There’s no One to fill Mahalanobis’s Shoes

Context: Framing data-based policies during the COVID-19 outbreak is needed for the welfare and development of people affected by the pandemic.

Role played by P.C. Mahalanobis to promote data-driven governance:

• Bengal Famine Survey: He conducted a large-scale sample survey of Bengal’s famine-ravaged villages to assess the estimate of the number of people affected. 

• Statistical heritage: He built up a strong and trustworthy statistical heritage in India. 

• Large-scale sample surveys: He envisaged large-scale sample surveys as statistical engineering rather than a pure theory of sampling.

• Institutional setup: He was instrumental in establishing the NSS and the Central Statistical Organization.

• Uncompromised Data Accuracy: He set up independent supervisory staff during the conduct of field operations by the National Sample Survey (NSS) for the collection of reliable data.

• Technology promotion: The Mahalanobis led Indian Statistical Institute procured India’s ?rst computer in 1956 and the second in 1959.

Issues with data collection in India:

• Socio-political reasons: Even in the pre-COVID era, the surveyors were widely reported to have face resistance from the people.

• Conducting a survey in the age of contagion is even much harder.

• Non-accurate data: Accuracy of different sorts of data from, economic data to Covid-19 data is under the scanner.

Need for data-based policies to overcome the impact of COVID-19 crisis:

• Data collection: We need estimates of the millions whose jobs and economic conditions will deteriorate in COVID-19-hit India. 

• For E.g. The extent of feasibility, success, and problem of online access needs to be properly estimated.

• Importance given to accuracy: Need to have built-in cross-checks and to get an estimate of errors in sampling like Inter-Penetrating Network of Subsamples to improve data accuracy.

• Use of technology: In addition to using masks and sanitizers while sending investigators for a socioeconomic survey.

•  Using different sorts of secondary data.
•  Adopting big data technology.

• Need for a statistician for analyzing the data for framing data-based policy decisions for human welfare and national development.