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Agriculture and Food Sciences Research 
Vol. 11, No. 2, 87-95, 2024 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/aesr.v11i2.6169 

© 2024 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Impact of bank of agriculture credit’s on agricultural productivity in South Western 
Nigeria 

 
Rasaki, Wasiu Adedamola1   
Omotoso, Abeeb Bababtunde2   
 
  

( Corresponding Author) 1Department of Agribusiness, Oyo State College of Agriculture and Technology, Igboora, Nigeria. 
Email: awrasaki@oyscatech.edu.ng  
2Department of Agricultural Technology, Oyo State College of Agriculture and Technology, Igboora, Nigeria. 
Email: omotosoabeebtunde@yahoo.com  

 
Abstract 

This study examined the impact of Bank of Agriculture (BOA) credit facilities on agricultural 
productivity in the South-West region of Nigeria. A multi-stage sampling technique was employed 
to select 225 beneficiaries and 630 non-beneficiaries farmers. Descriptive statistics were 
implemented to investigate the socio-economic attributes of the respondents. Impact of credit was 
analyzed using Propensity Score Matching (PSM), and the productivity of producers was analyzed 
using Total Factor Productivity (TFP). The respondents' average age was 47.6 years, and 87.0% of 
them were male. The average PSM score was 0.269, with four matching methods (Nearest 
Neighbour, Radius, Kernel, and Stratification matching) being implemented. The maximum and 
minimum propensity were 0.8207 and 0.0525, respectively. Both the balancing property and the 
region of common support (0.0503, 0.8209) were satisfied. The average treatment effect on the 
productivity differences of the treated (ATT) was approximately 0.347, and this difference was 
statistically significant at the 1% level. Productivity of ATT varied by approximately 21.0%. In 
summary, this investigation provided compelling evidence that the BOA credits have a positive 
impact on agricultural productivity in the South West. It was suggested that administrations at all 
levels and financial stakeholders should collaborate to ensure that producers have access to credit. 
  

Keywords: Credit, Productivity, Impact, Radius, Kernel, Stratification matching. 
 

 
Citation | Adedamola, R. W., & Bababtunde, O. A. (2024). Impact of 
bank of agriculture credit’s on agricultural productivity in South 
Western Nigeria. Agriculture and Food Sciences Research, 11(2), 87–95. 
10.20448/aesr.v11i2.6169 
History:  
Received: 16 October 2024 
Revised: 18 November 2024 
Accepted: 22 November 2024 
Published: 29 November 2024 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher: Asian Online Journal Publishing Group 
 

Funding: This research is supported by the Tertiary Education Trust Fund 

(Grant number: OYSCATECH/RG/2021/08). 

Institutional Review Board Statement: The Ethical Committee of the Oyo 

State College of Agriculture and Technology, Igboora, Nigeria has granted 

approval for this study on 12 March 2023 (Ref. No. OYSCATECH/ET/08). 

Transparency: The authors confirm that the manuscript is an honest, accurate, 

and transparent account of the study; that no vital features of the study have 

been omitted; and that any discrepancies from the study as planned have been 

explained. This study followed all ethical practices during writing. 

Competing Interests: The authors declare that they have no competing 

interests. 

Authors’ Contributions: Developed the topic, and drafted the proposal, 
R.W.A.; developed the questionnaire, O.A.B. Both authors have read and agreed 
to the published version of the manuscript. 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 88 
2. Literature Review ............................................................................................................................................................................ 89 
3. Methodology ..................................................................................................................................................................................... 90 
4. Result and Discussion ..................................................................................................................................................................... 92 
5. Conclusions and Policy Recommendations ................................................................................................................................ 95 
References .............................................................................................................................................................................................. 95 
 

 

 

 

 

mailto:awrasaki@oyscatech.edu.ng
mailto:omotosoabeebtunde@yahoo.com
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/aesr.v11i2.6169
https://orcid.org/0009-0005-9203-3874


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Contribution of this paper to the literature 
This study uniquely compares the productivity impacts between beneficiaries and non-
beneficiaries of Bank of Agriculture credit in South Western Nigeria, providing a dual-
perspective analysis. Unlike prior research, it incorporates farmers’ socio-economic and 
contextual variables to reveal nuanced insights into credit utilization and barriers, guiding 
targeted policy and program improvements. 

 
1. Introduction 

Agricultural sector has been characterized as the most significant sector of the economy, and it has the potential 
to significantly contribute to the nation's future economic development, as it has in the past [1]. The study also 
asserted that agriculture is a critical component of the socio-economic development of most developing economies 
and is a significant factor in national development. Agriculture was a significant source of foreign exchange revenues 
and it supplied the burgeoning population with sustenance and employment, as well as basic materials for the 
expanding industries. According to Ogen [2] agriculture was the most significant sector in the 1960s in terms of its 
contribution to the GDP and occupational distribution. Additionally, the Nigerian economy could be accurately 
described as an agricultural economy in the first decade following independence. Agriculture is a substantial 
contributor to Nigeria's GDP, with small-scale producers playing a dominant role in this contribution, as per Rahji 
and Fakayode [29]. Nevertheless, her productivity and development are impeded by their restricted access to credit 
facilities [3]. 

According to the World Bank Report [4] relationship between agricultural financing and economic development 
has been the subject of extensive theoretical and empirical research in both developed and developing countries. In 
order to examine the impact of agriculture on the Nigerian economy, Rhaji [5] implemented the ordinary least 
squares method. He concluded that the absence of accessible, affordable, and sufficient credit is the cause of the 
systemic decline in the agricultural sector's contribution to the Nigerian economy. However, agricultural credit is 
regarded as a strategic resource that can be used to elevate the living standards of our rural, impoverished 
agricultural community by promoting the production of crops and animals to new heights. Consequently, it is 
essential for the growth of the economy. Credits are critical factor in the enhancement of agricultural productivity. 
Farmers can acquire the necessary apparatus and inputs for conducting agricultural operations as a result of the 
timely availability of credit [6]. The quickest method of boosting agrarian productivity is through the provision of 
inexpensive and accessible credit [7]. Credit is granted for the acquisition of seed, fertilizer, cattle, and implements, 
as well as for the alleviation of distress. Farmers must have the necessary funds to increase the utilization and 
diversity of their inputs. The producers' reserves are either negligible or nonexistent, necessitating that they borrow 
to finance their productive endeavours. The majority of farmers, particularly those who are minor, are unable to 
obtain essential commodities (such as fertilizer, enhanced seed, advanced technology, and plant protection) from their 
sources due to a dearth of funding. Therefore, agricultural credit is a critical component in providing the necessary 
investment to stimulate production growth [8]. Also, credit facilitated the adoption of yield-enhancing technologies 
while the demand for inputs was enhanced by the increased flow of credit to producers [9]. 

Consequently, credit to farmers is usually recognized as a successful strategy for increasing agricultural output 
[10]. The process of improving agriculture and transforming the rural economy is said to be reliant on agricultural 
finance. According to Mahmood, et al. [11] the fastest way to increase agricultural output is to provide affordable 
and easy loans. The agricultural sector, according to the thesis, is more dependent on credit than any other sector of 
the economy because of seasonal changes in farmer returns and the financial needs involved with the shift from 
subsistence to commercial farming. Credit allows people to improve their level of life and earn more money [11]. As 
a result, there is growing concern over the producers' incapacity to access credit, despite the presence of credit rules. 
For example, in 2000, the Nigerian Agricultural Cooperative and Rural Development Bank (now the Bank of 
Agriculture) set aside N5 million for each local government area in the nation as an investment revolving fund. 
Farmers who had access to finance faced much higher mean input expenditures per hectare, independent of their 
socioeconomic standing. The acquisition, administration, and repayment of credit are plagued by a multitude of 
issues, despite its importance in agricultural production. The Nigerian Agricultural Cooperative and Rural 
Development Bank (NACRDB), founded in 1973 and formerly known as the Nigerian Agricultural and Cooperative 
Bank, is a government-sponsored lending organization dedicated to meeting the credit needs of agriculture. The 
efficacy of NACRDB has been inadequately reviewed, due mostly to a high default rate among receivers [12]. 

The BOA, which is entirely under the control of Nigeria's federal government, is the country's most significant 
agency for supporting agricultural and rural development. The Federal Ministry of Finance Incorporated owns 60% 
of the structure, while the Central Bank of Nigeria (CBN) owns 40%. The Federal Ministry of Agriculture supervises 
the Bank of Agriculture Limited. The Nigerian Agricultural Bank (NAB) was formed in 1973 and renamed the 
Nigerian Agricultural and Cooperative Bank (NACB) in 1978. The Nigerian Agricultural Cooperative and Rural 
Development Bank Limited (NACRDB) was founded in 2000 as a result of a merger with the People's Bank of Nigeria 
(PBN) and the acquisition of the risk assets of the Family Economic Advancement Programme (FEAP). In 2010, a 
plan was adopted to turn the Bank into a long-term and efficient national agricultural and rural development finance 
organization. This technique resulted in a future rebranding as BOA Limited [13]. 

It is a nationally owned development bank entrusted with providing low-cost loans to smallholder and 
commercial farmers, as well as small and medium-sized rural businesses. It also provides microfinancing to small and 
medium-sized non-agricultural businesses. The goal is to ensure the sustainable provision of agricultural and rural 
finance services, thereby supporting the national economic development agenda, which includes food security, 
poverty alleviation, job creation, reducing rural-to-urban migration, reducing reliance on imported food, and 
increasing foreign exchange earnings. 
 

1.1. Statement of the Problem 
According to Phillip, et al. [10] the provision of financing to farmers is generally regarded as a successful 

technique for augmenting agricultural productivity. Mahmood, et al. [11] said that credit gives farmers the ability 



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increasing the income and enhance level of life. Agricultural financing is seen vital for enhancing agriculture and 
transforming the rural economy. The assertion is that the agriculture industry relies heavily on credit than any other 
input due to the fluctuations in farmers' earnings and the finance needed for the transition from subsistence to 
commercial farming. Despite Nigeria's huge fertile land, a large percentage of the population suffers from hunger, 
poverty, and unemployment as a result of agricultural neglect. Several agro-industries depend largely on the 
importation of vital raw materials for manufacture, while a considerable proportion of Nigerian youths remain 
unemployed. Various laws have been implemented to address these issues, with banks designated to play a significant 
role in financing via the supply of loans. Nonetheless, the truth persists that banks, namely commercial banks, have 
not adequately addressed the issue, as seen by the limited impact on agricultural loans [14]. 

Conversely, in instances when loans are poised for approval, some farmers or customers lack the requisite 
collateral and integrity demanded by banks as a safeguard against potential losses or unexpected liabilities in the 
event of failure. Consequently, several loans are diverted to non-agricultural projects due to the tendency of certain 
individuals to prioritize extravagant home expenditures, so undermining the intended aim of the credit. Some 
reputable customers, who may be shielded from this allegation, are inevitably burdened by uncontrolled elements, 
like change in policies, and challenges in securing official permits [15]. 

Numerous studies indicate that the provision of accessible and inexpensive financing is the most effective method 
for enhancing agricultural productivity [31]. The claim is that the agriculture industry relies more heavily on the 
finance necessary for transitioning from subsistence to commercial farming. In response Nigeria Government 
established financial institution (BOA) saddled with responsibility of providing easy and cheaper credit to farmer. 
But the drawback is that many farmers have collateral, while some diverted the loan instead of using in it to boost 
the production. 

Given the foregoing, the study investigated impacts of agricultural credit on agricultural productivity in 
Southwest. Specific objectives are to: 

1. Profile the socio-economic characteristics of the BOA credit facilities beneficiaries and non-beneficiaries. 
2. Analyze the productivity differentials between non-beneficiaries and beneficiaries of BOA credit facilities in 

South-Western Nigeria. 
3. Analyze the effect of BOA credit on farm productivity of the beneficiaries in the South-Western Nigeria. 

 

2. Literature Review 
2.1. Empirical Framework 

The demand for commodities to enhance crop production had increased as a consequence of the credit transfer 
to producers, according to Siddiqi, et al. [9].  Omotoso and Omotayo [16] and Omotayo, et al. [17] found that the 
annual household income, per capita annual household income, rice income, rice output, and rice yield of producers 
are significantly and positively impacted by the utilization of credit in rice cultivation. Furthermore, a study 
conducted by Akinola and Oladejo [18] highlighted that direct lending mechanisms offered by BOA played a pivotal 
role in fostering innovation among agribusinesses within the region. This was evident through investments made by 
farmers towards modern farming techniques after securing funds from BOA-based credit programs. Ayegba and 
Ikani [19] revealed that less has been done to boost agriculture by assisting farmers through sufficient credit. On an 
institutional level, Omotoso and Omotayo [20] noted that while there were positive impacts associated with 
accessing BOA loans for agriculture purposes - including improved infrastructure and technology adoption - 
challenges related to loan accessibility processes hindered broader participation among small-scale farmers. 

 
2.2. Theoretical Framework 
2.2.1. Theory of Impact Evaluation 

According to the World Bank [21] impact assessment assesses the intended and, preferably, unforeseen 
consequences of a particular intervention. Impact evaluation differs from outcome monitoring, which focusses on the 
achievement of objectives. It seeks to determine how participants' well-being changed in the absence of the 
intervention. This entails counterfactual analysis, which is defined as "a comparison between the actual events and 
the events that would have occurred in the absence of the intervention" [22]. Impact evaluations aim to address 
cause-and-effect enquiries. They seek to identify outcome changes that can be directly linked to a program [23]. 
Impact evaluation assists in addressing critical enquiries essential for evidence-based policymaking: what is effective, 
what is ineffective, the context, the reasons, and the associated costs. There has been a growing focus on this issue 
in policy-making across Western and developing countries [24]. This component is crucial in the evaluation toolkit 
and essential for global initiatives to enhance aid delivery's effectiveness and public expenditure, ultimately 
contributing to improved living standards [25]. 

Since it is impossible to see program participants' outcomes if they were not beneficiaries, an impact evaluation 
is fundamentally an issue of missing data. In the absence of counterfactual data, the most viable alternative is to 
assess the outcomes of treated individuals or households against those of a non-treated comparison group. In this 
process, a comparison group is selected that closely resembles the treated group, ensuring that individuals receiving 
treatment would have experienced outcomes akin to those in the comparison group had treatment not been 
administered. Effective impact evaluations depend on identifying an appropriate comparison group [26]. Researchers 
employ two primary approaches to replicate the counterfactual of a treated group: (a) establishing a comparator 
group via a statistical design, or (b) adjusting the program's targeting strategy to eliminate pre-existing differences 
between the treated and non-treated groups prior to outcome comparison. 

The equation below illustrates the fundamental evaluation problem by comparing outcomes Y between treated 
and nontreated individuals i: 

𝑌𝑖 =  𝛼𝑋𝑖 +  𝛽𝑇𝑖 +  𝜀𝑖  (1) 
Where T is a dummy equal to 1 for those who participate and 0 for those who do not. X is a collection of additional 

noted traits of the person and maybe of his or her home and local surroundings. At last, ε is an error term indicating 
latent traits influencing Y as well. The equation shows a method often employed in impact assessments—that of 



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gauging the direct influence of the program “T” on results Y. Changes in pricing within program regions might 
potentially be of importance as indirect impacts of the program—that is, those unrelated to participation. 

The problem with estimating equation (𝑌𝑖 =  𝛼𝑋𝑖 +  𝛽𝑇𝑖 +  𝜀𝑖) is that Because of these elements—a deliberate 
program placement and self-selection within the program—treatment assignment is not usually random. Programs 
are therefore positioned in line with the needs of the communities and people, who then self-select depending on 
program design and location. One might base self-selection on observable traits, unseen elements, or both. Regarding 
unobserved components, the estimating equation's error term will include variables likewise linked with the 
treatment dummy T. One cannot quantify and hence explain these unseen properties in the equation above, thereby 
generating unobserved selection bias. 

That is, cov (T, ε) ≠ 0 indicates the failure of one of the main assumptions of ordinary least squares in acquiring 

unbiased estimates: independence of regressors from the disturbance factor λ. Naturally, the correlation between T 

and ε biassed the other estimates in the equation, including the estimate of the program impact β. One might also 
depict this issue in a more conceptual context. Imagine one is assessing an antipoverty initiative meant to increase 
family earnings, a credit intervention.  

𝐷 =  𝐸(𝑌𝑖(1) | 𝑇𝑖 =  1) –  𝐸(𝑌𝑖(0) | 𝑇𝑖 =  0)       (2) 
Where D= different in productivity of credit beneficiary and non-beneficiary Yi stand in for household i's per 

capita income. Ti = 1 represents for participants the value of Yi during treatment as Yi (1). Ti = 0 for 
nonparticipators allows Yi to be expressed as Yi (0). The average impact of the program may be shown as follows if 
Yi (0) is utilized across nonparticipating homes as a comparative result for participant outcomes Yi (1) 

One may also assume, less strongly, conditional exogeneity of program placement. The validity of the effect 
estimations is determined by the exogeneity of program targeting across treated and non-treated regions, as well as 
the justified assumptions on the comparability of participant and comparison groups. Still, one cannot evaluate the 
degree of bias "B" without any strategies or presumptions [26]. 

 

3. Methodology 
3.1. Study Area 

The research was carried out in Southwest Nigeria. Southwest Nigeria consists of six states: Ekiti, Lagos, Ogun, 
Ondo, Osun, and Oyo. It is designated as the South West geopolitical or geographical region of Nigeria. The area 
mostly speaks Yoruba; nevertheless, several variants occur even within a single state. The area is located between 
longitudes 20° 31' E and 60° 0' E, and latitudes 60° 21' N and 80° 37' N. The area's population is 28,767,752, covering 
a total of 77,818 km² [26]. Southwest Nigeria is delineated to the North by Kwara and Kogi States, to the East by 
Edo and Delta States, to the South by the Gulf of Guinea, and to the West by the Republic of Benin. 

The climate of Southwest Nigeria is tropical, marked by distinct wet and dry seasons. The climatic conditions 
vary between Nigeria's two distinct seasons: the rainy season (March - November) and the dry season (November - 
February). The dry season signifies the onset of Harmattan dust, as frigid, arid winds from the northern deserts 
infiltrate the southern regions at this time. The temperature ranges from 21°C to 34°C, and annual precipitation 
varies between 1,500mm and 3,000mm [13]. Agriculture is the primary occupation of the population in the area. 
They farm food crops such as rice, yam, cassava, maize, and cowpea, while cash crops include cocoa, oil palm, kola 
nut, plantain, banana, cashew, citrus, and timber. Animal husbandry, including fisheries, poultry, pig farming, goat, 
sheep, and cow raising, is evident.   

Figure 1 is the map of Nigeria shows all the regions and States. Green portion is the North West region, yellow 
part is North West, red part is North central, blue part South East, purple part is south south region and white part 
is the South West region where this research was carried out. 
 

 
Figure 1. Map of Nigeria. 

 
3.2. Population of the Study 

Study populations were made up of farmers (those that benefited and those that did not benefit from BOA credits) 
in the Southwest, Nigeria.  
 
 



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3.3. Sampling Techniques and Sample Size 
Multi-stages sampling technique was adopted. In stage one, three States were randomly selected from six States 

in the South-west, Nigeria (which are Ogun, Osun and Oyo States). In stage two, all the three branches of Bank of 
Agriculture in each of the state were selected and list of applicants (successful and non-successful) were collected; 
make total number of nine (9) branches. Branches of bank of agriculture limited in each of the three States are: Ogun 
State (Abeokuta, Imeko and Abigi Branches), Osun State (Osogbo, Ode-Omu and Ile-Ife Branches) Oyo State (Igbo-
ora, Ibadan and Iseyin branches). Stage involved systematic selected of 25 beneficiaries (successful applicants) from 
each of the lists that was collected from each of the nine (9) branches; make a total of 225 beneficiaries. In stage four, 
70 non- beneficiaries (non-successful applicants) were also systematically selected from the list provided by the bank; 
make a total of 630 non-beneficiaries. Both beneficiary and non-beneficiaries were 855 respondents. 
 

3.4. Type of Data and Instrument used for Data Collection 
This study used primary data acquired via a well-structured questionnaire. The questionnaire was framed in such 

a way to attain the objectives of the research. The questionnaire contained some useful information such as access to 
credit, constraint faced, inputs used, Output and revenue. 
 

3.5. Methods of Data Analysis 
Data were analyzed using descriptive statistics, Total Factor Productivity and Propensity Score Matching.  

 

3.6. Model Specification  
3.6.1. Total Factor Productivity (TFP) 

𝑇𝐹𝑃 =  𝑙𝑜𝑔𝑌 − 𝛽1𝑙𝑜𝑔𝐾– 𝛽2𝑙𝑜𝑔𝐿                      (3) 
Where Y = Output. 
 K= Capital. 
And L= Labour costs. 
Other necessary factors like Land, Fertilizer, Feed cost etc. 

𝐸𝑥𝑝𝑙𝑖𝑐𝑖𝑡𝑙𝑦: 𝑇𝐹𝑃 =  𝑙𝑜𝑔𝑌 −  𝛽1𝑙𝑜𝑔𝑋1 − 𝛽2𝑙𝑜𝑔𝑋2 − 𝛽3𝑙𝑜𝑔𝑋3 − 𝛽4𝑙𝑜𝑔𝑋4 − 𝛽5𝑙𝑜𝑔𝑋5(8) 
Where TFP = Total Factor Productivity  
  logY = Natural log of output 
  logX1-logX5 = Natural logs of inputs like; land, labour, Capital, chemical, Feed etc 

  β1-β5 = Coefficient of parameters  
 

3.6.2. Propensity Score Matching (PSM) 
Since the 1990s, propensity score matching has been widely used in Economics to assess the effects of a program 

or intervention on the economy. Ravallion [27] reiterated by Mapila, et al. [28] posits that propensity score 
matching involves the identification of two groups: those who participated in the intervention (access to credit in this 
study), represented by Ht=1 for beneficiary farmers, and those who did not participate, represented by Ht=0 for non-
beneficiary farmers. Farmers receiving intervention benefits are paired with non-beneficiary farmers based on the 
likelihood that the latter would have benefitted from the intervention, referred to as the propensity score.  

Propensity Score Matching was used to analyze impact of Agricultural Credit on farmers’ productivity. It was 
used to compare the outcome of those benefited from agricultural credit and those who did not. 

𝑌𝑖 =  𝑋1  +  𝑋2  +  𝑋3  +  − − −𝑋𝑛 +  є             (4) 
Let Y1 = Beneficiary of Agricultural Credit. 
       Y0 = Non-beneficiary. 
If a farmer is a recipient of Agricultural Credit, Z, an indicator variable, equals 1 (Z=1); if not, Z=0. This indicates 

if the farmer really got treatment (Credit). X represents a vector of control variables. Data on program recipients 
indicate the average result among the treated E(Y1|X, Z=1). 

Thus, ATT of the scheme can be estimated as: 

𝐴𝑇𝑇 =  𝐸(𝑌1 − 𝑌0|, 𝑍 = 1)                                        (5) 

=  𝐸(𝑌1|, 𝑍 = 1) −  𝐸𝑝|𝑧 = 1 {𝐸𝑌(𝑌0|𝑍 = 1, 𝑃)}     (6) 

 =  𝐸(𝑌1|, 𝑍 = 1) −  𝐸𝑝|𝑧 = 1 {𝐸𝑌(𝑌0|𝑍 = 0, 𝑃)}    (7) 
Where the first term of the above expression can be estimated from the treatment group and the second term 

from the mean outcome of the Matched (on P) comparison groups. 
 
Table 1, shows the summary of the objectives of the study and analytical technique(s) used for each of the objective. 
 
Table 1. Summary of objectives and analytical techniques. 

S/N Objectives Description Analytical technique 

1 Describe the socio-economic 
characteristics of the BOA credit 
beneficiaries and non-beneficiaries. 
Nigeria. 

Age, gender, marital status. Descriptive statistics 
such as frequency, 
percentages and means. 

2 Analyze the productivity differentials 
between the beneficiaries and non-
beneficiaries of BOA credits in South-
West Nigeria. 

The diverse ways in which farmer 
improve their productivity. The factors 
to be considered are production 
factors: Inputs and output. 

Total factor productivity 
(TFP) and t-test. 

3 Analyze the effect of BOA credit on farm 
productivity of the beneficiaries in the 
South-Western Nigeria. 

Different ways in which farmers make 
a living and build their worlds, e.g. 
improve productivity, increased 
income, improved welfare etc. 

Propensity score 
matching (PSM). 



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4. Result and Discussion  
Sex: Table 2 results showed that majority (87.1 percent) of respondents were male while only 12.9 percent were 

female. It also indicated that 91.1 percent of credit beneficiaries were male while female beneficiaries were just 8.9 
percent, 85.7 percent of non-beneficiaries were male while 14.3 percent were female. This shows that male dominated 
agricultural production in the study area and that male benefited more from Bank of Agriculture Credit than female.  

Age: It was revealed that 8 percent of beneficiaries have age below 30 years while 10.2 percent of non-
beneficiaries belong to this age bracket and the pooled response was 9.6 percent. Only 8 percent and 9.7 percent of 
beneficiaries and non-beneficiaries were above 60 years respectively. The mean age were 48.5 years, 47.6 years and 
47.8 years respectively for the beneficiaries, non-beneficiaries and pooled data.  The average age of beneficiaries is 
slightly higher than that of non-beneficiaries. This is in tandem with findings of Nosiru [29] in his work where the 
average age for beneficiaries was 45.48 years while that of non-beneficiaries was 43.54years. 

Marital Status: It was revealed that about 86 percent of beneficiaries and 85 percent of non-beneficiaries were 
married.  Percentage of singles beneficiaries and non-beneficiaries were 4.9 percent and 9.7 percent respectively while 
9.3 percent of beneficiaries and 5.8 percent of non-beneficiaries were either divorced, widowed or separated. This 
implies that married respondents had access to credit more than others. This conform with Olagunju [30] in her 
studywhere 78.30 percent of the respondents were married/engaged. 

Education Qualification: Results in Table 2 showed that 14.2 percent of beneficiaries had no formal education. 
The percentage of beneficiaries and non-beneficiaries that had education up to tertiary level were 15.6 and 6.7 percent 
respectively. This implies that majority of beneficiaries (85.8 percent) and non-beneficiaries (89.7 percent) had formal 
education. This result contradicted the finding of Tilahun [31] in his study where majority (53.6 percent) of credit 
unconstrained and (56.2 percent) credit constrained had no formal education. 

Farm Size: Revealed that majority of beneficiaries (72.0%) and non-beneficiaries (81.4%) had less or 5 hectares of 
land. The average farm size owned by beneficiaries was 5.9 hectares while that of non-beneficiaries was 3.7 hectares.  
This implies that on average beneficiaries had more land area than non-beneficiaries. This result contradicted that of 
Kiplimo, et al. [32] where mean farm size of farmers that accessed credit was 2.6 hectares and those that did not 
access credit was 2.9 hectares. 

Contact with Extension Agents: It was further revealed that 59.1 percent of beneficiaries and 30.2 percent of 
non-beneficiaries had contact with extension agents. Beneficiaries and non-beneficiaries that did not have contact 
with extension agents were 40.9 percent and 69.8 percent respectively. This indicates that beneficiaries engaged with 
extension agents more frequently than non-beneficiaries. Results contradicted the findings of Kiplimo, et al. [32] 
which indicated only 21.5 percent of beneficiaries had contact with extension agents. 

Type of Labour: The table also showed that 16.9 percent of beneficiaries and 45.6 percent of non-beneficiaries 
used family labour. Beneficiaries and non-beneficiaries that used hired labour were 48.9 and 21.2 percent respectively. 
Percentage of beneficiaries and non-beneficiaries that used both family and hired labour were 34.2 percent and 33.2 
percent respectively. This implies that beneficiaries used more of hired labours than family labours. This result 
negated the findings of Takane [33] in his study “Labour use in smallholder agriculture in Malawi” where family 
labour accounted for 88% of total labour used. 

 
Table 2. Socioeconomic distribution of respondents. 

Socio-economic       
characteristics 

Beneficiaries frequency % 
(N=225) 

Non-beneficiaries frequency % 
(N=630) 

Pooled frequency % 
(N=855) 

Sex 
Male 205 91.1 540 85.7 745 87.1 
Female 20 8.9 90 14.3 110 12.9 

Age (Years) 
≤ 30 18 8.0 64 10.2 82 9.6 
31-40 36 16.0 103 16.4 139 16.3 
41-50 83 36.9 223 35.3 306 35.8 
51-60 70 31.1 179 28.4 249 29.1 

Above 60 18 8.0 61 9.7 79 9.2 
Mean 48.5 47.6 47.8 
Marital status     
Married 193 85.9 533 84.6 726 84.9 
Single 11 4.9 61 9.7 72 8.4 
Divorced 11 4.9 7 1.1 18 2.1 
Widow/Widower             5 2.2 23 3.7 28 3.3 
Separated 5 2.2 6 1.0 11 1.3 
Education qualification 
Non-formal 32 14.2 65 10.3 97 11.4 
Primary 85 37.8 289 45.9 374 43.7 
Secondary 73 32.4 234 37.1 307 35.9 
Tertiary 35 15.6 42 6.7 77 9.0 
Farm size (Ha) 
≤ 5 162 72.0 513 81.4 675 78.9 
6-10 34 15.1 89 14.1 123 14.4 
Above 10 29 12.9 28 4.5 57 6.7 
Mean 5.9 3.7 4.3 
Source of land 
Inheritance 109 48.5 386 61.3 495 57.9 
Gift 41 18.2 107 18.0 148 17.3 
Rent/Lease 30 13.3 70 11.1 100 11.7 
Purchase 45 20.0 67 10.6 112 13.1 
Contact with extension agents     



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Socio-economic       
characteristics 

Beneficiaries frequency % 
(N=225) 

Non-beneficiaries frequency % 
(N=630) 

Pooled frequency % 
(N=855) 

Yes 133 59.1 190 30.2 323 37.8 
No 92 40.9 440 69.8 532 62.2 
Type of labour 
Family labour 38 16.9 287 45.6 325 38.0 
Hired labour 110 48.9 134 21.2 244 28.5 
Family & hired labour 77 34.2 209 33.2 286 33.5 

 

4.1. Productivity Differentials among the Respondents 
Table 3 showed the mean productivity of beneficiaries compared to non-beneficiaries. Mean productivity of 

beneficiaries and non-beneficiaries was 1.64 and 1.29, respectively. The mean difference in productivity was 0.35, 
which was significant at the 1%. This indicates a significant difference in productivity between beneficiaries and non-
beneficiaries.  

 
Table 3. T-test analysis (Productivity by credit). 

Categories    Obs. Mean Std. err. Std. dev. [95% Conf. interval] 

Non-beneficiaries 630 1.29 0.007 0.17 1.28 1.31 
Beneficiaries 225 1.64 0.031 0.46 1.58 1.70 
Combined  855 1.38 0.011 0.32 1.36 1.40 
Difference   -0.35 0.022  -0.39 -0.30 
diff = Mean (0) – Mean (1) t = -16.1 

 

4.2. Descriptive Statistics of Propensity Score and Region of Common Support 
Results in Table 4 showed that the total was 833 farmers and average propensity score was 0.269. Maximum 

and minimum propensity was 0.8207 and 0.0525 respectively. This implies that the average probability to access 
credit from BOA by respondents was about 27 percent.  

The region of common support (0.0503, 0.8209), as presented in Table 4, indicates the point at which treatment 
(beneficiaries) and control (non-beneficiaries) exhibit similar propensity scores, thereby satisfying the balancing 
property. Minimum propensity score is 0.0503, while maximum propensity score is 0.8207. 

 
Table 4. Descriptive statistics of propensity score and region of common support. 

Intervention Obs. Mean Std. dev. Min. Max. 

Credit (Propensity score)  833 0.27 0.17 0.05 0.82 

Credit (Common support) 833 - - 0.05 0.82 

 
4.3. Distribution of Treated and Controls Across Blocks 

Result in Table 5 showed that optimum number of blocks was 6 which was determined by the programme. 
Beneficiaries and non-beneficiaries that were matched at lowest p-score (0.05) were 7 and 133 respectively while only 
1 farmer each for beneficiaries and non-beneficiaries were matched at highest p-score.  This implies that very few 
respondents were matched at level of high propensity score. 

 
Table 5. Distribution of treated and controls across blocks. 

Access to BOA credit 

Block of P score Non-beneficiaries Beneficiaries Total 

0.05 133 7 140 
0.10 167 38 205 
0.20 224 84 308 
0.40 74 66 140 
0.60 9 29 38 
0.80 1 1 2 

Total 608 225 833 

 
4.4. Predictive Performance of Probit 

The summary of predictive performance of the BOA credit was presented in Table 6. It showed a very high 
performance (92.70 percent) in non-treatment case and about 26 percent performance in the treatment case. The 
overall performance was about 75 percent. This implies the BOA credit performed well in agricultural productivity 
in study area.  
 
Table 6. Predictive performance of probit (In %). 

Performance indicator Credit 

Overall correct classification rate 75.09 
Correct non-treatment classification rate 92.70 
Correct treatment classification rate 25.78 

 
4.5. Average Treatment Effect on the Treated (ATT) Productivity Differences Result  

Table 7 presents ATT regarding productivity differences. ATT was determined by calculating the difference in 
contract productivities between the treated group (beneficiaries) and the control group (non-beneficiaries) with 
comparable propensity scores. The methods employed for ATT productivity comparisons included Nearest 
Neighbour, Radius, Kernel, and Stratification matching.  Table 7 presents the total number of treated contracts, the 
corresponding number of control contracts utilized for matching, ATT productivity differences, the ATT 
productivity differences expressed as a percentage of average productivities, and the t-statistics for the productivity 
comparisons. Radius, kernel, and stratification matching utilize all control contracts, whereas nearest neighbour 



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matching employs only a subset of these contracts (151 out of 608) that exhibit the closest propensity scores to the 
treated contracts. Each treated contract was paired with one or more control contracts. 

Nearest Neighbour matching involved pairing each treated contract with one or more control contracts that 
exhibited the closest propensity score. All 225 beneficiary farmers were matched with 151 non-beneficiary farmers. 
ATT for productivity differences is 0.347, which is significant at the 1%. The ATT percent productivity difference 
was 25.04%. This result implies that BOA credit increases the agricultural productivity by 25.0 percent in the study 
area.  Radius matching matched each treated contract with one or more control contracts that has the similar radius. 
It matched all 225 beneficiaries with 608 non-beneficiaries farmers. It used more control than Nearest Neighbour. 
ATT productivity for radius matching was 0.351 which was significant at 1% level of significance and ATT percent 
productivity difference was 25.33%. This result implies that BOA credit increases the agricultural productivity by 
25.33 percent in the study area. Kernel matching paired each treated contract with a weighted average of all control 
contracts, assigning weights that are inversely proportionate to the distance between the propensity scores of the 
treated and control contracts. It correlated all 225 beneficiaries with 608 non-beneficiary farms. It used the same 
quantity of controls as Radius matching. The Average Treatment Effect (ATT) productivity for kernel matching 
was 0.358, significant at the 1% level, with an ATT percent productivity difference of 25.84%. This result implies 
that BOA credit increases the agricultural productivity by 25.84 percent in the study area. 

Stratification matching, matched each treated contract with one or more control contracts that have the similar 
strata. It matched all 225 treatments with 608 controls farmers. ATT productivity for stratification matching was 
0.360 which is significant at 1% and ATT percent productivity difference was 25.98%. This result implies that BOA 
credit increases the agricultural productivity by 25.84 percent in Southwest. 
 
Table 7. ATT productivities differences. 

Matching 
method 

Number 
of treated 

Number 
of control 

ATT 
productivity 
differences 

ATT percent 
productivity 
differences 

Standard 
error 

t- 
statistic 

Nearest neighbour 1.641(225) 1.294(151) 0.347 21.04% 0.036 9.66*** 
Radius 1.641(225) 1.290(608) 0.351 21.39% 0.031 11.18*** 
Kernel 1.641(225) 1.283(608) 0.358 25.84% - - 
Stratification 1.641(225) 1.281(608) 0.360 25.98% - - 

Note: Values in parentheses are numbers of observations. *** represents 1% level of significance. 

 

4.6. Bootstrapped Standard Errors  
Table 8 presented the result of bootstrapped standard error. The essence of bootstrapping was to adjust the 

standard errors and therefore, correct the bias in the estimate. The result showed very little or no difference in the 
standard errors of the estimate and that of bootstrapped standard errors. This implies that there were little or no 
bias in the estimate of the four matching methods used. The t-statistics for all the four matching methods used were 
significant at 1% level of significance. Table 8 presents compelling evidence that the BOA credit positively influences 
agricultural output in the research region. 
 
Table 8. Bootstrapped standard errors of the matching methods. 

Matching method Number of 
treated 

Number of 
control 

ATT productivity 
differences 

Standard 
error 

t-statistic 

Nearest neighbour 1.641(225) 1.294(151) 0.347 0.038 9.15*** 
Radius 1.641(225) 1.290(608) 0.351 0.034 10.24*** 
Kernel 1.641(225) 1.283(608) 0.358 0.027 13.35*** 
Stratification 1.641(225) 1.281(608) 0.360 0.034 10.56*** 
Note: Values in parentheses are numbers of observations. *** represents 1%. 

 

4.7. Result of the T-Test 
Table 9 presented the results of the hypotheses. Results revealed that, there were significant differences between 

BOA credit beneficiary farmers and non-beneficiary farmers with respect to sex, years of schooling, household size, 
main occupation, contact with extension agents, membership of organizations and farm size. Household size was 
significant at 10%. Sex, Years of schooling were significant at 5% while Main occupation, Contact with extension 
agent, membership of organizations and farm size were significant at 1%; hence, null hypotheses were rejected. There 
were no significant difference between the beneficiaries and non-beneficiaries with respect to age, marital status and 
farming years of experience; hence, null hypotheses were accepted.  
 
Table 9. T-test estimates of the variables (Difference between means). 

Variables     Mean Diff Std. error T-value P-value     Decision 

Treated Control 

Age 48.480 47.630 0.847 0.858 0.99 0.324 Accept HO 
Sex 0.910 0.850 0.054 0.026 2.08 0.038** Reject HO 
Married 0.857 0.846 0.011 0.028 0.42 0.673 Accept HO 
Years of schooling    7.836 7.108 0.728 0.360 2.02 0.044** Reject HO 
Household size          7.862 7.405 0.457 0.259 1.76 0.078* Reject HO 
Main occupation        0.738 0.529 0.209 0.038 5.56 0.000*** Reject HO 
Farming experience  18.711 18.051 0.660 0.899 0.73 0.463 Accept HO 
Extension contact 0.591 0.301 0.290 0.036 7.96 0.000*** Reject HO 
Member of organization 0.449 0.230 0.288 0.034 6.36 0.000*** Reject HO 
Farm size 5.861 3.700 2.161 0.433 4.99 0.000*** Reject HO 
Note: ***, ** and * represent 1%, 5% and 10%. 

 



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5. Conclusions and Policy Recommendations 
Subsequent conclusions were derived from the results of this investigation: The study area was dominated by 

masculine agricultural production, and males derived greater benefits from Bank of Agriculture Credit than females. 
Both beneficiaries and non-beneficiaries were married and in their prime years. It was also determined that there was 
a substantial disparity in productivity between beneficiaries and non-beneficiaries, and that these disparities were 
genuine. The BOA credit performed exceptionally well in the study area. From the Nearest Neighbour, Radius, 
Kernel, and Stratification matching results, it was determined that agricultural productivity in Southwest increases 
approximately by 25% as a consequence of BOA credit. The policy implications of the findings were to enhance and 
maintain access to agricultural credit, as it was determined that agricultural productivity was positively influenced 
by agriculture. Consequently, educational stakeholders and governments at all levels (Federal, State, and Local 
governments) should collaborate to enhance farmers' access to formal education. Farmers should not be apprehensive 
about utilizing credit in their farming operations, as research has demonstrated that credits has beneficial impacts on 
agricultural productivity.  

 

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https://doi.org/10.1016/j.jclepro.2023.137487
https://doi.org/10.1007/s10668-024-04681-8
https://doi.org/10.1007/s10668-024-04681-8
https://doi.org/10.3390/agriculture12030363
https://doi.org/10.1007/s10584-024-03775-6

