Poverty mapping

Nikos Tzavidis, CCSR.

A Google search of the term poverty mapping returns the following Wikipedia definition:

'Poverty mapping is a very powerful tool of targeting mechanisms. It provides a detailed description of the spatial distribution of poverty and inequality within a country. It combines individual and household (micro) survey data and population census (macro) data with the objective of estimating welfare indicators for specific geographic areas as small as village or hamlet.'

I will start by presenting the historical origins of poverty research and the more recent interest in estimating poverty at disaggregated geographical levels. Although the aim is not to present the 'best' way of measuring poverty, a review of the most popular poverty measures is provided.

Poverty mapping is synonymous to small area estimation. A non-technical description of some popular small area models alongside the data requirements for estimating these models and for performing poverty mapping will be presented. Having defined the targets of estimation, the data requirements and appropriate statistical models, I will then review three poverty mapping methodologies that have been at the centre of academic literature and applied work.


The practice of poverty mapping has been dominated by the World Bank method (Elbers et al., 2003). More recently researchers in small area estimation have extensively studied the World Bank method and have proposed alternative small area models for poverty mapping.

Two recent methods are the Empirical Best Prediction (EBP) approach (Molina and Rao, 2010) and the M-quantile approach (Chambers and Tzavidis, 2006 and Tzavidis et al., 2010).

I will conclude the talk by presenting an application of poverty mapping to EU-SILC data from Italy and the current trends in methodological work and software development.

EU funded project websites (online reading material and software)

Articles in peer reviewed journals

  • Elbers, C., Lanjouw, J. O. & Lanjouw, P. (2003). Micro-level estimation of poverty and inequality. Econometrica, 71, 355-364
  • Chambers, R. and Tzavidis, N. (2006). M-quantile models for small area estimation. Biometrika ,93, 255-268
  • Molina, I, and Rao, J.N.K. (2010). Small area estimation of poverty indicators. To appear in the Canadian Journal of Statistics
  • Tzavidis, N., Marchetti, S., and Chambers, R. (2010). Robust estimation of small area means and quantiles. To appear in the Australian and New Zealand Journal of Statistics.

PDF slides

Download PDF slides of the presentation 'What is poverty mapping?'