Editor’s note: Continuing the narrative from the previous part, the author delves in the depth of capacity crisis for sustainable development data. He is optimistic that the challenges are going to open up new vistas for national and international agencies to cooperate on mutually reinforcing efforts.
Not grabbing the opportunity for creating the appropriate capacity to address the data gaps, in the opinion of the author, could be as costly a mistake as failing the people of their rights. The narrative ends with a sigh for the dream data remaining elusive.
I presented some pieces of data and a chart in the last part related to some selected countries to demonstrate how the countries have progressed in developing the national capacity for production and dissemination of official statistics. Any intelligent person who knows that one must always look into the elements the data are made of would be reasonably justified to conclude that the countries of the developing world are doing well in gradually improving their statistical capacity in terms of a defined set of parameters.
At the same time, however, what message these impressive ‘score-lines’ give to a layman is a question that may have bothered none. Could it be simply a notion as being understood by a common man that some countries have already crossed the 90% mark and therefore, have not much to achieve further? If so, is it not an impression NSO’s would love to project in unreserved exhibitionism?
Now there lies a fallacy in this notion.
Let’s face it as a question that a common man could ask: Does the score in a true sense reflect a complete image of a county’s statistical capacity? Or, it’s just a fractured image?
One must not miss seeing what the SCI metadata has to say about this by way of explaining the structure, constituents, method, and rationale of the indicator. However, before we are done with the metadata thing, let’s try to understand the fallacy a bit more clearly.
Countries are in fact severely constrained in statistical capacity to meet the challenges confronting the statistical systems as of 2018. By 2016, the nations of the world were already seized with the 2030 Agenda of Sustainable Development Goals (SDGs) that their leaders had committed to achieving in the interest of the humanity and the planet earth.
Attaining the SDG targets under the goals by the year 2030, as they say, crucially depend on the countries’ ability to track the progress with good penetrative statistics. Knowing just the national count of the people to be reached with the good effects is not enough; a much stronger statistical capacity is required to know who they are, where they are located and what challenges they are facing.
A new statistical framework of 230+ indicators has been prescribed for tracking progress towards attaining the targets. An assessment of the IAEG-SDGs of the UN has revealed that as of 13 February 2019: The updated tier classification contains 101 Tier I indicators, 84 Tier II indicators, and 41 Tier III indicators.
This means, over 125 indicators (over 54%) belonging to Tier II, Tier III or multi-tier category have either no internationally established standards or methodology available, or data are not regularly published by the countries. This is a serious capacity deficit and a global syndrome.
Apart from these conceptual and methodological challenges, the overarching SDG principle of leaving no one behind has raised the bar (for the national statistical offices as well as for global monitoring agencies). UNICEF’s report ‘Progress for Every Child in the SDG Era‘ underscores the criticality:
It is no longer enough to monitor progress by global aggregates or national averages alone. Results need to be disaggregated to monitor progress among sub-national groups of people, especially those who are vulnerable such as the girls, children living in remote rural areas or informal urban settlements.
Children and Gender equality are central to the whole of the SDG agenda. 44 child-related indicators are situated under the 17 SDGs. Analyzing these indicators, UNICEF’s report maps them thematically into 5 dimensions of children’s rights:
- the right to survive and thrive (17 indicators under SDG 2 and SDG 3)
- the right to learn (5 indicators under SDG 4)
- the right to be protected from violence (10 indicators under SDG 5, SDG 8 and SDG 16)
- the right to live in a safe and clean environment (10 indicators under SDG 1, SDG 3, SDG 6, SDG 7 and SDG 13)
- the right to a fair chance / to have an equal opportunity to succeed (4 indicators under SDG 1)
The report reveals that data are not available for each of these dimensions in substantial proportions (of the 202 countries covered): 22% missing the data for the dimension ‘survive & thrive’; 63% for the dimension ‘learning’; 64% for the dimension ‘protection’; 24% for the dimension ‘environment’; and 63% missing for the dimension ‘fair chance’.
If we look at the gender responsive indicators of the SDGs (there are 54 in number spread over 12 of the 17 SDGs), the story is no different – “only about 26% of the data necessary for global monitoring of the gender-specific indicators are currently available”.
When this is the picture at the national level, the stories of non-availability of data at the sub-national levels for the child- or gender-related indicators, or for any other sub-populations/groups of people are obviously all the more disquieting, not to speak of the untrodden areas of environment, climate, life below water and the like affecting livability on earth.
So, a person struggling for the SDG data would wonder whether the 2018 SCI score is any indication of the actual current statistical capacity of a country or what? As its structural constitution is defined for a pre-SDG framework and not redesigned post-2015 to account for the data on SDG indicators, SCI in the present form just reflects a statistical capacity for an incomplete basket of statistical deliverables.
The varying degrees of challenges confronting national statistical offices with the advent of the SDGs in not being able to produce the required data, especially disaggregated data, for the lack of capacity could be somewhat fathomed if realistically assessed statistical capacity were known overall for the countries and for the constituent dimensions. A warrant for action is already spelled out in what Target 18 under SDG 17 states:
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.
The target essentially calls for action on the part of development agencies which will eagerly seek information on the intensity and depth of capacity deficit in the face of the SDGs across the world to assess how much effort and energy they may have to apply and where.
The SCI, if re-engineered taking into account the SDG indicators, could provide much of that all-important insight in terms of the score-levels of statistical capacity. Undoubtedly, a new score for 2018 by the hypothetical SDG-laden SCI should invariably be much lower than the existing 2018 score for even the very-high-scoring countries in the World Bank database. Now these hypothetically discordant scores as compared to the existing scorelines could potentially instigate debate within the number cognoscenti.
If it were so happening, anyone, in the same way as what happened to Mark Twain, might easily be tempted to ascribe the denigrated numbers to incredibility of statistics. Save probably those who studied the metadata and appreciated the plausibility of how scores could dwindle on loading the construction framework of SCI with additional parameters as of SDGs; they might easily argue, ‘a student of the fourth standard scoring 90 % overall in the fourth grade examination will in all probability do extremely badly if allowed to sit for an eighth grade test.’ Then that’s a matter of capacity gap – a gap due to the difference in the frame of reference as it would be the case if the SDGs were embedded in the SCI framework.
In the chart depicted in the previous section (also see above), the temporal ups and downs of the lines, however, are happening in spite of no change in the frame of reference of the SCI. These are somewhat akin to fluctuations in productivity (I spoke of in part 1).Change in the frame of reference by placing the SDGs on the SCI framework could materialize if and only when the data would be forthcoming on the majority of SDG indicators.
Since 2004, when Marrakesh Action Plan for Statistics was developed, strategic planning has been recognised to be a powerful engine for guiding the national statistics development programmes (NSDP), increasing political and financial support for statistics, and ensuring that countries are able to produce the data and statistics needed for monitoring and evaluating their development outcomes. A Global Action Plan for Sustainable Development Data has come into being at Cape Town in January 2017 for coordinated action on capacity development for sustainable development data.
Is it still too soon to expect the governments and statistics agencies to be investing resources and energies for augmenting the capacity to produce sustainable development data?
Dreaming a dream
of the spring setting in when the agencies of change go on waving the magic
wands (to transform the data eco-system), the ‘goose’ of golden data lies in
slumber. And thus, SCI keeps on serving with the scores as they are – the only
presentable measures of statistical capacity that does not lay the ‘golden
This blog originated out of a dinner table conversation at the Chakrabarti household, where Satyabrata Chakrabarti (the dad and former Deputy Director General at Central Statistics Office, Government of India), tries to convince his two daughters of the impact and current assessment of a statistical tool for social and economic sectors. While Meghna and I go in a trajectory to assess, its impacts in our fields.
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