Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It
By Morten Jerven. 2013
316 citations recorded by the source corpus.
Subfields: Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/d619ac3c256392716358ebd79adc8fec063b432f
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In Sociology and Political Science
- Valentina Rollo
- Nuria Oliver
- William Yang Wang
- Jacopo Staiano
- Justine Falciola
- Joshua Blumenstock
- G. D. de Vries
- Bryan R. Routledge
Citations in the corpus
- Patterns of structural Change in Developing Countries (2015, background): “Recently, scholars have pointed out anew that the statistical foundations underlying GDP and employment estimates in many developing countries, notably but not exclusively Africa and China, are subject to substantial measurement error (Devarajan, 2013; Jerven, 2013).”
- The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “res in developing countries represents a major challenge to researchers and policy-makers1, thus hampering eorts to target interventions eectively to areas of greatest need (e.g. African countries) [26, 40]. Recently, several researchers have started to use mobile phone data [8, 49, 76], social medi”
- Predicting poverty and wealth from mobile phone metadata (2015, background): “Inmuch of Africa, for instance, national statistics on economic production may be off by as much as 50% (3).”
- FinQA: A Dataset of Numerical Reasoning over Financial Data (2021, background): “Financial analysis is a critical means of assessing business performance, and the consequences of poor analysis can involve costs of billions of dollars (Jerven, 2013; MacKenzie, 2008).”
- Defining firm competitiveness: A multidimensional framework (2020, background): “While we highlight the importance of including firm level data in competitiveness analysis, we acknowledge well known concerns about the quality (and quantity) of firm level data available for many developing countries (e.g. Jerven, 2013).”