Gulf Shock Transmission and Macroeconomic Fragility in Developing Economies: A Bayesian-Calibrated Monte Carlo Vulnerability Assessment
Description
This dataset supports the research paper titled “Gulf Shock Transmission and Macroeconomic Fragility in Developing Economies: A Bayesian-Calibrated Monte Carlo Vulnerability Assessment.” The study examines how a Gulf-centred geopolitical shock may transmit to selected developing economies through GCC remittance exposure, energy-import pressure, shipping and logistics disruption, external-demand compression, foreign-exchange stress, inflation, reserve loss, policy-rate response, growth vulnerability and governance-fiscal fragility. The dataset covers six economies: Pakistan, Bangladesh, Sri Lanka, India, the Philippines and Bhutan. It includes raw remittance source files, processed remittance exposure files, macroeconomic and structural exposure inputs, recursive simulation outputs, Bayesian-calibrated Monte Carlo results, final tables, figures, audit files and R scripts used to reproduce the analysis. GCC remittance exposure is calculated as GCC-origin remittances divided by total recipient-country remittances, using Saudi Arabia, United Arab Emirates, Kuwait, Qatar, Oman and Bahrain as GCC source economies. The research develops a recursive Macroeconomic Vulnerability Index (MVI) and applies symmetric and asymmetric Gulf-shock scenarios. The Bayesian-calibrated Monte Carlo layer uses 10,000 draws to generate median vulnerability estimates, 90% credible intervals, uncertainty widths and rank-probability matrices. The results show a stable but intensity-sensitive vulnerability hierarchy, with Pakistan and Sri Lanka emerging as the highest-vulnerability cases under the extreme-combined scenario, followed by Bangladesh, India, the Philippines and Bhutan. The dataset is organised to allow replication of the paper’s tables, figures and Monte Carlo results. The R scripts can be run from the IRANPAK project folder to reproduce the cleaned remittance architecture, scenario-simulation outputs, Bayesian uncertainty tables, figures and audit logs. The files are provided for transparency, verification and future extension of the study, including possible full-panel macroeconomic modelling, alternative governance-fiscal amplification structures and robustness analysis. Data sources include public central-bank remittance publications, World Bank remittance and macroeconomic data, IMF and UNCTAD policy sources, and processed scenario-simulation files generated by the authors. Users should cite the original public data sources where relevant and cite this dataset when using the processed replication files, simulation outputs or R scripts.
Files
Steps to reproduce
1. Download and extract the complete dataset folder. 2. Open RStudio and set the working directory to the main IRANPAK project folder. 3. Confirm that the following folders are available: raw remittance files, processed macro and exposure data, remittance architecture files, final results tables, Bayesian Monte Carlo outputs, final figures, audit files and R scripts. 4. Run the remittance-processing script to reproduce the GCC remittance exposure architecture. GCC remittance exposure is calculated as GCC-origin remittances divided by total recipient-country remittances. 5. Run the recursive scenario-simulation script to reproduce the deterministic Macroeconomic Vulnerability Index results under symmetric and asymmetric Gulf-shock scenarios. 6. Run the Bayesian-calibrated Monte Carlo script to reproduce the uncertainty-adjusted results using 10,000 draws. This generates median MVI estimates, 5th and 95th percentile credible intervals, uncertainty-width measures and rank-probability matrices. 7. Run the final figure-generation script to reproduce the publication-ready figures used in the manuscript. 8. Compare the generated outputs with the final tables and figures included in the dataset package. Audit and manifest files are included to document file structure, replication sequence and data provenance.
Institutions
- Iqra UniversitySindh, Karachi