The impact of contactless payment on cash usage at an early stage of diffusion

Trütsch, Tobias Article The impact of contactless payment on cash usage at an early stage of diffusion Swiss Journal of Economics and Statistics Provided in Cooperation with: Swiss Society of Economics and Statistics, Zurich Suggested Citation: Trütsch, Tobias (2020) : The impact of contactless payment on cash usage at an early stage of diffusion, Swiss Journal of Economics and Statistics, ISSN 2235-6282, Springer, Heidelberg, Vol. 156, Iss. 1, pp. 1-35, https://doi.org/10.1186/s41937-020-00050-0 This Version is available at: https://hdl.handle.net/10419/259747 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Swiss Journa l o f Economics and Statistics Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 https://doi.org/10.1186/s41937-020-00050-0 ORIGINAL ARTICLE Open Access The impact of contactless payment on cash usage at an early stage of diffusion Tobias Trütsch Abstract This paper explores the impact of contactless payment on consumers’ demand for cash at an early stage of diffusion. The specific devices that are investigated are debit and credit cards, in which the feature is embedded. A novel balanced panel dataset drawn from representative surveys on consumer payment behavior in the USA from 2009 to 2013 is analyzed to account for unobserved heterogeneity in cash usage. The results show that contactless credit and debit cards exert no statistically significant effect on cash usage after controlling for unobserved heterogeneity. Consumers’ decision to use contactless payment is an endogenous choice. Card-affined individuals replace conventional card payments with contactless card payments. Hence, the overall effect on cash usage remains unaffected. Keywords: Contactless payment, Money demand, Cash usage, Credit cards, Debit cards JEL classification: C33, D12, E41, E42 1 Introduction Cash is still the most prominent payment method at the point-of-sale (POS) in numerous developed countries, especially at low transaction values (e.g., von Kalckreuth et al. (2014); Bouhdaoui and Bounie (2012); Arango et al. (2015); Bagnall et al. (2016)). However, the promotion of various technological innovations in retail payment markets such as credit, debit, and prepaid cards has led to a decline in cash usage in recent years (e.g., Lippi and Secchi (2009); Amromin and Chakravorti (2009); Stix (2003)). Recent innovative payment means (e.g., contactless payment) attempt to mimic the desirable features of cash. They promise efficient and convenient payment services that may reduce the transaction costs of payment for consumers. Contactless payment is therefore seen as a more competitive payment alternative to traditional cash payments compared to conventional payment cards. Thus, discussing the prospects of cash usage is high on the agenda of central banks, which are responsible for cash distribution. Correspondence: [email protected] University of St. Gallen, Holzstrasse 15, 9010 St. Gallen, Switzerland This paper explores the effect of contactless credit and debit cards on cash usage in the early stage of diffusion. The contactless antenna is usually embedded in conventional payment cards such as debit and credit cards. Contactless cards include a chip and a simple wireless sign. The sign is the only distinction to traditional chip cards. Contactless payment is based on near-field communication (NFC) technology. This standard radio communication technology allows paying within a 4-cm range by waving or tapping the payment card. A signature or PIN verification is not necessary below a certain transaction value. Contactless payment therefore offers instantaneous payment, speed, and convenience compared to traditional cards. Polasik et al. (2013) found that contactless payment cards compete with cash payments with respect to speed and under certain conditions even outperform cash. The speed of a transaction is key to determining the choice of payment instruments (e.g., Klee (2006); Jonker (2007)). Thus, I hypothesize that contactless payment adopters are more likely to exhibit lower cash usage. The effect on the number of cash transactions is expected to be larger comparedtocashexpenses.Thisisbecausecontactless © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 2 of 35 cards—due to their improved speed and convenience— are likely to substitute low cash value payments, which are high in frequency but have low budget impact. Analyzing the effect of contactless payment on cash usage is relevant for three reasons. First, one of the main responsibilities of central banks is to provide efficient payment services to ensure financial system stability. The number and transaction size of cash payments affect the efficiency of payment systems, as expressed in social welfare costs. van Hove (2008) measured the costs of cash usage in the Netherlands as being 0.48% of GDP. Schmiedel et al. (2013) estimated the substantial costs of cash, which amount to one half percent of GDP for the EU-27 member states. Thus, understanding the demand for cash is crucial to evaluating the costs of payment systems. Second, central banks are the sole institutions that are entitled to issue legal tender money. The assessment of future trends in cash demand is a relevant monetary policy issue. More contactless payment cards could imply lower cash in circulation and hence lower seignorage income. Third, the literature has shown that money demand might react less sensitively to interest rates due to technological improvements in payment processing. This might result in lower welfare costs of inflation (Alvarez and Lippi (2009))1. Three papers have so far examined how contactless payment impacts cash demand. Fujiki and Tanaka (2014) found that average cash balances do not decrease with the adoption of contactless payment and under some specifications even increase. They used household-level survey data from Japan. Fung et al. (2014)showedthatcontactless credit and stored-value cards reduce average cash usage for transactions in terms of both value and volume2. They analyzed consumer-level survey data from Canada. However, both studies failed to purge unobserved heterogeneity due to data restrictions. Chen et al. (2017) used household panel data from Canada to account for endogeneity. They encountered a high attrition rate of about 50%. However, they applied refreshment samples to account for this high attrition rate. They found no statistically significant impact of contactless credit cards on cash usage, neither in terms of value nor of volume after controlling for non-ignorable attrition and unobserved heterogeneity. This paper contributes to existing literature in three respects. First, it is essential to control for unobserved heterogeneity when examining the effect of contactless 1Welfare costs of inflation are the amount of less seigniorage revenue due to higher nominal interest rates (real rate plus expected inflation) (Briglevics and Schuh (2013)). 2Fung et al. (2014) estimated a decline in cash value due to contactless credit and stored-value cards by roughly –14 and –12% and a reduction in cash volume by around −13 and −15%, respectively. payment on cash usage Chen et al. (2017). I draw on a unique balanced panel dataset from 2009 to 2013. Using such rich datasets represents a novel approach, which does not suffer from non-ignorable attrition. Second, I investigate the effect of contactless debit cards on cash demand and thereby fill an important gap in the literature. This is because debit cards are the most popular cashless payment method. Third, I analyze the impact of contactless payment on cash usage in the USA, one of the biggest payment markets. This is important as there is still missing empirical evidence of contactless payment in the USA payment landscape. I find evidence that contactless credit and debit cards exert no statistically significant effect on cash usage in the early stage of diffusion. I account for unobserved heterogeneity in cash usage by using the fixed-effects model. Consumers’ decision to adopt contactless payment is an endogenous choice. Card-affined individuals replace conventional card payments with contactless card payments. The overall effect on cash usage therefore remains unaffected. I proceed as follows. Section 2reviews the relevant literature. Section 3provides background information on the theoretical framework of the estimation strategy as well as the institutional background of contactless payment in the USA. The data are described in Section 4, followed by empirical specification in Section 5.Section6discusses theresultswhileSection7draws conclusions and provides a research outlook. 2 Literature review This paper is related to the literature of money demand and the future use of cash with regard to technological improvements. Efforts to estimate precise parameters of the traditional money demand function in light of technological change have produced an important body of literature (e.g., Attanasio et al. (2002); Lippi and Secchi (2009); Alvarez and Lippi (2009); Briglevics and Schuh (2013)). Some scholars have estimated the share of cash transactions at the POS and its future usage with respect to payment enhancement. The effect of payment innovations on aggregate cash demand is not clear from an empirical point of view. Columba (2009) studied the effect of ATMs and POS terminals on the demand for currency and narrow money M1. He showed that the impact on cash in circulation is negative, whereas it positively affects narrow money. Others have found that modern payment technologies have little effect on currency usage, mainly due to its superior characteristic of anonymity. Amromin and Chakravorti (2009) showed that demand for low denomination notes and coins decreases as debit card usage increases. This is because merchants need less purse Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 3 of 35 money for change. The demand for high denomination notes is less affected because individuals use them for non-transactional purposes such as hoarding and illegal activities. This was highlighted by Drehmann et al. (2004), who pointed out that POS terminals negatively and ATMs positively affect demand for small banknotes. Snellman et al. (2001) argued that debit and credit cards are the main drivers of substituting away from cash, while the effect of ATMs remains ambiguous (cf. Humphrey (2004)). Another strand of the literature has employed household survey data to more precisely study cash usage. Stix (2003) found that debit cards negatively affect demand for purse cash in Austria. von Kalckreuth et al. (2009)argued that credit cards have no impact on the number of cash transactions in Germany. However, Huynh et al. (2014) reported that merchants’ acceptance of payment cards has a substantial negative impact on the demand for cash in Austria and Canada. 3 Background information 3.1 Theoretical background I derive the theoretical background for the estimation strategy and the empirical methodology used here from McCallum and Goodfriend (1987) framework. Attanasio et al. (2002) presented this framework as an extension of the traditional Baumol-Tobin model (Baumol (1952); Tobin (1956)). The extended model takes into account innovations in transaction technologies. Accordingly, individuals adopt payment innovations if the benefits of adopting the technology exceed the costs. Adoption costs of contactless payment may include (one-time) operational learning costs, monetary costs of using and adopting the payment card (e.g., annual fees, surcharges), and the availability of contactless terminals. Benefits of payment innovations increase with improving transaction efficiency. This makes adopting contactless payment more likely since it allows for a fast payment process. Polasik et al. (2013) showed that contactless paymentcardsarethefirstpaymentmethodtobefasterthan cash. The transaction speed is one of the most important factors to determine the choice of a payment instrument (e.g., Klee (2006); Jonker (2007)). This is because it reduces queue lines and thus consumers’ payment costs (Brits andWinder (2005)). Younger consumers in particular react more negatively to longer payment processing than older consumers and are therefore more likely to adopt contactless payment (Borzekowski and Kiser (2008)). Benefits also tend to rise with more consumption expenditures and higher transaction values because more spending is subject to longer transaction times. Consequently, the rate of adopting contactless payment varies by consumers’ demographic characteristics (e.g. income, age, education), which determine their opportunity costs of paying. High-income individuals are therefore more likely to adopt contactless payment to reduce their transaction costs of paying. This is because their opportunity costs of paying tend to be higher than for low-income individuals. For the same reason, cash demand tends to be lower for contactless adopters than non-adopters because cash payments take more time to settle than contactless payments (cf. Polasik et al. (2013)). In general, individuals need time to undertake transactions. As a form of exchange and financial innovations, money reduces the transaction time (Attanasio et al. (2002)). In the traditional Baumol-Tobin setting, individuals face a trade-off between holding liquidity in form of money, in order to carry out transactions, and the forgone interest paid on deposited assets. However, in Attanasio et al. (2002) extended version of the model, consumers choose optimal money holdings to trade off transaction costs against the costs of holding cash. Transaction time costs originate from the shadow value of time and from the “shoe-leather” costs of withdrawing cash. Hence, consumers demand optimal money holdings by minimizing both the transaction time costs and the forgone interest paid on deposited assets subject to their consumption expenditures. Improvements in transaction technology (e.g., contactless payment) and lower transaction costs therefore lessen the demand for cash. Contactless payment also enables instantly accessing liquid assets in accounts for making payments. This further reduces the demand for cash and maximizes the return of interest paid on deposited assets. Thus, higher interest rates on deposited accounts create more incentives to park money holdings that in turn reduce the demand for cash. Conversely, higher consumption expenditures increase the demand for cash. 3.2 Institutional background Contactless payment was first launched in the USA in 2005 by only very few issuing banks. The survey data used in this study show that the rate of contactless card adoption for credit and debit cards remained relatively stable (at around 10%) between 2009 and 2013 (see Fig. 1). The low adoption rate approximately agrees with actual data about contactless card adoption provided by the Federal Reserve System for the year 2012 (see Table 1). The actual rate of contactless credit cards was 7%, that of debit cards 8%. At the time, adopting contactless payment was an endogenous decision in the USA. Only a few banks reissued contactless cards by default when traditional payment cards expired. Some issuers provided contactless cards only upon request or exclusively to new customers. These were not required to pay extra for the contactless feature (cf. Chai (2017)). Compared to other countries like Canada or Australia, the adoption of contactless cards in the USA in the 2009– 2013 period failed to take off for various reasons. First, the Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 4 of 35 10.3% 8.2% 10.5% 10.0% 9.1% 9.9% 11.6% 11.3% 10.0% 8.1% 0.02% 0.10% 0.20% 1.00% 2.00% 7.00% 17.00% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 2009 2010 2011 2012 2013 2014 2015 Contactless Credit Contactless Debit NFC-Terminals Fig. 1 The evolution of contactless cards and NFC terminals. Note: The shares refer to the contactless data related to their corresponding total data. For instance, 9.1% of all credit cards and 2% of all POS terminals were contactless in 2013 in the USA. The Surveys of Consumer Payment Choice (SCPC) provide the share of contactless cards. They no longer included any information on contactless payment after 2013. The share of contactless terminals for the year 2015 is derived from LTP (2015), for the year 2013 from SPA (2016). The remaining fractions are computed according to the logistic regression analysis. Other data points are not available US banking and merchants sectors were very fragmented. Both sectors pursued different contactless payment strategies (SPA (2016)). Second, banks were legally obliged to shift completely to EMV (Europay International, MasterCard and VISA) standard payment cards by 2015. These types of cards enable storing data on chips rather than on magnetic stripes. Banks incurred significant manufacturing costs to overhaul these card portfolios given the immense US market for payment cards. Therefore, most banks decided to issue single-interface chip cards with no contactless antenna for saving money3. Third, and as a consequence, US retailers had never been eager to install contactless-enabled POS terminals due to lack of contactless card adopters. As a result of missing acceptance, individuals’ contactless payment adoption lagged behind and further led banks to slow down issuing contactless payment cards (SPA (2016)). Figure 1shows the very low level of contactless card acceptance at the POS during the years 2009–2013, ranging from roughly 0.02 to 2%. This goes hand in hand with actual usage of contactless cards (see Table 1): Only around 0.1% of all credit and debit card payments in the USA were made contactless in 2009 and 2012. Such payments accounted for approximately 0.1% of total transaction value. In other words, an average of less than one payment per card was made using contactless technology in 2012 (see Table 1). This made contactless payment a rare novelty in terms of usage. 3Canada, for instance, skipped first generation single-interface (EMV) chip cards and deployed contactless cards from the outset. 4Data 4.1 Source Data are drawn from the Federal Reserve Bank of Boston, which has conducted the Survey of Consumer Payment Choice (SCPC) since 2008. The surveys are performed in autumn (fourth quarter)—primarily in October—by the RAND Corporation as unique, comprehensive, and representative online surveys using RAND’s American Life Panel (ALP). They provide detailed payment information about individuals with respect to nine payment instruments (including cash) used in the USA4. The ALP’s sampling unit is an individual US consumer older than 18 years, whose responses to each survey are weighted to represent all US consumers aged 18 years and older. The 2008 responses are not comparable due to major revisions in the questionnaire and methodology across years. The survey series aims to provide a consumer-level longitudinal dataset and forms a valuable longitudinal balanced panel from 2009–2013 with respect to payment choice. The surveys conducted after 2013 no longer include information about contactless payment. One thousand one hundred thirty-two respondents completed all five surveys, which included similar and identical questions (see Table 2)5. Table 2depicts the number of respondents per survey and the various panelists. It shows an annual rate of attrition of roughly 10% until 2012, whereas this increased to 4These include cash, checks, money orders, traveler’s checks, debit, credit, and prepaid cards, online banking bill payments, and bank account number payments. 5I refer to Foster et al. (2013); Schuh and Stavins (2014) and Schuh and Stavins (2015) for a comprehensive description of each dataset, a synopsis of the results and detailed information about the collection process. Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 5 of 35 Table 1 Actual adoption and usage of contactless payment cards in the USA 2009 in % 2012 in % Credit cards Number of contactless cards (m) n/a 23.35 7.0% Contactless transaction volume (m) 20 0.10% 13 0.07% Contactless transaction value (m) 1000 0.06% 600 n/a Debit cards Number of contactless cards (m) n/a 22.62 8.0% Contactless transaction volume (m) 30 0.15% 27 0.07% Contactless transaction value (m) 1000 0.08% 378 n/a Average number of contactless transactions per Credit card n/a 0.57 Debit card n/a 1.19 Source: Federal Reserve System (cf. FED (2011;2014). Newer data are not available. Contactless payments are labeled “chip” card payments in the report provided by the Federal Reserve in 2014. “m” is millions. The shares refer to the contactless data related to their corresponding total data. For instance, 0.1% of all credit card transactions in 2009 were made contactless. In other words, contactless credit card transaction volume is divided by the total credit card transaction volume around 35% in 2013. This is because the SCPC incorporated the novel payment diary in 2012, thus more strongly emphasizing demographic coverage (cf. Angrisani et al. (2015))6. The retention rate between 2009 and 2012 was around 70% (1515 individuals). Around 90% of respondents who once participated in the SCPC before 2013 also participated in 2013 (Angrisani et al. (2015)). Among the 2169 individuals observed in 2009, 52% (1132) participated throughout (i.e., 2009–2013). Tables 10 and 11 (see Appendix) provide first-year summary statistics of stayers participating for five consecutive years versus attritors, in order to check whether panel attrition is systematic. The statistics reveal that attrition is likely to be random, i.e. exhibiting no systematic pattern7. The SCPC asks consumers what payment instruments they have and how often they use these instruments. The survey employs a flexible reporting strategy to enhance recall and to optimize the accuracy of the number of payments8. It also collects comprehensive data on consumer cash holdings and cash withdrawal behavior. Low-value payments tend to be more easily forgotten due to their high frequency and low budget impact. They are mostly effected in cash, which may lead to underreporting. Thus, cleaning procedures were applied to identify and edit invalid data entries for the number of monthly payments of all payment instruments and the typical value of cash withdrawals. The dataset also provides rich information 6The 2012 SCPC included an additional 1111 new respondents to the 2065 respondents with previous experience due to the novel payment diary. Many of the new respondents came from demographic strata poorly represented in the pool of respondents with previous SCPC experience (Angrisani et al. (2015.)) 7Statistically, attritors significantly differ with respect to three variables (among the 45 characteristics): They are more likely to earn between 100,000 and 124,000 USD, to be retired and to withdraw cash more frequently. However, overall differences are suggested to be unsystematic. 8Typical periods that measure the number of payments are during a week, a month, or a year. They are quite consistent with the implicit average that represents consumers’ trend behavior (Schuh and Stavins (2015.)) about consumer demographic characteristics, financial status, and the rating of payment instrument attributes. However, there are several limitations. The 2009–2013 estimates are not consistently adjusted for seasonal variation, inflation, or item non-response (missing values). The calendar time period of the 2009 survey also differs slightly from that of the 2010–2013 surveys. The latest surveys are very similar in terms of size, composition and timing of the sample. Survey comparability across years may suffer from different survey timing if crucial monthly seasonal differences occurred in individual payment behavior. Also, consumers may have underreported the number of payments and withdrawals in the years 2009–2010 (i.e., during the financial crisis and the corresponding severe recession). The rationale is that consumers generally relied more on cash payments in those days. These may be harder to recall due to their high frequency and low budget impact. Additionally, no longitudinal sample weights are available. 4.2 Description This section describes the 2009–2013 panel dataset used here for estimation. The surveys specifically ask respondents if one of their credit and debit cards is equipped Table 2 Panel data structure 2009 2010 2011 2012 2013 Nr. of respondents 2173 2102 2151 2065 2089 2009–2010 panelists 1913 1913 2010–2011 panelists 1801 1801 2011–2012 panelists 1926 1926 2012–2013 panelists 1330 1330 2009–2012 panelists 1515 1515 1515 1515 2009–2013 panelists 1132 1132 1132 1132 1132 Source: Schuh and Stavins (2014) and Angrisani et al. (2015) Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 6 of 35 Table 3 Adoption and usage rate of payment cards in the 2009–2013 surveys Variable Mean SD Obs. Contactless credit cards 0.095 0.294 5659 Contactless debit cards 0.103 0.304 5657 Credit cards 0.759 0.428 5628 Debit cards 0.78 0.414 5620 Credit card usage 0.613 0.487 5625 Debit card usage 0.63 0.483 5619 Usage describes the fact that respondents make the corresponding type of payment at least once in a typical month. Survey weights used with the contactless feature. This estimate is likely to be fairly robust since the decision to adopt contactless payment is endogenous. Some consumers actively applied for contactless cards. Unfortunately, the surveys provide no information on the specific usage patterns of contactless payment. Contactless adopters are labeled as innovators, or as non-innovators, irrespective of having any payment cards. Non-innovators are a relatively homogenous group of payment card adopters. Roughly 76% of respondents owned a conventional credit card and 78% a debit card within the observed period (see Table 3)9. Credit and debit cards were used at least once a month by around 61 and 63% of all respondents between 2009 and 2013. Around 10% of consumers in the overall period reported that one of their credit cards had the embedded contactless feature (see Table 3). Approximately 10% stated that they possess a contactless debit card. The surveys also collect data on consumer cash withdrawal behavior. Consumers were asked about the amount of cash they most often withdraw and the number of withdrawals they usually make in a typical period (week, month, or year). Both questions were asked for two separate withdrawal locations: the primary one, where consumers most often obtain cash, and all other sources10. Like Briglevics and Schuh (2013); this study focuses on the figures for the primary location. These estimates tend to be more precise. Reporting the usual rather than the actual withdrawal amount reduces the mental burden to compute averages of potentially diverse cash withdrawals (cf. Briglevics and Schuh (2013)).TheSCPCalsostates the number of cash payments and the total number of all purchases made in a typical month at the POS. Its ratio measures the cash share in terms of volume. This is a robust measure towards outliers. Table 4describes the summary statistics of the main cash measure variables in the panel dataset. The average 9These numbers are higher in the estimation sample since only checking account holders is considered. Additionally, more than half of total payments in the survey were made by payment cards. 10Cash withdrawal locations include ATMs, bank tellers, check cashing stores, cash back at retail stores, family or friends and others as well as being paid in cash. Table 4 Summary statistics of cash measures Statistics Usual Nr. of Cash Cash share withdrawal withdrawals in wallet in volume Mean 128.845 3.716 72.586 0.355 SD 172.734 6.610 134.691 0.285 Median 80.000 2.000 40.000 0.312 Min. 0.000 0.000 0.000 0.000 P-10% 20.000 0.833 1.000 0.000 P-99% 850.000 26.089 500.000 1.000 Max. 5000.000 434.821 3500.000 1.000 Obs. 5561 5572 5577 5527 Cash management measures are reported in USD except the number of withdrawals and cash share. The usual cash withdrawal amount and the number of withdrawals relate to the primary location. Cash share is the ratio of the total number of cash transactions in a typical month at the POS to the total number of all purchases in a typical month at the POS. Survey weights used amount of cash in wallet (73 USD) is roughly half of the average usual withdrawal amount (130 USD). The average number of withdrawals at the primary location per month amounts to around 4. Roughly 36% of all POS payments are made in cash (cash share in volume). Half of the consumers reported a cash ratio both lower and higher than 28.5%. Median values of the remaining cash measures were roughly half of the average values. This indicates that a small number of respondents relied heavily on cash, resulting in high standard errors. The maximum values of the cash variables support this finding (see Table 4). For this reason, I winsorize the usual cash amount withdrawn, the number of withdrawals and the average cash value in wallet at the 99% level. This enables properly analyzing the mean difference between innovators and non-innovators. Tables 5and 6report (winsorized) statistics of the relevant cash measures distinguished by contactless credit and debit card innovators and noninnovators. I also provide univariate mean comparison tests and Wilcoxon rank-sum tests between innovators and non-innovators in order to detect statistically significant differences11. Table 5shows that statistically contactless credit card adopters significantly make fewer cash withdrawals within a month than non-adopters (roughly 2.9 vs. 3.5). Another notable statistical difference is that adopters also have a 9 percentage point lower cash ratio in volume than nonadopters. Further, while their usual withdrawal amount tends to be smaller than that of non-innovators (around 8 USD), they carry slightly more cash in wallet (+1 USD). The Wilcoxon rank-sum test supports these results. Statistically, contactless debit card innovators make significantly more cash withdrawals (+ 0.8) compared to non-innovators (see Table 6). However, they withdraw 11The Wilcoxon rank-sum test tests if the samples of innovators and non-innovators come from populations with the same distribution. Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 7 of 35 Table 5 Cash Measures of Contactless Credit Card Innovators and Non-Innovators Innovator Non-Innovator t-Test Ranksum-Test Variable Mean SD Med. Min. Max. Obs. Mean SD Med. Min. Max. Obs. Mean Diff. z-values Usual Withdrawal 120.855 139.313 60.000 0.000 850.000 550 127.032 155.492 80.000 0.000 850.000 5011 -7.820 0.073* Nr. of Withdrawals 2.934 2.872 2.000 0.000 21.741 549 3.494 3.505 2.000 0.000 21.741 5023 -0.551*** 0.000*** Cash in Wallet 68.860 92.425 40.000 0.000 500.000 554 67.282 90.598 35.000 0.000 500.000 5023 0.871 0.048** Cash Share 0.275 0.234 0.229 0.000 1.000 544 0.363 0.289 0.319 0.000 1.000 4983 -0.088*** 0.000*** All variables are winsorized at the 99%-level except cash share. Cash management measures are reported in USD except the number of withdrawals and cash share. The usual cash withdrawal amount and the number of withdrawals relate to the primary location. Cash share is the ratio of the total number of cash transactions in a typical month at the POS to the total number of all purchases in a typical month at the POS. Survey weights used. T-tests of mean differences of innovators and non-innovators are displayed. Differences may stray from true values due to rounding and weighting. The Wilcoxon rank-sum test is displayed (z-values). Significance levels are denoted as *** p<0.01, ** p<0.05, * p<0.1. Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 8 of 35 Table 6 Cash Measures of Contactless Debit Card Innovators and Non-Innovators Innovator Non-Innovator t-Test Ranksum-Test Variable Mean SD Med. Min. Max. N Mean SD Med. Min. Max. N Mean Diff. z-values Usual Withdrawal 126.164 160.103 60.000 0.000 850.000 422 126.575 153.521 80.000 0.000 850.000 5137 -1.280 0.000*** Nr. of Withdrawals 4.114 4.247 3.000 0.000 21.741 424 3.361 3.346 2.000 0.000 21.741 5147 0.754*** 0.008*** Cash in Wallet 59.209 89.023 30.000 0.000 500.000 425 68.512 91.019 40.000 0.000 500.000 5150 -9.611 0.000*** Cash Share 0.329 0.280 0.302 0.000 1.000 424 0.357 0.286 0.312 0.000 1.000 5102 -0.026 0.000*** All variables are winsorized at the 99%-level except cash share. Cash management measures are reported in USD except the number of withdrawals and cash share. The usual cash withdrawal amount and the number of withdrawals relate to the primary location. Cash share is the ratio of the total number of cash transactions in a typical month at the POS to the total number of all purchases in a typical month at the POS. Survey weights used. T-tests of mean differences of innovators and non-innovators are displayed. Differences may stray from true values due to rounding and weighting. The Wilcoxon rank-sum test is displayed (z-values). Significance levels are denoted as *** p<0.01, ** p<0.05, * p<0.1. Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 15 of 35 Table 10 First-year demographics of stayers vs. attritors Stayers Attritors ttest Variable Mean SD N Mean SD N Mean diff. Income (in 1000) < 25 0.185 0.389 1128 0.181 0.386 1039 −0.026 0.3 cm 25–49 0.358 0.480 1128 0.313 0.464 1039 −0.014 50–74 0.219 0.414 1128 0.251 0.434 1039 0.029 75–99 0.132 0.338 1128 0.116 0.321 1039 −0.025 100–124 0.037 0.188 1128 0.061 0.239 1039 0.026** 125–199 0.049 0.216 1128 0.052 0.223 1039 0.009 > 200 0.021 0.142 1128 0.025 0.158 1039 0.001 Education < High school 0.046 0.209 1132 0.078 0.268 1041 0.023 High school 0.432 0.496 1132 0.343 0.475 1041 −0.072* Some college 0.265 0.442 1132 0.292 0.455 1041 0.033 College 0.168 0.374 1132 0.193 0.395 1041 0.015 Post graduate 0.089 0.285 1132 0.093 0.291 1041 0.002 Employment Working 0.789 0.408 1018 0.753 0.431 959 −0.036 Retired 0.119 0.324 1018 0.168 0.374 959 0.050** Unemployed 0.019 0.135 1018 0.005 0.074 959 −0.013* Other 0.062 0.242 1132 0.064 0.245 1041 −0.001 Marital status Single 0.200 0.400 1132 0.195 0.396 1041 0.003 Married 0.622 0.485 1132 0.641 0.480 1041 0.019 Separated 0.134 0.340 1132 0.120 0.325 1041 −0.021 Widowed 0.044 0.206 1132 0.044 0.206 1041 −0.001 Age < 25 0.099 0.299 1132 0.157 0.364 1040 0.047 25–34 0.178 0.383 1132 0.188 0.391 1040 0.026 35–44 0.186 0.389 1132 0.177 0.382 1040 −0.013 45–54 0.239 0.427 1132 0.154 0.361 1040 −0.095*** 55–64 0.135 0.342 1132 0.156 0.363 1040 0.015 > 65 0.161 0.368 1132 0.167 0.373 1040 0.021 Ethnicity White 0.733 0.443 1132 0.750 0.433 1041 0.035 Black 0.139 0.346 1132 0.098 0.298 1041 −0.064** Asian 0.036 0.187 1132 0.030 0.171 1041 −0.0001 Latino 0.133 0.34 1132 0.168 0.374 1041 0.037 Other 0.092 0.289 1132 0.122 0.327 1041 0.031 Others Male 0.485 0.500 1132 0.480 0.500 1041 0.004 HH members 1.316 1.528 1132 1.331 1.584 1041 0.034 Revolver 0.403 0.491 1122 0.403 0.491 1019 −0.015 Home owner 0.671 0.470 1129 0.693 0.462 1018 0.058 HH refers to household. Survey weights used. ttests of mean differences of stayers and attritors are displayed. Differences may stray from true values due to rounding and weighting. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1. Variables are displayed for 2009. Stayers participate five years in a row. Attritors participate in 2009 but not in all 5 years Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 16 of 35 Table 11 First-year payment card and cash usage characteristics of stayers vs. attritors Stayers Attritors ttest Variable Mean SD N Mean SD N Mean diff. Contactless credit cards 0.103 0.303 1131 0.089 0.285 1027 −0.015 Contactless debit cards 0.099 0.298 1129 0.130 0.336 1029 0.034 Credit cards 0.726 0.446 1131 0.717 0.451 1029 −0.023 Debit cards 0.757 0.429 1129 0.785 0.411 1028 0.029 Credit card usage 0.567 0.496 1120 0.542 0.499 1019 −0.036 Debit card usage 0.641 0.480 1118 0.671 0.470 1019 0.033 Usual withdrawal 122.368 161.119 1122 125.448 193.411 1023 3.974 Nr. of withdrawals 3.258 2.956 1123 3.757 3.710 1023 0.479* Cash in wallet 72.698 120.903 1116 64.834 104.088 1016 −8.315 Cash share in volume 0.392 0.300 1074 0.36 0.301 968 −0.027 Usage describes the fact that respondents make the corresponding typ of payment at least once in a typical month. Survey weights used. ttests of mean differences of stayers and attritors are displayed. Differences may stray from true values due to rounding and weighting. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1. Variables are displayed for 2009. Stayers participate five years in a row. Attritors participate in 2009 but not in all 5 years Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 17 of 35 Table 12 Sample summary statistics Entire sample Credit cards Debit cards IN-Ittest I N-I ttest Variable Mean SD N Mean Mean Mean diff. Mean Mean Mean diff. Income (in 1000) < 25 0.202 0.402 5645 0.140 0.209 −0.060*** 0.302 0.189 0.115*** 25–49 0.28 0.449 5645 0.253 0.283 −0.029 0.285 0.280 −0.004 50–74 0.199 0.4 5645 0.201 0.199 0.003 0.169 0.203 −0.035 75–99 0.13 0.336 5645 0.133 0.129 0.001 0.097 0.134 −0.038** 100–124 0.084 0.278 5645 0.090 0.084 0.004 0.058 0.088 −0.027* 125–199 0.076 0.264 5645 0.111 0.072 0.037** 0.053 0.078 −0.024* > 200 0.029 0.169 5645 0.072 0.025 0.044*** 0.037 0.029 −0.012 Education < High school 0.042 0.2 5660 0.058 0.040 0.025 0.059 0.040 0.019 High school 0.379 0.485 5660 0.250 0.393 −0.132*** 0.461 0.368 0.098** Some college 0.278 0.448 5660 0.277 0.278 −0.010 0.274 0.279 −0.015 College 0.171 0.377 5660 0.198 0.168 0.022 0.145 0.174 −0.026 Post graduate 0.13 0.336 5660 0.216 0.121 0.095*** 0.060 0.138 −0.076*** Employment Working 0.648 0.478 5546 0.709 0.641 0.070*** 0.733 0.637 0.099*** Retired 0.208 0.406 5546 0.196 0.209 −0.015 0.126 0.218 −0.088*** Unemployed 0.062 0.242 5546 0.045 0.064 −0.018 0.080 0.060 0.020 Other 0.175 0.38 5660 0.140 0.178 −0.041** 0.158 0.177 −0.022 Marital status Single 0.142 0.349 5660 0.110 0.145 −0.030* 0.209 0.134 0.047 Married 0.659 0.474 5660 0.674 0.657 0.010 0.610 0.664 −0.027 Separated 0.147 0.354 5660 0.145 0.148 −0.005 0.166 0.145 0.016 Widowed 0.052 0.222 5660 0.072 0.050 0.025* 0.015 0.056 −0.036*** Age < 25 0.045 0.206 5660 0.052 0.044 0.006 0.085 0.040 0.031 25–34 0.152 0.359 5660 0.119 0.155 −0.031 0.245 0.139 0.095** 35–44 0.169 0.375 5660 0.228 0.163 0.066** 0.165 0.170 −0.004 45–54 0.246 0.431 5660 0.242 0.246 −0.010 0.271 0.243 0.041 55–64 0.182 0.386 5660 0.141 0.186 −0.042** 0.114 0.190 −0.071*** > 65 0.207 0.405 5660 0.219 0.206 0.012 0.120 0.218 −0.092*** Ethnicity White 0.765 0.424 5660 0.740 0.768 −0.029 0.588 0.787 −0.201*** Black 0.142 0.349 5660 0.076 0.149 −0.070*** 0.198 0.133 0.065** Asian 0.031 0.174 5660 0.104 0.024 0.074*** 0.058 0.028 0.038** Latino 0.093 0.29 5660 0.113 0.091 0.023 0.180 0.081 0.107*** Other 0.062 0.241 5660 0.081 0.060 0.025 0.155 0.051 0.098** Others Male 0.452 0.498 5660 0.434 0.454 −0.026 0.507 0.445 0.050 HH members 1.276 1.566 5660 0.975 1.307 −0.353*** 1.619 1.237 0.368** Revolver 0.419 0.493 5602 0.499 0.410 0.086*** 0.339 0.429 −0.078** Home owner 0.71 0.454 5607 0.752 0.706 0.055* 0.499 0.737 −0.223*** N-I and Idenote non-innovators and innovators, respectively. HH refers to household. The minimum numbers equal zero for every variable. Survey weights used. ttests of mean differences of innovators and non-innovators are displayed. Differences may stray from true values due to rounding and weighting. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 18 of 35 Table 13 OLS regression results of contactless credit on usual cash withdrawn (1) (2) (3) Variable bse bse bse Contactless credit −5.301 (14.613) −5.507 (14.760) −10.587 (15.396) log(income)30.885*** (6.308) 35.589*** (6.301) 37.976*** (6.451) Interest rate −6.433 (11.463) −5.329 (10.787) −1.799 (9.505) Education High school −24.227 (31.741) −23.819 (33.539) −16.421 (36.158) Some college −14.669 (32.814) −13.565 (34.835) −10.931 (37.197) College −33.381 (33.496) −28.655 (35.686) −20.106 (38.322) Post graduate −10.333 (35.484) −6.421 (37.492) 1.853 (40.304) Employment Working −24.375* (13.123) −23.459* (13.400) −23.946** (12.172) Retired 3.797 (24.020) 7.888 (23.649) 9.055 (23.476) Other 31.713** (15.364) 35.325** (15.512) 33.888** (15.257) Marital status Single −35.151 (34.478) −32.307 (33.662) −33.348 (30.590) Married −57.976* (32.407) −58.621* (31.320) −59.704** (29.269) Separated −27.190 (34.106) −22.297 (33.602) −16.506 (30.831) Age 25–34 15.620 (19.975) 22.015 (20.370) 36.509** (18.243) 35–44 11.993 (22.396) 15.083 (22.467) 34.465* (19.610) 45–54 38.200* (22.869) 36.187 (22.962) 39.305** (20.018) 55–64 36.924 (25.361) 31.116 (25.617) 34.154 (22.811) > 65 44.779 (31.808) 30.569 (32.846) 31.962 (30.458) Ethnicity White −21.338 (44.445) −22.980 (45.108) −30.039 (45.024) Black 0.618 (48.577) −5.700 (48.927) −10.741 (48.945) Latino 23.720 (15.654) 18.551 (14.956) 21.396 (14.878) Other −26.842 (47.219) −28.048 (48.138) -33.685 (48.045) Other Male 25.931*** (9.259) 27.375*** (9.215) 22.688** (8.926) HH members 0.582 (4.399) −0.194 (4.387) −1.627 (4.491) CC revolver −41.217*** (9.535) −38.676*** (9.681) −30.695*** (9.370) Home owner 17.532 (12.353) 16.992 (12.130) 11.995 (11.307) Rel. characteristics Security 19.570*** (7.492) 14.066** (6.883) Setup −18.465 (11.395) −32.118*** (11.052) Acceptance 22.753 (16.290) 22.957 (16.678) Costs −1.936 (13.064) −2.073 (12.444) Records 13.911 (8.519) 6.240 (8.207) Convenience 37.702*** (11.137) 33.056*** (10.417) Withdrawal method Bank teller 83.374*** (13.958) Check casher 209.064*** (60.061) Cashback −60.506*** (7.440) Employer 103.629** (40.740) Family 8.894 (28.750) Other 98.102** (45.644) Constant −154.330* (88.997) −130.304 (90.618) −206.721** (94.081) R20.083 0.112 0.208 Individuals 1464 1452 1452 bare the point estimates and se the standard errors. Cluster-robust standard errors and survey weights are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian, and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 19 of 35 Table 14 OLS regression results of contactless debit on usual cash withdrawn (1) (2) (3) Variable bse bse bse Contactless debit −0.289 (15.510) 1.223 (15.558) 9.603 (14.638) log(income)30.668*** (6.243) 35.371*** (6.236) 37.663*** (6.382) Interest rate −6.450 (11.468) −5.359 (10.789) −1.879 (9.476) Education High school −23.307 (32.287) −22.802 (34.366) −14.868 (36.797) Some college −13.768 (33.322) −12.553 (35.593) −9.257 (37.827) College −32.630 (33.807) −27.787 (36.219) −18.655 (38.716) Post graduate −9.745 (35.788) −5.694 (37.963) 3.112 (40.608) Employment Working −24.510* (13.185) −23.680* (13.460) −24.508** (12.242) Retired 3.974 (23.963) 8.098 (23.549) 9.707 (23.396) Other 31.836** (15.398) 35.356** (15.511) 33.521** (15.309) Marital status Single −35.054 (34.424) −32.225 (33.655) −32.793 (30.590) Married −57.853* (32.409) −58.531* (31.351) −59.349** (29.342) Separated −27.270 (34.134) −22.438 (33.636) −16.686 (30.905) Age 25–34 15.708 (19.864) 21.953 (20.344) 36.273** (18.138) 35–44 12.183 (22.272) 15.325 (22.446) 35.682* (19.596) 45–54 38.522* (22.684) 36.575 (22.859) 40.826** (19.913) 55–64 37.198 (25.172) 31.414 (25.512) 35.368 (22.644) > 65 44.811 (31.841) 30.574 (32.932) 32.553 (30.558) Ethnicity White −20.823 (44.223) −22.444 (44.902) −28.547 (44.897) Black 1.536 (47.930) −4.914 (48.348) −9.532 (48.363) Latino 23.581 (15.556) 18.363 (14.851) 20.854 (14.828) Other −26.397 (46.944) −27.731 (47.925) −33.294 (47.947) Other Male 25.788*** (9.225) 27.224*** (9.187) 22.361** (8.901) HH members 0.623 (4.312) −0.164 (4.309) −1.601 (4.375) CC revolver −41.239*** (9.468) −38.696*** (9.623) −30.571*** (9.314) Home owner 17.635 (12.448) 17.170 (12.158) 12.646 (11.296) Rel. characteristics Security 19.551*** (7.470) 13.900** (6.843) Setup −18.688* (11.126) −32.062*** (10.874) Acceptance 22.779 (16.298) 22.936 (16.708) Costs −1.600 (13.078) −1.383 (12.515) Records 13.805 (8.518) 5.961 (8.191) Convenience 37.822*** (11.228) 33.467*** (10.451) Withdrawal method Bank teller 83.496*** (14.043) Check casher 211.711*** (59.847) Cashback −59.936*** (7.464) Employer 105.128** (40.819) Family 10.774 (29.041) Other 98.630** (45.704) Constant −154.078* (89.384) −130.316 (90.949) −209.598** (94.679) R20.083 0.112 0.208 Individuals 1464 1452 1452 bare the point estimates and se the standard errors. Cluster-robust standard errors and survey weights are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian, and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 20 of 35 Table 15 OLS regression results of contactless credit on number of withdrawals (1) (2) (3) Variable bse bse bse Contactless credit −0.723 (0.667) −0.611 (0.619) −0.455 (0.607) log(income)−0.816 (0.543) −0.880 (0.547) −0.804* (0.456) Interest rate −0.216 (0.345) −0.210 (0.358) −0.132 (0.360) Education High school −4.241 (3.365) −1.537 (1.920) −1.858 (1.803) Some college −5.073 (3.278) −2.369 (1.795) −2.621 (1.742) College −4.302 (3.217) −1.714 (1.846) −1.946 (1.754) Post graduate −4.301 (3.332) −1.636 (1.965) −1.938 (1.811) Employment Working 1.685* (0.999) 0.957 (0.691) 0.671 (0.637) Retired 0.782 (1.170) −0.390 (0.587) −0.386 (0.603) Other 0.341 (1.032) −0.550 (0.606) −0.591 (0.597) Marital status Single −0.181 (2.167) −0.463 (2.130) −0.464 (2.148) Married −1.721 (1.933) −2.179 (1.859) −2.166 (1.873) Separated −2.621 (2.038) −2.909 (2.016) −2.781 (2.041) Age 25–34 −1.942 (2.781) −2.190 (2.776) −1.676 (2.410) 35–44 −0.964 (2.584) −0.989 (2.533) −0.581 (2.246) 45–54 −0.614 (2.681) −1.013 (2.552) −0.614 (2.277) 55–64 −1.012 (2.581) −1.166 (2.455) −0.866 (2.181) >65 −0.767 (2.692) −1.088 (2.424) −0.561 (2.153) Ethnicity White 0.788 (1.041) 0.950 (1.092) 0.587 (0.952) Black 4.407** (1.840) 3.045** (1.532) 2.715* (1.474) Latino 0.314 (0.633) 0.484 (0.573) 0.409 (0.587) Other 5.129** (2.397) 5.319** (2.460) 4.874** (2.155) Other Male 0.985* (0.586) 0.728 (0.509) 0.578 (0.463) HH members −0.103 (0.197) −0.133 (0.203) −0.104 (0.200) CC revolver −0.636 (0.429) −0.519 (0.433) −0.455 (0.451) Home owner −1.527** (0.773) −1.125* (0.628) −0.962 (0.670) Rel. characteristics Security 0.131 (0.287) 0.051 (0.285) Setup −0.098 (0.729) −0.098 (0.718) Acceptance −0.937 (0.777) −0.971 (0.735) Costs −0.547 (0.661) −0.370 (0.586) Records −0.029 (0.423) −0.109 (0.406) Convenience 0.456 (0.702) 0.510 (0.701) Withdrawal method Bank teller −0.394 (0.524) Check casher 14.496 (11.068) Cashback −0.019 (0.759) Employer 1.186 (1.587) Family −0.932 (1.013) Other 2.537 (1.799) Constant 18.501*** (6.682) 17.340*** (6.624) 16.594*** (5.579) R20.092 0.086 0.111 Individuals 1464 1452 1452 bare the point estimates and se the standard errors. Cluster-robust standard errors and survey weights are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian and ATM. Significance levels are denoted as *** p<0.01, ** p<0.05, * p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 21 of 35 Table 16 OLS regression results of contactless debit on number of withdrawals (1) (2) (3) Variable bse bse bse Contactless debit 1.043 (1.299) 0.028 (0.899) 0.156 (0.863) log(income)−0.840 (0.546) −0.905 (0.553) −0.820* (0.457) Interest rate −0.232 (0.354) −0.212 (0.359) −0.134 (0.362) Education High school −4.155 (3.334) −1.417 (1.916) −1.775 (1.778) Some college −4.972 (3.227) −2.250 (1.775) −2.536 (1.705) College −4.213 (3.171) −1.613 (1.835) −1.873 (1.726) Post graduate −4.218 (3.283) −1.551 (1.963) −1.876 (1.793) Employment Working 1.622* (0.961) 0.935 (0.687) 0.653 (0.630) Retired 0.796 (1.158) −0.368 (0.589) −0.362 (0.608) Other 0.279 (0.972) −0.540 (0.606) −0.592 (0.596) Marital status Single −0.137 (2.173) −0.458 (2.130) −0.450 (2.147) Married −1.700 (1.933) −2.170 (1.859) −2.154 (1.873) Separated −2.652 (2.035) −2.923 (2.017) −2.788 (2.042) Age 25–34 −2.019 (2.755) −2.189 (2.748) −1.672 (2.391) 35–44 −0.903 (2.610) −0.967 (2.552) −0.546 (2.265) 45–54 −0.518 (2.720) −0.976 (2.575) −0.568 (2.299) 55–64 −0.943 (2.608) −1.138 (2.471) −0.827 (2.195) >65 −0.731 (2.725) −1.092 (2.448) −0.548 (2.172) Ethnicity White 0.900 (1.025) 1.006 (1.084) 0.641 (0.945) Black 4.444** (1.815) 3.140** (1.553) 2.783* (1.495) Latino 0.258 (0.645) 0.466 (0.580) 0.393 (0.594) Other 5.126** (2.426) 5.360** (2.488) 4.904** (2.175) Other Male 0.952 (0.580) 0.712 (0.509) 0.567 (0.463) HH members −0.103 (0.196) −0.130 (0.201) −0.102 (0.197) CC revolver −0.618 (0.424) −0.523 (0.437) −0.455 (0.455) Home owner −1.463** (0.742) −1.110* (0.629) −0.943 (0.669) Rel. characteristics Security 0.131 (0.284) 0.048 (0.281) Setup −0.131 (0.745) −0.112 (0.731) Acceptance −0.934 (0.775) −0.972 (0.730) Costs −0.509 (0.658) −0.340 (0.586) Records −0.040 (0.423) −0.118 (0.405) Convenience 0.467 (0.712) 0.521 (0.710) Withdrawal method Bank teller −0.397 (0.521) Check casher 14.577 (11.053) Cashback 0.005 (0.762) Employer 1.233 (1.576) Family −0.869 (1.015) Other 2.551 (1.801) Constant 18.355*** (6.748) 17.354*** (6.673) 16.527*** (5.613) R20.093 0.085 0.110 Individuals 1464 1452 1452 bare the point estimates and se the standard errors. Cluster-robust standard errors and survey weights are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 22 of 35 Table 17 OLS regression results of contactless credit on cash in wallet (1) (2) (3) Variable b se b se b se Contactless credit 3.606 (9.568) 4.114 (9.588) 3.644 (9.590) log(income)22.446*** (4.547) 22.969*** (4.569) 23.999*** (4.627) Interest rate 0.189 (4.865) −0.159 (5.080) 1.013 (5.229) Education High school 3.021 (19.040) 5.792 (19.713) 7.755 (20.328) Some college −1.895 (19.759) 1.181 (20.455) 2.198 (21.104) College 6.686 (20.460) 10.365 (21.135) 12.963 (21.838) Post graduate −0.388 (21.309) 3.125 (21.859) 5.460 (22.622) Employment Working −0.812 (7.003) −1.902 (6.968) −2.606 (6.863) Retired 4.829 (12.600) 7.901 (12.779) 8.339 (12.343) Other 5.847 (7.943) 6.809 (7.767) 6.417 (7.663) Marital status Single −23.529 (16.831) −22.292 (16.930) −22.157 (16.365) Married −37.789** (15.762) −36.988** (15.771) −36.980** (15.222) Separated −19.454 (15.889) −16.866 (15.985) −14.790 (15.458) Age 25–34 −8.511 (12.364) −8.941 (12.580) −2.698 (11.661) 35–44 0.027 (13.513) −0.100 (13.677) 7.076 (12.889) 45–54 10.213 (13.987) 9.080 (14.195) 11.900 (13.194) 55–64 25.764* (14.509) 23.771 (14.812) 26.734* (13.945) > 65 27.009 (17.671) 21.299 (17.972) 23.995 (17.261) Ethnicity White −11.911 (15.435) −11.541 (15.553) −13.642 (15.659) Black −13.987 (16.829) −13.339 (16.991) −14.787 (17.077) Latino −2.992 (7.632) −2.791 (7.856) −2.092 (7.895) Other −4.479 (17.283) −5.710 (17.424) −7.681 (17.707) Other Male 31.668*** (4.811) 30.825*** (4.872) 29.563*** (4.812) HH members 1.085 (2.962) 0.912 (2.918) 0.629 (2.942) CC revolver −15.320*** (5.121) −14.110*** (5.244) −12.023** (5.169) Home owner 17.379*** (5.441) 15.936*** (5.395) 15.149*** (4.984) Rel. characteristics Security −0.793 (3.269) −2.355 (3.236) Setup −3.409 (6.377) −7.041 (6.319) Acceptance 1.427 (9.089) 0.830 (9.227) Costs −8.512 (7.372) −7.981 (7.201) Records 0.457 (4.830) −1.543 (4.720) Convenience 12.896* (6.699) 11.800* (6.435) Withdrawal method Bank teller 20.917*** (6.983) Check casher 94.271** (40.477) Cashback −13.049** (6.096) Employer 36.990** (15.603) Family 4.733 (10.735) Other 28.496 (19.593) Constant −176.079*** (49.450) −177.989*** (50.897) −206.114*** (52.532) R20.153 0.155 0.182 Individuals 1465 1453 1453 bare the point estimates and se the standard errors. Cluster-robust standard errors and survey weights are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian, and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 23 of 35 Table 18 OLS regression results of contactless debit on cash in wallet (1) (2) (3) Variable b se b se b se Contactless debit −4.297 (8.632) −2.101 (8.698) 0.426 (8.579) log(income)22.570*** (4.533) 23.138*** (4.532) 24.163*** (4.586) Interest rate 0.261 (4.868) −0.116 (5.072) 1.036 (5.223) Education High school 2.530 (19.833) 5.129 (20.836) 7.041 (21.495) Some college −2.438 (20.582) 0.488 (21.607) 1.440 (22.324) College 6.204 (21.106) 9.803 (22.082) 12.375 (22.850) Post graduate −0.831 (21.867) 2.645 (22.718) 4.967 (23.545) Employment Working −0.532 (7.003) −1.727 (6.989) −2.544 (6.873) Retired 4.759 (12.582) 7.709 (12.759) 8.146 (12.309) Other 6.109 (8.026) 6.877 (7.822) 6.326 (7.713) Marital status Single −23.694 (16.853) −22.452 (16.982) −22.318 (16.391) Married −37.883** (15.795) −37.085** (15.815) -37.094** (15.245) Separated −19.301 (15.917) −16.765 (16.034) −14.785 (15.485) Age 25–34 −8.175 (12.195) −8.866 (12.419) −2.950 (11.503) 35–44 −0.213 (13.365) −0.347 (13.579) 6.858 (12.795) 45–54 9.806 (13.872) 8.696 (14.121) 11.577 (13.133) 55–64 25.473* (14.316) 23.488 (14.655) 26.461* (13.766) > 65 26.882 (17.618) 21.233 (17.976) 23.932 (17.258) Ethnicity White −12.432 (15.334) −11.990 (15.489) −14.012 (15.560) Black −14.257 (16.713) −13.823 (16.918) −15.384 (16.975) Latino −2.734 (7.618) −2.590 (7.836) −1.968 (7.864) Other −4.511 (17.154) −5.844 (17.351) −7.955 (17.618) Other Male 31.826*** (4.800) 30.983*** (4.856) 29.676*** (4.791) HH members 1.076 (2.949) 0.895 (2.914) 0.601 (2.933) CC revolver −15.400*** (5.098) −14.107*** (5.223) −11.953** (5.141) Home owner 17.090*** (5.481) 15.767*** (5.434) 15.089*** (5.014) Rel. characteristics Security −0.734 (3.257) −2.321 (3.222) Setup −3.304 (6.158) −6.769 (6.106) Acceptance 1.383 (9.081) 0.816 (9.215) Costs −8.711 (7.340) −8.160 (7.192) Records 0.583 (4.814) −1.448 (4.716) Convenience 12.647* (6.708) 11.532* (6.438) Withdrawal method Bank teller 20.921*** (7.011) Check casher 93.853** (40.348) Cashback −13.221** (6.138) Employer 36.721** (15.692) Family 4.369 (10.858) Other 28.434 (19.534) Constant −175.522*** (49.091) −177.964*** (50.477) −206.167*** (52.098) R20.153 0.155 0.182 Individuals 1466 1454 1454 bare the point estimates and se the standard errors. Cluster-robust standard errors and survey weights are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian, and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 24 of 35 Table 19 OLS regression results of contactless credit on cash share volume (1) (2) (3) Variable b se b se b se Contactless credit −0.053** (0.025) −0.052** (0.024) −0.059** (0.023) log(income)−0.019 (0.012) −0.009 (0.012) −0.008 (0.011) Interest rate −0.041*** (0.016) −0.032** (0.013) −0.029** (0.012) Education High school −0.026 (0.073) −0.062 (0.077) −0.047 (0.071) Some college −0.026 (0.071) −0.062 (0.075) −0.052 (0.070) College −0.049 (0.071) −0.076 (0.076) −0.063 (0.070) Post graduate −0.042 (0.071) −0.080 (0.076) −0.067 (0.072) Employment Working −0.012 (0.032) −0.004 (0.031) −0.013 (0.029) Retired −0.059 (0.038) −0.036 (0.035) −0.047 (0.034) Other 0.013 (0.030) 0.021 (0.029) 0.017 (0.028) Marital status Single 0.097* (0.051) 0.091* (0.051) 0.092** (0.044) Married 0.059 (0.044) 0.055 (0.044) 0.049 (0.037) Separated 0.037 (0.047) 0.038 (0.047) 0.040 (0.040) Age 25–34 0.042 (0.065) 0.063 (0.065) 0.064 (0.060) 35–44 0.093 (0.067) 0.099 (0.067) 0.103* (0.061) 45–54 0.118* (0.065) 0.126* (0.065) 0.123** (0.060) 55–64 0.144** (0.066) 0.144** (0.066) 0.143** (0.061) > 65 0.185*** (0.072) 0.177** (0.070) 0.183*** (0.065) Ethnicity White −0.022 (0.066) −0.046 (0.066) −0.064 (0.064) Black 0.017 (0.075) −0.005 (0.074) −0.021 (0.073) Latino 0.008 (0.027) 0.004 (0.027) 0.008 (0.026) Other 0.034 (0.081) 0.014 (0.078) −0.013 (0.077) Other Male 0.066*** (0.018) 0.064*** (0.017) 0.056*** (0.017) HH members 0.002 (0.007) 0.000 (0.007) −0.001 (0.007) CC revolver −0.053*** (0.016) −0.053*** (0.015) −0.045*** (0.015) Home owner −0.058*** (0.020) −0.057*** (0.020) −0.059*** (0.019) Rel. characteristics Security −0.005 (0.011) −0.008 (0.010) Setup 0.043* (0.022) 0.032 (0.021) Acceptance −0.066** (0.033) −0.064** (0.031) Costs 0.066** (0.026) 0.061** (0.025) Records 0.008 (0.016) 0.004 (0.015) Convenience 0.118*** (0.024) 0.116*** (0.022) Withdrawal method Bank teller 0.027 (0.021) Check casher 0.126 (0.170) Cashback −0.086*** (0.019) Employer 0.209*** (0.078) Family −0.101* (0.060) Other −0.088* (0.046) Constant 0.442** (0.189) 0.499*** (0.190) 0.497*** (0.171) R20.083 0.134 0.173 Individuals 1475 1463 1463 bare the point estimates and se the standard errors. Cluster-robust standard errors and survey weights are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian, and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 31 of 35 Table 26 FE regression results of contactless debit on cash in wallet (1) (2) (3) Variable b se b se b se Contactless debit 3.789 (5.966) 4.256 (7.965) 3.206 (7.945) log(income)14.059*** (5.240) 13.057*** (4.988) 13.154*** (5.006) Interest rate −3.960 (4.175) −7.424 (5.338) −7.002 (5.358) Education High school 22.962*** (5.741) 23.694** (9.272) 13.458 (10.292) Some college 8.841 (40.354) −42.988 (49.328) −46.158 (35.830) College 33.112 (43.942) −25.073 (52.826) −27.669 (40.513) Post graduate 60.546 (52.828) −16.242 (58.945) −20.329 (47.134) Employment Working 2.339 (7.593) 4.849 (8.873) 5.420 (8.717) Retired 6.492 (8.506) 25.442** (12.730) 24.980** (12.509) Other 7.170 (7.620) 4.548 (9.582) 5.150 (9.593) Marital status Single −5.355 (21.662) 3.977 (32.038) 4.851 (31.983) Married −17.728 (20.232) −11.518 (30.069) −9.448 (29.966) Separated −25.935 (24.999) −29.421 (36.135) −26.059 (35.745) Age 25–34 5.047 (13.856) −33.830 (26.245) −31.345 (26.809) 35–44 4.502 (17.233) −32.614 (29.194) −31.246 (29.329) 45–54 2.512 (19.327) −40.682 (31.522) −37.907 (31.710) 55–64 17.968 (21.568) −24.783 (33.801) −21.768 (33.750) > 65 25.009 (23.794) −17.044 (35.696) −13.001 (35.650) Other HH members −0.748 (2.545) −0.673 (3.064) −0.704 (3.049) CC revolver −3.961 (4.785) −1.643 (5.826) −1.743 (5.844) Home owner −1.334 (7.684) −7.772 (9.411) −8.442 (9.467) Rel. characteristics Security −3.354 (2.297) −3.030 (2.304) Setup 3.689 (4.183) 3.683 (4.189) Acceptance −0.064 (4.609) −0.270 (4.640) Costs −2.416 (4.978) −2.466 (4.982) Records 1.844 (3.087) 2.089 (3.093) Convenience −3.301 (4.358) −3.054 (4.361) Withdrawal method Bank teller 14.425** (6.723) Check casher 4.667 (13.153) Cashback −3.940 (5.414) Employer −17.593 (14.111) Family 16.414 (15.787) Other 5.485 (9.757) Constant −108.235 (67.233) −13.011 (73.219) −17.689 (67.989) R20.011 0.014 0.020 Observations 3882 3093 3093 Individuals 905 898 898 FE is the fixed-effects estimator obtained on the balanced panel. bare the point estimates and se the standard errors. Cluster-robust standard errors are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian, and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 32 of 35 Table 27 FE regression results of contactless credit on cash share volume (1) (2) (3) Variable b se b se b se Contactless credit −0.011 (0.027) −0.013 (0.030) −0.013 (0.030) log(income)−0.007 (0.013) 0.007 (0.015) 0.006 (0.015) Interest rate 0.013 (0.010) 0.013 (0.011) 0.013 (0.011) Education High school 0.268*** (0.069) 0.358*** (0.030) 0.346*** (0.033) Some college 0.308** (0.127) 0.536*** (0.102) 0.522*** (0.103) College 0.160 (0.140) 0.288** (0.119) 0.277** (0.119) Post graduate 0.155 (0.166) 0.281** (0.137) 0.271** (0.135) Employment Working −0.011 (0.016) −0.000 (0.018) −0.002 (0.019) Retired −0.005 (0.019) −0.026 (0.027) −0.027 (0.027) Other −0.000 (0.020) −0.036 (0.025) −0.035 (0.025) Marital status Single 0.040 (0.060) 0.031 (0.065) 0.033 (0.064) Married 0.008 (0.046) 0.037 (0.050) 0.039 (0.050) Separated 0.026 (0.052) 0.067 (0.054) 0.068 (0.054) Age 25–34 −0.073 (0.073) −0.296*** (0.102) −0.298*** (0.102) 35–44 −0.058 (0.078) −0.287*** (0.105) −0.289*** (0.105) 45–54 −0.090 (0.081) −0.320*** (0.108) −0.323*** (0.108) 55–64 −0.130 (0.083) −0.364*** (0.111) −0.365*** (0.111) >65 −0.099 (0.087) −0.375*** (0.114) −0.375*** (0.114) Other HH members −0.004 (0.007) 0.006 (0.008) 0.006 (0.008) CC revolver −0.006 (0.013) −0.017 (0.015) −0.016 (0.015) Home owner −0.034 (0.022) −0.032 (0.028) −0.032 (0.028) Rel. characteristics Security −0.008 (0.006) −0.008 (0.006) Setup 0.023* (0.013) 0.023* (0.013) Acceptance −0.009 (0.016) −0.009 (0.016) Costs 0.011 (0.016) 0.011 (0.016) Records 0.010 (0.009) 0.011 (0.009) Convenience −0.003 (0.013) −0.003 (0.013) Withdrawal method Bank teller 0.017 (0.017) Check casher 0.003 (0.041) Cashback −0.015 (0.015) Employer 0.002 (0.039) Family −0.009 (0.032) Other −0.004 (0.027) Constant 0.304 (0.192) 0.229 (0.206) 0.251 (0.207) R20.013 0.033 0.035 Observations 3556 2860 2858 Individuals 852 846 845 FE is the fixed-effects estimator obtained on the balanced panel. bare the point estimates and se the standard errors. Cluster-robust standard errors are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian, and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 33 of 35 Table 28 FE regression results of contactless debit on cash share volume (1) (2) (3) Variable b se b se b se Contactless debit −0.003 (0.033) 0.003 (0.038) 0.004 (0.038) log(income)0.015 (0.013) 0.022 (0.014) 0.020 (0.014) Interest rate 0.012 (0.010) 0.011 (0.010) 0.011 (0.010) Education High school 0.269*** (0.067) 0.357*** (0.029) 0.344*** (0.032) Some college 0.264* (0.158) 0.488*** (0.087) 0.454*** (0.097) College 0.090 (0.169) 0.226** (0.109) 0.195* (0.116) Post graduate 0.092 (0.185) 0.211* (0.125) 0.181 (0.129) Employment Working −0.009 (0.014) 0.009 (0.017) 0.007 (0.017) Retired −0.010 (0.017) −0.033 (0.026) −0.035 (0.026) Other −0.016 (0.018) −0.032 (0.023) −0.031 (0.023) Marital status Single 0.013 (0.067) 0.028 (0.072) 0.027 (0.071) Married 0.026 (0.055) 0.053 (0.058) 0.053 (0.057) Separated 0.036 (0.058) 0.077 (0.059) 0.076 (0.058) Age 25–34 −0.101 (0.063) −0.227* (0.124) −0.227* (0.124) 35–44 −0.093 (0.067) −0.222* (0.126) −0.223* (0.126) 45–54 −0.105 (0.070) −0.230* (0.129) −0.233* (0.129) 55–64 −0.133* (0.073) −0.260** (0.131) −0.261** (0.131) >65 −0.101 (0.076) −0.253* (0.133) −0.254* (0.133) Other HH members 0.000 (0.007) 0.004 (0.008) 0.005 (0.008) CC revolver −0.013 (0.012) −0.024* (0.014) −0.023 (0.014) Home owner −0.040* (0.021) −0.032 (0.026) −0.031 (0.025) Rel. characteristics Security −0.009 (0.006) −0.008 (0.006) Setup 0.018 (0.012) 0.018 (0.012) Acceptance −0.006 (0.014) −0.006 (0.014) Costs 0.014 (0.015) 0.013 (0.015) Records 0.003 (0.009) 0.003 (0.009) Convenience 0.003 (0.012) 0.004 (0.012) Withdrawal Method Bank teller 0.020 (0.016) Check casher −0.017 (0.043) Cashback −0.014 (0.015) Employer 0.018 (0.040) Family −0.015 (0.028) Other −0.014 (0.025) Constant 0.117 (0.213) 0.006 (0.207) 0.047 (0.210) R20.013 0.028 0.030 Observations 3832 3075 3074 Individuals 905 899 898 FE is the fixed-effects estimator obtained on the balanced panel. bare the point estimates and se the standard errors. Cluster-robust standard errors are used. HH and CC refers to household and credit card, respectively. Base category of categorical variables is lower than high school, unemployed, widowed, lower than 25 years, Asian, and ATM. Significance levels are denoted as ***p<0.01, **p<0.05, *p<0.1 Trütsch Swiss Journal of Economics and Statistics (2020) 156:5 Page 34 of 35 Abbreviations ALP: American life panel; ATM: Automated teller machine; EMV: Europay international, MasterCard and VISA; EU: European union; FE: Fixed-effects; FED: Federal reserve system; GDP: Gross domestic product; LTP: Let’s talk payments; NFC: Near-field communication; OLS: Ordinary least squares; PIN: Personal identification number; POS: Point-of-sale; SCPC: Survey of consumer payment choice; SPA: Smart payment association; USA: United States; USD: United States dollar Acknowledgements I would like to thank Martin Brown for his extremely valuable inputs and recommendations regarding the analysis in the paper. His comments helped to substantially improve the work. I am also grateful to the anonymous referees for their critical review, which has led to significant improvements of the paper. Authors’ contributions Not applicable. There is only one author. The author(s) have read and approved the manuscript. Funding Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the Federal Reserve Bank of Atlanta, Consumer Payment Research Center, https://www.frbatlanta.org/banking-and-payments/consumerpayments/survey-of-consumer-payment-choice. Competing interests The author declares that he has no competing interests. Received: 21 January 2019 Accepted: 22 April 2020 References Alvarez, F., & Lippi, F. (2009). Financial innovation and the transactions demand for cash. Econometrica,77(2), 363–402. Amromin, G., & Chakravorti, S. (2009). Whither loose change? The diminishing demand for small-denomination currency. 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