Who might be left behind in the SDGs
September 1, 2017
Written by Tom Moultrie
The Sustainable Development Goals that nations have committed themselves to striving to reach by 2030 represent a watershed in the global development agenda. Vast resources will be allocated internationally and at all levels of government to maximise their effect. Yet, while this agenda and the efforts that will be expended to meet it must be welcomed, the global development community should not be blinded to aspects of the agenda that should give pause for thought. Here, two such aspects are highlighted. The first relates to the mantra that no one should be left behind. The second, to the risk that in the rush to measure, monitor and track progress towards meeting the SDGs, countries in the global South may find themselves disempowered.
“Leaving no one behind”
A core guiding principle of the SDG framework is that “no one should be left behind”. The clear intention behind the phrase is that the fruits of achieving the SDGs should be shared equitably: that meeting the goals means meeting them for everyone, not just on average. Expressed like that, it is certainly a laudable ambition. However, while certain dimensions of “no one left behind” are laid out in the SDG framework: “income, sex, age, race, ethnicity, migratory status, disability and geographic location, or other characteristics, in accordance with the Fundamental Principles of Official Statistics”, this masks a conceptual problem. As theorists of official statistics have noted, the classifications and categories employed in official statistics are themselves ‘named into existence’ – the act of not classifying or categorising certain groups can render them invisible in official statistics. The use of simple binaries, such as sex for example, do not provide the space for transgender or intersex communities to be counted. Equally, not all minority populations (particularly those that fear, or experience, state-based discrimination or harassment) will want to be able to be identified in bureaucratic data. The question of who is to be counted so as to not be left behind is, fundamentally, political.
Disempowering the global South
A further concern with the SDG agenda also stems from the principle of leaving no one behind. It is readily acknowledged that the monitoring, measuring and tracking of the more than 200 indicators associated with the SDGs will require data of a far finer granularity and precision than is currently routinely collected in the global South. The challenges posed to national statistical systems in the global South will be formidable, not only in terms of budgets and finances, but also in terms of building and sustaining the required capacity. Attention is drawn to two ancillary risks associated with these challenges.
The first is that the centre of gravity for designing interventions and strategies to meet the challenge lies in the global North: at internationally-leading universities, corporations, and think tanks. These organisations have larger budgets, and greater capacity, than their counterparts in the global South. With this comes the risk of solutions being designed, piloted and implemented on a one-size-fits-all basis: a project that is shown to have some efficacy in one setting may be assumed to be as efficacious elsewhere. However, the failure to take local specificities, dynamics and politics into account may result in those solutions not being fit for purpose. Alternatively, without solid buy-in from local partners, these interventions may come to be seen as being as heavy-handed and removed from local realities as were the Structural Adjustment Programmes pursued by the World Bank in the 1980s.
The second risk, in the absence (or failure) of sustained efforts to rebuild and recapacitate the national statistical systems of the global South, is that the data for measuring, monitoring and tracking the progress towards the SDGs will increasingly be drawn from complex statistical and econometric models. Already, the Institute for Health Metrics and Evaluation (IHME) produces model-based estimates of child mortality for all countries in the world, and new versions of the model are capable of producing estimates at a sub-national level. Similarly, the Spectrum suite of demographic and epidemiological projection modelsis often used in the global South to produce estimates of population, HIV prevalence, or numbers of people in a country requiring antiretroviral therapy. While there is undoubtedly need for such models, it would be a grave error to conflate the model results with the reality of what is happening: no model is perfect, and the results should – at best – be regarded as providing an approximation to the measure in the absence of empirically observed and derived estimates. But the risks extend beyond that of model-based error. Models of this kind are frequently ‘black boxes’ – with their inner working only fully understood by a very few. One should ask how many health researchers, epidemiologists, statisticians, and demographers there are in countries in the global South who are capable of interrogating and questioning the results of such models, based on their own observations and experience. Where this knowledge is either not drawn upon, or is absent, the risk is that countries in the global South may become increasingly dependent on the results of those models to provide the data required for monitoring progress towards meeting the SDGs.
Beyond a catchphrase
Leaving no-one behind is a catchphrase that seeks to ensure that all people benefit from the global development agenda. However, the power to name those categories of people that should be monitored to ensure they are not left behind is neither neutral, nor necessarily benign. States should be engaging positively with domestic institutions and civil society organisations to determine for themselves the delineations of those at risk of being left behind.
At the same time, states in the global South should also guard against interventions for data collection and management, or model-based substitutes for those data, that may work to disempower local data communities. If not, these communities as a whole, may find themselves ‘left behind’.
Moultrie is a professor of demography and director of the Centre for Actuarial Research (CARe) at the University of Cape Town, South Africa. Contact him at firstname.lastname@example.org.
Originally published at UNSDSN.org on September 1, 2017.