Finding the Poor vs. Measuring Their Poverty: Exploring the Drivers of Targeting Effectiveness in Indonesia

Abstract

Centralized targeting registries are increasingly used to allocate social assistance benefits in developing countries. There are two key design issues that matter for targeting accuracy: (i) which households to survey for inclusion in the registry; and (ii) how to rank surveyed households. We attempt to identify their relative importance by evaluating Indonesia's Unified Database for Social Protection Programs (UDB), among the largest targeting registries in the world, used to provide social assistance to over 25 million households. Linking administrative data with an independent household survey, we find that the UDB system is more progressive than previous, program-specific targeting approaches. However, simulating an alternative targeting system based on enumerating all households, we find a one-third reduction in undercoverage of the poor compared to focusing on households registered in the UDB. Overall, there are large gains in targeting performance from improving the initial registration stage relative to the ranking stage.

Targeting Poor Students With Proxy Means Test

“…For a number of variables to be used as a poverty proxy, Grosh and Baker (1995) studied by using Living Standards Measurement Survey data in Jamaica, Bolivia and Peru, where they found that more information was preferred to less, although there were diminishing returns for more information added. This diminishing return also found in study by Bah, Bazzi, Sumarto, and Tobias (2018) where she found that out of 340 candidate variables, using a model random sampling method to select good predictions of household welfare in Indonesia, the best model to predict poverty in terms of undercoverage rate was the model with total 20 predicting variables. Also, Schreiner (2008aSchreiner ( , 2009Schreiner ( , 2010aSchreiner ( , 2011Schreiner ( , 2012aSchreiner ( , 2016 developed a Poverty Probability Index (PPI) to be used as a poverty scorecard by using only 10 household characteristic variables to simplify the questionnaire and to reduce cost of data collecting.…”

Rural poverty and labour force participation: Evidence from Indonesia’s Village fund program

“…This literature includes studies [ 33 , 34 ] using Randomised Control Trial (RCT) methods in a select group of villages, and nationally representative studies that have, for example the Askeskin and Jamkesmas programs [ 35 , 36 ] or the Raskin program [ 37 , 38 ]. Others evaluate the complementarity of various social welfare programs, and examine whether they reached the intended beneficiaries [ 39 , 40 ]. However, the focus of this literature has been on the targetting of social welfare programs for poor households, and not on a nation-wide reform of governance and fiscal transfers to rural villages.…”

Cash transfers in the context of carbon pricing reforms in Latin America and the Caribbean

“…This study addresses two limitations of the predominant proposal to combine carbon pricing with cash transfers in the context of low-and middle-income countries. First, while lump-sum cash transfers are effective in theory and may entail several benefits for economic development (Bastagli et al 2019), existing cash transfer programs may be imperfect instruments to channel money to households in practice: Existing cash transfer programs are imperfectly targeted (e.g., reach richer households), and suffer from imperfect coverage (e.g., do not reach all poor households) (Robles et al 2019;Bah et al 2019). It is therefore not clear if existing cash transfer programs can play the role in practice that lumpsum rebates play in theory (Malerba et al 2021;Vogt-Schilb et al 2019;Renner 2018).…”