## _wp1627

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### Introduction and context
- After rising dramatically for almost a decade, prices of many commodities, especially energy and metals, dropped sharply since 2011.
- Commodity futures prices suggest future spot prices will remain low or rebound only moderately over the next five years.
- The decline in commodity prices has been accompanied by stark slowdowns in economic growth among commodity-exporting emerging market and developing economies.
- Almost 1 percentage point has been shaved off the average of five-year-ahead growth forecasts for commodity exporters since 2012; medium-term growth forecasts of other emerging market and developing economies remained broadly unchanged as of October 2015.
- Focus: emerging market and developing economies that are net exporters of commodities; analysis of cyclical versus structural components of output growth during the commodity price cycle.

### Key research questions
- Macroeconomic effects: How do swings in the commodity terms of trade affect output, spending, employment, capital accumulation, and total factor productivity (TFP)? How different are responses of actual and potential output?
- Policy influences: Do policy frameworks influence variation in growth over the cycle?
- Sectoral effects: How do swings affect commodity producing, manufacturing, and nontradables sectors?
- Growth outlook: What do empirical findings imply for growth prospects over the next few years?

### Definitions and sample criteria
- “Commodity terms of trade”: country-specific weighted average of international commodity prices with weights equal to ratios of net exports of relevant commodities to the country’s total commodity trade.
- “Commodity exporters”: emerging market and developing economies where gross exports of commodities ≥ 35 percent of total exports and net exports of commodities ≥ 5 percent of exports-plus-imports on average (1960–2014).
- Sample: countries selected to have a balanced sample from 1990 to 2015; outliers with any annual growth rate exceeding 30 percent (in absolute value) excluded.

### Main empirical findings
- Cyclical versus structural:
  - Cyclical components of output growth tend to be about twice the size of structural components.
  - In prolonged terms-of-trade booms, annual actual output growth tended to be 1.0 to 1.5 percentage points higher on average during upswings than in downswings.
  - Potential output growth tended to be only 0.3 to 0.5 percentage point higher on average during upswings versus downswings.
- Investment and drivers:
  - Strong response of investment to swings in commodity terms of trade is the main driver of changes in potential output growth over the cycle.
  - Employment growth and TFP growth contribute little to variations in potential output growth.
- Heterogeneity and policy influences:
  - Stronger responses in countries specialized in energy commodities and metals.
  - Stronger responses in countries with low financial development.
  - Less flexible exchange rates and more procyclical fiscal spending tend to exacerbate the cycle.
- Event-study quantitative differences (upswing minus downswing, statistical significance):
  - Real GDP: about 1.5 percentage points more per year during upswings.
  - Real consumption: about 2.0 to 2.5 percentage points more.
  - Investment: about 8.0 to 8.5 percentage points more.
  - Credit to the private sector and government spending expand faster in upswings (differences significant at the 5 percent level).
- Local projection results:
  - A 10 percentage point increase in a country’s commodity terms of trade leads to slightly more than 1 percentage point increase in GDP after three years; effects significant up to five years.
  - Consumption and investment respond positively and significantly over a seven-year period; total fixed investment response almost double that of consumption.
  - Public investment response is more immediate and longer lasting than private investment.
  - Capital stock changes cumulatively by 1 percentage point steadily for seven years after the shock.
  - Employment response muted (not statistically significant); TFP only weakly significant in first two years.

### Frequency and characteristics of commodity cycles
- Algorithm identifies 115 cycles since 1960 (78 with peaks before 2000 and 37 with peaks after 2000).
- Approximately two cycles per country.
- Upswings slightly longer than downswings: mean (median) for upswings seven (six) years; for downswings six (five) years.
- Event-study cycles with pre-2000 peaks: upswings averaged eight years for extractive exporters and five years otherwise; commodity terms of trade improved by 63 percent on average for those episodes.
- The most recent upswing (2000s) was longer and larger than pre-2000 upswings, especially for energy and metals:
  - For net exporters with at least two upswings, cumulative net terms-of-trade increase averaged slightly more than 70 percent in the 2000s vs 50 percent in past episodes.
  - When all net exporters included, average cumulative increase in the 2000s was about 140 percent.

### Sample composition and summary statistics (Memorandum for 52 economies)
- Number of Economies: 52
- Maximum total commodities share: 96.8
- Maximum extractive share: 96.7
- Maximum nonextractive share: 72.4
- Maximum energy share: 60.0
- Maximum raw materials share: 71.5
- Maximum net commodity exports (percent of total exports-plus-imports): 58.2
- Mean total commodities share: 67.1
- Mean extractive share: 34.6
- Mean nonextractive share: 11.6
- Mean energy share: 14.5
- Mean raw materials share: 6.7
- Mean net commodity exports (percent of total exports-plus-imports): 24.2
- Median total commodities share: 65.9
- Median extractive share: 30.4
- Median nonextractive share: 1.3
- Median energy share: 6.2
- Median raw materials share: 2.7
- Median net commodity exports (percent of total exports-plus-imports): 20.8
- Standard Deviation total commodities share: 14.5
- Standard Deviation extractive share: 32.6
- Standard Deviation nonextractive share: 18.2
- Standard Deviation energy share: 16.5
- Standard Deviation raw materials share: 11.0
- Standard Deviation net commodity exports (percent of total exports-plus-imports): 14.5

### Transmission channels and amplifiers
- Income channel: income windfall raises domestic demand; part met by imports; real domestic income and demand respond more than real output; Dutch disease dynamics imply supply response concentrated in nontradables.
- Investment channel: expectations of persistent favorable terms of trade increase investment, altering productive capital and potential output; overoptimism can magnify boom-bust cycles.
- Fiscal channel: much of windfall accrues to government (especially energy exporters); procyclical fiscal policy during booms can aggravate dynamics; investing windfalls in productivity-enhancing capital can benefit long-term income.
- Financial channel: boom increases firm net worth, reduces leverage, lowers financing premia, eases financial frictions; effects reverse in downswing and unlikely to persist beyond cycle absent sustained financial development.
- Sectoral reallocation: factors shift toward commodities and nontradables at expense of noncommodity tradables; if manufacturing has positive externalities, reallocation may harm aggregate TFP.

### Methodology and data
- Event studies: cycles identified with a modified Bry-Boschan Quarterly algorithm applied to a five-year centered moving-average of the price index; focus on cycles with upswing ≥ five years.
- Local projection regressions (Jordà 2005) used for horizon-specific impulse-response estimates with country and time fixed effects.
- Commodity terms-of-trade indices: trade-weighted averages of global prices of 41 commodities grouped into Energy, Metals, Food, Raw materials.
- Annual change formula preserved in source: ∆logCTOT_{i,t} = Σ_{j=1}^J ∆logP_{j,t} τ_{i,j,t} with τ_{i,j,t} = (x_{i,j,t−1} − m_{i,j,t−1}) / (Σ_{j=1}^J x_{i,j,t−1} + Σ_{j=1}^J m_{i,j,t−1}).
- Price series start in 1960; oil price = simple average of Dated Brent, West Texas Intermediate, and Dubai Fateh.

### Projections and growth outlook (using commodity futures as of end August 2015 and October 2015 WEO assumptions)
- Weak commodity price outlook (as of August 2015) projected to subtract about 1 percentage point annually from average economic growth in commodity-exporting economies over 2015–17 compared with 2012–14.
- In energy exporters the drag estimated at about 2¼ percentage points on average.
- Projected decline in growth of potential output in 2015–17 vs 2012–14:
  - About 1/3 percentage point on average for commodity exporters.
  - About 2/3 percentage point on average for energy exporters.
- Estimation note: many commodity prices declined further in late 2015 and early 2016; hence estimates are likely a lower bound.
- Projection methodology: converts impulse-response level contributions (β_h) into year-over-year growth contributions and aggregates cumulative contributions from shocks up to five years before 2015 via multiplicative accumulation:
  - Cumulative growth contribution in 2015 = (1+γ_{∆s_{i,2010}}^5)(1+γ_{∆s_{i,2011}}^4)(1+γ_{∆s_{i,2012}}^3)(1+γ_{∆s_{i,2013}}^2)(1+γ_{∆s_{i,2014}}^1)(1+γ_{∆s_{i,2015}}^0) − 1
  - Conversion uses e^{β_h ∆s_{i,t}} − 1 and differences of β_h across horizons to obtain γ_{∆s_{i,t}}^h.

### Sectoral case studies: Australia, Canada, Chile (2000–10 boom vs 1990–99)
- Common patterns:
  - Income growth exceeded output growth; domestic demand grew in line with or more than incomes.
  - Investment as a share of GDP rose strongly, exceeding change in savings as a share of GDP.
  - Clear pickup in capital and labor growth in extractive sectors during the boom.
  - Little evidence of large-scale shifts of labor and capital into nontradables overall.
- Country-specific:
  - Australia: capital accumulation in manufacturing picked up; nontradables capital declined; much manufacturing capital increase related to mining and construction demand.
  - Chile: manufacturing employment growth strengthened; capital accumulation slowed in nontradables and declined in manufacturing.
  - Canada: factor accumulation favored extractive and nontradables; manufacturing saw declines in capital and employment.
- Aggregate TFP:
  - Aggregate TFP growth declined in all three countries during the boom relative to previous decade; TFP growth turned negative in Australia and Chile.
  - Decline driven entirely by within-sector effects; between-sector effects attenuated the decline (notably in Canada).
  - Weak within-sector TFP growth in extractive industries and manufacturing common across cases; nontradables TFP decline notable in Australia and Chile.

### Policy implications and recommendations
- Structural vs cyclical: on average about two-thirds of decline in output growth in commodity exporters during a downswing should be cyclical.
- Macroeconomic policy stance:
  - If slowdown is mostly cyclical and policy space exists, expansionary macroeconomic policies could be used to support aggregate demand.
  - If declines weaken potential output or revenue losses threaten debt sustainability, fiscal adjustment may be required.
  - Policymakers should avoid overestimating output gaps and the scope for expansionary policies after booms; if slack is limited, expansionary policies risk raising inflation rather than sustainably boosting investment and employment.
- Structural agenda:
  - Targeted structural reforms are needed to alleviate supply-side bottlenecks and boost productivity to restore stronger growth.
- Mitigating factors:
  - Many commodity exporters have moved toward less procyclical fiscal policies, more flexible exchange rates, and deeper financial systems—factors that could mitigate macroeconomic impacts of terms-of-trade fluctuations.

*Source: Excerpt from the IMF working paper (_wp1627) — IMF staff estimates, event studies, local projections, and country case studies as presented in the source content.*

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

### _wp1627 - References

### Introduction and context
- After rising dramatically for almost a decade, the prices of many commodities, especially those of energy and metals, have dropped sharply since 2011 (Figure 1).  
- Commodity futures prices suggest that, depending on the commodity, future spot prices will remain low or rebound only moderately over the next five years.  
- The decline in commodity prices has been accompanied by stark slowdowns in economic growth among commodity-exporting emerging market and developing economies, most of which had experienced high growth during the commodity price boom (Figure 2).  
- Almost 1 percentage point has been shaved off the average of five-year-ahead growth forecasts for commodity exporters since 2012, while medium-term growth forecasts of other emerging market and developing economies had, as of October 2015, remained broadly unchanged.  
- The paper focuses on emerging market and developing economies that are net exporters of commodities and analyzes changes in cyclical versus structural components of output growth during the commodity price cycle.

### Key research questions
- Macroeconomic effects: How do swings in the commodity terms of trade affect output, spending, employment, capital accumulation, and total factor productivity (TFP)? How different are the responses of actual and potential output?  
- Policy influences: Do policy frameworks influence the variation in growth over the cycle?  
- Sectoral effects: How do swings affect commodity producing, manufacturing, and nontradables sectors?  
- Growth outlook: What do the empirical findings imply for growth prospects over the next few years?

### Definitions and sample criteria
- “Commodity terms of trade” in this paper: the price of a country’s commodity exports in terms of its commodity imports, calculated as a country-specific weighted average of international commodity prices with weights equal to ratios of net exports of relevant commodities to the country’s total commodity trade.  
- “Commodity exporters” are emerging market and developing economies for which gross exports of commodities constitute at least 35 percent of total exports and net exports of commodities constitute at least 5 percent of exports-plus-imports on average, based on available data for 1960–2014.  
- Sample selection: Countries are selected to have a balanced sample from 1990 to 2015; outliers with any annual growth rate exceeding 30 percent (in absolute value terms) are excluded.

### Main findings (empirical summary)
- Swings in the commodity terms of trade lead to fluctuations in both cyclical and structural components of output growth, with cyclical components tending to be about twice the size of structural components.  
  - In previous prolonged terms-of-trade booms, annual actual output growth tended to be 1.0 to 1.5 percentage points higher on average during upswings than in downswings.  
  - Potential output growth tended to be only 0.3 to 0.5 percentage point higher on average during upswings versus downswings.  
- The strong response of investment to swings in the commodity terms of trade is the main driver of changes in potential output growth over the cycle.  
- Employment growth and TFP growth contribute little to variations in potential output growth.  
- Country characteristics and policy frameworks influence the amplitude of growth responses:  
  - Stronger responses in countries specialized in energy commodities and metals.  
  - Stronger responses in countries with low financial development.  
  - Less flexible exchange rates and more procyclical fiscal spending patterns tend to exacerbate the cycle.  
- Case studies for Australia, Canada, and Chile indicate that investment booms are mostly booms in the commodity sector itself; evidence of large-scale movements of labor and capital toward nontradables is mixed.  
- Growth outlook (based on commodity futures prices as of end August 2015 and other assumptions shown in the October 2015 World Economic Outlook):  
  - The weak commodity price outlook (as of August 2015) is projected to subtract about 1 percentage point annually from the average rate of economic growth in commodity-exporting economies over 2015–17 as compared with 2012–14.  
  - In energy exporters the drag is estimated to be about 2¼ percentage points on average.  
  - The paper notes that many commodity prices declined further in late 2015 and early 2016; hence the estimates are likely a lower bound.

### Interpretation: cyclical versus structural effects and policy implications
- On average, about two-thirds of the decline in output growth in commodity exporters during a commodity price downswing should be cyclical.  
- The extent to which the decline in growth has opened significant economic slack varies across countries and depends on:  
  - The cyclical position of the economy at the start of the commodity boom.  
  - The extent to which macroeconomic policies smoothed or amplified the commodity price cycle.  
  - The extent to which structural reforms bolstered potential growth.  
  - Other shocks to economic activity.  
- Policy implication emphasized: attaining growth rates as high as those experienced during the commodity boom will be challenging under the current commodity price outlook unless supply-side bottlenecks constraining growth are alleviated rapidly.  
- Macroeconomic policy stance: price declines that lead to a mostly cyclical slowdown could call for expansionary macroeconomic policies (if policy space is available) to pick up aggregate demand; if declines lead to weaker potential output, fiscal adjustments may be required where revenue losses threaten public debt sustainability.

### Concepts: potential output and transmission channels
- Potential output over a commodity cycle is defined as the level of output consistent with stable inflation—captured by output under flexible prices; the short-term divergence of actual output from potential output is the output gap.  
- In a growth-accounting framework, potential output decomposes into capital, labor, and TFP. Terms-of-trade booms can affect each component:  
  - Capital: Persistent booms raise investment in the commodity sector and supportive industries, raising the level (but not permanent growth rate) of potential output.  
  - Labor supply: Persistent booms may reduce structural unemployment and raise trend participation rates, affecting the level of potential output.  
  - TFP: Booms can induce faster technology adoption and higher R&D spending; sectoral reallocation effects on TFP are ambiguous.  
- Transmission channels for commodity cycles: income and investment.  
  - Income channel: The income windfall raises domestic demand and stimulates domestic production; real domestic output responds less than income and domestic demand. Dutch disease dynamics imply domestic supply response occurs disproportionately in nontradables.  
  - Investment channel: Expectations of persistent favorable terms of trade increase investment, altering productive capital and potential output, but investment may be unsustainable if prices fall.

*Source: Excerpt from the IMF working paper (figures, notes, and numerical values as presented in the source content).*

### 1. Domestic Income and Output Growth during the Boom

### 1. Domestic Income and Output Growth during the Boom

### Model predictions and simulated macroeconomic effects
- The IMF’s Global Economy Model predicts that a commodity price boom induces:
  - higher investment, consumption, output, and labor effort in commodity-exporting economies.
  - factor reallocation toward the nontradables and commodity sectors.
  - a real appreciation of the currency (real exchange rate appreciation).
- Gains in output and labor effort have cyclical and structural components.
- Model simulations (Figure 4) show responses in:
  - Real GDP, Potential GDP, Income (percent deviations).
  - Real Consumption and Investment.
  - Relative prices: REER (CPI based), Nontradables/Tradables.
  - Labor by sector and Real Value Added by sector.
- The income and investment channels are interrelated: larger investment responses in the commodity sector amplify the income windfall and vice versa.

### Transmission channels and amplifiers
- Terms-of-trade income windfall and demand composition:
  - A commodity price boom sharply improves terms of trade for commodity exporters and induces an income windfall.
  - Real domestic income and demand in the median commodity-exporting economy increased considerably more than real output (2000–10).
  - Part of the income-driven increase in domestic demand is met by a rise in imports, contributing to sectoral price changes.
- Relative prices and Dutch-disease dynamics:
  - Prices of relatively scarce nontradable goods and services increase relative to tradables.
  - Real exchange rate (REER) appreciates.
  - Investment incentives increase in the commodity sector and supporting industries (construction, transportation, logistics).
- Investment and supply response:
  - Commodity price booms heighten incentives to invest in the commodity sector and supporting industries (resource movement effect).
  - The supply response depends on sector maturity; more potential for new discoveries and lower costs to ramp up production imply larger supply responses.
  - In relatively mature sectors, higher investment may be associated with only a modest rise in commodity output (higher costs, deeper extraction).
- Expectations about commodity prices:
  - Consumption and investment increase only if the boom is expected to be long lasting.
  - Overly optimistic expectations regarding the persistence of the boom can aggravate the boom-bust cycle by generating a greater boom in domestic demand during the upswing and a larger correction during the downswing (Figure 5).
  - Overoptimism is more likely in persistent global upswings (e.g., early 2000s); futures prices may not materialize.
- Fiscal policy:
  - Much of the commodity price windfall accrues to government in commodity-producing economies—especially in energy exporters.
  - Procyclical fiscal policy during the boom (reducing taxes or increasing consumption spending) can aggravate boom-bust dynamics.
  - If governments invest windfalls in productivity-enhancing capital (infrastructure or human capital), productive capacity and income can benefit over the longer term.
- Financial frictions and sovereign spreads:
  - The commodity boom increases returns, improving companies’ net worth and reducing leverage.
  - Reduced leverage lowers financing premia and cost of capital, easing financial frictions.
  - Increased global risk appetite during the boom can magnify this channel.
  - Empirical illustration: during 1997–2014 commodity-exporting economies had lower spreads on sovereign bond yields when their country-specific terms of trade were higher (negative relationship between country-specific terms of trade and J.P. Morgan EMBI Global spreads).
  - The reduction in the cost of financing boosts income and potential output during the upswing; effects reverse during the downswing. Such effects are unlikely to persist beyond the commodity cycle unless they lead to a sustained improvement in financial sector development.
- Sectoral reallocation:
  - Factors shift away from noncommodity tradables toward commodities and nontradables.
  - Potential concerns if manufacturing is associated with positive externalities (learning-by-doing); shrinking manufacturing share may harm broader economy.
  - Reallocation can change sectoral weights and affect measured aggregate TFP growth.

### Empirical approach: event studies and regression analysis
- Sample: 52 commodity-exporting emerging market and developing economies (classification uses data available for 1962–2014).
- Country classification criteria:
  1. Commodities constitute at least 35 percent of total exports.
  2. Net exports of commodities are at least 5 percent of gross trade (exports plus imports) on average.
- Two-step empirical strategy:
  - Step 1: Event studies of actual and potential output during prolonged upswings in country-specific commodity terms of trade (cycles with prolonged upswings that peaked before 2000).
  - Step 2: Regression approach to isolate impact of changes in terms of trade controlling for contextual factors (e.g., output growth in trading partners).
- Identification of commodity terms-of-trade cycles:
  - Modified Bry-Boschan Quarterly algorithm applied to a five-year centered moving-average of the price index.
  - Algorithm allows asymmetry between upswings and downswings; focus on cycles with upswing at least five years long even if downswing is sudden.
  - This differs from studies focusing on magnitude thresholds or disjointed price changes.

### Commodity terms-of-trade index construction and data
- Country-specific commodity terms-of-trade indices constructed as trade-weighted averages of global prices of 41 commodities, grouped into four categories: Energy, Metals, Food, Raw materials.
- Annual change formula (as given in the source):
  - ∆logCTOT_{i,t} = Σ_{j=1}^J ∆logP_{j,t} τ_{i,j,t}
  - Weights: τ_{i,j,t} = (x_{i,j,t−1} − m_{i,j,t−1}) / (Σ_{j=1}^J x_{i,j,t−1} + Σ_{j=1}^J m_{i,j,t−1})
  - x_{i,j,t−1} and m_{i,j,t−1} denote average export and import values of commodity j by country i between t−1 and t−5 (in U.S. dollars).
- Commodity price series and sources:
  - Price series start in 1960; 41 commodities used.
  - Energy includes coal, crude oil, and natural gas.
  - Metals include aluminum, copper, iron ore, lead, nickel, tin, and zinc.
  - Food includes bananas, barley, beef, cocoa, coconut oil, coffee, corn, fish, fish meal, groundnuts, lamb, oranges, palm oil, poultry, rice, shrimp, soybean meal, soybean oil, soybeans, sugar, sunflower oil, tea, and wheat.
  - Raw materials include cotton, hardwood logs and sawn wood, hides, rubber, softwood logs and sawn wood, soybean meal, and wool.
  - Oil price = simple average of Dated Brent, West Texas Intermediate, and Dubai Fateh.
  - World Bank’s Global Economic Monitor used to extend certain series; Australian coal price and U.S. Energy Information Administration data used for coal.
- Cycle characteristics in event study sample:
  - The event studies focus on cycles with prolonged upswings that peaked before 2000.
  - On average, upswings were eight years long for exporters of extractive commodities and five years long otherwise.
  - The commodity terms of trade improved by 63 percent on average for those episodes.

### Sample composition and summary statistics (Table 1 memorandum)
- Number of Economies: 52
- For the 52 economies (Memorandum):
  - Maximum total commodities share: 96.8
  - Maximum extractive share: 96.7
  - Maximum nonextractive share: 72.4
  - Maximum energy share: 60.0
  - Maximum raw materials share: 71.5
  - Maximum net commodity exports (percent of total exports-plus-imports): 58.2
  - Mean total commodities share: 67.1
  - Mean extractive share: 34.6
  - Mean nonextractive share: 11.6
  - Mean energy share: 14.5
  - Mean raw materials share: 6.7
  - Mean net commodity exports (percent of total exports-plus-imports): 24.2
  - Median total commodities share: 65.9
  - Median extractive share: 30.4
  - Median nonextractive share: 1.3
  - Median energy share: 6.2
  - Median raw materials share: 2.7
  - Median net commodity exports (percent of total exports-plus-imports): 20.8
  - Standard Deviation total commodities share: 14.5
  - Standard Deviation extractive share: 32.6
  - Standard Deviation nonextractive share: 18.2
  - Standard Deviation energy share: 16.5
  - Standard Deviation raw materials share: 11.0
  - Standard Deviation net commodity exports (percent of total exports-plus-imports): 14.5

*Source: IMF staff estimates and calculations (content unit: 1. Domestic Income and Output Growth during the Boom).*

### 1. Frequency of Upswings and Downswings of Given Durations

### 1. Frequency of Upswings and Downswings of Given Durations

### Frequency and duration of commodity cycles
- The algorithm identifies 115 cycles since 1960 (78 with peaks before 2000 and 37 with peaks after 2000).
- There are approximately two cycles a country.
- Upswings are slightly longer than downswings, with a mean (median) of seven (six) years for upswings and six (five) years for downswings.
- Most peaks were in the 1980s and the most recent years, particularly for extractive commodities.
- The duration of phases and the amplitude of price movements are correlated.

*Sources for figures and cycle dating: Gruss 2014; IMF, Primary Commodity Price System; U.S. Energy Information Administration; World Bank, Global Economic Monitor database; and IMF staff calculations. Note: cycles shown are for country-specific commodity terms-of-trade indices.*

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### Event studies: cycles with pre-2000 peaks (method)
- Event studies are carried out for cycles with peaks before 2000, where the full downswing has been observed.
- Average growth rates over upswings (downswings) are computed by first averaging for a given country over all upswing (downswing) years, then taking simple averages of these across countries.
- Samples are fully balanced (they include the same country cycles for upswings and downswings).

---

### Macroeconomic effects during upswings versus downswings (key quantitative findings)
- Output and domestic spending grow faster during upswings than during downswings.
- Average differences (upswing minus downswing), statistical significance:
  - Real GDP: about 1.5 percentage points more per year during upswings.
  - Real consumption: about 2.0 to 2.5 percentage points more.
  - Investment: about 8.0 to 8.5 percentage points more.
  - Differences for GDP, consumption, and investment are statistically significant at the 5 percent level.
- Investment and consumption contribute about equally to the difference in GDP growth; stronger investment response offsets its smaller share in spending.
- Factors supporting domestic demand expand more strongly during upswings:
  - Credit to the private sector expands faster in upswings.
  - Government spending expands faster in upswings; differences are significant at the 5 percent level.
- Exchange rate behavior:
  - Pre-2000 upswing episodes did not show average REER appreciation (differences not significant).
  - The most recent upswing shows about 2.0 to 2.5 percent average real appreciation per year.
  - Flexible regimes have been associated with currency appreciations during upswings (and depreciations during downswings), while fixed regimes experienced depreciations during both phases.
- External accounts and financing:
  - Net commodity exporters received, on average, slightly higher net capital inflows during upswings than during downswings.
  - Current account balances were stronger in upswings.
  - Average ratio of net foreign assets to GDP tended to rise during upswings (driven by a few oil exporters); the median ratio tended to decline more in upswings than in downswings.

---

### Supply-side and trend output dynamics
- Aggregate production factors and TFP tend to move in tandem with changes in the commodity terms of trade.
- Rate of change in the capital stock comoves strongly with terms-of-trade changes (significant at the 10 percent level), consistent with faster investment growth during upswings.
- TFP growth is significantly different between upswings and downswings (at the one percent level), though TFP may capture utilization or hours worked cyclicality.
- Employment growth variation is much smaller and not statistically significant overall; in Latin America employment grew 1.5 percentage points more during upswings than in downswings.
- Trend output (from smoothed employment and TFP and estimated capital stock) is smoother than actual output:
  - Annual actual output growth tended to be 1.0 to 1.5 percentage points higher on average during upswings than in downswings.
  - Potential output growth tended to be only 0.3 to 0.5 percentage point higher during upswings.

---

### Role of policy frameworks and financial depth
- Differences in growth variation between upswings and downswings depend on:
  - Exchange rate regime
  - Cyclicality of fiscal policy
  - Depth of financial markets (credit-to-GDP)
- Empirical patterns:
  - Countries with fixed exchange rates experience slightly stronger variation in growth relative to countries with flexible exchange rates (consistent with a flexible exchange rate acting as a shock absorber); differences are not significant at conventional levels.
  - The difference in output growth between upswings and downswings is larger in countries with more procyclical fiscal spending; this is significant at the one percent level.
  - Countries with a lower level of credit to the private sector (relative to GDP) exhibit stronger variation in growth; this is statistically significant at the one percent level.
  - The growth slowdown in low-credit countries is sharper during downswings, likely reflecting greater tightening of borrowing constraints.
- Sample counts for regimes: the sample includes 34 cycles with fixed exchange rates and 8 cycles with flexible exchange rates (regimes classified using Reinhart and Rogoff 2004).

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### Heterogeneity across commodity exporters and country groups
- Commodity-exporting countries differ by commodity intensity, exhaustible versus renewable resources, and levels of development.
- Emerging markets (EMs) versus low-income developing countries (LIDCs):
  - EMs tend to have greater commodity intensity (GDP share of gross commodity exports).
  - A greater share of LIDCs operate fixed exchange rates.
  - EMs have greater financial depth (higher credit-to-GDP ratios).
  - EMs tend to have more procyclical fiscal stances.
- Growth patterns are more marked for less diversified economies and for exporters of extractive commodities.
- LIDCs have less procyclical fiscal spending and slightly lower commodity intensity but less flexible exchange rates and lower financial development.
- LIDCs exhibit greater variability in growth rates for investment, employment, and TFP compared with EMs, though differences are not statistically significant.

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### The 2000s commodity boom and recent upswing characteristics
- The most recent commodity boom (2000s) differed from earlier booms:
  - Larger upswing in the terms of trade, especially for energy and metals.
  - A greater number of oil exporters in the recent upswing.
- For net exporters with at least two upswings (one in the 2000s and one in 1960–99):
  - The cumulative net terms-of-trade increase averaged slightly more than 70 percent in the 2000s, compared with 50 percent in past episodes.
  - When all net exporters are included, the average cumulative increase in the commodity terms of trade in the 2000s was about 140 percent.
- Despite larger commodity price increases, average annual growth rates of key macroeconomic variables during the most recent upswing were very similar to pre-2000 upswing averages; investment, capital accumulation, and trend growth were somewhat lower in the most recent upswing.
- Improvements since earlier episodes:
  - Fiscal policy was considerably less procyclical during the most recent upswing: the correlation of government spending growth with changes in the commodity terms of trade fell to half of what it was in pre-2000 episodes.
  - Financial depth and exchange rate flexibility increased in most commodity exporters; the share of commodity exporters with hard pegs declined relative to the 1960s and 1970s.
- These improvements help explain a more muted macroeconomic reaction to the commodity price boom and put commodity exporters in a better position to deal with subsequent downswings.

*Sources: External Wealth of Nations Mark II; IMF, Balance of Payments Statistics; IMF, Fiscal Monitor; IMF, International Financial Statistics; Penn World Table 8.1; World Bank, World Development Indicators; Gruss 2014; U.S. Energy Information Administration; IMF, Primary Commodity Price System; IMF staff calculations.*

### 2.2 for the cycle identification methodology. CPI = consumer price index; FDI

### _wp1627 - 2.2 for the cycle identification methodology. CPI = consumer price index; FDI = foreign direct investment; NFA = net foreign assets; REER = real effective exchange rate; TFP = total factor productivity.

### Key empirical findings
- The most recent upswing in the commodity terms of trade was longer and larger than pre-2000 upswings, notably for energy exporters, but it coincided with average annual growth rates in key macroeconomic variables that were similar to those in the earlier booms.
- Commodity exporters entered the current downswing with stronger external positions: the median annual current account balance and the average annual change in the net foreign asset position were 5 percentage points of GDP stronger in the 2000s upswings than earlier.
- Terms-of-trade shocks have positive, fairly long-lasting, and symmetric effects on output.
- A 10 percentage point increase in a country’s commodity terms of trade is estimated to lead to a slightly more than 1 percentage point increase in GDP after three years; effects remain statistically significant up to five years.
- Negative terms-of-trade shocks produce somewhat larger and more persistent effects than positive shocks, though symmetry of responses cannot be statistically rejected.

### Local projection regression methodology
- Baseline estimations follow the local projection method (Jordà 2005; Teulings and Zubanov 2014) using separate horizon-specific regressions to produce nonparametric impulse-response estimates.
- Regression specification (notation preserved):
  - ∆ℎy_{i,t} = α_{i}^h + γ_{t}^h + β_{h} ∆s_{i,t} + ∑β_{1,h} ∆s_{i,t−j}^{p}_{j=1} + ∑β_{2,h} ∆s_{i,t+h−j}^{h−1}_{j=1} + ∑θ_{1,h} ∆y_{i,t−j}^{p}_{j=1} + ∑θ_{2,h} x_{i,t−j}^{p}_{j=0} + ∑θ_{3,h} x_{i,t+h−j}^{h−1}_{j=1} + ε_{i,t}^h
  - Definitions:
    - ∆ℎy_{i,t} ≡ y_{i,t+h} − y_{i,t−1}
    - ∆s_{i,t} ≡ s_{i,t} − s_{i,t−1}
    - y_{i,t} ≡ ln(GDP_{i,t}); s_{i,t} ≡ ln(CTot_{i,t})
  - Controls x_{i,t} include trade-weighted output growth of trading partners, political regime transition, and conflict in the domestic economy.
  - Regressions include country fixed effects α_{i}^h and time fixed effects γ_{t}^h.
- Balanced panel for baseline: period 1960–2007; sample sizes vary by variable (Table 3). Example coverage:
  - Real GDP: 32 commodity exporters (18 Emerging Markets, 14 Low-Income Developing Countries)
  - Real Consumption: 30 (16 EM, 14 LIDC)
  - Real Total Fixed Investment: 33 (17 EM, 16 LIDC)
  - Real Total Factor Productivity: 19 (14 EM, 5 LIDC)

### Impulse responses and magnitudes
- Output: positive and statistically significant response to a 10 percentage point positive terms-of-trade shock; slightly more than 1 percentage point increase in GDP after three years; statistically significant up to five years.
- Consumption and investment: both respond positively and significantly over a seven-year period; average response of total fixed investment is almost double that of consumption.
- Public vs private investment: public investment response is more immediate and longer lasting than private investment.
- Capital accumulation: capital stock changes cumulatively by 1 percentage point (increase or decrease) steadily for seven years after the shock.
- Employment and TFP: muted responses.
  - Employment: response not statistically significant.
  - TFP: only weakly significant in the first two years after the shock.
- Event-study consistency: results align with event studies suggesting potential output effects arise mainly through capital accumulation.

### Heterogeneity and robustness
- Stronger output response among low-income developing countries (LIDCs) than emerging markets (EMs).
- Faster effects in countries specializing in extractive commodities; nonextractive commodity specialists show later-building but potentially more persistent responses (estimates for nonextractive group not statistically significant due to smaller sample and varied responses).
- Sample sensitivity:
  - Extending start date to 1970 adds nine additional exporters (including Gulf oil exporters) and yields broadly robust magnitudes but weaker significance.
  - Starting from 1980 (omitting 1970s oil shocks) boosts GDP response in outer years and sharpens statistical significance.
- Shocks occurring during a persistent commodity terms-of-trade cycle generate stronger and more persistent investment and consumption responses than isolated shocks.

### Projections and implications for growth (using commodity price forecasts as of August 2015)
- On average, weaker commodity price outlook implies annual output growth for net commodity exporters will decline by almost 1 percentage point in 2015–17 compared with 2012–14.
- Energy exporters: projected reduction in growth of about 2¼ percentage points over the same period (reflecting larger and more recent declines in energy prices).
- Potential output effects via capital accumulation:
  - Projected decline in growth of potential output in 2015–17 compared with 2012–14: about 1/3 percentage point on average.
  - For energy exporters: about 2/3 percentage point decline in potential output growth.
- Projection methodology converts impulse-response level contributions (β_h) into year-over-year growth contributions and aggregates cumulative contributions from shocks originating up to five years before 2015 via multiplicative accumulation:
  - Cumulative growth contribution in 2015 = (1+γ_{∆s_{i,2010}}^5)(1+γ_{∆s_{i,2011}}^4)(1+γ_{∆s_{i,2012}}^3)(1+γ_{∆s_{i,2013}}^2)(1+γ_{∆s_{i,2014}}^1)(1+γ_{∆s_{i,2015}}^0) − 1
  - Conversion to growth uses e^{β_h ∆s_{i,t}} − 1 and differences of β_h across horizons to obtain γ_{∆s_{i,t}}^h.

### Sectoral reallocation during the 2000s commodity boom: case studies (Australia, Canada, Chile)
- Analytical focus: how sectoral capital and labor growth changed in boom period 2000–10 versus preboom 1990–99; contribution of sectors to aggregate investment and employment; shifts in nontradables and manufacturing shares; impact on aggregate TFP growth.
- Data sources: Latin America KLEMS and World KLEMS for sectoral output, capital, and labor stocks; analysis limited by data availability.
- Stylized observations:
  - All three countries saw income growth exceed output growth during the boom; domestic demand grew in line with, if not more than, incomes.
  - Investment as a share of GDP rose strongly in all three countries, surpassing the change in savings as a share of GDP.
  - Relative share of commodity sector: largest in Chile, followed by Australia, and smallest in Canada.
  - Australia and Chile experienced larger terms-of-trade gains over the decade than Canada.
  - Chile experienced the smallest real currency appreciation over the boom; Canada experienced the largest real appreciation relative to its terms-of-trade gain.
- Theoretical expectation: commodity booms reallocate output and factors from manufacturing toward commodity and nontradables sectors; sectoral shifts can affect aggregate TFP growth depending on sectoral TFP levels and growth rates.

*Source: IMF staff estimates.*

### 1. Net Commodity Terms of

### 1. Net Commodity Terms of

### Commodity booms and macroeconomic indicators (Australia, Canada, Chile)
- During the 2000–10 commodity terms-of-trade boom:
  - Real incomes grew faster than real output in all three countries.
  - Investment picked up strongly across the three countries.
  - The extent of real currency appreciation differed across the three countries.
- House prices:
  - Real house prices rose faster than the average real house price in commodity importers in all three countries, especially in Canada.

### Reallocation of capital and labor across sectors
- Common pattern:
  - Clear pickup in growth rates of both capital and labor in the extractive sector during the boom.
  - Labor and capital did not shift notably into the nontradables sector overall.
- Country-specific patterns:
  - Australia:
    - Pace of capital accumulation in manufacturing picked up during the boom, while it declined in the nontradables sector.
    - Evidence suggests much of the manufacturing capital increase was related to meeting mining and construction demand (e.g., fabricated metal output growth).
  - Chile:
    - Manufacturing employment growth strengthened during the boom.
    - Capital accumulation slowed in nontradables and declined in manufacturing.
  - Canada:
    - Sectoral factor accumulation favored extractive and nontradables sectors as predicted by standard theory: capital accumulation and employment fell in manufacturing while increased in extractive and nontradables sectors.

### Comparison with commodity importers
- Relative to a sample of commodity importers (Denmark, Finland, Germany, Japan, Sweden, the United Kingdom, and the United States):
  - The three commodity exporters saw a faster reallocation of output shares toward nontradables during the boom, but:
    - Only in Canada did this represent a change relative to the preboom years; in Australia and Chile it continued a preexisting trend.
  - For capital and labor, only Canada shows a steepening trend relative to importers during the boom.
  - Conclusion: Little evidence of a faster shift from manufacturing toward nontradables during the boom among the three countries, except in Canada.

### Impact on aggregate TFP
- Aggregate outcome:
  - Aggregate TFP growth declined in all three case-study countries during the commodity boom relative to the previous decade; TFP growth turned negative in Australia and Chile.
  - The decline in aggregate TFP was entirely due to the within-sector effect in the decomposition; the between-sector effect attenuated the decline, particularly in Canada.
- Sectoral drivers:
  - Declining TFP growth in extractive industries and manufacturing was a common factor behind weak within-sector TFP performance in all three cases.
  - A marked decline in TFP growth in nontradables also contributed in Australia and Chile (including lower TFP growth in construction toward the latter part of the boom).
  - Weak TFP growth in extractive sectors likely associated with time-to-build for large-scale mining investments and tapping less efficient mines; remoteness of extractive sites may have raised marginal costs in supporting nontradables services.
- Broader evidence:
  - The negative within-sector contribution is consistent more broadly for Latin American economies.

### Key quantitative projections and comparisons (paper-wide)
- Regression-based analysis projects:
  - The recent commodity price declines and weak commodity price outlook could subtract about 1 percentage point on average from the growth rate of commodity exporters in 2015–17 relative to 2012–14.
  - For energy exporters, the reduction in growth could be about 2¼ percentage points on average.
  - Projected drag on growth of potential output:
    - About 1/3 percentage point on average for commodity exporters.
    - About 2/3 percentage point on average for energy exporters.
- Structural vs cyclical:
  - The change in the cyclical component of output is about twice the size of the change in potential output (the structural component).

### Policy implications and recommendations
- Macroeconomic policy stance:
  - Policymakers must avoid overestimating output gaps and the scope for expansionary monetary and fiscal policies after booms.
  - If indicators show little slack, expansionary policies risk raising inflation rather than sustainably raising investment and employment.
- Country-specific policy constraints:
  - Exchange rate flexibility: currency depreciation may have eased monetary conditions; easing monetary stance risks further depreciation and higher inflation.
  - Fiscal revenues: declining resource-based fiscal revenues may necessitate fiscal adjustment to secure debt sustainability.
- Structural agenda:
  - Targeted structural reforms are needed to alleviate binding supply-side bottlenecks and boost productivity growth to restore stronger growth in commodity exporters.
- Mitigating factors:
  - Many commodity exporters have moved toward less procyclical fiscal policies, more flexible exchange rates, and deeper financial systems—factors that could mitigate some macroeconomic impacts of terms-of-trade fluctuations.

*Source: IMF staff calculations and analysis (figures, country case studies, and regression-based projections reported in the source content).*

### REFERENCES

### _wp1627 - REFERENCES

### Terms-of-Trade, Commodity Prices, and Booms/Busts
- Adler, Gustavo, and Nicolás E. Magud. 2015. “Four Decades of Terms-of-Trade Booms: A Metric of Income Windfall.” Journal of International Money and Finance 55: 162–92.
- Arezki, Rabah, and Kareem Ismail. 2013. “Boom-Bust Cycle, Asymmetrical Fiscal Response and the Dutch Disease.” Journal of Development Economics 101: 256–67.
- Cashin, Paul, Luis Felipe Céspedes, and Ratna Sahay. 2004. “Commodity Currencies and the Real Exchange Rate.” Journal of Development Economics 75 (1): 239–68.
- Céspedes, Luis Felipe, and Andrés Velasco. 2012. “Macroeconomic Performance during Commodity Price Booms and Busts.” IMF Economic Review 60 (4): 570–99.
- Chen, Yu-chin, and Kenneth Rogoff. 2003. “Commodity Currencies.” Journal of International Economics 60 (1): 133–60.
- Deaton, Angus, and Ronald Miller. 1996. “International Commodity Prices, Macroeconomic Performance and Politics in Sub-Saharan Africa.” Journal of African Economies 5 (3): 99–191.
- Dehn, Jan. 2000. “The Effects on Growth of Commodity Price Uncertainty and Shocks.” Policy Research Working Paper 2455, World Bank, Washington.
- Erten, Bilge, and Jose Antonio Ocampo. 2012. “Super-cycles of Commodity Prices since the Mid-Nineteenth Century.” DESA Working Paper No. 110, United Nations Department of Economic and Social Affairs, New York.
- Kilian, Lutz. 2009. “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market.” American Economic Review 99 (3): 1053–69.
- Schmitt-Grohé, Stephanie, and Martín Uribe. 2015. “How Important Are Terms of Trade Shocks?” NBER Working Paper 21253, National Bureau of Economic Research, Cambridge, Massachusetts.
- Spatafora, Nikola, and Irina Tytell. 2009. “Commodity Terms of Trade: The History of Booms and Busts.” IMF Working Paper 09/205, International Monetary Fund, Washington.
- Spatafora, Nikola, and Andrew Warner. 1995. “Macroeconomic Effects of Terms-of-Trade Shocks.” Policy Research Working Paper 1410, World Bank, Washington.
- Roache, Shaun K. 2012. “China’s Impact on World Commodity Markets.” IMF Working Paper 12/115, International Monetary Fund, Washington.
- Gruss, Bertrand. 2014. “After the Boom—Commodity Prices and Economic Growth in Latin America and the Caribbean.” IMF Working Paper 14/154, International Monetary Fund, Washington.
- Erten, Bilge, and Jose Antonio Ocampo. 2012. “Super-cycles of Commodity Prices since the Mid-Nineteenth Century.” DESA Working Paper No. 110, United Nations Department of Economic and Social Affairs, New York.

### Dutch Disease, Resource Curse, and Structural Effects
- Bjørnland, Hilde Christiane. 1998. “The Economic Effects of North Sea Oil on the Manufacturing Sector.” Scottish Journal of Political Economy 45 (5): 553–85.
- Corden, Walter M. 1981. “The Exchange Rate, Monetary Policy and North Sea Oil: The Economic Theory of the Squeeze on Tradeables.” Oxford Economic Papers 33: 23–46.
- Corden, Walter M., 1984, “Booming Sector and Dutch Disease Economics: Survey and Consolidation”, Oxford Economic Papers, New Series, Vol. 36, No. 3 (Nov., 1984), pp. 359-380.
- Corden, Walter M., and J. Peter Neary. 1982. “Booming Sector and De-industrialisation in a Small Open Economy.” Economic Journal 92 (368): 825–48.
- Ismail, Kareem. 2010. “The Structural Manifestation of the ‘Dutch Disease’: The Case of Oil Exporting Countries.” IMF Working Paper 10/103, International Monetary Fund, Washington.
- Magud, Nicolás, and Sebastián Sosa. 2013. “When and Why Worry about Real Exchange Rate Appreciation? The Missing Link between Dutch Disease and Growth.” Journal of International Commerce, Economics, and Politics 4 (2): 1–27.
- van Wijnbergen, Sweder J. G. 1984a. “The ‘Dutch Disease’: A Disease after All?” Economic Journal 94 (373): 41–55.
- van Wijnbergen, Sweder J. G. 1984b. “Inflation, Employment, and the Dutch Disease in Oil-Exporting Countries: A Short-Run Disequilibrium Analysis.” Quarterly Journal of Economics 99 (2): 233–50.
- Benigno, Gianluca, and Luca Fornaro. 2014. “The Financial Resource Curse.” Scandinavian Journal of Economics 116 (1): 58–86.
- Gelb, Alan, and Associates. 1988. Oil Windfalls: Blessing or Curse? New York: Oxford University Press.
- Frankel, Jeffrey A. 2012. “The Natural Resource Curse: A Survey of Diagnoses and Some Prescriptions.” In Commodity Price Volatility and Inclusive Growth in Low-Income Countries, edited by Rabah Arezki, Catherine A. Patillo, Marc Quintyn, and Min Zhu. Washington: International Monetary Fund.
- van der Ploeg, Frederick. 2011. “Natural Resources: Curse or Blessing?” Journal of Economic Literature 49 (2): 366–420.
- Collier, Paul, Rick van der Ploeg, Michael Spence, and Anthony J. Venables. 2010. “Managing Resource Revenues in Developing Economies.” IMF Staff Papers 57 (1): 84–118.
- Sala-i-Martin, Xavier, and Arvind Subramanian. 2003. “Addressing the Natural Resource Curse: An Illustration from Nigeria.” IMF Working Paper 03/139, International Monetary Fund, Washington.

### Public Investment, Fiscal Policy, and Growth in Resource Contexts
- Albino-War, Maria A., Svetlana Cerovic, Francesco Grigoli, Juan Carlos Flores, Javier Kapsoli, Haonan Qu, Yahia Said, Bahrom Shukurov, Martin Sommer, and SeokHyun Yoon. 2014. “Making the Most of Public Investment in MENA and CCA Oil-Exporting Countries.” IMF Staff Discussion Note 14/10, International Monetary Fund, Washington.
- Berg, Andrew, Edward F. Buffie, Catherine Pattillo, Rafael Portillo, Andrea Presbitero, and Luis-Felipe Zanna. Forthcoming. “Some Misconceptions about Public Investment Efficiency and Growth.” IMF Working Paper, International Monetary Fund, Washington.
- Berg, Andrew, Rafael Portillo, Shu-Chun S. Yang, and Luis-Felipe Zanna. 2013. “Government Investment in Resource-Abundant Developing Countries.” IMF Economic Review 61: 92–129.
- Melina, Giovanni, Shu-Chun S. Yang, and Luis-Felipe Zanna. 2014. “Debt Sustainability, Public Investment, and Natural Resources in Developing Countries: The DIGNAR Model.” IMF Working Paper 14/50, International Monetary Fund, Washington.
- Warner, Andrew M. 2014. “Public Investment as an Engine of Growth.” IMF Working Paper 14/148, International Monetary Fund, Washington.
- Husain, Aasim M., Kamilya Tazhibayeva, and Anna TerMartirosyan. 2008. “Fiscal Policy and Economic Cycles in Oil-Exporting Countries.” IMF Working Paper 08/253, International Monetary Fund, Washington.
- Gupta, Sanjeev, Alvar Kangur, Chris Papageorgiou, and Abdoul Wane. 2014. “Efficiency-Adjusted Public Capital and Growth.” World Development 57: 164–78.
- Gupta, Pranav, Bin Grace Li, and Jiangyan Yu. 2015. “From Natural Resource Boom to Sustainable Economic Growth: Lessons for Mongolia.” IMF Working Paper 15/90, International Monetary Fund, Washington.
- International Monetary Fund (IMF). 2015. “Making Public Investment More Efficient.” Staff Report, Washington.

### Productivity, Structural Change, and Sectoral Dynamics
- Baldwin, John R., Wulong Gu, Ryan Macdonald, and Beiling Yan. 2014. “Productivity: What Is It? How Is It Measured? What Has Canada’s Performance Been over the Period 1961 to 2012?” Canadian Productivity Review 38.
- Barnes, Paula., Leo Soames, Cindy Li, and Marcela Munoz, 2013, “Productivity in Manufacturing: Measurement and Interpretation,” Productivity Commission Staff Working Paper, November 2013.
- Dabla-Norris, Era, Si Guo, Vikram Haksar, Minsuk Kim, Kalpana Kochhar, Kevin Wiseman, and Aleksandra Zdzienicka. 2015. “The New Normal: A Sector-Level Perspective on Productivity Trends in Advanced Economies.” IMF Staff Discussion Note 15/03, International Monetary Fund, Washington.
- Hofman, André, Matilde Mas, Claudio Aravena, and Juan Fernandez de Guevara. 2015. “LA KLEMS: Economic Growth and Productivity in Latin America.” In The World Economy: Growth or Stagnation? edited by Dale W. Jorgenson, Kyoji Fukao, and Marcel P. Timmer. Cambridge: Cambridge University Press.
- Grigoli, Francesco, Alexander Herman, Andrew Swiston, and Gabriel Di Bella. 2015. “Output Gap Uncertainty and Real-Time Monetary Policy.” IMF Working Paper 15/14, International Monetary Fund, Washington.
- Parham, Dean. 2012. “Australia’s Productivity Growth Slump: Signs of Crisis, Adjustment or Both?” Visiting Researcher Paper, Australian Government, Productivity Commission, Melbourne.
- Tulip, Peter, 2014, “The Effect of the Mining Boom on the Australian Economy”, Reserve Bank of Australia, December Quarterly Bulletin.
- Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2010. Africa’s Infrastructure: A Time for Transformation. Washington: Agence Française de Développement and World Bank.
- McMahon, Gary, and Susana Moreira. 2014. “The Contribution of the Mining Sector to Socioeconomic and Human Development.” Extractive Industries for Development Series 30, World Bank, Washington.

### Exchange Rates, External Wealth, and Macro Frameworks
- Eichengreen, Barry. 2008. “The Real Exchange Rate and Economic Growth.” Commission on Growth and Development Working Paper 4, World Bank, Washington.
- Kohli, Ulrich. 2004. “Real GDP, Real Domestic Income, and Terms-of-Trade Changes.” Journal of International Economics 62 (1): 83–106.
- Lane, Philip R., and Gian Maria Milesi-Ferretti. 2007. “The External Wealth of Nations Mark II: Revised and Extended Estimates of Foreign Assets and Liabilities, 1970–2004.” Journal of International Economics 73 (2): 223–50.
- Reinhart, Carmen M., and Kenneth S. Rogoff. 2004. “The Modern History of Exchange Rate Arrangements: A Reinterpretation.” Quarterly Journal of Economics 119 (1): 1–48.
- International Monetary Fund (IMF). 2006. “Methodology for CGER Exchange Rate Assessments.” Unpublished, Washington.
- Pesenti, Paolo. 2008. “The Global Economy Model: Theoretical Framework.” IMF Staff Papers 55 (2): 243–84.
- Lalonde, René, and Dirk Muir. 2007. “The Bank of Canada’s Version of the Global Economy Model (BoC-GEM).” Bank of Canada Technical Report 98, Bank of Canada, Ottawa.
- Gruss, Bertrand. 2014. “After the Boom—Commodity Prices and Economic Growth in Latin America and the Caribbean.” IMF Working Paper 14/154, International Monetary Fund, Washington.

### Methodology, Econometrics, and Identification Techniques
- Jordà, Òscar. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” American Economic Review 95 (1): 161–82.
- Harding, Don, and Adrian Pagan. 2002. “Dissecting the Cycle: A Methodological Investigation.” Journal of Monetary Economics 49 (2): 365–81.
- Teulings, Coen N., and Nikolay Zubanov. 2014. “Is Economic Recovery a Myth? Robust Estimation of Impulse Responses.” Journal of Applied Econometrics 29 (3): 497–514.
- Rosenbaum, Paul R., and Donald B. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects” Biometrika 70 (1): 41–55.
- Kilian, Lutz. 2009. “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market.” American Economic Review 99 (3): 1053–69.
- Blagrave, Patrick, Roberto Garcia-Saltos, Douglas Laxton, and Fan Zhang. 2015. “A Simple Multivariate Filter for Estimating Potential Output.” IMF Working Paper 15/79, International Monetary Fund, Washington.
- Teulings, Coen N., and Nikolay Zubanov. 2014. “Is Economic Recovery a Myth? Robust Estimation of Impulse Responses.” Journal of Applied Econometrics 29 (3): 497–514.

### Country and Regional Case Studies, Historical Analyses
- Aravena, Claudio, Juan Fernández, André Hofman, and Matilde Mas. 2014. Structural Change in Four Latin American Countries: An International Perspective. Macroeconomics of Development Series No. 150. Santiago: Economic Commission for Latin America and the Caribbean.
- Fornero, Jorge, Markus Kirchner, and Andrés Yany. 2014. “Terms of Trade Shocks and Investment in Commodity Exporting Economies.” Paper presented at “Commodity Prices and Macroeconomic Policy,” 18th Annual Conference of the Central Bank of Chile, Santiago, October 23.
- Francis, Michael. 2008. “Adjusting to the Commodity-Price Boom: The Experiences of Four Industrialized Countries.” Bank of Canada Review (Autumn): 29–41.
- Steenkamp, Daan. 2014. “Structural Adjustment in New Zealand since the Commodity Boom.” Reserve Bank of New Zealand Analytical Note AN 2014/2, Reserve Bank of New Zealand, Wellington.
- Tulip, Peter, 2014, “The Effect of the Mining Boom on the Australian Economy”, Reserve Bank of Australia, December Quarterly Bulletin.
- Bjørnland, Hilde Christiane, and Leif A. Thorsrud. Forthcoming. “Boom or Gloom? Examining the Dutch Disease in Two-Speed Economies.” Economic Journal.
- De Gregorio, José. 2015. “From Rapid Recovery to Slowdown: Why Recent Economic Growth in Latin America Has Been Slow.” Policy Briefs PB15-6, Peterson Institute for International Economics, Washington.
- Gupta, Pranav, Bin Grace Li, and Jiangyan Yu. 2015. “From Natural Resource Boom to Sustainable Economic Growth: Lessons for Mongolia.” IMF Working Paper 15/90, International Monetary Fund, Washington.

_References list from _wp1627 - REFERENCES_

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