The Geography of FinTech∗

Hyun-Soo Choi†and Roger K. Loh‡

May 2019

Abstract

Banking services can now be delivered with technology (FinTech) and many banksare downsizing their physical operations. Bank customers are also relying less on physicallocations because of FinTech-enabled banking. Is geography indeed now less importantfor banks? Using quasi-exogenous closures of ATMs in a densely populated city, weexamine how small changes in physical banking access can affect FinTech adoption. Wefind that after such closures, affected customers’ travel distances to ATMs increase. Thisinduces them to increase their usage of the bank’s digital platform. Using closures asan instrument for FinTech adoption, we find that adopters become less likely to incurminimum-balance penalties. Our results show that very slight frictions to geographycan have an impact on FinTech adoption and financial inclusion. Small nudges can helpovercome the status-quo bias and facilitate significant behavior change.

Keywords: FinTech; Household Finance; Geography; Banking; Financial InclusionJEL Classification Codes: D12, D14, G21, G40, O33

∗We thank participants of the second Toronto FinTech conference and a seminar at SMU, Antonio Gargano,Jianfeng Hu, Clemens Otto, Melvyn Teo, and Chishen Wei for helpful comments and suggestions. We arealso grateful to DBS for providing data and Piyush Gupta (Group CEO) and Royce Teo (Group Head, DataManagement) for their comments. This paper is a project under the Financial Inclusion pillar at SMU’s SimKee Boon Institute for Financial Economics, where Roger is the faculty lead for this pillar and Hyun-Soo is aVisiting Research Fellow.†Assistant Professor of Finance, KAIST College of Business, 85 Hoegiro, Dongdaemoon-gu, Seoul 02455,

Korea. hschoi19@kaist.ac.kr‡Corresponding author: Associate Professor of Finance, Lee Kong Chian Fellow, Singapore Management

University, Lee Kong Chian School of Business, 50 Stamford Road, Singapore 178899. rogerloh@smu.edu.sg

“FinTech: Computer programs and other technology used to support or enable banking andfinancial services.”

— https://en.oxforddictionaries.com/definition/fintech

1. Introduction

Technology-enabled banking through digital and mobile platforms (FinTech) is now changing

the banking industry. While competing with new entrants, traditional banks themselves are

undergoing transformation in their business strategy, adding digital means to deliver their

services. For example, in October 2018, the Federal Deposit Insurance Corp (FDIC) reported

a rapid change in how U.S. households accessed banking services, with mobile banking users

doubling from about 20% of bank retail customers in 2013 to 40% in 2017.1 Digital means

of banking has the advantage that it is cheaper than physical means, and can enable greater

financial inclusion across the usual geographical reach of physical locations. Philippon (2016)

argues that the historical cost of providing financial intermediation has been high and FinTech

provides the opportunity for incumbents to reduce such costs.

While traditional views of banking reach typically rely on the number of physical locations

(e.g., Beck, Demirguc-Kunt, and Peria (2007) and Jayaratne and Strahan (1996)), many banks

are downsizing their physical operations. This is motivated by the belief that geography—

i.e. the customer’s distance to a physical banking location—is becoming less important in

the age of digital banking.2 While distance is traditionally an important friction in limiting

the ability of banks to serve customers, Petersen and Rajan (2002), albeit in the setting of

corporate banking, show that distance matters less in an information age.

Banks cite changing customer behavior as the reason for their downsizing. The underlying

principle is that many services can be delivered using FinTech and this substitutes the need for

physical banking locations. However, might the causality also run in the opposite direction?—

1https://www.wsj.com/articles/u-s-unbanked-population-continues-to-fall-15403165432Deustche Bank for example in 2016 announced a plan to reduce the number of branches by 25% citing the

behavior of customers as a key reason. https://www.reuters.com/article/us-deutsche-bank-branches/deutsche-bank-to-shut-188-german-branches-and-cut-3000-staff-idUSKCN0Z9205

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that a reduction in physical access actually nudges customers towards digital banking, hence

saving costs for the bank while maintaining its reach? Of course, closing physical locations

too quickly might result in the loss of some customers. Given the importance of knowing the

answers to these questions, there is surprisingly little scientific evidence on these questions

and we aim to fill this gap.

In this paper, we examine the importance of physical distance to consumer banking using a

representative sample of 500,000 retail customers of DBS Bank, the largest bank in Singapore,

from 2015–2016. Like many developed countries, Singapore has the means for digital banking

but still retains a sizable reliance on traditional means like cash. We first show that physical

distances are very important to customers. Customers predominantly use ATMs within the

first kilometer (km) of their address. However, there is a large heterogeneity in the mean

ATM usage distance among customers. Assuming that customers who travel longer distances

face more frictions in their access to physical banking, we find evidence that such customers

are in general more frequent users of digital banking services.

However, is there any causal relation between physical distance and digital banking usage?

In other words, can reductions to physical banking locations increase FinTech adoption? Ex-

amining such a question with branch closures would be fraught with reverse causality concerns

since banks may close locations where customers are more likely to switch to digital banking.

We shed light on the impact of physical location closures by studying quasi-exogenous closures

to ATMs, many of which are motivated by building renovations. We show that customers who

experience ATM closures face an increase in the average distance that they travel to an ATM.

Using instrumental variable estimations with customer fixed effects and year-month fixed ef-

fects, we find that this added distance friction increases their usage of digital banking. Our

results are also robust to using a propensity-score matching approach. In terms of economic

magnitude, a one km increase in distance results in about 4 more digital transactions per

month (the sample average is 22). In the aggregate, while the customer uses ATMs less fre-

quently after experiencing closures, the increased use of digital banking compensates for this so

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that the total banking activity in the short-term is slightly higher. In terms of cross-sectional

differences, we find that all our results are in general stronger in magnitude for the older age

groups, which is consistent with the view that older customers can benefit more from such

nudges.

We then use ATM closures as an instrument for FinTech adoption itself so that we can

identify if there is any potential benefit of this plausibly exogenous increased digitalization.3

Using the incurring of a minimum-balance fee as an outcome, we show that increased adoption

of FinTech due to closures results in a significant decline in the likelihood of paying such

penalty fees.

We believe these results have important implications. Kahneman, Knetsch, and Thaler

(1991) show that investors exhibit the status quo bias and even the simple switch of a default

option can have large effects on the eventual action taken. In a city where ATMs are readily

available in adjacent buildings, we show that small nudges in the form of an increase in

the friction of travel can induce customers to move more towards digital banking. This is

consistent with Cole, Sampson, and Zia (2011) who find that very small subsidies can greatly

increase the demand for financial services, compared to financial literacy education which has

only a small impact on the demand for financial services.

Second, our results highlight the importance of geography in financial markets in the

context of consumer banking. Geography has been shown in the literature to be an important

determinant in many areas of finance, for example, the home or familiarity bias of investment

(Grinblatt and Keloharju (2001) and Coval and Moskowitz (1999)), the accuracy of sell-side

research (Malloy (2005)), dividend policy (John, Knyazeva, and Knyazeva (2011)), and even

financial misconduct (Parsons, Sulaeman, and Titman (2018)). In banking, distance to the

bank has been shown to be related to corporate loan pricing due to information asymmetry

3The topic of closures of physical banking locations has also recently been examined by Nguyen (2018).She finds that branch closures in the U.S. due to bank mergers result in a negative impact to the credit supplyfor small businesses especially during the credit crisis. Our focus is on physical locations closures that aremore unremarkable (i.e. ATMs) and less endogenous than merger-motivated branch closures, and we examinethe impact on retail banking and digital banking, not corporate banking.

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(e.g., Herpfer, Mjos, and Schmidt (2018), Agarwal and Hauswald (2010), and Degryse and

Ongena (2005)). In consumer banking, Lippi and Secchi (2009) model the role for the density

of bank branches and ATM networks on an agent’s cash holding choices. Our key finding

is that shocks to the ease of accessing ATM services (in the form of a disappearance of an

often-used ATM) can produce spillover changes to banking behavior. The key source of this

friction is an agent’s geographical preference for closer distances compared to longer distances.

Given that our evidence is based on a densely populated city, we believe that the magnitude

of such impact is likely a lower bound when it is extrapolated to less dense cities.

Third, our paper contributes to the financial inclusion literature. Recent studies find that

when an individual with latent demand for banking services receives an exogenous increase

in banking access, they are likely to continue usage of those services even after the treat-

ment period. India for example launched an ambitious program to give bank accounts to

225 million unbanked individuals in 2014. Chopra, Prabhala, and Tantri (2017) and Agar-

wal, Alok, Ghosh, Ghosh, Piskorski, and Seru (2017) show that households with new bank

accounts exhibit subsequent usage that converges to that of banked households with similar

demographics. Higgins (2019) finds that welfare payments in Mexico disbursed using debit

cards caused merchants and other consumers to also adopt point-of-sale transactions. In our

setting, the nudge from ATM closures provides a quasi exogenous increase in their access to

digital banking services, a form of digital financial inclusion.

In both academia and the industry, there is a growing interest in FinTech, the rate of its

adoption by consumers, and the post-adoption implications. Alvarez and Lippi (2009) describe

how consumers use ATMs to obtain cash in the face of financial innovation. They model

financial innovation as the increasing availability of cash access points due to the diffusion of

branches and ATMs. One can think of the current FinTech as exponentially increasing the

number of “cash” access points to include all merchants who accept cashless payments. While

most might expect developed countries to take the lead in FinTech adoption, there is a wide

dispersion in the FinTech adoption rate among developed countries. For example, using cash

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reliance as a proxy for low FinTech adoption, we see that Germany and Japan are still reliant

on cash, while countries like Sweden are almost cashless.4 Countries like Singapore and the

U.S. are somewhere in between in their adoption of FinTech. While we do not examine country

differences in FinTech adoption, our within-country study suggests that small frictions can

result in sizeable cross-sectional differences in FinTech adoption rates.

Some studies have examined whether FinTech adoption leads to financial inclusion benefits.

Carlin, Olafsson, and Pagel (2017) analyzes how FinTech adoption changes the use of consumer

credit. They use an introduction of a personal financial management phone application in

Iceland and find that FinTech adoption reduces financial fee penalties. Agarwal, Qian, Yeung,

and Zou (2018) explore consumers’ response to the introduction of a cashless payment wallet

in Singapore and find that its use helps improve the sales of small companies relative to larger

ones. Beck, Pamuk, Ramrattan, and Uras (2018) show that an introduction of mobile money

in Kenya benefits entrepreneurial growth. Our paper is related to these studies in that we

explore FinTech adoption. One might argue that in studies that examine voluntary adoption,

those choosing to adopt the new technologies might be fundamentally different from the non-

adopters. In contrast, our setting relies on digital shocks obtained through ATM closure and

are likely less endogenous. Our finding that affected customers reduce their likelihood of

incurring minimum-payment penalties provides new evidence on how “involuntary” adoption

of FinTech can be associated with outcomes that improve financial inclusion.

One important caveat for the interpretation of our results is that our sample is not long

(with only two years of data) and that the bank has a large customer base in Singapore.

Hence, we might have less power to identify the potential negative effects of ATM closures,

such as customers switching to other banks. It is possible that our results might be more

applicable for established banks with a loyal customer base rather than for small banks that

are competing in new markets.

4See https://www.bloomberg.com/news/features/2018-02-06/germany-is-still-obsessed-with-cash, andhttps://www.bloomberg.com/news/articles/2018-04-04/banks-rush-to-turn-japan-cashless-ahead-of-looming-tech-rivals.

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The rest of the paper is organized as follows. Section 2 provides a short summary of

the banking industry in Singapore, Section 3 discusses the sample and how we measure the

variables of interest. Section 4 reports the results, and Section 5 concludes.

2. Retail Banking in Singapore

Singapore is a developed city-country with 5.5 million residents in our sample period of 2015

to 2016. Its banking industry is dominated by three local banks. Although there are foreign

banks that compete for retail deposits, foreign banks face restrictions on their total number of

physical locations. In contrast, using network sharing, each of the local banks provide a huge

network of ATMs for their customers. A typical local bank’s customer has access to about

1,000 ATMs in the country’s small land area of 721.5 square km. Singapore’s central bank

estimates that the majority of the population has access to an ATM within 1 km of their

residence.5 Figure 1 shows a map of Singapore with the locations of the ATMs indicated.

Banking customers are also well served by bank branches, with each local bank having more

than fifty branches.

In terms of FinTech adoption, cash reliance is still high in Singapore even though there

is existing technology for banking and payments to be done electronically without cash. The

primary reason for a customer to visit a branch or an ATM is still related to cash needs.

Debit cards and credit cards are widespread and customers are able to use these as payment

methods for the majority of merchants. However, a significant fraction of small businesses

continue to use cash as the only means of payment so as to avoid the fees associated with

renting payment equipment. Checks are still a common form of payment method between

customers and businesses or between customers, even though electronic payment of bills and

fund transfer services are free. Fund transfers can be done physically at a branch, at an ATM,

or on a bank’s website/mobile app.

5http://www.mas.gov.sg/News-and-Publications/Parliamentary-Replies/2017/Reply-to-parliamentary-question-on-accessibility-of-ATMs.aspx

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Overall, the infrastructure for FinTech in the form of digital banking and cashless payments

is mostly in place during the sample period. However, although FinTech adoption is growing,

it is not yet widespread. In our random sample of customers, only about 60% use the bank’s

digital platform. This is slightly larger than the 40% reported in the introduction about the

mobile banking usage of U.S. retail banking customers.

3. Data

Our data is from DBS bank from January 2015 to December 2016. Known as a leading

financial services group in Asia, DBS is headquartered and listed in Singapore and has a large

retail market share in Singapore.6 Our unique proprietary dataset contains transaction-level

banking activity for 500,000 randomly sampled retail customers from the bank’s customer

base in Singapore as at December 2016.

The data used in this study can be broadly classified into three parts. First, we have the

transaction-by-transaction data of all of the customer’s savings and checking accounts with

the bank. Second, we have all the ATM transactions of the customer, specifying the ATM

location, amount used, type of usage (cash withdrawal, deposit, fund transfer, balance enquiry,

etc.), and date and time of usage. Finally, we also obtain a large dataset containing all of the

customer’s digital transactions with the bank. These digital transactions are either financial

in nature (transactions with non-zero amounts associated with them such as fund transfer,

bill payment, etc.) or non-financial in nature (log-ins, viewing account summary, transaction

enquiry, request for SMS pin, etc.). We believe we are the first study that attempts to link

three important aspects of retail banking activity (traditional accounts, spatial ATM use, and

digital banking) at the micro level.

Besides banking data, we obtain demographics data on each customer, namely race, mar-

ital status, gender, and age. The most important information we need is the mailing address

of the customer. To maintain confidentiality and adhere to privacy regulations, the bank pro-

6Please see https://www.dbs.com/about-us/ for more information on DBS.

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vided addresses only at the postal code level and anonymized all customers’ original national

identifiers with pseudo identifiers. Unlike the U.S. where a ZIP code identifies a sizable area

within a city, the Singapore postal code identifies an address at the building level. Since more

than 90% of residents in Singapore live in high-rise apartments with hundreds of individuals

in one building, this sufficiently masks actual customer identities within the same building.

As an additional safeguard, the bank excluded customers associated with postal codes where

it had fewer than 50 customers. This will automatically exclude low rise apartments or stand-

alone houses from our sample. In other words, our sample would not contain the small fraction

of households who reside in these typically very expensive locations.

The bank provided two snapshots of the customer’s mailing postal code, one at January

2015 and another at January 2016. Only 1.4% of customers moved to a different postal

code between these two periods. In Singapore, physical mail is still important and customers

typically change their mailing addresses immediately upon moving as mail forwarding service

is costly. For movers in our sample, since we do not observe the actual month which they

moved, there will be some noise in the assumed customer location in some of the months

between January 2015 and January 2016. We also do not observe address changes between

January 2016 and December 2016. However, as customer moves are infrequent in our sample,

we believe that the noise in customer location is minimal and unlikely to bias our results.

Since the bank serves a large fraction of the population in Singapore, this random sample

is likely to be representative of a customer’s overall banking activity in the dimensions that

we have data on. We cannot rule out that a customer has accounts with other banks. To be

more certain that the customers we use have an active banking relationship with the bank

as their main bank, we focus only on customers who have at least one salary credit with

the bank during our sample period (e.g. the bank flags salary credits as “SAL” in savings

accounts). Customers who are informally employed or have private income (e.g. from odd

jobs or private tutoring) would be excluded by this screen since their income would not be

flagged as a salary. Note that if they are entrepreneurs, they will still have a salary entry

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as long as their company pays them an salary. Second, we exclude customers who hold any

joint-named accounts so that we can cleanly identify customer-level banking activity. If a

customer has multiple single-named accounts, the variables in each account are aggregated to

the customer level. Joint accounts are tricky to deal with because we cannot tell which of the

joint account holders are receiving the salary credit, even though we can observe their ATM

and digital activity at the individual level. After these screens, our final sample consists of

166,282 customers, the drop in sample size coming mainly from requiring salary credit and

non-joint accounts. This final sample is about 3% of total population in Singapore in 2015.

3.1. Summary Statistics

Table 1 reports the summary statistics of variables for our analysis. Our sample includes more

than 3 million customer-month observations from January 2015 to December 2016. These are

single-name account holding customers associated with at least one salary credit in the sample

period. For months which the customer does not have a salary credit, the salary is assumed to

be zero in the computation of averages. To mitigate the impact of outliers, we winsorize the

continuous variables (except for Age) at the 99th percentile. Panel A reports demographic

characteristics of age, monthly salary, and the beginning-month account balance. The average

customer age is 37.59. The average monthly salary of customers is 2,730 Singapore dollars

(SGD) and the average beginning-month balance is SGD16,912. During our sample period

the exchange rate is about 0.7 USD per SGD.

Panel B of Table 1 reports the mean usage distance, Distance to ATM, of a customer to

an ATM. To compute this, for each ATM transaction, we obtain the GPS distance between

the customer’s address postal code and the ATM location postal code. Postal codes are

converted to latitudes and longitudes using www.gps-coordinates.net. The average distance

per customer for each month is weighted by the number of transactions at each ATM. If we

cannot measure the distance for a customer due to lack of ATM usage in a month, we replace

it with the most recent distance of the customer (7.6% of customer-months contain such filled

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distance measures). The average Distance to ATM for a customer-month is 5.64 km and it

ranges from 0 to 22.16 km.

How do we interpret this average? While customers always have an ATM located very

close to their homes, they can also use ATMs when they are at other locations such as at

a shopping mall or at a transport hub. Our average weighs the importance of each location

to a customer using the number of transactions. We obtain similar average distances if we

weigh the importance of each location with the absolute dollar amount transacted instead.

The disadvantage of using dollar weights is that we would not be able to include transactions,

such as a balance enquiry, that do not have dollar amounts associated with them.

Panel C reports statistics of various customer banking activities. For ATM activity, we

report the total number of transactions, the number of non-financial transactions, and the

average dollar transaction amount. On average, a customer does 9.07 total ATM transactions

per month. Non-financial ATM transactions, i.e. balance enquiry or password change, occur

1.77 times per month. Financial ATM transactions (defined as transactions that are associated

with a non-zero dollar amount) have a mean S$305.10.

For digital activity, we report the total number of transactions, the number of financial

transactions, and average dollar transaction amount. On average, a customer does 21.89 digital

transactions per month, which includes 1.95 financial transactions (defined by transactions

that are associated with non-zero amounts). Total summed dollar amount of financial digital

transactions done in a month are S$1,306.99 per month on average. Note that the above

averages are computed by setting non-user usage activity and amounts to zero.

We also report the total number of transactions at the account level as a proxy for their

overall banking activity with the bank. This counts the total number of transactions across

the customer’s savings and checking accounts. Savings accounts form the majority (more than

90%) of account activity. The average customer has 24.35 transactions per month.

Panel D reports the summary statistics of the quasi exogenous shocks on the Distance to

ATM due to ATM closures. We define an ATM closure at the postal code level. A closure is

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defined as an ATM postal code where no more ATM transactions occur at this postal code

for at least 30 days in our sample. Defining ATM closures at the postal code (building) rather

than a specific ATM machine avoids the problem that in a large mall, an ATM is closed on

the first floor, but there is another ATM on the second floor. We find that there are 58 ATM

closures at the postal code level in our sample period.

For each closure, we identify customers who are significantly affected as those who had

at least 5 transactions at the relevant ATM postal code in 120 day-window before closure.

For these customers, we define two measures of shocks with different after-treatment periods.

The first measure uses [0,+3] months from ATM closure events as the treated period, i.e. a

short-term three month impact of closures. ATM Closure Shock [0,+3] equals to 1 for [0,+3]

months from ATM closure event for treated customers, and 0 otherwise. About 1% of our

sample is defined as treated using this approach. The second measure defines all periods after

the ATM closure as treated, i.e. a longer term effect. ATM Closure Shock [0,+] equals to 1

for [0,+] months from ATM closure events for the treated customer, and 0 otherwise. About

4% of our sample is defined as treated using this second approach.

3.2. Descriptive Statistics on Distance to ATM

We now report more detailed summary statistics on the distance measure. Because our main

analysis uses distance as a friction, this section will add to our understanding of how often

retail banking customers access proximate versus distant banking locations.

Figure 2 plots the time-series of the average Distance to ATM of our sample of single-

name account customers with at least one salary credit. While the average distance is 5.64

km, there is a slight upward trend in the distance measure, even though the total number

of ATMs actually increased in our sample period. This means that customers are slightly

shifting their transactions from proximate locations to farther away locations.

In Figure 3, we split ATM transactions according to the time of usage and compute the

Distance to ATM by weekday working hours (8AM to 6PM on non-public holiday weekdays),

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weekday non-working hours, and weekends (we also treat public holidays as “weekends”). If

the measure correctly captures a customer’s physical distance from home to the near-home

banking services, we would expect that the distance should be similar during weekday non-

working hours and during weekends when the customer is more likely to be at home. And the

distance should be longer during weekday working hours as the customer is at work and hence

more likely to use banking services close to their work location. The red-dashed line represents

the average Distance to ATM during working hours. We indeed find that usage distance is

the longest during working hours. The remarkable similarity between the Distance to ATM

during non-working hours and weekends gives us confidence that the measure is correctly

picking up the proximity of the customer from home to their typical banking location.

Although we know that the average distance of a customer is about 5-6 km, this does not

mean that a typical customer predominantly prefer banking locations that are exactly at this

distance. To see the usage preference of a customer by distance, we plot a histogram that

describes the probability that a typical customer uses an ATM that is x km away from their

postal address. The top chart of Figure 4 shows this plot. Red bars denote the fraction of

amounts that a typical customer transacts at ATMs at a particular km from their location.

We can see that about 38% of a customer’s ATM transaction amounts are done at ATMs

within the first km from their location. The second km is much less important, where the

usage fraction declines to less than 10%. This fraction declines further the farther away the

ATMs are.7 To make sure this skewness is not driven by the distribution of ATMs across the

city, we plot (with blue bars) as a benchmark the fraction of ATMs that are within x km of

a typical customer in the city. You can see that for a typical customer, only about 1% of the

ATMs in the country are within 1 km of their location but they will use it with almost a 40%

likelihood. This forms solid evidence that distances are important to customers when they

7To reconcile this histogram with the 5-6 km mean we reported in the prior Figure, one can simply take theweighted average of the km value in each bin using the fraction of usage as the weights and we would recoveran average of about 5-6 km. Hence, while the average distance of a typical customer is about 5-6 km, the mostfrequently used ATM is the one within the first km. Also note that the longest distance in this histogram is42 km which represents a customer with an address at one end of the island (Tuas at the extreme West of theisland) using an ATM at the airport (at the extreme East).

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choose from available physical locations.

Thus far, one might assume that the postal address is the home location. This is true

90% of the time. For the rest, the address is associated with the postal code of a commercial

building, for example an office building in a downtown area.8 For customers with commercial

addresses, which we assume to be their work location, we plot the histogram of their ATM

usage in increasing distance from their workplace in the second chart in Figure 4. We find

that the distribution is less skewed compared to the first chart. Although such customers are

still more likely to use ATMs close to their work addresses, the strong reliance on the closest

ATMs is not as stark. We conclude that the home location compared to the work location

is a more reliable anchor when customers access ATMs. Also, this means that the distance

measure computed from commercial addresses might be a noisier proxy for the friction faced

by a customer. Part of our later analysis will remove such commercial address customers.

Finally, to help the reader visualize the usage pattern of an individual customer, we plot

in Figure 5 an ATM usage heatmap by distance. We randomly pick 1,000 customers from our

sample and arrange them in the chart from left to right in order of increasing mean distance.

Each column represents one customer and each cell in a column depicts the probability that the

customer uses ATMs at that particular distance in the sample period, with the color intensity

representing the probability of usage at that particular km (e.g. red for high probability).

One can see immediately from this heatmap that the lowest distance customers rely almost

exclusively on ATMs within the first km of their address. Second, even the median customer

transacts about half of their dollar amounts at ATMs in the first km. Finally, even for far

away customers, the first km retains some importance for their usage of ATMs. Overall,

the heatmap shows banking services that are provided through ATMs are very important for

customers who are close to those locations.

8The address category is determined by searching for the postal code with an “(S) ” prefix in streetdirec-tory.com.

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4. Empirical Results

4.1. Physical Distance and Banking: Potential Endogeneity

We have shown that customers are more likely to use banking services very close to where

they are located. One can hence view distance as a friction, where customers who use banking

services very close to them have little friction in their access to banking, while customers who

use banking services farther away have greater frictions in access to banking. We now examine

the relation between distance and banking activity in general.

In Table 2, we run a panel regression to study the relation between the distance to ATM

and customers’ banking activity. Panel A uses all customers in our sample. In Columns (1)-

(3), dependent variables are related to ATM transactions. Column (1) uses the total number

of ATM transactions as the dependent variable. The main independent variable is Distance to

ATM, a transaction-weighted distance to ATM from the address of customers. Other controls

include monthly beginning account balance and their monthly salary (both in thousands).

We include year-month fixed effects and customer fixed effects to estimate changes in the

dependent variable within a customer. We cluster standard errors by customer. We find that

customers’ ATM usage are positively correlated with the Distance to ATM.

The dependent variable in Column (2) is the total number of non-financial ATM transac-

tions and in Column (3) is the average ATM transaction amount. We find that a customer’s

non-financial ATM transactions are also positively correlated with the Distance to ATM but

average ATM transaction amount is negatively correlated with the Distance to ATM. Are

these results affected by the measurement errors in Distance to ATM due to the customers

with commercial address? To minimize this concern, we also estimate our regressions using

only customers with residential addresses and we find similar results (in Panel B).

These results suggest that customers are more likely to use ATMs, and have smaller dollar

amounts transacted when the Distance to ATM is longer, which is counter-intuitive if we

consider distance to be a friction. However, the results are subject to a severe endogeneity

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problem. Although we estimate within-customer effects using customer fixed effects, the

increase in activeness of a customer can increase both the number of ATM transactions and

Distance to ATM. Note that our measure of Distance to ATM is constructed in a way that

active travellers within Singapore using ATMs all over the city tend to get a longer mean

distance measure. Unless we can control for customer activeness dynamically or find an

instrument for distance, this endogeneity issue limits our interpretation of these results.

Similarly, in Columns (4)-(6), we use dependent variables related to digital banking. Col-

umn (4) uses total number of digital transactions as a dependent variable, Column (5) uses

total number of financial digital transactions, and Column (6) uses total dollar amount of

digital transactions. We find a positive correlation between the number of digital transactions

and the Distance to ATM. We find similar results in Figure 6 where we compare digital trans-

actions of customers by Distance to ATM. Each month, we sort customers independently into

quintiles by their salary (excluding no salary credit months) and by their Distance to ATM.

We report the total number of digital transactions by each groups and find that there is some

evidence of a positive association between total digital transactions and Distance to ATM.

This positive relation is more obvious for the higher salary quintiles. Although the relation

between distance and digital banking is positive and is consistent with physical distance as a

friction, these results are also subject to the endogeneity issue as discussed earlier.

In Column (7) of Table 2, we use the total number of transactions as a dependent variable.

This is the total number of transactions recorded at customer’s current and savings accounts

and is highly correlated with the total number of financial transactions including both ATM

and digital transactions (for example, some transactions in ATM and Digital arenas do not

appear in the eventual accounts, like balance enquiries or logins). We find that the total

number of account transactions is also positively correlated with the Distance to ATM.

15

4.2. ATM Closure as an Instrument for Distance to ATM

To establish a causal relationship between the Distance to ATM and customers’ banking

activity, we need an instrument that changes the customer distance without changing the

banking behavior except through the channel of changes in distance. In this paper, we use

ATM closure events as a quasi-exogenous shock to distance. We have 58 closures of ATMs

at the postal code level in our sample period.9 The 58 closures at the postal code level are

widely spread out across the city as shown by the red markers in Figure 1.

We acknowledge that these events are only quasi-exogenous and not fully exogenous be-

cause the bank does not open or close any ATM randomly but carefully optimizes the location

based on the demand for ATM service. But there are two reasons why we believe that the

closures of ATM might be quasi-exogenous for our analysis. First, while the decision may be

endogenous from the bank’s point of view, it is not as endogenous from a single customer’s

point of view. When the bank decides an ATM to be closed due to a lack of demand in that

area, the closure is still quasi-exogenous to a customer who is still utilizing that ATM because

of convenience. On the day that the ATM disappears for a frequent user of that ATM, it can

be argued that the closure is like a random shock for that particular customer. Second, some

of major ATM closures in our sample are strictly not due to the banks’ optimization decisions

but due to the closure of shopping malls or office buildings. For example, Compass One is a

shopping mall located in the town centre of Sengkang and was closed for extensive renovation

works in late October 2015 and re-opened on 1 September 2016. Due to the renovation, the

bank had to close the ATM in the mall from 22 September 2015 to 24 September 2016.

Figure 7 reports the change in Distance to ATM around the event date. For each ATM

closure, we first identify customers who are significantly affected by the closure. We define

these as customers who did at least 5 transactions at the relevant ATM postal code in a

9As discussed earlier, we do not consider a closure of an ATM machine as a closure at the postal code levelif there exist other ATMs in the same postal code. We also do not include the closures of temporary ATMsset up to cater to seasonal demand such as ATMs for Chinese New Year or for the Formula One race event.Usage at such temporary ATMs are also not included when computing the distance measure.

16

120 day-window before closure. We compute the average Distance to ATM of these treated

customers in the window of [-120, +120] around the closure. Note that in the pre-closure

period, the daily mean distance measure for a customer includes all of the customer’s ATM

transactions, not just the transactions at the about-to-close ATM. Post-closure, the mean

distance measure for a customer would include only other ATMs since the treated ATM has

now been closed. We average these mean distance measures across all customers at each

closure event. We then plot the daily average across all 58 closures by weighting each closure

by the number of treated customers in each closure. The average Distance to ATM before

the closures ([-120,-1]) is plotted in a blue horizontal line and the average Distance to ATM

after the closures ([0,+120]) is plotted in a red horizontal line. We find a significant jump of

about 0.3 km in Distance to ATM at the closure date. This shows that affected customers

face a significant increase in friction when an often-used ATM closes. Bachas et al. (2018)

show that reducing access distance to bank accounts is a good thing for consumers. Hence,

in our setting, these closures introduce a distance inconvenience to consumers.

We report the effect of ATM closures on Distance to ATM using panel regressions in Table

3. For the treated customers, we define two measures of shocks with different after-treatment

periods. The first measure uses [0,+3] months from ATM closure events as the treated period.

In Column (1), we report the result using all customers with Distance to ATM as a dependent

variable and the ATM Closure Shock [0,+3] as the main independent variable. We include

monthly beginning balance and monthly salary of the customer as control variables. We also

include year-month fixed effects and customer fixed effects. We find that the ATM closure

shock significantly increases the Distance to ATM for treated customers compared to other

customers who were not treated. For treated customers, Distance to ATM increased by 129

meters, which is about 2.3% of the average Distance to ATM.

Instead of using all customers, in Column (2), we restrict our sample to the customers

with residential addresses in the bank data to mitigate the concern that Distance to ATM is

a noisier proxy for distance frictions when the customer location is based on a commercial

17

address. We find that the result is robust and stronger than the results in Column (1).

The second measure of ATM closure shock uses all periods after the ATM closure events as

the treated period. ATM Closure Shock [0,+] equals to 1 for [0,+] months from ATM closure

events for the treated customer. In Column (3), we report the result using all customers

with Distance to ATM as a dependent variable and the ATM Closure Shock [0,+] as a main

independent variable. Again, we find statistically and economically significant increase of

Distance to ATM closures. The magnitudes are smaller since the effect of shock may decay

over time (customers may adjust their travel patterns). Column (4) uses the residential address

sample and finds similar results.

4.3. Impact of Distance on Customers’ Banking Activity: using IV

Based on the first stage results in Table 3, we use the ATM closure shocks to estimate an

instrumental variable (IV) regression. Table 4 reports the IV regression estimates of the effect

of the Distance to ATM on customers’ banking activity.

First, we find that treated customers choose to use ATM less often but with a larger dollar

amount per transaction. Panel A uses all customers in our sample and uses ATM Closure Shock

[0,+3] as an instrumental variable. We report the results on ATM transactions in Columns

(1)-(3). The dependent variable in Column (1) is the total number of ATM transactions.

The main independent variable is the Distance to ATM instrumented by the ATM Closure

Shock [0,+3]. We also include the monthly beginning account balance and monthly salary as

control variables. We include year-month fixed effects and customer fixed effects so that we

estimate the changes within customer. We find that increased Distance to ATM due to ATM

Closure Shock [0,+3] decreases the total number of ATM transactions. In terms of economic

significance, a 1 km increase in Distance to ATM reduces ATM Transactions by 0.885. 0.885

is 9.8% of the mean of 9.07 ATM transactions.

We verify these estimates visually in Figure 8. Using the same affected customer sample

as in Figure 7, we compute the average number of ATM transactions in the [-120, +120]

18

window around closures. We then compute the daily average weighted by the number of

treated customers at each closure. To reduce survivorship bias in our activity plots for this

and the next two figures, for each treated customer, we fill in their activity for an event date

(within the sample period of Jan 2015 to Dec 2016) as zero if the customer becomes inactive in

the sample. We show a significant drop in the number of ATM transactions around closures,

consistent with Column (1) of Table 4.

Column (2) reports the results using the total number of non-financial ATM transactions

(e.g. balance enquiries, password changes). We find a significant drop—a 1 km increase in

Distance to ATM reduces non-financial ATM transactions by 0.727. 0.727 is 41% of the mean

of 1.77 non-financial ATM transactions. This shows that while customers still manage to use

ATMs despite the increased distance for financial transactions, they are much less inclined to

go to farther away ATMs just for non-financial transactions.

Column (3) reports the result using the average dollar amount of ATM transactions. We

find that the average dollar amount of ATM transactions increases by S$20.63 for a 1 km

increase in Distance to ATM. S$20.63 is 6.8% of the mean of S$305.10 transacted a month at

an ATM. That is, when customers get an ATM closure shock have longer distances to their

ATMs, they choose to use ATMs less frequently but with larger dollar amounts each time.

The results on ATM transactions activity are robust in different subsamples or with dif-

ferent definitions of ATM closure shocks. We use a subsample of customers who provided

residential addresses to bank in Panel B, to avoid the concern of measurement errors in Dis-

tance to ATM due to commercial addresses. Columns (1)-(3) report the change in ATM

activity due to the closure-induced changes in Distance to ATM and we find very similar

results in terms of statistical and economic significance. Panel C uses all customers in our

sample but uses ATM Closure Shock [0,+] as an instrumental variable. The main difference

from Panel A is that customers who are treated more than 4 months before are in the control

group in Panel A but are in the treated group in Panel C. Columns (1)-(3) report the change

in ATM activity due to the change in Distance to ATM and we find qualitatively similar

19

but economically stronger effects. This suggests that there can be some persistent long-term

changes to banking behavior after the shock. Panel D focuses only on customers with resi-

dential addresses and uses ATM Closure Shock [0,+] as the instrumental variable and we find

similar results as in the earlier panels.

Second, we report our main result that customers adopt digital transactions more after

ATM closure shocks increases their distance to ATMs. Columns (4)-(6) in Panel A report

the results on digital transactions using all customers with ATM Closure Shock [0,+3] as an

instrumental variable. The dependent variable in Column (4) is the total number of digital

transactions. We find that a 1 km increase in Distance to ATM increases digital transactions

by 4.23. 4.23 is 19.3% of the mean of 21.89 digital transactions per month for a customer.

Figure 9 shows this finding visually. Similar to Figure 8, Figure 9 reports the change in the

number of digital transactions around the event time, where we find an increase in the number

of digital transactions around the closure.

Column (5) uses the total number of financial digital transactions as a dependent variable

and Column (6) uses total dollar amount of digital transactions as a dependent variable.

Both coefficients are positive with large economic significance. For example, a 1 km increase

in distance increases the number of financial digital transactions by 0.288, which is 14.8% of

the mean of 1.95 financial digital transactions.

The results on digital transactions are robust in different subsamples or a different def-

inition of ATM closure shock. Columns (4)-(6) in Panel B use a subsample of customers

who provided residential addresses to the bank. We find similar results on increased digital

transactions due to the increased Distance to ATM as in Panel A. Panel C uses all customers

in our sample and Panel D only uses customers with residential addresses, but both use ATM

Closure Shock [0,+] as an instrumental variable. We also find similar results.

Third, we find mixed evidence on the total number of account transactions. Column

(7) in Panel A report the results on total number of transactions using all customers with

ATM Closure Shock [0,+3] as an instrumental variable. We find that increased Distance to

20

ATM increases the total number of transactions by 2.773. Using only the customers who

provided residential addresses to bank, Column (7) in Panel B finds similar results. However,

the coefficient becomes insignificant in Panels C and D. Hence the results are mixed here.

Visually, in Figure 10, we also do not observe any obvious change in the total number of

account transactions in event time around the ATM closure.

4.4. Subsample Analysis with IV: by Age Group

To estimate the differential effects across age groups, Table 5 reports the IV regression results

by age group. We divide the sample of customers into quartiles based on customer age. The

bottom quartile group includes ages under 27, “2/4” quartile includes ages from 27 to 35,

“3/4” quartile includes ages from 35-47, and the top quartile includes ages above 47.

Panel A uses all customers in our sample with ATM Closure Shock [0,+3] as an instru-

mental variable. In Columns (1)-(4), we use the total number of ATM transactions as the

dependent variable. Column (1) reports the results for the bottom quartile group, Column

(2) reports the results for the “2/4” quartile group, Column (3) reports the results for “3/4”

quartile group, and Column (4) reports the results for top quartile group. The main inde-

pendent variable is the Distance to ATM instrumented by the ATM closure shock. Other

controls include monthly beginning account balance, and monthly salary. We do not report

the coefficients of control variables for brevity. We also include year-month fixed effects and

customer fixed effects. We find that the reduction in the total number of transactions are

monotonic by age group and the reduction is largest in top age quartile.

In Columns (5)-(8), we use the total number of non-financial ATM transactions as a

dependent variable. We find that the reduction in the total number of non-financial ATM

transactions in most of the groups but the impact is the largest in top quartile. We use the

average dollar amount of ATM transactions as a dependent variable in Columns (9)-(12). The

coefficients are mostly positive with the largest significant impact in the “3/4” age group.

The results suggest that quasi-exogenous increases in Distance to ATM reduces ATM

21

usage more in the aged group. The results are robust with different subsamples or different

definitions of ATM closure shock. Panel B uses a subsample of customers who provided

residential addresses to the bank, Panel C uses all customers but with ATM Closure Shock

[0,+] as an IV, and Panel D uses the subsample of customers with residential addresses but

with ATM Closure Shock [0,+] as an IV. Results are stronger than those in Panels C and D.

Columns (13)-(24) in Panel A report the results on digital transactions. The Dependent

variable is the total number of digital transactions in Columns (13)-(16), the total number

of financial digital transactions in Columns (17)-(20), and the total dollar amount of digital

transactions in Columns (21)-(24). We find the largest increase in the total number of digital

transactions and the total dollar amount of digital transactions in top quartile. The results

are also robust in other specifications reported in Panels B, C, and D.

Our results suggest that the substitution from traditional to digital transactions is strongest

among the top quartile of ages when Distance to ATM exogenously increases. However, it

is not clear that such a substitution effect is monotonic with age across the board since our

sample does not include other older-aged customers who no longer receive salary credit.

4.5. Robustness Test: Propensity Score-Matched Sample

Thus far, we control for heterogeneous characteristics by including customer fixed effects. As

an alternative, we now identify a control group with similar characteristics as the treated

group using propensity score-matching. We first estimate a logit regression of the likelihood

to be affected by a closure with five lagged-month variables, namely, distance to ATM, number

of ATM transactions, number of digital transactions, number of account-level transactions,

and monthly salary. Then, for each treated customer (i.e. a customer-closure observation), we

identify another customer who was not affected by the closure but had the closest predicted

probability of facing a closure based on these five characteristics.

Table 6 reports IV regression estimates following Table 4 using the propensity score-

matched sample. Instead of the full panel, only treated and control-group customers are

22

included. We find similar results as in Table 4, although with smaller magnitudes.

We also conduct an even stricter propensity score matching approach where we now require

the control customer to be from the same district as the treated customer. This addresses a

potential concern that the bank is more likely to close ATMs in districts where they expect

that customers are more ready to move to digital platform (although we believe this is unlikely

to be the case as many of our closures are triggered by mall renovations).

We use the first two digits of the six-digit postal code to define a district. These two

digits represents postal districts in Singapore. For example the downtown area is divided into

eight districts from 01 to 08. Table 7 reports IV regression estimates using this same-district

propensity score-matched sample. Again, we find similar results.

4.6. FinTech Benefits: Evidence from Minimum Balance Fees

Is there any benefit for a customer who becomes more digital after an ATM closure shock? We

conjecture that digital customers, compared to traditional customers, might be more attentive

to their accounts after increasing their digital activity. An increase in attention could allow

them to avoid unnecessary charges such as minimum account balance fees.

The incidence of a minimum account balance fee is denoted as a service charge “SC” by

the bank. In our data, about 25% of the customer-year-month observations show a non-zero

service charge. The minimum balance requirement depends on the account type but most

minimum balance amounts are either $500 or $5,000 and the charge is typically $2. We argue

that the incurring of such charges are sub optimal as it could reflect a lack of attentiveness

to their accounts. If digital adoption reduces the costs of monitoring their balances, such

penalties can be avoided. A reduced occurrence of minimum balance fees can be argued as

some form of increased financial welfare.

Most studies on digital adoption such as Carlin, Olafsson, and Pagel (2017) and Agarwal,

Qian, Yeung, and Zou (2018) use adoption as the shock and measures its impact. However,

the agent’s choice of whether to adopt makes it unclear whether it is the adoption itself

23

that produces the effects, or whether those who choose to adopt are fundamentally different

from those who do not. Our advantage is that our digital adoption relies on an innocuous

instrument—the closure of oft-used ATM—and is more likely to be exogenous.

Using the ATM closure shock as an IV for the digitalness of customers, we examine the

impact of an increase in digitalness on the likelihood of a service charge payment. Unlike our

main results, we skip the distance channel at the first stage, i.e. we now directly measure the

impact of a closure on the customer’s digital banking activity regardless of how it affects the

customer’s travelled distance to ATM.

Table 8 shows in the first stage that the ATM closure shocks significantly increase the

number of digital transactions and the dollar amount of digital transactions. Similar results

are obtained in Panel A (short-term effects) and Panel B (longer-term effects).

Table 9 reports IV regression estimates of the effect of customers’ digital usage on the

incidence of service charge payments. Using ATM Closure Shock [0,+3], Columns (1)-(3)

in Panel A uses all customers. In Column (1), we instrument the total number of digital

transactions with the ATM closure shock. We find that for every increase of one total digital

transaction the likelihood of observing a service charge payment significantly decreases by

0.012. This is about 4.8% of the 0.25 mean of a service charge occurrence likelihood. Column

(2) uses the number of financial digital transactions and Column (3) uses the dollar amount of

digital transactions and finds similar results. Columns (4)-(6) use customers with residential

address and the results are similar. Panel B uses ATM Closure Shock [0,+] to find similar

but weaker longer-term effects.

Overall, the increase in digital banking usage due to quasi-exogenous closures of ATMs

has at least one benefit—reducing the incidence of below-minimum balance fees.

24

5. Conclusion

We examine the importance of physical distance to banking using a representative sample of

500,000 retail customers of a large bank in Singapore from 2015–2016. We provide evidence

of a relation between physical distance and digital banking (FinTech) usage using quasi-

exogenous ATM closures. We show that customers who experience ATM closures indeed face

an increase in the average distance that they travel to an ATM. Using instrumental variable

estimations with customer fixed effects and year-month fixed effects, we find that this added

distance friction increases their usage of digital banking. In terms of economic magnitude,

a one km increase in distance results in about 4 more digital transactions per month (the

sample average is 22). In the aggregate, while the customer uses ATMs less frequently after

experiencing closures, the increased use of digital banking compensates for this so that the total

banking activity in the short-term is slightly higher. In terms of cross-sectional differences, we

find that all our results are in general stronger in magnitude for the older age groups, which

is consistent with the view that older customers can benefit more from such nudges.

When customers face an increase in their physical distances to banking, they are more likely

to consider digital alternatives to access banking services. That minor changes in distances can

have such impact shows that physical distance remains an important friction for customers.

But these results also reveal that the preference of customers to have easier physical access to

banking locations can be substituted by digital access to banking.

We also use the closure shocks directly as an instrument for digital banking to examine

if such increased digital adoption helps customers. The outcome measure we use that we

argue can be associated with welfare, is the avoidance of a below-minimum balance fee. We

find that increased digital adoption as a result of ATM closures help reduce the likelihood of

minimum-balance fees. This provides evidence that FinTech adoption is beneficial in at least

one dimension.

25

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28

Table 1: Summary Statistics

We report summary statistics of the variables for our analysis. Our sample includes customer-year-monthobservations from January 2015 to December 2016. Panel A reports the demographic details of our samplecustomers, namely, age, monthly salary in thousands, and monthly account beginning balance in thousands.Panel B reports summary statistics for Distance to ATM, a transaction-equal-weighted distance to ATMs fromthe address of customers. Panel C reports customers’ banking activities. For the ATM activities, we reporttotal number of transactions, number of non-financial transactions, and average dollar amount of transactions.For the Digital activities, we report total number of transactions, number of financial transactions, and thetotal dollar amount of transactions. We also report the total number of transactions recorded in the customer’ssavings and checking accounts. Panel D reports summary statistics of the ATM Closure Shock. ATM ClosureShock [0,+3] equals to 1 for [0,+3] months from ATM closure events for a customer who did at least 5transactions at the relevant ATM in the 120 day-window before closure. ATM Closure Shock [0,+] equals to1 for [0,+] months from ATM closure events for a customer who did at least 5 transactions at the relevantATM in the 120 day-window before closure. We winsorize variables at the top 1%.

Variable Obs Mean Std.Dev. 10th Perc. 90th Perc.

Panel A: Customer Demographics

Age 3896921 37.59 13.28 22.00 57.00Monthly Salary (thousands) 3896921 2.73 6.45 0.00 6.09Beginning Balance (thousands) 3896921 16.91 65.74 0.01 38.52

Panel B: Distance to ATM

Distance to ATM 3896921 5.64 4.85 0.53 12.66

Panel C: Customers’ Banking Activities

ATM TransactionsNumber of Transactions Total 3896921 9.07 7.55 2.00 19.00

non-Financial 3896921 1.77 3.41 0.00 6.00Dollar Amount of Transactions 3896921 305.10 328.55 50.00 700.00

Digital TransactionsNumber of Transactions Total 3896921 21.89 35.20 0.00 68.00

Financial 3896921 1.95 3.63 0.00 7.00Dollar Amount of Transactions 3896921 1306.99 3561.76 0.00 3719.52

Total Account TransactionsTotal Number of Transactions 3896921 24.35 17.60 6 48

Panel D: ATM Closure Shock

ATM Closure Shock [0,+3] 3896921 0.01 0.11 0 0ATM Closure Shock [0,+] 3896921 0.03 0.18 0 0

29

Table 2: The Effect of the Distance to ATM on Customers’ Banking Activities

We report panel regression estimates of the effect of the distance to ATM on customers’ banking activities.Panel A uses all customers in our sample. In Columns (1)-(3), dependent variables are related to ATMtransactions. Column (1) uses the Total Number of ATM Transactions as a dependent variable, Column (2)uses the Total Number of non-Financial ATM Transactions, and Column (3) uses Average Dollar Amountof ATM Transactions. The main independent variable is Distance to ATM, a transaction-equal-weighteddistance to ATMs from the address of customers. Other controls include monthly beginning account balancein thousands (Beginning Balance), and Monthly Salary in thousands. We include year-month fixed effects andcustomer fixed effects. In Columns (4)-(6), dependent variables are related to Digital transactions. Column(4) uses the Total Number of Digital Transactions as a dependent variable, Column (5) uses the Total Numberof Financial Digital Transactions, and Column (6) uses the Total Dollar Amount of Digital Transactions. InColumn (7), the dependent variable is the Total Number of Transactions in the customer’s savings and checkingaccounts. Panel B only uses customers with residential address. The table reports coefficient estimates witht-statistics in parentheses. All the standard errors are clustered at the customer level. ***, **, * denotes 1%,5%, and 10% statistical significance. We winsorize the continuous variables at the top 1%.

Panel A: All Customers(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM 0.033*** 0.006*** -1.122*** 0.036*** 0.003*** 1.458*** 0.079***(28.84) (10.25) (-16.99) (7.99) (6.13) (2.89) (32.07)

Beginning Balance 0.002*** 0.0004*** 0.214*** 0.012*** 0.002*** 6.090*** 0.012***(5.89) (4.25) (5.11) (8.37) (7.35) (6.68) (7.68)

Monthly Salary 0.024*** 0.004*** 0.786*** 0.141*** 0.014*** 33.74*** 0.094***(9.48) (8.73) (9.04) (8.85) (8.61) (9.00) (8.62)

Observations 3,609,584 3,609,584 3,582,588 3,896,825 3,896,825 3,896,825 3,896,825R-squared 0.677 0.607 0.529 0.750 0.769 0.581 0.768Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes

Panel B: Customers with Residential Address(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM 0.037*** 0.006*** -1.252*** 0.042*** 0.003*** 1.576*** 0.092***(30.83) (9.53) (-18.20) (8.23) (6.59) (2.72) (34.02)

Beginning Balance 0.002*** 0.0003*** 0.212*** 0.013*** 0.002*** 6.125*** 0.012***(5.58) (3.73) (4.82) (8.17) (7.03) (6.33) (7.46)

Monthly Salary 0.028*** 0.004*** 0.904*** 0.179*** 0.018*** 43.27*** 0.117***(9.91) (9.02) (9.76) (10.91) (10.41) (10.38) (10.98)

Observations 3,189,014 3,189,014 3,168,583 3,440,453 3,440,453 3,440,453 3,440,453R-squared 0.688 0.607 0.536 0.745 0.765 0.576 0.757Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes

30

Table 3: The Effect of ATM Closures on the Distance to ATM

We report panel regression estimates of the effect of ATM Closure Shock on the distance to ATM. Thedependent variable is the Distance to ATM, a transaction-equal-weighted distance to ATMs from the addressof customers. In Columns (1)-(2), the main independent variable is the ATM Closure Shock [0,+3]. ATMClosure Shock [0,+3], measuring short-term effects, equals to 1 for [0,+3] months from ATM closure eventsfor a customer who did at least 5 transactions at the relevant ATM in the 120 day-window before closure.Column (1) includes all customers in our sample. Other controls include monthly beginning account balancein thousands (Beginning Balance), and Monthly Salary in thousands. We include year-month fixed effectsand customer fixed effects. Column (2) includes customers with residential address only. In Columns (3)-(4),the main independent variable is the ATM Closure Shock [0,+], measuring longer-term effects, equals to 1 for[0,+] months from ATM closure events for a customer who did at least 5 transactions at the relevant ATM inthe 120 day-window before closure. Coefficient estimates are reported with t-statistics in parentheses based onstandard errors clustered at the customer level. ***, **, * denotes 1%, 5%, and 10% statistical significance.We winsorize the continuous variables at the top 1%.

All Residential All Residential(1) (2) (3) (4)

Variables Distance to ATM

ATM Closure Shock [0,+3] 0.129*** 0.158***(6.92) (8.60)

ATM Closure Shock [0,+] 0.096*** 0.121***(4.23) (5.39)

Beginning Balance 0.0002** 0.0003*** 0.0002** 0.0003***(2.35) (2.58) (2.35) (2.58)

Monthly Salary 0.002*** 0.003*** 0.002*** 0.003***(5.22) (7.18) (5.22) (7.18)

Observations 3,896,825 3,440,453 3,896,825 3,440,453R-squared 0.643 0.600 0.643 0.600Year-Month FE Yes Yes Yes YesCustomer FE Yes Yes Yes Yes

31

Table 4: The Effect of the Distance to ATM on Customers’ Banking Activities(IV using ATM Closure Shock)

We report Instrumental Variable (IV) regression estimates of the effect of distance to ATM on customers’banking activities using ATM Closure Shock as an IV. Panel A uses all customers in our sample and ATMClosure Shock [0,+3] as an IV. ATM Closure Shock [0,+3], measuring short-term effects, equals to 1 for [0,+3]months from ATM closure events for a customer who did at least 5 transactions at the relevant ATM in the120 day-window before closure. In Columns (1)-(3), dependent variables are related to ATM transactions.The dependent variable for Column (1) is the Total Number of ATM Transactions, for Column (2) is theTotal Number of non-Financial ATM Transactions, and for Column (3) is the Average Dollar Amount ofATM Transactions. The main independent variable is Distance to ATM instrumented by the ATM ClosureShock. Distance to ATM is a transaction-weighted distance to ATMs from the address of customers. Othercontrols include monthly beginning account balance in thousands (Beginning Balance), and Monthly Salary inthousands. To mitigate the impact of outliers, We include year-month fixed effects and customer fixed effects.In Columns (4)-(6), the dependent variables are related to Digital transactions. Column (4) uses the TotalNumber of Digital Transactions as a dependent variable, Column (5) uses the Total Number of FinancialDigital Transactions, and Column (6) uses the Total Amount of Digital Transactions. In Column (7), thedependent variable is the Total Number of Transactions in the customer’s savings and checking accounts.Panel B only uses customers with residential address and uses ATM Closure Shock [0,+3] as an IV, measuringshort-term effects. Panel C uses all customers in our sample and uses ATM Closure Shock [0,+] as an IV,measuring longer-term effects. ATM Closure Shock [0,+] equals to 1 for [0,+] months from ATM closure eventsfor a customer who did at least 5 transactions at the relevant ATM in the 120 day-window before closure.Panel D only uses customers with residential address and uses ATM Closure Shock [0,+] as an IV, measuringlonger-term effects. Coefficient estimates are reported with t-statistics in parentheses based on standard errorsclustered at the customer level. ***, **, * denotes 1%, 5%, and 10% statistical significance. We winsorize thecontinuous variables at the 99th percentile.

Panel A: All Customers, short-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -0.885*** -0.727*** 20.63** 4.230*** 0.288*** 203.2* 2.773***(-2.83) (-3.69) (2.30) (3.38) (2.61) (1.91) (4.07)

Beginning Balance 0.003*** 0.0005*** 0.211*** 0.012*** 0.002*** 6.044*** 0.012***(5.92) (4.36) (5.06) (7.79) (7.19) (6.66) (7.48)

Monthly Salary 0.026*** 0.006*** 0.734*** 0.133*** 0.013*** 33.33*** 0.089***(9.04) (6.81) (8.67) (8.79) (8.56) (9.00) (8.60)

Observations 3,610,267 3,610,267 3,583,302 3,896,921 3,896,921 3,896,921 3,896,921Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes

Panel B: Customers with Residential Address, short-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -0.823*** -0.699*** 15.09** 3.932*** 0.259*** 166.2* 2.415***(-3.11) (-4.19) (2.12) (3.67) (2.71) (1.80) (4.32)

Beginning Balance 0.003*** 0.0004*** 0.209*** 0.012*** 0.002*** 6.083*** 0.012***(5.68) (4.08) (4.78) (7.55) (6.86) (6.31) (7.24)

Monthly Salary 0.031*** 0.007*** 0.850*** 0.167*** 0.017*** 42.73*** 0.110***(9.54) (7.25) (9.30) (10.53) (10.14) (10.32) (10.72)

Observations 3,189,674 3,189,674 3,169,269 3,440,554 3,440,554 3,440,554 3,440,554Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes

32

Table 4 Continues

Panel C: All Customers, longer-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -13.43*** -7.365*** 71.52*** 10.24*** 1.008*** 346.4** -1.593(-4.35) (-4.35) (3.51) (3.45) (3.43) (2.03) (-1.52)

Beginning Balance 0.005*** 0.002** 0.203*** 0.010*** 0.001*** 6.012*** 0.013***(3.27) (2.17) (4.90) (5.89) (6.20) (6.64) (7.60)

Monthly Salary 0.057*** 0.022*** 0.613*** 0.120*** 0.012*** 33.03*** 0.098***(5.35) (4.11) (7.00) (8.13) (7.89) (8.98) (8.40)

Observations 3,610,267 3,610,267 3,583,302 3,896,921 3,896,921 3,896,921 3,896,921Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes

Panel D: Customers with Residential Address, longer-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -11.03*** -6.108*** 48.83*** 8.412*** 0.840*** 260.2* -1.059(-5.35) (-5.35) (3.57) (3.89) (3.90) (1.91) (-1.24)

Beginning Balance 0.004*** 0.001** 0.203*** 0.010*** 0.002*** 6.059*** 0.013***(3.67) (2.38) (4.67) (6.19) (6.14) (6.30) (7.44)

Monthly Salary 0.065*** 0.025*** 0.739*** 0.152*** 0.015*** 42.43*** 0.121***(6.39) (4.96) (7.97) (9.47) (9.05) (10.28) (10.65)

Observations 3,189,674 3,189,674 3,169,269 3,440,554 3,440,554 3,440,554 3,440,554Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes

33

Table 5: The Effect of the Distance to ATM on Customers’ Banking Activities (IV using ATM Closure Shockby Age Group)

We report IV regression estimates of the effect of the distance to ATM on customers’ banking activities by the age group of customers using ATMClosure Shock as an IV. Panel A uses all customers in our sample and uses ATM Closure Shock [0,+3] as an IV, measuring short-term effects. ATMClosure Shock [0,+3] equals to 1 for [0,+3] months from ATM closure events for a customer who did at least 5 transactions at the relevant ATM in the120 day-window before closure. Customers in the panel are sorted in age quartiles. In Column (1)-(4), we use the Total Number of ATM Transactionsas the dependent variable. Column (1) reports the result for the age group of under 27, Column (2) reports the result for the age group of 27-35,Column (3) reports the result for the age group of 35-47, and Column (4) reports the result for the age group of above 47. The main independentvariable is Distance to ATM instrumented by the ATM Closure Shock. Distance to ATM is a transaction-equal-weighted distance to ATMs from theaddress of customers and ATM Closure Shock equals to 1 for a customer after an ATM closure if the customer used the closed ATM more than 5 timesin the 120 day-window before closure. Other controls include monthly beginning account balance in thousands (Beginning Balance), and MonthlySalary in thousands. We do not report the control variables coefficients for brevity. We include year-month fixed effects and customer fixed effects. InColumn (5)-(8), we use the Total Number of non-Financial ATM Transactions as the dependent variable. In Column (9)-(12), we use Average DollarAmount of ATM Transactions as a dependent variable. In Column (13)-(16), we use the Total Number of Digital Transactions as the dependentvariable. In Column (17)-(20), we use the Total Number of Financial Digital Transactions as the dependent variable. In Column (21)-(24), we usethe Total Dollar Amount of Digital Transactions as a dependent variable as the dependent variable. Panel B only uses customers with residentialaddress and uses ATM Closure Shock [0,+3] as an IV, measuring short-term effects. Panel C uses all customers in our sample and uses ATM ClosureShock [0,+] as an IV, measuring longer-term effects. ATM Closure Shock [0,+] equals to 1 for [0,+] months from ATM closure events for a customerwho did at least 5 transactions at the relevant ATM in the 120 day-window before closure. Panel D only uses customers with residential addressesand uses ATM Closure Shock [0,+] as an IV, measuring longer-term effects. Coefficient estimates are reported with t-statistics in parentheses basedon standard errors clustered at the customer level. ***, **, * denotes 1%, 5%, and 10% statistical significance. We winsorize the continuous variablesat the 99th percentile.

Panel A: All Customers, short-term effects(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

# of ATM Transactions (Total) # of ATM Transactions (non-Financial) $ of ATM TransactionsAge Group Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4

Distance to ATM 0.096 -0.143 -1.146* -1.750*** -0.857* -0.426 -0.236 -0.880** 8.545 15.54 43.85** 7.244(0.14) (-0.30) (-1.92) (-2.90) (-1.76) (-1.58) (-0.74) (-2.36) (0.52) (1.00) (2.17) (0.49)

Observations 852,818 864,028 970,009 923,412 852,818 864,028 970,009 923,412 843,964 858,602 964,043 916,693Year-Month FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

(13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24)# of Digital Transactions (Total) # of Digital Transactions (Financial) $ of Digital Transactions

Age Group Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4

Distance to ATM 7.644 5.103* 1.305 4.113*** 0.463 0.360 0.263 0.191 -79.02 75.87 189.7 326.0*(1.50) (1.95) (0.69) (2.71) (1.20) (1.59) (1.35) (1.56) (-0.43) (0.44) (0.86) (1.94)

Observations 939,445 932,867 1,033,990 990,619 939,445 932,867 1,033,990 990,619 939,445 932,867 1,033,990 990,619Year-Month FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

34

Table 5 Continues

Panel B: Customers with Residential Address, short-term effects(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

# of ATM Transactions (Total) # of ATM Transactions (non-Financial) $ of ATM TransactionsAge Group Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4

Distance to ATM 0.317 -0.107 -1.050** -1.932*** -0.555* -0.262 -0.343 -1.077*** 2.018 14.83 33.22** 4.449(0.61) (-0.25) (-2.15) (-3.18) (-1.80) (-1.18) (-1.26) (-2.81) (0.19) (1.19) (2.21) (0.31)

Observations 749,227 698,478 849,792 892,177 749,227 698,478 849,792 892,177 741,945 695,719 845,746 885,859Year-Month FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

(13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24)# of Digital Transactions (Total) # of Digital Transactions (Financial) $ of Digital Transactions

Age Group Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4

Distance to ATM 6.469** 4.889** 0.921 4.246*** 0.414 0.330* 0.210 0.205 -16.59 61.46 125.7 323.6*(2.06) (2.15) (0.54) (2.75) (1.64) (1.67) (1.22) (1.64) (-0.13) (0.40) (0.64) (1.91)

Observations 828,660 753,764 902,524 955,606 828,660 753,764 902,524 955,606 828,660 753,764 902,524 955,606Year-Month FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Panel C: All Customers, longer-term effects(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

# of ATM Transactions (Total) # of ATM Transactions (non-Financial) $ of ATM TransactionsAge Group Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4

Distance to ATM -5.750*** -18.14 -13.37*** -12.36*** -2.698*** -9.306 -6.862*** -7.640*** 30.16** 114.5 104.0** 46.96*(-3.59) (-1.09) (-2.74) (-2.91) (-3.54) (-1.10) (-2.74) (-2.90) (2.39) (1.02) (2.44) (1.69)

Observations 852,818 864,028 970,009 923,412 852,818 864,028 970,009 923,412 843,964 858,602 964,043 916,693Year-Month FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

(13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24)# of Digital Transactions (Total) # of Digital Transactions (Financial) $ of Digital Transactions

Age Group Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4

Distance to ATM 4.289 33.12 8.919** 9.570*** 0.424* 4.192 1.029** 0.449* 20.01 1,539 -113.5 397.2(1.62) (1.00) (2.13) (2.63) (1.72) (1.01) (2.20) (1.86) (0.18) (0.97) (-0.37) (1.63)

Observations 939,445 932,867 1,033,990 990,619 939,445 932,867 1,033,990 990,619 939,445 932,867 1,033,990 990,619Year-Month FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

35

Table 5 Continues

Panel D: Customers with Residential Address, longer-term effects(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

# of ATM Transactions (Total) # of ATM Transactions (non-Financial) $ of ATM TransactionsAge Group Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4

Distance to ATM -5.151*** -11.16* -10.72*** -11.90*** -2.405*** -5.650* -5.541*** -7.393*** 22.94** 72.24 73.15*** 36.47(-3.93) (-1.78) (-3.32) (-3.17) (-3.87) (-1.79) (-3.33) (-3.15) (2.24) (1.43) (2.63) (1.51)

Observations 749,227 698,478 849,792 892,177 749,227 698,478 849,792 892,177 741,945 695,719 845,746 885,859Year-Month FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

(13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24)# of Digital Transactions (Total) # of Digital Transactions (Financial) $ of Digital Transactions

Age Group Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4 Bottom 1/4 2/4 3/4 Top 4/4

Distance to ATM 3.744 19.35* 6.861** 9.465*** 0.372* 2.532* 0.810** 0.463** 27.89 837.3 -219.6 396.6*(1.55) (1.80) (2.14) (2.79) (1.67) (1.87) (2.28) (1.98) (0.26) (1.60) (-0.80) (1.71)

Observations 828,660 753,764 902,524 955,606 828,660 753,764 902,524 955,606 828,660 753,764 902,524 955,606Year-Month FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

36

Table 6: Propensity Score-Matched Sample

We repeat the IV regression estimates of Table 4 using a propensity score-matched sample. We constructa control group using propensity score matching with five lagged-by-one-month controls, namely, Distanceto ATM, total number of ATM transactions, total number of digital banking transactions, total number ofaccount transactions, and monthly salary. For each treated customer at each ATM closure event, we choosea control customer who has the closest likelihood of facing an ATM closure but was not treated. Regressionspecifications are same as Table 4 except the sample is restricted to only treated customers and the selectedcontrol customers. Panels A and B measure short-term effects that use ATM Closure Shock [0,+3] as an IV,while Panels C and D measures longer-term effects that use ATM Closure Shock [0,+] as an IV. Coefficientestimates are reported with t-statistics in parentheses and standard errors are clustered at the customer level.***, **, * denotes 1%, 5%, and 10% statistical significance. We winsorize the continuous variables at the 99thpercentile.

Panel A: All Customers, short-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -0.694** -0.504*** 17.27* 3.305*** 0.212* 210.8* 1.893***(-2.18) (-2.63) (1.84) (2.61) (1.87) (1.87) (2.88)

Observations 589,452 589,452 586,583 612,291 612,291 612,291 612,291Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes

Panel B: Customers with Residential Address, short-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -0.693** -0.545*** 11.51 3.211*** 0.202** 180.2* 1.727***(-2.55) (-3.29) (1.55) (2.92) (2.04) (1.84) (3.10)

Observations 526,870 526,870 524,805 545,710 545,710 545,710 545,710Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes

Panel C: All Customers, longer-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -6.776*** -1.659*** 54.56*** 4.980*** 0.428*** 311.4** -5.065***(-5.61) (-4.65) (4.02) (3.26) (2.95) (2.56) (-4.43)

Observations 589,452 589,452 586,583 612,291 612,291 612,291 612,291Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes

Panel D: Customers with Residential Address, longer-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -5.233*** -1.228*** 35.88*** 4.087*** 0.354*** 258.6*** -3.809***(-6.83) (-4.96) (3.88) (3.39) (3.04) (2.64) (-4.79)

Observations 526,870 526,870 524,805 545,710 545,710 545,710 545,710Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes

37

Table 7: Propensity Score-Matched Sample within a Postal Region

We repeat the IV regression estimates of Table 4 using a propensity score-matched sample. We constructa control group using propensity score matching with five lagged-by-one-month controls, namely, Distanceto ATM, total number of ATM transactions, total number of digital banking transactions, total number ofaccount transactions, and monthly salary. For each treated customer at each ATM closure event, we choose acontrol customer who lived in the same district (proxied by the first two digits of the Singapore postal code)who has the closest likelihood of facing an ATM closure but was not treated. Regression specifications aresame as Table 4 except the sample is restricted to only treated customers and the selected control customers.Panels A and B measures short-term effects that use ATM Closure Shock [0,+3] as an instrumental variable,while Panels C and D measures longer-term effects that use ATM Closure Shock [0,+] as the instrument. Thetable reports coefficient estimates with t-statistics in parentheses. All the standard errors are clustered atthe customer level. ***, **, * denotes 1%, 5%, and 10% statistical significance. We winsorize the continuousvariables at the 99th percentile.

Panel A: All Customers, short-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -0.676** -0.505*** 17.31* 3.160** 0.202* 172.9 1.910***(-2.14) (-2.64) (1.85) (2.54) (1.80) (1.57) (2.93)

Observations 568,063 568,063 565,410 590,616 590,616 590,616 590,616Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes

Panel B: Customers with Residential Address, short-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -0.676** -0.555*** 12.16 3.192*** 0.202** 156.8 1.826***(-2.48) (-3.32) (1.62) (2.89) (2.02) (1.60) (3.23)

Observations 513,445 513,445 511,453 532,306 532,306 532,306 532,306Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes

Panel C: All Customers, longer-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -5.681*** -1.645*** 39.91*** 2.671** 0.275** 43.78 -4.399***(-6.83) (-5.77) (4.13) (2.45) (2.54) (0.49) (-5.21)

Observations 568,063 568,063 565,410 590,616 590,616 590,616 590,616Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes

Panel D: Customers with Residential Address, longer-term effects(1) (2) (3) (4) (5) (6) (7)

# of ATM Transactions $ of ATM # of Digital Transactions $ of Digital Total # ofVariables Total non-Financial Transactions Total Financial Transactions Transactions

Distance to ATM -4.656*** -1.349*** 27.44*** 2.511*** 0.257*** 47.73 -3.430***(-7.92) (-6.22) (3.73) (2.62) (2.69) (0.60) (-5.30)

Observations 513,445 513,445 511,453 532,306 532,306 532,306 532,306Year-Month FE Yes Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes Yes

38

Table 8: The Effect of ATM Closures on the Digital Banking Activities

We report panel regression estimates of the effect of ATM closure shock on digital transactions. Panel A usesATM Closure Shock [0,+3] as main independent variable, measuring short-term effects. ATM Closure Shock[0,+3] equals to 1 for [0,+3] months from ATM closure events for a customer who did at least 5 transactionsat the relevant ATM in the 120 day-window before closure. Columns (1)-(3) use the sample of all customers.Column (1) uses total number of digital transactions as a dependent variable, Column (2) uses total numberof financial digital transactions, and Column (3) uses total dollar amount of digital transactions. Othercontrols include monthly beginning account balance in thousands (Beginning Balance) and Monthly Salary inthousands. We do not report the coefficients on the control variables for brevity. We include year-month fixedeffects and customer fixed effects. Columns (4)-(6) use the sample of customers with residential addresses only(removing those with commercial addresses). Panel B uses ATM Closure Shock [0,+] as main independentvariable, measuring longer-term effects. ATM Closure Shock [0,+] equals to 1 for [0,+] months from ATMclosure events for a customer who did at least 5 transactions at the relevant ATM in the 120 day-windowbefore closure. The table reports coefficient estimates with t-statistics in parentheses. All the standard errorsare clustered at customer level. ***, **, * denotes 1%, 5%, and 10% statistical significance. We winsorize thecontinuous variables at the 99th percentile.

Panel A: Short-term effects: ATM Closure Shock [0,+3]All Customers Customers with Residential Address

(1) (2) (3) (4) (5) (6)# of Digital Transactions $ of Digital # of Digital Transactions $ of Digital

Variables Total Financial Transactions Total Financial Transactions

ATM Closure Shock [0,+3] 0.576*** 0.041*** 28.52** 0.660*** 0.046*** 29.14**(4.07) (3.10) (2.18) (4.26) (3.16) (2.04)

Observations 4,145,599 4,145,599 4,145,599 3,684,181 3,684,181 3,684,181R-squared 0.752 0.770 0.579 0.748 0.767 0.574Year-Month FE Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes

Panel B: Longer-term effects: ATM Closure Shock [0,+]All Customers Customers with Residential Address

(1) (2) (3) (4) (5) (6)# of Digital Transactions $ of Digital # of Digital Transactions $ of Digital

Variables Total Financial Transactions Total Financial Transactions

ATM Closure Shock [0,+] 1.088*** 0.111*** 39.48*** 1.151*** 0.119*** 39.66**(6.33) (6.48) (2.74) (6.20) (6.44) (2.57)

Observations 4,145,599 4,145,599 4,145,599 3,684,181 3,684,181 3,684,181R-squared 0.752 0.770 0.579 0.748 0.767 0.574Year-Month FE Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes

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Table 9: The Effect of the Digital Banking Activities on the Service ChargePayments (IV using ATM Closure Shock)

We report IV regression estimates of the effect of customers’ digital usage on the service charge payments(incurred for dropping below the minimum-balance requirement in the account) using ATM closure shock asan IV. The main dependent variable is an indicator for the occurrence of a service charge payment in a month.Panel A uses ATM Closure Shock [0,+3] as an instrumental variable, measuring short-term effects. ATMClosure Shock [0,+3] equals to 1 for [0,+3] months from ATM closure events for a customer who did at least 5transactions at the relevant ATM in the 120 day-window before closure. Columns (1)-(3) use the sample of allcustomers. We use ATM Closure Shock [0,+3] as an instrument for the number of total digital transactionsin Column (1), for the number of financial digital transactions in Column (2), and for the dollar amount ofdigital transactions in Column (3). Other controls include monthly beginning account balance in thousands(Beginning Balance) and Monthly Salary in thousands. We include year-month fixed effects and customerfixed effects. Columns (4)-(6) use the sample of customers with residential addresses only. Panel B uses ATMClosure Shock [0,+] as main independent variable, measuring longer-term effects. ATM Closure Shock [0,+]equals to 1 for [0,+] months from ATM closure events for a customer who did at least 5 transactions at therelevant ATM in the 120 day-window before closure. The table reports coefficient estimates with t-statisticsin parentheses. All the standard errors are clustered at the customer level. ***, **, * denotes 1%, 5%, and10% statistical significance. We winsorize the continuous variables at the 99th percentile.

Panel A: Short-term effects: ATM Closure Shock [0,+3]All Customers Customers with Residential Address

(1) (2) (3) (4) (5) (6)Variables I Service Charge

# of Digital Transactions (Total) -0.012*** -0.011***(-2.77) (-2.78)

# of Digital Transactions (Financial) -0.171** -0.153**(-2.40) (-2.39)

$ of Digital Transactions -0.0002* -0.0002*(-1.93) (-1.82)

Observations 4,145,599 4,145,599 4,145,599 3,684,187 3,684,187 3,684,187Year-Month FE Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes

Panel B: Longer-term effects: ATM Closure Shock [0,+]All Customers Customers with Residential Address

(1) (2) (3) (4) (5) (6)Variables I Service Charge

# of Digital Transactions (Total) -0.003 -0.003*(-1.47) (-1.78)

# of Digital Transactions (Financial) -0.029 -0.034*(-1.47) (-1.78)

$ of Digital Transactions -7.99e-05 -0.0001(-1.34) (-1.53)

Observations 4,145,599 4,145,599 4,145,599 3,684,187 3,684,187 3,684,187Year-Month FE Yes Yes Yes Yes Yes YesCustomer FE Yes Yes Yes Yes Yes YesOther Controls Yes Yes Yes Yes Yes Yes

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Figure 1: ATM Network in Singapore

We mark the bank’s ATM network in Singapore for the 2015-2016 sample period. The $ icons represent buildinglocations (postal codes) that have at least one ATM. Red colored $ icons are the 58 locations associated withATM closures in our 2015-2016 sample period.

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Figure 2: Distance to ATM

We report the time-series of the average Distance to ATM, the average distance to ATM from the postal addressof customers. For each ATM transaction, we first compute a GPS distance between customer’s address andATM location. Using the total number of ATM transactions as the weight, we compute the weighted averagedistance per customer for each month. Observations where customers do not make any ATM transactions inthat month are excluded. The average across all customers every month is then plotted.

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Figure 3: Distance to ATM by the Time of Usage

We report the time-series of the average Distance to ATM by the time of usage. The red dashed line representsthe mean Distance to ATM during working hours. Working hours are defined as 8am to 6pm on weekdaysexcept for public holidays. The blue solid line represents the average Distance to ATM during non-workinghours in weekdays. The green long-dashed line represents the average Distance to ATM during weekends andpublic holidays. The distance averages are computed as follows. For each ATM transaction, we first computea GPS distance between customer’s address and ATM location. Using the total number of ATM transactionsas the weight, we compute the weighted average distance per customer for each month. Observations wherecustomers do not make any ATM transactions in that month are excluded.

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Figure 4: Distribution of ATM Usage by Distance

We report the average distribution of customers’ ATM usage by the distance from customers’ address. Thetop chart includes all customers in our analysis. Red bars show the average fraction of a typical customers’ATM usage by the distance at each km. For comparison with the total number of ATMs available at each km,blue bars show the average fractions of available ATMs in Singapore by the distance from a typical customer’saddress. The bottom chart shows the average distribution of customers’ actual ATM usage by distance forthe subset of customers who provide a commercial address (orange bars) in the bank’s record instead of aresidential address. The address category is determined by searching for the postal code with an “(S) ” prefixin streetdirectory.com.

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Figure 5: Customer-level Distribution of ATM Usage by Distance

We report the customer-level distribution of ATM usage by distance. We randomly select 1,000 customers who have at least 1 salary credit and 5ATM transactions. On the horizontal axis, the customers are arranged increasing mean Distance to ATM. On the vertical axis, we report the fractionof ATM usage by distance for each customer. Each column hence represents an actual customer and the color intensity signifies the probability thatthe customer uses ATMs at every km.

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Figure 6: Distance to ATM and the number of Digital Transactions

We compare the number of digital transactions of customers by their Distance to ATM. We sort customersindependently each month into quintiles by their salary (excluding no salary credit months) and by theirDistance to ATM. We report the total digital transactions of customers in each group. Customers with nodigital transactions have their number of digital transactions set to zero.

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Figure 7: The Effect of ATM Closures on Distance to ATM

We compare Distance to ATM of customers before and after ATM closures. For each ATM closure, wecompute Distance to ATM in the window of [-120, +120] around the closure for the customers who did atleast 5 transactions at the relevant ATM in the 120 day-window before closure. Blue dots report the averagesacross closures for [-120, +120] weighted by # of treated customers at each closure. For the pre-closure period,these daily averages from actual usage include all the customer’s ATM transactions, whether at the about-to-close ATM or at other ATMs. For the post-closure period, these daily averages include all other ATMs sincethe closed ATM no longer exists. The blue horizontal line reports the average Distance to ATM before theclosures ([-120,-1]) and red horizontal line reports the average Distance to ATM after the closures ([0,+120]).

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Figure 8: The Effect of ATM Closure on Total ATM Transactions of Customers

We compare total ATM transactions of customers before and after ATM closures. For each ATM closure,we compute the daily average number of ATM transactions in the window of [-120, +120] around the closurefor customers who did at least 5 transactions at the relevant ATM in the 120 day-window before closure.Blue dots report the averages across closures for [-120, +120] weighted by the # of treated customers at eachclosure. A treated customer with no ATM transactions for a day has the number of total ATM transactionsthat day set to zero. The blue horizontal line reports the daily average number of total ATM transactionsbefore the closures ([-120,-1]) and red horizontal line reports the average number of total ATM transactionsafter the closures ([0,+120]).

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Figure 9: The Effect of ATM Closure on Total Digital Transactions of Customers

We compare total digital transactions of customers before and after ATM closures. For each ATM closure, wecompute the daily average number of digital transactions in the window of [-120, +120] around the closure forcustomers who did at least 5 transactions at the relevant ATM in 120 day-window before closure. Blue dotsreport the averages across closures for [-120, +120] weighted by the # of treated customers at each closure.A treated customer with no digital transactions for a day has the number of total digital transactions thatday set to zero. The blue horizontal line reports the average number of total digital transactions before theclosures ([-120,-1]) and red horizontal line reports the average number of total digital transactions after theclosures ([0,+120]).

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Figure 10: The Effect of ATM Closure on Total Account Transactions ofCustomers

We compare total account transactions of customers in their savings and checking accounts before and afterATM closures. For each ATM closure, we compute the daily average number of savings and checking accounttransactions in the window of [-120, +120] around the closure for the customers who did at least 5 transactionsat the relevant ATM in 120 day-window before closure. Blue dots report the averages across closures for [-120, +120] weighted by the # of treated customers at each closure. A treated customer with no savings andchecking account transactions for a day has the number of total account transactions that day set to zero. Theblue horizontal line reports the average number of total account transactions before the closures ([-120,-1])and red horizontal line reports the average number of total account transactions after the closures ([0,+120]).

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