Tilburg University

Payment Technology Adoption and Finance

Dalton, Patricio; Pamuk, Haki; Ramrattan, R.; van Soest, Daan; Uras, Burak

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Citation for published version (APA):Dalton, P., Pamuk, H., Ramrattan, R., van Soest, D., & Uras, B. (2018). Payment Technology Adoption andFinance: A Randomized-Controlled-Trial with SMEs. (CentER Discussion Paper; Vol. 2018-042). CentER,Center for Economic Research.

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Download date: 06. Sep. 2020

No. 2018-042

PAYMENT TECHNOLOGY ADOPTION AND FINANCE: A RANDOMIZED-CONTROLLED-TRIAL WITH SMES

By

Patricio S. Dalton, Haki Pamuk, Ravindra Ramrattan, Daan van Soest,

Burak Uras

16 October 2018

ISSN 0924-7815 ISSN 2213-9532

Payment Technology Adoption and Finance:

A Randomized-Controlled-Trial with SMEs∗

Patricio S. Dalton† Haki Pamuk‡ Ravindra Ramrattan§

Daan van Soest† Burak Uras†

October 16, 2018

Abstract

What determines the adoption of electronic-payment instruments? Do these instru-

ments impact business outcomes, in particular access to finance? To shed light on

these questions, we conducted a Randomized-Controlled-Trial with Kenyan SMEs.

Our experiment released barriers to adopt a novel payment instrument. We uncover

that the adoption barriers were binding for a large portion of the firms and that firms’

financial transparency interacted with the decision to adopt. After sixteen months,

treated businesses were more likely to feel safe and had more loans. The impact on

loans was especially pronounced for smaller size establishments, which also experi-

enced a reduction in sales-volatility.

Keywords: SME Finance; Transparency; Technology Adoption; Lipa Na M-Pesa.

JEL Classification: D22, G00, G21, O33.

∗We are grateful to Abhilash Maji, Wasike Nambuwani and Edoardo Totolo for early constructive discussions about thisproject, and to FSD, specially Wasike Nambuwani, for their assistance with the field work. We also thank Thorsten Beck,William Jack and Jeremy Tobacman for their recommendations on earlier drafts of this paper. This paper has benefitedfrom comments of participants at 2017 Wageningen Development Economics Workshop, 2018 IMF-DFID Conference onFinancial Inclusion and seminars at Bogazici University and Tilburg University. This paper is written in the framework ofthe research project “Enabling Innovation and Productivity Growth in Low Income Countries (EIP-LIC/PO5639)”, fundedby the Department for International Development (DFID) of the United Kingdom and implemented by Tilburg Universityand partners. Website: www.tilburguniversity.edu/dfid-innovation-and-growth.†Tilburg University, Department of Economics and CentER, Warandelaan 2, 5037 AB, Tilburg, The Netherlands. E-mail:

p.s.dalton@uvt.nl, D.P.vanSoest@uvt.nl and r.b.uras@uvt.nl .‡Wageningen University and Research, Hollandseweg 1 6706 KN Wageningen, The Netherlands. E-mail:

haki.pamuk@wur.nl.§This paper is dedicated to the memory of our dear friend Ravindra Ramrattan, who inspired us to begin research in

Mobile Money and lost his life at the tragic Westgate Mall terrorist attacks in Nairobi, Kenya. Ravi’s soul has guided us untilthe completion of this paper.

1 Introduction

Technological progress in financial intermediation plays a key role in economic development.

Innovations in financial technologies, such as electronic payment instruments, can foster

market exchange and expand financial connectedness by reducing transaction costs and

anonymity. Despite their economic advantages, the adoption of such technologies has been

slow across the globe, and this holds especially for small- and medium-sized enterprises

(SMEs).1 A better understanding of the reasons for the reluctance of SMEs to adopt these

technologies and the consequences of adoption for business outcomes is essential to inform

financial policy reform.

Most of the empirical evidence on the determinants of adoption of cashless payment

technologies comes from studies using observational data. These studies flag the importance

of such technologies for, among others, financial access, ATM usage and safety.2 However,

observational studies typically suffer from endogeneity issues. For this reason, scholars like

Camera, Casari, and Bortolotti (2016) and Arifovic, Duffy, and Jiang (2017) have recently

resorted to designed laboratory experiments to complement the insights obtained by the

observational analyses.3 While these lab-experiments are able to identify important barriers

to adopt payment instruments, it is an open question what impact the removal of these

barriers will have on actual business outcomes in the real world.

To the best of our knowledge, no field-experimental studies are available yet that identify

the determinants and consequences of the adoption of electronic payment technologies by

actual businesses. This study aims to fill this important gap by conducting a Randomized

Controlled Trial (RCT) in which we offer SMEs in the central business district of Nairobi,

Kenya, the opportunity to adopt a mobile payment technology, allowing us to analyze up-

take as well as the outcome effects of adoption. Our identification strategy takes advantage

of a particular moment in time in which the country’s major electronic money provider,

1Klee (2008), Bolt, Janker, and van Renselaar (2010), Arango, Huynh, and Sabetti (2011), Wakamoriand Welte (2017) provide evidence that in US, Netherlands and Canada cash is the dominant form ofpayment for small transactions (below $25).

2See Humphrey, Pulley and Vesela (1996) and Chakravorti (2007).3Using experimental settings that capture some essential features of prototypical retail markets, these

authors vary the available types of payment methods, and find that fixed-adoption costs and service feescould be relevant for the diffusion of a cashless instrument.

1

Safaricom, launched a new payment instrument, called Lipa Na M-Pesa. Lipa Na M-Pesa

is a product specially designed to facilitate retail transactions by SMEs.

For our study, we sampled 1222 restaurants and pharmacies and randomly assigned

half of each business-group to a treatment aimed at encouraging merchants to adopt the

Lipa Na M-Pesa technology. The technology was available for all interested businesses at

the time of our intervention, but we increased adoption in the treatment group compared

to the control group by mitigating (or even completely removing) three potential adoption

barriers: product information, registration costs and technology know-how. Specifically, we

visited all merchants in the treatment group, providing them with a) leaflets highlighting

the benefits and the costs of the technology, b) a short movie featuring the experiences

of successful similar merchants who use the technology, and c) the possibility to have a

Lipa Na M-Pesa account opened on their behalf, at zero cost. Regarding c, we informed

businesses in the treatment group that the registration paperwork required to sign up for

the technology would be done by our research team and also that the business owner would

be provided with a short training on technology know-how, if the owner is willing to open

an account.4

We focus on restaurants and pharmacies because they share a similar business structure:

they have high frequency daily transactions and the payment per transaction is relatively

large. This implies that adopting an electronic payment technology is expected to be

particularly relevant for these businesses. In addition, cash theft – both external and

internal – is an important concern for both restaurants and pharmacies, and the incidence

of theft may be reduced by using Lipa Na M-Pesa.5 By means of our experiment, we

analyze the determinants of take-up of Lipa Na M-Pesa, while we also measure the impact

of Lipa Na M-Pesa on business outcomes.

We obtain four sets of key results. First, we confirm earlier findings regarding the impor-

tance of seemingly small adoption barriers in preventing the diffusion of cashless payment

4This included filling out a registration form and handing in copies of additional required documents toSafaricom, collecting the technology from Safaricom (once the till-number is issued) and bringing it backto the shop premises with Lipa N M-Pesa advertisement flyers and posters.

5According to Global Retail Theft Barometer Survey 2014-2015 conducted in 24 countries from AsiaPacific, Europe, Latin America, and US, among retailers, pharmacies have highest rate of losses due tointernal employee theft and shoplifting (external theft) (an equivalent of about 1.99% of sales).

2

instruments. Our experimental intervention uncovered a significant unmet demand for the

technology: 62% of the restaurants and 21% of the pharmacies in the treatment group in-

dicated that they would like to sign up. This result is in line with Bertrand, Mullainathan

and Shafir (2004), who argue that small situational barriers could play a decisive role in

preventing people to take advantage of available technologies. Quoting from Bertrand et al.

(2004, p. 420) “[small situational] barriers might be a testy bus ride, challenging hours, or

the reluctance to face a contemptuous [agent].” Indeed, many businesses who expressed in-

terest in the technology reported that, prior to our intervention, these barriers were reasons

for not adopting the technology.

Second, we find that business owners who are more open about their businesses’ financial

situation are more likely to be willing to adopt the technology. This is consistent with a-

priori expectations, because every Lipa Na transaction is traceable by Safaricom as business

activity. We also find that businesses with past exposure to mobile money products and

those who are more future biased and more trusting are more likely to want to adopt the

technology.

Third, we find that sixteen months after the intervention, the financial connectedness of

SMEs in the treatment group had increased compared to the control group, especially in

the form of improved access to mobile loans. More specifically, we observe that the amount

of mobile loans provided by Safaricom had increased, without significantly affecting access

to loans by other financial institutions, formal and informal. Lipa Na M-Pesa’s electronic

payment mechanism allows Safaricom to track business transactions, observe creditworthi-

ness of SMEs and lend mobile loans to those with higher probability of repayment. In

connection to this, we also find that the access to mobile loans that our treatment stim-

ulated is significantly more pronounced for small scale establishments, which are likely to

suffer the most from opacity and the lack of hard information in proving creditworthiness.

We conclude that financial transparency induced by Lipa Na M-Pesa fosters fast access

to short-term loans and replaces the necessity to build-up long-term relationships with

providers of working capital finance. Our results also reveal that small scale establishments

in the treatment group end up having less volatile sales over the course of the past 12

months compared to the control group. This business performance outcome is consistent

3

with our financial access finding on mobile loans, because mobile loans are designed to cope

with short-term uncertainty.

Finally we find that, sixteen months after the intervention, safety concerns were less

of an issue in the treatment group. This outcome is significantly more pronounced for

businesses operating in relatively unsafe areas, providing us with an underlying mechanism

for the result at hand.

We interpret the changes induced by our treatment in financial connectedness, reduced

sales volatility and improved safety as welfare improving, because perceived safer and less

uncertain business environments typically induce growth in the long-run and also because

the change in financial structure is reflective of improved financial inclusion.

Overall, our results show the importance that seemingly small adoption barriers have in

preventing the diffusion of cashless payment instruments. In addition, we suggest that the

electronic traceability of business transactions can be an important factor slowing down

the adoption of high-tech payment instruments among business owners who prefer to keep

transactions anonymous. However, we also show that once the technology is adopted,

electronic visibility is likely to have a positive impact on financial integration, which we

interpret as an opportunity cost of anonymity. These key findings are important for design-

ers of electronic payment instruments and policy makers in both advanced and developing

societies, as small adoption barriers and anonymity of transactions could pose challenges

across the globe.

The rest of the paper is organized as follows. Section 2 relates this paper to the existing

literature. Section 3 introduces the context and the technology. Section 4 describes the

RCT design and data. Section 5 presents the results on the adoption, usage and impact of

the technology. Section 6 concludes.

2 Contribution to the literature

This paper primarily contributes to the literature of cashless payment instruments by con-

ducting what constitutes, to our knowledge, the first field experiment with a payment

technology. Existing studies in this literature utilize non-experimental data or data from

4

laboratory experiments. Using survey data, for instance, Humphrey, Pulley and Vesela

(1996) document a positive association between debit card usage and the availability of

ATMs and a negative association between aggregate crime rates and debit card adoption.

Chakravorti (2007) argues that market competition is important in determining firms’ elec-

tronic payment adoption behavior, while Schuh and Stavins (2010) show that technological

developments in debit-card payments drive out checks and suggest that there are strong

substitutions between comparable payment methods that differ in the degree of their effi-

ciency. Bolt, Jonker and v. Renselaar (2010) refer to the issue of surcharges and suggest

that allowing for surcharges at retailers could be an important determinant of electronic

payment adoption. Different from the previous research, Arifovic, Duffy, and Jiang (2017)

develop a game theoretic framework, which the authors bring to a laboratory environ-

ment, to show that fixed adoption fees could be important in inhibiting the adoption of

an electronic money instrument. Similarly, Camera, Casari, and Bortolotti (2016) design

a laboratory experiment and show that eliminating service fees or introducing rewards can

have significant implications on adoption of electronic money instruments. We contribute

to this literature in that we identify and measure the barriers preventing adoption of a

cashless payment technology by SMEs in their own environment, as well as we measure the

impact of adopting such technology.

We also contribute to the recent and rapidly growing literature on the economic effects

of mobile money technologies, such as M-Pesa, in developing countries. Most of the studies

in this line of research aim to understand the implications of M-Pesa on household finance.

For instance, Mbiti and Weil (2011) find that the increased use of M-Pesa lowers the use

of informal savings mechanisms (for instance ROSCAS), and raises the propensity to save

via formal bank accounts. Jack, Ray and Suri (2013) and Jack and Suri (2014) show that

M-PESA help households manage financial uncertainties caused by crop failures, or health

issues and smooth consumption. The overarching conclusion of these studies is that the

users of M-PESA can access a wider network of support whenever financial needs arise, and

receive funds more quickly. The conclusions of our paper complement those of the studies

on household finance effects of M-Pesa, which reveal that Lipa Na M-Pesa could improve

5

financial connectedness and reduce sales volatility especially for small-scale enterprises.6

Different from the papers on household finance, Beck, Pamuk, Ramrattan and Uras

(2018) develop a dynamic general equilibrium model - calibrated with firm-level survey data

from Kenya - in order to evaluate the interactions between mobile-money, entrepreneurial

finance and macroeconomic activity. The authors quantify a substantial impact of M-Pesa

on SME trade credit arrangements and aggregate outcomes. While we also study mobile

money in the context of SMEs, and also uncover a complementarity between payment-

technologies and access to finance, our focus and method is substantially different. Our

focus is on the identification of the determinants to adoption of a technology designed to

cater the needs of SMEs, and on the implications for the SMEs of adopting the technology.

We also relate to the vast literature on SME finance. In particular, the findings presented

in this paper and the key underlying mechanism are relevant to studies on relationship lend-

ing. Seminal papers in this line of research, such as Petersen and Rajan (1994), Berger and

Udell (1995) and Degryse and Cayseele (2000), suggest that because of apparent difficulty

associated with collateral-based lending, extending loans to small and opaque businesses

requires the build-up of soft information, necessitating the formation of long-term bank-

firm relations. In a recent paper, Beck, Degryse, De Haas and van Horen (2018) show, for

instance, that long-term lending relationships help especially during business downturns

and significantly more so the smallest firms with non-transparent operations. Our findings

in this paper are in line with this research, while we also propose a policy-relevant novel an-

gle: connecting payment and lending means of financial services spectrum - using electronic

financial instruments - can overcome transaction frictions associated with the formation of

long-term lending relations and help small businesses to have fast access to loan products.

Finally, our paper also contributes to the technology adoption literature in the context

of developing countries.7 The studies that are most closely related to our research are the

field experiments on adoption of efficient technologies. Most studies in this literature con-

6Also in this literature, Suri and Jack (2016) show evidence of notable long-term effects of mobile-moneyon poverty reduction in Kenya. The authors estimate that since 2007, access to mobile-money servicesincreased daily per capita consumption levels considerably, lifting thousands of Kenyan households out ofextreme poverty.

7A large literature argues that differences in technology adoption rates explain per-capita income dif-ferences across countries (Caselli and Coleman (2001) and Comin and Hobijn (2004)).

6

centrate on the agricultural sector and in particular on the adoption of farming techniques

by small and micro enterprises, such as the seminal papers by Duflo, Kremer and Robinson

(2004, 2008, 2011) and Foster and Rosenzweig (2010). We add to this important literature

by studying the financial technology adoption decisions of SMEs in the service sector and

understanding the barriers to adopt and business outcome effects to this end. Importantly,

several papers in this literature highlighted the role of behavioral factors on technology

adoption, such as complexity of information, present bias and loss aversion.8 Our exper-

imental design reduces the complexity of information required to evaluate the benefits of

Lipa Na M-Pesa, while our survey design allows us to measure important behavioral factors

such as present bias, future bias, trust and cognitive capacity.9

3 Institutional Context: Mobile Money in Kenya

Over the past decade, mobile money has created a profound transformation in cashless

money circulation in the developing world. In 2007, Kenya’s Safaricom introduced the

first mobile-phone based money instrument, called M-Pesa, to enhance P2P (Person to

Person) money transfers. In this section we provide an overview of the standard M-Pesa

and then focus on Lipa Na M-Pesa, an extension of M-Pesa designed specially to facilitate

P2B (Person to Business) money transfers.

3.1 Standard M-Pesa

M-Pesa is the brand name of the most commonly utilized P2P mobile money service in

Kenya, which allows users to transfer money via mobile-phone text messages (SMS) to other

mobile money users.10 The way standard M-Pesa works is as follows. Users sign up for an

8See Hanna et al. (2014), and Drexler et al. (2014).9Also relevant for our paper are the empirical studies on technology adoption, which are interested in

understanding the heterogenity in technology adoption decisions across firms, such as Jack and Suri (2011)and Foster and Rosenzweig (2013). This strand of literature finds a positive correlation between technologyadoption and firm characteristics. Our empirical findings also uncover a heterogeneity in the adoption ofthe Lipa Na M-Pesa payment instrument, based on which we argue that the heterogeneity in relative costsand benefits of the technology could be important to explain differences in payment instrument take-uprates.

10At the time of the study, there were other mobile money providers in Kenya like Airtel Money, OrangeMoney, Equitel, Mobikash, and Tangaza. However, according to the Communications Authority of Kenya(2015), 77% (about 21 million) of the people who hold a mobile money account in June 2015 were M-Pesa

7

M-Pesa account and top it up by converting cash in M-Pesa units at specialized agents,

called M-Pesa kiosks. The electronic units can then be transferred to any other mobile

money user, utilized to make payments, or stored in the mobile phone. The recipients of

M-Pesa transfers can use the units to make new transfers or can redeem them at M-Pesa

kiosks.11

Signing up for a standard M-Pesa account does not entail any monetary cost. All that is

required is to visit an M-Pesa kiosk with an official identity document and a mobile phone.

Money can be exchanged for M-Pesa units free of charge, and the account holder does not

incur any costs either when receiving M-Pesa units. But a transfer fee is charged when

spending units by transferring them to another account, and another fee also applies when

converting M-Pesa units into cash. Both fees are step-wise increasing in the size of the

transaction, as shown in Figures 1 and 2.

Figures 1 and 2 here

With this electronic money technology, Safaricom has revolutionized P2P money trans-

fers in Kenya. By 2013 and only six years after being launched, more than 70% of Kenyan

households had an M-Pesa account.12 In that year 733 million M-Pesa transactions took

place, with an aggregate value of about 1.9 trillion Kenyan shillings (about 22 billion US

dollars), an equivalent of 40% of Kenyan GDP at the time.13 However, despite the high

adoption rate of M-Pesa by households, businesses did not show the same enthusiasm to

use M-Pesa for P2B and B2B money transactions. According to a nation-wide survey

conducted by FSD-K, only 35 percent of SMEs accepted M-Pesa as a common method of

payment over the years of 2013-2014, especially because of technological challenges and

financial considerations.14 Safaricom was well-aware of these barriers and, recognizing the

niche in the P2B market, introduced Lipa Na M-Pesa in 2014, a mobile money facility

users.11In December 2014, there were about 124,000 M-Pesa kiosks scattered across all Kenya. Around 20

percent of them are located in Nairobi (FSP interactive maps, 2013) with approximately 25 million cus-tomers.

12See Jack and Suri (2014).13The annual transaction volumes of mobile money transactions are the sum of the

monthly transaction volumes reported by the Central Bank of Kenya National Paymenthttps://www.centralbank.go.ke/national-payments-system/mobile-payments/.

14See FinAccess Business Survey, 2014.

8

developed to stimulate P2B transactions. In what follows we briefly describe the main

characteristics of Lipa Na M-Pesa, which constitutes the core of this paper.

3.2 Lipa Na M-Pesa

Lipa Na M-Pesa is a product especially designed for merchants, allowing them to receive

payments in retail transactions. When a business owner signs up for a Lipa Na M-Pesa

account, unlike for the case of standard M-Pesa, the account gets registered under the

name of the business. For this reason, the transactions made through Lipa Na M-Pesa

become visible to Safaricom as business activity. In addition to reducing cash-based transfer

frictions in the same way as standard M-Pesa does, Lipa Na M-Pesa offers monetary and

technological benefits that make the product attractive for P2B purposes.

With respect to the monetary benefits, standard M-Pesa users can make Lipa Na M-Pesa

payments without incurring any monetary costs. This is a major difference compared to the

P2P transactions made between two standard M-Pesa accounts, where the account holder

making the transaction (in this case, the buyer) needs to pay a transaction fee (Figure 1).

Second, while the business has to incur a payment receipt fee of 1% of the transaction value,

the costs are lower than those incurred by the customer when using standard M-Pesa for

all transactions below 8500 KSh (more than US$ 80). Importantly, the typical transaction

values the merchants tend to have in our sample are well below that threshold, implying

that using Lipa Na M-Pesa raises the surplus generated by an economic transaction between

a customer and a business.

Lipa Na M-Pesa also has two key technological advantages compared to standard M-

Pesa. First, there are restrictions on the amount of money one can store in a standard

M-Pesa account, while such restrictions are virtually absent for Lipa Na M-Pesa. This

implies that a business owner with Lipa Na M-Pesa does not need to cash-out so often

as with standard M-Pesa, substantially reducing the costs of cash withdrawals.15 Second,

Safaricom records all the transactions made via Lipa Na M-Pesa, and allows the business

owner free-of-charge access to daily transaction-records over a six-month period; all that

15The fees charged for cash withdrawal from Lipa Na M-Pesa and standard M-Pesa accounts are thesame; see Figure 2.

9

is required is to send the request via a simple e-mail. In this way, Lipa Na M-Pesa offers

business owners an option for bookkeeping system at zero cost.

3.3 Potential Factors affecting Lipa Na M-Pesa Adoption

The positive features of Lipa Na M-Pesa do not imply that the adoption of this technology

would be immediate among merchants. The literature of technology adoption – in devel-

oped as well as in developing countries – has consistently shown that adoption of a new

technology, regardless of how efficient the technology might be, tends to be slow due to the

existence of pecuniary and non-pecuniary factors. In the context of Lipa Na M-Pesa we

identify a number of factors that might prevent adoption of this new payment instrument.

First, the merchants may lack information about the costs and benefits of the technology,

since the technology was new in the market. Second, even though there is no fee to opening

an account, there are transaction costs associated with the time needed to do the paperwork,

e.g. filling out a form and handing it in at a Safaricom office. Such costs, even if seemingly

low compared to the benefits of the technology, have proven to be consequential especially

in the context of developing countries (Bertrand, Mullainathan and Shafir, 2004). Third,

there could be technology implementation barriers: merchants might not know how to

(make optimal) use of the various Lipa Na M-Pesa features and services. We consider these

three as “soft barriers”, because they could be mitigated at relatively low cost.

The decision to adopt Lipa Na M-Pesa can also be explained by other potential factors.

Notably, Safaricom traces all transactions made through Lipa Na M-Pesa as business activ-

ity. And, in addition to its money transfer and payment services, Safaricom provides also

mobile loans (called M-Shwari). For financially constrained firms, which otherwise do not

have the chance to signal the flow of their business activity to external financiers, sharing

business transaction volumes with Safaricom could thus help with accessing external finance

in the form of M-Shwari loans. For other businesses, however, transparency could be more

of a concern if the owner is not willing to disclose business transactions to third parties.

Additional potential factors that can explain adoption relate to exposure to sophisticated

payment products in the past, exposure to cash theft and behavioral characteristics of the

business owner.

10

4 Research Design

The purpose of our research is twofold. The first is to test the relevance of the above-

mentioned soft adoption barriers by exogenously lifting them for randomly selected enter-

prises, and then to compare adoption rates between the treatment and control groups. The

second purpose is to estimate the impact of Lipa Na M-Pesa adoption on a number of busi-

ness outcomes. We do so by exploiting the exogenous difference in adoption rates between

the treatment and control groups our intervention gave rise to. Our research thus consists

of an RCT with detailed baseline and endline surveys. The RCT is an intention-to-treat

field experiment with two possible forms of non-compliance. Businesses in the treatment

group are of course free to not adopt the technology. Moreover, the technology is also freely

available on the market, which means that at least some businesses in the control group

may also end up adopting the technology. That means that our experimental design is

closest to a standard encouragement design (Bradlow 1998).

The detailed baseline survey allows us to identify the other potential factors that can

explain the decision to adopt the technology, and that are not released experimentally. We

complement the baseline survey with an endline survey sixteen months after the intervention

to evaluate the effect of releasing the barriers on business outcomes. In what follows, we

introduce the experimental design and the survey data.

4.1 Experimental Design

4.1.1 Sampling and Randomization

The study took place in the periphery of Nairobi’s central business district. We chose

this location because lower business densities mitigate possible spillover concerns between

businesses in the treatment and control groups. To construct a sample of comparable

businesses, we focused only on two sectors: restaurants and pharmacies. The choice of

these two activities was based on the fact that both share characteristics which potentiate

the benefits of a cashless payment technology. They both are sectors in which SMEs are

overrepresented, they handle relatively many transactions per day, have relatively many

customers, and are relatively vulnerable to internal and external theft.

11

To draw the list of eligible firms for the study, we randomly assigned enumerators to

visit specific areas in the city and requested them to list restaurants and pharmacies that

had one or more employees, that were located at a distance not less than 50 meters from

the closest other business in the same sector, did not have a Lipa Na M-Pesa account yet,

and whose owner or manager was willing to participate in a study on mobile money use by

businesses. Following this procedure, we listed in total 1222 merchants, 669 restaurants and

553 pharmacies. Out of the 1222 eligible merchants, we randomly assigned 607 merchants

(331 restaurants and 276 pharmacies) to the treatment group and 615 merchants to the

control group. Random assignment was stratified by geographic location and by firm size

- measured by the number of employees.16 Figures 3, 4 and 5 provide the geographic

distribution of the sample of businesses over districts of Nairobi, by treatment and control

groups.

Figures 3, 4 and 5 here

4.1.2 Experimental Intervention

The experimental intervention was conducted right after concluding the baseline survey

interview. Firms assigned to the treatment group were informed that the researchers in-

volved in this study had done research on Lipa Na M-Pesa’s potential as a new cashless

payment instrument, and they were interested in informing merchants about the costs and

benefits of the technology. After receiving this information, the merchant was asked if he

or she would like us to open an account on his or her behalf at no cost.17 The answer to

this question is what constitutes the measure of willingness to adopt the technology. The

timing of the events is decribed in the following timeline.

Timeline here

The intervention itself consisted of three components, which we describe next.

16We consider a restaurant (respectively pharmacy) with more than 5 (respectively 2) employees to bebig and we use this categorization to stratify the sample. The geographic division of the area was madead-hoc by the survey company for logistical reasons.

17We made it very clear to the merchant that we did not have any relationship or commercial ties withSafaricom. We explicitly stated that our aim was purely academic.

12

Provision of Information about the Technology

The objective of this component was to provide information on the advantages and

disadvantages of Lipa Na M-Pesa compared to other payment methods. The information

was provided with an information leaflet and a video. The leaflet consisted of concrete and

easy-to-understand information on Lipa Na M-Pesa’s costs and benefits. A recent literature

emphasizes that the complexity of information concerning the benefits of a new technology

can limit adoption. People only pay attention to the slice of the whole information set

which they think is the most relevant to them. Hanna, Mullainathan and Joshua (2014)

suggest that this issue could be handled with the provision of simplified information. We

design our experiment with this insight in mind.

The video complemented the leaflet by showing a short clip of an interview with a fel-

low business owner from the same sector who had already adopted Lipa Na M-Pesa and

benefited from its usage.18 The inclusion of the video as a component of the intervention is

motivated by an emerging literature highlighting the effectiveness of role models to induce

behavioral change. This literature shows that successful peers can act as role models and

are particularly effective in the context of low-income households in developing countries

(see for instance Bernard et al. (2014) and La Ferrara (2016)).

Support for the Registration Process

Besides providing information, we also offered to handle the paperwork needed to open a

Lipa Na M-Pesa account on behalf of business owners. The motivation for this component is

based on the literature pioneered by Bertrand, Mullainathan and Shafir (2004), who argue

that small situational barriers play a decisive role in preventing people to take advantage

of efficient investment opportunities. The paperwork associated with opening an account

can be perceived as a hassle for a small firm owner and can prevent him or her to adopt

the technology. Specifically, we offered to fill out the registration form on behalf of the

business owner, bring the required forms and copies of the documents to Safaricom, pick

18There were thus two videos – a 5.2 minute clip for the restaurant sector and a 3.2 minute clip forpharmacists. The videos as well as the information leaflet that we produced are available upon request.

13

up the SIM-card and advertisement material from Safaricom and deliver this material at

the business premises.

Technology Implementation Assistance

We offered the business owner to take the business to a “transaction ready” mode,

should he or she be willing to adopt the technology. Specifically, this component of the

intervention consisted of inserting the Lipa Na M-Pesa SIM-card in the mobile phone the

business owner would use for Lipa Na M-pesa transactions, testing whether the SIM-card

was functional, and performing a transaction test worth of 100 KShs to illustrate how to

use the account in practice.

4.2 Survey Measures

A key objective of our baseline survey is to measure the factors that could potentially be

associated with the adoption of Lipa Na M-Pesa but which were not exogenously varied by

our experimental intervention. This subsection describes these factors and the way they

were measured in the survey instrument.

4.2.1 Financial Transparency Aversion

Transactions made through Lipa Na M-Pesa get recorded as business activity by Safaricom.

Therefore, firms that are averse to disclose their financial activities with a third party

might be reluctant to adopt the technology. These transparency concerns are difficult

to measure directly, but they can be captured in an indirect way. For this purpose, we

proxied transparency concerns with a dummy variable indicating the willingness of the

business owner to disclose revenue and profit figures during the baseline survey interview.

This measure, of course, could also be correlated with trust of the survey respondent in

the interviewer. For this reason, we gather a measure for “trust in a person at a first-time

contact” and include it as a control variable in the empirical analysis.19

Financial transparency concerns may be correlated with key business characteristics,

19As complementary information, we also created a financial sophistication index, which covers formalloan use from banks and mobile money providers, as well as a dummy regarding keeping business records.

14

such as financial sophistication20, profitability, size, and holding an up-to-date business

license. To mitigate these concerns, we control for these variables too.

4.2.2 Behavioral Factors

Recent studies stress the importance of behavioral factors in the technology adoption de-

cisions of small enterprises. Our design is, in part, motivated by these behavioral insights,

as we include role models to foster social learning and account for limited attention and

seemingly small bureaucratic hassles as important potential determinants of technology

adoption. However, there are other potential behavioural aspects that can interfere the

decision to adopt the technology. We measure these aspects in the baseline survey as

follows.

First, we collect a proxy measure of each entrepreneur’s cognitive function, as a plau-

sible determinant of the ability to grasp the information on Lipa Na M-Pesa costs and

benefits. We measure cognitive function by means of the Digit Span Task, in which the

entrepreneur is read a list of numbers and then asked to repeat these numbers in the same

order.21 Outcomes for this task are the longest correctly remembered span, and also overall

accuracy.22

Second, we elicit time preferences, as well as present and future bias. The reason we are

interested in these is that the decision to adopt a new technology involves planning (costs)

in the present, while benefits are obtained in the future. We elicited these preferences and

biases in an incentive-compatible way adopting the method used by, among others, Dupas

and Robinson (2013).23 Merchants were asked to choose between receiving either 500 Ksh

(US$4.93) the next day, or receiving a larger amount in 31 days. The larger the amount

needed to induce the merchant to choose for the later payment, the more impatient he

or she is coded to be. To measure time inconsistency, we also asked merchants to choose

between Ksh 500 in 31 days and a larger amount in 61 days. A merchant is defined to be

20For example, by being connected to a formal credit network, offering the option to customers topurchase on credit and keeping business records.

21Daneman and Carpenter (1980 and 1983).22This task is widely used in field experiments in economics as a proxy for cognitive ability, particularly

in the context of developing countries. See Dean, Schilbach, and Schofield (2017) for a review.23By incentive-compatible measures we mean that the decisions were actually paid.

15

present biased if he or she is more impatient in the present than in the future, i.e. exhibits

a higher discount rate between tomorrow and 31 days than between 31 days and 61 days.

In contrast, a merchant that is more patient in the present than in the future is defined to

be future biased.

Finally, we measure trust, as we conjecture that trust is particularly relevant in explain-

ing adoption of Lipa Na M-Pesa. Access to finance is quite limited in developing economies,

also because trust in financial contract enforcement is weak.24 Therefore, specially in our

context, lack of trust in customers, in people met for the first time, in Safaricom, and in

institutions in general, are potential important explanatory factors. We measure all these

dimensions with direct survey questions.

4.2.3 Other Potential Factors

We measure additional factors that we conjecture can contribute to explain the decision to

adopt an electronic money instrument. First, based on results from a laboratory experiment

by Arifovic, Duffy, and Jiang (2017), we conjecture that merchants who have a positive

previous experience with electronic money are more likely to adopt Lipa Na M-Pesa. For

this purpose, we ask if the merchant uses the (standard) personal mobile money account

for business purposes.

Second, we conjecture that merchants who are exposed to high risk of theft may value

Lipa Na M-Pesa relatively more (see also Jack and Suri (2014) and Economides and

Jeziorski (2016)). To capture this, we ask the business owner about his or her own percep-

tion of safety in the business premises, and about the frequency and magnitude of theft he

or she experienced over the last six months.

Third, we also conjecture that Lipa Na M-Pesa is less appealing for businesses who

frequently transfer money into saving accounts. Exchanging Lipa Na M-Pesa credits for

cash is subject to a withdrawal fee, and so is sending money to a bank account. By the

time our experiment was conducted, there was only two banks that provided real time

cash transfer services from Lipa Na M-Pesa to business bank accounts. At other financial

institutions, when business owners wanted to transfer money from Lipa Na M-pesa accounts

24See e.g. La Porta et al. (1997 and 1998).

16

to their business bank accounts, they were required to use Pay-Bill or M-Pesa transfer

services, which are costly and take at least 6 hours to complete the transfer. To account

for this in our analysis, we ask the merchant about the use of bank accounts for business

purposes to deposit cash.

Finally, we conjecture that smaller firms are expected to benefit more from a release of

fixed adoption costs, but bigger firms are expected to benefit more from the technology

due to higher sales, large pool of customers and higher likelihood of theft by employees. In

our survey we proxy size with the number of employees and total sales.

4.3 Sample Characteristics

Table 1 describes the characteristics of the firms in our sample and compares the firms

assigned to control and treatment groups.

Table 1 here

The average business in our sample is rather small. On average, pharmacies employ

three workers and restaurants five. The average monthly sales and profits of pharmacies

is about 1470 and 600 U.S dollars PPP respectively, and restaurants have about 3225 US

dollars PPP in sales and 820 dollars in profits. Only 19% of the pharmacies and 36% of

restaurants have made investments in their businesses in the past six months and only few

businesses have received loans in the past twelve months. Moreover, 91% of pharmacies

and 54% of restaurants have a business license. Almost all business owners in our sample

posses a personal M-Pesa account at the time of the baseline. 43% of restaurant owners and

31% of pharmacy owners report mobile money to be the most frequent method of payment

to suppliers, while 40% of restaurants and 25% of pharmacies have received payments from

their customers through personal mobile money account of the business owner.

Moreover, more than 90% of pharmacies and restaurants in our sample knew about the

existence of Lipa Na M-pesa, although they had not adopted the technology yet at the time

of our baseline. The primary reason reported for not having a Lipa Na M-Pesa account was

perceived lack of net benefits. Business owners also perceived that it was too costly to open

the account, that Lipa Na M-pesa transaction fees were too high, and that they did not have

17

the time to do the registration paperwork. This pre-treatment information is enlightening

in two ways. First, it reveals that merchants had wrong beliefs about the cost and benefits

to adopt and use the technology, which our intervention aimed to correct. Second, it

becomes clear that the paperwork for the registration was perceived as an impediment to

open an account, an aspect that was directly targeted by our experimental intervention.

Finally, it is important to note that no business in the sample sells its main product for

more than 8500 Kenyan Shillings (about 80 US$ by the time of the baseline survey), which

is the threshold amount of payment above which transferring money to a Lipa Na M-Pesa

account becomes more expensive than transferring to a standard M-pesa account in a P2B

transaction (Figure 1). Since surcharging is allowed, this means that all businesses in our

sample could generate a transaction surplus by asking the customers to pay through the

business Lipa Na M-Pesa account instead of business owners’ personal M-Pesa.

Columns 4 and 8 of Table 1 report the differences in average baseline firm characteris-

tics between treatment and control groups and the statistical significance for the difference

between the two groups resulting from t-tests. Overall, the sample characteristics of restau-

rants are balanced between the treatment and control groups across the board. Treated

pharmacies are more likely to use standard mobile money vis-a-vis their customers and pay

salaries through the mobile money accounts of their employees more often. Likewise, more

pharmacies in the treatment group opened a Lipa Na M-pesa account on their own in the

period between the listing and the baseline survey.25 We implement only the information

component of our treatment in those businesses, which were assigned to the treatment

group and opened a Lipa Na M-Pesa account before our experimental intervention.

Finally, relative to the control group, the treated pharmacies have also higher sales

and profits. However, as we present in Table OA4 of the online appendix, statistically

significant differences in sales and profits between the two pharmacy groups cease to exist

when we concentrate on businesses, which participate in the endline survey and possess

a business license.26 In addition, we perform a test for joint orthogonality by estimating

2582 restaurants (12%) and 25 pharmacies (5%) adopted the Lipa Na M-pesa technology in the periodsbetween listing and baseline/intervention.

26Because of a Safaricom policy change that we will explain below our outcome analysis concentrates onbusinesses with a business license.

18

a linear regression where the dependent variable is being assigned to treatment and the

explanatory variables are all the variables in Table 1. We test the null that the coefficients

of explanatory variables are jointly zero and find that the joint orthogonality test cannot

reject the null hypothesis.27 The observed characteristics cannot jointly explain treatment

assignment, suggesting that the two groups are well-balanced. Notwithstanding this, to be

conservative, in our outcome regression analyses we control for the baseline values that are

unbalanced at baseline.

5 Analysis and Results

We present the empirical analysis and results in two parts. In Section 5.1 we investigate

the factors explaining the willingness to adopt the technology and in Section 5.2 we study

the impact of our intervention on business outcomes.

5.1 Willingness to Adopt the Technology

Interest of SMEs in the Lipa Na M-Pesa technology can be gauged from the response of the

firms in the treatment group to our offer to open an account on their behalf. Acceptance

rates to our offer were 62% among the restaurants and 21% among the pharmacies. These

rates are high, given that the technology had been available for more than a year before our

intervention. This means under normal market conditions there was a substantial “unmet

demand” for the technology. It also shows that the “soft” adoption barriers were quite

important in preventing actual adoption.

Figure 6 presents the main reasons why firms had not adopted Lipa Na M-Pesa prior to

the intervention. Perceived lack of benefits was an important reason for not having adopted,

and especially more so for the pharmacies. Additional reasons were the (time) costs of

opening an account, and perceived complexity of using the technology. Interestingly, our

intervention was targeted at (i) providing good information on the benefits of the technolgy,

(ii) reducing the (time) costs of opening an account, and (iii) explaining usage. We conclude

that our intervention indeed targeted the key factors preventing adoption. Similar insights

27An F-test for the joint significance of 23 coefficient estimates after a linear regression produces a p-valueof 0.18 for pharmacies and 0.70 for restaurants.

19

are obtained when comparing the main reasons for not having adopted yet between those

who ended up accepting our offer, and those who did not. Results are reported in Figures

7 and 8. As can be gauged from Figure 7, our intervention increased take-up among

restaurants especially because it solved the time constraint, but we did not manage to

convince all respondents of the benefits of using the technology. The latter was also an

issue with the pharmacies, as shown in Figure 8, and the same holds for the costs of opening

the account, and of using it.

Next, we analyze what types of firms are interested in receiving an account. Table 2

explores in which respects firms accepting our offer differ from those that rejected it. Prior

usage of electronic payment systems, and then especially standard M-Pesa, is an important

factor. In case of restaurants this holds for nearly all mobile money usages; for pharmacies

receiving payments and storing money are the most important ones. Pharmacies that

accepted our offer tend to be larger and more profitable, while restaurants that tend to

invest more are also more prone to be willing to receive an account.

Figures 6, 7 and 8 here

Table 2 here

Exploring in what respects adopters and non-adopters tend to differ is one approach

to gauge insight into the determinants of the adoption process; using formal regression

analysis is another. We specify the following regression model for the latter purpose:

Adopti = α+ β′Exposurei +ω′Transparencyi + γ′Behaviori +φ′Sizei +χd +µj + εi, (1)

where i denotes the business, Adopti is a dummy variable equal to one if the business accepts

the offer to open an account, Exposurei is a vector of measures of past experience with

mobile money, Transparencyi is a vector of financial transparency measures, Behaviori is

a vector of behavioral factors, Sizei is a vector of business size measures, µj controls for

sectoral differences (pharmacies vs restaurants) and χd is the enumerator-and-district fixed

effects.28

28Table OA1 in Online Appendix provides the full list of variables utilized in this analysis and TableOA2 provides summary statistics of the main variables.

20

We estimate three specifications of Equation (1). In the first specification, we use ag-

gregated exposure and transparency-concern proxies in addition to behavioral factors and

business size indicators. In the second and third specifications we replace the aggregated

indicators with the indices’ individual components. Table 3 provides results from the full

sample of firms, while tables 4 and 5 report the results for restaurants and pharmacies

separately. In all tables the variables in panel A measure ex-ante mobile money exposure,

variables in panel B measure transparency concerns, those in panel C capture behavioral

aspects and the variables in panel D are business size indicators.

Table 3 here

The results reported in Table 3 confirm that “pre-treatment exposure to mobile money”

is a significant determinant of a firm’s willingness to adopt the technology. This echoes

some recent findings from laboratory experiments.29 Column (1) shows that firms that

use mobile money for business purposes are 14.2 percentage points more likely to adopt

the technology compared to other businesses. The more detailed measures of prior mobile

money usage indicate for example that firms that use mobile money to buy inputs are

more prone to request to receive Lipa Na M-Pesa. Also importantly, we observe that those

businesses which do not surcharge when transacting with M-Pesa are also more eager to

accept our offer to adopt the Lipa Na M-Pesa technology.

As an interesting piece of evidence we obtain that businesses that are non-transparent

with respect to their profits and sales in the baseline interview are less likely to sign up.

While other transparency aversion proxies (like not having an official business license, or

measures of financial sophistication) turn out to be insignificant, all coefficient estimates

have expected signs. As far as we are aware, this is a very novel finding, which is critical

both for the design of financial products as well as for policies aiming to promote electronic

payment usage. We will return to this important adoption channel when analyzing the

outcome effects of Lipa Na M-Pesa.

The behavioral factors perform more unevenly across various specifications, though one

key behavioral aspect stands out: cognitive capacity of the business owner is a significant

29Camera, Casari, and Bortolotti (2016) and Arifovic, Duffy, and Jiang (2017).

21

determinant of adoption throughout regression specifications in Table 3, confirming a priori

expectations.

With respect to other determinants, we observe that the decision to adopt is by and

large independent of (indicators of) firm size. Finally, results in Table 3 also show that

businesses which save at a business bank account are less likely to adopt. This pattern

holds most likely because banked firms can access payment and other financial services

through their bank and also importantly because transferring funds from Lipa Na M-Pesa

to a bank account is costly, as highlighted before.

Tables 4 and 5 confirm that pre-treatment use of mobile money turns out to be impor-

tant in both sectors. Also transparency aversion has negative coefficient estimates for both

pharmacies and restaurants, while the coefficient is not statistically significant in restau-

rants sub-sample, it is significant at 5% level for pharmacies. Having a business saving

account at a bank is negatively associated with the adoption of Lipa Na M-Pesa for phar-

macies (Table 5), but not as much for restaurants (Table 4). Similar conclusions apply to

behavioral biases, which show up significantly for pharmacies but not for restaurants.

Tables 4 and 5 here

5.2 Adoption, Use and Impact of the Technology

As we will delineate below, our intervention was successful in increasing the take-up in the

treatment compared to the control group, and hence we can measure the impact of Lipa Na

M-Pesa on business outcomes. The sample that we use for the outcome analysis consists of

618 businesses – all those that possessed a business license at baseline and that participated

in the endline30, which took place more than 16 months after the baseline intervention. A

change in Safaricom’s operating procedure is the reason why we were forced to conduct

our outcome analysis using only licensed businesses: after our baseline research had been

completed and during the process of registering businesses for Lipa Na M-Pesa, Safaricom

changed one of the formal requisites to open a Lipa Na M-Pesa account – any applying

firm should have an up-to-date business license. Because pharmacies tend to be more

30Table OA3 in Online Appendix provides summary statistics for the endline survey.

22

likely to have a business license, the change in the rule had a larger impact on our pool

of restaurants than on our pool of pharmacies. Importantly, as Table 1 illustrates with

respect to possessing a business license treatment and control groups are balanced for both

restaurants and pharmacies.31

We first examine whether attrition at endline is non-random. We regress “not having

participated in the endline survey” on the treatment dummy as well as on other business

characteristics. The estimates reported in Table 6 indicate that assignment to treatment

is not significantly related to attrition, implying that attrition does not bias our impact

estimates for the treatment. However, we also find that there is more attrition among

restaurants than among pharmacies, and also that there is more attrition for establishments

that were larger at baseline, experienced more external theft, received mobile loans, or had

more trust in mobile money companies.

5.2.1 Lipa Na M-Pesa Adoption and Usage

We first check to what extent our intervention was successful in exogenously increasing

adoption in the treatment group compared to the control group. We do so for various

measures of take-up: “actual registration of the Lipa Na technology”, “usage of Lipa Na

M-Pesa over the last 30 days”, “having utilized Lipa Na M-Pesa to receive payments from

the customers over the last 30 days”, and “sales through Lipa Na M-Pesa over the last

30 days”. The results are presented in Table 7A. The coefficients can be interpreted as

percentage-point differences in actual take-up between the treatment and the control group

(in columns 1-3) or as the percentage increase in take-up itself (in the fourth column).32

All four of the adoption measures are significantly higher in the treatment group. About

7 percentage points more businesses in the treatment group ended up having a Lipa Na

M-Pesa account by the time of the endline, usage is about 8 percentage points higher

among treated businesses, and the same holds for the increase in the propensity to receive

31The change in the business license requirement made 292 restaurants - which participated in ourbaseline survey - ineligible to adopt the technology since they did not have a business license. 47 pharmaciesstated that they had a business license in the sampling visits; however, baseline data collection revealedthat they did not have one.

32For presentational purposes, the coefficients of control variables have been omitted from the Table.The control variables we use are baseline measures of sales, not reporting sales in baseline, number ofemployees, use of Lipa Na M-Pesa in the baseline, enumerator FE, gender and business sector.

23

payments via the new technology. According to these three measures the uptake was

increased by 30% to 35% through our treatment. As shown in column 4 of Table 7A, the

treatment increased monthly sales via Lipa Na M-pesa by 26%, or 32 US$ (or 3256 Ksh).

This amount corresponds to 15% of the average sales of an SME from our pooled sample

in the endline.

Table 7A here

Table 7B shows intention-to-treat (ITT) estimates for a split sample based on merchant’s

aversion towards financial transparency. Panel A shows the results for the transparent firms,

Panel B for the non-transparent ones. Consistent with the findings reported in Section

5.1, the intervention induced Lipa Na M-Pesa usage significantly more among transparent

firms that disclosed their sales figures to enumerators during the baseline interview. These

results indicate that financial transparency concerns might play an important role not only

to explain the adoption of the technology, but also to explain usage when it is adopted.

This reveals potentially persistent effects of transparency in understanding the diffusion of

electronic payment products.

Table 7B here

5.2.2 Business Outcomes

In Section 3.3 we hypothesized that Lipa Na M-Pesa usage may affect business outcomes,

such us perceived safety and access to finance. In what follows, we report results of the

ITT regressions to assess the average treatment effect on these business outcomes as well

as on sales and investment. We also report the treatment-on-the-treated estimates (ToT)

throughout our outcome analysis.

Business Safety

Table 8A presents ITT estimates on the impact of treatment assignment on perceived

safety, proxied by the endline measure of “feeling more safe when conducting the business

operations” - measured from a scale from 1 to 10.33 On average, the group of treated firms

33The control variables we use are baseline value of corresponding outcome variable, baseline measuresof sales, not reporting sales in baseline, number of employees, use of Lipa Na M-Pesa in the baseline,enumerator FE, gender and business sector.

24

experienced a 3.5% increase in perceived safety, an effect that is statistically significant.

Given that exposure to the intervention increased Lipa Na M-Pesa usage by about 40%,

we document a ToT effect of just below 9% increase in perceived safety. This is an impor-

tant result as it confirms the role of an electronic money instrument in raising the retail

transaction safety among SMEs.

Table 8A here

In order to understand the mechanism behind the increased perceived safety, we split the

sample by exposure to theft at baseline. Column 1 of Table 8B provides ITT estimates for

the sub-sample of firms which reported to have experienced external theft one year before

the intervention, while column 2 presents ITT estimates for those which did not experience

theft. We show that the effect documented in Table 8A is caused by a substantial increase

in perceived safety by firms that had experienced theft six months prior to the intervention.

This finding reinforces the relevance of Lipa Na M-Pesa on business safety, as it shows that

in areas where using cash is relatively more risky, safety outcome effect is stronger.

Table 8B here

Business Finance

Access to finance is an important driver of business performance. Table 3 already doc-

uments that non-banked firms are more likely to adopt Lipa Na M-Pesa. That possibly

means that firms considered that having access to mobile loans is one of the advantages of

having a Lipa Na M-Pesa account, because Safaricom is not just the provider of M-Pesa

products but also a provider of mobile-loans, M-Shwari. Lipa Na M-Pesa allows Safaricom

to have access to detailed information on electronic payments made to the firm, which is

likely to relax hard information constraints and stimulate access to external finance. We

test this mechanism in tables 9 and 10.

Table 9 reports ITT estimates of treatment impact on formal (Panel A) and informal

finance (Panel B). For each source of finance we report the impact of treatment assignment

on the extensive margin financial inclusion (columns 1 and 3) as well as on the intensive

25

margin financial deepening (measured in logarithm of the borrowed amount in columns 2

and 4).

A novel source of external finance that we study in Table 9 refers to mobile-loans, which

exhibit the following characteristics. The dominant mobile loan provider of the country is

Safaricom. Similar to the M-Pesa service, the provision of mobile loans are made to cellular

phones of borrowers. Safaricom charges 7.5% interest on mobile loans and this interest

charge does not go up with the loan amount. The loan limit is determined by the history

of transactions incurred by the borrower via M-Pesa and in particular through Lipa Na

M-Pesa payment history for the case of a business owner. In this respect, creditworthiness

signalled to Safaricom through Lipa Na M-Pesa use help borrowers to expand credit limits.

Finally, since mobile-loans are typically extended for 30 days, they are utilized to curb

short-term fluctuations in business activity - and not so much for long-term investment.

Results in Table 9 show that treatment assignment significantly influenced mobile-loan

usage at the extensive (column 1) margin, and also at the intensive margin (column 2). Both

effects are statistically significant at 5% level and economically large. Both the financial

inclusion and financial deepening effects correspond to an increase of 60% compared the

control group mean. Interestingly, we do not observe a contraction in any of the other

sources of external finance – neither formal nor informal. This implies that the exogenous

increase in Lipa Na M-Pesa usage unambiguously increased financial access for the treated

businesses.

Table 9 here

Having established that Lipa Na M-pesa increases financial access, we proceed with un-

derstanding the mechanism behind this result by studying heterogeneous treatment effects

with respect to business size. If Lipa Na M-Pesa use is resolving financial opacity and

implied hard information constraints, we should expect the mobile loan impact to be the

most significant among small-scale establishments. We classify a business as small if its

baseline number of employees is smaller than the median number of employees in the re-

spective sector and study heterogenous treatment effects with respect to business size.34

34In the baseline median business size is 3 employees for pharmacies and 5 employees for restaurants.

26

Table 10 shows that the business size is an important factor affecting the mechanism via

which the treatment affects financial connectedness. Specifically, as indicated by the posi-

tive and significant interaction terms associated with “Small x Treated” in Panel A, Lipa

Na M-Pesa treatment increases the probability of receiving a loan as well as the size of the

loan for small firms more than the rest of the sample: “Small x Treated” has a statistically

significant effect on having a mobile loan at 1% level, while the intensive margin effect on

mobile loan size is statistically significant at 5% level.35

This differential impact is consistent with the literature arguing that the smaller estab-

lishments are likely to suffer from credit market exclusion because of financial opacity. For

instance, the literature on relationship lending suggests that extending loans to small and

non-transparent businesses requires the build-up of soft information and trust and therefore

necessitates formation of long-term bank-firm relations.36 What we propose here is that an

electronic payment technology, such as Lipa Na M-Pesa, could induce transparency, reduce

the necessity to rely on a bank-firm relationship to prove creditworthiness and thereby

allow for fast and low-cost access to external finance - to compensate short-term liquidity

needs.

Another interesting piece of evidence we derive from Table 10 is that our treatment also

stimulated small businesses’ informal finance, such as trade credit and loans from informal

financial networks. Although coefficient estimates are not very significant, we see that

at both extensive and intensive margins there is an overall increase in access to informal

finance among treated small businesses. This result echoes the findings in the literature

that suggest that access to formal loans (such as mobile loans in our analysis) could work

as a signalling device for creditworthiness and enhance access to informal loans as well.37

Table 10 here

Business Sales

35In all heterogeneous treatment regressions we control for the dummy variable “Small” on the RHS.36See Petersen and Rajan (1994), Berger and Udell (1995), Degryse and Cayseele (2000), and Beck, De

Haas, Degryse and v. Horen (2018).37Such complementarities are highlighted, among others, in Demirguc-Kunt and Maksimovic (2001),

Burkart and Ellingsen (2004) and Burkart, Ellingsen and Giannetti (2011).

27

What is the impact of Lipa Na M-Pesa on business sales? As shown in the first column

of Table 11, our treatment did not have a significant impact on the overall level of sales.

We do not find this outcome surprising for two reasons. First, if the real performance effect

of Lipa Na M-Pesa works its way through stimulated financial integration (especially by

mobile loans), then this effect is expected to be relatively more present for small businesses

and not so much for medium-sized establishments. Indeed in column 2 of Table 11 we

observe a positive coefficient estimate for “Small x Treated” - though insignificant - in an

ITT regression with sales, a finding that is consistent with what we observed in Table 10.38

Table 11 here

Second, and more importantly, mobile loans are much more likely to help coping with

cyclical shocks, such as inventory shortages, and as a result with “production smoothing”:

as emphasized previously mobile loans are designed to compensate short-term liquidity

needs and are not likely to be associated with long-term capital investment in stimulating

business expansion. In columns 4 and 6 of Table 11 we find evidence for this argument.

Our treatment resulted with a reduction in the volatility of sales for small businesses,

where sales volatility is proxied by ln(Salesmax) − ln(Salesmin) with ln(Salesmax) and

ln(Salesmin) measuring the maximum and the minimum amount of the natural logarithm

of sales, respectively, that the business experienced during particular months over the

past 12 months. The volatility reduction that we capture is statistically significant at the

level of 10% and the coefficient estimate in column 4 corresponds to as high as 55% of

the sales volatility observed in the control group. This reduction in sales volatility is also

quantitatively in line with the expansion in financial access allowed through mobile loans.39

The sales smoothing induced by our Lipa Na M-Pesa treatment complements the findings

by Jack and Suri (2014), who show that the standard M-Pesa product smooths household

consumption by allowing easy access to liquidity. Our research shows that an analog

38We would like to note that if the dummy variable “Small” is dropped on the RHS of the specificationpresented in the second column of Table 11, “Small x Treated” turns out to have a significant coefficientestimate.

39Columns 5 and 6 in Table 11 perform a robustness check, where we fill missing information in thebaseline survey on salesmax or salesmin using average monthly sales data. When compared against column4, in column 6 we observe that the economic significance through “Small x Treated” goes down, but thestatistical significance of the coefficient estimate improves.

28

property exists for SMEs, where especially for small-scale establishments Lipa Na M-Pesa

causes financial access and a reduced sales volatility.

Finally, in Table 12 we present the impact of our treatment on total investment and

the inventory investment component of total investment. Total investment, a potential

determinant of overall business growth in the long-run, is not significantly influenced by

our treatment, though both “Treatment” and “Small x Treated” have positive coefficient

estimates. Complementing our findings on reduced sales volatility, we find that in inventory

investment regression the coefficient estimate associated with “Small x Treated” is positive,

and borderline statistically significant at 10% level. Inventory investment primarily serves

the purpose of absorbing shocks and short-run smoothing of sales.40 Therefore, this nearly

significant treatment effect of our experimental intervention on small firms’ inventory in-

vestment is in line with our highlighted mechanism. In this respect, this finding provides

an insight for how improved financial access and reduced sales volatility outcomes that our

experimental intervention caused might relate to each other.

Table 12 here

6 Conclusion

We used a Randomized-Controlled-Trial experiment to estimate the factors affecting the

decision to adopt a novel P2B mobile-money payment technology for small- and medium-

sized merchants in Nairobi, and measured the impact of using such technology on business

outcomes. We found causal evidence that seemingly small situational barriers to adoption

are decisive for the adoption of this instrument. Providing information about the product

and eliminating the paperwork to open an account was enough to increase the interest to

adopt the technology by 62% in restaurants and by 21% in pharmacies.

As a highlighted result we showed that financial transparency concerns of the business

owner persistently affect adoption and usage of the payment technology. Sixteen months

after our intervention, we observed that firms that were induced to adopt the technology

40Eichenbaum (1988) and Dynan, Elmendorf and Sichel (2006) provide theoretical and empirical argu-ments for the role of inventory investment in absorbing production shocks and smoothing sales over thebusiness cycle.

29

feel safer and have more mobile loans. The impact on mobile-loan usage is especially

pronounced for small-size establishments, which also experience a reduction in their sales

volatility.

Our key results are generalizable to a wide range of settings, because we studied the

adoption of a mainstream product provided by a large intermediary connecting “payment”

and “lending” means of financial services spectrum. Also, we conducted our research with

actual SMEs and (in particular) those which are licensed and hence are taxable. Therefore,

our findings are highly relevant for researchers and policy makers across the globe interested

in promoting both cashless transformations and financial access.

Finally, the key mechanism behind our results revealed that transparency aversion is

likely to be an important barrier for the diffusion of high-tech payment products. Fi-

nancial regulators and designers of financial instruments might need to pay attention to

this novel empirical finding. Our recommendation to regulators and advocates of financial

products is to emphasize the benefits of financial transparency when promoting electronic

payment instruments to SMEs - and especially to those which are expected to suffer the

most from opacity: as our impact analysis revealed, becoming transparent to a financial

service provider can be associated with better access to external finance and eventually with

real outcome effects, especially for small businesses which otherwise might be hindered by

barriers to visibility and hard information constraints.

30

References

[1] Arango, C. A., Huynh, K. P., and L. Sabetti, 2011. “How do you pay? The roleof incentives at the point-of-sale”, ECB Working Paper No. 1386.

[2] Arifovic, J., J. Duffy, and J.H. Jiang, 2017. “Adoption of a new payment method:theory and experimental evidence”, Bank of Canada Working Paper.

[3] Bertrand, M., Mullainathan, S. and E. Shafir, 2004. “A behavioral-economicsview of poverty”, American Economic Review, 94-2, 419-423.

[4] Beck, T., H. Pamuk, R. Ramrattan, and B.R. Uras, 2018. “Payment Instru-ments, Finance, and Development”, Journal of Development Economics, 133, 162-186.

[5] Beck, T., Degryse, H., De Haas, R., and N. Van Horen, N., 2018. “When arm’slength is too far: Relationship banking over the credit cycle”. Journal of FinancialEconomics, 127(1), 174-196.

[6] Berger, A. N., and G. F. Udell, 1995. “Relationship lending and lines of credit insmall firm finance.” Journal of Business, 351-381.

[7] Bolt, W., N. Jonker, and C. Van Renselaar, 2010. “Incentives at the counter:An empirical analysis of surcharging card payments and payment behaviour in theNetherlands”, Journal of Banking and Finance, 34(8), 1738-1744.

[8] Bradlow, E.T., 1998. Encouragement Designs: An Approach to Self-Selected Samplesin an Experimental Design. Marketing Letters 9(4), 383-391.

[9] Burkart, M., and T. Ellingsen, 2004. “In-Kind Finance: A Theory of Trade Credit”,American Economic Review, 94 (3): 569-590.

[10] Burkart, M., Ellingsen, T., and M. Giannetti, 2011. “What you sell is what youlend? Explaining trade credit contracts”, Review of Financial Studies, 24(4), 1261-1298.

[11] Camera, G., M. Casari, and S. Bortolotti, 2016. “An experiment on retail pay-ments systems”, Journal of Money, Credit and Banking, 48(2-3), 363-392.

[12] Caselli, F. and W.J. Coleman II, 2001. “The US structural transformation andregional convergence: A reinterpretation”, Journal of Political Economy, 109(3), 584-616.

[13] Chakravorti, S., and T. To, 2007. “A theory of credit cards”, International Journalof Industrial Organization, 25(3), 583-595.

[14] Comin, D. and B. Hobijn, 2004. “Cross-country technology adoption: making thetheories face the facts”, Journal of Monetary Economics, 51(1), 39-83.

[15] Communications Authority of Kenya, 2015. “Fourth quarter sector statisticsreport for the financial year 2014-2015”, retrieved from http://ca.go.ke/index.php/

statistics.

[16] Degryse, H., and P. V. Cayseele, 2000. “Relationship lending within a bank-based system: Evidence from European small business data.” Journal of FinancialIntermediation, 9, 1, 90-109.

31

[17] Demirguc-Kunt, A., and Maksimovic, V., 2001. “Firms as financial intermedi-aries: Evidence from trade credit data”. Policy Research Working Paper, No. 2696.World Bank.

[18] Drexler, A., Fisher, G. and A. Schoar, 2014. “Keeping It Simple: FinancialLiteracy and Rules of Thumb”, American Economic Journal: Applied Economics, vol.6, No. 2, pp. 1-31.

[19] Duflo, E., M. Kremer and J. Robinson, 2004. “Understanding technology adop-tion: Fertilizer in Western Kenya, preliminary results from field experiments”, Unpub-lished manuscript, Massachusetts Institute of Technology.

[20] Duflo, E., M. Kremer and J. Robinson, 2008. “How High are Rates of Return toFertilizer? Evidence from Field Experiments in Kenya”, American Economic Review,P&P, 98, 2, 482-488.

[21] Duflo, E., M. Kremer and J. Robinson, 2011. “Nudging Farmers to Use Fertilizer:Theory and Experimental Evidence from Kenya”, American Economic Review, 101, 6,2350-90.

[22] Dupas, P., and J. Robinson, 2013. “Savings constraints and microenterprise de-velopment: Evidence from a field experiment in Kenya”, American Economic Journal:Applied Economics, 5(1), 163-92.

[23] Dynan, K. E., Elmendorf, D. W., and D.E. Sichel, 2006. “Can financial innova-tion help to explain the reduced volatility of economic activity?”. Journal of MonetaryEconomics, 53(1), 123-150.

[24] Economides, N., and P. Jeziorski, 2017. “Mobile money in Tanzania”, MarketingScience, 36(6), 815-837.

[25] Eichenbaum, M. S., 1988. “Some empirical evidence on the production level andproduction cost smoothing models of inventory investment”.

[26] Finaccess, 2014. FinAccess Business Survey

[27] Foster, A. and M.R. Rosenzweig, 2010. “Microeconomics of Technology Adop-tion”, Annual Review of Economics, 2:394-424.

[28] Hanna, R., Mullainathan, S., S. Joshua, 2014. “Learning through noticing: The-ory and evidence from a field experiment”, The Quarterly Journal of Economics 129(3), 13111353.

[29] Humphrey, D. B., L.B. Pulley and J.M. Vesala, 1996. “Cash, paper, and elec-tronic payments: a cross-country analysis”, Journal of Money, Credit and Banking,28(4), 914-93

[30] Jack, W., A. Ray and T. Suri, 2013. “Transaction Networks: Evidence from MobileMoney in Kenya”, American Economic Review, 103, 3, 356-61.

[31] Jack, W. and T. Suri, 2011. “Mobile Money: The Economics of M-PESA”, NBERWorking Paper No. 16721.

32

[32] Jack, W. and T. Suri, 2014. “Mobile Risk sharing and transactions costs: Evidencefrom Kenya’s mobile money revolution”, The American Economic Review 104, 1, 183-223.

[33] Klee, E., 2008. “How people pay: Evidence from grocery store data”, Journal ofMonetary Economics 55(3), 526-541.

[34] Kremer, M. and E. Miguel, 2007. “The illusion of sustainability”, The QuarterlyJournal of Economics, 122(3), 1007-1065.

[35] La Ferrara, E., 2016. “Mass Media and Social Change: Can We Use Television toFight Poverty?” Journal of the European Economic Association, 14, 4(1), Pages 791827.

[36] La Porta, R., Lopez-de-Silanes, F., Shleifer, A., and R.W. Vishny, 1997.“Legal determinants of external finance”. The Journal of Finance, 52(3), 1131-1150.

[37] La Porta, R., Lopez-de-Silanes, F., Shleifer, A., and R.W. Vishny, 1998.“Law and finance”. Journal of Political Economy, 106(6), 1113-1155.

[38] Petersen, M.A., and R.G. Rajan, 1994. “The benefits of lending relationships:Evidence from small business data.” The Journal of Finance 49, no. 1: 3-37.

[39] Mbiti, I., and D.N. Weil, 2011. “Mobile banking: The impact of M-Pesa in Kenya(No. w17129)”, National Bureau of Economic Research.

[40] Schuh, S. and J. Stavins, 2010. “Why are (some) consumers (finally) writing fewerchecks? The role of payment characteristics”, Journal of Banking and Finance, 34(8),1745-1758.

[41] Suri, T. and W. Jack, 2016. “The long-run poverty and gender impacts of mobilemoney”, Science, 354, 6317, 1288-1292.

[42] Wakamori, N., and Welte, A., 2017. “Why do shoppers use cash? Evidence fromshopping diary data”, Journal of Money, Credit and Banking, 49(1), 115-169.

33

Figure 1: Lipa Na M-Pesa vs. standard personal M-Pesa transfer fees

050

100

150

200

mpe

sa tr

ansf

er fe

e, K

enya

n S

hilli

ngs

10 5000 8500 15000 20000amount of transfer, Kenyan Shillings

Personal Mpesa Lipa Na M-pesa

Notes: The figure depicts the transfer fees charged when transacting with Lipa Na M-Pesa andstandard M-Pesa. The red line shows the fee that Safaricom deducts from a merchant (y-axis)for the corresponding transfer amount made by a customer (x-axis) through Lipa Na M-Pesa(the marginal cost of transaction is constant at 1% of the payment). The blue line shows the feethat Safaricom deducts from a customer (y-axis) for the corresponding amount of transfer madefrom a customer to the merchant (x-axis) through a standard M-Pesa account (the marginalcost of transaction is a step function).

34

Figure 2: M-Pesa and Lipa Na M-Pesa withdrawal fees

050

100

150

200

with

draw

al fe

e, K

enya

n S

hilli

ngs

10 2500 5000 10000 15000 20000amount of withdrawal , Kenyan Shillings

Notes: The blue line depicts cash withdrawal fees (when converting M-Pesa or Lipa Na M-Pesaunits into cash). Same fees apply for M-Pesa and Lipa Na M-Pesa.

35

Figure 3: Geographic distribution of restaurants at baseline

Notes: The figure shows the geographic distribution of restaurants in treatment (blue) andcontrol (red) groups at baseline.

36

Figure 4: Geographic distribution of pharmacies at baseline

Notes: The figure shows the geographic distribution of pharmacies in treatment (blue) andcontrol (red) groups at baseline.

37

Figure 5: Geographic distribution of a random sub-sample of merchants at baseline

Notes: The figure shows the geographic distribution of a sub-sample of merchants in treatment(blue) and control (red) groups at baseline.

38

Figure 6: Stated reasons at baseline for not adopting Lipa Na: by sector

0.1

.2.3

Too co

mplex t

o use

Too co

stly t

o ope

n an a

ccou

nt

Not se

eing t

he be

nefits

of Li

pa N

a M-P

esa

Wou

ld no

t incre

ase m

y sale

s

Don't h

ave t

ime t

o ope

n an a

ccou

nt

No trus

t in m

obile

mon

ey pr

ovide

r

High tra

nsac

tion f

ees

Pharmacy Restaurant

95 percent confidence interval

Notes: The figure shows the fraction of businesses (restaurants and pharmacies), who provideda particular reason for not having a Lipa Na M-Pesa account before our study. We also report95% statistical confidence levels for each bar. Not seeing the benefits of Lipa Na M-Pesa, Toocostly to open an account, High transaction fees via Lipa Na M-pesa , Don’t have time to openan account, Would not increase my sales, No trust in mobile money provider, Too complex touse are reasons of not adopting the Lipa Na M-pesa before our experiment. These variablesequal 1 if the business stated the corresponding reason, 0 otherwise.

39

Figure 7: Stated reasons at baseline for not having adopting Lipa Na prior to the interven-tion, by Restaurants who accepted or rejected our offer to open an account

0.1

.2.3

.4

Too co

mplex t

o use

Too co

stly t

o ope

n an a

ccou

nt

Not se

eing t

he be

nefits

of Li

pa N

a M-P

esa

Wou

ld no

t incre

ase m

y sale

s

Don't h

ave t

ime t

o ope

n an a

ccou

nt

No trus

t in m

obile

mon

ey pr

ovide

r

High tra

nsac

tion f

ees

Wants to open account Does not want to open account

95 percent confidence interval

Notes: The figure compares the fraction of adopters against non-adopters after the treatment- with a particular reason for not having a Lipa Na M-Pesa account before our study. We alsoreport 95% statistical confidence levels for each bar.

40

Figure 8: Stated reasons at baseline for not having adopting Lipa Na prior to the interven-tion, by Pharmacies who accepted or rejected our offer to open an account

0.1

.2.3

.4

Too co

mplex t

o use

Too co

stly t

o ope

n an a

ccou

nt

Not se

eing t

he be

nefits

of Li

pa N

a M-P

esa

Wou

ld no

t incre

ase m

y sale

s

Don't h

ave t

ime t

o ope

n an a

ccou

nt

No trus

t in m

obile

mon

ey pr

ovide

r

High tra

nsac

tion f

ees

Wants to open account Does not want to open account

95 percent confidence interval

Notes: The figure compares the fraction adopters against non-adopters after the treatment -with a particular reason for not having a Lipa Na M-Pesa account before our study. We alsoreport 95% statistical confidence levels for each bar.

41

Restaurant

May 2015-June 2015 • ListingJuly 2015-August 2015 • Baseline survey and offers to open Lipa accountsSeptember 2015-February 2016 • Lipa account openingMarch 2017-May 2017 • Endline Survey

Pharmacy

August 2015 • ListingSeptember 2015-November 2015 • Baseline survey and offers to open Lipa accountsOctober 2015-February 2016 • Lipa account openingMarch 2017-May 2017 • Endline Survey

Timeline: Experiment and survey timeline.

42

Tab

le1:

Bas

elin

ebusi

nes

sch

arac

teri

stic

san

dbal

ance

test

Ph

arm

acy

Res

tau

rant

All

Con

tT

reat

Diff

All

Con

tT

reat

Diff

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Sta

ndard

mobi

lem

on

eyu

seU

sin

gm

ob

.m

oney

for

bu

sin

ess

pu

rpos

es(0

/1)

0.41

0.40

0.43

-0.0

30.

590.

570.

62-0

.05

Usi

ng

mob

.m

oney

tore

ceiv

ep

aym

ents

(0/1

)0.

250.

210.

28-0

.07*

0.40

0.38

0.42

-0.0

4U

sin

gm

ob

.m

oney

tost

ore

mon

ey(0

/1)

0.09

0.09

0.10

0.00

0.25

0.26

0.24

0.02

Usi

ng

mob

.m

oney

top

ayb

ills

(0/1)

0.26

0.26

0.27

-0.0

10.

360.

370.

350.

02U

sin

gm

ob

.m

oney

top

aysa

lari

es(0

/1)

0.04

0.02

0.06

-0.0

4**

0.07

0.07

0.07

0.00

Usi

ng

mob

.m

oney

top

ayin

pu

ts(0

/1)

0.31

0.32

0.29

0.03

0.43

0.41

0.45

-0.0

4A

ware

nes

sof

Lip

aan

dre

aso

ns

of

not

adopti

ng

Aw

are

of

Lip

aN

aM

-Pes

a(0

/1)

0.94

0.95

0.93

0.02

0.96

0.95

0.97

-0.0

2H

ave

Lip

aN

aM

-Pes

a(0

/1)

0.05

0.01

0.08

-0.0

6***

0.12

0.12

0.12

0.00

Not

seei

ng

the

ben

efits

ofL

ipa

Na

M-P

esa

(0/1

)0.

270.

270.

270.

000.

250.

270.

240.

03T

oo

cost

lyto

open

an

acco

unt

(0/1

)0.

170.

180.

160.

020.

050.

030.

06-0

.02

Hig

htr

an

sact

ion

fees

via

Lip

aN

aM

-pes

a(0

/1)

0.25

0.25

0.24

0.01

0.09

0.08

0.09

-0.0

1D

on

’th

ave

tim

eto

open

anacc

ount

(0/1

)0.

090.

110.

070.

040.

140.

140.

15-0

.01

Wou

ldn

otin

crea

sem

ysa

les

(0/1

)0.

090.

080.

11-0

.02

0.06

0.06

0.06

0.00

No

tru

stin

mob

ile

mon

eypro

vid

er(0

/1)

0.03

0.03

0.04

-0.0

10.

020.

020.

010.

00T

oo

com

ple

xto

use

(0/1

)0.

080.

080.

080.

000.

110.

090.

13-0

.03

Bu

sin

ess

size

loga

rith

m(S

ale

s,m

onth

ly(1

000

Ksh

.),

win

sori

zed

5%)

4.67

4.59

4.74

-0.1

6**

5.30

5.29

5.31

-0.0

1lo

gari

thm

(Pro

fits

,m

onth

ly,

(1000

Ksh

.),

win

sori

zed

5%)

3.74

3.65

3.83

-0.1

9**

3.93

3.93

3.94

-0.0

1lo

gari

thm

(Em

plo

yee

s)1.

181.

171.

20-0

.02

1.65

1.66

1.64

0.01

Inve

stm

ent

an

dacc

ess

tofi

nan

ceIn

vest

men

t(0

/1),

inth

ep

ast

6m

onth

s0.

190.

190.

20-0

.01

0.37

0.39

0.35

0.04

Ban

klo

an

(0/1

),in

the

pas

t12

month

s0.

070.

070.

060.

010.

120.

100.

14-0

.04

Info

rmal

loan

(0/1

),in

the

past

12

mon

ths

0.03

0.03

0.02

0.00

0.04

0.04

0.05

-0.0

1M

obil

elo

an

(0/1

)in

the

past

12

mon

ths

0.08

0.07

0.09

-0.0

20.

120.

120.

120.

00In

form

ali

tyB

usi

nes

sli

cen

se(0

/1)

0.92

0.93

0.91

0.02

0.56

0.55

0.57

-0.0

1

Notes:

Th

ista

ble

des

crib

esth

etr

eatm

ent

an

dco

ntr

ol

gro

up

mea

ns

for

ph

arm

aci

esan

dre

stau

rants

.V

ari

ab

led

escr

ipti

on

s:S

ale

sis

the

bu

sin

ess

tota

lre

ven

ues

inth

ep

ast

month

.P

rofi

tsis

the

tota

lin

com

eth

eb

usi

nes

sea

rned

du

rin

gth

ep

ast

month

aft

erp

ayin

gall

exp

ense

s.E

mp

loyee

sis

the

tota

lnu

mb

erof

emp

loyee

sp

lus

the

ow

ner

.In

ves

tmen

tis

the

tota

lca

pit

al

inves

tmen

tfo

rb

usi

nes

sp

urp

ose

s.b

ank

equ

als

1(0

oth

erw

ise)

ifth

eb

usi

nes

sev

erre

ceiv

eda

new

loan

from

aco

mm

erci

al

ban

k,

SA

CC

O,

or

oth

erfo

rmal

fin

an

cial

inst

itu

tion

over

the

last

12

month

s.In

form

al

loan

equ

als

1(0

oth

erw

ise)

ifth

eth

eb

usi

nes

sor

bu

sin

ess

ow

ner

sb

orr

ow

edm

on

ey(n

ewlo

an

)fo

rb

usi

nes

sp

urp

ose

sfr

om

any

bu

sin

ess

ass

oci

ati

on

,m

on

eyle

nd

er,

fam

ily

or

frie

nd

over

the

last

12

month

s.M

ob

ile

loan

equ

als

1(0

oth

erw

ise)

ifth

eb

usi

nes

sor

bu

sin

ess

ow

ner

sb

orr

ow

edm

on

ey(n

ewlo

an

)fo

rM

ob

ile

Mic

rofi

nan

ceso

urc

eslike

KC

B-M

pes

a,

M-K

esh

a,

M-S

hw

ari

over

the

last

12

month

s.A

ware

of

Lip

aN

aM

-Pes

aeq

uals

1(0

oth

erw

ise)

ifth

eb

usi

nes

sis

aw

are

of

Lip

aN

aM

-Pes

a.

Bu

sin

ess

lice

nse

equ

als

1if

the

bu

sin

ess

has

an

up

tod

ate

bu

sin

ess

lice

nse

from

an

au

thori

ty.

Usi

ng

mob

.m

on

eyfo

rb

usi

nes

sp

urp

ose

s(t

ore

ceiv

ep

aym

ent/

store

mon

ey/to

pay

bills

/to

pay

sala

ries

/to

pay

inp

uts

)eq

uals

1(0

oth

erw

ise)

.N

ot

seei

ng

the

ben

efits

of

Lip

aN

aM

-Pes

a,

Too

cost

lyto

op

enan

acc

ou

nt,

Hig

htr

an

sact

ion

fees

via

Lip

aN

aM

-pes

a,

Don

’th

ave

tim

eto

op

enan

acc

ou

nt,

Wou

ldn

ot

incr

ease

my

sale

s,N

otr

ust

inm

ob

ile

mon

eyp

rovid

er,

Too

com

ple

xto

use

are

reaso

ns

of

not

ad

op

tin

gth

eL

ipa

Na

M-p

esa.

Th

eyeq

ual

1(0

oth

erw

ise)

ifth

eb

usi

nes

sst

ate

dit

isa

reaso

nn

ot

toad

op

tL

ipa

Na

M-p

esa

bef

ore

the

trea

tmen

t.p<

0.1

.**

p<

0.0

5.

***

p<

0.0

1

43

Tab

le2:

Busi

nes

sch

arac

teri

stic

sin

the

trea

tmen

tgr

oup

by

willingn

ess

toad

opt

Lip

aN

aM

-pes

a

Ph

arm

acy

Res

tau

rant

Does

wan

tto

Wan

tsto

Diff

,D

oes

wan

tto

Wan

tsto

Diff

.op

enac

cou

nt

open

acco

unt

open

acco

unt

open

acco

unt

Per

son

al

mobi

lem

on

eyu

sefo

rbu

sin

ess

pu

rpose

sU

sin

gm

ob

.m

oney

for

bu

sin

ess

pu

rpos

es(0

/1)

0.41

0.48

-0.0

70.

520.

68-0

.17*

**U

sin

gm

ob

.m

oney

tore

ceiv

ep

aym

ents

(0/1

)0.

260.

35-0

.09

0.33

0.48

-0.1

5***

Sh

are

ofm

obil

em

on

eycu

stom

ers

0.02

0.04

-0.0

20.

010.

03-0

.02*

**U

sin

gm

ob

.m

oney

tost

ore

mon

ey(0

/1)

0.08

0.15

-0.0

70.

170.

29-0

.12*

**U

sin

gm

ob

.m

oney

top

ayb

ills

(0/1)

0.27

0.28

-0.0

10.

310.

38-0

.07

Usi

ng

mob

.m

oney

top

aysa

lari

es(0

/1)

0.06

0.07

-0.0

20.

060.

07-0

.01

Usi

ng

mob

.m

oney

top

ayin

pu

ts(0

/1)

0.28

0.35

-0.0

70.

350.

52-0

.17*

**A

ware

nes

sof

Lip

aN

aM

-Pes

aan

dre

aso

ns

of

not

adopti

ng

Aw

are

of

Lip

aN

aM

-Pes

a(0

/1)

0.93

0.96

-0.0

40.

980.

970.

01N

ot

seei

ng

the

ben

efits

ofL

ipa

Na

M-P

esa

(0/1

)0.

300.

130.

16**

0.31

0.18

0.13

***

Too

cost

lyto

open

an

acco

unt

(0/1

)0.

190.

080.

11*

0.05

0.05

0.00

Hig

htr

an

sact

ion

fees

via

Lip

aN

aM

-pes

a(0

/1)

0.27

0.13

0.14

**0.

100.

090.

00D

on

’th

ave

tim

eto

open

anacc

ount

(0/1

)0.

060.

13-0

.08*

*0.

070.

21-0

.14*

**W

ould

not

incr

ease

my

sale

s(0

/1)

0.13

0.06

0.07

0.11

0.03

0.08

***

No

tru

stin

mob

ile

mon

eypro

vid

er(0

/1)

0.05

0.00

0.05

*0.

030.

010.

02T

oo

com

ple

xto

use

(0/1

)0.

080.

12-0

.04

0.15

0.12

0.03

Bu

sin

ess

size

loga

rith

m(S

ale

s,m

onth

ly(1

000

Ksh

.),

win

sori

zed

5%)

4.75

4.75

0.00

5.30

5.34

-0.0

4lo

gari

thm

(Pro

fits

,m

onth

ly,

(1000

Ksh

.),

win

sori

zed

5%)

3.81

3.90

-0.0

83.

953.

940.

01lo

gari

thm

(Em

plo

yee

s)1.

181.

23-0

.04

1.67

1.62

0.06

Inve

stm

ent

an

dacc

ess

tofi

nan

ceIn

vest

men

t(0

/1),

inth

ep

ast

6m

onth

s0.

190.

24-0

.06

0.28

0.39

-0.1

1*B

ank

loan

(0/1

),in

the

pas

t12

month

s0.

060.

060.

010.

130.

16-0

.03

Info

rmal

loan

(0/1

),in

the

past

12

mon

ths

0.02

0.02

0.00

0.06

0.04

0.02

Mob

ile

loan

(0/1

)in

the

pas

t12

month

s0.

090.

080.

010.

110.

14-0

.02

Info

rmali

tyB

usi

nes

sli

cen

se(0

/1)

0.92

0.86

0.06

0.52

0.60

-0.0

7

Notes:

Th

ista

ble

com

pare

sth

ed

escr

ipti

ve

chara

cter

isti

csof

ou

rsa

mp

lefo

rp

harm

aci

esan

dre

stau

rants

by

willin

gn

ess

toad

op

tth

eL

ipa

Na

M-p

esa

tech

nolo

gy

inou

rex

per

imen

t.W

eon

lyu

setr

eatm

ent

gro

up

bu

sin

esse

san

db

usi

nes

ses

that

do

not

have

aL

ipa

Na

M-P

esa

acc

ou

nt.

We

rep

ort

mea

nvalu

esfo

rea

chch

ara

cter

isti

c.V

ari

ab

led

escr

ipti

on

s:S

ale

sis

the

bu

sin

ess

tota

lre

ven

ues

inth

ep

ast

month

.P

rofi

tsis

the

tota

lin

com

eth

eb

usi

nes

sea

rned

du

rin

gth

ep

ast

month

aft

erp

ayin

gall

exp

ense

s.E

mp

loyee

sis

the

tota

lnu

mb

erof

emp

loyee

sp

lus

the

ow

ner

.In

ves

tmen

tis

the

tota

lca

pit

al

inves

tmen

tfo

rb

usi

nes

sp

urp

ose

s.b

an

keq

uals

1(0

oth

erw

ise)

ifth

eb

usi

nes

sev

erre

ceiv

eda

new

loan

from

aco

mm

erci

al

ban

k,

SA

CC

O,

or

oth

erfo

rmal

fin

an

cial

inst

itu

tion

over

the

last

12

month

s.In

form

al

loan

equ

als

1(0

oth

erw

ise)

ifth

eth

eb

usi

nes

sor

bu

sin

ess

ow

ner

sb

orr

ow

edm

on

ey(n

ewlo

an

)fo

rb

usi

nes

sp

urp

ose

sfr

om

any

bu

sin

ess

ass

oci

ati

on,

mon

eyle

nd

er,

fam

ily

or

frie

nd

over

the

last

12

month

s.M

ob

ile

loan

equ

als

1(0

oth

erw

ise)

ifth

eb

usi

nes

sor

bu

sin

ess

ow

ner

sb

orr

ow

edm

on

ey(n

ewlo

an

)fo

rM

ob

ile

Mic

rofi

nan

ceso

urc

eslike

KC

B-M

pes

a,

M-K

esh

a,

M-S

hw

ari

over

the

last

12

month

s.A

ware

of

Lip

aN

aM

-Pes

aeq

uals

1(0

oth

erw

ise)

ifth

eb

usi

nes

sis

aw

are

of

Lip

aN

aM

-Pes

a.

Bu

sin

ess

lice

nse

equ

als

1if

the

bu

sin

ess

has

an

up

tod

ate

bu

sin

ess

lice

nse

from

an

au

thori

ty.

Usi

ng

mob

.m

on

eyfo

rb

usi

nes

spu

rpose

s(t

ore

ceiv

ep

aym

ent/

store

mon

ey/to

pay

bills

/to

pay

sala

ries

/to

pay

inp

uts

)eq

uals

1(0

oth

erw

ise)

ifth

eb

usi

nes

su

sep

erso

nal

mob

ile

acc

ou

nt

for

bu

sin

ess

pu

rpose

s(t

ore

ceiv

ep

aym

ent/

store

mon

ey/to

pay

bills

/to

pay

sala

ries

/to

pay

inp

uts

).N

ot

seei

ng

the

ben

efits

of

Lip

aN

aM

-Pes

a,

Too

cost

lyto

op

enan

acc

ou

nt,

Hig

htr

an

sact

ion

fees

via

Lip

aN

aM

-pes

a,

Don

’th

ave

tim

eto

op

enan

acc

ou

nt,

Wou

ldn

ot

incr

ease

my

sale

s,N

otr

ust

inm

obil

em

on

eyp

rovid

er,

Too

com

ple

xto

use

aer

reaso

ns

of

not

ad

op

tin

gth

eL

ipa

Na

M-p

esa.

They

equ

al

1(0

oth

erw

ise)

ifth

eb

usi

nes

sst

ate

dit

isa

reaso

nn

ot

toad

op

tL

ipa

Na

M-p

esa.

p<

0.1

.**

p<

0.0

5.

***

p<

0.0

1

44

Tab

le3:

Reg

ress

sion

esti

mat

esfo

rw

illi

ngn

ess

toad

opt

Lip

aN

aM

-Pes

a:F

ull

trea

tmen

tsa

mp

le

Var

iab

le(1

)(2

)(3

)P

an

elA

:M

-Mon

eyE

xposu

reM

eth

od

sof

Use

for

bu

sin

ess

0.1

41***

(0.0

44)

mob

ile

mon

eyu

seR

ecei

vep

aym

ents

0.0

94*

(0.0

53)

Sto

rem

on

ey0.0

93

(0.0

64)

Pay

bil

l-0

.073

(0.0

57)

Pay

inp

ut

0.1

23**

(0.0

56)

Pay

sala

ries

-0.0

25

(0.0

92)

Sav

ing

inm

ob

.m

on

.acc

ou

nt

-0.0

48

(0.0

74)

%of

uti

lity

exp

.via

per

s.m

ob

.-0

.067

(0.0

56)

%of

inp

ut

exp

.via

per

s.m

ob

.0.2

37***

(0.0

78)

Pay

ing

wages

via

mp

esa

-0.0

30

(0.1

18)

No

incr

ease

inp

rice

s0.2

01***

(0.0

61)

Th

eft

Th

eft

an

dsa

fety

0.0

28

(0.0

24)

Inte

rnal

thef

t0.0

12

(0.0

54)

-0.0

07

(0.0

62)

Exte

rnal

thef

t,fire

,et

c.0.0

40

(0.0

79)

0.0

82

(0.0

80)

Fee

lin

gn

ot

safe

0.0

08

(0.0

12)

0.0

09

(0.0

14)

Bu

sin

ess

savin

gS

avin

gat

ab

an

kor

mic

ro.

-0.0

32

(0.0

48)

acco

unts

Sav

ing

at

ap

ers.

ban

kacc

.0.0

29

(0.0

48)

0.0

34

(0.0

54)

Sav

ing

at

ab

us.

ban

kacc

.-0

.189***

(0.0

68)

-0.1

95***

(0.0

75)

Sav

ing

at

am

icro

f.in

st.

-0.0

31

(0.1

22)

0.0

36

(0.1

37)

Pan

elB

:T

ran

spare

ncy

Not

rep

orti

ng

Not

share

dsa

les

-0.1

34*

(0.0

68)

-0.1

50**

(0.0

69)

bu

sin

ess

acti

vit

yN

otsh

are

dp

rofi

ts-0

.214***

(0.0

74)

Bu

sin

ess

lice

nse

Bu

sin

ess

lice

nse

0.0

42

(0.0

54)

0.0

34

(0.0

58)

0.0

89

(0.0

67)

Fin

anci

alF

inan

cial

Soph

isti

cati

on

0.0

21

(0.0

20)

sop

his

tica

tion

Ban

klo

an

0.0

54

(0.0

61)

-0.0

26

(0.0

72)

Mob

ile

loan

0.0

22

(0.0

75)

0.0

60

(0.0

80)

Bu

sin

ess

reco

rds

0.0

83

(0.0

64)

0.0

70

(0.0

72)

Sal

escr

edit

Sel

lson

cred

itto

cust

.0.0

58

(0.0

42)

0.0

48

(0.0

41)

0.0

57

(0.0

46)

Pan

elC

:B

ehavi

ora

lP

rese

nt

and

Pre

sent

bia

s0.0

25

(0.0

72)

0.0

54

(0.0

69)

0.0

46

(0.0

78)

Fact

ors

futu

reb

ias

Fu

ture

bia

s-0

.021

(0.0

54)

-0.0

00

(0.0

55)

-0.0

23

(0.0

57)

Cog

nit

ive

cap

acit

y#

of

dig

its

rem

emb

ered

0.0

30*

(0.0

17)

0.0

28*

(0.0

17)

0.0

39**

(0.0

20)

Tru

stT

rust

infi

rst

tim

e-0

.024

(0.0

25)

-0.0

37

(0.0

25)

-0.0

16

(0.0

29)

Tru

stin

cust

om

ers

0.0

18

(0.0

35)

0.0

20

(0.0

34)

0.0

09

(0.0

37)

Tru

stin

cou

rts

-0.0

44*

(0.0

26)

-0.0

35

(0.0

28)

-0.0

49*

(0.0

29)

Tru

stin

mob

.m

on

.co

mp

.0.0

42

(0.0

35)

0.0

42

(0.0

35)

0.0

29

(0.0

38)

Pan

elD

:B

usi

nes

ssi

zelo

g(E

mp

loye

es)

0.0

48

(0.0

69)

0.0

97

(0.0

69)

log(

Sale

s,m

onth

ly,

win

.5%

)0.0

37

(0.0

30)

0.0

56*

(0.0

29)

log(

Pro

fits

,m

onth

ly,

win

.5%

)0.0

24

(0.0

31)

Enu

mer

ator

and

dis

tric

tF

EY

esY

esY

esO

bse

rvat

ion

s493

490

394

R-s

qu

ared

0.2

85

0.3

16

0.3

51

Notes:

This

table

show

sth

eest

imati

on

resu

ltfo

rth

ere

lati

onsh

ipb

etw

een

wil

lingness

toadopt

Lip

aN

aM

-pesa

,and

valu

ati

on,

vis

ibil

ity,

behavio

ral

facto

rs,

busi

ness

size

for

all

sam

ple

.W

eest

imateYi

=β0

+X

′ iβ1

+εi

thro

ugh

OL

Sfo

rall

specifi

cati

ons

wherei

denote

sth

ebusi

ness

andX

iis

the

vecto

rin

clu

din

gvari

able

sdesc

rib

ed

inT

able

sO

A1

and

OA

2.

We

rep

ort

coeffi

cie

nt

est

imate

sfo

rβ1

and

robust

standard

err

ors

inpare

nth

ese

s.T

ocontr

ol

for

unobse

rved

regio

nal,

enum

era

tor

facto

rs,

and

merc

hant

typ

e,

we

add

enum

era

tor

and

dis

tric

tand

merc

hant

fixed

eff

ects

toall

est

imati

ons.

*p<

0.1

.**

p<

0.0

5.

***

p<

0.0

1.

45

Tab

le4:

Reg

ress

sion

esti

mat

esfo

rw

illi

ngn

ess

toad

opt

Lip

aN

aM

-Pes

a:T

reat

edR

esta

ura

nts

Vari

ab

le(1

)(2

)(3

)P

an

elA

:M

-Mon

eyE

xposu

reM

eth

od

sof

Use

for

bu

sin

ess

0.1

63**

(0.0

65)

mob

ile

mon

eyu

seR

ecei

vep

aym

ents

0.0

50

(0.0

74)

Sto

rem

on

ey0.0

38

(0.0

82)

Pay

bil

l-0

.038

(0.0

74)

Pay

inp

ut

0.1

98**

(0.0

79)

Pay

sala

ries

-0.0

69

(0.1

22)

Sav

ing

inm

ob

.m

on

.acc

ou

nt

-0.0

53

(0.0

95)

%of

uti

lity

exp

.via

per

s.m

ob

.0.0

29

(0.0

77)

%of

inp

ut

exp

.via

per

s.m

ob

.0.2

41**

(0.0

99)

Pay

ing

wages

via

mp

esa

-0.0

54

(0.1

84)

No

incr

ease

inp

rice

s0.1

54**

(0.0

78)

Th

eft

Th

eft

an

dsa

fety

0.0

29

(0.0

31)

Inte

rnal

thef

t0.0

29

(0.0

62)

0.0

12

(0.0

77)

Exte

rnal

thef

t,fire

,et

c.-0

.017

(0.0

95)

0.0

42

(0.0

99)

Fee

lin

gn

ot

safe

0.0

04

(0.0

17)

-0.0

00

(0.0

21)

Bu

sin

ess

savin

gS

avin

gat

ab

an

kor

mic

ro.

0.0

32

(0.0

67)

acco

unts

Sav

ing

at

ap

ers.

ban

kacc

.0.0

77

(0.0

70)

0.1

22

(0.0

81)

Sav

ing

at

ab

us.

ban

kacc

.-0

.121

(0.1

05)

-0.0

94

(0.1

20)

Sav

ing

at

am

icro

f.in

st.

-0.0

33

(0.1

22)

0.0

41

(0.1

73)

Pan

elB

:T

ran

spare

ncy

Not

rep

orti

ng

Not

share

dsa

les

-0.1

06

(0.1

28)

-0.0

83

(0.1

31)

bu

sin

ess

acti

vit

yN

ot

share

dp

rofi

ts-0

.164

(0.1

77)

Bu

sin

ess

lice

nse

Bu

sin

ess

lice

nse

0.0

17

(0.0

63)

0.0

11

(0.0

71)

0.0

64

(0.0

83)

Fin

anci

alF

inan

cial

Soph

isti

cati

on

0.0

22

(0.0

25)

sop

his

tica

tion

Ban

klo

an

0.0

17

(0.0

81)

-0.0

56

(0.1

02)

Mob

ile

loan

0.0

64

(0.0

95)

0.1

65

(0.1

16)

Bu

sin

ess

reco

rds

0.0

74

(0.0

80)

0.0

45

(0.0

93)

Sal

escr

edit

Sel

lson

cred

itto

cust

.0.0

93

(0.0

61)

0.0

99

(0.0

62)

0.0

82

(0.0

71)

Pan

elC

:B

ehavi

ora

lP

rese

nt

and

Pre

sent

bia

s-0

.020

(0.1

06)

0.0

15

(0.1

00)

0.0

49

(0.1

15)

Fact

ors

futu

reb

ias

Fu

ture

bia

s0.0

95

(0.0

77)

0.1

13

(0.0

79)

0.0

98

(0.0

92)

Cog

nit

ive

cap

acit

y#

of

dig

its

rem

emb

ered

0.0

21

(0.0

24)

0.0

25

(0.0

24)

0.0

34

(0.0

28)

Tru

stT

rust

infi

rst

tim

e-0

.048

(0.0

39)

-0.0

69*

(0.0

39)

-0.0

42

(0.0

48)

Tru

stin

cust

om

ers

0.0

72

(0.0

47)

0.0

61

(0.0

48)

0.0

40

(0.0

57)

Tru

stin

cou

rts

-0.0

60

(0.0

37)

-0.0

52

(0.0

39)

-0.0

63

(0.0

45)

Tru

stin

mob

.m

on

.co

mp

.-0

.015

(0.0

51)

-0.0

10

(0.0

54)

-0.0

36

(0.0

59)

Pan

elD

:B

usi

nes

ssi

zelo

g(E

mp

loye

es)

0.0

44

(0.0

85)

0.0

87

(0.0

86)

log(S

ale

s,m

onth

ly,

win

.5%

)0.0

62

(0.0

40)

0.0

57

(0.0

40)

log(P

rofi

ts,

month

ly,

win

.5%

)0.0

18

(0.0

41)

Enu

mer

ator

and

dis

tric

tF

EY

esY

esY

esO

bse

rvat

ion

s277

276

216

R-s

qu

ared

0.2

13

0.2

49

0.2

82

Notes:

This

table

show

sth

eest

imati

on

resu

ltfo

rth

ere

lati

onsh

ipb

etw

een

willingness

toadopt

Lip

aN

aM

-pesa

,and

valu

ati

on,

vis

ibilit

y,

behavio

ral

facto

rs,

busi

ness

size

for

rest

aura

nt

sam

ple

.W

eest

imateYi

=

β0

+X

′ iβ1

+εi

thro

ugh

OL

Sfo

rall

specifi

cati

ons

wherei

denote

sth

ebusi

ness

andX

iis

the

vecto

rin

clu

din

gvari

able

sdesc

rib

ed

inT

able

sT

able

sO

A1

and

OA

2.

We

rep

ort

coeffi

cie

nt

est

imate

sfo

rβ1

and

robust

standard

err

ors

inpare

nth

ese

s.T

ocontr

ol

for

unobse

rved

regio

nal,

enum

era

tor

facto

rsw

eadd

enum

era

tor

and

dis

tric

tfi

xed

eff

ects

toall

est

imati

ons.

*p<

0.1

.**

p<

0.0

5.

***

p<

0.0

1.

46

Tab

le5:

Reg

ress

sion

esti

mat

esfo

rw

illi

ngn

ess

toad

opt

Lip

aN

aM

-Pes

a:T

reat

edP

har

mac

ies

Var

iab

le(1

)(2

)(3

)P

an

elA

:M

-Mo

ney

Exp

osu

reM

eth

od

sof

Use

for

bu

sin

ess

0.1

15*

(0.0

59)

mob

ile

mon

eyu

seR

ecei

vep

aym

ents

0.1

32

(0.0

84)

Sto

rem

on

ey0.1

67

(0.1

44)

Pay

bil

l-0

.057

(0.0

94)

Pay

inp

ut

0.0

53

(0.0

91)

Pay

sala

ries

0.0

95

(0.1

74)

Sav

ing

inm

ob

.m

on

.acc

ou

nt

-0.2

29**

(0.0

88)

%of

uti

lity

exp

.via

per

s.m

ob

.-0

.155*

(0.0

82)

%of

inp

ut

exp

.via

per

s.m

ob

.0.1

61

(0.1

38)

Pay

ing

wages

via

mp

esa

0.0

73

(0.2

01)

No

incr

ease

inp

rice

s0.2

25**

(0.1

13)

Th

eft

Th

eft

an

dsa

fety

0.0

21

(0.0

43)

Inte

rnal

thef

t-0

.022

(0.1

24)

-0.0

78

(0.1

29)

Exte

rnal

thef

t,fire

,et

c.0.1

79

(0.1

71)

0.0

39

(0.1

50)

Fee

lin

gn

ot

safe

0.0

05

(0.0

20)

0.0

35

(0.0

23)

Bu

sin

ess

savin

gS

avin

gat

ab

an

kor

mic

ro.

-0.1

05

(0.0

75)

acco

unts

Sav

ing

at

ap

ers.

ban

kacc

.-0

.038

(0.0

80)

-0.0

40

(0.0

85)

Sav

ing

at

ab

us.

bank

acc

.-0

.237**

(0.0

91)

-0.1

92*

(0.1

06)

Sav

ing

at

am

icro

f.in

st.

-0.0

97

(0.2

12)

-0.0

99

(0.1

87)

Pan

elB

:T

ran

spare

ncy

Not

rep

orti

ng

Not

share

dsa

les

-0.1

73**

(0.0

83)

-0.2

21**

(0.0

89)

bu

sin

ess

acti

vit

yN

ot

share

dp

rofi

ts-0

.261***

(0.0

86)

Bu

sin

ess

lice

nse

Bu

sin

ess

lice

nse

-0.0

22

(0.1

34)

-0.0

16

(0.1

40)

0.0

80

(0.1

68)

Fin

anci

alF

inan

cial

Sop

his

tica

tion

0.0

34

(0.0

38)

sop

his

tica

tion

Ban

klo

an

0.0

98

(0.1

27)

0.0

28

(0.1

21)

Mob

ile

loan

0.0

17

(0.1

50)

-0.1

26

(0.1

23)

Bu

sin

ess

reco

rds

0.1

72**

(0.0

83)

0.1

99

(0.1

30)

Sal

escr

edit

Sel

lson

cred

itto

cust

.0.0

08

(0.0

61)

-0.0

08

(0.0

61)

0.0

38

(0.0

68)

Pan

elC

:B

ehavi

ora

lP

rese

nt

and

Pre

sent

bia

s0.0

88

(0.1

24)

0.0

82

(0.1

19)

0.0

65

(0.1

35)

Fact

ors

futu

reb

ias

Fu

ture

bia

s-0

.199***

(0.0

75)

-0.1

82**

(0.0

79)

-0.1

64**

(0.0

76)

Cog

nit

ive

cap

acit

y#

of

dig

its

rem

emb

ered

0.0

45

(0.0

28)

0.0

40

(0.0

28)

0.0

58*

(0.0

31)

Tru

stT

rust

infi

rst

tim

e-0

.014

(0.0

36)

-0.0

11

(0.0

38)

-0.0

10

(0.0

39)

Tru

stin

cust

om

ers

-0.0

73

(0.0

63)

-0.0

57

(0.0

62)

-0.0

72

(0.0

60)

Tru

stin

cou

rts

-0.0

36

(0.0

42)

-0.0

29

(0.0

44)

-0.0

58

(0.0

45)

Tru

stin

mob

.m

on

.co

mp

.0.1

00*

(0.0

51)

0.0

94*

(0.0

52)

0.1

06**

(0.0

53)

Pan

elD

:B

usi

nes

ssi

zelo

g(E

mp

loye

es)

0.1

34

(0.1

31)

0.1

82

(0.1

33)

log(S

ale

s,m

onth

ly,

win

.5%

)0.0

26

(0.0

50)

0.0

63

(0.0

51)

log(P

rofi

ts,

month

ly,

win

.5%

)0.0

64

(0.0

61)

Enu

mer

ator

and

dis

tric

tF

EY

esY

esY

esO

bse

rvat

ion

s216

214

178

R-s

qu

ared

0.1

68

0.2

18

0.2

88

Notes:

This

table

show

sth

eest

imati

on

resu

ltfo

rth

ere

lati

onsh

ipb

etw

een

willingness

toadopt

Lip

aN

aM

-pesa

,and

valu

ati

on,

vis

ibilit

y,

behavio

ral

facto

rs,

busi

ness

size

for

pharm

acy

sam

ple

.W

eest

imateYi

=

β0

+X

′ iβ1

+εi

thro

ugh

OL

Sfo

rall

specifi

cati

ons

wherei

denote

sth

ebusi

ness

andX

iis

the

vecto

rin

clu

din

gvari

able

sdesc

rib

ed

inT

able

sT

able

sO

A1

and

OA

2.

We

rep

ort

coeffi

cie

nt

est

imate

sfo

rβ1

and

robust

standard

err

ors

inpare

nth

ese

s.T

ocontr

ol

for

unobse

rved

regio

nal,

enum

era

tor

facto

rsw

eadd

enum

era

tor

and

dis

tric

tfi

xed

eff

ects

toall

est

imati

ons.

*p<

0.1

.**

p<

0.0

5.

***

p<

0.0

1.

47

Table 6: Relationship between business attrition in the endline and baseline business character-istics for all businesses with business license.

(1) (2)Baseline characteristics coef se

Treatment =1 if assigned to treatment group -0.010 (0.031)Pharmacy =1 if business is a pharmacy -0.144*** (0.043)Methods of Have Lipa Na M-pesa account -0.130*** (0.049)

Saving in mob. mon. account -0.033 (0.061)% of utility exp. via pers. mob. -0.027 (0.038)% of input exp. via pers. mob. -0.002 (0.022)Paying wages via mpesa 0.036 (0.034)

Theft and safety Internal theft -0.007 (0.044)External theft -0.117** (0.053)Feeling safe 0.002 (0.009)

Business saving Saving at a pers. bank acc. -0.058 (0.036)accounts Saving at a bus. bank acc. 0.013 (0.040)

Saving at a microf. inst. 0.030 (0.124)Financial Bank loan 0.003 (0.058)sophistication Mobile loan -0.100** (0.052)

Business records -0.101 (0.074)Present and Present bias -0.050 (0.050)future bias Future bias -0.068* (0.044)Cognitive capacity # of digits remembered -0.007 (0.012)Trust Trust in first time 0.030 (0.020)

Trust in customers 0.028 (0.027)Trust in courts 0.027 (0.018)Trust in mob. mon. comp. -0.033 (0.024)

Business size Employees -0.135*** (0.035)Constant 0.669*** (0.172)

Observations 855R-squared 0.075

Notes: This table shows the estimation result for the relationship between business attrition in the endline and baseline business characteris-

tics. we use the sample of businesses with business license in the baseline. We estimate Yi = β0 +X′iβ1 +εi through OLS for all specifications

where i denotes the business and Xi is the vector including variables listed in the first column. Yi equals 1 if the business did not participatein the baseline survey. We report coefficient estimates for β1 and robust standard errors in parentheses. * p<0.1. ** p<0.05. *** p<0.01.

48

Table 7A: Intention to treat (ITT) estimates for LPN usage

(1) (2) (3) (4)Opened LPN (0/1) Used LPN (0/1) Received payment via LPN (0/1) LPN sales, log(1+x)

Treatment 0.07** 0.08** 0.08** 0.26**(0.03) (0.03) (0.03) (0.11)

Control Mean 0.23 0.21 0.20 0.63Control StDev 0.42 0.40 0.40 1.43

N 619 618 618 618Notes: This table shows the ITT estimates for Lipa Na use indicators. Dependent variables are having Lipa Na M-pesa account (0/1), usingLipa Na M-pesa account for business in the past 30 days, receiving payment via Lipa Na M-pesa in the past 30 days, and Lipa Na M-pesasales (log(1+x)). Control Vector in each ITT regression: Baseline value of ln(sales-winsorized), not reporting sales in baseline, ln(baselineemployee #), use of LPN in baseline, enumerator FE, dummy variables for gender and business type and small business type. * p<0.1. **p<0.05. *** p<0.01.

Table 7B: Intention to treat (ITT) estimates for LPN usage for visible and non-transparent busi-nesses

(1) (2) (3) (4)Panel A: Transparent firms: shared sales figures in the baselineTreatment 0.07* 0.09** 0.09** 0.32**

(0.04) (0.04) (0.04) (0.12)

Contol Mean 0.24 0.22 0.21 0.65Contol StDev 0.43 0.22 0.22 1.45

N 486 485 485 485Panel B: Non-transparent firms: did not share sales figures in the baselineTreatment 0.00 0.01 0.01 0.02

(0.08) (0.08) (0.08) (0.24)

Control Mean 0.19 0.18 0.18 0.58Contol StDev 0.40 0.39 0.39 1.35

N 131 131 131 131Notes: This table shows the ITT estimates for Lipa Na use indicators. Dependent variables are having Lipa Na M-pesa account (0/1), usingLipa Na M-pesa account for business in the past 30 days, receiving payment via Lipa Na M-pesa in the past 30 days, and Lipa Na M-pesasales (log(1+x)). Control Vector in each ITT regression: Baseline value of ln(sales-winsorized), not reporting sales in baseline, ln(baselineemployee #), use of LPN in baseline, enumerator FE, dummy variables for gender and business type and small business type. * p<0.1. **p<0.05. *** p<0.01.

49

Table 8A: ITT estimates for business safety

Feeling safe

Treatment 0.24*(0.14)

Control Mean 6.88Control StDev 1.84

N 619Notes: This table shows the ITT estimates for business safety feeling (1 not feel safe - 10 feel safe). Control Vector: Baseline values offeeling safe and ln(sales-winsorized), not reporting sales in baseline, ln(baseline employee #), use of LPN in baseline, enumerator FE, dummyvariables for gender and business type and small business type. * p<0.1. ** p<0.05. *** p<0.01.

Table 8B: ITT estimates for business safety and theft exposure in the baseline

(1) (2)Theft in Baseline No Theft in Baseline

Feeling safe Feeling Safe

Treatment 1.22*** 0.17(0.44) (0.15)

Control Mean 6.36 6.96Control StDev 1.95 1.82

N 75 543Notes: This table shows the ITT estimates for business safety feeling (1 not feel safe - 10 feel safe) for sub-samples of firms based on theftexposure in the baseline. The first column reports the coefficient estimate for the sub-sample of businesses which reported in the baselinethat they were exposed to external theft - over the last 12 months - and the second column for those which did not report any external theftin the baseline. Control Vector: Baseline values of feeling safe and ln(sales-winsorized), not reporting sales in baseline, ln(baseline employee#), use of LPN in baseline, enumerator FE, dummy variables for gender and business type and small business type. * p<0.1. ** p<0.05.*** p<0.01.

50

Table 9: ITT estimates for business finance

(1) (2) (3) (4)Panel A: External finance (Formal)

Mobile loans Mobile loans Bank loans Bank loans(Yes/No) (ln(Amount)) (Yes/No) (ln(Amount))

Treatment 0.06** 0.47** 0.01 0.07(0.03) (0.23) (0.02) (0.25)

Control Gr Mean 0.10 0.74 0.08 0.84Control Gr StDev 0.30 2.35 0.26 2.86

N 612 581 609 580

Panel B: External finance (Informal)

Trade credit Trade credit Informal Loan Informal Loan(Yes/No) (ln(Amount)) (Yes/No) (ln(Amount))

Treatment -0.02 -0.51 0.01 -0.03(0.04) (0.37) (0.02) (0.09)

Control Gr Mean 0.35 3.17 0.05 0.15Control Gr StDev 0.48 4.71 0.22 1.12

N 619 564 576 575Notes: This table shows the ITT estimates for financial access outcomes. Control Vector in each regression: Baseline values of each financialvariable and ln(sales-winsorized), not reporting sales in baseline, ln(baseline employee #), use of LPN in baseline, enumerator FE, dummyvariables for gender and business type and small business type. * p<0.1. ** p<0.05. *** p<0.01.

51

Table 10: Heterogeneous treatment effects for business finance

(1) (2) (3) (4)Panel A: External finance (Formal)

Mobile loans Mobile loans Bank loans Bank loans(Yes/No) (ln(Amount)) (Yes/No) (ln(Amount))

Treatment 0.04 0.38* 0.02 0.10(0.03) (0.23) (0.02) (0.26)

Small x Treated 0.28*** 1.88** -0.01 -0.27(0.11) (0.92) (0.11) (1.21)

Control Gr Mean 0.10 0.74 0.08 0.84Control Gr StDev 0.30 2.35 0.26 2.86

N 612 581 609 580

Panel B: External finance (Informal)

Trade credit Trade credit Informal Loan Informal Loan(Yes/No) (ln(Amount)) (Yes/No) (ln(Amount))

Treatment -0.03 -0.61 0.00 -0.08(0.04) (0.38) (0.02) (0.09)

Small x Treated 0.19 2.10 0.18* 1.01(0.15) (1.39) (0.10) (0.69)

Control Gr Mean 0.35 3.17 0.05 0.15Control Gr StDev 0.48 4.71 0.22 1.12

N 619 564 576 575Notes: This table shows the ITT estimates with heterogeneous treatment effects for financial access outcomes. Control Vector in eachregression: Baseline values of each financial variable and ln(sales-winsorized), not reporting sales in baseline, ln(baseline employee #), use ofLPN in baseline, enumerator FE, dummy variables for gender and business type and small business type. * p<0.1. ** p<0.05. *** p<0.01.A firm is classified as small in a respective sector if in the baseline it has # Employees<Median(# Employees). “Small x Treated” is theinteraction between being a small-firm in the baseline and being assigned to the treatment group. Regressions include the dummy variable“Small” on RHS.

52

Table 11: ITT estimates and heterogenous treatment effects for business sales

(1) (2) (3) (4) (5) (6)ln(Sales) ln(Sales) Sales Sales Sales Sales

Volatility Volatility Volatility Volatility[robust] [robust]

Treatment -0.04 -0.04 0.001 0.02 0.000 0.002(0.07) (0.07) (0.052) (0.05) (0.004) (0.004)

Small x Treated 0.13 -0.40* -0.037*(0.36) (0.21) (0.020)

Control Gr Mean 4.90 4.90 0.72 0.72 1.062 1.062Control Gr StDev 0.95 0.95 0.59 0.59 0.043 0.043

N 539 539 435 435 515 515Notes: This table shows the ITT estimates with heterogeneous treatment effects for sales outcomes. Control Vector in each regression:Baseline values of LHS variables, ln(sales-winsorized), not reporting sales in baseline, ln(baseline employee #), use of LPN in baseline,enumerator FE, dummy variables for gender and business type and small business type. * p<0.1. ** p<0.05. *** p<0.01. A firm is classifiedas small in a respective sector if in the baseline it has # Employees<Median(# Employees). “Small x Treated” is the interaction betweenbeing a small-firm in the baseline and being assigned to the treatment group. Sales Volatility is computed using the difference betweenln(Salesmax) and ln(Salesmin), where ln(Salesmax) and ln(Salesmin) stand for the maximum and the minimum sales, respectively,during a particular month over the last 12 months. The heterogenous treatment regressions include the dummy variable “Small” on RHSinstead of baseline employee #. Columns 5 and 6 perform a robustness check, where we fill missing information in the baseline survey onsalesmax or salesmin using average monthly sales data.

Table 12: ITT estimates and heterogenous treatment effects for business investment

(1) (2) (3) (4)Total Investment Total Investment Inventory Investment Inventory Investment

Treatment 5.30 4.78 1.51 0.96(6.71) (6.88) (2.62) (2.72)

Small x Treated 11.20 10.15(21.32) (7.38)

Control Gr Mean 32.44 32.44 14.14 14.14Control Gr StDev 75.32 75.32 32.79 32.79

N 525 525 584 584Notes: This table shows the ITT estimates with heterogeneous treatment effects for investment outcomes. Control Vector in each regression:Baseline values of each investment variable, ln(sales-winsorized), not reporting sales in baseline, ln(baseline employee #), use of LPN inbaseline, enumerator FE, dummy variables for gender and business type and small business type. * p<0.1. ** p<0.05. *** p<0.01. A firmis classified as small in a respective sector if in the baseline it has # Employees<Median(# Employees). “Small x Treated” is the interactionbetween being a small-firm in the baseline and being assigned to the treatment group. The heterogenous treatment regressions include thedummy variable “Small” on RHS instead of baseline employee #.

53

Online Appendix

54

Tab

leO

A1:

Var

iable

san

dth

eir

defi

nit

ions

use

din

the

regr

essi

onan

alysi

s

Var

iab

leD

efin

iton

Pan

elA

:V

alu

ati

on

Met

hod

sof

Use

for

bu

sin

ess

=1

ifth

eb

usi

nes

su

sep

erso

nal

mob

.m

on

eyin

the

last

30

day

s.m

obil

em

oney

use

Rec

eive

pay

men

ts=

1if

the

bu

sin

ess

rece

ives

pay

men

tsto

per

son

al

mob

ile

mon

ey.

Sto

rem

oney

=1

ifth

eb

usi

nes

sst

ore

mon

eyin

per

son

al

mob

ile

mon

ey.

Pay

bil

l=

1if

the

bu

sin

ess

pay

bil

lsvia

per

son

al

mob

ile

mon

ey.

Pay

inp

ut

=1

ifth

eb

usi

nes

sp

ayin

pu

tsvia

per

son

al

mob

ile

mon

ey.

Pay

sala

ries

=1

ifth

eb

usi

nes

sp

aysa

lari

esvia

per

son

al

mob

ile

mon

ey.

Sav

ing

inm

ob.

mon

.acc

ou

nt

=1

ifth

eb

usi

nes

ssa

ve

bu

sin

ess

reven

ues

inm

ob.

mon

eyacc

ou

nt.

%of

uti

lity

exp

.via

per

s.m

ob

.%

of

bu

sin

ess

uti

lity

exp

ense

sp

aid

via

per

son

al

mob

.m

on

inu

tili

tyex

pen

ses.

%of

inp

ut

exp

.via

per

s.m

ob

.%

of

bu

sin

ess

inp

ut

exp

ense

sp

aid

via

per

son

al

mob

.m

on

ein

inp

ut

exp

ense

s.P

ayin

gw

ages

via

mp

esa

=1

ifth

eb

usi

nes

spay

wages

toth

ep

erso

nal

M-p

esa

acc

ou

nts

of

work

ers.

No

incr

ease

inp

rice

s=

1if

the

bu

sin

ess

do

not

incr

ease

pri

cew

hen

cust

om

erp

ayvia

mob

ile

money

.T

hef

tT

hef

tan

dsa

fety

ind

exS

um

mati

on

of

inte

rnal,

exte

rnal

thef

t,an

dfe

elin

gn

ot

safe

vari

ab

les,

stan

dard

ized

.In

tern

alth

eft

=1

ifem

plo

yees

stole

som

eth

ing

from

the

bu

sin

ess

inth

ep

ast

6m

onth

s.E

xte

rnal

thef

t,fire

,et

c.=

1if

exp

erie

nce

dlo

sses

as

are

sult

of

fire

,th

eft,

rob

ber

y,et

c.in

the

past

6m

onth

s.F

eeli

ng

safe

Th

esa

fety

of

the

are

aw

her

eth

eb

usi

nes

sis

loca

ted

(1ve

ryu

nsa

fe-1

0sa

fe)

Bu

sin

ess

savin

gS

avin

gat

ab

an

kor

mic

ro.

=1

ifth

eb

usi

nes

ssa

ves

at

ban

kor

mic

rofi

nan

ceacc

ou

nt

acco

unts

Sav

ing

ata

per

s.b

an

kacc

.=

1if

the

bu

sin

ess

save

at

ap

erso

nal

ban

kacc

ou

nt.

Sav

ing

ata

bu

s.ban

kacc

.=

1if

the

busi

nes

ssa

veat

ab

usi

nes

sb

an

kacc

ou

nt.

Sav

ing

ata

mic

rof.

inst

.=

1if

the

bu

sin

ess

save

at

am

icro

fin

an

ceacc

ou

nt.

Pan

elB

:T

ran

spare

ncy

Not

rep

orti

ng

Not

shar

edsa

les

=1

ifth

eb

usi

nes

sh

as

not

share

dit

ssa

les.

bu

sin

ess

acti

vit

yN

otsh

ared

pro

fits

=1

ifth

eb

usi

nes

sh

as

not

share

dit

sp

rofi

ts.

Bu

sin

ess

lice

nse

Bu

sin

ess

lice

nse

=1

ifth

eb

usi

nes

sh

as

an

up

tod

ate

gov

ern

men

tb

usi

nes

sp

erm

it.

Fin

anci

alF

inan

cial

index

Su

mm

ati

on

of

ban

klo

an

,m

ob

ile

loan

an

dre

cord

sst

an

dard

ized

.so

ph

isti

cati

onB

ank

loan

=1

ifth

eb

usi

nes

sre

ceiv

eda

ban

cklo

an

inth

ep

ast

12

month

s.M

obil

elo

an=

1if

the

busi

nes

sre

ceiv

eda

mob

ile

mic

rom

on

eyp

rovid

erlo

an

inth

ep

ast

12

month

s.B

usi

nes

sre

cord

s=

1if

the

bu

sin

ess

keep

sb

usi

nes

sre

cord

s.S

ales

cred

itS

ells

oncr

edit

tocu

st.

=1

ifth

eb

usi

nes

ssa

veat

am

icro

fin

an

ceb

an

kacc

ount.

Pan

elC

:B

ehavi

ora

lP

rese

nt

and

Pre

sent

bia

s=

1if

bu

sin

ess

own

eror

man

ager

isp

rese

nt

bia

sed

.F

act

ors

futu

reb

ias

Fu

ture

bia

s=

1if

bu

sin

ess

own

eror

man

ager

isfu

ture

bia

sed

.C

ogn

itiv

eca

pac

ity

#of

dig

its

rem

emb

ered

maxim

um

nu

mb

erof

dig

its

rem

emb

ered

from

nu

mb

er.

Tru

stT

rust

infi

rst

tim

eT

rust

inp

eople

you

mee

tfi

rst

tim

e,1

(not

at

all

)-4

(com

ple

tely

).T

rust

incu

stom

ers

Tru

stin

cust

om

ers,

1(n

ot

at

all

)-4

(com

ple

tely

).T

rust

inco

urt

sT

rust

inco

urt

s,1

(not

at

all

)-4

(com

ple

tely

).T

rust

inm

ob.

mon

.co

mp

.T

rust

inm

ob

ile

mon

eyco

mp

an

ies,

1(n

ot

at

all

)-4

(com

ple

tely

).B

usi

nes

ssi

zeE

mp

loyee

sN

um

ber

of

tota

lem

plo

yes

s,in

logari

thm

s.S

ales

,in

log.

Sale

sin

the

past

month

,in

logari

htm

.P

rofi

t,in

log

Pro

fits

inth

ep

ast

month

,in

logari

htm

.

55

Tab

leO

A2:

Des

crip

tive

stat

isti

csof

the

vari

able

suse

din

the

regr

essi

ones

tim

atio

ns

All

Sam

ple

Ph

arm

acy

Res

tau

rant

Var

iab

leN

mea

nsd

Nm

ean

sdN

mea

nsd

Pan

elA

:V

alu

ati

on

Met

hod

sof

Use

for

bu

sin

ess

545

0.5

20.5

0251

0.4

20.4

9294

0.6

00.4

9m

obil

em

oney

use

Rec

eive

pay

men

ts543

0.3

40.4

7250

0.2

70.4

4293

0.4

00.4

9S

tore

mon

ey543

0.1

70.3

8249

0.0

90.2

8294

0.2

40.4

3P

ayb

ill

544

0.3

00.4

6250

0.2

60.4

4294

0.3

30.4

7P

ayin

pu

t544

0.3

70.4

8250

0.2

90.4

6294

0.4

30.5

0P

aysa

lari

es544

0.0

60.2

4250

0.0

60.2

4294

0.0

60.2

5S

avin

gin

mob

.m

on

.acc

ou

nt

542

0.1

00.3

0249

0.0

40.2

0293

0.1

50.3

6%

ofu

tili

tyex

p.

via

per

s.m

ob

.474

0.3

30.4

5225

0.3

40.4

6249

0.3

10.4

5%

ofin

pu

tex

p.

via

per

s.m

ob

.485

0.1

50.3

1224

0.1

40.2

8261

0.1

70.3

3P

ayin

gw

ages

via

mp

esa

538

0.0

50.2

3244

0.0

60.2

4294

0.0

50.2

1N

oin

crea

sein

pri

ces

546

0.1

30.3

3252

0.1

10.3

1294

0.1

50.3

5T

hef

tT

hef

tan

dsa

fety

index

537

0.0

01.0

0246

-0.1

10.8

5291

0.0

91.1

0In

tern

al

thef

t538

0.2

70.4

4247

0.0

70.2

6291

0.4

40.5

0E

xte

rnal

thef

t,fire

,et

c.545

0.1

00.3

0251

0.0

20.1

5294

0.1

70.3

7F

eeli

ng

safe

545

7.3

21.8

9251

7.0

61.8

6294

7.5

41.8

8B

usi

nes

ssa

vin

gS

avin

gat

ab

an

kor

mic

ro.

542

0.5

70.5

0249

0.5

30.5

0293

0.6

10.4

9ac

cou

nts

Sav

ing

at

ap

ers.

ban

kacc

.542

0.3

70.4

8249

0.2

90.4

5293

0.4

50.5

0S

avin

gat

ab

us.

ban

kacc

.542

0.2

00.4

0249

0.2

50.4

3293

0.1

50.3

6S

avin

gat

am

icro

f.in

st.

542

0.0

30.1

6249

0.0

20.1

5293

0.0

30.1

6P

an

elB

:T

ran

spare

ncy

Not

rep

orti

ng

Not

share

dsa

les

546

0.1

40.3

5252

0.2

10.4

1294

0.0

90.2

8b

usi

nes

sac

tivit

yN

otsh

are

dp

rofi

ts546

0.1

40.3

5252

0.2

10.4

1294

0.0

80.2

7B

usi

nes

sli

cen

seB

usi

nes

sli

cen

se538

0.7

10.4

6246

0.9

10.2

9292

0.5

40.5

0F

inan

cial

Fin

ance

ind

ex515

0.0

01.0

0229

0.0

40.7

7286

-0.0

31.1

5so

ph

isti

cati

onB

ank

loan

522

0.1

00.3

0234

0.0

60.2

5288

0.1

40.3

4M

obil

elo

an

517

0.1

00.3

1231

0.0

70.2

6286

0.1

30.3

4B

usi

nes

sre

cord

s546

0.8

70.3

4252

0.9

60.2

0294

0.7

90.4

1S

ales

cred

itS

ells

on

cred

itto

cust

.545

0.5

10.5

0252

0.4

50.5

0293

0.5

70.5

0P

an

elC

:B

eha

viora

lP

rese

nt

and

Pre

sent

bia

s543

0.1

20.3

3251

0.1

10.3

2292

0.1

30.3

4F

act

ors

futu

reb

ias

Fu

ture

bia

s543

0.1

50.3

5251

0.1

30.3

3292

0.1

60.3

7C

ogn

itiv

eca

pac

ity

#of

dig

its

rem

emb

ered

545

5.2

11.5

0252

5.4

41.4

8293

5.0

21.4

9T

rust

Tru

stin

firs

tti

me

543

2.1

10.8

5251

2.1

50.8

9292

2.0

70.8

2T

rust

incu

stom

ers

544

3.4

20.6

5251

3.4

20.6

1293

3.4

10.6

9T

rust

inco

urt

s544

2.4

00.9

4252

2.3

70.9

8292

2.4

20.9

1T

rust

inm

ob

.m

on

.co

mp

.544

3.5

80.6

4252

3.4

30.6

8292

3.7

10.5

7B

usi

nes

ssi

zeE

mp

loyee

s546

1.4

10.4

2252

1.1

90.2

7294

1.5

90.4

3S

ales

,in

log.

546

5.0

11.0

1252

4.7

40.8

9294

5.2

31.0

5P

rofi

t,in

log

546

3.8

40.9

6252

3.8

30.8

9294

3.8

51.0

2

Notes:

Th

ista

ble

sum

mari

zes

des

crip

tive

stati

stic

sfo

rth

evari

ab

les

for

regre

ssio

nan

aly

sis

pre

sente

din

Tab

les

3,

4,

an

d5.

Th

evari

ab

led

efin

itio

ns

are

giv

enin

Tab

leO

A1.

56

Tab

leO

A3:

Des

crip

tive

stat

isti

csfr

omen

dli

ne

surv

eyfo

ral

lb

usi

nes

ses

wit

hb

usi

nes

sli

cen

ses.

Var

iab

les

All

sam

ple

Contr

ol

Tre

atm

ent

Defi

nit

ion

NM

ean

Med

ian

NM

ean

Med

ian

NM

ean

Med

ian

Lip

aN

aM

-pes

au

seH

ave

Lip

an

aM

-pes

aac

cou

nt

(0/1

)=

1if

bu

sin

ess

hav

ea

regis

tere

dL

ipa

na

618

0.2

70

309

0.2

30

309

0.3

11

0M

-pes

aac

cou

nt

Use

dL

ipa

na

M-p

esa

acco

unt

for

bu

sin

ess

(0/1

)=

1if

bu

sin

ess

use

dL

ipa

na

618

0.2

49

0309

0.2

07

0309

0.2

91

0M

-pes

aac

cou

nt

over

the

last

30

day

sR

ecei

ved

pay

men

tvia

Lip

an

aM

-pes

a(0

/1)

=1

ifb

usi

nes

su

sed

Lip

an

aM

-pes

aacc

ou

nt

618

0.2

46

0309

0.2

04

0309

0.2

88

0re

ceiv

edp

aym

ents

over

the

last

30

day

sL

ipa

na

M-p

esa

sale

s,m

onth

ly(1

000

Ksh

.)T

otal

sale

sre

ceiv

edvia

Lip

aN

aM

-pes

a618

15.5

50

309

12.5

60

309

18.5

40

ata

typ

ical

month

div

ided

by

the

sale

sla

stm

onth

Man

age

men

tpra

ctic

esan

dsa

fety

Rec

ord

kee

pin

gvia

mob

ile

mon

ey(0

/1)

=1

ifth

eb

usi

nes

skee

ps

reco

rds

via

618

0.1

08

0309

0.1

0309

0.1

17

0p

erso

nal

mob

ile

mon

eyor

Lip

aN

aM

-pes

aN

oth

avin

gch

ange

(0/1

)=

1if

the

bu

sin

ess

exp

erie

nce

da

fore

gon

e618

0.2

54

0309

0.2

56

0309

0.2

52

0op

por

tun

ity

tose

llgood

sd

ue

ton

oth

avin

gch

an

ge

Saf

ety

(1n

otfe

elsa

fe-

10fe

elsa

fe)

Saf

ety

ofth

eare

aw

her

eth

eb

usi

nes

s618

7.0

26

7309

6.8

74

7309

7.1

78

8is

loca

ted

inte

rms

of

the

thre

ats

of

fire

,th

eft,

robb

ery,

etc.

Inve

stm

ent

an

dacc

ess

tofi

nan

ceC

apit

alin

ves

tmen

t,(1

000

Ksh

.)In

vest

men

tin

the

cap

ital

good

s599

10.1

40

301

9.2

33

0298

11.0

50

inth

ela

st6

month

sR

ecei

ved

loan

(0/1

)=

1if

the

bu

sin

ess

has

rece

ived

alo

an

from

605

0.2

23

0301

0.1

96

0304

0.2

50

info

rmal

ly,

or

thro

ugh

mob

ile

ab

ank,

orm

on

eyacc

ou

nts

inth

ep

ast

12

month

sB

ank

loan

(0/1

)=

1if

the

bu

sin

ess

rece

ived

alo

an

from

608

0.0

86

0303

0.0

76

0305

0.0

95

0a

ban

k.

inth

ep

ast

12

month

sIn

form

allo

an(0

/1)

=1

ifth

eb

usi

nes

sre

ceiv

eda

loan

from

605

0.0

55

0302

0.0

53

0303

0.0

56

0fr

ien

ds,

rela

tive

s,et

c.in

the

pas

t12

month

sM

obil

elo

an(0

/1)

=1

ifth

eb

usi

nes

sre

ceiv

eda

loan

thro

ugh

611

0.1

33

0305

0.1

02

0306

0.1

63

0m

obil

em

on

eym

on

eyco

mp

an

ies

inth

ep

ast

12

month

sB

usi

nes

ssi

zeS

ales

,m

onth

ly(1

000

Ksh

.)S

ales

over

the

past

month

538

213.3

120

266

218.8

135

272

208

120

Pro

fits

,m

onth

ly,

(100

0K

sh.)

Pro

fits

over

the

past

month

.530

66.9

946

265

68.8

345

265

65.1

448

Em

plo

yees

Nu

mb

erof

tota

lp

erm

an

ent

592

4.3

78

3299

4.4

05

3293

4.3

52

3an

dte

mp

ora

ryem

plo

yees

Notes:

This

table

sum

mari

zes

the

desc

ripti

ve

stati

stic

sfr

om

endline

surv

ey

for

all

busi

ness

es

wit

hbusi

ness

license

.

57

Tab

leO

A4:

Bas

elin

ech

arac

teri

stic

san

dbal

ance

test

for

the

busi

nes

ses

that

par

tici

pat

edin

the

endline

surv

eyan

dhav

ebusi

nes

slice

nse

Ph

arm

acy

Res

tau

rant

All

Con

tT

reat

Diff

All

Con

tT

reat

Diff

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Sta

ndard

mobi

lem

on

eyu

seU

sin

gm

ob

.m

oney

for

bu

sin

ess

pu

rpos

es(0

/1)

0.45

0.40

0.49

-0.0

90.

670.

650.

68-0

.03

Usi

ng

mob

.m

oney

tore

ceiv

ep

aym

ents

(0/1

)0.

270.

200.

34-0

.14*

**0.

490.

480.

50-0

.02

Usi

ng

mob

.m

oney

tost

ore

mon

ey(0

/1)

0.12

0.11

0.13

-0.0

20.

240.

290.

180.

11U

sin

gm

ob

.m

oney

top

ayb

ills

(0/1)

0.29

0.25

0.33

-0.0

70.

460.

440.

47-0

.03

Usi

ng

mob

.m

oney

top

aysa

lari

es(0

/1)

0.05

0.02

0.09

-0.0

7***

0.09

0.09

0.10

-0.0

1U

sin

gm

ob

.m

oney

top

ayin

pu

ts(0

/1)

0.34

0.33

0.36

-0.0

20.

500.

500.

500.

00A

ware

nes

sof

Lip

aan

dre

aso

ns

of

not

adopti

ng

Aw

are

of

Lip

aN

aM

-Pes

a(0

/1)

0.94

0.95

0.94

0.01

0.98

0.98

0.98

0.00

Hav

eL

ipa

Na

M-P

esa

(0/1)

0.05

0.02

0.09

-0.0

7***

0.23

0.25

0.20

0.05

Not

seei

ng

the

ben

efits

ofL

ipa

Na

M-P

esa

(0/1

)0.

260.

260.

250.

010.

190.

230.

150.

08T

oo

cost

lyto

open

an

acco

unt

(0/1

)0.

150.

170.

120.

040.

050.

030.

07-0

.04

Hig

htr

an

sact

ion

fees

via

Lip

aN

aM

-pes

a(0

/1)

0.24

0.23

0.24

-0.0

10.

090.

070.

11-0

.04

Don

’th

ave

tim

eto

open

anacc

ount

(0/1

)0.

070.

090.

050.

050.

190.

210.

170.

04W

ould

not

incr

ease

my

sale

s(0

/1)

0.10

0.09

0.11

-0.0

20.

080.

070.

08-0

.01

No

tru

stin

mob

ile

mon

eypro

vid

er(0

/1)

0.04

0.03

0.05

-0.0

20.

020.

030.

010.

02T

oo

com

ple

xto

use

(0/1

)0.

080.

080.

070.

010.

130.

090.

16-0

.06

Bu

sin

ess

size

loga

rith

m(S

ale

s,m

onth

ly(1

000

Ksh

.),

win

sori

zed

5%)

4.78

4.72

4.84

-0.1

25.

705.

755.

660.

09lo

gari

thm

(Pro

fits

,m

onth

ly,

(1000

Ksh

.),

win

sori

zed

5%)

3.88

3.82

3.94

-0.1

24.

324.

344.

290.

05lo

gari

thm

(Em

plo

yee

s)1.

211.

201.

22-0

.02

1.86

1.88

1.84

0.05

Inve

stm

ent

an

dacc

ess

tofi

nan

ceIn

vest

men

t(0

/1),

inth

ep

ast

6m

onth

s0.

210.

190.

22-0

.03

0.42

0.47

0.37

0.10

Ban

klo

an

(0/1

),in

the

pas

t12

month

s0.

080.

080.

080.

010.

090.

090.

10-0

.01

Info

rmal

loan

(0/1

),in

the

past

12

mon

ths

0.03

0.02

0.03

-0.0

10.

040.

040.

05-0

.01

Mob

ile

loan

(0/1

)in

the

past

12

mon

ths

0.11

0.11

0.11

0.00

0.09

0.09

0.09

0.00

Info

rmali

tyB

usi

nes

sli

cen

se(0

/1)

1.00

1.00

1.00

0.39

1.00

1.00

1.00

0.00

Notes:

This

table

sum

mari

zes

the

desc

ripti

ve

stati

stic

sfr

om

base

line

surv

ey

for

all

busi

ness

es

wit

hbusi

ness

license

and

who

rem

ain

ed

inth

eendline.

*p<

0.1

.**

p<

0.0

5.

***

p<

0.0

1.

58