Measuring Income and Wealth Effects on Private-Label Demand with Matched Administrative Data

Calogero Brancatelli, Adrian Fritzsche, Roman Inderst and Thomas Otter

No 16918, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: Industry sentiment and practitioners’ observations link income and wealth to private-label demand. The intuition is that decreasing income and wealth increase the demand for (cheaper) private labels. Recent academic research focusses on measuring the causal effect of income and wealth changes on demand for private labels. Whereas plausible causality is harder to establish in aggregate, time series analyses, such analyses suggest large effect sizes (e.g. Lamey, Deleersnyder, Dekimpe, and Steenkamp 2007). An individual-level perspective greatly facilitates plausibly causal estimates (Dubé, Hitsch and Rossi 2018) but poses measurement challenges. We overcome these challenges by linking household scanner data to administrative data. We analyse individual-level private-label shares measured in household scanner data as a function of income and wealth, both from a linked administrative database in the Netherlands in the period from 2011–2018 and both aggregated over all household members (rather than only from the main earner). We find that relying on within-household variation in surveyed income data attenuates income effects relative to using that from administrative data. Yet, we find an economically small effect. Using our direct measures of wealth, we find a statistically reliable but again economically small effect of financial wealth on private-label shares. Wealth from housing does not proxy this effect, nor does housing wealth seem to independently influence private-label shares.

Keywords: Private-label demand; Income and wealth effects; Recession; Consumer demand (search for similar items in EconPapers)
JEL-codes: D12 M30 (search for similar items in EconPapers)
Date: 2022-01
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Journal Article: Measuring Income and Wealth Effects on Private-Label Demand with Matched Administrative Data (2022)
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