This paper examines whether concentration in electricity generation affects the deployment of wind and solar capacity across the 27 European Union member states over 2000–2022. Market concentration is measured by the generation share of the largest electricity producer, and deployment is measured as installed wind and solar capacity relative to total national capacity.
Using dynamic estimation methods that account for persistence, unobserved heterogeneity, and reverse causality, the analysis finds no systematic relationship between market concentration and subsequent wind or solar deployment — a result robust across alternative measures, bias-corrected estimators, balanced samples, and extensive heterogeneity analyses.
Source: Eurostat / European Commission. Own calculations.
The table below reports estimates across pooled OLS, fixed-effects OLS, and Arellano–Bond GMM, for both the full sample and a bias-corrected balanced panel following Chen et al. (2019). The coefficient of interest is the lagged market share of the largest producer (MSt−1).
| Base Sample | Balanced Sample | ||||||
|---|---|---|---|---|---|---|---|
| Dependent variable: Renewable sharet | (1) OLS | (2) FE-OLS | (3) AB-GMM | (4) FE-OLS | (7) FE-OLS | (8) AB-GMM | |
| Renewable sharet−1 | 1.016***(0.010) | 1.003***(0.021) | 0.747***(0.087) | — | 0.996***(0.023) | 0.823***(0.087) | |
| Market sharet−1 (MSt−1) — key coefficient | −0.003(0.003) | 0.006(0.007) | −0.006(0.025) | −0.063***(0.019) | 0.007(0.013) | −0.010(0.025) | |
| Observations | 515 | 515 | 485 | 516 | 399 | 378 | |
| AR(2) p-value | — | — | 0.152 | — | — | 0.115 | |
| Bias correction [bootstrapped SE] | — | — | — | — | 0.003 [0.033] | 0.049 [0.058] | |
All models include full sets of country and year fixed effects. Column (4) excludes the lagged dependent variable — its significant negative coefficient cannot be interpreted causally due to dynamic panel bias. Robust standard errors in parentheses. *** p<0.01.
The strongest predictor is lagged renewable share — deployment is self-reinforcing through accumulated know-how, supply chains, and learning-by-doing.
The only policy variable consistently significant across FE-OLS and AB-GMM specifications. Support scheme design matters more than market structure.
Eastern and Western Europe show more positive within-country concentration slopes than Northern Europe — reflecting deployment acceleration despite incumbent dominance.
Concentration, unbundling, competitive market rules, independent power producers, and fossil fuel subsidies all yield null or fragile effects.
The baseline specification is a dynamic panel model with country and year fixed effects, estimated to address endogeneity from reverse causality between concentration and renewable deployment. The dynamic structure captures the strong persistence in renewable capacity stocks.
Pooled OLS, Fixed Effects OLS, and Arellano–Bond (1991) GMM. Triangulated to guard against dynamic panel bias and instrument proliferation.
Split-sample bias-correction following Chen, Chernozhukov & Fernández-Val (2019) on a balanced panel of 21 countries, with bootstrapped standard errors.
Lagged levels as internal instruments under Arellano–Bond orthogonality conditions. AR(1)/AR(2) and Hansen tests reported throughout.
Alternative outcome variables (solar/wind separate, log capacity, incl. hydro), alternative concentration proxies, 5-year interval data, and retail-market measures.
This paper comes with a fully reproducible Python pipeline alongside the original Stata code. Data is publicly available from Eurostat, the European Commission, World Bank, and OECD.
Full data, cleaning scripts, analysis code, and output tables.
The Python replication uses linearmodels for panel estimation,
with the original Stata do-files preserved for reference.
Eurostat installed capacity. European Commission electricity market pocketbook. Annual, 2000–2022.
World Bank Global Power Market Structure Database (GPMSD). Regulatory regime classifications.
OECD feed-in tariff database (wind + solar, USD/kWh). OECD fossil fuel support as % of GDP.
World Bank: GDP per capita, natural resource rents. European Commission: electricity consumption, net imports.
Financial support from the Swedish Competition Authority (Konkurrensverket) is gratefully acknowledged.