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Farmers’perception and
preference of Islamic Banking
in Turkey
Mehmet Bulut and Harun Celik
Istanbul Sabahattin Zaim Universitesi, Istanbul, Turkey
Abstract
Purpose –The purpose of this paper is to examine the factors that influence farmers’preference for the use of
Islamic banks in Turkey and to investigate their knowledge level and perception about Islamic finance.
Design/methodology/approach –Survey data used in this study is obtained by drawing a sample of 1902
farmers who are members of the Agricultural Credit Cooperatives Union (ACCU) from 37 provinces of Turkey.
Pearson’s Chi-square test is used to analyze the association between the demographic features of farmers,
conventional bank usage and Islamic bank usage. Binary logistic regression model is used to estimate the
factors influencing the preference for Islamic banks. Explanatory variables include knowledge on Islamic
banking and finance, perception of compliance to religion, saving ability and cost concern along with the
control variables of Islamic bank branch number in the region and age of respondent. Robustness check is
conducted via alternative models using ordinary least squares (OLS) and logistic regression.
Findings –Less than 10% of the participant farmers use Islamic banks and 59% declare they know nothing
about Islamic banking. Age, education level, income level, nonagricultural income level, saving ability, duration
of working in agriculture, land size and region are significantly related to farmers’preference of using Islamic
banks. Knowledge level, perception of religious compliance, saving ability and cost concern are statistically
significant factors that influence the probability of using Islamic banks.
Research limitations/implications –This study does not include the analysis of the relationship between
being religious and using Islamic banks because questions related to the assessment of religious practice were
excluded due to the ACCU’s sensitivity to investigate personal beliefs. Therefore, future studies can expand the
scope of this research by investigating religiousness. The sample is chosen from the ACCU members who are
already benefiting from a formal source of credit; therefore, the results should not be attributed to all farmers.
Practical implications –Islamic banks and microfinance institutions’further engagement in the
agricultural sector and ACCU’s implementation of Islamic finance instruments.
Social implications –Islamic banks’further diversification in the agricultural sector and ACCU’s
implementation of Islamic finance instruments.
Originality/value –To the best of the authors’knowledge, this paper is the first to investigate the farmers’
perception and preference of Islamic banking in Turkey. The sample size of 1902 is much larger and
geographically diversified compared to studies in agricultural finance. This study will be valuable for the
agricultural finance empirical studies in Turkey as well as an important addition to the emerging literature on
Islamic finance.
Keywords Agricultural credit, Islamic finance, Logistic regression, Turkey
Paper type Research paper
1. Introduction
Turkey is one of the leading agricultural producer countries in the world in terms of value
added with constant prices. As of the end of 2019, Turkey, with 89.2bn dollars, ranked #7
after China, India, the USA, Indonesia, Nigeria and Brazil. Taking into consideration
Turkey’s much less population and arable land compared to the competitors, its agricultural
production performance is remarkable. The average annual growth rate of agricultural
production in Turkey between 1999 and 2019 was calculated to be 2.6%. The share of
agriculture in the GDP has been decreasing in Turkey over the years from 14.5% in 1997 to
6.4% in 2019 [1] in line with the general trend for the whole world as well as the upper middle-
income countries Turkey belongs to. However, agriculture sector still constitutes 18.2% of
employment with 5.1m employees (TURKSTAT, 2020), which makes it significant for the
Preference of
Islamic
Banking in
Turkey
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0002-1466.htm
Received 25 February 2021
Revised 12 May 2021
22 October 2021
Accepted 22 October 2021
Agricultural Finance Review
© Emerald Publishing Limited
0002-1466
DOI 10.1108/AFR-02-2021-0022
social and economic welfare of the country. Agriculture sectoral credits amounted to
$18.94bn, which is approximately 4% of the total credits of the banking sector in Turkey as of
the end of 2019 (BRSA, 2020).
Numerous empirical studies are identifying the significant positive impact of agricultural
credits on agricultural production in Turkey (Duramaz and Tas, 2018;Yalcinkaya, 2018;
Bahsi and Cetin, 2020). On the other hand, Terin et al. (2014) suggest that agricultural
production Granger-causes agricultural credits –not the other way around. Sahin (2020) finds
no significant relationship between the share of agricultural credits in total credits and the
share of agricultural production in total GDP in Turkey.
Ziraat Bank (“Agriculture Bank”in Turkish) is the first national bank in Turkey and the
largest bank today with asset size exceeding $121bn. The bank was established in 1888 based
on the experience of the preceding agricultural credit institution, which was found in 1863
during the Ottoman Period (Quataert, 1975). Since then, agricultural finance has been
provided predominantly in conventional banking manners. Ziraat Bank and private banks
provide most of the agricultural credits in Turkey (more than 90%) alongside agricultural
cooperatives as the formal source of credit (Gunes and Movassaghi, 2017). Agricultural Credit
Cooperatives Union (ACCU) is the umbrella organization of 1,625 local cooperatives
countrywide with its 810,000 members/partners.
There were 53 banks in Turkey in 2019 licensed according to the Banking Law no. 5411
under three categories: 34 deposit banks, 13 development and investment banks and 6
participation banks. Deposit banks and development and investment banks operate as
conventional banks, whereas the participation bank is a name given to the Islamic banks
(IBs)/interest free banks in Turkey (Genc et al., 2016). In line with the commonly used
terminology in the literature, the term Islamic banks (IBs) will be used in this study.
The asset size of global Islamic finance industry is estimated as $2.44 trillion (IFSB, 2020).
Islamic banking emerges from the Shari’a (Islamic law), which restricts riba (interest), gharar
(transactions involving uncertainty or speculation) and businesses associated with particular
forbidden products or activities such as alcohol or gambling (Khan, 2012). Major Islamic
financial instruments that can be used in the agricultural sector are Murabaha, Muzara’a,
Salam and Ijara where:
(1) Murabaha is selling goods at a purchase price plus a profit margin (Haron et al., 1994),
which can be used to finance agricultural inputs such as seeds, fertilizer, pesticides
and so on.
(2) Muzara’ais a partnership in agriculture where parties invest in an agricultural
enterprise or specific operation and the output is shared (Gulaid, 1995), which can be
used for agricultural projects.
(3) Salam is an advanced sale of commodities with deferred delivery (Islahi, 2018), which
can be used to finance the production of agricultural commodities.
(4) Ijara is a financial lease contract applicable to lease equipment in agriculture (Fianto
et al., 2019).
The principles of Islamic finance in theory and the historical practice revolve around trade
and agriculture, where several modes of Islamic financing such as Murabaha and Salam have
been implemented by Muslims since the early ages of Islam in the 7th century, AD (Islahi,
2018). Since the inception of Islamic banking in Turkey in 1984, IBs still have a marginal
share in the banking sector with a 5.1% share in total credits. Moreover, the share of IBs in
agricultural credits in Turkey is even smaller with a 1.9% share. The share of agricultural
credits in total credits of the whole banking industry in Turkey is 4.0%, while the share of
agricultural credits in IBs’credit portfolio is 1.5% (Table 1).
AFR
IBs’growth has been triggered with the support of the Islamic finance friendly
government, which has an ambitious target to increase the share of IBs in the banking sector
to 15% by 2025. In this regard, the underperformance of the IBs in agricultural credit market
is a genuine topic of interest.
In this study, we will try to understand Turkish farmers’preference of the IBs.
Specifically, we investigate the factors that affect the farmers’usage of IBs in Turkey by
using quantitative research methodology. The originality of this research stems from the
unique dataset that was compiled from the original survey collection from farmers and what
it fills the gap in the literature in terms of Islamic bank prevalence and farmers’perception
and preference. The paper will be structured as follows: Section 2 will provide a literature
review and a theoretical framework; Section 3 will be on the data, empirical strategy and
model; Section 4 will discuss the findings, and finally Section 5 concludes the study.
2. Literature review and hypothesis
There is a broad literature on the determinants of customers’preference for banks including
convenient service fees as well as demographic and economic factors such as gender, age,
income level, occupation (Kaynak and Harcar, 2005); safety, size and strength of the bank,
accuracy, general service quality, speed of delivery, proximity, cordiality of staff, price and
service charges, product packaging, general public impression (Seshaiah and Narender,
2007); accessibility, assurance, reliability, convenience, responsiveness and value-added
services (Saleh et al., 2013).
Similarly, patronizing IBs depends on factors such as fast and efficient services, speed of
transactions, friendliness of bank personnel, confidentiality, reputation and image of the
bank (Haron et al., 1994). Other factors can be listed as efficiency, banking services and
confidence (Rashid and Hassan, 2009); profitability and low service charges, demographic
features (Awan and Bukhari, 2011). Moreover, friendliness of staff, interest-free nature,
efficient services, financial counseling by the staff (Genc et al., 2016); cost/benefit, service
delivery, size and reputation (Ahmad and Haron, 2002) are among the significant factors for
IB preferences. Religious and financial reputation, quality service, social responsibility
service practices, convenience and cost (Dusuki and Abdullah, 2007) are taken into
consideration by prospective customers when they make their banking decisions.
Sumer et al. (2018) analyzed the reasons for not preferring to use IBs and argued that high
costs, unavailability of direct lending, insufficiency of products, branches and ATMs are the
main reasons. These findings are based on empirical studies, which are conducted with the
customers of certain banks, reflecting the banking relationship experience of the participants.
However, in our study the participants are not exclusively bank customers; therefore, we
have excluded specific factors related to standard banking service quality in our study.
Share in Islamic
banks’total credits
Share in banking
sectors’total credits
Sector’s share
in GDP
Islamic banks’share
in total credits
Manufacturing 29.0% 19.9% 18.3% 7.5%
Trade 19.2% 12.5% 12.4% 7.9%
Construction 17.4% 8.8% 5.4% 10.2%
Agriculture 1.5% 4.0% 6.4% 1.9%
Total 5.1%
Source(s): BRSA (2020) and TURKSTAT (2020)
Table 1.
Selected sectoral
credits’share in
Turkish banking
industry and sectors’
share in GDP-2019
Preference of
Islamic
Banking in
Turkey
With regard to the relationship between religious factors and IB preference, there are two
major views in the literature: Several studies identify religion related factors such as
compliance to religion, religious concerns, piety, religiousness level as dominant factors for IB
preference (Metawa and Almossawi, 1998;Khan, 2012;Amin et al., 2013;Polat et al., 2014;
Okumus
¸, 2015;Do
gan, 2020). Al-Ajmi et al. (2009) suggest that religious tendency and
perception with social responsibility are major elements that shape the bank selection of
customers. Moreover, they find that customers prefer more Islamic finance if they think the
products/services comply with Islamic law (shari’a).
On the other hand, several studies are highlighting nonreligious factors such as low costs,
customer satisfaction and service quality as affecting the preference for IBs (Haron et al.,
1994;Dusuki and Abdullah, 2007;Rashid and Hassan, 2009;Awan and Bukhari, 2011;Ozsoy
et al., 2013;Saleh et al., 2013;Genc et al., 2016;Aysan et al., 2018;Halis and Cengiz, 2019).
Recent studies show that the awareness of Islamic banking and Islamic finance literacy level
is low in Turkey (Er et al., 2015;Er and Mutlu, 2017;Comlekci, 2017;Durmus and
Yardimcioglu, 2018;Dogan, 2020). Even the empirical studies conducted with the customers
of IBs reveal a low level of awareness of common Islamic finance instruments such as
Murabaha and Mudaraba (Genc et al., 2016) and Ijara (Okumus, 2005). Islam and Rahman
(2017) suggest that there is a significant level of lack of understanding and lack of information
on how IBs work in India.
The perception of the compatibility of Islamic finance with the rules of Sharia is among the
factors affecting IB usage. According to Halis and Cengiz (2019)’s study conducted with bank
customers, some Muslims consider Islamic banking as only a matter of branding, only 5.8%
of the participants strongly disagree with the statement that IBs are not 100% Sharia
compatible and the ratio of participants who are very well or well knowledgeable about
Islamic finance is less than 15%. Bekereci et al. (2018) argue that belief tendency and financial
application dimension are positively related to religiousness.
The determinant factors of credit/banking preferences of the farmers include low-interest
rates, convenient tenor, flexible repayment structure, customer loyalty (G€
unes
¸and
Movassaghi, 2017); land size, cooperative membership, age (Hayran and G€
ul, 2018); saving
size, expenditure size (Erdas
¸and Oraman, 2016); trust and convenience (Koçt€
urk et al., 2013);
easiness of procedures (€
Unl€
uer and G€
unes
¸, 2016); household size, enterprise size (Kusek et al.,
2017); the number of land pieces, number of tractors (Akdemir et al., 2019); enterprise size, land
ownership ratio (Abay et al., 2017); expertise and market alignment (Kızılaslan and Adıg€
uzel,
2007); land size, capital size and income level (Gunes et al., 2016).
Major issues farmers face while using agricultural credits include insufficient credit
amount, high interest rates (Fırat and Çiçek, 2000); high interest rates, bureaucracy and
lengthy procedures (Kızılaslan and K€
oksal, 2002); insufficient finance (Tanrıvermis
¸and
Bayaner, 2006;Yahuza, 2018); efficiency, tenor and leverage ratio (€
Unl€
uer and G€
unes
¸, 2013).
Although the average annual total Interest (Profit Share) Income/Interest (Profit) Bearing
Assets Average (%) ratio for the period 2005–2019 is almost the same for the IBs and the
whole banking sector in Turkey [2], Cetin (2017) finds that the commercial credits of the IBs
are 72 bps cheaper compared to CBs while the housing loan rates of IBs are 92 bps higher.
Whether the IBs are costlier than the CBs or not, the perception of the costliness matters as it
can become the major reason for not using the IBs (Sumer et al., 2018).
With respect to the role of Islamic financial institutions inagricultural finance, Kocturk et al.
(2013) argue that the farmers do not consider IBs as a significant credit source. Meutia and
Mohamad (2019) compare the effectiveness of financing by conventional and Islamic banks,
and they assert that agricultural productivity does not depend on bank type and loan amount.
Obaidullah (2015) explores the effect of Islamic microfinance institutions on the farming
communities and claims that composite models involving the integration of not-for-profit and
for-profit components in Islamic microfinance institutions would provide a solutionfor the rural
AFR
poor. Fianto et al. (2019) posit that demographic and socioeconomic factors affect the rural
household for accessing finance provided by Islamic microfinance institutions in Indonesia and
find that experienced and high-socioeconomic level farmers are more likely access to finance.
Shafiai and Moi (2015) study the possibility of collaboration of Islamic agricultural finance in
agricultural land development and suggest that Islamic agricultural finance can play a positive
role for the farmers and eventually promote agricultural development in Malaysia.
Some research in the literature focuses on the specific Islamic finance tools and their
efficiencies on the agricultural production, alleviation of the poverty and improvement on the
Islamic bank usage. Munezza et al. (2011) examine the theoretical nature of the Salam contract
in-depth and suggest that a feasible banking product based on a Salam contract could be
formulated to help poor farmers in Malaysia. Hossain et al. (2019) examine Zakah and Salam
mechanisms, and they find that these mechanisms may decrease the income volatility of local
farmers, increase access to new market channels, food insecurity and improve fair price
conditions in Bangladesh. Kaleem and Wajid (2009) conduct a similar research to explore the
possible application of Salam contract as an alternative financial instrument in the agriculture
sector of Pakistan. Saeed et al. (2013) compare the agricultural finance products of four
conventional banks and one IB in Malaysia, and Yahuza (2018) focuses on Muzara’a and argues
that this product could be instrumental in matching the excess land of the landowners and the
need for land.
The theoretical framework of this study is based on the literature review in three different
areas, namely banking preference factors, Islamic banking preference factors and farmers’
credit usage factors. We assumed that the Islamic banking preference of farmers is affected
by demographic and economic factors. Although there is no evidence from the literature on
the impact of religious factors or knowledge level on farmers’credit usage from IBs, we
assumed that those factors would play a significant role based on the comparative studies on
the determinants of Islamic banking. Consequently, we will focus on Islamic finance
knowledge level and perception of compatibility to religion as our primary explanatory
variables and investigate cost concern of the farmers and saving ability as a reflection of
income level in farmers’decision to use IBs. Based on the literature review and the theoretical
framework, we will be testing three hypotheses as follows:
H1. Farmers’bank usage does not differ according to demographic/economic factors.
H2. Farmers’IB usage does not differ according to demographic/economic factors.
H3. Farmers’knowledge level on Islamic finance, perception of religious compatibility,
cost concern and saving ability does not influence their Islamic banking preference.
3. Data, empirical strategy and model
A survey was conducted on randomly selected 1902 farmers who are members of ACCU in 37
provinces of Turkey under eight regional cooperative unions between July 1 and 5, 2019. The
total population is 810,000 members/partners of ACCU in 17 regional unions and 1,625
cooperatives across the 81 provinces of Turkey (Figure 1).
Survey variables are the Likert scale, binary and multiple categorical variables related to
demographic, economic, credit usage, Islamic finance knowledge and perception of Islamic
finance. We combined five questions on the knowledge level of Islamic finance. These
questions reflect the information level of farmers on Islamic banking and widely used Islamic
finance tools in agricultural sector (Murabaha, Muzara’a, Salam and Ijarah). Prior to the study
we were assuming that the knowledge level of farmers in Turkey would be very low.
Therefore, we used a three-point scale for knowledge-level questions instead of a five-point
scale. Then, by adding these five variables we computed a variable called “KNOWLEDGE.”
Preference of
Islamic
Banking in
Turkey
With regard to the compliance of Islamic finance with the religion, we computed a variable by
taking the average of two variables related to compliance to religion and demonstrate the new
variable with “COMPLIANCE.”These two variables “KNOWLEDGE”and “COMPLIANCE”
are the primary explanatory variable we investigate in our model. We also included cost and
income related variables in our models as explanatory variables. Whereas control variables of
age group of the farmers and the number of the IB branches in the region are included in our
model (Table 2).
Dependent variables in our analysis are binary variables: Bank usage “BANKUSE”and IB
usage “IBUSE.”The relationship between the dependent variables and demographic/
economic factors is investigated via Chi-square-
χ
2
tests. Moreover, to identify the factors that
affect the IBUSE, we use a binary logistic regression model. Finally the robustness of our base
Variables Description
Variable
type
Expected
impact
Dependent variable
IBUSE Farmers’use of Islamic banks Binary
Explanatory variables
KNOWLEDGE Knowledge level of farmers on Islamic banking and
financial instruments. Computed by adding five variables
on Table 4
Likert scale þ
COMPLIANCE Perception of the farmers on religious compliance of using
Islamic banks. Computed by taking average of two
variables on Table 5
Likert scale þ
COSTCONCERN Whether farmers’major criteria when using agricultural
credit is cost
Binary ±
SAVING Whether farmers are able to save from income Binary ±
Control variables
IBBRANCH Number of the Islamic bank branches in the region
reflecting the supply side
Categorical þ
AGE Age group of the farmer Categorical ±
Figure 1.
Map of the provinces
and ACCU regional
unions where the
survey was conducted
Table 2.
Description of the
variables used in the
logistic
regression model
AFR
model is tested by conducting alternative ordinary least squares (OLS) and logistic regression
models. Dichotomous variables can be analyzed by using different distribution functions, and
due to the flexibility and easiness of use, we prefer logistic distribution (Hosmer and
Lemeshow, 2000), and using the same notation with these authors, we can summarize our
binary logistic model as follows:
Conditional mean for the expected value of dependent variable Ygiven the value of
independent variable xis assumed to be in a linear format as:
EðYjxÞ¼β0þβ1x
Where β0and β1are regression coefficients.
In order to simplify the expression, we denoted
π
ðxÞ¼EðYjxÞand used the following
special form of the logistic distribution:
π
ðxÞ¼ eβ0þβ1x
1þeβ0þβ1x
When we add the logit transformation gðxÞ¼ lnh
π
ðxÞ
1−
π
ðxÞi¼β0þβ1x, which will be a
univariate logistic regression model.
The logit for the multivariate logistic regression can be shown as follows:
gðxÞ¼β0þβ1x1þβ2x2þ...þβpxp
And the multivariate logistic regression model is expressed as:
π
ðxÞ¼egðxÞ
1þegðxÞ
β0,β1,β2,...,βpare regression coefficients, and Pgives the probability of the dependent variable
outcome 1. Hence the probability of having the outcome 0 becomes PðY¼0jxÞ¼ 1
1þegðxÞ
Subsequently the ratio of the probability of having the outcome 1 to probability of having
the outcome 0 is
PðY¼1jxÞ
PðY¼0jxÞ¼egðxÞ
Which is called the odds ratio and can be expressed as Exp (β). Odds ratio gives the relative
odds for an independent variable, given that the other independent variables are fixed in
the model.
We apply the standard multivariate logistic regression model for our analysis and have
our model:
lnPðIBUSE¼1Þ
PðIBUSE¼0Þ¼β0þβ1KNOWLEDGEiþβ2COMPLIANCEiþβ3COSTCONCERNiþ
β4SAVINGiþβ5IBBRANCHiþβ6AGEi
Statistical analyses in this paper comprise descriptive analyses, Chi-square tests and the
binary logistic regression model. Robustness tests by running alternative logistic regression
and OLS models are added. Calculations were conducted by using IBM SPSS Statistics
version 22.
Preference of
Islamic
Banking in
Turkey
4. Findings and discussion
Descriptive statistic for 1902 farmers reveals that 92.1% of the sample are male; 86.2% are
married; 9.2% are aged 18–29, 21.3% are aged 30–39, 27,4% are aged 50–59, 18.3% are aged
60þ; 42,4% are primary school graduates, 26,7% are secondary school graduates, 22% are
high school graduates and 8.9% are university graduates. In total, 18.3% of the sample have a
monthly income level of less than TRY2,000 [3], 52.3% earn between TRY2,000 and 5,000,
18.6% between TRY5,000 and 10,000 and 4.8% above TRY10,000. In total, 43.4% of the
farmers declared that they have non-agricultural income and 83.8% have social security.
Approximately 50% of the participants have been farming for more than 21 years, 33% of
them have agricultural land larger than 100 Decares and 67% do not have any savings from
their agricultural income (see Table 3).
In total, 81% of the farmers’main reason for using agricultural credit is for acquiring
agricultural inputs such as fertilizer, pesticides and diesel fuel; 60% of the farmers’first
of credit source is ACCU, which is much higher than the share of ACCU in agricultural
n%
Gender Female 151 7.9
Male 1,751 92.1
Marital status Married 1,639 86.2
Single 263 13.8
Age 18–29 175 9.2
30–39 406 21.3
40–49 522 27.4
50–59 451 23.7
60 þ348 18.3
Education Primary school 807 42.4
Secondary Schoos
¸507 26.7
High school 418 22.0
University 170 8.9
Monthly income (TRY) Less than 2,000 349 18.3
2,000–5,000 1,107 58.2
5,000–10,000 354 18.6
More than 10,000 92 4.8
Non-agricultural income Yes 826 43.4
No 1,076 56.6
Islamic bank usage Yes 184 9.7
No 1,718 90.3
Bank usage Yes 1,668 87.7
No 234 12.3
Number # 1 concern when using agricultural credit Cost 1,508 79.3
Tenor 301 15.8
Collateral 45 2.4
Service quality 48 2.5
Main reason for using agricultural credit Working capital 176 9.3
Agricultural inputs 1,536 80.8
Investment 97 5.1
Other 93 4.9
First choice of agricultural credit source ACCU 1,134 59.6
Ziraat Bank 625 32.9
Other banks 120 6.3
Other cooperatives 23 1.2
Table 3.
Descriptive statistics
for the sample
AFR
credit market, but since the survey is conducted with ACCU members, this figure is
understandable. In total, 79.3% of the participants say that they are most concerned
about the interest rates/costs of financing. In total, 88% of the farmers use banks and only
9.7% use IBs.
In total, 59% of the participants declare that they know nothing about Islamic banking
and only 5.3% says they know it very well. This is in line with the literature suggesting that
the knowledge level of Islamic finance is low in Turkey. In total, 88% of the farmers do not
know anything about Islamic financial instruments Murabaha, Muzara’a and Salam (see
Table 4). This ratio is smaller for Ijarah, which is probably reflecting a confusion due to a
similar term used in the Turkish language (Icar: Renting). Arabic terminology used in Islamic
finance is not well understood (Genc et al., 2016). Therefore, it would not be the best marketing
strategy for IBs in Muslim countries where Arabic is not a lingua franca. As explained before,
we have aggregated these five variables and created the Islamic finance knowledge-level
variable, that is, “KNOWLEDGE.”
As per Table 5, only 21% of the participants agree or strongly agree that using IBs is more
convenient from the religion point of view (term “Religion”is used instead of Islam). The
agreement level on the perceived difference between interest and mark-up, which is the
fundamental of Islamic banking, is slightly higher. The first question is more of a practical
nature, whereas the second question is conceptual. In both cases, farmers disagree more than
they agree with the compatibility of using Islamic banking to the religion.
Table 6 summarizes the Pearson Chi-square test results for farmers’preference for IBs and
conventional banks by their demographic/economic groups.
The usage of IBs statistically differs by age, education level, income level, nonagricultural
income, agricultural activity duration and land size (p< 0.05). While the usage ratio of IBs and
conventional banks increases as the educational level and income increase, the relationship is
negative with age and agricultural activity duration. In parallel with the income level, farmers
who have nonagricultural income and savings tend to use IBs more compared to those who do
Mean S.d
12345
%%%%%
The usage of Islamic banks is more convenient from
religion point of view
2.7403 1.08930 16.9 19.1 43.0 15.1 5.9
There is a difference between interest and mark-up
from religion point of view
2.8922 1.22891 17.2 18.8 32.4 20.6 10.9
Note(s): 15Strongly disagree, 2 5Disagree, 3 5Neutral, 4 5Agree, 5 5Strongly Agree
Item Mean S.d.
012
%%%
Islamic banking 0.465 0.596 58.8 35.9 5.3
Murabaha 0.155 0.425 87.0 10.6 2.5
Muzara’a 0.144 0.415 88.1 9.4 2.5
Salam 0.136 0.408 88.8 8.8 2.4
Ijarah 0.433 0.718 70.2 16.4 13.5
Note(s): 05None; 1 5A little knowledgeable; 2 5Very knowledgeable
Table 5.
Perception of farmers
on the compliance of
using Islamic banking
to religion
Table 4.
Knowledge level on
Islamic banking and
major Islamic financial
instruments
implementable for
agricultural finance
Preference of
Islamic
Banking in
Turkey
Variables Groups
%To
all
Usage of
Islamic
banks (%)
χ
2
Usage of conventional
banks (%)
Yes No Yes No
χ
2
Sex Female 7.9 10.6 89.4 0.160 84.1 15.9 1.961
Male 92.1 9.6 90.4 88.0 12.0
Age 18–29 9.2 14.3 85.7 15.335
***
90.9 9.1 6.018
30–39 21.3 13.1 86.9 86.5 13.5
40–49 27.4 9.2 90.8 89.1 10.9
50–59 23.7 7.8 92.2 88.2 11.8
60þ18.3 6.6 93.4 84.8 15.2
Education level Primary 42.4 6.6 93.4 19.070
***
85.6 14.4 10.677
**
Secondary 26.7 10.5 89.5 87.0 13.0
High 22.0 12.4 87.6 90.4 9.6
University 8.9 15.3 84.7 92.9 7.1
Martial status Married 86.2 9.2 90.8 2.884
*
88.1 11.9 1.805
Single 13.8 12.5 87.5 85.2 14.8
Monthly income level Less than
TRY2,000
18.3 6.9 93.1 9.452
**
82.2 17.8 12.050
***
TRY2,000–5,000 58.2 9.8 90.2 89.0 11.0
TRY5,000–10,000 18.6 9.9 90.1 88.4 11.6
TRY10,000þ4.8 17.4 82.6 90.2 9.8
Nonagricultural income Yes 43.4 11.6 88.4 6.342
**
89.3 10.7 3.680
**
No 56.6 8.2 91.8 86.4 13.6
Saving Yes 33.3 13.4 86.6 15.165
***
88.8 11.2 1.075
No 66.7 7.8 92.2 87.1 12.9
Agricultural activity
length
1–10 Years 18.1 11.9 88.1 9.946
***
89.6 10.4 4.927
*
11–20 Years 32.2 11.7 88.3 89.2 10.8
21þyears 49.6 7.5 92.5 86.0 14.0
Land size 0–10 Decares 10.3 4.1 95.9 8.890
**
81.0 19.0 30.953
***
10–25 Decares 22.8 10.9 89.1 85.7 14.3
25–100 Decares 33.9 9.3 90.7 85.6 14.4
100þDecares 33.1 11.0 89.0 93.3 6.7
Note(s):
*
,
**
,
***
Significant at 10, 5 and 1% levels, respectively
Usage of Islamic
banks
χ
2
Pvalue
Usage of
conventional
banks
χ
2
PvalueYes No Yes No
Tekirda
g 11.4% 88.6% 47.622 0.000 81.9% 18.1% 60.890 0.000
_
Izmir 12.0% 88.0% 90.3% 9.7%
K€
utahya 5.3% 94.7% 93.0% 7.0%
Konya 8.1% 91.9% 83.7% 16.3%
Kayseri 18.4% 81.6% 95.8% 4.2%
Samsun 1.7% 98.3% 83.2% 16.8%
Erzurum 9.7% 90.3% 92.6% 7.4%
Malatya 6.0% 94.0% 77.5% 22.5%
Table 6.
The usage of Islamic
banks and
conventional banks by
demographic/economic
groups
Table 7.
Usage of Islamic banks
and banks by region
AFR
not. Farmers who have less than 10 Decares of land use IBs significantly less. When we
compare the demographic/economic groups by using different types of banks, age and
saving lose their significance for conventional banks. Accordingly, we reject the null
hypotheses H1 and H2 and conclude that farmers’use of conventional banks and IBs differs
according to demographic and economic factors.
Survey is conducted in credit cooperatives from eight different ACCU regions, which have
different economic, social and geographic features. Table 7 summarizes the Pearson
Chi-square test results for farmers’preference for IBs and conventional banks by their
regions.
Comparison of the IB and conventional bank usage by region reveals that farmers in the
Kayseri region tend to use both IBs and conventional banks compared to other regions. On
the other hand, farmers in the Samsun region use fewer IBs than other regions.
Our baseline model for this paper is presented in Table 8. Our key explanatory variables
“KNOWLEDGE”and “COMPLIANCE”are statistically significant at 1% level. Statistically,
both the knowledge level of farmers on Islamic banking and the farmers’perception of
compliance of using IBs to the religion have significant positive impact on usage of IBs.
The interpretation of the logistic regression parameters is different compared to standard
linear probability models that are using OLS since the estimated coefficients of the logistic
model reflect the change in log-odds ratios in the left-hand side variable’s (dependent
variable) response to change in explanatory variables. As in the conventional literature, we
will look at the Exp(B) values in order to understand the partial effect of variable changes in
our model. Exp(B) value, which is the exponentiation of the B logistic coefficient, is given by
default because odds ratios can be easier to interpret than the coefficient. For example, the
coefficient 0.358 of knowledge variable implies that a unit change in knowledge level results
in a 0.358 unit change in the log of the odds. Instead of interpreting the log of the odds, the
odds ratio can be interpreted easier by referring to the Exp(B). For instance, for every one-unit
increase in KNOWLEDGE level, the predicted value of the odds ratio of using IBs, that is, the
ratio of the probability of using IBs to the probability of not using IBs, increases by a factor of
1.431 while holding other predictors in the model constant. In simpler words, farmers whose
knowledge level is higher are more likely to use IBs.
In terms of knowledge element, the results are compatible with our initial hypothesis since
we posit that the IB usage in the agricultural sector is associated with the information level of
farmers. So, we assert that the usage of IBs in the agricultural sector can increase as the level
of information of farmers on Islamic banking and finance increases as a policy suggestion.
With regard to the compliance element, for every one-unit increase in COMPLIANCE variable
while keeping other variables in the model constant, we expect a 0.252 increase in the log-odds
of IBUSE. In terms of the odds ratio, for every one-unit increase in COMPLIANCE, the odds
BS.E. Wald df Sig Exp(B)
Constant 3.402 0.413 67.709 1 0.000 0.033
KNOWLEDGE 0.358 0.033 116.243 1 0.000 1.431
COMPLIANCE 0.252 0.083 9.217 1 0.002 1.286
COSTCONCERN 0.340 0.191 3.165 1 0.075 0.712
SAVING 0.465 0.170 7.473 1 0.006 1.592
IBBRANCH 0.009 0.004 5.148 1 0.023 1.009
AGE 0.176 0.067 6.876 1 0.009 0.839
Hosmer and Lemeshow test Chi-square 514.308; Sig 50.074
Nagelkerke Rsquare 0.182
Omnibus test Chi-square 5169.979; Sig 50.000
Table 8.
Binary logistic
regression model for
Islamic bank usage of
farmers
Preference of
Islamic
Banking in
Turkey
ratio of using IBs increases by a factor of 1.286. In other words, the more the farmers perceive
using IBs is more compliant to religion, the more likely they use IBs.
Although farmers’perception on the compliance of IBs to religious rules is significant for
Islamic bank usage, our results show that knowledge variable is comparatively more significant
than the compliance variable in terms of IB usage. As explained before, we have included cost
concernand saving as explanatoryvariables in our model in line with the theoretical framework.
Almost 80% of the participants of our survey say that the cost is the first item they are
concerned with while they use agricultural credits, and we find in our study that farmers for
whom costis the major concern are less likely touse IBs. Farmerswho consider costas the major
factor have an odds ratio of using IBs 28.2% lower than the farmers who do not. According to
the results of our analysis, the odds ratio of IB usage of a farmer who can save ispredicted to be
1.592 times higher assuming other factors are identical. These findings would be interpreted as
the farmers who use IBs have less cost concern and more savings, which help them consider
alternative credit channels although the IB agricultural credits are not subsidized.
The number of IB branches by region is inserted in the model as a supply shifter for a more
accurate demand analysis, and we use it as a control variable. The impact of the IB branch
number is statistically significant at a 1.0% level. However, the magnitude of the coefficient is
very small and negligible. This can be interpreted as the number of IB branches in a region
has a very little impact on the probability of using IBs by farmers. Age is also used in our
model as a control variable, and it has a statistically significant influence on the usage of IBs.
One level increase in age category of the farmers decreases the odds ratio of IB usage by
16.1%. In simpler words, older farmers are less likely to use IBs. This finding would be
interpreted as younger farmers are more progressive, open-minded for alternative solutions
and more knowledgeable about Islamic finance.
The goodness of fit test for our baseline model is satisfactory as per the Hosmer and
Lemeshow Test (p> 0.05), and the Nagelkerke R-square figure is 0.182, which means 18.2% of
the variance in IBUSE is explained by our model. Although the Nagelkerke R
2
appears to be
low, according to Hosmer and Lemeshow (2000, p. 167) “low R
2
values in logistic regression
are the norm.”The overall test of the model, that is, the Omnibus Test shows our model is
significantly predictive of IB usage (p< 0.01).
In the next section, we will check the robustness with additional specifications and
alternative models.
4.1 Robustness checks
Our dependent variable in different specifications is IBUSE as mentioned before, and key
variables of interest are “knowledge”and “compliance.”In Table 9, we report our models for
alternative linear probability model (LPM) and logistic regression models with various
specifications. The LPMs are estimated using OLS. Our focus in the estimation process is on
the logistic regression, but we discuss different specifications and OLS results as a standard
robustness check procedure. Moreover, we inserted a “Region Fixed Effect”variable in all
alternative models to show how our baseline model is robust against regional effect.
We control the results of various models between the second and seventh columns of
Table 9. In the second model, we exclude IB Branch; in the third we exclude Saving; in the
fourth we exclude cost concern variables; while we selected a random sample from the main
sample before the estimation in the seventh column. The significance level and magnitude of
knowledge and compliance coefficients are almost identical across various models. We can
conclude that the effects of knowledge and compliance on the IB usage are similar among
various model specifications.
We control the validity of knowledge and compliance in the fifth and sixth columns for
LPMs. When we exclude the compliance variable, the magnitude of knowledge variable
AFR
1234567 8 9 101112131415
OLS Logistic regression
IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE IBUSE
KNOWLEDGE 0.047
***
0.003
0.047
***
0.003
0.048
***
0.003
0.047
***
0.003
0.048
***
0.003
0.043
***
0.004
0.357
***
0.034
0.357
***
0.034
0.370
***
0.034
0.357
***
0.034
0.365
***
0.034
0.333
***
0.042
0.251
***
0.092
COMPLIANCE 0.022
***
0.007
0.022
***
0.007
0.022
***
0.007
0.022
***
0.007
0.030
***
0.007
0.023
***
0.008
0.271
***
0.084
0.271
***
0.084
0.272
***
0.084
0.270
***
0.084
0.346
***
0.084
0.305
***
0.103
0.155
0.124
COST.C –0.027
*
0.016
–0.027
*
0.016
–0.025
0.016
–0.025
0.016
–0.032
*
0.017
–0.018
0.019
–0.361
*
0.194
–0.361
*
0.194
–0.334
*
0.193
–0.354
*
0.193
–0.345
*
0.184
–0.216
0.237
–0.360
*
0.194
SAVING 0.036
***
0.014
0.036
***
0.014
0.034
**
0.014
0.037
***
0.014
0.059
***
0.014
0.027
*
0.016
0.461
***
0.174
0.461
***
0.174
0.444
***
0.173
0.466
***
0.173
0.676
***
0.163
0.328
0.210
0.468
***
0.174
IBBRANCH 0.001
0.000
0.000
0.000
0.001
0.000
0.000
0.000
0.000
0.000
0.001
0.000
–0.014
0.018
–0.014
0.018
–0.014
0.018
–0.014
0.018
–0.024
0.017
–0.003
0.021
–0.012
0.018
AGE –0.013
**
0.005
–0.013
**
0.005
–0.013
**
0.005
–0.014
**
0.005
–0.013
**
0.005
–0.020
***
0.005
–0.007
*
0.006
–0.177
**
0.069
–0.177
**
0.069
–0.164
**
0.069
–0.184
***
0.069
–0.171
**
0.069
–0.246
***
0.067
–0.194
**
0.084
–0.181
***
0.070
INTER 0.037
0.030
Constant 0.016
0.037
0.058
*
0.031
0.021
0.037
–0.004
0.035
0.073
**
0.033
0.115
***
0.032
–0.023
0.045
3.025
***
0.588
3.534
***
0.487
2.925
***
0.586
3.291
***
0.572
2.261
***
0.526
2.183
***
0.544
3.227
***
1.697
2.743
***
0.631
Reg. Fixed E Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Obs 1902 1902 1902 1902 1902 1902 1300 1902 1902 1902 1902 1902 1902 1300 1902
Sample select Full Full Full Full Full Full Sampled Full Full Full Full Full Full Sampled Full
R
2
(Nagelkerke
for logit)
0.136 0.136 0.133 0.134 0.131 0.051 0.128 0.224 0.224 0.217 0.220 0.213 0.110 0.187 0.225
Note(s):
*
,
**
,
***
Significant at 10, 5 and 1% levels, respectively
Table 9.
Regression model
alternatives
Preference of
Islamic
Banking in
Turkey
remains almost same at 0.048. When we exclude the knowledge variable, the coefficient for
compliance increases slightly to 0.03. Again, the magnitudes and directions are quite similar
across models. As a conclusion, the results survive across different models. The effects of
knowledge and compliance variables on the IB usage are very similar across the table and
between the logistic model estimation and linear probability model. This situation supports
validity of our results of the baseline model estimation.
With regard to the sensitivity of logistic regression models, we exploit different
specifications in order to control the validity of results in the baseline model between the eight
to the thirteenth columns. There is no difference across different specifications and logistic
models in terms of the direction of the coefficients. In terms of magnitude, coefficients for the
knowledge and compliance variables are quite similar among different models. In terms of
statistical significance levels, knowledge and compliance variables do not lose any statistical
significance level across different models. These results show that our logistic regression
estimation is not sensitive to different specifications and model changes. However, other
variables in the models tend to lose their statistically significance as the sample size
decreases, which is reported in the 14th column. When we run the model for lower-size
samples, compliance loses its statistical significance level slightly. Adding a variable to show
the regional fix effect causes losing the significance of IB Branch variable. In fact, IB Branch
variable in the baseline logistic regression model was a control variable, which can be
considered as a categorical regional variable too.
With regard to the issue of logical collinearity between knowledge and compliance
variables, we expect that farmers who do not have any initial information on the Islamic
banking system would not have any perception on the religiosity of IBs. In order to eliminate
possible confounding factor, we run the baseline logistic regression model with interaction of
knowledge and compliance variables (“inter”variable, which is computed by multiplication).
The coefficient is reported in the 15th column. The direction of coefficient of knowledge
variable is same, and it does not lose level of statistical significance. However, compliance
loses its all statistically significance after the new model estimation. Compatible with our
results in previous parts, when we consider the interaction possibility between knowledge
and compliance variables, the knowledge continues its significance on the IB usage among
farmers. However, the compliance loses its explanatory power due to the statistical
insignificance. Therefore, we continue to claim the noticeable significance of knowledge over
compliance and other variables.
5. Conclusion
IBs have a marginal share in the Turkish banking sector with a 5.1% share in total credits.
The share of IBs in agricultural credits in Turkey is even smaller with a 1.9% share. This
study identifies the factors that influence farmers’preference for IBs. According to our survey
data from randomly selected 1902 farmers who are members of ACCU in 37 provinces of
Turkey under eight regional cooperative unions, only 9.7% of farmers use IBs.
The logistic regression model results show knowledge level of Islamic banking and
finance, perception of compliance of using IBs with religion, farmers’ability to save, cost
concern, age and the number of IB branches in the region influence the probability of farmers’
usage of IBs. Knowledge level, perception of compliance to religion and ability to save from
agricultural income have a positive and significant impact on farmers’usage of IBs. On the
other hand, cost concern and age have negative impact. Those who have not identified cost as
the major factor while using agricultural credit and younger farmers are more likely to
use IBs.
The impact of cost concern on the IB usage implies that a revision in the current scheme of
the Government’s subsidized agri-credit channels of Ziraat Bank and ACCU by including IBs
AFR
in the scheme would enhance the usage of IBs in the agricultural sector. The significant
impact of knowledge level and perception of compliance to religion imply that in order to
increase the share of Islamic finance in the agri-credit market, there is a need for PR
campaigns and training programs organized by IBs in collaboration with the ACCU and
Universities to create awareness about Islamic finance among the farmers.
The results of our survey can be used by IBs, microfinance institutions, cooperatives and
ACCU to understand the factors that influence the farmers’agri-credit usage and preference
for IBs and conventional banks in Turkey. Moreover, the investigation of farmers’perception
of Islamic finance and preference of using IBs gives this study an edge in the Islamic finance
literature and the agricultural finance literature. Further studies may elaborate on the
relationship between religiousness and IB usage. These results also imply that, for the
Government to succeed in its plan to increase the share of Islamic finance in the Banking
industry, one focus area should be the agricultural sector, where the IBs market share is even
less than their already small share in the banking sector in general. The Government should
extend the implementation of the subsidized agri-credits by including IBs, allow and
encourage the ACCU to offer more Islamic finance products and perhaps consider
establishing a full-fledged IB by the ACCU. Access to Islamic finance must be
accompanied by an education program designed for farmers about Islamic finance
products and services.
Notes
1. Data retrieved from World Bank database available at https://data.worldbank.org/indicator/
accessed on 24/10/2020
2. Retrieved from BRSA data base available at https://www.bddk.org.tr/BultenAylik/en/Home/
Gelismis
3. US$/TRY 55.63 on the survey dates.
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Corresponding author
Mehmet Bulut can be contacted at: profmehmetbulut@gmail.com
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Preference of
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