Having Reliable Statistical Information

Having reliable statistical information is crucial for policy analysis and decision-making of government and international organisations. Yet, the sources of these statistical data are rarely questioned. In Poor Numbers: How We Are Misled by African Statistics and What to Do about it?

Morten Jerven, an economic historian, argues that economic statistics and national income data of sub-Saharan countries are not reliable. He bases his argument on several field visits (seven countries) and email surveys (fifteen countries) of the National Statistical Offices (NSOs). He also supports his research by comparing published data of the NSOs, library archives, and international organisation reports.

Poor statistical quality in Sub-Saharan Africa is both a governance and a knowledge problem. Jerven analyses population censuses and agriculture statistics, but mostly concentrates on the gross domestic product (GDP). He argues that the NSOs are unable to produce correct national statistics. This is due to the unavailability of means and resources to calculate different parts of the GDP, i.e. informal sector. Moreover, many countries are relying on a GDP base year, which is often outdated, e.g. Ghana revision of base year led to a more than 60% increase in GDP and prompted the move to a middle-income country category overnight (pp. 26–28). Alternatively, the three main database providers – The World Bank Group development indicators, the Penn World Tables, and the Maddison database – undertake ‘gap filling procedure’ and provide the data users with different results despite using the same source. This is illustrated through the ranking of countries, e.g. Guinea is ranked by Maddison as the seventh poorest country, while Penn World Tables puts it closer to the ten richest African countries in term of GDP per capita (p.18–19).

The production of statistical information in sub-Saharan Africa is contingent to political pressure. To support his point, the author gives a statistical history of African States including the state of statistical data during colonialism, post-independence and structural adjustment period. Statistical data, mostly household surveys, were important during the colonial era due to tax collection. Its importance continued post independence (1960–1970) due to the central role of the state and the need of this data for state planning. After the economic crisis of the 1970s, the importance of statistical offices has waned especially with the structural adjustment programme and the reduction of the role of the state. Today, despite the acknowledgment of the necessity of data in decision making, statistical data remain incomplete and erroneous and mostly subject to national political interest and international influence (World Bank and International Monetary Fund). Moreover, the pressure of the Millennium Development Goals and the UN to increase data quality and reliability had diverse effects depending on the country. In some countries, it helped to increase the statistical information quality; meanwhile, in others, it led to favouring some data over others mostly to achieve the donors’ targets (p.105). These viewpoints are supported through case studies in Nigeria and Tanzania. Political pressure in Nigeria influences the population numbers between the North and the South regions and the ambiguity linked to using different sources in Nigeria, led to the interpretation of the adjustment programmes as positive or negative. In Tanzania different accounting for the informal and formal sectors leads to different growth dynamics and an unsure interpretation of the results at hand (p.57–72). Rwanda and the uncertainty over the rate of poverty, is a recent case shows statistics flexibility and bending to political and donors’ will (Germain, 2015).

Jerven proposes a number of solutions. First, there is a need to increase funding to national statistical offices in order to ensure their independence and that they accomplish their mission of assessing economic activity. This should be reinforced by proper training of staff, in lieu of sophisticated software that few people can use as in the case of the Norwegian organisation in Malawi (p.94). The second suggestion is that of updating the base year. Most countries still use the 1993 base year to account for new economic realities of the African countries, such as the emergence of new industries, e.g. telecom industries (for example, Ghana and recently Nigeria). Finally, improving metadata will give more transparency to the statistics produced, collection of data, and the adjustments made.

The book is an important study that questions the reliability of statistics that are used daily and on which decisions are made. However, it has a number of shortcomings. First, the author’s sample and field work in Sub-Saharan Africa includes only Anglophone Africa. Indeed, despite reaching other countries through email surveys, it is not sufficient to generalise his findings about all the African states. Other studies that concentrate on other regions in Africa are necessary to give more weight to his findings. While reading the book, one would wonder whether this problem of statistical reliability is an African problem, i.e. India has the same puzzling results for GDP and statistical data and has the same issues for accounting for the informal sector (Nayyar, 2015). In terms of sources, it would have been beneficial to get insights from other African institutions, such as Africa’s Committee on Development Information (CODI) and Paris21 and their role in peer-reviewing national statistical systems. (Balzyk et al. 2010). The author’s recommendations also have some limitations. For example, for the author, improvement of metadata is a priority. However, there is no guarantee that the information that is provided as metadata _just like the rest of the data_ would be reliable and accurate. Moreover, increasing the quality of data used by policy makers should be reinforced by institution building, in order to ensure independence of statistical offices.

Previous works on statistics like Collier’s Bottom Billion, reduce false statistics to an underestimation of economic decline (p.58). Poor Numbers is a ground-breaking Morten Jerven have succeeded in opening the debate about statistics reliability. Increasing the awareness about he necessity to adopt critical approach toward the statistics is useful diverse data users and is the first step towards having a better data quality in Africa. Bibliography:

Blazyk, S., Charumbira, G., Diop, L., Strode, M. and Williams, T. (2010) ‘Peer reviews of African national statistical systems’, The African Statistical Journal, 10.

Germain, N. (2015) Africa – Rwanda accused of manipulating poverty statistics. Available at: http://www.france24.com/en/20151102-rwanda-accused-manipulating-poverty-statistics (Accessed: 7 February 2016).

Jerven, M. (2013) Poor numbers: How we are misled by African development statistics and what to do about it. United States: Cornell University Press.

Nayyar, D. (2015) Why India Needs Better Numbers. Available at: http://www.bloombergview.com/articles/2015-02-06/india-policymakers-need-better-economic-data (Accessed: 10 February 2016).

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