Institutional dissonance and foreign divestment in Central and Eastern Europe: a hybrid econometric–machine learning analysis

Abstract Why do foreign investors exit Central and Eastern Europe (CEE) despite formal alignment with EU institutional standards? We argue that divestment reflects not cyclical volatility but strategic responses to institutional fault lines, the non-linear interaction between de jure legal frameworks and de facto enforcement credibility. Analysing panel data (1990–2024) for 20 CEE economies through a hybrid econometric–machine learning framework (ARDL bounds testing, Dumitrescu–Hurlin causality, Random Forest with SHAP diagnostics), we find that: (1) rule-based governance (corruption control) Granger-predicts divestment (Z = 3.897, p = 0.001), whereas formal rule-of-law indicators show ambiguous effects; (2) macroeconomic instability robustly elevates exit risk (β = 0.277, p = 0.001); and (3) trade openness and human capital amplify divestment only when enforcement credibility is weak. Economic scale dominates predictive power (72.0%) but reflects historical FDI exposure, not causal drivers. Critically, machine learning reveals that divestment tipping points emerge from combinations of high inflation, low corruption control, and high openness, patterns invisible to linear models. These findings reframe capital flight as a rational response to institutional dissonance rather than market failure. For policymakers, the implication is clear: EU cohesion policy should shift from formal harmonisation towards performance-based governance that prioritises verifiable anti-corruption enforcement and macroeconomic credibility.