Better performance in statistics

India performs reasonably well on data use and data services, but performance is middling on data sources and inferior on data products and data infrastructure.

data use | data services
In 2019, India’s scores were 70.4 overall, 80.1 for data use, 88 for data services, 60 for data products, 68.9 for data sources and 60 for data infrastructure. (Representational Image)

Bibek Debroy & Aditya Sinha

Earlier (2004-21), World Bank had a Statistical Capacity Indicator (SCI) for different countries.  Post-Covid, this has been replaced by a Statistical Performance Indicator (SPI). There are five pillars—data use, data services, data products, data sources and data infrastructure.  Indicators under each pillar are aggregated, and those are aggregated again to get SPI.  While there is rationale for such a comprehensive view, often, when we think of government data, we have Pillar 2 and Pillar 4 in mind.  On Pillar 2, “The data services (output) pillar is segmented by four service types—(i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access.”  On Pillar 4, “The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen-generated data.”  

India performs reasonably well on data use and data services, but performance is middling on data sources and inferior on data products and data infrastructure. Importantly, between 2016 and 2019, there has been little change in scores.  Across 174 countries, India may be in the fourth quintile.  But that’s not true of every pillar, and India ought to perform better.

In 2019, India’s scores were 70.4 overall, 80.1 for data use, 88 for data services, 60 for data products, 68.9 for data sources and 60 for data infrastructure.  The relative weakness is for Pillars 3, 4 and 5.  Obviously, SPI is still a work in progress, and every conceivable indicator under the pillars isn’t included yet.  If one scrutinises indicators under Pillar 3, those have to do with indicators for SDGs 1-17.  Pillar 4 covers censuses and surveys, administrative data and geospatial data.  “The following censuses and surveys are considered: Population & Housing census, Agriculture census, Business/ establishment census, Household Survey on income/consumption/ expenditure/ budget/ Integrated Survey, Agriculture survey, Labour Force Survey, Health/Demographic survey, Business/ establishment survey.” 

Administrative data today covers civil registration and vital statistics.  “An ideal indicator for … would include a score based on the density of administrative data available in sectors including social protection, education, labour, and health. However, social protection, education, health, and labour admin data indicators are not included because of lack of established methodology.”  For geospatial data, “Geospatial data available at 1st Admin Level. This data source from Open Data Watch focuses on data availability at the sub-national level and provides a partial understanding of a country’s ability to produce geospatial data.”

Finally, other than SDGs, Pillar 5 is about standards and methods.  “This set of indicators is based on countries’ use of internationally accepted and recommended methodologies, classifications and standards regarding data integration. These indicators help facilitate data exchange and provide the foundation for the preparation of relevant statistical indicators. The following methods and standards are considered: System of national accounts in use, National Accounts base year, Classification of national industry, CPI base year, Classification of household consumption, Classification of status of employment, the Centre’s accounting status, Compilation of government finance statistics, Compilation of monetary and financial statistics, Business process.”

Sometimes, when India doesn’t perform well on cross-country rankings, valid questions are raised about methodology, especially if perception-based questionnaires are involved.  That’s not true of something like SPI.  Other World Bank rankings have been used to ensure improvements.  A similar trigger should now be used to improve the statistical system.  Take Pillar 5.  India uses international standards and methods, for the most part.  Why is the score low?  Probably because the base years are old.  India’s administrative data are remarkably good and almost real-time.  India doesn’t get the credit for this because administrative data are restricted to civil registration and vital statistics.  We know the problems with census and surveys:delays, faults with sampling design of surveys, inconsistency in questions across various surveys, inability to use technology and so on.  These weaknesses of Pillar 4 spill over into Pillar 3.  Even then, on Pillar 3, we are told, “The primary data source is the UN SDG database.”  If you sift through this database, there is no obvious reason why India should score badly on data for Pillar 3.  Data, across indicators, are reasonably recent. Perhaps we have three problems.  First, there is indeed a problem with censuses, surveys, and base years.  Second, even when there is no real problem, there is a perceived problem because MoSPI doesn’t take these issues up with relevant agencies.  Third, India should not only use rankings like SPI to specifically target improvements but also interact with World Bank to improve and refine SPI further.  An instance is getting credit for administrative data.  The blame, if not the buck, partially stops with MoSPI.

(Authors are Respectively, chairman, and additional private secretary (policy & research), EAC-PM

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First published on: 02-05-2023 at 04:30 IST