Thursday, March 21, 2019

The Ugly Secret About Needs-Based Poverty Eradication: It Doesn't Work

Nicholas Freeland takes us through the preconditions needed for poverty targeting to work (excerpt):

Poor targeting: a response to Pathways’ paper on how best to reach those in poverty

In an ideal world, poverty targeting would be sensible, which is why it intuitively appeals to those who are new to social protection, or who don’t understand the true complexities of poverty. I would even go so far as to accept that, if all of five conditions were met, there are circumstances in which poverty targeting might be the optimal choice. But we don’t live in an ideal world, and Development Pathways’ paper clearly demonstrates how very unlikely it is – in the real world – to fulfil each one of those five conditions, let alone meet all five simultaneously!

For poverty targeting to be the best option, all of the following five assumptions need to hold true:

  • The poor represent a small residual group;
  • The poor will remain poor (and the non-poor will remain non-poor);
  • Inequitable outcomes are acceptable;
  • The resource envelope is fixed; and,
  • You can accurately identify the poor.

Why does this matter? You can read the Pathway working paper here. From the executive summary (excerpt, emphasis added):

Hit and Miss: An Assessment Targeting Effectiveness in Social Protection

…The research examined the effectiveness of poverty targeting by assessing programmes or registries targeted at the poorest 25 per cent or less of their intended category. When tested against their effectiveness in reaching their intended recipients, the errors were high across all programmes and registries errors. Brazil’s Bolsa Familia scheme – which uses a simple means-test – was the most effective, yet still excluded 44 per cent of its intended recipients. The worst performing programme was Rwanda’s Vision 2020 Umurenge Programme (VUP) which employed community-based targeting: its exclusion error was 97 per cent. It was closely followed by Guatemala’s Mi Bono Seguro scheme, which uses a proxy means-test and had an error of 96 per cent among intended recipients. In fact, out of 25 programmes or registries with coverage under 25 per cent, 12 had exclusion errors above 70 per cent and 5 had errors above 90 per cent….

…There are many reasons for the limited effectiveness of poverty targeting and, often, they are particular to the specific scheme and the local design of the mechanism. However, a key cause of ineffective poverty targeting underpins all mechanisms everywhere: the belief that there is a fixed group of the population called ‘the poor.’ In reality, this is a fictional construct and, therefore, not a good basis for determining social policy, including who should be targeted by social protection schemes.

The main purpose of poverty targeting, apart from pandering to the prejudices of taxpayers, seems primarily to be saving money. There are mainly two types of errors with any targeting scheme – inclusion (giving to those who don’t “deserve” it) and exclusion (missing out those who need the assistance). Inclusion errors tend to be easier to deal with, since evidence is typically concrete, visible, and on record (asset ownership for example). Exclusion is much, much harder, and focusing on one of these errors generally means relaxing constraints on the other – worrying about exclusion means potentially allowing more of the “undeserving” access to benefits, and vice-versa.

Increasingly, I’m finding the evidence supports high-coverage or universal programmes. These can achieve better results, and equitability can be maintained through higher taxation, with much the same effect as intended by targeted programmes. Of course, the political economy of wider coverage/higher taxes versus targeted coverage/lower fiscal outlay are radically different, even if they are designed to have the same redistributive effect.


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  5. I think it is better if you can examine whether such program is successful or otherwise in Malaysia. In fact, we have successfully reduce a huge chunk of poverty rates to just a few percentage.

  6. @OneOmar

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