## fiscalmonitordatabase-october2020-chapter-2 (dataset overview)

## Source details

**Canonical URL:** [fiscalmonitordatabase-october2020-chapter-2 (dataset overview)](https://www.imf.org/-/media/files/publications/fiscal-monitor/2020/october/data/fiscalmonitordatabase-october2020-chapter-2.xlsx)

## Other formats

- [Markdown version](/-/media/files/publications/fiscal-monitor/2020/october/data/fiscalmonitordatabase-october2020-chapter-2.xlsx.md)
- [Structured JSON version](/-/media/files/publications/fiscal-monitor/2020/october/data/fiscalmonitordatabase-october2020-chapter-2.xlsx.json)

---

### Dataset scope and structure
- Workbook contains multiple worksheets tied to Chapter 2 of the Fiscal Monitor dataset, including: "FM Database Oct. 2020", "Table of Contents", numerous Figures (Figure 2.1 through Figure 2.12), Box Figure 2.1.1., Annex Figures, and Annex Tables.
- Worksheets include time series, cross-country panels, figure data points, and panel/annex sensitivity tables. Sample rows include country-level indicators, percentiles, cost estimates, multipliers, and scenario outputs.

### Key figures and aggregate metrics (selected exact values from sample rows)
- Figure 2.1 sample values:
  - 1992: AEs = 75.687124; EMs excl. China = 62.525613; China = 153.8944
  - 2007: AEs = 68.812121; EMs excl. China = 52.421529; China = 159.92887
  - 2017: AEs = 67.059408; EMs excl. China = 58.017099; China = 164.93802
  - Group means in row: "ADV" = 0.7; "EMs excl. China" = 1.7000000000000002; "LIDC" = 2.72
  - Regional percent shares in row: "AEs" = 40.184089; "EMs excl. China" = 53.008994; "LIDC" = 88.226134
- Figure 2.2 average indices and RHS averages:
  - Average 2000-10 baseline = 100 (for categories)
  - 2011 Economic Affairs index = 101.37628653375705; RHS = 1.178371641794504
  - 2012 Education index = 100.76581403964484; RHS = 0.4094130414328648
  - Average 2000-18 (RHS) listed as 3.5 in a cell
- Figure 2.3 March–June 2019–2020 percentiles:
  - AE 75th percentile% = 0.10516752299701679; EME/LIDC 75th percentile% = 0.19922023087673496
  - AE 25th percentile = 0.037602615864552436; EME/LIDC 25th percentile = -0.07352408948310345
  - AE average = 0.07171866347953948; EME/LIDC average = 0.023250849496559742
- Figure 2.4 sample country points (sh_spent, wbgi_gee):
  - AT: 0.5909706950187683, 1.4791269302368164
  - BE: 0.38437479734420776, 1.4384405612945557
  - BG: 0.34826865792274475, 0.208683118224144
  - FI: 0.7375991344451904, 1.8130110502243042
  - FR: 0.508523166179657, 1.4367960691452026
- Figure 2.5 examples of preparation/implementation shares:
  - AUSTRALIA: preparation = 3.3; implementation = 4; total result cell = 7.3
  - CANADA: preparation = 12; implementation = 5.8; total result = 17.8
  - CHINA entries: 5 and 3 (total 8), and separate entry 2 and 5 (total 7)
  - INDIA: preparation = 9; implementation = 8; total result = 17
- Figure 2.6 regression/sample coefficients with associated labels:
  - scaleup10_mean_w = -5.0516988; cost_overrun = -0.19038851; label "Real GDP growth" = -0.055
  - Other labeled entries: "Government effectiveness" = -0.1036; "Public investment scale up" = 0.0435; "Rate of return computed at appraisal (1 = yes; 0 = no)" = -0.0379; "Share of grant financing" = 0.2046; "Project complexity" = -0.0391; "Project size" = -0.0712
- Figure 2.8 impulse-response sample (1-year and 2-year horizons):
  - Output (All sample) 1-year = 0.2334563; Output (Advanced economies) 1-year = 0.5510444
  - Private Investment (All sample) 1-year = 1.161128; Private Investment (Advanced economies) 1-year = 3.549421
  - Employment (All sample) 1-year = -0.0247785; Employment (Advanced economies) 1-year = 0.5302148
  - STD for Output (All sample) = 0.0807458; STD for Private Investment (All sample) = 1.009072
  - Beta - 2STD Output (All sample) = 0.07196469999999999
- Figure 2.9 multiyear series sample rows (Years 0–5 first column values):
  - Year 0: ncashknfig_gd_multlow_ols_f = 6.70945; ncashknfig_gd_multhigh_ols_f = 2.32844; Years = 0; associated "Lower" = 8.989200678418472; "Upper" example = 3.7810484294368063
  - Year 1: 10.35196, 5.23173, Years = 1; "Lower" = 15.969135136039279
  - Year 2: 13.245370000000001, 8.088610000000001, Years = 2; "Lower" = 19.949365721511196
  - Year 5: 6.556140000000001, -1.37173, Years = 5; "Lower" = 16.368514311579013
- Figure 2.10 sector 2-year effects (selected):
  - Construction (2 year) = 13.217586278915405
  - Communication (2 year) = 10.127400606870651
  - Manufacturing (2 year) = 9.265054762363434
  - Transport (2 year) = 8.25219377875328
  - Health (2 year) = 7.2170257568359375
  - Social (2 year) = 9.21088457107544
- Figure 2.11 country sample (IHRscore and Respiratory Medical Product Imports (%)):
  - Afghanistan: IHRscore = 42.26; Respiratory Medical Product Imports (%) = 0; Fitted Line = 0.0929505250581747
  - Australia: IHRscore = 99.8933333333333; Respiratory Medical Product Imports (%) = 0.136950211874042; Fitted Line = 0.212585032256924
  - Austria: IHRscore = 80.74; Respiratory Medical Product Imports (%) = 0.255677538731824; Fitted Line = 0.172826797533731
  - Armenia: IHRscore = 95.67; Respiratory Medical Product Imports (%) = 0.304005652589556; Fitted Line = 0.203818293065437
- Figure 2.12 bubble-chart country samples (bubble size, X, Y, risk/color):
  - Central African Republic: bubble size = 0.004666; X axis = 0.21543116839999998; Y axis = 0.26716286570000003; For color = "Low Risk"
  - Kenya: bubble size = 0.04803288; X axis = 4.711340476; Y axis = 4.040188319; For color = "Low"
  - Ethiopia: bubble size = 0.094138101; X axis = 7.661913897000001; Y axis = 3.40554082; For color = "High"
- Box Figure 2.1.1. — estimates of costs to improve public infrastructure resilience (selected GDP-weighted averages and totals):
  - AEs: Upgrading costs (GDP weighted average) = 0.012385038540025626; Retrofitting costs = 0.3581975229026143; Coastal protection costs = 0.07480434367339539; total USD billions = 229.51480850527258
  - EMEs: Upgrading costs = 0.32002237962114966; Retrofitting costs = 1.0794101581597226; Coastal protection costs = 0.09297835768761181; total = 494.38982243032086
  - LICs: Upgrading costs = 0.12986875499547126; Retrofitting costs = 0.52423101822239; Coastal protection costs = 0.4477682781340031; total = 24.55261314287109
  - SSCs simple average entries include values such as 0.05026412621396943 and total 1.5241049355471294
- Annex Figure 2.1.1 (Change in Sovereign Spread vs Public Investment Multiplier - GDP):
  - Row samples: Change in Sovereign Spread = 0; Multiplier = 0.7589645188041731
  - Change in Sovereign Spread = 248.94616096454936; Multiplier = 0.3239846394929993
  - Change in Sovereign Spread = 348.7427438010049; Multiplier = 0.15690910481848772
  - Change in Sovereign Spread = 935.8606930719083; Multiplier = 0.07792126796953767
- Annex Figure 2.1.2 (Mexico (2015 2.5% of GDP) consumption volatility and spread strength):
  - AEs, fast scaling up: Change in Consumption volatility = 0.020868759157586653; Strength = 0
  - EMs/LICs, gradual scaling up: Change in Consumption volatility = 0.017024071115078143; Strength = 0.329926
  - EMs/LICs, fast scaling up: 0.024074426158357476; Strength = 0.380514
  - EM/LICs, aggressive scaling up: 0.03563185456366405; Strength = 0.475224

### Policy-relevant items and model inputs present in the dataset
- Public investment multipliers and sovereign spread sensitivities are present, with sample multiplier values for benchmark and policy variants (Annex Figure 2.1.3):
  - Benchmark Change in Spreads = 248.94616096454936 with Multiplier-Benchmark = 0.3239846394929993
  - Higher debt (>60 pp GDP) Change in Spreads = 294.01687421294787 with Multiplier-High-Debt = 0.3168364918967254
  - Consumption tax rate +3 pp scenario Multiplier = 0.3396129914897548
- Project appraisal and implementation characteristics linked to cost overruns and scale-up effectiveness:
  - "Rate of return computed at appraisal (1 = yes; 0 = no)" coefficient = -0.0379
  - "Share of grant financing" coefficient = 0.2046
  - "Project size" coefficient = -0.0712
  - "Project complexity" coefficient = -0.0391
- Infrastructure resilience cost estimates (Box Figure 2.1.1.) provide GDP share averages and USD billions totals for AEs, EMEs, LICs, useful for fiscal planning and resilience budgeting.

### Sample country panels and governance/capacity indicators
- IHRscore paired with import shares and fitted lines (Figure 2.11) for 123 countries; sample entries include:
  - Afghanistan: IHRscore = 42.26; Respiratory Medical Product Imports (%) = 0; Fitted Line = 0.0929505250581747
  - Australia: IHRscore = 99.8933333333333; Respiratory Medical Product Imports (%) = 0.136950211874042; Fitted Line = 0.212585032256924
- Governance and capacity indicators linked to capital spending or capability (Annex Figures 2.6.1 and 2.6.2):
  - Benin: Average.Score = 29.155; CaptoGDPTotal = 0.173039569823629; Mean of CaptoGDPTotal = 0.15
  - Cuba: Average.Score = 99.1; CaptoGDPTotal = 0.283597140624947; Mean = 0.15
  - Benin (Annex Figure 2.6.2) CaptoGDPperIHR = 0.00593515931482179; Slope for whole sample = 0.00385997880300972

### Scenarios, confidence intervals, and uncertainty reporting
- Several worksheets include confidence-interval computations and significance settings (e.g., Annex Figure 2.5.2 has "Significance" = 5 and formulas for lower/upper bounds).
- Time series and panel outputs include STD and Beta - 2STD rows (Figure 2.8), enabling uncertainty characterization:
  - Output STD (All sample) = 0.0807458; Beta - 2STD Output (All sample) = 0.07196469999999999
- Annex sensitivity tables show alternative parameterizations (e.g., Annex Figure 2.1.4. presents matrices for "Lower correlation corporate and sovereign spreads (<0.5)" with values -46.70913074200422, -172.11626017907156, -586.3864159092835 and "Higher share of concessional debt (>30 percent of GDP)" with values -53.995537499249565, -253.19198918434705, -839.956498021388)

### Notes on data usage
- The workbook provides granular numeric series suitable for replication of chapter figures: time series, cross-country panels, impulse-response tables, sectoral 2-year impacts, infrastructure cost estimates, and scenario/sensitivity grids.
- Exact numeric values above are provided as sampled in worksheet rows and should be used verbatim for analysis, replication, or ingestion.

*Data extracted from fiscalmonitordatabase-october2020-chapter-2 (workbook sample rows).*

---


_Source: https://www.imf.org/-/media/files/publications/fiscal-monitor/2020/october/data/fiscalmonitordatabase-october2020-chapter-2.xlsx_