Not-so-classical measurement errors: a validation study of Homescan
National Bureau of Economic Research, 2008 - Business & Economics - 29 pages
We report results from a validation study of Nielsen Homescan data. We use data from a large grocery chain to match thousands of individual transactions that were recorded by both the retailer (at the store) and the Nielsen Homescan panelist (at home). First, we report how often shopping trips are not reported, and how often trip information, product information, price, and quantity are reported with error. We focus on recording errors in prices, which are more prevalent, and show that they can be classified to two categories, one due to standard recording errors, while the other due to the way Nielsen constructs the price data. We then show how the validation data can be used to correct the impact of recording errors on estimates obtained from Nielsen Homescan data. We use a simple application to illustrate the impact of recording errors as well as the ability to correct for these errors. The application suggests that while recording errors are clearly present, and potentially impact results, corrections, like the one we employ, can be adopted by users of Homescan data to investigate the robustness of their results.
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Aguiar and Hurst algorithm analysis average column conditional conditional distribution consumers data construction process demographics distinct households distinct UPCs distribution of rl Economics entire Homescan example expenditure Female age focus fraction fully employed greater than 0.7 Homescan Household Homescan panelist Homescan price Retailer Homescan record Homescan trip item UPC latent variable loyalty card discount matched items matched large trips matched trips measurement error medium trips mismeasured NBER Nielsen Homescan data number of distinct number of households observations opportunity cost overall p-value paper percent price Homescan price price imputation price paid ratio recording errors regression reported in Homescan reported price reported quantity researchers retailer trip retailer's data rl greater scan second step shopping trips small number statistics store and day store-days store-level data summary statistics transactions trip in Homescan trip information trips reported UPC fixed effects users of Homescan validation data validation sample validation study zip code