Agricultural credit plays an important role in agricultural development.
Agricultural household models suggest that farm credit is not only necessitated
by the limitations of self-finance, but also by uncertainty pertaining
to the level of output and the time lag between inputs and output (De
Janvry and Sadoulet, 1995). Recent studies show the growth rate of investment
in agriculture is less than other economic sector. Agricultural financing
is one of the most important factors to develop rural areas in developing
countries. Payment of bank credit is a way of financing. In fact, facilitation
of access to credit can raise amount of productive investment. Credit
has a crucial role for elimination of farmer`s financial constraints to
invest in farm activities, increasing productivity and improving technologies.
Generally, credit accessibility is important for improvement of quality
and quantity of farm products so, that it can increase farmer`s income
and avoid from rural migration. On the other hand, some policy makers
believe that payment of credit with low interest rate to farmers can support
them against some results of development policies that threat their welfare
(Ghorbani, 2005). Therefore, with limited access to credit, the budget
balance becomes a constraint, where expenditures have to remain less or
equal to the sum of revenues during the period, accumulated savings and
credit availability. Hence, credit constraint limits the optimum production
or consumption choices (De Janvry and Sadoulet, 1995). In other words,
if a producer faces an infinite supply of liquidity at a given price,
the production decisions will be independent of consumption decisions.
When credit is rationed, some borrowers cannot obtain the amount of credit
they desire at the prevailing interest rate, nor can they secure more
credit by offering to pay a higher interest rate. In such circumstances,
liquidity can become a binding constraint on many farmers` operations.
Facing such a situation, households have to choose how to invest and what
inputs to buy, depending on the level of credit they receive.
One of the financial institutes has an important role in financing agriculture
sector is agricultural bank. This bank can direct agricultural credit
flow such that helps general economic policies of government. So, duty
of agricultural bank is financing of farmers and related industries and
participation in activities that private sector can`t invest in it. In
fact, access to credit for farmers is accompanied with some problems (Ghorbani,
Recent theoretical and empirical study in economics has established that
credit markets in developing countries work inefficiently due to a number
of market imperfections. The literature cites a number of market imperfections
which lead some potential borrowers to be rationed out of the credit market.
These imperfections include:
||Interest rate ceilings usually imposed by the government
||Monopoly power in credit markets often exercised by informal lenders
(Bell et al., 1997)
||Large transaction costs incurred by borrowers in applying for loans
||Moral hazard problems (Carter, 1988)
In many cases a number of these imperfections combine to ration farmers
out of the loan market. Zeller et al. (2001) found that in Bangladesh
credit access had a significant and strong effect on both income and food
consumption. In contrast, Diagne and Zeller (2001) found that lower profit
levels can come from a number of sources including lower investment levels
and a misallocation of variable inputs. The literature suggests that credit
rationing can cause a misallocation of resources in farm production. This
misallocation of inputs can then cause the credit rationed farmer to have
lower profit levels than his unconstrained neighbor (Carter, 1989; Feder
et al., 1990). Petrick (2004) indicated that access to subsidized
credit has a statistically significant role in determining investment
behavior of farmers. In various specifications of the credit-investment
relationship, the average marginal effect of credit on investment was
smaller than one, which implies that credit is partly used for purposes
other than productive investment. Ghorbani (1997) believes that because
of high transaction costs and interest rate, efficiency of formal credit
payment to farmers in Mazandaran Province of Iran is low. Chizari and
Zare (2000) showed the effect of credit on agricultural production is
positive and significant.
Regards to results of rural credit literature, farmers with credit access
problems will invest less in capital assets and their land. Credit rationed
farmers will not be able to smooth their expenses over time implying that
they will not make long-term investments, especially those which entail
sunk costs. A main strategy of governments in developing countries like
Iran is help to develop the rural areas and increase agricultural production
through investment in the sector, so farmer`s access to credit and conduct
of it to productive investment projects seems to be required. Although
in Khorasan-Razavi Province of Iran, 64% of total credit demand of farmers
is covered in 2006 but it hasn`t investigated how received credit is used.
Thus in this study, the role of credit access on agricultural investment
in Khorasan-Razavi Province has been identified and effect of different
policy options on investment probability is compared.
MATERIALS AND METHODS
Data: In this study stratified random sampling is used. Data collected
by interview with farmers in 4 cities of Khorasan-Razavi Province of Iran
in 2007. According to sampling method, total sample size is 177 and using
proportional allocation, 133 observations for credit used group and 44
observations for non credit used group is determined.
Theoretical framework: The subsequent empirical analysis is based
on a potentially non-linear, reduced-form investment equation of the following
type (Petrick, 2004):
where, I denotes the investment volume, K is the amount of long-term
credit, Z denotes the existing capital stock or, more generally the initial
farm size, ρ is a vector of dummies capturing regional and farmer
specific effects and ε is a random error term. There are two important
peculiarities compared with conventional neo-classical investment equations
(Elhorst, 1993). First, the equation contains a financial variable, K.
Second, there are neither user costs of capital nor prices included in
the equation. The first peculiarity is due to the assumed relevance of
the financial constraint (Petrick, 2004). The second is due to the fact
that the investment equation is estimated on a cross-sectional data set,
so that prices are assumed to be equal for all farms and hence excluded
(Feder et al., 1992). The relation between credit access and investment
is unambiguously positive. The effect of Z on I depends on the size of
the desired capital stock or farm size. A negative sign implies that farm
sizes converge over time, whereas a positive sign implies diverging farm
sizes. ρ includes a dummy indicating some farmer`s characteristics.
Logit model: Logit is a model to assess choosing behavior of person
who faced with events have just two options and one of them should be
selected. In this study for investigating the effects of access to credit
on agricultural investment, logit model on the basis of Eq.
1 is used. In order to test the effects of credit access on farm investments,
the dependent variable is defined as purchases of capital equipment, new
technologies. Capital equipment includes investments in tractors and machinery,
irrigation pumps and green houses. The new technology is drip irrigation,
resource saving and production increasing technology. So, If farmer uses
loan to invest in agriculture, value of dependent variable will be one
and otherwise zero.
Independent variables include: Received loan (X1),
age (X2), level of education (X3), family size (X4),
none farm income (X5), farm land (X6), farm income
(X7), No. of installments (X8), amount of saving
(X9) and previous investment (X10).
Relation between independent variables and investment is showed as follow:
where, Ii is latent (unobservable) variable, so dummy variable
of investment is used which get 0 and 1 values:
Probability of Ii = 1 will be pi that is shown
in relation 3:
Logit model limits probabilities between 0 and 1.
Marginal effect (ME) of independent variables indicate probability variation
of being in group Ii = 1 if Xi changes one unit.
Elasticity of dependent variables is calculated as follow:
where, Exi shows elasticity of ith variable and Λ(.)
represents logistic cumulative distribution function. Also, in order to
evaluation of applying policy options, following relation is used (Maddala,
RESULTS AND DISCUSSION
Effect of credit access on investment: Results in Table
1 shows that farm land, No. of installments and previous investment
by farmer are significant at 5% level but other variables like credit
volume are not significant. Signs of variables are as expected such that
sign of credit volume is positive that shows increasing of credit amount
led to agricultural investment and small loans mostly is used for current
consumption expenditures of farmer family. Positive sign of age variable
confirms the effect of farmer`s experience on usage of received loan.
Family size and education level has negative relation with dependent variable.
In fact, with higher level of education, farmer can earn from non farm
sources so, he does not tend to invest in agriculture. Increasing of farm
income causes to increment of probability of investment while sign of
none farm income shows reverse relation with variable of investment. Saving
variable indicates a direct relation with dependent variable, although
it is not significant at 5% level.
Based on Table 1 three variables include farm land (X6),
of installments (X8) and previous investment (X8) are
significant at 5 percent level, so these variable are the most important factors
influencing on investment behavior of farmers. Logit coefficients shows the
ratio of variation the logarithm of investment probability to none investment
probability [Ln(pi/1-pi)], if independent variables varied
one unit. So, increasing of X6, X8, X10 will
increase probability of I. In other word, land indicates farmer`s ability for
preparing required collateral to access the great loans that are more suitable
for agricultural investment. More No. of installments let farmer to use received
loan for investment in agricultural production that often has longer pay back
period. Positive effect of previous investment on dependent variable is quite
expected, because shows experiment of farmer in this field that can facilitate
new investment decision.
|| Logit model estimation for investment behavior of farmer
LR test = 48.4, Estrella R2 = 0.344, Maddala
R2 = 0.305, Cragg-Uhler R2 = 0.416, Mcfadden
R2 = 0.262, Right prediction = 0.74, *Significant at 1%
|| Effect of policy options on investment probability
To find effect of unit variation of dependent variable on independent
probability, marginal effect is used. Also, elasticity of variables is
calculated to determine relative importance of them. Two kind of elasticity:
elasticity at mean and aggregated weighted elasticity estimated that aggregated
weighted is more reliable. So, in this study, values of marginal effect,
elasticity at mean and aggregated weighted elasticity are used.
According to results shown in Table 1, elasticity and
ME for farm land (X6) are 0.1825 and 0.019, respectively. It
means, if farm land increases on hectare then probability of applying
loan for agricultural investment will increment 0.019 units. Also, with
1% growth of farm land, will increase investment probability up to 0.1825%.
Elasticity and M.E for X8 are 0.349 and 0.013 while for X10
are 0.073 and 0.31, respectively.
Comparison of significant variables of logit model shows that number
of loan installments is the most important factor in investment decision
of farmer. Farm land and previous investment are the next factors, respectively.
These results should be considered in the field of rural financing to
motivate investment in agriculture sector and increase efficiency of credit
LR test is used to determine signification of model. Results showed that
estimated model is significant at 1% level. Values of McFadden, Maddala,
Estrella and Cragg Uhler statistics are 0.26, 0.30, 0.34 and 0.41, respectively.
Also, right prediction is 74% that is acceptable.
Policy options and agricultural investment: Effect of different
policy options on probability of investment is shown in Table
2. If single policies are used, No. of installments (X8),
farm land (X6), previous investment (X10) have the
most effect on investment probability, respectively.
Increasing farm land and No. of installments simultaneously is the best
supplementary policy that increments probability of investment up to 0.385
units. If three variables are used together as a policy then investment
probability will be increased up to 0.441 units.
In fact, farm land represents financial situation of farmers and ability
of control the repayment risk. Because of seasonally nature of agriculture
and long period of pay back for initial investment in some agricultural
activities such as horticulture, longer repayment period led to increase
the probability of investment. These results should be considered in the
process of credit payment in agriculture bank. With attention to results
of this study following suggestions are introduced:
||Results showed that longer repayment period and more
number of installments led to motivate the desired investment in agriculture.
So, bank can investigate the financial situation of farmers and profitability
of farm production to set a financial profile and ranking customers
to pay the loans with longer repayment period and more of installments
that are suitable for agricultural investment
||Increasing of farm land and No. of installments simultaneously have
the most effect on probability of investment as a policy option so
should be considered in financing strategies
||Results confirmed positive effect of credit on agricultural investment.
Thus, the gap between supply and demand of credit should be decreased
by efficient credit payment program