## _wp1443

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---

### I. Introduction and objectives
- Resource-rich countries face fiscal policy challenges from volatile international prices of hydrocarbons and minerals.
- Key mechanisms causing problems:
  - "Dutch Disease": appreciation of real exchange rates and weakened competitiveness of domestic tradable sectors.
  - "Voracity effect" (Tornell and Lane, 1999): positive shocks in government revenues can produce more-than-proportional increases in discretionary spending.
- Stabilization funds defined (Balding, 2012): “a government account designed to smooth public expenditures and consumption by setting aside revenue during periods of rapid growth that then could be drawn on during economic contractions” (p. 8).
- Main objective: examine if the presence of stabilization funds helps governments in resource-rich countries reduce their expenditure volatility.
- Primary empirical result:
  - The volatility of government spending in countries with stabilization funds is 13 percent lower than that in countries without such funds.
  - Robustness tests: impacts around 15 to 20 percent in many robustness specifications.

### II. Data, sample and variable construction
- Sample and period:
  - Panel data set of 68 resource-rich countries over a 25-year period (sample period 1988–2012; estimation samples start in 1993 because of 5-year lagging of time-variant explanatory variables).
  - Note: precisely, 63 countries from IMF (2012b) plus five countries (Colombia, Kiribati, Kuwait, Nauru and Tuvalu) considered; six countries excluded from main analysis due to data availability in some specifications.
- Key dependent variable:
  - Discretionary government spending volatility — The log of 5-year moving standard deviation of the residuals from the IV estimation of Equation (2) (country-by-country estimation; estimation period 1980–2012).
  - Volatility uses residuals after controlling for country-specific cycle and includes IV to address endogeneity between Δln(G_{i,t}) and Δln(Y_{i,t}).
- Presence of stabilization fund:
  - Dummy variable = 1 if a stabilization fund exists in a given year; operation assumed to start the year after establishment (establishment year 2000 → dummy = 0 for t = 2000, 1 for t = 2001).
  - In the main empirical model stabilization_fund is used with a 5-year lag (values in year t-5).
  - Sample coverage: 32 countries have—or had—stabilization funds; number of funds considered is 37 (countries may have more than one; first fund’s establishment year used for the dummy).
- Control groups of variables (all time-variant variables lagged 5 years in main regressions):
  - econ_structure: government expenditure (% of GDP, logs); population (logs); GDP per capita, PPP (logs); export product diversification (Theil’s entropy index).
  - econ_management: real GDP growth volatility (log of 5-year moving standard deviation); CPI inflation (log difference, percent; extreme values adjusted).
  - financial_market: liquid liabilities (broad money % of GDP, logs); volatility of liquid liabilities (log of 5-year moving standard deviation); international financial remoteness (log great-circle distance to London/New York/Tokyo); capital account openness (Chinn and Ito index).
  - political_inst: political freedom index (average of political rights and civil liberties, range 1–7, higher = less freedom); political regime (Polity IV, -10 to 10) used in robustness checks.
- Summary statistics (selected):
  - Volatility of discretionary government expenditure (in logs) — N 807 — Mean 2.17 — S.D. 0.67 — Min 0.21 — Max 4.19
  - Presence of stabilization fund (dummy) — N 807 — Mean 0.26 — S.D. 0.44 — Min 0.00 — Max 1.00
  - Population (logs) — N 807 — Mean 15.89 — S.D. 1.59 — Min 11.23 — Max 19.24
  - Political freedom (higher, less freedom) — N 807 — Mean 4.44 — S.D. 1.75 — Min 1.00 — Max 7.00
  - Sub-soil resource rents (percent of GDP) — N 797 — Mean 18.47 — S.D. 20.62 — Min 0.00 — Max 140.48

### III. Empirical strategy and identification
- Main estimating equation:
  - Dependent variable σ_{i,t} = measure of government expenditure volatility for country i at year t (t = 1993, 1994, ..., 2012).
  - stabilization_fund dummy (values in year t-5) and four sets of controls (econ_structure, econ_management, financial_market, political_inst).
  - α constant term and ε_{i,t} error term.
- Estimation techniques:
  - Primary: Ordinary least squares (OLS) with panel-corrected standard errors (Beck and Katz, 1995), correcting for heteroskedasticity and contemporaneous correlation across countries.
  - Justification: stabilization_fund is primarily time-invariant/binary in sample; fixed-effects may absorb most variation in dummy.
  - Robustness: fixed-effects, random-effects, IV country-specific regressions, difference-in-differences, and alternative volatility definitions.
- Endogeneity controls:
  - Country-specific IV estimation for Equation (2) (discretionary spending regression): instruments for current real GDP growth Δln(Y_{i,t}) include two lagged values of output growth (t-1, t-2), lagged inflation (t-1), lagged government expenditure growth (t-1), and hydrocarbon price index in year t.
  - Main explanatory variables lagged 5 years to address reverse causality with 5-year moving standard deviation dependent variable.

### IV. Main empirical findings
- Baseline OLS panel results (no interaction terms):
  - Stabilization fund dummy negative and statistically significant (1 percent and 5 percent levels in various specifications).
  - Estimated effect: volatility of government expenditure in countries with stabilization funds is 13 percent lower than in countries without funds (column 4 reported).
  - Robustness specifications commonly show negative relationships; impacts around 15 to 20 percent in many robustness specifications.
- Key control variable magnitudes and findings:
  - Population: one-percent increase in population size → around 0.15 percent decrease in expenditure volatility (statistically significant across specifications).
  - Export product diversification: a one-point decrease in the equality index (more diversified exports) → around 4 percent reduction in volatility.
  - Output growth volatility: 1 percent increase → 0.15 percent increase in government expenditure volatility.
  - Inflation: 1 percentage-point positive change → around 0.5 percent increase in expenditure volatility.
  - Domestic financial market volatility: when domestic financial market becomes more volatile by 1 percent, government expenditure volatility increases by around 0.06 percent.
  - Better institutional quality consistently associated with lower expenditure volatility; relationship statistically significant and strong.

### V. Interaction with institutions and heterogeneity
- Interaction term: political institutions × stabilization fund dummy:
  - Weaker political institutions associated with higher government expenditure volatility in countries with stabilization funds relative to those without.
  - For countries without stabilization funds: a one-unit increase in political freedom score → around 4 percent increase in government expenditure volatility.
  - If a country has a stabilization fund, the country is likely to face an additional increase in volatility by 4 percentage points under the same institutional weakening (Appendix 4 result).
- Restricted sample (countries that ever had stabilization funds):
  - With restricted sample of fund countries, stabilization fund dummy remains significant; spending volatility when stabilization funds present is 22 percent lower (column 4).
  - Interaction: with stabilization funds, one unit increase in political freedom index results in a 10 percent increase in volatility; without funds, effect is 4 percent.

### VI. Robustness and sensitivity analyses
- Five robustness approaches:
  - (A) Alternative indicators of government expenditure and CAFB (cyclically-adjusted fiscal balance).
  - (B) Different institutional indicators, time-invariant measures (legal origins, latitude), and fiscal rules.
  - (C) Restricting samples to only those with stabilization funds.
  - (D) Alternative estimation methods: fixed-effects (FE) or random-effects (RE).
  - (E) Difference-in-differences estimation.
- Selected robustness outcomes:
  - Alternative volatility measures (consumption volatility, CAFB volatility): presence of stabilization funds negative and significant; CAFB on average 20 percent more volatile in countries without stabilization funds.
  - Fiscal rules: number of fiscal rules and fiscal rules quality index negatively related to spending volatility; impact of fiscal rules index larger when stabilization funds are present.
  - Fixed-effects/random-effects results:
    - Hausman tests guide FE vs RE selection; in FE/RE specifications stabilization fund dummy often remains negative and significant (e.g., around 17 percent lower volatility in some FE/RE results).
    - Institutional interaction terms sometimes insignificant in FE/RE models.
  - Difference-in-differences:
    - Panel DID (4-, 6-, 8-year windows) — estimated effect: expenditure volatility in countries that have stabilization funds is lower by around 18 percent in many specifications.
    - Two-period DID (averaging pre/post windows): pre period volatility in treatment group 24 percent higher than control; post period volatility 7 percent lower than control; DID = negative 30 percentage points and statistically significant (adoption of stabilization funds associated with sizable reduction in spending volatility).

### VII. Global distribution and fund characteristics (summary)
- 32 countries have ever created stabilization funds; 37 funds considered.
- Timing: half (16 countries) created their first funds after 2000.
- Regional notes:
  - Asia and the Pacific: many funds created before 1980 (notably Pacific island economies).
  - Africa and Central Asia (part of MCD): many funds created in the 2000s.
  - Middle East created new stabilization funds in all four periods.
  - Western Hemisphere fund establishment concentrated in the 1990s.
- SWF scoreboard (2009 Truman):
  - 18 countries with stabilization funds have scoreboard data; overall scores range from 0.16 (Sudan) to 0.94 (Norway).
  - Structure scores close to maximum for most; governance scores vary widely.
- Assets under management (mid-2009 or most recent):
  - Assets as percent of GDP: more than 300 percent for Kiribati and Tuvalu; less than 0.5 percent for Sudan and Venezuela.

### VIII. Policy implications and limitations
- Policy implications:
  - Stabilization funds can contribute to smoothing government expenditure in resource-rich countries; effect magnitudes in main specifications are substantial (13 percent lower volatility; larger in many robustness checks).
  - Strong institutional frameworks and fiscal rules enhance the effectiveness of stabilization funds; weak political institutions may undermine fund benefits and be associated with increased volatility when funds exist.
  - Complementary policies (diversifying export products, improving economic management, deepening domestic financial markets, and strengthening fiscal rules) are associated with lower expenditure volatility.
- Limitations and avenues for future research:
  - Potential masking of stabilization fund–specific heterogeneity (fund design, rules, assets, operational gaps between establishment and activation).
  - Need for country-specific and cross-country work with detailed quantitative and qualitative information on stabilization funds and political economy to examine heterogeneity and mechanisms.

*Source: IMF working paper _wp1443 (sections I–IV, Appendices 1–5, References).*

### References .............................................................................................................

### _wp1443 - References

### I. Introduction: context and challenges
- Resource-rich countries face fiscal policy challenges from volatile international prices of hydrocarbons and minerals.
- Key mechanisms causing problems:
  - "Dutch Disease": production of natural resources attracts large foreign capital inflows, causing appreciation of the real exchange rates and weakening competitiveness of domestic tradable sectors.
  - "Voracity effect" (Tornell and Lane, 1999): a positive shock in government revenues can result in a more-than-proportional increase in discretionary spending due to rent-seeking or political behavior; spending patterns can be reversed by incoming administrations (Dixit et al., 2000; Humphreys and Sandbu, 2007).
- Stabilization funds have been introduced to save and set aside revenues for future use in stabilizing economies; first establishment in 1953 in Kuwait.
- Balding (2012) definition cited: a stabilization fund is “a government account designed to smooth public expenditures and consumption by setting aside revenue during periods of rapid growth that then could be drawn on during economic contractions” (p. 8).
- Design and institutional arrangements (rules on accumulation and investment, transparency, accountability) are critical to whether a stabilization fund achieves expenditure smoothing (Engel and Valdés, 2000; Bacon and Tordo, 2006; Asfaha, 2007; Le Borgne and Medas, 2007; Villafuerte et al., 2010).
- Empirical evidence is inconclusive on whether stabilization funds effectively reduce expenditure volatility (Devlin and Lewin, 2005; Barma et al., 2012).

### Main objective and empirical result
- Main objective: examine if the presence of stabilization funds helps governments in resource-rich countries reduce their expenditure volatility.
- Primary finding:
  - The volatility of government spending in countries with stabilization funds is 13 percent lower than that in countries without such funds.
- Robustness tests:
  - In most cases, robustness tests also show a negative relationship between the presence of stabilization funds and spending volatility.
  - The impacts are found to be around 15 to 20 percent in many robustness specifications.

### Contributions and scope
- Distinguishing features of the paper:
  - Assesses the impact of stabilization funds while accounting for other potential factors of expenditure fluctuations.
  - Uses different indicators, specifications and estimation methods.
  - Employs a panel data set consisting of 68 resource-rich countries over a 25-year period.
  - Provides additional evidence on determinants of expenditure volatility, covering indicators related to economic structure, real sector management and financial markets.

### Conceptual and definitional notes
- Stabilization funds are commonly treated as a type or function of Sovereign Wealth Funds (SWFs); stabilization funds can be recognized as SWFs whose purpose is stabilization of the economies.
- Definitions of SWFs vary; generally SWFs are government investment (or saving) funds to manage foreign assets and invest them for earnings (e.g., Aizenman and Glick, 2009; Sun and Hesse, 2010).
- The IMF (2012a) uses the definition in the Santiago Principles and classifies 30 SWFs where the data are publicly available into four types: (1) stabilization funds, (2) pension reserve funds, (3) reserve investment funds, and (4) saving funds.
- For the Santiago Principles, refer to the web site of the International Forum of Sovereign Wealth Funds (IFSWF) at http://www.ifswf.org.

### Structure of the paper (as presented)
- Section II: reviews existing literature that explores the relationship between stabilization funds and their impacts on fiscal conditions, focusing on quantitative assessments in the macroeconomic context.
- Appendices and figures/tables included in the source document (listed in the content table of the source):
  - Appendices: List of sample countries; List of stabilization funds; Definitions and sources of variables; Summary statistics; List of growth acceleration episodes.
  - Figures: regional counts, characteristics, assets as percentage of GDP, trends in expenditure volatility.
  - Tables: estimation results including discretionary spending volatility, alternative volatility definitions, political institutions and fiscal rules, fixed/random effects, difference-in-differences estimations, and country listings.

*Source: _wp1443 - References*

### Section III explains the data and empirical methodology employed in this paper. Section IV

### _wp1443 - Section III explains the data and empirical methodology employed in this paper. Section IV

### Literature review
- Existing empirical evidence on the association between stabilization funds and fiscal outcomes is mixed.
- Case studies and single-country analyses cited:
  - Fasano (2000): reviews natural resource funds in Norway, Chile, Venezuela, Kuwait, Oman, and Alaska; finds effects vary by country and fund objectives; successful schemes linked to governments’ commitments to fiscal discipline and good macroeconomic management.
  - Davis et al. (2001): uses Chile, Kuwait, Norway, Oman, Papua New Guinea and seven comparators; time-series and structural break tests find government expenditure tends to be less correlated with resource export receipts in countries with resource funds, but establishment of funds does not have a significant impact on government spending; suggests reverse causality (prudent spenders establish funds).
  - Clemente et al. (2002): Venezuela stabilization fund evaluated with a dynamic CGE model; actual fund ineffective in reducing volatility under positive oil price shocks, hypothetical funds more effective; both actual and hypothetical funds reduce volatility under negative oil price shocks, with hypothetical more effective.
  - Merlevede et al. (2009): Russia’s oil stabilization fund mitigated oil-price-induced fluctuations (data 1995–2007); highlights vulnerability to negative oil price shocks and private sector confidence; fiscal policy and funds can ease shocks.
- Panel-data studies:
  - Crain and Devlin (2003): 71 countries 1970–2000; find natural resource funds increase fiscal volatility, particularly in oil exporters; but Chile and Norway saw reduced expenditure volatility after fund establishment; mixed results attributed to fund management rules and broader fiscal frameworks.
  - Ossowski et al. (2008): 1992–2005 for oil producers; three fiscal outcomes used (non-oil primary balance, growth in government expenditure, ratio of changes in expenditure and oil revenue); concludes fiscal outcomes are not due to introduction of oil funds and fiscal rules; emphasizes the importance of governance institutions.
  - Bagattini (2011): 12 countries 1992–2007; constructs a “success” indicator based on six fiscal variables; finds stabilization funds positively related to non-resource fiscal balance and reduce public debt; political stability and fund rules are important for success.

### Estimation strategy (overview)
- Sample: total of 68 economies rich in non-renewable natural resources (mainly as defined in IMF (2012b)) over the period of 1988–2012.
  - Note: precisely, 63 countries from Appendix 1 Tables 1–3 in IMF (2012b) plus five countries (Colombia, Kiribati, Kuwait, Nauru and Tuvalu) that have or had stabilization funds are considered; due to data availability six countries excluded from the main analysis.
- Annual data used; all time-variant explanatory variables are 5-year lagged to avoid reverse causality arising from the dependent variable construction (expenditure volatility).
- Estimation samples start in 1993 (because of 5-year lag).
- Data set is an unbalanced panel.

### Empirical model
- Main estimating equation (notation preserved from source):
  - σ_{i,t} is a measure of government expenditure volatility in country i in year t (t = 1993, 1994, 1995, ..., 2012).
  - Dependent variable: discretionary government expenditure volatility (obtained from separate country-specific estimations).
  - stabilization_fund: dummy variable equal to 1 if a stabilization fund is present in country i; in Equation (1) values in year t-5 are used.
  - Four sets of indicators included as controls:
    - econ_structure: four variables (indexed by s) measuring economic structure.
    - econ_management: two measures of management of real sector (indexed v).
    - financial_market: four indicators showing capacities of financial markets (indexed m).
    - political_inst: two indicators related to political institutions (indexed p).
  - α is a constant term and ε_{i,t} is an error term.
- Primary estimation technique:
  - Ordinary least squares (OLS) with panel-corrected standard errors (Beck and Katz, 1995).
  - Correction for heteroskedasticity and contemporaneous correlation across countries: each country has its own variance and each pair of countries has different covariance.
  - Justification for OLS over fixed-effects: variable of interest (stabilization fund dummy) is binary and remains unchanged in most samples; when it changes it seldom changes again.
  - Robustness: specifications with additional explanatory variables (especially country-specific characteristics little changed over time) are tried; robustness tests include fixed-effects (or random-effects) and difference-in-differences methods.

### Data and variable construction
- Volatility of government expenditure (dependent variable):
  - Discretionary government spending obtained via country-specific regression estimations based on Fatás and Mihov (2003, 2006).
  - Equation estimated (preserved form):
    - Δln(G_{i,t}) = α_i + β Δln(G_{i,t-1}) + γ Δln(Y_{i,t}) + δ Z_{i,t} + ν_{i,t}  (notation paraphrased to match source)
  - G_{i,t} and Y_{i,t} are real general government expenditure, deflated by GDP deflator, and real GDP in country i in year t, respectively.
  - Controls Z_{i,t}: inflation in year t, average hydrocarbon price indices in years t and t-1 (expressed in logs), computed from coal price in Australia, crude oil price in Dubai and natural gas prices in the United States and Europe.
  - Country-specific constant α_i and error ν_{i,t}.
  - Estimation period for Equation (2): 1980–2012, done country by country.
  - IV estimation employed to address possible reverse causality from current real government expenditure growth Δln(G_{i,t}) to real output growth Δln(Y_{i,t}) in the same year.
    - Instruments used for current real GDP growth: two lagged values of output growth (t-1 and t-2), lagged inflation (t-1), lagged government expenditure growth (t-1), and hydrocarbon price index in year t.
  - Volatility measure: log of 5-year moving standard deviation of the residuals from the IV estimation using values in years from t-4 to t.
- Presence of stabilization fund (stabilization_fund):
  - Dummy variable equals 1 if a stabilization fund exists in a given year; for simplicity operation is assumed to start the following year (e.g., establishment year 2000 → dummy = 0 for t = 2000, but 1 for t = 2001).
  - In Equation (1) values in year t-5 are used (5-year lag).
  - Binary approach used due to lack of detailed time-series information on fund assets/liabilities; approach consistent with literature (Shabsigh and Ilahi, 2007; Ossowski et al., 2008).
  - Relies on multiple sources for fund definitions and establishment years.
  - Uses establishment years; acknowledges gaps between establishment and actual operation in some countries.
  - Sample coverage for funds: 32 countries that have—or had if abolished—stabilization funds. Number of funds considered is 37 (countries can have more than one fund). If multiple funds exist, the year when the first fund was established is used in creating the dummy variable.
  - Basic characteristics of these funds are presented in Figures 1–3 in the source.

### Identification and robustness checks
- Lagging all time-variant explanatory variables by 5 years to address reverse causality because dependent variable is computed from 5-year moving standard deviation of discretionary expenditure residuals.
- IV estimation in country-specific regressions for discretionary expenditure to address endogeneity between government expenditure growth and contemporaneous output growth.
- Robustness tests include:
  - Adding country-specific characteristics that are little changed over time (to mitigate omitted variable bias).
  - Employing fixed-effects, random-effects, and difference-in-differences estimation methods to check whether main specification results hold with alternative estimators.

*Source: IMF working paper _wp1443 (Section III: data and empirical methodology).*

### 1995. Also, as discussed in Kalyuzhnova (2006), the accumulation of assets (with no spending) was required for the initi

### _wp1443 - 1995. Also, as discussed in Kalyuzhnova (2006), the accumulation of assets (with no spending) was required for the initi

### Global distribution and timing of stabilization funds
- 32 countries have ever created stabilization funds; countries are grouped by geographical region and by establishment year (of the first fund).
- Half of them (16 countries) created their (first) funds after 2000.
- Region-specific trends:
  - Most funds in the Asia and the Pacific region, especially in Pacific island economies, were created before 1980.
  - Many funds in Africa and Central Asia (part of MCD) are relatively new (i.e., created in the 2000s).
  - Only the Middle East region created new stabilization funds in all four periods.
  - Fund establishment in Western Hemisphere is concentrated in the 1990s (only one in the 1980s), likely related to structural adjustment programs endorsed by the IMF and the World Bank.

### Sovereign Wealth Fund (SWF) scoreboard scores (2009 version, Truman, 2010)
- 18 countries with stabilization funds have scoreboard data.
- The scoreboard considers four areas: structure, governance, accountability and transparency, and behavior; scored using 33 questions.
- In Figure 2 each of the four categories is adjusted to have equal weight, overall maximum score = 1 (i.e., 0.25 assigned to each area).
- Overall scores:
  - Range from 0.16 for Sudan to 0.94 for Norway.
  - Most countries’ structure scores are close to the maximum; governance scores vary widely.
  - Norway is the only country whose score in the governance area is above 90 percent of the maximum score (i.e., more than 0.225).
  - Timor-Leste and Trinidad and Tobago join Norway for high accountability and transparency scores.

### Assets under management (mid-2009 or most recent date; Truman, 2010)
- Assets expressed as a percentage of each country’s GDP in 2009.
- Percentage shares:
  - More than 300 percent for Kiribati and Tuvalu.
  - Less than 0.5 percent for Sudan and Venezuela.
- Observations:
  - Pacific island countries: high assets-to-GDP ratios but relatively small absolute amounts (important domestically, not necessarily major international players).
  - Western Hemisphere and African countries: funds relatively small both internationally and domestically.
  - Middle East and Central Asia: funds large players both internationally and domestically.
- Note: Although multiple stabilization funds may exist in a country, only one fund is identified per country in Figures 2 and 3.

### Control variables — Economic structure (econ_structure)
- Government size: measured by log of general government expenditure as a percentage of GDP in year t-5.
  - Expectation: larger government size negatively related to expenditure volatility.
- Size of the economy: initial level (lagged 5 years) of total population (in logs).
  - Empirical finding: one-percent increase in population size is associated with an around 0.15 percent decrease in the expenditure volatility.
- Level of development: PPP-adjusted real per capita GDP (5-year lagged values).
  - Expectation: higher income level leads to less volatile expenditure.
- Export product diversification: Theil’s entropy index (computed from 6-digit HS 1988/92 UN Comtrade mirror data).
  - Lower number indicates more equality (more diversified exports).
  - Finding: a one-point decrease in the equality index (i.e., more diversified export baskets)—roughly saying, corresponding to a change in the index from the 75th percentile to the median—reduces the volatility by around 4 percent.
  - Resource-rich countries dependent on fewer export products may experience larger government expenditure volatility.

### Control variables — Economic management (econ_management)
- Output volatility: 5-year moving standard deviation of annual real GDP growth (initial level, year t-5).
  - Finding: a one percent increase in the volatility of output growth raises that of government expenditure by 0.15 percent.
- Inflation: log difference of CPI from the previous year, expressed as a percent, in year t-5 (extremely high inflation cases transformed per Ghosh et al. (2005)).
  - Finding: a one percentage-point positive change in inflation is related to an increase in expenditure volatility by around 0.5 percent.

### Control variables — Financial market (financial_market)
- Domestic market:
  - Liquid liabilities: broad money as a share of GDP; both level and volatility (log of 5-year rolling standard deviation) included.
  - Expectation and findings: deeper domestic financial markets reduce expenditure volatility; domestic financial market volatility increases expenditure volatility.
- International market:
  - Physical accessibility: distance to the closest major financial center (London, New York or Tokyo) measured by great-circle distance (time-invariant). Expected positive relationship with expenditure volatility.
  - Capital account openness: index from Chinn and Ito (2006), updated April 2013. Sign ambiguous: greater openness can provide options to mitigate volatility but also expose economies to capital flow fluctuations.

### Control variables — Political institutions (political_inst)
- Political freedom index: simple average of “political rights” and “civil liberties” from Freedom in the World 2013 (Freedom House), range 1 to 7 (higher = less freedom).
  - Expectation: stronger political institutions (lower index values) associated with less volatile fiscal policy.
  - Chosen for wide time and country coverage; alternative institutional indicators tested in robustness checks.
- Interaction term: political institutions × stabilization fund dummy (stabilization_fund).
  - Expectation: political institutions more important in countries with stabilization funds for reducing expenditure volatility—effective fund management under strong institutions contributes to expenditure stability.

### Empirical results (Section IV)
- Dependent variable: volatility of discretionary government expenditure.
- Specification highlights:
  - In specifications without interaction terms (columns 1–4), coefficients of the stabilization fund dummy are negative and statistically significant at the 1 percent and 5 percent levels.
  - Estimated effect: everything else held constant, volatility of government expenditure in countries with stabilization funds is 13 percent lower than in countries without funds (column 4).
- Economic-structure variables:
  - Larger government size and greater population associated with less volatile government spending.
  - Population statistically significant across specifications; one-percent population increase → ~0.15 percent decrease in expenditure volatility.
  - Income level generally reduces spending volatility, though not always significant.
  - Export product diversification strongly related to stable expenditure; one-point decrease in equality index → ~4 percent reduction in volatility.
- Economic-management variables:
  - Output growth volatility and high initial inflation increase government fiscal volatility:
    - 1 percent increase in output growth volatility → 0.15 percent increase in government expenditure volatility.
    - 1 percentage-point increase in inflation → ~0.5 percent increase in expenditure volatility.
- Financial markets:
  - Domestic financial depth reduces volatility; domestic financial market volatility increases spending volatility.
- Sample notes:
  - Six countries excluded due to data availability. Half of them have stabilization funds: Nauru, Timor-Leste and Tuvalu; the remaining three excluded countries are Afghanistan, Brunei Darussalam and Iraq.

*Source: Excerpt from the provided IMF working paper content.*

### Appendix 4.

### Appendix 4.

### Determinants of government expenditure volatility — key empirical findings
- When the domestic financial market becomes more volatile by one percent, the volatility of government expenditure is increased by around 0.06 percent.
- Better institutional quality is associated with lower expenditure volatility; the relationship is always statistically significant and found to be strong.
- Interaction of institutional quality with the stabilization fund dummy:
  - Weaker political institutions are associated with higher government expenditure volatility in countries with stabilization funds, compared to those without.
  - For countries without stabilization funds, an increase in the political freedom score by one unit leads to around 4 percent increase in government expenditure volatility.
  - If a country has a stabilization fund, the country is likely to face an additional increase in the volatility by 4 percentage points under the same institutional weakening.

### Sensitivity analysis — overview
- Five approaches to robustness checks:
  - (A) Alternative indicators of government expenditure and a measure of cyclically-adjusted fiscal balance (CAFB).
  - (B) Different institutional indicators, time-invariant measures (legal origins, geographical characteristics), and fiscal rules.
  - (C) Restricting samples to only those with stabilization funds.
  - (D) Alternative estimation methods: fixed- or random-effects.
  - (E) Difference-in-differences estimation.

### A. Alternative volatility measures (specification details and findings)
- Volatility measures used:
  - real overall general government expenditure growth,
  - growth in real public investment,
  - real public consumption expenditure growth,
  - CAFB as a share of potential GDP.
- Volatility defined as 5-year moving standard deviation (years t-4 to t) and logged.
- CAFB computed as (Equation (4) in source) under assumption elasticity of overall revenue, GREV, and overall expenditure, GEXP, is one and zero, respectively; Y and Y^p are actual and potential GDP; potential GDP obtained by HP filter with smoothing parameter of 6.25.
- For non-cyclically-adjusted expenditure series, estimations focus on boom periods only (growth acceleration framework).
  - Growth acceleration episode start criteria:
    - average annual real GDP growth rate over the following 5 years >= 3.5 percent, and
    - difference in average annual GDP growth rates between previous 5 years and following 5 years >= 2 percentage points.
  - Episode end criteria:
    - average annual growth rate over the next 5 years <= 2 percent and annual growth of the following year < 3 percent, or
    - negative GDP growth recorded.
  - Episodes continuing <= 3 years are dropped.
- Empirical sample sizes: 57 countries in columns 1 and 2; fewer in columns 3–6 due to growth acceleration identification; CAFB estimations use same sample as main specification.
- Main robustness findings:
  - Presence of stabilization funds has a negative and significant effect on consumption expenditure volatility and on volatility of CAFB.
  - In countries without stabilization funds, on average, CAFB is found to be 20 percent more volatile than in countries having them.
  - Political freedom is a significant determinant in unadjusted expenditure series.
  - Interaction terms: weak political institutions are more associated with volatile government expenditure in countries with stabilization funds than in those without (coefficients significant for most volatility indicators except one measure of overall expenditure).

### B. Different institutional indicators and fiscal rules
- Alternative institutional variables tested:
  - political regime (Polity IV, range -10 to 10),
  - sub-soil resource rents (percent of GDP; sum of oil, natural gas, coal and minerals rents).
- Time-invariant and near time-invariant controls:
  - legal origin (five types), latitude (absolute value), location dummies (landlocked, island), presidential system dummy.
- Fiscal rules indicators (IMF Fiscal Rules Dataset 2013; 21 countries available in sample):
  - number of rules in place,
  - fiscal rules strength index via principal component analysis of six characteristics (monitoring outside government; formal enforcement; coverage; legal basis; well-specified escape clause; supporting procedures/institutions); final score standardized (mean zero, SD one).
- Findings:
  - Use of latitude, landlocked/island dummies and legal origin does not affect the stabilization fund dummy result; stabilization fund dummy remains negative and significant at the 1 percent level.
  - Political regime variable yields negative and significant coefficients for stabilization fund and political regime (interaction insignificant).
  - Higher sub-soil resource rents (percent of GDP) are associated with more volatile government expenditure; relationship stronger and statistically significant when stabilization fund is present.
  - Number of fiscal rules and fiscal rules quality index are negatively related to spending volatility.
  - Interaction: impact of fiscal rules index on spending volatility is larger when stabilization funds are present.

### C. Restriction of sample countries
- Restricted sample: 32 countries that have ever had stabilization funds (three Pacific countries excluded for data availability).
- Findings:
  - Stabilization fund dummy remains significant and contributes to reduction in expenditure volatility.
  - With this restricted sample, spending volatility when stabilization funds are present is 22 percent lower (column 4).
  - Political institutions remain significant; interaction term positive:
    - With stabilization funds, one unit increase in political freedom index results in a 10 percent increase in volatility; without funds, effect is 4 percent.
  - Other determinants: economy size, income level, export product diversification, better economic management, and financial markets associated with lower expenditure volatility.
  - Capital account openness found significant but positively related to public expenditure volatility in this restricted sample.

### D. Fixed-effects vs random-effects models
- Hausman tests used to choose FE vs RE.
  - In all estimations in Panel A and columns 1 and 3 in Panel B, Hausman tests reject null at least at the 5 percent level → FE used.
  - In columns 2 and 4 in Panel B, p-values > 0.5 → RE chosen.
- Results:
  - Stabilization fund dummy negative and significant in Panel A (columns 1, 3 and 5): volatility in countries where stabilization funds are created is around 17 percent lower than in countries without such funds.
  - Institutional indicators and their interactions with stabilization fund dummy are insignificant in these FE/RE specifications.
  - Panel B (fiscal rules) yields no significant results except for stabilization fund dummy in column 2.

### E. Difference-in-differences estimation (two approaches and findings)
- Caveat: introduction of stabilization funds is non-random; selection bias possible. Fixed-effects used to control for unobserved time-invariant factors; standard errors clustered by country.
- Reference year definitions:
  - For treatment group (countries with stabilization funds): use establishment year of the (first) stabilization fund; actual base year t defined as five years ahead of this reference year (because explanatory variables are five-year lagged).
  - For control group (never established funds): use average establishment year of 32 first stabilization funds = 1994, thus t = 1999; sensitivity checks move base year by ±1 year.
- Panel-data difference-in-differences model:
  - Uses panel data covering 4-, 6- and 8-year periods (2, 3 and 4 years before and after establishment).
  - Pre-trend test (modification of model using pre-period only): null that year dummies are same between treatment and control not rejected in all specifications — suggests parallel trends plausible.
  - Estimated effect: expenditure volatility in countries that have stabilization funds is lower than those without by around 18 percent in many specifications (left part of Table 6).
- Two-period difference-in-differences:
  - Construct two-period data by averaging volatility and covariates over 2, 3 and 4 years before and after base year.
  - Results (Table 7, Panel B): in the “pre” period, volatility in treatment group is 24 percent higher than control; in “post” period, volatility of treatment group is 7 percent lower than control; difference-in-differences = negative 30 percentage points and statistically significant — adoption of stabilization funds has significant impact on reduction in spending volatility.
  - Effects remain negative and sizable though not always significant when base year t = 2000 (Panel C).

### Concluding remarks — summary and limitations
- Data and scope:
  - Panel data set covers 68 countries rich in natural resources, including 32 that have ever created stabilization funds, over the period of 1988–2012.
  - Main measure of fiscal volatility: discretionary government expenditure; alternative measures used in robustness checks.
- Main empirical conclusions:
  - Stabilization funds contribute to smoothing government expenditure.
  - Main specification: expenditure volatility in countries with stabilization funds is 13 percent lower than in economies without them.
  - Result robust across alternative specifications, expenditure definitions, and econometric models.
  - Interaction: effect of institutions is stronger when stabilization funds are present — underscores importance of strong institutional framework for managing stabilization funds and their resources.
- Other consistent findings:
  - Size of economy and government negatively related to fiscal volatility.
  - Export product diversification reduces expenditure volatility.
  - Better-managed real sector and functioning domestic and international financial markets reduce spending volatility.
  - Better institutions consistently reduce fiscal volatility.
- Limitations and avenues for future research:
  - Potential masking of stabilization fund–specific heterogeneity.
  - Need for country-specific and cross-country work with detailed quantitative and qualitative information on stabilization funds and political economy to examine heterogeneity and mechanisms.

*Source: Appendix 4 of the supplied IMF working paper content unit.*

### References

### _wp1443 - References

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### Appendix 1. List of sample countries
- Country and Income listing (as presented):
  - Afghanistan                                LIC
  - Albania                                       UMC
  - Algeria                                      UMC
  - Angola                                        UMC
  - Azerbaijan                                  UMC
  - Bahrain                                       HIC
  - Bolivia                                        LMC
  - Botswana                                    UMC
  - Brunei Darussalam HIC
  - Cameroon                                   LMC
  - Central African Republic LIC
  - Chad                                            LIC
  - Chile                                           HIC
  - Colombia                                    UMC
  - Congo, Dem. Rep. LIC
  - Congo, Rep. LMC
  - Côte d’Ivoire LMC
  - Ecuador                                       UMC
  - Equatorial Guinea HIC
  - Gabon                                          UMC
  - Ghana                                          LMC
  - Guatemala                                   LMC
  - Guinea                                         LIC
  - Guyana                                         LMC
  - Indonesia                                     LMC
  - Iran, Islamic Rep.                            UMC
  - Iraq                                              UMC
  - Kazakhstan                                   UMC
  - Kiribati                                       LMC
  - Kuwait                                         HIC
  - Kyrgyz Republic LIC
  - Lao PDR LMC
  - Liberia                                        LIC
  - Libya                                           UMC
  - Madagascar                                LIC
  - Mali                                           LIC
  - Mauritania                               LMC
  - Mexico                                       UMC
  - Mongolia                                 LMC
  - Mozambique                               LIC
  - Nauru                                          –
  - Niger                                         LIC
  - Nigeria                                      LMC
  - Norway                                       HIC
  - Oman                                          HIC
  - Papua New Guinea                 LMC
  - Peru                                         UMC
  - Qatar                                       HIC
  - Russian Federation                 HIC
  - São Tomé and Príncipe LMC
  - Saudi Arabia HIC
  - Sierra Leone                               LIC
  - Sudan                                        LMC
  - Suriname                                     UMC
  - Syrian Arab Republic LMC
  - Tanzania                                   LIC
  - Timor-Leste                               LMC
  - Togo                                        LIC
  - Trinidad and Tobago              HIC
  - Turkmenistan                          UMC
  - Tuvalu                                     UMC
  - Uganda                                   LIC
  - United Arab Emirates HIC
  - Uzbekistan                                LMC
  - Venezuela, RB                        UMC
  - Vietnam                                    LMC
  - Yemen, Rep.                              LMC
  - Zambia                                      LMC

- Income category counts (exactly as stated):
  - Low income (LIC): 15
  - Lower middle income (LMC): 22
  - Upper middle income (UMC): 18
  - High income (HIC): 12
  - Unclassified: 1
  - Total: 68

- Note (verbatim):
  - Note: Countries underscored are those who have ever established stabilization funds. The classification by income level is taken from the list constructed by the World Bank (as in October 2013). Nauru is not included in the list.

*References and Appendix content as presented in the source document.*

### Appendix 2. List of stabilization funds

### _wp1443 - Appendix 2. List of stabilization funds

### Appendix 2 — List of stabilization funds
- Algeria — Revenue Regulation Fund (RRF) — 2000
- Azerbaijan — State Oil Fund of the Republic of Azerbaijan (SOFAZ) — 1999
- Bahrain — Bahrain Mumtalakat Holding Company — 2006
- Botswana — Revenue Stabilization Fund 1/ — 1972
- Botswana — Pula Fund — 1993
- Chad — Revenue Management Plan — 2008
- Chile — Economic and Social Stabilization Fund 2/ — 1985
- Colombia — Oil Stabilization Fund (FAEP) — 1995
- Ecuador — Oil Stabilization Fund (FEP) — 1999 — 2007
- Ecuador — Fund for Stabilization, Social and Productive Investment, and Reduction of Public Debt (FEIREP) — 2002 — 2005
- Ecuador — Savings and Contingency Fund (FAC) 3/ — 2005 — 2008
- Ghana — Ghana Stabilization Fund 4/ — 2011
- Indonesia — Government Investment Unit — 2006
- Iran, Islamic Rep. — National Development Fund 5/ — 1999
- Kazakhstan — National Fund of the Republic of Kazakhstan — 2000
- Kiribati — Revenue Equalization Reserve Fund (RERF) — 1956
- Kuwait — Kuwait Investment Authority (KIA) — 1953
- Kuwait — General Reserve Fund (GRF) 6/ — 1960
- Libya — Oil Reserve Fund (ORF) — 1995
- Mauritania — National Fund for Hydrocarbon Reserves — 2006
- Mexico — Oil Revenues Stabilization Fund of Mexico — 2000
- Mongolia — Fiscal Stabilization Fund — 2011
- Nauru — Nauru Phosphate Royalties Trust (NPRT) — 1968
- Nigeria — Nigerian Sovereign Investment Authority 7/ — 2004
- Norway — Government Pension Fund – Global — 1990
- Oman — State General Reserve Fund of Oman — 1980
- Papua New Guinea — Mineral Resources Stabilization Fund (MRSF) — 1974 — 2001
- Papua New Guinea — Papua New Guinea Sovereign Wealth Fund — 2011
- Peru — Fiscal Stabilization Fund (FEF) — 1999
- Qatar — Stabilization fund — 2000
- Russian Federation — Reserve Fund 8/ — 2004
- Sudan — Oil Revenue Stabilization Account — 2002
- Timor-Leste — Petroleum Fund of Timor-Leste — 2005
- Trinidad and Tobago — Heritage and Stabilization Fund (HSF) 9/ — 2000
- Turkmenistan — Stabilization fund — 2008
- Tuvalu — Tuvalu Trust Fund — 1987
- Venezuela, RB — Macroeconomic Stabilization Fund (FIEM) — 1998

Footnotes (as listed):
- 1/ Established together with the Public Debt Service Fund.
- 2/ Former Copper Stabilization Fund.
- 3/ Special Account for Productive and Social Reactivation (CEREPS) also established.
- 4/ Established together with Ghana Heritage Fund.
- 5/ Former Oil Stabilization Fund.
- 6/ Managed by KIA.
- 7/ Former Excess Crude Account.
- 8/ Former Stabilization Fund of the Russian Federation.
- 9/ Former Interim Revenue Stabilization Fund (IRSF).

Note: The list is mainly based on IMF (2008, 2010 and 2012b), Ossowski et al. (2008), UNCTAD (2008), Truman (2009 and 2010), Kojo (2010), Kunzel et al. (2010), Sinnott et al. (2010), Bagattini (2011) and Baunsgaard et al. (2012).

### Appendix 3 — Definitions and sources of variables
- Dependent variable
  - Discretionary spending volatility — The log of 5-year moving standard deviation of the residuals from the IV estimation of Equation (2). The estimation is done by country and excludes country-specific business cycle effect. — IMF World Economic Outlook.
- Alternative dependent variables
  - Overall spending volatility — 5-year moving standard deviation of annual growth rate of real total government expenditure, in logs. The nominal expenditure series is deflated by GDP deflator. — IMF World Economic Outlook.
  - Investment spending volatility — 5-year moving standard deviation of annual growth rate of real public gross fixed capital formation, in logs. The nominal expenditure series is deflated by GDP deflator. — IMF World Economic Outlook.
  - Consumption spending volatility — 5-year moving standard deviation of annual growth rate of real public consumption expenditure, in logs. — IMF World Economic Outlook, and World Bank World Development Indicators.
  - Volatility of cyclically-adjusted fiscal balance — 5-year moving standard deviation of cyclically-adjusted fiscal balance, expressed as a percentage of potential GDP, in logs. Cyclically-adjusted balance is computed, as in Equation (4). — IMF World Economic Outlook.
- Presence of stabilization fund
  - Presence of stabilization fund — A dummy variable taking a value of 1 if a stabilization fund exists in a given year. The stabilization funds and their established/abolished years are included in Appendix 2. — Author’s compilation.
- Economic structure
  - Government expenditure — General government expenditure as a percentage of GDP, in logs. — IMF World Economic Outlook.
  - Population — Total population, in logs. — IMF World Economic Outlook.
  - GDP per capita, PPP — PPP-adjusted real GDP per capita, expressed in constant 2005 international dollars, in logs. — World Bank World Development Indicators.
  - Diversification of export products — Theil’s entropy index measuring inequality in export product baskets (higher numbers represent more inequality and less diversified). The index is based on the 6-digit-level export product data classified by the Harmonized System 1988/92. Due to the data availability, the mirror data are used in the calculation. — UN Comtrade.
- Real sector management
  - Real GDP growth volatility — The log of 5-year moving standard deviation of annual real GDP growth rate. — IMF World Economic Outlook.
  - CPI inflation — The log difference of consumer price index (CPI), expressed as a percent. The extreme values are adjusted, as in Ghosh et al. (2005). — IMF World Economic Outlook.
- Financial market
  - Liquid liabilities — Broad money as a percentage of GDP, in logs. — IMF World Economic Outlook.
  - Volatility of liquid liabilities — The log of 5-year moving standard deviation of broad money as a percentage of GDP. — IMF World Economic Outlook.
  - International financial remoteness — The log of great-circle distance to the closest major financial center (London, New York or Tokyo). — Rose and Spiegel (2009).
  - Capital account openness — An index measuring the degree of capital account openness. Higher values indicate more openness. — Chinn and Ito (2006), updated April 2013.
- Political institutions
  - Political freedom — An index measuring freedom in terms of political rights and civil liberties. It ranges from 1 to 7, showing higher values mean less freedom. A simple average of two indices, civil liberties and political rights, is used. — Freedom House Freedom of the World 2013.
  - Political regime — An index reflecting political regime, ranging from -10 (strongly autocratic) to 10 (strongly democratic). The series, polity2, is used. — Center for Systemic Peace Polity IV Project.
  - Sub-soil resource rents — A sum of rents from oil, natural gas, coal and minerals, as a percentage of GDP. Rents are the difference between the value of production at world prices and total costs of production. Minerals include tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite and phosphate. — World Bank World Development Indicators.
  - Legal origin — A set of dummy variables on legal origin. There are five types: British, French, German, Scandinavian and Socialist. — La Porta et al. (1999).
  - Latitude — The absolute value of latitude from the equator. — Rose and Spiegel (2009), and Mayer and Zignago (2011).
  - Location – landlocked — A dummy variable taking a value of 1 if a country is landlocked. — Rose and Spiegel (2009), and CIA World Factbook 2013.
  - Location – Island — A dummy variable (taking a values of 1) for an island country. — Rose and Spiegel (2009), and CIA World Factbook 2013.
  - Presidential system — A dummy variable that equals 1 if the presidential system is employed. The original variable has three categories: presidential, assembly-elected president, and parliamentary, and the second one, “assembly-elected president,” is included in the non-presidential group. — Beck et al. (2001), updated January 2013, and CIA World Factbook 2013.
- Fiscal rules
  - Number of rules in place — The total number of fiscal rules in place. It is a sum of rules related to budget balance, debt, revenue and expenditure. Both national and supranational levels are considered. — Schaechter et al. (2012), updated September 2013.
  - Fiscal rules index — An index showing the strength of fiscal rules in place. The index is computed by principal component analysis with the information of rule characteristics. Higher values reflect that rules are more strengthened. The index is standardized (i.e, mean of zero and standard deviation of one). — Schaechter et al. (2012), updated September 2013.

### Appendix 4 — Summary statistics
- Expenditure volatility and its determinants (Table 1)
  - Volatility of discretionary government expenditure (in logs) — N 807 — Mean 2.17 — S.D. 0.67 — Min 0.21 — Max 4.19
  - Presence of stabilization fund (dummy variable) — N 807 — Mean 0.26 — S.D. 0.44 — Min 0.00 — Max 1.00
  - Government expenditure (percentage of GDP, in logs) — N 807 — Mean 3.24 — S.D. 0.38 — Min 1.86 — Max 4.68
  - Population (in logs) — N 807 — Mean 15.89 — S.D. 1.59 — Min 11.23 — Max 19.24
  - GDP per capita, PPP (in logs) — N 807 — Mean 8.15 — S.D. 1.27 — Min 5.57 — Max 11.21
  - Diversification of export products (higher, less diversified) — N 807 — Mean 4.39 — S.D. 1.14 — Min 1.56 — Max 7.33
  - Real GDP growth volatility (in logs) — N 807 — Mean 0.92 — S.D. 0.87 — Min -1.81 — Max 3.54
  - CPI inflation (percent) — N 807 — Mean 8.42 — S.D. 9.64 — Min -9.41 — Max 66.33
  - Liquid liabilities (percentage of GDP, in logs) — N 807 — Mean 3.27 — S.D. 0.62 — Min 0.77 — Max 4.77
  - Volatility of liquid liabilities (in logs) — N 807 — Mean 1.00 — S.D. 0.76 — Min -2.06 — Max 3.86
  - International financial remoteness (km, in logs) — N 807 — Mean 7.99 — S.D. 0.35 — Min 6.70 — Max 8.65
  - Capital account openness (higher, more openness) — N 807 — Mean -0.08 — S.D. 1.48 — Min -1.86 — Max 2.44
  - Political freedom (higher, less freedom) — N 807 — Mean 4.44 — S.D. 1.75 — Min 1.00 — Max 7.00
- Alternative volatility measures (Table 2)
  - Volatility of overall expenditure (in logs) — N 507 — Mean 2.32 — S.D. 0.60 — Min 0.46 — Max 3.95
  - Volatility of public investment (in logs) — N 473 — Mean 2.93 — S.D. 0.62 — Min 0.64 — Max 4.59
  - Volatility of government consumption (in logs) — N 474 — Mean 1.67 — S.D. 1.01 — Min -1.43 — Max 4.03
  - Volatility of cyclically-adjusted fiscal balance (percentage of potential GDP, in logs) — N 507 — Mean 1.02 — S.D. 0.97 — Min -1.35 — Max 4.05
- Alternative Institutional Indicators and fiscal rules (Table 3)
  - Political regime (higher, more democratic) — N 787 — Mean 0.22 — S.D. 6.54 — Min -10.00 — Max 10.00
  - Sub-soil resource rents (precentage of GDP) — N 797 — Mean 18.47 — S.D. 20.62 — Min 0.00 — Max 140.48
  - Number of fiscal rules in place (number) — N 299 — Mean 0.71 — S.D. 0.88 — Min 0.00 — Max 2.00
  - Fiscal rules index (higher, more strengthened) — N 299 — Mean -0.24 — S.D. 0.73 — Min -0.72 — Max 1.35

*Source: _wp1443 - Appendix 2. List of stabilization funds; Appendix 3. Definitions and sources of variables; Appendix 4. Summary Statistics.*

### Appendix 5. List of growth acceleration episodes

### Appendix 5. List of growth acceleration episodes

### List of countries and growth acceleration periods
- Afghanistan: 2008-2012
- Albania: 1993-1996; 1998-2012
- Algeria: 1972-1977; 1981-1984; 1998-2012
- Angola: 1983-1987; 1994-2012
- Azerbaijan: 1998-2012
- Bahrain: 1969-1974; 1988-1993; 1999-2012
- Bolivia: 1987-1998; 2004-2012
- Botswana: 1968-1991; 1995-2008
- Cameroon: 1977-1985; 1995-2012
- Chad: 1969-1974; 1982-1991; 2001-2004
- Chile: 1976-1980; 1984-1998; 2003-2008
- Colombia: 1968-1994; 2001-2012
- Congo, Dem. Rep.: 2002-2012
- Congo, Rep.: 1970-1974; 1978-1989; 2000-2006; 2008-2012
- Cote d’Ivoire: 1974-1980; 1993-1997
- Ecuador: 1988-1994; 2000-2012
- Equatorial Guinea: 1969-1974; 1980-1985; 1992-2008
- Gabon: 1972-1975; 1988-1991
- Ghana: 1984-2012
- Guatemala: 1987-2012
- Guinea: 1984-2008
- Guyana: 1991-1996; 2006-2012
- Indonesia: 1966-1996; 2000-2012
- Iran, Islamic Rep.: 1968-1976; 1981-1984; 1989-1992; 1998-2011
- Kazakhstan: 1999-2012
- Kiribati: 1990-1994; 1996-2000
- Kuwait: 1968-1971; 2000-2006
- Lao PDR: 1969-1974; 1980-1986; 1989-2012
- Liberia: 2006-2012
- Libya: 2003-2006
- Madagascar: 1966-1969; 1996-2001; 2003-2007
- Mali: 2001-2011
- Mauritania: 1969-1973; 2002-2008
- Mexico: 1988-1994; 1996-2000
- Mongolia: 1994-2008
- Mozambique: 1969-1976; 1987-1991; 1993-2012
- Niger: 1974-1979
- Nigeria: 1968-1973; 1997-2012
- Norway: 1982-1985; 1991-2004
- Oman: 1980-1986; 1990-1998; 2004-2012
- Papua New Guinea: 1969-1973; 2003-2012
- Peru: 1993-1997; 2002-2012
- Qatar: 1969-1976; 1994-2012
- Russian Federation: 1999-2007
- São Tomé and Príncipe: 1973-1978; 2001-2012
- Saudi Arabia: 1968-1980; 1988-1993; 2002-2012
- Sierra Leone: 2000-2012
- Sudan: 1974-1977; 1986-1989; 1993-2010
- Suriname: 2001-2012
- Syrian Arab Republic: 1990-1994; 2004-2012
- Tanzania: 1984-1990; 1996-2012
- Timor-Leste: 2007-2012
- Trinidad and Tobago: 1994-2007
- Turkmenistan: 1998-2012
- Uganda: 1986-2012
- United Arab Emirates: 1989-1992; 1996-2001; 2003-2008
- Uzbekistan: 1997-2012
- Venezuela, RB: 1973-1976; 1984-1988; 2004-2008
- Vietnam: 1974-1979; 1989-2012
- Zambia: 1999-2012

### Note on methodology and projections
- Boom periods are based on real GDP growth rates. Refer to the text about how each episode is identified.
- Episodes without end years indicate that growth accelerations are projected to continue at least until 2013.

*Source: Appendix 5, _wp1443 - Appendix 5. List of growth acceleration episodes*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2014/_wp1443.pdf_