effects of credit on national and agricultural gdp, and
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Vol.:(0123456789)
SN Bus Econ (2021) 1:140https://doi.org/10.1007/s43546-021-00146-6
ORIGINAL ARTICLE
Effects of credit on national and agricultural GDP, and poverty: a developing country perspective
Tanni Roy1 · Md. Emran Hossain2 · Md. Jahid Ebn Jalal3 · Jiban Krishna Saha1 · Eshrat Sharmin4 · Md. Akhtaruzzaman Khan5
Received: 24 March 2021 / Accepted: 1 September 2021 / Published online: 29 September 2021 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
AbstractA developing country’s economy, such as Bangladesh’s, mainly focuses on agri-culture, and credit plays a vital role in economic development and poverty allevia-tion. Therefore, this study aims to narrate the effects of credit on the national Gross Domestic Product (GDP), agricultural GDP, and poverty. Forty-three years’ data (from 1976 to 2018) on credit, GDP, and poverty are collected from different sec-ondary sources, whereas four prominent credit disbursement organizations are con-sidered for this study. A fixed-effect model is employed to reveal the effect of credit. The study finds that credit has significant positive effects on both national and agri-cultural GDP. An increase in the amount of loans, number of loanees, and number of banks’ branches indicate an increasing number of people getting access to finance, which would increase their production-related activities and eventually contributed to increasing GDP. The study also finds that credit has a significant favorable effect on poverty alleviation. The findings highlight the importance of credit in Bangladesh and other emerging economies to flourish the economy and ease poverty. Therefore, the study suggests that financial institutions should expand their credit programs for rural entrepreneurs and farmers to ensure sustainable rural development as well as economic development.
Keywords Agricultural GDP · Credit · Poverty · Fixed-effect model · Bangladesh
Introduction
The structure of Bangladesh’s economy is conventionally divided into three sectors, viz., agriculture, industry, and service, while agriculture has been playing the domi-nant role in forming GDP from the country’s inception (BER 2018). But industry increases its share in GDP as the economy progresses. However, the rural economy
* Md. Akhtaruzzaman Khan [email protected]
Extended author information available on the last page of the article
SN Bus Econ (2021) 1:140140 Page 2 of 20
plays an essential role in GDP since 63.4% of the total population lives in villages (World Bank 2019). The rural economy consists mainly of the agriculture sector, which employs the lion’s share of the labor force, 43% as of 2018 (BBS 2018a, b). The rural population of Bangladesh is poor and disadvantaged by economic status compared to their urban counterparts (Islam and Azad 2008). Hence, they face capi-tal shortage which results in a lower rate of investment and production. Credit can help rural people to meet the capital requirement through generating employment and income (Karim et al. 2012). In addition, it can further improve the nutrition and education of the future generation and alleviate households from vicious poverty traps through proper investment in productive purposes (Nawaz 2010). However, the government’s existing effort toward the industry sector is quite promising, but the action for the agriculture sector is inadequate, inconsistent, and infested with inter-mediaries. Besides, modern agriculture is capital intensive, and credit is necessary to ensure capital flow throughout the production process.
In Bangladesh, credit requirement is very high in the agricultural sector due to the presence of a large number of individual producers, the majority of whom are marginal farmers (Quddus and Kropp 2020). The credit requirement has increased further following the popularization of high-yielding varieties (HYV), as these vari-eties require a greater investment than the local ones. Consequently, credit is a pre-requisite to accelerate production and improve the growth rate of agricultural GDP and national GDP. This study considered the "credit"1 which is being distributed by various institutions in rural areas of Bangladesh. The main sources are the Bang-ladesh Krishi Bank (BKB), Nationalized Commercial Banks (NCB), and different Non-governmental Organizations (NGOs). However, NCB provides mainly short-term production loans, while BKB is the only government institution that offers medium- and long-term loans besides catering to the seasonal credit needs of the farmers.
Apart from the credit disbursed through the state-owned and commercial banks, the agriculture sector has been familiar with the concept of credit through non-bank financial institutions and NGOs since the inception of Bangladesh. Those institu-tions came forward with microcredit theory to foster income generation and pov-erty alleviation through enhancing self-employment (McKernan 2002). But in recent years, those institutions started to provide medium- and large-scheme credit for agricultural production and business-related activities. Although the agriculture sector is a key source of employment in the country, it has been unable to create rewarding employment opportunities due to a lack of proper expansion. This raises some questions about the current financing level available to the marginal people employed in the agricultural sector. Lack of financing would contribute to the lower level of mechanization and efficiency in the agriculture sector and other productive
1 This study considered only “rural credit”, which is taken by the rural households for the purpose of agricultural activities or operating small and medium business in rural areas. The rural people are in most adverse situation on taking loan because of limited number of credits providing institutions oper-ated in rural areas but the urban manufacturing industries can take loan from any commercial bank or non-bank financial institutions (NBFIs) due to easy accessibility. Therefore, this research particularly focused on rural credit and the institutions provide credit in rural areas.
SN Bus Econ (2021) 1:140 Page 3 of 20 140
sectors of the economy. Thus, it may result in a lower level of employment creation, income, and eventually national GDP. Considering the overall situation, the NGOs and government are working on poverty eradication by directly addressing the pov-erty-stricken population through providing financial support to the rural poor, but not as much as required. This study selects four major credit disbursing institutions; BKB disburses the highest amount of loans among them (Bangladesh Bank 2019). Similarly, the amount of credit disbursed by the Association for Social Advancement (ASA), Grameen Bank, and Bangladesh Rural Advancement Committee (BRAC) has increased significantly over the last decades in rural areas, especially focusing on women to magnify the household income and agricultural production, since most of the affiliated activities of agricultural sector take place in rural areas (Fig. 1).
Although Bangladesh’s government and NGOs have been disbursing credit for poverty alleviation, rural development, and hence, economic growth, very few empirical studies were conducted on credit’s impact (Sharmeen and Chowdhury 2013; Islam 2014; Afrin et al. 2010; Ara 2009). However, most of them were focused on poverty alleviation through microcredit provided by NGOs, but none of them focused on the effects of credit supplied by state-owned banks and NGOs com-bined. With this backdrop, this study intends to contribute to the gap of evidence to assist in the evidence-based policymaking process and enrich existing knowledge of the role of credit. Therefore, this research makes an effort to document Bangladesh’s GDP, agricultural GDP, and poverty situation and aims to answer the research ques-tion substantially "Does the credit have a significant effect on national GDP, agricul-tural GDP, and poverty reduction?"
The rest of the paper is organized as follows. “Empirical evidence of the cred-it’s effect on GDP, agricultural GDP, and poverty” presents an extensive review of empirical studies with a research gap. “Data and descriptive statistics” describes data and presents scenarios of Bangladesh’s national GDP, agricultural GDP, and
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Fig. 1 Comparison of loan disbursement among the selected four institutions. Source: Bangladesh Bank annual report 2019
SN Bus Econ (2021) 1:140140 Page 4 of 20
poverty situation. “Econometric methodology” presents the methodology of the study. “Results and discussion” is on empirical findings to address the research question and discusses the empirical results, while “Conclusions and recommenda-tions” concludes the study.
Empirical evidence of the credit’s effect on GDP, agricultural GDP, and poverty
Credit support to the rural poor should be assessed as to whether their goals aug-ment the production activities or economic growth or alleviate poverty in rural households. The impact assessment of credit on GDP, agricultural GDP, and pov-erty measures to what extent credit borrowers have been able to put themselves in economic activities to boost up the economic growth of the country. Credit is neces-sary for the development of the rural economy (Akinlo 2014), because it improves incomes through productive investment. In addition, it aids in creating employ-ment possibilities and lessens the vulnerability of the poor by providing consistent income opportunities over time (Abraham 2018). Further, a lack of access to credit not only detains economic growth, but also increases poverty (Abraham 2018) and, therefore, the amount of GDP might be choked off.
Duican and Pop (2015) analyze the relationship between credits and economic growth in Romania for the period of 2005–2014. The results confirmed that cred-its have a significant effect on the progression of GDP. Credits offered to house-holds pony up a greater extent to the evolution of the GDP than credits offered to public administration (Banu 2013). Seven and Tumen (2020) found that agricultural credits positively impact agricultural productivity; particularly, doubling the agricul-tural credits provokes an approximately 4–5% increase in agricultural GDP. Credit positively and significantly influences agricultural GDP in the long run, while in the short run, this effect is insignificant in Nigeria for 1981–2014 (Ogbuabor and Nwosu 2017). Agricultural GDP is highly sensitive to agricultural bank credit and found a one-way association between agricultural bank credit and GDP of agricul-ture in Pakistan (Khan et al. 2017) and Bangladesh (Rahman 2011). Assessing the determinants of agricultural production in Pakistan employed the Cobb–Douglas production function, which revealed that agricultural bank credit had a strong and positive influence on agricultural GDP along with seed and fertilizer (Rehman et al. 2019). Besides, Islam (2014) investigates that most agricultural credits are small-scale loans for the poor entrepreneurs in Bangladesh. It allows them to access funds and start their own business for rural development. In the same context, bank’s credit allocation fosters economic growth by increasing the money supply, lower-ing unemployment, raising income through increasing labor demand, and ultimately decreasing poverty (Musau et al. 2018; Sipahutar 2016; Korkmaz 2015).
Likewise, Roy and Biswas (2014) revealed that Grameen Bank and Bangla-desh Rural Development Board (BRDB) covered a higher proportion of landless households in Bangladesh and recommended establishing effective monitoring and evaluation system on the utilization of microcredit, accompanied by appro-priate training and increasing credit size is mandatory for accelerating poverty
SN Bus Econ (2021) 1:140 Page 5 of 20 140
reduction. Microcredit provided by NGOs has a positive impact on the standard of living of poor people and their lifestyles (Ferdous 2014). Despite the debate about the higher interest rate of NGO’s credit, they contribute not only to alleviating pov-erty and improving the living standards of poor people, but also in offering exten-sive human development through empowering women (Yu et al. 2020; Pomi 2019; Chirkos 2014; Karim et al. 2012; Rahman 2007; Chowdhury et al. 2005). However, by examining the extensive literature on whether credit affects GDP and poverty, a substantial research gap exists since most studies focus on agricultural credit and microcredit. Besides, some studies focused on only economic growth or poverty alleviation separately using time-series or cross-sectional data. Therefore, this paper contributed to the simultaneous effects of providing any kind of credit by govern-ment and NGOs to achieve inclusive economic growth and poverty reduction using panel data that would help redirect the focus of government policy.
Data and descriptive statistics
Nature and sources of data
The model developed in this study uses data from 1976 to 2018 of national GDP, agricultural GDP, and poverty collected from the Bangladesh Bureau of Statistics (BBS) and World Bank. As Grameen Bank, BRAC, ASA, and Bangladesh Krishi Bank (BKB) are the core credit organizations in rural areas of Bangladesh and BKB is the largest state-owned agricultural loan distributor in Bangladesh, these four loan disbursement organizations were selected purposively for this study. The nature of data considered for this study is panel, because data were collected from secondary sources on different dimensions over the years. Data on loan disbursement amount, number of loanee members, and number of branches were collected from the head offices of the respective NGOs and banks. The description of variables and sources of data are presented in Table 1.
Documentation of national GDP, agricultural GDP, and poverty situation in Bangladesh
Gross Domestic Product (GDP) is widely accepted as one of the significant indi-cators of economic growth and standard of living of a country. Since the mode of measuring GDP is uniform around the globe, GDP can be used to compare the pro-ductivity of various countries with a relatively higher degree of accuracy. Inflation adjustment enables the seamless comparison of current GDP measurement with measurements from previous years or quarters, allowing economists to compare a country’s progress throughout time, given that reliable data are available. Figure 2 gives the picture of the absolute amount of GDP per year from 1976 to 2018. The GDP of Bangladesh has constantly been growing throughout the period, but it took around two-and-half decades to reach USD 50 billion. After that, in the next decade, it touched USD 100 billion GDP landmark. Within the last 8 years, Bangladesh’s
SN Bus Econ (2021) 1:140140 Page 6 of 20
Tabl
e 1
Des
crip
tion
of v
aria
bles
*1 U
SD =
84.9
5 B
DT
as o
f Aug
ust 2
021
Varia
bles
Defi
nitio
n/un
it of
mea
sure
men
tSo
urce
Justi
ficat
ion
Nat
iona
l gro
ss d
omes
tic
prod
uct (
GD
P)Th
e m
onet
ary
valu
e of
all
finis
hed
good
s and
se
rvic
es m
ade
with
in a
cou
ntry
(mill
ion
BD
T* a
t cur
rent
pric
e)
BB
S an
d W
orld
Ban
kA
wad
and
Al K
arak
i (20
19),
Dui
can
and
Pop
(201
5)
Agr
icul
tura
l GD
P (A
g. G
DP)
GD
P co
mes
from
the
agric
ultu
ral s
ecto
r (m
il-lio
n B
DT)
BB
S an
d W
orld
Ban
kRe
hman
et a
l. (2
017)
, Har
tars
ka e
t al.
(201
5)
Pove
rtyPe
rcen
tage
of e
mpl
oyed
pop
ulat
ion
belo
w
$1.9
0/da
y pu
rcha
sing
pow
er p
arity
BB
SA
mpa
h et
al.
(201
7), S
ipah
utar
(201
6)
Am
ount
of l
oan
(loan
)Th
e to
tal a
mou
nt o
f loa
n di
sbur
sed
(mill
ion
BD
T)Re
spec
tive
bank
’s a
nd N
GO
’s h
ead
office
Aw
ad a
nd A
l Kar
aki (
2019
), En
imu
et a
l. (2
017)
, Sip
ahut
ar (2
016)
, Dui
can
and
Pop
(201
5)N
umbe
r of m
embe
rsTo
tal n
umbe
r of m
embe
rs/b
orro
wer
s who
ta
ke c
redi
t fro
m se
lect
ed b
ank
and
NG
O’s
Resp
ectiv
e ba
nk’s
and
NG
O’s
hea
d offi
ceM
oahi
d an
d M
ahar
jan
(202
0), D
evel
tere
and
H
uybr
echt
s (20
05)
Num
ber o
f bra
nche
sTo
tal n
umbe
r of s
elec
ted
bank
’s a
nd N
GO
’s
bran
ches
that
pro
vide
cre
dit
Resp
ectiv
e ba
nks a
nd N
GO
’s 0
ffice
Ber
nini
and
Brig
hi (2
018)
SN Bus Econ (2021) 1:140 Page 7 of 20 140
GDP has reached USD 274 billion (BBS 2019). This figure gives the visual impres-sion of the astounding economic growth that has been taking place in the least developed country like Bangladesh.
GDP growth rate in Bangladesh has averaged 5.1% from 1976 to 2018, reaching an all-time high of 7.9% in 2018 and a record low of 3.8% in 2002 (Fig. 2). Figure 2 also shows the relative stability that Bangladesh has achieved in economic growth throughout the time, particularly in the last decade. If the time period has been bro-ken down into decades, the average GDP growth rate was 4.1% in the first two dec-ades (1976–1995). The average GDP growth rate increased to 5% in the third decade (1996–2005), which again rose to 6.1% in the next decade (2006–2015). From 2011 to 2018, the average GDP growth has been 6.7%.
The agricultural sector’s contribution includes crop, fisheries, livestock, and for-estry sub-sectors on GDP is immense. Therefore, the monetary value of all the fin-ished goods and services produced by the agriculture sector is considered as agri-culture value added. Figure 3 provides information regarding the yearly increase of agriculture value added at constant (2010) price (billion USD). According to the illus-tration, from 1976 to 2018, agriculture value added has risen gradually. In 1971, the country had experienced a total agricultural value added of USD 7.6 billion, which slowly increased to USD 10.4 billion in 1994. In 2018, the country experienced agri-cultural GDP worth USD 25.7 billion. While the first two decades have encountered a little under USD 8 billion worth of growth in agricultural GDP, the last two decades have observed a doubling of the agricultural GDP. This remarkable growth is achieved through the concerted effort of government and non-government organizations by pro-viding credit. A large number of credit schemes have been formulated aiming toward the livestock, fisheries, poultry, and especially the crop sector.
Traditionally, the agriculture sector has depended on the ancestral knowledge trickled down from generation to generation. While the world has embarked on modernized agriculture, Bangladesh is still lagging behind, partially because of
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
0.00
50.00
100.00
150.00
200.00
250.00
300.00
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
GD
P gr
owth
rate
(%)
GD
P (in
Bill
ion
USD
)
Year
Fig. 2 GDP and its growth rate in Bangladesh (1976–2018). Source: WDI 2019
SN Bus Econ (2021) 1:140140 Page 8 of 20
7.5 7.2 7.7 7.98.9 9.7 10.4
11.613.3
16.0
19.6
22.5
25.7
y = 1.4742x + 2.6038R² = 0.8626
0.0
5.0
10.0
15.0
20.0
25.0
30.0
1971 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 2018
Agric
ultu
ral G
DP (b
illio
n US
D)
Fig. 3 Crops, fisheries, livestock, and forestry value added (constant 2010 USD). Source: WDI 2019
48.9
40
31.5
21.8
52.3
43.8
35.2
26.4
35.228.4
21.3 18.9
0
10
20
30
40
50
60
2000 2005 2010 2018 2000 2005 2010 2018 2000 2005 2010 2018
Na�onal Rural Urban
% o
f pov
erty
Year
Fig. 4 Poverty rate in Bangladesh based on the upper poverty line (%). Source: BBS 2018a
the employed population in agriculture living in poverty for generations. The poor farmers lack access to education, entrapping them into the vicious circle of pov-erty. Figure 4 depicts the poverty rate in Bangladesh based on the upper poverty line during the time period of 2000–2018. As shown, the poverty rate in Bangladesh reduced from 48.9% in 2000 to 21.8% in 2018, with significant variance based on locality. In 2000, the rural and urban poverty rate was 52.3% and 35.2%, respec-tively. The poverty rate reduced to 26.4% and 18.9% in rural and urban regions in
SN Bus Econ (2021) 1:140 Page 9 of 20 140
2018, respectively. The poverty rate was consistently higher in the rural area than the urban counterparts and the national rate over the period mentioned above. Urban poverty is constructed by the migrated poor or extremely poor population from rural areas to some extent. Although the difference between rural and urban poverty rates has decreased over time, the presence of such difference provides a strong argument for increasing access to finance via credit.
Econometric methodology
Panel data regression was used to estimate the effects of credit on national GDP, agricultural GDP, and poverty over time, including time and institutions/firm invari-ant variables. To handle the panel regression models, three linear estimation tech-niques, i.e., pooled OLS, the fixed and random-effects model are widely applicable (Asteriou and Hall 2007). Among them, the fixed-effect and random-effect models have conventionally been preferred (Maloba and Alhassan 2019). The assumption of the fixed-effects model is the heterogeneity of each of the individuals in the model through the constant of regression function (Islam et al. 2020). On the other hand, the heterogeneity issue of each individual has been incorporated in the random-effects model by assuming that homogeneity variance prevails between the varia-bles; that is, the individual time-explicit effects are uncorrelated with the explana-tory variables (Maloba and Alhassan 2019). The fixed- and random-effect models can be specified in the following manner:
where �i + �it is treated as an error term consisting of two components: an individ-ual-specific component, which does not vary over time, and a remainder component, which is assumed to be uncorrelated over time, allowing for the time-invariant vari-ables to play the role of explanatory variables.
The study employed the Hausman (1978) specification test to statistically validate the compatible approach between the fixed- and random- effect models. The hypoth-esis for the Hausman test is:
Ho = the suitable effect is random [i.e., differences in coefficient are not systematic].
Ha = the fixed effect is appropriate [i.e., differences in coefficient are random].If the probability of Chi-square (χ2) is more than the 5% level, we do not reject
the null hypothesis implying that the random-effect estimators would be appropri-ate to explain the model. In the case of less than 5% Chi-square value, we reject the null hypothesis, which allows for using fixed-effect estimators (Rashid 2020). The estimated value of χ2 (3) = 8.28 and prob > χ2 = 0.040 for GDP, χ2 (3) = 8.24 and prob > χ2 = 0.041 for agricultural GDP and χ2 (3) = 3.15 and prob > χ2 = 0.039 for poverty, which were below the threshold p value of 0.05; hence, the fixed-effect
yit = �i + ��
Xit +�it, where �̂i = yi −
�
X �̂ (Fixed effects model)
yit = ��
Xit +(
�i + �it)
, where �it ∼ IID(0, �2�) and �i ∼ IID(0, �2
�) (Random effects model)
SN Bus Econ (2021) 1:140140 Page 10 of 20
model was the efficient estimator for this study. Empirical models estimated in this study are as follows:
Here, x1 is the amount of loan disbursed, x2 is the number of members, and x3 is the numbers of branches. This study also employed the Breusch–Pagan/Cook–Weis-berg test (Cook and Weisberg 1983) to diagnose heteroscedasticity. Null and alterna-tive hypotheses of this test are stated in the following manner:
Ho: the model is homoscedastic.Ha: the model is heteroscedastic.The estimated Chi-square value was 9.49, 4.0, and 5.15 for GDP, agricultural
GDP, and poverty in the three respective models, and their p value was less than 0.05. This finding confirmed that heteroscedasticity is a problem in the OLS model of our study. Therefore, a robust standard error was used to control the heteroscedas-ticity in this model. This study also employed the bias-corrected Born and Breitung HR test to assess the panel serial correlation for fixed-effects regressions (Wursten 2018). Results confirmed no panel serial correlation presented in the data; hence, this study used the fixed-effects regression model appropriately.
Results and discussion
Summary statistics and correlation matrix
Table 2 represents the summary statistics of the selected data from 1976 to 2018. The national GDP of Bangladesh is estimated as an average of BDT 29,990.681
(1)Impact of credit on GDP ∶ yGDPti = �0 + �1x1ti + �2x2ti + �3x3ti + �ti
(2)Impact of credit on Agricultural GDP ∶ yAGDPti = �0 + �1x1ti + �2x2ti + �3x3ti + �ti
(3)Impact of credit on poverty ∶ yPovti = �0 + �1x1ti + �2x2ti + �3x3ti + �ti
Table 2 Summary statistics of selected variables
National GDP Agri. GDP Poverty Loan Members Branch
Mean 29,990.681 5262.409 34.706 31,380.29 1,988,404 945Maximum 106,906.362 10,468.821 74.000 169,490.6 8,640,225 3333Minimum 3016.750 1396.310 21.800 301.010 10,995 130Std. Dev 28,421.901 2527.183 18.338 42,134.77 2,580,145 920Skewness 0.006 0.000 0.026 0.007 0.039 0.012Kurtosis 1.007 0.969 2.185 1.003 0.477 2.474Jarque–Bera 7.462 10.972 8.901 7.317 9.309 10.354Probability 0.013 0.004 0.003 0.013 0.001 0.001
SN Bus Econ (2021) 1:140 Page 11 of 20 140
million, with a range of 3016.750–106,906.362, and a standard deviation of 28,421.901. The study shows that the average value of agricultural GDP over the study period is BDT 5262.409 million. However, the contribution of agriculture to the national GDP is falling, but the total agricultural output has been significantly rising. Results also indicate that a significant variation in poverty has prevailed over the study period with a range of 21.8–74.0% of the total employed population. In recent years, Bangladesh has made substantial progress in terms of reducing pov-erty. There are considerable variations in the independent variables too. The amount of loans, the number of beneficiaries, and the number of branches of the selected organization have increased considerably over the study period. For instance, the average loan amount provided to rural people is BDT 169,490.6 million through four key loan-providing institutions. The results of skewness and kurtosis show that the variables are approximately symmetric and platykurtic. Also, the Jarque–Bera probability test of normality indicates all variables were normally distributed.
However, this study requires a test to assess the degree of correlation among inde-pendent variables, since it is based on panel data. Having a collinearity issue in the data set may influence the model that prompts the twisting of the regression results. The multicollinearity problem ensues when the correlation coefficient is above the threshold of 0.80 between any two explanatory variables (Farrar and Glauber 1967). This study considered the Pearson pairwise correlation among independent vari-ables to identify multicollinearity. The findings revealed that among the independ-ent variables, the highest degree of correlation was found between the number of branches and number of members, which is still lower than the threshold value of 0.80 (Table 3). To confirm the results, the study also tests the variance inflation fac-tor (VIF) to validate whether the model is collinear. If the value of VIF of all vari-ables is less than the threshold value of 10, then there is no multicollinearity prevail-ing in the model (Hair et al. 1984). The estimated value of VIF2 for all variables was
Table 3 Pairwise correlations for dependent and independent variables
**p < 0.05***p < 0.01
Loan Member Branch NGDP Ag. GDP Poverty
Loan 1.0000Member 0.6754*** 1.0000Branch 0.6696** 0.7329*** 1.0000NGDP 0.9245*** 0.7653*** 0.7395*** 1.0000Ag. GDP 0.8948*** 0.8030*** 0.8078*** 0.9577*** 1.0000Poverty -0.8444*** -0.8233*** -0.7999*** -0.8701*** -0.9479*** 1.0000
2 VIF was 4.12, 5.51 and 1.28 for amount of loan, number of members and number of branches, respec-tively, in model 1 and 2 while it was 3.24, 5.59 and 5.99 for amount of loan, number of members and number of branches, respectively, in model 3.
SN Bus Econ (2021) 1:140140 Page 12 of 20
less than the threshold value of 10 in three different models; thus, multicollinearity was not a considerable problem for our data set.
Effect of credit on national GDP
This section has set out to assess credits’ impact on the national GDP, agricul-tural GDP, and poverty. Results depict the positive relationship between credit and national GDP (Table 4). The model shows that the variations in the independent variables (loan, member, and branches) can explain 72.3% of the variation of the dependent variable (national GDP). These variables are statistically significant at 5%, 1%, and 10% levels, respectively. It also means that some alternative variables affect the increase in GDP other than these variables.
In the case of the amount of loan disbursed, it can be assumed that the amount of loan disbursed and national GDP are positively related, given that other things remain constant. The targeted credit recipients of the four organizations (Grameen Bank, BRAC, ASA, BKB) are generally spreading throughout the country and in places where other means of loan are often less accessible due to collateral guaran-tee-related issues, the complex procedure, and absence of such organizations alto-gether. These organizations also provide loans to a smaller scale, which would likely be sought out by the poor and extremely poor people. Grameen Bank, BRAC, and ASA have a group-based loan disbursement process where the credit has no collat-eral guarantee. It makes accessible credit to people without a significant amount of assets and lets them contribute to their economic activities (production and invest-ment) or basic needs (consumption), resulting in an increase in GDP since GDP is the accumulated results of domestic production, investment, and consumption.3 In economic activities, such as agriculture or small and medium businesses, access to that small amount of loans increases production, leading to an increase in income and standard of life. Besides, when small and medium businesses have access to credit, they might finance a new production facility, buy new raw material, or start a
Table 4 Effects of credit on national GDP, agricultural GDP, and poverty
***, ** and * indicate significance at 1%, 5% and 10% level of significance. Parenthesis figures indicate the standard error
Variable National GDP Agri. GDP Poverty
Amount of loan 0.086** (0.038) 0.051** (0.021) − 0.022** (0.009)Number of members 0.068*** (0.010) 0.040*** (0. 006) − 0.023*** (0.003)Number of branches 0.108* (0.065) 0.070* (0.038) − 0.056** (0.017)Constant 12.179*** (0.156) 11.635*** (0.093) 4.591*** (0.048)R2 0.723 0.748 0.709F-statistics 79.26 [0.000] 93.34 [0.000] 123.52 [0.000]
3 Domestic production, investment and consumption influence the others components of national GDP (e.g., government spending, net export, etc.) directly or indirectly.
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new product line or service, or can implement new production technologies, or can hire skilled labor, which eventually augments the overall production of the country. Hence, the investment in productive purposes might be boosted; in turn, this invest-ment positively affects the production and income of individuals and the country as a whole. This increased income would be further spent on investment and consump-tion expenditures, increasing the GDP as a result. Even if the access to such loans leads to a seemingly unproductive expenditure such as basic needs (food, health, education, etc.), the absence of these organizations’ programs would make the bor-rowers vulnerable to the informal lenders’ exploitation, dependent on charity or lack of the basic needs altogether. Ultimately, these can reduce the consumption level and their ability to work in the productive sectors. On the other hand, the trust agree-ments between credit providers and credit receivers are represented by the quantity of credit available. This system of trust agreements allows the greatest number of economic agents to participate in economic activity and contribute to the GDP. The more extensive the network, the more inclusive is the economy and the greater is the potential for a country’s GDP to grow.
Other than the amount of loans, the number of recipients or members has been taken into account, as the amount of loans can increase to some extent without increasing the number of recipients. An increase in the number of members refers to a rise in the number of people having access to credit. In the case of organizations such as Grameen Bank, ASA, and BRAC, women are preferred as the recipients of loans. An increase in the number of members, therefore, means an increasing number of women getting access to finance which would increase the participa-tion of women alongside men in production-related activities. The involvement of both men and women in economic activities ensures a regular income flow to the household. Thus, women’s access to credit, banking, and financial services will strengthen women’s rights, boost household productivity, eliminate hunger, and pro-mote economic growth. On the other hand, women’s higher economic participation and earning power through utilization of credit translate into greater investment in children’s education, health, and nutrition, which also leads to economic growth in the long-term.
The number of branches and GDP are also positively related. This is because the additional branches of the organizations mentioned above would more likely open in places where there are fewer branches, or no branch existed previously, and hence access to formal credit-providing institutions was absent for the targeted group. This also considers that the credit disbursing organizations that have been considered are active throughout the remote areas of Bangladesh, resulting in easier access to loans, which are usually used in economic activities and thus contribute to increasing GDP. Consequently, a positive correlation between these three independent variables and GDP can contribute to the conclusion that credit has a positive impact on GDP.
Effect of credit on agricultural GDP
The essential part of the credit in agriculture is to provide cash flow to obtain any sort of productive resources, land, and/or machinery. Therefore, credit gives way to
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numerous farmers changing their tasks to stay aware of the steady changes and, thus, to improve their activities. Table 4 also depicts the results to determine the effect of credit on agricultural GDP by observing the relationship between three independent variables and the dependent variable (agricultural GDP). The model shows that the explanatory variables can explain 74.8% of the variation of the dependent variable (agricultural GDP). However, loan amount, number of members, and number of branches positively correlate with agricultural GDP, and it is statistically significant at 5%, 1%, and 10% levels, respectively.
The credit-providing organizations selected for this study have the targeted loan schemes, e.g., crop loans, farm and irrigation equipment loans, etc., which are eas-ily accessible to the marginal and landless farmers as well as farmers with a plan to modernize their production practices. However, some farmers lack the primary financing necessary for production practices, e.g., buying seeds and fertilizers, trac-tors, deep tube well, motors for irrigation, and poultry equipment. Therefore, they have to depend heavily on sharecropping on unfavorable terms or informal lenders. On the other hand, more amount of loan disbursed may have increased the scale of production for farmers who want to increase production level, resulting in increas-ing the employment or the general wage level of the agriculture laborers. However, most of the farmers in Bangladesh operate their farms at the subsistence level, and they are often running after cash to manage their production inputs promptly. Due to insufficient cash and credit during the planting and harvesting season, they cannot involve themselves in the process of modernization and commercialization of their farms. Therefore, the provision of low-cost credit to farmers enables them to pur-chase sufficient fertilizers, pesticides, and other production inputs at the right time, allowing the production process to continue without interruption, increasing pro-duction and, ultimately, agricultural GDP (Islam 2020). Besides, farmers may pur-chase the necessary inputs, modern machinery, advanced production technologies, new practices, and experienced manpower to increase agricultural production when easily accessible and cheap credit is available on time and in sufficient amounts (Ahmad and Heng 2012). On the other side of the coin, agriculture is confronted with uncertain production risks caused by the possibility of variations in crop yields, seasonal weather variation, and diseases (Akhtar et al. 2019). However, there is no "one size fits all" criterion; instead, subsistence farmers needed risk management planning to combat production risk decision-making (Saqib et al. 2016). Low-cost credit plays a crucial role in this decision-making process through enabling diver-sified production activities, which ultimately minimize the risk and increase farm productivity as well as agricultural GDP as a whole.
The increase in the number of members is positively related to the rise in agri-cultural GDP. The targeted loan recipients are generally habituated in the remote or less economically advanced areas, most of which accounts for rural areas. In rural areas, 51.7% of people are employed in the agriculture sector, according to HIES 2016–17 preliminary report (BBS 2017). An increase in the number of loan recipi-ents can be translated to an increase in the number of people employed in the agri-cultural industry. This can increase the agricultural GDP by increasing investment in equipment, human capital, land, raw materials such as seeds, fertilizers, etc. The increasing investment in human capital means increasing investment in agricultural
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labor, whose wage can increase in general with the increased amount of loans and members, and ability to pay wage increases. Increased production and consump-tion fueled by the income generated by the production increase agricultural GDP. Even when the aforementioned organizations’ members are not directly employed in the agricultural sector, their overall consumption of farm products rises due to their increased income level by newly invested economic activities or simply because of the borrowed amount. This increased demand of the respective agricultural products creates a positive impact on agricultural GDP as well.
Increased number of branches is also positively related to agricultural GDP. An increased number of branches helps to provide loans to a broader geographical area or population covered by the organizations. In rural areas, formal means of a loan are scarce due to the infrastructural hurdle to reach the financial institutions. So, the poor people are deprived of access to finance and adequate investment in farm-ing activities. The increase in economic activities by rural people through those increased number of brunches creates more consumption or investment opportuni-ties in the agriculture sector which they were deprived of beforehand; thus, the agri-culture productivity upsurge.
Effect of credit on poverty reduction
Credit has substantiated itself as a solid catalyst for economic development and poverty alleviation. Credit beneficiaries uphold themselves through their increased income, as well as employing others and generating business for their improvement in work. Table 4 also shows the effect of credit on poverty reduction. Similarly, loan disbursement amount (million BDT), number of members, and branches of the selected organizations (Grameen Bank, ASA, BRAC, and Bangladesh Krishi Bank) are used as independent variables, and poverty trend is used as the dependent vari-able. Results reveal that the independent variables can explain 70% of the variation of the dependent variable. It is assumed that there are other factors apart from credit influencing the change in poverty trend.
The loan amount is negatively associated with poverty, and it is statistically sig-nificant at a 5% level. This study shows that the amount of loans provided by these four organizations has risen over the years. The amount of loans and the conditions make it more plausible for the people living under poverty or extreme poverty to be the recipients. With zero collateral and easier disbursement procedures, these loans provide the people living in poverty and consequently less educated and unac-customed to complex procedures the much-needed access to finance. This primary financial access protects them from informal lenders with unfavorable terms or depending on charity. People living below the poverty line have little or no assets and savings. The absence of formal institutions and the absence of credit mean they have to suffer in the event of an accident or economic shock. In case of such unintentional or accidental expenses, they naturally have to depend on lenders. The absence of credit lending institutions to the poor has made the cycle of poverty inev-itable. However, poverty is seemingly reducing with the increase in loan amount
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through different credit programs, as the people living under poverty are not being automatically forced to extreme poverty due to random economic shocks. Also, they are being able to invest in economic activities generating income and consumption. The increased consumption and investment are usually associated with an upgraded standard of life and reduced poverty.
The number of members of credit programs of the four organizations has increased over the period. This increase also can be translated into an increased number of female recipients receiving loans due to the policies of such programs. Clientele for such credit-providing institutions is typically low-income, economi-cally active, and impoverished rural women. Women are perceived as more docile, subservient, and fragile as debtors, making them more liable to repay. When the number of members increases, more people gain access to loans and spend the bor-rowed money for consumption and investment. In some organizations, the nature of these credit programs is such that it requires a group of people to disburse the loan. This means the whole group’s concerted effort helps everyone economically gain a better situation and reimburse the borrowed money together. This means that people without any kind of collateral securities and different kinds of employment work together to improve their conditions. The collaborative credit or group-based lending models of ASA, Grameen Bank, and BRAC are very familiar all over Bangladesh, especially in rural areas. These organizations work to link credit provision with the goal of society or community development and reduce poverty in its entirety. In the absence of collateral or credit-rating systems, the group-lending model relies on information ’impacted’ in the village about who is a trustworthy borrower, and group members reveal such reports by using their judgment. In this way, the group-based lending model builds its borrowers’ social capital and ensures the proper utili-zation of credit. As a result, group-based credit is efficient and effective for the pro-vider, with low transaction costs and a high utilization rate. Any additional income earned as a result of participating in a credit scheme can be spent on whatever cli-ents (or their households) consider being their top priority, enabling them to involve economic development activities, thus reducing vulnerability to poverty.
The increase in the number of branches is negatively related to poverty reduc-tion as well. The rise in the number of branches refers to more members or groups of members collecting loans who had not borrowed money before or simply used a more expensive method of borrowing money. This increased number of branches, in turn, disburses a greater amount of money. This mobilization of finance helps the poor people to improve their lives by investing in economic activities and consump-tion. As a result, the overall level of poverty diminishes due to these newly invested activities in the lives of the most deprived ones.
Conclusions and recommendations
In Bangladesh, credit can play a crucial role in achieving the expected economic growth. Therefore, in this study, an attempt has been taken to evaluate the impact of credit on GDP, agricultural GDP, and poverty reduction of Bangladesh. Research findings reveal that credit has a positive effect on GDP and agricultural GDP.
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Besides, the loan amount is negatively associated with poverty. An increase in the number of branches of different areas leads to an increase in the number of mem-bers, corresponding to an increase in the loan disbursement amount. The result also reveals that the variation caused by different credit programs in poverty is signifi-cant. Poor people can easily borrow money due to easy application procedures from various NGOs without collateral and put themselves in economic activities, thus may result in increasing their income and away from poverty.
The Bangladesh government should provide all the support necessary to the financial institutions to undertake credit programs in rural areas aimed at rural development. To generate income, proper utilization of credit as per the approved plan by the beneficiaries should be ensured. In this regard, financial institutions need to be established for an effective monitoring and evaluation system of credit utiliza-tion by the beneficiaries. In most cases, credit helped in generating seasonal self-employment that has little contribution to poverty alleviation. Therefore, emphasis should be given to generate full-time employment through credit.
Author contribution All authors listed have significantly contributed to the preparation and the writing of this manuscript.
Funding This research did not receive any specific grant from funding agencies in the public, commer-cial, or not-for-profit sectors.
Data availability Data included in the article are available from the corresponding author on reasonable request.
Declarations
Conflict of interest The authors declare no conflict of interest.
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Authors and Affiliations
Tanni Roy1 · Md. Emran Hossain2 · Md. Jahid Ebn Jalal3 · Jiban Krishna Saha1 · Eshrat Sharmin4 · Md. Akhtaruzzaman Khan5
Tanni Roy [email protected]
Md. Emran Hossain [email protected]
Md. Jahid Ebn Jalal [email protected]
Jiban Krishna Saha [email protected]
Eshrat Sharmin [email protected]
1 Department of Agricultural Finance and Banking, Sylhet Agricultural University, Sylhet, Bangladesh
2 Department of Agricultural Finance and Banking, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
3 WaterAid Bangladesh, Dhaka, Bangladesh4 South Asian Network on Economic Modeling (SANEM), Dhaka, Bangladesh5 Department of Agricultural Finance and Banking, Bangladesh Agricultural University,
Mymensingh, Bangladesh