Читать книгу The Uncounted - Alex Cobham - Страница 8

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 Desrosières identifies four attitudes of statisticians and others to the reality or otherwise of statistics.³ The most obvious, which he labels ‘metrological realism’, rests on the assumption of some permanent reality, which is independent of any observation apparatus – so that quantitative social sciences could ultimately attain equivalent status to natural sciences.

In contrast, ‘accounting realism’ relates to a more limited space (internal to an enterprise or economic), but offers the illusory promise of rational, testable, provable numbers through double-entry bookkeeping. Illusory, because the numbers necessarily depend on a whole series of judgements – from those involved in the underlying business (or government of, say, a national economy), to the accountants involved directly in compiling the public record, and eventually to those behind the accounting frameworks in use.

The validity of this ‘reality’ depends in turn upon trust in those accountants and others – not in some objectively verifiable and unique set of data. If you’re tempted to think accounting realism is anything but illusory, just ask anyone who has ever looked at the annual report of a multinational company to try to work out whether they paid the right tax, at the right time, in the right place.

Desrosières labels the third attitude ‘proof in use’. Here, researchers using a given dataset prepared by some other body may evaluate its ‘reality’ on the basis of its internal consistency and/or of how well the results of their analysis conform with their priors. Good data with genuine inconsistencies may be undervalued, and institutions publishing statistics may have more incentive to ensure internal consistency of their data – even to the point of deliberately censoring genuine datapoints that do not conform with expectations. Finally, these three attitudes are contrasted with a fourth: one that recognizes explicitly ‘that the definition and coding of the measured variables are “constructed”, conventional, and arrived at through negotiation’.⁴ There is no objective truth, but the constructed data can be more or less legitimate as a reflection of the different concerns and interests.

This point is extended in an important contribution by Wendy Espeland and Mitchell Stevens, which makes the case that ‘quantification is fundamentally social – an artefact of human action, imagination, ambition, accomplishment, and failing’.⁵ Measures not only reflect a view of people or things, but also lead people to change their behaviour – including policymakers, thereby creating the possibility of circular feedback between the (in any case overlapping) processes of government and measurement. That circularity is an inevitable feature, and can be both vicious and virtuous. In the case of the uncounted, poor data can promote poor policy, which in turn undermines the scope to improve data; but data improvements can also be self-reinforcing, driving a positive loop of better policy.

Specific metrics, above all where they allow ranking, can impose powerful (Foucaultian) discipline on the people and groups measured; that is, forms of accountability through transparency.⁶ And so influence over the choice of metric, even for a fixed dataset, is an important form of power. Sakiko Fukuda-Parr and others examined a wide range of metrics selected in the context of the UN Sustainable Development Goals (SDGs), illustrating exactly how competing actors have sought (and achieved) political influence over supposedly technical processes and decisions.⁷

The choice of who and what go uncounted, excluded either from the gathered statistics or from the chosen metrics, is equally a question of power. And the roles of power and social construction in counting are not optional. There is no ‘neutral’ option in which counting decisions are taken in a vacuum, free from political concerns. And there are no meaningful counting decisions that do not have political implications.

We cannot design a system that inoculates societies from these core characteristics of counting. But we can inoculate ourselves to a degree, from the ‘seduction of quantification’, by opening our eyes to it: by understanding the central dynamics, and the possible nature and extent of the biases that result. We can count better. And if we do, the world can be better.

In this book, I look at a range of important counting choices as they are actually made. I consider the implications for inequality, for governance and for human progress. I use the term ‘uncounted’ to describe a politically motivated failure to count. This takes two main forms, and each has direct implications for inequalities.

First, there may be people and groups at the bottom of distributions (e.g., income) whose ‘uncounting’ adds another level to their marginalization – for example, where they are absent from statistics that underpin political representation (‘who decides’) and also inform policy prioritization (‘what people get’). Second, there may be people and groups at the top of distributions who are further empowered by being able to go uncounted – not least by hiding income and wealth from taxation and regulation (‘what people are required to do’). The uncounted at the bottom are excluded; the uncounted at the top are escaping.

A further distinction in each case lies between what we can call ‘relative’ and ‘absolute’ uncounting. In the former, people or groups are included within the data sample, but are not differentiated. For example, household surveys may include (some) transgender people but without differentiation will fail to generate comparative data on how this group fares. Or consolidated company accounts may reveal information about a multinational’s global profits and tax, but without revealing the relative positions of its operations in Luxembourg, say, or Kenya. Absolute uncounting, meanwhile, reflects the complete failure to include a group – whether that be the absence of high net-worth tax evaders from wealth distribution data, or of some rural, indigenous populations from census data.

Questions of power being complex, there are also cases when marginalized groups may seek to be uncounted precisely in order to exert some power. Any desire to be counted in order to provide a basis for curtailing inequalities will be remote, when the purpose of a state’s counting is to impose greater inequalities.⁸ Think of oppressed populations fighting to avoid being singled out – whether against the use of the Star of David to isolate Jews in Nazi Germany, for example, or against the use of ‘pass books’ as tools of racial discrimination in South Africa, from the eighteenth century up until the apartheid regime; or the resistance in certain cases to group identification in census surveys (the ‘I’m Spartacus’ response).⁹

Hidden identity through collective pseudonyms has a long history as a tool of resistance also. Marco Deseriis tracks the use of ‘improper names’ in groups from the Luddites of the nineteenth century, to the modern-day Luther Blissett Project and the Anonymous hacker collective, and argues that they share three features:¹⁰

1 Empowering a subaltern social group by providing a medium for identification and mutual recognition to their users.

2 Enabling those who do not have a voice of their own to acquire a symbolic power outside the boundaries of an institutional practice.

3 Expressing a process of subjectivation characterized by the proliferation of difference.

Depending on the external conditions – the power faced, and its legitimacy to count or identify – the case for being uncounted, and the space to do so, will vary. There is a clear difference, however, between the ‘guerrilla’ tactics of the relatively powerless seeking to go uncounted in the face of a quantifying bureaucracy, and the exertion of power by those at the top to escape or circumvent counting.

Inevitably, counting at the national level is imperfect. Survey and census data tend to have major flaws, as does the administrative data used for taxation and voting – and yet these are the basis for any number of crucial policy decisions about where and how to allocate resources.

If the missing data were more or less random, any overall distortions would be limited. If, on the other hand, there were systematic patterns to the distortions, then we should be less sanguine. And, of course, it turns out that what goes uncounted is not random after all.

Instead, the failure to count is directly related to the power of those involved. At the bottom, those who are excluded tend to be from already marginalized groups. The established weaknesses of these data include the almost universal absence of good statistics on lesbian, gay, bisexual and transgender (LGBT) populations and persons living with disabilities, as well as country-specific failures around indigenous populations and racial and ethnolinguistic groups. And the absence of good statistics can lead, in turn, to the absence of a profile for these groups in public policy discussions and prioritization decisions.

Sometimes, supporters of the status quo will use the fact of a phenomenon being uncounted as an argument against addressing the underlying inequalities. The fight for international tax justice, for example, has seen a number of phases. First, policymakers would deny that tax avoidance and tax evasion were a significant problem. Then, when presented with estimates of substantial scale, the response would often be that the big numbers are not sufficiently robust. Only under the pressure that stems from media coverage and public awareness of those big numbers have international institutions themselves begun to estimate the scale of the problem – often coming up with bigger numbers than had campaigners, and finally leading to sustained policy engagement.

There is important value to the process of testing and probing estimates, both to improve their quality and as part of the social legitimation of their construction. But this can also often provide cover for defenders of the status quo simply to raise doubts about the validity of the underlying concerns. In the tax justice space, this is typically expressed as the view that the big numbers are not robust, so there is no ‘pot of gold’ to be had from stopping multinationals’ tax avoidance. It is clearer now, when that view continues to be expressed despite a range of new data sources and research by independent academics and from international institutions, that much of the support for it is politically rather than technically motivated. But in the early stages of the tax justice movement, there was a genuine risk that the absence of better data could have provided the defenders of the status quo with a conclusive argument against progress.

Bad statistics used to advocate for change can be exploited by what we might call the ‘uncounted lobby’. A paradigmatic example has been the claim made that the informal settlements in Kibera, Nairobi, constitute ‘Africa’s largest slum’. Over time, the challenges to this deeply flawed statistical claim have contributed both to improve the true understanding of the area and its development challenges, and to raise the standard to which development nongovernmental organizations (NGOs) hold themselves in respect of similar claims. But at the same time, the absence of good data backing this particular claim has provided fodder to those with a broader political agenda to downplay concerns about global poverty, and to attack NGOs and others arguing for justice.¹¹ The claim was comprehensively debunked by Kenya’s census, and covered with high profile in the Daily Nation newspaper as the results were released in 2010. But two years later, international reporting was claiming credit for applying their scepticism to ‘Africa’s propaganda trail’ – a much wider claim that seemed intent on undermining a whole sector.

Or consider the ‘zombie stats’ around women’s inequality: in particular, the claims that women make up 70 per cent of the world’s (extreme income) poor, and that women own only 1 per cent of the world’s land.¹² For the uncounted lobby, the fact that neither of these can be stood up by available data is evidence that supporters of women’s equality are misguided, extreme, talking about a problem that doesn’t exist, etc. For the rest of us, the fact that we (still) don’t have the data to know how extreme the income and wealth inequalities facing women are, is itself an obvious part of the problem.

Being counted does not guarantee that inequalities will be addressed. But being uncounted certainly makes inequalities less visible, and progress less likely. James Baldwin put it better: ‘Not everything that is faced can be changed, but nothing can be changed until it is faced.’¹³

At the top, inequality is hidden in three main ways. First, inequality is hidden through missing data: while the poorest groups are underrepresented in surveys, high-income households are much less likely to respond to surveys and are therefore omitted. This can be fixed by using data from tax authorities, where available, which has been seen to add significantly to observed inequality.

Second, and most blatantly, there is deliberate hiding of income and assets by those with most power. This means that the tax data itself contains important omissions. Through the use of anonymous companies and the exploitation of bank secrecy, substantial sums of wealth and the resulting income streams are hidden overseas by high-income households all around the world. Thomas Piketty, in his landmark study Capital in the Twenty-First Century, suggested that an estimate of the volume of undeclared assets of around 10 per cent of global gross domestic product (GDP) should be thought of as the lowest possible level. This is part of Piketty’s reasoning for a global wealth tax – that, actual revenue and redistributive effects aside, such a policy would ensure that the distribution data at least exist.

Multinational companies use similar secrecy mechanisms, coupled with accounting opacity, to shift massive volumes of profits out of the tax jurisdictions where they arise. Together, individual and corporate tax abuses drain hundreds of billions of dollars in revenue from governments around the world each year, undermining the effectiveness of progressive, direct taxation – and broader attempts to hold accountable these largest of the world’s economic actors.

The third way in which income inequality goes uncounted is more subtle, but perhaps equally poisonous. The Gini coefficient, the default measure for inequality, is inherently flawed – and in such a way that it is relatively insensitive to the tails of the distribution (the parts we care about most), and increasingly insensitive at higher levels of inequality (the times when we care most). So what is presented as a neutral, technical measure in fact imparts a serious bias to our understanding of inequality and its development over time. Overall, we know much less about inequality of wealth and income at the top than we do for most of the distribution – and the most common inequality measure exacerbates this problem still further. Metrics, as well as statistics, are distorted by power and in turn distort political outcomes.

Similar problems play out in the area of what we might call ‘privately counted’ data. These include cases where data of significant public value is indeed gathered but is held by private companies with an interest in what part of it is publicly countable, and how. The roles of Facebook and Cambridge Analytica in the UK’s Brexit referendum and the election of Donald Trump as US President highlight the potential threat to democracy.¹⁴ Another example is that of the limitations of access to medical trial data generated by pharmaceutical companies. Ben Goldacre has shown how the failure to regulate for effective access to medical trial data results in inferior medical treatments continuing to be widely marketed and used – with human impacts up to and including large-scale excess mortality.¹⁵

Cathy O’Neil’s analysis of the biases in algorithms reveals just how far we are from the dream that big data or its use in the public or private sectors could be a tool for equalizing power. Instead, there are multiple, opaque avenues for discrimination, both deliberate and accidental, with grave implications for a range of inequalities.¹⁶ More trivially, unless you take seriously Bill Shankly’s suggestion that football surpasses life and death in importance, the Tax Justice Network’s Offshore Game project has shown how the unregulated financial secrecy used by owners can lead football fans to suffer all sorts of risks – including exploitation and even liquidation of their clubs.¹⁷

The common thread across all the cases touched on here is the relationship between power, inequality and being uncounted – a relationship that demands we pay much more attention to who and what are and are not counted. The sociologist William Bruce Cameron observed that not everything that can be counted counts, and not everything that counts can be counted.¹⁸ While this antimetabole is undoubtedly true, so, too, is another: much that goes uncounted matters, and much that matters goes uncounted.

There are reasons to be optimistic: as attention to inequality has grown, so, too, has awareness of the need to reveal the uncounted. Concerted efforts to challenge the uncounted at the bottom and the top are possible now in a way that simply was not the case even ten years ago.

The first part of this book explores the uncounted at the bottom of the distribution, ranging from international development findings to evidence of the exclusion of marginalized groups in high-income countries. The second part focuses on the uncounted at the top of the distribution. This includes the core tax justice analysis of the nature and extent of financial secrecy, the scale of revenue losses through ‘tax havens’ promoting individual tax evasion and multinational profit shifting, as well as the largely unacknowledged bias that overreliance on the Gini coefficient introduces into common perceptions of inequality. Together, the evidence points towards great damage being done to the world’s prospects for genuinely sustainable human progress – and quite unnecessarily.

The book concludes with an Uncounted Manifesto: a political and technical call to action, to change the relationships we tolerate between data, power and inequality. Before they change us any further …

Our societies owe a debt to those we have caused to go uncounted, through their marginalization. At the same time, we are owed a debt by those who conspire to hide from tax and other responsibilities. Both debts are rising by the day, and both are driving up inequality, uncounted. Before we can make the past right, we must stop the clock. We need to understand the systematic flaws that make us ignorant of ourselves and our world – and start counting as if we cared.