Poverty has been a topic of conversation throughout human history. As Ravallion (2016Ravallion, Martin. 2016. The Economics of Poverty: History, Measurement and Policy. 1st ed. New York, USA: Oxford University Press.) points out, Aristotle and Confucius discussed ideas about poverty. In fact, Aristotle’s ideas influenced Thomas Aquinas, one of the pillars of Western philosophy. Since then, societies changed, modifying the theories of justice underlying the idea of poverty.
As the concept and the ethics towards poverty change, so does its measurement. From basic measures like the headcount rate to more complex metrics, such as the Foster-Greer-Thorbecke (FGT) index, poverty measurement has evolved. Nowadays, poverty estimates are calculated using household surveys and censuses (Deaton 1997Deaton, Angus. 1997. The Analysis of Household Surveys: A Microeconomic Approach to Development Policy. World Bank Publications.). However, only (relatively) recently have the aspects of statistical inference combining such measures and complex sample survey designs been explored (for more discussion on this topic, see Deville (1999Deville, Jean-Claude. 1999. “Variance Estimation for Complex Statistics and Estimators: Linearization and Residual Techniques.” Survey Methodology 25 (2): 193–203. http://www.statcan.gc.ca/pub/12-001-x/1999002/article/4882-eng.pdf.), Berger and Skinner (2003Berger, Yves G., and Chris J. Skinner. 2003. “Variance Estimation for a Low Income Proportion.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 52 (4): 457–68. https://doi.org/10.1111/1467-9876.00417.), Bhattacharya (2007Bhattacharya, Debopam. 2007. “Inference on Inequality from Household Survey Data.” Journal of Econometrics 137. https://doi.org/10.1016/j.jeconom.2005.09.003.), Osier (2009Osier, Guillaume. 2009. “Variance Estimation for Complex Indicators of Poverty and Inequality.” Journal of the European Survey Research Association 3 (3): 167–95. http://ojs.ub.uni-konstanz.de/srm/article/view/369.) and references therein). These advances became even more important given the recent efforts in poverty mapping, an analytical method that combines poverty analysis and small area estimation, like Elbers, Lanjouw, and Lanjouw (2003Elbers, Chris, Jean O. Lanjouw, and Peter Lanjouw. 2003. “Micro–Level Estimation of Poverty and Inequality.” Econometrica 71. https://doi.org/10.1111/1468-0262.00399.), Bedi, Coudouel, and Simler (2007Bedi, Tara, Aline Coudouel, and Kenneth Simler. 2007. More Than a Pretty Picture: Using Poverty Maps to Design Better Policies and Interventions. World Bank Publications.) and Molina and Rao (2010Molina, Isabel, and J. N. K. Rao. 2010. “Small Area Estimation of Poverty Indicators.” Canadian Journal of Statistics 38 (3): 369–85. https://doi.org/https://doi.org/10.1002/cjs.10051.).
This chapter shows how poverty estimates and their sampling errors can be estimated using simple commands from the convey package.