R FO RES © A Publication of the Centre for Research and Development (CERAD), JoST 2019 E E R A T R N C The Federal University of Technology, Akure, (www.jost.futa.edu.ng) E H

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N T D N D E M EVELO P Factors Influencing Default among Participants in Rotating Savings and Credit Associations (ROSCAS) in Division of , Nigeria

Akerele, E.O. and Obasanya, S. Department of Agricultural Economics and Farm Management, College of Agricultural Sciences, Olabisi Onabanjo University, Yewa Campus, Ayetoro, Ogun State, Nigeria *Corresponding Author: Akerele, E.O.; [email protected] ABSTRACT: Against the background of susceptibility of Rotating Savings and Credit Associations (ROSCAs) in the Yewa Division of Ogun State, Nigeria to default which poses high risk to their sustainability, this study assessed the factors influencing default in the association with a view to providing empirical link between participants’ socio-economic characteristics and default in the association for decision-making. Towards achieving this research objective, data were collected from 400 randomly selected residents within the division through a structured questionnaire. Descriptive and inferential statistics including frequency counts, percentages and measures of Central Tendencies, Probit Regression Model, Analysis of Variance (ANOVA) as well as Chi- square model were used in analyzing the study data. Results revealed significant difference (p<0.05) in the average age of defaulters (37.12 years) and non-defaulters (37.91 years). Household sizes of defaulters (4.66) and non-defaulters (4.11) were found to be significantly different (p<0.05) as well as their educational levels, marital statuses and main occupations. The income level of defaulters (N245,527) and non-defaulters (N200,426) was also found to differ significantly (p<0.05). However, no significant difference (p<0.05) was found in the gender, membership of social groups, tribes and religions of defaulting and non-defaulter ROSCA participants. Household size, annual income, what the pot was used for, proportion of working household members to household size had significant association with default in the association. Spending the lump sum received solely on personal consumption rather than business, having to repay too many slots at a time and having too many dependants to cater for at the home front were the major challenges militating against repayment of ROSCA pots in the study area. The study concluded that default in the associations was high and influenced by some of the participants’ socio-economic circumstances. The paper therefore recommended that ROSCAs’ foremen should be wary of respondents taken too many slots per ROSCA round to avoid default tendency.

Keywords: ROSCA, Default, Socio-economics, Determinants, Yewa Division, Ogun State JoST. 2019. 10(1): 94-103. Accepted for Publication, April 30, 2019 INTRODUCTION In spite of the proliferation of formal financial participants, the bidding ROSCAs do this by a bidding institutions in Nigeria in general and in Yewa Division process (Hevener, 2006). The consumer durable of Ogun State in particular, informal savings and ROSCA is a variation of the aforementioned ROSCAs credit schemes are still very prevalent. These informal but rather than involving allocation of funds, the savings and/or credit schemes have different variants participants receive physical goods agreed upon at but they include the collector type of ajo (daily the commencement of a round. contribution or utaki), the group savings and With the random ROSCA, a fixed amount is borrowing schemes proprietary to licensed contributed at a regular time interval and handed over individuals or firms (e.g. LAPO, GAPO, GROOMY, to a participant whose turn is to receive the pot. The Grace and Mercy, and Astra Polaris) and unregistered random assignment can be done at the beginning of revolving group savings and credit schemes that are the ROSCA round or at every ‘cycle period’. If the self-organised by the participants through a foreman random assignment is done at the beginning (i.e. at or foremen (esusu) (Preliminary study, 2017). This cycle period T0), every participant will know his/her study focused on the esusu otherwise referred to as own turn ab initio but if the latter is the case, Rotating Savings and Credit Associations (ROSCAs). participants will only know the person to receive the

\ROSCAs are in various forms. Basically, there are pot at every cycle period (i.e. at cycle period Tn). random, bidding and consumer durable ROSCAs. Most importantly, contributions across cycle periods While the random ROSCAs rotate funds by random are fixed. Unlike the random ROSCA, the bidding assignments of the lump sum (pot) among its ROSCA requires participants to bid for the pot at

Pp 94 Journal of Sustainable Technology, Vol. 10, No. 1 (April 2019), ISSN: 2251-0680 Factors Influencing Default among Participants in ROSCAS in Yewa Division of Ogun State, Nigeria every cycle period by discounting contributions made ii. ascertain the relationship between socio- to the pot by other participants (Biggart, 2001). The economic characteristics of the participants and participant that offers the best discount receives the default in ROSCA. pot per cycle period. In the bidding ROSCA, there is iii. examine the challenges that promote default in an obvious element of borrowers and lenders as well ROSCAs in the study area. as cost of capital. The cost of capital reflects in the discount received (shortfall in the regular amount Hypotheses of the Study payable) by other participants. H01 = There is no significant relationship between The third type of ROSCAs, referred to as the number of slots taken by participants and default in consumer durable ROSCAs, are variants of the ROSCA. aforementioned ROSCA types but involves the H02 = Default incidence is not location (community) distribution of physical goods to participants instead specific. of physical cash. This ROSCA type is common among women who use it to acquire household Justification for the Study utensils like big iron cooking pots, electricity Focusing on factors influencing default in ROSCAs generators, dish washers, freezers among other within the Yewa Division of Ogun State, this study household equipment and other indivisible goods contributes to the empirical record of factors (Kabuya, 2015; Tsai, 2000; Bouman, 1977). influencing default associated with ROSCAs (focusing One important issue among the ROSCAs, on socio-economic variables) in Yewa Division of irrespective of their types or variants, is the Ogun State, in Ogun State and by extension, in Nigeria. possibility of non-repayment after the pot has been More importantly, results of this study can help in collected. The associations generally have no legal effective management of default associated with framework for debt recovery or legal sanctions which ROSCA. By minimizing incidence of default, having make them entirely susceptible to irrecoverable debt. been armed with information on factors (particularly Default has tendency to undermine ROSCA as an socio-economics) that could increase probability of alternative to the formal microcredit/microfinance default in the association, participants in the schemes and deserving of an empirical assessment. association can be assured that they will recoup their An important means of minimizing the occurrence savings at a certain cycle period within a ROSCA of default in ROSCA is to understand the variables ‘round’. The assurance that participants’ that can most likely contribute to default. These contributions will not be lost to default has tendency variables may be socio-economic, institutional or to increase number of willing participants. With more based on group dynamics. Managers of the funds in the pot (by way of increased participation), associations have traditionally relied on their participants can receive large sum that can be invested discretionary judgments in selection of participants relatively more profitably (considering the benefits and how they are dealt with (Bisrat et al., 2012; of economic of scale or size). Increased lump sum Biggart, 2001). The success of the discretionary and zero risk of default can create the pareto approach in the selection of participants and in optimality in the use of funds among all participants. determining the order of pot collection does not depend on any objective metric but on the personal Operational Definition of Terms judgment of the foremen which can be erroneous. The operational definitions of terms used in this study Provision of an empirical link between socio- are provided below to avoid ambiguity. economic and other variables associated with would- Pot: The sum total of the money contributed by all be participants and likelihood of default can help in participants at a contribution cycle to be given to a minimizing default in the association. To this end, participant. this study was conducted. Cycle or Round: A session within a ROSCA lifespan in which every participant is expected to have Objectives of the Study received the pot for that session. The general objective of this study is to assess the Cycle periods: Frequency with which contributions factors influencing default among Rotating Savings have to be made in each cycle. This can be daily, and Credit Associations (ROSCAs) in Yewa Division weekly, biweekly, monthly and half-yearly, every of Ogun State, Nigeria. The specific objectives are five days etc. to: Default: Failure to make contributions as at when i. describe the socio-economic characteristics of due and in full within a ROSCA round. the respondents in the study area

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Number of slots: The multiplicity of contribution Foreman: The individual or few group of individuals made by a participant at every cycle period and which who initiate and/or organise the ROSCAs. also determines the number of times a pot is collected within a round by a participant. EMPIRICAL LITERATURE ON DEFAULT DETERMINANTS Generally, this study reviewed relevant literature on repayment capacity of the farmers, the farmers’ default in informal savings and credit association and household size had negative effect on their capacity microcredit schemes. Technically, when ROSCA to repay the cooperative loan. participants (apart from the last pot receiver) receive Addisu (2006) investigated informal microfinance the pot, the difference between the total regular fixed repayment problem in Addis Ababa, Ethiopia with a savings made and the value of the pot (excluding view to understanding factors contributing to the deductions) represents a loan to be repaid. In many repayment problem. Using a multinomial logit ROSCAs, however, such loan is usually interest- regression model, the author analysed data collected free. The following empirical literature provides through mixed-methods data collection procedures information on factors influencing credit and loan from individuals that operate in the informal sector default. within the study area. The study findings revealed Essien, Ibekwe, Akpan, and Ben-Chendo (2016) that educational level of borrowers positively assessed the factors influencing credit delinquencies predicted repayment performance but, inadequacy among self-help food crop farmers in Akwa-Ibom of loan received and unplanned businesses activities State, Nigeria. The authors used Probit regression negatively influenced the borrowers’ repayment model to analyse the effect of beneficiaries’ socio- performance. economic variables as well as loan characteristics on Oni, Oladele and Oyewole (2005) examined the factors probability of credit delinquency among 94 randomly influencing default in loan repayment among poultry selected farmer-beneficiaries in the study area. The farmers in Ogun State, Nigeria. Logit regression model study results revealed that magnitude of non-farm used to analyse cross sectional data collected from income, credit amount received, interest charged farm 100 randomly selected poultry farmers in the study size, net farm profit and household size are significant area, the study revealed that farmers’ age, education, variables influencing credit delinquencies in the study income as well as flock size significantly predicted area. While the magnitude of credit received and default in loan repayment by the farmers. interest charged positively influenced credit Paxton (1996), examined the determinants of delinquencies, magnitude of non-farm income, repayment in informal group loan scheme in Burkina household size, farm size and net farm profit had Faso. The focus of the study was on group dynamics negative effect on credit delinquencies among the and externalities that could have stabilizing or farmer-beneficiaries. destabilizing effect of group loan repayment. The Magali (2013) examined factors influencing credit study used a mean and covariance structural default risks in savings and credit association in econometric model in analysing data obtained from Tanzania. A set of demographics, institutional factors 140 credit groups in the country. Findings revealed and some selected externalities were assessed. that ‘domino’ and ‘matching’ have significant Through a multivariate regression model analysis of destabilizing effects on repayments in the group. cross-sectional data from borrowers and savings & While domino effect describes a situation whereby a credit institutions, the author found that years of participant default (fail to repay) as a result of default schooling of borrowers and loan size had positive by other participants in the group, the matching effect and significant influence on credit risk default. Age, depicts a situation where credit terms and condition marital status and family size of borrowers, however, are no longer appropriate for group members as the reportedly had no significant influence on credit cycles linger. Eze (1993) assessed the factors default risks. influencing default in informal financial organisations Ojiako and Ogbukwa (2012) assessed the loan in Nsukka Agricultural zone, Nigeria. In a quantitative repayment capacity of small-holder cooperative survey research design, the authors used descriptive farmers in Local Government Area of statistics and multiple regression models were used Ogun State, Nigeria. The authors used correlation to analyse data collected from 72 beneficiaries and 10 and regression model in analyzing data obtained from officials of informer lenders in the study area. The 110 farmers in the study area. They reported average findings revealed that poor loan management, small repayment rate of 49% and found that while size of size of loan and short loan duration positively loan and farm size had positive effect on loan influenced default in the organisations.

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Summarily, it is evident from the foregoing studies (or farm) of the borrowers. The loan characteristics conducted within and outside Nigeria that socio- that influenced loan repayment or default include: economics of borrowers, loan characteristics, duration of installment repayments and adequacy of institutional and macroeconomic variables influenced the loan for intended purpose. The institutional repayment performance of loan or default. factors that influenced repayment or default of loan Specifically, the socio-economic characteristics of include: loan management prowess and group size. borrowers that reportedly influence default or Exigencies that affected loan repayment include repayment of loan in the foregoing studies include: ‘domino’ and ‘matching’ effects. age, income, education, family size and size of firm

RESEARCH METHODOLOGY Study Area associations are widespread in the study area, it was The study area is Yewa Division of Ogun State, assumed that the more the population of the Local Nigeria. The division has a population figure of Government Areas, the more the likelihood of finding 1,112,761 (National Population Commission: NPC, more participants. To determine the appropriate 2006). The division has five (5) Local Government sample size, the Guilford and Frucher (1973) Slovin’s Areas (LGAs) which consists of Ado Odo-Ota, formula was used. The formula is given as: n=N/ Ipokia, , Yewa North and Imeko/Afon (1+Ne2). Local Government Areas (Ambali, 2012). Where: n =Sample size, N = Sample population = Data used for this study include: qualitative and 1,112,761, e =error term = 0.05 (i.e. 95% confidence quantitative data from both primary and secondary level). With population of 1,112,761, the ‘n’ was sources. The first data set was quantitative obtained estimated at 400 respondents. Therefore, a total of from respondents through structured questionnaire 400 respondents (participants and non-participants) within the division while the second data set was were sampled proportional to the population of each qualitative data collected through semi-structured of the Local Government Areas as shown in Table 1. interview with ROSCA foremen in the study area. For the qualitative data, 5 ROSCAs were sampled in The aforementioned data types were primary data. each of the Local Government Areas. From each of The secondary data were obtained from scholarly the ROSCAs, one foreman was interviewed. In the journal articles and peer-reviewed publications, study area, a total of 25 foremen of ROSCA were theses, official websites, textbooks and other quality interviewed. The qualitative data supported the internet sources. A proportional sampling technique quantitative data obtained from the questionnaire. was used in selecting the sample for the study. This Both descriptive and inferential analytical techniques was because the Rotating Savings and Credit were used in analysing the study data in line with the Associations have widespread practice in the Yewa study objectives. For the quantitative data, the Division (Preliminary survey, 2017). Since the analysis was done using the Statistical Package for

Figure 1: Map of Ogun State Source: Adapted from Ogunstate.gov.ng (2017).

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Table 1: Sampling Frame Local Government Areas Population Number of respondents (2006 census) (Pi) (Pi/TP x 400) Pi = P1, P2, …, P5.

Yewa North 183,844 (P1) 66 Yewa South 168,336 (P2) 60 Imeko-Afon 82,952(P3) 30 Ipokia 150,387 (P4) 54 Ado-Odo/Otta 527,242 (P5) 190 Total 1,112,761 (TP) 400 Source: (NPC, 2006)

Social Sciences (SPSS 15) while the qualitative data V4 = Occupation (Dummy: Farming = 1, were analysed using the Nvivo11 developed by the 0 = otherwise)

QSR international, United States in 2015. How each V5 = Sex (Male = 1, Female = 0) objective was analysed is presented below. V6 = Household size (number)

V7 = Membership of another social group Socio-economic Characteristics of the (member = 1, Non-member = 0)

Respondents V8 = Annual income (Naira)

Frequency distribution tables and percentages were V9 = Tribe (Yoruba = 1, otherwise= 0) used in describing the respondents’ socio-economic V10 = Religion (Dummy: Islam = 1, characteristics. 0 = otherwise)

V11 = Proportion of working member of Determinants of Default in ROSCA households to household size

Binomial probit regression model was used in V12 = Number of slots taken (number) analyzing the determinants of default in ROSCAs. V13 = What the pot collected was used for 퐷 = V푖′훽 + ε …………………….... (1) (Consumed = 1, 0 = invested) Where Εi= Error term, which is assumed to be D = Default (Defaulted = 1, 0 = no default) distributed as standard normal and has a variance of 1. Vi = V1, V2, V3,…, V13 훽 = Coefficient Factors Militating against Successful Operation V = Age of respondents (years) 1 of the Associations V = Level of education of respondents (years) 2 This objective was achieved using descriptive V = Marital status of respondents (married =1, 3 statistics such as bar charts, frequency counts and otherwise = 0) percentages. RESULTS AND DISCUSSION Socio-economic Characteristics Unlike the differentials in formal education level of The results obtained on the socio-economic participating and non-participating respondents, the characteristics of the respondents are presented in majority of both defaulters (52.0%) and non- Table 2a and Table 2b. The average age of participants defaulters (55.8%) shared similar education level that defaulted was 37.12 years while those that did (secondary). Irrespective of the respondents’ ROSCA not default had an average age of 37.91 years. The participation status, a typical respondent had about sex distribution statistics for defaulters (67.0%) and two members of his/her household working. The same non-defaulters (62.5%) showed significant difference applies to defaulters and non-defaulters. In terms of (p<0.05). The married were in the majority within main occupation, substantial percentage of defaulters the group of defaulters (78.0%) and non-defaulters (44.1%) and non-defaulters (52.9%) engaged in (83.7%). Irrespective of default status, participants trading. The defaulters and non-defaulters, had an average of four individuals per household. respectively, had an average working experience of While the majority (67.3%) participating 9.17 and 12.5 years respectively. The average annual respondents possessed tertiary level formal income of defaulters and non-defaulters were education, their non-participating counterparts N245,527 and N324,299 respectively. (53.2%) possessed secondary school education.

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Socio-Economic Determinants of Default and estimated at 64.36 implying that the model is a

Binary Probit regression model was used to analyse good fit. Age (V6), Household size (V6), and the effect of selected socio-economic characteristics proportion of working members of households to of the participants on probability of defaulting in household size (V11) positively influenced ROSCA. The results are presented in Table 3. The propensity to default in ROSCA while annual income log likelihood function was estimated -215.84 while (V8) and what the pot was used for (V13) had inverse the chi-square statistics was significant at 1% level relationship with tendency to default in ROSCA. Table 2a: Distribution of Respondents by Socio-Economic Characteristics Defaulters Non-defaulters Pooled Data Variables Value F % F % F % <25 25 11.0 7 6.7 32 9.7 25-34 78 34.4 23 22.1 101 30.5 35-44 55 24.2 54 51.9 109 32.9 45-54 62 27.3 17 16.4 79 23.9 Age >55 7 3.1 3 2.9 10 3.0 Total 227 100 104 100 331 100 2 Defaulters: Mean = 37.12, SD = 10.52, Non-Defaulters: Mean = 37.91, SD = 8.31; χ cal = 25.07, 2 df = 4, and χ tab = 9.49, Comment: Significant Male 75 33.0 39 37.5 114 34.4 Female 152 67.0 65 62.5 217 65.6 Sex Total 227 100 104 100 331 100 2 2 χ cal = 0.628; df = 1; and χ tab = 3.84; Comment: Not Significant Single 48 21.1 2 1.9 50 15.1 Married 177 78.0 87 83.7 264 79.8 Divorced 2 0.9 1 1.0 3 0.9 Marital Status Separated - 14 13.4 14 4.2 Total 227 100 104 100 331 100 2 2 χ cal = 48.29; df = 3; and χ tab = 7.81; Comment: Significant 1-3 69 30.4 40 38.5 109 32.9 4-6 123 54.2 64 61.5 187 56.5 7-9 32 14.1 - - 32 9.7 Household size >10 3 1.3 - - 3 0.9 Total 227 100 104 100 331 100 2 Defaulters: Mean = 4.66, SD = 2.32, Non-defaulters: Mean = 4.11, SD = 2.14;χ cal = 18.12; 2 df = 3; and χ tab = 7.81; Comment: Significant None 28 12.3 5 4.8 33 9.9 Primary 10 4.4 16 15.4 26 7.9 Secondary 118 52.0 58 55.8 176 53.2 Education Tertiary 71 31.3 25 24.0 96 29.0 Total 227 100 104 100 331 100 2 2 χ cal = 16.48; df = 3; and χ tab = 7.81; Comment: Significant 1 member 41 18.0 17 16.3 58 17.5 2 members 182 80.2 85 81.7 267 80.7 Number of 3-4 members 4 1.8 2 2.0 6 1.8 working household >5 members ------member Total 227 100 104 100 331 100 2 Defaulters: Mean = 1.90, SD = 0.43, Non-defaulters: Mean = 1.75, SD = 0.88, χ cal = 11.87; 2 df = 3; and χ tab = 7.81; Comment: Significant Source: Field Survey, 2018

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Table 2b: Distribution of the Respondents by Membership of Social Group Defaulters Non-defaulters Pooled Data Variables Value F % F % F % Trading 100 44.1 55 52.9 155 46.8 Artisanship 54 23.8 32 30.7 86 26.0 Farming 38 16.7 16 15.4 54 16.3 Civil service 26 11.5 1 1.0 27 8.2 Main Occupation Food vending ------Unemployed 9 3.9 - - 9 2.7 Total 227 100 104 100 331 100 2 2 χ cal = 16.35; df = 4; and χ tab = 7.81; Comment: Significant. None 10 4.4 1 1.0 11 3.3 <5 52 22.9 13 12.5 65 19.6 5-9 101 44.5 13 12.5 114 34.4 10-14 13 5.7 39 37.5 52 15.7 Experience in Main occupation (years) 15-19 12 5.3 26 25.0 38 11.5 >20 39 17.2 12 11.5 51 15.5 Total 227 100 104 100 331 100 2 Defaulters: Mean = 9.17, SD = 7.72, Non-defaulters: Mean = 12.5, SD = 5.9; χ cal = 99.127; 2 df = 5; and χ tab = 11.07; Comment: Significant None 10 4.4 1 1.0 11 3.3 <100,000 29 12.8 2 1.9 31 9.4 100,000-199,999 69 30.4 20 19.2 89 26.9 200,000-299,999 82 36.1 38 36.5 120 36.2 Annual income 300,000-399,999 22 9.7 28 26.9 50 15.1 >400,000 15 6.6 15 14.5 30 9.1 Total 227 100 104 100 331 100 Defaulters: Mean = 245,527, SD = 253,489, Non-defaulters: Mean = 324,299, SD = 200,426; 2 2 χ cal = 33.65; df = 5; and χ tab = 11.07; Comment: Significant Member 127 55.9 65 62.5 192 58.0 Membership of Social Non-member 100 44.1 39 37.5 139 42.0 group 2 2 χ cal = 1.257; df = 1; and χ tab = 3.84; Comment: Not Significant Yoruba 188 82.8 87 83.7 275 83.1 Igbo 27 11.9 6 5.8 33 10.0 Hausa 5 2.2 4 3.8 9 2.7 Tribe Other tribe(s) 7 3.1 7 6.7 14 4.2 Total 227 100 104 100 331 100 2 2 χ cal = 5.641; df = 3; and χ tab = 7.81;Comment: Not Significant Christianity 88 38.8 71 68.3 159 48.0 Religion Islam 132 58.1 23 22.1 155 46.8 Traditional 7 3.1 10 9.6 17 5.2 Total 227 100 104 100 331 100 Source: Field Survey, 2018

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Analysis of the marginal effect of the significant that could lead to default in ROSCA and the results variables revealed that a unit increase in age, presented in Table 4. It is evident in the Table 4 that household size and proportion of working members the top three challenges militating against successful to total household size (say by 1%) will increase the operation of the association was ‘spending the lump probability of defaulting in ROSCA by about 1.58% sum received solely on personal consumption rather and 7.34% respectively whereas a unit increase in than business (61.3%), having to repay too many annual income and use of the pot for investment will slots at a time (65.0%) and participation in too many decrease the probability of defaulting in ROSCA by ROSCAs at a time (54.1%). Other prominent 0.01%, 0.19% and 13.75% respectively. The challenges that might lead to default include too many implication of these findings is that participants with dependants and expenses at the home fronts (54.1%), higher annual income will be less likely to default favoritism by foreman in determining the order of than those with lower annual income. However, a pot collection (42.3%) and business failure participant with large household size would more (50.2%).The implication of these findings is that what likely default in ROSCA than those with lower the pots collected was invested on, the number of household size. An old participant might not have slots taken in a ROSCA and participation in too many required agility to generate more income to repay the ROSCAs at a time predispose participants to pot, hence, they were more prone to default than the ROSCA default in the study area. younger participants. When pot collected is used for investment, probability of default was found to be Test of Hypotheses lower than when it was consumed. Against a priori H01 = There is no significant relationship expectation, the participants with higher proportion between number of slots taken by participants of household members working were more likely to and default in repaying the pot. default than those with lower proportion of Chi-square test shows that the chi-square value household members working. calculated is lower than the one tabulated. This implies significant relationship and, on this basis, the null Challenges that Promote Default in ROSCAs hypothesis which states that there was not significant Understanding the challenges that promote default relationship between number of slots taken by an in ROSCAs can help in improving the sustainability individual participant and default in ROSCA is of the association among the people. This is the basis rejected. for analyzing the challenges facing the respondents Table 3: Determinants of Default in ROSCA Variables Coefficient Standard t-value Marginal error effect Constant 0.4238 0.2625 1.615 - Age (V1) 0.3117*** 0.0532 5.859 0.0001 Education (V2) 0.0094 0.0062 1.512 0.1037 Marital status (V3) -0.0039 0.0954 -0.041 0.9213 Occupation (V4) 0.0698 0.0653 1.069 0.2926 Sex (V5) 0.0824 0.0540 1.524 0.2037 Household size (V6) 0.0533** 0.0226 2.352 0.0158 Membership of another social group (V7) 0.0003 0.0004 1.232 0.6702 Annual income (V8) -0.3261E-06*** 0.1123E-06 -2.904 0.0019 Tribe (V9) -0.0869 0.0733 -1.186 0.1921 Religion (V10) -0.0037 0.0028 -1.305 0.2452 Proportion of working member of households to 0.3484* 0.2150 1.620 0.0734 household size (V11) Number of slots taken(V12) -0.0192 0.0306 -0.627 0.4029 What the pot collected was used for (V13) -0.0953* 0.0532 -1.793 0.1375 Log likelihood function -215.64 Chi-square 64.36 Degree of freedom 13 Source: Field Survey, 2018, * Significant at 10%, ***, Significant at 1% level

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H03 = Default incidence is not location no significant relationship between location and (community) specific default in ROSCA given that the F-value statistics To examine whether default incidence is location- (0.042) is not significant at 5% level. On this basis, dependant, the ANOVA statistical model was carried the null hypothesis was not rejected, that means out and the results presented in Table 6. There was default is not location dependent. Table 4: Distribution of respondents by challenges that promotes default in ROSCA Challenges militating against Serious Not Serious Not a challenge Rank Freq. % Freq. % Freq. % Spending on personal consumption rather than 203 61.3 103 31.1 25 7.6 1st business The size of the amount received is too small 88 26.6 38 11.5 192 58.0 15th I didn't get the funds when needed 88 26.6 103 31.1 140 42.3 13th I participated in too many ROSCAs 193 58.3 37 11.2 101 30.5 5th I had a business failure and unable to pay 166 50.2 78 23.6 75 22.7 6th Too many dependents and expenses at the home front 179 54.1 76 23.0 76 23.0 3rd The contribution amount is too much for me 127 38.4 51 15.4 128 38.7 12th There is no penalty for non-payment 115 34.7 126 38.1 90 27.2 9th Familiarity with the foreman 89 26.9 125 37.8 117 35.3 11th Poor manner of approach or demand for contribution 113 34.1 64 19.3 154 46.5 13th by the foreman Favoritism by foreman in determining order of pot 140 42.3 127 38.4 64 19.3 4th collection If I don't pay, nobody can force me to pay 86 26.0 142 42.9 90 27.2 10th I do not provide collateral, so I don't see the urgency to 128 38.7 113 34.1 90 27.2 7th repay the pot as at when due I do not sign any agreement or court paper, so I am not 164 49.5 51 15.4 116 35.0 8th under compulsion to repay as at when due I am repaying too many slots 215 65.0 52 15.7 64 19.3 2nd Source: Field Survey, 2018 Table 5: Chi-square analysis of relationship number of slots taken and default in ROSCA 2 2 Variables df χ cal χ tab Significance Decision (0.05) (0.05) (P < 0.05) Number of slots vs default 4 3.639 9.49 S Fail to reject H0 Source: Field Survey, 2018 2 2 2 Decision Rule: reject H0 if χ cal >χ tab, otherwise: fail to reject H0, χ cal = Chi- square value calculate or 2 computed, χ tab = Chi- square value tabulated, S = Significant, NS = Not significant, H0= as previously defined. Table 6: ANOVA test of relationship between location of participants and default in ROSCA Sum of Degree of Mean F- Significance Decision Squares freedom Square value level Between Groups 0.037 4 0.009 0.042 0.997 null Within Groups 71.287 326 0.219 hypothesis Total 71.323 330 was not rejected

CONCLUSION AND RECOMMENDATIONS Conclusion working household members to household size and Based on the study findings, it is concluded that age are associated with default among ROSCAs’ default level among participants was high. The participants in the study area. In terms of challenges foremen employed a similitude of insurance strategies that could promote default among the participants in mitigating the effect of the default. Household spending the lump sum received solely on personal size, income, what pot is used for, proportion of consumption rather than business, having to repay

Journal of Sustainable Technology, Vol. 10, No. 1 (April 2019), ISSN: 2251-0680 Pp 102 Factors Influencing Default among Participants in ROSCAS in Yewa Division of Ogun State, Nigeria too many slots at a time and having too many be careful in enlisting those found to be dependents to cater for at the home front were the participating in many ROSCAs at a time. major challenges. 3. The ROSCA’s foremen should consider other variables such as income (daily, weekly, Recommendations monthly or annual) before enlisting Arising from the findings of the study,, it is participants with large household size to avoid recommended that: being prone to default in the association. 1. ROSCAs’ foremen should be wary of 4. Foremen in ROSCA where default is high respondents taken too many slots per should consider a semblance of insurance or ROSCA round to avoid default tendency. sustainable strategy to minimize the effect of 2. ROSCAs’ foremen should enquire about the the default on the smooth operation of the number of ROSCAs a would-be participant ROSCAs. engaged in before enlistment as member and REFERENCES Addisu, M. (2006). Micro-finance repayment Hevener, C. C. (2006). Alternative Financial problems in the informal sector in Addis Ababa, Vehicles: Rotating Savings and Credit 1(2), 29–50. Associations (ROSCAs) (Discussion Paper) (pp. Ambali O.I. (2012). Comparative analysis of 1–31). Philadelphia, United States: Federal production efficiency of beneficiary and non – Reserve Bank. beneficiary cassava farmers of Bank of Kabuya, F. I. (2015). The Rotating Savings and Credit Agriculture loan scheme in Ogun State. Nigeria. Associations (ROSCAs): Unregistered sources An Unpublished M.Sc. Thesis Federal of credit in local communities. IOSR Journal of University of Agriculture, , Ogun Humanities and Social Science, 20(8), 95–98. State, Nigeria. pp. 32 – 68 Magali, J. J. (2013). Factors affecting credit default Biggart, N. W. (2001). Banking on Each Other: The risks for rural Savings and Credits Cooperative Situational Logic of Rotating Savings and Credit Societies (SACCOS) in Tanzania. European Associations. Advances in Qualitative Journal of Business and Management, 5(32), 60– Organization Research, 3, 129–153. 73. Bisrat, A., Kostas, K. and Feng, L. (2012). Are NPC (2006). Population Distribution by Age & Sex there financial benefits to join RoSCAs? (Vol. IV). Nigeria. Retrieved from http:// Empirical evidence fromequb in Ethiopia. www.ibenaija.org/uploads/1/0/1/2/10128027/ Procedia Economics and Finance, 1, 229–238. priority_table_vol_4.pdf https://doi.org/10.1016/S2212-5671(12)00027- Ogunstate.gov.ng (2017). Fact File [online], 5 Assessed on 4th December, 2017, Retrieved Bouman, F. J. (1977). Indigenous savings and credit from http://ogunstate.gov.ng/ogun-state societies in the Third World. In Savings and Ojiako, I. A., and Ogbukwa, B. C. (2012). Economic development (pp. 181–219). San Diego, analysis of loan repayment capacity of small- California. Retrieved from http://www.jstor.org/ holder cooperative farmers in Yewa North Local stable/25829637 Government Area of Ogun State, Nigeria. African Essien, U. A., Ibekwe, U. C., Akpan, S. B., and Journal of Agricultural Research, 7(13), 2051– Ben-Chendo, N. G. (2016). Determinants of 2062. https://doi.org/10.5897/AJAR11.1302 informal credit delinquency among food crop Oni, O. A., Oladele, O. I., and Oyewole, I. K. farmers in rural Niger Delta of Nigeria. Review (2005). Analysis of factors influencing loan of Agricultural and Applied Economics, 19(01), default among poultry farmers in Ogun State 50–55. https://doi.org/10.15414/raae.2016. Nigeria. Journal of Central European 19.01.50-55 Agriculture, 6(4), 619–624. Eze, B. S. (1993). Factors affecting default rate in Paxton, J. A. (1996). Determinants of successful the informal financial institutions in Nsukka group loan repayment: An application to Burkina agricultural zone of Enugu State (M.Sc.). Faso (PhD). The Ohio State University, United University of Nigeria, Nsuka, States. Guildford, J. P. and Fruchter, B. (1973). Tsai, K. S. (2000). Banquet banking: gender and Fundamental Statistics in Psychology and rotating savings and credit associations in South Education, 5th Edition, McGraw Hill, New China. The China Quarterly, 161, 142–170. York.

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