I argued in the previous blog that new directions in development data and demand for those data are opening up new possibilities for empirical data collection, and for feeding it into policy. I argued too that these new developments will not make it necessarily any easier to produce data-informed policy. There are all sorts of obstacles at work.

But we must now consider a slightly different order of problem. These derive not from any failings of data per se – because the facts in this case are plainly known and indisputable. Rather they derive from thinking that monitored, incremental change will be sufficient to bring transformation. This can be a dangerous lie.

In some instances, instead of looking to data to provide evidence for policy we need to recognise that the most powerful elements of the worldviews we are dealing with can be the myths and narratives that underpin them. Part of the condition of evidence free policy is that it exists in a realm where data may not have any purchase. The empirics depend upon the myths and narratives that lie behind them. Or to put this differently, some development goals are simply fantasies that cannot be combatted in the realm of fact or data.

For example consider the goal achieving full and productive employment. The stated Millenium Development Target 1B was achieving full and productive employment and decent work for all, including women and young people. This was plainly and indisputably not achieved by 2015. And yet the very same target has been set for 2030. This again will not happen. Only very particular forms of capitalism have ever got close to full employment, and that entailed not counting women’s work well. Taken at face value this target suggests a radical re-ordering and restructuring of society and a transformation of our norms and values. It is not the sort of thing which can be done through better use of data or more fact.

This kind of transformation is not achieved by charting progress over time. Rather, as Thomas Pogge has argued, examining

‘[W]hether an institutional order is harming people . . .  depends . . . on a counterfactual comparison with its feasible institutional alternatives’ (page 25).

Pogge had in mind slavery – you don’t chart progress from slavery by showing how slaves homes or health are improving. Ultimately the ills of slavery can only be addressed through emancipation. You have to imagine the alternative – the counterfactual comparison, of a world with slavery compared to one without – and then realise it.

But he goes on to point out that counterfactual alternatives already exist for many development injustices, and that where they exist then we cannot feel content with limited progressive change. This is a significant challenge for anyone keen on monitoring progress towards the sustainable development goals. As Pogge says

‘Most citizens of the affluent countries take comfort in the asserted decline of global poverty . . . They should instead take intense discomfort in the fact that a feasible alternative global order could have avoided most life-threatening poverty and its associated evils.’ (ibid)

What then are the important myths and their counterfactuals that we might want to recognise and take on in international development affairs? There are many, but, let me just touch on a couple. Perhaps the most problematic is the enduring myths of the primacy of GDP and economic growth, the alternatives to which are oddly both radical and obvious.

GDP is widely recognised to be the wrong way of measuring progress. GDP measures production, whether that be in pursuit of good things, or cleaning up the problems. If you produce stuff without causing pollution or health problems your GDP will be so high, but if you pollute and you additionally then have to spend money cleaning up the river, or curing the diseases that that pollution causes, then your GDP will get higher still. A sick or violent society in which millions of people are prescribed expensive anti-depressants or which imprisons large proportions of its population in expensive jails, and spends a huge amount on security and policing will, despite all that misery, enjoy a higher GDP than societies who are not so productive. As Lorenzo Fioramonti has shown growing GDP does not progress other indicators of well-being. And yet GDP remains the main indicator of progress and prosperity.

Constructing counterfactual measures of well-being and using them as the key indicators by which we will guide our collective economic lives is proving difficult. Nevertheless there are ever increasingly auspicious authorities supporting this shift. It is found in the work of Nobel Prize Winners such as Amartya Sen and Joseph Stiglitz. Tim Jackson’s ESRC funded research centre is precisely based on a vision of prosperity which may be possible without economic growth. Geographers JK Gibson-Graham insist that so much of what we actually do with our time, creativity and resources is not captured by capitalist enterprise. More generally, the degrowth movement, which argues for reduced GDP, is gathering strength with a variety of models of what degrowth might constitute and how it might come into being.

So while we seek better development data, I am also looking for the counterfactuals, that will allow us to imagine different ways of doing things, and of ordering our lives, and that make us feel discomfort when they present valid alternatives to present misfortune. Only with such counterfactuals can we find the home, the nurturing environment, for the new development data that I look forward to contributing.

This blog first appeared on the SIID site here.