## 10. Simulation Results from a Global Double-Dip Recession

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### I. Introduction — scope and questions
- Focus: economic linkages between low-income countries (LICs) and a set of systemic emerging market (EM) leaders and the spillover effects of EM growth on LIC economic activity.
- Research questions:
  - Do growth shocks in EM leaders produce spillover effects on LIC economic activity?
  - What is the size and nature of these spillovers across LIC regions?
- Methods:
  - VAR methodologies and dynamic panel regressions to estimate spillovers.
  - Simulations using IMF’s Global Projection Model (GPM) and trading patterns from IMF Direction of Trade.

### II. Trade linkages — stylized facts and exposure
- Advanced economies remained the largest destination of LIC exports, accounting for about 60 percent of total LIC exports in 2008; their share fell by over 10 percentage points in the last 30 years.
- Regional patterns and product composition shifts (1995 → 2008):
  - SSA: decline in advanced-economy share of about 30 percentage points; non-traditional partners account for about half of SSA exports in 2004-2008.
  - Asia and ECA LICs: expansion of intra-regional trade.
  - By 2008 fuels, manufactures, and other products account for the largest share of LIC export baskets (notably to advanced economies and inter-regional leaders).
  - Intra-regional exports tend to have a larger share of products with higher local value added across LIC regions.
- Representative exporter-share statistics (means):
  - India (Asia): Share of Intra-Regional LIC Exports, mean: 10.7% — Share of Inter-Regional LIC Exports, mean: 5.9%
  - China (Asia): 10.8% — 8.2%
  - Russia (ECA): 13.6% — 0.9%
  - Turkey (ECA): 9.4% — 0.6%
  - Mexico (LAC): 2.9% — 0.4%
  - Brazil (LAC): 15.1% — 0.5%
  - Saudi Arabia (MNA): 2.5% — 0.5%
  - South Africa (SSA): 3.3% — 1.0%
- Top LICs with strong export dependence on EM leaders (selected examples):
  - Mongolia: LICs Exports to EM Leaders: 64.6 — Major EM Leader: China — Exports to Major EM Leader: 64.5
  - Bolivia: 61.0 — Brazil: 60.1
  - Nepal: 55.5 — India: 54.8
  - Guinea-Bissau (Inter-regional example): LICs Exports to EM Leaders: 65.0 — Major EM Leader: India — 64.0

### III. Other linkages — remittances, FDI, cross-border banking
- Remittances:
  - India accounts for about 30 percent of total remittance inflows to LICs in Asia.
  - Russia and Saudi Arabia account for respectively 50 and 65 percent of total remittance flows to LICs within their own regions.
  - Remittances from Russia account for 28 and 36 percent of GDP in the Kyrgyz Republic and Tajikistan, respectively.
  - Saudi Arabia: close to 10 percent of total remittance flows to LICs in Asia and about 1 percent of flows to SSA.
  - LICs in LAC: advanced economies (in particular the United States) account for over 80 percent of remittance inflows.
  - SSA remittances: over 75 percent originate from the U.K. and the euro area.
- Foreign Direct Investment (FDI):
  - Advanced countries remain the most important source of FDI for LICs, but FDI from EM leaders is increasing.
  - Between 2003 and 2009, the stock of Chinese FDI to LICs increased 19 fold.
  - India: between 2001 and 2005 the stock of Indian FDI in LICs grew almost four-fold.
  - South Africa: share of African host economies in South Africa’s outward FDI stock reached almost USD11 billion in 2008 (22 percent, compared with 5 percent in 2000).
- Top recipients of Chinese FDI, average 2003-09 (millions of U.S. dollars / Percent of total FDI to LICs):
  - Nigeria — 1311 — 14.4
  - Mongolia — 1271 — 11.1
  - Myanmar — 1221 — 10.6
  - Cambodia — 79 — 6.8
  - Zambia — 79 — 6.8
  - Laos PDR — 74 — 6.4
  - Vietnam — 62 — 5.4
  - Guyana — 60 — 5.2
  - Congo, Dem. Rep. of — 52 — 4.5
  - Sudan — 52 — 4.5
  - Papua New Guinea — 44 — 3.9
  - Afghanistan — 26 — 2.3
  - Niger — 26 — 2.3
  - Tajikistan — 21 — 1.8
  - Madagascar — 19 — 1.6
  - Ethiopia — 18 — 1.6
- Cross-border bank lending:
  - Advanced economies dominate; cross-border bank flows from EM leaders are around 1 percent of total cross-border flows to LICs (using available data).
  - Lending from EM leaders to LICs within their own regions increased rapidly from 2006 onwards.
  - Notable entrants among emerging-market banks: Industrial and Commercial Bank of China, Itau Unibanco (Brazil), Sberbank (Russia), State Bank of India.

### IV. Evidence of coupling / decoupling and variance decomposition
- Growth correlations (selected figures):
  - Correlation, GDP Growth (1995-99 vs 2000-08):
    - Advanced Countries — 1995-99: 0.79 — 2000-08: 0.605
    - EM Leaders — 1995-99: 0.566 — 2000-08: 0.958
  - Correlation, GDP Growth and LIC Exports:
    - Advanced Countries — 1995-99: 0.598 — 2000-08: 0.701
    - EM Leaders — 1995-99: -0.055 — 2000-08: 0.630
- Variance decompositions (selected magnitudes):
  - In 1980-2008, external shocks contributed, on average, about 45 to 60 percent of the variation in LIC economic activity at a two-year horizon.
  - Over 1990-2008, this range increased on average for Asia, LAC, and SSA.
  - Shocks to advanced economies: responsible for over 28 percent of business cycle variation in 1990-2008 on average; LICs in LAC account for over 50 percent of variation due to advanced economy shocks.
  - Shocks to EM leaders: on average over 17 percent of variation in economic activity in the average LIC; range: less than 10 percent for MNA and SSA LICs to around 20 percent or more for LICs in LAC, ECA, and Asia.
  - Commodity price shocks: responsible for around 48 percent of the typical SSA LIC’s cycle.
  - Domestic shocks: roughly 30 to 50 percent of the typical country’s cycle, but contribution has declined over time.
  - Regional EMs: shocks in regional EMs can contribute around 45 percent of variation for LICs in Asia when EM leaders are replaced by all EMs in the region.

### V. VAR analysis — impulses, elasticities, timing
- VAR configuration:
  - Annual regional VARs since 1980 (MNA and ECA from 1990 onwards) with m = 2 lags in baseline (MNA and ECA with one lag).
  - Endogenous variables Y: real GDP growth for advanced countries, global commodity prices, GDP growth of EM leaders (or intra-regional EMs), real GDP growth of LICs within the region.
  - GDP measured in US$ PPP summed before computing growth rates.
  - Identification via Cholesky decomposition ordering: advanced economies → EM leaders → global commodity prices → LIC region.
  - Impulse-response functions estimated with one-standard deviation error bands using 1000 Monte Carlo replications.
- Key impulse-response magnitudes:
  - Advanced-economy shock:
    - A one-standard deviation positive growth shock to advanced country activity (i.e., a 1.5 percentage point shock in GDP) is associated with a rise in activity in LICs in Asia and ECA of about 1-2 percentage points.
    - LICs in LAC and MNA are affected with some delay; LICs in SSA are least affected after controls.
  - EM leaders shock:
    - A one-standard deviation positive shock to EM leaders (i.e., a one percentage point increase in GDP growth in EM leaders) raises activity by about:
      - 1 percentage point in MNA and ECA LICs;
      - between ½ and one point in SSA LICs;
      - smaller amounts in Asian LICs.
    - These spillovers are statistically significant at various horizons for LICs in these regions.
  - Commodity-price shock:
    - A one-standard deviation shock to global commodity prices (i.e., a 6 percent increase in commodity prices) raises activity by slightly less than ½ percentage point in Asian and LAC LICs.
    - Commodity-price effects are statistically insignificant for LICs in ECA and MNA once activity in advanced countries and EM leaders is controlled for.
    - Commodity-price shocks have the most sizeable impact on SSA LICs, reaching a cumulative 2.8 percent 3 years after the initial shock; the impact is statistically significant.
- Elasticities and timing (selected statements preserved):
  - Three elasticities calculated from impulse responses: average response in year one, additional lagged response in year two, total effect over two years.
  - Accounting for lagged effects raises the impact on the ECA region to around 2.0 over a two-year period, and around 1 or more percent on Asia and MNA LICs.
  - Spillovers from shocks in EM leaders are typically front-loaded; a one percent shock to growth in EM leaders shifts economic activity by 0.3 to over 1 percent, on average.

### VI. Panel regressions — specification and key elasticities
- Empirical framework: g_i = c_i + βX_i + ε_i (g_i = real GDP growth; X includes trading partner growth, lagged FDI/GDP, interaction of TOT changes with very-open dummy).
- Sample and methods: 55 EMs and 54 LICs, annual data 1980–2008; fixed effects and Arellano-Bond difference GMM; time dummies included.
- Main regression findings:
  - For the entire sample (EMs and LICs) growth spillovers from partner countries show elasticity around 0.5-0.7 depending on estimation procedure.
  - FDI matters for growth with a semi-elasticity of around 0.1-0.2.
  - Strength of spillovers from partner country growth increased in the post-1995 period.
  - Elasticity to partner growth is higher for EMs than LICs.
  - Changes in terms of trade matter for LIC growth in the post-1995 period for very open economies, with an elasticity of 0.04 (implying a 25 percent increase in commodity prices increases growth in these LICs by around 1 percentage point).
- Heterogeneity (post-1995):
  - Partner country growth is not a significant determinant of growth in SSA and MNA LICs.
  - Elasticities to partner country growth are close to or above 1 for LICs in Asia and ECA.
  - For non-commodity exporting LICs, elasticity to trading partner growth estimated around 1.2.
  - A 100 percent increase in trade openness increases the elasticity to partners growth by 0.56.
  - Trade openness averages (1995-2008), (Exports+Imports)/GDP, in percent: SSA 68.4, MNA 70.9, LAC 75.9, ASIA 77.7, ECA 91.5.

### VII. EM leaders vs other partners and robustness
- Splitting partner GDP growth into EM Leaders (average of 8 EM leaders, trade-weighted) and other partners (post-1995):
  - For full LIC sample, significant spillovers from both EM Leaders and other trading partners with elasticities around 1.
  - Growth in EM leaders matters particularly for commodity exporters (elasticity around 1).
  - Growth in other trading partners matters for non-commodity exporters (elasticity of 1.3).
- Robustness checks with global variables (post-1995):
  - Tested global controls: world trade growth, world real GDP growth, change in oil prices, change in non-fuel commodity prices, Federal Funds rate.
  - Lagged world trade growth remains the only global variable consistently significant.
  - Growth spillovers from partner countries remain significant with elasticity around 0.5 when global variables are included.
  - For commodity exporters, spillovers from partner countries remain significant with elasticity higher than 1 after controlling for global variables.
  - For SSA LICs, controlling for global variables, statistically significant spillover effects from EM leaders were found (but not from other trade partners).

### VIII. Simulation: global double-dip recession (2011-12 downside scenario) — GPM-based
- Scenario source: October 2010 WEO downside scenario via IMF’s Global Projection Model (GPM).
- GPM assumption: escalation of financial stress, particularly in the Euro area, and contagion prompted by rising sovereign risk concern results in lower global growth of 1.4 percentage points in 2011, relative to the WEO baseline.
- GPM differences from baseline (2011) — (Region / Baseline / Difference):
  - GPM World: 3 — -1.4
  - United States: 1.5 — -1.9
  - Euro Area: -0.9 — -2.2
  - Japan: 0.2 — -1.3
  - Emerging Asia: 7.2 — -0.9
  - Latin America: 3.2 — -0.8
  - Remaining GPM Countries: 2.5 — -1.1
- Simulation approach:
  - Use GPM projections for global and regional growth and country-by-country TOT projections.
  - Use trading patterns from IMF Direction of Trade.
  - Apply elasticities of LIC growth to partner GDP growth from panel regressions (Column 1 in Table 7).
- Simulation outcomes and magnitudes:
  - Depressed external demand expected to lower LIC growth prospects for 2011, shaving off close to 2 percentage points from baseline growth projections for nearly a quarter of LICs.
  - Overall growth expected to remain positive in most countries.
  - Decline in growth expected to be more significant for LICs in LAC, given strong trade and tourism linkages with the U.S.
  - Higher growth in EM leaders expected to support LIC growth prospects in the ECA region.
  - LICs with higher export shares to EM leaders are expected to show a smaller decline in their growth rates.
  - Regression line reported for LICs linking export shares with EM Leaders (in percent) to decline in GDP Growth, 2011 (difference from baseline): y = 0.0217x -1.5813

### IX. Conclusion — implications and policy-relevant findings
- Integration trends and exposure:
  - LICs have become increasingly integrated with EMs via stronger trade links, rising cross-border financial asset holdings and capital flows, and higher remittance flows, increasing exposure to external shocks.
- Empirical magnitudes summarized:
  - In 1980-2008, 45 to 60 percent of the average LIC cycle was determined by external factors; this proportion increased to 60-90 percent over 1990-2008.
  - The bulk of this increase is attributable to new relationships developed with large EM leaders.
  - Growth elasticity to partners’ GDP growth is higher for LICs in Asia, ECA and LAC compared to commodity-exporting LICs in MNA and SSA.
  - Spillovers to LICs in SSA and MNA are primarily channeled through terms of trade changes and economic activity in the EM leaders.
- Policy-relevant inference:
  - Increasing trade and financial ties between LICs and EM leaders will strengthen business cycle synchronization.
  - These links were beneficial to LICs during the Great Recession of 2008-09 (EMs less affected than advanced economies) but could be a source of vulnerability because historically economic contractions in EMs have tended to be deeper and more frequent.

*Source: IMF staff working paper — "10. Simulation Results from a Global Double-Dip Recession" (excerpts from _wp1249).*

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

### _wp1249 - References

### Tables
- 1. EM Leaders’ Export Shares from LICs .................................................................................5
- 2. Top Ten Ranking of LICs based on Exports Share to Regional Leaders, 2008 ....................6
- 3. Top Recipients of Chinese FDI..............................................................................................9
- 4. LICs Correlation with other Economies ..............................................................................11
- 5. Elasticity of LIC Domestic Growth to External Growth .....................................................15
- 6. Baseline Regressions for EMs and LICs .............................................................................19
- 7. Regressions by LIC region, Exporter-type ..........................................................................20
- 8. Robustness Check: Global Variables ...................................................................................21
- 9. The Downside: GPM Projections fof Real GDP Growth ....................................................22

### Figures
- 1. Share of LIC Exports by Destination .....................................................................................5
- 2. Exports Shares from all LICs, by Products and Destination Income Group .........................6
- 3. Exports Shares from LICs, by Product and Destination ........................................................7
- 4. Remittance Flows to LICs, 2006 ...........................................................................................8
- 5. EM Leaders Stock of FDI in LICs .........................................................................................9
- 6. Consolidated Foreign Claims of Reporting Banks by Region, 2006-09 .............................10
- 7. GDP Rare by Income Groups, 1995-2008 ...........................................................................11
- 8. Impact of External Shocks on LICs Growth (Annual VAR) ...............................................14
- 9. Contributions to Variations in Growth in LIC Regions .......................................................15

*Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp1249.pdf*

### 10. Simulation Results from a Global Double-Dip Recession ................................................23

### 10. Simulation Results from a Global Double-Dip Recession

### I. Introduction — scope and questions
- Focus: economic linkages between low-income countries (LICs) and a set of systemic emerging market (EM) leaders and the spillover effects of EM growth on LIC economic activity.
- Research questions:
  - Do growth shocks in EM leaders produce spillover effects on LIC economic activity?
  - What is the size and nature of these spillovers across LIC regions?
- Methods: VAR methodologies and dynamic panel regressions to estimate spillovers; simulations (including a euro area-driven recession in 2011) are described later in the paper.

### II. Trade linkages — stylized facts
- Advanced economies remained the largest destination of LIC exports, accounting for about 60 percent of total LIC exports in 2008; their share fell by over 10 percentage points in the last 30 years.
- Regional patterns:
  - The decline in advanced-economy share is particularly pronounced in SSA: a decline of about 30 percentage points, with non-traditional partners accounting for about half of SSA exports in 2004-2008.
  - Asia and ECA LICs: trend driven mainly by expansion of intra-regional trade.
  - SSA LICs: inter-regional trade features more prominently.
- Product composition shift (1995 → 2008):
  - In 1995 LIC exports to advanced economies (and to a lesser extent intra-regional leaders) were dominated by agricultural raw materials and food and beverage products.
  - By 2008, fuels, manufactures, and other products accounted for the largest share of overall LIC export baskets, particularly to advanced economies and inter-regional leaders.
  - SSA and MNA LIC exports: marked increase in fuels share since 1995.
  - Intra-regional exports tend to have a larger share of products with higher local value added across LIC regions.

- Classification and specific shares (Table 1 style figures preserved):
  - India — Region: Asia — Share of Intra-Regional LIC Exports, mean: 10.7% — Share of Inter-Regional LIC Exports, mean: 5.9%
  - China — Region: Asia — 10.8% — 8.2%
  - Russia — Region: ECA — 13.6% — 0.9%
  - Turkey — Region: ECA — 9.4% — 0.6%
  - Mexico — Region: LAC — 2.9% — 0.4%
  - Brazil — Region: LAC — 15.1% — 0.5%
  - Saudi Arabia — Region: MNA — 2.5% — 0.5%
  - South Africa — Region: SSA — 3.3% — 1.0%

- Top LICs with strong export dependence on EM leaders (Table 2 style):
  - Rank by Intra-Regional Leaders — examples:
    - Mongolia: LICs Exports to EM Leaders (in percentage of total exports): 64.6 — Major EM Leader: China — Exports to Major EM Leader (in percentage of total exports): 64.5
    - Bolivia: 61.0 — Brazil: 60.1
    - Nepal: 55.5 — India: 54.8
    - Tajikistan: 35.1 — Turkey: 26.5
    - Zimbabwe: 32.3 — South Africa: 32.3
  - Rank by Inter-Regional Leaders — examples:
    - Guinea-Bissau: LICs Exports to EM Leaders: 65.0 — Major EM Leader: India — 64.0
    - Yemen, Republic of: 54.3 — China: 30.9
    - Sudan: 52.4 — China: 48.0
    - Congo, Dem. Rep. of: 50.2 — China: 47.3
    - Mauritania: 44.0 — China: 41.5

### III. Other linkages — remittances, FDI, cross-border banking
- Remittances:
  - India accounts for about 30 percent of total remittance inflows to LICs in Asia.
  - Russia and Saudi Arabia account for respectively 50 and 65 percent of total remittance flows to LICs within their own regions.
  - Remittances from Russia account for 28 and 36 percent of GDP in the Kyrgyz Republic and Tajikistan, respectively.
  - Saudi Arabia: close to 10 percent of total remittance flows to LICs in Asia and about 1 percent of flows to SSA.
  - LICs in LAC: advanced economies (in particular the United States) are the major source of remittances, accounting for over 80 percent of the total.
  - SSA remittances: over 75 percent originate from the U.K. and the euro area.
  - Note: EM leaders exclude China from remittance bilateral data because bilateral data on remittances from China are not available.

- Foreign Direct Investment (FDI):
  - Advanced countries continue to be the most important source of FDI for LICs, but FDI from EM leaders is becoming increasingly important.
  - China dominates the expansion of FDI to LICs in available bilateral data, especially in the resource sector (SSA) and manufacturing (Asia).
  - Between 2003 and 2009, the stock of Chinese FDI to LICs increased 19 fold.
  - India: between 2001 and 2005 the stock of Indian FDI in LICs grew almost four-fold, with increasing presence in SSA; majority of Indian FDI to LICs within Asia flows to Vietnam, Nepal and Bangladesh.
  - Brazil: less prominent as source of FDI to most LICs; Bolivia receives 74 percent of Brazilian FDI to LICs.
  - South Africa: second most important developing-country investor in Africa after China; share of African host economies in South Africa’s outward FDI stock reached almost USD11 billion in 2008 (22 percent, compared with 5 percent in 2000).

- Top recipients of Chinese FDI, average 2003-09 (Table 3 style: amounts preserved)
  - Nigeria — In millions of U.S. dollars: 1311 — Percent of total FDI to LICs (Percent): 14.4
  - Mongolia — 1271 — 11.1
  - Myanmar — 1221 — 10.6
  - Cambodia — 79 — 6.8
  - Zambia — 79 — 6.8
  - Laos PDR — 74 — 6.4
  - Vietnam — 62 — 5.4
  - Guyana — 60 — 5.2
  - Congo, Dem. Rep. of — 52 — 4.5
  - Sudan — 52 — 4.5
  - Papua New Guinea — 44 — 3.9
  - Afghanistan — 26 — 2.3
  - Niger — 26 — 2.3
  - Tajikistan — 21 — 1.8
  - Madagascar — 19 — 1.6
  - Ethiopia — 18 — 1.6

- Cross-border bank lending:
  - Advanced economies overwhelmingly dominate LIC banking sectors, but cross-border bank flows from EM leaders have increased; their share (using available data) remains around 1 percent of total cross-border flows to LICs.
  - Lending from EM leaders to LICs within their own regions increased rapidly from 2006 onwards; lending to other LICs also increased but is more volatile.
  - Examples of expanding patterns:
    - Indian cross-border lending expanded mostly within the region (Maldives and Bangladesh) and opened markets in SSA (Kenya, Nigeria, Niger and Ghana).
    - Brazil’s cross-border flows to LICs in SSA increased (Nigeria, Senegal, and Kenya).
    - Turkey: greater lending outside its own region, mainly with SSA (Liberia and Sudan) and ECA (Georgia, Kyrgyz Republic).
  - Notable bank entrants: Industrial and Commercial Bank of China, Itau Unibanco (Brazil), Sberbank (Russia), State Bank of India — cited as emerging-market banks in top-25 globally.

### IV. Evidence of coupling / decoupling
- Observed patterns:
  - Last business cycle exhibits some decoupling from advanced economies while growth correlations between LICs and EM leaders strengthened.
  - Strong correlation exists between GDP growth rate of EM leaders and growth rate of LIC exports over GDP over 2000-08.
- Correlation summary (1995-99 vs 2000-08; Table 4 figures preserved):
  - Correlation, GDP Growth:
    - Advanced Countries — 1995-99: 0.79 — 2000-08: 0.605
    - EM Leaders — 1995-99: 0.566 — 2000-08: 0.958
  - Correlation, GDP Growth and LIC Exports:
    - Advanced Countries — 1995-99: 0.598 — 2000-08: 0.701
    - EM Leaders — 1995-99: -0.055 — 2000-08: 0.630

### V. VAR analysis — approach and key quantitative findings
- VAR setup:
  - Annual regional VARs since 1980 (MNA and ECA from 1990 onwards) with m = 2 lags in baseline (MNA and ECA with one lag).
  - Endogenous variables Y: real GDP growth for advanced countries, global commodity prices, GDP growth of a group of emerging markets (EM leaders or intra-regional EMs), real GDP growth of LICs within the region.
  - GDP measured in US$ PPP summed before computing growth rates (akin to PPP-weighted growth rates).
  - Identification via Cholesky decomposition with ordering: advanced economies → EM leaders → global commodity prices → LIC region.
  - Impulse-response functions with one-standard deviation error bands estimated using 1000 Monte Carlo replications.

- Key impulse-response magnitudes (preserved wording and numeric magnitudes):
  - Advanced-economy shock:
    - A one-standard deviation positive growth shock to advanced country activity (i.e., a 1.5 percentage point shock in GDP) is associated with a strong and statistically significant rise in activity in LICs in Asia and ECA of about 1-2 percentage points.
    - LICs in LAC and MNA are affected with some delay.
    - LICs in SSA are least affected by a shock to advanced countries, after controlling for commodity prices and growth in EM leaders.
  - EM leaders shock:
    - A one-standard deviation positive shock to economic activity in the EM leaders (i.e., a one percentage point increase in GDP growth in EM leaders) raises activity by about:
      - 1 percentage point in MNA and ECA LICs;
      - between ½ and one point in SSA LICs;
      - smaller amounts in Asian LICs.
    - These spillovers are statistically significant at various horizons for LICs in these regions.
  - Commodity-price shock:
    - A one-standard deviation shock to global commodity prices (i.e., a 6 percent increase in commodity prices) raises economic activity by slightly less than ½ percentage point in Asian and LAC LICs.
    - Commodity-price effects are statistically insignificant for LICs in ECA and MNA once activity in advanced countries and EM leaders is controlled for.
    - Commodity-price shocks have the most sizeable impact on SSA LICs, reaching a cumulative 2.8 percent 3 years after the initial shock; the impact is statistically significant.

- Elasticities and timing (methodology preserved; final numeric elasticity table referenced but not fully reproduced because source excerpt truncates):
  - The impulse responses are used to compute elasticities of domestic growth to external growth in advanced countries and EM leaders:
    - Three elasticities calculated: average response in year one, additional lagged response in year two, total effect over two years.
  - Historical example (partial sentence preserved verbatim from source):
    - "Historically, a one percentage point positive shock to annual growth in advanced economies has caused, on average, an increase in growth in year one ranging from 0.1 in SSA LICs to" — (source excerpt ends here; full two-year elasticities and complete numeric table are outside the supplied content).

*Source: IMF staff working paper — section "10. Simulation Results from a Global Double-Dip Recession" (excerpts provided).*

### 1.1 in ECA LICs. The lagged effects are also relevant for the LICs in the LAC, MNA, and

### _wp1249 - 1.1 in ECA LICs. The lagged effects are also relevant for the LICs in the LAC, MNA, and

### Impact of external shocks on LIC growth (VAR results)
- Accounting for lagged effects raises the impact on the ECA region to around 2.0 over a two-year period, and around 1 or more percent on Asia and MNA LICs.
- Spillovers from shocks in EM leaders are typically front-loaded, and end up having an important effect on all LIC regions.
- A one percent shock to growth in EM leaders shifts economic activity by 0.3 to over 1 percent, on average.
- In contrast to strong lagged effect of advanced country shocks, the impact elasticity tends to be higher across all LIC regions.

### Variance decompositions of real GDP growth (two sample periods)
- Two time periods considered: 1980-2008 and 1990-2008.
- In 1980-2008, external shocks contributed, on average, about 45 to 60 percent of the variation in LIC economic activity at a two-year horizon, with a higher contribution for LICs in Asia and LAC.
- Over 1990-2008, this range increased on average for Asia, LAC, and SSA (where full-sample data are available).
- Shocks to advanced economies:
  - Responsible for over 28 percent of business cycle variation in 1990-2008 on average.
  - LICs in LAC depend the most on advanced economy events (accounting for over 50 percent of the variation).
  - Lowest contribution of advanced economy shocks is amongst LICs in SSA.
- Shocks to EM leaders:
  - Contribution increased in post-1990 period for LICs in Asia, SSA, and LAC.
  - On average responsible for over 17 percent of variation in economic activity in the average LIC.
  - Range: less than 10 percent for MNA and SSA LICs to around 20 percent or more for LICs in LAC, ECA, and Asia.
- Commodity price shocks:
  - Spillovers in SSA LICs are primarily channeled through commodity prices shocks, which are responsible for around 48 percent of the typical country’s cycle.
- Domestic (country-specific) shocks:
  - Responsible for roughly 30 to 50 percent, on average, of the typical country’s cycle, but their contribution has declined over time.
- Regional EMs:
  - Replacing EM leaders with all EMs in a region suggests shocks in regional EMs matter most for LICs in Asia, contributing around 45 percent of the variation of economic activity of the typical LIC.

### Panel regressions: specification and key elasticities
- Empirical framework: panel growth regression g_i = c_i + βX_i + ε_i, where g_i is real GDP growth, c_i varies by country, X includes foreign shocks (trading partner growth), lagged ratio of FDI to GDP, and interaction of changes in terms of trade (TOT) with a dummy for very open economies (top quartile of trade openness).
- Sample: 55 EMs and 54 LICs, annual data 1980–2008.
- Estimation methods: fixed effects and Arellano-Bond difference GMM; time dummies included in regressions.
- Main regression findings:
  - For the entire sample (EMs and LICs) growth spillovers from partner countries show elasticity around 0.5-0.7 depending on estimation procedure.
  - FDI matters for growth with a semi-elasticity of around 0.1-0.2.
  - Strength of spillovers from partner country growth increased in post-1995 period.
  - Comparing LICs and EMs:
    - Elasticity to partner growth is higher for EMs than LICs.
    - FDI is a significant determinant of growth in LICs across specifications, particularly in the post-1995 period.
  - Changes in terms of trade matter for LIC growth in the post-1995 period for very open economies, with an elasticity of 0.04.
    - Implied effect: a 25 percent increase in commodity prices increases growth in these LICs by around 1 percentage point.
- Heterogeneity by region and exporter type (post-1995):
  - Partner country growth is not a significant determinant of growth in SSA and MNA LICs.
  - Elasticities to partner country growth are close to or above 1 for other LIC regions (notably Asia and ECA).
  - Spillover effects strongest for LICs in Asia and ECA.
  - Commodity vs non-commodity exporters:
    - Growth in commodity exporters is typically insensitive to trading partner growth.
    - Elasticity to trading partner growth in non-commodity exporting LICs estimated around 1.2.
    - Robustness check with larger group of commodity exporters (including MICs) confirmed this result.
  - Trade openness interaction:
    - A 100 percent increase in trade openness increases the elasticity to partners growth by 0.56.
    - Trade openness averages (1995-2008): SSA 68.4, MNA 70.9, LAC 75.9, ASIA 77.7, ECA 91.5 (Exports+Imports)/GDP, in percent.

### EM leaders vs other partners (post-1995, by exporter type)
- Splitting partner GDP growth into EM Leaders (average of 8 EM leaders, trade-weighted) and other partners:
  - For full LIC sample, statistically significant spillover effects from both EM Leaders and other trading partners with elasticities around 1.
  - Growth in EM leaders matters particularly for commodity exporters (elasticity around 1).
  - Growth in other trading partners matters for non-commodity exporters (elasticity of 1.3).
  - For commodity exporters, other partners’ growth effect is offset by a -1.6 elasticity (Column 6).

### Robustness checks with global variables (post-1995)
- Global variables tested: world trade growth, world real GDP growth, change in oil prices, change in non-fuel commodity prices, and the Federal Funds rate.
- Starting from general specification and removing least significant variables, the only global variable that remains significant for overall sample is lagged world trade growth.
- In the specification with global variables and no time dummies:
  - Growth spillovers from partner countries remain significant with elasticity around 0.5.
  - For commodity exporters, spillovers from partner countries are statistically significant with elasticity higher than 1, even after controlling for global variables.
  - Changes in country terms of trade were highly significant; global commodity price indices (not country-specific) were insignificant.
  - For SSA LICs, controlling for global variables, statistically significant spillover effects from EM leaders were found (but not from other trade partners).

### Simulation: global double-dip recession (2011-12 downside scenario)
- Scenario source: October 2010 WEO downside scenario via IMF’s Global Projection Model (GPM).
- GPM assumption: escalation of financial stress, particularly in the Euro area, and contagion prompted by rising sovereign risk concern results in lower global growth of 1.4 percentage points in 2011, relative to the WEO baseline.
- Table of GPM differences from baseline (2011):
  - GPM World: 3 -1.4 (difference from baseline)
  - United States: 1.5 -1.9
  - Euro Area: -0.9 -2.2
  - Japan: 0.2 -1.3
  - Emerging Asia: 7.2 -0.9
  - Latin America: 3.2 -0.8
  - Remaining GPM Countries: 2.5 -1.1
- Simulation approach:
  - Use GPM projections for global and regional growth and country-by-country TOT projections.
  - Use trading patterns from IMF Direction of Trade.
  - Apply elasticities of LIC growth to partner GDP growth from panel regressions (Column 1 in Table 7).
- Simulation outcomes:
  - Depressed external demand expected to lower LIC growth prospects for 2011, shaving off close to 2 percentage points from baseline growth projections for nearly a quarter of LICs.
  - Overall growth expected to remain positive in most countries.
  - Decline in growth expected to be more significant for LICs in LAC, given strong trade and tourism linkages with the U.S.
  - Higher growth in EM leaders expected to support LIC growth prospects in the ECA region.
  - LICs with higher export shares to EM leaders are expected to show a smaller decline in their growth rates.
- Figure metrics shown:
  - Decline in GDP Growth, by region, 2011 (Difference from baseline projections): median and mean values plotted for Asia, ECA, LAC, MNA, SSA (visuals described).
  - Regression line for LICs: y = 0.0217x -1.5813 relating export shares with EM Leaders (in percent) to decline in GDP Growth, 2011 (difference from baseline projections).

### Conclusion: implications and policy-relevant findings
- LICs have become increasingly integrated with EMs via stronger trade links, rising cross-border financial asset holdings and capital flows, and higher remittance flows, increasing exposure to external shocks.
- Empirical findings:
  - In 1980-2008, 45 to 60 percent of the average LIC cycle was determined by external factors; this proportion increased to 60-90 percent over 1990-2008.
  - The bulk of this increase attributable to new relationships developed with large EM leaders.
  - Extent of growth spillovers varies across LIC regions, depending on strength of trade and commodity price linkages.
  - Growth elasticity to partners’ GDP growth higher for LICs in Asia, ECA and LAC compared to commodity-exporting LICs in MNA and SSA.
  - Spillovers to LICs in SSA and MNA primarily channeled through terms of trade changes and economic activity in the EM leaders.
- Policy-relevant inference:
  - Increasing trade and financial ties between LICs and EM leaders will strengthen business cycle synchronization.
  - These links were beneficial to LICs during the Great Recession of 2008-09 (EMs less affected than advanced economies) but could be a source of vulnerability because historically economic contractions in EMs have tended to be deeper and more frequent.

*Source: _wp1249 - 1.1 in ECA LICs. The lagged effects are also relevant for the LICs in the LAC, MNA, and (IMF staff estimates and analysis as presented in the supplied content).*

### REFERENCES

### _wp1249 - REFERENCES

### References
- Akın, Ç., and M. Kose, 2007, “Changing Nature of North-South Linkages: Stylized Facts and Explanations,” IMF Working Paper 07/280 (Washington).
- Alturki, F., J. Espinosa-Bowen, and N. Ilahi, 2009, “How Russia Affects the Neighborhood: Trade, Financial, and Remittance Channels,” IMF Working Paper 09/277 (Washington).
- Arora, V., and A. Vamvakidis, 2005a, “How Much Do Trading Partners Matter for Economic Growth?” IMF Staff Papers, Vol. 52, No. 1, pp. 24-40 (Washington).
- Arora, V., and A. Vamvakidis, 2005b, “The Implications of South African Economic Growth for the Rest of Africa,” IMF Working Paper 05/58 (Washington).
- Bayoumi, T., and Swiston, 2007, “Foreign Entanglements: Estimating the Source and Size of Spillovers across Industrial Countries,” IMF Working Paper 07/182 (Washington).
- Berg, A., C. Papageorgiou, C. Pattillo, M. Schindler, N. Spatafora, and H. Weisfeld, 2011, “Global Shocks and their Impact on Low-Income Countries: Lessons from the Global Financial Crisis,” IMF Working Paper 11/27 (Washington).
- Drummond, P., and G. Ramirez, 2009, “Spillovers from the Rest of the World into Sub-Saharan African Countries,” IMF Working Paper 09/155 (Washington).
- Holtz-Eakin, D., W. Newey, and H. Rosen, 1988, “Estimating Vector Autoregressions with Panel Data,” Econometrica, Vol. 56, pp. 1371-96.
- Imbs, J., 2004, “Trade, Finance, Specialization, and Synchronization,” The Review of Economics and Statistics, Vol. 86(3), pp. 723-34.
- International Monetary Fund, 2011, “New Growth Drivers for Low–Income Countries—The Role of BRICs.” Available via the internet at: www.imf.org/external/np/pp/eng/2011/011211.pdf
- International Monetary Fund, 2011 “Managing Volatility-A Vulnerability Exercise for Low-Income Countries.” Available via the internet at: www.imf.org/external/np/pp/eng/2011/030911.pdf
- International Monetary Fund, 2010, “Emerging from the Global Crisis: Macroeconomic Challenges Facing Low-Income Countries.” Available via the internet at: www.imf.org/external/np/pp/eng/2010/100510.pdf
- Massa, I., 2010, “Cross-border Bank Lending to Developing Countries: The New Role of Emerging Markets,” in The G-20 Framework for Strong, Sustainable, and Balanced Growth: What Role for Low-Income Small and Vulnerable Countries? ed. by Dirk Willem te Velde (London: Overseas Development Institute).
- Roodman, D., 2006, “How to Do xtabond2: An Introduction to “Difference” and “System” GMM in Stata,” Center for Global Development Working Paper No. 103 (Washington).
- Shiells, C. R., M. Pani, and E. Jafarov, 2005, “Is Russia Still Driving Regional Growth?” IMF Working Paper 05/192 (Washington).

### Appendix A. List of Countries
- Afghanistan, I.R. of
- Albania
- Algeria
- Angola
- Argentina
- Armenia
- Azerbaijan, Rep. of
- Bangladesh
- Belarus
- Benin
- Bolivia
- Bosnia & Herzegovina
- Brazil
- Bulgaria
- Burkina Faso
- Burundi
- Cambodia
- Cameroon
- Central African Rep.
- Chad
- Chile
- China, P. R.: Mainland
- Colombia
- Congo, Dem. Rep. of
- Congo, Republic of
- Côte d'Ivoire
- Ecuador
- Egypt
- El Salvador
- Ethiopia
- Gambia, The
- Georgia
- Ghana
- Guinea
- Guinea-Bissau
- Haiti
- Honduras
- India
- Indonesia
- Iran, I.R. of
- Iraq
- Jamaica
- Jordan
- Kazakhstan
- Kenya
- Kyrgyz Republic
- Lao People's Dem. Rep
- Liberia
- Madagascar
- Malawi
- Mali
- Mauritania
- Mauritius
- Mexico
- Moldova
- Mongolia
- Morocco
- Mozambique
- Myanmar
- Nepal
- Nicaragua
- Niger
- Nigeria
- Pakistan
- Papua New Guinea
- Paraguay
- Peru
- Philippines
- Poland
- Romania
- Russian Federation
- Rwanda
- Senegal
- Sierra Leone
- Sri Lanka
- Sudan
- Syrian Arab Republic
- Tajikistan
- Tanzania
- Togo
- Uganda
- Uzbekistan
- Vietnam
- Yemen, Republic of
- Zambia
- Zimbabwe
- Guatemala
- Latvia
- Lebanon
- Lithuania
- Macedonia, FYR
- Malaysia
- Mauritius
- Morocco
- Namibia
- Paraguay
- Philippines
- Poland
- Romania
- Serbia, Republic of
- South Africa
- Thailand
- Tunisia
- Turkey
- Ukraine
- Uruguay
- Venezuela, Rep. Bol.
- (Note: Original formatting includes group headings "Emerging Market Countries" and "Low-income Countries".)

### Appendix B. Data and Sources
- Real GDP growth — IMF World Economic Outlook
- Trade shares — IMF DOTS Database, UN Comtrade Database
- FDI — IMF World Economic Outlook, UNCTAD Database
- Terms of Trade — IMF World Economic Outlook
- Fed Funds Rate — IMF World Economic Outlook
- Oil prices — IMF World Economic Outlook
- Nonfuel Commodity prices — IMF World Economic Outlook
- World trade — IMF World Economic Outlook
- Real Effective Exchange Rate — IMF World Economic Outlook
- remittance — Ratha and Shaw (2006)
- Consolidated foreign claims — Bank for International Settlements (BIS) Consolidated Banking Statistics

*Content derived from _wp1249 - REFERENCES*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2012/_wp1249.pdf_