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

Morten Jerven
5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)

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Descriptions du produit

Présentation de l'éditeur

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.

Détails sur le produit

  • Format : Format Kindle
  • Taille du fichier : 1343 KB
  • Nombre de pages de l'édition imprimée : 176 pages
  • Editeur : Cornell University Press; Édition : 1 (1 février 2013)
  • Vendu par : Amazon Media EU S.à r.l.
  • Langue : Anglais
  • ASIN: B00B7GGG7W
  • Synthèse vocale : Non activée
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  • Word Wise: Activé
  • Composition améliorée: Activé
  • Moyenne des commentaires client : 5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)
  • Classement des meilleures ventes d'Amazon: n°187.685 dans la Boutique Kindle (Voir le Top 100 dans la Boutique Kindle)

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5.0 étoiles sur 5 Très innovent 11 décembre 2014
Format:Format Kindle|Achat vérifié
Un livre à lire pour sortir du mythe des donnés "fiable à 100%".
Je le conseille pour tous les passionnés de veille de l'information.
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Amazon.com: 4.6 étoiles sur 5  17 commentaires
7 internautes sur 7 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Be careful with those aggregated data sets! 6 janvier 2014
Par David Last - Publié sur Amazon.com
Morten Jerven is an economic historian from the LSE, with a good track record of publication and some World Bank and UNDP consultancies to his name. This book got the nod from Bill Gates and several good reviews. It makes a strong and succinct case for NOT relying on African GDP statistics as indicators of growth. The data from most sub-saharan countries are unreliable and misleading. One reason is that the aims of those who produce the figures and those who use them are in conflict. Jerven points out that scholars in the 1960s-1980s relied on national accounts, but by the 1990-2000s, Penn World Tables and World Bank data dominated footnotes; their ‘brand’ was better, although the ingredients were the same tainted data. Development studies, Jerven says, are now dominated by economists who prefer econometric analysis using global datasets in cross-country regressions; they are more interested in economics than economies, so they don’t notice the poor source data for the big data sets. The heart of Jerven’s argument rests (Ch 3) on case studies in which he dissects contradictory data on basic variables: population, agricultural production, and change in national income. The process of counting population in Nigeria is fraught with practical and ideological problems. Agricultural production figures have not adequately accounted for subsistence production--a major component (see sources on the informal economy, which dwarfs the formal economy). GDP and rates of change show huge variation from different sources. The reason for these basic data problems can be found in the bureaucracies of national capitals - civil servants without the tools to do what is expected, lack of investment in basic surveys and data collection, and poor institutions. “It requires a massive exercise of social power to establish valid numbers.” (Porter, 1995).

This book changed the way I think about aggregate data; it might NOT be getting better all the time. Canada’s experience with the long-form census, and the general retreat from big government science may mean that the world’s data are becoming less reliable rather than the reverse.
7 internautes sur 8 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Very good book; African economic indicators have no face value 20 avril 2014
Par ncooty - Publié sur Amazon.com
If you work in or are interested in international development--in Africa or elsewhere--read this book.

By (i) assessing the techniques and influences related to the generation of economic data for Africa and (ii) placing that information in a cultural and historical context, Jerven has provided an exceptionally useful and cautionary view of the tragically poor state of economic data from most of the African continent. Most readers will likely be astonished at the arbitrariness of economic indicators from these countries and the extent to which enormous levels of measurement error are ignored by public institutions, academics, and other users. The book predominantly comprises reviews of economic history (mostly from 1950s to present) and insights from his interviews and surveys involving international financial institutions (e.g., World Bank and IMF) and officials/ civil servants from statistical agencies and central banks in 20-something African countries over about 4.5 years.

He advocates (i) strengthened and consistent support (funding, facilities, staff, technical assistance, etc.) to autonomous or semi-autonomous national statistical agencies--both for collection and reporting, (ii) more locally appropriate measures of economic indicators, with a greater focus on data quality (e.g. regular, accurate surveys) than comprehensiveness (e.g., complete decadal censuses), (iii) strengthened methodological transparency--largely via improved meta-data--so that users are informed of the methods and assumptions that produced each component of the statistics and can estimate their validity for decision-making, (iv) closer attention from users to the methods, assumptions, and influences that underpin the production of data, and (v) greater reliance on qualitative methods and local expertise to verify data and check for misinterpretations and omissions (blind spots).

I've given it 5 stars for the sheer value of this book and Jerven's recommendations. I was tempted to deduct a star, because (i) Jerven at times seems to lose sight of the point he's making, (ii) there are redundancies (despite its mere 120 pages of text), and (iii) there are numerous typographical and grammatical errors. His perspective also seemed at times surprisingly narrowly focused on the work of economists. (E.g., citing Esther Duflo regarding fertilizer efficacy seemed absurd, given that surely even an economist will concede that other fields have done more and better research on agricultural productivity.) Lastly, I think Jerven could have done a better job of considering and presenting the legitimate concerns of civil servants at the World Bank and IMF. At times, he seems to use them as an all-too-easy patsy for some issues. Granted, he levels some very fair criticisms of those institutions, but skates past a couple of opportunities to interrogate underlying influences (e.g., when an IMF technical advisor noted that sharing confidential country information with third parties compromises trust with country representatives).
4 internautes sur 4 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Bad data 1 février 2013
Par Dascholar - Publié sur Amazon.com
Format:Format Kindle|Achat vérifié
Bravo to Morten Jervens on a fine and informative book. May the stats get better!!!!

I just finished reading this book. Since I had read other things by author, I did not discover anything that I did not already know. For those who are not aware of his work, this book will come as quite informative and for some hopefully a wake up call. My only quibble with the book is that it too nice. Probably, this is a question of personality and style. He references epistemological and methodological problems that arise from using bad data (bad plus noisy data is, in my view, a priori junk); however, he does not, borrowing from a Seinfeld episode, bother to name names as much as I think he should have. In my view, there is no justification for someone who is methodologically sophisticated using bad data to draw inferences about whether institutions/regime types impact economic development and public goods distribution. There is no basis for claiming that there is a positive and robust relationship between post-structural adjustment, democratization and an uptick in economic growth in Africa based on national income data. As Jerven shows clearly, the problems with the data is just too much to rest such an inferential oomph onto. Even with pristine data, this proposition is problematic. It is down right silly with bad data! In a nutshell, I like the book a lot and see it as having a major impact but I would have liked it to have been more forceful in its criticism of those who have used bad data and should have known better
2 internautes sur 2 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Necessary work on data quality and its implications 9 septembre 2013
Par Leo - Publié sur Amazon.com
Format:Broché|Achat vérifié
Since Morgenstern, few works have examined extensively the accuracy of economic observations. This book do it for Africa, but only for national accounting. It deals with the (extreme) deficiencies of african GDP estimations, and how these (should) affect the (in)conclusions about development. If you plan to use african data, this is a mandatory reading. If you do not plan to use african data, but you do work with cross section macro variables from FMI, WB or Penn Tables, you still should read this to get a real feel of how unreliable these datasets can be.
4 internautes sur 5 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Compelling overview of a pressing yet overlooked global issue 4 février 2013
Par T. Russo - Publié sur Amazon.com
Format:Format Kindle|Achat vérifié
I've done a masters in Economic History and International Development and I found Jerven's book Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It (Cornell Studies in Political Economy) to be a well-written overview of a pressing issue that no one has ever talked about before. The subject matter is weighty and data-centric. But the author's clear and compelling writing style, as well as entertaining anecdotes from his research trips throughout sub Saharan Africa, make this a first-rate read both for experts and novices curious about international development, aid and global inequality. Jerven does an excellent job of answering the subtitle's question: "How We Are Misled by African Development Statistics and What to Do about It." Hope he writes more soon. Kudos!
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