Demand Analysis with a Thin Price Sample

TL;DR

This paper introduces a new sampling method for demand analysis that reduces respondent burden by collecting price data from only one household per first stage unit, while maintaining accuracy in estimating demand elasticities.

Contribution

The study proposes a novel, less burdensome sampling scheme for demand elasticity estimation that requires fewer data points without sacrificing precision.

Findings

The new sampling scheme accurately estimates demand elasticities.

It significantly reduces interview burden compared to conventional methods.

Results are validated using NSS 2011-12 vegetable data.

Abstract

For about 125 items of food, the Consumer Expenditure Survey (CES) schedule of the Indian National Sample Survey asks the interviewer to obtain both quantity and value of household consumption during the reference period from the respondent. This would appear to put a great burden on the respondent. But it is likely that the price usually paid is almost the same within each first stage unit (fsu). The present work proposes a new sampling scheme to estimate demand elasticities of essential food items. While the conventional sampling method used in practice (e.g. in NSS consumer expenditure survey) involves seeking price information from many households sampled from a fsu, the proposed procedure involves only one household chosen randomly from every fsu for price data collection and thus requires much less interview burden. Using unit records for vegetable items in the NSS's 2011-12 CES, our results show that in spite of requiring much less data, the new scheme captures the household food consumption behavior as precisely as before.