A quantitative assessment of CEE-5 export dependence and value-chain integration with Germany and France over 2010–2023, based on Eurostat FIGARO data and gravity-model estimation.
A guided walk-through from the gravity-model framework to the policy implications, grounded in fourteen years of FIGARO bilateral data.
Key findings, headline elasticities, and the structural insights that frame the report.
Read sectionTheoretical framework, multilateral resistance, and the strand of literature this report builds on.
Read sectionSources, country and year coverage, descriptive statistics for the CEE-5 panel.
Read sectionDVA growth profiles across the five CEE economies and two destinations, 2010–2023.
Read sectionFive model specifications side-by-side, robustness checks, and the headline supply elasticity.
Read sectionTwo gravitational poles, two distinct integration patterns — magnitudes and composition.
Read sectionLong-run domestic-share dynamics and what they say about value-chain integration.
Read sectionFastest DVA growth in the CEE-5 and the most resilient domestic share — what's driving it.
Read sectionFrom statistical findings to actionable strategy on supply capacity, FDI, and infrastructure.
Read sectionData sources, full model equation, robustness specifications, and complete bibliography.
Read sectionKey findings and structural insights
This report examines how much economic value five Central and Eastern European countries — Romania, Poland, Hungary, Czechia, and Bulgaria — generate through their trade relationships with Germany and France. Using fourteen years of data (2010–2023) and formal statistical modelling, we find that productive capacity in CEE economies is the primary engine of trade growth toward both destinations, and that Germany plays a structurally more intensive role than France.
Standard trade statistics count goods crossing borders multiple times, overstating the genuine economic benefit. This report instead uses "domestic value added" (DVA) — the share of export revenues that actually stays in the exporting country as wages, profits, and tax. This is a more honest measure of how much each country benefits from its trade relationships.
Romania shows the fastest DVA growth to Germany (+232%) and France (+187%) among CEE-5 economies over 2010–2023. Bulgaria is close behind (+248% to Germany, +151% to France), signalling genuine structural upgrading of both economies' export positions despite starting from smaller bases.
Romania also stands out for retaining a larger domestic share of export value than its peers — only 8.7 percentage points lost toward Germany and 3.2 toward France over 1995–2022.
Theoretical framework and literature
Romania, Poland, Hungary, Czechia, and Bulgaria have deepened their economic ties with Western Europe dramatically since EU accession. Germany in particular anchors a vast manufacturing network that stretches across Central and Eastern Europe — supplying components to German car and machinery factories is the single largest source of export income for several of these economies. France represents a second, less intensively integrated relationship with different trade composition and dynamics.
When a Romanian car plant exports a gearbox to Germany, much of the steel, electronics, and tooling embedded in that gearbox was itself imported. The genuine Romanian contribution — the wages of the workers, the profits of the factory, the taxes paid — is the "domestic value added" (DVA). This report tracks DVA in CEE-5 bilateral exports from 2010 to 2023 based on the Eurostat–FIGARO database.
Just as Newton's law of gravity says that two objects attract each other more strongly when they are larger and closer together, the gravity model of trade says that two countries trade more when their economies are bigger and when they are geographically closer. This deceptively simple idea has been shown to fit trade data remarkably well across decades and dozens of country pairs.
In this framework, bilateral DVA flows depend on: the productive capacity of the exporter (GDP), the absorption capacity of the importer (GDP), and trade costs — measured by geographic distance and shared land borders. The standard derivation (Anderson and van Wincoop, 2003) implies trade is also influenced by costs with all other partners — "multilateral resistance". We account for this through country and year fixed effects.
Practical interpretation: when the model estimates an "elasticity" of, say, 0.88 for exporter GDP, a 1% increase in the exporting country's GDP is associated with a 0.88% increase in domestic value added shipped to Germany or France — holding everything else constant.
The intellectual origins of the gravity framework trace to Jan Tinbergen's 1962 monograph Shaping the World Economy, which received the first systematic empirical application of the model to bilateral trade flows. Subsequent theoretical work — Anderson (1979), Bergstrand (1985, 1989), and eventually Anderson and van Wincoop (2003) — demonstrated that the gravity equation emerges necessarily from a wide class of trade models in which goods are differentiated by country of origin.
Sources, coverage, descriptive statistics
The dataset covers ten bilateral trade relationships (five CEE exporters × two destinations) across fourteen years, giving 140 data points. Value-added trade data come from Eurostat's FIGARO database. GDP figures come from Eurostat's national accounts (retrieved March 2026). Distances are standard capital-to-capital great-circle distances.
Five CEE exporters × Germany & France × 14 years = 140 observations
| Variable | Average | Std. Dev. | Smallest | Largest | Pairs |
|---|---|---|---|---|---|
| Value added in exports (€ thousand) | 7,702 | 8,490 | 386 | 45,103 | 140 |
| GDP of exporting country (€ million) | 214,266 | 159,720 | 38,289 | 751,931 | 140 |
| GDP of importing country (€ million) | 2,792,171 | 610,512 | 1,996,075 | 4,219,310 | 140 |
| Distance between capitals (km) | 1,074 | 598 | 280 | 2,053 | 140 |
| Shared border (yes=1, no=0) | 0.20 | — | 0 | 1 | 140 |
The DVA averages conceal enormous variation: Poland's average annual DVA in exports to Germany (€28 billion) is thirty times larger than Bulgaria's (€0.9 billion). This cross-country variation is a core input to the statistical estimation.
DVA growth across CEE-5, 2010–2023
Several patterns are immediately apparent in the time-series. Poland is the largest contributor in both corridors during the period 2010–2023. Romania and Bulgaria show the fastest growth, particularly toward Germany: Romania's DVA to Germany grew by 232% (from €3.2 billion to €10.6 billion), while Bulgaria's grew by 248%. The 2020 pandemic caused a visible dip across all series, followed by an uneven recovery.
Source: Eurostat FIGARO; CPAG calculations
Source: Eurostat FIGARO; CPAG calculations
Germany's total DVA intake from CEE-5 countries is roughly 3.5 times larger than France's, reflecting deeper supply-chain integration between Central Europe and German manufacturing.
€ billions, 2010 / 2019 / 2023
| Country | DVA to Germany (€ bn) | DVA to France (€ bn) | DE/FR ratio | Growth DE | Growth FR | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| 2010 | 2019 | 2023 | 2010 | 2019 | 2023 | 2010 | 2023 | 2010–23 | 2010–23 | |
| Bulgaria | 0.90 | 2.28 | 3.13 | 0.39 | 0.81 | 0.97 | 2.3× | 3.2× | +248% | +151% |
| Czechia | 12.15 | 16.73 | 22.15 | 2.50 | 3.81 | 4.89 | 4.9× | 4.5× | +82% | +96% |
| Hungary | 5.59 | 8.65 | 10.92 | 1.29 | 1.90 | 2.01 | 4.3× | 5.4× | +95% | +55% |
| Poland | 18.38 | 31.21 | 45.10 | 5.76 | 10.38 | 12.62 | 3.2× | 3.6× | +145% | +119% |
| Romania | 3.19 | 8.31 | 10.61 | 2.03 | 3.85 | 5.81 | 1.6× | 1.8× | +232% | +187% |
Five model specifications
Five different modelling approaches were tested to check whether the patterns in the data hold up under different statistical assumptions.
Standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05
| Specification | Supply elasticity (exporter GDP) | Demand elasticity (importer GDP) | Distance effect | Notes |
|---|---|---|---|---|
| Spec. 1 — Pooled OLS (baseline) | +0.88 *** (0.024) | +1.58 *** (0.095) | −0.51 *** (0.078) | R² = 0.93. Benchmark. |
| Spec. 2 — Country & Year effects | +1.62 *** (0.608) | +1.23 (0.923) | absorbed | R² = 0.97. Country controls. |
| Spec. 3 — Within-pair (strictest) | +1.62 *** (0.436) | +1.23 * (0.726) | absorbed | Within-R² = 0.91. |
| Spec. 4 — PPML (preferred) | +0.79 *** (0.023) | +1.22 *** (0.094) | −0.38 *** (0.069) | Pseudo-R² = 0.95. Preferred. |
| Spec. 6 — Corridor split (DE vs FR) | DE: +1.68 *** FR: +1.57 *** | not significant | absorbed | R² = 0.97. Wald F=11.0, p=0.004. |
Each row shows what one statistical model found. Supply elasticity = how much DVA increases when the exporting country's GDP grows by 1%. Demand elasticity = same for the importing country's GDP. Stars (*** / ** / *) indicate statistical confidence: *** means we are more than 99% confident the effect is real. "Absorbed" means the model design prevents us from measuring that variable separately — its effect is captured in the country-specific constants.
The single most consistent result across all five specifications is the supply elasticity: every 1% increase in a CEE country's GDP is associated with a 0.79–1.68% increase in domestic value added shipped to Germany and France. This finding is robust to every modelling variation tested.
The demand elasticity is large and significant in baseline and PPML models (1.22–1.58), but when the model controls for year-by-year common shocks — isolating only the within-country-pair variation — the demand effect becomes statistically undetectable. This suggests that what looks like a "demand-pull" effect in simple models is actually driven by common business-cycle movements that affect all pairs at the same time.
In Spec. 4 (PPML), demand elasticity (1.22) appears to exceed supply elasticity (0.79). This dissolves once one notes that pooled specifications cannot separate structural size differences (Germany's GDP is ~50% larger than France's throughout the sample) from genuine year-to-year fluctuations. Once Specs. 2, 3, and 6 introduce country/pair fixed effects, the apparent demand effect collapses while supply elasticity remains stable. The supply-led narrative survives all robust controls.
Two gravitational poles, two patterns
The corridor-comparison model (Specification 6) tests formally whether Germany and France operate as different kinds of trade relationships. The answer is yes, but only modestly: Germany shows a slightly higher supply elasticity (1.68) than France (1.57), and the supply elasticity difference is statistically significant at the 0.1% level (p = 0.001). The importer demand effect is statistically indistinguishable from zero in both corridors.
Germany's edge reflects geographic proximity, shared borders with Poland and Czechia (the +46% premium), deeper manufacturing integration in automotive and machinery, and historical trade relationships predating EU enlargement.
France's elasticity is statistically significant at the 0.1% level below Germany's, but the difference is modest. Both relationships are primarily supply-driven; France's intake is roughly one-third of Germany's in absolute volume.
Each 1% of additional distance reduces trade by ~0.38–0.51%. EU single-market integration cushions this; shared land borders with Germany add ~46% for Poland and Czechia.
The data do not support a sharp Germany = supply-chain / France = demand-pull distinction. Both relationships are primarily supply-driven. Germany's edge reflects the greater density and contractual intensity of its CEE supply-chain network, not a fundamentally different trade mechanism.
Fastest growth, most resilient domestic share
Romania shows the fastest percentage growth among CEE-5 economies in the German corridor (+232%) and the second fastest in the French corridor (+187%), starting from a relatively low base. This trajectory reflects an upgrading of Romania's export position within European value chains over the measurement period — a structural shift rather than a cyclical fluctuation.
Romania shows fastest growth and retains a larger domestic share of export value than its peers. While DVA share has declined across all CEE-5 economies since the mid-1990s, Romania's reduction has been far more limited — only 8.7 percentage points toward Germany and 3.2 toward France over 1995–2022. This indicates a more gradual integration into import-intensive European value chains, with proportionally more economic value retained domestically per euro of gross exports.
What this means in practice: Bulgaria is close behind Romania (+248% to Germany, +151% to France) — both economies show genuine structural upgrading of their export positions despite starting from smaller absolute bases than Poland and Czechia. This signals successful capacity-building and FDI absorption in both countries.
From statistical findings to actionable strategy
The dominant driver of domestic value added in CEE exports is the productive capacity of the exporting economy itself. When Romania, Poland, or Czechia expands its industrial base — through investment, FDI absorption, or labour market development — bilateral DVA flows increase proportionally. Changes in German or French GDP, while relevant in raw data, lose explanatory power once common economic shocks are properly separated.
The Germany corridor consistently shows a slightly higher supply elasticity than France, and CEE economies direct between 1.6 and 5 times more domestic value added to Germany than to France. This asymmetry is most pronounced for Czechia and Hungary, least pronounced for Romania and Bulgaria.
Romania shows the fastest percentage growth among CEE-5 economies in both corridors, starting from a relatively low base. This trajectory reflects an upgrading of Romania's export position within European value chains — a structural shift rather than a cyclical fluctuation.
For policymakers in CEE economies, the supply-dominance finding points clearly toward investment in productive capacity, skills, infrastructure, and supply-chain integration as the primary levers for raising DVA exports. Exchange rate policy and demand-side subsidies are secondary channels. Diversification toward France requires supply-chain development, not merely demand-side market access negotiations.
Data sources, model equation, full bibliography
Value-added trade data are from Eurostat FIGARO (dataset naio_10_fgdm), with cross-validation from OECD. GDP figures are from Eurostat national accounts (nama_10_gdp, indicator B1GQ), retrieved March 2026. All values in nominal euros; no price deflation applied. Distance data follows CEPII GeoDist conventions.
ln(DVAij,t) = α + β1 ln(GDPi,t) + β2 ln(GDPj,t) + β3 ln(distij) + β4 contigij + μij + λt + εij,t
where β1, β2 are supply and demand elasticities, β3 is distance elasticity (negative), β4 is the border premium (positive), μij are bilateral fixed effects, λt are year fixed effects, ε is the error term.
Five statistical specifications were estimated: pooled OLS (baseline), three-way fixed effects (country and year), bilateral pair fixed effects with year controls, Poisson pseudo-maximum likelihood (PPML) — the statistically preferred estimator given heteroscedasticity in the data — and a pooled interaction model. Full technical details are in the companion Methodology & Diagnostics document.
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