How best to interpret CMIE’s consumer survey findings

Context: Centre for Monitoring Indian Economy (CMIE) has been at the forefront of providing necessary data that provide information about the average Indian household. However, some discrepancies in the survey methodology brought the CMIE data under question.

CMIE provided the surveys during lockdowns that helped to understand the scale of urban unemployment and distress migration of urban workers to rural farms. It received admiration for providing real-time data, free from state influence.

What are the discrepancies in CMIE survey?

CMIE reports fewer women in the workforce than the Periodic Labour Force Survey (PLFS).

Women’s labour force participation rates estimated by CMIE are roughly half the ‘official rate’ estimated by the PLFS.

CMIE shows a higher share of respondents with post-office savings, pension (or provident fund) plans, and insurance products (life and health) compared to the All India Debt and Investment Survey (AIDIS) 2019.

What are the concerns raised against the CMIE survey?

First, Very poor, uneducated, and very rich have found less representation in the survey, compared to National Family Health Survey (NFHS, 2019-21).

Second, the sampling theory demands that surveys select households in the primary sampling unit (typically villages or urban wards) randomly from a list of all households in that unit. However, CMIE did not follow this theory. It asked field staff to count the number of households in the street after entering there and pick a random number, between 5 and 15, to select households. After completing the main street, the person moves to the inner streets. Thus, there was a lack of a complete listing, the absence of a random start, and the use of an ‘ad-hoc’ interval (5-15) to select households.

Third, this method is mainly problematic in rural areas, where residential arrangements are not random. Richer households often tend to be clustered on the Main Street and poorer households on the periphery.

CMIE is currently investigating its survey methods for any biases. Thus, until these reviews and corrections are done, these surveys should be used cautiously.

Source: This post is created based on the article “How best to interpret CMIE’s consumer survey findings” published in Live Mint on 7^th June 2022.