I was part of a short, but interesting discussion last night regarding this very good article on the political implications of data analysis. The argument made (assuming I understood it correctly) was simply that statistical measures are inherently ideological since they impose a particular view of the world from one social group (us, the elite) on another (the non-elite). She takes this further, stating that though the voice of the elite can be heard through anecdotes (and opinionated blog posts), the experience of the non-elite relies on statistics and numbers. Statistics, then, is the language of power.
The conversation went further to discuss the implications of statistical methods themselves, particularly the measures of central tendency: the mean, median and mode. With perfectly symmetrical data, these measures are all the same, but, of course, no set of data is perfectly symmetrical, so that the application of each will produce different results. Though any responsible statistician would make statements of assumptions, limitations and appropriateness, with politics, these statements are overlooked and the method chosen is often that which best supports one’s political position, asking for trouble.
Moreover, the measure of central tendency itself in inherently flawed since it concentrates on the center and silences the extremes, supporting the status quo, or so it was argued. The choice of measure, I would argue, depends on the goals of the particular study. For example, a study which sought to determine if average graduation rates lower for blacks than whites would necessarily use a measure of central tendency, while a study on which students in a particular school are the least likely to graduate might look at outliers and extremes.
Either way, I agreed with the writer that, no matter what, we are influenced by our ideology. However, there is a difference between performing a study which seeks to maintain impartiality for the greater good and one which seeks to deceive in order to merely win a political battle, particularly among those who benefit from marginalizing, for example, the poor and disenfranchised.
However, I found this passage quite interesting and it can be applied to a post on this blog regarding what we do and don’t know about the poor:
Perhaps statistics should be considered a technology of mistrust—statistics are used when personal experience is in doubt because the analyst has no intimate knowledge of it. Statistics are consistently used as a technology of the educated elite to discuss the lower classes and subaltern populations, those individuals that are considered unknowable and untrustworthy of delivering their own accounts of their daily life. A demand for statistical proof is blatant distrust of someone’s lived experience. The very demand for statistical proof is otherizing because it defines the subject as an outsider, not worthy of the benefit of the doubt.
Part of my academic work focuses on the refinement of measurements of poverty. I am keenly aware of the “othering” of this process and how these measurements use a language of the educated elite (me) to speak for the daily experiences of people not like me.
This “othering” is not limited to statistics at all. Even merely referring to “the poor” is a condescending labeling of a group of people who are mostly powerless to speak for themselves within global power structures. Moreover, “the poor” ignores the diverse and varied experiences of most of humanity.
When I first entered the School of Public Health at UM, I was extremely uncomfortable with the language used in studies of ethnicity and public health in the United States. Studies would simply throw people into simplistic categories of black, white, hispanic, asian and “other” (whatever that is), ignoring the great diversity of people within, for example, urban slums. The method of categorization seemed to be a horrible anachronism and bought back awful memories of Mississippi. Simply putting people into neat categories risked continuing an already divisive view of the world.
However, the more I thought about it, the method is justified since we are looking at the effects of a racist view of the world on the very people who are the most burdened by it. Certainly, there are better ways of viewing the world, but when criticizing social power structures, it can be advantageous to speak its language. I still don’t like it, but I’m at least more understanding of it.
It’s a fine thread to walk. On the one hand, as advocates for “the poor,” we have to work within the very structures which oppress, exploit and ignore them. To succeed, however uncomfortable it may be, we may be required to adopt the language of those structures. On the other, we must remain aware of the potentially dire implications of the ways in which we describe those we advocate for and how they can be misused.
Often people will mention that we are “adapted” to do this or another thing, either indicating some crime of modernity (of course, ignoring the fact that a larger percentage of babies are surviving and people are living longer and healthier than at any time in human history) or trying to point out some example of the glaring perfection of our creation, with either an implicit or vocal reference to divine creation.
For example, obesity is attributed to fat and protein rich modern diets since we aren’t “adapted” to eat these types of foods (despite having found the food in East Africa so unpalatable that we had to learn to crush or cook it to digest it efficiently). Our bad disposition is blamed on a lack of sleep since we aren’t “adapted” to sleep as little as we do (this might be true). Most recently, a book writer blamed our problems with depression on a divorced relationship to nature, given that we are “adapted” to hunt and kill for food and then revel over the blood stained corpse (of course, the writer doesn’t consider that people in antiquity might have been depressed, too).
There may be some truth to some of this. However, “adaptation” implies something about the individual, when evolution, in fact, is about reproduction. We aren’t “adapted” to anything. Rather, certain traits are selected for based on the survival of at least two generations of living things, at least for complex social animals like ourselves.
Simply surviving as an individual does not insure the survival of a species. Living things must first survive long enough to reproduce and then, at least in humans, insure that the children make it to reproductive age. Human babies are horribly weak in contrast to sharks, which are ready to go even before they leave the mother. Further, in the case of humans, a full three generations must live at once to insure long term survival.
Thus, we maintain a tenuous relationship with out environment, where traits do not necessarily favor a single individual, but rather an entire family unit, and these traits may or may not imply perfect harmony with an environment, but rather do the job at least satisfactorily.
Nature cares little for quality as numerous examples throughout our physiology show. To claim that we are somehow “perfectly suited” to a specific environment is just simply wrong. Merely, we have come to a brokered peace (after generations of brutal trial and error, what we eat today is thanks to the deaths of millions, mostly children, who had to die to allow us to do so) with wherever we live in order to allow a few of our kids and grandkids to survive.
This, of course, has deep implications for public health. Some public health problems are known to be passed down from parents to children, but in a multi-generational evolutionary framework, it is possible that certain public health problems can be passed through 3 or more generations at a time, complicating interventions. Certainly, the multi-generational health problems of the descendants of African slaves can be an example of this. How can we intervene to protect the public health over a full century?
OK, back to work.
Same thing. Wrong way down an unmarked one way. Cop at the end. After arguing with him for a bit, I threw 1000 schillings at him and just left.
I was just reading a post from development economist Ed Carr’s blog, where he reflects on a book he wrote almost five years ago. Reflection is a pretty depressing excercise for any academic, but Carr seems to remain positive about his book.
He sums it up in three points:
“1. Most of the time, we have no idea what the global poor are doing or why they are doing it.
2. Because of this, most of our projects are designed for what we think is going on, which rarely aligns with reality
3. This is why so many development projects fail, and if we keep doing this, the consequences will get dire”
Well, yeah. This is a huge problem. In academics, we filter the experiences of the poor through a lens of academic frameworks, which we haphazardly impose with often no consultation with our subjects. Granted, this is likely inevtiable, but when designing public health interventions, it helps to have some idea of what the poorest of the poor do and why or our efforts are doomed to fail.
I remember a set of arguments a few years back on bed nets. Development and public health people were all upset because people were seen using nets for fishing. The reaction, particularly from in country workers was that poor people are stupid and will shoot themselves in the foot at any opportunity.
I couldn’t really understand the condescension and was rather fascinated that people were taking a new product and adapting it to their own needs. Business would see this as an opportunity and would seek to figure out why people were using nets for things other than malaria prevention and attempt to develop some new strategy to satisfy both needs (fishing and malaria prevention) at once. Academics simply weren’t interested.
To work with the poor, we have to understand them and understanding them requires that we respect their agency. If we don’t do this, we risk alienating the people we seek to help.
It is pretty obvious that after July, something happened and I stopped posting with any sort of regularity. I really need to fix this or whatever is keeping me from posting. I don’t get a whole lot of traffic on this blog, but it seems that every day I don’t post is a missed opportunity for me.
Anyway, to all of you who read this blog in 2014, I thank you. It’s great to have you around. I wish everyone a great 2015.
The WordPress.com stats helper monkeys prepared a 2014 annual report for this blog.
Here's an excerpt:
Madison Square Garden can seat 20,000 people for a concert. This blog was viewed about 62,000 times in 2014. If it were a concert at Madison Square Garden, it would take about 3 sold-out performances for that many people to see it.
African countries are blessed with ample cropland and resources, but suffer from crippling and unforgivable levels of poverty, have some of the shortest lifespans on the planet and the highest rates of infant mortality in the world. Meanwhile, Japan, Korea, Sweden, Switzerland and Singapore are wholly the opposite, yet mostly lacking in everything that Africa has. Clearly, the picture is more complicated than merely having access to a natural resources.
However, within countries, the picture might be different. African countries are complex and diverse places. Poverty is often confined to the most unproductive regions, areas with poor soils, poor rainfalls or dangerous terrains.
I was just working with some socio-economic data from one of our field sites, and noticed some interesting patterns (note the map up top). In Kwale, a small area along the Coast, socio-economic levels vary widely, but neighbors tend to be like neighbors and patterns of socio-economic clustering emerge.
Note that the poorest of the poor are concentrated to an area in the middle, which I know to be extremely dry, difficult to get to, difficult to farm and generally tough to live in.
I tried to see if socio-economic status (as measured through a composite material wealth index a la Filmer and Pritchett but using multiple correspondence analysis rather than PCA) was related to any environmental variables that I might have data for.
I fit a generalized additive model using the continuous measure of of wealth from the MCA as an outcome. Knowing that very few things in nature or human societies are linear, I also applied smoothing to the predictors to relax these assumptions. The results can be seen in the plot at the bottom.
A few interesting things came out. While it is hard to tell much about the poorest of the poor, we can tell something about the most wealthy. The richest in this poor area, tend to live in areas with the richest vegetation (possibly representing water), a high altitude (low temperature), high relief (no standing water) and in locations distant from a wildlife reserve (far from annoying and dangerous wildlife).
I’m not sure the wildlife reserve is meaningful (unless the reserve was an area undesirable for human habitation to begin with), but the others might be and represent a trend seen in other Sub-Saharan contexts. Areas without malarious swamps and ample farm land tend to do the best. Central Province, one of the most developed areas of Kenya, would be an example.
But the question has to be, does a harsh environment doom people to poverty, or do people self shuffle into and compete for access to more favorable areas? Is environmentally determined poverty (or wealth) an accident of birth, or the result of competitive selection?
Alright, back to work. Oh wait, this is my work. Well….