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Agricultural Finance ReviewAgricultural credit rationing in Ghana: what do formal lenders look for?Dadson Awunyo-Vitor Ramatu Mahama Al-Hassan Daniel Bruce Sarpong Irene Egyir
Article information:To cite this document:Dadson Awunyo-Vitor Ramatu Mahama Al-Hassan Daniel Bruce Sarpong Irene Egyir , (2014),"Agriculturalcredit rationing in Ghana: what do formal lenders look for?", Agricultural Finance Review, Vol. 74 Iss 3 pp.364 - 378Permanent link to this document:http://dx.doi.org/10.1108/AFR-01-2013-0004
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Agricultural credit rationingin Ghana: what do formal
lenders look for?Dadson Awunyo-Vitor
Department of Agricultural Economics, Agribusiness and Extension,Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, and
Ramatu Mahama Al-Hassan, Daniel Bruce Sarpong andIrene Egyir
Department of Agricultural Economics and Agribusiness,University of Ghana, Legon, Ghana
Abstract
Purpose – The purpose of this paper is to investigate the determinants of agricultural credit rationingby 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 farmersand 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 BrongAhafo Region of Ghana.Findings – The result reveals that farmers face three types of rationing. Evidence from the Heckmantwo-stage models shows that engagement in off farm income generating activities, increase in farmsize, positive balances on accounts and commercial orientation of the farmers has the potential toreduce rationing of credit applicants by formal lenders.Practical implications – The results provide information on the factors that need to be consideredas 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 improveaccess to required amount of agricultural credit from formal financial institutions. The information wouldalso benefit stakeholders in the agricultural sector, particularly youth in agriculture program organized byMinistry of Food and Agriculture in Ghana as how to improve access to credit and reduce rationing ofprogram participants by formal financial institutions.
Keywords Ghana, Farmers, Agricultural credit, Formal lenders, Heckman two-stage model,Rationing
Paper type Research paper
IntroductionCredit rationing is referred to as a condition in which individuals who need credit arenot able to apply for credit or applicants are not offered the desired amount theyapplied for (Boucher and Guirkinger, 2007). Rural credit market is characterized byasymmetry information and adverse selection which gives rise to credit rationingby lenders which they use as optimal behavior (Stiglitz and Weiss, 1981). Also, thetime lag between incurring debt by borrowers and receipt of repayment of the debtobligation exposes the credit transactions to considerable risks. These risks arise as aresult of asymmetry in information as borrowers have better information about theirpotential risk of default than the lenders (Aleem, 1990).
However, any attempt by the lenders to increase the interest rate to cover the cost ofcollecting such information necessary to reduce their risk would lead to adverse
The current issue and full text archive of this journal is available atwww.emeraldinsight.com/0002-1466.htm
Received 13 January 2013Revised 29 September 2013Accepted 24 October 2013
Agricultural Finance ReviewVol. 74 No. 3, 2014pp. 364-378r Emerald Group Publishing Limited0002-1466DOI 10.1108/AFR-01-2013-0004
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selection which may negatively affect their return on loans. This is because borrowerswith safe and low-default risk projects may decide to opt out of the credit market in theface of high-interest rates. Often the riskier projects have potentially higher returns,but a higher probability of default may be attracted into the market (Stiglitz andWeiss, 1981). Under these conditions, interest rates are disabled from playing theirclassical market clearing role (Baydas et al., 1994). Thus lenders use other non-pricemechanisms to allocate loans (Hoff and Stiglitz, 1990). One such non-price mechanismis rationing of credit applicants on the loan amount (quantity rationing).
Also lenders’ policies can result in potential credit applicants self-selecting not toapply for credit due to risk and transaction costs. These types of rationing are referredto as risk and transaction cost rationing, respectively (Boucher and Guirkinger, 2007).A credit market outcome for a rationed household is characterized by under investmentand low consumption as the level of investment carried out by borrowers ispersistently below the socially desirable level (Petrick, 2005).
Boucher et al. (2006) also observed that each form of non-price rationing adverselyaffects household resource allocation and thus should be accounted for in any empiricalanalyses of credit market performance. Therefore, the study of credit rationing has ahigh-practical value. This is particularly critical in developing countries where a largemajority of the active population is involved in agriculture. Although Bendig et al.(2009) have identified the prevalence of credit rationing within the rural financialmarket in Ghana, they did not evaluate the type of rationing faced by borrowers andthe things formal lenders look for in deciding whether or not to ration credit applicants.Thus, the aim of this study is to identify types of credit rationing faced by maizefarmers and the key determinants of quantity rationing by formal lenders.
Review of related literatureWhen lenders ration credit, some borrowers cannot obtain the amount of credit theydesire at the prevailing interest rate, nor can they secure more credit by offering to paya higher interest rate, resulting in a binding credit constraint for such households.A significant number of households are not able to access credit because they lackcollateral to put up for formal credit. Ghosh et al. (2000) argued that most householdsare rationed by lenders as a result of asymmetric information. He argued that there area number of strands within the credit literature which focus on adverse selection(hidden information), moral hazard (hidden action) and contract enforcement problems.The theory on adverse selection in credit markets originated with the study by Stiglitzand Weiss (1981). This theory was developed on two main assumptions. First, theyassumed that lenders cannot distinguish between borrowers and different degrees ofrisk and that the contract is subject to limited liability; and second that the repaymentof any loan is limited to the assets which arise from the returns of the investment onlyand that other assets of the borrower could not be used to cover loan repayment.
This introduces the issue of risk aversion on the part of lenders leading to quantityrationing. Based on this assumption (limited liability), lenders bear all the downsiderisk and all the returns from the investment above the loan repayment obligationsaccrued by the borrower. Lenders may use price rationing by raising the interest rate toincrease their return, however, this would lead to adverse selection. This is because theinterest rate will affect the profitability of low-risk borrowers which will cause them toopt out of the credit applicant pool. Therefore the lender may hold the interest rate ata level below market clearing rate and ration credit applicants in order to achievebetter loan composition and a lower risk portfolio.
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Traditionally, credit rationing was limited to quantity rationing (Stiglitz and Weiss,1981) where the lender rejects credit applications by not offering credit applicants loansor offering an amount less than what the applicants have applied for. Boucher et al.(2006) have developed a more complete framework of credit rationing that includes thetwo non-price mechanisms; transaction cost and risk rationing. In line with thisframework a household is regarded as credit rationed if their demand for credit exceedsthe loan amount offered by the lender or they could not apply for credit because theyperceive that they have not satisfied sufficient conditions to meet the requirement forthe supply of credit although they need credit.
Lenders screen borrowers before advancing loans and also monitor them after theloans have been advanced. This coupled with efforts involved in satisfying collateralrequirements impose significant cost on the borrowers resulting in some of themrefraining from borrowing and are thus rationed out.
While quantity rationed households are denied access to the desired amount ofloans, risk and transaction cost rationed households instead voluntarily withdraw fromthe credit market because of the non-price terms of available contracts (Boucher et al.,2006). Thus, some potential borrowers do not apply for credit although they need theloan, due to risk of default. Boucher and Guirkinger (2007) identified quantityrationing, risk rationing and transaction cost rationing within the financial market inPeru. Meyer and Ceuvas (1992) have asserted that loan transaction costs are theultimate reason for credit rationing of certain borrowers particularly small farmhouseholds.
Perraudin and Sorensen (1992) empirically undertook analysis on credit constrainedhouseholds in the USA with data from a consumer finance survey. Models of demandand supply of loans were estimated simultaneously using a discrete choice model of theconsumer decision on whether or not to apply for credit. This analysis was combinedwith a reduced form logit model to analyze banks’ credit granting decisions. The studyfound that households face significant transaction costs in applying for loans and thatthe credit granting decisions made by the banks are highly dependent on theborrower’s demographic characteristics. Jappelli (1990) estimated the logit equationwith the probability of being rationed by formal lenders as a dependent variable inorder to assess determinants of credit rationing by lenders. His results reveal thatincome, wealth and age are the most important determinants of loan rationing byformal lenders. Rahji and Fakayode (2009) used a multinomial logit (MNL) model toidentify factors influencing commercial banks’ decisions to ration agricultural credit insouthwestern Nigeria. They found that the borrowers are heterogeneous and that keydeterminants of credit rationing by commercial banks are farm size, previous income,enterprise type, co-operative membership, household net worth and agriculturalcommercialization level.
MethodologyThe sample was selected in three stages; first was the purposive selection of tworegions followed by the purposive selection of districts. The selection of the tworegions was guided by the level of agricultural activities and the districts were selectedbased on the level of maize production using official statistics from Ministry of Foodand Agriculture (MoFA) (2009). After a consideration of the objectives of the study andresources available, seven districts were selected including two from Ashanti Regionand five from Brong Ahafo Region. Table I indicates the selected districts’ levels ofmaize production, area cultivated under maize and sample sizes.
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The third stage of the sampling involved random selection of maize farmers from a listof maize farmers provided by the Agricultural Extension Agents (AEAs) in charge ofthe operational areas in each of the selected districts. AEAs are frontline staffsof MoFA who work directly with farmers in educating them on new farmingtechniques and appropriate cultural practices. An operational area is a spatial unitcanvassed by one AEA.
The definition of the limit of the operational area is based on the fact that theoperational area forms the building block of a district and it does not cut acrossthe boundary of any other administrative district.
Using the procedure recommended by Bartlett et al. (2001) we determined theminimum sample size to be 374. The sample size was increased by 59 percent to ensurefair distribution of respondents within the seven districts. Thus, the total sample size isapproximated to 595; this was proportionally distributed across the districts usingpopulation in agriculture (Table I). A structured questionnaire was used to collect datafrom farmers between May and August 2011. Descriptive statistics were used topresent the types of rationing faced by the farmers within the formal financial marketsegment. An Analysis of Variance (ANOVA) was used following Okurut and Thuto(2009) to investigate whether there is a difference in the key features of formal credit(namely: lending rate, loan duration and amount) used by the farmers. Finally a seriesof regressions were run to evaluate factors influencing formal lenders’ credit allocationdecisions. The analysis applies to both logit and MNL models using Heckman’stwo-stage procedure.
Theoretical and analytical frameworkQuantity rationing by formal lenders has been conceptualized as a sequentialdecision-making process involving farmers and lenders at two different stages(see Figure 1).
The farmer decides at Stage 1 whether to apply for a loan or not and in stage twothe lender decides whether to ration a borrower or not. A farmer’s decision to applyfor credit is assumed to be a function of his or her individual and householdcharacteristics, and of lending policies and procedures of the institutions within the
Region Districts
Maizeoutputin Mta
Number ofhouseholdsb
Proportionemployed in the
agriculturalproduction (%)b
Householdsinvolved inagriculturalproductionc
Sample sizeselected fromeach districtc
Ashanti Afigya-Sekyere 23,401 22,253 42.2 9,391 53EjuraSekye-dumase 21,871 14,148 36.5 5,164 30
BrongAhafo
Sunyani 43,153 37,978 45.9 17,432 98Dormaa 72,270 31,499 68.5 21,577 121Techiman 27,500 34,332 57 19,569 110Nkoranza 74,719 23,729 71.1 16,871 95Kintampo 73,308 23,786 65.9 15,675 88Total 336,222 187,725 � 105,679 595
Sources: aStatistics, Research and Information Directorate (SRID) of MoFA (2009); bPopulation andHousing Census, Ghana Statistical Service (2008); cAuthor’s calculation
Table I.Selected districtsand sample size
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financial market. If a farmer’s demand for credit is greater than zero he or she maydecide to apply for credit. The lender upon receipt of the application would then decidewhether to ration the farmer or not.
Assuming that DD is credit demand (amount of credit requested) by the farmer andSS is amount of credit supplied (amount of credit offered to an applicant) by the lender,then the credit applicant is rationed if DD4SS. A farmer can either be partiallyrationed where he or she receives an amount of credit less than what was applied for orcompletely rationed when the application is rejected and no credit is offered.
Stage 1 is assumed to depend on a farmer’s assessment of utilities Wt to be derivedfrom using the credit for current consumption, and next period consumption, throughuse of credit for production and capital valuation function when he or she uses thecredit to invest. The farmer’s assessment of utility is influenced by his or herindividual, farm and household characteristics as well as institutional factors (Zi.). Thiscan be represented as:
Wi ¼ a0Zi þ mi: ð1Þ
With the following empirical specification:
W ¼ b0 þ b1GENþb2YEDUþb3VALMOUTPUþb4OATIVþ b5HSIZþ b6TASETþb7HMSIKþb8BVESTþb9HMDETþb10PROXIFAþ b11LenProFORþb12INTERþe
ð2Þ
In the second stage, the lender decides whether to allocate the applicant all the creditapplied for, reduce the amount requested or totally reject the application. From theseoutcomes the applicant can be categorized into three groups (see Figure 1). Thesegroups are fully satisfied, partially satisfied, or completely rejected.
CREDIT DEMAND
DEMAND >0
DEMAND <0
DO NOT APPLY
DO APPLY
PARTIALLY SATISFIED
RATIONED
NOT RATIONED
COMPLETELY REJECTED
FULLY SATISFIED
STAGE ONE STAGE TWO-
Source: Developed from the author’s literature review
SS < DD
SS < DD
SS = DD
DD = SS
SS = 0SS = 0
Figure 1.Framework for creditallocation decisionof formal lenders
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To investigate the determinants of these decisions logit and MNL regressions wererun. Because our interest is in quantity rationing the regression models used includedtwo logit models and a MNL model. The direct estimation of these equations may leadto biased parameter estimates because there is a potential sample selection bias, asthose who do not apply for credit are not represented (see Figure 1). Heckman (1990)has shown that this type of bias is equivalent to missing variable bias and can beovercome by including a variable called Inverse Mills Ratio (IMR). Thus IMR d hasbeen included in the estimation model as follows:
The logit models were specified as follows:
FCL1 ¼ b0 þ b1GENþb2AGEþb3OATIVþb4FSIZEþb5PROXIþb6YEARSþ b7BALDEPOþb8ACOMþb9VALUMAIZþb10dþ m
ð3Þ
where the dependent variable FCL1 is a dummy variable with a value of 1 if therespondent is partially satisfied and 0 if the respondent is fully satisfied:
FCL2 ¼ b0 þ b1GENþb2AGEþb3OATIVþb4FSIZEþb5PROXIþb6YEARSþ b7BALDEPOþb8ACOMþb9VALUMAIZþb10dþ m
ð4Þ
where the dependent variable FCL2 is a dummy variable with a value of 1 if therespondent’s credit application is completely rejected and 0 if the respondent isfully satisfied.
The MNL model is specified as:
FCL0 ¼ b0 þ b1GENþb2AGEþb3OATIVþb4FSIZEþb5PROXIþb6YEARSþ b7BALDEPOþb8ACOMþb9VALUMAIZþb10dþ m
ð5Þ
where FCL0 is the dependent variable and specified as 2 is the partially satisfied, 1 isfully satisfied, and 0 is completely rejected.
This model is valid under the assumption that characteristics of one particularchoice alternative (partially satisfied, fully satisfied and completely rejected) do notimpact on the relative probability of choosing the other alternative. This is referred toas assumption of Independence of Irrelevant Alternatives (IIA). The violation of thisassumption would affect the parameter estimate of the model. This assumption canbe validated or tested using the Hausman test for IIA. Under the null hypothesis,irrelevant alternatives are independent hence omitting the irrelevant alternative(partially satisfied, fully satisfied and completely rejected) will lead to a biasedparameter estimate. Under the alternative hypothesis, irrelevant alternatives are notindependent hence irrelevant alternatives need to be eliminated to obtain an unbiasedparameter estimate. The Hausman test was carried out to test the IIA assumptionfor the MNL model (Equation (5)). Equations (3), (4) and (5) were estimated using themaximum likelihood estimation method.
Choice and description of explanatory variablesThe choice of independent variables was based on related studies such as Ayamgaet al. (2006) and Rahji and Fakayode (2009).
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Gender (GEN)This is included because male farmers are known to have greater access to formalcredit than females (Omonona et al., 2010). This variable is specified as a dummy andtakes a value of 1 if the respondent is male and 0 otherwise. It is hypothesized that thisvariable would have a negative relationship with credit rationing.
Age (AGE)The age of the respondent was included in the model because it is used as a proxy formaturity and the potential ability to utilize and repay credit by borrower (Rahji andFakayode, 2009). Age of the farmer is a continuous variable, defined as the farmer’s ageat the time of interview, measured in years. This variable is expected to be positivelyrelated to credit rationing by formal lenders.
Off-farm income generating activities (OATIV)Some of the maize farmers are also engaged in off-farm income generating activitiessuch as food processing and petty trading. These farmers have a higher probability ofapplying for credit and receiving the full amount, since they have a higher requirementfor credit and also have additional income which they can save and use to guaranteetheir loans. Therefore we expect that it will exhibit a negative relationship withrationing. This variable is specified as a dummy variable which takes a value of 1 if therespondent is engaged in off-farm income generating activities and 0 otherwise.
Farm size (FSIZE)Farm size is measured in hectares. It can be used to estimate the expected income of thefarmer. It is also used as a proxy for the scale of operation of the farmer being classifiedinto the different groups. Large farm sizes are expected to lead to increased creditaccess.
Financial services proximity (PROXI)Farmers who are nearer to formal financial institutions can be contacted by thefinancial institution easily. Therefore, proximity is expected to decrease the probabilityof farmers being rationed. This variable is specified as a dummy variable which takesthe value of 1 if the distance between farmer’s residence and the formal financialinstitution is between 0-2 km and 0 otherwise. The dummy variable was used becauseit was difficult to access the actual distance in km from the farmers. Also 2 km isconsidered walking distance which would not attract transportation cost based onthe distances farmers walk to their farms. With this distance, it is assumed that therespondent can easily walk to the financial institution without having to incurtransportation cost which might influence their use of the institution positively.
Length of period of saving (YEARS)The duration of a farmer’s relationship with the financial institution is used as a proxyfor customer loyalty and trust. It is measured as the number of years the borrower hasbeen saving with a formal financial institution. It is expected that the longer the yearsthe lower the probability of being rationed.
Savings with the financial institution (BALDEPO)This variable is specified as the positive balance on the farmer’s account with theformal financial institution measured in Ghana Cedis (GHb) at the time of the loan
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application. A farmer with a positive balance in his bank account has a higherprobability of securing a loan in full. It is expected that the higher the balance the lowerthe probability of being rationed.
Maize commercialization (ACOM)The level of maize commercialization is conceptualized as the ratio of the average valueof output(s) of market sales to the average value of total output(s) (von Braun andEileen, 1994). It embodies the concept of marketable surplus and market orientation ofthe farmers to maize production and their links to the market economy. It is used todetermine the repayment capacity and subsequently used to allocate credit to farmershence this variable is expected to have a negative relationship with credit rationing.This variable is specified as a dummy and takes the value of 1 if the ratio is equal to ormore than 0.7 and 0 if less than 0.7.
Previous year’s maize income (VALUMAIZ)The previous year’s maize income is used as a proxy for the ability of the farmer toself-finance. This variable connotes the profitability and it is a key decision variable incredit provision by the formal financial institution in the study area. It is expected thatlarger maize income would reduce rationing and increase the amount of credit offeredby the formal institutions.
Results and discussionFormal financial institutions operating in the study areaThe study revealed that there are: 11 universal banks, 14 rural banks, seven savings andloans companies, six credit unions and one financial non-governmental organization(NGO) operating within the study area. The formal financial institutions most patronizedby the respondents are rural banks (61 percent) followed by the AgriculturalDevelopment Bank with 16 percent. A universal bank is a full-service bank thatparticipates in all kinds of banking activities. It is a bank that embraces the three-pillarbanking model development, merchant and commercial banking. A rural bank is afinancial institution that is established to provide banking services to the ruralpopulation, providing credit to small-scale farmers and businesses and supportingdevelopment projects. The banks are locally owned and managed. They are supervisedby the Association of Rural Banks Apex Bank (as the clearing bank) under theregulations of the Bank of Ghana, which owns shares in the banks. The headquarters arelocated in the rural areas and they are unit banks which operate within specificcatchment areas with agencies. A Savings and Loans Company is a registered financialinstitution which is mandated to provide a limited range of financial services. They arealso supervised by the Bank of Ghana. A Credit Union is a member-owned financialco-operative, democratically controlled by its members, and operated for the purpose ofpromoting thrift, providing credit at competitive rates and providing other financialservices to its members.
Types of credit rationing faced by respondentsIn total, 81 percent of the 595 farmers interviewed faced three forms of rationing.About 34 percent of the farmers were risk rationed as they self-selected themselves anddid not apply for credit because of the risk of crop failure and low producer priceswhich may affect loan repayment. In all, 30 percent did not apply for credit because ofthe cost of traveling to the institution and time spent at the banking hall as well as
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membership dues in the case of group loans (transaction cost rationed) (Table II).These results imply that rationing plays a significant role in farmers’ access to creditparticularly risk rationing.
Features of formal creditThe universal banks offered the highest mean loan amount of GHb650.52 followed byrural banks with an average amount of GHb533.33 and savings and loan companiesand credit unions with GHb420.87 and GHb200.50, respectively (Table III).
Based on the ANOVA test, none of the formal loan characteristics is statisticallydifferent across the various groups of formal financial institutions. Formal financialinstitutions granted loans to the respondents with an average period of 13 months.Universal banks generally granted the highest mean loan period of 18 months, whilesavings and loans companies extended the shortest mean loan period of eight months.The overall mean interest rate on formal credit was 3.5 percent per month. The highestmean interest rate of 6 percent per month was charged by savings and loan companiesfollowed by credit unions (3 percent), rural banks (2.8 percent) and universal banks(2 percent). The interest rate charged by the lender may be influenced by the sourceof funds for lending. Institutions which access managed funds appear to have lowerinterest rates.
Credit allocation by formal financial institutionIn all, 136 farmers applied for formal credit. Of this number, 41.9 percent had an amountthey applied for, that is, they were fully satisfied while 34 percent had an amount less
Mechanism Frequency %
Yes rationed Quantity 94 15.800Risk 204 34.290Transaction cost 184 30.920Sub total 482 81.010
Not rationed Offered full amount 65 10.920Does not need credit 48 8.070Sub total 113 18.990Total 595 100.000
Source: Survey data
Table II.Credit rationingforms faced by therespondents
Formal financial institutionsLoan amount
granted (GHb)Mean loan
period (months)Mean interestper month (%)
Universal banks 650.520 18 2.000Rural banks 533.330 12 2.800Savings and loans 420.870 8 6.000Credit unions 200.500 14 3.000Average 451.310 13 3.450F-statistics 0.982 0.57 0.774Sig. 0.431 0.687 0.379
Source: Survey data
Table III.Features offormal credit
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than what they had applied for (partially satisfied). Those rejected amounted to24 percent of the applicants (see Figure 2).
Results of the estimated logit model for partial satisfaction of credit applicants (Model 1)and complete rejection of credit applicants (Model 2)The result of the logit model on factors influencing quantity rationing by formallenders is presented in Table IV: Model 1 presents the results of the logit model forpartial satisfaction of credit applicants: Model 2 presents the results of the logit modelfor rejection of credit applications. The IMR is significant at 1 percent for both models
Source: Survey data
Fully satisfied
Partially satisfied
Completely rejected
41.9%
34.08%
24.02%
0 5 10
percentage of credit applicants
15 20 25 30 35 40 45 Figure 2.Credit allocation
by formal financialinstitution
Model 1 partially satisfied Model 2 completely rejected
Independent variables CoefficientMarginal
effect CoefficientMarginal
effect
Constant �14.283 (6.678) � �12.011 (4.120) �Gender �0.410 (0.295) �0.002 �0.009 (0.016) �0.550Age �0.015 (0.008) �14.809 �0.048 (0.036) �0.182Engaged in off-farm income activities �1.923** (0.857) �0.308 �0.306** (0.032) �0.401Farm size �0.165** (0.081) �0.324 �6.288*** (0.525) �0.547Proximity to farmer’s residence �0.308 (0.409) �0.002 �0.003 (0.025) �0.919Length of period of saving �0.308 (0.409) �0.232 0.291*** (0.077) �0.151Savings with the financial institution �2.891*** (0.901) �0.128 �0.267*** (0.063) �0.202Agric commercialization �0.927** (0.616) �0.035 �0.010*** (0.005) �0.110Previous years farm income �0.954 (0.682) �0.139 �0.528* (0.957) �0.032invmills1 0.304*** (0.021) 0.021 4.010*** (0.634) 0.075Number of observations 162 162LR w2 (10) 39.84 32.56Prob.4w2 0.0000 0.0000Pseudo R2 0.4934 0.5406
Notes: The dependent variable for each model is listed in the column heading. Figures in parenthesisstand for standard error. *,**,***Significant at 10, 5 and 1 percent, respectivelySource: Survey data
Table IV.Logit model for partial
satisfaction of creditapplicants (Model 1)
and complete rejectionof credit applicants
(Model 2).
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indicating that sample selection bias does exist, and that direct estimation of themodels would have produced biased parameter estimates. Therefore, the inclusion ofthe IMR has corrected the sample selection bias. The log-likelihood ratio (LR) statisticis significant at po0.001 for the two models meaning that at least one of theindependent variables included in the model has a coefficient different from zero. In thiscase, the model is statistically significant. Thus, the logit models used has integrityand is appropriate.
Engagement in off-farm income generating activities was found to be significantand has a negative coefficient for the two models. This result means that creditapplicants who are engaged in off-farm income generating activities are less likelyto be quantity rationed by formal lenders. This result supports those of Zeller (1994)in Madagascar where he found that household members with additional sourcesof income are less likely to be rationed by formal credit groups. Credit applications offarmers who engaged in off-farm income generating activities are 30 percent less likelyto be partially rationed and 40 percent less likely to be rejected.
Farm size has a negative coefficient which significantly influences a formal lender’sdecision to partially honor or reject a credit application. The negative sign andsignificance of the coefficient implies that farmers with larger farm sizes have a higherprobability of their credit application being fully honored. This may be attributed tothe fact that lenders use farm size as a proxy for scale of production and profitabilitywhich increases the likelihood of loan repayments. This result is consistent with thefindings of Rahji and Fakayode (2009). Their study on the determinants of creditrationing by commercial banks in Nigeria revealed that credit applicants with largerfarm sizes were less likely to be rationed by commercial banks. A unit increase in farmsize reduces the probability of a farmer’s credit application being partially rationed by32 percent and rejected by 54 percent.
As expected, loyal customers of formal financial institution are less likely to bequantity rationed. The length of period of saving with a formal financial institutionby the farmers met the a priori expectation of a negative relationship with theprobability of the credit application being rejected, however, it is statisticallysignificant for only Model 2. An additional year of saving with a formal lender wouldreduce the probability of a farmer’s credit application being rejected by 15 percent.
The balance of the saving account was found to be a significant variable for creditrationing by formal lenders. The negative sign for the coefficient of this variablesuggests that credit applications by farmers with higher positive account balances areless likely to be rationed. A Cedi (Ghanaian currency) increase in a positive accountbalance would reduce the probability of a farmer’s credit application being partiallysatisfied by 12 percent and rejected by 20 percent.
The coefficient of agriculture commercialization has an expected sign and issignificant at 5 and 1 percent, respectively, for the two models. This means thatcredit applications of commercially oriented farmers are less likely to be quantityrationed by formal lenders. Credit applications of farmers who sell a largerproportion of their produce are 3 percent less likely to be partially satisfied and11 percent less likely to be rejected as compared with their counterparts who arenot commercially oriented.
Previous year’s farm income from maize farming negatively influences a lender’sdecision to reject a credit application. The coefficient of this variable is found to besignificant at 10 percent. This is consistent with the findings of Rahji and Fakayode(2009). They noted that income reduces the probability of rationing by commercial
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banks in Nigeria. A unit increase in the previous maize income would reduce thelikelihood of a farmer’s credit application being rejected by 3 percent.
Results of the estimated MNL model for quantity rationing by formal lendersThe estimated coefficients of the MNL model, along with the marginal effects arepresented in Table V. The IMR has corrected the sample selection bias as the coefficientis significant. The model was run and tested for the validity of the independence of theirrelevant alternatives (IIA) assumptions by using both the Hausman test for IIA andthe MNL specification test, respectively.
The Hausman test failed to reject the null hypothesis of independence of the creditallocation options suggesting that the MNL specification is appropriate to modelcredit allocation decisions of formal lenders. The log-likelihood ratio (LR) statistic issignificant at 1 percent, suggesting the model has a strong explanatory power.Given these goodness of fit measures, it can be concluded that the MNL model usedis appropriate.
The coefficient of engagement in off-farm income generating activities is negativeand significant with both complete rejection and partial satisfaction. Based on themarginal effect a loan application of a farmer who is engaged in off-farm incomegenerating activities is 18 percent less likely to be rejected and 12 percent less likely tobe partially satisfied. This is because the lender knows that in case of crop failure,off-farm income can be used to repay the loan thus a farmer who is engaged in off-farmincome generating activities has a greater chance of receiving the full amount of theloan applied for as compared to farmers who do not have other sources of income apartfrom maize cultivation.
The coefficient of the farm size is negative and significant for both options. It can beinferred that the larger the size of the farmer, the lower the chance of rationing byformal lenders. This is because lenders used farm size as an indication of scale of
Completely rejected Partially satisfied
Independent variables CoefficientMarginal
effect CoefficientMarginal
effect
Constant �23.217 (17.533) � �42.052 (17.679) �Gender �0.439 (0.641) �0.435 �0.207 (0.678) �0.609Age �0.043 (0.033) �0.435 �0.006 (0.030) �0.342Engaged in off-farm income activities �0.944*** (0.157) �0.185 �0.277** (0.119) �0.123Farm size �0.364*** (0.124) �0.035 �0.267** (0.116) �0.080Proximity to farmer’s residence �0.0562 (0.144) �0.058 �0.838 (0.720) �0.278Length of period of saving �0.218*** (0.072) �0.307 �0.748** (0.329) �0.118Account balance with the financialinstitution �1.528** (0.618) �0.238 �2.501*** (0.831) �0.168Agric commercialization �3.128*** (0.772) �0.150 �1.952** (0.722) �0.082Previous years farm income �2.923*** (1.022) �0.264 �3.164*** (0.835) �0.184invmills1 15.087*** (4.422) 0.995 10.517** (4.224) 2.516Number of observations¼ 159LR w2 (16)¼ 45.84Prob4w2¼ 0.0000Pseudo R2¼ 0.327
Notes: Figures in parenthesis stand for standard error. **,***Significant at 5 and 1 percent, respectivelySource: Survey data
Table V.Multinomial logit model
for quantity rationedmaize farmers
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operation and profitability. The marginal effect indicates that a one hectare increase infarm size decreases the probability of a farmer’s application being completely rejectedby 3 percent and partially satisfied by 8 percent. This result supports the findings ofRahji and Fakayode (2009). They used the MNL model to identify factors influencingcommercial banks’ decisions to ration agricultural credit in southwestern Nigeria.They found that farmers with larger farm sizes have a higher chance of being offeredthe full amount of loan they applied for.
One of the factors which significantly influence a lender’s decision to offer the fullamount of the loan the applicant applied for is the number of years the applicants hassaved with the formal financial institution. Length of period of saving decreases theprobability of rationing by formal lenders. As can be seen from Table V, the coefficientof the number of years a farmer has saved with the formal financial institution isnegative and significant for both options of rationing. This implies that a farmer’sloyalty to the financial institution over time significantly reduces the likelihood of hisloan application being completely rejected or partially satisfied. A unit increase in thenumber of years of saving would result in a 30 percent decrease in the probability ofa farmer’s credit application being rejected and 11 percent decrease in it being partiallysatisfied.
The coefficient of the amount of savings a farmer has with the formal financialinstitution at the time of the loan application is negative and significant for bothrationing options. This means that credit applicants with higher positive balances intheir accounts are less likely to be rationed. For instance a cedi increase in a farmer’ssavings account balance results in a 24 percent decrease in the probability of completerationing and a 17 percent decrease in partial rationing by formal lenders. This isbecause lenders used borrowers’ savings as a proxy for account turnover and collateralto offer loans.
As expected the level of agriculture commercialization has a negative andsignificant impact on rationing by formal lenders. Farmers who are commerciallyoriented (sell 70 percent or more of harvested produce) are more likely to be offered thefull amount of the loan applied for. The marginal effect revealed that farmers who arecommercially oriented or sell at least 70 percent of their harvested maize have a 15 percentless likelihood that any credit application would be completely rejected and an8 percent less probability that the credit application would be partially rejected.
The previous year’s farm income of surveyed farmers decreases the likelihood ofrationing. Based on the marginal effect, a cedi increase in the previous year’s farmincome decreases the probability of a farmer’s application being rejected or partiallysatisfied by 26 percent and 18 percent, respectively. This result is similar to thefindings of Jappelli (1990) in the USA. Jappelli (1990) estimated a logit equation withthe probability of being rationed by formal lenders as a dependent variable in order toassess determinants of credit rationing by lenders.
Conclusion and policy implicationsThe purpose of this paper is to investigate the type of rationing faced by farmers andfactors that influence formal lenders decision to ration agricultural credit applicants inGhana. The key findings are that farmers faced three types of rationing namely:quantity, transactions cost and risk rationing. A large proportion of the respondentsare risk rationed because of the possibility of crop failure as a result of poor weatherconditions and unstable producer prices. Also the results from the logit and MNLmodels reveal the factors that formal lenders look for when making decisions on
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whether or not to ration agricultural credit applicants. These factors are farm size,previous year’s maize income, engagement in off-farm income generating activities,commercial orientation of the farmer, and the period of saving and account balance.
Therefore, a farm size expansion policy through the block farming concept wouldreduce rationing by formal lenders. A farm income improvement policy in terms ofadequate remunerations for farmers (stable producer prices) is also an importantpolicy option for improving farmers’ access to credit through reduced rationing byformal lenders. Also improvement in farmers’ income levels through off-farm incomegenerating activities would encourage formal lenders to offer the full amount of creditapplied for by the farmers. Thus governmental and NGOs that work to empowerfarmers’ livelihoods should train the farmers in off-farm income generating activities toimprove their income levels and reduce rationing by formal lenders. Furthermore,farmers should be educated to take farming as a business and a saving culture shouldalso be promoted among the farmers so as to reduce the level of rationing by formallenders.
Due to the level of risk rationing among the respondents it would be of interestto examine individual factors and their level of influence on risk rationing as a furtherstudy.
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Corresponding authorDadson Awunyo-Vitor can be contacted at: [email protected]
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