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CHAPTER ONE: INTRODUCTION
1.1 Background of Study
Agricultural output is a critical component of Nigeria’s economy, contributing approximately 25% to Gross Domestic Product (GDP) and employing about 35% of the labour force (CBN, 2022). The agricultural sector provides food for the nation’s growing population, raw materials for agro-industries, foreign exchange earnings (cocoa, rubber, oil palm, cotton), and employment for millions of rural households (FMARD, 2021). However, agricultural productivity in Nigeria has remained low compared to potential, with yields of major crops (cassava, maize, yam, rice) at 30-60% of achievable levels (World Bank, 2021). One of the major constraints to agricultural productivity is inadequate access to credit for smallholder farmers, who constitute over 80% of the farming population (NBS, 2022).
Commercial bank credit to agriculture refers to loans and advances provided by commercial banks to farmers, agribusinesses, and agricultural cooperatives for agricultural purposes, including land preparation, input purchase (seeds, fertilizers, pesticides), equipment acquisition (pumps, sprayers, tractors), labour hire, and post-harvest handling (CBN, 2022). Commercial banks are the primary source of formal credit in Nigeria, but their lending to agriculture has historically been low due to perceived high risk (climate risk, price risk, pest/disease risk), lack of collateral (land titles), high transaction costs (small loan sizes, remote rural locations), and information asymmetry (banks cannot easily assess farmers’ creditworthiness) (Adebayo and Ogunyemi, 2020).
The period 1982-2007 is a significant era in Nigerian economic history, encompassing major policy shifts that affected agricultural credit (CBN, 2022). 1982-1986: Early 1980s oil price collapse led to economic crisis; Structural Adjustment Programme (SAP) was not yet implemented. 1986-1993: SAP period (introduced 1986) included financial sector reforms, deregulation of interest rates, and removal of agricultural credit subsidies. 1994-1998: Period of economic stagnation under military rule; banking sector crisis (1994-1995) led to bank failures and reduced credit. 1999-2007: Return to democracy (1999); banking consolidation (2004-2005) reduced number of banks from 89 to 25; increased capital base from ₦2 billion to ₦25 billion. Agricultural credit policies included Agricultural Credit Guarantee Scheme (ACGS, 1977), Agricultural Credit Support Scheme (ACSS, 2007), and Commercial Agriculture Credit Scheme (CACS, 2009 – after the period). This period provides a rich empirical setting for examining the relationship between commercial bank credit and agricultural output.
The Agricultural Credit Guarantee Scheme (ACGS), established in 1977, was the major government intervention to encourage commercial bank lending to agriculture (Okonkwo, 2020). Under ACGS, the government guarantees up to 75% of loan defaults, reducing bank risk. ACGS loans are targeted at smallholder farmers. However, uptake of ACGS loans has been low due to: low awareness among farmers, bureaucratic procedures, delays in disbursement, and banks adding additional collateral requirements despite the guarantee (Eze and Nweze, 2019). By 2007, cumulative ACGS loans guaranteed amounted to billions of naira, but the impact on agricultural output is debated.
The theoretical relationship between credit and agricultural output is well-established (Schultz, 1964; Lewis, 1954; Diamond, 1984). Agricultural Development Theory (Schultz, 1964) argues that investment in agriculture (including credit for inputs and technology) is essential for transforming traditional agriculture into a productive, modern sector. Lewis Dual Sector Model (Lewis, 1954) explains that agricultural surplus (output above subsistence) provides resources for industrial development; credit increases agricultural productivity, increasing surplus. Financial Intermediation Theory (Diamond, 1984) explains the role of banks in channeling savings to productive investments (including agriculture), reducing information asymmetry and transaction costs.
Empirical studies on the relationship between commercial bank credit and agricultural output in Nigeria have produced mixed findings (Adebayo and Ogunyemi, 2020; Eze and Nweze, 2019; Okafor and Nwosu, 2020). Some studies find a positive, significant relationship; others find weak or insignificant effects. Differences in time periods, variables, methods, and data quality contribute to these mixed findings. Few studies cover the specific period 1982-2007 using rigorous time-series econometric methods (cointegration, error correction, Granger causality). This period is important because it includes major policy shifts (SAP, banking consolidation, ACGS) that affected agricultural credit.
The graph below shows the trend in commercial bank credit to agriculture and agricultural output in Nigeria (1982-2007) (illustrative description):
- 1982-1986: Low credit levels (agricultural credit <5% of total bank credit). Agricultural output growth modest (2-4%).
- 1987-1993: SAP period; interest rates deregulated; commercial bank lending to agriculture declined (banks preferred trade and services). Agricultural output growth low (1-3%).
- 1994-1998: Banking crisis; credit to agriculture fell further. Agricultural output growth stagnant (0-2%).
- 1999-2007: Democracy; banking consolidation (2004-2005). Commercial bank credit to agriculture increased (from <2% to >6% of total credit). Agricultural output growth increased (4-6%).
The period 1982-2007 includes 26 years of annual data, sufficient for time-series econometric analysis. Variables include: agricultural output (index of agricultural production, or agricultural GDP), commercial bank credit to agriculture (₦ million, real terms or share of total credit), Agricultural Credit Guarantee Scheme (ACGS) loans (₦ million), commercial bank lending interest rate (%), inflation rate (CPI), government agricultural expenditure (₦ million), and exchange rate (₦/USD). Control variables: rainfall, fertilizer use, population growth.
From a theoretical perspective, this study is supported by three theories: Agricultural Development Theory (Schultz, 1964), which posits that investment in agriculture (credit, inputs, technology) transforms traditional agriculture; Lewis Dual Sector Model (Lewis, 1954), which explains how agricultural surplus (increased by credit) provides resources for industrial development; and Financial Intermediation Theory (Diamond, 1984), which explains the role of banks in channeling savings to productive investments, reducing information asymmetry and transaction costs.
In summary, commercial bank credit is a critical determinant of agricultural output, enabling farmers to purchase inputs, adopt technology, and invest in productivity. However, commercial bank lending to agriculture in Nigeria has been low (<10% of total credit) due to perceived risk, lack of collateral, and high transaction costs. The period 1982-2007 includes major policy shifts (SAP, banking consolidation, ACGS) that affected agricultural credit. Empirical evidence on the relationship between commercial bank credit and agricultural output is mixed, and few studies have used rigorous time-series methods for this period. This study aims to examine the relationship between commercial bank credit and agricultural output in Nigeria from 1982 to 2007, using time-series econometric methods (stationarity tests, cointegration, error correction, Granger causality).
1.2 Statement of Problems
Despite the recognized importance of credit for agricultural productivity, commercial bank lending to agriculture in Nigeria has been persistently low (typically <5-10% of total bank credit). The agricultural sector contributes 25% of GDP but receives less than 5% of commercial bank credit (CBN, 2022). This credit gap is estimated at over ₦1 trillion annually. Consequently, smallholder farmers (over 80% of farmers) lack access to formal credit, constraining their ability to purchase improved seeds, fertilizers, pesticides, and equipment, resulting in low yields (30-60% below potential), low output, and low income. The relationship between commercial bank credit and agricultural output during the period 1982-2007 has not been adequately quantified. It is unclear whether commercial bank credit Granger-causes agricultural output, or whether agricultural output Granger-causes credit demand (reverse causality). The long-run relationship (cointegration) between credit and output has not been firmly established. The problem this study addresses is the need to empirically examine the relationship between commercial bank credit and agricultural output in Nigeria from 1982 to 2007, using time-series econometric methods (unit root tests, cointegration, error correction modelling, Granger causality tests) to determine the long-run relationship, short-run dynamics, and direction of causality.
1.3 Aim of the Study
The specific aim of this research work is to examine the relationship between commercial bank credit and agricultural output in Nigeria from 1982 to 2007, using time-series econometric methods (stationarity tests, cointegration analysis, error correction modelling, Granger causality tests) to determine the long-run equilibrium relationship, short-run dynamics, and direction of causality between commercial bank credit and agricultural output.
1.4 Objectives of the Study
- To determine the time-series properties (stationarity) of commercial bank credit to agriculture and agricultural output in Nigeria from 1982 to 2007.
- To examine the long-run relationship (cointegration) between commercial bank credit to agriculture and agricultural output.
- To estimate the short-run dynamics (error correction mechanism) of the relationship between commercial bank credit and agricultural output.
- To determine the direction of causality (Granger causality) between commercial bank credit and agricultural output.
- To quantify the magnitude of the effect of changes in commercial bank credit on agricultural output.
1.5 Research Questions
- What are the time-series properties (stationarity) of commercial bank credit to agriculture and agricultural output in Nigeria from 1982 to 2007?
- Is there a long-run relationship (cointegration) between commercial bank credit to agriculture and agricultural output?
- What are the short-run dynamics (error correction mechanism) of the relationship between commercial bank credit and agricultural output?
- What is the direction of causality (Granger causality) between commercial bank credit and agricultural output?
- What is the magnitude of the effect of changes in commercial bank credit on agricultural output?
1.6 Research Hypotheses
Hypothesis One
- H₀ (Null): Commercial bank credit to agriculture has no significant effect on agricultural output in Nigeria.
- H₁ (Alternative): Commercial bank credit to agriculture has a significant effect on agricultural output in Nigeria.
Hypothesis Two
- H₀ (Null): There is no long-run relationship (cointegration) between commercial bank credit to agriculture and agricultural output.
- H₁ (Alternative): There is a long-run relationship (cointegration) between commercial bank credit to agriculture and agricultural output.
Hypothesis Three
- H₀ (Null): There are no significant short-run dynamics (error correction) between changes in commercial bank credit and changes in agricultural output.
- H₁ (Alternative): There are significant short-run dynamics between changes in commercial bank credit and changes in agricultural output.
Hypothesis Four
- H₀ (Null): Commercial bank credit to agriculture does not Granger-cause agricultural output.
- H₁ (Alternative): Commercial bank credit to agriculture Granger-causes agricultural output.
Hypothesis Five
- H₀ (Null): Agricultural output does not Granger-cause commercial bank credit to agriculture.
- H₁ (Alternative): Agricultural output Granger-causes commercial bank credit to agriculture.
1.7 Justification of the Study
This study is justified on several grounds. First, despite the importance of credit for agricultural productivity, commercial bank lending to agriculture in Nigeria is low. Quantifying the relationship between credit and output is essential for policy. Second, the period 1982-2007 (26 years) is sufficiently long for time-series econometric analysis (cointegration, error correction, Granger causality). Third, understanding whether the relationship is long-run (cointegrated) or only short-run has different policy implications: cointegration suggests a stable equilibrium relationship; absence suggests shocks have permanent effects. Fourth, determining the direction of causality (Granger causality) is essential for policy: if credit Granger-causes output, increasing credit will increase output; if output Granger-causes credit, then output growth will stimulate credit demand. Fifth, the findings will inform agricultural credit policy (CBN, FMARD, Bank of Agriculture, commercial banks).
1.8 Significance of the Study
The findings of this research will be significant to several stakeholders. To the Central Bank of Nigeria (CBN) , the study will provide evidence on the effectiveness of commercial bank credit to agriculture, informing credit policy (credit to agriculture targets, interest rate subsidies). To the Federal Ministry of Agriculture and Rural Development (FMARD) , the findings will inform agricultural credit programme design (ACGS, ACSS, CACS, ABP). To commercial banks, the findings will inform agricultural lending strategies and risk assessment. To development partners (World Bank, IFAD, FAO, AfDB) , the findings will inform project design for agricultural finance programmes. To academic researchers, the study will contribute empirical evidence on credit-output linkages, testing and extending agricultural development theory, Lewis dual sector model, and financial intermediation theory.
1.9 Scope of the Study
The scope of this study is delimited to the relationship between commercial bank credit and agricultural output in Nigeria from 1982 to 2007 (26 years). Variables include: agricultural output (Index of Agricultural Production, or Agricultural GDP at constant prices, ₦ million); commercial bank credit to agriculture (₦ million, nominal and real). Control variables: Agricultural Credit Guarantee Scheme (ACGS) loans (₦ million), commercial bank lending interest rate (%), inflation rate (CPI, %), government agricultural expenditure (₦ million), exchange rate (₦/USD). The study employs time-series econometric methods: unit root tests (Augmented Dickey-Fuller ADF, Phillips-Perron PP), cointegration tests (Engle-Granger, Johansen), error correction model (ECM), Granger causality tests within VECM/VAR framework. The study does not extend to other sources of agricultural credit (microfinance banks, cooperative credit, informal credit), nor to other sectors of the economy (manufacturing, services, oil), nor to micro-level analysis (household/farm level). The study period ends at 2007 to avoid structural breaks caused by global financial crisis (2008-2009) and major policy changes (Anchor Borrowers’ Programme 2015, Finance Acts 2019-2021).
1.10 Definition of Terms
Commercial Bank Credit to Agriculture: Loans, advances, and overdrafts provided by commercial banks to farmers, agribusinesses, and agricultural cooperatives for agricultural purposes (land preparation, input purchase, equipment acquisition, labour hire, post-harvest handling). Measured in ₦ million (nominal and real).
Agricultural Output: The total value of agricultural production (crops, livestock, fisheries, forestry) measured as Agricultural GDP (Gross Domestic Product) at constant prices (₦ million), or Index of Agricultural Production (base year).
Agricultural Credit Guarantee Scheme (ACGS): A Nigerian government programme (established 1977) that guarantees bank loans to smallholder farmers (up to 75% of loan amount), reducing bank risk and encouraging lending to agriculture.
Agricultural GDP (Gross Domestic Product): The value added of the agricultural sector (crops, livestock, forestry, fisheries) as a percentage of total GDP, measured in constant prices (real agricultural GDP) to remove the effect of inflation.
Real Credit: Nominal credit adjusted for inflation. Real credit = (Nominal Credit / GDP Deflator) × 100, or deflated using Consumer Price Index (CPI).
Interest Rate (Lending Rate): The interest rate charged by commercial banks on loans to the agricultural sector (percentage per annum). High interest rates discourage borrowing.
Cointegration: A statistical property of two or more non-stationary time series that move together over the long run such that a linear combination of them is stationary; cointegration indicates a long-run equilibrium relationship.
Error Correction Model (ECM): A time-series model that captures the short-run dynamics of how variables adjust to deviations from long-run equilibrium; the error correction term (ECT) measures the speed of adjustment.
Granger Causality: A statistical concept of predictive causality (not necessarily true causal mechanism) where one time series (X) is said to “Granger-cause” another (Y) if past values of X help predict current Y better than past values of Y alone, controlling for other variables.
Unit Root Test: A statistical test (Augmented Dickey-Fuller ADF, Phillips-Perron PP) to determine whether a time series is stationary (no unit root) or non-stationary (has a unit root). Non-stationary series require differencing before regression to avoid spurious results.
Spurious Regression: A regression that shows statistically significant relationships between unrelated variables, typically arising from regressing two independent non-stationary time series. Cointegration tests avoid spurious regression.
Agricultural Development Theory: A theory (Schultz, 1964) arguing that investment in agriculture (credit, inputs, technology, extension, research, infrastructure) is essential for transforming traditional agriculture into a productive, modern sector.
Lewis Dual Sector Model: A theory (Lewis, 1954) explaining how agricultural surplus (output above subsistence) provides the resources (food, labour, capital) for industrial development; increased agricultural productivity (enabled by credit) increases surplus, accelerating structural transformation.
Financial Intermediation Theory: A theory (Diamond, 1984) explaining the role of financial institutions (banks) as intermediaries between savers and borrowers, reducing information asymmetry (adverse selection, moral hazard) and transaction costs.
CHAPTER TWO: LITERATURE REVIEW
2.1 Conceptual Framework
The conceptual framework for this study is organized around the key concepts of commercial bank credit, agricultural output, the channels through which credit affects agricultural output, and the factors affecting credit supply and demand. These concepts are defined, operationalized, and related to one another below.
2.1.1 Concept of Commercial Bank Credit to Agriculture
Commercial bank credit to agriculture refers to loans, advances, and overdrafts provided by commercial banks to farmers, agribusinesses, and agricultural cooperatives for agricultural purposes (CBN, 2022). Commercial banks are the primary source of formal credit in Nigeria, but their lending to agriculture has historically been low due to perceived high risk, lack of collateral, high transaction costs, and information asymmetry (Adebayo and Ogunyemi, 2020).
Types of Commercial Bank Credit to Agriculture:
| Type | Description | Repayment Period |
| Short-term credit | Seasonal loans for input purchase (seeds, fertilizers), labour hire | Less than 1 year (repaid after harvest) |
| Medium-term credit | Loans for equipment purchase (pumps, sprayers, planters), land improvement | 1-5 years |
| Long-term credit | Loans for irrigation, tree crops (cocoa, oil palm, rubber), land purchase | More than 5 years |
Measures of Commercial Bank Credit to Agriculture:
| Measure | Definition | Unit |
| Nominal credit | Credit in current naira value (not adjusted for inflation) | ₦ million |
| Real credit | Nominal credit adjusted for inflation (deflated by CPI or GDP deflator) | ₦ million (constant prices) |
| Credit share | Agricultural credit as percentage of total commercial bank credit | % |
| Credit to GDP ratio | Agricultural credit as percentage of agricultural GDP | % |
| ACGS loans | Loans guaranteed under Agricultural Credit Guarantee Scheme | ₦ million |
Trends in Commercial Bank Credit to Agriculture (1982-2007):
| Period | Credit Share (%) | Real Credit Trend | Characteristics |
| 1982-1986 | 5-8% | Low | Pre-SAP |
| 1987-1993 | 3-6% | Declining | SAP (interest rate deregulation) |
| 1994-1998 | 2-4% | Very low | Banking crisis |
| 1999-2007 | 4-10% | Increasing | Democracy; banking consolidation (2004-2005) |
(Source: CBN, 2022)
2.1.2 Concept of Agricultural Output
Agricultural output refers to the total value of agricultural production (crops, livestock, fisheries, forestry) measured as Agricultural GDP or Index of Agricultural Production (NBS, 2022).
Measures of Agricultural Output:
| Measure | Definition | Unit |
| Agricultural GDP (nominal) | Value of agricultural output at current prices | ₦ million |
| Agricultural GDP (real) | Value of agricultural output at constant prices (deflated) | ₦ million |
| Index of Agricultural Production | Index (base year = 100) tracking production volume | Index |
| Crop production index | Index for crops only | Index |
| Yield (major crops) | Output per hectare (cassava, maize, yam, rice) | tons/ha |
Trends in Agricultural Output (1982-2007):
| Period | Agricultural GDP Growth (%) | Characteristics |
| 1982-1986 | 2-4% | Modest growth |
| 1987-1993 | 1-3% | Low growth (SAP) |
| 1994-1998 | 0-2% | Stagnation (banking crisis) |
| 1999-2007 | 4-6% | Recovery (democracy, banking consolidation) |
(Source: NBS, 2016; CBN, 2022)
2.1.3 Channels Through Which Credit Affects Agricultural Output
Commercial bank credit affects agricultural output through multiple interconnected channels (Schultz, 1964; Okafor and Nwosu, 2020).
Channel 1: Input Use Channel
| Credit Enables | Effect on Agriculture | Effect on Output |
| Purchase of improved seeds | Higher yields (30-100%) | Higher output |
| Purchase of fertilizers | Higher yields (40-60%) | Higher output |
| Purchase of pesticides | Reduced pest/disease losses (20-50%) | Higher effective yield |
| Purchase of equipment (pumps, sprayers, planters) | Labour saved, timeliness, precision | Higher output |
Channel 2: Investment Channel
| Credit Enables | Effect on Agriculture | Effect on Output |
| Land improvement (irrigation, drainage) | Higher yields, dry season cultivation | Higher output |
| Storage facilities (silos, warehouses) | Reduced post-harvest losses (20-50%) | Higher marketable surplus |
| Processing equipment (mills, dryers) | Value addition | Higher agricultural GDP |
| Tree crop establishment (cocoa, oil palm, rubber) | Long-term income stream | Future output |
Channel 3: Technology Adoption Channel
| Credit Enables | Effect on Agriculture | Effect on Output |
| Mechanization (tractors, planters) | Labour productivity increased | Higher output per worker |
| Improved varieties | Higher yields, disease resistance | Higher output |
| Modern irrigation systems | Year-round production | Higher output |
Channel 4: Labour Channel
| Credit Enables | Effect on Agriculture | Effect on Output |
| Hired labour during peak seasons | More area cultivated, timely operations | Higher output |
| Labour-saving technology | Reduced labour demand; labour shifts to other tasks | Efficiency |
Channel 5: Risk Management Channel
| Credit Enables | Effect on Agriculture | Effect on Output |
| Diversification | Reduced risk | Stability of output |
| Insurance (with credit) | Reduced risk | Higher investment |
2.1.4 Factors Affecting Commercial Bank Credit Supply to Agriculture
| Factor | Effect on Credit Supply | Description |
| Interest rate (lending rate) | Negative | Higher rates reduce credit demand; banks may reduce supply |
| Reserve requirement | Negative | Higher reserves reduce loanable funds |
| Credit guarantee (ACGS) | Positive | Reduces bank risk, encourages lending |
| Government agricultural expenditure | Positive | May stimulate credit demand |
| Inflation | Negative | Erodes real value of loan repayments; banks reduce lending |
| Economic growth | Positive | Higher demand for credit |
| Banking sector health (capital adequacy) | Positive | Healthier banks lend more |
| Collateral availability | Positive | More collateral → more credit |
| Information asymmetry (credit history) | Negative | Banks cannot assess risk → ration credit |
2.1.5 Factors Affecting Credit Demand by Farmers
| Factor | Effect on Credit Demand | Description |
| Interest rate (lending rate) | Negative | Higher rates reduce demand for loans |
| Farm size | Positive | Larger farms need more credit |
| Input prices (fertilizer, seeds, pesticides) | Positive | Higher input prices increase credit need |
| Output prices (crop prices) | Positive | Higher output prices increase expected profit → more credit demand |
| Farmer education | Positive | More educated farmers more likely to use credit |
| Cooperative membership | Positive | Members have better access to credit |
| Land tenure (collateral) | Positive | Formal land title enables credit access |
2.1.6 Conceptual Framework Diagram (Described in Text)
The conceptual framework can be visualized as follows:
Credit Supply → Credit Disbursement → Credit Utilization → Agricultural Output
Independent Variables (Credit Supply Factors):
- Commercial bank credit to agriculture (₦ million, real)
- Agricultural Credit Guarantee Scheme (ACGS) loans (₦ million)
- Lending interest rate (%)
↓ Credit Disbursement (Access to Credit by Farmers):
- Number of farmers receiving credit
- Average loan size (₦)
- Loan purpose (inputs, equipment, labour, land improvement)
↓ Credit Utilization (Mediating Variables):
- Input use (fertilizer kg/ha, improved seeds kg/ha, pesticide L/ha)
- Investment (irrigation, storage, processing, tree crops)
- Technology adoption (mechanization, improved varieties)
- Labour (hired labour, labour-saving technology)
↓ Dependent Variable (Agricultural Output):
- Agricultural GDP (real, ₦ million)
- Index of Agricultural Production
- Crop yields (tons/ha)
Control Variables (Contextual Factors):
- Interest rate (lending rate, %)
- Inflation rate (CPI, %)
- Government agricultural expenditure (₦ million)
- Rainfall (mm)
- Exchange rate (₦/USD)
The framework posits that commercial bank credit supply (independent variables) affects credit disbursement to farmers. Credit utilization (inputs, investment, technology, labour) mediates the relationship between credit and agricultural output. The magnitude of the effect is moderated by contextual factors: interest rates, inflation, government spending, rainfall, and exchange rates.
2.2 Theoretical Framework
This study is anchored on three supporting theories that provide a comprehensive theoretical foundation for understanding the relationship between commercial bank credit and agricultural output. These theories are Agricultural Development Theory, Lewis Dual Sector Model, and Financial Intermediation Theory.
2.2.1 Agricultural Development Theory
Agricultural Development Theory, associated with Nobel laureate Theodore Schultz (1964), argues that investment in agriculture (credit, inputs, technology, extension, research, infrastructure) is essential for transforming traditional agriculture into a productive, modern sector (Schultz, 1964).
Core Propositions (Schultz, 1964):
- Traditional agriculture is poor but efficient: Farmers in traditional agriculture allocate resources efficiently given the constraints they face (limited technology, no credit, poor infrastructure). However, traditional agriculture is “poor” (low output, low income) because of limited investment.
- Low productivity is not due to farmer irrationality: Farmers are rational but constrained. They do not adopt improved practices because they lack credit to purchase inputs, lack information (extension), or face high risk.
- Investment in agriculture yields high returns: Investment in agricultural research (improved seeds), human capital (farmer education, extension), credit (inputs), and infrastructure (roads, irrigation) generates high economic returns.
- Transforming traditional agriculture requires: (a) new technology (high-yielding varieties, fertilizers), (b) incentives (profitable prices for outputs), (c) credit (to purchase inputs), (d) education (extension to teach practices), and (e) infrastructure (roads, storage, markets).
Application to Commercial Bank Credit and Agricultural Output
Agricultural Development Theory predicts (Schultz, 1964; Timmer, 2019):
- Agricultural credit (commercial bank loans) is a critical input for transforming traditional agriculture. Without credit, farmers cannot purchase improved seeds, fertilizers, or irrigation equipment.
- The returns to agricultural credit are high (increased yields, increased farm income, increased agricultural GDP).
- Credit constraints (lack of access to affordable credit) keep farmers trapped in low-productivity traditional agriculture.
- Policy should remove credit constraints through subsidized credit, credit guarantees (ACGS), or microfinance.
Limitations: Schultz’s theory was developed before the widespread availability of microfinance and mobile banking. It focuses on formal credit and does not fully address informal credit markets (money lenders, traders) (Schultz, 1964).
2.2.2 Lewis Dual Sector Model
The Lewis Dual Sector Model, developed by Nobel laureate Arthur Lewis (1954), explains how agricultural surplus (output above subsistence) provides the resources (food, labour, capital) for industrial development (Lewis, 1954).
Core Propositions (Lewis, 1954):
- Dual economy: The economy is divided into a traditional agricultural sector (low productivity, subsistence wages, surplus labour) and a modern industrial sector (higher productivity, higher wages).
- Unlimited supply of labour: The agricultural sector has surplus labour (disguised unemployment) where marginal product of labour is zero or below subsistence wage. This surplus labour can be withdrawn for industrial employment without reducing agricultural output.
- Capital accumulation in industry: Industrial capitalists reinvest profits to expand production, creating more industrial jobs, drawing more labour from agriculture.
- Turning point: Once surplus labour is exhausted, agricultural wages rise, and both sectors share in productivity gains.
Role of Agricultural Credit in the Lewis Model
Agricultural credit (commercial bank loans) can increase agricultural productivity, generating surplus (output above subsistence) that can be used to (Lewis, 1954; Timmer, 2019):
- Feed the industrial workforce (food surplus)
- Provide labour (workers released from agriculture as productivity increases)
- Provide capital (savings from agriculture can be invested in industry)
- Provide foreign exchange (agricultural exports earn currency to import industrial machinery)
Application to Nigeria (1982-2007)
| Indicator | 1982 | 1993 | 2007 | Lewis Model Implication |
| Agricultural employment share | ~60% | ~45% | ~35% | Still high; surplus labour exists |
| Agricultural productivity | Low | Low | Moderate | Low surplus for industry |
| Agricultural credit share | 5-8% | 3-6% | 4-10% | Credit constraints limit productivity |
| Industrial employment share | ~8% | ~10% | ~12% | Low; limited absorption of surplus labour |
Limitations: The Lewis model assumes that the industrial sector can absorb unlimited labour without raising wages (due to unlimited supply). In reality, absorptive capacity may be limited (unemployment in cities). Also, the model does not fully account for rural-urban migration costs or urban informal sector (Todaro and Smith, 2020).
2.2.3 Financial Intermediation Theory
Financial Intermediation Theory, developed by Diamond (1984) and extended by Freixas and Rochet (2019), explains the role of financial institutions (banks, microfinance banks) as intermediaries between savers (surplus units) and borrowers (deficit units), reducing information asymmetry and transaction costs (Diamond, 1984).
Core Propositions (Diamond, 1984; Freixas and Rochet, 2019):
- Information asymmetry: Lenders (savers) cannot easily assess the creditworthiness of borrowers (farmers) or monitor their use of funds. Borrowers have private information about their risk and effort (adverse selection, moral hazard).
- Transaction costs: Direct lending between savers and borrowers is costly (search costs, contracting costs, monitoring costs, enforcement costs).
- Financial intermediaries reduce information asymmetry and transaction costs: Banks specialize in screening borrowers (reducing adverse selection), monitoring borrowers (reducing moral hazard), diversifying risk (lending to many borrowers), and achieving economies of scale (reducing transaction costs per loan).
- Credit rationing: Even with financial intermediation, some borrowers (especially smallholders) may be rationed (denied credit) because screening and monitoring costs are high relative to loan size, or because they lack collateral.
Application to Commercial Bank Credit to Agriculture
Financial Intermediation Theory explains several features of agricultural credit markets in Nigeria (Diamond, 1984; Freixas and Rochet, 2019):
- Why commercial banks are reluctant to lend to agriculture: Information asymmetry is severe (farmers’ risk difficult to assess); transaction costs are high (small loan sizes, remote rural locations); collateral is lacking.
- Why credit is rationed (less than 5-10% of total credit): Banks prefer lending to trade, manufacturing, and services where information asymmetry is lower and transaction costs are lower.
- Why ACGS is needed: The Agricultural Credit Guarantee Scheme reduces bank risk (guarantees 75% of default), encouraging lending to agriculture.
- Why credit constraints persist: Even with ACGS, banks may add additional collateral requirements, and many smallholders are unaware of the scheme.
Limitations: Financial Intermediation Theory focuses on formal financial intermediaries and does not fully explain informal credit markets (money lenders, traders) that dominate agricultural finance in Nigeria (Freixas and Rochet, 2019).
Integration of the Three Theories
The three theories are complementary and collectively provide a robust theoretical framework for this study:
| Theory | Focus | Contribution to Study |
| Agricultural Development Theory | Investment in agriculture for transformation | Explains why agricultural credit is essential for increasing agricultural output |
| Lewis Dual Sector Model | Surplus labour and structural transformation | Explains how agricultural growth (enabled by credit) supports industrial development |
| Financial Intermediation Theory | Role of banks in reducing information asymmetry | Explains why commercial banks are reluctant to lend to agriculture and why ACGS is needed |
Together, these theories support the study’s examination of the relationship between commercial bank credit and agricultural output, recognizing that: (1) agricultural credit is a critical input for transforming traditional agriculture (Agricultural Development); (2) agricultural growth (enabled by credit) generates surplus for industrial development (Lewis); and (3) financial intermediaries (banks) and credit guarantees (ACGS) are needed to overcome credit constraints (Financial Intermediation).
2.3 Review of Related Empirical Studies
This section reviews empirical studies relevant to the relationship between commercial bank credit and agricultural output, organized by geographic focus and key findings.
2.3.1 Studies on Commercial Bank Credit and Agricultural Output (Nigeria)
Adebayo and Ogunyemi (2020) conducted a study on the effect of commercial bank credit on agricultural output in Nigeria (1981-2018). Using a Vector Error Correction Model (VECM), they found that commercial bank credit had a positive and significant effect on agricultural GDP in the long run (coefficient 0.28, p<0.05). A 1% increase in real agricultural credit increased agricultural GDP by 0.28% in the long run. In the short run, the effect was positive but not significant. The study recommended increasing agricultural credit to smallholders.
Eze and Nweze (2019) studied the relationship between Agricultural Credit Guarantee Scheme (ACGS) loans and agricultural output in Nigeria (1990-2018). Using Ordinary Least Squares (OLS) regression, they found a positive and significant relationship (R² = 0.62, p<0.01). However, they did not test for stationarity or cointegration; OLS on non-stationary data may produce spurious results. The study recommended expanding ACGS coverage.
Okafor and Nwosu (2020) studied the effect of commercial bank credit to agriculture on agricultural GDP in Nigeria (1981-2019). Using Autoregressive Distributed Lag (ARDL) bounds testing, they found a long-run relationship (cointegration) between agricultural credit and agricultural GDP. The long-run elasticity was 0.32 (p<0.05). The study concluded that commercial bank credit significantly affects agricultural output.
Okonkwo (2020) studied the effect of agricultural financing (commercial bank credit, ACGS, government expenditure) on agricultural productivity in Nigeria (1981-2018). Using a VECM, he found that: commercial bank credit had a positive effect (0.25 elasticity), ACGS loans had a positive but smaller effect (0.08 elasticity), and government agricultural expenditure had a negative effect (attributed to corruption and inefficiency). The study recommended increasing commercial bank credit to agriculture.
2.3.2 Studies on Credit and Agricultural Output (Other Countries)
| Study | Country | Period | Key Findings |
| Chisasa and Makina (2013) | South Africa | 1970-2010 | Agricultural credit positively affects agricultural output |
| Abate (2019) | Ethiopia | 2000-2015 | Microfinance credit increases agricultural productivity (household-level) |
| Khan and Bashir (2019) | Pakistan | 1980-2015 | Positive long-run relationship between agricultural credit and GDP |
| Ibrahim and Aliero (2012) | Nigeria (Northern) | 1975-2010 | Positive relationship between ACGS loans and agricultural output |
2.3.3 Studies Using Time-Series Econometric Methods
| Method | Application | Advantage | Disadvantage |
| Unit root tests (ADF, PP, KPSS) | Test for stationarity | Determines appropriate model | Low power with small samples |
| Cointegration (Engle-Granger, Johansen) | Test for long-run equilibrium | Avoids spurious regression | Requires non-stationary variables |
| Error Correction Model (ECM) | Short-run dynamics | Captures adjustment to equilibrium | Requires cointegration |
| Granger causality (VAR/VECM) | Direction of causality | Informs policy | Correlational, not true causality |
2.3.4 Studies Using ARDL Bounds Testing
| Study | Variables | Key Findings |
| Okafor and Nwosu (2020) | Credit, agricultural GDP | Cointegration; long-run elasticity 0.32 |
| Akinyemi et al. (2019) | Credit, interest rate, agricultural GDP | Credit Granger-causes GDP; interest rate negative |
2.3.5 Summary of Empirical Findings
The empirical literature reveals consistent findings: (1) commercial bank credit has a positive effect on agricultural output in Nigeria; (2) the effect is stronger in the long run than the short run; (3) ACGS loans positively affect agricultural output; (4) commercial bank lending to agriculture is low (<5-10% of total credit); (5) constraints include high interest rates, lack of collateral, high transaction costs, and information asymmetry; (6) most Nigeria studies use time-series methods (VECM, ARDL) but some use OLS without testing for stationarity (spurious regression risk); (7) few studies cover the specific period 1982-2007; (8) period coverage varies (1981-2018, 1990-2018, 1981-2019). This study addresses these gaps by focusing specifically on the period 1982-2007 using rigorous time-series methods.
2.4 Summary of Literature Review
The table below summarizes key theoretical and empirical literature relevant to commercial bank credit and agricultural output.
| Author(s) and Year | Focus of Study | Strength | Weakness | Limitation | Gap Identified |
| Schultz (1964) | Agricultural Development Theory | Investment in agriculture transforms traditional agriculture | Pre-microfinance era | General theory | Application to Nigeria needed |
| Lewis (1954) | Lewis Dual Sector Model | Agricultural surplus supports industrial development | Assumes unlimited labour absorption | General theory | Application to Nigeria needed |
| Diamond (1984); Freixas and Rochet (2019) | Financial Intermediation Theory | Banks reduce information asymmetry, transaction costs | Focuses on formal finance | General theory | Application to agricultural finance needed |
| Adebayo and Ogunyemi (2020) | Credit and agricultural output (1981-2018) | VECM; long-run elasticity 0.28 | Period includes post-2007 structural breaks | Period gap | 1982-2007 focused study needed |
| Eze and Nweze (2019) | ACGS and output (1990-2018) | Positive relationship | OLS (no stationarity test) | Methodological gap | Cointegration test needed |
| Okafor and Nwosu (2020) | Credit and agricultural GDP (1981-2019) | ARDL; cointegration; elasticity 0.32 | Period includes post-2007 breaks | Period gap | 1982-2007 focused study needed |
| Okonkwo (2020) | Agricultural financing (1981-2018) | Credit positive, government expenditure negative | Period includes post-2007 breaks | Period gap | 1982-2007 focused study needed |
| Chisasa and Makina (2013) | Credit and output (South Africa) | Positive relationship | South Africa, not Nigeria | Geographic gap | Nigeria replication needed |
| Abate (2019) | Microfinance and productivity (Ethiopia) | Positive effect (household-level) | Ethiopia, not Nigeria | Geographic gap | Nigeria replication needed |
| Khan and Bashir (2019) | Credit and GDP (Pakistan) | Positive long-run relationship | Pakistan, not Nigeria | Geographic gap | Nigeria replication needed |
| CBN (2022) | Statistical bulletin | Official data | Not research; descriptive | No analysis | Analytical study needed |
| NBS (2016, 2022) | GDP and agricultural survey | Official data | Not research; descriptive | No analysis | Analytical study needed |
| FMARD (2021) | Agricultural sector report | Official data | Not research; descriptive | No analysis | Analytical study needed |
| World Bank (2021) | Nigeria agricultural sector review | Overview | Not primary research; descriptive | No primary data | Primary research needed |
| Todaro and Smith (2020) | Economic development (textbook) | Comprehensive theory | Not empirical | Not Nigeria-specific | Nigeria empirical needed |
