing trade interdependence has heightened sensitivity to disruptions, including pandemics, geopolitical tensions and armed conflicts, with significant implications for trade volumes and patterns (Baldwin and Freeman, 2022; Evenett, 2023). Reports from the World Trade Organization [World Trade Organization (WTO), 2023] and the World Bank (2023) document a slowdown in global trade growth driven by logistical disruptions, financial sanctions and rising geopolitical fragmentation. In this context, small open economies with limited product and destination diversification are particularly vulnerable to external shocks due to narrow adjustment margins and high exposure to volatility [ECLAC, 2023; United Nations Conference on Trade and Development (UNCTAD), 2023a, 2023b].
Within this framework, geopolitical risk (GPR) has emerged as a key determinant of international trade performance. Empirical evidence shows that increases in GPR are associated with lower trade openness, reduced bilateral trade flows and persistent contractions in economic activity, operating through heightened uncertainty, higher transaction costs and disruptions to trade finance and logistics (Yan et al., 2025). Quantitative estimates suggest that periods of elevated geopolitical tension can reduce trade by 30%–40%, effectively acting as implicit trade barriers (Mulabdic et al., 2025). Related studies highlight the sensitivity of maritime transport to geopolitical risk, including declines in container volumes and rerouting of shipping networks (Atacan, 2023).
Beyond contractionary effects, geopolitical shocks also induce trade reorientation, as firms and countries shift commercial relations toward geopolitically stable or strategically aligned partners. This pattern has been documented for emerging economies, where trade flows realign in response to changes in political risk and alliance structures (Sohag et al., 2024). Complementary evidence from the International Monetary Fund shows that geopolitical conflicts increase macroeconomic uncertainty, raise energy and food prices and generate logistical frictions, thereby amplifying adverse trade effects (IMF, 2022–2024).
The Russia–Ukraine conflict represents one of the most significant geopolitical shocks of the last decade, operating through multiple interconnected channels of transmission. Energy supply disruptions raised transportation and production costs, weakening trade competitiveness (IIEA, 2023), while interruptions in food and fertilizer exports – given the central role of Russia and Ukraine in global markets – contributed to rising agricultural prices and food insecurity (IFPRI, 2023). In parallel, financial sanctions restricted access to international payment systems and trade finance, altering bilateral trade patterns (García-Herrero and Tan, 2023). Network-based analyses further document reconfigurations in trade connectivity and routing, as well as significant trade-diversion effects toward alternative suppliers (Zhang et al., 2024; Steinbach, 2023; Fan et al., 2025).
Small open economies are particularly exposed to these mechanisms due to high import dependence, limited diversification and constrained short-term adjustment capacity. Empirical evidence indicates that terms-of-trade shocks lead to pronounced declines in real income and external balances in smaller economies (Shafiullah et al., 2020), while higher logistics costs and infrastructure constraints further amplify vulnerability (Bednarski et al., 2025). Firm-level adaptability, diversification strategies and resilience capabilities play a critical role in shaping adjustment outcomes under geopolitical uncertainty (Althaqafi, 2025).
Despite the expanding literature on geopolitical shock and trade, important gaps remain. There is no empirical evidence on the impact of the Russia–Ukraine conflict on Ecuador’s bilateral trade, despite the country’s exposure as a small open economy. Existing studies focus primarily on aggregate trade effects, overlooking product-level heterogeneity and sector-specific adjustment mechanisms. Moreover, although recent methodological advances emphasize counterfactual approaches such as synthetic control or dynamic difference-in-differences, their application is constrained in small-sample contexts characterized by common shocks and limited data availability.
Given these constraints, this study adopts a methodological strategy based on structural break tests, elasticity analysis, cluster classification and panel data models with intervention effects. This approach is well suited to the available data and enables the identification of asymmetric impacts, reorientation patterns and structural disruptions without overstating causal claims.
Recent empirical research examining the effects of major disruptions on international trade has increasingly relied on counterfactual identification strategies. Approaches such as difference-in-differences designs, event-study frameworks and synthetic control methods have been widely used to estimate the causal effects of policy shocks, geopolitical events and trade restrictions on bilateral trade flows (Autor et al., 2013; Abadie et al., 2010; Larch et al., 2024). These methodologies are particularly valuable when suitable comparison groups and sufficiently long time series are available, allowing researchers to isolate the impact of specific shocks from broader global trends. In contexts where such conditions are not fully met, complementary approaches based on structural break analysis, intervention models and descriptive comparative frameworks remain useful for identifying significant changes in trade dynamics associated with major external disruptions (Egorov et al., 2025; Gavoille, 2025).
These gaps justify the study’s scientific and practical relevance and position it as an original contribution to the literature on international trade and geopolitical shocks in small open economies. Based on the preceding analysis, the following hypotheses are proposed:
The Russia–Ukraine conflict led to a significant reduction in Ecuador’s exports to Russia from 2022 onward.
The conflict caused a more pronounced decline in Ecuador’s exports to Ukraine than in exports to Russia.
The relative participation of Russia and Ukraine in Ecuador’s total exports to the world changed significantly after 2022.
The impact of the conflict varies across product categories, with stronger effects on products more dependent on the Russian market.
The decline in exports to Russia and Ukraine cannot be explained solely by global trade conditions, but reflects a conflict-associated geopolitical shock.
Overall, the literature reviewed highlights that the trade effects of geopolitical disruptions are heterogeneous, context-dependent and often mediated by preexisting structural vulnerabilities. This is particularly relevant for small open economies such as Ecuador, whose export performance may react asymmetrically across destinations and products when exposed to major external shocks. These considerations frame the analytical approach developed in this study.
Methodology
Data and sample construction. The empirical analysis uses foreign trade data from the ITC, a joint agency of the UNCTAD and the WTO. The data set covers Ecuador’s exports to Russia, Ukraine and the rest of the world over the period 2017–2024 and provides product-level information across more than 220 reporting economies. Export values are reported in nominal terms, expressed in current US dollars. Unless otherwise indicated, the values shown in tables and figures are reported in US$ thousands, following the standard ITC reporting format.
Standard data harmonization procedures were applied, including aligning year identifiers across destinations, converting trade values to a consistent numerical format and standardizing Harmonized System product codes. Two complementary data sets were constructed. Data set A is a product–year panel in long format containing annual export values by destination, supporting time-series and distributional analyses. Data set B is an analytical data set derived from Data set A and includes variables required for impact assessment, such as conflict-period indicators, export shares, logarithmic transformations and product-level shock measures.
Analytical strategy and identification logic. The empirical strategy assesses whether the Russia–Ukraine conflict was associated with a structural and asymmetric shock to Ecuador’s bilateral exports. The analysis proceeds in three stages:
descriptive and relative sensitivity analysis;
identification of structural breaks associated with the onset of the conflict in 2022; and
econometric estimation using intervention and panel data models.
Given the small-sample context and the absence of a credible counterfactual, this framework is intended to identify associations, structural discontinuities and asymmetric export adjustments linked to the conflict, rather than causal effects in a strict econometric sense. The year 2022 is treated as the analytically relevant breakpoint because it marks the onset of the Russia–Ukraine war and constitutes the first observable postshock period in Ecuador’s annual bilateral export series. It therefore allows the identification of the presence and relative magnitude of conflict-associated changes in bilateral export dynamics and directly addresses H1, H2 and H5, and conditionally H4.
Identification strategy and causal interpretation. The empirical strategy evaluates whether Ecuador’s bilateral export dynamics exhibit statistically detectable changes after the onset of the Russia–Ukraine conflict in 2022. However, the analysis does not assume that the conflict constitutes the sole driver of the observed trade adjustments. During the same period, several global shocks may also have influenced export performance.
First, international trade experienced a gradual post-COVID recovery, particularly during 2021–2022, as global demand and production networks resumed after pandemic-related restrictions. Second, commodity price fluctuations affected export revenues and trade incentives across multiple sectors. Third, persistent disruptions in global logistics and shipping systems, including container shortages, higher freight costs and port congestion, continued to influence international trade flows during the early 2020s.
Given these concurrent factors, the empirical results should be interpreted as identifying conflict-associated structural changes in export patterns within a broader global adjustment context, rather than isolating the conflict as a strictly causal determinant. The combination of structural break analysis, intervention models and panel estimation, therefore, provides evidence on the timing and magnitude of export adjustments during the conflict period, while accounting for other macroeconomic and logistical influences on global trade.
In the broader empirical literature, trade shocks are often examined using counterfactual identification strategies such as difference-in-differences, event-study models and synthetic control methods. These approaches are especially useful when suitable comparison groups and longer time series are available. In the present study, however, the short annual series and the limited number of bilateral units constrain the feasibility of such designs. For this reason, the empirical strategy is based on descriptive comparison, structural break analysis, intervention modeling and complementary panel estimation, which together provide evidence on conflict-associated changes in bilateral export dynamics without claiming strict causal identification.
Descriptive, distributional and pre–post analysis. The descriptive analysis compares pre- and postconflict export dynamics across Russia, Ukraine and global markets, identifying exposed products, changes in export shares and evidence of trade reorientation. To characterize the statistical properties of the shock, export shocks were defined as the product-level difference between 2022 and 2021 export values. Given the short time dimension (2017–2024), normality was assessed using cross-sectional tests rather than individual time-series diagnostics. The Shapiro–Wilk test was applied as the primary procedure, complemented by the Kolmogorov–Smirnov test for robustness (n = 146 products for Russia and Ukraine).
Because normality was rejected, pre- and postconflict differences (2017–2021 vs 2022–2024) were evaluated using the Wilcoxon signed-rank test for paired samples, supplemented by average percentage changes to assess economic magnitude.
Cluster analysis. To capture heterogeneity in exposure and resilience across products, a k-means cluster analysis was conducted. The clustering procedure was based on a synthetic shock index constructed from three normalized indicators:
pre/post export variation, intended to capture the magnitude of change associated with the conflict period;
export continuity in the postconflict period, intended to reflect the persistence of trade flows after 2022; and
a resilience index, defined as the share of years with positive exports over 2017–2024.
Because these indicators are measured on different scales, they were normalized before clustering to ensure that no single component dominated the distance calculation.
The number of clusters was set to , corresponding to high-shock, moderate-shock and resilient profiles. This specification was chosen on substantive grounds, as it provides an interpretable classification of product responses while preserving analytical parsimony. As a robustness check, alternative specifications with and were also examined. The three-cluster solution was retained because it offered the most meaningful balance between differentiation and interpretability for the product categories analyzed.
Structural break and elasticity analysis. To evaluate whether the onset of the Russia–Ukraine conflict coincides with a structural change in Ecuador’s export trajectories, the Chow test was applied using 2022 as the breakpoint. This year was selected because it marks the beginning of the Russia–Ukraine war and is the first annual observation in the available data set that reflects the postinvasion trade environment. Consequently, 2022 serves as an analytically motivated breakpoint for testing whether export patterns changed significantly after the geopolitical shock.
The Chow test compares the stability of regression coefficients before and after the selected breakpoint. In this study, the preconflict period corresponds to 2017–2021 and the postconflict period to 2022–2024. Although alternative multiple-break procedures, such as the Bai–Perron test, are widely used in structural break analysis, their implementation generally requires longer time series to produce reliable results. Given the limited sample size available in this study, the Chow test provides a parsimonious and theoretically grounded approach for assessing whether export dynamics changed significantly after the onset of the conflict.
The baseline trend model is specified as:
The test compares parameter stability between the preconflict (2017–2021) and postconflict (2022–2024) periods. Rejection of the null hypothesis of parameter stability indicates a structural discontinuity associated with the onset of the conflict, providing empirical support consistent with H1 and H2 and reinforcing H5 by helping distinguish a localized geopolitical shock from broader global trade fluctuations.
As a complementary measure, export elasticities to Russia and Ukraine were calculated with respect to changes in Ecuador’s total world exports. In this context, an elasticity below unity suggests a comparatively weaker bilateral response relative to Ecuador’s total exports, whereas higher absolute values indicate greater relative sensitivity. These elasticity measures should be interpreted as indicative indicators of relative export sensitivity rather than as structural parameters. They are calculated as the ratio between the percentage change in Ecuador’s bilateral exports to each destination and the percentage change in Ecuador’s total exports to the world over the same period. Under this formulation, the resulting values indicate whether bilateral exports responded more or less intensely than Ecuador’s overall export performance. Given the volatility that can arise from small denominators and large percentage changes, these elasticities are used here as descriptive comparative measures of relative responsiveness and should be interpreted with caution.
Econometric models. Two econometric specifications were estimated to quantify the conflict’s impact. Semi-logarithmic intervention model (product–year):
where denotes exports of the product in year, is a dummy variable equal to one from 2022 onward, captures product-specific fixed effects and measures the average percentage change associated with the conflict.
Panel model with country fixed effects:
where denotes the destination country (Russia or Ukraine). This specification captures structural differences between the two markets and estimates the average conflict effect across destinations.
Robustness and diagnostic tests. Model validity was assessed using standard diagnostic procedures, including the Breusch–Pagan and White tests for heteroskedasticity, the Wooldridge test for serial correlation in panel data, the Hausman test to compare fixed- and random-effects specifications and comparisons between logarithmic and linear functional forms. Where appropriate, robust standard errors were used to improve the consistency of inference under potential specification issues.
Small-sample considerations. The empirical analysis is based on annual export data covering the period 2017–2024 for two bilateral trade relationships (Russia and Ukraine). As a result, the panel data set contains a limited number of observations. This small-sample structure restricts the statistical power of econometric inference and requires cautious interpretation of estimated coefficients. Within this context, the panel regression model is used primarily as a complementary analytical tool to illustrate comparative export dynamics across the two destinations. The study’s main empirical insights rely on the combined interpretation of descriptive patterns, structural break analysis and intervention models. Consequently, the panel estimates should be interpreted as indicative rather than definitive evidence of conflict-associated export adjustments.
Results
General export dynamics and market asymmetries
During 2017–2024, Ecuador exported 145 distinct products to the Russian market, spanning agricultural products, seafood, flowers, industrial goods, raw materials and specialized manufactured goods. Despite this broad product base, export continuity was limited, as only 19 products were exported without interruption throughout the period. This reflects intermittent market access and considerable product-level volatility even prior to the geopolitical shock.
Exports to Ukraine were substantially more concentrated. Only eight products were exported during the period – mainly flowers and agricultural or seafood products – and just four maintained uninterrupted export flows. This narrower export structure implies greater vulnerability to external disruptions and a more limited capacity for short-term adjustment. Table 1 reports the annual evolution of Ecuadorian exports to Russia, Ukraine and the rest of the world, considering only products common to both bilateral markets.
Annual export performance
| Year | Russia | Ukraine | World |
|---|---|---|---|
| 2017 | 1,725,924 | 150,222 | 28,038,942 |
| 2018 | 1,737,032 | 125,831 | 31,105,975 |
| 2019 | 1,759,345 | 169,612 | 32,606,755 |
| 2020 | 1,839,232 | 212,707 | 30,901,557 |
| 2021 | 1,999,590 | 246,577 | 38,590,725 |
| 2022 | 1,988,677 | 67,300 | 50,892,977 |
| 2023 | 1,847,220 | 26,924 | 46,143,046 |
| 2024 | 1,796,573 | 35,727 | 51,994,869 |
The table reveals a clear post-2022 divergence across destinations. Exports to Russia increased steadily between 2017 and 2021, then declined moderately but persistently from 2022 to 2024, without evidence of a complete collapse, as export values remained positive and relatively stable relative to preconflict levels. By contrast, exports to Ukraine followed a markedly different trajectory. After sustained growth up to 2021, exports collapsed sharply in 2022, falling by more than two-thirds and remained at extremely low levels thereafter. Although a partial recovery is observed in 2024, export values remain far below preconflict levels, indicating a severe and persistent disruption of bilateral trade.
At the same time, exports of the same product set to the world market expanded strongly in 2021 and 2022 and remained comparatively high in subsequent years. This divergence suggests that contractions in Russia and Ukraine do not reflect a generalized decline in Ecuador’s export capacity or competitiveness, but rather a destination-specific shock.
Table 2 summarizes these dynamics by combining average export performance before and after the conflict’s onset with each market’s share of Ecuador’s total exports. The results show positive average growth across destinations in the preconflict period, followed by sharply asymmetric outcomes after 2022: severe contraction in Ukraine, moderate adjustment in Russia and continued expansion globally. The table also shows a decline in Russia’s share of total exports and a near-collapse of Ukraine’s share between 2021 and 2024, patterns consistent with a shift away from conflict-affected markets.
Export dynamics and market shares of Ecuador’s exports to Russia and Ukraine
| Market | Mean growth 2017–2021 | Mean growth 2022–2024 | SD | Min shock | Max shock | Share in world exports, 2021 | Share in world exports, 2024 |
|---|---|---|---|---|---|---|---|
| Russia | +36.5% | +22.9% | −74.7 | −56785 | +81,999 | 5.18% | 3.46% |
| Ukraine | +86.9% | −34.1% | −1228 | −89917 | 0 | 0.64% | 0.07% |
| World | +14.6% | +6.0% | +534,881 | −21228 | +9,111,134 | 100.00% | 100.00% |
Mean growth compares the average annual export variation in the preconflict and postconflict periods. Market shares indicate each destination’s participation in Ecuador’s total exports to the world
Overall, the results indicate that the 2022 geopolitical shock generated highly asymmetric destination-specific effects. Trade relations with Ukraine experienced an abrupt breakdown, exports to Russia adjusted without structural collapse and global exports continued to grow, consistent with a localized geopolitical shock rather than a systemic crisis in Ecuadorian exports.
Strategic products and sectoral heterogeneity
Beyond aggregate dynamics, the impact of the Russia–Ukraine conflict exhibits substantial heterogeneity across product categories (see Table 3).
Strategic products and shock (2022)
| Product | Share Russia2017–2021 | Shock Russia2022 | Share Ukraine2017–2021 | Shock Ukraine2022 |
|---|---|---|---|---|
| Fresh roses | 19.0% | −56785 | 1.30% | −1080 |
| Frozen shrimp and prawns | 2.49% | −8164 | 2.60% | −12960 |
| Jams and preserves | 13.0% | −6095 | 2.20% | −1440 |
| Frozen mackerel | 12.6% | −4783 | – | – |
| Frozen fish n.e.s. | 13.0% | −3,69 | 1.20% | −480 |
| Other fresh flowers | 3.30% | −3724 | – | – |
| Fresh carnations | 32.7% | −3185 | 1.10% | −860 |
| Copper waste | 2.54% | −3104 | – | – |
| Coffee extracts | 26.9% | −2716 | 1.50% | −1236 |
| Fresh bananas | – | – | 1.80% | −1755 |
| Various legumes | – | – | 1.00% | −600 |
| Frozen vegetables | – | – | 1.40% | −550 |
| Preserved fruits | – | – | 1.10% | −350 |
| Total | 4.17% | −5456 | 0.64% | −179277* |
Shock (an abrupt, unexpected and significant change in an economic variable). Shock2022 = Exports2022 − Exports2021
Table 3 reports the performance of selected strategic products exported by Ecuador to Russia and Ukraine, including their average participation during the preconflict period (2017–2021) and the absolute shock observed in 2022, measured as the difference between exports in 2022 and 2021.
For the Russian market, the results indicate significant but noncatastrophic disruptions across a broad range of strategic products. Fresh flowers – particularly roses and carnations – show large absolute declines, reflecting their strong preconflict dependence on the Russian market. Similar contraction patterns are observed for frozen fish, coffee extracts, fruit preserves and copper waste. Despite these declines, exports for most strategic products do not disappear entirely, indicating continuity of commercial relations and suggesting that the shock primarily manifests as a contraction rather than a complete market exit.
In contrast, exports to Ukraine exhibit a markedly more severe adjustment. For most strategic products common to both markets – flowers, seafood, coffee-based products and preserved foods – the 2022 shock corresponds to declines exceeding 80%, often followed by near-zero export levels in subsequent years. This pattern reflects not only reduced demand and logistical constraints but also the effective interruption of trade channels. Even products with relatively small preconflict participation shares experience pronounced post-2022 collapses, underscoring the depth of the disruption.
This comparison highlights a clear asymmetry in sectoral exposure. While Russia remains an active, though diminished, destination for Ecuadorian strategic exports, Ukraine effectively ceases to function as a viable market for most products in the postconflict period. This distinction holds across both agricultural and nonagricultural categories, suggesting that broader market-level conditions, rather than product-specific characteristics, drive the observed differences.
To evaluate whether the contraction of exports to Russia and Ukraine was accompanied by expansion in alternative markets, Table 4 compares the export performance of selected product categories affected by the conflict with their export evolution in the rest of the world. This comparison provides a more direct basis for assessing whether post-2022 export growth outside the conflict-affected destinations reflects trade diversion rather than general export expansion. In this study, trade diversion is inferred only when product categories that contract in Russia and/or Ukraine simultaneously expand in other markets during the postconflict period.
Export performance of selected products affected in Russia and Ukraine and growth in alternative markets
| Product category | Export change to Russia/Ukraine (2022–2024 vs 2017–2021) | Export change to rest of the world | Interpretation |
|---|---|---|---|
| Cut flowers | Strong contraction | Moderate expansion | Evidence consistent with market reorientation toward alternative destinations |
| Processed coffee products | Bilateral contraction | Significant expansion | Trade growth outside conflict markets suggests partial trade diversion |
| Preserved foods | Decline in Ukraine | Stable to moderate growth in other markets | Export resilience with possible diversification |
| Seafood products | Bilateral reduction | Expansion in global markets | Evidence consistent with reallocation of export flows |
| Agricultural processed goods | Moderate decline | Moderate growth | Partial compensation through broader market expansion |
Export changes are calculated as the difference between the postconflict period (2022–2024) and the preconflict period (2017–2021). Positive values indicate export expansion, while negative values indicate contraction
To further assess heterogeneity across the full product set, a k-means cluster analysis (k = 3) was conducted using a composite shock metric capturing pre/post variation, export continuity and resilience over 2017–2024. Table 5 reports the resulting cluster centroids and interpretations for all exported products (n = 146).
Cluster analysis of shock (k = 3)
| Cluster | Shock Russia | Shock Ukraine | Shock World | Interpretation |
|---|---|---|---|---|
| Cluster 0 | −418 | −3752 | +24,390 | Market substitution |
| Cluster 1 | −5456 | −89917 | +9,111,134 | Macro shock signature |
| Cluster 2 | +4,048 | −10567 | +2,678,931 | Asymmetric resilience |
The largest cluster groups products experiencing bilateral declines in Russia and Ukraine, but positive performance in world markets, consistent with market substitution dynamics. A second cluster captures the aggregate national pattern of strong bilateral contractions combined with global expansion, mirroring the macrolevel signature of the geopolitical shock. The third cluster identifies a small number of asymmetric cases in which exports decline sharply in Ukraine but increase in Russia alongside robust global growth, indicating exceptional resilience and successful reorientation.
Overall, the product-level evidence reinforces the aggregate findings by showing that the conflict’s impact is highly uneven across sectors. While some products experience severe bilateral contractions, others can redirect exports to alternative destinations. Importantly, this reallocation capacity differs markedly between Russia and Ukraine, with disruptions in the latter being substantially more pervasive and persistent.
Distributional properties of the shock and pre–post contrasts
To assess the statistical properties of the export shock associated with the onset of the Russia–Ukraine conflict, the analysis examines the distribution of the absolute change in exports between 2022 and 2021 at the product level. This shock is constructed as a cross-sectional variable, with one observation per product, capturing interproduct heterogeneity in the immediate impact of the conflict.
Table 6 reports the results of the Shapiro–Wilk and Kolmogorov–Smirnov tests applied to the shock distribution for Russia, Ukraine and the world market. In all cases, the null hypothesis of normality is strongly rejected. For Russia and Ukraine, extremely low p-values indicate highly skewed and heavy-tailed distributions, consistent with large negative shocks affecting a subset of products. The world market also exhibits non-normality, reflecting heterogeneous product-level adjustments.
Normality tests (shock 2022 vs2021)
| Market | n | Shapiro–Wilk W | p-value | Kolmogorov–Smirnov | p-value |
|---|---|---|---|---|---|
| Russia | 146 | 0.176 | 4.38E-25 | 0.435 | 1.25E-25 |
| Ukraine | 146 | 0.113 | 7.86E-26 | 0.502 | 1.26E-34 |
| World | 23 | 0.311 | 2.04E-09 | 0.481 | 2.05E-05 |
The rejection of normality directly informs the choice of inferential procedures. Given the presence of extreme values and distributional asymmetries, parametric mean-based tests are unsuitable as the primary tool for inference. Accordingly, the analysis relies on nonparametric methods. Table 7 presents the results of the Wilcoxon signed-rank test comparing average export levels in the preconflict (2017–2021) and postconflict (2022–2024) periods at the product level.
Pre- vs postconflict comparison
| Market | n | Pre mean | Post mean | Wilcoxon W | p-value Wilcoxon | t-test (t) | p-value t-test |
|---|---|---|---|---|---|---|---|
| Russia | 145 | 6.249 | 6.474 | 2.493 | 3.27E-08 | −1.47 | 0.142 |
| Ukraine | 145 | 614 | 148 | 7.00 | 0.123 | 1.09 | 0.279 |
| World | 22 | 469.986 | 728.844 | 32.0 | 0.00126 | −2.97 | 0.007 |
For Russia, the Wilcoxon statistic indicates a statistically significant change between periods, revealing substantial reallocation across products despite the absence of an aggregate collapse. The lack of significance in the corresponding t-test further confirms that the adjustment is driven by distributional shifts rather than uniform changes in mean export values.
For Ukraine, the Wilcoxon test does not reach conventional significance levels despite the sharp aggregate contraction reported earlier. This reflects the underlying export structure, characterized by many products with very small or zero trade flows, which concentrates the collapse in a limited number of items and reduces the power of paired product-level tests.
By contrast, results for the world market show a statistically significant postconflict expansion under both non- parametric and parametric tests, confirming that Ecuador’s global exports continued to grow after 2022. Overall, these findings indicate that the conflict generated highly asymmetric and non-normal product-level effects, producing restructuring in Russia, a concentrated collapse in Ukraine and continued expansion in world exports, thereby providing a robust basis for the subsequent elasticity, structural break and econometric analyses.
Elasticities, relative sensitivity and structural breaks
To assess whether contractions in exports to Russia and Ukraine reflect destination-specific shocks rather than global export dynamics, the analysis examines relative elasticities and structural breaks in bilateral export relationships. This approach evaluates the sensitivity of exports to each destination with respect to changes in Ecuador’s total exports to the world and identifies potential discontinuities associated with the onset of the 2022 conflict.
Table 8 reports annual percentage variations in exports to Russia, Ukraine and the world, together with the corresponding bilateral export elasticities.
Variations and elasticities
| Year | Exports | Variation (Δ%) | Elasticity Russia | Elasticity Ukraine | ||||
|---|---|---|---|---|---|---|---|---|
| World | Russia | Ukraine | Δ% World | Δ% Russia | Δ% Ukraine | |||
| 2017 | 19,092, 352 | 862,960 | 76,771 | – | – | – | – | – |
| 2018 | 21,627,978 | 868,516 | 64,497 | 13.28 | 0.64 | −15.99 | 0.05 | −1.20 |
| 2019 | 22,329,379 | 879,670 | 86,664 | 3.24 | 1.28 | 34.37 | 0.40 | 10.60 |
| 2020 | 20,226,568 | 919,618 | 107,867 | −9.42 | 4.54 | 24.47 | −0.48 | −2.60 |
| 2021 | 26,269,228 | 999,796 | 124,323 | 29.87 | 8.72 | 15.26 | 0.29 | 0.51 |
| 2022 | 35,380,362 | 994,340 | 34,406 | 34.68 | −0.55 | −72.33 | −0.02 | −2.09 |
| 2023 | 31,126,424 | 923,611 | 13,466 | −12.02 | −7.11 | −60.86 | 0.59 | 5.06 |
| 2024 | 34,420,386 | 898,287 | 17,863 | 10.58 | −2.74 | 32.65 | −0.26 | 3.09 |
For Russia, elasticities are generally small in magnitude and frequently change sign across years. In several cases, exports to Russia grow more slowly than global exports or move in the opposite direction, indicating an unstable and relatively inelastic response to global export dynamics. This pattern suggests that postconflict bilateral flows are shaped primarily by destination-specific factors – such as sanctions, payment restrictions and logistical frictions – rather than by overall export performance.
Elasticities for Ukraine display a markedly different behavior. They are highly volatile and often large in absolute value, particularly in 2022 and 2023, reflecting the sharp collapse in exports alongside continued growth or a moderate contraction in global exports. Because elasticities are computed over a very small postconflict export base, even modest absolute changes generate large relative responses. This extreme sensitivity underscores the fragility of the Ukrainian market and its decoupling from global export trends during the conflict period.
Importantly, elasticity estimates for both destinations remain well below unity in key postconflict years, especially in 2022. This indicates that exports to Russia and Ukraine contracted more sharply than total world exports, supporting the interpretation of a localized, conflict-associated shock rather than a response to global trade conditions.
To formally test for structural changes, the Chow test is applied using log–log specifications linking bilateral exports to world exports (Table 9). For Russia, the test does not reject parameter stability, indicating that export adjustments occurred within a relatively stable long-term relationship despite moderate postconflict declines. For Ukraine, the test strongly rejects stability, revealing a statistically significant structural break that reflects a collapse followed by limited recovery and confirms a fundamental disruption of the bilateral export trajectory.
Chow test results
| Destination | RSS_pooled | RSS_pre | RSS_post | F-statistic | p-value | Decision |
|---|---|---|---|---|---|---|
| Russia | 0.0155 | 0.0057 | 0.0044 | 1.07 | 0.423 | No structural break |
| Ukraine | 2.0897 | 0.1924 | 0.1240 | 11.21 | 0.023 | Structural break |
Log–log specification where exports to the destination are regressed on total world exports
These results are consistent with the visual evidence in Figures 1 and 2. While Russia shows only moderate divergence between pre- and postconflict trajectories, Ukraine exhibits a pronounced break, with post-2022 observations lying well outside the preconflict pattern. Overall, the combined evidence on elasticity and structural breaks confirms that the conflict generated destination-specific export disruptions of varying intensity, reinforcing the asymmetric nature of the geopolitical shock identified earlier.
The scatter plot compares log worldwide exports with log exports to Russia for pre-conflict years 2017 to 2021 and post-conflict years 2022 to 2024. The x-axis is labelled log Total exports worldwide, and the y-axis is labelled log Exports to Russia. Orange cross markers represent pre-conflict observations, and blue square markers represent post-conflict observations. Dashed and dash-dot trend lines show positive relationships for both periods. The pre-conflict trend line remains consistently higher than the post-conflict trend line, indicating lower exports to Russia relative to worldwide exports during the post-conflict period. A legend identifies the data groups and corresponding trend lines.Log–log relationship between exports to Russia and world exports
The scatter plot compares log worldwide exports with log exports to Russia for pre-conflict years 2017 to 2021 and post-conflict years 2022 to 2024. The x-axis is labelled log Total exports worldwide, and the y-axis is labelled log Exports to Russia. Orange cross markers represent pre-conflict observations, and blue square markers represent post-conflict observations. Dashed and dash-dot trend lines show positive relationships for both periods. The pre-conflict trend line remains consistently higher than the post-conflict trend line, indicating lower exports to Russia relative to worldwide exports during the post-conflict period. A legend identifies the data groups and corresponding trend lines.Log–log relationship between exports to Russia and world exports
The scatter plot displays the relationship between log worldwide exports and log exports to Russia for pre-conflict years 2017 to 2021 and post-conflict years 2022 to 2024. The x-axis is labelled log Total exports worldwide, and the y-axis is labelled log Exports to Russia. Orange cross markers indicate pre-conflict observations clustered around values above 11, while blue square markers represent post-conflict observations with lower values between approximately 9.5 and 10.5. A dashed orange regression line shows a gradual positive increase during the pre-conflict period, whereas a blue dash-dot regression line rises sharply across the post-conflict observations. The legend identifies the data periods and regression lines.Log–log relationship between exports to Ukraine and world exports
The scatter plot displays the relationship between log worldwide exports and log exports to Russia for pre-conflict years 2017 to 2021 and post-conflict years 2022 to 2024. The x-axis is labelled log Total exports worldwide, and the y-axis is labelled log Exports to Russia. Orange cross markers indicate pre-conflict observations clustered around values above 11, while blue square markers represent post-conflict observations with lower values between approximately 9.5 and 10.5. A dashed orange regression line shows a gradual positive increase during the pre-conflict period, whereas a blue dash-dot regression line rises sharply across the post-conflict observations. The legend identifies the data periods and regression lines.Log–log relationship between exports to Ukraine and world exports
Econometric intervention and panel model results
To quantify the impact of the Russia–Ukraine conflict on Ecuadorian exports while controlling for underlying trends and unobserved heterogeneity, two complementary econometric approaches are used:
semi-logarithmic intervention models estimated separately for each destination; and
a panel data model combining Russia and Ukraine with country-specific effects.
Together, these specifications allow identification of both destination-specific and average bilateral effects associated with the conflict.
Semi-logarithmic intervention models.
Table 10 reports the estimated coefficients of the semi-logarithmic intervention models for exports to Russia and Ukraine. The dependent variable is the natural logarithm of export values, and the specification includes a linear time trend and a postconflict dummy equal to one from 2022 onward.
Semi-log intervention model coefficients
| Country | Parameter | Estimate | Std. error | t | p-value |
|---|---|---|---|---|---|
| Russia | β (intercept) | −16.234 | 7.010 | −2.32 | 0.062 |
| β (annual trend) | 0.00189 | 0.00363 | 0.52 | 0.620 | |
| β (postconflict effect) | −0.1077 | 0.1109 | −0.97 | 0.366 | |
| Ukraine | β (intercept) | 139.412 | 26.799 | 5.20 | 0.002 |
| β (annual trend) | −0.0646 | 0.0139 | −4.62 | 0.004 | |
| β (postconflict effect) | −1.831 | 0.424 | −4.32 | 0.005 |
For Russia, neither the time trend nor the postconflict intervention dummy is statistically significant. Although the estimated postconflict coefficient is negative, indicating a reduction in exports after 2022, the effect is not statistically significant at conventional levels. This suggests that, once gradual temporal dynamics are accounted for, the conflict does not induce a statistically detectable structural shift in the export trajectory to Russia. Export variations, therefore, appear to reflect moderate stability combined with idiosyncratic fluctuations rather than a sharp regime change.
In contrast, the results for Ukraine reveal a markedly different pattern. The time trend is negative and statistically significant, indicating that exports to Ukraine were already declining prior to the conflict. More importantly, the postconflict intervention coefficient is large in magnitude and highly significant, implying a substantial proportional reduction in exports after 2022 even after controlling for the preexisting downward trend. This confirms that the conflict generated an additional, economically meaningful disruption to an already-weakening bilateral trade relationship.
These results are summarized in Table 11, which highlights the strong asymmetry between destinations: no statistically significant structural break is detected for Russia, while exports to Ukraine experience a pronounced and robust contraction attributable to the conflict.
Semi-log intervention summary
| Country | β (trend) | p | Signif. | β (postconflict) | p | Signif. | R² | Conclusion |
|---|---|---|---|---|---|---|---|---|
| Russia | 0.00189 | 0.620 | No | −0.108 | 0.366 | No | 0.191 | No evidence of a significant structural break |
| Ukraine | −0.0646 | 0.004 | Sí | −1.831 | 0.005 | Sí | 0.895 | Strong and statistically significant impact of the conflict |
Figures 3 and 4 visually reinforce these dynamics, showing a moderate downward shift for Russia and an abrupt collapse for Ukraine in the postconflict period.
The time series graph plots log exports to Russia from 2017 to 2024. The x-axis is labelled Year, and the y-axis is labelled log Exports to Russia. Orange cross markers represent actual data values, while a dashed line labelled Model Adjustment shows adjusted export trends across the years. A vertical dotted line marks interventions in 2022. Export values rise gradually between 2017 and 2021, decline after 2022, and remain below pre-2022 levels through 2024. A legend identifies Actual Data, Model Adjustment, and the 2022 intervention marker.Semi-logarithmic intervention model – Russia
The time series graph plots log exports to Russia from 2017 to 2024. The x-axis is labelled Year, and the y-axis is labelled log Exports to Russia. Orange cross markers represent actual data values, while a dashed line labelled Model Adjustment shows adjusted export trends across the years. A vertical dotted line marks interventions in 2022. Export values rise gradually between 2017 and 2021, decline after 2022, and remain below pre-2022 levels through 2024. A legend identifies Actual Data, Model Adjustment, and the 2022 intervention marker.Semi-logarithmic intervention model – Russia
The time series graph plots log exports to Ukraine from 2017 to 2024. The x-axis is labelled Year, and the y-axis is labelled log Exports to Ukraine. Cross markers represent actual data values, and a dashed line labelled Model Adjustment displays adjusted export trends. A vertical dotted line identifies interventions in 2022. Export values remain relatively stable from 2017 to 2021 before dropping sharply in 2022 and remaining lower through 2024. The graph shows a partial recovery after 2022, although values stay below pre-2022 levels. A legend identifies Actual Data, Model Adjustment, and the 2022 intervention marker.Semi-logarithmic intervention model – Ukraine
The time series graph plots log exports to Ukraine from 2017 to 2024. The x-axis is labelled Year, and the y-axis is labelled log Exports to Ukraine. Cross markers represent actual data values, and a dashed line labelled Model Adjustment displays adjusted export trends. A vertical dotted line identifies interventions in 2022. Export values remain relatively stable from 2017 to 2021 before dropping sharply in 2022 and remaining lower through 2024. The graph shows a partial recovery after 2022, although values stay below pre-2022 levels. A legend identifies Actual Data, Model Adjustment, and the 2022 intervention marker.Semi-logarithmic intervention model – Ukraine
Panel data model with country effects
To assess the average bilateral impact of the conflict while accounting for structural differences between destinations, a panel data model is estimated using annual export data for Russia and Ukraine over 2017–2024, yielding a balanced panel of 16 observations. Tables 12 and 13 compare pooled and fixed-effects specifications.
Coefficient estimates of the panel model under fixed effects and pooled specifications
| Parameter | FE (with country dummy) | FEp-value | RE/pooled | REp-value |
|---|---|---|---|---|
| Constant | −76.252 | 0.713 | −77.694 | 0.912 |
| Year | 0.0447 | 0.663 | 0.0447 | 0.898 |
| Postconflict | −0.905 | 0.080 | −0.905 | 0.584 |
| Ukraine dummy | −2.884 | 0.000 | – | – |
Model fit statistics and global tests for the panel estimations
| Model | R² | Adjusted R² | Global F | p(F) | N obs |
|---|---|---|---|---|---|
| FE (country dummy) | 0.924 | 0.906 | 48.92 | 5.29e-07 | 16 |
| RE/pooled | 0.053 | −0.093 | 0.36 | 0.702 | 16 |
The pooled model exhibits very low explanatory power and yields no statistically significant coefficients, indicating that the assumption of homogeneity across destinations is inappropriate. By contrast, the fixed-effects model achieves a substantially higher goodness of fit and captures pronounced structural differences between Russia and Ukraine. In this specification, the country dummy for Ukraine is large and highly significant, confirming that export levels to Ukraine are structurally lower than those to Russia throughout the sample period. The postconflict dummy is negative and marginally significant, indicating that conflict is associated with a contraction in bilateral exports, after controlling for country-specific effects.
Although the Hausman test does not reject the null hypothesis favoring random effects, the random-effects (or pooled) model performs poorly and fails to capture observed heterogeneity. Given the small number of cross-sectional units and the strong structural differences between destinations, the fixed-effects specification is retained as the preferred model.
Diagnostic tests and robustness
Diagnostic tests are conducted to assess the validity of the fixed-effects panel model. Tests for heteroskedasticity (Breusch–Pagan and White) provide, at most, weak evidence of heteroskedasticity, while the Wooldridge test does not detect first-order autocorrelation in the residuals. To ensure robustness, the final estimates are reported with heteroskedasticity-consistent (HC1) standard errors, as shown in Table 14.
Fixed-effects panel model (robust standard errors)
| Variable | Coefficient | Robust standard error | t | p-value |
|---|---|---|---|---|
| Constant | −76.252 | 136.23 | −0.56 | 0.589 |
| Year | 0.0447 | 0.100 | 0.45 | 0.662 |
| Postconflict | −0.905 | 0.491 | −1.84 | 0.087 |
| Ukraine dummy | −2.884 | 0.261 | −11.06 | 0.000 |
| N | 16 | |||
| R² | 0.924 | |||
| Adjusted R² | 0.906 |
The results based on robust standard errors confirm the main findings of the baseline panel model. The Ukraine dummy remains large and highly statistically significant, indicating that export levels to Ukraine are structurally lower than those to Russia throughout the sample period. The postconflict coefficient remains negative and marginally statistically significant, suggesting an average contraction in bilateral exports after controlling for country-specific effects. By contrast, the common time trend remains statistically insignificant, indicating that export dynamics are not driven by a shared temporal pattern.
Overall, the econometric evidence is consistent with the descriptive, distributional and product-level analyses. The post-2022 bilateral contraction is driven mainly by the collapse of exports to Ukraine, while the effect on exports to Russia is more moderate and does not appear as a clear structural break once underlying trends are considered.
Discussion
Asymmetric trade responses to geopolitical shocks in a small open economy
The results provide clear evidence of a highly asymmetric impact of the Russia–Ukraine conflict on Ecuador’s export performance, showing how major geopolitical shocks propagate unevenly across destination markets in small open economies. By linking the econometric findings with the international trade and geopolitical risk literature, this section clarifies the main transmission mechanisms and situates Ecuador’s experience within broader theoretical debates.
Asymmetric exposure to geopolitical shocks. The most salient result is the severe and persistent collapse of exports to Ukraine, in sharp contrast to the more moderate adjustment observed in exports to Russia. The contraction of Ecuadorian exports to Ukraine – exceeding 80% – together with the statistically significant structural break identified by the Chow test and the intervention model, reflects a near-total disruption of trade channels. This pattern is consistent with evidence showing that armed conflict abruptly undermines trade connectivity, payment systems and import capacity in directly affected economies (IFPRI, 2023; Steinbach, 2023). The magnitude of the Ukrainian shock also exceeds the bilateral trade contractions commonly associated with geopolitical shock, often estimated at 30%–40% and interpreted as implicit trade barriers (Mulabdic et al., 2025). This suggests that when geopolitical shock escalates into full-scale war, trade effects may become nonlinear and substantially more destructive than standard risk-based models imply.
By contrast, exports to Russia display partial resilience. Although export values decline after 2022, the absence of a statistically significant structural break indicates that the bilateral relationship remains broadly intact. This finding aligns with studies showing that larger economies have greater capacity to reorganize logistics, redirect trade routes and sustain import demand despite sanctions and financial frictions (García-Herrero and Tan, 2023). For Ecuador, this implies that geopolitical shocks do not translate mechanically into a trade collapse; their effects depend on the destination market’s institutional and economic characteristics.
Preexisting vulnerabilities and amplification effects. Differences in preconflict trajectories help explain these divergent outcomes. Exports to Ukraine had already been declining before 2022, whereas exports to Russia remained relatively stable. This supports the argument that geopolitical shocks tend to amplify preexisting vulnerabilities rather than generate disruptions ex nihilo (Baldwin and Freeman, 2022; Freund et al., 2022). In this case, the conflict accelerated structural weaknesses in Ecuador–Ukraine trade while exerting a more limited effect on the more established trade relationship with Russia.
Product-level heterogeneity and trade reorientation. The product-level analysis reveals selective resilience and evidence consistent with trade reorientation. Several strategic export categories – such as flowers, coffee-based products, preserved foods and seafood – experienced bilateral losses in Russia and Ukraine while also recording stronger performance in other destinations or in aggregate world markets. Where these bilateral contractions coincide with export expansion in alternative markets, the pattern is consistent with trade diversion; where only aggregate world growth is observed, the evidence is more appropriately interpreted as export resilience or broader market expansion rather than direct reallocation (Fan et al., 2025).
These results suggest that Ecuadorian exporters partially reallocated supply to alternative destinations, thereby mitigating aggregate losses. This flexibility supports arguments that firm-level adaptability and market diversification can buffer geopolitical shocks in open economies (Althaqafi, 2025). At the same time, the uneven distribution of this adjustment capacity shows that resilience is sector-specific rather than universal.
Elasticities, structural breaks and localized shocks. Elasticity estimates reinforce the interpretation of a localized geopolitical shock. Exports to Russia exhibit low and unstable elasticities relative to global exports, indicating increasing decoupling from Ecuador’s overall export performance. In Ukraine, elasticities become highly volatile after 2022, reflecting both the collapse of trade flows and the narrow postconflict export base. Structural break tests confirm that only Ukraine experiences a statistically significant regime shift, underscoring that geopolitical shocks do not affect all trading partners uniformly.
Small open economies under geopolitical fragmentation. Finally, the coexistence of bilateral contractions with strong growth in Ecuador’s global exports is inconsistent with explanations based solely on generalized loss of competitiveness or a broad global trade slowdown. Instead, the findings align with the literature on geopolitical fragmentation, which emphasizes that contemporary trade shocks are increasingly destination-specific and politically mediated [Bown, 2023; World Trade Organization (WTO), 2023]. From a theoretical perspective, the results show how small open economies can face high bilateral vulnerability while still preserving aggregate resilience through diversification and market reorientation, a tension that is central to current debates on trade resilience amid rising geopolitical shock.
Policy and strategic implications
Taken together, the results yield clear policy and strategic implications for small open economies exposed to geopolitical shocks. First, armed conflict can generate severe and persistent trade ruptures with directly affected partners, as illustrated by the collapse of Ecuador’s exports to Ukraine. Second, large sanctioned economies may remain viable – though riskier – export destinations, as reflected in the partial resilience of exports to Russia. Third, global market diversification can partially offset bilateral losses, underscoring the role of export flexibility in mitigating localized disruptions. More broadly, Ecuador’s experience confirms the structural vulnerability of small open economies to externally driven geopolitical shocks, consistent with the literature on trade risk exposure and terms-of-trade volatility (CEPAL, 2023; Shafiullah et al., 2020).
Rather than triggering a generalized export crisis, the Russia–Ukraine conflict appears to have accelerated a process of reactive trade reorientation. This highlights the need for proactive trade policies that strengthen market intelligence, diversification strategies and institutional support to reduce future geopolitical exposure.
From a strategic and managerial perspective, the findings show that geopolitical shocks generate asymmetric effects across destinations and therefore require differentiated responses by sector and market. The contrasting outcomes for Russia and Ukraine suggest that uniform export strategies are ineffective. In markets such as Russia, where no structural break is detected, maintaining a calibrated commercial presence and adjusting product portfolios may remain viable. In contrast, markets facing abrupt collapse, such as Ukraine, require reorientation toward substitute destinations and caution regarding sunk investments with limited short-term recovery prospects.
The evidence also highlights the importance of strengthening firm-level capabilities in risk assessment, trade intelligence and logistical resilience. Firms better able to monitor transport conditions, financial constraints and cost volatility are more capable of adjusting contracts, pricing and destination choices under stress. At the same time, the ability of several sectors to expand in other markets suggests that commercial flexibility – supported by trade promotion institutions and product upgrading – is a key source of resilience.
Overall, the results indicate that improving export diversification, strengthening coordination between firms and trade promotion agencies and explicitly incorporating geopolitical shocks into strategic decision-making are central to enhancing the resilience of export-oriented firms, particularly SMEs, in an increasingly volatile and geopolitically fragmented trade environment.
Limitations and directions for future research
While this study provides robust empirical evidence on the effects associated with the Russia–Ukraine conflict on Ecuadorian exports, several limitations related to data availability and methodological scope should be acknowledged.
First, the analysis relies on annual trade data for 2017–2024, which limits the ability to capture short-term adjustments and high-frequency responses to the onset of the conflict. Future research using monthly or shipment-level data could better trace the temporal propagation of geopolitical shocks and identify more immediate effects on logistics, prices and demand.
Second, the study adopts an aggregate product–destination approach and does not incorporate firm-level export data, which prevents analysis of heterogeneity in adjustment capacity across firms. Microlevel studies could examine whether resilience varies systematically with firm size, diversification strategies, technological intensity or access to finance.
Third, although the econometric analysis identifies structural breaks and significant changes in export trajectories, it cannot fully disentangle the effects of the conflict from other contemporaneous shocks, such as postpandemic recovery dynamics or international price volatility. Future research could strengthen identification through counterfactual approaches, including synthetic control methods, event-study designs or dynamic difference-in-differences frameworks where suitable comparison groups are available.
Fourth, the panel analysis is limited to two destination markets, which constrains statistical power and external validity. Extending the analysis to additional countries affected, directly or indirectly, by geopolitical tensions would enable a broader comparative assessment of trade adjustment patterns.
Finally, the study focuses on exports and does not incorporate import disruptions or indirect logistical effects, such as changes in freight costs, route availability or transport risk premia. Integrating import data, logistics indicators and simulation-based approaches would provide a more comprehensive assessment of trade vulnerability under geopolitical uncertainty.
Overall, these limitations point to the need for more granular, high-frequency and multidimensional analyses to advance understanding of how geopolitical shocks reshape international trade in an increasingly fragmented global environment.
Conclusions
This study shows that the Russia–Ukraine conflict was associated with sharply asymmetric effects on Ecuador’s external trade. Exports to Ukraine experienced an abrupt and structural collapse from 2022 onward, with declines exceeding 80%, a statistically significant structural break and no evidence of sustained recovery. By contrast, exports to Russia declined more moderately and did not exhibit a structural rupture, remaining relatively stable despite the logistical and financial constraints associated with the conflict.
The contrasting evolution of Ecuador’s exports to the rest of the world indicates that these bilateral contractions are not consistent with a generalized loss of export competitiveness or a broad global trade downturn. Instead, the results are consistent with a localized geopolitical shock, reinforcing recent evidence on trade fragmentation and the heightened exposure of small open economies to externally generated disruptions. Product-level findings further suggest that some sectors partially mitigated bilateral losses by expanding into alternative markets, pointing to adaptive capacity and supply-side flexibility.
The econometric results support this interpretation. Ukraine displays a large, statistically significant and persistent postconflict effect, whereas exports to Russia adjust without a detectable structural break. The panel estimates likewise indicate that the average bilateral contraction observed after 2022 is driven mainly by the collapse of exports to Ukraine rather than by changes in trade with Russia.
Overall, the findings indicate that the conflict affected specific export destinations rather than triggering a generalized export crisis. They underscore the importance of destination diversification, logistical resilience and systematic monitoring of geopolitical shocks to sustain trade performance amid increasing global instability.
During the preparation of this manuscript, the authors used ChatGPT (OpenAI) only for language editing, grammar correction, wording refinement, and improvement of academic writing style. The tool was not used to generate the article’s scientific content, formulate the methodology, produce or analyze results, derive conclusions, or create the proposed decision framework. All conceptual development, methodological design, mathematical formulation, data processing, interpretation of findings, and final intellectual content were carried out, reviewed, and approved by the authors. The authors take full responsibility for the accuracy, integrity, and originality of the final manuscript.
Funding
This research received no external funding.
