This paper explores the potential correlations between financial development and state fragility, using a sample of 137 countries observed over the period from 1998–2019. The countries are grouped into clusters that capture the different joint states of financial development and fragility. The paper introduces a new switching methodology to further allow for a qualification of the evolution of countries in terms. of fragility scores with and without controlling for other variables. Irrespective of the precise methodology and state fragility measure as used in this paper, the findings indicate a negative correlation between financial development and state fragility, after controlling for several forms of observed and unobserved heterogeneity.
Authors
João, Igor Custodio, Calice, Pietro, Lucas, Andre, Schaumburg, Julia
- Citation
- “ João, Igor Custodio ; Calice, Pietro ; Lucas, Andre ; Schaumburg, Julia . 2024 . Financial Development and Fragility: A Clustering Analysis . Policy Research Working Paper; 10850 . © Washington, DC: World Bank . http://hdl.handle.net/10986/41895 License: CC BY 3.0 IGO . ”
- Collection(s)
- Policy Research Working Papers
- DOI
- http://dx.doi.org/10.1596/1813-9450-10850
- Identifier externaldocumentum
- 34364723
- Identifier internaldocumentum
- 34364723
- Pages
- 54
- Published in
- United States of America
- RelationisPartofseries
- Policy Research Working Paper; 10850
- Report
- WPS10850
- Rights
- CC BY 3.0 IGO
- Rights Holder
- World Bank
- Rights URI
- https://creativecommons.org/licenses/by/3.0/igo/
- UNIT
- EFI-FCI-Finance-GD (EFNDR)
- URI
- https://hdl.handle.net/10986/41895
- date disclosure
- 2024-07-17
- region geographical
- World
Files
Table of Contents
- Introduction 4
- Clustering methodologies 7
- The model 7
- Static clustering membership 8
- Cluster switching 9
- Identification and the number of clusters 11
- The data 11
- Results 16
- Linear model with two-way fixed effects 16
- Clusters and heterogeneity 17
- Allowing for cluster membership switches 23
- Robustness 28
- Clustering CPIA and all FD components (7 variables) 30
- Clustering CPIA and FI components (4 variables) 32
- Robustness: FSI instead of CPIA 35
- Conclusions 38
- Additional results 46
