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Poor Numbers: A Critical Review of African Development Statistics by Morten Jerven

Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It

If you are interested in African economic development, you might want to read a book called Poor Numbers by Morten Jerven. This book is the first analysis of the production and use of African economic development statistics. It shows how the statistical capacities of sub-Saharan African economies have fallen into disarray, and how this affects our understanding and policy making on the continent. In this article, I will give you an overview of the book's main argument, its key findings, and its implications for improving poor numbers.

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Why are African statistics poor?

The first question that Jerven asks is why are African statistics poor in the first place? He identifies three main reasons:

The historical legacy of colonialism and independence

Jerven traces the origins of poor numbers to the colonial era, when European powers imposed their own systems of measurement and classification on their African colonies. These systems were often arbitrary, inconsistent, and incomplete, reflecting the interests and biases of the colonizers rather than the realities and needs of the colonized. After independence, many African countries inherited these systems without much modification or adaptation. They also faced political instability, civil wars, and economic crises that disrupted their statistical operations and reduced their funding and capacity.

The political economy of data production and use

Jerven also examines how political factors influence the production and use of data in Africa. He argues that data are not neutral or objective, but rather reflect the interests and agendas of those who produce and use them. For example, governments may manipulate or censor data to enhance their legitimacy or attract aid. Donors may impose their own standards or preferences on data collection and analysis. Researchers may cherry-pick or ignore data that do not fit their theories or hypotheses. All these practices can result in biased, inaccurate, or misleading data.

The technical challenges of measuring economic activity

Finally, Jerven acknowledges that measuring economic activity in Africa is not an easy task. He points out that many African economies are dominated by informal sectors, subsistence agriculture, and non-market transactions that are hard to capture by conventional methods such as surveys, censuses, or national accounts. He also notes that many African countries lack reliable population data, price indices, exchange rates, or other basic indicators that are essential for calculating economic aggregates such as GDP or income per capita. He argues that these technical challenges require more attention and innovation from statisticians and policymakers.

What are the consequences of poor numbers?

The second question that Jerven asks is what are the consequences of poor numbers for African economic development? He identifies three main consequences:

The distortion of development policies and priorities

Jerven shows how poor numbers can distort our understanding of the economic performance and potential of African countries. He provides examples of how GDP estimates can vary widely depending on the methods and sources used, and how this can affect the ranking and classification of countries by income level, growth rate, or poverty rate. He argues that these variations can have significant implications for the design and evaluation of development policies and priorities, such as structural adjustment, debt relief, or the Millennium Development Goals.

The misallocation of aid and resources

Jerven also shows how poor numbers can affect the allocation of aid and resources to African countries. He explains how donors use data to determine the eligibility, amount, and conditionality of their assistance. He argues that poor data can lead to misallocation of aid, either by rewarding or punishing countries based on unreliable or outdated indicators, or by creating perverse incentives for data manipulation or fabrication. He also notes that poor data can hamper the monitoring and evaluation of aid effectiveness and impact.

The erosion of trust and accountability

Finally, Jerven shows how poor numbers can undermine the trust and accountability between data producers and users in Africa. He argues that poor data can erode the credibility and legitimacy of national statistical offices and governments, as well as the confidence and cooperation of donors and researchers. He also warns that poor data can foster a culture of skepticism and cynicism among the public and the media, who may question or dismiss any data or evidence that challenge their preconceptions or expectations.

How can we improve poor numbers?

The third question that Jerven asks is how can we improve poor numbers in Africa? He offers three main suggestions:

The need for more and better data

Jerven argues that the first step to improve poor numbers is to invest more in data collection and analysis in Africa. He calls for more funding, training, and equipment for national statistical offices and other data producers. He also advocates for more frequent and comprehensive surveys, censuses, and national accounts to update and expand the data available. He stresses that more data are not enough; they also need to be better quality, meaning more accurate, reliable, consistent, and comparable.

The role of national statistical offices and international organizations

Jerven argues that the second step to improve poor numbers is to strengthen the role and capacity of national statistical offices and international organizations in Africa. He urges national statistical offices to assert their independence and authority over data production and dissemination, and to resist political interference or pressure from governments or donors. He also urges international organizations such as the World Bank, the IMF, or the UN to coordinate their efforts and standards on data collection and analysis, and to support and respect the work of national statistical offices.

The importance of transparency and quality control

Jerven argues that the third step to improve poor numbers is to enhance the transparency and quality control of data production and use in Africa. He recommends that data producers disclose their methods, sources, assumptions, and limitations when they publish their data, and that they subject their data to peer review or external verification. He also recommends that data users check the validity, reliability, consistency, and comparability of the data they use, and that they acknowledge their uncertainties or errors when they present their findings or conclusions.


In conclusion, Poor Numbers is a book that challenges us to rethink our assumptions and expectations about African economic development statistics. It reveals how poor numbers are produced and used in Africa, how they affect our understanding and policy making on the continent, and how they can be improved. It is a book that should be read by anyone who cares about Africa's economic development.


Who is Morten Jerven and what is his expertise?

Morten Jerven is a professor of development studies at the Norwegian University of Life Sciences. He has a PhD in economic history from the London School of Economics. He has been researching African economic development statistics since 2006. He has published several articles and books on this topic, including Africa: Why Economists Get It Wrong (2015) and Measuring African Development: Past and Present (2020).

How reliable are the sources and methods used in the book?

How does the book compare to other works on African development statistics?

The book is unique in its focus and scope on African development statistics. It is the first book to provide a comprehensive and critical analysis of the production and use of these statistics. It covers a wide range of topics and issues, such as GDP, income, poverty, growth, aid, and development indicators. It draws on a variety of sources and methods, such as historical records, official documents, interviews, surveys, and case studies. It is also written in an accessible and engaging style, with clear explanations, examples, and illustrations.

What are some examples of poor numbers in action?

The book provides many examples of poor numbers in action in Africa. Here are some of them:

  • In 2010, Ghana revised its GDP estimate upwards by 60%, making it a lower-middle-income country overnight.

  • In 2014, Nigeria rebased its GDP estimate upwards by 89%, making it the largest economy in Africa.

  • In 2015, Tanzania revised its poverty rate downwards by 10 percentage points, making it a success story in poverty reduction.

  • In 2016, Zambia revised its inflation rate upwards by 20 percentage points, making it a hyperinflationary country.

  • In 2017, Kenya revised its population estimate downwards by 10 million people, making it a less populous country.

How can I access or download the PDF version of the book?

You can access or download the PDF version of the book from various sources online. Here are some of them:

  • The publisher's website:

  • The author's website:

  • The Project MUSE website:

  • The OceanofPDF website:



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