Health Organisation
Introduction
We may pride ourselves on being the generation of open data, of big data, of transparency and accountability, but the truth is less palatable. We are the generation of the uncounted – and we barely know it.
Imagine a world of such structural inequality that even the questions of who and what gets counted are decided by power. A world in which the ‘unpeople’ at the bottom go uncounted, as does the hidden ‘unmoney’ of those at the very top. Where the unpeople are denied a political voice and access to public services, while the unmoney escapes taxation, regulation and criminal investigation, allowing corruption and inequality to flourish out of sight.
This is the world we live in. A world of inequality, uncounted.
Actual and even perceived inequalities have large and wide-ranging, negative social impacts. Higher rates of child mortality, lower life expectancies, increased likelihood of conflict, reduced trust and social cohesion, increased corruption, lower economic growth and shorter growth cycles, on and on. When we accept higher inequality, we accept these inferior outcomes. We accept the absolute and certain waste of human potential that they impose.
We accept that women will work longer and for less income. We accept that indigenous populations and marginalized ethnolinguistic groups will be systematically excluded from educational opportunities and suffer poorer health. That people with disabilities will live poorer, shorter lives. As will people from marginalized geographic regions. And as for people at the intersections of one or more of these inequalities …
Wait, though: we have broadly functional democracies, right? Surely it follows that those inequalities, those losses, are freely chosen by a majority. And if you don’t like it, couldn’t you just campaign for more people to care more about inequalities?
But debate over acceptable inequalities and acceptable redistributions is itself circumscribed by our failure to count. Political parties take positions on the basis of their ideological stance and their reading of popular concerns. Those positions mark out the space for mainstream political debate. And those positions, plus the underlying popular concerns, reflect in turn the data that is available and how it is presented.
What if common measures consistently understate the degree of income inequality, for example? Can the political debate still represent democratic preferences accurately? Or what if data is simply not collected on the inequalities facing certain groups: is the resulting absence of political debate legitimate? Or the absence of steps to reduce those groups’ exclusion? What if the failure to count means that swathes of legal, regulatory and tax responsibilities do not fall equally on certain groups? And what if the very nature of our political processes is compromised, by a failure to count all votes or all voters equally, or to ensure that political funding is appropriately regulated, so that underlying economic inequalities can map themselves onto political outcomes?
The threat that this book addresses is that decisions seen as technical go unchallenged even though they are, in fact, powerfully political, creating a systematic bias towards levels of inequality that are needlessly high and do not reflect people’s preferences. At the heart of the argument is the role played in society by statistics. I focus primarily on the analysis of data relating to the state, to which the term ‘statistics’ originally referred.
As a starting point, we can identify three core features of a state, and the related aspects of counting. First, take the state as a form of political representation. In whichever way it performs this ‘who decides’ function, it will reflect more or less well the views of citizens. This applies as much to the authoritarian state, which nonetheless requires some degree of popular support for its continuing legitimacy, as it does to the avowedly democratic state.
The state also performs a distributive role. For ease, I suggest a somewhat rough division between the second core feature, that of determining the distribution of benefits; and the third core feature, that of determining the distribution of responsibilities. There is inevitably overlap, and the split could be made elsewhere; but it is broadly useful to think of the categories as ‘what people get’ and ‘what people are required to do’.
The distribution of benefits covers everything from the most direct to the least – from, say, levels of household transfers, regional investment decisions and the provision of public services and infrastructure, to the quality and resourcing of, for example, administrative and military functions. A state can perform this role more or less well, and more or less inclusively of the whole population.
The distribution of responsibilities covers the array of legal, judicial and regulatory functions, broadly defined, from the identification and policing of criminal behaviour, the design and enforcement of regulation and – crucially – of taxation. Again, a state can perform this role more or less well, and can ensure the application of regulation is more or less inclusive of the whole population.
Each of these three roles of the state depends on data. The questions of ‘who decides’, ‘what people get’ and ‘what people are required to do’ are answered in part by the underlying processes of counting. Political representation is determined by the counting of votes and of voters. The relative weighting of particular people and groups in the distributions of benefits, and of responsibilities, is determined by the counting of people for each purpose.
Crucially, the gathering of this data is far from unbiased (what Foucault terms ‘governmentality’). Statistics and metrics do not simply appear fully formed, nor do they emerge from some neutral process of knowledge search, ready to be applied objectively to an optimal policy analysis. Instead, the interests of those who govern will be reflected in the very means of counting. Data is constructed in such a way as to support the emergence of social structures that are more ‘governable’.
In Foucault’s conception, this process can be a positive one:1
We must cease once and for all to describe the effects of power in negative terms: it ‘excludes’, it ‘represses’, it ‘censors’, it ‘abstracts’, it ‘masks’, it ‘conceals’. In fact, power produces; it produces reality; it produces domains of objects and rituals of truth. The individual and the knowledge that may be gained of him belong to this production.
The key point for our purposes is that the production of statistics and metrics, the process of counting that underpins state functions, is not abstract but deliberately willed. Most bluntly, this applies to the planning approaches that James C. Scott memorably dissects in Seeing Like a State:2
The power and precision of high-modernist schemes depended not only on bracketing contingency but also on standardizing the subjects of development … What is striking, of course, is that such subjects – like the ‘unmarked citizens’ of liberal theory – have, for the purposes of the planning exercise, no gender, no tastes, no history, no values, no opinions or original ideas, no traditions, and no distinctive personalities to contribute to the enterprise. They have none of the particular, situated, and contextual attributes that one would expect of any population and that we, as a matter of course, always attribute to elites. The lack of context and particularity is not an oversight; it is the necessary first premise of any large-scale planning exercise.
The experience of the UN Millennium Development Goals (MDGs), discussed later, provides a good illustration of the point that being blind to the characteristics of people, households and groups does not result in neutral progress – quite the opposite. Recognizing the ‘markings’ of ‘subjects’, or refusing to do so, is likely to change significantly the processes of both planning and policy enactment.
Alain
