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EVAIA: INTERNATIONAL JOURNAL OF ETHICS AND VALUES Volume 1, NO. 1,  July, 2020 

 

MEZE MICHAEL EJIKE                                                                                                                                                     Page 160 
 

EFFECTS OF FOREIGN DIRECT INVESTMENT ON 

AGRICULTURAL PRODUCTION IN NIGERIA: 1985 TO 2018. 

 

Meze Michael Ejike 

Department of Economics Nwafor  

Orizu College of Education, Anambra State 

Email: mezemichael386@gmail.com 

 

 

Abstract 

The study determines the effect of foreign direct investment inflow into the 

agricultural sector and the productivity of the sector in Nigeria from 1985 to 

2018. It employed the Ordinary Least Squares Technique, Co-integration Test 

and Error Correction Mechanism which minimizes the possibility of 

estimating spurious relations while at the same time, retaining long run 

information. Amongst other findings, the research reveals that foreign direct 

investment has significant positive impact on agricultural production. The 

findings also revealed that corruption perception index and exchange rate have 

negative impact on agricultural production in Nigeria. The study thus 

recommends, among other things, an enactment and reformation of extant 

laws and policies that could boost the ease of doing business in Nigeria. This 

could be done by enforcing investment attracting mechanisms like tax 

holidays and tax waivers in order to attract more investment in the agricultural 

sector.  

 

Keywords:  Foreign, Agriculture, investment, production, investment 

inflow. 

 

 

Introduction 

Agricultural sector is an important area in which Foreign Direct Investment 

(FDI) has proven effective in tackling the fundamental issues confronting 

Africa. This is as Foreign Direct Investment (FDI) has brought increase in 

agricultural productivity and growth and this is critical for reducing poverty 

enhancing sustainability and food sufficiency in the developing world (Msuya, 

2007). Prior to FDI lending by commercial banks to agriculture has fallen 

across developing nations with sub-Sahara African countries recording less 

than 10% of the total credit. Along this line, sectoralcredit allocation in 

Nigeria shows that 3.0% of the total volume of credit was allocated to 

agricultural sector in 2014 and 2015 (Food and Agricultural Organization and 

Central Bank of Nigeria (2016). To compound the problem of credit to 

agricultural sector, the issue of collateral and moratorium period attached to 

agricultural borrowing as well as the volume of capital required to boost the 

 

 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 161 
 

agricultural sector makes borrowing from microfinance and other specialized 

financial institutions unsuitable either (Akpan, Udoh&Umoren,2017).  

 

Worse still, the development assistance directed to agricultural sector in 

developing countries like Nigeria sometimes does not meet the critical need of 

beneficiaries. This assistance often tied to project of interest of the donor 

rather than that of the recipient. For instance the Maputo declaration on 

Agriculture food Security and the 10 percent national budget allocation to 

agricultural and rural developments implementable within five years was an 

illusion to many African countries including Nigeria (FAO, 2016).Following 

these shortfalls in funding of agricultural sector in developing society such as 

Nigeria, the need for alternative sources of funding with less stringent 

condition became overwhelmingly necessary. Hence Nigeria resorted to FDI 

to boost her agricultural sector. Consequently Nigeria has absorbed new 

technologies made use of their low-cost labor and her agricultural products 

have become largely present in the international market (Tvaronavičienė, 

Grybaitė and Korsakienė, 2008). Indeed, FDI is a globalization strategy that 

enables investing firms to retain world market share by rearranging their 

production lines both at home and abroad. 

 

It constitutes one of the fundamental exogenous sources of capital available to 

several developing economies especially in the Sub-Saharan Africa (Dabour, 

2000). It represents a viable channel through which issues like increasing 

poverty incidence; low capital accumulation, low savings and relatively weak 

capital and money markets are resolved in developing and sub-saharam Africa 

countries (Iddrisu, Immurana & Halidu, 2015). It contributes immensely, to 

financing of agricultural projects and dissemination of technologies among 

farmers in developing economies like Nigeria (Msuya, 2007). In Nigeria 

therefore, there has been a tremendous rise in foreign investment inflow over 

the years. Between 1980 and 1990, FDI inflows in Nigeria averaged over 

$740 million, over $2 billion between 1991 and 2000, over $ 7 billion 

between 2001 and 2014 and over 8 billion between 2017 and 2018 (CBN 

Statistical Bulletin, 2018). This substantial increase notwithstanding, there 

seem to be varying opinions on the effect of such increases on agricultural 

production in Nigeria with some analysts claiming that such increases have 

not yielded expected effects. And so, this paper investigates the effects of 

foreign direct investment in boosting Nigeria’s agricultural sector.  

 

 

Conceptual Analysis  
For the purpose of this study FDI in this context points at oversees 

investments by private multinational corporations. Similarly, agricultural 

production has to do with the total annual national output or monetary value 

of agricultural sector in Nigeria.  



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 162 
 

Foreign Direct Investment includes external resources that embrace 

knowledge and capital in technology, managerial and advertising. All of these 

have a significant effect on the productive capacity of the host nation, and the 

achievement of public measures to stimulate the productive base of the 

economy mainly depends on its ability to regulate appropriate amounts of FDI 

comprising managerial, capital, and technological resources to boost current 

manufacturing capacity (Omankhanlen, 2011).  

 

Lipsey and Chrystal (2004) describe FDI as a non-resident investment in the 

form of an acquisition or capital investment in a national branch, plant or 

subsidiary in which the investor has voting control. The International 

Monetary Fund (IMF, 1993, section 359) describes FDI as an investment 

reflecting the goal of acquiring a resident's lasting interest in another 

economy... lasting interest means the presence of a long-term connection 

between the immediate investor and the (foreign) undertaking and a 

significant degree of impact on the leadership of the undertaking by the 

investor. 

 

Foreign Direct Investment simply implies transfer of resources in the form of 

capital and technology to another country. Examples of this include 

management and advertising. These have been seen to significantly affect the 

productive capacity of the host nation and the achievement of measures to 

stimulate the productive base of the economy, therefore, depends on its ability 

to appropriately regulate the amount of FDI comprising managerial, capital 

and technological resources that boost current manufacturing capacity 

(Omankhanlen, 2011). Baraja (2010) defined agriculture as the science and 

practice of producing plants, other crops and animals for food, other human 

needs for economic gain David and Fuller (2014) contend that agriculture is 

the most comprehensive word used to denote the many ways in which crops, 

plants and domestic animals sustain the global human population by providing 

food and other products. Agricultural output on the other hand deals with 

quantifying the output of the sector. According to Olabanji, Adebisi, Ese and 

Oduntan (2017) agricultural output is the value of agricultural products which, 

free of intra branch consumption are produced during the accounting period 

and before processing are available for export and consumption. 

 

Statement of the Problem  

In spite of the concentration of government on oil revenue, the agricultural 

sector still contributed to the economy in terms of gross domestic product, 

foreign earnings and employment creation. Yusuff, Afoloyan and 

Adamu(2015) aver that agriculture accounts for about 40 percent of GDP and 

provides employment for about 60 percent of Nigeria’s over 170 million 

people. With this huge contribution, one would expect that the sector would 

attract greater attention from the government, but this has not been the case 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 163 
 

owing to the total reliance of the economy on crude oil (Akpan et al, 2017). 

Agricultural sector continues to witness drop in productivity of major farm 

produce which has resulted in the importation of consumable products that 

could be produced in the country. Also agricultural sector has been 

experiencing under funding in the recent years which is due to fall in 

allocation of funds to the sector in the budget. Financial institutions have also 

turned away from providing credits to agricultural sector as they prefer 

lending to industrial and oil companies who could give them more returns, a 

situation which has made agricultural sector to remain in subsistence nature 

and rely on crude equipment (Yusuff et al, 2015). Though, the Nigerian 

economy has benefited from foreign direct investment, however, the recipient 

sectors have been the communication, banking, education, and industrial 

sectors with manufacturing sector being the major recipient.  

 

Considering the unstable nature of oil price which has resulted in dwindling 

oil revenue successive governments have made diversification of the economy 

into agricultural sector their leading agenda (Udoh&Akpan, 2007; 

Aya&Akpan, 2009 &Akpan et al, 2017). Over the past 20 years, statistics on 

Nigeria have shown that, value-added per capita in agriculture in the country 

was less than one percent per annum. The Federal Ministry of Agriculture and 

Rural Development in 2008, revealed that, food (crop) production growth rate 

was far below the population growth, resulting in rising food imports and 

declining levels of national food self-sufficiency. Indeed, the dominance of 

small holder farmers with inadequate resources to practice large scale 

mechanized agriculture for the past twenty years especially in developing 

countries like Nigeria has resulted in low productivity and dwindling 

agricultural production (Iddrisu et al 2015). However, the poor fundamental 

infrastructure such as power supply and transportation further complicate 

issues in the agricultural sector. And questions regarding the effects of foreign 

direct investment inflow into the ageicultural sector and the 

relationshipagricultural production and trade openness have remained 

unattained to especially in the work of Akpan et al (2017), Idrissu et al (2015) 

and Anyanwu (2011). Hence this study seek to provide answers to these 

questions while at the same time accounting for Nigeria’s capacity in 

attracting FDI and its inflow into the agricultural sector from 1985 to 2018. 

 

Purpose of the Study  

This study is geared towards determining the impact of foreign direct 

investment on agricultural production in Nigeria. Specifically, the study will 

determine if  

a. Increasing foreign direct investment in Nigeria has resulted in 

improved agricultural production. 

b. Trade openness and credit to private sector help to boost productivity 

of the agricultural sector. 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 164 
 

 

Research Questions  

- To what extent does increase in foreign direct investment in Nigeria 

result in improved agricultural production? 

- How do trade openness and credit to private sector help to boost 

productivity of the agricultural sector in Nigeria? 

- How do corruption perception index and exchange rate affect 

agricultural production in Nigeria? 

 

Theoretical Framework  

The Cobb-Douglas Production Function 

 

This production function is used to show the relationship between the 

quantities of two or more inputs employed in the production of specified 

quantities of an output. These inputs can be physical capital and labour. Hence 

Cobb Douglas production function can be used to explain the relationship 

between FDI inform of capital (technology or funds) transfer and output of the 

agricultural sector. (Ogbuabor&Nwosu, 2017). This function relates input and 

output in a multiplicative function thus: 

 

Q = f(K, L) = AKaLb 

Where A, a, and b are all positive constants and Q is the output level. Q can be 

assumed to be the monetary value of all goods produced annually in the 

economy, L = labour input which is the total number of person-hours worked 

in a year, K = capital input or the monetary worth of all machinery, 

equipment, and buildings; A is total factor productivity, a and b are the output 

elasticities of labour and capital, respectively. Koutsoyiannis (2002) opined 

that these constants are determined by available technology. The predictions 

of this theory indicate that if agricultural output were to be represented by Q, 

given that inflow of foreign direct investment yields more capital to thehome 

country,  K can be taken to mean foreign direct investment while L which is 

the total labour force can represent the absorptive capacity of the recipient 

country. 

 

The Stochastic Frontier Production Function 
The stochastic frontier production function (SFPF) was developed by Aigner, 

Lovell and Schmidt (1977) and Meeusen and Van den Broeck (1977) and 

utilized by Battese and Coelli (1993)to estimate production and goal 

attainment functions of a firm. Production function shows the technical 

relationship between given input and output of a firm (Battese and Coelli 

1993) and (Hossain, Alam and Uddin, 2015). Based on this theory, 

agricultural production is aggregated for all the farmers to obtain the 

aggregate farmer therefore defines the frontier. Deviation from this frontier 

could emanate from mismeasured production factors. Estimation of the 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 165 
 

frontier presupposes that the boundary of the production function is defined 

by optimal farmers. If a firm moves away from the boundary, this is 

accommodated since the estimation procedures are stochastic. This theory can 

therefore, be employed to depict the relationship between the input inform of 

capital (funds and technology) and labor transferred as FDI and agricultural 

output. 

 

Theory of Productive Efficiency  

The theory of productive Efficiency was proposed by Farrel (1957). He 

classified the theory into Allocative Efficiency and Technical Efficiency. 

Technical Efficiency implies the ability of a firm to produce a given level of 

output with minimum quantity of input under a given technology. Allocative 

efficiency is a measure of the degree of success in achieving the best 

combination of inputs in producing a specific level of output with 

consideration given to the relative prices of these inputs (Arene, 2003). Based 

on this theory, therefore, the agricultural farmer is assumed to allocate and 

utilize inputs obtained through FDI in an efficient way to generate an optimal 

level of production. According to Umoh, (2006), maximum efficiency of a 

firm is attained when it is impossible to reshuffle a given resource 

combination without decreasing total output. 

 

The Link between Foreign Direct Investment and Agricultural 

Production in Nigeria  

 

There is a significant connection between FDI and agricultural production in 

Nigeria. EL-Wassal (2002) pointed out that there is connection between FDI 

and growth in agricultural production. He categorized the connection into the 

positive view, the negative view and the dependent impact view. According to 

him, the positive impact view which stems from the neoclassical economic 

growth theory that classifies the positive impact into direct and indirect 

impacts. The direct impact considers the impacting of FDI on growth from 

augmentation of domestic capital based on the proposition of the philosophy 

that it is capital that drives growth. this implies that given the increased capital 

inflow through FDI more capital will be available for agricultural production. 

The indirect impact view on the other hand results from knowledge and 

technology that are included as factors of production, the transfer of which in 

form of FDI will lead to improvement in techniques of agricultural production 

(Romar, 1994), (Kumar and Pradham, 2002) and (Moran, Grabam and 

Blostrom, 2005). The negative impact view implie that FDI is connected to 

agricultural production by widening agricultural income inequality hence 

negatively impacting agricultural output in the recipient economies 

(Bomschier, Chase-Dunn and Rubinson, 1978) and (Nolan, 1983). 

 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 166 
 

As for the dependent impact view, FDI is connected to agricultural production 

through its absorptive ability of the host country’s characteristics. These 

characteristics include rate of domestic investment, well developed financial 

market, export orientation and trade openness (Blomstrom, Lipsey and Zejan, 

1994), Lautier and Moreaub (2012), (Alfaro and Charlton 2007), 

(Balasubramaryan, Salisu and Spasford, 1996). 

 

Effect of Foreign Direct Investment on Agricultural Production in 

Nigeria 

On the impact of FDI on agricultural production, there have been divergent 

views and findings by researchers with these findings revolving around the 

positive view, negative view and the dependent impact view. For example, 

Oloyede (2014) studied the impact of FDI on the development of the 

agricultural sector in Nigeria using time series data covering the period 1981 

and 2012 and employing the Ordinary Least Square (OLS) estimation 

technique. The author finds FDI to positively impact agriculture. The author 

also finds instability of the political environment to inversely affect the 

agricultural sector. Adeyeye (2016) looked at the impact of foreign direct 

investment on the growth of Nigeria's agricultural sector. The research 

covered the 1981-2015 periods. Multiple regression method was used in the 

research. The findings showed a favorable and substantial impact of FDI on 

agricultural production. Thus, the research found that foreign direct 

investment has a beneficial impact on Nigeria's agricultural sector growth. In 

2016, Fauzel, Seetanah and Sannasee also conducted a survey to tackle the 

significant issue of whether foreign direct investment in the agricultural sector 

enhances the sector's productivity in Mauritius using information from the 

1980-2010 time series. Using a dynamic vector error correction model, 

catering for dynamic endogeneity and causality, the findings indicate that FDI 

in the agricultural sector has actually contributed to overall productivity factor 

as well as long-term labor productivity while inflation and exchange rate are 

negative contributors to the productivity of the agricultural sector. Analyzing 

the outcomes of the short run, the research discovered that FDI continues to 

affect productivity in the agricultural sector, but the effect is very low.  

 

On the negative view, Ogbanje, Okwu and Saror (2010) used Pearson Product 

Moment Correlation analysis to determine the relationship between 

agricultural FDI and agricultural GDP and found a strong negative 

relationship. Olayide (2014) examined the impact of foreign direct investment 

on agricultural sector in Nigeria by employing secondary time series data 

which spanned from 1981 to 2012 using multiple regression. It was found in 

the study that FDI impacted negatively on agriculture not only in the short-run 

but also in the long-run. Abu et al., (2011) explored the relationship between 

foreign direct investment and agricultural production in Nigeria. It was 

revealed that foreign private investment had negative but insignificant effect 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 167 
 

on agricultural output while government expenditure had positive effect on 

agricultural output.  

 

On the dependent impact view, Yusuff et al (2015), researched on analysis of 

foreign direct investment on agricultural sector and its contribution to gross 

domestic product in Nigeria in which they investigated the impact of 

agricultural foreign direct investment on agricultural sector productivity in 

Nigeria and used descriptive statistics and simple linear regression with the 

result showing that the inflow of Foreign direct investment to agricultural 

sector did not follow a regular pattern, but that the sector’s contribution to 

gross domestic product was in direct relationship with the inflows of Foreign 

Direct Investment. Daniel and Maiwada (2015) analyzed the nature and 

volume of Chinese trade and investment in Nigeria’s agricultural sector and 

its impact on the Nigerian economy. The study revealed that the agricultural 

sector which hitherto dominated the economy especially as a source of 

revenue soon gave way to crude oil and it was revealed that Chinese trade and 

investment in Nigeria’s agriculture is very low compared to other sectors and 

has not focused much in the development of the sector in Nigeria owing to its 

dependence on the characteristics of the Nigerian economy. 

 

 

Agricultural Production and Trade Openness 

Djokoto (2013) looked at how trade openness impacts agricultural 

performance in Ghana by using FDI as a proxy for openness and data 

covering the period 1995 and 2009. The study revealed that trade openness is 

a major determining factor of agricultural production in Ghana as it positively 

affects the performance of the agricultural sector. Sakyi (2011) found a long 

run relationship among agricultural growth, openness of trade and foreign aid 

in Nigeria by using autoregressive distributed lag (ARDL) bounds test 

approach and data covering the period 1984 and 2007. Insah (2013) found a 

positive relationship between trade openness and agricultural sector growth in 

Ghana. Using the same data coverage of between 1980 and 2010, Antwi, 

Mills and Zhao (2013) found a similar result on Ghana by establishing that 

trade openness has a positive relationship with agricultural sector growth in 

Ghana. 

 

Saqib, Masnoon and Rafique (2013) reviewed the effect of trade openness on 

Pakistan's agricultural sector growth from 1981 to 2010. They used six 

variables where agricultural sector contribution to GDP is specified as 

dependent variable while independent variables were trade openness, Total 

Debt Service, Gross Domestic Savings, inflation. The results suggested an 

important adverse connection between trade openness and agricultural sector 

growth. There was also an adverse connection between debt, inflation and 

trade and the performance of the sector. Koojaroenprasit (2012) used 

 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 168 
 

secondary data for the period 1980–2009 to explore the effect of openness to 

trade on South Korea's agricultural sector development. The research used 

multiple regression analysis. This research discovered that the effect of 

openness on South Korean agricultural sector growth is significant and 

positive. Moreover, the research showed that human capital, jobs and exports 

also have a favorable and substantial effect, while national investment has no 

significant effect on agricultural sector growth in South Korea. The empirical 

relationship between openness to trade and agricultural sector growth in 

Nigeria between 1970 and 2008 was also explored by Umoh, Jacob and 

Chukwu (2012). They used concurrent and single equation schemes to 

examine this connection. Their findings indicate that from agricultural growth 

to openness and from openness to agricultural growth, there is a beneficial 

cause.  

 

Methodology and Instrumentation  
Data for this study will be obtained through secondary sources and analyzed 

to answer the research questions. A multivariate ordinary least square model 

will be adopted. in their study on the impact of FDI on agricultural 

productivity, Fauzel et al (2016) specified a model where FDI, inflation rate 

exchange rate and interest rate served as the explanatory variables. Given the 

nature of the present study, the model used by Fauzel et al (2016) will be 

adopted with little modification. Given that the present study intends to 

account for Nigeria’s characteristics which form the absorptive capacity of the 

country and given the objectives of this study, the model used by Fauzel et al 

(2016) will be modified through the addition of the variables like trade 

openness and absorptive capacity variables such as institution quality and 

financial institutions capacity, while excluding some variables from their 

model. Following from the above modifications therefore, the model for this 

study is specified as follows: 

 

),( .  iii xfAy
 - - - - - - - -

 - 3.1        
 

Where yi is the observed output by the producer i; xi is a vector of K inputs 

used by the farmer i, ),( ixf  is the production frontier and  is a vector of 

technology parameters to be estimated. iA represents technical efficiency 

defined as ratio of observed input to maximum feasible output. Let us assume 

that Ai is a stochastic variable with a normal distribution function that is 

common to all rice farmers, then, we can write }exp{ ii uA  where 0iu . 

Suppose we add a stochastic component; }exp{ iv , that captures random 

shocks affecting the production of rice such that each rice farmer faces 

different shocks, the production function becomes: 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 169 
 

)exp{ . )exp{- ).,( iiii vuxfy 
 - - - - - -

  3.2
 

Now if we assume a log-linear Cobb-Douglas production, then (3.2) would 

become a translog stochastic production frontier of the form: 

 

iiik

k

i uvxy    ln    ln k  0
 - - - - - -

 - 3.3 

      

 

Where  k   is a 1 x k vector of unknown parameter to be estimated 

The systematic error component, vi, is assumed to be independently and 

identically distributed (i.i.d) random error having normal distribution with 

mean zero and variance 
2

v , that is, vi ~N(0, 
2

v ).  iu are non-negative random 

variables associated with the technical inefficiency of production. Given that 

FDI also involves technology transfer, k   in the model will capture this 

technology transfer through FDI while xi which is a vector of inputs will 

capture all other explanatory variables in the model. 

 

 

AGDP = F(FDI, DI, TRO, CPI, CPS, EXR)  - - - - -

 - 3.4 

The mathematical form of the model is specified as: 

AGDP = α + β1FDIt + β2DIt + β3TROt + β4CPIt+ β5CPSt + β6EXRt -` -

 - 3.5    

Given that there are other variables that affect agricultural production, but for 

simplicity, these other variables were not explicitly included in the model, the 

error term will be introduced in the econometric form of the model; the log 

form of some variables will also be introduced into the model in order to 

reduce the extreme values of those variables and avoid the problem of 

explosive modelling. The log-linear econometric form of the model is 

therefore as specified below: 

LogAGDP = α + β1LogFDIt + β2LogDIt + β3TROt + β4CPIt +β5LogCPSt + 

β6EXRt + µt -   3.6  

Where:  
LogAGDP = Log of agricultural sector contribution to GDP (proxy for 

agricultural production) 

LogFDI = Log of Foreign Direct Investment. 

LogDI = Log of domestic investment 

TRO = Trade Openness (calculated as the ratio of export and import to GDP) 

CPI = Corruption perception index (proxy for institutional quality) 

CPS = Credit to private sector (proxy for financial institutions capacity) 

EXR = Exchange rate 

α = Intercept. 

9 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 170 
 

β1, β2, β3,β4,β5and β6 are the coefficients. 

µ = error term (representing other variables that affect agricultural production 

but are not explicitly specified in the model) 

t = Time factor  

 

The choice of the Multivariate Ordinary Least Square is hinged on the fact 

that among other estimation techniques for linear models, the Ordinary Least 

Squares (OLS) is the best because if produces parameters that are unbiased, 

consistent and efficient (Gujarati, 2007). The multiple regression analysis will 

also be carried out in view of the multiple nature of the models. It is expected 

that all the variables will positively affect agricultural production except for 

corruption perception index and exchange rate which are expected to have 

negative effect on agricultural production. 

 

The “t statistics” will be employed to answer research questions while the 

“R2” and Adjusted R2 statistics will be employed as the coefficient for 

determination to measure the goodness of fit of the regression line to the 

observed sample values of the variables while the “F statistics” will be used to 

test the overall significance and predictive power of the model. The unit root 

test,conitegration test and the error correction modeling techniques will be 

adopted. The secondary data will be sourced from the publications of the 

central bank of Nigeria (CBN) statistical Bulletin (2018) and the World 

Bank’s World development Indicators (WDI, 2018). 

 

Presentation and Analysis of Results 

 

Unit Root Test 

The analytical techniques discussed in the previous chapter were applied to 

the models of the study and the results are presented in this section. Since 

empirical analysis based on time series data would be biased if the underlying 

data are non stationary, the unit root test is therefore necessary to check for 

the stationarity of the variables. The test used for observing the stationarity of 

the time series data used for analysis in this study is the Phillips-Perron unit 

root test. The results are summarized in table 1 below: 

 

 

 

 

 

 

 

 

 

 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 171 
 

Table 1 Summary of Phillips-Perron Unit Root Test Result 

Variables ADF Statistics Order of Integration R e m a r k s 

D(AGDP) - 6 . 1 2 0 6 4 1 ( 1 ) Stationary at first difference 

D ( F D I ) - 4 . 4 5 4 5 1 0 1 ( 1 ) Stationary at first difference 

D ( D I ) - 9 . 9 1 0 5 6 5 1 ( 1 ) Stationary at first difference 

D ( T R O ) - 6 . 0 5 8 6 9 1 1 ( 1 ) Stationary at first difference 

D ( C P I ) - 4 . 0 5 5 8 0 7 1 ( 1 ) Stationary at first difference 

D ( C P S ) - 9 . 9 7 5 6 6 6 1 ( 1 ) Stationary at first difference 

D ( E X R ) - 5 . 0 1 8 3 7 9 1 ( 1 ) Stationary at first difference 

E C M - 5 . 2 5 9 5 8 7 1 ( 0 ) S t a t i o n a r y  i n  l e v e l 

Source: Researcher’s compilation using Eview 9 (2019).  

 

As seen in table 1, all the variables were stationary at first difference. The 

ECM was stationary in level. This provides a strong criterion for the 

cointegration analysis. 

 

Cointegration Test 
Since short run equilibrium has been revealed to exist among the series, there 

is the need to investigate the existence or otherwise of long run equilibrium 

among these series. This test will be done using the Johansen and 

JuseliusCointegraion Test. 

 

Hypothesis: 

H0: The series do not cointegrate 

H1: The series cointegrate 

 

Decision Rule 

Reject the null hypothesis if the trace statistic > the 5% critical value or if the 

probability value < 0.05. The cointegration test tables are presented below: 

Table 2: Cointegration test result 

Hypothesized No. of CE(s)  Eigen value Trace statistic 0.05 critical value P r o b * * 

N o n e * 0 . 5 7 4 8 3 8 7 5 . 0 3 5 7 6 6 9 . 8 1 8 8 9 0 . 0 1 8 0 

A t  m o s t  1 * 0 . 4 7 8 7 9 7 4 5 . 9 5 6 0 6 3 7 . 8 5 6 1 3 0 . 0 4 4 6 

A t  m o s t  2 0 . 3 6 5 2 9 9 2 3 . 8 0 1 1 0 2 9 . 7 9 7 0 7 0 . 2 0 9 0 

A t  m o s t  3 0 . 2 1 0 9 2 3 8 . 3 4 4 6 7 7 1 5 . 4 9 4 7 1 0 . 4 2 9 3 

A t  m o s t  4 0 . 0 0 8 5 0 5 0 . 2 9 0 3 9 0 3 . 8 4 1 4 6 6 0 . 5 9 0 0 

Source: Author’s computation using Eviews 9(2019) 

 

From table 2 above, there were two cointegrating equations in the series. 

Hence we reject their respective null hypotheses while concluding that the 

series has long run equilibrium relationship. This therefore means that in the 

long run (that is, the period when all factors are variable) the variables directly 

or indirectly influence the behavior of each other.  



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 172 
 

 

Since the short run and long run relationships cannot individually explain the 

exact behavior and nature of relationships between variables, it is necessary to 

combine both the short run and long run components in order to ascertain the 

speed of adjustment and the level of disequilibrium/discrepancies in the 

previous period that were actually corrected in the present period. This 

therefore necessitates the error correction model. Again, to determine the 

maximum lag length so as to enable a parsimonious regression result, the 

optimal lag selection criteria was adopted and lag 1 was adopted as the 

maximum lag for the three models using the Akaike Information criteria as 

evidenced in the table below: 

 

Table 3: Optimal Lag Length Selection Criteria 

L a g L o g L L R F P E A I C S C H Q 

0 -479.2687 N A 4.28e+16 52.60404 52.82851 52.68059 

1 -712.9348 257.222* 2.17e+13* 44.87852* 46.22530* 45.33781 

2 -890.1344 21.34875 4.13e+13 45.41967 47.88878 46.26171 

Source: Author’s compilation (2019) Where * indicates lag order selected 

by the criterion 

 

 

 

Summary of The Regression Result: Dependent Variable- D(AGDP) 

ECM (SHORT RUN) MODEL 

V a r i a b l e s C o e f f i c i e n t S t d .  E r ro r t - s t a t i s t i c P r o b . 

C 0 . 0 2 2 0 5 8 0 . 0 1 0 5 9 4 2 . 0 8 2 1 7 9 0.0463 

D ( F D I ) 0 . 0 6 9 3 6 9 0 . 0 1 5 3 6 7 2 . 2 6 0 4 2 5 0.0176 

D ( D I ) 0 . 0 5 0 5 1 9 0 . 0 0 0 3 2 3 3 . 6 0 9 0 9 9 0.0184 

D ( T R O ) 0 . 0 5 1 6 0 9 0 . 5 4 5 4 1 0 2 . 1 0 2 7 4 5 0.0443 

D ( C P I ) - 0 . 0 1 3 3 3 2 0 . 0 3 2 2 5 6 - 1 . 5 1 3 4 7 8 0.4015 

D ( C P S ) 0 . 0 3 0 0 6 9 0 . 0 4 7 4 3 9 1 . 0 4 8 7 3 0 0.2109 

D ( E X R ) - 0 . 0 1 0 3 3 1 0 . 2 1 0 4 9 2 1 . 0 8 2 7 4 7 0.3201 

E C M ( - 1 ) - 0 . 5 8 2 3 7 6 0 . 1 5 2 8 4 8 - 3 . 8 1 0 1 7 5 0.0007 

R2= 0.527281 F stat= 46.69436 R2Adj= 0.445777 F proby=0.00037  

 

Source: Researcher’s compilation using Eview 9(2019)  

 

 

 

 

 

 

 

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MEZE MICHAEL EJIKE                                                                                                                                                     Page 173 
 

Table 5: Summary of the Regression Result: Dependent Variable- AGDP 

LONG RUN MODEL 

V a r i a b l e s C o e f f i c i e n t S t d .  E r r o r t - s t a t i s t i c P r o b . 

C 6 . 8 5 5 8 4 7 0 . 4 5 3 8 8 4 1 5 . 1 0 4 8 6 0.0000 

F D I 0 . 0 8 1 2 5 3 0 . 0 0 7 1 9 0 4 . 3 0 1 1 2 7 0.0009 

D I 0 . 0 6 0 2 8 0 0 . 0 0 0 3 4 2 3 . 8 1 6 7 8 2 0.0203 

T R O 0 . 0 6 7 1 1 1 0 . 0 2 3 6 1 0 2 . 0 8 0 6 3 8 0.0362 

C P I - 0 . 0 3 1 3 6 1 0 . 0 1 9 9 8 0 - 1 . 0 7 1 1 2 7 0.3044 

C P S 0 . 5 0 3 8 3 0 0 . 9 3 7 3 8 7 2 . 3 4 7 4 8 2 0.0103 

E X R - 0 . 2 0 4 7 4 9 0 . 9 8 3 1 6 3 - 1 . 5 3 7 3 9 1 0.4592 

R2= 0.743972 F stat= 130.5745 R2Adj= 0.736743 F proby=0.00000  

Source: Researcher’s compilation using Eview 9(2019)  

 

From tables 4 and 5 above, the estimated coefficients of 0.069369 in the short 

run and 0.081253 in the long run for foreign direct investment shows that a 

percentage increase in foreign direct investment will subsequently increase 

agricultural production in Nigeria by about 6% and 8% in the short and long 

run periods respectively. Similarly, the coefficients of 0.051609 in the short 

run and 0.067111 in the long run for trade openness show a positive 

relationship between trade openness and agricultural production in Nigeria. 

This indicates that a percentage increase in trade openness will increase 

agricultural production by about 5% and 6% in the short and long run periods 

respectively. The coefficient of -0.013332 in the short run and -0.031361 in 

the long run for corruption perception index shows that a percentage increase 

in corruption perception index will decrease agricultural production by about 

1% and 3% in the short and long run periods respectively. The coefficient of 

0.030069 and 0.053830 in the short and long run periods respectively for 

credit to private sector shows that a percentage increase in credit to private 

sector will increase agricultural production by about 3% and 5% in the short 

and long run periods respectively. Again, the coefficient of -0.010331 in the 

short run and -0.204749 for exchange rate shows that a percentage increase in 

exchange rate will bring about a 1% and 2% decrease in agricultural 

production in the short and long run periods respectively. Finally, the negative 

coefficient for the ECM shows that in the previous period, the long run 

component of the ECM model (i.e., εt-1) had a value that was > 0 which means 

that the value of the regressor and in the previous period was above its 

equilibrium value, hence, the ECM short run component (i.e., α) needed to 

take a negative value to restore the value of the regressor and back to 

equilibrium in the long run. Therefore, from the result, the coefficient value of 

-0.582376 for the ECM shows that about 58% of the 

disequilibrium/discrepancies in the previous period (short run) were corrected 

in the present period (long run). This therefore shows a high speed of 

adjustment to long run equilibrium. 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 174 
 

Discussion of Findings 
Since the long run period represents the period when all factors of production 

and all variables vary, the discussion of findings in this section will be based 

on the long run regression output.  The result indicates that a percentage 

increase in FDI inflow into the agricultural sector brings about an 8% increase 

in agricultural production. This implies that as FDI inflow into the agricultural 

sector increases, agricultural production will also increase. This is expected 

given that FDI inflow leads to technology transfer. These technologies may 

come inform of machineries and other mechanized farming equipment and 

improved seedlings and fertilizer which goes a long way in improving capital 

formation in the sector. The improved capital formation will consequently 

lead to an improvement in labour efficiency and effectiveness thereby leading 

to an increase in output per labour and the general productivity of the sector. 

This finding is in line with that of Oloyede (2014) and Adeyeye (2016). 

 

The result also shows that a percentage increase in trade openness will 

increase agricultural production by about 6%. Given that trade openness will 

increase is the ratio of import and export to the GDP, it goes to determine the 

extent to which engagement in trade affects the domestic economy. Hence, an 

increase in trade openness would mean that the domestic economy benefits 

from international trade. Such benefits increase foreign exchange earnings 

leading to further appreciation of domestic currency which increases the gains 

from agriculture thereby leading to an increase in farmers’ income with an 

attendant increase in agricultural production through expansion and 

acquisition of improved farming implements. These findings were in line with 

those of Agrawal (2015); Melnyk et al (2014) and Djokoto (2013). 

 

On corruption perception index, the findings showed that increases in 

corruption perception index which is a sign of deteriorating institutional 

quality will decrease agricultural production. This result is predicated on the 

fact that a decline in institutional quality will lead to a decline in the ease of 

doing business index which will further exacerbate FDI inflow into the 

economy and agricultural sector in particular leading to further decline in 

domestic investment and general agricultural production.  

 

The findings further showed that increases in exchange rate will decrease 

agricultural production in Nigeria. Given that an increased exchange rate 

means a depreciation of the domestic currency, such depreciation leads to an 

increase in the cost of importation which invariably affects the importation of 

agricultural implements leading to a decline in agricultural production. These 

findings are in line with that of Fauzel et al (2016). 

 

 

 



EVAIA: INTERNATIONAL JOURNAL OF ETHICS AND VALUES Volume 1, NO. 1,  July, 2020 

 

MEZE MICHAEL EJIKE                                                                                                                                                     Page 175 
 

Summary 
 

This study was undertaken to empirically evaluate foreign direct investment 

and agricultural production in Nigeria from 1985 to 2018. The study was 

specifically carried out to determine the effect of foreign direct investment 

inflow into the agricultural sector on the productivity of the sector, the 

agricultural production-domestic investment nexus, and the relationship 

between agricultural production and trade openness in Nigeria. In order to 

achieve these objectives of the study, an econometric model was specified. 

The data used for the research were from secondary source obtained from 

Central Bank of Nigeria (CBN) Statistical Bulletin (2018) and World Bank’s 

World Development Index (WDI, 2018). 

 

The findings of the study showed that foreign direct investment, domestic 

investment, trade openness and credit to private sector (financial institutions 

capacity) have significant positive effect on agricultural production while 

corruption perception index (proxy for institutional quality) and exchange rate 

have insignificant negative impact on agricultural production in Nigeria. 

 

Recommendations  
Given the findings of the study, the policy recommendations are given below: 

 An enactment and reformation of extant laws and policies that could 

boost the ease of doing business index for Nigeria. This could be done 

by reinvigorating investment attracting mechanisms in the less 

invested sectors of the economy such as the mining and quarrying and 

the agricultural sector.  

 Enhancement of international competitiveness through an improved 

exportation which will increase the trade openness index and further 

improve the already existing positive effect of trade openness on the 

performance of agricultural sector. This will also help to minimize the 

negative effect of exchange rate vagaries on agricultural production. 

 

Conclusion 

 

In a bid to ascertain whether the research questions of the study were upheld, 

the following conclusions were drawn from the findings of the study: 

 Increasing foreign direct investment helps boost agricultural 

production in Nigeria. 

 Trade openness also helps to boost agricultural production in Nigeria. 

 Corruption perception index and exchange rate are inhibitors to the 

growth of agricultural production in Nigeria. 

 

From the above, it is evident that foreign direct investment is a catalyst to 

agricultural production in Nigeria. 



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MEZE MICHAEL EJIKE                                                                                                                                                     Page 176 
 

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MEZE MICHAEL EJIKE                                                                                                                                                     Page 177 
 

 

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MEZE MICHAEL EJIKE                                                                                                                                                     Page 180 
 

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MEZE MICHAEL EJIKE                                                                                                                                                     Page 181 
 

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