Earlier this week, Oxford Dictionaries announced ‘post-truth’ as the word of the year. Beating ‘woke’ and ‘adulting’ for the coveted prize, ‘post-truth’ has grown in popularity mainly due to the seismic political shifts occurring in politics in the West over the past two years.
According to Oxford Dictionaries, post-truth is defined as ‘Relating to or denoting circumstances in which objective facts are less influential in shaping public opinion than appeals to emotion and personal belief.’ This definition might imply that facts and emotion/personal belief are on opposite ends of the ‘truth’ spectrum. In reality, post-truth shows how data can be egregiously manipulated and still have emotional and political clout despite the awareness that facts have been used in a misleading manner.
This has important implications for the concept and power of data in global politics and global development. The Sustainable Development Agenda (SDGs) text has 17 references of the word ‘data’ and offers some interesting insights for global development. As a negotiated document, the Sustainable Development Agenda is an explicitly political text. Every single word in this agreement, has gone through a global discursive exercise of negotiating ideals and framing values to ultimately create a written document palatable to all countries. As with any negotiation, there are losses (= ‘compromises’) and there are wins.
Data plays a critical role in monitoring and reviewing the agenda. It is also one of the targets for Goal 17, ‘Strengthen the means of implementation and revitalize the global partnership for sustainable development.’ Section 48 of the Declaration summarises the role of data as follows,
‘48. Quality, accessible, timely and reliable disaggregated data will be needed to help with the measurement of progress and to ensure that no one is left behind. Such data is key to decision-making. Data and information from existing reporting mechanisms should be used where possible. We agree to intensify our efforts to strengthen statistical capacities in developing countries, particularly African countries, least developed countries, landlocked developing countries, small island developing States and middle-income countries. We are committed to developing broader measures of progress to complement gross domestic product (GDP).’
There are three key themes in this text which are interesting to the politics of data, and more broadly, the politics of knowledge, in international development.
Data, information and evidence
‘Data and information from existing reporting mechanisms should be used where possible.’
This sentence differentiates data, information and what is presented as evidence through the reporting mechanisms. Systems-thinking.org has provided some of the most useful definitions of data and information I have come across:
‘Data… data is raw. It simply exists…and can exist in any form, usable or not. It does not have meaning of itself.
Information… is data that has been given meaning by way of relational connection. This “meaning” can be useful, but does not have to be.’
Evidence is what I define as information that has been used to create understanding of a phenomenon for the pursuit of an intended action. The process of understanding itself is also a process of generating new information as you apply whatever information you have, to make new assumptions (a type of information) about the phenomenon in question.
In the context of development policy and programming, the relation between these three concepts is not just one of logical thinking but also of power. Prior to the increase of global terrorist attacks, Brexit referendum, and the US presidential elections, the difference between data and information and evidence was not a prominent feature of public life in the West.
However, as politicians struggle to gain popularity (closely interlinked with authority), their use of data to manipulate information, and information to generate ‘evidence’, has left public life in a state of disarray.
The prominence of Full Fact, an independent fact-checking civil society organisation, during the Brexit referendum is an indication of just how powerful (and blurred) the lines between data, information and evidence have become.
An example of Full Fact in action is below:
For international development, this means that the way data is used to create information and present evidence is not an apolitical process. What is collected as facts, and how these facts are used to make conclusions about existing realities is a matter of selectivity, and selectivity in this case is also a matter of power. Being conscious of this politics when monitoring the SDGs and when using evidence to design development policies and programmes can be a complicated process but an important one if we are not looking for quick wins but systemic gains to improve our world.
The Great Divide
‘Quality, accessible, timely and reliable disaggregated data will be needed to help with the measurement of progress’
In March 2016, the United Nations Statistical Commission’s Interagency and Expert Group on SDG Indicators (IAEG-SDGs), agreed on 230 indicators to monitor progress on the 169 targets used to measure the SDGs.
The 230 indicators are quantitative data which allows for objective cross-comparison of results because we are using numbers. This is especially important in development implementation because measuring and defining what ‘success’ looks like, requires you to know where you are starting from; this is commonly referred to as a ‘baseline.’ Although, there are currently many issues regarding the 230 indicators identified, mainly to do with feasibility of data collection and availability of existing data, I often wonder what role qualitative data can play in monitoring the SDGs.
One of the key shifts in global development policy from the Millenium Development Goals era (the predecessor of the SDGs) to the Sustainable Development Goals, has been our willingness to embrace new forms and new combinations of measuring development.
The 230 indicators reflect this shift, however, the basis of measurement is still mainly quantitative. This qualitative/quantitative divide is not just a technical debate but a philosophical one extending to the social sciences and the arts versus sciences divide in modern Western intellectual thought.
Qualitative data makes important contributions in our quest to gain knowledge about the complexities we exist in. Monitoring progress on the SDGs will be a great opportunity to find progressive ways to use both quantitative and qualitative data to measure results.
Political Economy of Data Collection
‘We agree to intensify our efforts to strengthen statistical capacities in developing countries, particularly African countries, least developed countries, landlocked developing countries, small island developing States and middle-income countries.’
There have been several research pieces such as this one by European Centre for Development Policy Management and this article by Center for Global Development assessing the politics of data and statistics collection. The reason a government agency may not be collecting or reporting adequate quality data is not just down to competence as the SDGs agreement alludes to. Political interests by political authorities may also limit the amount of data that is collected or reported. As reported by United Nations Statistical Commission’s Interagency and Expert Group on SDG Indicators (IAEG-SDGs), who developed the 230 SDG indicators, there are ‘constraints faced by many countries in producing the data…including policies and regulations that prevent data collection by race and/or ethnicity and confidentiality issues, among others.’
Furthermore, strengthening statistical capacities at the country level does not consider other development actors (NGOs, private consultancies) also collecting data to monitor their projects and how this might hinder the abilities of governments to improve the coherence and consistency of data collection.
It is also worth mentioning that the use of data and statistics for monitoring and accountability has been incredibly decentralised since the MDGs era. Civil society play a greater role in collecting and using data to hold governments and development actors to account. Recognising the role grassroots and local actors play in data collection is a great opportunity to strengthen statistical capacities of countries.
Acknowledging the political economy of context and the diversity of actors involved is part and parcel of effective development policy and planning. It is crucial if we are to create a credible space to monitor progress over the next 14 years.
What these three segments of the text above show, is a fluid dialogue on the ways of knowing in global development and the way power influences the context of inquiry. Whether you are a development policy maker, practictioner, activist or a mix of everything, data plays a big part of the work you do.
I’m curious to find out the role of power in the way we collect and use data. What constitutes ‘credible’ data and who accords credibility? Which actors and which geographies have ownership and influence over the process and use of data in global development?
I’m leaving the last word to the master of words, Chinua Achebe. In his 1988 conversation with Bill Moyers, Achebe offers a poignant commentary relevant to this reflection on the subjectivity of data, and essentially the ways of knowing. In response to the Igbo proverb, “Wherever something stands, something else will stand beside it”, Achebe shared his interpretation,
“There is no one way to anything. The Ibo people who made that proverb are very insistent on this — there is no absolute anything. They are against excess — their world is a world of dualities… If there is one God, fine. There will be others as well… If there is one point of view, fine. There will be a second point of view.”