Reassembling Rubbish

How do we know e-waste? Electronic discards and the double social life of trade statistics.

Another important genre of knowing e-waste is the use of trade statistics. In a forthcoming paper (Lepawsky, 2014) I describe some of the key difficulties in measuring the international trade and traffic of e-waste using such data so that I can then go on and map their patterns (see here and here). I'll summarize those difficulties, but hold in the back of your mind that adjective 'international'. It might have rolled by you without much of a thought, but, as we'll see in a moment, when we're thinking about the social life of methods it is a little piece of language that turns out to be highly significant.

The difficulties for measuring the international trade and traffic of e-waste are many, but can be summarized as follows:
Is a toaster with a digital clock 'e-waste'? Is a Toyota Prius? According to [oops... modest witness alert!] Manfred Broy, a professor of informatics at Technical University, Munich, a contemporary "premium-class" car "probably contains close to 100 million lines of software code" running on 70-100 microprocessors (Charette, 2009). The gamut of objects and materials that could potentially be considered waste of an electronic kind is immense, growing, and there are no non-arbitrary ways to include or exclude their legions. This inherent indeterminacy of what is or is not to be categorized as an electronic object makes coming up with robust statistical categories for them as a distinct class of objects to be recorded and counted by statistical agencies probably impossible.
Whether a piece of equipment that might be more readily recognized as an electronic item (e.g., a CPU, a monitor, a cellphone, a printer) is crossing a border because it is a new manufacture, fresh from the factory floor or old model sold to be reused again by some one else somewhere else is not directly distinguished in international trade data. Estimates must be made based on price thresholds that are themselves derivatives of price data available from sources other than international trade data itself (see Duan et al 2014).
This problem, while obvious in a certain way, is actually staggering in its scale well beyond the specific issue of e-waste (see Nordstrom, 2007 and here). The illicit and unrecorded is also, for the most part, invisible to the state and its statistical agencies, yet much of yours and my daily lives are dependent on those invisibilities.

These three difficulties ensure that the use of international trade data make a variety of things visible, but at the cost of much, much more being or becoming invisible. But there is more here to be said, about the 'international', about what is going on methodologically when state-centric data are used to track e-waste.

One of the key entities that are generated by international trade data are states 'themselves'. Data I use in several of my papers are from state based statistical agencies and collected by an international body: the United Nations Commission on Trade and Development (COMTRADE). In that data the fundamental units are 'territories', one cannot disaggregate to a more fine grained detail in such data. Most often, these territories in the COMTRADE data are states, but there are some non-state territories that are also interesting for how they flag up the 'state' as an arbitrary category by which to group data. A non-exhaustive list of examples of these other territorial groupings include "United States Minor Outlying Islands", "Other Asia nes (not elsewhere specified), "North Africa, nes", and "Occ. Palestinian Terr." (as in Occupied Palestinian Territories).

The inability to disaggregate such data into other scales, especially intra-territorial ones such as 'the urban' has major consequences for how we know e-waste via trade data. Much of the e-waste debate is couched in terms of so-called 'developed' countries dumping waste in 'developing' countries. As such the issue of e-waste is framed at the scale of international relations. A consequence of that framing and all the data collected at that scale is that we are blind to the growing rise of 'middle-class' consumers within 'developing' countries, particularly in urban centres, that can and do purchase new and used electronics domestically - and have been doing so for some time - before subsequently ridding themselves of them; we're also blind to the fact that such sites have major institutional users of electronics - banks, hospitals, government offices - that are important domestic sources of discarded electronics. So by framing the issue of e-waste in terms of international trade we partially format the phenomenon in such a way that is not necessarily the most relevant to it. For example, a recent multi-method study of e-waste in Ghana - site of the infamous Agbogbloshie 'dump' - found that more than half of electronic discards in the country are from domestic sources, not imports of e-waste (Amoyaw-Osei et al. 2011). 

More generally, we can say that trade data, in its statistical grouping work, forces us to consider what statisticians refer to in technical language as the 'ecological fallacy'. This is the error of making inferences about individuals that belong to a statistical aggregate - like a state - and where the individuals in question happen to be states themselves that have been aggregated into groups like 'developed' and 'developing' countries and the like. The latter issue raises a second: the modifiable areal unit problem (or MAUP), which amounts to a form of geographical and statistical indeterminacy (there are others, see Kwan, 2012) in which one can arrive at different summary figures (such as totals, rates, or proportions) that change based on how the boundaries are drawn to define group membership. The problem is those boundary definitions are arbitrary and cannot help but be thus. While these two issues might seem confined to the arid realms of statistical arcana, versions of them play out directly in real laws and policies designed to institute trade restrictions on e-waste imports/exports between various territorial groupings such as 'developed' and 'developing' countries, Organization of Economic Co-Operation and Development (OECD) and non-OECD countries, or under the Basel Convention, Annex VII and non-Annex VII countries (see Lepawsky and McNabb, 2010; Lepawsky, 2012; Lepawsky, 2014).

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