Can the SDGs wipe out poverty? Let's use data to plan attack

A person begging in the Czech Republic
ShutterstockArnont Boosarawongse
A person begging in the Czech Republic. The Sustainable Development Goals call for wiping out poverty everywhere. 

Delegates of the 193 United Nations member states have reached consensus on the new Sustainable Development Goals for the next 15 years. With 169 targets amidst 17 goals, the SDGs have size and ambition, anchored by one exceptional poverty target to:

By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day.

Such an ambitious (some would say impossible) agenda demands that the global development apparatus function as a well-oiled machine. Somehow the international system must miraculously achieve, within a hybrid intergovernmental-public-private institutional framework, a scale and efficiency that has rarely been achieved by a single state, with the possible exception of China, a success story that relatively few stakeholders seek to emulate.

Unless the United Nations is supplanted by a benevolent despot from outer space, the success of the SDGs and successor frameworks will depend on our ability to systematically mobilize an unprecedented mix of human, financial and institutional resources in the most efficient way possible.

In today’s world, analytics — meaning the analysis of quantitative and qualitative data in pursuit of a logical goal — have nearly completed its long ascent to prominence in the logic of the public and private sector action.

We may disagree about the perceived end of logical action — be it development, human rights, systems, security, profit or power — but we mostly agree that goals can be analyzed and pursued in a logical, directed, and evidence-based fashion. At whatever level of spatial and temporal specificity we are operating, our logical model is roughly the same:

Need → Intervention → Impact

The problem is that, in spite of our daily encounters with the growing volume and impact of data both big and small, the current global development data space remains more characterized by gaps, holes and noise than by anything like an emerging corpus of evidence that could support dramatically heightened efficiency or impact, much less long-term goals like self-sufficiency or broad participation spelled out by the UN’s Data Revolution Group.

Big data from mobile phones and Internet searches can play a critical role in measuring human need and programmatic impact, but they must be a complement, not a substitute, for program- and policy-relevant data collected purposefully at the appropriate spatial and temporal scale for analysis.

Efforts to systematize evidence from program evaluations scattered across numerous settings and scales worldwide have driven home the reality that evidence-based interventions must be routinely optimized to suit local conditions that change over time. We need room for trial-and-error, experimentation, consultation, and yes, mistakes, but we need to know where, when, who and how these errors are emerging at a granular level in something approximating real time.

Progress over the past 15 years in improving the collection of national statistical data has been extraordinary when compared to the past, but looks pitiful when compared to what we need.

It is in the full context of progress, opportunities, risks, and the drastic expansion of targets from Millennium Development Goal to Sustainable Development Goals that the near-total neglect of the data revolution in the actual SDG framework is so deeply troubling. Here is SDG target 17.18, buried deep within the means of implementation 

By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing States, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts

Target 17.18 has “fear of commitment” issues

It can’t even commit to building capacity, but merely to “enhance capability-building support.” By how much will capacity-building support be enhanced? We have no idea. “High-quality, timely and reliable” have no meaning without specific definitions.

In light of technological advances, the mention of various forms of disaggregated data might be a step forward, were it not for the definitive declaration that such data must be “relevant in national context,” not so that they might meet the needs of individual people, communities, or subpopulations to achieve the stated goal of leaving no one behind.

The original Open Working Group Proposal for the SDGs was anchored by a more powerful expression of the purpose of data, a point that was completely excised from the ratification draft:

 17. "In order to monitor the implementation of the SDGs, it will be important to improve the availability of and access to data and statistics disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts to support the monitoring of the implementation of the SDGs. There is a need to take urgent steps to improve the quality, coverage and availability of disaggregated data to ensure that no one is left behind."

Here we see the three key things that must be said about the purpose of data within the post-2015 framework:

1. That we must improve quality and coverage of disaggregated data for the explicit purpose of leaving no one behind.

2. That the purpose of data is to support the implementation of the SDGs at all levels.

3. That data must be made available for accountability and eventually participation at all levels.

The failure to retain this vision or to put specific targets on improving the quality of local data betrays a fundamental misunderstanding of the role of data in the development process. This failure likely reflects the fact that most people charged with negotiating the SDGs, while well-intentioned and well-versed in the language of leaving no one behind, don’t actually have much contemporary experience of carrying out this hard work.

Throughout the top tiers of the development industry, there remains a conception that the main point of collecting data is to generate accountability, and specifically to enforce the kind of “report card” accountability that would keep global and national leaders on side.

The MDGs came in for much criticism around this issue, for setting goals without participation, for setting targets that could barely be measured at baseline and certainly not for measuring change over time, and for including no sanctions for failure to achieve targets. In reality, this sort of high-level accountability is a mirage, and certainly not a goal that could begin to justify the kind of data expansion that the world needs.

Everyone responsible for negotiating the SDGs will be gone in 15 years, or at least not remotely in the same position. The notion that heads should roll if targets aren't met is one only put forward by cynics who largely reject the entire process of goal-setting. Of course we can use country-level data to refocus certain efforts after 5 and 10 years, and to take stock after 15 years. But by then it will be pretty late.

We might also potentially use country-level data to recalibrate our expectations for what can be realistically achieved over a 15-year time horizon, though if data were truly leading to asense of realism, would we currently have SDGs that are anchored by a poverty target that is almost certainly out of reach?

Ultimately, the real reason we need better data is for improved targeting and delivery of services, and the SDGs are effectively counterproductive in that respect. The SDGs barely contain the power to close the data and evidence gaps exposed by the MDGs. They certainly offer no plan to produce the data that would actually be needed to achieve the 169 targets that will be on the books as of September 27.