SearcharxivSearch

arXiv · 2604.12368

A Diagnostics-First Composite Index for Macro-Financial Resilience to Socioeconomic Challenges: The Gondauri Index with Benchmarking and Scenario Evidence

Abstract

In the face of socioeconomic challenges, this paper develops and empirically demonstrates the Gondauri Index (GI) as a reproducible diagnostics-first composite framework for benchmarking macro-financial resilience across heterogeneous economies on a unified 0-100 scale. The GI addresses a key limitation of conventional surveillance dashboards: resilience is multi-dimensional and only partially substitutable, so strength in one area cannot sustainably offset fragility in another. The index integrates three interpretable pillars: Inequality Resilience Score (IRS), Liquidity and Systemic Resilience (LNSR), and Inflation Forecast Coherence (IFC). Cross-country comparability is ensured through robust percentile normalization (p5-p95), a consistent annual country-year design, and explicit missing-data handling via component-level weight renormalization. Empirically, the paper provides a 2024 benchmark snapshot and dynamic evidence for 2005-2024 using 5-year rolling diagnostics and Delta log(GI) contribution decomposition, allowing transparent attribution of resilience changes to pillar-level drivers. A forward-looking extension constructs 2026-2030 scenario pathways and introduces a binding-pillar diagnostic that identifies the dominant constraint on resilience across horizons. Overall, the GI offers a scalable tool for comparative resilience assessment, early-warning diagnostics, and evidence-based policy sequencing.

Explore related subjects

Keep this discovery

BibTeXRIS

Davit Gondauri. 2026-04-14. A Diagnostics-First Composite Index for Macro-Financial Resilience to Socioeconomic Challenges: The Gondauri Index with Benchmarking and Scenario Evidence. https://doi.org/10.61093/sec.10(1).50-83.2026

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM