The impact of electricity price shocks triggered by russia’s invasion of Ukraine on inflation in European countries: Insights for public governance

  • 17 Views
  • 3 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Type of the article: Research Article

Abstract
Europe’s post-2022 energy shock has renewed concern about electricity markets as an inflation channel. This paper quantifies how shocks to day-ahead electricity prices and the share of renewables are transmitted to consumer inflation and tests whether the Russia–Ukraine war altered these pass-through mechanisms, thus informing public governance. A harmonized monthly panel for 26 European countries from 2019 to 2025 combines HICP inflation and industrial producer prices with electricity prices and the share of RES, and generates estimations using the TWFE model, event-study dynamics, and generalized synthetic control. Results show that the direct pass-through from wholesale electricity prices to monthly HICP is small and short-lived: Event-time profiles indicate one-month responses that revert to zero within two to three months once producer-price pressures and common shocks are controlled for. In contrast, industrial producer prices have a significant impact, adding approximately 0.05 percentage points to the monthly HICP for each 1 percentage point increase in producer prices. In comparison, the war-period average treatment effect on inflation is close to zero (≈ 0.004 percentage points) after accounting for latent factors. A higher share of RES is associated with modestly lower inflation and attenuates the marginal impact of electricity-price spikes, leading to smaller and less persistent responses in such systems. Public governance should prioritize de-risking renewable investment, strengthening system flexibility, and managing broader cost-push pressures rather than relying on price suppression in electricity markets. Targeted consumer protection, transparent retail pass-through rules, and forward-looking risk monitoring emerge as key elements of a more sustainable price-stability strategy.

Acknowledgment
The project was funded by the European Union’s Horizon 2020 Research and Innovation Programme on the basis of the Grant Agreement under the Marie Skłodowska-Curie funding scheme No. 945478 – SASPRO 2 and through the MSCA4Ukraine project 06030419. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union, the European Research Executive Agency, or the MSCA4Ukraine Consortium. Neither the European Union nor the European Research Executive Agency, nor the MSCA4Ukraine Consortium as a whole, nor any individual member institutions of the MSCA4Ukraine Consortium can be held responsible for them.

view full abstract hide full abstract
    • Table 1. Descriptive statistics (pooled monthly observations)
    • Table 2. Joint test of pre-treatment leads (event-study with covariates)
    • Table 3. Joint pre-trend test (all leads k ≤ –2)
    • Table B1. Two-way FE with Driscoll–Kraay SEs
    • Table B2. Two-way FE with conventional SEs
    • Table B3. Two-way RE with country-clustered SEs
    • Table B4. Two-way RE with PCSE
    • Table B5. Mundlak RE coefficients (including *_bar terms)
    • Table B6. Joint tests of the *_bar terms (cluster and DK variants)
    • Table C1. FE DiD with Driscoll–Kraay SEs
    • Table C2. FE DiD with two-way clustered SEs
    • Table C3. Marginal (pre- vs post-war) slopes for x1–x4 (point estimates)
    • Table C4. FE continuous exposure (dose–response DiD) with covariates with two-way clustered SEs
    • Table C5. Marginal (pre- vs post-war) slopes for x1–x4 (point estimates)
    • Table C6. Event-study (Treat × Relative month) with covariates (x1–x4) and country-specific trends; two-way FE; two-way clustered SEs
    • Table C7. Event-study (Treat × Relative month) with covariates (x1–x4) and post interactions; two-way FE; Driscoll–Kraay SEs (maxlag = 12)
    • Table C8. Event-study (Treat × Relative month) with covariates (x1–x4) and post interactions; two-way FE; Panel Newey–West SEs (maxlag = 12)
    • Table C9. Weighted event-study (Treat × Relative month) with covariates x1–x4 and Post × x interactions
    • Table C10. Event-study (Treat × Relative month) with covariates (x1–x4) and post interactions; two-way FE; Driscoll–Kraay SEs (Unweighted plm)
    • Table C11. Event-study (Treat × Relative month) with covariates (x1–x4) and post interactions; two-way FE; Panel Newey–West SEs (Unweighted plm)
    • Table D1. Generalized synthetic control (matrix completion) – average ATT on monthly HICP growth (Post-Feb 2022), two-way FE, nonparametric bootstrap, covariates x1–x4
    • Table D2. Generalized synthetic control (matrix completion) – event-time ATT on monthly HICP growth (Post-Feb 2022), two-way FE, nonparametric bootstrap, covariates x1–x4
    • Conceptualization
      Tetiana Vasylieva, Ihor Vakulenko, Andreas Horsch
    • Data curation
      Tetiana Vasylieva, Ihor Vakulenko
    • Formal Analysis
      Tetiana Vasylieva
    • Investigation
      Tetiana Vasylieva
    • Methodology
      Tetiana Vasylieva
    • Validation
      Tetiana Vasylieva
    • Visualization
      Tetiana Vasylieva
    • Writing – original draft
      Tetiana Vasylieva, Ihor Vakulenko, Andreas Horsch
    • Writing – review & editing
      Tetiana Vasylieva, Ihor Vakulenko, Andreas Horsch
    • Funding acquisition
      Ihor Vakulenko
    • Resources
      Ihor Vakulenko
    • Software
      Ihor Vakulenko
    • Project administration
      Andreas Horsch
    • Supervision
      Andreas Horsch