使用家用扫描仪数据了解支付卡的偏好

Federal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print) Understanding Preferences for Payment Cards using HouseholdScanner Data 2025-096 Rysman, Marc, Shuang Wang, and Krzysztof Wozniak (2025).“Understanding Prefer-ences for Payment Cards using Household Scanner Data,” Finance and Economics Dis-cussion Series 2025-096. Washington: Board of Governors of the Federal Reserve System,https://doi.org/10.17016/FEDS.2025.096. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment.The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or the Understanding Preferences for Payment Cards using Marc RysmanBoston UniversityShuang Wang†Charles River AssociatesKrzysztof WozniakFederal Reserve BoardSeptember 15, 2025 Abstract We use consumer panel scanner data to examine households’ payment choices, a new ap-plication of such data. In particular, we study the long-term shift towards payment cards, aswell as the role of transaction size in determining choices. We find that idiosyncratic householdpreferences are a key driver of payment choice.Our estimates suggest that transaction size,while important, may have a smaller effect on payment choice than previously thought, and 1Introduction Over the past several decades, the US payments system has shifted from paper payment instru-ments, namely cash and check, to digital instruments, such as debit cards and credit cards. Thisshift is important because digital payments are typically regarded as superior in many dimensions: play a large role in the United States. Anecdotal evidence of young people adopting digital paymentwhile older households persist with cash and check suggests that demographics and heterogeneitybetween households could be key to explaining the enduring popularity of paper payment instru- This paper studies the determinants of payment method choice in both the short and longterm. In the short term, across shopping trips, we focus on the transaction size as an importantdeterminant. Transaction size has been central to the discussion of payment choice, with householdsmore likely to pay with non-cash instruments for larger transactions. Previous papers, such as Klee(2008) and Wang and Wolman (2016), have studied the effect of transaction size on payment choice We also study the long-term evolution of payment method choice. While it is natural to ascribechanges in card use to changes in household preferences, alternative explanations are that thereare shifts in the composition of transaction volumes or transaction sizes.For instance, if olderhouseholds prefer cash and check while younger households prefer cards, gradual growth over timein the number of transactions made by younger households would result in an aggregate increase in This paper leverages a consumer scanner dataset to obtain transaction-level data on paymentchoice. NielsenIQ maintains a panel of households that tracks in great detail their purchases of foodand non-food items for home use across all retail outlets in all U.S. markets (except Alaska and of Chicago, which recently made the payment choice data available.2 academic work has used such data to study payment choice. A recent paper that makes use of thatpayment information in the NielsenIQ data is Wang (2025), which integrates it into a larger study In order to fully capture the many factors driving payment choice, we estimate a multinomialdiscrete choice model with household-quarter-choice fixed effects.With over 110,000 households,28 quarters of data, and 3 payment choices, our richest specification translates into more than 2.4million fixed effects. Such a setting presents challenges to maximum likelihood estimation. We rely While our analysis confirms that transaction size is an important determinant of short-termpayment choice, accounting for household heterogeneity suggests the effect is not just smaller inmagnitude, it also varies considerably across households.In particular, we find that going fromthe 1st quartile of the empirical distribution of transaction size,$11.46, to the 3rd quartile,$55.40,leads to, on average, a 21.6 percentage point increase in the probability of the payment being madeusing a card.Notably, we find that our model specification with a full set of household-quarter- The long-term analysis focuses on the increase in card usage share of more than 13 percentagepoints over the seven-year period in our data. We use our model to decompose the factors drivingthis change into (a) changes in individual household preferences, (b) changes in the number andvalue of transactions, and (c) entry and exit of households from the sample. Our results show that Overall, our paper makes several contributions.We demonstrate that consumer scanner datacan be a powerful tool for studying payment choice. We present new results on