




































American International Journal of Agricultural Studies  

Vol. 1, No. 1; 2018 

Published by American Center of Science and Education, USA 

 

38 

 

 

Effectiveness Analysis of Agricultural Protection Policy on Food 

Supply, Export and Farmer-Welfare in Nigeria: Generalized Method of 

Moment Approach 

Mr. Emmanuel Ejiofor Omeje 

University of Nigeria Nsukka, Faculty of Agriculture, Department of Agricultural Economics, Nsukka, Enugu State, 

Nigeria. ejiofor.omeje@unn.edu.ng, Phone 08038090907 

 

Prof. Chukwuemeka John Arene 

University of Nigeria Nsukka, Faculty of Agriculture, Department of Agricultural Economics, Nsukka, Enugu State, 

Nigeria. cjarene@yahoo.com 

 

Dr. Benjamin Chiedozie Okpukpara 

University of Nigeria Nsukka, Faculty of Agriculture, Department of Agricultural Economics, Nsukka, Enugu State, 

Nigeria. Benjamin.okpukpara@unn.edu.ng 

 

 

 

Received: November 9, 2018      Accepted: November 13, 2018             Online Published: November 17, 2018        

 

Abstract 

 

This study examined the effectiveness of agricultural protection policy and other macroeconomic variables on food 

supply, agricultural export, and farmers welfare in Nigeria, from 1980-2016 with a special interest in their 

relationship with the political economy. The specific objectives were to (i) estimate the degrees of agricultural 

protection, domestic agricultural food supply and economic welfare to farmers in Nigeria, (ii) determine the 

effectiveness of agricultural protection on food self-supply, agricultural export; and farmer-welfare. Data were 

obtained from secondary sources. Descriptive statistics and generalized method of moment (GMM) were used. 

Nigeria’s self-food supply was slightly above 50% while the rest of the consumption depended on importation. The 

welfare measure to farmers was relatively poor and not good enough to motivate them. There was a positive and 

significant relationship between export and agricultural protection. A significant and positive relationship also exists 

between farmer-welfare and protection in the sector. 

 

Keywords: Agricultural protection, effectiveness, self-food supply, agricultural export, farmer-welfare, generalized 

method of moment. 

 

1. Introduction 

 

Agriculture has been a major source of income for many Nigerian people, source of export earnings by the 

government and source of own food for its growing population.  According to Okumadewa, (1997), agriculture 

contributes immensely to Nigeria’s economy in various ways including provision of food for the increasing 

population, supply of adequate raw materials (and labour input) to the growing industrial sector, a major source of 

employment; generation of foreign exchange earnings, and, provision of market for the products of the agrarian 

sector. Asirvatham (2016) opined that agricultural protection represents an effort by the political class to increase 

agricultural growth by improving national food security and minimizing food dependence on foreign countries.  This 

means that the agricultural sector has a strong relationship with the economy and hence, the concern for agricultural 

policies.  

 

http://www.acseusa.org/journal/index.php/aijas
mailto:ejiofor.omeje@unn.edu.ng
mailto:cjarene@yahoo.com


www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

39 

 

 

In economic and political terms, agricultural protection policies are not new in Africa. According to the 

Organisation for Economic Co-operation and Development [OECD] (2018), agricultural protection could be defined 

as the ratio between the average price received by producers (measured at the farm gate), including net payments per 

unit of current output, and the border price (measured at the farm gate). For instance, a coefficient of 1.10 suggests 

that farmers, overall, received prices that were 10% above international market levels. This indicator reflects the 

level of price distortions and is measured by the Producer Nominal Protection Coefficient expressed as the ratio of 

farm price to border reference price. Also, any policy that puts up tariffs or other trade barriers on agricultural 

products so as to prevent or discourage imports or any policy that seeks to promote agriculture in a given area is 

termed agricultural protection policy. 

 

The patterns of agricultural protection policies in Africa suggest that developing nations strongly subsidize 

agriculture (Olper, 1998). Other scholars like Inhwam (2008) and Barrette (1999) had argued that agricultural 

protection is capable of creating negative externalities to developing countries because agricultural protection 

distorts trades of agricultural products which some developing countries have a comparative advantage in producing. 

Goldin and Knudsen (1990) opined that since agriculture is a sector of comparative advantage for many developing 

countries now and for some time in future, agricultural protection does not materially impair their potentials for 

economic growth. Swinnem (1996), Inhwam, (2008) and Barrett (1999) noted that some political environment 

surrounding food price policy differs markedly across time in a given country and cross-sectional among countries 

at any given time. 

 

The political economy of financing and or supporting agricultural policy by the government is determined by the 

government’s interests. To determine where, when or how much resources any rational government would commit 

into agricultural protection, some important political economy factors or indicators are expected to guide the 

decision. Bratton and van de Walle (1994) viewed political economy variables as those factors taken into 

consideration as economic and political exigencies when analyzing determinants of protectionism. Such political 

economy variables may include the state of food security or food self-sufficiency status; the contribution of foreign 

exchange earnings from the sector’s export; general economic welfare to farm producers; GDP of the sector; policy 

changes; budgetary allocation to the sector; and political or structural changes in the economy. In the same line of 

thought, Amin (1972) explained that the different regimes reflected varying economic and political interests and 

similar commodity biases. It is expected that a nation whose food supply is grossly dependent on import would be 

politically vulnerable. Food production during the World Wars generated the revenues necessary for administrative 

independence for some countries (Cooper, 2002). Pejout opined that food riots and violence became more prevalent 

in African cities following the rapid escalation of food prices in 2008 (Pejout, 2010) and this resulted in political 

instability and drove governments to re-analyze their agricultural policy. General economic welfare to farmers is 

also a political indicator that determines the demand push for protection from voters/farmers. It is expected that 

when the farmers are not making much of profit, their demand for protection may likely increase. Sometimes, it is 

suspected that political class purposively increases the budget for protection or subsidies in order to gain political 

support during elections. In line with this, Bratton and van de Walle (1994), opined that political class or elite 

mobilize political support by using their public position to distribute rent-seeking opportunities such as subsidies, 

interest free-loan, or grants.  Nations’ GDP or GNP appears to be a quick tool in the hands of politicians for 

measuring the progress of policies. The GDP situation during a specific period or policy regime may guide the 

political class on whether more funds should be pushed into the sector or not. Also, the government usually would 

like to increase revenue through agricultural export since financial constraints can weaken administrative capacity 

and threaten political stability (Gardner, 2012).  

 

Governments’ decision to intervene and finance agricultural sector is usually spurred by a number of factors but 

common among them is to protect the farmers’ commodity price from domestic and international market forces; to 

improve farmers’ income and standard of living; and to protect the consumers from excessive prices (Inhwam, 

2008). Therefore, agricultural projects and policy reforms are expected to be subjected to analyses to examine its 

effectiveness in ensuring production of own food, increasing export and improving the wellbeing of the producers. 

Problem statement 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

40 

 

 

 

It is notable that between 1980 and 2016, the federal government of Nigeria has promulgated various agricultural 

policies and implemented many projects and programmes with the views to improving the standard of livelihood of 

producers, increase food supply and agricultural export. For example, in 1981, Nigeria pursued import substitution 

industrialization strategy of which import prohibition policy was pronounced; from 1981, the policy shifted towards 

export promotion policy. At this point, the government was made to embark on the Economic Stabilization Policy 

(temporal provisions) act in April 1982. 

 

To consolidate the policy, Nigeria further protected agriculture sector when the policy brought about the prohibition 

of frozen poultry while tariff on 49 agricultural commodities was raised. Between 1983 - 1985, 152 items were 

brought specific import license and foreign exchange policy regulations became more stringent (Briggs, 2007). In 

1986, a noticeable shift in policy was directly attributed to greater liberalization policy and the adoption of structural 

adjustment programme (SAP). Between 1988 and 1994, protection policy became more evident when the 

government provided a seven-year import tariff where imports attracted ad valorem rates applied in Most Favoured 

Nation (MFN) basis. Also, between 1995 and 2001, another tariff regime succeeded in the previous regime.  

 

The restructuring and recapitalization of the Nigeria Agricultural Bank to provide loans to peasant farmers at single 

digit interest rates. The launching of the Growth Enhancement Scheme to cater for farmers using the E-wallet 

system. The substitution of 20% of wheat bread flour with cassava flour. Agricultural commodity marketing and 

pricing policy in 1977, six national commodity boards were established which include; commodity boards for cocoa, 

groundnuts, palm produce, cotton, rubber and food grains. Land use policy was promulgated by the Federal 

Government in 1978 vesting the ownership of all lands on the government as to giving genuine farmers access to 

farmlands. Agricultural extension and technology transfer policy aimed at improving the adoption of improved 

agricultural technology by farmers with the National Accelerated Food Production Project (NAFPP) and agricultural 

development projects (ADPs) as implementing agencies. Input supply and distribution policy were promulgated to 

ensure adequate and orderly supply of agricultural inputs notably fertilizers, agrochemicals, seeds, machinery and 

equipment such that agricultural input subsidy policy on fertilizer was 50%, agro-chemicals (50%) and tractor hiring 

services (50%). 

 

Also, agricultural research policy was established in 1971, followed by Agricultural co-operatives policy in 1979 

and water resources and irrigation policy which brought about the establishment of eleven River Basin Development 

Authorities in 1977 charged with the responsibility of developing Nigeria’s lands and water resources and 

Agricultural mechanization policy which was instrumental to the creation of the Ministry of Science and 

Technology and the establishment of some Universities of Science and Technology, the operation of tractor hiring 

units in all the states of Nigeria, reduced import duty on tractors and agricultural equipment and implements, 

generalized and liberalized subsidies on farm clearing and establishment of a centre for agricultural mechanization. 

Despite all these efforts, poverty has persisted in the sector where over 70% of its rural population is engaged in 

agricultural activities (International Food and Agricultural Development [IFAD] 2016, National Bureau of Statistics, 

[NBS], 2017). According to Olawepo (2010), income has generally remained low from agricultural production in 

Nigeria. Also, International Fund for Agricultural Development (IFAD, 2016; NBS, 2017) reported that despite all 

these many efforts, poverty is still widespread in the country and has been on increase since the 1990s. Also, past 

studies have shown that most of these poorest households are found working in agriculture (Ikpi, 1989; Ayoola, 

Aina, Mamma, Nweze, Odebiyi, Okumadewa, Shehu, Williams & Zasha, 2000; Alayande & Alayande, 2004; 

Poulton, Doward & Kydd, 2005). Prices are also not stable as agricultural commodities were hit with inflation of 

about 18.72% in January 2017, one of the highest in the history of Nigeria (National Bureau of Statistics (NBS, 

2017). For this suspected paradox of multiple protection policies with stagnated policy outcomes, there is an urgent 

need for a proper assessment of the effectiveness of agricultural protection policy since all these protectionist 

interventions may have not yielded the much-expected results in Nigeria. The objective of this research was to 

empirically evaluate the effectiveness of agricultural protection policies, programmes and projects on the general 

economy with a special interest in domestic food supply, export and producer welfare. 

 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

41 

 

 

2. Literature Review 

 

2.1 Effects of Agricultural Protection in the Economy 

 

In Olatomide and Omowumi (2013), the assessment of the effects of the agricultural support reforms and policies on 

the agricultural sector is with respect to the fundamental roles of agriculture which include provision of adequate 

food for a growing population and raw materials for industries, provision of an expanding market for non-

agricultural products, generation of savings for investment in agriculture as well as other sectors and release of 

surplus or underutilized resources to other sectors, and generation of foreign exchange. 

Joachim and Valdes (1993) are of the view that Agricultural protectionism in industrialized countries is well known 

to heavily burden consumers and/or taxpayers but it also reaches far beyond a country’s borders.  It negatively 

affects actual or potential agricultural exporters. On the contrary, Abou and Takor in 2003 conducted a study on “the 

Relevance of Tax Incentives in Export-oriented Enterprises in Lebanon”. 117 managers of export businesses in 

Lebanon were considered for the study to indicate whether tax incentives significantly promote their investment. 

The data generated from the study, which were analyzed using simple percentages, revealed that a tax incentive is a 

strong tool for investment promotion in export-oriented businesses like agriculture. Similarly, Gomes (1999) 

conducted a pilot study of twenty-five (25) business executives and his findings indicated several forms of tax 

incentives applicable to businesses in Brazil.  

 

Holland (1996) conducted a study on “Income tax incentives for investment in agro-allied business” and the 

researcher operationalized tax incentive into investment allowance and loss relief. The main objective of that study 

was to examine the extent to which tax incentives influence the investment of agro-allied business in free-trade zone 

areas. In order to collect the necessary data for the study, Holland considered eighty-three business executives 

whose businesses are located in the nine free-trade zones in Uruguay. Part of her findings showed that investment 

allowance and loss relief has a positive significant impact on corporate investment of agro-allied businesses. 

 

According to Philip (1995), the tax incentive is a deliberate reduction in or total elimination of tax liability granted 

by the government in order to encourage particular economic units to act in some desirable ways-invest more, 

produce more, employ more, save more, consume less, import less, pollute less and so on.  

 

From the political point of view, agricultural protection could be a tool for gathering voters’ support. Public choice 

and collective action theories hypothesize that the interests of politicians, bureaucrats, and farm organizations are the 

driving forces increasing government protection (Swinnen & van Der Zee, 1993; Josling et al, 2010). Supporting 

this view, Gardner (1994) suggests that the nature of agricultural protectionism has shifted from problem-solving to 

interest-group politics. 

 

Gardner (1987) had earlier examined why the extent of government intervention differs from commodities in the 

US. The study showed that self-sufficiency rates in agricultural products were negatively related to the protection 

rates: i.e. if the commodity faces import competition, it is likely to receive greater protection. Low elasticity of 

demand and supply was positively associated with it. The share of the commodity in aggregate agricultural output 

had a positive effect on the protection. In addition, Swinnen (1994) highlighted the role of relative farm incomes and 

the countercyclical nature of agricultural protection. After controlling for the effects of economic development, 

terms of trade, comparative advantages, and constraints on tax collection feasibility, Beghin and Kherallah (1993) 

showed that agricultural protection level increases as the political system moves to a more pluralistic form. Yet, the 

study showed that further transition to democratization causes partial dissipation of protection and agricultural 

protection may persist if transactions costs in connection with eliminating/reducing farm programs/policies are 

substantial. In line with this importance of the political system, Thies and Porche (2005) examined political 

institutional factors on a more detailed level and showed that veto players, federalism, party fragmentation and the 

timing of elections are as important as other economic factors. 

 

 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

42 

 

 

2.2 The System of Protectionism in Agriculture 

 

According to Akanegbu (2015), Protection can be given through tariffs - taxing particular imports but not locally 

produced versions of the same product or through quantitative restrictions on imports, such as quotas or outright 

embargoes, limiting the imported supply, or through both together. However, a major disadvantage of the 

widespread use of protection is that it discourages and decreases trade, thereby, destroying or giving up many of the 

advantages from trade. There is overwhelming empirical evidence that suggests a strong link between price 

distortions and economic growth, especially in developing countries; Harberger (1959) attempted to explore the 

possible results of eliminating misallocations of resources in economies like Chile, Brazil, and Argentina. It was 

concluded that policies aimed at eliminating distortions in the price mechanism can raise the long-term rate of 

growth of national income.  

 

Also, results from global and single country studies of subsidy reform suggest that on an aggregate level, changes to 

GDP are likely to be positive due to the incentives resulting from price changes leading to more efficient resource 

allocation (Von Moltke, Mckee, & Morgan, 2004). Also, there is evidence that tax policy has influenced the pattern 

of investment with consequent effects on overall efficiency. Lower taxes have resulted in higher real returns to 

savings and then investments. Higher returns have stimulated a larger aggregate supply of these factors of 

production and thus raised total output. Also, in low-tax countries, different types of financial incentives that were 

provided appear to have shifted resources from less productive to more productive sectors and activities, thus 

increasing the overall efficiency of resources utilization. 

 

Some analysts have attempted to estimate the costs of protection in models using general equilibrium methods to 

examine the general effects of trade liberalization. For example, De Melo (1978), in his study, divided the loss of 

real income due to protection into two elements: (a) the consumption costs resulting from the distorted prices facing 

consumers as domestic prices differ from world prices, and (b) the production costs resulting from distortions of 

prices facing producers. Therefore, the total cost of protection is measured by the total reduction in utility from the 

above effects. However, a general equilibrium approach was used which is not restricted to small departures from 

free trade. The analysis is based on a Walrasian approach, which emphasizes the importance of substitution effects 

in both product and factor markets on the grounds that a removal of trade barriers entails a large change in relative 

prices that is likely to affect both producer’s and consumer’s choices. 

 

 More so, protection has a joint effect of being a consumption tax and a production subsidy, and it reduces the utility 

enjoyed by the community both by reducing real output below the maximum attainable from the expenditure of the 

real output below the potential maximum. It was noted that removing quota alone in Turkey in 1978 would have 

increased its GDP by as much as 5.4% (Grais, De Melo & Urata, 1986). Also, Ubogu (1988) conclude that a liberal 

trade regime with low tariffs and without quotas up to 1973 led to export-led growth in the world economy and 

relative stability in Nigeria’s export earnings and inflow of foreign capital. 

 

Also, further study by the World Bank monitored agricultural support policies in transition economies (World Bank, 

2000). This study covers six transition economies during the period 1994-97. Using a direct price comparison 

approach, the study presents various estimates of agricultural support policies including trade and price policy 

interventions and government non-price related subsidies on production incentives and on net farm income. The 

study examined to what extent the economic environment prevailing in 1994-97 provided an appropriate and sound 

basis for adjustment towards a more internationally competitive agricultural sector. Based on a common 

methodology for all countries, the study reports estimates of NPR, ERP, and ERA for the major agricultural import-

competing and export activities. The report presents a synthesis of the various indicators for all the countries 

included, which is followed by individual country agricultural policy notes describing the salient features of 

agricultural policies at the time. This study does not adjust for a possible misalignment of the exchange rate, though 

it does present a “decomposition analysis” to examine the relative effect of fluctuations in the real exchange rate, 

border prices and domestic trade policy on the evolution of domestic real farm prices. 

 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

43 

 

 

In addition to those studies mentioned above, Josling and Valdes (2004) noted that the on-going FAO study on the 

roles of agriculture includes a component on agricultural support measures. The study includes a total of 11 

countries in Asia, sub-Saharan Africa, North Africa and Latin America. The analysis presents estimates of nominal 

and effective rates of protection for the late 1990s, for the major agricultural import-competing and export activities 

(approximately six activities in total). 

 

In summary, the patterns of agricultural protection vary among countries. The political market framework of 

protection provides factual evidence to show that producers in industrialized countries such as Australia, Canada, 

Japan, United States, etc. are subsidized while the low-income countries such as China, Pakistan, India, Turkey, 

Bangladesh, Argentina, etc. tax the farmers. Also, an analysis of the evolution of producer prices shows that 

between 1986 and 1995, in seven out of eight countries, all major agricultural producer prices declined in real terms 

in which most countries, the decline for both import and export was larger during the reform period than in the 

previous years. 

 

At the root of everything in an economy is the force of demand and supply. It is not farfetched to think of these as 

basically human characteristics. In this context, the government or the ruling class represents the supply side while 

the consumers or farmers represent the demand side of agricultural protectionism (Zietz & Valdes, 1993). Downs’s 

(1957) neoclassical economic theory of politics provides a simple starting point. The model allows one to think of 

protection as the result of demand and supply forces. Downs’s model assumes that politicians adopt policies that 

maximize their chances of staying in office. The beneficiaries of a particular policy, such as government assistance, 

invest in lobbying effort up to the point where an additional investment of resources is expected to have no net 

benefits (Baldwin 1982). Baldwin added that the expected losers of a particular policy also invest in lobbying effort, 

similarly balancing marginal cost and expected marginal benefit. Since one can think of the beneficiaries of 

government assistance as on the demand side for protection, whereas politicians are on the supply side. Politicians 

supply protection up to the point where the marginal cost of lost support from those opposing government assistance 

or protection is just equal to the marginal gain in support from those groups demanding assistance or protection. 

 

Zietz and Valdes (1993) explain that the emphasis of Downs’s model on marginal changes clearly entails one 

problem: it is ill-equipped to deal with large changes, such as major structural shifts or regime switches. In those 

instances, political coalitions tend to break down or are realigned, and formal models based on marginal changes 

can, therefore, predict little. However, the loss of comparative advantage in agriculture tends to occur gradually 

rather than at distinct points in time. Hence, sufficient political stability may be maintained, at least in principle, 

even though not in each case, for Downs’s model to remain a useful framework for thinking about the growth of 

agricultural protection. 

 

From the earlier discussion, it followed that the basic demand of farmers for protection or, more generally, 

government intervention results from the high cost, relative to investing in political lobbying, of avoiding income 

losses through raising productivity growth; diversifying into products with high-income elasticity; or more off-farm 

employment. According to Downs’s theory and Olson’s (1965) work, the demand for protection gains political clout 

with a decline in group size and the consequent cost of the organization.  

 

The empirical work by Honma and Hayami (1986) has provided support for this hypothesis for agriculture that as 

agriculture shrinks in the development process, the demand for protection becomes more effective ceteris paribus, 

reaches a maximum, and eventually declines again as the size of the agricultural sector shrinks under a critical level. 

In addition to smaller group size, there are other changes taking place as employment shrinks in agriculture that help 

organize a farming lobby and that raises the demand for government support. Education levels rise and as 

information and transportation become more easily accessible, better and lower-cost communication links are 

established with the city centers. According to Zietz and Valdes (1993), the lower cost of information, in turn, 

allows the rural sector to identify more quickly and more reliably any emerging income disparity in relation to other 

groups in the country. It also reduces the cost of becoming informed and taking part in and organizing political 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

44 

 

 

activity. In addition, as agriculture becomes more commercialized, agricultural support services establish themselves 

in the rural sector. 

 

In general, Arene (2008) notes that supply could be affected by changes in input prices, changes in profitability of 

substitute commodities, changes in technology, and prices of joint products. Zietz and Valdes (1993), specify that 

the supply of protection depends on the perceived costs of protection to individuals or groups inside or outside the 

sector and; the power of the bureaucracy that is charged with overseeing the particular sector demanding protection. 

Zietz and Valdes (1993) opine that high perceived costs of protection are likely to generate resistance or 

countervailing power. For politicians, this is equivalent to a rise in their supply curve of protection. Higher bribes 

are required from the sector seeking protection, while less protection is provided at the same time. Zietz and Valdes 

further identify a number of factors that tend to shift the supply curve for agricultural protection or support. 

 

i. An agricultural sector that is large relative to other sectors tends to raise the supply curve for agricultural support 

measures, in particular, income transfers. To provide support to agriculture under this scenario, politicians would 

have to impose heavy costs on the sectors outside of agriculture. This has been done, for example, in Indonesia 

starting in the 1970s (Barichello, 1989). 

ii. As income increases, food items take up an increasingly smaller budget much of an incentive to get informed or 

organized to oppose agricultural protection. The costs associated with becoming active are simply much higher than 

the expected benefits.  

iii.  Industry resistance to agricultural protection has the potential to shift up the supply curve for protection. 

iv.  In the long run, as income levels reach a certain critical level, resistance to agricultural protection may also arise 

out of environmental concerns of the public.  

v.Resistance against agricultural protection may also arise from within agriculture itself. Two factors that have 

already been mentioned separately combine to bring this about. First, agricultural protection raises land prices, that 

is, the return to the fixed factor. Second, more land is needed by farmers to achieve economies of scale for such 

land-intensive products as cereals.  In sum, agricultural protection actually makes it more difficult for farmers to halt 

the decline in comparative advantage.  

vi.A tight government budget is likely to shift upward the supply curve of protection. This is particularly true if 

agricultural protection is provided in the form of support prices, as for many commodities in the EC, or when 

government support of the agricultural sector is provided mainly through budgetary expenses, as in the United 

States. Its importance can be measured by the fact that it is generally credited with being one of the driving forces 

behind the Uruguay Round and its focus on agriculture (Zietz & Valdes 1988). 

vii.Protection can be contained by credible pressure from outside the country. This point has already been touched 

on in the context of industry resistance to agricultural protection. Outside pressure can take the form of bilateral 

pressure applied by a powerful trading partner that is negatively affected by agricultural support programs or legal 

obligations such as those driven from General Agreement on Tariff and Trade (GATT). 

 

In summary, the relationship between agricultural protection and demand and supply first, characterized the subject 

into two basic concepts.  The demand side (farmers or consumers) and the supply side (government in power). To 

supply protection, it has economic consequences – forgone alternative. Despite this, those in power or politicians 

supply protection up to the point where the marginal loss in support from those opposing government assistance or 

protection is just equal to the marginal gain in support from those groups demanding assistance or protection. The 

supply of protection depends on input price, profitability in the sector, change in technology, perceived cost of the 

protection, groups within and outside the economy. 

 

3. Methodology 

 

3.1 The Study Area  

 

The study area is officially known as the Federal Republic of Nigeria, but here often referred to as Nigeria. The 

major exports of the country are: crude oil (petroleum), natural gas, cashew nuts, skin and fur, tobacco, cocoa, 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

45 

 

 

cassava, rubber, food, live animals, aluminium alloys and other solid minerals, (CIA World Factbook 2018) while 

major imports are refined petroleum products, wheat, rice, sugar, herbicides, fertilizers, chemicals, vehicles, aircraft 

parts, vessels, vegetable products, processed food, beverages, spirits and vinegar, equipment, machines, and tools 

(NBS 2015). Despite its considerable agricultural resources, Nigeria is still a net importer of food and agricultural 

products in general (USAID 2009) and as such the agricultural sector has been one of the least attractive sectors 

(Owutuamor and Arene, 2018) and has lost its leading contribution to Nigeria's GDP (CBN 2018; FAO 2012). 

 

3.2 Data Specification 

 

This work made use of secondary data. The annual time series data of agricultural output, measured by the share of 

agriculture to GDP, and FDI inflows into the sector were collected from CBN, spanning from 1980-2015 while 2016 

was extrapolated. Also, NPC was calculated from annual data of domestic price collected from FAOSTAT and 

World price collected from the World Bank. This study covering a 37-year period, spanning from 1980 to 2016 

employed descriptive statistics aided by the use of Microsoft Excel and inferential statistics in the form of the 

econometric regression methods of the multiple linear regression and Granger causality test were applied as the 

estimation technique in evaluating the relationships and causality between the dependent variable (agricultural 

growth) and the political economy variables (agricultural protection level, foreign direct investment inflows to 

agriculture, Gross Domestic Product (GDP) inflows from the agricultural sector into the economy, political structure 

changes in national policy reforms and form of government in power). 

 

The regression equation was estimated after carrying out pre-estimation tests for stationarity in order to avoid 

multicollinearity of explanatory variables. To eliminate the presence of autocorrelation in the model, this study 

applied the Augmented Dickey-Fuller (ADF) test to detect the stationarity of the variables at the 5% level of 

significance and also identify the order of integration of the variables in the model. The study was initially designed 

to be approached with ordinary least square method but due to endogeneity found in the model, instrument variable 

(IV) generalised method of moment (GMM) was adopted to take care of the endogeneity. 

 

The level of protection in agriculture was estimated using the nominal protection coefficient (NPC) model, level of 

economic welfare was measured using trade measure (TM),  and domestic own food production was estimated using 

food self-sufficiency ratio (SRR). The effectiveness model was analysed using the generalised method of moment 

(GMM)  with Eviews 9.0 software. 

 

3.3 Model specification 

 

The coefficients of the protection level in the agricultural sector are widely estimated using the nominal protection 

coefficient (NPC). According to De Gorter and Tsur, (1991), Krueger, Schiff, and Valdés, (1991), the most simple 

and widely used measurement of the price wedge is the nominal rate of protection (NRP) and the nominal protection 

coefficient (NPC) (Krueger, Schiff and Valdés, 1991; Miller  &   Anderson, 1992 and Arene, 2008). The level of 

protection estimation equation is given as:  

NPC = PD/PW                      eq1. 

Where: 

PD = Domestic Producer Price (Naira);  

PW = World price (Naira). 

 

The coefficient of estimation of Nigeria’s own food production was estimated using trade measure (TM) tools. 

Trade measure is a tool used to evaluate the economic welfare accrued to producers in relation to international prices 

in a given sector like agriculture. This measurement standard was also adopted as standard procedure on national 

policies’ evaluation and agricultural trade (OECD, 1987). This measurement concept was based on the general 

phenomenon that a fall in price will lead to fall in farmers’ economic welfare. This fall in real farm prices is 

expected to lead to increased political pressure by farmers as their demand for protection of import-competing sub-

sectors increases (Josling & Valdes, 2004). 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

46 

 

 

The trade measure model for Producers adopted in this study is given as: 

TM = (PD - PW) x DP.         eq2. 

Where: 

TM = Trade Measure in Agriculture 

PD = Domestic Price 

PW = World price and 

DP = Domestic Production.  

 

The effectiveness of agricultural protection on some growth indicators such as own food production (SSR), 

agricultural export and farmers welfare (TM) was analyzed in the standard growth accounting framework. The 

relationship between growth and other macroeconomic variables such as agricultural growth is often analyzed using 

the standard models of economic growth (Owutuamor and Arene, 2018), we apply the Solow (1956) growth model 

in which the growth of economies is broken down into basics in the production function:  

𝑌 = 𝑓 (𝐾, L)           eq3. 

To account for time factor in the model and adopt equation 1 to the study according to Mankiw, Romer and Weil 

(1992), adopting the Cobb Douglas model, we make the indicators of effectiveness i.e., domestic agricultural food 

supply (SSR), agricultural export (AE) and farmer-welfare (FM) functions of NPC and related independent variables 

at time (𝑡). That is:  

 eq4.  

Where: Y = output indicators or measures of effectiveness and  = NPC and other factors that 

determine the rate of output. To account for time factor in the model, according to Mankiw, Romer and Weil 

(1992), the model is estimated at time (𝑡).  

 eq5. 

Assuming there is a steady state, say a linear relationship, as seen in standard output models; output is estimated by 

multiple linear equations in the linear form in Eq. 3, which formed the basis for the estimation of the model in this 

study. This study is also based on the assumption that there may be other influential factors affecting growth but this 

study is only restricted to political economy variables as indicators for quick and easy policy considerations. In order 

to establish the mathematical function of this model, the intercept , measure of error term  and parameters of 

estimations β1,2,3…n are added in equation 5. 

     eq5. 

 Following this, the GMM model used here followed the same econometric derivatives from equations 3 to 5 thereby 

giving the GMM econometric models as equations 6,7 and 8. 

𝑙𝑛SSRFOODR(t)= β0+β1𝑙𝑛FDIAGR(t)+β2𝑙𝑛TMGRt)+β3𝑙𝑛POCH(t)+β4𝑙𝑛BUDAGR(t)+β5    

 𝑙𝑛CPI(t)+β6 𝑙𝑛NPC(t)+β7𝑙𝑛INTRATE(t) + ε        eq6. 

𝑙𝑛AGREXP(t)=β0+β1𝑙𝑛FDIAGR(t)+β2𝑙𝑛INTRATE(t)+β3𝑙𝑛POCH(t)+β4𝑙𝑛BUDAGR(t)+β5 

 +𝑙𝑛CPI(t)+β6𝑙𝑛NPC(t)+β7𝑙𝑛TMGR(t)+β8𝑙𝑛EXRATE(t) +ε     eq7. 

𝑙𝑛TMGR(t)=β0+β1𝑙𝑛EXRATE(t)+β2𝑙𝑛+β3𝑙𝑛BUDAGR(t)+β4𝑙𝑛NPR(t)+β5𝑙𝑛GOTYPE(t)+β6𝑙𝑛C

 PI(t)+β7𝑙𝑛POCH(t)+𝑙𝑛β7AGREXP(t)+ε       eq8. 

Where: GDP = agricultural GDP (GDP percentage share to agricultural sector is an indicator tool for making quick 

adjustments in the economy), 

EXR = exchange rate in percentage, 

NPC = nominal protection coefficient (used as proxy for measuring the degree of agricultural protection), 

SSR = national food situation of political economy (SSR was modeled to capture the dependency ratio of Nigeria’s 

own food production), 

FDIAGR = Foreign Direct Investment (FDI percentage share to agricultural sectors represents the economic and 

political will of individuals to invest in the sector), 

INTRATE = Interest rate in percentage, 

 EXTDEBT = external debt in percentage and 

CPI = consumer price index as a general measure of effect of price on protection dynamics. 

 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

47 

 

 

4. Results and Discussion 

 

4.1 Pre-Estimation Techniques 

 

Some form of data modification and pre-estimation processes were carried out before the main analyses were 

conducted. The set of data was tested for a unit root in the study. ADF was used to carry out the test under its 

traditional conditions, hypotheses and decision rules as adopted by Nwosu and Okafor (2014). In a related study, 

Njoku, Chigbu and Akujobi (2015) also adopted the use of unit root test on some residuals using the ADF test. The 

variables were further tested for endogeneity and corrections made. Also, the variables were further subjected to co-

integration test to check for a long-term association.  

The decision rule showed that the prob (t-stat) > 0.05 which implied that the null hypothesis of no integration be 

rejected and we, therefore, concluded that the variables in the model have a long-term relationship. The results of the 

ADF and co-integration tests are shown in Tables 1 and 2. The result shows that all the variables were stationary at 

their first difference (i.e. 1(1)). The result of the table also confirms that the variables were co-integrated in the long-

run at the same rate by the normalized co-integration coefficient with the highest log likelihood in absolute term. 

 
Table 1: ADF Unit Root Test Result 

 

VARIABLES 

 

ADF 

SATS 

CRITICAL 

VALUE 

1% 

CRITICAL 

VALUE 

5% 

CRITICAL 

VALUE 

10% 

ORDER OF 

INTEGRATIO

N 

 

REMARK 

EXPORT -5.308594 -3.632900 -2.948404 -2.612874 1(1) stationary 

BUDGET -5.550498 -3.639407 -2.951125 -2.614300 1(1) stationary 

GDPAGRIC -6.192236 -3.639407 -2.951125 -2.614300 1(1) stationary 

NPC -5.890904 -3.639407 -2.951125 -2.614300 1(1) stationary 

SSR -3.657007 -3.657007 -2.967767 -2.622989 1(1) stationary 

TM -6.692878 -3.632900 -2.948404 -2.612874 1(1) stationary 

FDI -11.91513 -3.639407 -2.951125 -2.614300 1(1) stationary 

Source: computed output with e-view 

 

Table 2: Johansen Cointegration Test Result 

 

EIGEN 

VALUE 

 

LIKELIHOOD RATIO  

(LR) 

CRITICAL 

VALUE 

5% 

HYPOTHESIZE

D NO OF C.E 

0.734822 - 46.23142 None** 

0.673258 1464.734 40.07757 At most 1 

0.581074 1446.277 33.87687 At most 2 

0.5410732 1431.921 25.58434 At most 3** 

0.458023 1419.082 21.13162 At most 4** 

0.363664 1408.976 14.26460 At most 5* 

0.090478 1401.517 3.841466 At most 6** 

*(**) denotes rejection of the hypothesis at 5 percent (1 percent) significance levels. L.R. test indicates 5 co-

integration equations (s) at 5 percent level. C.E represents Co-integrating Equations 

Source: computed output with e-view 

 

4.2 Estimates of Agricultural Protection Coefficient in Nigeria 

 

The level of protection in the agricultural sector in Nigeria (Table 3) shows an unsteady trend with mean value of 

1.4%, minimum of -7.1% and maximum of 9.8%. This suggests that Nigeria protects agricultural sector. This result 

is in line with previous studies (Olper, 1998) which states that the patterns of agricultural policies in Africa suggest 

that developing nations strongly subsidize or protect agriculture. This concept aimed at measuring the level of price 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

48 

 

 

protection or intervention using the nominal rate of protection (NRP) or the nominal protection coefficient (NPC). A 

coefficient of 1.10 implies that farmers, overall, received prices that were 10% above international market levels. 

This indicators as contained in figures 1and 2 reflect the level of price distortions across selected commodities which 

were measured using the Producer Nominal Protection Coefficient (NPC)  and nominal protection rate (NPR) 

expressed as the ratio of farm price to border reference price. 

 
Table3: Computations Showing Nominal Protection Coefficient of Agricultural Protection in Nigeria from, 1980 – 2016 

 

YEAR NPC maize NPC p-oil 

NPC 

rice 

NPC 

rubber NPC wheat 

AVERAGE 

NPC 

   NPR 

(%) 

1980 0.6 0.9 1.1 1.1 1.0 0.93 -7.1 

1981 0.6 1.0 1.1 1.1 1.0 0.95 -5.3 

1982 0.7 1.0 1.1 1.1 1.0 0.98 -2.3 

1983 0.6 1.0 1.1 1.1 1.0 0.94 -5.5 

1984 0.6 1.0 1.1 1.1 1.0 0.95 -5.5 

1985 0.8 0.9 1.1 1.1 1.0 0.96 -3.5 

1986 0.9 1.0 1.1 1.1 1.0 1.01 0.7 

1987 1.0 1.0 1.1 1.0 1.0 1.02 2.3 

1988 1.0 1.0 1.1 1.0 1.0 1.02 1.8 

1989 1.0 1.0 1.1 1.0 1.0 1.01 0.7 

1990 1.0 1.0 1.1 1.0 1.0 1.02 1.6 

1991 1.0 1.0 1.1 1.1 1.0 1.03 2.7 

1992 1.0 1.0 1.1 1.1 1.0 1.03 2.9 

1993 1.0 1.0 1.1 1.1 1.0 1.05 4.9 

1994 1.0 1.0 1.3 1.1 1.0 1.10 9.8 

1995 1.0 1.0 1.0 1.0 1.0 1.01 1.4 

1996 1.0 1.0 1.0 1.0 1.0 1.00 -0.2 

1997 1.0 1.0 1.0 1.1 1.0 1.02 1.9 

1998 1.0 1.0 1.0 1.1 1.0 1.02 2.3 

1999 1.0 1.0 1.0 1.1 1.0 1.04 3.6 

2000 1.0 1.0 1.0 1.1 1.0 1.03 3.1 

2001 1.0 1.0 1.0 1.1 1.0 1.02 2.1 

2002 1.0 1.0 1.0 1.1 1.0 1.03 3.1 

2003 1.0 1.0 1.0 1.0 1.0 1.02 1.8 

2004 1.0 1.0 1.0 1.1 1.0 1.02 2.3 

2005 1.0 1.0 1.0 1.0 1.0 1.02 1.5 

2006 1.0 1.0 1.0 1.0 1.0 1.01 1.2 

2007 1.0 1.0 1.0 1.0 1.1 1.03 2.7 

2008 1.0 1.0 1.1 1.0 1.1 1.04 4.0 

2009 1.0 1.0 1.1 1.0 1.1 1.04 3.5 

2010 1.0 1.0 1.1 1.0 1.1 1.04 3.6 

2011 1.0 1.0 1.0 1.0 1.0 1.01 1.1 

2012 1.0 1.0 1.0 1.0 1.0 1.02 2.0 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

49 

 

 

2013 1.0 1.0 1.0 1.0 1.0 1.01 1.2 

2014 1.1 1.0 1.0 1.0 1.1 1.03 2.8 

2015 1.0 1.0 1.0 1.0 1.1 1.04 3.6 

2016 1.1 1.0 1.0 1.0 1.2 1.01 3.4 

Min 

Max 

Mean 

     

0.93  

1.10 

1.01  

-7.1% 

9.8% 

1.4% 
 

Nominal rate of protection (NRP) is also given as (NPC-1) x100 in percentages. 

Source: Author’s Computation, 2018. 

 

 

Figure 1: Graphical Representation of Nominal Protection Coefficient (Agricultural Protection) of selected 

agricultural commodities in Nigeria from 1980 – 2016 

Source: Author’s Computation, 2018. 

 

 
 

Figure 2: Graphical Representation of Nominal Protection Rate in Nigeria from 1980 – 2016 

Source: Author’s Computation, 2018 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

50 

 

 

4.2 Estimates domestic food supply in Nigeria 

The ability to produce own food and reduce dependency on importation has been one of the major policy objectives 

among many African countries. Nigeria’s food self-sufficiency ratio on major staple foods in Nigeria from 1980 to 

2016 was calculated and the average presented in Table 4 and Figure 3. The results show that apart from the period 

between 1987 and 1991 when the SSR was highest, ranging from 70.8 to 84.8 percent, the rest of the period was 

characterized by huge dependency on food importation and lesser food sufficiency ratio. The result also shows that 

from 1999 to 2016, which represents the period under Nigeria’s democratic rule, the NPC has stayed steadily 

between 54.5% - 62.2% showing that no meaningful increase has taken place since this democratic regime. 

 
Table 4: Table presentation of average estimate of food self sufficiency ratio (SSR) in Nigeria between 1980 and 2016 

 

YEAR SSR YEAR SSR YEAR SSR YEAR SSR 

1980 50.5 1990 84.8 2000 61.5 2010 57.7 

1981 47.2 1991 70.8 2001 54.5 2011 57.2 

1982 46.9 1992 67.4 2002 57.7 2012 57.1 

1983 56.2 1993 64.2 2003 56.3 2013 56.9 

1984 58.5 1994 64.2 2004 57.6 2014 57.0 

1985 61.1 1995 65.9 2005 58.9 2015 57.1 

1986 64.3 1996 65.2 2006 60.9 2016 56.8 

1987 78.4 1997 62.8 2007 58.0   

1988 71.5 1998 63.8 2008 61.0   

1989 73.0 1999 62.2 2009 59.1   

This average estimate of food self sufficiency ratio (SSR) was calculated using the SSR of major staple foods in 

Nigeria. Source: Author’s Computation, 2018 

 

 

 

 
 

Figure 3: Graphical Area Representation of food SSR from 1980 – 2016.  

Source: Author’s Computation, 2018. 

 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

51 

 

 

4.3 Estimates of farmer welfare in Nigeria 

 

The proxy for measuring the price distortion advantage in financial values as accrued to Nigerian farmers in Table 5 

and Figure 4 shows that the most favourable period for farmers was between 2010 and 2012 when Growth-Based 

Agricultural Policy, Agricultural Transformation Agenda and Growth Enhancement Support (GES) schemes were 

executed. From the table 5 and figure 4, the results show that the welfare package to farmers were merely staggering 

between 1980 and 2009. This suggests that the policies have not been very favourable to farmers when compared to 

international prices. In line with expectations, Harberger (1959) had opined that there is overwhelming empirical 

evidence suggesting a strong link between price distortions and economic growth, especially in developing 

countries.  During this period too, average GDP share to the sector was 25.3 percent and above the mean value of 

21percent. Also, in line with results from global and single country studies of subsidy reform suggest that on an 

aggregate level, changes to GDP are likely to be positive due to the incentives resulting from price changes leading 

to more efficient resource allocation (Von Moltke, Mckee, & Morgan, 2004). Therefore, GDP share from agriculture 

could be used to check if efficient resources were allocated to appropriate policies in a particular place and time. 

Table 5: Table presentation of farmers’ economic welfare in Naira between 1980 and 2016 

YEAR AV TM YEAR AV TM YEAR AV TM YEAR AV TM 

1980 60686281 1990 145833703 2000 161373522 2010 242727700 

1981 75046349 1991 163540719 2001 114436819 2011 344972298 

1982 57469514 1992 134441975 2002 64915892 2012 174308887 

1983 46761825 1993 118367860 2003 150367319 2013 148967140 

1984 35386468 1994 180348350 2004 146206977 2014 110192896 

1985 24983884 1995 156189188 2005 140515254 2015 18721698 

1986 23813465 1996 178140863 2006 156739490 2016 31910376 

1987 57217767 1997 155126058 2007 122805774   

1988 93590225 1998 153492256 2008 150913174   

1989 123421022 1999 94136451 2009 82122932   

AVTM is the Average Trade Measures in Naira accrued to farmers in Nigeria. 

Source: Author’s Computation, 2018 

 

Figure 4: Graphical charts of FARMER-WELFARE in Nigeria from 1980 – 2016. Note: 1to 37 represent 1980 to 

2016; AVTM represents Average Trade Measures. 

Source: Author’s Computation, 2018 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

52 

 

 

4.4 Effectiveness of Agricultural Protection on Agricultural Food Self Supply, Agricultural Export, and Farmer-

Welfare. 

 

The result of the regression analysis of the effects of agricultural protection and other political economy variables on 

growth outputs i.e, Y1, Y2, and Y3 in the model are shown in Table 6. The variables were first tested of 

endogeneity. Knowing that if the p < 0.5, accept the null hypothesis that the model contains endogenous variables or 

reject it if otherwise. From the result, the probability was lesser than 0.05 (0.0003). Therefore, the variables are 

endogenous and fit for instrumental variable GMM regression. The regression output as analysed with GMM is 

presented in Table 6 and each dependent variable is discussed under the following sub-headings: 

 

4.4.1 Domestic Food Supply (SSR) 

 

From the table, about 83% variations in agricultural exports were explained by variations in the political economy 

variables which was statistically significant (p<0.01). This means that at least, one or more variables specified in the 

model significantly affected the export growth from agricultural sector. From this result in table 4.7, the null 

hypothesis which states that agricultural protection policy does not have significant effect on agricultural export in 

Nigeria be rejected.  The effect of agricultural protection on agricultural export was negative and significant 

(p<0.001). Therefore, a unit increase in the predictor variable showed that the outcome of agricultural export 

decreased by 0.07 %. This means that in the long run, as protection increased, export from the sector declined 

significantly. The effect of structural changes in policy outlook was negative and significant (p<0.001) on 

agricultural export level. Statistically, when the political/policy structure outlook appeared to favour liberalization, 

the outcome of export decreased by 0.48%. The structural outlook content is another determinant in decision making 

processes. On one part, it forms a basis for level of demand or pressure from the farmers and on the second hand, the 

level of response or supply of protection instruments by the government. According to Virender (1993), the 

structural changes that occur during the course of economic development have been shown as instrumental in 

bringing about a switch in export considerations. This means that negative coefficient of POCH i.e., liberalization 

favours export more than protection. The effect of CPI on GDP share from agricultural sector was positive and 

significant (p<0.001). In other words, a unit increase in predictor variable showed that the outcome of export 

increased by 0.01%.  This is in line with the apriori expectation that increase in price would lead to increase in 

production thereby increasing the export from the sector.  

4.4.2 Agricultural Export 

 

The result shows that about 83% variations in agricultural exports were explained by variations in the political 

economy variables which was statistically significant (p<0.01). This means that at least, one or more variables 

specified in the model significantly affected the export growth from agricultural sector. From this result in table 6, 

the null hypothesis which states that agricultural protection policy does not have significant effect on agricultural 

export in Nigeria be rejected. This is because NPR is significant in the regression. The effect of agricultural 

protection on agricultural export was negative and significant (p<0.001). Therefore, a unit increase in the predictor 

variable showed that the outcome of agricultural export decreased by 0.07 %. 

Table 6: Parameter estimates of GMM regression on effectiveness of agricultural protection and other political 

economy variables on Nigeria’s self-sufficiency ratio, agricultural export and FARMER-WELFARE. 

 

Variables Parameters Standard Error T-ratio 

Y1=Self Sufficiency Ratio 

Constant 57.13534 9.407739 6.073227*** 

LnNPR 0.695291 0.452162 1.537702 

LnFDIAGR -0.910298 2.203268 -0.413158 

lnTM 2.164594 1.917257 1.129006 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

53 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Key:  ***, * *, * - represent 1%, 5% and 10% levels of significance respectively 

Y1 : R
2
 - 31%, instrument specification:   lncpi2, lngdpagr, lnnpr, lnfdiagr, lnbudagr, lntm, lnagrexp, ln poch. 

constant added to instrument 

Y2: R
2
 - 83%. IV specification:  lngdpagr, lnnpr, lnfdiag, lnbudagr,  lntm, lnagrexp, ln poch,constant added to 

instrument 

Y3: R
2
 - 54%. Instrument specification: lntm, lngotype, lnbudsgr, lncpi(2), lnexr, lnagrexp, lnnpr, constant added to 

instrument 

Source: Author’s computation from Eviews 9.0, 2018.  

 

This means that in the long run, as protection increased, export from the sector declined significantly. The effect of 

structural changes in policy outlook was negative and significant (p<0.001) on agricultural export level. Statistically, 

when the political/policy structure outlook appeared to favour liberalization, the outcome of export decreased by  

0.48%. The structural outlook content is another determinant in decision making processes. On one part, it forms a 

basis for level of demand or pressure from the farmers and on the second hand, the level of response or supply of 

protection instruments by the government. According to Virender (1993), the structural changes that occur during 

the course of economic development have been shown as instrumental in bringing about a switch in export 

considerations. This means that negative coefficient of POCH i.e., liberalization favours export more than 

protection. The effect of CPI on GDP share from agricultural sector was positive and significant (p<0.001). In other 

words, a unit increase in predictor variable showed that the outcome of export increased by 0.01%.  This is in line 

with the apriori expectation that increase in price would lead to increase in production thereby increasing the export 

from the sector.  

4.4.3 Farmer-Welfare  

 

The result shows that about 54% variations of the economic welfare accrued to agricultural producers were 

explained by variations in NPR and other  political economy variables in the model. From this result in Table 4.8, 

LnPOCH -6.232640 4.787480 -1.301862 

LnCPI 0.049667 0.028094 1.767856* 

Y2= Agric Export 

Constant 6.869506 0.374818 18.32759*** 

ln NPR -0.059485 0.015828 -3.758097*** 

ln FDIAGR -0.066068 0.032830 -2.012452 

ln BUDAGR 0.004435 0.016790 0.264168 

lnTM -0.032595 0.082989 -0.392769 

ln POCH -0.489985 0.071195 -6.882311*** 

ln CPI 0.008697 0.001220 7.127612*** 

ln GDPAGR -0.006024 0.006732 -0.894832 

Y3=Farmer-Welfare 

Constant 5.816706 3.002052 1.937577*** 

LnGOTYPE -0.297168 0.284175 -1.045722 

LnPOCH 0.669714 0.247875 2.701821** 

LnBUDAGR -0.015157 0.038023 -0.398618 

LnCPI 0.005888 0.008609 0.683886 

LnEXR -0.000457 0.004718 -0.096883 

LnAGREXP -0.265387 0.461839 -0.574631 

LnNPR 0.066129 0.032906 2.009644* 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

54 

 

 

the null hypothesis which states that agricultural protection does not have significant effect on farmers’ economic 

welfare in Nigeria was rejected. The specified political economy variables that had significant effects on their 

economic welfare in Nigeria were policy structural changes (POCH) and level of agricultural protection (NPR). The 

policy structure outlook in the sector was vectorized into protection (+) and non-protection (-) eras.  The effect of 

POCH on farmers’ economic welfare was negative and significant (p<0.05). In other words, a change from 

protection structure to liberalisation showed that the farmers’ economic welfare also increased by 0.69%. This is 

often the situation because many of the investors in the sector consider the nature and structure of the policy on 

ground before increasing or reducing their capital shares. The effect of agricultural protection on FARMER-

WELFARE was positive and significant (p<0.05). Therefore, a unit increase in the predictor variable showed that 

the outcome of famer welfare increased by 0.07 %. This means that in the long run, as protection increased, gains 

made by farmers increased significantly. 

5. Conclusions/Recommendations 

The average level of agricultural protection in Nigeria was poor between 1980 and 1985 but became significantly 

pronounced afterward and varies across food commodities and time. Protection is not without cost implications on 

the supply side (government) but these expenditures seem not to be commensurate with the results of the expected 

outcomes of the protection policy especially on domestic food production, and agricultural export. This study 

showed that the level of protection policy promulgated in the country during these periods under study only affected 

level of export and economic advantage received by the local farmers. However, the policy did not affect the 

measure of domestic food supply significantly.  

 

This study, therefore, recommends among others that these forms of agricultural protection policies be reformed to 

ensure that policies motivate the production of staple food for the domestic consumption. This will reduce the 

nation’s dependence on importation. To do this, stakeholders are expected to hold conferences on how a new and 

all-inclusive protection policy could be promulgated to motivate the sector out of stagnation and subsistence 

farming.  

 

6. References 

ABOU, N., & TAKOR, W. (1999). Tax incentives in Lebanon. A Paper Presented at the United Nations Conference 

on Trade and Development (UNCTAD) at the Expert Meeting on Tax Incentives; 7-8. 

AKANEGBU, B.N. (2015). Agricultural price distortions and their effects on the Nigerian economy: An Empirical 

Analysis. European Journal of Research in Social Sciences. 3 (1). ISSN 2056-5429 

AMIN, S. (1972). Underdevelopment and dependence in black Africa: Origins and contemporary forms.  Journal of 

Modern African Studies, 10(4), 503-24. 

ARENE C.J. (2008
b
). Agricultural Economics: a functional approach. Prize Publishers, Enugu Nigeria. 

AYOOLA, G.B., AINA, B.M., NWEZE, N., ODEBIYI, T., OKUNMADEWA, F., SHEHU D., WILLIAM O.  & 

ZASHA, J. (2000). Nigeria: Voice of the Poor, Country Synthesis Report, World Bank. 

BALDWIN, ROBERT, (1982). The Political Economy of Protectionism: In Import Competition and Response, ed. 

Jagdish N. Bhagwati. Chicago: University of Chicago Press. 

BARETTE, C.B. (1999). Micro-economics of the development paradox on the political economy of food price 

policy. Agricultural Economics 20(1999) 159-172. 

BEGHIN, J.C & M. KHERALLAH (1994), “Political Institutions and International Patterns of Agricultural 

Protection”, Review of Economics and Statistics 76(3): 482-489.  

BEGHIN, J.C & M. KHERALLAH (1994), “Political Institutions and International Patterns of Agricultural 

Protection”, Review of Economics and Statistics 76(3): 482-489.  



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

55 

 

 

BRIGGS, I.N. (2005). Nigeria: protecting domestic production in terms of liberalization and the short, medium and 

long term implication for regional integration. A paper presented at the ECOWAS Regional workshop on 

Multilateral Trade Agreements, parliament of Ghana and Friedrich-Ebert-Stiftung, Accra, Ghana, November 

27, 2005. 

CIA WORLD FACTBOOK (2015). The World Factbook. Retrieved November 26, 2015, from www.cia.gov 

COOPER, F. (2002). Africa since 1940: The Past of the Present. Cambridge. 

DE GORTER, H. & TSUR, Y. (1991). Explaining price bias in world agriculture: The calculus of support 

maximizing politicians. American Journal of Agricultural Economics, 73,1244-54.  

DE MELO, J.A.P. (1978). Protection and resources allocation in walrasian trade model. International Economic 

Review, 19, 25-43. 

FAO. (2012). Foreign Agriculture Investment Profile - Nigeria. Food and Agriculture Organization (FAO) 

Investment Policy Support. Retrieved February 16, 2016, from 

http://www.fao.org/fileadmin/user_upload/tcsp/docs/Nigeria_Country_Profile_FINAL.pdf 

GARDNER, B.L. (1987), “Causes of U.S. Farm Commodity Programs”, Journal of Political Economy 95(2), 290-

310. 

GARDNER, L. (2012). Taxing colonial Africa: The political economy of British imperialism. (Oxford U. Press, 

2012), Ch. 1, an Introduction to the Problem of Colonial Taxation. pp. 1-13. 

GOLDIN, I. & KNUDSEN, O. (ED). (1990). Agricultural Trade Liberalization: Implication for developing 

countries. Paris OECD and World Bank. 

GOMES, G.M. (1999). State level tax incentives in Brazil.  A Paper presented at the expert meeting on tax 

incentives; pp 8-9. 

GRAIS, W., DE MELO, J., & URATA, S. (1986). A general equilibrium estimation of the reduction of tariffs and 

quantitative restrictions in Turkey in 1978. In Thirukodikaval Srinivasan, John Whalley, (Eds.), General 

equilibrium trade policy modeling. Cambridge, MA. 

HARBERGER, A.C. (1959). The fundamentals of economic progress in underdeveloped countries: Using the 

resources at hand more efficiently. American Economic Review, 49, 134-146. 

HOLLAND, M. (1996). Income tax incentives for investment.  A Ph.D Dissertation Submitted to the Department of 

Economics and Finance, National University of Singapore. 

HONMA, M. & HAYAMI, Y. (1986). Structure of agricultural protection in industrial countries. Journal of 

International Economics, 20, 115-129. 

IKPI, A.E (1989). Understanding the rural farmers for effective adoption of improved agricultural technology and 

impact modelling, IITA, Research Monograph No4, Ibadan, Nigeria. 

INHWAM, J. (2008). Determinants of Agricultural Protection in Industrial Countries. An Empirical Bulletin, 17 (1), 

1-11. 

International Food and Agricultural Development (IFAD). (2016). Poverty in Rural Nigeria. 

www.ruralpovertyportal.org/country/home/tags/nigeria/IFAD 

JOACHIM, Z. & VALDES, A. (1993). Growth of Agricultural Protection. Trade and Protectionism, NBER-EASE 

Volume 2, University of Chicago Press ISBN: 0-226-38668-6. Accessed from URL: 

http://www.nber.org/chapters/c8073 

JOSLING, T. (1975). The world food problem. Food Policy, 1 (1), 3–14.  



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

56 

 

 

JOSLING, T. E. & VALDES, A. (2004). Agricultural Policy Indicators. ESA Working Paper No. 04-04. 

Agricultural and Development Economics Division. The Food and Agriculture Organization (FAO) of the 

United Nations. 

KIABEL, B.D. & NWIKPASI, N.N. (2001). Selected aspect of Nigerian taxes. Owerri; Springfield Publishers 

KRUEGER, A. O. (1992). The Political Economy of Agricultural Pricing Policies: Volume 5: A Synthesis of the 

Political Economy in Developing Countries. A World Bank Comparative Study, Baltimore, MD: John 

Hopkins University Press for the World Bank.  

KRUEGER, A.O. (1978). Liberalization attempts and consequences. New York: National European Journal of 

Research in Social Sciences Vol. 3 No. 1, 2015 ISSN 2056-5429 Progressive Academic Publishing, UK 

Page 98 www.idpublications.org Bureau for Economic Research. 

KRUEGER, A.O., M. SCHIFF & VALDES, A. (1988). Agricultural Incentives in Developing Countries: Measuring 

the Effects of Sectoral and Economy-wide Policies, World Bank Economic Review 2(3), 255-72, 

September. 

National Bureau of Statistics [NBS], (2017). National population figures.  Retrieved November 22, 2017, from 

http://www.nigerianstat.gov.ng  

NJOKU,C.O., CHIGBU, E.E. & AKUJUOBI, A.B.C. (2015). Public Expenditure and Economic Growth in Nigeria 

(A Granger Causality Approach) 1983-2012. Management Studies and Economic Systems (MSES), 1 (3), 

147-160, Winter 2015.  

OECD (2018).  Producer protection (indicator). doi: 10.1787/f99067c0-en (Accessed on 04 October 2018).  

OGHOGHOMEH, T. (2014). An assessment of agribusiness tax incentives in Nigeria. international journal of 

business and economic development Vol. 2 Number 1 March 2014. www.ijbed.org 

OKUMADEWA, F. (1997). Poverty and income in Nigeria: Measurements and Strategies for reform: A Paper 

Presented at the Vision 2010 Workshop, Abuja. 

OLATOMIDE W.O. & OMOWUMI A.O. (2013). Policy interventions and public expenditure reform for pro-poor 

agricultural development in Nigeria.  African Journal of Agricultural Research. Vol. 9(4), pp. 487-500, 

(http://www.academicjournals.org/AJAR). 

OLAWEPO R. A. (2010). Determining rural farmers’ income: A rural Nigeria experience. Journal of African 

Studies and Development, 2(4), 99-108. 

OLPER, A. (1998). Political Economy Determinants of agricultural protection levels in EU Member States: an 

empirical investigation. Eoropean Review of Agricultural Economics, 25,463-487. 

OWUTUAMOR, Z.B. AND ARENE, C.J. (2018). The impact of foreign direct investment on agricultural growth in 

Nigeria (1979-2014).  Review of Agricultural and Applied Economics. The Successor of the Acta 

Oeconomica et Informatica, ISSN 1336-9261, XXI (Number 1, 2018): 40-54 doi: 

10.15414/raae/2018.21.01.40-54. 

PEJOUT, N. (2010). Agriculture policy in Africa-renewal or status quo? A spotlight on Kenya and Senegal. The 

political economy of Africa. V. Padayachee, London, Abingdon: 247-265. 

PHILIP, D. (1995). Corporate tax incentives and economic growth in Nigeria. Nigeria Tax News; 2(1) April; pp 

163. 

POULTON, C., EDWARD, A. & KYDD, J. (2005). The Future of small farmers: New direction for services, 

institutions and intermediations. In proceedings of research workshop on the future of small farms, Wye, 

UK. Organized by International Food policy Research Institute (ODI), Imperial College, London. 

SWINNEN, J. (1996). Endogenous price and trade policy developments in central European Agriculture. Euro Rev. 

Agric. Econ. 23(2), 133-166. 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

57 

 

 

SWINNEN, J. F. M. &. VAN DER ZEE, F. A. (1993). The political economy of agricultural policies: A survey. 

European Review of Agricultural Economics, 20, 261-290. 

THIES, C.G., & PORCHE S. (2007), “The Political Economy of Agricultural Protection”, Journal of Politics 

69(1),116-127. 

TIMMER, C.P. (1980). Food prices and food policy analysis in LDCs. Food Policy, 5, 188-199. 

VAN DE WALLE, N. (1999). African economies and the politics of permanent crisis, 1979 Cambridge: Cambridge 

University Press, 2001. 

VIRENDER, G. (1993). The political economy of international Agricultural Protection. Retrospective Theses and 

Dissertations, Iowa State University. 

VON MOLTKE, A., MCKEE, C. & MORGAN, T. (2004). Energy Subsidies: Lessons Learned in assessing their 

impact and designing policy reforms. Sheffield: Greenleaf Publishing. 

ZIETZ, J. & VALDES, A. (1993). Growth of agricultural protection, trade and protectionism. NBER-EASE Volume 

2, University of Chicago Pres ISBN: 0-226-38668-6. Accessed 25/03/2017 from URL: 

http://www.nber.org/chapters/c8073. 

 

Appendixes 

 

BUDGETARY ALLOCATION FOR AGRICULTURE IN NIGERIA, 1980-2016 

 

 

 

           

Years 

All Sectors        

Agric 

      

Total 

% 

Agric % 

1980 NA NA 100 NA 

1981 7965.9 141.9 100 1.8 

1982 10277.9 127.3 100 1.2 

1983 10684.3 166.2 100 1.6 

1984 11827.8 167.0 100 1.4 

1985 12628.6 166.1 100 1.3 

1986 12628.6 150.8 100 1.2 

1987 14318.7 139.9 100 1.0 

1988 15751.1 221.1 100 1.4 

1989 20080.9 263.5 100 1.3 

1990 22070.7 443.7 100 2.0 

1991 35282.7 494.0 100 1.4 

1992 54109.7 698.9 100 1.3 

1993 119460.3 1824.0 100 1.5 

1994 135824.4 1805.4 100 1.3 

1995 333019.2 1807.7 100 0.5 

1996 403947.7 1807.7 100 0.4 

1997 419864.7 1819.9 100 0.4 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

58 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source: CBN, 2018. 

Years with NA were extrapolated. 

 

NIGERIA’S  GROSS DOMESTIC PRODUCT (GDP) 1980-2016 

 

Year Agric (Total) Agric % Construction Total GDP % total 

1981 17052 11.8 11038 144831 100 

1982 20126 13.0 9904 154978 100 

1983 23798 14.6 8980 163000 100 

1984 30365 17.8 7587 170378 100 

1985 34237 17.8 6098 192273 100 

1986 35703 17.6 7643 202436 100 

1987 50287 20.2 8658 249439 100 

1988 73765 23.0 9820 320329 100 

1989 88264 21.1 15343 419196 100 

1990 106627 21.3 17318 499677 100 

1991 123236 20.7 19506 596045 100 

1992 184116 20.2 24320 909803 100 

1993 295325 23.5 31920 1259070 100 

1994 445273 25.3 41097 1762813 100 

1995 790142 27.3 54869 2895201 100 

1998 445238.1 1904.0 100 0.4 

1999 444956.4 1904.0 100 0.4 

2000 416237.0 1907.8 100 0.5 

2001 461083.5 1910.8 100 0.4 

2002 469958.6 1913.9 100 0.4 

2003 499839.9 1913.9 100 0.4 

2004 636865.4 1913.9 100 0.3 

2005 698089.1 1913.9 100 0.3 

2006 896847.8 2553.5 100 0.3 

2007 1077318.4 33824.4 100 3.1 

2008 1400333.6 3171.8 100 0.2 

2009 878103.1 11217.9 100 1.3 

2010 891142.2 1588.9 100 0.2 

2011 1470098.4 6815.5 100 0.5 

2012 2616996.6 14219.7 100 0.5 

2013 3520246.8 13756.8 100 0.4 

2014 6461139.1 3943.5 100 0.1 



www.acseusa.org/journal/index.php/aijas          American International Journal of Agricultural Studies            Vol. 1, No. 1; 2018 

 

 

59 

 

 

1996 1070515 28.3 63856 3779133 100 

1997 1211462 29.5 74737 4111641 100 

1998 1341041 29.2 99027 4588990 100 

1999 1426974 26.9 109574 5307362 100 

2000 1508409 21.9 121819 6897482 100 

2001 2015422 24.8 162183 8134142 100 

2002 4251521 37.5 191007 11332253 100 

2003 4585926 34.5 234474 13301559 100 

2004 4935264 28.5 311850 17321295 100 

2005 6032332 27.1 414761 22269978 100 

2006 7513298 26.2 551632 28662469 100 

2007 8551981 25.9 733670 32995384 100 

2008 10100325 25.8 975781 39157884 100 

2009 11625442 26.3 1297789 44285561 100 

2010 13048893 23.9 1570973 54612264 100 

2011 14037826 22.3 1905575 62980397 100 

2012 15815998 22.1 2188719 71713935 100 

2013 16816553 21.0 2676284 80092563 100 

2014 18018613 20.2 3188823 89043615 100 

2015* 19636969 20.9 3472255 94144960 100 

2016* 21523513 21.2 3606560 101489492 100 

 

SOURCE: CBN, 2018 

Years with asterisks were extrapolated 

 

Acknowledgement 

 

The authors acknowledge the useful comments from members of the Department of Agricultural Economics, 

University of Nigeria, Nsukka during the seminar sessions. 

 

 

 

Copyrights  

Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an 

open-access article distributed under the terms and conditions of the Creative Commons Attribution license 

(http://creativecommons.org/licenses/by/4.0/). 

 

 


