Escaping the poverty trap

(I conned GrrlScientist into posting this on her Guardian blog)

Migrant Mother, Nipomo, California (1936)
Image: Dorothea Lange (1895-1965)
Common domain.
As an old fashioned liberal, I want us all to be happy, and for the State to play a role by giving us the opportunity to accomplish that. One way to increase general public happiness is to help them to climb out of poverty, something the last Labour government in the UK recognised was important when it created the Social Exclusion Unit (which later became a Task Force. Even the best of intentions run foul of Government Shuffling). But can social policy really make a difference to people’s lives and make them more prosperous?

Economic theory suggests that people can get caught in “poverty traps” — if a community isn’t prosperous enough, it can’t generate the economic momentum necessary to create wealth and escape poverty. Thus, a simplistic way of reducing poverty is to give money to the really poor, so that they can use it to generate their own wealth. But a lack of money isn’t the only reason people can’t get out of poverty traps. Ill health can also play a role: obviously you can’t work as efficiently if you’re sick. A new paper by Mateusz Plucinski, Calistus Ngonghala and Matthew Bonds uses a mathematical model to explore this problem, and to ask whether health interventions can be effective in alleviating poverty.
The authors look at how economic development is affected by epidemics. Their disease is a classical one that causes “SIS epidemics”. Basically, anyone can be susceptible or infected. If you’re infected, you either die or recover, after which you may become infected again. So, this model is appropriate for diseases like malaria, rather than ‘flu (although my guess is that it also works reasonably as a model for all the diseases floating around a population: if you recover from one, you may still come down with another). They also assume that your ability to generate wealth is affected by whether you’re ill. But they also assume that both transmission of disease and recovery rates depend on how rich you are — if you’re poorer, you’re more likely to catch a disease, and also less likely to recover from it.
All in all, this model is stacked against the poor. So it’s no surprise that if a community is poor enough and unhealthy enough, they can’t improve their lot. This is known as the “Poverty Trap”. The Poverty Trap can be represented graphically as the white area below:

Phase plot illustrating the effect of initial conditions on determining the stable equilibrium point to which the system relaxes in the long time limit (grey shaded region, development; unshaded region, poverty trap). DOI:10.1098/rsif.2011.0153
When a community moves from the white to the grey area in the graph, they are moving out of poverty. This is achieved in this model either by providing income (which moves them up the graph), or by providing health care (which moves them to the left). Obviously this could mean quite a substantial investment, and it also suggests that targeting those people who are worst-off is a bad idea — because they are the ones in the bottom right-hand corner.
That would be a bit depressing, but this model has a big flaw. It is deterministic, so it assumes that everyone reacts in the same way. Reality, of course, is different: people come down sick at different times, and the amount of money one earns also varies, depending on how the weather has affected the harvest, or how well the wider economy is doing, for example. So Plucinski and co. include this randomness, to see if this makes a difference.
We can get some feeling for what might happen if we visualise that the effect of this randomness is to move the population around the graph above. If a little more money comes in, the population moves up the graph and hence, can find itself out of the poverty trap. Of course, it could also be moved the other way, a bad run can make a previously healthy group crash into illness and poverty. The effect is to make things less certain, but not to fundamentally change the overall pattern. What this does mean, though, is that there is always a chance that a group can escape the poverty trap. It might need luck, but it’s possible:

Phase plot illustrating probabilities that an initial condition will lead to development or a poverty trap. Each solid line corresponds to a single probability of reaching either the development or poverty-trap equilibrium. The arrows indicate the quickest path to the development equilibrium. Note that this path depends on the status of the level of income and disease. DOI: 10.1098/rsif.2011.0153
How about interventions? A simple intervention is to apply a social safety net, so that a community that drops below a certain poverty or health threshold is rescued by pumping in aid (money or medical care!). Obviously, a population can now never be sucked into the poverty trap, so we know that it must escape (there is some basic mathematical theory that shows this), but how long will it take? The figure below shows this:

Phase plot illustrating the average time in years required to attain the development equilibrium from initial conditions that are reinforced by safety nets. DOI: 10.1098/rsif.2011.0153.
What’s important about this figure is that it also shows which interventions are most effective. The fastest change in time to development (and hence the optimal intervention) is perpendicular to the lines in the plot. So, for very poor communities, it appears that health interventions may be better: it is better to stop people becoming ill so that they can make money rather than to just give them money.
So, with a relatively simple model, the authors have shown that health interventions can be effective in helping to raise people out of poverty. But the model is rather abstract, and the ideas still have to be applied to reality. Although the general conclusion probably holds in practice, questions about the effectiveness of specific interventions and their cost, have to be considered.
Interestingly, although the authors are all based in the USA, they frame their analysis in terms of aiding communities in developing countries. But their analysis is generic: it will apply just as well to the US economy, and pulling poor American citizens out of poverty. With the recent health care debate over there, it’s interesting to see that there is evidence supporting the positive effects of providing health benefits for all. If the American Dream is to “make it” — to pull yourself out of poverty — this paper suggests that the government can help by making sure that the poor have adequate health care, so they are less sick, and so they are then able to earn their riches. This is a wonderful liberal irony: the best way to free people to fulfill their potential is for the state to intervene by giving them a helping hand.
Source:
Plucinski, M., Ngonghala, C., & Bonds, M. (2011). Health safety nets can break cycles of poverty and disease: a stochastic ecological model Journal of The Royal Society Interface DOI: 10.1098/rsif.2011.0153