## _wp0665 — introduction in 1999 of various initiatives anchored in Poverty Reduction Strategy Papers (PRSPs)

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### Main findings and framing
- The 1999 introduction of initiatives anchored in Poverty Reduction Strategy Papers (PRSPs) aimed at strengthening coordination among donors, improving the design of financial support programs, and improving domestic records of policy implementation.
- Empirical finding: "We find no evidence of any fundamental changes in the way aid has been delivered in the past five years."
- Observed trends:
  - "If anything, aid volatility has worsened somewhat and the information value of long-term lending commitments has declined."
  - Interpretation: "the main causes of the volatility and unpredictability of aid, and the broader issue of macroeconomic instability in low-income countries, have not been addressed in a systematic manner by the donor community."

### Data, definitions, and measurement methodology
- Database: 76 countries, 1975–2003; unbalanced panel (balanced-sample checks for 50 countries with complete 1975–2003 series).
- Primary aid definition: narrow gross aid (ODA grants and disbursed ODA loans, excluding food and emergency aid, and debt relief); results robust to net aid and broader definitions.
- Three denominators used for robustness:
  - percent of nominal GDP,
  - constant U.S. dollars per capita,
  - percent of purchasing-power-parity (PPP) GDP.
- Transformations and filtering:
  - Natural logarithms taken for aid and revenue series.
  - De-trended using Hodrick-Prescott filter with smoothing coefficient λ = 7 (robustness checks with λ = 100 and first differences).
- Relative volatility measure Φ defined as ratio of variances of filtered aid and revenue series (Φ = Aθ / Rθ).
- Aid procyclicality assessed by correlation of de-trended aid and revenue.
- Exogenous shock identification: fitted real GDP growth ŷ_t from OLS on ToT (terms of trade) and A (agriculture share) used to focus on external shocks.

### Key empirical findings — volatility and procyclicality
- Aid is substantially more volatile than domestic fiscal revenue; relative volatility increased in the early 2000s compared with the late 1990s.
- Representative statistics (variables expressed in percent of GDP; constant U.S. dollars per capita; percent of PPP GDP):
  - Full sample (1975–2003; sample = 76):
    - Average Φ = 14.2*
    - Median Φ = 6.2*
    - Number of countries where Φ > 1 = 73
    - Procyclicality of aid (average correlation) = 0.04
    - Number of countries where corr > 0 = 38
    - Aid-to-revenue ratio (in percent) = 32.8
  - Variables expressed in constant U.S. dollars per capita:
    - Average Φ = 5.4*
    - Median Φ = 2.5*
    - Number of countries where Φ > 1 = 60
    - Procyclicality = 0.12*
    - Number of countries where corr > 0 = 49
    - Aid-to-revenue ratio (in percent) = 29.6
  - Variables expressed in percent of PPP GDP:
    - Average Φ = 10.2*
    - Median Φ = 5.5*
    - Number of countries where Φ > 1 = 73
    - Procyclicality = 0.00
    - Number of countries where corr > 0 = 36
    - Aid-to-revenue ratio (in percent) = 12.1
  - '*' denotes significance at the 5 percent level.
- Medians (less sensitive to outliers) give lower estimates: Φ ≈ 6 (percent of GDP) and Φ ≈ 2.5 (US$ per capita).
- Relative volatility highest in subsamples of the least aid-dependent countries (aid-to-revenue < 25%).
- Instances where aid is less volatile than revenue (Φ < 1) are rare; examples listed in source (GDP-based and US$ per capita series).
- Temporal pattern:
  - Aid became more volatile in 2000–03 than in 1995–98 (post-PRSP period compared with pre-PRSP period).
  - Average Φ rose sharply between 1995–98 and 2000–03 for percent-of-GDP series (1999 excluded from averages): e.g., average Φ rose from 14.2* (full period) to 25.9* (1995–98) and 50.0* (2000–03).
- Decomposition shows increase in Φ primarily driven by increased aid volatility (Aθ) while revenue volatility (Rθ) remained stable or declined.

### Key empirical findings — predictability (commitments versus disbursements)
- Long-term loan commitments and disbursements (World Bank Global Development Finance) used as imperfect proxy for predictability.
- Average finding: actual loan delivery falls short of promises by more than 40 percent; predictability worsened in 2000–03.
- Specifics:
  - In 2000–03 disbursements fell short of commitments by about one-third (sample average C-to-D ratio highest in 20 years).
  - During 2000–03 average commitments grew by about 4 percent relative to 1995–98, while average disbursements fell by some 5 percent over the same interval (1999 excluded).
- Cross-sectional relationship with development level:
  - An increase in constant PPP GDP by US$ 100 is associated with a reduction of almost 0.2 in the C-to-D ratio (better reliability).
  - 10th and 90th percentiles of the C-to-D series ordered by GDP per capita were 2.0 and 1.0, respectively.
- HIPC vs non-HIPC (2000–03):
  - Median C-to-D ratio in non-HIPC countries = 1.2 (2000–03).
  - Median C-to-D ratio in HIPC countries = 2.0 (2000–03).
  - Regression slopes relating GDP per capita to C-to-D ratio: β = -0.20* for HIPC; β = -0.13* for non-HIPC; full-sample slope β = -0.18* (all slope coefficients statistically significant).
- Conclusion: long-term lending predictability remains low on average and is significantly worse for poorer countries; no evidence of improvement attributable to PRSP-related initiatives.

### Key empirical findings — aid and macroeconomic shocks (insurance role)
- Aid did not compensate for large GDP shortfalls during 1975–2003:
  - Countries hit by negative GDP shocks equivalent to 5 percent or more received a meaningful increase in aid in only one out of seven cases.
- Sample and event counts:
  - Sample period: 1975–2003.
  - Total number of observations = 2,010.
  - Six occurrences of fitted real GDP falling by 10 percent or more.
  - Twenty-seven occurrences of aid increasing by 10 percentage points of GDP.
  - Example interpretation: out of 6 cases of annual GDP declines of 10 percent or more one of these declines (or 16.7 percent) coincided with an aid increase of 10 percent or more (of which there were 27 in the sample).
  - For aid increases of 5 percentage points of GDP coinciding with 5 percent negative GDP shocks: these increases coincided with the GDP shock in one case only, or 7.1 percent.
  - The probability of receiving compensating aid in the wake of such GDP shocks was between 5 and 30 percent for off-diagonal combinations of GDP and aid shocks.
- Timing and persistence:
  - Aid arriving contemporaneously with large negative output shocks is uncommon.
  - Probability of delayed aid (negative GDP shock at time t and positive aid shock in time t+1) was on average substantially smaller than for contemporaneous aid.
  - Even when considering contemporaneous and lagged aid jointly, joint coincidence remained well below one-half for most combinations of negative output shocks and additional aid.
  - Aid was just as likely to decrease as to increase in the wake of a negative GDP shock: probability of a cut in aid in the wake of a negative output shock was between 10 and 20 percent.

### Policy implications and recommendations
- Core conclusion: No prima facie evidence that PRSP-related or late-1990s initiatives produced meaningful improvements in aid delivery stability, predictability, or countercyclical behavior.
- Recommended donor and IFI actions:
  - Give macroeconomic stability the prominence it deserves and make it an explicit goal of development assistance.
  - Discuss and adopt mechanisms through which aid can help achieve macroeconomic stability, including disbursing aid in a more stable and predictable manner and responding more quickly and efficiently to large adverse shocks.
  - Reassess effectiveness of PRSP-anchored coordination and commitment mechanisms.
  - Strengthen measures that enhance predictability and stability of aid flows and improve the information value of long-term lending commitments.
  - Allow use of bilateral aid flows for stabilization purposes and design modalities that reduce volatility and procyclicality.
- Country-level and interim measures:
  - Aid-dependent countries may adopt conservative fiscal policies supported by international financial institutions.
  - Formulate more ambitious targets for reserve accumulation in IMF-supported programs, taking into account vulnerability to external volatility, and consider using aid to fund this accumulation with clear replenishment rules.
  - Build cushions of international reserves to compensate for shortfalls in aid or other budgetary revenue.
- Need for a comprehensive approach that addresses sources of economic instability in low-income countries in an integrated manner, rather than piecemeal activation of separate lending facilities.

### Measurement robustness and sensitivity checks
- Smoothing techniques and aid definitions produce broadly similar qualitative results:
  - Relative volatility (percent of GDP; Hodrick-Prescott λ = 7) for gross aid narrow definition = 12.1 (all countries) and 5.3 (countries with aid ≥ 50 percent of revenue).
  - Alternative smoothing (λ = 100) and first-difference transformations yield similar relative-volatility magnitudes (e.g., 11.5 and 11.2).
- Results robust to using net aid (WDI definition) and broader gross-aid definitions.

*Source: _wp0665 — introduction in 1999 of various initiatives anchored in Poverty Reduction Strategy Papers (IMF working paper, excerpts and appendices, sample period 1975–2003).*

### introduction in 1999 of various initiatives anchored in Poverty Reduction Strategy Papers

### introduction in 1999 of various initiatives anchored in Poverty Reduction Strategy Papers (PRSPs)

### Main findings and framing
- The 1999 introduction of initiatives anchored in Poverty Reduction Strategy Papers (PRSPs) aimed at strengthening coordination among donors, improving the design of financial support programs, and improving domestic records of policy implementation.
- Empirical finding: "We find no evidence of any fundamental changes in the way aid has been delivered in the past five years."
- Observed trends:
  - "If anything, aid volatility has worsened somewhat and the information value of long-term lending commitments has declined."
  - Interpretation: "the main causes of the volatility and unpredictability of aid, and the broader issue of macroeconomic instability in low-income countries, have not been addressed in a systematic manner by the donor community."

### JEL Classification, keywords, and authors
- JEL Classification Numbers: F35, 019
- Keywords: External aid, ODA, volatility, predictability
- Author E-Mail Address: abulir@imf.org, ahamann@imf.org
- Author affiliations: A. Bulíř is with the IMF Institute and A. J. Hamann is with the Independent Evaluation Office of the IMF.

### Acknowledgements (as reported)
- Gratitude expressed to: Christopher Adam, Andy Berg, Oya Celasun, Tom Crowards, Tina Daseking, David Goldsbrough, Tim Lane, Alex Mourmouras, Mark Plant, Russell Kincaid, and various other colleagues at Department for International Development (DFID) and the IMF.
- Thanks to participants of the "Seminar on Foreign Aid and Macroeconomic Management" (Maputo, March 14-15, 2005) and several IMF seminars for comments and suggestions.

### Structure of the paper (table of contents)
- I. Introduction
- II. Volatility and Predictability of Aid: What Exactly Is the Issue?
- III. Data and Measurement Issues
  - A. Choices, Choices...
  - B. Data Transformations
- IV. Measuring the Variability of Aid: Three Approaches
  - A. Aid Volatility and Procyclicality
  - B. Predictability
  - C. Aid and Macroeconomic Shocks: Beyond Procyclicality of Aid
- V. Conclusions and Policy Implications
- References
- Appendices
  - 1. A Selective Glossary of Aid-Related Initiatives
  - 2. Data Sources and Transformations

### Key empirical claims and indicators (as presented)
- Periods and coverage referenced: 1975–2003; 1995–2003; 2000–03; 1975–2003.
- Table and figure captions indicating empirical emphases:
  - Table 1: "Aid Is More Volatile than Revenue and Procyclical, 1975–2003"
  - Table 2: "During 2000–03 Aid Has Been More Volatile than Ever Before"
  - Table 3: "The Patterns of Aid Volatility in HIPC and Non-HIPC Countries Remain Broadly Unchanged, 1995–2003"
  - Table 4: "Aid Has Been Poor Insurance against Negative GDP Shocks, 1975–2003"
  - Figure 1: "Aid Is Important, Albeit Declining, Resources for Aid Recipients, 1975–2003"
  - Figure 2: "Countries Receive Unstable Aid Flows, 1975–2003"
  - Figure 3: "Selected Countries: Relative Volatility of Aid and Revenue, Φ, 1975–2003"
  - Figure 4: "Aid Is Getting More Volatile; Revenue Remains Stable, 1975–2003"
  - Figure 5: "Commitments Are Poor Predictors of Disbursements, 1975–2003"

### Measurement and methodological emphasis
- Sections dedicated to data and measurement issues, including "Choices, Choices..." and "Data Transformations," indicating substantial attention to how aid variability is defined and measured.
- The paper employs three approaches to measuring variability of aid:
  - A. Aid Volatility and Procyclicality
  - B. Predictability
  - C. Aid and Macroeconomic Shocks: Beyond Procyclicality of Aid

### Policy implications (summarized from conclusions described)
- Despite PRSP-anchored initiatives introduced in 1999, the donor community has not systematically addressed:
  - The main causes of aid volatility and unpredictability.
  - The broader issue of macroeconomic instability in low-income countries.
- Implicit policy direction derived from findings:
  - Reassess effectiveness of PRSP-anchored coordination and commitment mechanisms.
  - Strengthen measures that enhance the predictability and stability of aid flows.
  - Improve the information value of long-term lending commitments to better support recipient country macroeconomic management.

*Source: _wp0665 - introduction in 1999 of various initiatives anchored in Poverty Reduction Strategy Papers*

### Appendix Tables

### _wp0665 - Appendix Tables

### Introduction and framing
- Aid volatility and unpredictability are highlighted as macroeconomic management problems for low-income countries and have attracted attention from academia, bilateral donors, and IFIs.
- Key coordination problems cited: “uncoordinated donor practices” and “delays in disbursements.”
- Historical initiatives referenced as attempts to improve aid allocation and macroeconomic integration: Enhanced Structural Adjustment Facility (1987), Heavily Indebted Poor Countries (HIPC) Initiative (1996), Poverty Reduction Strategy Paper (PRSP) (1999).
- Implication: greater donor and IFI recognition of the cost of macroeconomic instability is required; program design and contingency planning need improvement; bilateral donors should avoid making aid a source of macroeconomic volatility.
- This paper updates Bulíř and Hamann (2003), extending the series from 1975–1997 to 1975–2003 and testing robustness to alternative aid definitions and smoothing techniques.

### Core empirical questions addressed
- Does aid continue to be more volatile than domestic revenue?
- Has aid become more predictable (are disbursements related to donor commitments)?
- Do aid inflows respond to macroeconomic shocks in recipient countries?

### Data, definitions, and transformations
- Database: 76 countries, 1975–2003, with gross and net aid series and domestic revenue series (tax and nontax). Unbalanced panel (see Table A1 for availability).
- Primary aid definition used: narrow gross aid (ODA grants and disbursed ODA loans, excluding food and emergency aid, and debt relief). Results robust to net aid and broader definitions.
- Three denominators used for robustness:
  - percent of nominal GDP,
  - constant U.S. dollars per capita,
  - percent of purchasing-power-parity (PPP) GDP.
- All series: natural logarithms, de-trended using Hodrick-Prescott filter (with robustness checks using first differences and HP parameter changes).
- Relative volatility measure Φ defined as ratio of variances of filtered aid and revenue series (logs).

### Key findings — Aid volatility and procyclicality
- Aid is substantially more volatile than domestic fiscal revenue; relative volatility increased in the early 2000s compared with the late 1990s.
- Representative statistics (variables expressed in percent of GDP; constant U.S. dollars per capita; percent of PPP GDP):
  - Full sample (1975–2003):
    - Average Φ = 14.2*
    - Median Φ = 6.2*
    - Sample size = 76; Number of countries where Φ > 1 = 73
    - Procyclicality of aid (average correlation) = 0.04
    - Number of countries where corr > 0 = 38
    - Aid-to-revenue ratio (in percent) = 32.8
  - Variables expressed in constant U.S. dollars per capita:
    - Average Φ = 5.4*
    - Median Φ = 2.5*
    - Sample size = 76; Number of countries where Φ > 1 = 60
    - Procyclicality = 0.12*
    - Number of countries where corr > 0 = 49
    - Aid-to-revenue ratio (in percent) = 29.6
  - Variables expressed in percent of PPP GDP:
    - Average Φ = 10.2*
    - Median Φ = 5.5*
    - Sample size = 76; Number of countries where Φ > 1 = 73
    - Procyclicality = 0.00
    - Number of countries where corr > 0 = 36
    - Aid-to-revenue ratio (in percent) = 12.1
  - '*' denotes significance at the 5 percent level.
- Medians (less sensitive to outliers) give lower estimates: Φ ≈ 6 (percent of GDP) and Φ ≈ 2.5 (US$ per capita).
- Relative volatility highest in subsamples of the least aid-dependent countries (aid-to-revenue < 25%).
- Instances where aid is less volatile than revenue (Φ < 1) are rare:
  - GDP-based series: Bolivia, Chad, Comoros (3 instances).
  - U.S. dollar per capita series: Angola, Bolivia, Burkina Faso, Chad, Comoros, Ecuador, Guinea-Bissau, Laos, Lebanon, Lesotho, Mongolia, Nigeria, Papua New Guinea, Sudan, Uganda, Vietnam (16 instances).
  - In many of these cases the result is driven by relatively unstable revenue rather than unusually stable aid.
- Aid tends to be mildly procyclical on average—declines in aid associate with declines in revenue—reducing aid’s effectiveness for consumption smoothing.
- Temporal pattern:
  - Aid became more volatile in 2000–03 than in 1995–98 (post-PRSP period compared with pre-PRSP period).
  - Table 2 highlights dramatic increases in average Φ between 1995–98 and 2000–03; e.g., average Φ rose from 14.2* (full period) to 25.9* (1995–98) and 50.0* (2000–03) when variables expressed in percent of GDP (1999 excluded from averages).
- Balanced-sample check (50 countries with complete 1975–2003 series): average aid volatility increased in the 2000s after a late-1990s decline; median volatility remained unchanged or increased slightly.
- Decomposition (Figure 4): increase in Φ primarily driven by increased aid volatility (Aθ), while revenue volatility (Rθ) remained stable or declined.

### Key findings — Predictability (commitments vs disbursements)
- Long-term loan commitments and disbursements (World Bank Global Development Finance) used as imperfect proxy for predictability.
- Average finding: actual loan delivery falls short of promises by more than 40 percent; predictability worsened in 2000–03:
  - In 2000–03 disbursements fell short of commitments by about one-third (sample average C-to-D ratio highest in 20 years).
  - During 2000–03 average commitments grew by about 4 percent relative to 1995–98, while average disbursements fell by some 5 percent over the same interval (1999 excluded).
- Cross-sectional relationship with development level:
  - An increase in constant PPP GDP by US$ 100 is associated with a reduction of almost 0.2 in the C-to-D ratio (better reliability).
  - 10th and 90th percentiles of the C-to-D series ordered by GDP per capita were 2.0 and 1.0, respectively.
- HIPC vs non-HIPC (2000–03):
  - Median C-to-D ratio in non-HIPC countries = 1.2 (2000–03).
  - Median C-to-D ratio in HIPC countries = 2.0 (2000–03).
  - Regression slopes relating GDP per capita to C-to-D ratio: β = -0.20* for HIPC; β = -0.13* for non-HIPC; full-sample slope β = -0.18* (all slope coefficients statistically significant).
- Conclusion: long-term lending predictability remains low on average and is significantly worse for poorer countries; no evidence of improvement attributable to PRSP-related initiatives.

### Key findings — Aid and macroeconomic shocks
- Hypothesis tested: whether donors provide countercyclical aid in response to large negative GDP shocks (shock-insurance nexus).
- Aid did not compensate for large GDP shortfalls during 1975–2003:
  - Countries hit by negative GDP shocks equivalent to 5 percent or more received a meaningful increase in aid in only one out of seven cases.
- Methodology note: focus on exogenous components of GDP variability using proxies for external shocks (terms of trade and agriculture share) to distinguish exogenous from domestically generated shocks; results reported up to the point documented in source text.

### Policy implications and interpretation
- No prima facie evidence that PRSP-related or late-1990s initiatives produced meaningful improvements in aid delivery stability, predictability, or countercyclical behavior.
- Suggested policy directions implied by analysis:
  - Donors and IFIs should explicitly recognize and target reduction of aid volatility as an objective of development assistance.
  - Improve program design with emphasis on contingency planning and modalities that reduce volatility and procyclicality.
  - Bilateral donors should prevent aid from being a source of macroeconomic volatility and consider allowing portions of aid to neutralize other sources of volatility.
  - Aid-recipient countries should commit to less erratic policy implementation to help stabilize disbursements.

*Source: _wp0665 - Appendix Tables (IMF), Appendix Tables and main text excerpts.*

### Annex II for further details).

### 1. Countries were aid recipients during the period under consideration (the minimum number

### _wp0665 - 1. Countries were aid recipients during the period under consideration (the minimum number

### Sample selection criteria
- Minimum number of annual observations is 9 for Tajikistan.
- Only countries with an average population of more than 500,000 were included (to address small-country bias), eliminating most small island countries (World Bank, 2000).
- Only countries where the sample aid-to-GDP ratio exceeds 1 percent were included (to focus on countries where aid has some minimal macroeconomic impact).
- Sample limited to countries with average U.S. dollar GDP per capita incomes below 3,000 (to concentrate on development aid), eliminating countries such as Argentina and Brazil.

### Data transformations and volatility measures
- Raw data downloaded: aid in U.S. dollars and revenue in domestic currency; both series expressed in common denominators (percentages of nominal GDP, percentages of PPP GDP, and constant per capita U.S. dollars).
- Natural logarithm taken for aid and revenue to put both series on the same scale.
- Aid and revenue series found to be nonstationary, or in a few cases stationary around a deterministic trend; series de-trended using the Hodrick-Prescott filter (HP) with smoothing coefficient λ = 7 as suggested by Pesaran and Pesaran (1997).
- Sample variances calculated for the aid and revenue series, Aθ and Rθ, respectively.
- Relative volatility defined as Φ = Aθ / Rθ (measure of the ratio of these variances).
- Relative aid variability assessment:
  - (i) Φ calculated for each country.
  - (ii) Significance of sample averages and medians across countries tested. Because Φ is a ratio of variances estimated with a common number of observations per country in numerator and denominator, statistical significance of sample averages checked using an F-test.
- Aid procyclicality assessed by the correlation coefficient of de-trended aid and revenue.

### Empirical approach to exogenous shocks ("Aid and macroeconomic shocks: beyond procyclicality of aid")
- Focus on the part of GDP variability explained by two proxies of external exogenous shocks: terms of trade and the share of agriculture in GDP.
- For each country, ordinary least squares (OLS) regressions estimated:
  - y_t = α + β ToT_t + γ A_t + ε_t
    - where y_t is the rate of growth of real GDP, ToT is the index of terms of trade, A is the agricultural output to GDP ratio, and ε is an error term.
- The fitted values of GDP growth, ŷ_t, were used in Table 4 in lieu of actual GDP growth.

### Notes on functional form and scale
- In Bulíř and Hamann (2003) logs were not taken because focus was on absolute size of aid and revenue shocks; for volatility analysis logs are preferable because they eliminate the scale problem.
- Example scale issue: the aid-to-revenue ratio is about 0.3; thus, all else being equal, variances of domestic revenue would tend to be about ten times larger than those of aid.

### Sample of countries and sample periods (Table A1)
- Albania 1992-2003
- Algeria 1975-2003
- Angola 1981-2003
- Armenia 1994-2003
- Bangladesh 1975-2003
- Benin 1975-2003
- Bhutan 1981-2003
- Bolivia 1975-2003
- Burkina Faso 1975-2003
- Burundi 1980-2003
- Cambodia 1987-2003
- Cameroon 1980-2003
- Central African Rep. 1980-2003
- Chad 1980-2003
- Colombia 1975-2003
- Comoros 1980-2003
- Congo, Dem. Rep. of 1980-2003
- Congo, Republic of 1980-2003
- Côte d'Ivoire 1980-2003
- Djibouti 1980-2003
- Dominican Republic 1975-2003
- Ecuador 1975-2003
- Egypt 1975-2003
- El Salvador 1975-2003
- Ethiopia 1980-2003
- Fiji 1975-2003
- Gambia, The 1975-2003
- Ghana 1975-2003
- Guatemala 1975-2003
- Guinea 1980-2003
- Guinea-Bissau 1980-2003
- Guyana 1975-2003
- Haiti 1975-2003
- Honduras 1975-2003
- Indonesia 1975-2003
- Jamaica 1975-2003
- Jordan 1975-2003
- Kenya 1975-2003
- Kyrgyz Republic 1993-2003
- Lao People's Dem. Rep 1975-2003
- Lebanon 1975-2003
- Lesotho 1975-2003
- Madagascar 1978-2003
- Malawi 1975-2003
- Mali 1975-2003
- Mauritania 1975-2003
- Mongolia 1975-2003
- Morocco 1992-2003
- Mozambique 1975-2003
- Nepal 1980-2003
- Nicaragua 1975-2003
- Niger 1975-2003
- Nigeria 1980-2003
- Pakistan 1975-2003
- Papua New Guinea 1975-2003
- Paraguay 1975-2003
- Peru 1975-2003
- Philippines 1975-2003
- Rwanda 1975-2003
- Senegal 1975-2003
- Sierra Leone 1975-2003
- Sri Lanka 1975-2003
- Sudan 1975-2003
- Swaziland 1975-2003
- Syrian Arab Republic 1975-2003
- Tajikistan 1992-2003
- Tanzania 1975-2003
- Thailand 1975-2003
- Togo 1975-2003
- Tunisia 1975-2003
- Turkey 1975-2003
- Uganda 1975-2003
- Vietnam 1981-2003
- Yemen, Republic of 1975-2003
- Zambia 1975-2003
- Zimbabwe 1978-2003
- (1/ Countries in italics in the original are eligible for debt relief under the HIPC Initiative.)

### Relative volatility results (Table A2: Relative volatility of aid and revenue (Φ), in percent of GDP)
- All countries / Countries with aid equivalent to at least 50 percent of revenue
- I. Aid definitions (Hodrick-Prescott filter, λ = 7)
  - A. Gross aid, narrow definition (loans and grants) 1/ : 12.1 5.3
  - B. Gross aid, broad definition (loans, grants, emegency and food aid) : 12.2 6.2
  - C. Net aid, WDI defintion : 13.5 7.7
- II. Smoothing techniques (Gross aid; loans and grants)
  - A. Hodrick-Prescott filter, λ = 7 1/ : 12.1 5.3
  - B. Hodrick-Prescott filter, λ = 100 : 11.5 5.5
  - C. First difference : 11.2 6.3
- Note: 1/ Results for gross aid and λ = 7 correspond to Table 1.
- Source: Authors' calculations.

*Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2006/_wp0665.pdf.*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2006/_wp0665.pdf_