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Poor Numbers: How We Are Misled by African Development Statistics
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 Jerven 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.
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 Jerven 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.
176 pages, Hardcover
First published February 1, 2013
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Displaying 1 - 14 of 14 reviews
February 19, 2023
three takeaways:
1 - african growth statistics stink. They under-report the informal sector, rely on faulty population and baseline year estimates, and are often politically motivated. Many countries may be due to revise GDP estimates upwards by 50% or more when they correct these in the coming years.
2 - international institutions (WB and IMF) dont help the problem - theyre emphasis on generating agreement and legitimacy comes at the expense of frankness about shortcomings in the data and providing accurate metadata.
3 - by far the greatest failure of data is the in the agriculture sector, where you cannot trust most national-level aggregate numbers.
1 - african growth statistics stink. They under-report the informal sector, rely on faulty population and baseline year estimates, and are often politically motivated. Many countries may be due to revise GDP estimates upwards by 50% or more when they correct these in the coming years.
2 - international institutions (WB and IMF) dont help the problem - theyre emphasis on generating agreement and legitimacy comes at the expense of frankness about shortcomings in the data and providing accurate metadata.
3 - by far the greatest failure of data is the in the agriculture sector, where you cannot trust most national-level aggregate numbers.
July 28, 2014
Had some solid points and was well researched but droned on. The arguments could've been conveyed in half the number of pages. It also could have been more clearly written but to be fair, it was most likely written for those in the field of development economics and not the general populace.
July 14, 2014
An important response to today's data obsession. Jerven calls attention to certain organisations' tendency to rank countries on all sorts of measures and figures, such as GDP, without taking a moment to stop and look at whether the estimates are worth the paper they are written on. "Datasets are like guns: if left lying around, someone will use them", he observes.
Moreover, he points out why it is a problem that these figures aren't accurate. If we don't know what a country's population is, how can we know how many doctors per capita there are?
This is not a popular science book. It makes fairly dense, dry reading - but if you care about the issue at stake, it's well worth it.
Moreover, he points out why it is a problem that these figures aren't accurate. If we don't know what a country's population is, how can we know how many doctors per capita there are?
This is not a popular science book. It makes fairly dense, dry reading - but if you care about the issue at stake, it's well worth it.
December 31, 2019
This book is important and necessary, and I am absolutely sure it will be essential reading for NGOs, activists, development experts and academics who work in the field of African development. I hope African statisticians are able to use it to claw in more support and funding for their clearly grossly underappreciated work.
For a lay reader though, well, there is only so many times an author can say “the World Bank makes shit up”. Especially when they never directly says it because they are very serious and sensible and objective and rational and this book is not meant to be a blood-boiling polemical attack - although it could be. The introduction has a faint hint of that kind of book, as the author briefly describes his field trips to those neglected statistical bureaus and decaying archives. But it’s designed to be cited by experts, not read by angry people.
I vacillated about how to rate it. It’s probable it deserves 5 stars for social usefulness, but the people who will use it in that way aren’t going to go on Goodreads recommendations. I decided on 3 stars for a worthy book that is dull to read.
For a lay reader though, well, there is only so many times an author can say “the World Bank makes shit up”. Especially when they never directly says it because they are very serious and sensible and objective and rational and this book is not meant to be a blood-boiling polemical attack - although it could be. The introduction has a faint hint of that kind of book, as the author briefly describes his field trips to those neglected statistical bureaus and decaying archives. But it’s designed to be cited by experts, not read by angry people.
I vacillated about how to rate it. It’s probable it deserves 5 stars for social usefulness, but the people who will use it in that way aren’t going to go on Goodreads recommendations. I decided on 3 stars for a worthy book that is dull to read.
October 26, 2013
Interesting read, but the Private Sector does possess data - so not as bad a situation as he makes out
February 12, 2017
Very good and important message on the problems with data collection and analysis in Africa. However, as others have mentioned, it was unnecessarily repetitive.
September 4, 2018
Basic premise:
• “Not everything that counts can be counted; and not everything that can be counted, counts” Einstein – I always have liked this quote.
• African statistics are of dubious quality, it is often hard to validate sources. The book focuses on the estimation of national accounts and GDP statistics, which is hard for any country to do (due to assumptions needed on income, population, etc.), but much harder for national statistics offices across Africa without the necessary resources, guidelines and structural independence.
• What is needed is new baseline estimates in most African countries, based on local applicability, not solely on theoretical or political preference. There is also a need to strengthen the legitimacy of statistical offices as providers of data.
Key points of contention:
1. We don’t know very much about income and growth in Africa. Comparing three major sources of national income data, World Development Indicators, Penn World Tables and datasets of Angus Maddison, highlights some large discrepancies in GDP estimates for African countries, due to different base years and methodologies. Regardless of how the national numbers are reached, more often than not, the World Bank disseminates different numbers than the ones reported by the national statistical office.
2. Very likely GDP today for African countries is underestimated, reflected by the large GDP increases that follow a rebasing for any country (e.g. Ghana by 60%).
3. There is ample opportunity for politics to tamper national statistics, but often the statistics are not important enough to motivate political leaders to influence the data, as they can adjust or choose statistical series at their discretion when it fits their political aims. There is a lack of knowledge about which statistics do matter politically.
4. The lack of transparency in reporting and the paucity of information accompanying the datasets mean that data users are easily misled.
5. Donors are part of the problem, ad hoc support directly linked to particular donor-funded projects distorts data production instead of building up statistical capacity. Statistical officers are richly remunerated with per diem allowances when they are engaged in data collection in the field, but this leaves fewer people and resources for analysis and dissemination back in the statistical offices.
6. Monitoring of specific projects, including the SDGs, should be tempered by a realistic assessment of the capacity of the statistical office to deliver information on the basis of which national leaders can confidently govern. Rather than asking what kind of development we should target, perhaps the question should be: What kind of development are we able to monitor?
A sad fact, colonial accounts for Rhodesia (like some other colonial countries) recorded “normal” versus “African” output, differentiating nationalities to measure national income.
• “Not everything that counts can be counted; and not everything that can be counted, counts” Einstein – I always have liked this quote.
• African statistics are of dubious quality, it is often hard to validate sources. The book focuses on the estimation of national accounts and GDP statistics, which is hard for any country to do (due to assumptions needed on income, population, etc.), but much harder for national statistics offices across Africa without the necessary resources, guidelines and structural independence.
• What is needed is new baseline estimates in most African countries, based on local applicability, not solely on theoretical or political preference. There is also a need to strengthen the legitimacy of statistical offices as providers of data.
Key points of contention:
1. We don’t know very much about income and growth in Africa. Comparing three major sources of national income data, World Development Indicators, Penn World Tables and datasets of Angus Maddison, highlights some large discrepancies in GDP estimates for African countries, due to different base years and methodologies. Regardless of how the national numbers are reached, more often than not, the World Bank disseminates different numbers than the ones reported by the national statistical office.
2. Very likely GDP today for African countries is underestimated, reflected by the large GDP increases that follow a rebasing for any country (e.g. Ghana by 60%).
3. There is ample opportunity for politics to tamper national statistics, but often the statistics are not important enough to motivate political leaders to influence the data, as they can adjust or choose statistical series at their discretion when it fits their political aims. There is a lack of knowledge about which statistics do matter politically.
4. The lack of transparency in reporting and the paucity of information accompanying the datasets mean that data users are easily misled.
5. Donors are part of the problem, ad hoc support directly linked to particular donor-funded projects distorts data production instead of building up statistical capacity. Statistical officers are richly remunerated with per diem allowances when they are engaged in data collection in the field, but this leaves fewer people and resources for analysis and dissemination back in the statistical offices.
6. Monitoring of specific projects, including the SDGs, should be tempered by a realistic assessment of the capacity of the statistical office to deliver information on the basis of which national leaders can confidently govern. Rather than asking what kind of development we should target, perhaps the question should be: What kind of development are we able to monitor?
A sad fact, colonial accounts for Rhodesia (like some other colonial countries) recorded “normal” versus “African” output, differentiating nationalities to measure national income.
October 23, 2018
Interesting read with useful lessons for policymakers
I have enjoyed this book, despite being quite specific (and therefore, unlikely to be interesting to the general reader). It identifies the problem with African statistics in a very clear and didactic manner and offers relevant and "easy-to-apply" recommendations for policymakers. It has certainly helped me look at country data in a totally different way.
I have enjoyed this book, despite being quite specific (and therefore, unlikely to be interesting to the general reader). It identifies the problem with African statistics in a very clear and didactic manner and offers relevant and "easy-to-apply" recommendations for policymakers. It has certainly helped me look at country data in a totally different way.
September 19, 2021
Did you know that many statistics concerning developing economies are very unreliable?
September 26, 2016
Important book, and a great introduction to some of the core challenges of producing and using data for better development policy and practice, with a focus on GDP. Unfortunately, it reads like an unedited final draft and the Kindle edition contains various syntax and grammar errors. That said, it does not take away from the content. Highlights are the brief history of statistical services in sub-Saharan Africa and the analysis of current challenges faced by national statistics offices.
May 10, 2015
Really enjoyed the main idea of this book. I saw the author speak about the book before reading it, so I was already familiar with the core concepts. I was hoping the book were digger deeper than it did into these examples. Overall, an important main idea for development practitioners and economists to be aware of.
August 13, 2014
Must read for users of African data! A good reminder to be cautious when creating new 'knowledge'. I would caution though that while some national statistical offices in Africa are in shambles, others are quite good and getting better. And non official data (private sector) needs to be better acknowledged, interrogated and utilized.
March 22, 2014
This is a brilliant read and should be on every African scholars reading list. There are one or two proofreading issues along the way, but these don't detract from the overall quality of the book and its insights.
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September 20, 2013338.967 J57 2013
Displaying 1 - 14 of 14 reviews