Search Results

You are looking at 1 - 3 of 3 items for :

  • "World Bank methodology assessment" x
Clear All
Yasmin Alem and Jacinta Bernadette Shirakawa
Based on internal data, this paper finds that the capacity development program of the IMF’s Statistics Department has prioritized technical assistance and training to fragile and conflict-affected states. These interventions have yielded only slightly weaker results in fragile states than in other states. However, capacity development is constantly needed to make up for the dissipation of progress resulting from insufficient resources that fragile and conflict-affected states allocate to the statistical function, inadequate inter-agency coordination, and the pervasive impact of shocks exogenous to the statistical system. Greater coordination with other capacity development providers and within the IMF can help partially overcome low absorptive capacity in fragile states. Statistical capacity development is more effective when it is tailored to countries’ level of fragility.
Yasmin Alem and Jacinta Bernadette Shirakawa

Conclusion Annexes on FCS Case Studies I. Case Study on Djibouti II. Case Study on Haiti III. Case Study on Kosovo IV. Case Study on Madagascar V. Case Study on Myanmar References BOXES 1. IMF’s Data Standards Initiatives and FCS 2. Statistical Innovation and Big Data in FCS FIGURES 1. High Risk Locations 2a. World Bank Methodology Assessment of Statistical Capacity for FCS and Non-FCS 2b. World Bank Methodology Assessment of Statistical Capacity for FCS and Non-FCS 3a. Countries Facing Resource Challenges 3b. Countries Facing Source

Yasmin Alem and Jacinta Bernadette Shirakawa

; 31% lower middle income; 17% upper middle income; 24% small states; 31% e-GDDS with NSDP; 29% with current program (ECF); 55% with RCF/RFI . 1 ECF: Extended Credit Facility; EFF: Extended Fund Facility; SA: Standby Arrangement 2 RCF: Rapid Credit Facility; RFI: Rapid Financing Instrument 3 World Bank methodology assessment of statistical capacity (scale 0 – 100) Figure 1. High Risk Locations (Percent of time as an HRL between 2016–2020) Source Authors’estimates based on IMF data The World Bank’s classification of FCS