Why Do Shoppers Use Cash? Evidence from Shopping Diary Data *

Transcription

1 Why Do Shoppers Use Cash? Evidence from Shopping Diary Data * Naoki Wakamori Department of Economics University of Mannheim Angelika Welte Currency Department Bank of Canada The Second Conference on Economic Growth and Productivity in Japan July 25, 2014 * Disclaimer: The views expressed in this presentation are those of the authors. No responsibility for them should be attributed to the Bank of Canada.

2 1/20 Introduction (1/2): Consumer Payment This paper studies consumers payment at the point-of-sale. Cash keeps its dominant position for small-value transactions: 25 AUD in Australia Simon, Smith, and West (2010) 25 CAD in Canada Arango, Huynh, and Sabetti (2011) 20 EUR in Netherlands Bolt, Jonker, and van Renselaar (2010) 25 USD in U.S. Klee (2008) Why do we still use cash? Demand driven: Consumers might want to use cash. Supply driven: Merchants might not accept cards. This paper answer this question by estimating a generalized multinomial logit model to separately identify demand factors, while controlling for supply factors.

3 2/20 Introduction (2/2): Policy Question If all merchants accepted any payment methods regardless of the transaction values, how much cash usage would decrease? This is policy relevant!! Policy makers Central banks Private banks Credit card companies Mobile phone companies Cash usage would decrease by only 8 percentage points (from 57% to 49%) implying that cash usage in small-value transaction is driven mainly by consumers preferences.

4 Data

5 3/20 Data (1/6): Method of Payment Survey Bank of Canada s 2009 Method of Payment Survey (i) Basic demographic and financial information for individuals Age, income, gender, education, and so on Bank account and debit cards information Credit cards information (ii) 3-day shopping diary Shopping types/values Actual payment method Perceived acceptance

6 4/20 Data (2/6): Intuitive Identification Two key features of the data: 1. Perceived Acceptance 2. Multiple Observations per Subject which enable us to separately identify demand and supply. Example: Alex s counterfactual payment method $7.5 at Cafe 1 - Cash among {Cash} Predict his choice if the choice set was {Cash, Credit, Debit} If we have multiple observations: Obs. 2: $80 at IKEA - Credit among {Cash, Credit, Debit} Obs. 3: $9 at Restaurant 1 - Cash among {Cash, Debit} Obs. 4: $6 at Cafe 2 - Credit among {Cash, Credit, Debit}

7 5/20 Data (3/6): Canadian Payment Landscape All Transactions Density Transaction Value Cash Debit Credit

8 6/20 Table : Number of Shopping Trips Data (4/6): Multiple Obs. per Subject Raw Raw Weight Freq. % ed % Total 2, Note: Raw Percent and Weighted Percent mean whether or not I use the sample weights to correct the sampling bias in the data, respectively. Density Transaction Value Remaining Observations Dropped Observations

9 7/20 Data (5/6): Heterogeneity in Payment Samples with more than 2 Shopping Opportunities #of avg # of avg Type of Consumers obs. % shopping TV (1) Cash Users (2) Debit Users Only Debit Cash & Debit (3) Credit Users Only Credit Cash & Credit (4) Mixed Users Debit & Credit All three Total # of individuals 1, Total # of transactions 7,

10 Data (6/6): Payment by Types Credit Committed Users Debit Committed Users Non Committed Users Transcation Value Transcation Value Transcation Value Cash Debit Credit Cash Debit Credit Cash Debit Credit 8/20

11 Model

12 9/20 G-MNL Model (1/5): Utility Function Notation i = 1, 2,, N: individuals t = 1, 2,, T i : shopping opportunities j {cash, credit, debit}: payment choices Each individual choose a method which gives the highest utility defined by: u ijt = X ijt β ij + ε ijt, X ijt = [Z it A ij D i ] : characteristics including.1 Z it : Transaction type/values of t for individual i.2 A ij : Attitudinal scores toward j for individual i.3 D j : Demographic information for individual i β ij = [β Z i β A ijβ D ij]: Coefficients vector for individual i.

13 10/20 G-MNL Model (2/5): Hetero. Coefficients Each element of the coefficients vector takes the form of β ij = σ ij ( β + µ ij ) (c.f. β ij = β j for Logit) where 1. µ ij N (0, βij u ): Random coefficients 2. σ ij : Scale coefficients σ ij = exp( τ 2 /2 + τϵ ij0 ), with ϵ ij0 N (0, 1). For identification, normalization is required: E[σ] = 1.

14 11/20 G-MNL Model (3/5): Illustration Example for large σ credit 1 Credit Committed Users Choice Probabilities cash credit debit Transaction Value (dollar)

15 12/20 G-MNL Model (4/5): Simulated Choices Assuming Type I extreme value distribution for ε ijt, choice probability for each (i, t) will be given by: Pr(y ijt = 1 X, θ, σ i, η i ) = exp(x ijt β i ) l J it exp(x ilt β i ). Each individual i s likelihood contribution should be T i L i (θ) = [Pr(y ijt = 1 X, σ i, η i )] d ijt, where d ijt = t=1 j { 1, if i choose j at t (observed decision), 0, otherwise.

16 13/20 G-MNL Model (5/5): SML Estimator Letting {σ s i, ηs i } s=1,,s denote a set of random draws for each individual i, we can define the simulated likelihood contribution for each individual: L i (θ) 1 S S [P (y ijt = 1 X ijt, σi s, ηi s )] d ijt. s=1 t d Using the sample weights, w i, for correcting the sampling bias, we can define the simulated maximum likelihood estimator: ˆθ SML = arg max θ N w i log(l i (θ)). i=1

17 Results

18 14/20 Summary of Estimation Results MNL MNL MNL S-MNL H-MNL G-MNL Model Charact. Choice Set No Yes Yes Yes Yes Yes Multiple Obs. iid iid Ind. Ind. Ind. Ind. Scale Coeff. No No No Yes No Yes Random Coeff. No No No No Yes Yes Summary Stat. No. of Param. (k) Log Likelihood Akaike I.C Bayesian I.C Note: I.C. means Information Criterion. Two information criteria are given by the following formula: AIC = 2k 2 ln(l) and BIC = 2 ln(l) + kn, where L denotes the log likelihood value and N denotes the number of observations.

19 15/20 A Policy Experiment Suppose the government regulates merchant fees to a very low level, possibly zero. Now, all merchants are willing to accept credit and debit cards. What would happen? Maintaining Assumptions (potential problems): Consumers do not change their decision rules. Those people who do not have credit/debit cards hypothetically have credit/debit cards. Merchants accept cards without any fees nor changing prices. Network providers do not change their pricing scheme.

20 16/20 Simulation Results - Overall Effects Prediction Data MNL S-MNL H-MNL G-MNL No Hetero. Scale Random Both Frequency Share Cash 56.99% -7.06% -7.47% -8.11% -7.89% Credit 18.91% 4.32% 4.43% 4.75% 4.80% Debit 24.10% 2.74% 3.04% 3.36% 3.09% Value Share Cash 30.06% -7.12% % -8.17% -8.05% Credit 36.01% 5.53% 5.41% 6.41% 6.34% Debit 33.93% 1.59% 1.66% 1.76% 1.71%

21 Simulation Results - Why so Small? All Transactions Transactions Accept All Methods Density Density Transaction Value Transaction Value Cash Debit Credit Cash Debit Credit 17/20

22 18/20 Simulation Results - Detailed Effects Prediction MNL S-MNL H-MNL G-MNL Data No Hetero. Scale Random Both Merchants only accepting Cash (Freq. Share) Cash % % % % % Credit 0.00% 15.13% 15.28% 15.64% 15.81% Debit 0.00% 18.10% 19.47% 18.81% 18.13% Merchants accepting Every Method (Freq. Share) Cash 42.24% -0.11% -0.11% -0.64% -0.45% Credit 28.70% -0.03% 0.25% 0.29% 0.37% Debit 29.05% 0.14% -0.13% 0.35% 0.08%

23 19/20 Welfare Changes Consumer Surplus: No explicit price - we cannot quantify consumer surplus! Merchant Surplus: Merchants need to pay some fees to card acquirers. Credit (Visa&Master) Debit (Interac) Value Freq Fees Value Freq Fees Current Simulation Difference Note: We assume that 2% fees of each transaction value for credit cards and 0.12 dollars/transaction for debit cards, according to Arango and Taylor (2008). The unit of all numbers is billion dollars. Merchants would need to pay 0.98 billion dollars more in fees to card acquirers can be seen as costs of implementing this policy.

24 Concluding Remarks

25 20/20 Conclusion This paper studies the consumer payment choice at the point-of-sale using G-MNL models and conducts counterfactual simulation. Based on the simulation results of couterfactual analysis, we conclude that cash usage is driven mainly by consumers preference. A couple of concerns and future directions: 1. Endogeneity and measurement errors in choice set 2. How to quantify consumer welfare? 3. A two-sided market aspect of the payment industry

26 Thank you

27 Appendix 1/4 Literature on Retail Payment Theoretical literature: 1. Cash Demand: Baumol (1952), Tobin (1956), Alvarez and Lippi (2009), Alvarez, Guiso, and Lippi (2012) 2. Search theoretic: Telyukova and Wright (2008) etc.. 3. Two-sided market: Rochet and Tirole (2002) etc.. Empirical literature: Check: Schuh and Stavins (2010) Debit: Borzekowski, Kiser, and Ahmed (2008), Zinman (2009) Credit: Ching and Hayashi (2010), Simon Smith, and West (2010) Single/Multi-homing: Rysman (2007) International Comparison: Bagnall at al (2014) Scanner Data: Klee (2008) Structural analysis: Koulayev, Rysman, Schuh, and Stavins (2012) Card acceptance: Huynh, Schmidt-Dengler, and Stix (2014)

28 Appendix 2/4 Discussion 1: Consumer Welfare Typically we have a price for each choise, i.e., max βx j αp j + ε ij. j J A calculation procedure of Compensation Variation (CV) requires the estimated parameter for price, ˆα, which is absent for our case.. What would be potential alternatives? Consumers fee for each method of payment Waiting time can be an interesting normalization (!)

29 Appendix 3/4 Discussion 2: Endogeneity and Measurement Error (1/2) Our model assumes exogeneity for choice sets J it : max X ijt β ij + ε ijt. j J it Some people who want to use cards are likely to go to shops accept cards, i.e., (this is mathematically sloppy but) conceptually Corr(J it, ε ijt ) 0. This survey also might have (non-classical) measurement errors: In the survey: { Cash, Credit, Debit }. It could be: { Cash, Credit, Debit } or { Cash, Credit } We need to instrument J it, but it is nontrivial..

30 Discussion 2: Endogeneity continued... (2/2) One possible way: Instrumenting acceptances Acceptance debit,t = f d (Z it δ) + ϵ j,t Acceptance credit,t = f c (Z it δ) + ε j,t and, instead of reported choice set, use predicted acceptance sets max j J it X ijt β ij + ε ijt. However, it is very hard to find appropriate instruments. Debit: the number of ATMs in each region Credit: the average acceptance rate in each region(?) Appendix 4/4