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The Potential Use of In-Home Scanner Technology for Budget Surveys

In: Improving the Measurement of Consumer Expenditures

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  • Andrew Leicester
Abstract
This paper considers the potential role of in-home scanners as a method of data collection for national budget surveys such as the Consumer Expenditure Survey. A detailed comparison is made between scanner data and diary-based budget survey data for food at home in the UK. Levels of recorded spending are lower in scanner data for all commodities, but patterns of spending are similar. A large part of the difference is explained by households in the scanner survey failing to record any food spending in a given week. The gaps are widened once demographic differences between the surveys are controlled for. There is clear evidence that short-term diaries do not accurately capture household food spending patterns given infrequency of purchase for some commodity groups. Conditional on store choice, demographics play little role in explaining food spending patterns in scanner data. This suggests that attempts to impute detailed spending patterns from aggregate store-level spending would be difficult.
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Suggested Citation

  • Andrew Leicester, 2014. "The Potential Use of In-Home Scanner Technology for Budget Surveys," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 441-491, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:12669
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    1. Chen Zhen & Justin L. Taylor & Mary K. Muth & Ephraim Leibtag, 2009. "Understanding Differences in Self-Reported Expenditures between Household Scanner Data and Diary Survey Data: A Comparison of Homescan and Consumer Expenditure Survey," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 31(3), pages 470-492, September.
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    Cited by:

    1. Suel, Esra & Polak, John W., 2017. "Development of joint models for channel, store, and travel mode choice: Grocery shopping in London," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 147-162.
    2. Christopher D. Carroll & Thomas F. Crossley & John Sabelhaus, 2014. "Introduction to "Improving the Measurement of Consumer Expenditures"," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 1-20, National Bureau of Economic Research, Inc.
    3. Kuroda, Yuta, 2022. "The effect of pollen exposure on consumption behaviors: Evidence from home scanner data," Resource and Energy Economics, Elsevier, vol. 67(C).
    4. Jonathan A. Parker & Nicholas S. Souleles & Christopher D. Carroll, 2014. "The Benefits of Panel Data in Consumer Expenditure Surveys," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 75-99, National Bureau of Economic Research, Inc.

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    More about this item

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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