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THINKING ABOUT GROWTH AND DEVELOPMENT IN AFRICA

Morten Jerven’s Poor Numbers is an entertaining and topical read, predating the ‘Data Revolution’ – that is, the current drive to improve data quality and transparency – in development statistics. The book provides an ethnography of national income accounting in Africa, assessing the history, underlying theory and process of production of gross domestic product (GDP) indicators. The book is organized into four chapters, following three main themes. Poor Numbers (1) gives an impression of the extent of data inaccuracy for poor African countries, and shows (2) why this matters and (3) what can be done about it. The limited reliability of statistics for most poor African economies is generally accepted. However, Poor Numbers demonstrates how severe these limitations are for even such a commonly used indicator as GDP, exposing the dangers of glossing over data problems – an all too frequent occurrence in quantitative economic analysis. Scrutinizing three main sources of GDP data – the World Development Indicators, the Penn World Tables and the Angus Maddison dataset – Jerven shows in Chapter 1 that levels of per capita income for African economies vary strongly across data sources. As a consequence, relative country rankings – when ordered from richest to poorest – become meaningless for one-fifth of African economies. The book’s persuasive power lies in Jerven’s ability to place abstract numbers into both historical perspective and local context, as demonstrated in Chapter 2. The author discusses those aspects of African statistics typically disregarded by their users: statistical offices in poor countries are often sparsely equipped and under pressure to generate comprehensive and regular economic indicators. This has been especially problematic during economic crises when budgets are cut. The presence of a large informal sector – which is notoriously difficult to monitor – makes this task even more daunting. An important argument of the book is that statistics are not simply standardized numbers gathered in a rule-based process, but rather convey power. Data can be used to push for certain economic policies and to prevent others. Therefore, economic figures and their gathering process can be highly politicized, as discussed in Chapter 3. It is typically not fraudulent figures that are the problem but the inadequacy or absence of data, paired with limited data literacy in developing societies. Therefore, in the final chapter, Jerven proposes changes to data collection and usage in poor economies. Given the bold message of Chapter 3, the policy recommendations seem timid, mostly focusing on technical aspects of data gathering: poor countries require greater resources for the collection of statistics. Help from donors is needed, but must not distort data collection with ad hoc projects Africa 85 (4) 2014: 722–40