I just finished reading “Decolonizing the Mind,” a short book from perhaps Kenya’s greatest living writer, Ngugi wa Thiong’o. Ngugi is an interesting figure. Born into a peasant Kikuyu family in the fabricated colonial village of Kamiriithu in Central Province, he managed to take advantage of new educational opportunities during the colonial period and attended Makere University in Uganda and eventually Leeds in the UK. He returned to Kenya and eventually became Chairman of University of Nairobi’s Literature Department.
Though highly critical of colonialism, having been in the heart of the worst of Kenya’s experience with it, he was even more critical of Kenya’s post-colonial trajectory. He started a political theater in his hometown and was eventually jailed under the dictator Daniel Arap Moi.
In “Decolonizing the Mind,” Thiongo seeks to dissociate Kenya’s literature from that of the colonialists. He seeks to create a new African literature, by and for Africans. He would eventually abandon writing in English, choosing instead to write works in his native Gikuyu. Despite Thiongo’s call for an African literature, his European pedigree can’t be denied. He is Brechtian in both rhetoric and action. Hs politics are wholly Marxist and it can even be noted that his medium itself (the novel) is decidedly un-African. Moreover, despite his clear hostility to Europe and the United States, it is interesting the he would be jailed by his own countrymen and then would receive asylum and employment from the US.
I found his ideas of language, however, quite interesting. The colonialists, like the Americans, worked to debase indigenous cultural practices to further an imperialist agenda. Locals were weakened through the apparent dominance of English as a language for communication and business, and the language itself was presented in such a way that social hierarchies were reinforced.
This phenomenon continues to this day. Children are taught from an early age, to greet white people on the street with a scripted “How are you?” “I’m fine, and you?” The formal distance between the stilted Kenyan English spoken in Palirament and the guttural Sheng spoken on the streets of Nairobi is hardly an accident. English the language of oppression, control and government exploitation, and Sheng the language of resistance.
Given my recent experiences at Governmental and NGO meetings, however, what strikes me is how language continues to be used as a tool of control, but hat this vocabulary has been internalized by Kenyans themselves. I grit my teeth now when I head the term “capacity building,” which basically implies that people lack the capacity to help themselves without the good graces of NGOs and governmental organizations. It implies that people are helpless without the assistance of formal authoritarian structures. This is, of course, untrue (though one has to allow for the possibility that people often do things that run counter to their long term self-interest).
People may argue that the term is innocuous, but in my experience “capacity building” is often used in place of “training.” To me, words matter, and where “capacity building” carries with it the implications that there is an inherent defect to be rectified, training implies that the capability exists, but the knowledge not yet there. To put this in perspective, I don’t think that anyone would call any of my academic degrees to have been an exercise in “capacity building.” I can’t help but think that white people are trained, while black people are “capacity built.”
Worse yet is “gender empowerment,” which implies that women weren’t sufficiently capable of managing their own affairs prior to the arrival of some dubious microloan project. Again, in my experience, women all of the world are sufficiently empowered. It’s the men who need to be de-powered. The term is condescending and fails to appropriately recognize the inherent capabilities of individuals while at the same time avoids challenging the paternalistic structures which created economic disparities reprehensible practices like FGM, the buying and selling of women and the inability for women to hold men accountable for violence. In essence, the term blames the victim.
Both “capacity building” and “gender empowerment” reinforce the weakness of the individual and offer that the poor of Africa’s only hope lie in international organizations and their own authoritarian though wholly inept governments. It’s worth noting that the strategy is very similar to that of Christianity, which requires followers to believe themselves powerless and to blame for whatever awful fate has befallen them.
Sadly, both of these terms have worked themselves so deeply into the consciousness of people in SSA, that questioning their validity is futile, which is exactly the nightmare that Thiong’o writes of in “Decolonizing the Mind”. Pointing out that “training” is a more appropriate term than “capacity building” to locals will be met with black stares.
Ebola is a cool disease. It transmits among fruit bats in the area in and around the Central African Republic. Apes live in and under the trees the bats live in and ingest their feces. Humans who ingest the apes pick up the virus when slaughtering the animal, or so some think. The truth is that no one really knows for sure.
Contacts between humans is increasing as settlements expand and a demand for meat increases. Lacking access to formal methods of employment, individual sellers happily take advantage of market demand and a thinly profitable trade in bushmeat profulgates. Meat equals success and in the place of professionally or pastorally raised beef, which is mostly unavailable to poor people in countries like Liberia and Sierre Leone, people eat the monkeys, chimps and many other of our cousins which are able to harbor the many of the same pathogens we do.
One person gets sick. He or she has no access to formal care because his or her government can’t or won’t provide it so he remains at home. The family consults the local herbalist who provides some medications which offer temporary psychological relief but nothing more. As time ticks on, the victim becomes even sicker until the situation becomes so serious that the family has no choice but to carry their dying loved one to a health clinic 20 km away from their house. Along the way, everyone carrying him or her touches infected feces and vomit and three weeks later the process is repeated.
This could have all been avoided if rural economies were developed enough so that a mass migration to urban areas wasn’t necessary, had there been safer sources of meat available for an affordable price, were there sufficient jobs which wouldn’t necessitate the bushmeat trade, were the governments of Liberia and Sierre Leone effective enough to place a proper health facility close by to patient 0’s house and if health care was dependable enough to be able to spot and deal with an Ebola case.
Ebola is a conflation of ecology, economics, sociology, culture and politics, all mixed together to create conditions for one of the worst health crises the African continent has seen since HIV. It’s going to erase any of the gains of the past decade and collapse the already struggling health systems of some of the poorest places on the planet.
Meanwhile, the United States is having another 9/11 moment and this is where I’m starting to get quite concerned. Panic is about to become policy. Fears of global terrorism prompted our entry into Afghanistan, which might have been justified. But it also paved the way for the invasion of Iraq, which, from the beginning, was a disaster waiting to happen. Out of 9/11, we got the Patriot Act, a massive expansion in government powers to search, seize and detain and America stood by and allowed it to happen with little debate.
I am not a Libertarian, though keep getting accused of being one. I believe in public schools, public health care and government oversight of dangerous industries. So there. John Galt wouldn’t be much into me (but I guess from the far, far left anyone looks like a Libertarian).
I am, however, despite my leftist pedigree, quite concerned with the rights of individuals and the potential for panic and ignorance to lead to a rhetoric that can quickly spiral out of control and veer seemingly caring people away from the direction that the moral compass would normally point us in. I am remembering how many Americans supported torture during Bush II and wondered how many of them would support torture were it to be practiced on their own children. Though seemingly alarmist, I think that we need to be extremely careful.
Enough about me. The reality of Ebola is that it is a man-made crisis. Forest dwelling locals have eaten bushmeat for as long as humans have lived there but there is little evidence that there has ever been a large scale outbreak like the one we are currently experiencing (though history in Africa is often obscure). As I noted earlier, many forces are at play, all of which are associated with the rapid social change that Sub-Saharan African states are currently experiencing.
Some of these forces are inevitable. Population growth, as it did in Europe and Asia before, has led to the creation of mega-cities. The connections, however, between the rural and the urban, however have not been severed. People are going to do what they do, regardless of risk, particularly if they can make a buck meeting some market demand.
Some forces, though, are avoidable. While health care did not initiate the crisis, it helped drag it along. Liberia and Sierre Leone can boast to have two of the worst health systems in the world, but their poor capabilities are hardly unique in Sub-Saharan Africa. NGOs and missionary groups work to plug some of the gaps, but the reality is that without a concerted and proactive effort from the governments of those countries, the system will never improve. International funding is too poor and weak national economies and top heavy tax structures can’t adequately fund these systems domestically. Poor funding leaves many clinics, particularly those in rural areas where these outbreaks begin, without supplies, trained staff and diagnostic equipment. In Kenya, Malawi and Tanzania, I’ve seen more than one rural clinic without power or clean water. Worse yet, Ebola outbreaks, though devastating, are infrequent so that more pressing needs like malaria, diarrheal disease and HIV eat up the brunt of the already scarce funds clinics receive. Pathogens not only compete in the wild, but also for funding and support. This leaves many rural health workers without the protective gear they need, so that they work are the highest risk for death from diseases like Ebola.
What can we do? First, we can calm down. In the United States, the reality is that one of far more likely to be killed by an oncoming car than from Ebola and the probability of sustained transmission extremely low. Though people like to view domestic transmission events such as the one in Texas as failure, the reality is that public health and medical resources move much more quickly and effectively in Texas than in troubled Liberia. Much is made over Ebola’s lethality, but a patient who is found to be infected in the United States has a vastly higher likelihood of surviving than one in Liberia.
Second, leaders can stop spreading and capitalizing on misinformation. While attractive, promoting hysteria only leads to bad policy. The tendency in America is to view as some kind of apocalyptic movie scenario. While fun (not to me), the reality is that there are people in the world who are dying who shouldn’t be. Moreover, closing schools because someone knows someone who knows a Liberian is just simply unwise and counterproductive in the long term.
Third, the international community needs to engage the governments of Liberia and Sierre Leone to improve their public health infrastructure. This is not an easy task. The histories of working relationships of international health bodies and developing countries governments are fraught with failure. Mutual distrust, corruption and indifference of political leaders to the plight of their constituencies has created a mostly untenable system. However, providing supplies and training come at little cost is a mostly uncontroversial affair.
How long will this last? No one knows but it is inevitable that, even if this epidemic is brought under control, it certainly won’t be the last of its kind. We don’t have time to waste.
Tunga penetrans is native to South America, was brought to West Africa through the slave trade. In the mid 19th century it was brought on an English shipping vessel and made its way through trade routes and is now found everywhere throughout the continent.
Bacteria opportunistically invades the site and super-infections (multiple pathogens) are common. Victims suffer from itching and pain and multiple fleas are common. Due to the location of the bite, people often have trouble walking and due to the disgusting nature of the infection, victims are stigmatized and marginalized. Worse yet, the site can becomes gangrenous and auto-amputations of digits and feet and eventually death are not uncommon.
The Parliaments of both Kenya and Uganda have introduced bills in the past calling for the arrest of people suffering from jiggers. Of course, these ridiculous bills don’t come with public health actions to control the disease.
Jiggers are entirely preventable, treatable through either surgical excision or through various medications but risk factors for it are mostly unknown and the data contradictory and mostly inconclusive.
It sometimes occurs in travelers and is easily treated in a clinic on an outpatient basis but is a debilitating infection for poor communities. Thus, it is not taken seriously by international public health groups who choose to focus on big issues like HIV and malaria.
Jiggers are a classic example of the neglected tropical disease: it devastates the poorest of the poor but gets almost no attention from donors or the international press.
We gathered some data on jiggers back in 2011 along the coast of Kenya. Without presenting these results as official, I was drawn to the attached map.
Animals of various species have been implicated as reservoirs for the disease, most notably pigs and dogs. Less understood is the role of wildlife in maintaining transmission. On the map below, the large yellow dots represent cases. Note that they are nearly all located along the Shimba Hills Wildlife Reserve. I calculated the distance of each household to the park’s border (see the funny graph at the bottom), and found a graded relationship between distance and jiggers infections. Past 5km away from the park, the risk of jiggers is nearly zero.
What does this mean? I have ruled out domesticated animals, at least as a primary reservoir. People in this area tend to all own the same types and numbers of animals. Being Islamic, there are no pigs here, but dogs are found everywhere. Despite this, there are distinct spatial patterns which are associated with the park. Note that all of the cases are found between the parks border and a set of lakes, perhaps implying that certain wild animals are traveling there for water and food.
The ecology of jiggers is very poorly understood and, like many pathogens (like Ebola, for example), wildlife probably play an important role.
It’s worth paying me a lot of money to study it.
I’m reflecting on an interesting discussion that some of my colleagues and I had over dinner last night. We were discussing the Demographic Surveillance Program (DSS) that I am currently managing.
For those not familiar, DSS programs, generally speaking, do what you local city hall and health department do. They track births, deaths and migration and keep track of specific health outcomes such as cause of death and incidence of particular diseases. It’s pretty basic stuff, but outside the capacity of many developing countries, and the focus on health issues allows for deeper investigation of public health outcomes.
One of us, who is Kenyan, remarked that the DSS program needs to coordinate with the Kenyan government to satisfy specific data needs. I took issue with this idea.
First, as a program sponsored by a research institution, our number one priority should be the accommodation of research projects. A university receives public money to perform research projects which benefit and are directed by the scientists who run them.
Second, a research institution is not a development NGO that seeks to provide public services that countries are unwilling or unable to provide themselves. It is a fact that the Kenyan Government, like many African governments, has a poor record of providing public services. I would argue that while we should certainly offer results of our activities to the government and local stakeholders, that a university sponsored DSS should not be considered a substitute for that which governments should be doing themselves. The assumption is often that African government are incapable of doing things like tracking births and deaths, but the reality is that they are simply unwilling and the activities of NGOs often exacerbate this problem.
Third, government involvement in foreign sponsored research is a slippery slope. Many African governments are wholly or partially autocratic. They do not exist or function to benefit their people, but rather exist only to enrich themselves and maintain power. Academic research should, in no way, contribute to keeping autocrats in power and should operate independently from any political body. In fact, academia has a duty to question power both domestically and internationally. Unnecessarily submitting to government demands opens up a number of potentially problematic issues, including the suppression of results which are inconvenient to government bodies.
While research projects require government approval to function, the power of government to influence the course and outcomes of research should be limited. While many bureaucrats would take issue with this, the reality is we can always just go somewhere else.
Mostly what we’re left with is a convenience sample of some kind, usually determined by introductions from the survey workers themselves. It is absolutely the worst way to run a survey and the data is usually crap, but, worse yet, unverifiable crap.
Ideally, in a household level survey, we’d run in establish target areas for sampling, do a complete census on target areas and then perhaps take a random sample within those areas. At the minimum this would be a relatively decent approach.
Unfortunately, I often encounter one of two situations. The first is the convenience sample I mentioned above, which is inherently biased toward the social connections and thus the demographic of the survey workers themselves. If you want to do a sample of someone’s friends and family, this might be a good start, otherwise its completely awful.
The second is the “school based survey,” a design I think I hate more than all others. This travesty of sample design depends on the good graces of families which send their children to school, being lucky that the kids you are interested in show up to school the day of the survey and reasonable connections with school administrations. Worse yet, if you’re doing a survey on health, the chances that you’ll the kids you’re really interested in at school is really low. People love this awful design because it’s convenient, cheap, can be done in a short time and has the added benefit of providing one with warm feelings.
I’ve resolved myself to do neither of these again. As the manager of a Health and Demographic Surveillance System based in Kenya which monitors more than 100,000 people in two regions of Kenya, I decided I have a unique opportunity to do something a little more interesting.
In gearing up for a pilot survey to improve measurement of socio-economic status in developing country contexts, I realized that I had an incredible set of resources at my disposal. I have a full sampling frame on two sets of 50,000 people in two areas of Kenya, basic demographic information and a competent staff with sufficient time to do a project which otherwise would interfere with their regular duties.
With some help from a friend (well, much more than a friend), I maneuvered the basic of complex survey design and came up with something that might work relatively well for my purposes.
The DSS of the area of Kwale, Kenya I’m working in is divided into nine areas, each delegated to a single field interviewer who visits each of the households three times a year. Each field interviewer area is then divided into a number of subgrids, the number of which arbitrarily follows the population surveyed and the logistics of the survey rounds. Some areas are easier to survey than others. Each grid then has a number of households within them, the number of which varies depending on population density.
I want to target three areas, each of which ostensibly will represent different levels of economic development, but in reality represent different types of economic activities and lifestyles. One is relatively urbanized, another is purely agricultural and the third is occupied by agro-pastoralists who keep larger herds of large animals.
I then decided to choose 20 grids in each area at random, and then want to select up to 10 households from each selected grid again at random. The reason for choosing this strategy was purely a logistic one. Survey workers can do about 10 households in a day and I’ve given them a month (20 working days) to do it before they have to start on their next round of regular duties. Normally, I’d like to do something fancier, but without any previous data on the variables I’m interested in, it just wasn’t possible.
I have discovered that this design is called a stratified two stage cluster design which makes it all sound fancier that I really believe it to be. The advantage to using this design is that I’m able to control for the selection probabilities, which can bias the results when doing statistical tests. I have no doubt that the piss poor strategies I’ve used in the past and the dreaded “school based survey” I mentioned above are horribly biased and don’t really tell us a whole lot about whatever it is we’re trying to find out.
I used the survey package in R to determine the selection probabilities and, as I suspected, found that the probability of selection is not uniform across the sampling frame. Some households are more likely to be included in the survey, biasing the data in favor of, for example, people in more densely populated areas.
Alright, enough for now….
I was just screwing around with some data we collected a bit ago. In a nutshell, I’m working to try to improve the way we measure household wealth in developing countries. For the past 15 years, researchers have relied on a composite index based up easily observable household assets (a la Filmer/Pritchett, 2001).
Enumerators enter a households and quickly note the type of house construction, toilet facilities and the presence of things like radios, TVs, cars, bicycles, etc. Principal Components Analysis (PCA) is then used to create a single continuous measure of household wealth, which is then often broken into quantiles to somewhat appeal to our sense of class and privilege or lack thereof.
It’s a quick and dirty measure that’s almost universally used in large surveys in developing countries. It is the standard for quantifying wealth for Measure DHS, a USAID funded group which does large surveys in developing countries everywhere.
First, I take major issue with the use of PCA to create the composite. PCA assumes that inputs are continuous and normally distributed but the elements of the asset index are often dichotomous (yes/no) or categorical. Further, PCA is extremely sensitive to variations in the level of normality of the elements used, so that results will vary wildly depending on whether you induce normality in your variables or not.
It’s silly to use PCA on this kind of data, but people do it anyway and feel good about it. I’m sure that some of the reason for this is the inclusion of PCA in SPSS (why would anyone ever use SPSS (or PASW or whatever it is now)? a question for another day…)
So… we collected some data. I created a 220 question survey which asked questions typical of the DHS surveys, in addition to non-sensitive questions on household expenditures, income sources, non-observable assets like land and access to banking services and financial activity.
The DHS focuses exclusive on material assets mainly out of convenience, but also of the assumption that assets held today represent purchases in the past, which can act as pretty rough indicators of household income. So I started there and collected what they collect in addition to all my other stuff.
This time, however, I abandoned PCA and opted for Multiple Correspondence Analysis, a technique similar to PCA but intended for categorical data. The end result is similar. You get a set of weights for each item, which (in this case) are then tallied up to create a single continuous measure of wealth (or something like it) for each household in the data set.
Like PCA, the results are somewhat weak. The method only captured about 12% of the variation in the data set, which sort of begs the question as to what is happening with the other 88%. However, we got a cool graph which you can see up on the left. If you look closely, you can see that the variables used tend to follow an intuitive gradient of wealth, running from people who don’t have anything at all and shit in the shrubs to people who have cars and flush toilets.
We surveyed three areas, representing differing levels of development. Looking at how wealth varies by area. we can see that there is one very poor area, which very little variation to the others which have somewhat more spread, and a mean level of wealth that is considerably higher. All of this agreed with intuition.
“Area A” is known to be very rural, isolated and quite poor. Areas B and C are somewhat better though they are somewhat different contextually.
My biggest question, though, was whether a purely asset based index can truly represent a household’s financial status. I wondered if whether large expenditures on things like school fees and health care might actually depress the amount of money available to buy material items.
Thus, we also collected data on common expenditures such schools fees and health care, but also on weekly purchases of cell phone airtime. Interetinngly we found that over all the two were positively correlated with one another, suggesting that higher expenses do not depress the ability for households to make purchases, but found that this relationship does not hold among very poor households.
There is nothing to suggest that high expenses are having a negative effect on material assets among extremely poor households located in Area A at all. It might be the case that there is no relationship at all. This could indicate something else. Though overall there might not be a depressive effect of health care and school costs on material purchases, they might be preventing households from improving their situation. It might only be after a certain point that the two diverge from one another and households are then able to handle paying for both effectively.
Also of interest were the similar patterns found in the three areas.