Analysis of Effect of Microfinance on the Performance of MSEs in Amhara National Regional State,Ethiopia

Abstract

This article examines the effect of microcredit on the performance of the micro and small enterprises (MSEs) in Amhara National Regional State, Ethiopia. A total of 340 MSEs were randomly selected, and a survey method was used. Average Sales volume was used to measure performances of MSEs. The findings suggested that the majority of MSEs in Ethiopia were engaged in manufacturing and urban agriculture sectors with a share of 48.53% and 26.76% of the total, respectively. Paired t-test analysis of the study confirmed that there was a significant difference between the sales, total asset, employment and net profit performance of MSEs after microcredit loan. The study concluded that loan size, savings and entrepreneurship training had a significant positive effect on the performance of MSEs. It is suggested that microfinancial institutions should strengthen their existing policies and strategies to increase credit to MSEs, enhancing the modalities of entrepreneurship training and mobilizing savings to achieve the envisioned targets of reducing unemployment and promoting the growth of MSEs in Ethiopia.

Keywords

Microcredit savings performance micro and small enterprises (MSEs)regression Ethiopia

Small and medium enterprises (SMEs) are critical in generating and supporting economic growth and equitable development around the globe. They are centres of employment for more labour, market-based economic growth, combating poverty and promoting democratisation in developing countries (Anne, 2014). The role of SMEs in employment and Gross Domestic Product (GDP) varies across countries. In developing countries, for instance, they contribute below 10% of GDP whereas in the advanced countries their share is above 50%. The SMEs offer about 45% of total employment and 33% of GDP in developing countries (Amoah & Amoah, 2018).

Ethiopia’s micro- and small-enterprise (MSE) strategy focuses primarily on guaranteeing fast and sustainable economic growth, creating job opportunities for unemployed youths, and transforming the agrarian economy to an industrial economy. The Ethiopian MSEs’ Development and Promotion Strategy launched in 1997 gave much more emphasis to Ethiopia’s Industrial Development Strategy. This strategy was developed to catalyse growth, ensure sustainable employment opportunities, reinforce collaboration among MSEs, lay foundations for the emergence of medium and large enterprises and subsequently encourage export. Even though MSEs were designed to create jobs in urban centres, as the core of the country’s development plan, the need to support MSEs development goes beyond the current priorities. This is because the MSEs’ development sector was taken as an engine for developing the manufacturing sector (FeMSEDA, 2011).

Despite MSEs being the target sector, its performance with its share to GDP, employment and export and total manufacturing output was not known in Ethiopia (Assefa et al., 2014). In the 2016/17 fiscal year, 157,768 new MSEs established and created employment opportunities for over 1.2 million youths, and over 7.1 billion ETB loans were disbursed. Of the total newly established MSEs, Oromia, Tigray and the Amhara Regional States took of 31.3%, 25.1% and 24.5%, respectively, while Southern Nations Nationalities People Region (SNNPR) and Addis Ababa received 13% and 3.5% of the MSEs. Of the total loans distributed, Amhara, Addis Ababa and the Oromia Regional States received 38.9%, 23.2% and 16% share, respectively, while Tigray and the SNNPR Regional States took 12.5% and 5.1% of the loans. The share of employment created in Oromia, SNNPR and Amhara was 40.5%, 17.3% and 16%, respectively, while 14% employment created in Tigray and 6.9% in Addis Ababa (NBE, 2016).

SMEs need a capital injection to facilitate their operations and growth, but microcredit is required to fill the financing gap (Waliula, 2013). Lack of access to finance arises as the main barrier for smaller, less established firms (Aldaba, 2012; Levy, 1993). The delivery of financial services to SMEs is vital for more productive activity through resource mobilisation (Watson & Everett, 1996). One’s own personal savings, borrowing from microfinance institutions (MFIs), borrowings from friends, relatives and families are the vital sources to raise start-up capitals, respectively, to small-scale enterprise in Ethiopia (Gerba & Viswanadham, 2016).

Microfinance as an institutionalised form evolved after the Ethiopian government issued the proclamation no.40/96 in 1996 to address the poorest segment of the society in the path of reducing poverty (Wolday, 2002). The poor households lack access to sufficient finance for well-organised intertemporal allocations of resources and risk managing. The prospects of these households in sustainably enhancing their productivity and welfare would be difficult without some sort of financial support. This is because formal financial sectors such as banks do not have the willingness to provide loan to the poor recognizing that they do not meet the lending requirements of banks such as collateral (Assefa et al., 2005). Thus, the distribution of financial services to the poor have been acknowledged as one of the poverty reduction strategies in the development process of the country since MFIs help improve income, housing condition, health and education status of the poor if it is comprehended appropriately (Wolday, 2003).

The achievement of MFIs as a development intervention through the provision of financial and non-financial services to the poor and SMEs across countries has become exciting. The MSEs in Ethiopia get finances from formal and informal sources, including family, friends, local moneylenders and MFIs. However, the main sources of funding are MFIs. MFIs have been playing a great role in promoting financial services to the poor. They stimulate the growth of MSEs by developing new markets, technologies and encouraging entrepreneurship culture (Oleka et al., 2016). The degree to which SMEs could access finance determines the level at which firms could save and accumulate capital for reinvestment (Hossain, 1988). MFIs are the options of small businesses by contributing to decrease risks associated with their businesses, increase profits, store excess liquidity and help obtain returns from their savings. It also helps for the expansion and diversification of SMEs and to operate their business with dignity (NG’ANA, 2013). However, so far, there is limited study on the effect of microfinance on MSEs’ performance in Ethiopia. Therefore, the present investigation was initiated to study the impact of microfinance on their performance.

Literature Review

Concept of MSEs

Various individuals and organisations conceive MSEs in many different ways. The concept and definition of MSEs vary across countries. A firm that is considered a small or medium in one country may not be in another. Some nations define SMEs in terms of their number of employees and sales turnover, while other countries define SMEs in terms of total assets, capital investment and labour force employed. Employment and total asset are two operational definitions of SMEs in the Philippines (Aldaba, 2012). In Nigeria, SMEs are defined based on business turnover and employment (Banji, 2003). Total assets, the number of workers employed, annual turnover and capital investments are the shared indicators in the definition framework of MSEs across countries. The FeMSEDA (2011) states the definition of MSEs based on the nature of the sector the enterprise engages, employment and total assets, as indicated in Table 1.

Table 1.

Definitions of MSEs in Ethiopia

Enterprise Category	Sector	Employment	Total Asset ETB	Total Asset USD
Microenterprise	Industry	≤5	≤100,000	≤4,630
	Service	≤5	≤50,000	≤2,310
Small enterprise	Industry	6–30	101,000–1,500,000	4,630–69,500
Small enterprise	Service	6–30	50,001–500,000	2,310–23,150

Source: FeMSEDA, 2011.

MSEs in Ethiopia’s Economy

For the last two decades, Ethiopia has experienced remarkable economic growth. MSEs contribute to the growth process through employment creation, investment and production. They play an important role in the socioeconomic development of Ethiopia as it needs small capital, promotion of savings, augmentation of investment and a base to serve medium- and large-scale enterprises. MSEs are also helpful in producing and delivering different goods and services to domestic markets, encourage innovation and entrepreneurship. They also played their fair share in the development of technology, research and reduction of poverty (GTP-I, 2010). MSEs constitute 93% of the total employment, 99% of the manufacturing sector, and 28% of the total manufacturing yield. Also, the share of the MSE sector to GDP is 3.4%, which is about 33% of the total contribution of the industrial sector and 52% of manufacturing in Ethiopia (CSA,1997).

Objectives of the National MSE Strategy of Ethiopia

MSEs’ development in Ethiopia is the vital industrial policy path paying to the envisioned structural transformation of the economy from agriculture to industry. The national MSE strategy stressed the imperative roles that the sector could play in employment creation, and different support systems designed to improve the performances of enterprises. The primary objective of the National MSE strategic framework is to create an enabling environment for MSEs such that they are responsible for the operation, growth and progress of their enterprises. These strategic objectives are focusing on promoting equitable and fast economic growth, creating permanent job opportunities, reinforce collaboration between MSEs, serve as the foundation for medium and large-scale enterprises, export promotion and balance preferential treatment between MSEs and bigger enterprises.

Growth Constraints of MSEs

Lack of access to finance arises as the main barrier for smaller, less established firms in Sri Lanka and the Philippines (Aldaba, 2012; Levy, 1993). Lack of managerial and entrepreneurial skills, limited government and institutional support, limited access to markets and delivery linkages, lack of competence and exposure, limited opportunities for promotion and participation, absence of technological skill and integration mechanism and deep-rooted corruption in disclosed or masked form were main holdups for small firms (Singh & Belwal, 2008). Wasihun and Paul (2010) attempted to find the factors that determine the growth of women-owned MSEs in Addis Ababa. They found that marketing problems, fluctuation in the market and the absence of market linkages are the essential growth restraints of women-operated MSEs. At the same time, working capital is the major growth deterrents for women’s enterprises. Lack of finance and marketing difficulties, asymmetric market information, lack of better technology and skilled workers, problems in purchasing factors of production, marketing and supply-related challenges and government policies and rules were major constraints of SMEs in Indonesia (Tambunan, 2011).

Amentie et al. (2016) conducted a study to determine the barriers of medium and small enterprises in Ethiopia and recognised that high market competition, unaffordable loan interest, insufficient infrastructure, a short period of loan repayment, absence of suitable resources, the nation’s economic status, low demand for outputs, competitors’ pricing, limited supply of inputs, perception of banks and liquidity shortage from lenders are described as the core restraints of growth of SMEs. Thus, the main hindrances for MSE’s growth were lack of finance, limited managerial and entrepreneurial skills, limited/less support from the government, lack of market linkages and access to technology. Asma et al. (2015) investigated the features of influencing SMEs’ performance in Algeria. Their study revealed that external factors including loanable funds, policies, rules and directives, workforce capacities, business and environment-related conditions were the bottlenecks for firm development. On the other hand, internal factors such as entrepreneurial characteristics, management capacities, marketing skills and technological capacities are also hindrances that hamper the performance of SMEs. Bilal et al. (2016) conducted a study to find the impediments related to external, institutional and socio-environmental factors of SMEs in China, India and Pakistan through a comparative analysis using 1,443 SMEs. The authors concluded that external funding was affecting the performance of SMEs in China, while it has a positive impact in Pakistan and India. On the basis of the literature surveyed related to SMEs’ barriers to growth, it can be concluded that, among all other factors, lack of access to finance, marketing and distribution problem, lack of technology and technological know-how, lack of entrepreneurial and managerial skills, and lack of government support are the major constraints for SMEs’ performance.

Sources of Finance for MSEs

According to the study by Gerba and Viswanadham (2016), own personal savings, borrowing from MFIs, borrowings from friends, relatives and families are the vital sources to raise start-up capitals for small-scale enterprise in Ethiopia. The authors also highlighted that Eqquib¹ and Iddir,² which are the traditional informal financing options, are additional sources of startup capital. Borrowing from formal banks is the least frequently used source of finance to raise start-up capital for MSEs. The stage or level of development of MSEs determined by the probability of accessing funds from financing institutions (Chirkos, 2014). If an enterprise recognised as growing, then it would be easy to access credit. However, the underlying criterion set by the funding institutions is the ability of MSEs to repay their liability and to secure collateral as per the requirements of MFIs.

The Role of Microfinance on the Performance of MSEs

MFIs have been widely recognised as a tool to elevate the poor from poverty circle and increase their future life prospects. At the initial age of an enterprise, SMEs funding is indispensable for procuring assets, financing the initial operations of the business and the expenses of owners. There is a direct relationship between the volume of initial capital investment and SMEs survival (Cooper et al., 1994). The amount of initial capital enables businesspersons to invest in profitable ventures, to cope with upmarket depressions or to correct management faults, and to derive the benefits from market prospects and enhance firm growth in succeeding periods (Bates, 1995; Cooper & Woo, 1988). Studies show that the effect of microfinance is not only at the individual level (Littlefield et al., 2003) but also at the country level (Khandker, 2005).

There are several studies on the effect of microfinance on the growth of SMEs. SMEs need a capital injection to facilitate their operations and growth, but the existence of the financing gap requires microcredit to fill the gap (Waliula, 2013). Lack of access to finance arises as to the main barrier for smaller, less established firms (Aldaba, 2012; Levy, 1993). The delivery of financial services to SMEs is vital for more productive activity through resource mobilisation (Watson & Everett, 1996). Thus, MFIs are the options of small businesses by contributing to decrease in risks associated with their businesses, increasing profits, storing excess liquidity, helping obtain returns from their savings. It also helps for the expansion and diversification of SMEs and to operate their business with dignity (NG’ANA, 2013). Lack of financial capital is also one of the important causal factors to the weak performance of SMEs (Xavier et al., 2012). Access to microcredit and performance of SMEs are positively related (Akingunola, 2011). Access to finance did not influence the growth of SMEs in Nigeria, but the size of the loan was found improving firm performance (Aldaba, 2012; Wanambisi, 2013).

Similarly, Olowe et al. (2013), on their study, showed that credit received from MFIs has a positive significant impact on the growth of SMEs while the duration of loans had a positive but not significant impact in Nigeria. The study also asserted that high loan interest rate, collateral requirement and speed of loan repayment can deter the growth of SMEs in Nigeria. They also stated that loan amount and loan duration have a positive and significant effect on the growth of SMEs, which implies that an increase in loan amount and loan duration leads to an improvement in the performance of SMEs. On the other hand, interest rate, loan repayment and loan collateral have a negative and significant impact on enterprise growth.

Mahmood and Rosli (2013) conducted a study on the effect of microcredit on the performance of MSEs in Malaysia and concluded that managerial and entrepreneurial practices, business experience, training, level of education, religious values and support from family were significant factors for increasing SMEs performance. Thus, microcredit services alone are not effective in improving MSEs performance. Akinboade (2015) found that the location of firms influences net sales of SMEs. The possibility of negative or zero growth declines with an increase in enterprise age while turnover growth increased with levels of education. According to the study by Ndife (2013), the relationship between microcredit and SMEs development was significant and positive. However, the finding indicates that microcredit is not the only factor that influences SME’s growth. Moreover, Ndife (2013) confirmed that access to formal credit positively improves the availability and quality of factors of production, such as land, labour, capital, machinery and equipment. This, in turn, improves SMEs performance by enhancing profit and the number of employees (Aliero & Yusuf, 2015).

Christopher (2010) conducted a study to assess the impact of microfinance on SMEs in Nigeria. A hundred SMEs were randomly selected, and structured questionnaire was employed for the study. The result reveals that MFIs loans benefitted SMEs even if only a few of them were able to access to secure enough loan. The study confirmed that MFIs loans contribute positively to improving the performance of SMEs in terms of product innovation, market share, achieving market excellence and to the overall SMEs competitive advantage. Similarly, Abiola (2012) conducted a study to investigate the effects of microfinance on micro and small business growth in Nigeria. The study employed panel data and multiple regression analysis to analyse a survey of 502 randomly selected enterprises financed by microfinance banks. The study concluded that access to microfinance does not augment the growth of SMEs, while the business size and location a positive significant effect of the growth of SMEs. Thus, the literature reviewed in this study revealed that microcredit has mixed effects in determining SME’s performance. Therefore, the following hypothesis was formulated for this study.

H ₀ The amount of microcredit has no positive effect on MSEs’ performance.

Entrepreneurship Training and MSEs Performance

The objective of training/educating an entrepreneur is to make them responsible, risk takers, make strategic decisions and enable them to learn from achievements and failures (Tendai, 2014). In addition to this, business owners learn how to overcome the challenges that they may face. It also enhances entrepreneurial attitudes, growth of firms and its achievements. Studies have shown varied results for the relationship between entrepreneurship training and entrepreneurial development. A study by Black et al. (1999) suggested that the association between business productivity and training was positive and feasible in large firms since these firms deliver extra training per worker than small firms. Entrepreneurship training is vital at the macro level since it drives economic growth through the accumulation of human capital (Bryan, 2006). The accumulation of capital contributes more to non-stop competitive advantage at the microlevels (Bryan, 2006; García, 2005). Entrepreneurship and managerial training can enhance the skill gap of MSEs operators. Entrepreneurship training is the way to disseminate appropriate information to increase the performance of SMEs. Owner and managerial training help entrepreneurs to have exposure to the internal and external business environment. Thus, entrepreneurship and managerial training is given to MSEs contribute to improving the performance of firms (Mescon, 1987; Webster et al., 2005). Knowledge acquired through entrepreneurship training increases business productivity and reduces failure (García, 2005; Mahmood & Rosli, 2013). Thus, the following null hypothesis was formulated.

H ₀ Entrepreneurship training and performance of MSEs are not significantly related.

Method

Research Design and Approaches

The researcher used a survey study design, which involved the collection of quantitative data from the Amhara National Regional State (ANRS) sampled MSEs managers/owners and MFIs managers’/loan officers. Qualitative research identifies people’s experience, culture, opinions, attitudes, behaviour and how people observe a problem or situation, while quantitative research is used to scrutinise the association between rigorous quantitative analysis (Creswell, 2014; Kothari, 2009).

Sample and Sampling Procedures

The target population of this study was the registered MSEs in ANRS and Amhara Credit and Saving Institution. The sample size was determined using purposive and systematic random sampling approaches. Hence, multi-stage sampling techniques were employed. First, the ANRS was selected purposely because it ranked third in the concentration of MSEs and first in the amount of loan distributed at the national level in 2017/18. Second, from the ANRS, Zones and cities were selected based on the relative concentration of MSEs and the amount of loan given to MSEs. Hence, there are 25,441 registered MSE. Finally, from the legally registered MSEs, 340 were randomly selected using Cochran (1977) sample size determination formula.

Validity and Reliability

Validity is the extent to which a test measures what we wish to measure while reliability has to do with the accuracy and precision of a measurement procedure. The questionnaire was given to experts that have better knowledge in the areas of research and small business development for content analysis. Their suggestions and comments were incorporated into the final document. According to Mugenda and Mugenda (2003), the reliability pre-test sample size can be between 1% and 10 % of the total sample. Thus, 10% of the total sample was used as a pilot study to ensure reliability.

Data Collection and Analysis

Data were collected using self-administered structured questionnaires. The questionnaire has three parts related to owners’/managers’ demographic characteristics (gender, age, marital status and level of education), MSEs characteristics (business age, type of ownership structure, kind and size of the business, location), and microfinance characteristics (amount of loan, duration of microcredit loan, loan repayment period and interest rate) characteristics because the performance of a business venture depends on all these factors in addition to the legal and institutional policies prevailing in a country. The collected data were encoded and analysed using statistical software Stata/SE14.0. Descriptive statistics such as mean, percentages, standard deviations and inferential statistics (t-test and multiple regression) were calculated.

Analytical Model Specification

To examine the impact of microcredit on the performance of MSEs, the study employed ordinary least square regression analysis, where the dependent variable was measured by average sales performance since it shows both the short- and long-run trends of MSEs, while the explanatory variables were loan size which was measured by the amount of credit to MSEs, microsavings, interest rate, duration of the loan, repayment of liability and entrepreneurship training. The predictors are selected from key microfinance characteristics. The hypothesis was set to discover the degree to which microcredit could improve the performance of MSEs in the study. The following multiple regression model was used to examine the effect of microcredit on the performance of MSEs:

L n (P) = α + β_{1} \ln (L S) + β_{2} \ln (S) + β_{3} R + β_{4} T + β_{5} L R + β_{6} D L + ε

where ln(P) is the dependent variable, which was measured by the natural logarithm of average sales revenue as suggested by (Azeref & Gelagil, 2018; Ishaq & Mishra, 2020; Zahra & Garvis, 2000), and the key explanatory variables were: ln(LS) = natural logarithm of loan size; ln(S) = natural logarithm of savings; R = interest rate; T = entrepreneurial training; LR = loan repayment; and DL= duration of the loan. α is constant, which represents the performance of MSEs that is not influenced by explanatory variables in the model, and ε is the error term.

Results and Discussion

Demographic and Business Characteristics of Respondents

The results from the study show that the majority (64%) of respondents were male, while 36% were female. The participation of female was less than that of men due to sociocultural factors. The study also indicates that the average age of the owners/managers of MSEs was 26.5, with a standard deviation of 3.21. According to FeMSEDA (2011), youths are allowed and supported by the government to access credit from MFIs if their age is between 18 and 32. For those whose age is above 32, the government may not support the enterprise but can help them to access credit on their own. Besides, the study reveals that the mean level of schooling of the respondents was 9.4 years, with a standard deviation of 3.21, which shows that the average respondents can read and write so that it would be easier to them to manage their business. Furthermore, the finding from the study reveals that 42.3% of respondents were single, while 52.94% were married (Appendix 1).

Regarding the business characteristics of MSEs (Appendix 2), the study shows that the average age of MSEs was 4.75 years, with a minimum business’ life of two years, and a maximum of nine. The MSEs are categorised in terms of initial total asset based on the Ethiopian national MSEs definition. Using this definition in this survey, the study indicates that 61% of the total enterprises were small enterprises, while 39% were micro. In terms of kind of business sectors, the result from this study reveals that the majority (48.53%) of MSEs were involved in manufacturing, followed by urban agriculture (26.76%), while 17.35% in trading, 5.88% in construction, and the remaining in-service sectors. This might be because of the governments’ support has been on manufacturing and urban agriculture. Most of the time, the business life of microenterprises is lower than small enterprises since the former face more challenges in accessing credit, work premises, training and support. However, the average age of microenterprises was 4.88 years with a standard deviation of 1.54, and small enterprises had 4.65 years with a standard deviation of 1.24. The average age difference between MSEs suggest that both MSEs established in the same year or the small enterprises understate their performance so as not to graduate to medium enterprises to seek the government’s continuous support. A two-sample t-test was also used to check whether there is a statistically significant difference between the average ages of MSEs. To this end, the following null hypothesis was formulated:

H ₀ There is no significant difference between the average age of small and microenterprises.

H ₁: There is a significant difference between the average age of small and microenterprises.

Table 2 indicates the null hypothesis, which states that there is no significant difference between the average age of small and microenterprises, could be accepted and the alternative hypothesis was rejected because the p-value (.1336) is greater than .05. Although large firms have a better lifespan in the business arena, this study confirmed that microenterprises survived on average, with the same age span as of small enterprises by exploiting supports provided by the governments and non-governmental organisations.

Table 2.

Average Business Age Comparison of MSEs

Group	Obs	Mean	Std. Err.	Std. Dev.	Sig (Two-tailed)
Small	208	4.65	0.0857	1.2370	0.1336
Micro	132	4.88	0.1337	1.5365
Combined	340	4.75	0.0739	1.3635
Diff		–0.2277	0.1514

Source: Author’s computation, 2019.

Net Profit Analysis of MSEs

Net profit measures annual net earnings of MSEs with netting out all incurred expenses to run the business. In other words, it is the difference between the revenue of incurred to run the business, including tax revenue in (2017/18 and 2018/19). Such net profit analysis enables the study to show the profitability of each sector as well as the performance of MSEs.

As displayed in Figure 1, the study reveals that the average net profit of MSEs except for the construction sector has shown relative and progressive increment in 2018/19 compared to the preceding years. The net profit of the construction sector did increase despite enterprises achieve positive profit because in the year 2018/19 relative to 2017/18, there was inflation, which affected the construction sector more seriously than others. The findings from the study also show that in 2018/19, the average annual net profit of construction sector was ETB 51,775 followed by urban agriculture and manufacturing with ETB 36801.35 and 34,654 ETB, respectively. The trade and service sectors performed the lowest relative to construction, urban agriculture and manufacturing with a net mean annual profit of ETB 25,419 and 23,760, respectively. Similarly, the study shows that, in 2017/18, the average yearly net profit of construction sector was ETB 51,800 followed by urban agriculture with ETB 36194.81, while the manufacturing sector registered to mean net profit of ETB 29771.61. The trade and service sector performed the lowest relative to construction, urban agriculture and manufacturing sectors with a net mean yearly profit of ETB 21,337 and 18,900, respectively. All sectors showed progressive performance in terms of mean net profit in 2018/19 relative to 2017/18.

Figure 1.

Source: Authors computation, 2019.

Job Creation and Employment Opportunities in MSEs

Current Employment of MSEs (2018/19)

Creating employment opportunities for jobless community groups, mainly youths, is a central objective of most MSEs development initiatives. A fully organised and functional enterprise creates diversified job opportunities. Thus, this study assessed employment opportunities created by each MSE in 2017/18 and 2018/19. The results from the study reveal that construction, in the 2018/19, has, on average, 6.5 permanent employees followed by urban agriculture and manufacturing with a mean employment level of 6.31 and 4.74, respectively. The three sectors employ more labour, which is consistent with the government’s MSEs development strategy. Also, the study indicates that the service and trade sectors have 3.6 and 2.32 employees on average, respectively. The maximum number of workers employed in manufacturing, construction and urban agriculture was 14, while for trade and service sectors, it was 8 and 6, respectively. The survey study also shows that all MSEs employ, on average, 4.70 workers with a standard deviation of 2.97 (Appendix 3).

Employment Level of MSEs at Business Startup

Unemployment is a key macroeconomic problem that affects the political economy of both the developed and developing world. The MSEs were given due attention in developing countries, including Ethiopia, to generate new jobs and foreign exchange reserves. The study showed that the average level of employment in urban agriculture was 4.30 with a standard deviation of 2.21, followed by construction and manufacturing with an average employment level of 3.40 and 2.67, respectively. Trade and service sectors registered the lowest job opportunities at business start-up with a mean level of workers 1.40 and 1.80, respectively. From the survey, it was found that the MSEs registered on average 2.90 workers, with a standard deviation of 2.57, at business start-up stage (Appendix 4).

Analysis of MSEs Performance Before and After Microcredit

The researcher assessed whether or not there was a significant difference in the performances of MSEs in terms of sales, profit, employment, and asset performances after and before microcredit using a paired t-test analysis. The study in Table 3 confirms that the mean sales revenue of MSEs after taking credits was ETB 154266.90, which was higher than before loan (ETB 94962.46). The paired t-test result showed that there was a significant mean difference between the sales revenue of MSEs before and after a loan. The study also reveals that the mean net profit of MSEs after the loan was ETB 44594.83, while before was ETB 16747.94. The paired t-test on net profit after and before microcredit loan indicated that there was a significant mean difference. Furthermore, the study indicates that the average total asset of MSEs after the loan was 224632.8 ETB, which was higher than that of before loan (ETB 136,214). The t-test result confirms that average total asset after loan significantly exceeds that of before loan. Similarly, the results from the study indicate that there was a significant mean difference between the employment level of MSEs after and before credit. On the whole, the paired t-test result suggested that microcredit contributed significantly to improve the performance of MSEs in terms of sales revenue, total assets, profits and level of employment in Ethiopia.

Table 3.

Analysis of MSEs Performance After and Before Microcredit

Variable	Obs	Mean	Std. Err.	Std. Dev.	Sig (Two-tailed)
Sales before loan	340	94962.46	4564.09	84157.82	0.0000
Sales after loan	340	154266.9	6187.53	114092.5
Profit before loan	340	16747.94	705.38	13006.72	0.0000
Profit after loan	340	44594.83	4187.38	77211.47
Asset before loan	340	136,214	5537.85	102,113	0.0000
Asset after loan	340	224632.8	9908.06	182695.6
Employment before loan	340	2.908824	0.1394	2.5717	0.0000
Employment after loan	340	4.332353	0.1708	3.1498

Source: Compiled by author, 2019.

Model Estimation and Test of Hypothesis

The researcher investigated the effect of microfinance on the performance of MSEs in ANRS, Ethiopia using a multiple regression model. The F-value of the model was 108.61, with a p-value of .000, which shows the overall significance of the model. That is, at least one of the variables has a significant effect on the dependent variable. The R-squared of the model was 0.73, which implies that the explanatory variables could explain 73% of the variation in the dependent variable.

Model Diagnostic Tests

Prior to running the regression model, the existence of homoscedasticity, multicollinearity and normality assumptions was checked. Robust regression was employed to test the assumption of homoscedasticity. The Kernel density histogram method was used to test a normality assumption, and the result is bell-shaped, which indicates that the normality assumption is not violated (Appendix 5). Besides, the predictors should be free of multicollinearity problems. The researcher tested the model using the variance inflation factor (VIF). Most studies argue that if the mean VIF is less than 10, the model has no problem with multicollinearity (Gujarati, 2004). In this study, the mean VIF is 2.76, which indicates that the model has no multicollinearity problem among predictors (Appendix 6).

Multiple regression analysis was applied to assess the effect of microcredit on the performance of MSEs. The dependent variable was the natural logarithm of the sales performance of MSEs, which shows both the short- and long-run fluctuations about the performance of firms. The explanatory variables are derived from the services and products of microcredit institutions: loan size, savings, duration of the loan, loan repayment and entrepreneurial training. From the regression estimation in Table 4, the study reveals that the amount of loan disbursed to MSEs in ANRS, Ethiopia, has a positive and significant effect on the sales performance of MSEs with a p-value of.000 at 1% level of significance. The result indicated that if the amount of loan received by MSEs increases by 1%, then the sales performance of the firms increases by 0.46%. Thus, the null hypothesis stating that there is no significant positive relationship between the amount of MSEs microcredit and performance of sales revenue was rejected, and the alternative one was accepted. Therefore, microcredit has a vital role in enhancing the performance of MSEs. The result is consistent with other findings (Gyimah, 2018; Irene et al., 2015; Sampong, 2011). The study also shows that MSEs savings have a significant and positive impact on the firm’s sales performance. The result is similar to the findings of (Kisaka, 2015). Size of loans and savings has a positive relationship with MSEs performance, and their impact is significant (Azeref & Gelagil, 2018). A 1% increase in savings of MSEs resulted in a 0.13% increase in sales performance. This is because the more the firms save part of their profit, the more they could access credit in addition to earning interest. Entrepreneurship and managerial training have a positive and significant effect on the performance of MSEs. If the number of training increased by one unit, MSE’s performance would increase by 0.028 units. The finding is consistent with other studies (García, 2005; Mahmood & Rosli, 2013; Simpson et al., 2004). Thus, the null hypothesis stating that entrepreneurship training and performance of MSEs are not positive and significantly related was rejected, and the alternative hypothesis was accepted at 5% level of significance.

Table 4.

Regression Estimation on the Effect of Microcredit on the Performance of MSEs

Ln (Sales)	Coefficient	Robust Std. Err.	t-value	p-value
Ln (loan size)	0.4621	0.0457	10.09	0.000^***
Ln (savings)	0.1328	0.0276	4.80	0.000^***
Interest rate	–0.0121	0.0097	–1.25	0.213
Training (yes)	0.1248	0.0565	2.21	0.028^**
Loan repayment (yes)	0.0611	0.0444	1.38	0.170
Duration of loan	–0.0254	0.0372	–0.68	0.496
Constant	5.3694	0.5010	10.72	0.000
Model Summary (Robust Regression)
R ²		0.7279
Root MSEs		0.32892
Prob > F		0.0000

Source: Authors computations, 2019.

Notes: ^***Significant at 1% and ^**significant at 5% levels of significance.

Conclusion

Ethiopia, being one of the poorest countries in the world, cannot pull out its people from poverty unless the MSEs’ sector plays its fair share in its development path. MSEs work for employment generation, market-based economic growth, combating poverty and promoting democratisation in developing countries like Ethiopia. The study also confirmed that there was a significant difference between the sales, total asset, employment and net profit performance of MSEs after microcredit loans. This study also concluded that other factors remaining constant, microcredit, savings and entrepreneurship training had a positive significant effect on the performance of MSEs in Ethiopia. MSEs need a capital injection to facilitate their operations and growth; hence, the existence of microcredit may help to fill that financing gap. On the other way, this study reveals that MFIs also help MSEs to increase profits, total asset and employment through expansion and diversification of their operation.

Limitations of the Study and Future Research Suggestions

The key limitation of the study is the dependability of data collected from MSEs operators who usually do not have the interest to provide the available information about their business for fear of being exposed to tax. Most of the MSEs operators do not have the habit of accounting and bookkeeping procedures so that they may supply inaccurate information during data collection. The results from the study rely on quantitative data collected through the use of semistructured questionnaires. The findings would be further comprehensive if it includes more qualitative data to answer the reasons and how MSEs performance is impacted by microcredit. Furthermore, the study is cross-sectional, that is, it disregards MSEs characteristics, which could be examined using longitudinal study design. However, scientific procedures were employed to collect and analyse the data, which enables us to generalise our findings. Future cross-sectional studies could employ both qualitative and quantitative research design or use longitudinal study for detailed examination of the impact of microfinance on the performance of MSEs.

Practical Implications

The study assesses the role of microcredit, savings and entrepreneurship training in promoting and enhancing the performance of MSEs. MSEs operators could utilise managerial and entrepreneurship training provided by MFIs and MSEs development office because it would enable them to acquire business management skills and knowledge. Strengthen the existing capacity of microcredit institutions is of paramount importance since the credit disbursed to MSEs is helpful in significantly affecting the performance of MSEs. Assessing the interest rate charged on asset loan, improving the modality of managerial and entrepreneurial training and strengthening the saving culture of MSEs should be the focus of both MSE development agencies and MFIs of nations.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

Appendix 6.

Variance Inflation Factor (VIF)

Variable \|	VIF	1/VIF
-----------+------------------------------------------------------------------
lnloan \|	2.40	0.417494
lnsaving \|	1.85	0.541756
durloan \|	4.79	0.208936
1.LR \|	1.62	0.617808
R \|	4.91	0.203601
1.training \|	1.02	0.977243
-----------+------------------------------------------------------------------
Mean VIF \|	2.76

Source: The authors.

Notes

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