Do Socio-economic Factors Drive Peer to Peer Lending?

Analysis in Indonesia

Rima Kusuma Rini

and Nanda Ayu Wijayanti

Graduate Program of Accounting, Universitas Indonesia, Depok, Indonesia

Keywords: Fintech Lending, Peer to Peer Lending, Loan, Borrowers, Social, Economic.

Abstract: This study aims to analyse the determinant of p2p lending by examine the social and economic factors to

accumulated loan disbursement. Since the increasing number of lender and borrower indicates the higher

potential to grow, this study examines the factor affected significantly in p2p lending transactions. The data

extracted from Financial Service Authority (OJK) report from 2019-2020 and The National Socioeconomic

Survey (Susenas) from Central Bureau of Statistics (BPS). The analysis method uses OLS regression to test

each of hypothesis. This study verified that there is relationship between social and economic factor especially

formal labour and high education with accumulated loan of borrower. The result leads the regulation and

fintech platform to make analysis of social-economic matter regards financial technology-based service. The

result of this study has significant implication to p2p lending theoretically since it analyses the social and

economic determinant factors by using the secondary data. Practically, the findings of the study could give

additional information to reach and engage the user by identify social and economic factors. Few studies

identified the determinant by using empirical analysis. Previous study mainly identified the determinant from

qualitative reviews and the majority of the studies employ interview to address the determinant factor.

1 INTRODUCTION

Financial technology is an application of digital

technology to solve financial intermediary problem

(Aaron, Rivadeneyra, & Sohal, 2017). As

intermediary for society around the world, it

impacted the new era of financial services (Milian,

Spinola, & de Carvalho, 2019). The use of technology

in financial services has significant growth since it

could increase the efficiency, reduce opportunity

cost, and encourage customer satisfaction by enabling

them to operate it flexibly as long as internet

connected (Thaker, Amin, Thaker, & Pitchay, 2019).

According to The Global Findex database (2017),

515 million adults worldwide opened an account

through mobile money provider between 2014-2017.

In developed economies about 94 percent have

account and developing economies about 63 percent.

It indicates that developing countries have quiet

significant number of people who have not account.

For those who have account, majority at bank,

https://orcid.org/0000-0002-1160-3371

https://orcid.org/0000-0002-1750-6783

microfinance institution, and another type of

regulated financial institution.

Globally, about 1.7 billion adults remain

unbanked-without an account at a financial institution

or through a mobile money provider (Asli Demirgu-

Kunt, 2017). Due to account ownership is nearly

universal in high-income economies, virtually all

these unbanked adults live in the developing

economies. Indeed, according to World Bank (2017)

nearly half live in just seven developing economies:

Bangladesh, China, India, Indonesia, Mexico,

Nigeria, and Pakistan.

Indonesia is developing countries which fintech is

growing rapidly (OJK, 2019). Besides, it has a

significant potential in strengthening economic

growth through the optimization the role of financial

technology as an intermediary between investors and

companies (Hendratmi, Ryandono, &

Sukmaningrum, 2019). Currently fintech business

models has funding, payments, wealth management,

capital markets, and services for insurance (Lee &

Shin, 2018). The popular one is funding business

102

Rini, R. and Wijayanti, N.

Do Socio-economic Factors Drive Peer to Peer Lending? Analysis in Indonesia.

DOI: 10.5220/0010744900003112

In Proceedings of the 1st International Conference on Emerging Issues in Humanity Studies and Social Sciences (ICE-HUMS 2021), pages 102-109

ISBN: 978-989-758-604-0

models since the online lending platform provide and

facilitate loans for small and medium enterprises

(SMEs) across the nation as archipelago country

(Suryono, Budi, & Purwandari, 2021).

Online lending platform or peer-to-peer (p2p)

lending is a platform that allowed the individual

investors provide microloans directly to individual

borrowers without the intermediation of financial

institutions (Lin, 2009). The regulation about p2p

lending referred to Financial Services Authority

(OJK) Regulation Number 77 / POJK.01 /2016 about

Information Technology-Based Lending Service

which regulates p2p lending services for all business

services. The main objective of the Law is to require

p2p lending providers to apply for OJK licenses to

operate p2p lending platforms. At the end of 2020,

the Financial Services Authority recorded 149

registered and licensed fintech companies (OJK,

2020). As of December 2020, the Investment Alert

Task Force (SWI) and Kemenkominfo (the Ministry

of Communication and Information Technology, also

known as the Ministry of ICT) have blocked 1.026

illegal fintech p2p platforms. This number decreasing

from 2019 about 1.493, but increasing from 2018

about 404 (Kontan, 2021). It indicates that the

number of illegal platforms still existed since the lack

of understanding particular of people about this

platform.

Borrowers come from various backgrounds and

most borrowers come from micro and SME (Small

Middle Enterprises) businesses which allows local

governments to believe that market mechanisms can

improve the economy. However, due to the

development of the business model and no previous

business configuration regarding p2p lending

platform owners, this has become a challenge for

regulators to consider existing regulations (Adriana

& Dhewantoa, 2018). Additionally, borrowers could

possibly use multiple platforms without

consideration of ability to pay. Therefore, the

majority of borrowers of p2p lending are unbanked

and un-bankable (Suryono et al., 2021).

According to that problem, it showed various

characteristics of user p2p lending that impacted to

the transactions issues. Hence, current study aims to

examine the social and economic determinant of p2p

lending. The determinants are reviewed based on

social and economic characteristics. In addition, this

study could lead as material for regulators and the

parties involved enable to determine the policy

directions and protect the rights of parties. Previous

study majority used a qualitative approach to explain

the determinant of funding in Indonesia by discussing

the issues, cases, and responsibility of p2p lending

(Sundjaja & Tina, 2019; Suryono et al., 2021). Then

study of Sitompul (2018) provides an analytical

description of the urgency of fintech legality,

especially peer to peer lending in Indonesia.

Another study identified and focus on behavioural

intentions in term of use p2p lending that diversified

characteristics such as gender, education background,

profession, job position, income, and expenditure

through purposive sampling by using questionnaires

(Darmansyah, Fianto, Hendratmi, & Aziz, 2020). In

addition, study Santoso, Trinugroho, and Risfandy

(2020) investigated the determinants of platform

interest rate and borrowers’ default status by

identified three sample of three samples of p2p

lending platform. In order to sum up everything that

has been stated, it indicates that previous studies still

focused on discussion from a legal perspective,

determinants factors by identified behavioural

intentions that using questionnaires, and

representative sample.

Therefore, to fill the gap this study provides

empirical method by using social and economic data

from National Socioeconomic Survey (Susenas) from

Central Bureau of Statistics (BPS) to demonstrate the

relationship between social and economic factor to

the number of loans in p2p lending.

Since National

Socioeconomic Survey origin from BPS, it could lead

the comprehensive multiple determinant analysis that

affected to the number of loan disbursement in fintech

lending. According to the research objective, the

research question is: how the social and economic

determinants affected p2p lending according to the

accumulated loan of borrower.

1.1 Literature Review

Practically, p2p lending contains information

asymmetry between the parties involved. Information

asymmetry is a fundamental problem in online p2p

lending. The challenge is to overcome the principal-

agent problem (Jensen & Meckling, 1976). On the

other hand, lenders want to get as much valid

information as possible about the borrower. The

borrower is potentially interested in hiding some of

his characteristics in order to get the lowest possible

interest rate. In order to enable the lender’s decisions

based on valid information, p2p lending platforms

encourage borrowers to provide financial information

that has been validated by external institutions. In

addition, many platforms require users to provide

demographic information, such as gender, race or age.

Borrowers are also often given the opportunity to

provide social information, which cannot be

Do Socio-economic Factors Drive Peer to Peer Lending? Analysis in Indonesia

103

validated, such as hobbies, family backgrounds or

photographs. This is a characteristic that determines

p2p lending because the determinant has a big

influence on the success of funding the list of

borrowers and the interest rate requested (Bachmann

et al., 2011).

1.1.1 Planned Behaviour Theory

Planned behaviour theory (TPB) emerged since it

proved to be efficient model to describe the

intentions, manage and control of perceived

behaviour and to predict behaviour (Hamzah &

Mustafa, 2019). Planned behaviour theory is

extension of reason action theory (TRA) that taking

degree of control over behaviour into account. Both

of them considered the determinant of intentions and

behaviour (Darmansyah et al., 2020). In the context

of this study, TPB could explained that characteristics

of borrowers such as education, gender, profession,

and other social conditions, drives their consideration

and intentions to use p2p lending.

1.1.2 Theory of Acceptance Model

Acceptance model theory (TAM) familiar model to

describe and predict of the system used (Chuttur,

2009). Then the development of acceptance model

theory frequently involved in behavioural intentions

as proxy that directly influenced by the benefit

recipients. As the study of Huei, Cheng, Seong, Khin,

and Bin (2018) who use TAM to identify the essential

factors that influence the intentions of costumer in

adoption of fintech products and service in Malaysia.

The result show that the perceptions ease the

borrower to adopt fintech. It also gives significant

positive effect of intentions towards product.

In term of social and economic factors such the

level of education and type of professions, TAM

could allow the borrower to have perceptions in order

to use p2p lending.

1.1.3 Hypothesis Development

Bachmann et al. (2011) emphasize the social capital

dimension such as structural, rational, and cognitive

that enable to the existence of entity or individual has

connected each other. Bachmann et al. (2011)

diversified the determinant in p2p lending by

financial, demographic, social capital and soft

factors. It showed the importance of characteristics

dimension of borrower. Besides, Darmansyah et al.

(2020) also demonstrated the behavioural intentions

to use fintech by diversified characteristics of

borrower such as gender, education background,

profession, job position, income, expenditure.

According to that research, this study

demonstrated social and economic factors by

diversified to Job, Gender according to Human

Development Index and Expenditure per capita,

Number of Non-bank institution, and Education that

might affected to the accumulated loan of borrower in

p2p lending.

a. Job Characteristic

Job characteristics explain the type of (1) micro and

small labour or (2) formal labour. Both type of labour

showed the differences of background which could

strongly associated to p2p lending (Darmansyah et al.,

2020). Possibly, micro and small labour are un-

bankable hence this situation leads them to use fintech

lending as the alternative. Due to the situation,

according to Theory of accepting model, it could

possibly that micro and small labour has positive

relationship to accumulated loan borrower in fintech

lending. Hence, the hypothesis:

H1a: Micro and small labour has positive

relationship to accumulated loan of borrowers

Then, formal labour also could possibly access

fintech lending since the requirement not complicated

as conventional institutions. Hence, this opportunity

allows them to have more intentions to easily utilize

the platform to fulfil consumptive behaviour for

example. According to theory of acceptance model,

this situation could lead borrower to have behavioural

intentions since they could receive benefits easily.

Hence the hypothesis:

H1b: Formal labour has positive relationship to

accumulated loan of borrowers

b. Gender

According to report of Global Financial Index (2017),

about 56 percent of all unbanked adults are women.

Women are overrepresented among the unbanked in

economies where only a small share of adults are

unbanked, such as China and India, as well as in those

where half or more are, such as Bangladesh and

Colombia (Asli Demirgu-Kunt, 2017).

In another side, according to Pope and Sydnor

(2011) and Barasinska (2009), women has less

interest and motives rather than men about lending

and borrowing money.

According to that problem, gender in term of this

study examined by identify human development

index and expenditure since it could reflect the

knowledge, empowerment, behaviour, and the roles.

Therefore, supported by planned behaviour theory

ICE-HUMS 2021 - International Conference on Emerging Issues in Humanity Studies and Social Sciences

104

they could directed the intentional behaviour towards

the woman and man, the hypothesis:

H2a: Human development index woman has

positive relationship to accumulated loan of

borrowers

H2b: Human development index man has positive

relationship to accumulated loan of borrowers

H2c: Expenditure woman has positive

relationship to accumulated loan of borrowers

H2d: Expenditure man has positive relationship to

accumulated loan of borrowers

c. Number of Non-bank Institutions

Non-bank institutions in this study using the number

of cooperatives. Before the significant growth of

fintech, we know that Indonesia has cooperative as

non-bank to ease the society based on populist

economic. Unfortunately, 30 percent of cooperative

in Indonesia have been inactive for various reason

(Azhari, Syechalad, Hasad, & Shabri, 2017).

Therefore, this study attempt to examine of the

existence of cooperative in era of fintech lending.

Hence the hypothesis is:

H3: The lower of total cooperative has positive

relationship to accumulated loan of borrowers

d. Education

According to Darmansyah et al. (2020), the user of

fintech lending is dominated by bachelor degree level

users. It means that, the high education people have

more access and knowledge about fintech lending. It

also supported by planned behaviour theory that high

education people lead them to have perception

towards fintech lending. Hence the hypothesis:

H4: High education has positive relationship to

accumulated loan of borrowers

2 METHODS

This study demonstrated the relationship of social and

economic factors to accumulated loan of borrowers

of fintech lending in Indonesia. Therefore, the sample

is all of fintech lending in Indonesia in 34 province

that captured and extracted from statistic fintech

lending from Financial Authority Services (OJK)

from 2019-2020. Since the year of 2018 does not

have complete data, we excluded the year of 2018.

Additionally, the data of accumulated loan of

borrowers (Billion IDR) as dependent variable are

available in monthly and annually report. Then, the

independent variable are social and economic factors

extracted from Central Bureau of Statistics (BPS).

Therefore, the final observations of this study consist

of 34 provinces in 2 year resulted 68 province-year

observations with balance panel data.

This research analysis adopted to Stern, Makinen,

and Qian (2017) that using three angles of p2p lending

platform, that are number of p2p platforms, lenders’

average yield of p2p loans, and outstanding balance

of p2p. Consequently, this study developed from one

of them by using the accumulated loan disbursement

(borrowers). Then, this study also consider the model

of Darmansyah et al. (2020) and Bachmann et al.

(2011) in order to identified the determinant of p2p

lending from social and economic aspects such as

demographic, gender, education background,

profession, job position, income, and expenditure.

Therefore, the independent variables in this analysis

are social and economic factors that diversified into 4

characteristics:

(1) Job Characteristics

Job characteristics identified by two type of labour

that are total micro and small labour each province

and formal labour each province;

(2) Gender

Gender diversified into woman and man according

two proxies that are human development index (in

percentage) and expenditure (in thousands IDR);

(3) Number of Non-bank Institutions

The number of total cooperatives in each province (in

unit);

(4) Education

Education proxy use the number of high education

enrolment each province (in percentage).

Then as control variable, this study uses the gross

domestic regional product since it reflected the gross

additional value in economic sectors each province.

GDRP calculated by distribution of GRDP each

province to total GDRP in 34 provinces at current

price. GDRP shows the capability from various

economic resources that produced in each province.

In order to demonstrate the hypothesis

development, the equations model of this study:

AccumLoan

= β

+ β

MicroLabour +

FormLabour + β

Hdpwoman + β

Hdpman

+ β

Expwoman + β

Expman + β

Tcoop +

hEdu + β

Gdrp + ɛ

(1)

Descriptions:

AccumLoan : Accumulated Loan (Borrower)

MicroLabour : Micro and Small Labour

FormLabour : Formal Labour

Hdpwoman : Human Development Index

(Woman)

Do Socio-economic Factors Drive Peer to Peer Lending? Analysis in Indonesia

105

Hdpman : Human Development Index (Man)

Expwoman : Expenditure per Capita (Woman)

Expman : Expenditure per Capita (Man)

Tcoop : Total Cooperatives

HighEdu : High Education

Gdrp : Gross Domestic Regional Product

3 RESULT AND DISCUSSIONS

According to descriptive statistics in Table 1, the

average of accumulated loan of borrower is 3.491,178

Billion with maximum loan is 45.768 billion Rupiah

during two years. Then total micro and small labour

show 279.554 people in average and 41,20% of

average formal labour from all around province. For

second characteristics, human development index

and expenditure show that men have higher average

than woman with value 75,24% for human

development index and 15.207,19 thousand Rupiah

for expenditure respectively. The number of high

education participation show the average percentage

is 44,55 while GDRP show 2,94% average from all

around province in Indonesia. It showed that about

44,55% people each province has access and

participation to high educations, then about 2,94% the

average distribution of each province in Indonesia to

GDRP in 34 provinces.

In order to ensure this analysis is best linear

unbiased estimation and prediction, we demonstrated

several statistical tests. According to thhe result of

Pair-wise Correlation Matrix in the Table 2 show

there is no correlation above 0,8. It indicates that no

multicollinearity problem. This model also does not

have autocorrelation problem since the value is 1,000.

In another side, according to Breusch-Pagan/ Cook-

Weisberg test, it detected had heteroskedasticity

problem, hence to solve this problem we use robust

standard error. Additionally, we have stages of test to

seek the appropriate model. We demonstrated

Hausman test, Chow test, and Lagrange Multiplier

test before demonstrated main test. The result

indicated to use common effect.

First, the result of main test in the table 3 show

that job characteristic especially formal labour has

positive significant with t value 0,039 in 5%. This

result indicates that formal labour has positive

relationship to accumulated loan of borrower in

fintech lending. In another side, micro and small

labour show negative coefficient. The result

emphasize that H1a not supported to hypothesis and

H1b supported to hypothesis. according to the result,

it might formal labour have more access and

knowledge toward fintech lending that enable their

behavioural intension to use the platform. Formal

labour has guarantee explicitly that allow them to

borrow money in fintech lending.

Second, as gender characteristics has 2 proxies in

the table 3, both human development index and

expenditure show the result not supported the

hypothesis. The result indicates there is not positive

significant relationship both woman and man with

great human development index and high expenditure

to accumulated loan of borrower in fintech lending.

This result inconsistent with the previous researches

(Bachmann et al., 2011; Barasinska, 2009).

According to the result H2a, H2b, H2c, H2d not

supported the hypothesis.

Third, according to number of non-bank

institutions in the table 3 show that the lower number

of total cooperatives in province has positive

relationship to accumulated loan of borrower in

fintech lending but not significant. It could indicate

that the role of cooperative still existed especially in

particular province since not all populations familiar

to use platform fintech lending. This result means not

supported the H3.

Fourth, education characters show that people

who has high education has positive relationship to

accumulated loan of borrower in fintech lending. It

could be supported the planned behaviour theory

since the educated people allow them to have

perception that could drive their intentions include

financial propensity. This result means H4 supported

the hypothesis.

The last, result of control variable show that

GDRP has positive significant to accumulated loan of

borrower in fintech lending. GDRP reflected the

consumption and production in the province, hence it

impacted to the accumulated loan. It also emphasized

that, in order to funding the various production in

economic sectors or household consumption, fintech

lending is an alternative of funding in each province.

Besides, the household with fewer assets and lower

income facilitating for using fintech lending

especially in consumption (Li, Wu, & Xiao, 2020).

Additionally, to check the robustness of the result,

this study attempt to use another measurement of

dependent variable from accumulated loan

disbursements to accumulated transaction according

to number of account (borrower). The result show

consistent with the main test that only formal labour

and high education has positive significant

relationship to accumulated transaction account of

borrower.

ICE-HUMS 2021 - International Conference on Emerging Issues in Humanity Studies and Social Sciences

106

Table 1: Descriptive Statistics.

Variable Obs Mean Std. Dev. Min Max

Accumloan 68 3491,178 8660,987 36,680 4,58e+07

Microlabo

68 279554,5 504299,4 11953 2380673

Formlabo

68 41,204,26 10,3767 20,080 70,430

woman 68 67,95706 4,799316 52,520 79,170

Hdpman 68 75,235 3,472995 65,990 83,660

woman 68 8670,353 2531,631 3999 17087

Expman 68 15207,19 2644,217 10455 22912

Tcoo

68 -3679 4308,265 -2464 -476

Highedu 68 44,545 0,0353 44,51 44,580

Gdr

68 2,941029 4,230104 0,250 17,560

Source: Data processed, 2021

Table 2: Pair-wise Correlation Matrix.

Variable

Accum

loan

Micro

labo

Form

labo

Hdp

woman

Hdpman Tcoop

Capex

woman

Capex

man

Highedu

Reg

Gdp

Accumloan 1.000

Microlabor 0,2335 1.000

Formlabor 0,1717 -0, 0161 1.000

Hdpwoman 0,1744 0,0736 0,2969 1.000

Hdpman 0,1893 0,0531 0,3835 0,4480 1.000

Tcoop -0,2616 -0,4766 -0,001 -0,0083 -0,0712 1.000

Expwoman 0,2211 -0,0875 0,269 0,4401 0,3722 -0,0932 1.000

Expman 0,2108 0,0511 0,3834 0,3564 0,4023 -0,0692 0,3784 1.000

Highedu 0,0637 0,0020 -0,1074 0,0016 0,0045 -0,0070 -0,0188 -0,0313 1.000

Gdrp 0,4272 0,3446 0,1762 0,1880 0,2127 -0,3734 0,2296 0,2427 -0,0000 1.000

1-tailed result, Source: Data processed, 2021

Table 3: Relationship of Social-Economic Factors to Accumulated Loan Disbursement 2019-2020.

Variables

Accumulated Loan

Coef, t P>

Microlabo

-1,863619 -0,71 0,239

Formlabo

141,2326 1,79 0,039**

Hdpwoman 147,8934 0,59 0,277

man -483,7818 -1,51 0,068*

Expwoman 417,6715 0,81 0,209

man -590,0066 -2,20 0,016**

Tcoop 323,8104 0,64 0,262

hedu 39597,33 2,38 0,010***

Gdrp 2196,662 4,88 0,000***

Cons -1,74e+09 -2,36 0,011**

1-tailed result, Source: Data processed, 2021

Table 4: Robustness Test.

Variables

Accumulated Transaction

Coef, t P> |t|

Microlabo

-1,50959 0,370

Formlabo

176,781 0,093**

Hdpwoman 272,000 0,269

man -651,008 0,108

Expwoman 412,874 0,337

man -773,158 0,059*

Tcoop 556,069 0,285

hedu 80156,4 0,005***

Gdrp 3022,88 0,003***

Cons -3,54e+09 0,005***

1-tailed result, Source: Data processed, 2021

Do Socio-economic Factors Drive Peer to Peer Lending? Analysis in Indonesia

107

4 CONCLUSIONS

This study aimed to examine the social and economic

determinant of p2p lending. The determinants are

reviewed based on social and economic

characteristics diversified into 4. That are job, gender,

number of non-bank institutions (cooperatives), and

high education.

According to the test, it shows that only H1b and

H4 supported the hypothesis. This result manifested

that formal labour has positive significant to

accumulated loan, so does high education. Both

formal labour and high education indicates the similar

value of information since it reflected the level of

populations. Hence, according to planned behaviour

and acceptance model theory, it possibly reason of

action that leads them have intentional and perception

to use the platform of fintech lending. Besides, in

order to access financial technology-based activity, it

requires them to have advance knowledge to operate

and understand the platform as well. This result

consistent with Najaf, Subramaniam, and Atayah

(2021) that found most of borrowers of p2p lending

affected by their background such as the higher level

of annual income and employment rate. It

emphasized that the formal labour which has higher

level income than micro and small labour.

Additionally, high educated borrower possibly

dominated the loan to funding their enterprise or fulfil

the consumptive behaviour without any complicated

requirement in traditional institutions for instance.

The findings of this study have several

implications. Theoretically, this study contributes to

further expand study that identify of determinant of

fintech lending especially by using secondary data

that available from BPS. Since this data origin from

national survey, we could match them to the trend of

fintech lending platform and attempt to demonstrate

multiple factors that could be impact to the loan

disbursement. Practically, this study could useful for

regulator and stakeholder since it provides additional

information and analysis especially formal labour and

high education people that affected to the number of

transactions. The phenomena could lead the further

development of platform to reach the non-high

education people and micro and small labour.

This study has several limitations that could lead

the future research. First, the gender factor needs to

explore comprehensively since it only uses two proxy

(human development index and expenditure), hence

future research could explore another variable to

describe gender such as the proportion of man and

woman labour in each province. Second, the non-

bank institutions only use the numbers of cooperation

in each province, therefore future study could

examine another non-bank institution such as

microloan organizations and venture capitalists.

Third, since this study only employ empirical method,

future study could demonstrate additional analysis by

using interview to stakeholders.

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