ABSTRACT
The
purpose of this research work is to investigate empirically agricultural
financing and its impact on reduction of poverty in Nigeria covering the period
(1980-2008). In the objective, effort was made to see how agriculture sector
financing will reduce the level of poverty in Nigeria. The regression model,
otherwise known as Ordinary Least Square (OLS) technique is chosen by the
researcher in the research method. The result showed that agriculture financing
has no significant relationship with reduction of poverty in Nigeria. Based on
the findings, it was recommended that government funding on agriculture should
be channeled to farm mechanization. This will help to create employment and
boost food production, thereby reducing poverty.
DEPARTMENT OF ECONOMICS
FACULTY OF SOCIAL SCIENCES
TABLE OF
CONTENTS
Title
Page i
Approval
Page ii
Dedication
iii
Acknowledgment iv
Table
of contents v
Abstract
CHAPTER ONE: Introduction
1.1 Background of the Study 1
1.2 Statement of Problem 6
1.3 Objective of the Study 7
1.4 Test of Hypothesis 7
1.5 Significance of the Study 7
1.6 Scope and Limitations of the Study 8
CHAPTER TWO: Literature Review
2.1 Review or Related Literature 9
2.2 The Structure of Nigerian Agriculture 13
2.3 Nigeria Agricultural Sector Development 14
2.4 Nigerian Agricultural Sector Performance 15
2.5 Agricultural Sector Policies 18
2.6 Effect (of the polices) on Agricultural
output and Growth 21
2.7 Incidence of Poverty in Nigeria 24
2.8 The Role of Agriculture in Poverty Reduction
28
2.9 Financing Agriculture and Poverty
Reduction in Nigeria 33
2.10 Agricultural funding in Nigeria (Government
Budget) 35
2.11 Agriculture Credit Guarantee Scheme in
Nigeria 36
CHAPTER THREE: Research Methodology
3.1 Model Specification 41
3.2 Model Evaluation 42
3.3 Sources of Data 44
CHAPTER FOUR: Presentation and Analysis of Results
4.1 Presentation of Results 45
4.2 Analysis of Results 45
4.3 Test of Hypothesis 47
4.4 Implication of the Result 48
CHAPTER FIVE: Summary, Conclusion and Recommendation
5.1 Summary of Findings 49
5.2 Conclusion 50
5.3 Recommendations 51
Bibliography 52
BIBLIOGRAPHY
Akanji O. O. (2004). Microfinance as a Strategy for Poverty Reduction CBN Economic & Financial Review, Vol. 39
No.4
Anderson, K. Kurzweil, M. Martin, W. Sandri, D. &
Valenzuela, E. (2008) “Methodology for Measuring Distortions to
Agricultural Incentives,” Agricultural
Distortions Working Paper 02, World Bank, Washington DC, Revised January.
Central Bank of Nigeria (2000) “Annual Report and
Statement of Accounts”, Abuja:
CBN.
Central Bank of Nigeria (2005). “Annual Report and
Statement of Accounts, Abuja:
CBN.
Central Bank of Nigeria (2006) “Annual Report and
Statement of Accounts” Abuja
CBN.
Cowitt, P. P. (ed) (Various years) World Currency
Yearbook, Brooklyn: “Currency Data
ancf Intelligence”, Inc.
Daramoia, A. Ehui, S., Ukeji , E., & McIntire, J.
(2007). Agricultural Export Potential,
in Collier P. and C. Pattillo (eds), Economic Policy Options for a Prosperous Nigeria, London: Palgrave
Macmillan.
Etuk, O. E. U. (1986) Fertilizer Pricing in Segura, L.
T. Shetty, and M. Nishimizu
(eds) Fertilizer Producer Pricing in Developing Countries: Issues and approaches, industry and
finance series, volume 11, World Bank:
Washington DC.
FAOSTAT (2006) “Food and Agricultural Organization
Statistics Database, electronic
data produc, Rome: Food and Agriculture Organization.
APPENDIX ONE
DATA FOR ANALYSIS
SOURCE: CBN STATISTICAL BULLETIN VOLUME
21, 2010
BUREAU OF STATISTICS ENUGU, ENUGU STATE
|
YEAR
|
PL
( %)
|
ACGSF
(
|
GEA
(
|
|
1980
|
27
|
30945
|
32.5
|
|
1981
|
30.8
|
35642.4
|
33.9
|
|
1982
|
34.6
|
31763.9
|
34.1
|
|
1983
|
38.4
|
36307.5
|
29.3
|
|
1984
|
42.2
|
25154.9
|
32.8
|
|
1985
|
46
|
44242.1
|
32.7
|
|
1986
|
45.4
|
68417.4
|
32.9
|
|
1987
|
44.8
|
102152.7
|
29.2
|
|
1988
|
44.2
|
118611
|
54.3
|
|
1989
|
43.6
|
129300.3
|
81.1
|
|
1990
|
43
|
98493.4
|
208.1
|
|
1991
|
42.4
|
82107.4
|
121.1
|
|
1992
|
42
|
91953
|
161.5
|
|
1993
|
48.3
|
80845.9
|
1015.3
|
|
1994
|
54.6
|
91821.1
|
919
|
|
1995
|
60.9
|
163938.6
|
2236
|
|
1996
|
67.2
|
243608
|
1681.2
|
|
1997
|
68.2
|
244025.2
|
1682.2
|
|
1998
|
69.2
|
217699
|
2963.8
|
|
1999
|
70.2
|
246993.5
|
31347.2
|
|
2000
|
67.1
|
357832
|
4834.7
|
|
2001
|
64
|
810821.1
|
7064.9
|
|
2002
|
60.8
|
1062391.8
|
12439.4
|
|
2003
|
57.7
|
1894281.4
|
7534.3
|
|
2004
|
54.6
|
3308704.3
|
11725.6
|
|
2005
|
51.5
|
760969
|
10858.8
|
|
2006
|
48.4
|
4265066.3
|
18739.8
|
|
2007
|
51.2
|
4427868.9
|
15781.42
|
|
2008
|
52.1
|
6721074.6
|
16328.51
|
|
2009
|
53.6
|
7183654.3
|
18407.73
|
|
2010
|
51.4
|
8035790.1
|
19467.13
|
APPENDIX TWO
|
REGRESSION RESULTS
Dependent Variable: PL
|
||||
|
Method: Least Squares
|
||||
|
Date: 06/29/13 Time: 14:29
|
||||
|
Sample: 1980 2010
|
||||
|
Included observations: 31
|
||||
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
|
C
|
47.56130
|
2.304774
|
20.63599
|
0.0000
|
|
ACGSF
|
-1.66E-06
|
1.11E-06
|
-1.494900
|
0.1461
|
|
GEA
|
0.000910
|
0.000316
|
2.881836
|
0.0075
|
|
R-squared
|
0.238421
|
Mean dependent var
|
50.81935
|
|
|
Adjusted R-squared
|
0.184022
|
S.D. dependent var
|
11.36757
|
|
|
S.E. of regression
|
10.26849
|
Akaike info criterion
|
7.587804
|
|
|
Sum squared resid
|
2952.376
|
Schwarz criterion
|
7.726577
|
|
|
Log likelihood
|
-114.6110
|
F-statistic
|
4.382850
|
|
|
Durbin-Watson stat
|
0.511563
|
Prob(F-statistic)
|
0.022081
|
|
|
Dependent Variable:
LOG(PL)
|
||||
|
Method: Least Squares
|
||||
|
Date: 06/29/13 Time: 11:21
|
||||
|
Sample: 1980 2010
|
||||
|
Included observations: 31
|
||||
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
|
C
|
4.063339
|
0.272161
|
14.92991
|
0.0000
|
|
LOG(ACGSF)
|
-0.072220
|
0.030999
|
-2.329773
|
0.0273
|
|
LOG(GEA)
|
0.110198
|
0.021679
|
5.083224
|
0.0000
|
|
R-squared
|
0.610540
|
Mean dependent var
|
3.902520
|
|
|
Adjusted R-squared
|
0.582721
|
S.D. dependent var
|
0.235539
|
|
|
S.E. of regression
|
0.152152
|
Akaike info criterion
|
-0.836113
|
|
|
Sum squared resid
|
0.648203
|
Schwarz criterion
|
-0.697340
|
|
|
Log likelihood
|
15.95975
|
F-statistic
|
21.94721
|
|
|
Durbin-Watson stat
|
0.454696
|
Prob(F-statistic)
|
0.000002
|
|