Microsoft Word - 5290-19171-1-SM-writer2-new.doc Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 198 Variables for Modeling SACCOS in Tanzania Joseph John Magali Accounting School, Dongbei University of Finance and Economics P.O Box 116025, Hei Shi Jiao, Dalian, P.R China E-mail: josephmagali@yahoo.com Received: March 15, 2014 Accepted: April 8, 2014 Published: June 1, 2014 doi:10.5296/ajfa.v6i1.5290 URL: http://dx.doi.org/10.5296/ajfa.v6i1.5290 Abstract In order to publish a quality article in a journal, an author must understand how to apply the econometric skills to construct models which explain the relationship between the independent and dependent variables. This study used the literature review to examine the variables used to establish relationship between the independent and dependent variables in Savings and Credits Cooperative Societies (SACCOS) in Tanzania. The findings reveal that currently few studies have been conducted to model SACCOS in Tanzania and scholars mostly applied the regression models. Moreover, the findings show that most scholars have empirically studied the outreach and sustainability of SACCOS in Tanzania compared to other topics. The studies also show that most of issues have not been addressed in the research. This study recommends that financial management researchers should learn econometrics so that they might gain skills which might help them to model SACCOS and other MFIs. Moreover, topics which were not addressed in research should be given priority by the researchers in Tanzania and in other countries. Keywords: Variables, modeling, SACCOS, Tanzania Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 199 1. Introduction 1.1 Background of the Study Tanzania is one of the least developing countries located in Eastern part of Africa with a population of 44 million. Liberalization of economy in Tanzania took place in 1980s where the socialism policies dominated the economy since independence in 1961. Majority of Tanzanians (more than 80%) engage in peasant agricultural production and dwell in rural areas (NBS 2013). Savings and Credits Cooperatives Societies (SACCOS) have been established since 1980s after liberalization of the financial services (Maghimbi 2010). Wangwe (2004) asserted SACCOS have helped to provide the financial services to many clients who the formal financial institutions such as banks did not serve them. Hence SACCOS have solved the problem of capital inadequacy for rural people in Tanzania. Qin and Ndiege (2013) and Bwana and Mwakujonga (2013) revealed that SACCOS significantly contribute to GDP of Tanzania. The government of Tanzania promotes the establishment of SACCOS even to very remote areas because of their importance contribution to the country economy (Wangwe, 2004). Therefore the SACCOS increase rapidly each year because of government sensitization. The total SACCOS in Tanzania were 5,559 in March 2013 whereas the number of members, amount of shares, savings and deposits were 1,153,248 and 463.5 billion TZS respectively (MOFT 2013). The government establishes various policies and regulations in order to supervise the SACCOS and cooperatives in Tanzania. Based on the history, the literatures indicate that improper management, frauds, inadequate capital, business misconduct and Non Performing Loans (NPL) are the problems facing cooperatives and SACCOS in Tanzania (TFC 2006; Bibby 2006; Maghimbi 2010; Magali 2014). Various scholars have designed models which accommodate different variables to investigate various issues in MFIs. The most famous models in MFIs measure the performance, credits risk, efficiency, impacts, outreach and sustainability. Scholars usually use the variables of returns of assets or equity (ROA, ROE) or profitability to measure performance while they use NPL to measure the credits risk management. Moreover, they use inputs and outputs to measure the efficiency whereas they use the livelihood variables to assess the efficiency and impacts of MFIs. Furthermore, authors use the average loan size, number of clients or borrowers, number of female clients to measure the outreach but they use ROA, Financial Self Sustainability (FSS) or Operational Self Sustainability (OSS) to measure financial sustainability of MFIs. Often regression models are used to measure the relationship between independent and dependent variables for MFIs when measuring performance, credits risk, efficiency, impacts, outreach and sustainability. However, in measuring efficiency data envelopment analysis (DEA) model is mostly used. Moreover, scholars use the logistic regression for the models which have dichotomous outcomes. Some models measure the social performance of the MFIs. Zeller et al (2003) asserted that outreach to the poor and excluded, adaptation of the services and products to the target clients, improving social and political capital of clients and communities and social responsibility are the four major dimensions of the social performance model of MFIs. Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 200 In MFIs modeling, one model can be applied to examine the relationship between the MFIs variables in various circumstances. What is important is the possession of knowledge by the applicant of the model. Most scholars apply rules based scoring, statistical method and neural network models for modeling of credits risk in MFIs (Sur 2008). Moreover, most scholars use Value at Risk (VaR) to measure the market risks of stocks and other assets (Alam and Masukujjaman 2011). Salari et al (2012) recommended VaR for measuring loans portfolio. Rouk (2008) argued that credit grades model (CGM) calculate the credits risk and indicates that the loans need to be impaired. Logistic regression model was used by Vasanthi and Raja (2006) in measuring the credits risks for state housing authorities in Australia. However, Haque et al (2011) applied the logistic regression model to assess the impacts of a MFI to the beneficiaries. Moreover, Doreitnera and Pribernya (2011) designed a model to measure the probability of default in MFIs by taking into consideration variables from the stock markets. Likewise, some scholars use multivariate discriminant analysis (MDA) to model the credits risk of default in banks (Castagnolo and Ferro 2014; Raj and Sindhu 2013; Chijoriga 2011). Moreover, some authors use the Bayesian models to predict the credits probability of default in banks and MFIs (Mileris 2010; Figini and Giudici 2013). The list is long, however the listed examples show that scholars explain the relationship between the MFIs variables by designing the new or apply the existing models. 1.2 Problem Statement and Justification The literatures show that most scholars modeled the credits risk in banks (Rouk 2008; Abdou and Pointon 2011; Lefcaditis et al 2011; Kruppa et al 2013). However, few scholars modeled the credits risk in MFIs (Doreitnera and Pribernya 2011; Kinda and Achonu 2012; Elías et al 2012). Likewise, various empirical studies have been conducted to assess the banks and MFIs performance, efficiency, outreach and sustainability. The literatures also show that SACCOS in Tanzania are important engine for the economic growth and development. Moreover, the literatures show that only few empirical studies concerned SACCOS are done in Tanzania. Likewise, most university academic staff specialized in finance or business management in Tanzania don’t publish scientific papers in journals because they lack skills in SACCOS or MFIs modeling. Lack of skills in modeling the MFIs and SACCOS variables, made the university academic staff to stay in their academic grades for long time (some for more than ten years). Furthermore, many students who are doing their researches in finance or financial management especially at undergraduate and master levels in Tanzania fail to structure their studies empirically because they don’t have knowledge and skills on how to model the MFIs or SACCOS’ variables. Thus they only design their studies qualitatively or descriptively. Therefore, this paper describes how to use the SACCOS variables to construct models. The paper might be very useful to students and university academic staff who want to model SACCOS and other MFIs. I believe that this paper will encourage many students and academic staff to conduct the empirical quantative studies by modeling the MFIs variables which is a key step toward articles’ publication. 2. Literature review 2.1 Descriptive and Qualitative Studies Done in SACCOS outside Tanzania Mostly scholars analysed the problems in SACCOS by using descriptive and qualitative Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 201 analysis. The following are some of studies which applied the descriptive and qualitative analysis. Waweru (2011) investigated the cash balance management approaches for Kenya SACCOS at Nakuru region. Fiorillo (2006) assessed the effects of wholesale lending to SACCOs in Uganda. Sebhatu (2011) studied the outreach and sustainability of SACCOS in Ethiopia. Sharma et al (2005) assessed the impact assessment of SACCOS in Nepal’s Hill district by using PRA and descriptive analysis. Matumo et al (2013) examined the impact of front office SACCOS’ activity on SACCOS’ performance in Kenya. Odera (2012) assessed the corporate governance problems SACCOS in Kenya. Friends Consult Ltd (n.d) investigated the delinquency management for the Umurenge SACCOS in Rwanda. Gingrich (n.d) examined a sustainable means for microfinance delivery for community based savings and credit cooperatives in Nepal while Hall (2005) studied the strengths and weaknesses of Koperasi Citra Lestari and Koperasi Mawar Putih savings and credit cooperatives microfinance institutions at East Java Indonesia. Chahayo et al (2013) analyzed the reasons for financial mismatch in SACCOS in Kenya. Mwangi, et al (2013) analysed the role of SACCOS in growth of youth entrepreneurship in Kenya. Mwangi and Wanjau (2013) examined the role of SACCO in growth of youth entrepreneurship in Kenya. Makori et al (2013) studied the challenges facing deposit-taking SACCOS’ regulatory compliance at Gusii region in Kenya and Ondieki et al (n.d) assessed the effect of external financing on financial performance of SACCOS at Kisii central district, Kenya. The literatures show that Kenyan scholars lead to study about SACCOS’ problems. 2.2 Quantative Studies Done Outside Tanzania Scholars use various models to study issues in SACCOS. Awotide et al (n.d) used the queue model to analyse the effectiveness of cooperative societies’ in delivering credits for agricultural enterprises in Southwest Nigeria. The three variables were modeled to measure the effectiveness of credits delivery as follows: Time rivalNumberofar eArrivalrat = Time rvedNumberofse eServicerat = eServicerat eArrivalrat ensityTraffic =int They asserted that arrival rate depicts the number of loan request per month, the service rate represents the number of application accepted while traffic intensity measures the efficiency in queue management, which is achieved when arrival rate =service rate. Lagat et al (2013) studied the effects of credits risk management practices on lending portfolio among SACCOS in Kenya by using a multiple regression model. The portfolio performance (PP) was used as a dependent variable and the independent variables were: Risk Identification (RI), Risk Assessment (RAA), Risk Monitoring (RM) and Credit Risk Analysis (CRA). The model was specified as follows: Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 202 iiiiii ECRARMRAARIURMPP ++++++= 543210 ββββββ Where 0β =intercept and iE is the error term. Moreover, Onsase et al (n.d) studied the effects of performance management practices on provision of financial services for SACCOS in Kenya by using the Likert scale and the univariate regression model. The regression model was written as: Y=a+bx Where; Y is the dependent variable, that is performance of financial services (PFS) and X, is the independent variable that is performance management practices (PMP). Olando et al (2013) examined the contribution of SACCOS’ financial stewardship to the growth of SACCOS’ wealth (GSW) in Kenya by using a multiple linear regression analysis. The financial stewardship was measured by loan evaluation (LE), loan disbursement (LD), loan recovery (LR), loan protection (LP), default risk (DS), staff competence (SA) and innovativeness (TEC). The model was constructed as: εββββββββ ++++++++= TECSADSLPLRLDLEGSW 76543210 Where: =0β constant term and =− 71 ββ coefficient of the independent variables and =ε error term. Onchangwa and Memba (2012) investigated whether investment in SACCOS have effect on members’ investment culture in Kenya. The univariate regression model expressed the relationship between savings and investment as follows: Y= a + bX1 Where: Y= investment, X1= savings in SACCOS, a = the intercept and b =the beta coefficient. Auka and Mwangi (2013) examined the factors influencing SACCOS’ members to seek services from other financial service providers in Kenya. The relationship between independent variables and dependent variable was established by the regression model as follows: ePEPSSPMDPTTPFI +++++++= 6543210 βββββββ Where: FI = Financial service provider, TP = type of financial product, PT = processing time of loan applications, SP =staff performance, PS = promotion strategies, PE = physical evidence, =0β Intercept, β1- β6=estimated coefficients of independent and e = Error term. Mosongo et al (2013) investigated the relationship between the financial innovation and financial performance for SACCOS at Nairobi County in Kenya by using the descriptive and multivariate regression analysis. The dependent variable (Y) was the financial performance and the independent variables were institutional (X1), process (X2) and product innovations (X3). The multivariate regression model was specified as follows: μββββ ++++= 3322110 XXXY Mpiira et al (2013) studied the factors influencing households’ participation in SACCOS programmes in Uganda using the ordered probit regression model which was presented as follows: Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 203 uDISTRICTURBANDISTSTABLEMARITALDDEP GENDEREDUCDINFOAGEAGEYkS ii i P n ++++++ +++++++==   == = 1211 3 1 1098 5 1 76 3 1 543210 2^)Pr( βββββ ββββββββ Where pr is the probability that the household will choose to participate in the programme and Sn =k represent the household status whether is a non-member or a member who save or don’t save. the independent variables of the model includes: PY which determines the presence of permanent income, AGE of the household head, age of spouse ( 2^AGE ), dummy of information about the famous banks used for depositing the children school fees (DINFO), the education level of the household (EDUC), GENDER of respondent, dummy variable of children going school whether in nursery, primary, secondary or university (DDEP), MARITAL status, dummy of STABLE income, distance from home to the SACCOS office (DIST), dummy of household location whether in rural or URBAN and DISTRICT of residence whether is a base district or otherwise. Ahimbisibwe (2007) studied the effects of SACCOS on members’ saving culture in Ntungamo district in Uganda. The author applied the multivariate linear regression model where saving culture was the dependent variable. The model was expressed as follows: tt TERTSECPRIMGOSCHNOCHILDINCOMEY εβββββββ +++++++= 6443210 Where: INCOME = A dummy for income, NOCHILD = A dummy for number of children in a family, GOSCH = A dummy for number of school going children, PRIM = A dummy for primary level of education, SEC = A dummy for secondary level of education, TERT = A dummy for tertiary level of education, Yt = Saving culture, β0 =intercept, β1 -β6 = estimated coefficients of independent variables and =tε error term. Similarly, Mohammed (2011) applied the regression model to examine how financing strategies and financial sustainability strategies which influenced the sustainability and outreach of SACCOS in Uganda. The independent variable was the financing strategies of SACCOS which was measured by debt, equity and individual savings while the independent variables were operational self sustainability, financial self sustainability, active borrowers and average loan size. The study used the first and the second two variables to measure the financial sustainability and outreach of the SACCOS respectively. Tesfamariam et al (2013) applied the data envelopment analysis to examine the relative efficiency of rural SACCOS in Ethiopia where they specified total expenses and savings and loans and total income as inputs and outputs respectively. Moreover, Dong and Featherstone (2004) studied the technical and scale efficiencies of Chinese rural credit cooperatives by using a Bootstrapping approach in DEA and treated deposits as inputs and loans as outputs. Similarly, Kipesha (2012) assessed the efficiency of microfinance institutions in East Africa by using DEA. Kipesha asserted that a simple DEA model to measure the technical efficiency using input oriented approach for MFI (including cooperative MFIs such as SACCOS) can be Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 204 constructed by assuming that there is K decision making units (DMUs or MFIs) which utilize N inputs to produce M outputs. Kipesha (2012) argued that if inputs are denoted by xjk with j=1…n and outputs by yik with i=1…m for each DMU, the technical efficiency for DMU under inputs specification can be expressed as: Kr vjxjk uiyis MinTE n j m i ....1 1 1 =−−−−−−=   = =  = ≥+−= m i wyiFuiyrSubjecto 1 0  = ≥− n j ujxkXjr 1 0 ui and vj 0≥ Where ui and vj are the weights of output and input correspondingly (Kipesha, 2012). 2.3 Empirical Descriptive Studies Done in SACCOS from Tanzania Some studies in Tanzania explain issues in SACCOS by using descriptive and qualitative analysis. Piprek (2007) examined the links between SACCOS and CRDB bank in expanding the access of financial service in rural areas. Magali and Qiong (2014) compared the commercial banks and rural SACCOS’ credits risk management. Magali (2014) investigated the influence of leadership, corporate governance and regulations on credits risk management on rural SACCOS. Kyessi and Furaha (2010) examined the access of housing loans by the urban poor clients from WAT-SACCOS in Dar es Salaam by using descriptive and pictorial presentation. Karumuna and Akyoo (2012) studied the challenges faced the Kibaigwa financial services and credit cooperative society (KIFISACCOS) in Kongwa district by using descriptive and SWOT analysis while Bwana and Mwakujonga (2013) studied the issues related with SACCOS’ development in Kenya and Tanzania by using the literature review. 2.4 Empirical SACCOS’ Studies Which Applied Models in Tanzania Since 2010 Tanzanian scholars are motivated to model SACCOS’ variables in Tanzania. Kushoka (2013) analysed the sustainability of an employee based SACCOS at Dar es Salaam city in Tanzania by using the Likert scale and linear regression model. The model was written as follows: μβ +++= 210 bXaXY where Y=Sustainability, 0β =intercept, X1=Capital base, X2=amount of loans issued, a & b = coefficients of X1 and X2 and μ =error term. Qin and Ndiege (2013) assessed the role SACCOS in economic growth in Tanzania. They applied econometric model which captures the time series data. The model was specified as: Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 205 ittttttt INTRINFLSGDPCGDPRPGDP μβββββ +++++= )()()()()( 4320 Where RPGDP =real GDP per capita which was used to measure the economic growth, (CGDP) = credits divided by real GDP per capita and SGDP =savings divided by real GDP per capita. The two proxies were used to measure the financial deepening in SACCOS while inflation rate (INFL) and real interest rate (INTR) were used as controlling variables. Magali (2013d) assessed the impacts of rural SACCOS’ loans on borrowers in Tanzania by using the logit model which was expressed as:       − = i i P P LogPLogit 1 )( ……………..(1) where the term within the square brackets is the odd of an event occurring, i.e odd of a SACCOS’ borrower to perceive the impacts from using loans Let: )1Pr( ii xXYP === ……………….(2) Then the model was written as: μβββββββ +++++++==      − 6655443322110)( 1 xxxxxxPLogit P P Log i i i ……(.3) Pi = Probability that of a borrower to realize positive impacts while (1-Pi) =probability of a borrower to realize the negative impacts from using the credit from the rural SACCOS. The independent variables specified in the model were: improvement in health and education, buying of assets, improvement of income, improvement of crop yields, increment of business’ capital and improvement of housing structure after receiving loan from the rural SACCOS. Similarly, Girabi and Mwakaje (2013) assessed the impact of rural SACCOS on smallholder farm productivity in Iramba District, Singida region Tanzania. The regression model was specified as follows: uXbXbXbXbXbXbaQ +++++++= 665544232211 .Where: Q = farm’s output (bags) a = constant bs = estimated coefficients, X1 = inputs (fertilizer, seeds, pesticides) X2 = technology (tractor, ox-plough or hand hoe); X3 = hired labour X4 = money (in Tanzania shillings-TZS), X5 = land u = error term. Magali (2013e) examined whether the rural SACCOS in Tanzania are currently sustainable by using the multivariate regression model. The study measured the outreach by using the log average loan size and the model was specified as follows: μβββββ +++++= 443322110 xxxxY . The independent variables were savings and deposits to total assets, age of SACCOS, log of cost per borrower and OSS. Moreover, OSS measured Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 206 the sustainability of the rural SACCOS and the model was written as: μββββββ ++++++= 55443322110 xxxxxY . In this model NPL to Equity, log of cost per borrower, age of SACCOS, grants to total loans and log of average loan size were regarded as independent variables. Ndiege et al (2013) examined relationship between sources of funds and outreach for SACCOS in Tanzania by using two similar regression models. The number of members (Memb) and average credits (AVC) were used as dependent variables for breadth and depth of outreach in the first and second model while credits per assets (CRAR), external source of funds (EQT), internal sources of funds (LIB) and external-internal sources ratio (LER), age of SACCOS (YEAR) and number of SACCOS (SACC) were used as independent variables. The two models were specified as follows: )1......(333221 itititititiit LERCRARLBLSACCOYEARMemb μββββββ ++++++= )2........(333221 itititititiit LERCRARLBLSACCOYEARAVC μββββββ ++++++= Temu and Ishengoma (2010) used the multivariate regression model to examine effects of financial linkages on performance and sustainability of the rural SACCOS in Tanzania by using the regression model. In the model the financial linkages was measured by a dummy variable of 1 if on a study year, a MFI received a loan from commercial banks or large MFI at least TZS 50000 per its individual member or 0 otherwise. The independent variables were membership growth, membership size, size of loan and interest income. The model was written as follows: P = a + bL + [SIGMA]bc + e Pi = A + [SIGMA][[beta].sub.i][C.sub.i] + [SIGMA][[beta].sub.2][L.sub.i] + [.sub.i] (1), where P, C, L, and e represented the SACCOs' performance, controllable variables, financial linkages and error term correspondingly while [[beta].sub.1], and [[beta].sub.2] presented the controllable variables and linkages parameters. Similarly, Nyamsogoro (2010) used dependent variable as Financial Self Sufficiency (FSS) to assess the sustainability of rural MFIs (SACCOS, NGOs and SACAS) in Tanzania. The independent variables were: MFIs capital structure (equity/capital), interest rates, lending type (group or individual), cost per borrower, product type, MFI size, age, number of borrowers (as a proxy for outreach), female clients, yield on gross loan portfolio, level of portfolio risk, liquidity level, staff productivity and operating model (regulated vs non regulated). Average loan size was used as a proxy measure for outreach while the size of MFI was measured by total assets. Difficulty in assessing data forced the author to measure the depth of outreach by assessing the relationship between number of borrowers and profitability instead of using poor clients. I include this study because 82% of the sample size was SACCOS and SACAS while the remaining sample items were NGO MFIs. Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 207 The model was specified as follows: itititit itititititit itititititit itititititi matigrTmatindTagrarea educareaborrowersregutedfemalemfisizeavoutloan mfitypemfiageloangrloanindiprodtypetpborr rinstainstalindtpdolstratecapstrucFSS εβββ βββββ ββββββ ββββββα +++ ++++++ ++++++ ++++++= 2ln2ln lnlnln minmincosln lglncoslnint 201918 1716151413 121110987 654321 Magali (2013a) assessed the factors affecting credit default risks for rural SACCOS’ borrowers in Tanzania by using the multivariate regression analysis where the default risk (dependent variable) was proxied by Non-Performing Loans (NPL). The model was specified by the following equation: μ+++++++++++= 10109988776655443322110 xbxbxbxbxbxbxbxbxbxbbY . The specified factors which determined the default risk were: age of the respondent, education of the respondent, size of household, marital status, interest rate, received loan amount, loan maturity, value of collateral, borrowers’ experiences in years and loan activity. Similarly, Magali (2013b) examined the rural SACCOS’ variables which influence the loans default risks in Tanzania by using the multivariate regression model where the loans default risk was measured by NPL. The model was specified as follows μ+++++= 443322110 bxbxbxbxbY . The independent variables were savings and deposits, total assets, year of schooling of the manager and the number of borrowers with outstanding loans. Moreover, Magali (2013c) assessed the impacts of credits risk management on profitability of rural SACCOS in Tanzania by using the univariate regression model where the credits risk management was measured by NPL ratio and profitability was measured by returns on asset (ROA) and returns on equity (ROE). The univariate regression model was expressed as: μβα ++= )(),( TL NPLROEROAP (1), By simplifying the model, it can be written as: )(),( LogTL LogNPLROEROAP βα += (2), where P represents profitability and diversity of NPL and TL were reduced by introduction of base 10 logarithm. Furthermore, Magali and Pastory (2013) assessed the technical efficiency of rural SACCOS in Tanzania by using DEA where number of members, total savings and deposits and total expenses were classified as inputs while the loans issued were classified as output while Marwa and Aziakpono (2013) assessed the technical and scale efficiency of SACCOS in Tanzania by using DEA where the total costs and total fixed assets were classified as inputs while the total deposits and total loans in a portfolio were used as outputs. Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 208 3. Discussion of the Findings The findings show that Kenyan scholars lead in studying SACCOS’ issues. The findings from Tanzania, Kenya and other countries show that most scholars apply the regression analysis to model the SACCOS’ variables. Clock Backward (2009) asserted that most scholars prefer to use the univariate or multivariate regression models which apply Ordinary Least Square (OLS) method because its application in computers is simple, it can handle problems of various fields, simple to analyse mathematically as opposed to other models and the results from the regression analysis can be interpreted easily even to a non-mathematician. However, the results from the multivariate regression models need to be tested against heteroscedasticity, autocorrelation and multicollinearity as proposed by Gujarat and porter (2010). Likewise the findings show that most scholars in Tanzania examined the outreach and sustainability of the SACCOS while few scholars analysed the efficiency. However, efficiency studies need to be extended so as to reveal the real situation of both rural and urban SACCOS’ efficiencies in Tanzania separately. The findings further indicate that Magali (2013a &b) assessed the credits risk management in rural SACCOS by using NPL as dependent variables with a multiple regression model. This is an innovation since majority of authors use the amount of loans repaid by borrowers with multiple regression models to assess the credits/loans repayment performance in MFIs (Oladeebo and Oladeebo 2008; Acquah and Addo 2011; Haque et al 2011; Ojiako and Ogbukwa 2012). However, studies which examined the borrowers’ loans default risk applied the logit models (Tra and Lensink n.d; Vasanthi and Raja 2006; Kohansal and Mansoori 2009). Moreover, Mensah (2013) measured loans default by counting the number of times the borrower defaulted. Magali (2013b) also adopted the model from commercial banks in measuring the impacts of credits risk management on profitability of the rural SACCOS. Scholars who assessed the impacts of credits risk management on profitability of banks include Achou and Tenguh (2008), Haneef et al (2012), Funso et al (2012) and Kaaya and Pastory (2013). The findings show that this is the first study to be done in rural MFI (i.e SACCOS) in Tanzania. The findings also indicate that to the best of my knowledge, only two studies (Magali and Pastory 2013 and Marwa and Aziakpono 2013) assessed the technical efficiency of SACCOS in Tanzania by using DEA. However, only Magali and Pastory (2013) focused the efficiency of rural SACCOS in Tanzania while Marwa and Aziakpono (2013) assessed the efficiency of both rural and urban SACCOS. The literatures show that most scholars are interested to the study the efficiency of banks or other MFIs (Misra 2006; Kipesha 2012, Gwahula 2012). Likewise the findings indicate that impacts of loans on clients were measured by using the logistic regression model (Magali 2013d). The use of logistic regression in measuring impacts of MFIs is not famous by many authors .The literature show that Haque et al (2011) applied the logistic regression model to investigate the impacts of a MFI in Bangladesh. 4. Conclusion and Recommendations This study used the literature review to examine the variables used to establish relationship between the independent and dependent variables in SACCOS modeling. The findings reveal Asian Journal of Finance & Accounting ISSN 1946-052X 2014, Vol. 6, No. 1 www.macrothink.org/ajfa 209 that currently few studies are done in Tanzania and scholars applied mostly the regression models. However, the findings show that most studies which modeled the SACCOS variables were conducted in Kenya. The findings further revealed that most scholars have empirically studied the outreach and sustainability of SACCOS compared to other topics. The studies from Tanzania also show that most of issues have not been addressed in the research. This article recommends that scholars to assess issues which limit the SACCOS’ performance such as current influence of regulations in financial performance of SACCOS, the effectiveness of the credits risk management and efficiency of SACCOS in all zones of Tanzania. Also I recommend that scholars should design research to assess the factors affecting the SACCOS’ business performance, compare the performance of rural and urban SACCOS, farmers’ and Workers SACCOS and SACCOS and village banks or other rotational funds. Moreover, I recommend scholars to study econometrics so that they might gain more skills which will help them to model the SACCOS’ and MFIs variables in Tanzania and in other countries. References Abdou, H. & Pointon, J. (2011). Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature. Intelligent Systems in Accounting, Finance and Management, 18 (2-3), 59-88. http://dx.doi.org/10.1002/isaf.325. Achou, T. F. & Tenguh, N. C. (2008). 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