EMPIRICAL ANALYSIS OF CREDIT SUPPLY AND AGRICULTURAL OUTPUT IN NIGERIA

BERNARD, OJONUGWA ANTHONYDepartment Of Economics

Kogi State University, Anyigba. Email: tondecobros@yahoo.com

08065499711, 08070539895AbstractA strong agricultural sector would enable a country like Nigeria to meet the challenges of the recent economic crises ravaging the whole world by providing food for the teeming population, generate employment, foreign exchange earnings and raw materials for industries. The paper therefore, empirically analyse the effects of Credit Supply on Agricultural Output in Nigeria. This study uses the time series data that span a period of 23years (1986-2008). The study specifies a Multiple regression Loglinear Model (base on the theoretical framework of Cobb-Douglas production function) with four explanatory variables. That is bank loans and advances, government capital expenditure on agriculture, agricultural credit guarantee scheme and foreign investment on agriculture. The study makes use of the OLS method to test the significance of the explanatory variables on output of agricultural sector in Nigeria. The result revealed that except the foreign direct investment on agriculture, other variables expressed significant influence on agricultural output in Nigeria. The researcher concludes that, there is need to enhance and monitor credit supplied for agricultural purpose to effectively attain the expected growth in the sector.

Introduction

Agricultural sector in Nigeria was the most dominant sector before the

early 1970s. Until the early 1970s, it was the major development drive of the

economy employing over 80% of the active population. (Anyanwu et’al,

1997). It also contributed to over 60% of the nation’s Gross Domestic Product

(GDP) and provided nearly 100% of the economy’s food requirement; raw

materials to industries and the country’s export earnings among others.

Prior to early 1970s there were significant growth in this sector, but

during the oil boom era when crude oil became a major export earner,

agriculture began to falter as its contribution to GDP began to decline from

over 60% in early 1970 to 30% and 40% (Aigbokhan, 2001) and less than

26% between 2000 and 2007(CBN, 2007). These indicate that the discovery 1

of oil as the fastest means of revenue brought about devastating neglect of

the agricultural sector.

Due to the aforementioned problems, over a decade, most government

policies have been directed towards accelerating economic development with

the ultimate aim of transforming the economy into an industrialized one, as

well as raising the welfare of the people. One of the sectors expected to act

as a catalyst towards the realization of this goal is the agricultural sector. This

is measured by increasing the output of agricultural sector to meet the

demand of the people and the industries.

In order to increase the output of agricultural sector, government over

the years has been given priority to agriculture in its budget, directing financial

institution to make credit available to farmers. Agricultural credit is expected

to play a vital role in agricultural development (Duong and Izumida, 2002).

Agricultural Credit has over the years been identified as a major input in the

development of the agricultural sector in Nigeria (CBN, 2005). The decline in

the contribution of the sector to the Nigerian economy has been attributed to

the lack of a formal national credit policy and paucity of credit institution,

which can assist farmers in the purchase of farm inputs (Rahji and Fakayode,

2009). The provision of these input by the sector is important because credit

or loan-able fund helps in determining access to all the needed inputs to

facilitate farming. Access to agricultural credit has been severely constrained

in developing countries. This is because of the imperfection and costly

information problems encountered in the financial markets (Swinnen and

Gow, 1999). Such problems are common and particularly important in

agriculture (Stiglitz, 1993).

To ameliorate the prevailing problem of credit supply to agricultural

sector, government of Nigeria came up with the Agricultural Credit Guarantee

Scheme (ACGS) in 1978, with the objective of providing guarantees in

respect of loans granted for agricultural purposes by any bank in accordance

2

with the provisions of the Act and with the aim of increasing the level of bank

credit to the agricultural sector (Anyanwu et’al 1997). In addition, were the

gentle appeals to make loans and advances available to agricultural sector by

commercial banks.

As the major objective of this research, we shall empirically estimate

how the various credit supplied by the government and other financial

institutions have contributed to the output growth of agricultural sector in

Nigeria.

While this section introduces the subject, section 2 and 3 are for

literature review and methodology of research respectively. Section 4

estimates the model for this study, hypotheses are tested and regression

results are analyzed. Section 5 concludes the work and provides policy

recommendation that will boast agricultural development in Nigeria.

Literature review

Theoretical Literature

Ekpebu (2006), reviews that the performance of the agricultural sector

has been unsatisfying over the years due to insufficient funding or credit

facilities, inadequate infrastructural facilities, low technology base, high cost

of farm input and inadequate extension services. Trzeciak-Daveal (2003), in

his own view opined that agriculture like all other sectors of the economy

needs credit for its development. Experience was drawn from Organization for

Economic Cooperation and Development (OECD) countries. He

demonstrated that in a competitive financial environment, profitable

agriculture can obtain the credit it needs, also suggested that government

have a vital role in channeling fund to agricultural sector through its policy

making. Radolphe (2005), bringing together loan commitment theories and

credit rationing theories, within a framework of asymmetric information

between lenders and borrowers and under costly termination of lending

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arrangements, commitment may explain the accumulation of non performing

loans by banks. In this theory, two additional results follow: That banks favour

borrowers with well known production functions and long-term credit history

and that interest rate may be large if significant market imperfection prevail. In

agricultural household models, farm credit is not only necessitated by the

limitation of self-finance and government expenditure, but also by uncertainty

pertaining to the level of farm inputs and outputs and the time lag between

inputs and outputs (Sighh et’al 1980). CBN (2003) identified access to

agricultural credit as factor responsible for the sustainable growth in the

agricultural sector. Also, government has a vital role in the growth of

agricultural sector in Nigeria (Obiechina, 2007).

Ekechi (1977) supported the view that raising the volume of financial

savings will increase the volume of total deposit of the banking sector which

will further lead to increase in the supply of credit to other sectors of the

economy (agricultural sector inclusive).

The theoretical basis of this study is anchored on the 2-Gap models or

the Harrod-Domar model and the Cobb-Douglas production function.

According to Harrod-Domar model, there exists a domestic saving gap and

foreign exchange gap in developing countries. The domestic savings gap

exist when the domestic savings capacity falls bellow that necessary to

permit the level of investment required to achieve a particular rate of growth in

the economy. While available inputs are adequate. In this situation foreign

financial resources cover this gap or make up the deficit and permit

achievement of the expected growth rate. By implication, the foreign

exchange gap exists if with adequate domestic savings, the flow of import is

not sufficient because there is inadequate foreign exchange to finance it.

Again, foreign capital breaks the import bottleneck and permits the target

growth rate to be realized.

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The Cobb-Douglas (CD) production function is also a substantial guidance for

specifying supply–side agricultural potential output which is primarily

determined by measurable input factor (Q=AkαLß). This theory is to a large

extent consistent with the theory of supply of production function that

underlies specification of the supply-side of agricultural output. The Cobb-

Douglas (CD) production function was derived from the observation by Cobb (1928)

and Douglas (1948) that over the long-run, the relative share of National Output

earned by Labour (L) and Capital (K) tends to be constant. The CD function further

assumes constant returns to scale and unitary elasticity of substitution. The CD

production is generally given by the equation:

Q = AKβLα 1Where: Q = Total Output

K = Capital L = Labour A Efficiency Factor β and α = Substitution Parameter β = (1- α) and β + α = 1

Linear homogeneity of CD Production Function

If we increase each factor in equation (1) by a constant λ, we have

Q = A (λK)β (λL)α 2

Q = Aλβ + α KβLα

Q = λAKβLα ( since β + α =1) 3 Therefore, λ = 1

From equation (3), we observed that the CD production is linearly homogeneous in

Labour and Capital. This implies that, if we increase all inputs by a constant multiple

(λ), output will increase by that same constant. Thus the Cobb-Douglas function is to

be characterised by constant return to scale.

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Slope and Convexity of Isoquant

dK = ־ β/L = - β . K 4dL α/K α L

Strict Convexity is established by the expression

dK 2 = ־ β . 1 LdK – K > 0 5 dL2 α L2 dL

Therefore, Isoquant is Strictly Convex

Average and Marginal Physical ProductFrom equation (1)

APPL = Q = AKβLα = AKβLα – 1 6 L LAPPk = Q = AKβLα = AKβ -1Lα 7 K K

MPPL = ∂Q = α AKβLα – 1 8 ∂L MPPK = ∂Q = βAKβ - 1Lα 9

∂KOutput Elasticity of Inputs

Output Elasticity of Capital MPPK = βAKβ - 1 L α = β 10 APPk AKβ -1Lα

MPPL = α AK β L α – 1 = α 11APPL AKβLα – 1

Elasticity of Substitution

Assuming that firms behave rationally (minimizing cost) then,

∂Q ∂Q∂L w ∂K r 12

Where w = wage rate and r = unit cost of Capital

∂K = w∂L r 13

From equation (4) above, α = ∂K . L 14

6

β ∂L K

Consequently, K = β . ∂K 15 L α ∂L

Combining equation (13) and (14), we have K = β .w

L α rThis implies that a given percentage change in ∂K will lead to an equal percentage in

∂LInput ratios.

However, due to the peculiarity of the objectives of the study, the

specification of the production function shall incorporate variables such as;

commercial banks loans and advances to agricultural sector, government

capital expenditure on agriculture, agricultural credit guarantee scheme and

foreign direct investment on agriculture. Most of the variables are not as

specified by Harrod-Domar and Cobb-Douglas but are regarded as capital

and fund for investment that strongly influence the domestic output growth.

Empirical Literature

Otu and Balogun (1991) in their study of credit policies and agricultural

development in Nigeria tested two hypotheses that credit policies influence to

a large extent the behaviour of both constitutional lenders and borrowers.

That is, credit policies can influence favourably the supply and demand for

agricultural credit. Secondly, that a positive relationship exists between

agricultural credit and a host of other variables such as output and use of

modern inputs. Empirically they concluded that credit policies play very little

role in influencing both lenders and borrowers behaviour. Credit subsidies are

also major sources of production disincentive. They further contend that there

is need to re-examine the overall objective of agricultural credit policies

largely because it will be erroneous to infer that finance plays little role in

agricultural development of the economy. Raji and Fakayode (2009) tried to

identify the determinants influencing commercial banks decision to ration

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credit in South-Western Nigeria. Data analyzed were from agricultural credit

transaction of banks in Nigeria. Evidence from the multinomial model

estimated shows that borrowers are heterogeneous.

Akpan (1999) uses time series data of 33 years, and the OLS method of

regression to analyze the contribution of government expenditures to the

growth process in Nigeria. He concluded that capital expenditure on

agriculture though not statistically significant but influence positively on

investment.

Oguamanam (1996) did an empirical work on commercial bank credit to

agriculture sector in Nigeria. From the analysis, commercial bank loans and

advances has positive relationship with the level of agricultural output, federal

government capital expenditure contributed positively to the growth of

agricultural output in Nigeria. Similar work was carried out by Nnanna (2001),

on bank lending behaviour and output growth with implication on monetary

policy in Nigeria. He revealed a significant relationship between banks lending

behaviour and output growth. He further suggested that in the medium-term,

the decline in output has negative influence on bank credit to private sector.

Also Isijola (2000) revealed a significant relationship between credit supply

and agricultural output in Nigeria. isijola also identified commercial banks’

loans and advances, Agricultural Credit Guaranteed Scheme as the

determinant of agricultural credit supply in Nigeria.

Shanggen et’al (1998) in their empirical analysis on government

spending, growth and poverty, supported the view that government spending

enhance the growth in agricultural productivity. His managerial analysis also

shows that additional government expenditures on agricultural research and

extension have the largest impact on agricultural productivity growth.

Conclusively, this study deviates a little bit from the studies reviewed by

segregating activities thereby looking at the effect of credit supply on

agricultural output in Nigeria.

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Methodology of Research

Secondary data on credit supply and agricultural outputs are employed

for this study. The data are obtained from Central Bank of Nigeria (CBN)

publications.

This study makes use of analytical tools which consist of the use of

ordinary least square (OLS) regression.

The research adopts the Harrod-Domar model but in a modified form

based on the theoretical assertion for this study.

Specification of Model and Definition of Variables

Q = BL1GE2AC3FI4℮µ…………………………….1

Applying the logarithm transformation:

InQ = ln+1lnBL+2lnGE+3lnAC+4lnFI+µ…………..2

Note lne =1, therefore, eµ = µ and ln =logarithmic

Q = Output of major Agricultural Commodities (staples and other crops)

BL = Bank’s loan and advances to Agricultural sector

GE = Government Capital Expenditure on Agricultural Sector

AC = Agricultural Credit Guarantee Scheme Fund

FI = Foreign Direct Investment on Agriculture

= Intercept term.

1, 2, 3 and 4 = Elasticity of Output (Q) or the Coefficients of the variables.

µ = error term.

The sum of the estimated coefficients (1+2+3+4) gives the

homogeneity of the functions. If the sum is = 1, we have a constant return to

scale in agricultural output, If >1 we have an increasing return to scale and

<1, we have a decreasing return to scale in output. On the a priori, 1>0, 2>0,

3>0 and 4 >0.

Q = lnQ9

= ln

BL =lnBL

GE =lnGE

AC =lnAC

FI = lnFI

Plug these into equation 2.

Q* = *+1BL*+2GE*+3AC*+4FI*+µ ……………3

Therefore, the OLS can be applied to the linearised model (eqn 3) to

obtain the estimate of the coefficients. The (*) indicates natural logarithms,

Hypothesis to be tested are:

1 =0, credit supply has significantly influenced the output of agriculture in

Nigeria

1 ≠ 0, Credit supply has not significantly influenced the output of agricultural

credit in Nigeria.

Where: i = 1-4.

Estimation of Model and Analysis of Results. Estimation of Model:

Variables Coefficient

t Change Statistics

Durbin-Watson

Std. Error

Constant BL GEACFI

0.3550.5010.3650.683-0.357

2.9810.2620.2300.2890.360

0.1191.9101.5842.363-1.034

R Square= 0.866Adjusted R2=0.837F = 29.140df1 = 4df2 = 18 2.065

n = 23Except the Foreign Direct Investment on agriculture, other variables

conformed to the economic a priori expectation. A 100 percent point increase

in bank loans, government capital expenditure and the agricultural credit

guarantee scheme lead to about 36%, 50% and 36% increase in agricultural

output as influenced by each variable respectively. And a 100 percent point

10

increase in foreign investment in agricultural leads to 35% fall in agricultural

output in Nigeria.

Comparing the calculated t-value of each variable and the theoretical t-

value of 1.730, other variables are significant at 90 percent level of significant

except the foreign direct investment that is not significant at that level of

significance. With the four variables employed, we can explain 87 percent of

the systematic variation in agricultural output in Nigeria. This result is quite

good. The remaining 13 percent may be explained by other variables that

could influence agricultural output though, not specified in the model. Such

variables could include fertilizer, pesticide, rainfall, soil fertility, availability of

farmland and the demand for agricultural products. The F-value is highly

significant at 95% level of significance. The result also suggests the absence

of autocorrelation and multicollineariity in the model. However, this satisfies

the desirable properties of unbiasedness, efficiency and consistency in the

use of OLS.

Conclusions and Recommendations

Conclusion

An increase in credit supply through the approval of commercial banks’ loans

and advances, government capital expenditure on agriculture and agricultural

credit guarantee scheme fund lead to increase in the output of agricultural

commodities in Nigeria. Also, over the period, foreign direct investment on

agriculture has not been significant in increasing the output of the sector. The

study also concluded that the sector experienced a slight increasing return to

scale over the years. Despite this, the rate of increase is not enough to meet

the challenges facing the agricultural sector vis-à-vis , food insecurity over the

years.

Recommendation

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The study confirmed the use of credit supplied to agricultural sector in Nigeria

as a panacea for the growth in the sector. The government of Nigeria should

see agriculture as the core of economic activities in terms of its employment

and income generation; inter linkages with other sectors of the economy.

Above all, is the supply of food to the teaming population.

As factor identified as the determinants of agricultural output, government

should make policies that will direct credit inflows through banks’ loans and

advances to the sector, good percentage of government budget should be

made available for agricultural activities. To encourage foreign investors to

the sector, government should make policies that can strengthen Public-

Private-Partnership in the sector and a conducive economic atmosphere for

their existence.

It is recommended that government should monitor credit meant for

agriculture purpose to facilitate the efficient utilization of the credit.

This study serves as baseline information to policy makers in the formulation

of policy measures on credit administration, allocation and provision in the

agricultural sector in Nigeria.

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APPENDIX I

COMMERCIAL BANKS’ LOANS AND ADVANCES, GOVERNMENT CAPITAL EXPENDITURE, AGRICULTURAL CREDIT GUARANTEE

15

SCHEME FUND, FOREIGN DIRECT INVESTMENT AND MAJOR OUTPUT OF AGRICULTURAL COMMODITIES IN NIGERIA

Year Major Output Of Agricultural Commodities

(‘000 tones)

Commercial Banks’ Loans And Advances

N’Mill

Government Capital Expenditure

N’Mill

Agricultural Credit Guarantee Scheme Fund N’Mill

Foreign Direct Investment In Agricultural N’Mill

19861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008

9200.09164.09849.010754.011364.011892.012227.011456.011448.011270.012891.013042.014302.01476.015230.015367.015645.016735.720389.617752.818385.918505.918882.5

1830.32427.13066.73470.54221.45012.76978.910753.017888.825278.733264.127939.327180.7118518.3146504.5200856.2227617.6242185.7261558.6262005.549393.482212.0520311.0

892.5365.1595.7981.51758.5551.2763.01820.02800.14691.73882.86247.48876.66912.66761.757879.032364.48610.948047.87939.415176.822618.729958.3

68417.4102152.7118611.0129300.398493.482107.491953.080845.991821.1163938.6243608.0244025.2217699.0246993.5357832.0810821.11062391.81894281.43308704.3706969.04265066.34427868.96721074.6

128.2117.3128.9134.8334.7382.8386.41214.91208.51209.01209.01209.01209.01209.01209.01209.01209.01209.01209.01209.01209.01329.91397.2

Source: Central Bank of Nigeria Statistical Bulletin, 50years Special Anniversary Edition, December, 2008.

APPENDIX II

COMMERCIAL BANKS’ LOANS AND ADVANCES, GOVERNMENT CAPITAL EXPENDITURE, AGRICULTURAL CREDIT GUARANTEE SCHEME FUND,

16

FOREIGN DIRECT INVESTMENT AND MAJOR OUTPUT OF AGRICULTURAL COMMODITIES IN NIGERIA

Year Major Output Of Agricultural Commodities

(‘000 tones)

Commercial Banks’ Loans And Advances N’Mill

Government Capital Expenditure

N’Mill

Agricultural Credit Guarantee Scheme Fund N’Mill

Foreign Direct Investment On Agricultural N’Mill

19861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008

9.1279.1239.1059.2839.3389.3849.4119.3469.3469.3309.4649.4769.5687.2979.6319.6409.6589.7259.9239.7849.8199.8269.846

7.5127.7958.0288.1528.3488.5208.8519.2839.74210.13810.41210.23810.21011.68311.89512.21012.33512.39712.47412.47610.80811.31713.162

6.7945.9006.3906.8897.4726.3126.6377.5077.9378.4548.2648.7409.0918.8418.81910.96610.3859.06110.7808.9809.62810.02710.308

11.13311.53411.68411.77011.49811.31611.42911.30011.42812.00712.40312.40512.29112.41712.78813.60613.87614.45415.01213.46915.26615.30314.334

4.8544.7654.8594.9045.8135.9485.9577.1027.0977.0987.0987.0987.0987.0987.0987.0987.0987.0987.0987.0987.0987.1937.242

Computed Logarithm*

Source: Central Bank of Nigeria Statistical Bulletin, 50years Special Anniversary Edition,

December, 2008.

17