Meeting universal needs requires meaningful statistics
THE UN Sustainable Development Goals (SDGs) that countries have committed themselves to striving to reach by 2030 are a watershed in the global development agenda.
Vast resources will be allocated internationally and at all levels of government to ensure that the effects of the 17 goals are maximised.
These range from ending poverty and hunger, to mitigating the effects of climate change.
The SDG agenda and the efforts that will be expended to meet these goals must be welcomed.
But the global development community should not be blinded to aspects of the agenda that appear to be neglected.
The first relates to a guiding principle of the SDG framework that “no one should be left behind”. The second relates to the risk that in the rush to measure, monitor and track the progress towards meeting the SDGs, countries in the global south may find themselves disempowered.
Meeting the goals means meeting them for everyone, not just on average. Certain dimensions of “no one left behind” are laid out in the framework. These include income, sex, age, race, ethnicity, migratory status, disability and geographic location, in accordance with the UN’s Fundamental Principles of Official Statistics. The principles lay out the framework for the collection, analysis and dissemination of official statistics.
But 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. 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 is, fundamentally, political.
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 routinely collected in the global south. Doing so will pose formidable challenges to national statistical systems in the region. There are two ancillary risks associated with these challenges.
First, internationally, leading universities, corporations and think-tanks lead the way. These organisations have larger budgets and greater capacity than their counterparts in the global south. With this comes the risk of solutions being designed in the north, and piloted and implemented on a one-size-fits-all basis in the south. 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 built and designed in the global north.
Similarly, the spectrum suite of demographic and epidemiological projection models is 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 a need for such models, it would be a grave error to conflate the model results with the reality of what is happening. 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.
The power to name those categories of people who 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 that may work to disempower local data communities. If not, these communities as a whole may find themselves “left behind”. – The Conversation
This is an edited version of a column written for the UN SDSN Thematic Research Network on Data and Statistics (UN SDSNTReNDS).
Tom Moultrie is a professor of demography at UCT