Chapter

Appendix

Author(s):
Kevin Carey, Sanjeev Gupta, and Catherine Pattillo
Published Date:
February 2006
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Table A1.Subgroups of Countries
Oil-Producing CountriesCFA Franc CountriesCoastal CountriesLandlocked CountriesResource-Intensive Countries1Conflict Countries2
1960s1970s1980s1990s
AngolaBeninBeninBurkina FasoAngolaAngolaAngolaAngolaAngola
CameroonBurkina FasoCape VerdeBurundiBotswanaCongo, Dem. Rep. ofEthiopiaChadBurundi
Congo, Rep. ofCameroonComorosCentral African Rep.CameroonMozambiqueEthiopiaCongo, Dem. Rep. of
Equatorial GuineaCentral African Rep.Côte d’IvoireCongo, Rep. ofGuinea-BissauZimbabweMozambique
GabonGambia, TheChadEquatorial GuineaMozambiqueNamibiaCongo, Rep. of
NigeriaChadGhanaCongo, Dem. Rep. ofGabonUgandaMali
Congo, Rep. ofGuinea-BissauGuineaSierra Leone
Côte d’IvoireKenyaEthiopiaNamibia
Equatorial GuineaMadagascarLesothoNigeria
GabonMauritiusMalawiSão Tomé and Príncipe
Guinea-BissauMozambiqueMali
MaliSenegalNigerSierra Leone
NigerSeychellesRwandaZambia
SenegalSouth AfricaSwaziland
TogoTanzaniaUganda
TogoZimbabwe
Program Countries3
Sources and Notes:

Resource-abundant countries, coastal countries without natural resources, and landlocked countries without natural resources are defined in Collier and O’Connell (2004).

For each half-decade, conflict countries are defined as those with a civil war in the past 10 years (Tsangarides, 2005; using data from Sambanis, 2001).

For the early and late 1990s, a program country is designated as off track if half or more of its programs in a given period experienced an irreversible interruption; that is, the program was either canceled or allowed to lapse because of policy slippages. Data from Nsouli, Atoian, and Mourmouras (2004) (see for more details on index derivations) were extended to include the full set of sub-Saharan African countries.

1990–941995–99
1980–841985–89On trackOff trackOn trackOff track
Central African Rep.MaliBeninMadagascarBeninBurkina FasoBurkina FasoBenin
Congo, Dem. Rep. ofMauritiusBurundiMalawiCameroonBurundiCape VerdeCameroon
Côte d’IvoireNigerCameroonMaliComorosCentral African Rep.ChadCentral African Rep.
Equatorial GuineaSenegalCentral African Rep.MauritiusCôte d’IvoireChadEquatorial GuineaCôte d’Ivoire
EthiopiaSierra LeoneMozambiqueCongo, Rep. ofCongo, Rep. ofGambia, TheEthiopia
GabonSouth AfricaChadNigerEthiopiaEquatorial GuineaGuineaGabon
Gambia, TheTanzaniaCongo, Dem. Rep. ofNigeriaGabonMalawiGuinea-BissauGhana
GhanaTogoSão Tomé and PríncipeGambia, TheNigerMalawiKenya
GuineaUgandaCongo, Rep. ofGhanaSenegalMaliLesotho
KenyaZambiaCôte d’IvoireSenegalGuineaTogoRwandaMadagascar
MadagascarZimbabweEquatorial GuineaSierra LeoneKenyaZimbabweSenegalMozambique
MalawiGabonTanzaniaLesothoSierra LeoneNiger
Gambia, TheTogoMadagascarTanzaniaZimbabwe
GhanaUgandaMaliTogo
GuineaZambiaMozambiqueUganda
KenyaNigeriaZambia
LesothoRwanda
São Tomé and Príncipe
Sierra Leone
Tanzania
Uganda
Sources and Notes:

Resource-abundant countries, coastal countries without natural resources, and landlocked countries without natural resources are defined in Collier and O’Connell (2004).

For each half-decade, conflict countries are defined as those with a civil war in the past 10 years (Tsangarides, 2005; using data from Sambanis, 2001).

For the early and late 1990s, a program country is designated as off track if half or more of its programs in a given period experienced an irreversible interruption; that is, the program was either canceled or allowed to lapse because of policy slippages. Data from Nsouli, Atoian, and Mourmouras (2004) (see for more details on index derivations) were extended to include the full set of sub-Saharan African countries.

Sources and Notes:

Resource-abundant countries, coastal countries without natural resources, and landlocked countries without natural resources are defined in Collier and O’Connell (2004).

For each half-decade, conflict countries are defined as those with a civil war in the past 10 years (Tsangarides, 2005; using data from Sambanis, 2001).

For the early and late 1990s, a program country is designated as off track if half or more of its programs in a given period experienced an irreversible interruption; that is, the program was either canceled or allowed to lapse because of policy slippages. Data from Nsouli, Atoian, and Mourmouras (2004) (see for more details on index derivations) were extended to include the full set of sub-Saharan African countries.

Table A2.Real Per Capita Growth, Gross Fixed Capital Formation, and Total Factor Productivity (TFP) Growth
1970–741975–791980–841985–891990–941995–992000–03
Percent
Real per capita growth of GDP
Sub-Saharan Africa2.20.5–0.41.0–1.12.01.4
Without Equatorial Guinea2.20.5–0.51.1–1.11.30.9
Oil-producing4.9–1.81.00.6–2.56.34.4
Without Equatorial Guinea5.2–1.90.90.9–3.51.01.4
Non-oil1.70.9–0.61.1–0.81.30.9
Percent of GDP
Gross fixed capital formation
Sub-Saharan Africa22.123.020.918.720.122.219.9
Without Equatorial Guinea21.722.620.618.719.720.619.0
Fast growers of the 1990s26.326.723.821.525.829.425.0
Without Equatorial Guinea25.526.023.221.924.824.522.3
Medium growers of the 1990s19.620.218.617.116.918.018.7
Slow growers of the 1990s20.021.620.317.317.918.916.0
Oil-producing27.528.726.922.924.235.726.8
Without Equatorial Guinea25.526.925.824.221.624.320.1
Non-oil21.222.119.918.019.420.018.8
CFA franc24.424.222.720.119.123.822.6
Without Equatorial Guinea23.423.221.920.317.718.519.8
Non–CFA franc20.822.420.018.020.721.218.5
Percent
Total factor productivity growth
Sub-Saharan Africa0.2–0.9–1.70.6–1.81.20.5
Without Equatorial Guinea0.2–0.9–1.60.7–1.90.80.1
Fast growers of the 1990s0.7–0.8–1.61.3–0.33.31.7
Without Equatorial Guinea0.7–0.8–1.41.5–0.32.30.5
Medium growers of the 1990s2.1–1.7–2.30.4–1.50.7–0.9
Slow growers of the 1990s–1.9–0.3–1.20.2–3.7–0.40.7
Oil-producing3.1–4.6–1.80.4–2.73.73.7
Without Equatorial Guinea3.1–4.6–1.20.7–3.31.20.9
Non-oil–0.3–0.3–1.70.7–1.70.80.0
CFA franc–1.20.0–1.10.6–1.12.01.4
Without Equatorial Guinea–1.20.0–0.80.8–1.20.90.1
Non–CFA franc1.3–1.6–2.00.6–2.20.80.1
Program on track–2.22.40.4
Program off track–1.01.0–1.0
Sources: IMF, World Economic Outlook/Economic Trends in Africa database, 2004; and staff calculations. Note: For the early 1990s, total factor productivity growth in countries with off-track programs, without Equatorial Guinea, is–1.1 percent and for the late 1990s, total factor productivity growth in countries with on-track programs, without Equatorial Guinea, is 1.4 percent.
Sources: IMF, World Economic Outlook/Economic Trends in Africa database, 2004; and staff calculations. Note: For the early 1990s, total factor productivity growth in countries with off-track programs, without Equatorial Guinea, is–1.1 percent and for the late 1990s, total factor productivity growth in countries with on-track programs, without Equatorial Guinea, is 1.4 percent.
Table A3.Real GDP Per Capita Growth Performance Classification, 1960–2003(Percent)
1960–20031990–941995–991990–992000–03
1.Equatorial Guinea7.1Mauritius5.4Equatorial Guinea32.9Equatorial Guinea17.8Equatorial Guinea19.2
2.Botswana6.9Uganda3.0Rwanda8.9Mauritius4.8Sierra Leone11.6
3.Mauritius4.1Seychelles3.0Mozambique6.8Mozambique3.8Botswana5.5
4.Seychelles2.4Mali2.8Cape Verde5.8Burkina Faso3.7Chad5.0
5.Cape Verde2.3Equatorial Guinea2.7Malawi5.7Uganda3.5Mozambique4.6
6.Swaziland2.2Burkina Faso2.7Angola4.7Cape Verde3.5Mauritius3.9
7.Burkina Faso2.1Ghana2.0Burkina Faso4.6Seychelles3.4Cape Verde3.8
8.Comoros1.8Botswana1.6Mauritius4.1Botswana2.7Tanzania3.8
9.Mali1.8Chad1.4Uganda3.9Mali2.7Burkina Faso3.2
10.Gambia, The1.6Lesotho1.3Botswana3.8Ghana1.9Angola3.0
11.Lesotho1.5Swaziland1.3Seychelles3.7Malawi1.9Mali2.8
12.Gabon1.5Guinea-Bissau1.1Mali2.5Lesotho1.8Nigeria2.4
13.Mozambique1.5Cape Verde1.1Côte d’Ivoire2.5Benin1.6São Tomé and Príncipe2.3
14.Cameroon1.4Benin0.9Lesotho2.4Swaziland1.2Zambia2.1
15.Burundi1.4Mozambique0.7Senegal2.4Namibia0.7Uganda2.0
16.Chad1.2Namibia0.7Ethiopia2.2Senegal0.6Cameroon1.9
17.Congo, Rep. of1.2Gabon0.5Guinea2.2Rwanda0.5Ghana1.8
18.Kenya1.1Gambia, The0.1Benin2.2Guinea0.5Benin1.8
19.Tanzania1.0Nigeria–0.1Ghana1.8Chad0.5Lesotho1.7
20.South Africa0.9Comoros–0.4Cameroon1.7Tanzania0.4Congo, Rep. of1.5
21.Guinea-Bissau0.9Tanzania–0.5Tanzania1.3Gambia, The0.3Rwanda1.5
22.Malawi0.8Zimbabwe–0.5Swaziland1.2Côte d’Ivoire0.3South Africa1.4
23.Nigeria0.8Burundi0.7Burundi0.7Burundi0.0Senegal1.3
24.Guinea0.7Guinea–1.2Namibia0.7Zimbabwe0.0Gambia, The0.9
25.São Tomé and Príncipe0.7Senegal–1.2Kenya0.6Gabon–0.1Swaziland0.5
26.Uganda0.7Kenya1.3Gambia, The0.5Nigeria–0.1Niger0.4
27.Benin0.5São Tomé and Príncipe–1.8Zimbabwe0.5Ethiopia–0.2Namibia0.0
28.Ethiopia0.4Côte d’Ivoire–1.9Central African Rep.0.4Kenya–0.3Guinea–0.1
29.Togo0.4Malawi–2.0South Africa0.4Comoros–0.6Ethiopia–0.2
30.Rwanda0.4South Africa–2.0Niger0.3South Africa–0.8Comoros–0.4
31.Namibia0.3Ethiopia–2.6Togo0.0Guinea-Bissau–0.8Togo–0.8
32.Côte d’Ivoire0.3Congo, Rep. of–3.1Madagascar0.0São Tomé and Príncipe–1.5Madagascar–1.0
33.Ghana0.2Togo–3.1Nigeria–0.1Niger–1.5Kenya–1.0
34.Angola0.1Madagascar–3.1Chad–0.4Togo–1.6Seychelles–1.2
35.Zimbabwe0.1Niger–3.2Congo, Rep. of–0.6Madagascar–1.6Gabon–1.8
36.Niger–0.1Central African Rep.–3.9Gabon–0.7Angola–1.7Burundi–2.2
37.Sierra Leone–0.2Sierra Leone–4.8Comoros–0.8Central African Rep.–1.7Guinea-Bissau–2.4
38.Zambia–0.3Zambia–5.4São Tomé and Príncipe–1.1Congo, Rep. of–1.8Central African Rep.–3.2
39.Senegal–0.8Cameroon–6.6Zambia–1.5Cameroon–2.4Congo, Dem. Rep. of–3.3
40.Central African Rep.–0.8Rwanda–7.9Guinea-Bissau–2.8Zambia–3.4Malawi–3.8
41.Madagascar–1.2Angola–8.2Congo, Dem. Rep. of–4.8Congo, Dem. Rep. of–8.1Côte d’Ivoire–4.7
42.Congo, Dem. Rep. of–2.7Congo, Dem. Rep. of–11.5Sierra Leone–13.5Sierra Leone–9.1Zimbabwe–6.5
Sources: World Bank, World Development Indicators database, 2004; IMF, World Economic Outlook database, 2004; and IMF staff estimates. Note: Data not available for Eritrea and Liberia.
Sources: World Bank, World Development Indicators database, 2004; IMF, World Economic Outlook database, 2004; and IMF staff estimates. Note: Data not available for Eritrea and Liberia.
Table A4.Forgone Growth in Africa Relative to Other Regions
Forgone Annual Growth
Robust Growth Determinant Estimated from a World SampleCoefficientsEast Asia/PacificEast Asia/Pacific 1990sEurope/Central AsiaLatin America/CaribbeanMiddle East/North AfricaSouth Asia
(Percent)
1.Log (inflation)–0.0088–0.1–0.10.40.10.00.0
2.Fiscal balance (to GDP)0.7031–2.9–3.4–2.2–1.91.9–1.4
3.Log (investment to GDP)0.0950–8.7–10.8–8.5–4.8–5.1–5.9
4.Log (government consumption to GDP)–0.0289–1.0–1.6–0.5–0.10.5–0.5
5.Log (initial income)–0.167823.926.017.718.114.36.0
6.Percentage of land in tropics–0.1454–9.7–6.1–13.5–1.3–12.2–4.7
7.Terms of trade (growth)0.02510.040.020.040.020.010.04
8.Black market premium–0.0015–0.06–0.030.02–0.04–0.010.1
9.Log (overall schooling)0.0556–6.9–5.4–5.2–5.0–3.4–2.5
10.Log (arable land)–0.0188–2.3–3.10.7–0.7–0.5–1.8
Forgone sub-Saharan Africa growth “total”–7.7–4.6–11.04.5–4.7–10.7
Forgone sub-Saharan Africa growth due to policy (variables 1, 2, 4, 8)–4.1–5.2–2.3–1.92.3–1.9
Forgone sub-Saharan Africa growth due to accumulation (variables 3, 9)–15.6–16.2–13.7–9.8–8.6–8.4
Source: Tsangarides (2005).Notes: Draws on an expanded model specification. Bayesian model averaging techniques are applied using a panel data system. Generalized method of moments (GMM) estimator.
Source: Tsangarides (2005).Notes: Draws on an expanded model specification. Bayesian model averaging techniques are applied using a panel data system. Generalized method of moments (GMM) estimator.
Table A5.Relative Impact of Robust Variables on Growth(Period-to-period changes from 1980–84 to 1995–2000, in percentage points)
All VariablesPolicy VariablesTerms of TradeInvestment
Sub-Saharan Africa2.44.90.1–0.4
Fast growers of the 1990s4.17.80.62.4
Medium growers of the 1990s1.23.00.9–3.1
Slow growers of the 1990s0.35.4–8.1–4.8
Oil producing–1.61.3–4.3–9.8
Non-oil-producing2.45.81.2–0.6
Program2.36.1–1.9–1.9
Nonprogram2.71.26.14.1
Program on track5.612.4–0.6–1.8
Program off track–1.4–0.7–3.3–1.9
CFA franc–0.51.3–2.9–3.6
Non–CFA franc3.86.71.61.2
Coastal2.27.1–5.5–1.5
Landlocked2.23.60.81.2
Resource-intensive1.23.93.3–5.9
Source: IMF staff calculations.Notes: Robust variables identified using expanded specification from Tsangarides (2005). Policy variables include inflation, government consumption to GDP, fiscal balance to GDP, black market and premium; other variables include terms of trade, investment to GDP, and overall schooling. Fixed factors such as percent of land in tropics, arable land, and initial income are included in the regression, but not in the above calculation.
Source: IMF staff calculations.Notes: Robust variables identified using expanded specification from Tsangarides (2005). Policy variables include inflation, government consumption to GDP, fiscal balance to GDP, black market and premium; other variables include terms of trade, investment to GDP, and overall schooling. Fixed factors such as percent of land in tropics, arable land, and initial income are included in the regression, but not in the above calculation.
Table A6.Quality of Macroeconomic Policies, 2003–04
Monetary PolicyFiscal PolicyComposition of Public ExpenditureConsistency of Policy MixGovernance in the Public SectorGovernance and Transparency in Monetary and Financial InstitutionsTrade Regime
20032004200320042003200420032004200320042003200420032004
Sub-Saharan Africa3.94.22.82.82.62.33.23.32.72.83.63.63.84.1
Oil-producing3.14.22.23.12.02.72.53.52.02.92.84.22.94.6
Non-oil3.24.12.32.82.22.42.63.32.32.92.93.73.14.2
Coastal3.14.22.23.12.02.72.53.52.02.92.84.22.94.6
Resource-intensive2.24.01.42.71.22.11.83.41.52.82.13.32.03.8
CFA3.94.52.32.62.02.12.63.21.92.33.34.43.14.6
Non-CFA2.73.92.12.82.02.52.43.42.23.12.63.22.83.8
Program3.74.22.82.92.52.43.13.52.52.83.43.63.54.1
Nonprogram1.24.00.52.50.72.10.73.01.02.81.23.71.34.0
Program on track3.74.32.92.92.52.53.23.52.62.83.43.53.54.1
Program off track3.82.01.52.01.81.51.83.01.32.82.84.33.54.5
Source: IMF staff assessments.Note: Indicators are rated 1 to 5, with 1 and 5 being, respectively, the most negative and most positive ratings.
Source: IMF staff assessments.Note: Indicators are rated 1 to 5, with 1 and 5 being, respectively, the most negative and most positive ratings.
Table A7.Strongest and Weakest Performers by Growth Benchmarking
1960–20001990–2000
Actual growthPredicted growthDifferenceActual growthPredicted growthDifference
Strongest 10Strongest 10
Botswana6.730.905.83Uganda3.25–0.193.44
Seychelles2.830.112.73Mauritius4.741.752.99
Mauritius3.861.921.94Botswana2.87–0.102.97
Mali0.35–0.661.01Seychelles3.190.372.82
Gambia, The1.440.441.01Burkina Faso3.210.452.77
Equatorial Guinea1.230.380.95Mali1.87–0.722.58
Nigeria0.65–0.180.83Mozambique2.920.712.21
Kenya1.280.500.78Ghana1.82–0.061.88
Congo, Rep. of1.230.470.76Benin1.60–0.161.76
Cote d’Ivoire0.50–0.090.59Namibia0.63–0.451.07
Weakest 10Weakest 10
Angola–0.720.17–0.89Rwanda–1.370.66–2.02
Togo–0.330.59–0.92Congo, Rep. of–1.430.66–2.09
Niger–0.600.48–1.07Togo–2.100.05–2.15
Madagascar–1.340.17–1.52South Africa–0.671.51–2.17
Senegal–0.850.73–1.58Central African Rep.–1.820.61–2.43
Central African Rep.–0.551.03–1.58Cameroon–2.580.45–3.03
South Africa–0.532.13–1.60Angola–2.620.68–3.30
Ethiopia–0.281.89–1.61Zambia–3.280.35–3.64
Zambia–0.920.78–1.70Sierra Leone–8.560.18–8.74
Sierra Leone–1.890.25–2.14Congo, Dem. Rep. of–8.220.18–8.40
Source: IMF staff calculations from data in O’Connell (2004) and the World Economic Outlook database.
Source: IMF staff calculations from data in O’Connell (2004) and the World Economic Outlook database.
Table A8.Real Per Capita GDP Growth Rates, 1980–2003
UnweightedGDP WeightsPopulation Weights
1980–891990–941995–992000–031980–891990–941995–992000–031980–891990–941995–992000–03
Sub-Saharan Africa0.13–1.062.031.36–0.71–1.661.021.15–0.26–1.371.231.24
Fast growers of the 1990s1.471.915.813.28–0.722.193.973.42–0.231.694.242.22
Medium growers of the 1990s–0.59–1.091.52–0.13–0.86–0.920.980.15–0.49–1.051.010.86
Slow growers of the 1990s–0.50–3.99–1.250.94–0.44–2.830.401.390.14–3.79–0.111.38
Oil-producing0.23–2.456.324.37–1.21–2.990.772.241.02–1.60.572.35
Non-oil-producing0.11–0.821.310.86–0.58–1.441.080.820.00–1.291.460.84
CFA–0.27–0.763.211.73–0.45–2.441.770.23–0.431.501.950.69
Non–CFA0.301.201.441.18–0.68–1.480.891.31–0.23–1.341.091.35
Program–0.50–1.052.420.75–0.18–1.331.791.210.16–0.921.951.40
Nonprogram0.87–1.621.041.83–1.54–2.020.601.091.19–3.040.200.33
Program on track–1.043.281.38–1.232.461.68–0.862.161.60
Program off track–0.981.26–1.91–1.681.41–4.55–1.181.82–4.32
Source: IMF, World Economic Outlook database, 2005.
Source: IMF, World Economic Outlook database, 2005.
Table A9.Differences Between Sample Averages: Sustained and Unsustained Accelerations
Difference in Means During an Episode
Openness7.3
Real export growth15.1
Debt service–5.8
Net present value of debt/exports–0.9
Aid/GDP3.5
Polity index1.8
Investment/GDP3.1
Source: IMF staff calculations.Notes: All reported differences are significant at the 10 percent level. A sustained acceleration is one where the average per capita growth was at least 2 percent for five years after an acceleration ended. Mozambique in 1986 was excluded as a case of sustained acceleration because the period in question overlapped with its postconflict recovery.
Source: IMF staff calculations.Notes: All reported differences are significant at the 10 percent level. A sustained acceleration is one where the average per capita growth was at least 2 percent for five years after an acceleration ended. Mozambique in 1986 was excluded as a case of sustained acceleration because the period in question overlapped with its postconflict recovery.
Table A10.Probit Coefficient Estimates for Probability of a Country Being in an Acceleration in a Year, 1980–2004, Robustness Checks
Random EffectsInstrumental Variables
Marginal coefficientp-valueMarginal coefficientp-value
Inflation–0.0050.480.0020.66
Deficit0.0310.10–0.0010.75
Aid–0.030.01–0.0090.32
Debt service–0.0020.850.0100.13
Debt net present value burden0.1900.010.0900.01
Real exchange rate–0.020.01–0.010.01
Partner growth0.2300.010.1200.06
Country risk0.0040.981.0700.01
Sachs-Warner0.5100.130.5200.04
Coastal0.8000.04–0.230.32
Resource-rich0.9900.020.0900.69
Investment0.0100.50–0.0070.64
Total factor productivity6.020.013.560.08
p-value for χ-squared test0.010.01
Pseudo R20.22
Percent of acceleration years predicted4073
Percent of predicted acceleration years incorrect6875
Source: IMF staff calculations.Notes: For computational reasons, the estimates reported here are the probit equation coefficients and not the marginal values. Thus, results can be compared with Table A4 in terms of which variables are significant, but not in terms of magnitude.
Source: IMF staff calculations.Notes: For computational reasons, the estimates reported here are the probit equation coefficients and not the marginal values. Thus, results can be compared with Table A4 in terms of which variables are significant, but not in terms of magnitude.
Table A11.Probit Marginal Estimates for Probability of an Acceleration Beginning in a Year, 1980–2004
1980s1990sAllAll (HPR)
Economic liberalization0.030.110.090.10
0.550.040.030.01
Big terms of trade shocks–0.020.0500.04
0.640.180.980.08
Political transition00.180.04
0.990.210.56
Regime change (democracy)0.120.090.060.12
0.50.090.250.01
Regime change (autocracy)0.320.220.240.02
0.010.010.010.68
Percent of acceleration years predicted16373423
Percent of predicted acceleration years incorrect60777882
Source: IMF staff calculations.Notes: Each cell in a variable row shows the marginal coefficient thereof with p-value shown below in italics. The goodness-of-fit statistics compare outcomes to estimated probabilities and are explained in the text. HPR stands for a set of accelerations identified using the Hausmann, Pritchett, and Rodrik.
Source: IMF staff calculations.Notes: Each cell in a variable row shows the marginal coefficient thereof with p-value shown below in italics. The goodness-of-fit statistics compare outcomes to estimated probabilities and are explained in the text. HPR stands for a set of accelerations identified using the Hausmann, Pritchett, and Rodrik.
Table A12.Probit Marginal Estimates for Probability of a Country Having a Sustained Acceleration in a Year, 1980–2004
Marginal Coefficientp-ValueMarginal Coefficientp-Value
Inflation0.0040.51–0.0050.30
Budget balance0.0130.31–0.0070.48
Aid0.0130.180.0160.04
Debt service0.0130.020.0070.17
Debt net present value burden–0.060.06–0.040.09
Real exchange rate–0.0070.090.0010.90
Partner growth0.0800.150.0190.56
Country risk0.0500.600.0900.26
Sachs-Warner–0.050.850.0090.97
Export growth0.0050.18
Coastal0.4900.01
Investment0.0170.030.0160.03
Total factor productivity growth0.6400.631.1000.16
p-value for χ-squared test0.010.33
Pseudo R20.310.14
Percent of acceleration years predicted8987
Percent of predicted acceleration years incorrect4738
Source: IMF staff calculations.Notes: The marginal coefficient is the change in the probability for an infinitesimal change in the independent variable x, evaluated at the mean value of x. The p-value is the analog of the usual regression test for the probit coefficient being zero. The country risk measure is the World Bank’s Country Policy and Institutional Assessment. Base set refers to the set of accelerations shown in Table 1. Results with a smaller set of accelerations are shown in the right two columns. These require 3.5 percent growth, as in Hausmann, Pritchett, and Rodrik (2004).
Source: IMF staff calculations.Notes: The marginal coefficient is the change in the probability for an infinitesimal change in the independent variable x, evaluated at the mean value of x. The p-value is the analog of the usual regression test for the probit coefficient being zero. The country risk measure is the World Bank’s Country Policy and Institutional Assessment. Base set refers to the set of accelerations shown in Table 1. Results with a smaller set of accelerations are shown in the right two columns. These require 3.5 percent growth, as in Hausmann, Pritchett, and Rodrik (2004).
Table A13.Probit Marginal Estimates for Probability of a Sustained Acceleration Beginning in a Year, 1980–2004
Base SetWith 3.5 Percent Growth
Economic liberalization0.420.21
0.040.20
Big terms of trade shocks0.190.08
0.160.60
Recent civil conflict–0.20–0.13
0.090.41
Regime change (democracy)0.310.36
0.090.05
Regime change (autocracy)0.090.12
0.550.69
Percent of acceleration years predicted2766
Percent of predicted acceleration years incorrect5036
Source: IMF staff calculations.Notes: The upper rows show the indicated variable’s marginal coefficient with the p-value below in in italics. Goodness-of-fit, in the lower two rows, is assessed at a 0.5 cutoff probability. Base set refers to 0.5 cutoff probability. Base set refers to the set of accelerations shown in Table 1. Results with a smaller set of accelerations are shown in the far right column. These require 3.5 percent growth, as in Hausmann, Pritchett, and Rodrik (2004).
Source: IMF staff calculations.Notes: The upper rows show the indicated variable’s marginal coefficient with the p-value below in in italics. Goodness-of-fit, in the lower two rows, is assessed at a 0.5 cutoff probability. Base set refers to 0.5 cutoff probability. Base set refers to the set of accelerations shown in Table 1. Results with a smaller set of accelerations are shown in the far right column. These require 3.5 percent growth, as in Hausmann, Pritchett, and Rodrik (2004).
Table A14.Changes in Real GDP Growth and Lagged Deficit Change(2002–04 compared with 1999–2001; in percentage points)
High DeficitLow Deficit
Lagged change in deficitChange in growthLagged change in deficitChange in growth
Deficit
worsened–1.9–0.8–2.0–0.3
Deficit
improved3.82.93.3–0.5
Sources: IMF, WEO/Economic Trends in Africa database, 2004; and IMF staff calculations.
Sources: IMF, WEO/Economic Trends in Africa database, 2004; and IMF staff calculations.
Table A15.Subperiod Averages for Budget Balance, Growth, and Domestic and Foreign Financing(Percent of GDP)
PeriodGroupGovernment BalanceGDP GrowthDomestic FinancingForeign Financing
2000–04Africa–3.54.21.13.1
CFA franc–1.14.8–1.02.4
Oil2.67.7–1.9–0.7
Program–2.74.20.93.6
1995–99Africa–4.94.61.72.3
CFA franc–3.55.60.52.7
Oil–6.29.01.60.6
Program–3.34.71.02.2
1990–94Africa–6.31.51.24.2
CFA franc–6.31.80.65.4
Oil–9.20.21.17.8
Program–5.61.50.74.1
Sources: IMF, World Economic Outlook/Economic Trends in Africa database, 2004; and staff calculations.
Sources: IMF, World Economic Outlook/Economic Trends in Africa database, 2004; and staff calculations.
Table A16.Growth Benefits of Infrastructurel Development: Improvements to Levels of Sub-Saharan African Leader
StocksQualityTotal
Botswana4.40.75.1
Burkina Faso7.80.98.7
Côte d’Ivoire4.20.85.0
Ethiopia8.40.59.0
Ghana5.00.45.4
Guinea6.50.87.4
Guinea-Bissau6.60.97.4
Kenya5.61.06.7
Lesotho0.8
Madagascar6.90.97.8
Mali9.00.99.8
Mauritius
Niger9.50.910.4
Nigeria5.31.26.5
Rwanda7.30.78.1
Senegal5.60.66.1
Sierra Leone5.80.86.6
South Africa0.60.20.8
Tanzania6.81.07.9
Uganda7.30.98.2
Zambia4.50.34.7
Zimbabwe3.70.54.2
Sources: Calderon and Servén (2004); and IMF staff calculations.Note: The sub-Saharan African country with the highest stock and quality of of infrastucture is Mauritius. The calculation of the potential growth payoffs are based on the preferred GMM-IV system estimates from Calderon and Servén (2004).
Sources: Calderon and Servén (2004); and IMF staff calculations.Note: The sub-Saharan African country with the highest stock and quality of of infrastucture is Mauritius. The calculation of the potential growth payoffs are based on the preferred GMM-IV system estimates from Calderon and Servén (2004).
Table A17.Country Classification by Financial Development and Growth Performance
PercentPercent of GDP
Real growthGrowth per capitaInflationFinancial developmentFiscal balancePrivate investmentPrivate savings
1960–2003
High financial development
Fast growth4.92.311.335.9–4.216.115.0
Slow growth3.00.416.433.3–8.610.110.6
Low financial development
Fast growth3.91.416.217.5–4.88.49.7
Slow growth2.4–0.314.817.3–4.88.14.9
Oil-producing4.52.09.218.0–5.221.010.9
1997–2003
High financial development
Fast growth5.43.29.037.5–5.414.612.6
Slow growth1.2–0.818.444.5–5.513.311.5
Low financial development
Fast growth5.32.34.915.3–3.711.17.6
Slow growth1.7–1.010.718.9–3.58.07.5
Oil-producing8.15.35.015.8–1.622.512.6
Source: IMF, World Economic Outlook database, 2004.Notes: Financial development is measured as liquid liabilities over GDP. Growth and financial development are averaged over 1960–2003. Investment and savings are available only since 1970 and fiscal balances since 1980. Angola and the Democratic Republic of the Congo are dropped from the sample to compute the average inflation rate.
Source: IMF, World Economic Outlook database, 2004.Notes: Financial development is measured as liquid liabilities over GDP. Growth and financial development are averaged over 1960–2003. Investment and savings are available only since 1970 and fiscal balances since 1980. Angola and the Democratic Republic of the Congo are dropped from the sample to compute the average inflation rate.
Table A18.Headcount Poverty and Inequality
World Bank DataNational Data
Head countGiniSurvey yearHead countGiniSurvey year
Burkina Faso63511994560.471994
30471998620.451998
470.452003
Senegal45541991681994
22411994572001
Ghana47351987
45361988520.371991/92
45411998400.391998/99
Uganda88441989560.361992
88431992450.351997
86371996340.402000
85431999380.432003
Zambia65601991690.591991
74531993
73501996790.501996
64531998750.491998
Sources: World Bank Global Poverty Monitoring database; and national data from authorities.Note: National data are drawn from the pro-poor growth case studies. Where the survey years between the two sources do not directly match, they are aligned at the closest corresponding year. The national data for Senegal are different from Pattillo and others (2005) owing to a subsequent revision of this case study.
Sources: World Bank Global Poverty Monitoring database; and national data from authorities.Note: National data are drawn from the pro-poor growth case studies. Where the survey years between the two sources do not directly match, they are aligned at the closest corresponding year. The national data for Senegal are different from Pattillo and others (2005) owing to a subsequent revision of this case study.
Table A19.Case-Study Countries
Mean Growth in Survey Income and Pro-Poor GrowthGrowth and Inequality Decomposition of the Change in Poverty
PeriodGrowthPro-Poor GrowthGrowth componentInequality component
Burkina Faso1994–20030.91.0–3.2–4.5
1994–98–4.7–5.27.5–3.4
1998–20035.66.6–13.7–1.7
Senegal (Dakar)1994–20012.61.6–13.0–2.2
Ghana1991/923.22.1–13.10.9
1998/99
Uganda1992–20033.02.7–26.38.3
1992–973.63.9–10.3–0.4
1997–20006.04.8–16.35.0
2000–03–0.9–1.7–1.45.3
Zambia1991–981.15.9–0.4
1991–96–1.19.8–0.5
1996–982.2–4.40.8
Source: Country case studies from the Operationalizing Pro-Poor Growth Work Program. Available via the Internet: www.worldbank.org/propoorgrowth.Notes: Statistics refer to data from the household survey. The change in the head count is decomposed into components due to growth and inequality, following the method of Datt and Ravallion (1992). The sum of the two components equals the total change in national head-count poverty over the indicated period.
Source: Country case studies from the Operationalizing Pro-Poor Growth Work Program. Available via the Internet: www.worldbank.org/propoorgrowth.Notes: Statistics refer to data from the household survey. The change in the head count is decomposed into components due to growth and inequality, following the method of Datt and Ravallion (1992). The sum of the two components equals the total change in national head-count poverty over the indicated period.

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