In Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It, Morten Jerven tackles a critical issue: the accuracy and reliability of economic data used to assess African development. Drawing from his own research and experiences, Jerven reveals that much of the data used to shape policies and drive development initiatives in Africa is unreliable, incomplete, or entirely misleading. The book highlights the consequences of these statistical inaccuracies, questioning the validity of commonly accepted narratives about African economies and emphasizing the importance of better data for informed decision-making. This book serves as a wake-up call for development agencies, economists, and policymakers who rely on faulty numbers, urging them to rethink their approach to African development.

In the opening chapters, Jerven explains the scope of the problem: Africa’s economic development statistics are often riddled with inaccuracies. This issue stems from the fact that many African countries lack the institutional capacity to gather reliable data. National statistical offices are often underfunded and understaffed, leading to outdated and incomplete records. As a result, many economists and policymakers are basing their decisions on flawed information. Jerven uses several compelling examples to illustrate this point, including Ghana’s revision of its GDP in 2010, which showed that the country’s economy was 60% larger than previously thought. This revision raises the question: how reliable are the numbers we use to assess African development?

Jerven argues that the implications of poor data are far-reaching. For instance, international organizations such as the World Bank and the International Monetary Fund (IMF) often use this faulty data to design and implement development policies, which can lead to ineffective or even harmful interventions.

One of the book’s strengths is its use of real-world examples to demonstrate the dangers of relying on poor data. Jerven highlights multiple cases where incorrect statistics have distorted our understanding of African economies. For instance, in Nigeria, the statistical base year used for calculating the country’s GDP was 20 years out of date, leading to significant misestimations of the economy’s size and structure. Another example is from Tanzania, where the agricultural sector’s growth was grossly overestimated due to incomplete data collection.

These examples demonstrate that poor statistics are not just academic concerns—they have real consequences for development. Policymakers relying on these numbers can make misguided decisions that negatively impact millions of people. For example, overestimation of agricultural growth might lead governments to neglect necessary reforms, leaving the sector underdeveloped despite optimistic reports.

Jerven is also critical of international organizations and their role in perpetuating the problem of poor data. He points out that these organizations often push for quick solutions to complex problems, relying on easily accessible but unreliable statistics. This has led to a culture where development success is measured by quantitative targets—such as GDP growth or poverty reduction—without questioning the validity of the data used to track progress.

Jerven explains that organizations like the World Bank and the IMF often fail to acknowledge the limitations of the data they rely on, leading to overly optimistic assessments of African economies. This creates a feedback loop where inaccurate data reinforces flawed development strategies, resulting in ineffective policies and missed opportunities for real progress.

In the second half of Poor Numbers, Jerven shifts from diagnosing the problem to offering solutions. He advocates for greater investment in national statistical systems across Africa, emphasizing the importance of reliable data for effective governance and development. Jerven argues that donors and international organizations should prioritize funding for statistical capacity building, rather than focusing solely on quick-fix development projects.

He also suggests that economists and policymakers adopt a more critical approach to data, questioning its accuracy and acknowledging its limitations. Jerven believes that this shift in mindset is crucial for creating a more accurate picture of African development.

Jerven calls for a more nuanced approach to economic analysis in Africa, one that takes into account the unique challenges faced by national statistical offices and the limitations of the data they produce. He argues that this approach will lead to more realistic assessments of African economies and better-informed development policies.

Poor Numbers has been praised for its bold critique of the development industry and its call for more accurate data. It has sparked important debates among economists, policymakers, and development practitioners about the reliability of African economic statistics and the need for better data collection methods. Critics have noted that Jerven’s analysis sheds light on a crucial but often overlooked aspect of development work, and his recommendations for improving data accuracy have been widely endorsed.

In today’s world, where data-driven decision-making is increasingly the norm, Jerven’s message is more relevant than ever. As the global community continues to push for development in Africa, the need for reliable statistics is paramount. Poor Numbers serves as a reminder that without accurate data, even the best-intentioned policies can go awry.

In Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It, Morten Jerven exposes the flaws in the data used to assess African development, demonstrating how unreliable statistics can lead to misguided policies and missed opportunities. Through compelling examples and insightful analysis, Jerven makes a strong case for the need to improve statistical capacity in African countries and adopt a more critical approach to data.

Jerven’s book is not just a critique of the current state of African development statistics—it is a call to action for economists, policymakers, and international organizations to demand better data and, ultimately, better development outcomes. As Jerven notes, the future of Africa’s development depends on it.