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Coping with intelligence deficits in poverty-alleviation policies in low-income countries

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Abstract

Poverty-alleviation initiatives in lower-income countries are challenged by intelligence deficits that cause suboptimal designs that threaten their effectiveness, targeting, and sustainability. The uncertainty of theory and information, the adverse consequences of conventional family-level “means testing,” and unpredictable future events and conditions call for auto-targeting and auto-correcting policy designs with built-in adaptive capacity. Numerous categories and examples of these designs from multiple countries are presented.

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Notes

  1. Although no measures can capture the full complex of intelligence strengths and weaknesses for particular countries, the World Bank’s Knowledge Assessment Methodology (KAM) (World Bank Institute Passerini 2015; Chen and Carl 2005) generated a Knowledge Economy Index (KEI) (though discontinued in 2012 after publishing the ratings for that year) designed to capture broad aspects of knowledge infrastructure along multiple dimensions. For the purpose of confirming the greater intelligence challenges of lower-income countries, this article, based on the 129 countries with reliably matching KEI ratings and GDP per capita, finds a correlation of 0.88 between KEI and the log10 of GDP per capita. Note that the focus is on lower-income countries, as this article eschews the contested term “developing” (Lepenies 2008).

  2. “Auto-targeting” is a somewhat awkward term, but the commonly used term “self-targeting” is ambiguous, because it can also refer to targeting relying on the individual choosing to make use of the benefit himself or herself.

  3. It should be noted, however, that other criteria of deservingness besides deprivation or need exist; sometimes disdained groups—of the “wrong” ethnicities, practices, or beliefs—may be denied eligibility (Ascher 2020, chapter 3).

  4. Ravallion (1989, 1223) reviews the prospects for targeting benefits to landless or land-poor rural families. He notes that “It is now common for poverty alleviation schemes in Bangladesh, India, and elsewhere to be targeted toward landless or near landless households. Policy instruments include grants, loans, food subsidies and employment on public works.”

  5. The usefulness criterion is in keeping with the pragmatist tradition of John Dewey (Hickman 1990), which is the philosophical basis of the policy sciences.

  6. This last caveat is to acknowledge that a very tight targeting may erode the support for the continuation of the policy or program (Pritchett 2005).

  7. Personal communication, Prof. Dileni Gunewardena, January 26, 2017.

  8. Even for means testing, the median is roughly six percent (Devereux et al. 2017; Grosh 1994).

  9. No consensus exists on a categorization of targeting mechanisms. Devereux et al. (2017, 167), for example, separate means testing, proxy means testing, categorically targeting, geographically targeting, community-based targeting, self-targeting and multiple mechanisms. Grosh (1994, 38) distinguishes among individual assessment, self-selection, and categorical/geographic targeting.

  10. Another geographic targeting approach, explained below, limits the areas where poverty-alleviation programs are applied, but provides benefits selectively within those areas.

  11. Greenhill and Rabinowitz (2017, 16) summarize: “Research on the incidence of universal subsidies applied to the consumption of goods and services suggests that these can be highly regressive, due to the fact that higher income groups have higher levels of consumption and therefore capture a greater share of these subsidies.”

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Ascher, W. Coping with intelligence deficits in poverty-alleviation policies in low-income countries. Policy Sci 54, 345–370 (2021). https://doi.org/10.1007/s11077-020-09412-0

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