Updating Poverty Estimates at Frequent Intervals in the Absence of Consumption Data: Methods and Illustration with Reference to a Middle-Income Country

Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Poverty Dynamics in India between 2004 and 2012: Insights from Longitudinal Analysis Using Synthetic Panel Data

“…12 Given these two assumptions, Dang et al (2014b) propose an approach to impute the poverty rate for round 2, where the parameter estimate 1 β and the distributions of the cluster random effects and the error term estimated from data in round 1 can be imposed on the data in round 2. Note that the standard errors of the imputation-based estimates can in fact be even smaller than that of the true (or design-based) rate if there is a good model fit (or the sample size in the target survey is larger than that in the base survey; see, e.g., Matloff, 1981).…”

“…Following the estimation procedures in Dang et al (2014b), our empirical implementation involves a two-stage process. First, we apply the estimated parameters from the 2004/05 round on the 2009/10 data to impute poverty for the latter.…”

To Impute or not to Impute? A Review of Alternative Poverty Estimation Methods in the Context of Unavailable Consumption Data

“…This assumption ensures that the estimated We note that survey-to-census imputation shares a similar issue as with across-survey imputation methods: the variables used in the imputation in both the (base) survey and the (target) census should have the same distribution. Perhaps we cannot overemphasize the importance of this condition, given both the theoretical results and empirical evidence (offered by Tarozzi and Deaton (2007) and Dang et al (2017b)). However, to our knowledge, few studies offer explicit 20 See, e.g., Bilton et al (2017) for a proposal to use a classification trees technique for poverty mapping, and Das and Chambers (2017) for alternative standard error formulae with the Elbers et al (2003) method.…”

“…15 While concerns exist that this assumption is likely to be valid only under normal circumstances, rather than during periods of fast (economic growth and) poverty reduction, it has been shown to hold during a period of dramatic economic growth in China and Vietnam where poverty incidence was cut by around half (Christiaensen et al, 2012). Furthermore, a weaker version of this assumption has been proposed and validated for data from various countries such as India, Jordan, and Vietnam (Dang et al, 2017a;Dang et al, 2017b;Dang and Lanjouw, in press). Yet, we would like to note that the validity of this assumption can be contextspecific, and it can be useful to check it using at least two previous rounds of household consumption surveys wherever such data are available.…”

“…This seemingly rather innocuous condition appears often taken for granted in many studies, but if it is not satisfied, it may result in severely biased estimates (Dang et al, 2017b) 16 . The intuition is rather straightforward: variables such as household size or labor force participation may be defined differently in a household consumption survey and a labor force survey, and the data can be collected accordingly in different ways.…”