The ripple effect of credit accessibility on the technical efficiency of maize farmers in Ghana

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anthony Siaw ◽  
Yuansheng Jiang ◽  
Martinson Ankrah Twumasi ◽  
Wonder Agbenyo ◽  
Gideon Ntim-Amo ◽  
...  
Keyword(s):  
Technical Efficiency ◽  
Farm Size ◽  
Farm Income ◽  
Agricultural Credit ◽  
Maize Production ◽  
Content Type ◽  
Frontier Analysis ◽  
Access To Credit ◽  
Variable Approach ◽  
The Impact

PurposeThe purpose of this study is to examine the impact of access to credit on technical efficiency (TE) of maize farmers in a developing country, Ghana.Design/methodology/approachThe study employed an instrumental variable approach and the stochastic frontier analysis (SFA) method for the estimation of the results.FindingsThe study found that farmers who have access to agricultural credit stand the chance of increasing TE by a margin of 8%, which also influences the maize production than those who did not have access to credit. The average TE score of the farmers was 74%. The study also found out that factors like membership, gender, farmers' access to credit, age and social network determine farmers' possibility of accessing agricultural credit. The study finds out that returns to size are increasing among the maize farmers and that significant improvement in efficiency can be realized by increasing the level of input used in production. Also, factors such as farm size, labor, seeds and fertilizer are the essential determinants of maize production output. Also, gender, extension, age, off-farm income, access to credit and membership were significant factors influencing technical inefficiency (TI).Originality/valueThe paper contributes to the existing literature on agricultural credit on rural agricultural development. The problem of endogeneity associated with access to credit, which has been considered by other researchers, is dealt with this study. This paper also provides information to government policymakers, practitioners and all other stakeholders in the maize sub-sectors and also will benefit small farmers outside the study area.

Agricultural credit and technical efficiency in Ghana: is there a nexus?

2016 ◽  
Vol 76 (2) ◽  
pp. 309-324 ◽  
Author(s):  
Abdul-Hanan Abdallah
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Probit Model ◽  
Estimation Procedure ◽  
Farm Size ◽  
Agricultural Credit ◽  
Content Type ◽  
The Impact ◽  
Unique Dataset

Purpose – The purpose of this paper is to examine the impact of agricultural credit on technical efficiency of Ghanaian maize farmers using a unique dataset drawn from the database of Sub-Saharan Africa’s intensification of food crops agriculture (Afrint II) in 2008 period. Design/methodology/approach – In this study, a two-stage estimation procedure is employed to determine impact of agricultural credit on technical efficiency of Ghanaian maize farmers. The first stage utilized probit model while the second stage utilized stochastic frontier approach to estimate impact of credit on technical efficiency of Ghanaian maize farmers. Findings – The study found that farmers are producing below the frontier with average technical efficiency of 47 percent. Policy variables such as credit access; education, extension access and farm size played a stronger role in technical efficiency. Agricultural credit in particular increased technical efficiency by 3.8 percent. Research limitations/implications – The results should not be extended to the impact of agricultural credit on economic efficiency since the allocative efficiency component is not considered in this study. Also, caution should be taken in the interpretation of these results because the data could not permit the incorporation of all variables that might affect technical efficiency. Originality/value – The originality of the paper and its contribution to existing literature largely lies from the use of a unique dataset to find evidence of the impact of credit on efficiency in Ghana.

The impact of microfinance services on the efficiency of family farms in Sub-Saharan Africa: the case of Cameroon

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alim Belek ◽  
Abega Ngono Jean Marie
Keyword(s):  
Technical Efficiency ◽  
Tobit Model ◽  
Sub Saharan Africa ◽  
Economic Factors ◽  
Family Farms ◽  
Agricultural Credit ◽  
Content Type ◽  
Frontier Analysis ◽  
The Impact ◽  
Socio Economic Factors

PurposeDoes MFIs agricultural credit influence the determinants of the efficiency of SFF which are socio-economic factors of the farmers but also agricultural endowments of family farms? This paper aims to study the contribution of MFI services on improving the technical efficiency of SFFs in Cameroon.Design/methodology/approachThe stochastic frontier analysis (SFA) model permits the estimation of the technical efficiency indicators for beneficiaries and nonbeneficiaries of agricultural credits on a sample of 130 cocoa farming households and four MFIs of the same area between 2008 and 2011. The censored tobit model is used to assess the determinants of technical efficiency.FindingsThe results show that the SFF beneficiaries of agricultural credit have an average technical efficiency of 0.68 inferior to that of nonbeneficiaries (0.72) as expected. They are, respectively, at 0.32 and 0.28 of their full productive capacities. The results of the censored Tobit model show that socioeconomic characteristics of the producer such as age and gender explain negatively, while experience explains positively the technical efficiency of SFFs.Research limitations/implicationsAlthough without any selectivity bias, this study indicates the essential character of the socioeconomic factors in the amplification of the role of the MFIs credit on the efficiency of SFFs.Practical implicationsStrategies to improve the efficiency of SFFs require an increase in MFI credits, primarily targeting young, experienced and female farmers.Originality/valueThis study examines the efficiency of SFFs by highlighting the interaction between the socio-economic factors of farmers and the credit of MFIs. It also points to the problem of monitoring the implementation of agricultural financing.

Agricultural credit rationing in Ghana: what do formal lenders look for?

2014 ◽  
Vol 74 (3) ◽  
pp. 364-378 ◽  
Author(s):  
Dadson Awunyo-Vitor ◽  
Ramatu Mahama Al-Hassan ◽  
Daniel Bruce Sarpong ◽  
Irene Egyir
Keyword(s):  
Financial Institutions ◽  
Agricultural Sector ◽  
Credit Rationing ◽  
Farm Size ◽  
Farm Income ◽  
Two Stage ◽  
Agricultural Credit ◽  
Content Type ◽  
Access To Credit ◽  
Improve Access

Purpose – The purpose of this paper is to investigate the determinants of agricultural credit rationing by formal lenders in Ghana. Design/methodology/approach – This study employed descriptive statistics, analysis of variance (ANOVA) and Heckman's two-stage regression model to identify types of rationing faced by farmers and investigate factors that influence agricultural credit rationing by formal financial institutions. Data used in this study are gathered through a survey of 595 farmers in seven districts within Brong Ahafo Region of Ghana. Findings – The result reveals that farmers face three types of rationing. Evidence from the Heckman two-stage models shows that engagement in off farm income generating activities, increase in farm size, positive balances on accounts and commercial orientation of the farmers has the potential to reduce rationing of credit applicants by formal lenders. Practical implications – The results provide information on the factors that need to be considered as important in an attempt to reduce agricultural credit rationing by formal lenders. Originality/value – The value of this study is that farmers would use the results of this study to improve access to required amount of agricultural credit from formal financial institutions. The information would also benefit stakeholders in the agricultural sector, particularly youth in agriculture program organized by Ministry of Food and Agriculture in Ghana as how to improve access to credit and reduce rationing of program participants by formal financial institutions.

Does household engagement in concurrent business affect the farm size-technical efficiency relationship in grain production?

2019 ◽  
Vol 11 (1) ◽  
pp. 125-142 ◽  
Author(s):  
Min Zhong ◽  
Yuchun Zhu ◽  
Qihui Chen ◽  
Tianjun Liu ◽  
Qihua Cai
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Negative Relationship ◽  
Northern China ◽  
Farm Size ◽  
Farm Income ◽  
Content Type ◽  
Frontier Analysis ◽  
Two Factors

Purpose The purpose of this paper is to examine how households’ engagement in concurrent business (CB), which is measured by the contribution of off-farm income to household income, affects the farm size–technical efficiency (TE) relationship in Northern China. Design/methodology/approach This paper applies a stochastic frontier analysis method to analyze data on 1,006 rural households collected from four major wheat-producing provinces in Northern China, adopting a translog specification for the underlying production function. Findings The analysis yields three findings. First, the farm size–TE relationship is inverted U-shaped for all CB engagement levels higher than 5 percent, and the most technically efficient farm size increases with the level of household CB engagement. Second, how TE varies with the level of CB engagement depends on farm size: an inverted-U relationship for relatively small farms (<10μ), a positive relationship for middle-size farms (10–20μ), and a negative relationship for large farms (>20μ). Finally, the overall TE score, 0.88, suggests that wheat output can be increased by 12 percent in Northern China if technical inefficiency were eliminated. Originality/value Unlike most previous studies that examine the impacts of farm size and households’ off-farm business involvement separately, this paper examines how these two factors interact with each other.

Impact of agricultural credit on farm income under the Savanna and Transitional zones of Ghana

2019 ◽  
Vol 79 (1) ◽  
pp. 60-84 ◽  
Author(s):  
Abdul-Hanan Abdallah ◽  
Micheal Ayamga ◽  
Joseph A. Awuni
Keyword(s):  
Selection Bias ◽  
Farm Size ◽  
Farm Income ◽  
Data Set ◽  
Content Type ◽  
Credit Access ◽  
Access To Credit ◽  
Mean Differences ◽  
On Farm ◽  
The Impact

PurposeThe purpose of this paper is twofold: to determine the factors contributing to farm income in the Transitional and Savanna zones of Ghana and to ascertain variations between in the same and across the two locations; and to determine the impact of credit on farm income in each of the two zones and to ascertain the variation in impact of credit across the two locations.Design/methodology/approachIn order to address endogeneity and sample selection bias, the authors draw from the theory of impact evaluation in nonrandom experiment, employing the endogenous switching regression (ESR) while using the propensity score matching (PSM) to check for robustness of the results.FindingsThe results show significant mean differences between some characteristics of households that have access to credit and those that did not have access. Further, the results revealed farm size, labor; gender, age, literacy, wealth and group membership as the significant determinants of both credit access and income in the two zones. With the ESR, credit access increases households farm income by GH¢206.56/ha and GH¢39.74/ha in the Transitional and Savanna zones, respectively, but with the PSM, credit increases farm income by GH¢201.50 and GH¢45.69 and in the Transitional and Savanna, respectively.Research limitations/implicationsThe mean differences in characteristics of the households revealed the presence of selection bias in the distribution of household’s covariates in the two zones. The results further indicate the importance of productive resources, information and household characteristics in improved access to credit and farm income. Also, the results from both methods indicate that credit access leads to significant gains in farm income for households in both zones. However, differences exist in the results of PSM and that of the ESR results.Practical implicationsThe presence of selection bias in the samples suggests that the use of ESR and PSM techniques is appropriate. Further, the results suggesting that enhanced credit access and farm income could be attained through improved access to household resources and information. The results also suggest the need for establishing and expanding credit programs to cover more households in both zones. The differential impact of credit between the two methods employed in each zone revealed the weakness of each model. The low values from PSM could indicate the presence of selection bias resulting from unobservable factors whiles the high values from the ESR could stem from the restrictive assumption of the model. This reinforces the importance of combining mixed methods to check robustness of results and to explore the weakness of each method employed.Originality/valueThe novelty of this study lies in the use of a very extensive and unique data set to decompose the determinants of credit access and farm income and as well as the impacts of credit into zones.

scholarly journals Technical Efficiency and Small Scale Maize Producers in Mwanza Region: A Stochastic Frontier Analysis

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Mukole Kongolo
Keyword(s):  
Technical Efficiency ◽  
Best Practice ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Farm Size ◽  
Small Scale ◽  
Maize Production ◽  
Frontier Analysis

This study measured technical efficiency and its determinants in maize production by small-scale producers in Mwanza region, using a stochastic frontier production function approach. A randomly selected sample of participants in the two districts was used. The Maximum Likelihood estimation procedure was followed to obtain the determinants of technical efficiency and technical efficiency levels of small-scale maize producers. The minimum and maximum values of technical efficiency were between 20% and 91%, indicating that the least practices of specific producer operates at a minimum level of 20%, while the best practice producers  operate  at 91% technical efficiency  level respectively. The summary results of the mean technical efficiency was 63%. The main determinants of technical efficiency were labour, farm size, producer’s experience, producer’s age, family size which were all positive and statistically significant. The findings suggest that the average efficiency of small-scale maize producers could be improved by 37% through better use of existing resources and technology. These findings highlight the need for action by government to assist small-scale maize producers improve efficiency.

scholarly journals Estimation of technical efficiency of Czech farms operating in less favoured areas

2019 ◽  
Vol 65 (No. 10) ◽  
pp. 445-453
Author(s):  
Tamara Rudinskaya ◽  
Tomas Hlavsa ◽  
Martin Hruska
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Positive Impact ◽  
Stochastic Frontier ◽  
Agricultural Area ◽  
Farm Size ◽  
Identification System ◽  
Accounting Data ◽  
Frontier Analysis ◽  
The Impact

This paper deals with the technical efficiency analysis of farms in the Czech Republic. The empirical analysis provides an evaluation of technical efficiency with regard to the farm size, farm specialisation, and farm location. Accounting data of Czech farms from the Albertina database for the years 2011–2015 were used for the analysis. The data were classified by the utilised agricultural area and location of the farm expressed as a less favoured area type from the Land Parcel Identification System (LPIS) database. Research was conducted using the translogarithmic production function and Stochastic Frontier Analysis. The results indicate positive impact of farm size, expressed by utilised agricultural area, on technical efficiency. The analysis of the impact of farm specialisation on technical efficiency verified that farms specialised on animal production are more efficient. The lowest technical efficiency is shown by farms situated in mountainous Less Favoured Areas (LFAs), the highest technical efficiency by farms located in non-LFA regions.

Non-performance of financial contracts in agricultural lending

2016 ◽  
Vol 76 (3) ◽  
pp. 362-377 ◽  
Author(s):  
Basri Savitha ◽  
Naveen Kumar K.
Keyword(s):  
Default Risk ◽  
Secondary Data ◽  
Binary Logistic Regression ◽  
Farm Size ◽  
Farm Income ◽  
Multinomial Regression ◽  
Agricultural Credit ◽  
Family Responsibilities ◽  
Content Type ◽  
Loan Defaults

Purpose Evaluating a portfolio of agricultural loans has become an important issue in recent years primarily due to a large number of loan defaults. The purpose of this paper is to investigate the factors influencing credit repayment behavior of farmers in Karnataka. Design/methodology/approach The study is based on secondary data of 590 farmers collected from a private bank in the state of Karnataka, India. Binary logistic regression and multinomial regression analysis was carried out to estimate the probability of non-payment of a loan. Findings The results of the regression confirm a significant relationship between non-repayment of agricultural credit and characteristics of borrowers such as the age, years of banking relationship, yield of the crop, distance to bank branch, size and tenure of the loan, farm size and leverage and efficiency ratio. Practical implications The factors predicted by the model do certainly help in improving the decision-making process in agricultural lending. A rigorous assessment of family responsibilities, farm size, credit-to-asset ratio, interest burden on the farmers and farm income is suggested to reduce the probability of doubtful assets. Originality/value The studies that predict default risk in agricultural loan are limited in India. This is one of the few studies that estimate the determinants of substandard and doubtful categories of credit in a private sector bank.

The Effects of Improved Cassava Variety Adoption on Farmers’ Technical Efficiency in Indonesia

2021 ◽  
Vol 11 (4) ◽  
pp. 269-278
Author(s):  
Syafrial ◽  
Hery Toiba ◽  
Moh Shadiqur Rahman ◽  
Dwi Retnoningsih
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Probit Regression ◽  
Cross Sectional ◽  
Factors Affecting ◽  
Frontier Analysis ◽  
Access To Credit ◽  
Farm Work ◽  
The Impact

The adoption of technological innovations, such as an improved variety, has been widely promoted worldwide to improve agricultural productivity. This study aimed to examine factors affecting farmers’ decision to adopt a new improved cassava varieties (NICV), and to estimate the effects of NICV adoption on farmers’ technical efficiency. This research used cross-sectional data from 300 cassava farmers in East Java, Indonesia. Furthermore, the data were analyzed by probit regression to examine factors affecting farmers’ decision to adopt NICV. Propensity score matching (PSM) procedures and stochastic frontier analysis were applied to evaluate the impact of NICV adoption on farmers’ technical efficiency. The results indicated that adoption was highly influenced by cooperative membership, access to credit, internet access, certified land, and off-farm work. The stochastic frontier analysis, by controlling the matched sample using PSM procedures, demonstrated that NICV adoption positively and significantly impacted farmers’ technical efficiency. Those who adopted NICV showed a higher technical efficiency level than those who did not. This finding implies that improved varieties could be further promoted to increase productivity. The research suggests that there is a need to improve NICV adoption to increase the levels of technical efficiency and productivity.

Assessing Production Efficiency by Farm Size in Rwanda: A Zero-inefficiency Stochastic Frontier Approach

2020 ◽  
Author(s):  
Jules Ngango ◽  
Seungjee Hong
Keyword(s):  
Technical Efficiency ◽  
Production Efficiency ◽  
Stochastic Frontier ◽  
Sub Saharan Africa ◽  
Farm Size ◽  
Farm Income ◽  
Maize Production ◽  
Positive Effects ◽  
Study Results ◽  
The Relationship

Abstract This study investigates the relationship between farm size and technical efficiency for maize production in Rwanda. Since levels of technical efficiency tend to vary considerably across farms in sub-Saharan Africa, with a mixture of both inefficient and fully efficient farms, the use of the conventional stochastic frontier method is not appropriate. In this paper, we apply a zero-inefficiency stochastic frontier method that manages both efficiency and inefficiency in the studied sample. The average technical efficiency of maize farms for the full sample is estimated at 0.64, demonstrating that maize output can be improved by approximately 36% without increasing the proportion of farm inputs used. Regarding the relationship between farm size and technical efficiency, the study results show a positive relationship between farm size and technical efficiency for maize production in Rwanda. Thus, the enforcement of land reforms such as land consolidation and enhanced aggregate productivity growth are needed. The results also indicate that education, cooperative membership, extension services, access to credit, off-farm income, land tenure, and livestock ownership have significant and positive effects on technical efficiency.

Sign in / Sign up

Export Citation Format

Share Document