Issuu

Scholarly Research Journal for Interdisciplinary Studies, Online ISSN 2278-8808, SJIF 2019 = 6.380, www.srjis.com PEER REVIEWED & REFEREED JOURNAL, MAY-JUNE, 2020, VOL- 7/59 DEBT RESCHEDULING, A NECESSARY STRATEGY IN MANAGEMENT OF NON-PERFORMING LOANS: INSIGHTS FROM MICROFINANCE INSTITUTIONS IN NAIROBI CITY COUNTY Stephen Mutuku Mumina1 & Kalui Fredrick Mukoma2, Ph.D. 1

Faculty of Commerce - Egerton University, muminamutuku88@gmail.com

Faculty of Commerce - Egerton University

Abstract The purpose of this study was to determine the influence of debt rescheduling on the level of nonperforming loans in MFIs in Nairobi County. Explanatory research design was adopted with the population of study comprising of all 57 MFIs in Nairobi County under umbrella body AMFI. Both primary and secondary data was collected. Primary data was collected with the help of a semi structured questionnaire while secondary data covering a period of five years from 2014-2018 was collected from AMFI published annual supervisory reports and MFIs final financial statements using a data collection sheet. Data was analysed using descriptive statistics (mean and standard deviation), correlation analysis, as well as linier regression analysis. The SPSS software was used to analyse the data. Results indicated that non-performing loans and debt rescheduling in the MFIs are strongly and negatively correlated (r=-0.682).Debt rescheduling had a statistical significant influence on NPLs (adjusted R2=0.652; F=96.550>4.0343; β=-0.355; P-value=0.008<0.05). Based on these results, the study concluded that debt rescheduling was significant in predicting the level of NPLs in MFIs in Nairobi County. Therefore, the study recommends that MFIs should carefully evaluate possibility of rescheduling repayment periods for struggling borrowers. The study contributes to the body of knowledge by proposing a management model for MFIs in managing their NPLs. The study also contributes theoretically by validating the applicability of the theoretical anchorage of creditor’s bargain theory which had been applied in other contexts but was significantly absent in microfinance institutions. Empirically, the study contributes by showing the relationship that exist between the study variables among microfinance institutions in Nairobi City County, a gap identified in previous scholarship. Future studies should be carried out on other debt restructuring strategies other than debt rescheduling to determine whether they have a significant influence on the level of NPLs in MFIs in Kenya. Key Words: Debt rescheduling, non-performing loans, microfinance institutions.

Scholarly Research Journal's is licensed Based on a work at www.srjis.com

Introduction Use of debt in the capital structure provides corporates an opportunity to enjoy interest tax shield/savings arising from the fact that interest expense is an allowable deduction for tax purposes (Graham, Hanlon, Shevlin, & Shroff, 2013). This provides the potential to increase the shareholder’s earning, particularly when economic conditions are conducive such that Copyright © 2020, Scholarly Research Journal for Interdisciplinary Studies

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financial leverage leads to increased earnings per share (Zeitun& Tian, 2014). However, during turbulent times, when the companies are exposed to high operating risks, high levels of debt may tug a firm into financial distress and in some casesbankruptcy Andinsolvency (Drehmann& Nikolaou, 2013). Literature supports that firms that over rely on use of debt capital are at a highest risk of falling into bankruptcy because over use of debt capital in the capital structure exposes the firm to financial obligations in form of interest rates and principal amount which are mandatory (Keener, 2013). When the firm is unable to meet its short-term maturing financial obligations it becomes exposed to financial risks which may lead to financial distress and ultimately bankruptcy (Zeitun& Tian, 2014). When this happens a company has two main options: To file for bankruptcy or renegotiate with its creditors privately by invoking out-of-court debt restructuring (Garrido, 2012). Due to the associated costs of bankruptcy as provided by the law, many companies opt for out-of-court debt restructuring, commonly referred to as private renegotiation(Moldogaziev, Kioko&Hildreth, 2017).Restructuring involves changing the composition and the structure of assets and liabilities of a financially distressed firm without resorting to a full judicial intervention aimed at promoting efficiency, restoring growth, and minimizing the costs associated with financial distress.One commonly adopted strategies in debt restructuring is debt rescheduling. Debt rescheduling, involves extending contractual payments into the future and sometimes lowering interest rates on those payments. Three types of rescheduling are available to MFIs. The first method postpones payments of loan principal only for a specified period.In the second strategy, involve deferring both principal and interest payments but interest continues to accrue over the period of deferment and accumulated interest paid at a later date. In the final strategy, both principal and interest payments are temporally stopped with no interest accumulation (Chua, 1999). Debt rescheduling is mostly applied by credit managers as an incentive to reduce non-performing loans in Microfinance institutions. The concept of debt rescheduling has also been practiced over the years, both in developed and under developed nations (Moldogaziev, et al., 2017). Drawing evidence from Peruvian, Indian and Ugandan MFIs, the author observed that, MFIs in these countries are more sympathetic to clients who face payment difficulties due to natural disasters and unfavorable Copyright ÂŠ 2020, Scholarly Research Journal for Interdisciplinary Studies

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macro-economic factors and tend to consider extending the loan repayment period through rescheduling (Solli, et al., 2015). In Kenya, this practice is more prevalent among commercial banks, microfinance institutions (MFIs) and Savings and Credit Cooperative Societies (SACCOs). However, there is very limited literature on the concept of debt restructuring among MFIs. This is because most of the existing scholarship has laid more emphasis and macro level and the few at firm level have concentrated on commercial banks. On the other hand, the concept of non-performing loans has been studied over and over again being linked to other concepts such as credit policies, operational efficiency, economic conditions, firm resources and firm performance (Fofack, (2005; Gaitho, 2010; Saba Kouser&Azeem, 2012; Mitman, 2016). Loans are said to be non-performing if interest and principal obligations remain outstanding for ninety days (IMF, 2009).Financial institutions face the risks of non-performing loans as a result of numerous factors such as unfavourable economic conditions, efficiency problem, insider lending, high interest rates, macroeconomic instability, inadequate credit risk management policies, financial mismanagement, and political interference(Gaitho, 2010). Moreover, moral hazard, in particular insider lending and lending at high interest rates to borrowers in the riskier segments of the credit markets are other causes of high levels of non-performing loans. The relevance of studying non-performing loans and its determinants is amplified by the fact that it may lead to failure of the lending institution. The Global Economy (2019) established that the global average of non-performing loans was 7.1 percent in the year ending 2017, with the highest values being observed in San Marino with 46.76 percent and Macao with the lowest value of 0.12 percent. Additionally, World Bank (2018) for the period 1998 to 2017, Kenya maintained an average of 16.3 percent of non-performing loans during the period, with a low percentage of 4.43 in 2011 and a maximum level of 34.9 percent in 2003. Microfinance institutions (MFIs) are defined as institutions that provide a range of financial services to micro and very small enterprises and retail customers (World Bank, 2018). These institutions serve the needs of the most economically marginalized groups through finance of livelihood, health care, housing improvements, small business creation, and other needs in underserved

populations,

specifically

poverty

and

near-poverty

level

individuals

worldwide(CBK, 2019). Most of them take the form of credit unions, commercial banks, Copyright ÂŠ 2020, Scholarly Research Journal for Interdisciplinary Studies

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nongovernmental organizations, and even government banks. It is estimated that approximately 1.7 billion people around the world don't have access to financial services (World Bank, 2018). Thus the relevance of microfinance in economic development cannot be under estimated. The Kenyan microfinance sector emerged in the 1980s and are broadly classified into two: deposit-taking institutions and non-deposit-taking institutions. Deposit-taking institutions are allowed to accept deposits from members of the public on current account or savings and payment on and acceptance of cheques and are regulated by the central bank under microfinance regulations of 2008. On the other hand, non-deposit-taking institutions, also known as credit only microfinance institutions, do not fall under the jurisdiction of the CBKâ€™s microfinance regulation and are thus not subject to the prudential regulations. This means that they are self-regulated and to some extent regulated by member body such as AMFI (CBK, 2019). Among the first institutions to offer microcredit in Kenya was the Small and MicroEnterprise Programme (SMEP) owned by the National Council of Churches of Kenya (NCCK) and K-Rep. However, before the enactment of Microfinance Act of 2006, the Microfinance Regulations for Deposit Taking MFIs Act of 2008 and the 2013 amendments which referred deposit-taking institutions as Microfinance Banks, Microfinance Institutions operated without a proper regulatory framework (Kibet&Wagacha, 2018). After the establishment of a regulatory framework, Kenya has experienced a rapid growth of Microfinance Institutions. By 2010 there were 24 large micro finance institutions in Kenya, which provided over sh. 1.5 trillion to approximately 1.5 million active borrowers. Currently, there are 57 institutions practicing micro-lending serving over 6.1 million clients with assets worth more than Ksh. 325 billion (AMFI, 2018). The institutions comprise of Commercial banks, wholesale businesses, SACCOS, development institutions, MFI banks and Credit only MFIs. Among them, Equity Bank had the largest market share of approximately 73.50% closely followed by Kenya Women Microfinance Bank (KWFT) with 12.06%. Others are KRep, now Sidian Bank, with 6.39%, Faulu (3.56%) and Jamii Bora (0.86%) (CBK, 2018). The Kenyan microfinance sector saw non-performing loans rise to 99.1 billion at the end of 2017 up from 73.71 billion in 2016. The situation aggravated micro-finance banking sector Copyright ÂŠ 2020, Scholarly Research Journal for Interdisciplinary Studies

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losses which hit 7.31 billion for the period ended December 2017, up from 3.77 billion over a similar period in 2016. At the same time, customer deposits dropped to394.16 billion, from 401.98 billion in 2016.These statistics paint a picture of a troubled microfinance sector (CBK, 2018). While the sector continues to struggle with non-performing loans, existing literature on the functions of its determinants among microfinance institutions remains scanty. Most of this literature has focused on commercial banks. The few on MFIs were conducted in developed countries. Given the integral part played by microfinance institutions in the Kenyan financial sector and the high levels of NPLs threatening the sustainability of the sector, the study has identified literature gaps and it is on this background that the study seeks to fill these gaps by answering the question: What is the influence of loan rescheduling on the level of non-performing loan in microfinance institutions in Nairobi City County? The objective of the study was to establish the influence of debt rescheduling on the level of nonperforming loans in microfinance institutions in Nairobi City County. Literature Review This study was anchored on the creditorâ€˜s bargain theory proposed by Jackson (1982). The theory asserts that the insolvency law reflect the notional agreement the creditors of a company themselves would strike if given the chance to bargain with each other before they lend anything to the firm. The model thus holds that bankruptcy should reflect the conjectural settlement that creditors would reach among themselves in the event they had an opportunity to negotiate before a bankruptcy petition (Senbet& Wang, 2012). According to the theory, there are two broad classes of creditors; senior creditors and junior creditors. The two represent conflicting interests in the claim on assets of a debtor with a view of exercising control over the debtor firm. The theory, argued that a creditor who obtains control of the debtorâ€™s assets well in time, when the debtor is still solvent, may get full payment while late arriving creditors may get nothing (Westbrook, 2015). When senior creditors gain control over the assets of the debtor firm, they will have an incentive to sell the company in a quick sale even when reorganization has a higher expected return for the estate (Jenkins & Smith, 2014). Thus, when senior creditors are exercising control, which they do in most cases, the result is an inefficient sale of the debtor's assets. Copyright ÂŠ 2020, Scholarly Research Journal for Interdisciplinary Studies

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On the other hand, junior creditors have an incentive to block the quick sale in favour of a drawn-out reorganization even when the sale has the higher expected return for the estate. As a result of these distortions, the assets of a bankrupt firm are not maximized because the assets of the firm are sold at a lower price than their market value. Conversely, when junior creditors gain control, the firm spends unnecessary resources on reorganization. Since the main focus of the theory is the maximization of the value of the debtorâ€˜s assets, so as to effectively confront the common-pool problemin case of the insolvency, the theory becomes relevant in explaining the need for debt restructuring through rescheduling. This may be achieved through delaying the due dates of required payments or sometimes reducing payment amounts by extending or lengthening of the time of debt repayment period. Assessing the country risk assessment model during the Asian crisis, Huberman (2001) established that globally, countries rescheduled their debts in the event that they were not able to service such external debts. More so, it was noted that countries such as Nicaragua, Panama and Argentina were rated as high-risk countries and therefore, rescheduled their debts in various degrees between 1997 and 1998. Korea exercised its major debt rescheduling in 1998. However, it was concluded that not all debt payment difficulties resulted into debt rescheduling covenants. For instance, despite the severe economic conditions and debt service difficulties, countries such as Thailand, Philippines and Malaysia did not exercise debt rescheduling during the Asian crisis compared to Indonesia and Korea. Marchesi (2013) conducted an empirical analysis to determine the relationship between adoption of an IMF programme and the concession of a debt rescheduling by commercial creditors to confirm that adoption of IMF programme had a significant effect on the subsequent concession of a debt rescheduling by private and commercial creditors. This study targeted private and commercial creditors in Italy while the current study targeted microfinance institutions in Nairobi City County. Therefore, the findings cannot be generalised. Siddiqui, Siddiqui and Kazmi (2014) conducted a study in Pakistan to investigate the probability of debt rescheduling. The study adopted a qualitative response model and employed both Economic (financial ratios) and political indicators to determine if any of these indicators or both of them had an effect on the countryâ€™s ability to meet the debt Copyright ÂŠ 2020, Scholarly Research Journal for Interdisciplinary Studies

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obligation and its prospects for debt rescheduling. The findings of the study showed that financial ratios are significant determinants of debt rescheduling for Pakistan. Besides, LauĹĄev, Stojanovic & Todorovic (2015) conducted a study to determine debt rescheduling probabilities in European countries. Utilizing Panel Logit Models applied to a set of macroeconomic, financial, and political variables. The study revealed that policy efforts focused on reducing government expenditure, attracting foreign direct investment, increasing export revenues, and keeping a good repayment record result in low debt rescheduling probabilities and, in turn, decrease the cost of debt for these countries. Finally, in a study to investigate the effects of debt rescheduling on debt capacity and overall firm value, Charalambides and Koussis (2018) revealed that Debt rescheduling reduces debt capacity and overall firm value. This is because rescheduling results in reduced agency costs between shareholders and debt holders. Debt rescheduling however had a positive effect in that more significant anticipated debt reduction in rescheduling results in reduced agency costs between shareholders and debt holders. This study investigated the effects of debt rescheduling on debt capacity and overall firm value therefore the findings may not be generalised to a study conducted on the influence of debt rescheduling on non-performing loans. Methodology The study adopted both explanatory and descriptive research designs. Descriptive research design was used because it is analytical and pinpoints one variable factor or subject and describes it in detail. On the other hand, explanatory research design was used because it studies the cause and effect of the relationship that exist between the independent and dependent variables. The population for this study comprised of microfinance institutions in Nairobi County. Microfinance institutions operating in Nairobi County were selected because all microfinance institutions in Kenya have an operational office in the County. Primary data was collected on the explanatory variables via structured questionnaire while secondary data was collected on the regressed variable covering a period of five years from 2014-2018 was collected from AMFI published annual supervisory reports and MFIs final financial statements using a data collection sheet. Copyright ÂŠ 2020, Scholarly Research Journal for Interdisciplinary Studies

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Pilot testing was conducted to establish the validity and reliability of the research instrument. Both face and content validity were tested. Reliability was measured using Cronbach’s alpha where a coefficient of 0.6 or more was considered adequate. Data analysis was conducted with the aid of Statistical Package for Social Sciences (SPSS) Version 23. Descriptive statistics such as frequencies, mean score, and standard deviation were utilised. In addition inferential analysis (correlation analysis and multiple regression analysis) were conducted. Descriptive statistics were preferred as they allow for the meaningful description of the results by use of minimal indices (Marshall & Rossman, 2014). Correlation analysis was conducted to determine the strength and direction of the relationship between debt rescheduling and non-performing loans. Regressions analysis was preferred because it enables the researcher to determine the effect of the dependent variable in predicting the dependent variable. The decision on hypothesis testing was made based on 0.05 significance level based on the recommendations of Field (2009). To test the model significance the researcher used the coefficient of determination (R2) to determine the variation in the level of non-performing loans as a result of the changes in the loan rescheduling. The researcher further computed the F-statistic at 95% confidence level to determine if a significant relationship does exist between loan rescheduling and the level of non-performing loans in microfinance institutions. The simple linear regression model for debt rescheduling was specified as; Y= β0 + β1X1 + ε Where: Y= Non-Performing Loans (Portfolio at Risk) β0 =constant β1 = Variable Coefficients X1= Debt Rescheduling ε - Error Term Diagnostic tests were carried out on the collected data before actual analysis to test the assumptions of the multiple regression models (Mutandwa, Grala&Grebner, 2016). The relevant diagnostics tests for the study were normality, autocorrelation and multicollinearity.

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Results and Discussions Reliability of the Research Instruments The study sought to determine the validity and reliability of the research instrument. Face validity and content validity were tested through research experts. Examination of the instrument confirmed completeness, understandability and plausibility of the research items. Reliability of the research instrument was tested using Cronbach’s alpha. Pilot results were summarised in table 1. Table 1: Reliability Coefficients Cronbach's Alpha

Variable

Conclusion

Coefficient

Debt Rescheduling

0.767

Reliable

Non-performing loans

0.789

Reliable

Overall

0.778

Reliable

Source: Research Data (2019) The results shown in table 1 indicated that the overall reliability index was 0.778 indicating that there was overall reliability of the study constructs. Particularly, the results showed that debt rescheduling had a coefficient of 0.767while non-performing loans had a coefficient of 0.789. Based on these results, the study concluded that all the study constructs were reliable since they had coefficients greater than 0.6 as recommended by Field (2009). Diagnostic Tests results Diagnostics tests were conducted on the study variables to establish the normality, autocorrelation, and multicollinearity of the data obtained. The results were summarised in table 2 Table 2: Summary of Diagnostic Test Results Diagnostics Test

Test Done

Test statistic

Conclusion

Normality

Shapiro-Wilk

0.976;P=0.381>0.05

Normally distributed

Autocorrelation

Durbin-Watson

1.873≃2

No autocorrelation

Multicollinearity

VIF

1.099<3

No multicollinearity

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Descriptive Results The study sought to establish the influence of debt rescheduling on the level of nonperforming loans in microfinance institutions in Nairobi City County. The descriptive statistics; mean scores and standard deviations were used. Descriptive results for debt rescheduling were as presented in table 3. Table 3: Descriptive Statistics for Debt Rescheduling N Repayment holidays are granted to clients facing financial distress It is the practice of the firm to negotiate with the clients for an appropriate grace period before they start repaying the loan. More clients start repaying their loans with ease upon extension of the repayment period Our amortization schedule is made flexible to accommodate clients who land in financial distress. To enable clients to make regular payments we normally reduce instalment amounts Debt roll overs are usually granted to struggling clients to ease their financial burden when debts fall due Our MFI usually extends loan repayment period for clients who are unable to repay We allow debtors to pay additional fee in order to extend the loan's due date. Reduced instalment amounts motivate clients to honour their obligation in time We accept changes in loan amortization schedule When clients have financial difficulties, we grant them grace period after which they resume repayment Our company critically evaluate clientâ€™s application for extension in their repayment period Repayment holiday makes clients consistently honour their repayment obligation

Min Max Mean Std. Dev

4.73

.689

4.48

.828

4.16

.769

4.06

.669

4.04

.885

4.03

.975

3.75

1.604

3.72

1.456

3.67

.810

3.60

.912

3.15

.916

2.21

.997

1.87

.768

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52 Aggregate Source Data: Research Data 2019

3.65

13808

0.848

The results in table 3 indicate that most of the respondents strongly agreed that microfinance institutions in Nairobi County grant repayment holidays to clients facing financial distress as indicated by a mean score of 4.73 and a standard deviation of 0.689, the respondents also agreed that institutions negotiate with the clients for an appropriate grace period before they start repaying the loan. Moreover, the respondents also agreed that more clients start repaying their loans with ease upon extension of the repayment period, the institutions make the amortization schedule flexible to accommodate clients who land in financial distress, the institution also normally reduce instalment amounts to enable clients to make regular payments and usually grant roll overs to struggling clients to ease their financial burden when debt fall due as indicated by means scores of 4.48, 4.16, 4.06, 4.04 and 4.03 respectively. Low standard deviations of 0.828, 0.769, 0.669, 0.885 and 0.975 show that most of the respondents had similar opinions on these statements. In addition, the respondents also agreed that the institutions usually extend loan repayment period for clients who are unable to repay as shown by a mean of 3.75 and a high standard deviation of 1.604 shows that there were disparities in the respondent’s opinion on the same. Similarly, respondents agreed that the institutions allow debtors to pay additional fee in order to extend the loan's due date as indicated by a mean score of 3.72 and a high standard deviation of 1.456 which indicates disparities in the respondent’s opinion. Moreover, reduced instalment amounts motivate clients to honour their obligation in time as indicated by a mean score of 3.67 and a low standard deviation of 0.810 which indicates a general agreement by the respondents. Similarly, the respondents agreed that the microfinance institutions accept changes in loan amortization schedule as indicated by a mean score of 3.60 and a standard deviation of 0.912 which shows that there were no disparities in the respondent’s opinion. The respondents could not form a decision on whether microfinance institutions grant grace period to clients with financial difficulties after which they resume repayment as indicated by mean of 3.15and a low standard deviation of .916. Lastly, the respondents did not agree that the microfinance institutions critically evaluate client’s application for extension of their repayment period and that repayment holiday makes Copyright © 2020, Scholarly Research Journal for Interdisciplinary Studies

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clients consistently honour their repayment obligation. This is shown by mean scores of 2.21 and 1.87 and low standard deviation of 0.997 and 0.768 respectively. Overall, the respondents agreed on the adoption of debt rescheduling among the microfinance institutions in Nairobi County this indicates that debt rescheduling was practised to moderate extent as shown by an aggregate mean of 3.65. There was less or minimal disparities regarding debt rescheduling by microfinance institutions in Nairobi County as shown by a low standard deviation of 0.871. Descriptive results for non-performing loans were as presented in table 4.

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Table 4: Descriptive Statistics for Non-Performing Loans Year

2014

2015

2016

2017

2018

Aggregate

Average outstandin g loans in 44,681,174. 59,609,128. 85,040,487. 87,778,799. 79,973,477. 71,416,613. arrears of

over 30 days Average refinanced or

9,302,558.1

restructur

11,215,765. 14,372,406. 22,863,693. 23,817,323. 16,314,349. 7

ed loans Average outstandin g gross

171,274,285 192,211,627 222,869,487 236,802,159 249,148,958 214,461,303 .4

Mean PaR

0.315

0.368

0.446

0.467

0.417

0.403

Std. Dev.

0.034

0.031

0.027

0.026

0.025

0.029

Minimum

0.010

0.031

0.017

0.028

0.064

0.030

Maximum

0.851

0.702

0.943

0.868

0.874

0.848

Skewness

1.361

1.207

0.851

1.062

0.945

1.085

Kurtosis

1.274

1.058

0.927

0.861

0.782

0.980

loan portfolio

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In table 4, the aggregate mean score for PaR was 0.403 suggesting that a number of MFIs were struggling with non-performing loans averaging 40.3% of their gross loan portfolio as shown by the high mean score. The aggregate standard deviation for PaR among MFIs in Nairobi City County was 0.029. The low standard deviation depicted that this was a common problem among MFIs struggling with high volumes of non-performing loans. At the same time the results showed that the data obtained on non-performing loans was positively skewed as shown by a Skewness level of 1.085 which was greater than zero. Similarly the data was slightly flat (platokurtic) as shown by a Kurtosis of 0.9804< 3. For the purpose of inferential analysis, PaR was converted into five point likert scale form such that a ratio less than 0.1=1, greater than 0.1 but less than 0.2= 2, greater than 0.2 but less than 0.3= 3, greater than but less than 0.3 but less than 0.4 = 4 while PaR above 0.4 = 5 Inferential Analysis Correlation Analysis Results The direction and strength of the relationship existing between the study variables was determined through correlation analysis. The results were as shown in table 5. Table 5: Correlations

Pearson Correlation Non-performing loans

Debt Rescheduling

Non-performing

Debt

loans

Rescheduling

Sig. (2-tailed) N

Pearson Correlation

-.682**

Sig. (2-tailed)

.000

*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Form table 5, it was established that non-performing loans and debt rescheduling in the MFIs had a strong, negative, and significant relationship (r = -0.682, p < 0.05). These findings concurred with the findings of McConnell (2016) who indicated that loan restructuring is beneficial to both the lender and the borrower as it enhances the ability of the debtor to come out of financial distress as well as improving chances of debt recovery thus reducing nonperforming loans and Odula (2015) who stated that loan restructuring results in improved

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performance, growth in shareholdersâ€™ value, increased loan recovery and performance improvement in Kenyan banking industry. Regression Analysis Results The study sought to establish the influence of debt rescheduling on the level of nonperforming loans in microfinance institutions in Nairobi County. The study used simple linear regression to test the hypothesis which stated that rescheduling repayment period has no significant influence on the level of non-performing loans in microfinance institutions in Nairobi County and results were as shown in the tables; 6,7 and 8 Table 6: Model Summary for Debt Rescheduling Model

R Square

Adjusted R Square

.812a

.659

.652

Std. Error of the Estimate .42429

a. Predictors: (Constant), Debt Rescheduling Table 7: ANOVAa for Debt Rescheduling Model

Sum of Squares

Mean Square

Sig.

Regression

17.381

96.550

.008b

Residual

9.001

.180

Total

26.382

a. Dependent Variable: non-performing loans b. Predictors: (Constant), Debt Rescheduling Table 8: Coefficientsa Debt Rescheduling

Model

Unstandardized

Standardized

Coefficients

Std. Error Beta

(Constant)

2.677

1.092

Debt Rescheduling

-.355

.128

-.278

Sig.

2.451

.018

-2.773

.008

a. Dependent Variable: non-performing loans From the results shown in model summary table 6, it was observed that adjusted R2 for the model was 0.652 suggesting that debt rescheduling predicted 65.2% of all variations in nonperforming loans in MFIs in Nairobi County while 34.8% of all variations in non-performing

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loans in MFIs in Nairobi County were instigated by other factors other than debt rescheduling. Based on the ANOVA results in table 4.12, the F-statistic for the model was 96.550>4.0343 F-critical and the P=0.008<0.05. The study thus established that the model is a good fit for the data and hence was used to predict the influence of debt rescheduling on the level of nonperforming loans in MFIs in Nairobi County. The regression coefficients results shown in table 4.13 showed un-standardized beta coefficients of; Î˛=2.677; P-value=0.018<0.05 constant and Î˛=-0.355; P-value=0.008<0.05 debt rescheduling, Therefore the simple regression model for debt rescheduling was summarised as follows; Y= 2.677- 0.355X1 From the model, holding debt rescheduling constant at zero, non-performing loans would be equal to 2.677 while holding all other factors constant, a unit increase in debt rescheduling would lead to a 0.355 decrease in non-performing loans in the MFIs in Nairobi County Since there was a statistical significant relationship between debt rescheduling and the level of non-performing loans in MFIs in Kenya, the null hypothesis (H01) that, debt rescheduling has no significant influence on the level of non-performing loans in MFIs in Kenya was rejected and concluded that, debt rescheduling has a significant influence on the level of nonperforming loans in MFIs in Kenya. The results of the study conforms to those of the study done byChua (1999) on ways in which MFIs can ease the impact of a natural disaster on clients by rescheduling loans where loan rescheduling was found to be effective since it allows borrowers to have time for reorganization and to regain better financial footing. Conclusion The study concluded that debt rescheduling has a significant influence on the level of nonperforming loans in microfinance institutions in Nairobi County. These conclusions are in line with the existing literature which shows how interest continues to accrue over the period of deferment, but clients pay accumulated interest at a later date and that both deferral measures are effective since they allow borrowers to have time for reorganization and to regain better financial footing. It is noted that the findings of this study are in line with those of other scholars which implies that the findings can be generalised in other studies. Recommendation The study concluded that rescheduling the repayment period has a significant influence on the level of non-performing loans in microfinance institutions in Nairobi County. The study Copyright ÂŠ 2020, Scholarly Research Journal for Interdisciplinary Studies

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therefore recommends that the management of the microfinance institutions should consider debt rescheduling as a good strategy of reducing the level of non-performing loans. Microfinance institutions should encourage their clients especially those struggling to pay to consider debt rescheduling to make it easier for them to pay. Contribution to the Body of Knowledge The study contributes to the body of knowledge by proposing a management model applicable by microfinance institutions in Nairobi City County and beyond in managing the level of non-performing loans. This is anchored on the adopted conceptual framework which indicated that debt rescheduling significantly influence the level of non-performing loan in microfinance institutions in Nairobi City County. Secondly, the study contributes theoretically to the body of knowledge by validating the applicability of the theoretical anchorage of creditorâ€™s bargain theory which had been applied in other contexts but was significantly absent in microfinance institutions. Reviewed literature indicated that creditorâ€™s bargain theory had been used to anchor studies in other contexts such as commercial banks and insurance companies. Thus, by successfully anchoring the conceptualisation of debt rescheduling, interest rate reduction and haircuts in this study fill the gap. Empirically, the study contributes to the body of knowledge by showing the relationship that exist between the study variables among microfinance institutions in Nairobi City County. Existing literature showed that although the study constructs had been studied previously, the scholarship considered their individual influence on Non-performing loans and not their combined influence. The study also noted that most of these relationships were studied at macro level and very little, if any was done at firm level. Suggestions for Further Research Findings of this study were based on 57 microfinance institutions in Nairobi County. The study therefore suggests that other studies should be conducted focusing on microfinance institutions in other counties to see whether the same results would be obtained. On the same note, the study also suggests that since the study focused on microfinance institutions only, there is need to carry out other studies focusing on all commercials banks in Kenya to determine the effect of debt restructuring strategies on the level of non-performing loans. The study established that 77.3% of the variations in non-performing loans among the MFIs in Nairobi County were explained by changes in debt rescheduling, interest rate reduction and haircuts. The remaining 22.7% of the variations in non-performing loans were explained by Copyright ÂŠ 2020, Scholarly Research Journal for Interdisciplinary Studies

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