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

By R. Garcia‐Verdu. 2013

527 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/db93d443ff5bfd381b31019c8751afcb251b604f

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Citations in the corpus

Predicting poverty and wealth from mobile phone metadata (2015, background): “In much of Africa, for instance, national statistics on economic production may be off by as much as 50% ( 3 ).”
Understanding the agricultural input landscape in Sub-Saharan Africa : recent plot, household, and community-level evidence (2014, unknown): “…from macro-level statistics, which cannot capture the heterogeneity in within-country conditions and are prone to issues of data reliability 1 (Jerven 2013), or studies using small or purposively chosen samples, which may not be reliably scalable for informing national-level policy priorities.”
The World Bank's Classification of Countries by Income (2016, background): “It is possible that, in some countries, under-estimation of formal activities may be as large as under-estimation of informal activities (Jerven, 2013).”
Do economists make policies? On the political effects of economics (2014, background): “GDP of a nation has effects both economic (for example, affecting eligibility for World Bank assistance; see Jerven, 2013) and geopolitical (China’s surpassing of Japan as the world’s second-largest economy was widely read as having military implications; e.g. Dawson and Dean, 2011).”
Aid and Growth at the Regional Level (2015, unknown): “24 Particularly in the developing world, uncertainty about official GDP estimates can be enormous (Henderson et al., 2012; Jerven, 2013).”
Global Oil Prices and Local Food Prices: Evidence from East Africa (2016, unknown): “error is a substantial concern (Jerven 2013).”
Structural Transformation in Africa: Static Gains, Dynamic Losses (2015, background): “…foundations underlying GDP and employment estimates in various African countries are subject to large measurement error, and have referred to these weak fundamentals for growth and productivity analysis as ‘Africa’s statistical tragedy’ (Devarajan, 2013; Jerven, 2013; Jerven & Johnston, 2015).”
Using night light emissions for the prediction of local wealth (2017, unknown): “In many developing countries, official economic statistics are either not available or of poor quality (Jerven, 2013).”
Left to Other Peoples’ Devices? A Political Economy Perspective on the Big Data Revolution in Development (2017, background): “In an era when the traditional apparatus for measuring social and economic life has been weakened by the legacies of structural adjustment policies (Jerven, 2012), one may think of the telecommunication network as a vast but diffuse surveillance infrastructure capable of generating ‘big data’…”
The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics (2014, unknown): “These numbers should be treated with caution though: while it is no surprise to see Mauritius score highly (76), it is perhaps surprising to see it tied with Nigeria, where Jerven (2013) documents considerable challenges in data reliability.”
Does Foreign Aid Target the Poorest? (2016, background): “Jerven 2013. surveys to produce estimates of the spatial distribution of wealth groupings across regions.”
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).”