Editor’s note: In this two-part blog series, the author explores the issue of the statistical capacity deficit in the current global context. In part 1, his main aim is to uphold the interplay of the system’s capacity, it’s efficacy and productivity, impacting the socio-economic sphere globally, especially the developing nations.
I have been tickling my imagination to mentally visualize the ‘golden eggs’ laid by a goose with ‘statistical capacity’. One day, no sooner I closed my eyes than my thought process veered off the track and entered the dull territory of numbers. The detour thereafter is the story here – passing on a road lined with numerical landmarks shining gloriously with images of statistics atop in some semblance of ‘jewels in the crown’.
Indeed, in the beginning, as I kept on stirring my imagination for a while, I did make an effort to identify something royal about numbers. It’s then I realized the connection that ‘statistical system’ has with the business of the king. The journey, therefore, started with me being captivated by the ideas and concepts of a system’s capacity that enables rulers to indulge in statistical stockpiling, although they turned out to be harmlessly mundane and theoretical as in the descriptions to follow.
A system, as the wise men say, has to perform a given set of tasks routinelyy. So, it must have some capacity to do so. So a system, or for that matter a person or a machine which can produce something has a capacity to create a certain quantity of something in a given period. This is quite trivial. However, what’s not so nugatory is that the capacity does not necessarily remain equally productive all the time. When capacity is not equally productive, i.e., sometimes less productive and sometimes more, then the wise men say that it’s a matter of efficiency of the system or the person.
So, how do I know the efficiency of the ‘goose that laid golden eggs’? Is that conceivable?
For a moment I’m inclined to believe: yes, it is. For there is a measure called productivity to quantify the efficiency of a person, machine, factory, or a system. Who doesn’t know that inputs (i.e., labor, material, energy, cost, time, etc.) are used by the system and converted into useful outputs? So this notion about the effectiveness of any productive effort termed productivity is measured in terms of the rate of output per unit of input.
A trick, however, lies here in the fact that even when you keep the inputs fixed, the productivity can vary.
That’s because there are factors which, uncontrollably, unnoticeably or otherwise, influence the output to change from time to time. These factors, even when known for the role they play, often remain unrecognized or unexplained. If they are not explainable or perceptible, variation in productivity may appear strange, or even intriguing. And you know it very well that Mark Twain was intrigued; intrigued by his own productivity figures. Mark Twain wrote in 1906,
Figures often beguile me, particularly when I have the arranging of them myself; in which case the remark attributed to Disraeli would often apply with justice and force: ‘There are three kinds of lies: lies, damned lies, and statistics….
What puzzled Mark Twain was the output-to-time ratio as a measure of his ability to write words (i.e., writing productivity) since the ratio when measured on two separate occasions differed violently in magnitude. This is quite an analogy to the cases where we observe variation in human performance (nay, productivity) due to factors not easily visible/ discernible at the time of measuring it. As for example, weather, time of the day or, level of comfort in the room or something of that sort having an effect on the mood or psychological/mental condition of the performer.
However, the great author in the pleasure of his own creative inspiration perhaps made himself imperceptive to the factors playing on his own productive capacity. And as a result, that remark of Disraeli that he immortalized in his auto-biography made statistics damnable forever.
Nevertheless, in a contrasting scene of reality, statistical numbers indicating the state of development often fail to reveal the vital things when they do matter a lot. Even those produced in the mills of highly reputed institutions or by the national statistical offices acclaimed for their high statistical standards are frequently damned for such reasons.
Heaps of literature on concepts and methods for collection of data, a compilation of statistics and dissemination of results including analytical tools have been produced and promoted under the aegis of the United Nations Statistical Commission (UNSC) over the last 70 years just to make official data all over the world sounding credible and dependable. These works covering almost all fields of economic and social relevance in development have helped to strengthen national statistical systems, especially the statistical capacity building.
By ‘statistical capacity’ they mean a nation’s ability to collect, analyze and disseminate high-quality data about its population and economy. In this sense, however, countries have attained varying levels of capacity at the national level, the most significant difference being in the ability to collect the data. The collection of data for calculating recommended statistical measures at regular intervals involves systemic rigor and requires considerable resources for conducting large scale operations. The situation becomes all the more challenging when the system gets to respond to new realities, e.g., evolution of the measurement paradigm necessitated by new statistics-based evaluation.
No system, in fact, can very quickly adapt to significant reforms in the statistical framework that may evolve as a necessity. The national policies which are able to quickly respond and make structural/procedural transformation are really robust in statistical capacity.
The World Bank developed a Statistical Capacity Indicator (SCI) for assessing the capacity of a country’s statistical system. It is a composite score based on a diagnostic framework assessing the areas: methodology, data sources, and periodicity and timeliness. Countries are scored against 25 criteria in these areas, using publicly available information and/or country input. The overall statistical capacity score is then calculated as a simple average of all the area scores on a scale of 0 – 100. From the scores of nearly 140 developing countries of the world in the last 15 years, what is evident is a clear trend of improvement for most of the nations, though there are times for falls after rises.
Afghanistan, for example, rose from almost a state of void (24.4 in 2004) to 50.0 in 2018; it’s a success story of a devastated nation. The period covered coincides with the countries being engaged with the Millenium Development Goals (MDGs) and therefore, the third dimension: ‘periodicity and timeliness’ looks at the availability of key socio-economic indicators, of which 9 are MDG indicators.
It’s interesting to see how some of the major developing countries have fared. Mexico, a high performing country with a scoreline of 74.4 in 2004, 85.6 in 2010, and 92.2 in 2015, finished at 96.7 in 2018 – a story of steady improvement; especially against an ordinary scenario for its own region (Latin America & the Caribbean) with the average score hovering in the range of 74 – 78 during the period, it’s remarkable.
Against South Asia’s regional score ranging between 65 and 76, India finished at 91.1 in 2018 moving through 78.9 in 2004, 81.1 in 2010 and 77.8 in 2015; whereas Bangladesh moves from 70.0 in 2004 to 72.2 in 2018 after a rise to 76.6 in 2015; and Pakistan moved from 73.3 in 2004 to 78.9 in 2018.
Indonesia, another major country in East Asia and the Pacific region having a regional score of 77.5 in 2018, moved from 86.7 in 2004 to 90.0 in 2018. On the other hand, the Philippines and Thailand of this region remained almost static during the period scoring in the range 81 – 88. In sub-Saharan Africa, Rwanda did remarkably well moving from 61.1 in 2004 to 78.9 in 2018 and exceeding the regional score of 62.4 in 2018 by a considerable margin, whereas Ghana, not so worse a beginner moved up by 20 points from 51.1 in 2004 to 71.1 in 2018; and Tanzania moved from 67.8 to 71.1 during the corresponding period.
But is this enough? In the coming blog, we will discuss the fallacy in the SCI system and how it fails to give a credible picture of the statistical capacity of the developing countries. I will try to answer the following question – is SCI a proper tool in the present context to measure with? Also, I will talk about how the Sustainable Development Agenda requires a new measurement paradigm and offers opportunities for national systems to evolve into core entities of country-specific data eco-systems for the same.
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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