- The paper develops the Gondauri Index, a diagnostics-first composite measure of macro-financial resilience integrating inequality dynamics, liquidity transmission, and inflation forecast coherence.
- The methodology employs regression analysis, robust percentile normalization, and a weighted geometric aggregation to form a 0-100 scale with binding-pillar diagnostics.
- Empirical findings and scenario analyses highlight that vulnerabilities, particularly in inflation forecast coherence, drive resilience variations across economies.
A Diagnostics-First Composite Index for Macro-Financial Resilience: The Gondauri Index
Introduction
The paper "A Diagnostics-First Composite Index for Macro-Financial Resilience to Socioeconomic Challenges: The Gondauri Index with Benchmarking and Scenario Evidence" (2604.12368) introduces a composite indicator— the Gondauri Index (GI)—constructed for systematic benchmarking and diagnostics of macro-financial resilience across diverse economies. The GI targets an analytical gap in existing surveillance and composite index approaches, prioritizing interpretability, within-pillar decomposability, and reproducibility on a robust 0-100 scale. By integrating inequality dynamics, liquidity transmission/systemic risk, and inflation forecast coherence, the GI establishes a multidimensional architecture—explicitly penalizing structural imbalances using geometric aggregation to reflect partial non-substitutability among resilience components.
Methodological Framework
Pillar Structure
The GI is underpinned by three distinctive, interpretable pillars:
- Inequality Resilience Score (IRS): Anchored in a regression-based approach, IRS measures the stability, macro-consistency, and structural explainability of Gini coefficient dynamics with respect to inflation, unemployment, and per-capita GDP. Notably, it does not serve as a normative welfare metric but evaluates the smoothness and macro-alignment of distributional change.
- Liquidity & Systemic Resilience (LNSR): LNSR quantifies the coherence and stability of liquidity transmission via proxies derived from broad money/GDP ratios, rolling variance of liquidity speed, residual stress diagnostics (inflation unexplained by core regressors), and cycle-alignment metrics. It operationalizes the integrity of monetary-financial transmission under systemic stress.
- Inflation Forecast Coherence (IFC): This pillar embeds the condition that inflation forecasts must demonstrably improve when augmenting structural Forecasting and Policy Analysis Systems (FPAS) models with a cyclical Riemann zeta-based signal (FPAS+3). Rolling RMSE comparisons furnish empirical attribution, while only meaningful forecast gains are allowed to contribute to resilience.
Data Handling and Scaling
Cross-country comparability is enforced through:
- Robust percentile normalization (p5-p95 bounds) to buffer outlier influence.
- Annualized country-year panels and a uniform data-vintage policy with explicit missingness handling via within-pillar weight renormalization.
- Each pillar is scored on a 0-100 interval, and the composite index is constructed as a weighted geometric mean (w₁=0.35 IRS, w₂=0.35 LNSR, w₃=0.30 IFC), amplifying the impact of the weakest pillar and punishing over-reliance on strength in only one domain.
Rolling Diagnostics and Scenario Analysis
Empirical validation encompasses a 2024 benchmark snapshot, rolling 5-year diagnostics (2005–2024), and scenario-based projections for 2026–2030 (baseline, adverse, optimistic), with dynamic attribution via Alog(GI) decomposition. The scenario module includes a binding-pillar diagnostic to explicitly identify the most constraining resilience factor under each projected macro-path.
Empirical Findings
Cross-Country and Regional Benchmarking
The GI outperforms conventional indices by dissecting resilience into interpretable, non-compensatory domains. For example, in the 2024 snapshot, Georgia exhibits a higher GI (52.52) than the United States (41.48), due mainly to considerably higher IRS (67.42 vs 29.60), highlighting distributional stability as a binding constraint in the US case despite its superior LNSR and IFC. IRS in this context signals stability and explainability of inequality dynamics, not absolute income equality.
Descriptive statistics reveal that resilience is structurally variable over time—Romania and the United States record the highest mean GI over 2005–2024, but with pronounced intra-period volatility (e.g., China std ~30.8). Scenario projections further demonstrate that GI trajectories and resilience bands are sensitive to regime changes in the pillars, confirming the non-substitutability principle.
Pillar-Specific Insights
- IRS: High IRS scores correlate with stable and macro-explainable Gini movements (e.g., Georgia R² ~0.79). The pillar is sensitive to labor-market conditions and macro shocks; missing Gini data is mitigated by weight adjustment rather than panel deletion.
- LNSR: Systems displaying low rolling variance in liquidity proxies, low RMS of inflation residuals, and consistently positive cycle-alignment display higher resilience. Instability in these metrics is a harbinger of systemic risk, especially under pre-crisis liquidity stress.
- IFC: The transition to FPAS+3 yields heterogeneous improvements across countries; only non-negative incremental RMSE gains contribute to resilience. This approach prevents artificial enhancement of IFC in cases where model complexity is misaligned with macro-regimes and actual forecast performance.
Scenario-Based Diagnostics
Binding-pillar analysis in the forward-looking component indicates that, under baseline and adverse scenarios, IFC frequently emerges as the binding constraint (e.g., Georgia, USA, China), suggesting that inflation forecast discipline and credibility will be the first point of failure in macro-financial stress situations for these economies. In contrast, IRS or LNSR bind in lower-resilience economies or under intense distributional instability or liquidity stress.
Theoretical and Practical Implications
The GI framework advances the literature by formalizing resilience as a multidimensional, partially non-substitutable property rather than an aggregate score reflecting compensatory strengths. It enforces economic realism—the systemic importance of bottlenecks, akin to the minimal-cut set in reliability theory, is hardwired into the aggregation rule. In practical terms, this provides actionable early-warning diagnostics suitable for macroprudential monitoring, inflation-targeting credibility assessment, and explicit risk-aware policy sequencing.
The scenario capability, with explicit identification of the binding constraint, implies GI can guide time-consistent intervention targeting the most structural vulnerability. Moreover, the GI’s transparency and replicability enable use across heterogeneous policy and research environments, particularly in data-constrained settings.
Limitations and Future Directions
Methodological limitations are acknowledged:
- Annual data smooths intra-year shocks, limiting crisis detection at high frequency.
- Sparse inequality data, especially Gini, reduces IRS granularity, although weight renormalization mitigates outright sample loss.
- The regime-sensitive performance of FPAS+3 models in different inflation environments suggests further calibration extensions are required.
Future research should focus on expanding the universe of benchmarked economies, integrating higher-frequency data for early-warning extensions, and regime-conditioned model calibration for the IFC pillar. The diagnostics-first, non-compensatory GI architecture offers a transferable template for resilience measurement, adaptable to evolving macro-financial data environments and policy priorities.
Conclusion
The Gondauri Index constitutes a significant methodological contribution to the comparative measurement of macro-financial resilience (2604.12368). By constructing a diagnostics-first, geometrically aggregated, and interpretable composite framework, the GI demonstrates that resilience is a structured, multidimensional phenomenon, inseparably linked to the interaction and stability of distributional, liquidity, and nominal-anchor dynamics. Its scenario-based, binding-pillar diagnostics extend practical utility to policy sequencing and risk management. With further extension, GI offers a scalable and empirically robust platform for resilient macro-financial surveillance and applied research.
