{"id":21137,"date":"2015-05-18T05:00:09","date_gmt":"2015-05-18T11:00:09","guid":{"rendered":"https:\/\/tested-neuron.flywheelsites.com\/?p=21137"},"modified":"2015-06-05T19:26:32","modified_gmt":"2015-06-06T01:26:32","slug":"poor-girls-and-girl-poverty-debunking-artificial-constructs","status":"publish","type":"post","link":"https:\/\/unreasonablegroup.com\/articles\/poor-girls-and-girl-poverty-debunking-artificial-constructs\/","title":{"rendered":"Poor Girls and Girl Poverty: Debunking Artificial Constructs"},"content":{"rendered":"

\u201cThe poor\u201d are an artificial construct.<\/strong><\/p>\n

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 \u201cprove\u201d it.<\/p>\n

The poor are an artificial construct.<\/i> Tweet This Quote<\/a><\/span><\/p><\/blockquote>\n

But before we get too excited, hear me out. By saying that \u201cthe poor\u201d is an artificial construct does not mean that people don’t experience poverty, or that poor people don’t exist. The idea that \u201cthe poor\u201d are an artificial construct is not new. In fact, my friend Charles Knox-Vydmanov highlights this in his paper Why \u2018The Poor\u2019 Don\u2019t Exist (And What This Means For Social Protection Policy)<\/a><\/em>. This idea stuck with me so I\u2019ll explain why I think it has validity, then talk about what this means for reaching poor adolescent girls as a distinct group.<\/p>\n

In order to understand what is meant by the poor being an artificial construct, I\u2019ll talk about three things: poverty dynamics, problems with poverty measurement, and multi-dimensional poverty. Think of this as a sort of \u201cPoverty 101\u201d 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.<\/p>\n

Poverty dynamics:<\/strong> 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.<\/p>\n

Poverty is dynamic. People move into and out of poverty<\/i> Tweet This Quote<\/a><\/span><\/p><\/blockquote>\n

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!<\/p>\n

Problems with poverty measurement:<\/strong> 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\u2014the differences in incomes between these groups are pretty infinitesimal. This is called a \u201cflat distribution curve\u201d in poverty analysis speak, and it is really common. The flat curve is illustrated in the below figure.<\/p>\n

\"The<\/a>

The cumulative distribution of consumption in Bangladesh, from the poorest household to the richest (BNLP = Basic Needs Poverty Line)<\/p><\/div>\n

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).<\/p>\n

You might be thinking, \u201cwell, 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\/day poverty line, and a person who earns $2.05\/day, but surely with the right measurement tools\u2014more sophisticated and objective than our own assessments\u2014one can tell this?\u201d Well, sorry to disappoint, but no.<\/p>\n

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\u2014difficult if not impossible in largely informal economies\u2014it 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<\/a>\u2014identifying 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.<\/p>\n

\"Proxy<\/a>

Proxy means test:?Theoretical exclusion and inclusion errors (Bangladesh)<\/p><\/div>\n

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.<\/p>\n

When we look at poverty multi-dimensionally, we see that there is no one group we can refer to as \u201cthe poor.\u201d<\/i> Tweet This Quote<\/a><\/span><\/p><\/blockquote>\n

And don\u2019t forget, as I discussed above, this is measuring poverty at one moment in time and doesn\u2019t take into account the \u201cchurning\u201d that is constantly going on around the poverty line. Think about the above scatter graph, but with all the dots moving!<\/p>\n

Multi-dimensional poverty:<\/strong> 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 \u201cthe poor.\u201d A group of \u201cpoor\u201d identified using one approach, are often distinct from another group of \u201cpoor\u201d identified using another approach. In other words, those who experience income poverty don’t necessarily experience poverty in other aspects of their lives.<\/p>\n

The below figure shows that, while deprivations in different areas\u2014such as housing and water and sanitation\u2014can be experienced by the same group of \u201cpoor\u201d (especially by the extreme poor), this is not always, or even usually, the case. Some \u201cpoor\u201d don’t experience these deprivations, and some \u201cnon-poor\u201d 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.<\/p>\n

\"Multi-dimensional<\/a>

Multi-dimensional poverty correlation<\/p><\/div>\n

What does this mean for girl poverty and for working with \u201cpoor girls?\u201d<\/h2>\n

What are the implications for the slipperiness of poverty as a concept and the inability to accurately identify the poor on how we understand girl poverty and work with \u201cpoor girls\u201d?<\/p>\n

First, as with the rest of \u201cthe poor\u201d apart from the extremes, it is impossible to know which girls are living in poverty and which are not. Why is this? In summary:<\/p>\n