IJBASSNET.COM VOL: 3, ISSUE: 5 ISSN: 2469-6501 MAY, 2017 … · Federal University Wukari, Taraba...

©Center for Promoting Education and Research (CPER) USA, www.cpernet.org

9 | P a g e

IJBASSNET.COM ISSN: 2469-6501

VOL: 3, ISSUE: 5 MAY, 2017 http://ijbassnet.com/

The Effect of Household Income on Agricultural Productivity in Nigeria: An Econometric Analysis

AJIE, Hycenth Amakiri

Associate Professor of Economics

And Head, Department of Economics,

Federal University Wukari, Taraba State, Nigeria

OJIYA, Emmanuel Ameh

Lecturer, Department of Economics,

Federal University Wukari, Taraba State

Email: [email protected]

(Corresponding Author)

MAMMAN, Andekujwo Baajon

Lecturer, Department of Economics

Federal University Wukari, Taraba State

Abstract:

Agricultural development is one of the most powerful tools to end extreme poverty, boost shared prosperity

and feed a projected 9.7 billion people by 2050. Growth in the agriculture sector is two to four times more

effective in raising incomes among the poorest compared to other sectors. Agriculture is also crucial to

economic growth. It is in consideration of this fact that this study empirically examines the effect Household

Income on Agricultural Productivity in Nigeria. In achieving the objectives of the study, stationarity and

long run tests including Ordinary Least Square Methods were adopted. In testing for the time series

properties, the evidence from estimated economic models suggests that all the variables examined are

stationary at first difference I(I) using the Augmented Dickey Fuller (ADF) and Phillips-Perron. Besides,

Johansen Co-integration test reveals that the variables are co integrated which confirms the existence of

long-run equilibrium relationship between the variables. Thus, this suggests that all the variables tend to

move together in the long run. Empirical evidence reveals that a naira increase in per capita gross domestic

product of individual households in Nigeria translates to about 0.92 million naira increase in agricultural

output between 1986 to 2016. Similarly, a naira increase in government expenditure on agriculture results

to 0.75 million naira increases in agricultural output also within the period under reference. It is therefore,

recommended that government must provide funds to acquire sophisticated farm tools (harvesters,

herbicides, fertilizer etc), build irrigation, dams, and storage facilities and establishes food processing

industries across the thirty-six states of the federation to enable farmers’ process and preserve their food

stuff. This will bring value addition and make our export competitive in the international market. Finally,

the peasant farmers who live in the rural areas, and incidentally the major providers of food for the nation

should be adequately catered for and motivated by making the rural areas more conducive and habitable

through the provision of adequate infrastructural facilities such as good road networks, recreation centres,

educational and farm institutes, pipe-borne water and electricity. The provision of these facilities will no

doubt impact positively on the rural farmers’ output.

Keywords: Agricultural Productivity, Personal income, Government Expenditure, OLS

http://www.cpernet.org/

mailto:[email protected]

http://datatopics.worldbank.org/hnp/popestimates

http://documents.worldbank.org/curated/en/700061468334490682/Ending-poverty-and-hunger-by-2030-an-agenda-for-the-global-food-system



10 | P a g e



Introduction:

According to Ahmed (1993), agriculture is been defined as the production of food and livestock and the

purposeful tendering of plants and animals. He stated further that agriculture is the mainstay of many

economies and it is fundamental to the socio-economic development of a nation because it is a major

element and factor in national development. In the same vein, Okolo (2004) described agricultural sector as

the most important sector of the Nigeria economy which holds a lot of potentials for the future economic

development of the nation as it had done in the past. In the view of Fulginiti and Perrin (1998), agricultural

productivity refers to the output produced by a given level of input(s) in the agricultural sector of a given

economy. More formally, it can be defined as “the ratio of the value of total farm outputs to the value of

total inputs used in farm production” (Olayide and Heady 1982). Notwithstanding the enviable position of

the oil sector in the Nigerian economy over the past three decades, the agricultural sector is arguably the

most important sector of the economy. Agriculture’s contribution to the Gross Domestic product (GDP) has

remained stable at between 30 and 42 percent, and employs 65 per cent, of the labour force in Nigeria

(Emeka 2007).

In the view of Obayan (2016) mechanised and commercial agriculture will assist in addressing Nigeria’s

current economic recession and other developmental challenges. Agriculture is essential for sub-Saharan

Africa to grow and achieve the Sustainable Development Goals aimed at ending poverty and hunger by

2030. Moreover, attaining success in the agricultural sector would reduce the country’s food importation,

which she noted as fuelling inflation and depleting the nation’s foreign reserves. She observed that Nigeria’s

famous agricultural profile had declined steeply, making her to slide from being a self-sufficient country in

food production to an importer of agricultural produce. This trend was however reversible and the aberration

could be addressed, with Nigeria taking her rightful lead again in feeding Africa considering her population

of about 180 million, and her land areas of 98.3m ha, 74 million of which are said to be good for farming but

yet to be explored maximally. Ghana was able to reduce her poverty by half by boosting cocoa farming

towards meeting the Millennium Development Goals of 2015, hence Nigeria should be able to boost food

production and feed her citizens with nutritious food and improve her economy via investment in agriculture

and its related value chains.

Agricultural development is one of the most powerful tools to end extreme poverty, boost shared prosperity

and feed a projected 9.7 billion people by 2050. Growth in the agriculture sector is two to four times more

effective in raising incomes among the poorest compared to other sectors. Evidence reveals that that 65% of

poor working adults relied on agriculture to make a living. Agriculture is also crucial to economic growth: in

2014, it accounted for one-third of global gross-domestic product (GDP). But agriculture-driven growth and

poverty reduction, as well as food security are at risk (World Bank, 2016).

Problem Statement:

The provision of an equitable standard of living, adequate food, clean water, safe shelter and energy, a

healthy and secured environment, an educated public, and satisfying job for this and future generations, is

one of the major challenges facing mankind. It is not an overstatement to assert that the growth and

development of any nation depend, to a large extent, on the development of agriculture. Low agricultural

yield has a negative effect on the Nigerian economy as a whole. Empirical evidence has it that in spite of

Nigeria’s rich agricultural resource endowment, there has been a gradual decline in agriculture's

contributions to the nation's economy (Manyong et al., 2005). These myriads of challenges confronting

farmers in the Nigerian society trace their root to lack of capital for optimum agricultural productivity

(Lawal, 2011). An examination of the effect of household income on agricultural productivity becomes

imperative owing to the fact that despite government avowed commitment to diversifying the economy


http://datatopics.worldbank.org/hnp/popestimates



http://documents.worldbank.org/curated/en/187011475416542282/pdf/WPS7844.pdf

http://documents.worldbank.org/curated/en/187011475416542282/pdf/WPS7844.pdf

http://data.worldbank.org/indicator/NV.AGR.TOTL.ZS


11 | P a g e



away from crude oil revenue to other sources of revenue, especially agriculture, the sing-song on funding the

sector has yielded little or no result due to misappropriation of funds meant for agricultural development in

the country. It is glaring that funds budgeted for the sector get siphoned by unscrupulous Nigerians and

corrupt politicians with little or nothing on ground to show. In the process, the modest output from the

agricultural sector is solely the efforts of peasant farmers and households’ income devoted to farming. It is

in view of this fact that this paper empirically examines the effect of household income and agricultural

productivity in Nigeria between 1986 to 2016.

Objective of the Study:

The main objective of this study is to examine the effect of household income to agricultural productivity in

Nigeria using econometric techniques. The study shall at the end test two hypotheses. First, “household

income has no significant positive effect on agricultural productivity in Nigeria between”; secondly,

“government expenditure on agriculture has no significant impact on agricultural productivity in Nigeria””.

Literature Review:

According to the United Nations Food and Agricultural Organization production year book, agriculture was

defined to include cereals, starchy roots, sugar, edible oil, crops, nuts, fruits, vegetables, wine, cocoa, tea,

coffee, livestock and livestock products. Also included in the group are industrial oil seeds, tobacco, fibre,

vegetable and rubber. Further to knowing the subject agriculture, Anyanwu et a1 (1979) defined agriculture

as the cultivation of land for the purpose of producing food for man, feed for animals and fibre or raw

materials for industries. It also includes the processing and marketing of crops. With regard to the above

viewpoint, the central role of agriculture in the individual and the country's life at large cannot be

overemphasized.

Although it depends heavily on the oil industry for its budgetary revenues, Nigeria is predominantly still an

agricultural society. Approximately 70 percent of the population engages in agricultural production at a

subsistence level. Agricultural holdings are generally small and scattered. Agriculture provided 41 percent

of Nigeria's total gross domestic product (GDP) in 1999. This percentage represented a normal decrease of

24.7 percent from its contribution of 65.7 percent to the GDP in 1957. The decrease will continue because,

as economic development occurs, the relative size of the agricultural sector usually decreases. Nigeria's wide

range of climate variations allows it to produce a variety of food and cash crops. The staple food crops

include cassava, yams, corn, coco-yams, cow-peas, beans, sweet potatoes, millet, plantains, bananas, rice,

sorghum, and a variety of fruits and vegetables. The leading cash crops are cocoa, citrus, cotton, groundnuts

(peanuts), palm oil, palm kernel, benniseed, and rubber. They were also Nigeria's major exports in the 1960s

and early 1970s until petroleum surpassed them in the 1970s. Chief among the export destinations for

Nigerian agricultural exports are Britain, the United States, Canada, France, and Germany http://www.nationsencyclopedia.com/economies/Africa/NigeriaAGRICULTURE.html#ixzz4ekK86Lbs

Decline in agricultural production in Nigeria began with the advent of the petroleum boom in the early

1970s. The boom in the oil sector brought about a distortion of the labor market. The distortion in turn

produced adverse effects on the production levels of both food and cash crops. Governments had paid

farmers low prices over the years on food for the domestic market in order to satisfy urban demands for

cheap basic food products. This policy, in turn, progressively made agricultural work unattractive and

enhanced the lure of the cities for farm workers. Collectively, these developments worsened the low

productivity, both per unit of land and per worker, due to several factors: inadequate technology, acts of

nature such as drought, poor transportation and infrastructure, and trade restrictions (World Bank, 2015).

According to U.S. Department of State FY2001 Country Commercial Guide, as food production could not


http://www.nationsencyclopedia.com/knowledge/Cassava.html

http://www.nationsencyclopedia.com/economies/Africa/Nigeria-AGRICULTURE.html#ixzz4ekK86Lbs

http://www.nationsencyclopedia.com/knowledge/Primary_sector_of_the_economy.html


12 | P a g e



keep pace with its increasing population, Nigeria began to import food. It also lost its status as a net exporter

of such cash crops as cocoa, palm oil, and groundnuts. Nigeria's total food and agricultural imports are

valued at approximately US$1.6 billion per year. Among the major imports from the United States are

wheat, sugar, milk powder, and consumer-ready food products

The Contribution of Agriculture to Nigeria’s Economy:

Agriculture is the largest sector of the Nigerian economy with GDP contribution of about 40%. Research

shows that Nigeria has over 80 million hectares of arable land. This accounts for about 23% of arable land

across all of West Africa. The necessary key for successful reform is to turn agriculture into a business that

makes money, with a focus on investments as opposed to aid and development. Prospects for the agricultural

sector is very bright, owning to the growing demand for food driven by a large population and growing

incomes as well as higher prices due to demand in the international market (World Bank, 2015).

The Federal Government, through the Ministry of Agriculture announced a supportive program towards

creating a Nigerian agricultural sector worth $256 billion by 2030. By this gesture, government intends to

stop food importation valued at over ₦1 trillion annually and ensure a massive growth in the sector, in

partnership with the private sector. As against the annual loss of funds to importation, ₦350 billion would

accrue to the nation’s economy by the end of 2016 following the import substitution policy for rice, while

the substitution of wheat flour content in bread with cassava flour is estimated to generate over ₦60 billion.

Currently, Nigeria is rolling out an ambitious reform programme across its agricultural sector aimed at

cutting the country’s dependency on food imports, creating jobs and generating growth. The reforms such as

the move to privatise the procurement and distribution of fertilizer and seed have resulted in more private

sector participation as well as increasing in foreign direct investments (National Bureau for Statistics (NBS),

2016).

The key to unlocking the growth potential of agriculture in Nigeria is to improve the lot of small scale

farmers. Empowering the millions of small holder farmers who have access to millions of hectares will

ensure they have access to appropriate inputs, sufficient financing that will significantly boost productivity.

The key model developed to this effect is the Agricultural Franchise Model. This makes the small holder

farmer a franchisee of a larger farm, with access to all the necessary inputs. This model stands to minimize

the risks associated with investing in the sector and thereby stimulates the financial sector to invest in the

Nigerian Agricultural Sector.

Trend of Agriculture contribution to GDP (1986 – 2016)

Source: World Bank (2015); National Bureau for Statistics (NBS, 2017)

0

20

40

60

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

% of GDP

% of GDP



13 | P a g e



Empirical Review:

Umaru and Zubairu (2012) made a comparative analysis of the contribution of agriculture and petroleum

sectors to the growth and development of Nigerian economy (1960-2010) and the results revealed that

agricultural sector contributed higher than the petroleum sector, though they both possessed a positive

impact on economic growth and development of the economy. They affirmed that good performance of an

economy in terms of per capita growth may therefore be attributed to a well-developed agricultural sector

capital. In the same vein, Suleiman and Aminu (2010) conducted research on the contribution of agriculture,

petroleum and manufacturing sector of the Nigerian economy and found out that agricultural sector is

contributing higher than both petroleum and manufacturing sectors. Their study reveals that agriculture is

contributing 1.7978 units to GDP while petroleum is contributing 1.14 units to GDP, which is less than the

contribution of agriculture.

Oji-Okoro (2011) also studied the contribution of agricultural sector on the Nigerian economic development

and found that foreign direct investment (FDI) on agriculture contributes the most (56.43) in terms of

agricultural development. This means that for every unit of change in FDI on agriculture there is a

corresponding change of 56.43 units in GDP in Nigeria. Ekpo and Umoh (2012) in their study revealed that

the contribution of agriculture to GDP, which was 63 percent in 1960, declined to 34 percent in 1988, not

because the industrial sector increased its share but due to neglect of agriculture sector. It was therefore not

surprising that by 1975, the economy had become a net importer of basic food items. The apparent increase

in industry and manufacturing from 1978 to 1988 was due to activities in the mining sub-sector, especially

petroleum.

Muhammad and Atte (2006) analysed agricultural production in Nigeria and revealed that the negative

coefficient of the value (-0.07) of the food imports indicates that as food import increases, domestic

agricultural production decreases. This might be due to the fact that food importation exposes the local

farmers to unfair competition by foreign producers who usually take advantage of economies of scale in

production due to their access to better production technology.

Methodology:

Variables and Data Source:

Variables for this study are Agricultural Output (proxy for agricultural productivity) used as dependent

variable with per capita gdp (proxy for household income), government expenditure on agriculture as

independent variables respectively. The data for the study were secondary in nature obtained from the World

Bank Development Indicators database and Central Bank of Nigeria (CBN) statistical bulletin, 2015 edition.

The time series data cover a 30-years period ranging from 1986-2016. An examination of the effect of

household income on agricultural productivity becomes imperative owing to the fact that despite

government avowed commitment to diversifying the economy away from crude oil revenue to other sources

of revenue, especially agriculture, the hype on funding the sector has yielded little or no result due to

misappropriation of funds meant for agricultural development in the country. It is glaring that funds

budgeted for the sector gets siphoned by unscrupulous Nigerians and corrupt politicians with little to show

for it. In the process, the modest output from the agricultural sector is solely the efforts of peasant farmers

and households’ income devoted to farming. This time frame is therefore purposely chosen to empirically

test the significance to which household income (per capital gdp) and government expenditure on agriculture

affected agricultural productivity despite several years of government neglect.



14 | P a g e



Model Specification:

A multiple regression model is used with agricultural productivity proxied by agricultural output (AG-

output) as dependent variable while per capita-GDP (Pc-GDP) and government expenditure on agriculture

(Gx_Agric) were taken as independent variables The functional form of the model is thus specified. As:

AG-output = f (Pc-GDP, Gx-Agric) …… (eqtn 1)

For the purpose of estimation we shall restate the above functional form explicitly as:

AG-output = β0 + β1(Pc-GDP) + β2(Gx-Agric) + μt ……. (eqtn 2)

Where:

AG-output = Agricultural output (expressed in Nigeria Naira)

Pc-GDP = Per capita gross domestic product (expressed in US dollars)

Gx-Agric = Government expenditure on Agriculture (expressed in Nigeria Naira)

μt = Error term

β0 = Intercept

β1 and β2 = Slope of the regression equation

The estimated models are further transformed into log-linear form. This is aimed at reducing the problem of

multi-collinearity among the variables in the models. Thus the log-linear models are specified as shown

below:

LnAG-output = β0 + β1(LnPc-GDP) + β2(LnGx-Agric) + μt ……. (eqtn 3)

Our a priori expectations are:

β1 and β2 > 0.

A priori Expectation:

This specifically has to do with sign expectation set by economic theory and it is expected that parameters in

this model have the correct signs and sizes that conform to economic theory. If they carry the expected

signs, then the hypothesis is accepted otherwise they are rejected. From the model, the expected theoretical

relationship between the explanatory and independent variables are as follows:

Agricultural Output (AG-output) and per capita GDP:

Here β1 is expected to have a positive sign as increase in per capita gross domestic product tends to bring an

increase in Agricultural output, all things being equal.

Agricultural Output (AG-output) and Government expenditure on agriculture:

In β2 above, the relationship is expected to be positive since the more government expenditure (funding)

received by the Federal Ministry of Agriculture, the more capital projects like dam, harvesters, ploughs,

irrigation and preservations equipments it can purchase towards strengthening the sector and ultimately

leads to greater agricultural output.



15 | P a g e



Justification of Chosen Variables:

Agricultural output reflects the total produce of the agricultural sector in the economy in a given year and is

measured in N’Billion. An increase in the level of agricultural output would imply an increase in the

economic growth and by implication the standard of living of the citizenry, and an increase in the

consumption level of the economy leading to an increase in the level of output and employment of the

labour.

Per capita income is a measure that reflects the value of goods and services produced per individual in the

economy in a given year, measured in N’million. It is used to capture household earnings (income) in this

study because it captures the total output produced by each individual in the country and as such provides a

more accurate figure. Therefore, per capita GDP is expected to have a positive relationship with agricultural

output, all things being equal. The higher the level of per capital gdp, the higher would be the level of

agricultural productivity / output in the country.

Government agricultural expenditure is the total amount spent by the public on the agricultural sector in a

fiscal year. It is measured in N’Billion. An increase in the level of agricultural expenditure is expected to

bring about an increase in the level of agricultural output. Hence, government agricultural expenditure is

expected to have a positive relationship with agricultural output, i.e. the higher the level of government

agricultural expenditure, the higher would be the level of agricultural output.

Method of Data Analysis:

The methods of data analysis include first and foremost descriptive statistic, then unit root test with

Augmented Dickey-Fuller (ADF) and Philips-Perron method, a test for longrun relationship (cointegration)

and then the ordinary least square (OLS) multiple regression method to determine the effect of the

independent variables in the model on the dependent variable. The study made use of E-views 8.0,

econometric software for the analysis.

Table 1: Data Presentation on Variables used in the study

AG_OUTPU

T PC_GDP GX_AGRIC

1986 7.71E+09 240.6174 0.020689

1987 8.44E+09 272.5077 0.046145

1988 9.11E+09 256.3758 0.083000

1989 7.39E+09 260.0476 0.151800

1990 9.21E+09 321.6684 0.258000

1991 8.11E+09 279.2758 0.208700

1992 7.66E+09 291.2835 0.455975

1993 5.12E+09 153.0757 1.803806

1994 6.68E+09 171.0248 1.183291

1995 8.81E+09 263.2880 1.510400

1996 1.05E+10 314.7399 1.592562

1997 1.18E+10 314.2998 2.058885

*1998 1.20E+10 273.8698 2.891705

1999 1.22E+10 299.3568 59.31617

2000 1.17E+10 377.5003 6.335779

2001 1.43E+10 350.2602 7.064546

2002 2.78E+10 457.3970 9.993554



16 | P a g e



2003 2.80E+10 510.2963 7.537355

2004 2.94E+10 645.7639 11.25663

2005 3.64E+10 804.0060 16.32596

2006 4.62E+10 1014.735 17.91903

2007 5.37E+10 1131.148 32.48423

2008 6.73E+10 1376.857 65.39901

2009 6.17E+10 1091.969 22.43520

2010 8.68E+10 2314.964 28.21795

2011 9.07E+10 2514.149 41.20000

2012 1.00E+11 2739.852 33.30000

2013 1.07E+11 2979.844 39.43101

-2014 1.14E+11 3203.244 36.70000

2015 9.93E+10 2640.291 41.27000

*2016 9.99E+10 2530.281 76.75000

Source: World Bank Development Indicators and Central Bank of Nigeria Statistical Bulletin (2015)

Empirical Result and Analysis:

Stationarity Test:

This study first commences its investigation by first testing the properties of the time series used for

analysis. We perform a unit root test on each of the variable since the variables are time series in nature and

prone to fluctuations. This enables us to avoid the problems of spurious result in the time series models. The

test is conducted using two different unit root models. That is, the Augmented Dickey Fuller (ADF) model

and the Philips-Perron (PP) model. The essence of using the two tests is for confirmatory testing.The result

of the unit root test is shown in table 1 below:

Table 1: Augmented Dickey Fuller and Philip-Perron Unit Root Test with Intercept

Variable P-value 1st Difference

t-statistic value

5% Critical

Value

Order of

Integration

Log(AG_output) ADF 0.0002 -5.183544 -2.967767 I(1)

P-P 0.0002 -5.179057 -2.967767 I(1)

Log(PC_GDP) ADF 0.0001 -5.749260 -2.967767 I(1)

P-P 0.0000 -5.779082 -2.967767 I(1)

Log(Gx_Agric) ADF 0.0000 -7.611650 -2.967767 I(1)

P-P 0.0000 -8.669888 -2.967767 I(1)

Source: Author’s computation using E-views 8.0

Interpretation:

From Table 1 above it is apparent that all the variables are I(1) series, that is, they were stationary at first

difference. When variables that are known to be I(1) produce a stationary series, then there is a possibility of

a long run co integration relationship among them, hence we proceed to estimate the long run relationship

among the series using Johansen co integration approach.



17 | P a g e



Table 3: Trace and Maximum Eigenvalue Cointegration Table

Series tested: AG_output; PC_GDP; Gx-Agric

Trace

Statistic

5% critical

value

Prob. Value Max- Eigen

statistic

5% critical

value

Prob. Value

35.44278 24.27596 0.0013 20.44633 17.79730 0.0195

14.99645 12.32090 0.0174 13.12960 11.22480 0.0229

Trace test indicates 2 cointegrating eqn(s) at the 0.05 level Series have long-run

relationship

Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level Series have long-run

relationship


Table 3 above indicates two cointegration equations at those ranks where the values of the trace statistics

exceed the 5% critical values. This occurred in two places in the table. In addition, this was confirmed by the

results of the maximum eigenvalues where cointegration exists at ranks where the value of eigenvalues is at

least 0.5. The discovery here is that both the trace and max-eigenvalue statistic yielded two cointegrating

equations. However, theory agrees that cointegration exists where there is at least one cointegrating

equation, hence we conclude that the series - agricultural output, per capita gross domestic product and

government expenditure on agriculture respectively have longrun relationship, i.e. they can both walk

together for a long time without deviating from such established path.

R-squared: 0.976452

Adjusted R-squared: 0.974770

F-Statistic: 580.5297

Prob(F-statistic): 0.000000


The results of the regression show that there is a positive relationship between the dependent variable (AG-

output) and the independent variables (Pc-GDP and Gx-Agric). Empirical evidence reveals that a naira

increase in per capital gross domestic product of individual households in Nigeria translates to about 0.92

million naira increase in agricultural output between 1986 to 2016. Similarly, a naira increase in government

expenditure on agriculture results to 0.75 million naira increases in agricultural output also within the period

under reference. The estimated model shows goodness of fit to the data as shown by the F-statistic value of

about 580.5297 with a p-value of 0.00000. This implies that agricultural sector performance for the period of

analysis was significantly influenced by per capital gross domestic product and government expenditure on

agriculture. The explanatory power of the regression model with an adjusted r-squared of 0.98 is impressive.

This indicates that 98 percent of variation in agricultural output is explained by the independent variables

PC-GDP and Gx-Agric. The remaining 2 percent is explained by variables outside this model. The Adjusted

Table 5: Ordinary Least Square Regression

Variable Coefficient Std. Error t-Statistic Prob.

C 17.84168 0.286087 62.36454 0.0000

LOG(PC_GDP) 0.922553 0.047722 19.33171 0.0000

LOG(GX_AGRIC) 0.075975 0.019824 3.832589 0.0007



18 | P a g e



R2 of 0.97 is close to the R

2 value of 0.98, meaning that the model is fit and useful for making

generalization. From our results the standard errors for each parameter is statistically significant. At 5%

level of significance for the variable, the t-statistic value shows that there is a positive relationship between

AG-output and Pc-GDP and Gx-Agric between 1986 to 2016.

Test of Hypotheses:

The two hypotheses earlier formulated are restated here for emphasis:

(i) Household income (Pc-GDP has no significant positive effect on agricultural productivity in Nigeria

between 1986 to 2016;

(ii) Government expenditure on agriculture has no significant impact on agricultural productivity in

Nigeria between 1986 to 2016.

T-statistic value for individual coefficients and their probability values shall be used as yardstick for

accepting or rejecting formulated hypothesis. In the first hypothesis, since the t-statistic value (19.33171)

and probability value (0.0000) of the coefficient of per capita gross domestic product (Pc-GDP) are within

permissible threshold by theory, we hasten to reject the null hypothesis and conclude that household income

has significant positive effect on agricultural productivity in Nigeria between 1986 to 2016. In the case of

the second hypothesis, the null hypothesis is equally rejected with the conclusion that Government

expenditure on agriculture has no significant impact on agricultural productivity in Nigeria between 1986 to

2016. The implication of these findings is that the independent variables in the model had significant

positive impact on agricultural output within the period being reviewed. It is therefore safe to conclude that

household earnings are the catalyst for the modest improvement in agricultural productivity in Nigeria

within the study period.

Robustness Test:

To buttress the empirical analysis above, it is also necessary to examine the statistical properties of the

estimated model. Robustness checks are crucial in this analysis, because if there is a problem in the residuals

from the estimation of a model, it is an indication that the model is not efficient, such that parameter

estimates from such model may be biased. The model was tested for normality, serial correlation (Durbin-

Watson), heteroscedasticity and stability.

From the OLS output, the Durbin Watson statistic value of 0.952029 is less than 2 thus indicating the

presence of positive serial autocorrelation. Also, the White heteroscedasticity test result in the appendix with

probability value of 0.3635 is greater than the 0.05 level of significance, thus leading to acceptance of the

null hypothesis of no heteroscedasticity; hence we conclude that the series are homoscedastic. Furthermore,

the diagnostics test indicates that the residuals are normally distributed and valid for empirical testing

because of the high probability value of 0.412832. The result of both the CUSUM and CUSUMQ stability

test indicates that the model is stable. This is because both the CUSUM and CUSUMQ lines fall in-between

the two 5% lines.

Conclusion / Recommendations:

The age-long advocacy for premium to be placed on agricultural development for self-reliance, sustenance

and food security in any nation is not in any way misleading. Results from this study indicates clearly that

per capita income has significant positive effect on output from this all-important sector upon which the



19 | P a g e



happiness, peace and survival of this nation depends. Empirical evidence further confirmed the earlier

assumption that government efforts at revitalizing the sector through budgetary allocation often do not bring

in desired result as the funds end up in the private accounts of some unscrupulous bureaucrats and

politicians with little or nothing to show for it. It is therefore, recommended that government should as

matter of urgency take decisive steps towards bringing improvement in other sectors of the national

economy so that collectively, output could grow, leading to improvement in per capita gross domestic

product of the country.

It is equally recommended that government should improve on its allocation to the sector. The less than

desirable input of 0.075 from the coefficient of government expenditure on agriculture is worrisome. It is not

in doubt that successive Nigerian government have not seen reason to come close to fulfilling international

benchmarked budgetary recommendations for the agricultural sector since independence in 1960. It is

equally disturbing, that, while other African countries like Ivory Coast, Ghana and Ethiopia dedicate a larger

percentage of their budget to the agricultural sector, Nigeria with a large population of over 180 million

mouths to feed has displayed lackadaisical and a carefree attitude towards recommendations from

international agencies on the need to give priority to the sector. A country either sinks or stands through its

commitment to making agriculture work, and working, very well.

Furthermore, government must provide funds to acquire sophisticated farm tools (harvesters, herbicides,

fertilizer etc), build irrigation, dams, and storage facilities and establish food processing industries across the

thirty-six states of the federation to enable farmer’s process and preserve their food stuff. This will bring

value addition and make our export competitive in the international market. Finally, the peasant farmers who

live in the rural areas, and incidentally the major providers of food for the nation should be adequately

catered for and motivated by making the rural areas more conducive and habitable through the provision of

adequate infrastructural facilities such as good road networks, recreation centres, educational and farm

institutes, pipe-borne water and electricity. The provision of these facilities will no doubt impact positively

on the rural farmers’ output.

References:

Adewuyi, S.A. (2006). “Resource Use Productivity of Rural Farmers In Kwara State, Nigeria.”

International Journal of Agricultural Sciences, Sciences, Environment and Technology 1(1). http://www.nationsencyclopedia.com/economies/Africa/Nigeria-AGRICULTURE.html#ixzz4ekLpmKgv

Anyanwu, A.C., Anyanwu, B. O. and Anyanwu, V, A. (1979) Agriculture: Its Importance and

Development. Africana Educational Publishers Nig.

Diewert, W. and A. Nakamura. 2005. “Concepts and Measures Of Productivity: An Introduction.” In Services, Industries and the Knowledge Based Economy, edited by Lipsey and Nakamura. University Of Calgary Press.

Emeka, O. (2007). Improving the agricultural sector toward economic development and poverty

reduction in Nigeria. CBN Bullion, 4, 23-56.

Fulginiti, L. and R. Perrin. 1998. “Agricultural Productivity In Developing Countries.” Agricultural

Economics 19: 45–51.

Manyong VM, Ikpi A, Olayemi JK, Yusuf SA, Omonona BT, Okoruwa V, Idachaba FS (2005).

Agriculture in Nigeria: Identifying opportunities for increased commercialization and investment.

Ibadan, Nigeria: International Institute of Tropical Agriculture


http://www.nationsencyclopedia.com/economies/Africa/Nigeria-AGRICULTURE.html#ixzz4ekLpmKgv


20 | P a g e



Muhammad-Lawal, A., & Atte, O. A. (2006). An analysis of agricultural production in Nigeria.

African Journal of General Agriculture,

NBS (2015) National Bureau for Statistics Database

Olayide, S.O. and E. O. Heady. 1982. Introduction to Agricultural Production Economics. First

Edition. Ibadan: Ibadan University Press.

Okolo, D. 2004. Regional Study on Agricultural Support, Nigeria’s Case. FAO. Http://Www. Fao.Org / Tc/Tca/Work05/Nigeria.Pdf

Oji-Okoro Izuchukwu (2011) “Analysis of the Contribution of Agricultural Sector on the Nigerian

Economic Development” World Review of Business Research Vol. 1. No. 1.

Oni, A. O., O. I. Y. Ajani, B. T. Omonona, and J. O. Lawal. 2009. “Effects of Social Capital on

Credit Access among Cocoa Farming Households In Osun State, Nigeria.” Agricultural Journal 494.

Punch Newspaper, published October 7, 2016

Umaru, Aminu and A. A. Zubairu (2012).An Empirical Analysis of the Contribution of

Agriculture and Petroleum Sector to the Growth and Development of the Nigerian Economy from

1960-2010, International Journal of Social Science and Education.2(4): 12.

U.S. Department of State FY2001 Country Commercial Guide

WORLD BANK. 2015. World development indicators 2015. Washington, D.C.: World Bank.

Wiebe, K., M. J. Soule, C. Narrod and V. E. Brenneman. 2003. “Resource quality and agricultural

productivity: A multi-country comparison” in land quality, agricultural productivity and food

security: Biophysical processes and economic choices at local, regional and global levels. Edward

Elgar publishing, 2003.

Zepeda, L. 2001. “gricultural Investment, Production Capacity and Productivity” in Agricultural

Investment and Prod/uctivity in Developing Countries, L. Zepeda, editor. Rome: FAO, 2001. http://www.nationsencyclopedia.com/economies/Africa/NigeriaAGRICULTURE.html#ixzz4ekK86Lbs


http://www.nationsencyclopedia.com/economies/Africa/NigeriaAGRICULTURE.html#ixzz4ekK86Lbs


21 | P a g e



Appendices

Dependent Variable: LOG(AG_OUTPUT)

Method: Least Squares

Date: 04/08/17 Time: 08:46

Sample: 1986 2016

Included observations: 31


C 17.84168 0.286087 62.36454 0.0000

LOG(PC_GDP) 0.922553 0.047722 19.33171 0.0000

LOG(GX_AGRIC) 0.075975 0.019824 3.832589 0.0007

R-squared 0.976452 Mean dependent var 23.86342

Adjusted R-squared 0.974770 S.D. dependent var 1.055900

S.E. of regression 0.167719 Akaike info criterion -0.641291

Sum squared resid 0.787628 Schwarz criterion -0.502518

Log likelihood 12.94001 Hannan-Quinn criter. -0.596054

F-statistic 580.5297 Durbin-Watson stat 0.952029

Prob(F-statistic) 0.000000

0

1

2

3

4

5

6

7

8

9

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

Series: ResidualsSample 1986 2016Observations 31

Mean 4.31e-16Median -0.055488Maximum 0.382071Minimum -0.270566Std. Dev. 0.162032Skewness 0.574196Kurtosis 2.773999

Jarque-Bera 1.769428Probability 0.412832



22 | P a g e



Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 1.049471 Prob. F(2,28) 0.3635

Obs*R-squared 2.161778 Prob. Chi-Square(2) 0.3393

Scaled explained SS 1.564325 Prob. Chi-Square(2) 0.4574

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Date: 04/08/17 Time: 08:53

Sample: 1986 2016

Included observations: 31


C 0.107907 0.058581 1.842006 0.0761

LOG(PC_GDP) -0.014008 0.009772 -1.433446 0.1628

LOG(GX_AGRIC) 0.004950 0.004059 1.219409 0.2329

R-squared 0.069735 Mean dependent var 0.025407

Adjusted R-squared 0.003287 S.D. dependent var 0.034400

S.E. of regression 0.034343 Akaike info criterion -3.813056

Sum squared resid 0.033025 Schwarz criterion -3.674283

Log likelihood 62.10237 Hannan-Quinn criter. -3.767819

F-statistic 1.049471 Durbin-Watson stat 1.905814

Prob(F-statistic) 0.363492

-16

-12

-8

-4

0

4

8

12

16

90 92 94 96 98 00 02 04 06 08 10 12 14 16

CUSUM 5% Significance



23 | P a g e



-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

90 92 94 96 98 00 02 04 06 08 10 12 14 16

CUSUM of Squares 5% Significance


IJBASSNET.COM VOL: 3, ISSUE: 5 ISSN: 2469-6501 MAY, 2017 … · Federal University Wukari, Taraba...

Documents

Transcript of IJBASSNET.COM VOL: 3, ISSUE: 5 ISSN: 2469-6501 MAY, 2017 … · Federal University Wukari, Taraba...