The Discrepancies in GDP Growth Estimates for Sub-Saharan Africa
Xiao Ye
Why are different GDP growth rates cited for Sub-Saharan Africa (Africa thereafter)
even for the same year? There are three main reasons: the data source, the aggregation
methodology, and the weighting variable. The basic unit for reporting GDP and its
growth is at the national level in the current local currency. Both the country economists
from the World Bank and from the IMF provide GDP growth estimates, which may differ
from each other. In addition, when the growth rates to be calculated at a more aggregated
level, say, the Africa GDP growth in 2004, it is necessary to aggregate all individual
country growth rates into a regional one. When aggregating, one must choose an
aggregation method and a weighting variable. The World Bank and the IMF have chosen
different aggregation methods and weighting variables. Below is a discussion of how the
choices of the data source, the aggregation method and the weighting variable create the
GDP growth discrepancies between the two institutions.
I. The Discrepancy due to Different Data Sources
There are three major databases supplying GDP growth rates: the World Bank Africa
Region Live Database (AFRLDB), the World Bank World Development Indicator (WDI)
database and the IMF World Economic Outlook (WEO) database. The most recent
estimates by these two institutions are shown in Figure 1. Historically, IMF has a
tendency over predicting SSA GDP growth by one percentage point (See Appendix I).
The 2004 growth rates will be likely revised again in September 2005. It is still too soon
to tell the GDP growth discrepancies in 2004 among the three databases. For historical
data, however, discrepancies do exist as in shown in Figure 1.
Figure 1 GDP growth by difference data sources
7
6
5.1
GDP growth %
5
4.3
4.0
4
3
2
1
0
-1
AFRLDB
DECDG
IMF WEO
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
-2
Data sources: WB WDI, AFRLDB and IMF WEO.
The two databases from the World Bank use exactly the same data sources, but the IMF
uses its separate data sources. While the AFRLDB and the World Bank WDI databases
take their data from the World Bank country economists, the IMF WEO database takes
its data from the IMF country economists. The IMF country economists make their own
calculations based on extensive consultation with the relevant government officials and
their own observations. The World Bank country economists often use the IMF

For further questions please contact Xiao Ye at XYE@WORLDBANK.ORG.
1
estimates, but with their own adjustments, especially when concerning the predictions.
There, however, is no formal mechanism for the Bank and the IMF country economists to
consolidate their growth estimates before reporting to their respective databases. After
the reported country level data are entered into the databases, different cleaning
procedures and consistency checks may apply. There seems to be no agreement on a set
of standard cleaning procedures among the three databases.
II. The Discrepancy Due to the Aggregation Method
The World Bank databases use the same aggregation method, while the IMF uses a
different one. GDP growth is essentially a ratio between the current and the last year’s
GDP, taking into account of the inflation effect. The essence of the difference between
the two aggregation methods is to aggregate GDP growth rates by the ratio of the sum
(The Bank’s method) or by the sum of ratios (The Fund’s method).
The World Bank aggregation method (the ratio of the sum)
First, one converts the individual country GDP estimates of the current local currency
into the base-year local currency based on the country specific GDP deflators. Second,
the GDP of the base-year local currency is converted to the GDP of the base-year $US,
using the base-year exchange rate. Third, the GDP estimates of the base-year $US from
all Africa countries are added up to obtain a series of the total Africa GDP in the baseyear $US (the sum). The Africa GDP growth is then calculated based on this series (the
ratio of sum).
The IMF aggregation methodology (the sum of the ratios)
First, one converts the individual country GDP estimates of the current local currency
into the base-year local currency based on the country specific GDP deflator. Second, the
GDP growth rate is calculated for each country based on the country series (the ratio).
Third, the growth rates from all counties are summed up using the country GDP of the
current $PPP as the weighting variable (the sum of ratios).
III. The Discrepancy Due to the Weighting Variable
The World Bank weighting variable
By calculating the aggregated growth rate using the series of Africa GDP in base-year
$US value, the World Bank has essentially chosen the GDP series of the base-year $US
as its weighting variable. Because the conversion is based on the base-year exchange
rate, the weight can change for the same country of the same year when the base year
changes. The two databases from the World Bank always use the same base year for
their aggregated GDP calculation.
The IMF weighting variable
The IMF weighting variable is the country GDP valued in current $PPP. The choice of a
base year does not affect the weight since the conversion is based on the current year
exchange rate.
2
IV. The Contribution to the GDP Growth Discrepancy by Data Sources, the
Aggregation Method and the Weighting variable
In summary, the Africa GDP growth discrepancy between the World Bank and the IMF
are due to three factors: the different data sources, aggregation methods, and weighting
variables. Figure 2 shows the percent contribution of these three factors in 2002 and
2004. In 2002, the majority discrepancy is due to the different aggregation methods and
weighting variables. In 2004, the data sources contribute a large proportion of the
discrepancy, but this is likely to decrease by September 2005 when the WDI and the
WEO revise their 2004 growth rates again. As shown in Figure 2, by agreeing on the
same aggregation method and the same weighting variable, the three databases can
eliminate a large proportion of the discrepancy.
200%
6.00
12%
150%
5.00
100%
4.00
67%
50%
156%
0%
3.00
48%
-16%
2.00
-68%
-50%
Africa GDP growth %
Contribution to the GDP discrepancy
Figure 2 The IMF vs. the World Bank: the Contribution of the Weighting Variable and
the Growth Rates to the Total Discrepancy of Africa GDP growth estimates
The data source
The weighting variable
The aggregation method
WDI estimated GDP growth
IMF estimated GDP growth
1.00
-100%
0.00
2002
2004
Data sources: see Appendix II.
V. Additional Information: Different Weighting Variables for Different Purposes
In addition to the GDP-weighted GDP growth, researchers also use the populationweighted or unweighted GDP growth for different purposes. The GDP-weighted growth
rates are perhaps the most often cited ones due to their ready availability in all databases.
This method is especially appropriate for monitoring regional or global GDP growth.
The GDP weighted growth, however, could be inappropriate for other purposes. In the
context of Africa, where South Africa and Nigeria account for 50 percent of the total
regional output, the GDP-weighted GDP growth at the regional level reflects mainly the
growth performances of these two countries. Therefore, some researchers prefer the
population-weighted GDP growth, which they argue representative of the growth
experienced by a typical African (Collier and O’Connell, 2005). In some cases, the
growth experience of each country counts. This is especially true when one wants to
examine the growth experience by a group of countries based on the average values of
country variables such as country policy performances or natural resource endowments.
3
The GDP-weighted GDP growth, the population-weighted GDP growth and the
unweighted GDP growth can yield quite different growth rates from the same data series.
Figure 3 presents the GDP growth rates estimated by each of these three weighting
variables, using IMF WEO series.
7
6
5
5.7
5.6
5.1
4
3
2
1
0
-1
-2
-3
GDP weighted
unweighted
population weighted
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
SSA GDP growth %
Figure 3 GDP growth rates by different weighting schemes
Data sources: IMF WEO and staff calculation.
The accumulated effect of the different weighting scheme could be even more
pronounced for the GDP per capita growth, which involves more complex weighting
scheme than that is used for the GDP growth, due to the added population variable.
Figure 4 presents such effects. The “Conventional calculation” in the graph is the
method used by WDI database. Based on the GDP and the population weighted GDP
growth, Africa region has experienced a negative GDP per capita growth since 1980.
This is probably due to the poor performance of a few large countries, including
Nigeria, South Africa and The Democratic Republic of Congo. Figure 4 shows that
the unweighted GDP per capita growth tells a different story, with more countries
having made a good growth performance than not. But these better performing
countries weigh less in terms of the population or GDP in the region.
Figure 4 GDP per capita growth for SSA, by weighting scheme
112
115
109
108
110
GDP per capita index 1980=100
105
105
100
Unweighted
102
101
99
99
102
98
99
95
100
Population
weighted
90
85
Conventional
calculation
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
80
Data sources: World Bank WDI and GDF and IMF WEO.
4
The aggregated GDP growth rates are likely to vary depending on aggregation methods
and data sources. One should use one data source as much as possible, but some time it
is necessary to combine data from different sources. All three weighting schemes
presented above are the correct ways to aggregate GDP growth, depending on the
purpose of the research. The best one can do is to provide a consistent series in the same
study and to clearly state the data sources and the aggregation methods used.
5
Appendix I 
Systematic overshooting in predicting near future GDP growth and
difficulties in predicting near future export
Each May, IMF puts out predicted values for selected macro economic indicators in its
“World Economic Outlook”. The forecasts are 18 months or 6 months ahead,
respectively. The information below shows the difficulty in accurately predicting even
the current year macro economic indicators, which may influence the decision on
lending. Table 1 and figure 1 show that the predicted GDP growth is consistently higher
than the realized ones. Table 2 and figure 2 show the volatility of exports. After 1997,
the export growth fluctuates a great deal, the predicted values miss the realized values by
a large margin. The actual average export (goods only) growth rate between 1997 and
2000 is 4 percent, but the average current year prediction is 8 percent.
Table 1 Forecasts of annual real GDP growth for Sub-Saharan Africa
1995
1996
1997
1998
1999
2000
Actual real GDP growth rate
3.8
5.2
3.5
2.6
2.2
3.1
6 month forecast
5.2
5.4
4.4
4.1
2.9
4.2
18 month forecast
5.1
5.4
5.2
5
4.8
5.2
Data source: IMF World Economic Outlook, May 1994, 95, 96, 97, 98, 99, 2000, 2001.
2001
3.9
3.9
4.6
2002
4.6
Figure 1 Forecasts of annual real GDP growth for Sub-Saharan Africa
6.0
5.0
4.0
3.0
2.0
1.0
0.0
1995
1996
One year forecast

1997
1998
Current year forecast
1999
2000
2001
Actual real GDP growth rate
This appendix was authored by Gelb and Ye in 2001.
6
Table 2 Sub-Saharan Africa: forecasts of annual growth rate for total exports in goods
1995
1996
1997
1998
1999
2000
One year forecast
14.2
7.9
6.6
-3.2
1.8
21.4
Current year forecast
12.3
9.4
6.1
6.6
9.1
11.9
Actual export growth
18.5
11.0
1.5
-13.8
5.6
22.0
Data source: IMF World Economic Outlook, May 1994, 95, 96, 97, 98, 99, 2000, 2001.
2001
1.4
-0.1
2002
3.1
Table 2 Sub-Saharan Africa: forecasts of annual growth rate for total exports in goods
25
20
15
10
5
0
-5
-10
-15
1995
1996
One year forecast
1997
1998
1999
Current year forecast
2000
2001
2002
Actual export growth
7
Appendix II
World Bank and IMF estimates of country growth rates and weights
GDP growth
GDP growth
estimates
Weights
estimates
2002
2002
2004 2004
2002
2002
WDI
IMF
WDI
IMF
WDI
IMF
Benin
6.0
6.0
0.6
0.8
3.0
3.0
Botswana
5.0
4.4
1.2
1.8
5.2
3.8
Burkina Faso
5.2
4.4
1.1
0.9
4.8
3.9
Burundi
4.5
4.5
0.4
0.2
5.5
5.5
Cameroon
6.5
4.2
2.8
3.0
4.3
5.0
Cape Verde
5.0
4.6
0.2
0.2
4.0
5.5
CAR
-0.6
-0.8
0.4
0.3
0.9
5.8
Chad
9.9
9.9
0.7
0.5
30.5
31.0
Comoros
2.3
2.5
0.1
0.1
1.9
1.8
Congo, Dem. R.
3.5
3.5
2.7
1.3
6.8
6.3
Congo, Rep.
5.4
3.5
0.3
1.1
4.0
4.0
Cote d'Ivoire
-1.5
-1.6
2.2
3.2
-0.9
1.8
Equatorial Guinea
9.6
17.6
1.0
0.5
34.2
10.0
Eritrea
0.6
0.7
0.3
0.2
1.8
1.8
Ethiopia
1.6
2.7
4.4
2.2
11.6
11.9
Gabon
0.0
0.0
0.7
1.5
1.9
2.0
Gambia, The
-3.2
-3.2
0.2
0.1
7.7
8.3
Ghana
4.5
4.5
3.7
1.7
5.5
5.2
Guinea
4.2
4.2
1.4
1.0
2.5
2.6
Guinea-Bissau
-7.2
-7.2
0.1
0.1
4.3
1.7
Kenya
1.1
1.1
2.7
3.3
3.1
2.4
Lesotho
4.5
3.8
0.4
0.3
2.3
3.0
Madagascar
-12.7
-12.7
1.0
1.1
5.2
5.3
Malawi
2.1
1.8
0.5
0.5
4.3
3.6
Mali
4.3
4.4
0.9
0.9
2.2
4.7
Mauritania
4.1
3.3
0.5
0.3
5.2
5.0
Mauritius
3.4
4.4
1.1
1.5
4.4
5.0
Mozambique
7.4
7.4
1.7
1.4
7.8
8.4
Namibia
2.5
2.5
1.0
1.1
4.4
3.8
Niger
3.0
3.0
0.8
0.6
0.9
4.1
Nigeria
1.5
1.5
11.2
13.4
3.5
4.1
Rwanda
9.4
9.4
0.9
0.6
4.0
5.9
Sao Tome and P.
5.0
4.1
0.0
0.0
6.0
6.5
Senegal
1.1
1.1
1.4
1.4
6.0
6.0
Seychelles
1.3
0.3
0.1
0.2
-2.0
-2.0
Sierra Leone
27.5
6.3
0.3
0.2
7.4
7.7
South Africa
3.6
3.6
38.8
41.4
3.7
2.6
Sudan
6.0
6.0
5.5
4.2
7.3
6.0
Swaziland
2.8
3.4
0.4
0.4
2.1
1.7
Tanzania
7.2
7.2
1.8
3.1
6.3
6.3
Togo
4.5
4.1
0.6
0.4
2.9
3.0
Uganda
6.8
6.8
3.3
2.0
5.9
5.7
Zambia
3.3
3.3
0.7
1.1
5.0
3.5
Data sources: World Bank WDI, AFRLDB and IMF WEO databases.
2004/
IMF
0.6
1.2
1.2
0.4
2.8
0.2
0.3
1.0
0.1
2.8
0.3
2.0
1.2
0.3
4.3
0.7
0.2
3.8
1.3
0.1
2.6
0.4
1.1
0.5
0.9
0.5
1.1
1.8
1.0
0.8
11.9
0.9
0.0
1.4
0.1
0.3
37.4
5.8
0.4
1.8
0.6
3.3
0.7
Weights
2004
WDI
0.8
1.8
0.9
0.2
3.0
0.2
0.3
0.7
0.1
1.4
1.0
2.9
0.6
0.2
2.2
1.5
0.1
1.7
1.0
0.1
3.1
0.3
1.2
0.5
0.9
0.3
1.5
1.5
1.1
0.6
14.3
0.7
0.0
1.5
0.2
0.2
40.0
4.3
0.4
3.3
0.4
2.1
1.1
8