Global poverty: A first estimation of its uncertainty
Michail Moatsos and
World Development Perspectives, 2021, vol. 22, issue C
The dollar-a-day method, applied in monitoring the UN’s development goals against poverty, provides no confidence interval for the official figures of global poverty reduction, a practice that does not allow statistical testing. Using Monte Carlo micro-simulations we construct confidence intervals that reflect the error introduced by the process of determining the International Poverty Line, as well as the uncertainty of the involved Purchasing Power Parity exchange rates. These estimates identify a reduction of 5.19% between 1990 and 2015 at 95% confidence level, in stark contrast with the remarkable 73% reduction of global poverty reported in the World Bank official statistics published on September 18, 2018. At the same time, MDG1 obtains with a 80% confidence level. The cost-of-basic-needs method paints a more promising picture identifying a 35.71% reduction at 95% confidence level, while the confidence level at which poverty in 2015 was half of 1990 stands at 46%. We conclude that the derivation method of the international poverty line introduces high levels of uncertainty in the estimates.
Keywords: Global Poverty; MDG1; Cost of basic needs; dollar a day; total error; confidence interval (search for similar items in EconPapers)
JEL-codes: I32 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:wodepe:v:22:y:2021:i:c:s2452292921000291
Access Statistics for this article
World Development Perspectives is currently edited by Ashwini Chhatre
More articles in World Development Perspectives from Elsevier
Bibliographic data for series maintained by Catherine Liu ().