Agriculture’s contribution to economic development cannot be over-emphasized. Numerous roles have been ascribed to agriculture in most economies; major among these are employment generation, food production, source of foreign exchange, provision of raw materials to industries. Although, the Nigerian agricultural sector has been described as the backbone of the economy at independence (1960-1970), however, the sudden decline in its contribution has raised concern over the years.

Endowed with abundant resources, Nigeria’s agricultural sector has potential for growth. Despite the huge potential of agriculture in Nigeria, the impact of the sector on economic growth through ensuring food security and self-sufficiency is still very low. Although several reasons could be deduced for this development, prominent among them are poor private investments in agriculture, inequitable access to assets and resources, and poor technology.

In combating the inconsistency of the sector in its contribution to output, several policies, programmes and reforms have been implemented. However, from available data, an appraisal of the outcome proves that the objectives of the sector have not been achieved. Furthermore, trend analysis of growth rate of output and outlook of the share of output in GDP show that performance in this sector has been dismal. 

The justification for this debacle has been linked to over-zealousness in policy formulation in contrast to financial commitment; budgetary allocation lower than released funds, unstable government leading to changing policies, absence of effective regulatory and monitoring system, lack of transparency on the part of government, bureaucratic practices, and corruption, Manyong et al. [1] and Ogen [2]. All of these reinforce weak and inefficient institutions. It is very clear that policies stand or fall according to the institutional support that they receive. It is not simple in practice to separate policy from institutions since in reality the two concepts overlie each other [3].

North [4] defines institutions as the formal and informal rules/norms governing human behaviour. Lin and Nugent defines institutions as a set of humanly devised behavioural rules that govern and sharpen the interaction of human beings by helping them to form expectations of what other people do. More precisely, institutions can be defined in terms of the extent of property rights' protection; the degree to which laws and regulation are fairly enforced; the ability of the government to protect the individual against economic shocks and provide social protection and the extent of political corruption.

In the context of the agricultural sector, institutions can be defined as the laws, regulations, policies, norms that are put in place to enhance the performance of the sector. For many developing countries, lack of quality institutions causes low productivity of conventional inputs like labor, land and other resources in the agricultural sector as Institutions help translate the potential for capital accumulation and savings from increased agricultural productivity into actual increase in investment. 

Review of Empirical literature

Several studies have examined the impact of institutions on economic growth and authors have established that better institutions leads to a higher income [5,6]. While there is a consensus in the literature that institutional quality matters for growth, the literature is quite ambiguous about the relative importance of “institutions” vis-à-vis other factors, including manufacturing growth, geography and trade [7]. There is an overall acknowledgement in literature that institutions matter and have a direct impact on growth. For example, Rodrik et al. [5] found in a study that the “estimated direct effect of institutions on incomes is positive. Institutions can lead to an increase in investment, to a better management of ethnic diversity and conflicts, to better policies and to an increase in the social capital stock of a community. All these factors have a recognized positive influence on growth. Therefore, most of the studies suggest a strong and robust relationship between institutional quality and growth and development outcomes [6].

Juttings [6] has been of the opinion that in most of the recent articles, institutions are defined in a broader sense, linking various different measures of institutional quality to development outcomes from various angles and disciplines. Few authors have examined the relationship between institutions and agricultural sector performance using different meaures of institutions.

Broadly based empirical analyses in agriculture have focused on global [8,9] regional [10] and country level performance [11,12]. Recent studies have investigated cross-country differences in agricultural productivity levels and growth rates, according to Coelli and Rao [8], this is most likely driven by the development of new empirical techniques and the desire to assess the degree to which the various agricultural development programmes have improved agricultural performance in developing countries. Fulginiti, et al. [10] explored agricultural productivity performance across some 41 African nations using innovative production techniques (in particular, the seldom-used Fourier functional form) and exploring the role of institutions (colonial heritage, for one) as an influence on differential productivity growth. Productivity change, estimated at an average 0.83% per year since 1960, is found to be higher in those African nations with a British heritage, less armed conflict, and higher levels of political freedom.

Olajide analysed agricultural productivity growth in Sub Saharan Africa in context of diverse institutional arrangement using data from 1961-2003 to measure the Malmquist index of total factor productivity, using Data Envelopment Analysis(DEA).The study examined the effect of land quality, malaria, education and selected governance indicators such as control of corruption and government effectiveness on productivity growth. Results showed that all variables with the exception of government effectiveness were significant and performed well in terms of expected relationship with total factor productivity, except land and education having an inverse relationship with total factor productivity.

Rizov [9] measured the link between institutions, reform policies and productivity growth in agriculture using evidence from 15 former communist countries. Applying a GMM-IV estimator, results indicated that economic reforms via democratic institutions positively contributed to the productivity growth of agriculture in former communist countries.

Eze in a study examined the agricultural financing policies of the government of Nigeria and effects on rural development .The study found that though the government has made serious efforts at making good agricultural policies through schemes, programmes and institutions, it has not been able to back them up with adequate budgetary allocation and financing coupled with corruption in the execution of the policies.

Bashiru, employed co-integration and error correction technique in examining institutional reforms, interest rate policy and agricultural sector financing in Nigeria with data covering 1980-2011.Results showed a negative relationship between agriculture value added, interest rate spread and inflation.

Omojimite [12] examined the nexus between institutions, macroeconomic policy and growth of the agricultural sector in Nigeria using the fully modified ordinary least square technique. Dummy variables were used to capture institutions this study and results indicated a positive relationship between deficit financing income, institutional reforms and credit to the agricultural sector while interest rate was negative.

Asgari and Nogueira in their work on institutional differences and agricultural performance in Sub Saharan Africa represented institutions with governance, health and economic indices using government expenditure, corruption control and lower mortality rate at birth to measure institutions using panel data for 22 Sub-Saharan Africa countries(1995-2011) and employing the Generalized Least square estimator .Results revealed that government expenditure and corruption control has a positive relationship with agriculture performance while mortality rate is negative implying that a low health status would impede the performance of the sector.

Overview of Agricultural Institutions in Nigeria

Although agriculture has been the mainstay of the Nigerian economy, there has been declining contributions of agriculture to the gross domestic product (GDP) in the past three decades. This could be associated with the gross neglect of the agricultural sector and over dependence on the oil sector. In the pre-and post-independence era (1930 to 1965), the Nigerian economy was dependent on agriculture. Agriculture employed about 70 to 80% of the country’s labour force and contributed 60% of the nation’s gross domestic product (GDP) and foreign exchange earnings (CBN, 1985). In the oil boom era (1966 to 1977), agriculture contributed only 12% to the GDP in 1970 which resulted in rising food import bill leading to the persistent huge deficit in the balance of payments over the years [13]. In the post oil boom era (1977 till date), the price of crude oil started falling and/or fluctuating and there has been a growing concern to restructure the agricultural sector as well as diversify the economy. In order to revamp the agricultural sector, several policies cum instititions have been formulated. Institutions established include:

• Nigerian Agricultural, Cooperative and Rural Development Bank (NACRDB) -1972 to date: Formerly Nigerian Agricultural and Cooperative Bank, NACB, it was jointly established by the Federal Government of Nigeria (FGN) and the Central Bank of Nigeria to give credit to individual small holder farmers at a subsidized interest rate. It is presently known as Family Economic Advancement Programme and even though it now collects deposits, it has not lived up to expectation due to poor funding 

• River Basin Development Authority (RBDA)- 1977 to date: Nine RBDAs were established in 1977 as part of the Third National Development Plan (1975 – 80) to add to the existing Sokoto and Rima RBDAs. Their focus is the provision of especially rural water infrastructure but also roads; N32.8 billion was budgeted for this plan. It was the first plan to make rural development and, especially rural electrification, a priority area of government (FGN, 1975). The scheme also involved a massive development of the nation’s water resources through creation of irrigation schemes to encourage all season farming

• National Grains production company (1979): Established for the expansion of grain production through giving the farmers improved seeds as credit 

• Directorates of Foods, Roads and Rural Infrastructure (DFRRI)-1986 to 1993: This agency adopted an integrated approach to rural development. It was established from the fact that increased food production was tied to development of rural economic infrastructure 

• Nigerian Agricultural Insurance Corporation (NAIC) - 1987 to date: This provides insurance cover for all types of farming and farming related activities, including insurance for stock in transit. The premium paid on NAIC policy is heavily subsidized by the CBN to make it affordable for small holder farmers. The indemnity paid in the event of occurrence of a risk insured against helps in ploughing the farmer back to business 

• National Agricultural Land Development Authority – 1991 to date: Established to open up more areas for agricultural production with supporting credit 

To achieve an improved performance of the agricultural sector, these institutions were established by the government. However, budgetary allocations to agriculture over the years when compared with the total budget, fall short of meeting policy intentions. For instance during the first to third (1962 to 1980) development plan periods, the federal government budgeted =N=3.57billion but only =N=2.41 billion was actually released for the sector(Federal Department of Agriculture, National Development Plan, 1992).The record also showed that in the first Plan, 11.6 percent of the budget was allocated to agriculture but only 9.8 percent was released, in the second Plan 9.9 percent was budgeted but 17.7 % was actually spent and in the third plan 7.2 allocation was budgeted and 7.1 0f this amount was released for the period. From the foregoing, it is evident that in Nigeria, the role of institutions in the development of the Nigerian agricultural-sector has not been fully addressed and the impact has not been fully felt largely due to the inconsistencies of the government in financing the sector. 

The data set for this study consists of annual time series for years ranging from 1981-2018. The choice of this period is based on data availability. The main source of data for this study is the Central Bank of Nigeria (CBN) Statistical Bulletin, ICRG (2004), and Political Risk Service. The variables considered are agricultural performance proxy by agricultural output(Q), labour employed in the agricultural sector(L), capital(K)proxy by agriculture machines and tractors, agricultural land(N) and institutional quality proxy by corruption (COR) and bureaucratic quality (BQ).

This study is based on the Neoclassical's production function, following Asgari and Noguiera. Using a production function with 3 factors of production, where Q is agricultural sector performance measured as agricultural output; K is the stock of capital measured by agricultural machines and tractors; and L is the labor force in the agricultural sector. The focus of this study is on the agricultural sector performance, we control for other factors that would affect agricultural production. Thus, we include N as agricultural land in the production function.

Qit = f (Kit, Lit, Nit)

 (1)

In establishing the relationship between agricultural sector performance and institutional quality, we include institutional variables in equation (1).

Q = f (L, K, N, COR, BQ, U)

(2)

where COR=corruption and BQ=bureaucratic quality, both measuring institutional quality, U is the stochastic error term.

Apriori Expectation

It is expected that negative relationship exist between corruption (R) and agricultural sector output, bureaucratic quality (BQ) and agricultural sector output, while a positive relationship is expected between land (N) and agricultural sector output, capital (K)and agricultural sector output, and finally, positive relationship between labour (L) an agricultural sector output.

Methodology

This study adopts unit root test, co-integration test and error correction model. Unit root test was carried out to avoid the problem of spurious regression. We examined the time series properties of the logged series using the standard Augmented Dickey Fuller (ADF) test by Dickey and Fuller. The tests are conducted with intercept and trend in each of the series. This can be determined as:

Equation 1 above represent intercept and trend, α represent the drift, t represent deterministic trend and m is a lag length large enough to ensure that  ε is a white noise Process. The co-efficient of interest in equation above is δ. If δ is less than one (1) i.e. δ<1, the series does not have unit root. The estimated t-statistic of the variable of interest is compared with the Dickey and Fuller critical values to determine if the null hypothesis is valid. If the variables are integrated, we test for the possibility of a co-integrated relationship using the Johansen Co-integration test by Johansen 1988; Johansen and Juselius1990.

Where result shows variables are stationary, we proceed to conduct co-integration test where the error correction model is expressed as:

In Q_t = a₀+a₁∑

lnQ represents the log of output, lnK represents log of capital, lnN represents log of land, lnL represents log of labour, lnBQ represents log of bureaucratic quality, lnR is the log of corruption, while ECM is the error correction model.

Empirical Results

Due to the nature of data, we began our analysis by examining the time series properties of the variables in the model. This is done using the Augmented Dickey Fuller (ADF) test. The result is summarized in (Table 1).

Table 1: ADF Unit Root Test At log level

 H₀: there is unit root, *** denotes rejection of H₀ at 5% significance level 

Source: Authors’ computation 

Table 1 reports the results of the stationarity tests in the level as well as in first difference and second difference for the variables. We estimate intercept & trend term in these tests. However, after taking the first difference of the logged variables, each series became stationary except for capital which became stationary at second difference. This is because the ADF calculated statistics for all the variables is more negative than the ADF critical values. Thus, we accept the hypothesis that the series contain a unit root at level or the variables are integrated of order one I (1) while capital became stationary at order I (2). Thus, we proceed to carrying out the co-integration test.

The Johansen Co-integration test by Johansen 1988; Johansen and Juselius1990 was used to carry out the co-integration test. The result is displayed in table 2 above. 

The Eigen value suggest on co-integrating relationship. The implication of this is that there exists a long-run relationship between agricultural sector output, corruption, bureaucratic quality, land, labor, and capital which could be given some Error Correction representations. The parsimonious result displayed in table 3 indicates that three of the variables conform to apriori expectation (land, capital, bureaucratic quality and corruption), while labour did not conform to apriori expectation. 

Specifically, the result indicates that there is a positive but insignificant relationship between land and agricultural output. A one percent increase in land would lead to an increase in agricultural output by 2.51%. There also exist a positive and significant relationship between capital and agricultural output. A one percent increase in capital would lead to 3.56% increase in agricultural output. In the same vein, there is a negative and significant relationship between corruption and agricultural output. A one percent increase in corruption reduces agricultural output by 2.2%. There is a negative but significant relationship between labour and agricultural output. A one percent increase in labour would lead to 4.31% decrease in agricultural output. There exists a negative and significant relationship between bureaucratic quality and agricultural output. A one percent increase in bureaucratic quality would lead to 0.5% decrease in agricultural output.The coefficient of the error correction term is negative, less than one and significant while the speed of adjustment is 0.8%

Table 3: Error Correction Model, Parsimonious, Dependent Variable: D(LNQ)

R-squared 0.686152 

Adjusted R-squared 0.501535

S.E. of Regression 0.128111

F-statistic 3.716628 *Significant at 1% 

**Significant at 5% 

Source: Authors’ Computation

In this study, we set out to empirically investigate the impact of institutional quality on agricultural sector performance in Nigeria using co-integration and an Error Correction Mechanism (ECM) technique with annual time series covering the period between 1981and 2018. Some statistical tools were employed to explore the relationship between these variables. The analysis starts with examining stochastic characteristics of each time series by testing their stationarity using Augmented Dickey Fuller (ADF) test, and then estimate error correction mechanism model. From the error correction model, several interesting conclusions are drawn. First, agricultural output has a positive relationship with land, capital and corruption. However, results indicate a negative relationship between agricultural output, labour and bureaucratic quality.

Recommendation

 It is therefore recommended that:

• There should be regular training and capacity building for staff of the institutions involved with implementation of policies – CBN, banks, ministry of agriculture, etc. to strengthen institutional capacity

• No country is void of corruption. However, less corrupt governments would allocate resources with less discrimination, more effectively and more efficiently. Therefore, strigent measures should be put in place to forestall excessive corruption at all levels of administration in the agricultural sector. Also, corruption weakens the capacity for bureaucracy to perform its function of supporting the agricultural sector effectively as it is expected that the bureaucracy in the relevant institutions should be efficient

1. Manyong, V., et al. Agriculture in Nigeria: Identifying Opportunities for Increased Commercialization and Investment. IITA, Ibadan, 2005.

2. Ogen, O. “The Agricultural Sector and Nigeria’s Development: Comparative Perspectives from the Brazilian Agro-Industrial Economy 1960–1995.” Nabula, vol. 4, 2007, pp. 184–194.

3. Ajayi, S. “Institutions: The Missing Link in the Growth Process?” Presidential Address Delivered at the 43rd Annual Conference of the Nigerian Economic Society, August 2003.

4. North, D.C. “The New Institutional Economics and Development.” Economic History, 9309002, University Library of Munich, Germany, 1993.

5. Rodrik, D., et al. “Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development.” IMF Working Paper, no. 02/189, Washington, 2002.

6. Jutting, J. “Institutions and Development: A Critical Review.” Technical Paper No. 210, OECD Development Centre, 2003.

7. Sachs, J. “Institutions Don’t Rule: Direct Effects of Geography on Per Capita Income.” NBER Working Paper, no. w9490, 2003.

8. Coelli, T., and D. Rao. “Total Factor Productivity Growth in Agriculture: A Malmquist Index Analysis of 93 Countries, 1980–2000.” CEPA Working Papers, no. 2/2003, School of Economics, University of New England, Armidale, 2003, pp. 31.

9. Rizov, M. “Institutions, Reform Policies, and Productivity Growth in Agriculture: Evidence from Former Communist Countries.” Wageningen Journal of Life Sciences, vol. 55, no. 4, 2007, pp. 307–323.

10. Fulginiti, L., et al. “Institutions and Agricultural Productivity in Sub-Saharan Africa.” Agricultural Economics, vol. 31, nos. 2–3, 2004, pp. 169–180.

11. Alabi, I. “The Determinants of Agricultural Productivity in Nigeria.” Food, Agriculture and Environment, vol. 3, no. 2, 2005, pp. 78–82.

12. Omojimite, B. “Institutions, Macroeconomic Policy and the Growth of the Agricultural Sector in Nigeria.” Global Journal of Human Social Science, vol. 12, no. 1, 2012.

13. Ugwu, O., and I.O. Ihechituru. “Effects of Agricultural Reforms on sthe Agricultural Sector in Nigeria.” Journal of African Studies and Development, vol. 4, no. 2, 2007, pp. 51–59.