Measuring GDP in Africa

World Economics - March 2016



Speed Read
  • Many of the problems with economic data in Africa stem from the low level of resources available to national statistics offices which has resulted in very poor quality macroeconomic data.
  • Many African economies are measured using outdated base years which if updated could lead to a collective upward revision in African GDP of 30.8%.
  • Most African countries also use outdated national income accounting standards rendering international comparisons between their economies and the developed world of little value.
  • African countries generally fail to adequately record the size of the informal economy which estimates suggest could account for between 21.9% and 62.7% of GDP in the countries across the continent.



African Economic Data: The problems

This paper analyses the provision and accuracy of official national income accounting data across 54 African countries.

 

1.    Resources Available to Measure National Income

The main factor impacting on the quality of national income statistics across Africa is the capacity of national statistical offices and the resources available for them to follow best international practice to ensure comparability with other parts of the world. This is a global problem, but it is particularly serious across Africa because of the relatively higher proportion of the world’s poorer nations that are situated in the continent.


Global standards are set by the United Nations for national accounts[1], but poorer countries generally report lower quality statistics because of the lack of resources available to national statistical offices. There are large differences in “official” GDP per capita across Africa although, as this report makes clear, the data are highly suspect and almost certainly under, rather than over, estimates of GDP per capita. Of the countries analysed, 37 had a GDP per capita measured in PPP terms of below US$5,000 while 22, had national income falling below US$2,000 per capita. This compares with typical developed world figures of approximately US$50,000 per capita.


According to an extensive study of African economic statistics by Morten Jerven reported in his book Poor Numbers:

“…in most African states the database for aggregating measures of income and growth are weak. For large shares of the economy we have little or no information and the figures involve a great deal of guesswork.”[2]   

The problem is more serious than the shortcomings that can arise from individual national statistics offices feeding inaccurate data into the public domain. There are also serious discrepancies in the data on African economies published in international economic databases. Jerven (2013) notes that the UN reported annual national accounts for 45 sub-Saharan African countries between1991-2004, but it had only received data for less than half of  the 1,410 observations, but for 15 countries no data had been received at all. The World Bank also provides data for African countries in constant and current prices even when no figures, accurate or otherwise, have been provided by national statistical offices to undertake the necessary adjustments for inflation. The missing figures are supplied The result is that different international databases give different rankings about the size, growth rates through “a method for filing the data gap”[3] a procedure which Jerven (2013) describes as “unclear.”[4]  and living standards of African economies.



The most significant specific data issues which impact on the accuracy of GDP estimates in Africa are:


  1. the failure to update base years regularly.
  2. the use of outdated standards of national income accounting;
  3. the degree to which the shadow economy (including informal activities) is measured.



2.    Base Year Problem

Even when the economic data produced by national statistical offices are published regularly serious distortions arise from the use of outdated base years. This biases estimates of the relative size of economies and the speed at which they are growing in real terms. This means that the estimates of GDP used in country rankings are likely to be seriously inaccurate with the degree of bias for each country being dependent on the length of time since the last base year was employed by the national statistics office.

The time elapsed between current estimates of GDP and the base year employed to assess the structure of an economy is of crucial importance to the accuracy of national income data. According to Jerven (2013) failures to regularly update a base year in which the structure and relative prices of an economy are monitored can lead to serious bias in the estimation of GDP. He refers to the case of Ghana when estimated GDP was uplifted by 60% overnight as a result of an update in the base year from 1993. In 2014, Nigeria the Nigerian National Bureau of Statistics released new estimates of GDP showing a rise of 59.5% after the economy’s base year was updated from 1990 to 2010. One of the reasons for these large uplifts is that better estimates of some economic activities such as telecommunications, tourism and financial services were made by statisticians while some previously unrecorded activities from the informal sector were included.

The results of a sample of recent rebasing exercises for 13 African countries is shown in Table 1. A scatter graph of this data shows that there is a strong positive relationship between the size of GDP uplift as a result of rebasing and the number of years since the exercise was last carried out. This is shown in Figure 1.


Table 1: Results of Rebasing in Africa


 

Figure 1: African Countries: % GDP Uplift and Years since last Rebasing


 

Reassessing African GDP

In this section we report on an exercise to estimate what the size of African GDP might be if most African countries updated their base years to 2014. In order to undertake a ranking that is not affected by exchange rate fluctuations we use World Bank GDP data which is estimated at (Purchasing Power Parity) PPP data in constant international US dollars order to make inter country comparisons of the 54 African nations for which official data is available. [5]

 

The methodology used is crude and involves applying an estimated constant cumulative annual rate of growth to the years between the last reported base year and 2014 on top of the underlying rate of real growth. The longer the period between 2014 and the last base year and the higher the assumed cumulative growth rate, the higher the uplift that would be expected from rebasing. Analysing the existing economic data after carrying out this procedure shows that aggregate African GDP may be underestimated by 30.8%. Analysing the existing economic data after carrying out this procedure shows that aggregate African GDP may be underestimated by 20.9%.

 

Two assumptions were necessary to carry OUT this exercise:

  1.   First the choice of a base year data series to apply on a country by country basis, a task made problematic given the    inconsistencies between the WDI, the IMF and the UN.
  2.  The second assumption is to estimate the cumulative annual rate which would act as a proxy by replicating over time the effect of the uplift created in one year by rebasing GDP (See Figure 1)


Inconsistent Base Year Data: The available data on the most recent base years for African countries are inconsistent. The three main international sources are the World Bank’s World Development Institute (WDI), the IMF, and the UN. Alternative sources are the base year recorded by each country’s national statistical offices, but often these are not reported. There are considerable differences in the reported base year between all of these sources which is particularly worrying when there are discrepancies between reputable international bodies such as the WDI, and the IMF.

The reported base years from the three international sources are shown by country in Table 2. Given the differences in reported base years, the source(s) generally used to calculate how many years the latest base year was out-of-date was higlighted in bold.



Table 2: Reported Base Years by Source

                                    

 

Cumulative annual rate: The assumed annual rate that would act as a proxy by replicating over time the effect of the uplift created in one year by rebasing GDP was 3.14% for African countries. This was based on an econometric model using data from the sample of 13 GDP national rebasing exercises shown in Table 1 and Figure 1.

Results: In 2014, the 54 African countries analysed had a combined GDP of US$5,502 billion. Applying the Africa uplift estimate produces a revised figure of US$7,196 billion, an average uplift of 30.8% (See Table 3). However, this is misleading since it includes Cape Verde, Malawi, Mauritius, Morocco, and Tunisia (0% uplift), which according to their national statistics offices, these countries have adopted chaining which is in effect rebasing every year. The uplift estimate also produce differences in the size rankings of African countries and the results are shown in Table 3 displaying each country ranked by current GDP and rebased GDP.

  

Table 3: Rebased GDP data and % Uplift


                   

 

3.    Outdated National Accounting Standards

The harmonized System of National Accounts (SNA) was created by the international community to facilitate the comparability of economic statistics and standards. Since 1953 there have been three revisions to recommended SNA standards, in 1968, 1993, and in 2008, all approved by the United Nations Statistical Commission. Most countries in Africa have now adopted SNA 1993, which aids comparisons between economies, but 8 of them still use the outdated SNA 1968 standard while 6 countries have adopted the latest SNA 2008 standard (See Table 4).

These standards are not mandatory and the adoption by a country of a standard such as SNA 1993 does not imply that all of the recommendations are implemented. The use of SNA 1993 across Africa means that economic statistics across the largest group of 41 countries are broadly comparable in terms of the definitions used and accounting methodologies applied, but not with the 14 using SNA 1968 or SNA 2008. Furthermore, the United Nations Statistical Commission formally adopted SNA 2008 in 2009 and the implementation of the recommendations of the new standard across most of the developed world means that there are problems in comparing measured GDP across most African countries with developed countries. Significant differences between SNA 1993 and SNA 2008 include the treatment of government accounts, capital expenditure, intellectual property and the measurement of the informal sector and illegal activities, areas which are particularly important in emerging markets. The longer it takes the bulk of African countries to adopt this standard the less reliable will be economic comparisons between themselves and the developed world.

 

Table 4: Accounting Standards by Country

                                                             
                                                             

  

4.    The Shadow Economy Problem

African economies have many unrecorded economic transactions which bias downwards official estimates of GDP, employment and poverty. Failing to measure the informal sector, or the shadow economy as it is often called, is particularly serious given its relative importance in African economies. Jerven (2013) notes that the decision by Zambia to incorporate estimates of the informal sector in 1994 led to an estimate of 42% of total value added. (Is this correct: “1994”?)

Estimating the size of the shadow economies in Africa without a set of full regular surveys which require significant resources is obviously problematic. But in his 2013 study, Freidrich Schneider, a leading expert on the size and importance of shadow or grey economic activity across the world, showed that for 2007 the size of the shadow economies across Africa ranged from 21.9% of GDP in Mauritius to 62.7% in Zimbabwe (See Table 5). [6]

 

Table 5:  The Shadow Economy % of National Income

  

                                                
                                        


5.    Conclusion

The quality of economic data across Africa is poor. The resources available to national statistics offices is limited, most countries use outdated national income standards and base years need updating in many countries and the shadow economy is largely uncounted. The result of all these deficiencies means that ranking countries by GDP and GDP per capita is meaningless.

 
Accurate and transparent statistics are essential indicators of economic potential. The issue of data quality needs to be urgently addressed to provide investors with more accurate guidelines. Given the increasing interest of investors in Africa into equity and bond markets and through direct investment these data issues are important. According to Jerven (2013) to improve matters “a change in the structure of funding for statistical offices is needed. We need not only more funding, but funding that is geared towards reliable, frequently disseminated survey
s.” [7] At present World Economics produces the only existing regular surveys of business conditions in Africa. Indicators of the speed and direction of the underlying economies are available from the Sales Managers’ Indexes for Africa and Nigeria. The Indexes which are published monthly provide data relating to the current month on the World Economics website.

 

References

 

Jerven, M. (2013), “Poor Numbers: How we are misled by African Development Statistics and what to do about it”, Cornell.

Schneider, F and Williams, C. C., (2013), “The Shadow Economy”, Institute of Economic Affairs, Hobart Paper 172.

World Economics, (2015) Sales Managers’ Index: Africa

World Economics, (2015) Sales Managers’ Index: Nigeria

 

 



[1] The United Nations System of National Accounts (SNA): http://unstats.un.org/unsd/nationalaccount/

[2] Jerven (2013).p3.

[3] Jerven(2013) p22

[5] Jerven (2013) p24

[5] No official economic data is available for Somalia.

[6] No Shadow Economy data was available for the following countries: Djibouti, Sao Tome, Seychelles, and South Sudan.

[7] Jerven (2013) p107.