Editor's Note: This post originally appeared on Unreasonable.is.
By Rebecca Calder
“The poor” are an artificial construct.
Impossible! I hear you say. I know poor people! We employ poor people in our value chains! Poor people buy our products! Not only that, but we have social impact investors who give us money because of our great track record in reaching and impacting the lives of the poor! And maybe, just maybe, some of you have the data to “prove” it.
But before we get too excited, hear me out. By saying that “the poor” is an artificial construct does not mean that people don’t experience poverty, or that poor people don’t exist. The idea that “the poor” are an artificial construct is not new. In fact, my friend Charles Knox-Vydmanov highlights this in his paper, Why ‘The Poor’ Don’t Exist (And What This Means For Social Protection Policy). This idea stuck with me, so I’ll explain why I think it has validity, then talk about what this means for reaching poor adolescent girls as a distinct group.
In order to understand what is meant by the poor being an artificial construct, I’ll talk about three things: poverty dynamics, problems with poverty measurement and multi-dimensional poverty. Think of this as a sort of “Poverty 101” class, which you can use to think about your own business models, how you think about and measure social impact, and how you communicate with funders.
Poverty dynamics: First, what do we know about identifying the poor? Well, we know that when it comes to identifying or targeting, the poor are a moving target. This is because of something called volatility, or poverty churning. While those in deep poverty often stay there for many years, or even generations, those closer to the poverty line often dip in and out of poverty depending on a range of shocks and stresses experienced by households. In Indonesia, while the poverty rate is estimated to be 13.3 percent, the World Bank suggests that at least 43 percent of the population falls under the poverty line at least once between 2008 and 2010.
Indeed, World Bank research from both Pakistan and Indonesia suggests that households at 1.5 times the Poverty Line should be regarded as being at risk of spending some time in poverty over a three to five year period. This recognizes the fact that poverty is dynamic: people move into and out of poverty, and poverty is experienced frequently by those we call the poor, the vulnerable, the near poor, and a plethora of other terms!
Problems with poverty measurement: So, how do we identify the poor? The poor, the near poor and the non-poor don’t look all that different from one another—the differences in incomes between these groups are pretty infinitesimal. This is called a “flat distribution curve” in poverty analysis speak, and it is really common. The flat curve is illustrated in the below figure.
So, in Bangladesh, for example, while 32 percent of the population fall below the official poverty line, 80 percent of the population falls below two times the poverty line. And, there is very little difference between those 32 percent at the bottom, and those 48 percent just above them. You, and I, would not be able to see the difference at all if we visited these households, except perhaps at the extreme left of the distribution, where you have a relatively small group called the extreme poor, and at the far right, where you have the rich).
You might be thinking, “Well, you and I might not be able to tell the difference between a person who earns $1.98 per day, so officially below the $2-a-day poverty line, and a person who earns $2.05 a day, but surely with the right measurement tools — more sophisticated and objective than our own assessments — one can tell this?” Well, sorry to disappoint, but no.
Even the most sophisticated poverty measurement approaches employed by poverty-analysis gurus in the World Bank cannot tell us this. The most sophisticated approach that the WB has for identifying the poor is called the proxy means test (like means tests in formal economies, but instead of measuring income — difficult if not impossible in largely informal economies—it uses proxies for wealth such as housing materials, education of household head, assets, etc.). It has been shown that the proxy means test has huge errors associated with it — identifying those who are not poor as poor (an inclusion error), and those who are poor as not poor (an exclusion error). And by “huge errors,” I mean that if you were trying to identify the ten percent of the population who are the poorest, the errors would be in the order of 70 percent. A failure by any measure. This is illustrated in the below scatter graph.
In this scatter graph, everyone below the horizontal red line is assessed as being poor. Everyone to left of vertical red line is below the poverty line. So, everyone to the right of the vertical line and below the horizontal line has been wrongly identified as poor. Everyone above the horizontal line has been identified as non-poor; this is correct for those to the right of the vertical line, but not for those to the left, who are indeed poor. Accurate identification of the poor would see all of the dots tightly clustered in the bottom left box (the poor who are rightly included) and the top right box (the non-poor who are rightly excluded). But you can see the magnitude of the errors by looking at the top left box, and the bottom right box.
And don’t forget, as I discussed above, this is measuring poverty at one moment in time and doesn’t take into account the “churning” that is constantly going on around the poverty line. Think about the above scatter graph, but with all the dots moving!
Multi-dimensional poverty: Finally, when we look at poverty multi-dimensionally (by which I mean looking at aspects of poverty not captured by income measures, such as health and education outcomes, or access to clean water or housing quality), we see that there is no one group we can refer to as “the poor.” A group of “poor” identified using one approach, are often distinct from another group of “poor” identified using another approach. In other words, those who experience income poverty don’t necessarily experience poverty in other aspects of their lives.
The below figure shows that, while deprivations in different areas—such as housing and water and sanitation—can be experienced by the same group of “poor” (especially by the extreme poor), this is not always, or even usually, the case. Some “poor” don’t experience these deprivations, and some “non-poor” do. The below graphic taken from data in Indonesia shows how, for example, income poverty and deprivations in housing and in water and sanitation do not correlate perfectly.
First, as with the rest of “the poor” apart from the extremes, it is impossible to know which girls are living in poverty and which are not. Why is this? In summary:
Firstly, the base of the pyramid in the most developing countries is so wide that reaching ANY girls outside of the most advantaged (the girls in that far right hand side of the distribution) will mean you are reaching girls on less than $4/day, and probably some on less than $2 a day. If we look at the graphics below, we see that 63 percent, 67 percent and 82 percent of households in Uganda, Kenya and Rwanda are living on less than $2 a day. If you include households not officially “poor” but vulnerable to falling into poverty, say on $3 or $4 a day, practically the whole pyramid would be a base.
So, though we might not be talking about the very poorest and most vulnerable girls who are “last mile” (or the far left side of the distribution), we are talking about girls who are vulnerable, and who experience poverty.
Secondly, poverty is experienced multi-dimensionally, and is more than just a lack of access to financial resources. And this is as true for adolescent girls as for anyone else. When girls talk about their experiences of “poverty” or “vulnerability,” they do talk about inability to buy basic necessities such as food, school supplies, and sanitary pads, and inability to pay school fees and pay transport costs. But they also talk about safety concerns, about feeling isolated and not having friends or anyone to talk to, about being tired because they have so much household work to do, about feeling worthless and hopeless, about having no control over what happens to them and not being able to achieve their dreams. These much more multi-dimensional notions of poverty are not anything that we can measure monetarily, but they are just as meaningful, if not more so, to girls, and can be just as powerful areas of impact.
In closing, I want to restate that poverty is real and so are poor people, but also emphasize that “the poor” are not a static, identifiable group. Poverty is dynamic, and people experience different types of vulnerability and deprivation at different times. Measuring poverty is a difficult endeavor, and the outcomes inevitably inaccurate, so don’t to get too caught up in trying to accurately identify whether it is “the poor” or “poor girls” – defined by income or consumption measures – who are benefitting from your products and services. Impact on aspects of well-being beyond income and consumption are often much more meaningful and easier changes to measure. For example, measuring change for girls who are “off track” and therefore most as risk, such as girls out of school before or at the age of 15. We know that these girls are at the greatest risk of the worst possible outcomes, including early marriage, early pregnancy, HIV, and trafficking.
I intend to explore this and other girl impact metric issues in the next post, but please give me your questions and ideas below! In particular, what would you like me to talk about in relation to gender and poverty – and in particular adolescent girls – in future blogs?
Image credit: Flickr/Rod Waddington
Charts courtesy of Development Pathways
Rebecca is the technical director at Spring Accelerator—a business accelerator supporting products and services that could change the lives of adolescent girls. She has more than 20 years of experience as a gender and social development specialist and has produced a significant body of research that has informed policy for national governments and multilateral sponsors. She is also a Girl Metrics specialist, working with M&C Saatchi on the Girl Effect Accelerator.