Models and obstacles for targeting vulnerable groups — an economics and computer science perspective
Tags: Social Good, Inequality, Mechanism Design, Governmental Subsidies, Economics

Method 3 of 3: Ordeal Targeting

Both methods above involve active targeting the population of interest, that is, actively looking to identify the ones that must be served. An alternative approach is called ordeal targeting or self-targeting.

Here, the idea comes from analyzing the problem from another perspective: we want to give benefits to impoverished communities through some program, but such benefits could also be attractive for the non-poor. Self-targeting deals with this problem by imposing costs that would reduce the attractiveness of the program for the non-poor, so only the ones in need get access to these services. Hence, by designing a cost scheme which has a different impact on poor and non-poor, the targeting efficiency is improved. Long applications, waiting times and required visits on working times are examples of cost schemes that will in theory have different impact to each group, and are expected to make programs less attractive for the non-vulnerable population.

What is the problem with ordeal targeting?

One of the problems with ordeal targeting is that ordeals are usually unproductive, they generate little or no value. The improved accuracy on targeting should balance against such waste and loss on productive efficiency. A related concern is that the poor and vulnerable populations are the ones actually paying such costs, while it is the behavior of the non-poor driving the necessity to rely on such distortions. Moreover, such costs could discourage applications from the poorest, which is probably the population we should care more about.

Limits of policy implementation

There are some theoretical and empirical considerations that could make self-targeting fail. For example, if there is a technology available that could allow some people to avoid or reduce the costs imposed, then the procedure could be ineffective. Another reason that could make ordeal targeting unsuccessful is the implementation of exemptions, that is, even if an incentive scheme is properly designed so that the poor would self-select into the program but we allow people to skip such requirements then the targeting will fail anyways, allowing non-vulnerable population to easily apply. Indeed, Alatas et al. (2016) shows that the PKG program contained exemptions that made increasing targeting requirements unsuccessful.

Trade-offs in ordeal targeting

Another related aspect discussed by Kleven and Kopczuk (2011) is how stricter selection criteria and the amount of benefits interact with both types of selection errors. On one hand, more complexity or screening intensity could improve targeting accuracy, reducing both false positives and increasing true positives among the ones that apply but could at the same time reduce applications from the vulnerable population, which is undesirable. On the other hand, increasing the program benefit would increase the number of applications but could potentially increase false positives. Hence, there is a trade-off between these two dimensions that we need to balance in order to implement the best targeting policy possible.

Behavioral considerations

The game-theoretic or traditional approach to self-targeting basically concludes that increasing screening rigor improves provision to vulnerable populations since it discourages those defined as non-poor from applying for benefits. However, in practice, several programs present a problem of under-coverage, i.e. only a fraction of the eligible group defined as ‘poor’ is being served. Therefore, something is missing, and so increasing complexity and screening rigor could indeed have the unintended consequence of providing less resources to ones in need.

Figure from Session 2A: a summary of the traditional and behavioral approaches to self-targeting.

Why are eligible individuals not applying for benefits?

Poverty could impact the performance of people, hence negatively impacting their capacity to respond to the incentives and benefits available from certain programs. Indeed, Mani et al. (2013) showed that for farmers in India, decision-making was impacted by whether they were before (low income state) or after the harvest (higher income state). This suggests that the actual income level could impact the quality of people’s decision making, in that there are cognitive costs related to poverty. However, the general evidence on the impact of these costs is still mixed.

Another reason why people are not applying could be impatience. That is, impatient people could be less willing to apply for benefits because the waiting time is restrictively long for them. While a correlation between income and impatience is documented in the literature (Tanaka et al. (2010), Dohmen et al. (2018), whether there is a causal relationship and what is the mechanism behind this relation are still open questions.

Deservingness: are the poor culturally perceived as lazy or unlucky?

Depending on what is our perspective on why a particular population is poor, we will be more or less willing to provide support. When people misperceive the poor as lazy they are less likely to provide assistance. For example, we will be less willing to support people living in poverty if we think that there are job opportunities available to them that they are willfully not taking, or because of stereotypes and stigmatization about substance abuse. In contrast, we will be more willing to help the poor and support social programs designed for vulnerable populations when we perceive them as unlucky. For instance, we would be more willing to help if we perceive their poverty condition coming from an accident, an underlying health condition or a disability. Moreover, some authors argue that such differences could explain the differences in redistributive policies across countries (Piketty (1995), Alesina et al. (2001); Alesina and Angeletos (2005)).

In this sense, the way a situation is framed could impact the public’s willingness to support a program or population.

Final remarks

The theory used for rationalizing individual behavior is key to computing the expected consequences of a policy. Different reasons behind the vulnerability of individuals, as well as different evaluations of such circumstances, would determine both the effectiveness of the implementation of certain policies on reducing poverty, as well as determine the willingness of the public or authorities to implement such policies.

More accuracy is not necessarily better: if the poorest face any type of limitation, then improving accuracy could have the unintended consequence of excluding the ones most in need. Moreover, the possibility of manipulation in order to be eligible could even increase the differences between poor and non-poor populations.

There could be an important gap between theory and practice: sometimes carefully planned policies do not work due to unexpected obstacles, from individuals or from institutions, that distort the outcome predicted by the theory.

All the different approaches to targeting have their pros and cons, and they should be seen as complementary in the goal of helping the poor.

The discourse used about impoverished communities is fundamental to obtain support for certain programs or populations. An example is the way people with drug or alcohol abuse are seen: if they are seen as people having bad habits and making bad decisions, then the public will be less willing to help them. However, if we recognize that indeed those people have an underlying health condition, and are experiencing something outside of their control, then people could start acting more empathetic and supportive to them.

Moreover, a particular way of thinking about the poor would be strongly influenced by the norms and politics of a particular society, and not necessarily have to do with any intrinsic characteristic of a vulnerable population.

Due to the limited resources available to help people in need, targeting is crucial in order to focus such resources on helping the most vulnerable population. This is especially true in the current pandemic situation where not only is this group the one that suffers more, but also that the pandemic crisis has exposed even more people to being part of this disadvantaged group. Hence, knowing the targeting tools available and their limitations have become even more important today for helping the disadvantaged to succeed.

Our working group is currently considering theoretical and practical approaches to some of these questions — we welcome thoughts and suggestions!