Journal of Mathematics and Statistics

Toward a Hierarchical Bayesian Framework for Modelling the Effect of Regional Diversity on Household Expenditure

Pudji Ismartini, Nur Iriawan, Setiawan and Brodjol Sutijo Supri Ulama

DOI : 10.3844/jmssp.2012.283.291

Journal of Mathematics and Statistics

Volume 8, Issue 2

Pages 283-291


Problem statement: Household expenditure analysis was highly demanding for government in order to formulate its policy. Since household data was viewed as hierarchical structure with household nested in its regional residence which varies inter region, the contextual welfare analysis was needed. This study proposed to develop a hierarchical model for estimating household expenditure in an attempt to measure the effect of regional diversity by taking into account district characteristics and household attributes using a Bayesian approach. Approach: Due to the variation of household expenditure data which was captured by the three parameters of Log-Normal (LN3) distribution, the model was developed based on LN3 distribution. Data used in this study was household expenditure data in Central Java, Indonesia. Since, data were unbalanced and hierarchical models using a classical approach work well for balanced data, thus the estimation process was done by using Bayesian method with MCMC and Gibbs sampling. Results: The hierarchical Bayesian model based on LN3 distribution could be implemented to explain the variation of household expenditure using district characteristics and household attributes. Conclusion: The model shows that districts characteristics which include demographic and economic conditions of districts and the availability of public facilities which are strongly associated with a dimension of human development index, i.e., economic, education and health, do affect to household expenditure through its household attributes."


© 2012 Pudji Ismartini, Nur Iriawan, Setiawan and Brodjol Sutijo Supri Ulama. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.