Stimulating Cashless Economy: The Role Of Tariff Regulations & National Payment Systems Creation

Egor Krivosheya, Moscow school of management SKOLKOVO (Egor_Krivosheya@skolkovo.ru)

Mark Kevin Mital, Independent researcher (mark.mital@gmail.com)

Agenda

• Introduction

• Motivation

• Theoretical framework

• Hypotheses

• Data sources

• Data sample

• Model

• Results

• Robustness checks

• Conclusions

• Practical implications2

Introduction

Cashless economy

Economy in which different payment methods have the same sets of barriers (are not discriminated against)

Interchange fees

Key tariffs in retail payments market, which is usually paid by acquiring bank to issuing bankupon every transaction. Interchange fees affect fees of all other market participants

National Payment System (NPS)

Payment system that is established for uses in a particular country or region. Could be implemented by bank association (JCB), commercial firm (EFTPOS, Bancontact) or regulator (Unionpay, NSPC)

Types of regulatory interference into retail payments market considered here:

• Interchange fees regulation

• Implementation of NPS3

Motivation

Idea: Empirically test the correlations between government interference (interchange fee regulation and NPS creation) into retail payments market and the market development (volume of cashless transactions)

Main Additions:

▪ Empirical evidence of consequences of government intervention in retail payments market development

▪ Cross-country analysis

Contribution: Empirical correlation between government intervention and retail payments market development allows understanding the directions for optimal stimulating policies development

4

Theoretical framework (1/2)

Interchange fee regulation effects

Lower interchange fee as a result of regulation leads to lower profit of payment systems => lower individuals’ benefits => lower incentive to use cards payments => in terms of welfare inefficient regulation (Rochet & Tirole, 2011; Krivosheya & Korolev, 2016, 2017; Krivosheya, 2018, 2020; Bedre-Defolie & Calvano, 2013)

Lower interchang fee => lower merchants’ costs => more merchants can afford accepting cashless payments => more card transactions => stimulation of cashless payements (Gurthie & Wright, 2007; Vickers, 2005; Rochet & Tirole, 2011)

Low interchange fee => lower merchants’ costs => lower market prices => stimulation of all types of purchases (Weiner & Wright, 2005; Evans. 2011; C. Arango & Taylor, 2008; Rochet & Tirole, 2011; Bolt & Mester, 2017)

5

Theoretical framework (2/2)

NPS implementation effects

• Better introduction into government payments (e.g., Multibanco) (Kayalidereden& Cetiner, 2018; Chaplin et. all, 2014)

• NPS serves as operator of POS terminals => more possibility to use cashless payment (e.g., Unionpay) (Yip & Yao, 2015)

• Targeting previous non-users (e.g., Rupay & Troy) (Kayalidereden & Cetiner, 2018; Gupta, 2017)

• Providing cards without fees mainly for social transfers (e.g., NSPC) (Krivosheya & Korolev, 2016; Krivosheya, 2018; Krivosheya, 2020)

6

Hypotheses

H1:

▪ National payment systems introduction correlates with higher cashless payments usage, i.e. volume of cashless transactions.

H2:

▪ Interchange fee regulation (cuts) correlate with higher volume of cashless transactions.

7

Data sources

Macroeconomic variables World Bank

Control variablesWorld Bank; Worldwide Governance Indicators

World Bank (Global Payment Systems Survey); Bank for International Settlements

Retail payments market data

8

Data sample

9

Key Facts:

Data: 34 countries in 2004-2017

Representative of global payments market

All the available data on global retail payments market

7

10

2

12

3Regulation & NPS

Regulation, No NPS

NPS, no regulation

No regulation, no NPS

Regulation on one of themarkets

Distribution of countries (number of countries on the chart):

Model

• Volumeit is the logarithm of volume of transactions in country i at time t

• Macro factorsit is the vector of control variables of macroeconomic factors (e.g., GDP per capita, population size, urbanization rate, government efficiency, etc)

• Market specific factorsit is vector of control variables of retail payments market controls (e.g., number of ATMs, number of POS terminals, etc)

• Governmentit is vector of two explanatory dummy variables: interchange fee regulation (1 if interchange fee was regulated; 0 otherwise) and implementation of NPS (1 if NPS is introduced & operating; 0 otherwise)

• ε is vector of robust standard errors

Models for debit and credit cards markets are observed separately due to market specificsEstimation method: Arellano-Bond estimation (GMM)Robustness checks for alternative estimation methods were performed

𝑉𝑜𝑙𝑢𝑚𝑒𝑖𝑡 = α + β ∗ Macro Factorsit + γ ∗ Market Specific Factorsit + Governmentit + εit

10

Results (1/3): anecdotal evidence

▪ Hypothesis H1 about interchange fees regulation may be rejected

▪ Hypothesis H2 about stimulation of debit card payments market by NPS introduction may be supported

▪ Formal econometric testing is needed

▪ Note: Singapore data is plotted on right-hand side axis, while Russian and Turkish data isdepicted on the left-hand side axis

0

50000000

100000000

150000000

200000000

250000000

300000000

350000000

400000000

450000000

0

5E+09

1E+10

1.5E+10

2E+10

2.5E+10

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

vo

lum

e o

f tr

ansa

ctio

ns

Debit card market

Russia Turkey Singapore

NSPC

Troy

0

50000000

100000000

150000000

200000000

250000000

300000000

350000000

400000000

450000000

0

1E+10

2E+10

3E+10

4E+10

5E+10

6E+10

7E+10

8E+10

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

vo

lum

e o

f tr

ansa

ctio

ns

Debit card market

Russia USA Singapore

NSPC

MIF regulation

11

Results (2/3): Debit card market

▪ Hypothesis H1 about interchange fees regulation is rejected

▪ Hypothesis H2 about stimulation of debit card payments market by NPS introduction cannot be rejected

▪ The results are economically significant.

*For details see Appendix; Herein: *** - 1% significance, ** - 5% significance; * - 10% significance

Baseline model NPS Regulation Both

Time fixed effects

included

Interchange regulation0.0872 0.0996 -0.0748

(0.266) (0.203) (0.482)

NPS0.457** 0.465** 0.190*

(0.0348) (0.0328) (0.0577)

Macroeconomic controls Yes Yes Yes Yes Yes

Card market controls Yes Yes Yes Yes Yes

Observations 220 220 220 220 220

12

Results (3/3): Credit card market

▪ Hypothesis H1 about interchange fees regulation is weakly rejected

▪ Hypothesis H2 about stimulation of credit card payments market by NPS introduction is also rejected

▪ The results are economically significant.

*For details ask authors

Baseline NPS Regulation BothTime fixed

effects included

Interchange regulation0.291** 0.290** 0.0666

(0.0114) (0.0104) (0.644)

NPS -0.00838 0.00269 0.195

(0.904) (0.97) (0.251)

Macroeconomic controls Yes Yes Yes Yes Yes

Card market controls Yes Yes Yes Yes Yes

Time fixed effects dummy No No No No Yes

Observations 184 184 184 184 184

13

Robustness checks

Robustness check Result

Inclusion of further control variables

Included variables, which may influence transaction volume and checked coefficient sensitivity of variables

Key results are robust to changes

Alternative methods

Checked other possible model estimation methods: Pooled OLS, FE, RE

Key results are robust to changes. Arelano-Bond outperforms the alternatives due to data structure

14

Conclusions

Outcomes: ▪ Interchange fee regulation is inefficient for both card markets (credit card market may be stimulated more)

▪ NPS introduction correlates with higher activity in retail payments market

Further research directions:

▪ More recent or previously omitted data in reports can alter results (although the sample is representative for global payments market)

▪ Other metrics for retail payments market development (e.g., transactions value)

▪ Cause-and-effect analysis of government intervention may be helpful

15

Practical implications

For policy makers:

▪ Understand the correlation between interference into retail payments market and its development

▪ Anticipate potential effects in the markets, where there was no/partial interference

▪ Use the estimate to improve the policies of cashless economy stimulation (especially, the efficiency of tariff stimulation)

For Banking & Financial services

▪ Estimate the potential consequences for consumer behavior after government interference

16

THANK YOU!

Egor Krivosheya, Moscow school of management SKOLKOVO

E-mail: Egor_Krivosheya@skolkovo.ru

Mark Kevin Mital, Independent researcher

E-mail: mark.mital@gmail.com

Appendix: Basic market scheme

18

Issuer Acquirer

Cardholder Merchant

p-a

Payment (p)

Good/service

p-mp+f

p – pricef – variable cardholder’s fees (e.g., cashback)

m – merchant discount feea – MIF (multilateral interchange fees)

Appendix: Debit card market estimation results

1 2 3 4 5

Debit card baseline

model

Debit card model with

NPS

Debit card model with

interchange regulation

Both interchange

regulation and NPS

Both dummy with

time fixed effect

VARIABLES

Lag of Log (Volume debit cards) 0.313*** 0.300*** 0.310*** 0.295*** 0.330***

0.00348 0.00597 0.00393 0.00688 0.00254

Regulation debit cards dummy 0.0872 0.0996 -0.0748

0.266 0.203 0.482

National Payment System dummy 0.457** 0.465** 0.190*

0.0348 0.0328 0.0577

Rule of Law index 0.00543 0.0956 0.0106 0.102 -0.0799

0.981 0.666 0.964 0.657 0.741

Inflation 0.0136 0.0109 0.014 0.0114 0.0167

0.356 0.465 0.337 0.441 0.204

Log(GDP) -0.15 -0.0602 -0.143 -0.0493 0.344

0.34 0.708 0.392 0.774 0.155

Log(Population) -1.38 -1.441 -1.626 -1.726 -3.780**

0.543 0.521 0.478 0.446 0.012

Log(Number of POS terminals) 0.707*** 0.662*** 0.694*** 0.649*** 0.478***

5.75E-06 4.90E-05 1.33E-05 0.000105 0.00529

Log(Number of ATMs) -0.188 -0.226 -0.169 -0.208 -0.0161

0.572 0.499 0.612 0.537 0.945

Log(Number of debit cards in

circulation)

1.242*** 1.239*** 1.261*** 1.259*** 0.804***

0.000164 9.51E-05 0.000111 5.96E-05 1.64E-05

Constant 13.19 12.89 16.98 17.24 50.36**

0.726 0.73 0.654 0.646 0.0487

Time fixed effects NO NO NO NO YES

Observations 220 220 220 220 22019

Appendix: Descriptive statistics

Mean S.D. Min Max

Regulation debit cards dummy 0.284 0.451 0 1

Regulation credit cards dummy 0.298 0.458 0 1

National Payment System

dummy

0.261 0.439 0 1

GDP 1.59E+12 2.97E+12 2.60E+09 1.94E+13

Population 1.36E+08 2.97E+08 2.88E+06 1.39E+09

Rule of law index 0.495 0.987 -1.407 2.038

Inflation 4.301 4.042 -1.404 29.502

Volume of debit card

transactions in units

4.30E+09 1.25E+10 4.00E+05 1.22E+11

Volume of credit card

transactions in units

2.28E+09 5.57E+09 8 3.77E+10

Value of debit card transactions

in US$

8.15E+11 5.48E+12 8.89E+06 6.08E+13

Value of credit card

transactions in US$

1.71E+12 1.20E+13 905.44 1.27E+14

Number of POS terminals 1.02E+06 2.71E+06 629 3.12E+07

Number of ATMs 59834.05 1.10E+05 71 9.61E+05

Number of credit cards in

circulation

7.66E+07 2.01E+08 17545 1.33E+09

Number of debit cards in

circulation

1.77E+08 6.36E+08 67695 6.16E+09

20

Appendix: Cross-correlations of variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1Regulation debit cards

dummy1

2National Payment System

dummy0.3

1

3 Rule of Law index 0.13 0.2 1

4 Inflation -0.21 -0.21 -0.59 1

5 Log(GDP) 0.35 0.35 0.42 -0.35 1

6 Log(Volume debit cards) 0.31 0.23 0.43 -0.34 0.77 1

7 Log(Value debit cards) 0.21 0.22 0.44 -0.37 0.76 0.91 1

8 Log(Population) 0.2 0.23 -0.24 0.1 0.7 0.47 0.45 1

9Log(Number of POS

terminals)0.35 0.34 0.25 -0.29 0.92 0.76 0.73 0.68 1

10 Log(Number of ATMs) 0.2 0.38 0.09 -0.19 0.9 0.65 0.64 0.82 0.91 1

11Log(Number of debit cards in

circulation)0.27 0.35 0.03 -0.13 0.8 0.59 0.59 0.78 0.87 0.81 1

12Consumption, as a percentage

to GDP-0.19 -0.2 -0.09 0.19 -0.3 -0.15 -0.12 -0.16 -0.14 -0.09 -0.19 1

13 Urban index 0.16 0.18 0.64 -0.49 0.33 0.37 0.46 -0.32 0.29 0.11 0.03 -0.15 1

14 Corruption index 0.15 0.18 0.97 -0.59 0.39 0.38 0.42 -0.29 0.27 0.05 0.01 -0.11 0.71 1

15 Government efficiency index 0.16 0.22 0.96 -0.64 0.46 0.46 0.46 -0.21 0.31 0.15 0.09 -0.19 0.69 0.95 1

21