Introduction
Rapid urbanization, climate change and disaster risks place pressure on
agricultural production and baseline vulnerability. While there has been
a steady increase of population growth during the past decade, it is
very important for future generations to be able meet sufficient
quantity of appropriate food available in an inclusive and sustainable
manner. Obviously, food security and sustainability can be achieved by
increasing productivity in agricultural production and finding
alternative sustainable ways to produce more food on less land. In
addition to parameters such as fertilizer, irrigation, medicine, seed,
labor, soil, tool-machine and technology usage, agricultural financing
with low-interest for elimination of farmers‘ financial constraints to
carry out their activities more comfortably and efficiently is vital to
sustainable rural development, particularly for the least developed
countries. Besides, advances in technology within agriculture
have forced businesses to use more modern inputs and increased their
capital requirements to access new knowledge, investments and innovative
farming methods. Furthermore, price risks due to the low elasticity of
demand in agricultural products can result in uncertainties in the
incomes of producers and thereby, necessitate the use of policy tools
such as measures to support producer prices, subsidies for agricultural
inputs, and short-run financing loans to producers (Thompson, 1916;
Leatham and Hopkin, 1988; Binswanger, 1989; Tweeten and Zulauf, 2008).
First and foremost, agricultural sector plays a strategic role in
reaching social welfare development through spreading employment and
income gains. Coinciding with steady growth in real wages, increasing
productivity growth in all sectors is critical to increase domestic
demand. To increase agricultural productivity, as well as inefficient
production methods, labor market distortions should also be prevented.
Besides, in agricultural development process, agricultural support
policies such as cash subsidies, credits, tax benefits make agriculture
competitive, improve investments, increase resource allocation
and profitability, and thereby, continue farming in a sustainable manner
(Kahan, 2013). Specifically, stimulating agricultural credit policies
ensure the quantity and continuity of agricultural production by
providing input to higher productivity gains. Especially, the existence
of mostly small sized farms in low-income countries, most of which are
family- owned and operated, the timing, techniques and conditions
of harvesting, adopting new technology and diversifying their production
are responsible for agricultural credit demand. Consequently, the
uncertainties inherent in agricultural production raises the possibility
that the producers do not always get the expected income and lead to
reduced productivity by increasing vulnerability (Kahan, 2013; Rajan and
Ramcharan, 2015).
The main challenge for developing countries is to improve their
agricultural credit systems. For those countries, first and foremost,
access to credit, especially for small-sized family farms, needs to be
improved. Generally, lending to agricultural sector can be classified
into mainly two types according to their intended use: Short-run or farm
operating loans to help with the day-to-day expenses and investment
loans to support agricultural enterprises to finance their investment
expenses (Salami and Arawomo, 2013). Another aspect that deserves
attention is the relationship between the agricultural credit and
agricultural growth, even its impact on economic growth. As a matter of
fact, agricultural sector has continued to show its important role as a
”buffer sector ” in the economy by providing employment during
economic downturns in other sectors. However, above and
beyond this, the ability of credit to induce agricultural
productivity is also an issue for many developed countries worldwide.
Besides this, access to adequate credit affects farm output by
contributing to sustainability of farming systems and technology. Since
managing risk to stabilize farm income is an important aspect of farming
business, access to credit is a key component in protecting farmers
against uncertainties. That is, easy availability and access to credit
increases farmers and entrepreneurs lead to more diversified options to
undertake new investments. One of the arguments for the contribution of
agricultural credits on agricultural productivity by Braverman and
Guasch (1986) is that agricultural loan interest rate should not be
market rate, otherwise these types of programmes may result in some kind
of subsidy and income transfer; that is, since income transfer and
subsidy is proportional to the size of loan, larger landholders and
larger farms receive larger income transfers and subsidies. However, it
is crucial to determine to what extent financing of agricultural sector,
especially through increasing the availability of bank credits to
agricultural businesses and farmers have an impact on agricultural
productivity. But more importantly, to what extent should we consider
that agricultural financing may be more effective in contributing to
higher agricultural productivity as well as stimulating growth? Going
one step further, do making efforts to subsidize agricultural credits in
developing countries and providing credit cheaper and easier to finance
new technology adoption give an opportunity to those countries to
promote rural development rapidly increasing agricultural output and
productivity?
A substantial number of recent studies were devoted to investigating the
effects of agricultural credits on agricultural productivity. As
regards agricultural productivity, a great deal of research (Siriram,
2007; Das et al., 2009; Ali et al., 2014; Misra et al., 2016; Rehman et
al., 2017; Seven &Tumen, 2020; Tuan Anh et al., 2020; Chandio et al.,
2021, Manoharan & Varkey, 2021) identified the role of credits on
agricultural productivity. While some of these studies used farm-level
data, some of them has identified and addressed linkages focusing
specifically on country-level data. For instance, Chandio et al. (2017)
provided evidence on the positive impact of formal credit on sugarcane
productivity with a farm-level data for Pakistan. Rehman et al. (2017)
did a discrimination among loan types using time series data and
concluded that total food production, loan disbursed by Modern
agriculture technology machinery and Agriculture loans in Pakistan
(ZTBL) and the total loan disbursed by various institutions had a
positive and significant influence on the agricultural gross domestic
product whereas cropped area and cooperatives loan had a negative but
insignificant influence on the agricultural gross domestic product using
time series data in Pakistan.
Misra et al. (2016) observed a
positive impact of the intensity of agricultural credit on total factor
productivity in agriculture under state-level panel model for the Indian
economy. A more comprehensive study by
Seven and Tumen (2020) used
country-level data covering 104 countries for the 1991-201 period.
Accordingly, agricultural credit expansion contributes to high
agricultural growth rates in almost all countries; however, this
positive effect may vary according to their level of development. To the
best of our knowledge, no prior work has yet been carried out to
investigate the nexus between agricultural credits and agricultural
value added as a proxy for agricultural productivity under a
heterogenous panel cointegration and panel autoregressive distributed
lag (ARDL) approach.
In the discussion of the role of agricultural credits in agricultural
productivity both in short-run and long-run, the ARDL of pooled mean
group (PMG), mean group (MG) and dynamic fixed effects (DFE) approaches
are employed using a dataset comprising 53 countries in the period from
2000 to 2018. FMOLS (Fully Modified OLS) and Dynamic OLS (DOLS) and
panel pairwise causality tests are used as robustness tests. According
to this narrative, we are interested in studying the effect of
agricultural credits on agricultural productivity with inflation, net
foreign direct investments and general government total expenditures as
our control variables to avoid omitted variable bias.
The rest of this study is structured as follows: section 2 presents the
methodology; section 3 presents the data; empirical results are
summarized in section 4 followed by conclusion in section 5.
Empirical Methodology
The aim of this empirical analysis is to investigate the short-run and
long- run effects of agricultural credits on agricultural productivity
by considering a global sample of 53 countries over the period
2000-2018. The countries in the panel are listed in Appendix A.
We initially present the conceptual framework that we follow and then
outline the statistical approach that we implement to estimate the
long-run equilibrium parameters.
Before the estimation, it is necessary to investigate the
characteristics of the cross sections and time series, as well as to
control for the existence of specificities which may lead to
inconsistent and incorrect results. In this context, a set of
preliminary tests should be performed before estimating the model of
interest as Variance Inflation Factor (VIF) to check for the existence
of multicollinearity, Cross-section Dependence (CSD) test
(Pesaran, 2004) to account for
serial correlation of an unknown form in the error term, second
generation unit root test (CIPS-test)
(Pesaran, 2007) to test the
stationarity of the data, and finally, second generation cointegration
test (Westerlund, 2007) to check the order of integration of the
variables under consideration for establishing long-run relationship
among them.
Based on the results of these tests, we consider a heterogeneous dynamic
panel model to estimate the relationship between agricultural credit and
agricultural productivity. A combined autoregressive distributed lag
(ARDL) panel approach, namely, the Mean group (MG) developed by
Pesaran and Smith (1995), the
Pooled mean group (PMG) developed by
Pesaran et al. (1999) and the
Dynamic Fixed Effect (DFE) estimator to estimate the short-run and
long-run linkages between agricultural value added and agricultural
productivity with other control variables.
Pesaran et al. (1999) developed two estimators to estimate the panel
ARDL model: MG (Mean Group Estimation) and PMG (Pooled Mean Group
Estimation). The MG estimator places no restrictions on the coefficients
in the long-run. PMG approach, associated with pooling and averaging of
the coefficients over the cross-sectional units, allows a greater degree
of parameter heterogeneity then the usual estimator procedures by
imposing common long-run relationship across countries while allowing
heterogeneity for the short-run. In other words, PMG restricts the
long-run coefficients but allows the constants, error term variances,
and short-run coefficients to vary. Therefore, PMG allows short-run
coefficients and error variances to vary across different groups while
assuming a homogeneous long-run relationship between dependent and
explanatory variables (Tong et
al., 2016). By contrast, DFE model further limits the speed of
adjustment coefficient and the short-run coefficient to be the same or
equal and subject to the bias between the error term and the lagged
dependent variable. However, the model features country-specific
intercepts allowing different intercepts and groups.
As shown in Pesaran and Shin
(1996), the aim of panel ARDL approach is to estimate the relationship
between agricultural productivity and agricultural credit and can be
specified by the following equation:
\(Y_{\text{it}}=\sum_{j=1}^{p}{\beta_{i,j}Y_{i,t-j}+\sum_{j=0}^{q}{\gamma_{i,j}X_{i,t-j}+\mu_{i}+\varepsilon_{\text{it}}}}\)(1)
By rearranging terms such as:
\(Y_{\text{it}}=\varnothing_{i}\left(Y_{i,t-1}-\theta_{i}X_{i,t}\right)+\sum_{j=1}^{p-1}{\beta_{\text{ij}}^{{}^{\prime}}Y_{i,t-j}+\sum_{j=0}^{q-1}{\gamma_{\text{ij}}^{{}^{\prime}}X_{i,t-j}}}+\mu_{i}+\varepsilon_{\text{it}}\)(2)
with i and t representing country and time respectively, Y is the
agriculture, forestry, and fishing
value added as a percentage of GDP, \(X_{i,t}\) is a \(kx1\) vector of
explanatory variables containing the credit to agriculture, net foreign
direct investments as a percentage of GDP, annual rate of changes in CPI
(%) and general government total expenditure as a percentage of GDP,\(\varnothing_{i}\) is the group-specific speed of adjustment
coefficient, \(\theta_{i}\) is the long-run coefficients of explanatory
variables, ECT=[\(Y_{i,t-1}-\theta_{i}X_{i,t}]\) is the
error correction term and finally, \(\beta^{{}^{\prime}}\) and \(\gamma^{{}^{\prime}}\) are
represent the short run
coefficients linking agriculture, forestry, and fishing value added with
its past values and the variables of interest\(X_{i,t}\)
Further, one more estimation technique is employed as a part of
robustness check. The Dynamic Ordinary Least Square (DOLS) proposed by
Stock and Watson (1993) and later
extended by Kao and Chiang
(2001). DOLS method can be applied with mixed and
higher orders of integration and frequently used in estimating long-run
nexus for heterogeneous panel by correcting simultaneity, endogeneity,
serial correlation and small sample bias among the regressor. Fully
Modified OLS (FMOLS) is also commonly used to check the robustness of
DOLS results in the literature; however, this approach essentially
requires that all variables must have the same integration order
(Yahyaoui and Bouchoucha, 2021).
As Kao and Chiang (2001) proposed, DOLS outperforms FMOLS estimators
in terms of mean biases. Ali et
al. (2017) notes that the most significant benefit of DOLS is that the
test considers the mixed order of integration of variables in the
cointegration framework. (Stock and Watson, 1993,
Masih and Masih, 1996; Kumar et
al., 2021).
Data
An annual balanced panel data set of 53 countries over the 2000-2018
period was used. The data for a selection of countries are drawn from
the World Development Indicators (WDI) database provided by the World
Bank (2019) and listed in Appendix A. The descriptions of the variables
used in this study are presented in Table 1. The selection of the
countries and time period is limited by data availability. Agriculture,
forestry, and fishing value added series (AGV), the output of the
agricultural sector less the value of intermediate inputs as identified
by FAO statistical annex, were used as a proxy for agricultural
productivity. (For more information, please see
https://www.fao.org/3/a0050e/a0050e10.htm.)
AGC is the Credit to agriculture, forestry and fishing, in constant LCU,
INF is Inflation as the annual percentage change in consumer prices, FDI
is the net foreign direct investments as a percentage of GDP, INF is the
annual rate of changes in CPI (%) and GOV is the general government
total expenditure as a percentage of GDP. AGV and AGC are converted into
natural logarithm for consistent and reliable empirical results. As it
can be seen on Figure 1., there is a strong correlation between
agriculture productivity and agricultural credits.