The Real-Time Impact of the War on Russian Imports: A Synthetic Control Method Approach

Abstract

In response to the invasion of Ukraine, the EU and most other advanced economies imposed extensive sanctions on Russia, intending to harm its production capabilities and hinder its economic activities by restricting its access to international trade and financial markets. This paper develops an empirical framework based on the synthetic control method to assess the impact of the war and the following sanctions on bilateral and sectoral exports to Russia almost in real time. The war and the following sanctions reduced aggregate exports to Russia by a third between March and December 2022, with the effects being stronger for sanctioning countries than for non-sanctioning ones, albeit with substantial country-level heterogeneity within each group. Exports to Russia in high-tech sectors – relatively more targeted by trade sanctions – have been disproportionately affected.

1. Introduction

Following the Russian invasion of Ukraine in February 2022, the EU and several countries (the USA, the UK, Japan, Canada, and Australia, among others) imposed extensive sanctions on Russia.¹ The restrictions restrained trade in goods and services, affected the financial sector (e.g., exclusion of most local banks from SWIFT), and even targeted specific individuals related to the Russian government, military, or state-owned enterprises. Moreover, several multinationals, including large transport and logistic firms, exited the Russian market or curtailed local operations.

Providing a credible quantitative assessment of the impact of the war and sanctions on the Russian economy remains complex because local institutions have discontinued the publication of several official statistics (including on monthly bilateral merchandise trade). Available figures and anecdotal information point to a significant blow to the Russian economy (Sonnenfeld et al., 2022), with some sectors recording dramatic contractions in activity (e.g., car production dropped 77% in September 2022 with respect to the previous year). The IMF projections before the conflict indicated Russia's GDP would expand by 3.8% in 2022, whereas actual data revealed a contraction of 2.1%. Despite the significant negative revision, it was milder than the initial estimates reported following the war outbreak and the first sanction packages. This evidence fueled the debate surrounding the effectiveness of the sanctions.

In this context, the evolution of Russia's imports provides a valuable metric to glean the implications of the conflict and sanctions on its economy as they relate tightly to short- and long-run growth.² Russian domestic production relies on foreign intermediate and capital goods in many sectors (Grzegorczyk et al., 2022). In addition, reducing available foreign goods may decrease consumer welfare as it will increase prices (Broda and Weinstein, 2004). However, evaluating the changes in nominal trade flows requires an appropriate counterfactual to account for the prevailing dynamics of international trade. This aspect becomes particularly crucial when examining the period following the outbreak of the conflict, which was marked by high inflation and still-growing trade volumes at the global level.

This paper contributes to the debate on the real consequences of the war and the following sanctions by developing an empirical framework to assess their impact on Russian imports almost in real time.³ This prompt and easy-to-update assessment could inform the debate among policymakers concerning the design of optimal sanctioning policies.

To the best of our knowledge, this paper is the first one evaluating the impact of the war and the following sanctions on exports to Russia.⁴ Another recent contribution, closely related to this paper, focuses instead on the impact of the war on exports from Russia and Ukraine using an event-study approach (Steinbach, 2023).

We find that the war and the following sanctions caused a substantial drop in exports to Russia from March to December 2022. Initially, both shipments from sanctioning and non-sanctioning countries were considerably affected. Then, when export bans and restrictions became more binding,⁵ exports from sanctioning countries did not rebound and stood below 50% of the corresponding benchmark. Conversely, exports from non-sanctioning countries bounced back, substantially exceeding the no-war counterfactual. Sectoral decomposition of exports shows that high-tech imports – such as motor vehicles and electronics industries, relatively more targeted by trade sanctions than other sectors – have been impacted the most (−60% compared to their counterfactual). Considering a naive counterfactual, such as the pre-war level of Russian imports, instead of the synthetic control, would lead to severely underestimating the drop in Russian imports by about two-thirds.

The rest of this paper is structured as follows. Section 2 describes the synthetic control method in more detail. Section 3 describes our data. Section 4 presents the main results concerning the impact of the war and following sanctions on Russian imports. Section 5 concludes.

2. The Synthetic Control Method

This section provides an overview of the synthetic control method. In subsection 2.1, we summarize in a non-technical way the features of such a methodology and highlight the main differences using a standard gravity framework. In subsection 2.2, we detail the methodology, especially the construction of the synthetic counterfactual.

2.1 Overview of the Methodology

We use the synthetic control method (Abadie and Gardeazabal, 2003; Abadie et al., 2010, 2015) to construct a no-war counterfactual for monthly Russian imports.⁶ The synthetic control method is a statistical tool used to estimate the causal impact of a treatment or intervention – in our case, the war and the following sanctions. The gist of the synthetic control method lies in its ability to create an artificial comparison term that is as similar as possible to the unit receiving the treatment, allowing researchers to make causal inferences even in non-experimental settings. More formally, the synthetic control method provides a data-driven procedure to construct a robust counterfactual from a weighted average of eligible control units.

Gravity models represent a workhorse tool for trade economists (Head and Mayer, 2014), also for the analysis of trade shocks. However, for specific research designs, the features of the synthetic control method might be more suitable. For instance, in the case of endogenous trade policy shocks, such as the war and following sanctions, the gravity coefficients might be highly sensitive to the chosen sample, which can be concerning when choosing a proper counterfactual (Saia, 2017). Without a proper exogenous shock, a standard gravity model may represent “a regression of endogenous variables on endogenous variables” (De Benedictis and Taglioni, 2011). Unlike a standard gravity framework, the synthetic control method may alleviate concern related to non-random assignment of the treatment (Saia, 2017) and the presence of time-varying heterogeneity (Xu, 2017).

In addition to the above issues related to the research design, the synthetic control method yields an immediate, time-varying, and easy-to-interpret measurement of the overall effect of a policy change, where a gravity equation provides a coefficient, indicating its partial effect (Addessi et al., 2019). Furthermore, a static gravity framework may fail to capture a time-varying impact. As we show in Section 4, the average effect of the war and related sanctions on exports to Russia is far from the actual time-varying value. For all these reasons, the synthetic control methodology has been used widely in the international trade literature (e.g., Billmeier and Nannicini, 2013; Saia, 2017; Breinlich et al., 2020; Douch and Edwards, 2022; Adarov, 2023) and to analyze the effects of sanctions on GDP (Gharehgozli, 2017).

In our setting, the war and the following sanctions represent the treatment, whereas the treated units are merchandise exports to Russia from each partner. For such trade flows, the synthetic control method builds a corresponding hypothetical no-war counterfactual, derived from a weighted average of third countries’ imports from the same partner. The above weights minimize the distance between the counterfactual exports and the observed ones before the war. In other words, the objective is to find a combination of export flows that may accurately track the targeted export flow before the war. Hence, after the treatment, one may carry over time such weights and use the untreated flows to compute the no-war hypothetical Russian imports from the given partner.

When aggregated, these distinct counterfactual imports constitute the total exports to Russia that would have been observed in a no-war scenario. At each point in time, the total effect of the war and following sanctions on Russian imports is the wedge between the observed Russian imports and their respective counterfactual. Notice that we can use this bottom-up approach also to disentangle the evolution of exports from distinct groups of countries (e.g., sanctioning vs. non-sanctioning countries), or for specific sectors, as we compute a counterfactual for each aggregate and sectoral trade flow to Russia.

2.2 Building the Counterfactual

We consider exports from country i to Russia, X _i→Russia as the treated unit. For such trade flow, there are J ⁱ untreated trade flows that can be used to construct the counterfactual. In line with the literature (e.g., Head and Mayer, 2014; Saia, 2017), we assume that the exports from country i to j are some function of k predictors, Z^i→j = Z _1,i→j, …, Z _k,i→j. We denote as Zⁱ the k × J ⁱ-dimensional matrix of predictors of untreated trade flows originating from country i to the J ⁱ destinations. By analogy, Z ^i→Russia represents the vector of predictors of the trade flows from country i to Russia. Let T ₀ = 2022M02 represent the starting time of the war.

We are interested in obtaining the effect of war and sanctions on trade flows to Russia, τ _i→Russia,t, at time t, i.e.,

(1)$$\tau _{i\to Russia, t} = X_{i\to Russia, t}^W -X_{i\to Russia, t}^{NW} $$

for t > T ₀, where $X_{i\to Russia, t}^W $ are the exports from i to Russia under war at time t and $X_{i\to Russia, t}^{NW} $ are the exports from i to Russia at time t under no war. It follows that the observed trade exports to Russia correspond to the war scenario, i.e., $X_{i\to Russia, t} = X_{i\to Russia, t}^W $ for t > T ₀. Hence, to obtain τ _i→Russia,t, we need to compute $X_{i\to Russia, t}^{NW} $. Given a set of appropriately chosen weights, $X_{i\to Russia, t}^{NW} $ is given by

(2)$$\hat{X}_{i\to Russia, t}^{NW} = \mathop \sum \limits_{\,j = 1}^{J^I} w_{i, j}X_{i\to j, t}$$

where w _i,j represents the weight for the jth untreated export from i to j, with w _i,j ∈ [0, 1] and $\sum\nolimits_j {w_{i, j} = 1} $. It follows that the synthetic counterpart for τ _i→Russia,t is given by

(3)$$\hat{\tau }_{i\to Russia, t}^{NW} = X_{i\to Russia, t}^W -\hat{X}_{i\to Russia, t}^{NW} $$

Let v_i = (v _i,1, …, v _i,k) denote a set of weights assigned to each of the k for the flows originating from country i. The problem of finding the optimal weights w _i,j and v_i can then be written as

(4)$$\eqalign{& \mathop {\min }\limits_{( {{\vector \bf w}^i, {\vector V}^i} ) } ( {{\boldsymbol Z}^{i\to Russia}-{\bf \vector w}^i{\boldsymbol Z}^i} ) ^{\prime}{\bf \vector V}^i( {{\boldsymbol Z}^{i\to Russia}-{\vector \bf w}^i{\boldsymbol Z}^i} ) \cr & {\rm s}.{\rm to}\,{\boldsymbol w}^i\in [ {0, \;1} ] ^J\,{\rm and}\,\mathop \sum \limits_{\,j = 1}^{J^i} w_{i, j} = 1} $$

where ${\bf \vector V}^i$ represents a k-dimensional diagonal matrix obtained from the k-dimensional vector v_i, and ${\bf \vector w}^i$ represents a J ⁱ-dimensional vector of weights w _i,j.

To obtain $\hat{X}_{i\to Russia, t}$, we repeat the described procedure for all origin countries i′ for which exports to Russia and other $J^{{i}^{\prime}}$ destinations are available. By aggregating over all origins i, we obtain the counterfactual total exports to Russia, $\hat{X}_{\to Russia, \;t}^{NW} $, as

(5)$$\hat{X}_{\to Russia, \;t}^{NW} = \mathop \sum \limits_i \hat{X}_{i\to Russia, t}$$

Our design also helps us delve into sectoral trade flows to assess the impact of the war and the following sanctions on specific goods (e.g., high-tech items exports). To this end, for the sector-level analysis, we run an ad-hoc synthetic control calibration that features industry-level predictors in the Zⁱ matrix, and compute the weights needed to obtain the counterfactual sectoral exports to Russia. We detail the data used in the analysis in Section 3.

3. Data

The empirical analysis relies on the Trade Data Monitor panel of monthly bilateral and sectoral trade flows for January 2020–December 2022.⁷ We focus on the top 60 exporters to Russia,⁸ and for each one of them, we build counterfactual exports to Russia by appropriately weighting its exports to 150 trading partners, i.e., our donor pool. To reduce the confounding effect of the war on counterfactual exports, we exclude from the donor pool those countries directly affected by the conflict or indicated as potential hubs for trade with Russia (Ukraine, Turkey, Kazakhstan, Belarus, Serbia, Armenia, Uzbekistan, Kyrgyzstan, Georgia, Azerbaijan).⁹ When a country's exports to Russia before the war are not close enough to their counterfactual, we drop the exporting country from the analysis.¹⁰ Our final sample includes 50 exporters whose synthetic exports to Russia are obtained by combining, on average, the flows to four other partners in the donor pool (see the table in the Appendix). Overall, this sample covers more than 80% of total Russian imports before the war.¹¹

We consider January 2020–January 2022 as the pre-treatment period and February 2022–December 2022 as the treatment period. In line with the literature on the synthetic control method approach and international trade, we consider the outcome of interest (i.e., export flow to Russia) to be a function of some predictors. In particular, the predictors used in the synthetic control, sourced from the Dynamic Gravity Dataset (Gurevich and Herman, 2018) and Trade Data Monitor are the following:

1. 1. the sum of the logs of real GDPs of the trading partners averaged between 2015 and 2019;

2. 2. a linear approximation of the multilateral resistance between the trading partners (Baier and Bergstrand, 2009) based on distance, common language, and contiguity;¹²

3. 3. the average value of the bilateral trade flows between January 2020 and January 2022;

4. 4. the export shares from the origin country to the trading partner of the four most important exporting sectors from the origin country to Russia in 2021;

5. 5. a dummy for preferential trade agreements covering merchandise trade between 2015 and 2019.

The first three predictors broadly follow Saia (2017) and are commonly used in international trade within the gravity framework (Head and Mayer, 2014) Instead, the sectoral export shares help us select importers from the donor pool whose sourcing of foreign goods is as close as possible to that of Russia. The last predictor allows us to control for the impact of policy variables affecting merchandise exports through the reduction of trade barriers.

In addition to multilateral resistance and preferential trade agreements, in the sectoral level analysis, we use three industry-level variables that allow us to take further into account heterogeneity at the sector level. In the calibration of the industry-level synthetic control, we use the following variables as predictors, instead of the corresponding aggregate variables:

1. 1. the sum of the logs of sectoral gross output of the trading partners averaged between 2015 and 2019, sourced from Eora Global Supply Chain Database;

2. 2. the average value of the sectoral–bilateral trade flows between January 2020 and January 2022;

3. 3. the export share in 2021 of the given sector from the origin country to the trading partner.

4. Results

4.1 The Effects of the War and the Following Sanctions on Aggregate Russian Imports

Figure 1 compares the evolution of observed exports to Russia with the synthetic counterpart. Before the conflict, the synthetic control matches observed exports to Russia. Following the invasion, we observe a sharp decoupling between the two series. Actual exports plummeted in March and April 2022 while the synthetic control increased, generating the widest gap between them in April (actual flows 55% below their counterfactual). In the latest data points, actual imports recorded a sharp rebound, suggesting an ongoing trend of import substitution and reorganization of logistics, payment systems, and supply chains. In December, exports to Russia stood 16% below their counterfactual, still 5% below their pre-war February level. This evidence suggests that a naive counterfactual, such as that implied by a no-change scenario, would underestimate the drop in Russian imports by about two-thirds.

Figure 1. Effects of war and sanctions on exports to Russia

Note: On the vertical axis, we report observed and synthetic exports to Russia in USD bn.

Exports from both sanctioning and non-sanctioning countries sharply contracted (Figure 2). Initially, exports from sanctioning economies were 66% lower than in the scenario without the war. Flows from non-sanctioning countries were markedly below their counterfactual (−43%). This suggests that export bans and other trade sanctions were not the only drivers at play in determining the sharp contraction of exports to Russia, at least at the initial stage of the war. Other factors may be critical (e.g., restrictions on international payments, logistics disruptions, the contraction of Russian demand, and the exit of multinationals from the Russian market). In the following months, exports from non-sanctioning countries strongly rebounded, while those from sanctioning countries remained subdued. In the last quarter of 2022, flows from sanctioning countries were 53% below the estimated no-war scenario. Instead, exports from non-sanctioning economies exceeded their counterfactual by 31%.¹³ Hence, the stabilization of the aggregate export losses observed in the last few months hides a more complex picture at the country group level.

Figure 2. Effects of war and sanctions on exports to Russia, sanctioning vs. non-sanctioning countries

Note: On the vertical axis, we report the percentage difference between the observed and counterfactual exports to Russia for sanctioning and non-sanctioning countries.

4.2 Country- and Sector-Level Evidence

The impact of the war has been very heterogeneous, even within sanctioning and non-sanctioning groups. In the former group, the US recorded the sharpest fall in exports to Russia (almost 90% below their counterfactual). Most sanctioning countries narrowed the initial gaps from their no-war counterfactual (Figure 3). Among non-sanctioning countries, Turkey is a stark outlier: after contracting in March and April, its exports to Russia rebounded sharply, more than doubling the no-war counterfactual in the last quarter of 2022.

Figure 3. Effects of war and sanctions on exports to Russia by origin country (March–April vs. October–December)

Note: Differences from the counterfactual are in percentage points.

The war has disproportionately affected exports to Russia in the motor vehicle and electronics sectors due to sanctions targeted at specific products related to these industries (e.g., dual-use items). In the first few months of the war, exports in these industries experienced a 60% reduction compared to the scenario in the absence of war (Figure 4). By the end of 2022, they remained nearly 30% below their respective counterfactual levels.¹⁴ This result is almost entirely driven by the reduction in exports from sanctioning countries, which is only partly offset by the increase in exports from non-sanctioning countries (Figure 5), additional evidence of the effectiveness of export bans and restrictions. Moreover, the sharp drop in these high- and mid-tech flows is likely to hurt the Russian economy in the short term and hinder its potential long-term growth. Russia is highly dependent on imports of such products, and substitution with domestic products seems not viable because of the high technological content.¹⁵

Figure 4. Effects of war and sanctions on manufacturing exports to Russia (March–April vs. October–December)

Note: Differences from the counterfactual are in percentage points.

Figure 5. Effects of war and sanctions on exports to Russia in selected sectors

Note: For sanctioning (right-hand-side axis, inverted scale) and non-sanctioning countries (left-hand-side axis), we report the average monthly difference in the last quarter of 2022 between the observed and synthetic exports to Russia in the given sector.

4.3 Country- and Sector-Level Evidence

Distinguishing trade diversion from potential triangulation attempts and assessing their relative importance would require more information than currently available (e.g., firm-level data). Nonetheless, disaggregated data at our disposal may provide some suggestive evidence regarding the possible presence of the aforementioned phenomena. Since sanctions imposed on exports to Russia have targeted items with relatively high technological content, we focus on high-tech products in this section.¹⁶ More specifically, we analyze the pattern of these flows from sanctioning countries to Turkey, given that its merchandise exports to Russia are well above their corresponding counterfactual in the last quarter of 2022 (as shown in Figure 6).

Figure 6. Effects of war and sanctions in the high-tech sectors, the case of Turkey

Notes: Left panel, exports from Turkey to Russia. Right panel, exports from sanctioning countries to Turkey. On the vertical axis, we report observed and synthetic trade flows in USD bn.

The first evidence is that Turkish exports to Russia in the high-tech sectors have increased considerably since April, after an initial decline following the outbreak of the war (Figure 6, left panel). High-tech sectors correspond to Chapters 84, 85, 87, 88, 89, and 90 of the Harmonized System.¹⁷ A necessary condition to support the evidence that exporters may use Turkey as a hub to circumvent sanctions is an increase in sanctioning countries’ exports to Turkey with respect to a no-war counterfactual in the high-tech sectors. The right panel of Figure 6 shows that the flows to Turkey associated with this product category are indeed above the corresponding counterfactual. The two pieces of evidence are consistent with the hypothesis that Turkey may represent a hub to circumvent sanctions, in line with other recent analyses (Borin et al., 2023b; Chupilkin et al., 2023).

Furthermore, we assess the sources of the rise in Turkish high-tech imports, with reference to the sanctioning countries group. We find a sizable rise in imports from Poland, South Korea, and Italy, larger than the corresponding counterfactual by around 30% (Figure 7, left panel). In absolute terms, imports from Germany exceed their counterfactual by around 1 USD bn (Figure 7, right panel), reflecting the sectoral specialization of the country.

Figure 7. Effects of war and sanctions in the high-tech sectors, the case of Turkey

Notes: Differences from the counterfactual are in percentage points in the left panel and in USD bn in the right panel. Both panels refer to the last quarter of 2022. High-tech sectors correspond to Chapters 84, 85, 87, 88, 89, and 90 of the Harmonized System.

4.4 Robustness Checks

To evaluate the robustness of our analysis, we perform an “in-time” placebo test, changing the date of the shock (i.e., the war) to June 2021. The test shows that the observed series and its synthetic counterpart are really close, without any clear decoupling since June 2021 (Figure 8). Furthermore, we rerun the analysis, including the observations starting from January 2015, without any major change in the results.¹⁸

Figure 8. “In-time” placebo effect (shock in June 2021)

Notes: Left panel, aggregate Russian imports, observed and synthetic exports to Russia in USD bn. Right panel, sanctioning vs. non-sanctioning countries, percentage difference between the observed and counterfactual exports to Russia for sanctioning and non-sanctioning countries.

5. Conclusions

We apply the synthetic control method to estimate trade flows in a no-war counterfactual using bilateral, sectoral trade statistics at monthly frequency. We find that the war and the following sanctions have significantly impaired the ability of Russia to import goods from sanctioning countries by drastically hitting some critical, high-technology sectors. Even if exports from non-sanctioning economies exceeded their no-war counterfactual in the last available periods underlying our analysis, suggesting that some trade diversion is taking place, the overall capability to source specific products has been crippled by the imposed restrictions. The substantial shortage of key inputs and consumer goods is likely to reduce substantially Russian welfare and growth in the short and long run.

Additional research on the impact of specific sanctions and bans is ongoing (see Borin et al., 2023b). For instance, the sharp contraction of exports from non-sanctioning countries at the initial stage of the war suggests that factors such as restrictions on international payments and logistics disruptions might have played a relevant role beyond the bans imposed on exports from sanctioning countries. However, the effects of these other factors might have been short-lived since shipments from non-sanctioning economies swiftly rebounded later on.

The empirical approach proposed in this study – which relies on disaggregated, timely bilateral and sectoral trade flows at a monthly frequency – could be useful to assess the effect of any shock on trade flows almost in real time and inform the debate among policymakers concerning the design of optimal sanctioning policies. Some caveats are in order. An increase in indirect exports to Russia through non-sanctioning countries to bypass sanctions (rerouting) might inflate the counterfactual, leading to an underestimation of the impact of the war and related sanctions on Russian imports. To reduce this bias, we excluded from the donor pool those countries which might have redirected flows to Russia. In addition, the deflection of trade flows previously destined for Russia to other final markets may bias the construction of the counterfactual, as exports to donors would not be exogenous to the shock.¹⁹ Right after the start of the war, these channels became limited, and reallocation may require some time. However, the synthetic counterfactual may be less reliable as we depart from the treatment date.