Right data, wrong data: Statistical sampling and the making of modern agriculture in India

Area of Science:

• Agricultural Economics
• Statistics
• Indian Economic History

Background:

• Post-independence India faced a critical food deficit, necessitating accurate data for policymaking.
• Early efforts focused on quantifying food production to bridge the gap between supply and demand.

Purpose of the Study:

• To analyze the historical development and adoption of statistical methods for food production estimation in India.
• To explore the influence of various factors on the selection and implementation of these statistical tools.

Main Methods:

• Examination of statistical survey methodologies, including random sampling and crop-cutting techniques.
• Analysis of historical data from P.C. Mahalanobis and the Indian Council of Agricultural Research (ICAR) under P.V. Sukhatme and V.G. Panse.

Main Results:

• The adoption of statistical tools was shaped by regional revenue systems, administrative structures, and inter-institutional power dynamics.
• Discrepancies between statistical approaches, particularly between the Indian Statistical Institute (ISI) and ICAR, contributed to data inaccuracies persisting until the late 1950s.

Conclusions:

• The evolution of statistical methods for food production assessment in India was a complex process influenced by scientific, political, and administrative factors.
• Persistent challenges with data accuracy highlight the ongoing struggle for reliable agricultural statistics in India.