Nicholas Freeland

Sometimes in life there are things that you know, instinctively, to be true; but you lack the proof with which to convince others. It is then particularly gratifying when the necessary proof emerges. I have experienced just such a moment of gratification with the appearance of Development Pathways’ paper on targeting effectiveness!

I have always felt that poverty targeting, in all its forms, is a fundamentally flawed approach to deliver social protection – see my blogs and paper on this. The new paper provides incontrovertible proof.

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.

Let’s look at each of these in turn, in the light of the evidence on targeting effectiveness presented in Development Pathways’ paper.

1) The poor represent a small residual group

It only makes sense to undertake the expensive and complicated attempt to target the poor if they represent a relatively small portion of the population. And yet, paradoxically, it is in exactly these circumstances that poverty targeting is most difficult, as the paper shows. In any case, it is absolutely clear from the paper that in all low- and most middle-income countries, the number of those who can be considered poor by any comprehensive metric is high: usually in the region of 80 per cent. It illustrates this with graphs of the wealth distribution in Brazil and South Africa, and with a telling figure showing incomes in purchasing power parity – an in actual dollars (to the right of the figures) – for five such countries:

In the USA, anyone living on less that US$10 a day would be considered extremely poor and the point of purchasing power parity is to reflect an equivalent standard of living in the USA. So, if some 80 per cent of the population is poor, it makes no sense to waste money on targeting: the cost of doing so would probably exceed the cost of including the extra 20 per cent in the programme, and you would lose many of the political economy benefits of an inclusive, universal approach. Conversely, if a much smaller proportion of the overall population is poor, then you still have a serious problem, because the paper shows clearly that poverty targeting is less and less accurate the lower the coverage – see point 5 below.

2) The poor will remain poor (and the non-poor will remain non-poor)

Poverty is extremely dynamic. The paper gives two examples of such churn in and out of poverty, from Uganda and Indonesia:

These graphs show that in Uganda, only 46 per cent of households that were in the poorest quintile in 2013 had been in the poorest quintile in 2011; and, in Indonesia, 48 per cent of households in the poorest quintile in 2015 had not been in that quintile just one year earlier. As the paper makes clear, all forms of poverty targeting would miss this: even the very best such programmes only undertake re-targeting every three or four years and, in the vast majority, the interval is between five and ten years (for instance, neither Pakistan nor the Philippines has updated its PMT since 2009). There is simply no extant example of a poverty-targeted approach that could capture such volatility in wealth.

3) Inequitable outcomes are acceptable

Even if it could, that would create another challenge. The main reason for such volatility is that, in most low- and middle-income countries, there is very little difference in the income (or consumption) of the poorest 80 per cent of the population,* so even a small change in income, caused by only a minor shock, can knock a household a long way down the wealth distribution. Conversely – and this is where the next challenge comes – making a cash grant to a selected poor household will inevitably propel it far up the wealth distribution, leap-frogging a number of almost equally poor households who are now poorer than the beneficiary household. And yet the beneficiary household is likely to go on receiving the same benefit for a number of years, while the now-poorer households get nothing. This is inequitable and frequently causes the resentment, jealousy and social tensions that are the inevitable by-products of poverty-targeting.

4) The resource envelope is fixed

If you assume that the fiscal space for social protection is fixed and immutable, then there is some justification in arguing that the limited resources should be focused on the poor. As Devereux** argues when presenting the case for targeting: “Given the reality of budget constraints, scarce public resources must be used optimally and allocated efficiently, where they can achieve the maximum impact. If poverty reduction is an objective of public policy, social spending should be directed towards the poor who need income support, not spread thinly over the entire population including to those who do not need it.” But it is wrong to assume that poverty reduction is the only – or even the main – objective of social assistance, and it is naïve to think that budgets are immutable: investment in social protection is a political choice. If politicians spy electoral benefit, the funding will inevitably follow. As a result, the value of transfers is likely to be much higher with universal programmes than with poverty-targeted ones, meaning – ironically – that the poorest do better out of such inclusive programmes than they do when they are specifically targeted.

5) You can accurately identify the poor

Finally, we come to what the Development Pathways’ paper demonstrates to be the killer assumption: that you can accurately identify the poor. It is abundantly clear that you cannot…or at least that current approaches cannot. The paper systematically analyses national household survey datasets from 23 countries and examines the targeting accuracy of 38 social protection schemes or targeting registries in those countries. It does this using two metrics of targeting effectiveness: (i) the proportion of households incorrectly excluded, when measured against the scheme’s intended coverage; and (ii) the degree of exclusion of the poorest 20 per cent of households from the scheme. Both give results that can only be described as lamentable, especially when considering schemes that aim to reach the poorest 25 per cent of the population or less (representing two-thirds of the programmes studied). In terms of exclusion against intended coverage, the very best performer amongst these has errors of 47 per cent, while 12 such schemes have exclusion errors above 70 per cent, 8 above 80 per cent and 5 above 90 per cent (meaning that fewer than one in ten of intended beneficiaries is actually selected). In terms of exclusion of the poorest 20 per cent of households, the results are similar: the very best programme excludes 46 per cent of the poorest quintile of households, while 12 schemes exclude more that 70 per cent of the poorest wealth quintile, 9 exclude more than 80 per cent and 4 exclude more than 90 per cent – indeed, the worst-performing manages to reach fewer than one in twenty of these poorest households!

The purpose of the paper is not to shame the worst performers, but to demonstrate that the whole approach is fatally flawed. Yet there is a remedy: to increase programme coverage and – ultimately – move to universality. The paper summarises this on a very instructive chart, showing the relationship between coverage and exclusion error:

The best-targeted programmes, using both metrics applied in the paper, are those which have higher coverage of their target populations. Thus, the best results using a PMT, though still not very good, are for the two such programmes where coverage exceeds 40 per cent of the intended population: the old age pension in Ecuador and Uruguay’s Asignaciones Familiares, both of which exclude “only” some 30 per cent of their intended recipients and 17 percent of the poorest quintile of households. Similarly, the best example of community targeting, admittedly out of an alarmingly poor set of results, is Rwanda’s Ubudehe classification, covering 30 per cent of the population, although this still excludes over half of those intended and half of the poorest quintile of households.

As coverage increases further, exclusion error decreases. South Africa’s Child Support and Old Age Grant, which are means-tested to exclude the most affluent, have over 70 percent coverage of their target populations: exclusion of the intended beneficiaries is only 13 percent and 8 percent respectively; and exclusion of the poorest quintile of households is zero in both cases – in other words every household in the poorest quintile is included, a remarkable achievement. As we move to the four universal programmes analysed in the paper, this finding is further reinforced. Bolivia’s Renta Dignidad and Bono Juancito Pinto, Georgia’s Old Age Pension and Mongolia’s Child Money Programme all exclude fewer than one in ten of their intended beneficiaries and fewer than one in ten of the poorest households.

The bottom line on targeting effectiveness? It does not work. The best way to help the poor is not to target them, but to move to higher-coverage programmes, and ideally to universal life-course approaches! I know I have said this before…but now I feel I have the evidence to prove it! Thank you, Development Pathways!

Notes

* It is much easier to distinguish the wealthiest 20 percent in such cases, which is probably why the high-coverage, affluence-tested programmes studied in the paper – in particular South Africa’s Old Age and Child Support Grants – perform so well.

** Devereux, Stephen, 2016. Is targeting ethical? Global Social Policy, I-16.