Are we too eager to build economic models and development policies and too slow to look at the quality of the data that underwrites them? In his new book, Poor Numbers. How We Are Misled by African Development Statistics and What to Do about It‘ Morton Jerven explores the issue
What do we know about growth and income in African economies? Much less than we would like to think. The World Bank Chief Economist for Africa, Shanta Devarajan, recently declared that the state of development data in Africa amounts to nothing less than a statistical tragedy. Indeed, it is no exaggeration to say that, due to data unreliability, we know little about the growth and income of African economies. Yet institutions and scholars routinely make statements regarding the pace and direction of development in Africa.
Recently, there has been an increased focus on the weakness of statistical systems in sub-Saharan Africa. The remarkable upward revision causing a doubling in GDP in Ghana has just been confirmed, and reports suggest that a similar upward revision of GDP is pending in Nigeria. These very visible events have raised the attention given to African statistics, especially for the measurement of growth, poverty and also for development in African economies in a broader sense.
One of the most urgent challenges in African economic development is to devise a strategy for improving statistical capacity. Reliable statistics, including estimates of economic growth rates and per-capita income, are basic to the operation of governments in developing countries and vital to nongovernmental organizations and other entities that provide financial aid to them. Rich countries and international financial institutions such as the World Bank allocate their development resources on the basis of such data. The paucity of accurate statistics is not merely a technical problem; it has a massive impact on the welfare of citizens in developing countries.
Where do these statistics originate? How accurate are they? Poor Numbers is the first analysis of the production and use of African economic development statistics. Morten Jerven’s research shows how the statistical capacities of sub-Saharan African economies have fallen into disarray. The numbers substantially misstate the actual state of affairs. As a result, scarce resources are misapplied. Development policy does not deliver the benefits expected. Policymakers’ attempts to improve the lot of the citizenry are frustrated. Donors have no accurate sense of the impact of the aid they supply. Jerven’s findings from sub-Saharan Africa have far-reaching implications for aid and development policy. As he notes, the current catchphrase in the development community is ‘evidence-based policy’, and scholars are applying increasingly sophisticated econometric methods—but no statistical techniques can substitute for partial and unreliable data.
The first chapter states and describes the problem. It introduces some of the basics that are essential for understanding the production of economic statistics in African countries and provides a guide to the different types of statistics and the central data providers. It also offers readers a map of the different stakeholders involved in the process. The rest of the chapter is devoted to an analysis of how much we currently know about income levels in African countries. Huge discrepancies and alarming gaps in the knowledge exist, and the chapter concludes with the observation that any ranking of African economies according to GDP levels is misleading.
The second chapter offers a short history of national income accounting in Africa and argues that income statistics need to be fully historicized and contextualized. While these data are often presented as facts, they are better considered as products, and the production of the data is subject to particular economic and political constraints. The statistical capacity of African states was greatly expanded in the late colonial and early postcolonial period, but it was greatly impaired during the economic crisis of the 1970s. The importance of statistical offices was neglected in the decades of policy reform that followed—the period of “structural adjustment” in the 1980s and 1990s. In retrospect it may be puzzling that the International Monetary Fund (IMF) and the World Bank embarked on growth oriented reforms without ensuring that there were reasonable baseline estimates that could plausibly establish whether the economies were growing or stagnating. For statistical offices, structural adjustment meant having to account for more with less: Informal and unrecorded markets were growing, while public spending was curtailed. As a result, our knowledge about the economic effects of structural adjustment is limited. More generally, the economic growth time series, or the cumulative record of annual growth between 1960 and today for African economies does not appropriately capture changes in economic development.
The third chapter is a direct response to the question economists most commonly ask: ‘Yes, we know that there are measurement problems. But does it really matter?’ The answer is, ‘yes, it really does matter’. The chapter presents some basic findings about controversy and disagreement between the datasets used in development economics and shows that policymakers, nongovernmental organizations, and scholars draw different conclusions based the different datasets they use in their analysis. The chapter presents some numerical examples that underscore the dangers of ignoring data quality. Numbers we are currently using to allocate scarce resources are not good enough for these purposes, and the econometric models we currently use to explain differences in development performance are far more impressive than the numbers they set out to explain.
The fourth chapter builds on survey information and interviews conducted in field research to paint a picture of the current situation at statistical offices in sub-Saharan Africa. The chapter discusses the policy implications related to the future of statistical systems in sub Africa. The current development agenda is set by the Millennium Development Goals of the United Nations. This has led to some statistical capacity building in a number of countries, while in others there have been perverse effects when statistical capacity is diverted to data collection in order to monitor particular donor targets. At present there is no coherent global strategy for improving the provision of data for development. What is needed is first and foremost transparency in reporting, meaning that international databases acknowledge their sources and report metadata appropriately. This will be helpful in turning the attention of the development community to the important role local statistical offices play. In turn, global standards for new baseline estimates must be based on local applicability, not just on theoretical preferences and wishful thinking in the development community. Currently we are misled by African development statistics, and there is an urgent need for reforming both data users and data producers. Poor numbers are too important to be dismissed as just that.
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Click here to see Morten Jerven’s other publications and read more about his book and research.