Income vulnerability of rural households in Bangladesh

a comparison between Bayesian and classical methods

verfasst von

Md Ershadul Islam, Ulrike Grote, Md Israt Rayhan

Abstract

The geographical location and the monsoon climate render Bangladesh highly vulnerable to natural hazards, deteriorating the country's socio-economic stability. This study is based on 500 randomly chosen rural households from the Household Income and Expenditure Survey [Bangladesh Bureau of Statistics, Planning Division, Ministry of Planning, Government of the People's Republic of Bangladesh, Dhaka, 2006]. The objectives are to estimate the income vulnerability of rural households and to check whether the Bayesian approaches (natural conjugate prior and non-informative prior estimates) have any superiority over the classical (feasible generalized least square (FGLS)) method. The poverty level, measured from the data, is 24%; whereas the vulnerability estimates, using FGLS, natural conjugate prior and non-informative prior are 31%, 69% and 82%, respectively. Vulnerability estimates by the Bayesian natural conjugate prior approach is found to have greater efficiency compared with FGLS and non-informative prior approaches.

Organisationseinheit(en)

Institut für Umweltökonomik und Welthandel

Externe Organisation(en)

University of Dhaka

Typ

Artikel

Journal

Journal of Statistical Computation and Simulation

Band

83

Seiten

1179-1187

Anzahl der Seiten

9

ISSN

0094-9655

Publikationsdatum

2013

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Statistik und Wahrscheinlichkeit, Modellierung und Simulation, Statistik, Wahrscheinlichkeit und Ungewissheit, Angewandte Mathematik

Ziele für nachhaltige Entwicklung

SDG 13 – Klimaschutzmaßnahmen

Elektronische Version(en)

https://doi.org/10.1080/00949655.2012.656310 (Zugang: Geschlossen)