## _wp08172

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### Introduction and research focus
- Sustained growth in global oil demand and steady increases in oil prices linked to improved export and growth performance for oil-producing African countries (OPAC).
- OPAC had average annual GDP growth of over 7 percent in 2000–06; Sub-Saharan Africa (SSA) grew at 4.7 percent in 2000–06.
- Paper examines competitiveness of OPAC (Angola, Cameroon, Chad, Republic of Congo, Côte d’Ivoire, Equatorial Guinea, Gabon, and Nigeria) and structural reasons for weak non-oil sector performance since 1970.
- Comparative analysis includes Indonesia, Mexico, Trinidad and Tobago, the United Arab Emirates (UAE), and Venezuela.
- Empirical approach: descriptive historical analysis and a gravity model of bilateral trade flows to evaluate the significance of structural factors—especially institutional quality—on non-oil exports.

### Key findings (summary)
- Oil abundance on average has a strongly negative effect on the export competitiveness of the non-oil tradable sector.
- Infrastructure and institutions positively affect non-oil exports and mitigate oil abundance effects.
- Institutional quality significantly determines the extent to which oil abundance affects non-oil sector competitiveness.
- OPAC can benefit from oil booms substantially if revenues are used to improve infrastructure, institutions, investment climate, and human capital.

### Export performance and concentration (exact statistics)
- OPAC contribution: eight major oil-exporting countries together contributed 7.5 percent of world exports in 2003–06.
- Trade-to-GDP ratio:
  - OPAC: increased from 70 percent to 88 percent between 1970 and 1999 and to 103 percent in 2000–06.
  - SSA: increased from about 55 percent in 1970–99 to 66 percent in 2000–06.
  - World: increased from 38 percent to 50 percent.
- Real exports growth (1970–2006):
  - OPAC goods and services: average annual rate of 6.9 percent.
  - SSA: 3.8 percent.
  - World: 6.5 percent.
- Oil dependence (2000–06 averages and shares):
  - Oil constitutes over 70 percent of total exports for Angola, Chad, Congo, Equatorial Guinea, Gabon, and Nigeria.
  - Oil constitutes 30–40 percent of total exports for Cameroon and Côte d’Ivoire.
  - Contribution of oil exports to GDP averages about 50 percent in 2000–06 for OPAC.
- World export shares:
  - OPAC share of world goods exports dropped from 1.5 percent in 1970–80 to 0.7 percent in 1990–2006.
  - OPAC share of world non-oil exports dropped from 0.6 percent in 1970 to 0.2 percent in 2006.
- Country-specific nominal devaluations:
  - Nigeria: nominal exchange rate devalued by about 34 percent in 1986.
  - CFA Franc zone (Cameroon, Chad, Congo, Côte d’Ivoire, Equatorial Guinea and Gabon): devalued by 49 percent in 1994.
  - Angola: stepped devaluation early 1990s resulting in average annual nominal exchange rate devaluation of about 88 percent during 1991–97.

### Export structure, diversification indices, and revealed comparative advantage
- Export structure remains dominated by primary commodities, especially fuels; manufactures share has held constant or fallen for most OPAC.
- Export concentration (ECI):
  - ECI close to 1 for Angola, Equatorial Guinea, and Nigeria; about 0.8 for Congo and Gabon; Cameroon and Côte d’Ivoire average about 0.5 for 1970–2005.
- RCA counts (number of product categories with RCA ≥ 1, out of 182) by decade (selected):
  - Angola: 15 (1970–79), 4 (1980–89), 3 (1990–99), 3 (2000–05).
  - Cameroon: 17, 15, 16, 16.
  - Congo: 18, 10, 10, 13.
  - Côte d’Ivoire: 16, 18, 21, 25.
  - Equatorial Guinea: 6, 9, 7, 5.
  - Gabon: 7, 7, 6, 6.
  - Nigeria: 7, 4, 6, 6.
  - Comparator examples: Indonesia 20, 25, 42, 50; Mexico 53, 30, 39, 33; United Arab Emirates 4, 9, 19, 28.
- Sectoral RCA distribution in 2005 (number of product categories with RCA ≥ 1):
  - Angola: 2 nonfuel primary commodities, 0 manufactures.
  - Cameroon: 13 nonfuel primary commodities, 1 manufacture.
  - Congo: 8 nonfuel primary commodities, 0 manufactures.
  - Côte d’Ivoire: 13 nonfuel primary commodities, 2 manufactures.
  - Equatorial Guinea: 1 nonfuel primary commodity, 2 manufactures.
  - Gabon: 4 nonfuel primary commodities, 1 manufacture.
  - Nigeria: 2 nonfuel primary commodities, 1 manufacture.
  - Comparators: Indonesia 21 nonfuel primary commodities and 23 manufactures; Mexico 13 and 22; United Arab Emirates 12 and 8.

### Productivity, infrastructure, and human capital (selected indicators)
- Productivity proxies: OPAC generally have much lower labor productivity than comparators; Gabon highest and Angola, Chad, and Nigeria lowest among OPAC (real non-oil GDP per capita averages referenced for 2000–06).
- ICT and transport infrastructure (2005 per-1,000 and percentage measures; selected values):
  - Telephone main lines (per 1,000 people): AGO 6; CMR 6; TCD 1; COG 4; CIV 14; GNQ 20; GAB 28; NGA 9; SSA 17; IDN 58; MEX 189; TTO 248; ARE 273; VEN 136.
  - Mobile subscribers (per 1,000 people): AGO 69; CMR 138; TCD 22; COG 123; CIV 121; GNQ 192; GAB 470; NGA 141; SSA 125; IDN 213; MEX 460; TTO 613; ARE 1,000; VEN 470.
  - Internet users (per 1,000 people): AGO 11; CMR 15; TCD 4; COG 13; CIV 11; GNQ 14; GAB 48; NGA 38; SSA 29; IDN 73; MEX 181; TTO 123; ARE 308; VEN 125.
  - Personal computers (per 1,000 people): AGO 2; CMR 10; TCD 2; COG 4; CIV 15; GNQ 14; GAB 33; NGA 7; SSA 15; IDN 14; MEX 136; TTO 79; ARE 197; VEN 82.
  - Internet bandwidth (bits per person): AGO 0; CMR 1; TCD 0; COG 0; CIV 3; GNQ 33; GAB 145; NGA 1; SSA 2; IDN 7; MEX 110; TTO 375; ARE 923; VEN 51.
  - Paved roads (% of total roads): AGO 10.4; CMR 11.7; TCD 0.8; COG 9.3; CIV 9.3; GAB 9.1; NGA 25.6; SSA 14.7; IDN 51.9; MEX 35.7; TTO 49.6; ARE 98.8; VEN 35.4.
- Human Development indicators (2005; selected exact values):
  - Angola: HDI 0.45, Rank 162, Adult literacy (% aged 15+) 67.4, Gross enrolment 25.6*, Life expectancy index 0.28.
  - Cameroon: HDI 0.53, Rank 144, Adult literacy 67.9, Gross enrolment 62.3, Life expectancy index 0.41.
  - Chad: HDI 0.39, Rank 170, Adult literacy 25.7, Gross enrolment 37.5, Life expectancy index 0.42.
  - Congo (Rep. of): HDI 0.55, Rank 139, Adult literacy 84.7, Gross enrolment 51.4, Life expectancy index 0.48.
  - Côte d’Ivoire: HDI 0.43, Rank 166, Adult literacy 48.7, Gross enrolment 39.6*, Life expectancy index 0.37.
  - Equatorial Guinea: HDI 0.64, Rank 127, Adult literacy 87.0, Gross enrolment 58.1, Life expectancy index 0.42.
  - Gabon: HDI 0.68, Rank 119, Adult literacy 84.0, Gross enrolment 72.4*, Life expectancy index 0.52.
  - Nigeria: HDI 0.47, Rank 158, Adult literacy 69.1, Gross enrolment 56.2, Life expectancy index 0.36.
  - SSA average: HDI 0.49, Adult literacy 60.3, Gross enrolment 50.4, Life expectancy index 0.41.
  - Comparator examples: Indonesia HDI 0.73 Rank 107; Mexico HDI 0.83 Rank 52; Trinidad and Tobago HDI 0.81 Rank 59; UAE HDI 0.87 Rank 39; Venezuela HDI 0.79 Rank 74.
  - Note: * = data for a year other than 2005.

### Institutional quality and business environment (exact rankings and metrics)
- Doing Business Indicator (DBI) 2007:
  - OPAC average DBI rank: 155 (out of 178 countries), worse than SSA and all other oil producers except Venezuela.
  - Individual OPAC ranks (best to worst among OPAC): Nigeria ranks 108; followed by Gabon, Cameroon, Côte d’Ivoire, Equatorial Guinea, Angola, Chad, and Congo.
  - Comparator rank examples: Mexico 44; UAE ahead of other oil exporters.
- Subcategory weaknesses:
  - Ease of obtaining credit, dealing with licenses, trading across borders, and protecting investors particularly weak.
  - Average days to export goods for OPAC: about 40 days.
    - Comparators: Indonesia 21 days; Mexico 17 days; Trinidad and Tobago 14 days; UAE 13 days.
  - Days required to import goods into OPAC is almost double that for other oil producers (exact comparator import-day values not listed in excerpt).
- Doing Business 2007 (selected exact country ranks and indicators):
  - Ease of Doing Business average rank (out of 178): AGO 167; CMR 154; TCD 173; COG 175; CIV 155; GNQ 165; GAB 144; NGA 108; SSA 136; IDN 123; MEX 44; TTO 67; UAE 68; VEN 172.
  - Time to export (days): AGO 64; CMR 27; TCD 78; COG 50; CIV 23; GNQ 29; GAB 19; NGA 26; SSA 36; IDN 21; MEX 17; TTO 14; UAE 13; VEN 45.
  - Cost to export (US$ per container): AGO 1850; CMR 907; TCD 4867; COG 2201; CIV 1653; GNQ 1403; GAB 1510; NGA 1026; SSA 1660; IDN 667; MEX 1302; TTO 693; UAE 462; VEN 2400.
  - Legal Rights Index (getting credit): NGA 7; MEX 5; UAE 5; SSA 4; AGO 3; CMR 3; TCD 3; COG 3; CIV 3; GAB 3; TTO 3; VEN 3; GNQ 2; IDN 4.

### World Governance Indicators and Global Competitiveness (selected)
- WGI dimensions: voice and accountability, regulatory quality, rule of law, control of corruption, political stability, government effectiveness.
- On average, OPAC perform poorly on all six indicators compared to SSA and other oil producers except Venezuela.
- Gabon ranks highest among OPAC in five of the six governance indicators; Equatorial Guinea, Côte d’Ivoire, and Chad have the lowest percentile ranks (rarely rising above the 10th percentile).
- GCI coverage and rankings:
  - GCI unavailable for Gabon, Equatorial Guinea, and Cote d’Ivoire.
  - UAE best performer overall; Indonesia and Mexico follow.
  - Chad and Angola rank 123rd and 125th (out of 125), respectively.
- Structural competitiveness summary:
  - OPAC’s structural competitiveness generally weak; Indonesia, Mexico, and UAE have relatively strong infrastructure and institutional quality and stronger non-oil export performance.

### Empirical model: gravity specifications and identification
- Benchmark and augmented specifications:
  - Baseline log-linear gravity includes exporter and importer real GDP, DIST, FTA, LANG, BORDER, COL, CURCOL, LAND, ISLAND, plus controls and εijt.
  - Augmented specification includes INF (infrastructure), INS (institutional quality), OIL (oil abundance), and INS*OIL interaction; country-pair fixed effects μij and year dummies included.
- Variable proxies:
  - Oil abundance proxied by share of oil exports in total exports (log oil export share used).
  - Infrastructure proxied by main telephone lines per 1,000 inhabitants (log).
  - Institutions proxied by PRS indices: corruption, bureaucratic quality, government stability; combined via principal component analysis (first component accounts for over 60 percent of variance).
- Estimation samples:
  - World sample: unbalanced panel of 190 countries, 1970–2006.
  - SSA sample: exporting country restricted to Sub-Saharan Africa; partner may be any country.

### Key empirical results — world sample (selected coefficients and interpretations)
- Baseline elasticities and effects (selected reported coefficients across columns):
  - Log real GDP (origin): 1.13; 1.31; 1.33; 1.29; 1.24; 1.35; 1.26.
  - Log real GDP (destination): 0.85; 0.98; 0.99; 0.88; 0.86; 0.89; 0.86.
  - Free trade agreement (dummy): 1.22; 0.36; 0.34; 0.19; 0.20; 0.21; 0.20.
  - Log distance: -1.22.
  - Log oil export share (Oil): -0.04; -0.03; -0.03; -0.03; -0.03 (significant). Interpretation: increasing the share of oil in total exports by 10 percent decreases real non-oil exports by about 0.4 percent.
  - Log of infrastructure: 0.12; 0.10; 0.16; 0.12.
- Institutional indices and interactions (selected estimates):
  - Corruption (Corr): 0.50; Corr * Oil: 0.01.
  - Bureaucratic quality (Burq): 0.59; Burq * Oil: 0.01.
  - Government stability (Stab): 0.28; Stab * Oil: 0.00.
  - Institutional quality (Inst, first principal component): 0.09; Inst * Oil: 0.01.
- Observations and diagnostics:
  - Observations: 338,056; subsets include 338,056; 335,394; 195,805; 195,534; 195,805; 195,528.
  - R-squared (within): 0.18 (and other R-squared reported per specification).
  - Hausman test (p-value): 0.00 (fixed effects preferred).

### Key empirical results — Sub-Saharan Africa sample (selected)
- Selected coefficients:
  - Log real GDP (origin): 0.98; 1.16; 1.22; 1.56; 1.40; 1.43.
  - Log real GDP (destination): 0.75; 0.83; 1.40; 0.77; 0.74; 0.76.
  - Log distance: -0.80.
  - Log oil export share (Oil): -0.02; -0.08; -0.03; -0.02; -0.01.
  - Log of infrastructure: 0.43; 0.39; 0.39; 0.40.
- Institutional coefficients and interactions:
  - Corruption (Corr): 1.88; Corr * Oil: 0.14.
  - Bureaucratic quality (Burq): 0.31; Burq * Oil: 0.02.
  - Government stability (Stab): 0.03; Stab * Oil: 0.02.
  - Institutional quality (Inst): 0.28; Inst * Oil: 0.02.
- Observations and fit:
  - Observations: 39,499; subsets include 39,499; 37,306; 24,487.
  - R-squared (within): 0.05; R-squared (overall) around 0.24–0.37.
  - Hausman test (p-value): 0.00 (fixed effects preferred).

### Results by institutional quality groups
- Grouping by institutional quality (below median = low; at/above median = high).
- Selected findings:
  - Log oil export share (low-quality group): -0.04 (significant) — a 10 percent increase in share of oil exports decreases real non-oil exports by about 0.4 percent in low-quality group.
  - Log oil export share (high-quality group): effect smaller; for world sample small but significant; for SSA sample insignificant.
  - Log of infrastructure: 0.10; 0.18; 0.15 (across specifications).
- Interpretation: negative relationship between net oil and non-oil exports is more pronounced for net oil exporters with low-quality institutions.

### Sensitivity analysis and robustness checks
- Zero trade observations:
  - Log-linear estimations drop zero values (~one-third of dataset), potentially causing selection bias.
  - PPML used as robustness check; including zeros yields similar signs and significance but generally smaller magnitudes.
- Fixed effects and omitted variables:
  - Country-pair fixed effects included in all estimations; separate exporter and importer fixed effects tried with no change in sign/significance.
- Comparative advantage control:
  - Trade specialization variable included with no effect on institutional/infrastructure coefficient results.
- Alternative proxies and deflators:
  - Oil exports as share of GDP; net exporter dummy; average WGI score; US CPI as deflator; inclusion of per capita income and population tested.
  - Main findings robust: oil abundance on average has a negative effect on non-oil exports; institutional quality determines extent of the negative effect.

### Quantified effects and magnitudes (selected estimates)
- Increasing the share of oil in total exports by 10 percent decreases real non-oil exports by about 0.4 percent (world sample, baseline).
- A 10 percent improvement in infrastructure is estimated to improve non-oil exports by 1.0–1.6 percent (reported range).
- Institutional quality coefficients (examples from world sample):
  - Corruption = 0.50; Bureaucratic quality = 0.59; Government stability = 0.28; Institutional quality (composite) = 0.09; interaction Inst * Oil = 0.01.
- For SSA, institutional and infrastructure marginal effects larger (e.g., Inst = 0.28; Inst * Oil = 0.02; log of infrastructure ≈ 0.40).

### Conclusion and policy implications (executive bullets)
- Main empirical conclusion:
  - Oil abundance has a strongly negative effect on the export competitiveness of the non-oil tradable sector on average.
  - Institutional quality (especially bureaucratic quality and corruption control) and infrastructure significantly mitigate this negative effect; interaction terms (Inst * Oil, Corr * Oil, Burq * Oil) are positive and significant in many specifications.
- Policy priorities for OPAC:
  - Improve bureaucratic quality and strengthen corruption control.
  - Enhance infrastructure (including telecommunications).
  - Stabilize government institutions.
  - Use oil revenues prudently to remove supply-side constraints and create an environment conducive to a dynamic tradable sector.
- Strategic objectives:
  - Diversify ahead of declining oil reserves and capture opportunities from changing global labor costs and demand patterns.
  - Diversification would reduce exposure to external shocks, enhance international competitiveness, broaden distribution of oil revenue gains, increase productivity, and support sustainable long-run growth.
- Comparative lesson:
  - OPAC countries have much to learn from Indonesia, Trinidad and Tobago, and the UAE, which improved their investment climate and diversified successfully.

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

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

### _wp08172 - References

### Introduction
- "In recent years, the sustained growth in global oil demand and the resultant steady increase in oil prices have allowed the oil-producing African countries (OPAC) to improve their export and growth performances."
- "The OPAC had average annual GDP growth of over 7 percent in 2000–06 while Sub-Saharan Africa (SSA) grew at 4.7 percent."

### Key findings highlighted in this unit
- Sustained growth in global oil demand and steady increases in oil prices are linked to improved export and growth performance for OPAC.
- OPAC experienced robust average annual GDP growth of over 7 percent in 2000–06.
- Sub-Saharan Africa (SSA) experienced average annual GDP growth of 4.7 percent in 2000–06.

*Source: _wp08172 - References..............................................................................................................*

### 4.3 percent. However, one of the major threats to sustaining this impressive growth rate is the

### _wp08172 - 4.3 percent. However, one of the major threats to sustaining this impressive growth rate is the

### Overview and research focus
- Paper examines competitiveness of OPAC (Angola, Cameroon, Chad, Republic of Congo, Côte d’Ivoire, Equatorial Guinea, Gabon, and Nigeria) and structural reasons for weak non-oil sector performance since 1970.
- Comparative analysis includes Indonesia, Mexico, Trinidad and Tobago, the United Arab Emirates (UAE), and Venezuela.
- Empirical approach includes descriptive historical analysis and a gravity model of bilateral trade flows to evaluate the significance of structural factors—especially institutional quality—on non-oil exports.

### Key findings on oil abundance, Dutch disease, and structural causes
- OPAC’s heavy reliance on oil revenues leaves them highly vulnerable to external shocks and makes long-run prospects uncertain.
- Appreciation of exchange rates after early 1970s oil shock is often cited as a main reason for tradable-sector contraction, but subsequent devaluations did not restore manufacturing performance.
- Structural factors identified as hindering diversification and external competitiveness:
  - ill-planned economic policies
  - low productivity
  - poor management of public finances
  - weak investment climate
- Literature gap: no prior study explicitly investigates institutional quality’s role in mitigating Dutch disease in resource-abundant countries; this paper extends literature by focusing on oil-rich SSA economies.

### Export performance and concentration (exact statistics preserved)
- OPAC contribution: eight major oil-exporting countries together contributed 7.5 percent of world exports in 2003–06.
- Trade-to-GDP ratio:
  - OPAC increased from 70 percent to 88 percent between 1970 and 1999 and to 103 percent in 2000–06.
  - SSA region increased from about 55 percent in 1970–99 to 66 percent in 2000–06.
  - World increased from 38 percent to 50 percent.
- Real exports growth (1970–2006):
  - OPAC goods and services: average annual rate of 6.9 percent.
  - SSA: 3.8 percent.
  - World: 6.5 percent.
- Oil dependence in exports and GDP (2000–06 averages and shares):
  - Oil constitutes over 70 percent of total exports for Angola, Chad, Congo, Equatorial Guinea, Gabon, and Nigeria.
  - Oil constitutes 30–40 percent of total exports for Cameroon and Côte d’Ivoire.
  - Contribution of oil exports to GDP averages about 50 percent in 2000–06 for OPAC.
- World export shares:
  - OPAC share of world goods exports dropped from 1.5 percent in 1970–80 to 0.7 percent in 1990–2006.
  - OPAC share of world non-oil exports dropped from 0.6 percent in 1970 to 0.2 percent in 2006.
- Country-specific nominal devaluations mentioned:
  - Nigeria’s nominal exchange rate devalued by about 34 percent in 1986.
  - Cameroon, Chad, Congo, Côte d’Ivoire, Equatorial Guinea and Gabon (CFA Franc zone) devalued by 49 percent in 1994.
  - Angola: stepped devaluation early 1990s resulting in average annual nominal exchange rate devaluation of about 88 percent during 1991–97.

### Export structure, diversification indices, and revealed comparative advantage
- Export structure remains dominated by primary commodities, especially fuels; manufactures share has held constant or fallen for most OPAC.
- Export concentration (ECI) and similarity measures:
  - ECI has risen for all OPAC; ECI close to 1 for Angola, Equatorial Guinea, and Nigeria; about 0.8 for Congo and Gabon; Cameroon and Côte d’Ivoire average about 0.5 for 1970–2005.
  - Similarity (Pearson r and Bray-Curtis S) indicate composition of exports shifted heavily toward oil and then stabilized.
- Revealed comparative advantage (RCA) counts (number of product categories with RCA ≥ 1, out of 182):
  - Angola: 15 (1970–79), 4 (1980–89), 3 (1990–99), 3 (2000–05).
  - Cameroon: 17, 15, 16, 16.
  - Congo: 18, 10, 10, 13.
  - Côte d’Ivoire: 16, 18, 21, 25.
  - Equatorial Guinea: 6, 9, 7, 5.
  - Gabon: 7, 7, 6, 6.
  - Nigeria: 7, 4, 6, 6.
  - Indonesia: 20, 25, 42, 50.
  - Mexico: 53, 30, 39, 33.
  - Trinidad and Tobago: 11, 14, 27, 22.
  - United Arab Emirates: 4, 9, 19, 28.
  - Venezuela: 6, 7, 18, 17.
- Sectoral RCA distribution in 2005 (number of product categories with RCA ≥ 1):
  - Nonfuel primary commodities vs Manufactures:
    - Angola: 2 nonfuel primary commodities, 0 manufactures.
    - Cameroon: 13 nonfuel primary commodities, 1 manufacture.
    - Congo: 8 nonfuel primary commodities, 0 manufactures.
    - Côte d’Ivoire: 13 nonfuel primary commodities, 2 manufactures.
    - Equatorial Guinea: 1 nonfuel primary commodity, 2 manufactures.
    - Gabon: 4 nonfuel primary commodities, 1 manufacture.
    - Nigeria: 2 nonfuel primary commodities, 1 manufacture.
    - Comparator examples: Indonesia 21 and 23; Mexico 13 and 22; United Arab Emirates 12 and 8.

### Productivity, infrastructure, and human capital (selected indicators)
- Productivity proxies indicate OPAC generally have much lower labor productivity than comparators; Gabon has highest and Angola, Chad, and Nigeria the lowest among OPAC (real non-oil GDP per capita averages referenced for 2000–06).
- ICT and transport infrastructure (2005 per-1,000 and percentage measures; table-based values preserved):
  - Telephone main lines (per 1,000 people) examples: AGO 6; CMR 6; TCD 1; COG 4; CIV 14; GNQ 20; GAB 28; NGA 9; SSA 17; IDN 58; MEX 189; TTO 248; ARE 273; VEN 136.
  - Mobile subscribers (per 1,000 people) examples: AGO 69; CMR 138; TCD 22; COG 123; CIV 121; GNQ 192; GAB 470; NGA 141; SSA 125; IDN 213; MEX 460; TTO 613; ARE 1,000; VEN 470.
  - Internet users (per 1,000 people): AGO 11; CMR 15; TCD 4; COG 13; CIV 11; GNQ 14; GAB 48; NGA 38; SSA 29; IDN 73; MEX 181; TTO 123; ARE 308; VEN 125.
  - Personal computers (per 1,000 people): AGO 2; CMR 10; TCD 2; COG 4; CIV 15; GNQ 14; GAB 33; NGA 7; SSA 15; IDN 14; MEX 136; TTO 79; ARE 197; VEN 82.
  - Internet bandwidth (bits per person): AGO 0; CMR 1; TCD 0; COG 0; CIV 3; GNQ 33; GAB 145; NGA 1; SSA 2; IDN 7; MEX 110; TTO 375; ARE 923; VEN 51.
  - Paved roads (% of total roads): AGO 10.4; CMR 11.7; TCD 0.8; COG 9.3; CIV 9.3; GNQ ...; GAB 9.1; NGA 25.6; SSA 14.7; IDN 51.9; MEX 35.7; TTO 49.6; ARE 98.8; VEN 35.4.
- Human Development indicators (2005; exact values preserved):
  - HDI and ranks (examples):
    - Angola: HDI 0.45, Rank 162, Adult literacy (% aged 15+) 67.4, Gross enrolment 25.6*, Life expectancy index 0.28.
    - Cameroon: HDI 0.53, Rank 144, Adult literacy 67.9, Gross enrolment 62.3, Life expectancy index 0.41.
    - Chad: HDI 0.39, Rank 170, Adult literacy 25.7, Gross enrolment 37.5, Life expectancy index 0.42.
    - Congo (Rep. of): HDI 0.55, Rank 139, Adult literacy 84.7, Gross enrolment 51.4, Life expectancy index 0.48.
    - Côte d’Ivoire: HDI 0.43, Rank 166, Adult literacy 48.7, Gross enrolment 39.6*, Life expectancy index 0.37.
    - Equatorial Guinea: HDI 0.64, Rank 127, Adult literacy 87.0, Gross enrolment 58.1, Life expectancy index 0.42.
    - Gabon: HDI 0.68, Rank 119, Adult literacy 84.0, Gross enrolment 72.4*, Life expectancy index 0.52.
    - Nigeria: HDI 0.47, Rank 158, Adult literacy 69.1, Gross enrolment 56.2, Life expectancy index 0.36.
    - SSA average: HDI 0.49, Adult literacy 60.3, Gross enrolment 50.4, Life expectancy index 0.41.
    - Comparator examples: Indonesia HDI 0.73 Rank 107; Mexico HDI 0.83 Rank 52; Trinidad and Tobago HDI 0.81 Rank 59; UAE HDI 0.87 Rank 39; Venezuela HDI 0.79 Rank 74.
  - Note: * = data for a year other than 2005.

### Institutional quality and business environment (exact rankings and metrics)
- Doing Business Indicator (DBI) 2007:
  - OPAC average DBI rank: 155 (out of 178 countries), worse than SSA and all other oil producers except Venezuela.
  - Individual OPAC ranks (best to worst among OPAC): Nigeria ranks 108; followed by Gabon, Cameroon, Côte d’Ivoire, Equatorial Guinea, Angola, Chad, and Congo.
  - Comparator ranks: Mexico 44; Trinidad and Tobago (rank not specified in excerpt but indicated better than OPAC); UAE ranks ahead of other oil exporters.
- Subcategory weaknesses for OPAC (qualitative and timing metrics preserved):
  - Ease of obtaining credit, dealing with licenses, trading across borders, and protecting investors are particularly weak.
  - Average days to export goods for OPAC: about 40 days.
    - Comparators: Indonesia 21 days; Mexico 17 days; Trinidad and Tobago 14 days; UAE 13 days.
  - Days required to import goods into OPAC is almost double that for other oil producers (exact comparator import-day values not listed in excerpt).

### Empirical model result summary and policy-relevant implication (as stated)
- Gravity model estimates:
  - Oil abundance negatively affects non-oil exports.
  - Infrastructure and institutions positively affect non-oil exports.
  - Institutional quality significantly determines the extent to which oil abundance affects non-oil sector competitiveness.
- Policy-relevant conclusion emphasized in analysis:
  - Oil revenues do not necessarily weaken the tradable sector if managed appropriately; countries may benefit substantially from booms if revenues are used to create an environment (infrastructure, institutions, investment climate, human capital) that promotes the non-oil sector.

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

### Appendix for the detailed breakdown of the DBI by subcategories.

### _wp08172 - Appendix for the detailed breakdown of the DBI by subcategories.

### World Governance Indicators (WGI) — summary observations
- The WGI cover six dimensions: voice and accountability, regulatory quality, rule of law, control of corruption, political stability, and government effectiveness (percentile ranks plotted for 1998–2006).
- On average, the OPAC perform poorly on all six indicators compared to SSA and other oil producers except Venezuela.
- Among OPAC:
  - Gabon ranks highest in five of the six governance indicators.
  - Equatorial Guinea, Côte d’Ivoire, and Chad have the lowest percentile ranks (rarely rising above the 10th percentile).
- Among comparator oil producers:
  - Mexico, Trinidad and Tobago and the UAE have the highest percentile ranks.
  - Indonesia performs well on government effectiveness, voice and accountability, and regulatory quality.
  - Venezuela lags in all indicators.
- Angola is the only OPAC that does not exhibit a drop in any of the six WGIs since 1998; Chad and Côte d’Ivoire fell in rank in every indicator. Nigeria improved in voice and accountability, government effectiveness, and the rule of law, but dropped in others.

*Source: Worldwide Governance Indicators (www.worldbank.org/wbi/governance/govdata/).*

### Global Competitiveness Index (GCI) — key points and rankings
- The GCI is based on nine pillars grouped into three categories: basic requirements; efficiency enhancers; innovation and sophistication factors.
- The three stages of development identified by the GCI:
  - factor-driven (per capita income below $2,000);
  - efficiency-driven (per capita income between $3,000-9,000);
  - innovation-driven (per capita income above $17,000).
- GCI coverage and data notes:
  - The GCI is unavailable for Gabon, Equatorial Guinea, and Cote d’Ivoire.
- Rankings (out of 125 countries) highlights:
  - UAE is the best performer overall and in all subcategories.
  - Indonesia and Mexico follow the UAE.
  - Chad and Angola rank 123rd and 125th, respectively.
- Structural competitiveness summary:
  - OPAC’s structural competitiveness is generally weak.
  - Indonesia, Mexico, and the UAE have relatively strong infrastructure and institutional quality and stronger non-oil export performance and diversification.
  - OPAC and Venezuela exhibit weak investment climates and weak non-oil tradable sectors.

*Source: World Economic Forum (2006).*

### Institutional quality and export competitiveness — conceptual summary
- Preliminary empirical observation:
  - Non-oil exports appear negatively related with oil abundance regardless of measure used.
  - When grouped by institutional quality (below-median WGI average vs above), the negative relationship between net oil and non-oil exports is more pronounced for net oil exporters with low-quality institutions.
- Interpretation:
  - Institutional quality may determine whether oil abundance leads to weaker non-oil trade (Dutch disease) or can be harnessed to promote non-oil exports.
  - Natural resource abundance tests institutional arrangements; better institutions and effective management of oil revenues can enable countries to benefit from oil abundance (citing Mehlum, Moehne, and Torvik, 2006).

### Methodology — gravity model specifications
- Benchmark gravity model (Tinbergen 1962 conceptual structure). Estimated specification (log-linear baseline):
  - Equation (5): Xijt = exp[...], with explanatory variables including logs of exporter and importer real GDP, DIST (distance), FTA, LANG, BORDER, COL, CURCOL, LAND, ISLAND, plus controls and error term εijt.
- Augmented specification to test institutions and oil interaction:
  - Equation (6): includes INF (infrastructure), INS (institutional quality), OIL (oil abundance), and INS*OIL interaction; country-pair fixed effects μij and year dummies included.
  - Oil abundance proxied by share of oil exports in total exports (log oil export share used).
  - Infrastructure proxied by main telephone lines per 1,000 inhabitants (log of infrastructure).
  - Institutions proxied by three PRS indices: bureaucratic quality, government stability, and corruption; normalized 0–1, combined via principal component analysis (first component accounts for over 60 percent of total variance) to form a composite institutional quality measure.
- Estimation samples:
  - World sample: unbalanced panel of 190 industrial and developing countries, 1970–2006.
  - SSA sample: exporting country restricted to Sub-Saharan Africa; trading partner may be any country.
- Data sources enumerated (UN-COMTRADE for non-oil exports; WDI 2007 for GDP and infrastructure; PRS and WGI for institutional indices; CIA World Factbook for distance/language/colonial ties; Tsangarides et al. (2007) for FTAs).

### Key empirical results — world sample (summary of Table 6)
- Baseline elasticities and effects (selected coefficients from fixed-effects estimations):
  - Log real GDP (origin): 1.13; 1.31; 1.33; 1.29; 1.24; 1.35; 1.26 (reported across columns with significance indicated; see table).
  - Log real GDP (destination): 0.85; 0.98; 0.99; 0.88; 0.86; 0.89; 0.86.
  - Free trade agreement (dummy): 1.22; 0.36; 0.34; 0.19; 0.20; 0.21; 0.20.
  - Log distance: -1.22.
  - Log oil export share (Oil): -0.04; -0.03; -0.03; -0.03; -0.03 (significant), interpreted as: increasing the share of oil in total exports by 10 percent decreases real non-oil exports by about 0.4 percent.
  - Log of infrastructure: 0.12; 0.10; 0.16; 0.12 (across specifications).
- Institutional indices and interactions (selected estimates):
  - Corruption (Corr): 0.50; Corr * Oil: 0.01.
  - Bureaucratic quality (Burq): 0.59; Burq * Oil: 0.01.
  - Government stability (Stab): 0.28; Stab * Oil: 0.00 (insignificant in some specs).
  - Institutional quality (Inst, first principal component): 0.09; Inst * Oil: 0.01.
- Observations and goodness-of-fit:
  - Observations: 338,056; subsets: 338,056; 335,394; 195,805; 195,534; 195,805; 195,528 across columns.
  - R-squared (within): 0.18; other R-squared (between/overall) reported per specification.
  - Hausman test (p-value): 0.00 (fixed effects preferred).

### Key empirical results — Sub-Saharan Africa sample (summary of Table 7)
- Selected coefficients (fixed-effects):
  - Log real GDP (origin): 0.98; 1.16; 1.22; 1.56; 1.40; 1.43 (across columns).
  - Log real GDP (destination): 0.75; 0.83; 1.40; 0.77; 0.74; 0.76.
  - Log distance: -0.80.
  - Log oil export share (Oil): -0.02; -0.08; -0.03; -0.02; -0.01 (across columns).
  - Log of infrastructure: 0.43; 0.39; 0.39; 0.40.
- Institutional coefficients and interactions:
  - Corruption (Corr): 1.88 (large positive), Corr * Oil: 0.14.
  - Bureaucratic quality (Burq): 0.31; Burq * Oil: 0.02.
  - Government stability (Stab): 0.03 (insignificant alone); Stab * Oil: 0.02 (significant).
  - Institutional quality (Inst): 0.28; Inst * Oil: 0.02.
- Observations and fit:
  - Observations: 39,499; subsets: 39,499; 37,306; 24,487 (consistent across columns).
  - R-squared (within): 0.05; R-squared (overall) around 0.24–0.37.
  - Hausman test (p-value): 0.00 (fixed effects preferred).

### Results by institutional quality groups (summary of Table 8)
- Grouping by institutional quality (below median = low; at/above median = high).
- Selected findings:
  - Log oil export share (low-quality group): -0.04 (significant) — a 10 percent increase in share of oil exports decreases real non-oil exports by about 0.4 percent in low-quality group.
  - Log oil export share (high-quality group): effect smaller; for world sample small but significant; for SSA sample insignificant.
  - Log of infrastructure: 0.10; 0.18; 0.15 (across specifications).
- Observations:
  - World sample observations reported: 74,198; 220,208; 220,208; 17,095; 17,095; 15,877; 15,877 across columns (see table for details).
  - Hausman test p-values vary across specifications.

### Sensitivity analysis — robustness checks
- Zero trade observations:
  - Concern: log-linear estimations drop zero values (~one-third of dataset), potentially causing selection bias.
  - PPML (Poisson pseudo maximum likelihood) used as robustness check; including zeros yields similar signs and significance but generally smaller magnitudes (reported in Table B5).
- Omitted variable bias and fixed effects:
  - Country-pair fixed effects included in all estimations; separate exporter and importer fixed effects tried with no change in sign/significance.
- Comparative advantage control:
  - Trade specialization variable (net exports relative to total trade for non-oil sector) included with no effect on institutional/infrastructure coefficient results.
- Alternative proxies and deflators:
  - Alternative oil abundance proxies (oil exports as share of GDP; net exporter dummy) and institutional proxies (average WGI score) used; US CPI as alternate deflator used; inclusion of per capita income and population as additional size measures tested.
  - Main findings robust: oil abundance on average has a negative effect on non-oil exports; institutional quality determines extent of the negative effect.

### Conclusion and policy implications — executive bullets
- Main empirical conclusion:
  - Oil abundance has a strongly negative effect on the export competitiveness of the non-oil tradable sector on average.
  - Institutional quality (especially bureaucratic quality and corruption control) and infrastructure significantly mitigate this negative effect: the interaction terms (Inst * Oil, Corr * Oil, Burq * Oil) are positive and significant in many specifications.
- Quantified effects and magnitudes:
  - Increasing the share of oil in total exports by 10 percent decreases real non-oil exports by about 0.4 percent (world sample, baseline).
  - A 10 percent improvement in infrastructure is estimated to improve non-oil exports by 1.0–1.6 percent (reported range).
  - Institutional quality coefficients: example magnitudes from world sample include Corruption = 0.50; Bureaucratic quality = 0.59; Government stability = 0.28; Institutional quality (composite) = 0.09; interaction Inst * Oil = 0.01.
  - For SSA, institutional and infrastructure marginal effects on non-oil exports are larger (e.g., Inst = 0.28; Inst * Oil = 0.02; log of infrastructure ≈ 0.40).
- Policy implications emphasized:
  - OPAC countries can benefit substantially from oil abundance if revenues are managed prudently to remove supply-side constraints and create an environment conducive to a dynamic tradable sector.
  - Priorities include improving bureaucratic quality, strengthening corruption control, enhancing infrastructure (including telecommunications), and stabilizing government institutions.
  - The recent oil boom offers a unique opportunity for OPAC to address economic and institutional shortcomings, diversify ahead of declining reserves, and capture opportunities from changing global labor costs and demand patterns.
  - Diversification would reduce exposure to external shocks, enhance international competitiveness, broaden distribution of oil revenue gains, increase productivity, and support sustainable long-run growth.
- Comparative lesson:
  - OPAC countries have much to learn from Indonesia, Trinidad and Tobago, and the UAE, which improved their investment climate and diversified successfully.

*Source: Author’s calculations based on UN-COMTRADE, WDI, PRS, WGI, WEO, Doing Business database, World Economic Forum, and related data as reported in the appendix.*

### REFERENCES

### _wp08172 - REFERENCES

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### Dutch disease, natural resources, and the resource curse
- Neary, J. P., and S. Van Wijnbergen, 1986, Natural Resources and the Macroeconomy (Oxford: Basil Blackwell).
- van Wijnbergen, Sweder, 1984, “Inflation, Employment, and the Dutch Disease in Oil-Exporting Countries: A Short-Run Disequilibrium Analysis,” Quarterly Journal of Economics, Vol. 99, No. 2, pp. 233–50.
- Krugman, P., 1987, “The Narrow Moving Band, the Dutch Disease, and the Competitive Consequences of Mrs. Thatcher on Trade in the Presence of Dynamic Scale Economies,” Journal of Development Economics, Vol. 27, pp. 41–55.
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- Mehlum, H., K. Moene, and R. Torvik, 2006, “Institutions and the Resource Curse,” Economic Journal, Vol. 116, No. 508, pp. 1–20.
- Leite, C., and J. Weidmann, 1999, “Does Mother Nature Corrupt? Natural Resources, Corruption, and Economic Growth,” IMF Working Paper No. WP/99/85 (Washington: International Monetary Fund).
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- Auty, R.M., 1990, Resource-Based Industrialization: Sowing the Oil in Eight Developing Countries (New York: Oxford University Press).
- Mehlum et al., 2006; Leite and Weidmann, 1999 cited among policy- and institution-focused works on resources.

### Competitiveness, exchange rates, and real exchange rate misalignment
- Chudik, A., and J. Mongardini, 2006, “In Search of Equilibrium: Estimating Equilibrium Real Exchange Rates in Low-Income African Countries,” IMF Working Paper No. 07/90 (Washington: International Monetary Fund).
- Di Bella, G., M. Lewis, and A. Martin, 2007, “Assessing Competitiveness and Real Exchange Rate Misalignments in Low-Income Countries,” IMF Working Paper No. 07/201 (Washington: International Monetary Fund).
- Dufrenot, G., and E. B. Yehoue, 2005, “Real Exchange Rate Misalignment: A Panel Co-Integration and Common Factor Analysis,” IMF Working Paper No. 05/164 (Washington: International Monetary Fund).
- Ghura, D., and T. Grennes, 1993, “The Real Exchange Rate and Macroeconomic Performance in Sub-Saharan Africa,” Journal of Development Economics, Vol. 42, pp. 155–74.
- Sekkat, K., and A. Varoudakis, 2000, “Exchange Rate Management and Manufactured Exports in Sub-Saharan Africa,” Journal of Development Economics, Vol. 61, No. 1, pp. 237–53.
- Ramirez, G., and C. Tsangarides, 2007, “Competitiveness in the CFA Franc Zone,” IMF Working Paper No. 07/212 (Washington: International Monetary Fund).
- Tsangarides, C., P. Ewenczyk, and M. Hulej, 2006, “Stylized Facts on Bilateral Trade and Currency Unions,” IMF Working Paper No. WP/06/31 (Washington: International Monetary Fund).
- Abdih, Y., and C. Tsangarides, 2008, “FEER for the CFA Franc,” Applied Economics (forthcoming).

### Africa trade, manufacturing, diversification, and policy-focused studies
- Carey, K., S. Gupta, and U. Jacoby, 2007, Sub-Saharan Africa: Forging New Trade Links with Asia (Washington: International Monetary Fund).
- Collier, P., 1994, “Devaluation: The Experience of Anglophone Africa”, Dialogure No. 2, (Paris: Développement Institutions et Analyses de Long Terme).
- Collier, P., 1997, “Globalization: Implications for Africa,” in Trade Reform and Regional Integration in Africa, ed. by Z. Iqbal and M. Khan (Washington: International Monetary Fund).
- Collier, P., and Jan Willem Gunning, 1999, “Explaining Africa’s Economic Performance,” Journal of Economic Literature, Vol. 37, No. 1, pp. 64–111.
- Collier, P., and A. Hoffler, 2004, “Greed and Grievance in African Civil Wars,” Oxford Economic Papers, Vol. 56. No. 4, pp. 563–95.
- Gupta, Sanjeev, and Yongzheng Yang, 2006, “What Impedes Africa’s Trade?” Finance and Development, Vol. 43, No. 4, pp. 22–25.
- UNCTAD, 2003, Economic Development in Africa: Trade Performance and Commodity Dependence (Geneva: United Nations Conference on Trade and Development).
- UNECA, 2007, Economic Report on Africa 2007: Accelerating Africa’s Development Through Diversification (Addis Ababa: United Nations Economic Commission on Africa).
- World Bank, 2004, Patterns of Africa-Asia Trade and Investment: Potential for Ownership and Partnership, Africa Region, Private Sector Unit (Washington: World Bank).
- WEF, 2007, African Competitiveness Report 2007, (Geneva: World Economic Forum).
- Fukunishi, Takahiro, 2004, “International Competitiveness of Manufacturing Firms in Sub-Saharan Africa: Why Has the Manufacturing Sector Remained Small?” IDE Discussion Paper No. 2 (Chiba: Institute of Developing Economies).
- Biggs, T., M. Shah, and P. Srivastava, 1995, “Technological Capabilities and Learning in African Enterprises,” World Bank Technical Paper No. 288 (Washington: World Bank).
- Biggs, T., and P. Srivastava, 1996, “Structural Aspects of Manufacturing in Sub-Saharan Africa,” World Bank Discussion Paper No. 345 (Washington: World Bank).
- Bigsten, A., A. Isaksson, M. Soderbom, P. Collier, A. Zeufack, S. Dercon, M. Fafchamps, J. W. Gunning, F. Teal, S. Appleton, B. Gauthier, A. Oduro, R. Oostendorp, and C. Pattillo, 2000, “Rates of Return on Physical and Human Capital in Africa’s Manufacturing Sector,” Economic Development and Cultural Change, Vol. 48, No. 4, pp. 801–27.
- Söderling, Ludvig, 2000, “Dynamics of Export Performance, Productivity, and Real Effective Exchange Rate in Manufacturing: the Case of Cameroon,” Journal of African Economies, Vol. 9, No. 4, pp. 411–29.
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- Pack, H., 1987, Productivity, Technology and Industrial Development: A Case-Study in Textiles (New York: Oxford University Press).
- Francois, J., and M. Manchin, 2007, “Institutions, Infrastructure and Trade,” CEPR Discussion Paper No. 6068 (London: Centre for Economic Policy Research).

### Macroeconomic crises, datasets, and methodological sources
- Cerra, V., and S.C. Saxena, 2002, “What Caused the 1991 Crisis in India?” IMF Staff Papers, Vol. 49, No. 3, pp. 395–425.
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- Di Bella, G., M. Lewis, and A. Martin, 2007, “Assessing Competitiveness and Real Exchange Rate Misalignments in Low-Income Countries,” IMF Working Paper No. 07/201 (Washington: International Monetary Fund).
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- International Monetary Fund, 2007, Regional Economic Outlook: Sub-Saharan Africa, April 2007 (Washington: IMF).

*Reference list as provided in _wp08172 - REFERENCES*

### APPENDIX A: MACROECONOMIC INDICATORS AND EXPORT PERFORMANCE

### APPENDIX A: MACROECONOMIC INDICATORS AND EXPORT PERFORMANCE

### Real GDP and GDP Per Capita Growth (Table A1)
- Real GDP growth (period averages, percent) for OPAC and selected countries:
  - OPAC: 1980-84 = 2.85; 1985-89 = 3.88; 1990-94 = 2.61; 1995-99 = 9.46; 2000-06 = 7.03
  - Angola: 1985-89 = 1.64; 1990-94 = 3.33; 1995-99 = -6.19; 2000-06 = 8.75; (2000-06 also listed = 10.62 in same row)
  - Cameroon: 1985-89 = 9.77; 1990-94 = 0.62; 1995-99 = -3.73; 2000-06 = 4.57; 2000-06 also = 3.74
  - Chad: 1985-89 = 1.90; 1990-94 = 5.40; 1995-99 = 3.88; 2000-06 = 2.65; 2000-06 also = 10.86
  - Congo, Republic of: 1980-84 = 4.49; 1985-89 = 10.04; 1990-94 = -0.09; 1995-99 = 1.76; 2000-06 = 4.87
  - Côte d'Ivoire: 1985-89 = 0.88; 1990-94 = 2.40; 1995-99 = 2.02; 2000-06 = 5.62; 2000-06 also = -0.51
  - Equatorial Guinea: 1980-84 = 3.77; 1985-89 = 3.29; 1990-94 = 19.03; 1995-99 = 47.79; 2000-06 = 19.87
  - Gabon: 1985-89 = 1.40; 1990-94 = 1.45; 1995-99 = 3.17; 2000-06 = 1.78; 2000-06 also = 1.10
  - Nigeria: 1985-89 = -1.05; 1990-94 = 4.51; 1995-99 = 2.82; 2000-06 = 2.79; 2000-06 also = 5.65

- Real non-oil GDP growth (selected values, percent):
  - OPAC: 1980-84 = -7.75; 1985-89 = 6.75; 1990-94 = -0.86; 1995-99 = 5.10; 2000-06 = 8.16
  - Angola: 1980-84 = -28.63; 1985-89 = 9.44; 1990-94 = -16.19; 1995-99 = 10.19; 2000-06 = 10.85
  - Congo, Republic of: 1980-84 = -9.25; 1985-89 = 10.31; 1990-94 = -1.73; 1995-99 = -2.69; 2000-06 = 8.28
  - Equatorial Guinea: 1980-84 = 3.77; 1985-89 = 3.29; 1990-94 = 3.64; 1995-99 = 13.98; 2000-06 = 27.72
  - Nigeria: 1980-84 = -13.40; 1985-89 = 5.32; 1990-94 = 2.76; 1995-99 = 3.28; 2000-06 = 6.42

- Real per capita GDP growth (selected values, percent):
  - OPAC: 1980-84 = -1.09; 1985-89 = 0.22; 1990-94 = -1.02; 1995-99 = 5.65; 2000-06 = 3.91
  - Angola: 1985-89 = -1.17; 1990-94 = -1.18; 1995-99 = -8.72; 2000-06 = 5.55; 2000-06 also = 7.47
  - Equatorial Guinea: 1980-84 = -4.24; 1985-89 = -3.60; 1990-94 = 11.09; 1995-99 = 36.97; 2000-06 = 14.72
  - Nigeria: 1980-84 = -5.17; 1985-89 = 1.49; 1990-94 = -0.10; 1995-99 = -0.12; 2000-06 = 2.90

- Note: "..." indicates data not available. Source: IMF, African Department database.

### Macroeconomic Indicators for Selected Oil Exporting Countries (Table A2)
- Real GDP growth (period averages, percent) for selected oil exporters:
  - AGO (Angola): 1980-89 = 2.7; 1990-99 = 1.0; 2000-06 = 10.4
  - CMR (Cameroon): 1970-79 = 7.3; 1980-89 = 4; 1990-99 = 0.41; 2000-06 = 3.8
  - TCD (Chad): 1970-79 = -1.0; 1980-89 = 5.4; 1990-99 = 2.2; 2000-06 = 11.1
  - COG (Congo, Rep. of): 1970-79 = 5.5; 1980-89 = 6.8; 1990-99 = 0.8; 2000-06 = 5.1
  - CIV (Côte d’Ivoire): 1970-79 = 7.6; 1980-89 = -0.2; 1990-99 = 2.6; 2000-06 = -0.2
  - GNQ (Equatorial Guinea): 1990-99 = 20.2; 2000-06 = 20.0
  - GAB (Gabon): 1970-79 = 9.9; 1980-89 = 1.9; 1990-99 = 2.5; 2000-06 = 1.1
  - NGA (Nigeria): 1970-79 = 7.0; 1980-89 = 0.9; 1990-99 = 3.1; 2000-06 = 5.6
  - IDN (Indonesia): 1970-79 = 7.8; 1980-89 = 6.4; 1990-99 = 4.8; 2000-06 = 4.9
  - MEX (Mexico): 1970-79 = 6.4; 1980-89 = 2.3; 1990-99 = 3.4; 2000-06 = 2.9
  - TTO (Trinidad and Tobago): 1970-79 = 4.6; 1980-89 = -1.3; 1990-99 = 2.7; 2000-06 = 8.4
  - ARE (UAE): 1970-79 = 12.6; 1980-89 = 1.2; 1990-99 = 5.6; 2000-06 = 7.6
  - VEN (Venezuela): 1970-79 = 4.0; 1980-89 = -0.2; 1990-99 = 2.5; 2000-06 = 4.2

- Oil exports as percent of total exports (selected values):
  - AGO: 1970-79 = 47.59; 1980-89 = 78.08; 1990-99 = 88.88; 2000-06 = 88.36
  - NGA: 1970-79 = 74.25; 1980-89 = 92.91; 1990-99 = 90.05; 2000-06 = 85.50
  - VEN: 1970-79 = 86.10; 1980-89 = 81.27; 1990-99 = 70.40; 2000-06 = 80.93

- Oil exports as percent of world oil exports (selected values):
  - AGO: 1970-79 = 1.14; 1980-89 = 0.77; 1990-99 = 1.25; 2000-06 = 1.59
  - NGA: 1970-79 = 6.26; 1980-89 = 4.49; 1990-99 = 4.06; 2000-06 = 3.91
  - VEN: 1970-79 = 8.43; 1980-89 = 5.05; 1990-99 = 4.79; 2000-06 = 4.34

- Sectoral shares of value added (percent of GDP), selected periods and countries:
  - Agriculture, value added (% of GDP), 2000-06:
    - AGO = 7.6; CMR = 21.4; TCD = 31.8; COG = 5.5; CIV = 22.9; GNQ = 5.5; GAB = 5.7; NGA = 25.4; IDN = 14.95; MEX = 3.97; TTO = 1.2; ARE = 3.08; VEN = 4.35
  - Manufacturing, value added (% of GDP), 2000-06:
    - AGO = 3.6; CMR = 19.9; TCD = 7.3; COG = 5.2; CIV = 17.3; GNQ = 6.1; GAB = 4.4; NGA = 4.0; IDN = 29.0; MEX = 18.6; TTO = 7.3; ARE = 13.6; VEN = 18.4
  - Services, value added (% of GDP), 2000-06:
    - CMR = 46.1; TCD = 36.8; COG = 27.4; CIV = 56; GNQ = 4.1; GAB = 38.7; NGA = 40.2; IDN = 40.16; MEX = 69.38; TTO = 47.5; ARE = 44.31; VEN = 46.4

- Note: AGO=Angola; CMR=Cameroon; TCD=Chad; COG=Congo (Rep. of); CIV=Côte d’Ivoire; GNQ=Equatorial Guinea; GAB=Gabon; NGA=Nigeria; IDN=Indonesia; MEX=Mexico; TTO=Trinidad and Tobago; ARE=United Arab Emirates; VEN=Venezuela; "..." indicates data not available. Source: World Bank (2007) and IMF (2007).

### Share of Total and Non-Oil Exports in World Exports (Table A3)
- Total exports (share of world exports, percent):
  - OPAC: 1970-89 = 1.09; 1990-99 = 0.44; 2000-06 = 0.68
  - Angola: 1970-89 = 0.13; 1990-99 = 0.07; 2000-06 = 0.13
  - Nigeria: 1970-89 = 0.61; 1990-99 = 0.23; 2000-06 = 0.34
  - SSA (Sub-Saharan Africa): 1970-89 = 2.76; 1990-99 = 1.52; 2000-06 = 1.60
  - Mexico: 1970-89 = 0.89; 1990-99 = 1.48; 2000-06 = 2.00
  - Indonesia: 1970-89 = 0.67; 1990-99 = 0.82; 2000-06 = 0.80

- Non-oil exports (share of world exports, percent):
  - OPAC non-oil: 1970-89 = 0.29; 1990-99 = 0.10; 2000-06 = 0.11
  - Nigeria non-oil: 1970-89 = 0.07; 1990-99 = 0.01; 2000-06 = 0.03
  - Mexico non-oil: 1970-89 = 0.43; 1990-99 = 1.14; 2000-06 = 1.65
  - Indonesia non-oil: 1970-89 = 0.23; 1990-99 = 0.61; 2000-06 = 0.64
- Source: World Economic Outlook database.

### Leading Export Sectors, 1970–2005 (Table A4)
- OPAC country export concentration (top export sectors and shares in merchandise exports, figures in parentheses are shares):
  - Angola: Petroleum (crude/partly refined) shares range across periods: 84%, 90%, 88%, 95%; other notable items: Precious/semi-precious stones (18%), Iron ore/concentrates (15%)
  - Cameroon: Cocoa (32% in 1970); Petroleum (crude/partly refined) 37% (1980), 48% (1990), 37% (2000), 45% (2005); Coffee and Wood are recurrent non-oil exports.
  - Congo (Rep. of): Wood (rough/roughly squared) 34% (1970); Petroleum (crude/partly refined) 72% (1980), 66% (1990), 70% (2000), 83% (2005); Copper and ores featured in some periods.
  - Côte d'Ivoire: Cocoa dominant across periods (e.g., 32%, 38%, 48% in various decades); Wood and fruit, fresh and nuts also recurring.
  - Equatorial Guinea: Shift from cocoa/wood to Petroleum (crude/partly refined) with shares 82%, 92% in later periods; Gas appears in later periods (Gas 2%, Gas 1%).
  - Gabon: Petroleum (crude/partly refined) dominant (71%–77% across 1980–2005); wood and ores also appear.
  - Nigeria: Petroleum (crude/partly refined) dominant across decades: 66%, 96%, 93%, 91%, 89%; Cocoa small share historically; Gas appears in later periods.
  - Comparative non-African oil exporters (selected highlights):
    - Indonesia: Petroleum-crude/partly refined and Petroleum products featured; Wood and Veneers, plywood boards & other worked wood important in various periods.
    - Mexico: Transition toward Road motor vehicles and Telecommunication apparatus by 2000s; Petroleum-crude/partly refined declined in share.
    - Trinidad and Tobago: Petroleum products and Gas dominant across periods; Inorganic chemicals and Organic chemicals present.
    - United Arab Emirates: Petroleum-crude/partly refined dominant (75%–91%); Petroleum products and Gas appear; Silver and platinum group metals appear in some periods.
    - Venezuela: Petroleum-crude/partly refined consistently major (48%–65%); Petroleum products also large; Aluminum and iron ores/concentrates appear.

- Note: Figures in parentheses are the shares of the respective sectors in total merchandise exports. Source: Author’s calculations based on UN-COMTRADE database.

### Doing Business Indicators, 2007 (Table A5)
- Ease of Doing Business: Average rank (out of 178)
  - AGO = 167; CMR = 154; TCD = 173; COG = 175; CIV = 155; GNQ = 165; GAB = 144; NGA = 108; SSA = 136; IDN = 123; MEX = 44; TTO = 67; UAE = 68; VEN = 172

- Starting a Business (selected metrics)
  - Procedures (number): AGO = 12; CMR = 13; TCD = 19; COG = 10; CIV = 10; GNQ = 20; GAB = 9; NGA = 9; SSA = 11; IDN = 12; MEX = 8; TTO = 9; UAE = 11; VEN = 16
  - Time (days): AGO = 119; CMR = 37; TCD = 75; GNQ = 136; GAB = 58; NGA = 34; MEX = 27; VEN = 141
  - Cost (% of income per capita): AGO = 344; CMR = 129; TCD = 189; GNQ = 105; NGA = 57; MEX = 13; TTO = 1; UAE = 37; VEN = 28.2
  - Rank (starting a business): AGO = 173; CMR = 160; TCD = 177; GNQ = 172; GAB = 147; NGA = 80; MEX = 75; TTO = 40; UAE = 158; VEN = 134

- Dealing with Licenses (selected metrics)
  - Time (days): AGO = 337; CMR = 426; TCD = 181; CIV = 628; GNQ = 201; GAB = 210; NGA = 350; MEX = 131; TTO = 261; VEN = 395
  - Cost (% of income per capita): AGO = 1110; CMR = 1203; TCD = 1064; COG = 566; CIV = 248; GNQ = 240; GAB = 48; NGA = 1016; SSA = 2549; IDN = 287; MEX = 104; TTO = 6; UAE = 2; VEN = 326

- Employing Workers (indices and costs)
  - Difficulty of Hiring Index: AGO = 78; CMR = 28; TCD = 39; COG = 78; CIV = 33; GNQ = 67; GAB = 17; NGA = 0; IDN = 72; MEX = 33; TTO = 0; UAE = 0; VEN = 78
  - Rigidity of Hours Index: AGO = 60; CMR = 40; TCD = 60; COG = 60; CIV = 60; GNQ = 60; GAB = 80; NGA = 0; IDN = 44; MEX = 0; TTO = 40; UAE = 0; VEN = 60
  - Nonwage labor cost (% of salary): AGO = 8; CMR = 16; TCD = 21; COG = 29; CIV = 18; GNQ = 23; GAB = 20; NGA = 9; IDN = 10; MEX = 21; TTO = 3; UAE = 13; VEN = 16
  - Firing costs (weeks of wages): AGO = 58; CMR = 33; TCD = 36; COG = 33; CIV = 49; GNQ = 133; GAB = 43; NGA = 50; SSA = 68; IDN = 108; MEX = 52; TTO = 67; UAE = 84; VEN = NP

- Registering Property (selected metrics)
  - Procedures (number): AGO = 7; CMR = 5; TCD = 6; COG = 7; CIV = 7; GNQ = 6; GAB = 8; NGA = 14; IDN = 7; MEX = 7; TTO = 5; UAE = 8; VEN = 8
  - Time (days): AGO = 334; CMR = 93; TCD = 44; COG = 137; CIV = 62; GNQ = 23; GAB = 60; NGA = 82; MEX = 74; TTO = 162; UAE = 6; VEN = 47
  - Cost (% of property value): AGO = 11; CMR = 18; TCD = 21; COG = 27; CIV = 17; GNQ = 6; GAB = 11; NGA = 22; IDN = 11; MEX = 5; TTO = 7; UAE = 2; VEN = 2

- Getting Credit (indices and coverage)
  - Legal Rights Index: AGO = 3; CMR = 3; TCD = 3; COG = 3; CIV = 3; GNQ = 2; GAB = 3; NGA = 7; IDN = 4; MEX = 5; TTO = 3; UAE = 5; VEN = 3; SSA = 4
  - Credit Information Index: AGO = 4; CMR = 2; TCD = 1; COG = 2; CIV = 1; GNQ = 2; GAB = 2; NGA = 0; IDN = 3; MEX = 6; TTO = 4; UAE = 2; VEN = 0
  - Public registry coverage (% adults): AGO = 2.3; CMR = 1; TCD = 0.2; COG = 2.4; CIV = 2.8; GNQ = 1.9; GAB = 2.4; NGA = 0; IDN = 2; MEX = 21; TTO = 0; UAE = 0; VEN = 1

- Protecting Investors (indices)
  - Investor Protection Index (composite): AGO = 5.7; CMR = 4.3; TCD = 4; COG = 3.3; CIV = 3.3; GNQ = 3.7; GAB = 3.3; NGA = 5.7; SSA = 4; IDN = 6; MEX = 6; TTO = 7; UAE = 4; VEN = 2.7
  - Rank (protecting investors): AGO = 51; CMR = 107; TCD = 122; COG = 147; CIV = 147; GNQ = 141; GAB = 147; NGA = 51; IDN = 111; MEX = 51; TTO = 33; UAE = 15; VEN = 165

- Paying Taxes (selected metrics)
  - Payments (number): AGO = 31; CMR = 41; TCD = 54; COG = 89; CIV = 66; GNQ = 45; GAB = 28; NGA = 35; SSA = 39; IDN = 51; MEX = 27; TTO = 40; UAE = 14; VEN = 70
  - Time (hours): AGO = 272; CMR = 1400; TCD = 122; COG = 606; CIV = 270; GNQ = 212; GAB = 272; NGA = 1120; IDN = 321; MEX = 266; TTO = 552; UAE = 12; VEN = 864
  - Total tax rate (% profit): AGO = 53.2; CMR = 51.9; TCD = 63.7; COG = 65.4; CIV = 45.4; GNQ = 62.2; GAB = 44.2; NGA = 29.9; SSA = 68; IDN = 37; MEX = 51.2; TTO = 33; UAE = 14; VEN = 53.3

- Trading Across Borders (selected metrics)
  - Time for export (days): AGO = 64; CMR = 27; TCD = 78; COG = 50; CIV = 23; GNQ = 29; GAB = 19; NGA = 26; SSA = 36; IDN = 21; MEX = 17; TTO = 14; UAE = 13; VEN = 45
  - Cost to export (US$ per container): AGO = 1850; CMR = 907; TCD = 4867; COG = 2201; CIV = 1653; GNQ = 1403; GAB = 1510; NGA = 1026; SSA = 1660; IDN = 667; MEX = 1302; TTO = 693; UAE = 462; VEN = 2400
  - Time for import (days): AGO = 58; CMR = 33; TCD = 102; COG = 62; CIV = 43; GNQ = 46; GAB = 35; NGA = 46; SSA = 44; IDN = 27; MEX = 23; TTO = 26; UAE = 13; VEN = 65
  - Cost to import (US$ per container): AGO = 2325; CMR = 1529; TCD = 5520; COG = 2201; CIV = 2457; GNQ = 1403; GAB = 1600; NGA = 1047; SSA = 1986; IDN = 623; MEX = 2411; TTO = 1100; UAE = 462; VEN = 2400

- Enforcing Contracts (selected metrics)
  - Procedures (number): AGO = 46; CMR = 43; TCD = 41; COG = 44; CIV = 33; GNQ = 40; GAB = 38; NGA = 39; SSA = 39; IDN = 39; MEX = 38; TTO = 42; UAE = 50; VEN = 29
  - Time (days): AGO = 1011; CMR = 800; TCD = 743; COG = 560; CIV = 770; GNQ = 553; GAB = 1070; NGA = 457; SSA = 643; IDN = 570; MEX = 415; TTO = 1340; UAE = 607; VEN = 510
  - Cost (% of debt): AGO = 44.4; CMR = 46.6; TCD = 77.4; COG = 53.2; CIV = 41.7; GNQ = 18.5; GAB = 34.3; NGA = 32; SSA = 49; IDN = 123; MEX = 32; TTO = 34; UAE = 26; VEN = 43.7

- Closing a Business (selected metrics)
  - Time (years): AGO = 6.2; CMR = 3.2; COG = 3; CIV = 2.2; GAB = 5; NGA = 2; IDN = 3; MEX = 6; UAE = 5; VEN = 4; TCD = NP; GNQ = NP; TTO = NP
  - Cost (% of estate): AGO = 22; CMR = 15; COG = 24; CIV = 18; GAB = 15; NGA = 22; IDN = 20; MEX = 18; UAE = 30; VEN = 38; TCD = NP; GNQ = NP; TTO = NP
  - Recovery rate (cents on the dollar): AGO = 10.8; CMR = 25.5; COG = 20.4; CIV = 33; GAB = 15.2; NGA = 27.5; IDN = 17; MEX = 13; TTO = 63.9; UAE = 10; VEN = 6.6; TCD = 0; GNQ = 0

- Note: NP = No Practice. Source: World Bank’s Doing Business database.

*Source: _wp08172 - APPENDIX A: MACROECONOMIC INDICATORS AND EXPORT PERFORMANCE (tables and notes as provided).*

### APPENDIX B: DATA SOURCES AND SENSIITIVITY RESULTS

### APPENDIX B: DATA SOURCES AND SENSIITIVITY RESULTS

### Data sources (Table B1)
- Trade: UN-COMTRADE database
- Real GDP (constant 2000 US$): World Bank's World Development Indicators, 2007
- Real GDP per capita (constant 2000 US$): World Bank's World Development Indicators, 2007
- Distance 1/: CIA’s World Factbook
  - 1/ Distance between two countries calculated using the great circle method.
- Common language: CIA’s World Factbook
- Common border: CIA’s World Factbook
- Landlocked; Island: CIA’s World Factbook
- Land area: CIA’s World Factbook
- Colonial ties: CIA’s World Factbook
- Currency unions and trade agreements: Tsangarides et al. (2007)
- Adult literacy rate: World Bank's World Development Indicators, 2007
- Human development index: Human Development Report 2007/08
- Transport infrastructure (% of paved roads): World Bank's World Development Indicators, 2007
- World governance indicators: World Bank
- Doing business indicators: World Bank
- Corruption, bureaucratic quality, government stability: The Political Risk Services Group

### Summary statistics (Table B2)
- Log of real non-oil exports: Mean -5.95, Std. Deviation 3.51
- Log real GDP (origin): Mean 23.28, Std. Deviation 2.33
- Log real GDP (destination): Mean 23.28, Std. Deviation 2.33
- FTA: Mean 0.02, Std. Deviation 0.16
- Log distance: Mean 8.25, Std. Deviation 0.78
- Common language: Mean 0.17, Std. Deviation 0.37
- Common land border: Mean 0.02, Std. Deviation 0.13
- Number landlocked in the pair: Mean 0.33, Std. Deviation 0.53
- Number islands in the pair: Mean 0.42, Std. Deviation 0.58
- Current colony: Mean 0.00, Std. Deviation 0.04
- Ever colony: Mean 0.01, Std. Deviation 0.10
- Log oil export share in total exports: Mean -3.78, Std. Deviation 6.43
- Corruption: Mean 0.51, Std. Deviation 0.23
- Bureaucratic quality: Mean 0.54, Std. Deviation 0.29
- Government stability: Mean 0.55, Std. Deviation 0.22

- Source: Author's calculations based on data obtained from sources listed in Table B1.

### Gravity model estimates and diagnostics (Table B3 and related notes)
- Dependent variable: log of real trade (exports and imports) between bilateral trade partners.
- Constant and year dummies included in all regressions.
- Key coefficient estimates (selected):
  - Log product of real GDP:
    - Pooled OLS (World): 1.07 ***
    - Pooled OLS (World) alternate column: 1.09 ***
    - Fixed effects (World): 1.19 ***
    - Fixed effects (World) alternate column: 0.90 ***
    - Reported standard errors: (0.00), (0.00), (0.01), (0.05)
  - Log product of population:
    - Pooled OLS: 0.03 ***
    - Pooled OLS alternate: -0.21 ***
    - Standard errors: (0.00), (0.02)
  - Log product of real GDP per capita:
    - Pooled OLS: -0.02 ***
    - Pooled OLS alternate: 0.16 ***
    - Standard errors: (0.00), (0.02)
  - Log distance:
    - Pooled OLS: -1.23 ***
    - Pooled OLS alternate: -1.26 ***
    - Standard errors: (0.01), (0.01)
  - Log product of areas:
    - Pooled OLS: -0.11 ***
    - Pooled OLS alternate: -0.08 ***
    - Standard errors: (0.00), (0.00)
  - Common language: 0.63 *** and 0.66 *** (standard errors (0.01), (0.01))
  - Common border: 0.74 *** and 0.69 *** (standard errors (0.03), (0.03))
  - Landlocked: -0.26 *** and -0.27 *** (standard errors (0.01), (0.01))
  - Island: 0.10 *** and 0.17 *** (standard errors 0.01 and (0.01))
  - Currency union: 0.80 ***, 0.79 *** (standard errors (0.04), (0.04))
  - Free trade agreement: 1.18 ***, 1.17 *** (standard errors (0.04), (0.03))
  - Current colony: 1.11 ***, 1.12 *** (standard errors (0.13), (0.13))
  - Ever colony: 1.20 ***, 1.21 ***
- Observations: 232,751 and 243,279 (reported twice)
- R-squared (within): 0.14 and 0.13
- R-squared (between): 0.65 and 0.64
- R-squared (overall): 0.70 and 0.70; additional reported overall 0.59 and 0.58
- Hausman test (p-value): 0.00 (reported as 0.000.00)

- Notes: Robust standard errors reported in parentheses. *, **, *** denote significance at the 10, 5 and 1 percent levels, respectively.

### Undertrading: differences between actual and predicted trade (Table B4 notes and excerpts)
- Estimates calculated using the average difference between actual and predicted (log of) real trade obtained from model (4) in Table B3.
- Table B4 reports "Undertrading in the OPAC and selected regions, 1970-2005" with country/region-specific average differences across subperiods 1970-2006, 1970-79, 1980-89, 1990-99, 2000-05 for several entries (excerpted):
  - OPAC: 0.02, 0.42, 0.27, -0.16, -0.24
  - SSA: 0.01, 0.50, 0.25, -0.14, -0.29
  - Indonesia: 0.17, -0.47, -0.06, 0.40, 0.55
  - Mexico: -1.64, -1.74, -1.94, -1.50, -1.29
  - Trinidad and T-: -0.27, 0.59, 0.03, -0.76, -0.66
  - UAE: 0.22, 0.22, 0.06, 0.17, 0.46
  - Venezuela: -0.96, -0.44, -0.58, -1.37, -1.35
- Source: Author's estimates.

### Regression of real non-oil exports with institutional and infrastructure interactions (Table B4 details)
- Dependent variable: real non-oil exports; constant, country-pair effects, and time effects included in all specifications.
- Selected coefficient estimates and standard errors (multiple specifications):
  - Log real GDP (origin):
    - Coefficients across specifications: 1.08 ***, 1.05 ***, 1.09 ***, 1.09 ***, 0.53 ***, 1.04 ***
    - Standard errors: (0.05), (0.05), (0.05), (0.05), (0.16), (0.03)
  - Log real GDP (destination):
    - Coefficients: 0.98 ***, 0.98 ***, 0.99 ***, 0.98 ***, 0.98 ***, 0.91 ***
    - Standard errors: (0.04), (0.04), (0.04), (0.04), (0.15), (0.03)
  - Free trade agreement: 0.41 ***, 0.39 ***, 0.40 ***, 0.42 ***, 0.97 ***, 0.41 ***
    - Standard errors: (0.03), (0.03), (0.03), (0.03), (0.13), (0.02)
  - Log oil export share (Oil): -0.01 *, -0.01 *, -0.01 *, -0.01 **, -0.03 *, 0.01 ***
    - Standard errors: (0.00), (0.01), (0.00), (0.00), (0.02), (0.00)
  - Corruption (Corr): 0.02 **
    - Standard error: (0.06)
  - Corr * Oil: 0.01 **
    - Standard error: (0.00)
  - Bureaucratic quality (Burq): 0.45 ***
    - Standard error: (0.07)
  - Burq * Oil: 0.02 **
    - Standard error: (0.01)
  - Government stability (Stab): 0.06 *
    - Standard error: (0.04)
  - Stab * Oil: 0.00
    - Standard error: (0.00)
  - Institutional quality (Inst): 0.02 *
    - Standard error: (0.01)
  - Inst * Oil: 0.01 ***
    - Standard error: (0.00)
  - Log of infrastructure: 0.30 ***, 0.30 ***, 0.30 ***, 0.30 ***, 0.13 *, 0.33 ***
    - Standard errors: (0.02), (0.02), (0.06), (0.02), (0.07), (0.02)
- Observations reported for specifications: 241,576; 241,263; 241,499; 241,221; 95,791; 268,632
- Wald chi-square (p-value): 0.000.00 (repeated)
- Hausman test (p-value): 0.000.00 (repeated)

### PPML estimates (Table B5 note)
- PPML estimates of the Gravity Model reported; notation:
  - *, **, *** denote significance at the 10, 5 and 1 percent levels, respectively.
  - Institutional quality refers to the first principal component of the corruption, bureaucratic quality and govt. stability indices.
- Columns labeled include Low institutions, High institutions, and World (multiple columns), but numeric PPML coefficient detail is not fully excerpted in the provided content.

*Source: _wp08172 - APPENDIX B: DATA SOURCES AND SENSIITIVITY RESULTS*

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