For central banks who study the use of cash, acceptance of card payments is an important factor. Surveys to measure levels of card acceptance and the costs of payments can be complicated and expensive. In this paper, we exploit a novel data set from Hungary to see the effect of stratified random sampling on estimates of payment card acceptance and usage. Using the Online Cashier Registry, a database linking the universe of merchant cash registers in Hungary, we create merchant and transaction level data sets. We compare county (geographic), industry and store size stratifications to simulate the usual stratification criteria for merchant surveys and see the effect on estimates of card acceptance for different sample sizes. Further, we estimate logistic regression models of card acceptance/usage to see how stratification biases estimates of key determinants of card acceptance/usage.
We thank the Magyar Nemzeti Bank, in particular Lóránt Varga and Gábor Sin, for facilitating access to the data. Tamás Ilyés is no longer an employee of the Magyar Nemzeti Bank. We also thank Jean-Louis Combes, Pierre Lesuisse and participants of the Doctoral Seminar at the University of Auvergne School of Economics. We are grateful to the editors and referees for their helpful comments. The views expressed in this paper are those of the authors. No responsibility for them should be attributed to the Bank of Canada or the Magyar Nemzeti Bank. All remaining errors are the responsibility of the authors.
Henry, C.S. and Ilyés, T. (2019), "Effectiveness of Stratified Random Sampling for Payment Card Acceptance and Usage", The Econometrics of Complex Survey Data (Advances in Econometrics, Vol. 39), Emerald Publishing Limited, Bingley, pp. 35-57. https://doi.org/10.1108/S0731-905320190000039002
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