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\title{Challenges for Small and Micro Enterprises in Accessing Finance (Case of Wolaita Soddo Town)}
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             \author[1]{Mesele Kebede  Manaye}

             \affil[1]{  KIIT university}

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\date{\small \em Received: 10 December 2016 Accepted: 31 December 2016 Published: 15 January 2017}

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\begin{abstract}
        


Small-scale enterprises have become an important contributor to Ethiopia?s economy. The purpose of this paper was to bring to light one of the major prerequisites, namely access to finance aspects, of the small scale enterprises which are foundation in accelerating economic growth. The main objective of the paper is to identify the challenges that small businesses in Wolaita soddo town face in accessing finance from financial institution through identifying the dominant means of financing small businesses and factors influence the extent of accessing finance. This study would have adopted descriptive statistics and econometric analysis. The target population would have been drawn from all micro and small enterprises in Wolaita Sodo town. The data for this study would have been gathered through the use of primary and secondary sources from 282 respondents. The study would have used stratified random sampling technique to select the sample. Both quantitative and qualitative data analysis methods were adopted. Besides, descriptive statistics, the linear regression model were used to determine the covariates of the likelihood of having a loan from a formal financial institution. From the study findings, it can infer that, sources of initial capital influences accessibility to credit facilities to a great extent followed by purchase of fixed assets financed by and major obstacles of SMEs access to credit facilities. This study recommends that the credit giving institutions come up with programs of educating the MSEs on how they can go about obtaining credit facilities.

\end{abstract}


\keywords{Access to Finance, SME?s, Challenges, Wolaita Sodo Town, Ethiopia.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
mall and Micro Enterprises (SMEs) are the lifeblood of developing economies. Finance is the backbone of SMEs and any other business enterprise  {\ref (McKernan \& Chen, 2005)}. Both in the developing and developed economies small firms have little access to external finance and are thus constrained in their operation and growth  {\ref (Galindo \& Schiantarelli, 2003)}.\par
The SME sector in Ethiopia is considered as an instrument in bringing about economic transition by effectively using the skill and talent of the people particularly women and youth with least training, limited capital and available technology. The Small and Medium Enterprises sector (SMEs) contributed nearly Birr 8.3 million in 1996 to the exchequer. Based on the 1992/93 data, this figure constitutes about 3.4\% of the GDP, 33\% of the industrial sector's contribution and 52\% of the manufacturing sector's contribution to the GDP of the same year  {\ref (Gebrehiwot, 2006)}.\par
The development of the sector in Ethiopia believed to be the major source of employment and income generation for a wider group of the society in general and urban youth in particular. The five-year Growth and Transformation Plan (GTP) of Ethiopia envisages creating a total of three million micro and small-scale enterprises at the end of the plan period  {\ref (NBE, 2011)}. Citing the source from the Federal Micro and Small Enterprise Development Agency (FMESDA), the EEA Research Brief noted that seventy thousand five hundred (70,500) new SMEs were established in 2011/12 employing eight hundred six thousand three hundred (806,300) people across the country. The performance is below the target set in GTP  {\ref (EEA, 2015)}.\par
In this project, we analyze the financing gap of SMEs in Ethiopia and recommend ways of addressing the financing gap. Specifically, the study addresses a) the financing needs and financing options of SMEs in Ethiopia, b) Key constraints of SMEs access to finance, c) extent of banks and MFIs involvement with SMEs, and the drivers and obstacles of SME bank financing, and d) the impact of existing government policies and potential areas of government involvement. Unlike many previous studies that address either the demand side or the supply side limitations of access to finance (see for e. In almost all economies of the world, especially in developing countries in Africa, micro and small enterprises are crucial and are a key factor for sustained growth and development. SMEs play pivotal roles in creating dynamic, market oriented economic growth, employing the growing workforce in developing countries, alleviating poverty and promoting democratization.  {\ref (Obwocha, 2006}).\par
Africa's SMEs have little access to finance, which thus hampers their emergence and eventual growth. Their main sources of capital are their retained earnings and informal savings and loan associations, which are unpredictable, not very secure and have little scope for risk sharing because of their regional or sector based focus. Access to formal finance is poor because of the high risk of default among SMEs and due to inadequate financial facilities. Small businesses in Africa can rarely meet the conditions set by financial institutions, which see SMEs as a risk because of poor guarantees and lack of information about their ability to repay loans.\par
The financial system in most of Africa is underdeveloped however and so provides few financial instruments. Capital markets are in their infancy, shareholding is rare and no long-term financing is available for SMEs. Non-bank financial intermediaries, such as microcredit institutions, could be a big help in lending money to the smallest SMEs but they do not have the resources to follow up their customers when they expand  {\ref (Kauffmann, 2005)}.\par
According to the World Bank recent report of Ethiopia, small-scale enterprises are the most marginalized in getting access to financial institutions. As to the report, the private sector plays a crucial role as a catalyst for economic change through offering financing options, which allow firms to expand and innovate. Despite the role of SMEs in the Ethiopian economy, the financial constraints they face in their operations are daunting and this has had a negative impact on their development and limited their potential to drive the national economy as expected. This is worrying for a developing economy without the requisite infrastructure and technology to attract big businesses in large numbers.\par
Thus, this research is attempted to analyze the gaps in accessing the finances by SMEs in Ethiopia and recommend measures to bridge those gaps. Specifically, the study aims to address the financing needs, key constraints in accessing finance, and the extent of involvement of banks and MFIs in driving away the obstacles thereby creating a room for reforms in Government policies. Moreover, unlike many previous studies that address either the demand side or the supply side limitations of access to finance (see for e.g.,  {\ref Petersen and Rajan, 1994}  
\section[{Literature Review}]{Literature Review}\par
Access to external sources of finance may increase growth possibilities since it facilitates the development and improvement of firm's products and services or hire new employees. In transition economies, the development that financial markets experience may create barriers linked to the access to finance. Hence, academic research considers financial constraints as an important obstacle for entrepreneurship and firm growth. Empirical evidence supporting the importance of access to external finance for business growth can be found in Brown, Earlem \& Lup, (2005), who examines firm growth determinants. Conversely, \hyperref[b17]{Johnson, McMillan and Woodruff (2000)} evaluate institutional reforms in five Eastern European countries (including Romania), and they conclude that access to bank finance does not prevent business growth.\par
Lack of access to credit facilities is almost universally indicated as key problem for small and micro enterprises. In most cases, even where credit is available mainly through banks, the entrepreneurs may lack freedom of choice because the banks' lending conditions may force the purchase of heavy, immovable equipment that can serve as collateral for the bank. Credit constraints operate in variety of ways in Kenya where undeveloped capital market forces entrepreneurs to rely on self-financing or borrowing from friends and relatives. Lack of access to long-term credit for micro, small and medium enterprise forces them to rely on high cost short term finance (Wanjohi \& Mugure, 2008).\par
Matavire et al.,  {\ref (2013)}, in their study on challenges facing SMEs in accessing finance from financial institutions: The case of Belaway, Zimbabwe found out that SMEs fail to secure loans because of restrictive requirements of the financial institutions, top among them being collateral security. Among their recommendations is that the government should play its role of enabling SMEs to obtain finance from financial institutions. Makena, et al.,  {\ref (2014)}, in their study on challenges facing women entrepreneurs in accessing business finance in Kenya: Case of Ruiru Township, Kiambu County, lack of collateral was one of the objectives. However, the study found out that lack of collateral was a greater hindrance to credit accessibility by women entrepreneurs. This is due to lack of tangible assets like land, which used as an asset to secure credits. Among their recommendations is that the government should play its role of enabling SMEs to obtain finance from financial institutions.\par
Gitari, (2012) in her study on factors affecting women entrepreneurs' financial performance in Kenya: a case of Ngara Market found out that lack of information on who is offering what and the cost of obtaining such services limit them and that high inventory costing are some of the major drawbacks for success in women entrepreneurship. The high cost of running the entrepreneurs is a big threat to the women development due to lack of adequate capital and on the other hand lack of information on how to access funds to boost the business also is a major threat. At Wolaita Sodo town (2017), did a study on factors affecting the performance of SMEs traders at Wolaita Sodo town, Ethiopia. The study concluded that access to finance affected performance of SME's largely because it limited the entrepreneurs' ability to take advantage of opportunity as and when they arose. 
\section[{III.}]{III.} 
\section[{Conceptual Framework}]{Conceptual Framework}\par
? A conceptual framework is a product of qualitative process of theorization, which interlinks concept that together provides a comprehensive understanding of a phenomenon or phenomena (Jabareen, 2009). ? The concepts that constitute a conceptual framework support one another, articulate their respective phenomena, and establish a frameworkspecific philosophy that defines relationships. The conceptual framework of this study relates to independent variables; collateral requirements, cost of credit, availability of information on finance, business risks and the dependent variable; access to credit facilities by micro and small enterprises in Wolaita Sodo\par
IV. 
\section[{Research Methodology a) Research Design}]{Research Methodology a) Research Design}\par
This study would have adopted descriptive statistics and econometric analysis. The target population would have been drawn from all micro and small enterprises in Wolaita Sodo town. Currently the total micro and small enterprises registered with Wolaita Sodo Town is 957. The businesses in operation are mainly in manufacturing, construction, trade, service and urban agriculture. b) Data Type\par
The data for this study would have been gathered through the use of primary and secondary sources. The primary data source for this study involves the use of questionnaire. The questionnaires would have been distributed to SME operators and/or owners for first hand information for processing towards answering the research questions. The questionnaire would have been divided into three sections. ii. Sampling Techniques\par
The study would have used stratified random sampling technique to select the sample. The study would grouped the population into strata. From each stratum, the study used simple random sampling to select 282 respondents. This sampling design would have been used because the population of study would have not homogenous and was to be sub-divided into sub-units namely manufacturing, construction, trade service and urban agriculture. \hyperref[b19]{Kothari (2004)} recommends stratified random sampling because it is accurate, easily accessible, divisible into relevant strata and it enhances better comparison; hence representation across strata. Another advantage of stratified random sampling is its ability to ensure inclusion of sub-groups, which would otherwise be omitted entirely by other sampling methods because of their small number in the population. 
\section[{d) Data Collection Instruments}]{d) Data Collection Instruments}\par
The study would have used questionnaire to collect data. Questionnaires would have been constructed based on the research objectives. The questionnaire contained closed-ended questions using a Likert scale (ranging from 1 = highly disagree; 2 = disagree; 3 = neutral 4 = agree; 5 = highly agree). 
\section[{e) Hypothesis Testing}]{e) Hypothesis Testing}\par
Research statements are tested using the Z-test statistical tool. The tests are measured within 95\% confidence interval having 0.05 significance levels. The following are the hypothesis drawn: H1: Sources of initial capital are not due to challenges of Small and Micro Enterprises in accessing the required finances.\par
H2: Availability of information on finance is not a serious challenge for Small and Micro Enterprises in accessing the required finances H3: The major constraints to SMEs are not a serious challenge for Small and Micro Enterprises in accessing the required finances. f) Data Analysis and Interpretation i. Data Analysis Under this section, the process by which outputs are generated through three steps: preparing data for analysis, analyzing the data, and interpreting the output data, i.e. testing the research hypothesis and drawing valid inferences. Qualitative analysis consisted of examining, categorizing, tabulating and recombining evidences to address the research questions. Qualitative data grouped into meaningful patterns and themes that observed to help in the summarizing and organization of the data.\par
Quantitative data analyzed with statistical techniques such as frequency counts, percentages, arithmetic means, standard deviations, bar charts and tabulation to show differences in frequencies. Qualitative data would have been analyzed descriptively with questionnaires. Statistical Package for Social Sciences (SPSS) version 20 was used to aid in coding, entry and analysis of quantitative data obtained from the closed ended questions.\par
Besides, descriptive statistics, the linear regression model would be used to determine the covariates of the likelihood of having a loan from a formal financial institution. The linear regression model is extremely flexible and widely used function, and leads itself to meaningful interpretations when the dependent variable is dichotomous outcome. It is a powerful tool in its ability to estimate the individual effects of the continuous or categorical variables on the qualitative dichotomous dependent variable (Wright, 1995). The dependent variable in model is whether SME's benefit from banking facilities or not. It is a categorical variable with a value of 1 when a SME's benefits from banking facilities and 0 otherwise. This is regressed against factors that possibly limit a firm's access to bank loan. The model specified as follows:Formula: Zi = ?0 + xji + µi\par
Where Zi is the dependent variable with a value of 1 when firm i benefits from micro finance facilities and 0 other wise. Xji are a vector of explanatory variables which include major obstacle of SME's, availability of financial information, sources of initial capital, working capital financed by firm size and purchase of fixed assets financed by. Finally, µi is the discrepancy term.\par
V. 
\section[{Findings and Discussion}]{Findings and Discussion} 
\section[{a) Introduction}]{a) Introduction}\par
This chapter presents the analysis of data collected from respondents. The collected data was edited and cleaned for completeness and consistency in preparation for coding. Once coded, the data was keyed into the Statistical Package for Social Sciences (SPSS-20) for analysis. 
\section[{b) Results of the Pilot Study i. Data Reliability}]{b) Results of the Pilot Study i. Data Reliability}\par
A pre-test of 5\% of the population size was conducted to give a representation of the selected population thus the designed questionnaire was tested on 12 potential respondents (Mugenda and Mugenda, 2003). To test the internal consistency of the Likert scale used in this study, reliability analysis was done using Cronbach's Alpha as the measure. A reliability coefficient of ? ? 0.7 was considered adequate. A coefficient of 0.761 was registered indicating that the scale used had a high level of internal consistency. This indicated that the scale was reliable enough to test the extent to which micro and small enterprises faced challenges in accessing finance facilities. The results of the reliability analysis are shown in Table \hyperref[tab_3]{4}    The researchers administered 282 questionnaires and 280 were completed and returned successfully. This represents a response rate of 99.3\%. This response rate was adequate to allow the researchers to continue with the analysis. The questionnaires were composed of questions that addressed the objectives of the study. The study sought to establish the influence of major obstacles of SMEs, sources of initial capital, working capital financed by firm size, availability of information on accessibility of credit facilities by MSEs in Wolaita Soddo town. 
\section[{VI.}]{VI.} 
\section[{Sex of the Respondents}]{Sex of the Respondents}\par
The study sought to know the distribution of the respondents by gender. The study findings indicated that 62.5\% of the respondents' were male while 37.5\% were female. This implied that the male participants dominated the businesses at Wolaita Soddo town. The findings also indicated that the researchers observed gender balance during the administration of questionnaires. It reveals that, the SME is largely dominated by the male participants. Thus, it needs attention to increase the number of women entrepreneur in the SMEs to sustained their economic independence and improve the level of employment of women. 
\section[{VII.}]{VII.} 
\section[{Level of Education}]{Level of Education}\par
The study also sought the distribution of the respondents by their level of education. The study established that most (48.9\%) of the respondents' were TVET levels followed by diploma levels at 37.5\% and then BA 13.6\%. This implies that the respondents were educated enough to understand the issues being sought by the researchers.\par
The results of the study are as shown in Table \hyperref[tab_3]{4}.2. 
\section[{Year ( )}]{Year ( )} 
\section[{2017}]{2017} 
\section[{C}]{C}\par
Challenges for Small and Micro Enterprises in Accessing Finance (Case of Wolaita Soddo Town)  VIII. 
\section[{Age of Respondent}]{Age of Respondent}\par
The survey result shows that about 66.4\% of the sampled respondents were under the age of 25-35 followed and 31.1\%, 2.5\% of respondents were under the age of <25 and 35-45 respectively. This implies that the researchers-sought opinions from respondents who had the age of respondents were enough to understand the challenges faced when trying to access credit facilities. The study findings are as shown in Table \hyperref[tab_3]{4}.3. 
\section[{IX.}]{IX.} 
\section[{Age of the Company}]{Age of the Company}\par
The study further sought to know the duration the businesses had been in existence. The study found out that most (49.3\%) of the businesses had been in existence for below two years followed by 43.6\% of the businesses that had been existence for 2-5 years. Only 7.1\% of the businesses had less than 6-10 years' existence. This implies that the researchers-sought opinions from respondents who had been in business long enough to understand the challenges faced when trying to access credit facilities. The study findings are as shown in Table \hyperref[tab_3]{4}.4. 
\section[{X.}]{X.} 
\section[{Nature of Organization}]{Nature of Organization}\par
In this section, the study sought to know the distribution of respondents by the nature of business they were engaged in. According to the study findings, most (29.6\%) of the respondents were engaged in the service followed by 23.6\% of construction and Traders accounted for 23.9\% respectively. Manufacturing and Urban agriculture (14.3\% and 8.6\%) accounted only. This indicates that the researchers sought views from people of diverse businesses to avoid any bias. The findings are shown in Table \hyperref[tab_3]{4}.5. 
\section[{XI.}]{XI.} 
\section[{Current Position in the Organization}]{Current Position in the Organization}\par
In this section, the study sought to know the distribution of respondents by their position in the organization.\par
The respondents position within the organization was categorized in to four categories namely accountants, cashiers, mangers and secretary.\par
According to the study findings, most (74.3\%) of the respondents were mangers in the organization, 13.6\% of the respondents were cashier and accountants and secretary were accounted for 2.1\% and 10\% respectively. This implies that the researchers-sought opinions from respondents who had been in business were mangers and this helped the researchers to get their feelings regarding the challenges they faced when trying to access credit facilities. The findings are shown in Table \hyperref[tab_3]{4}  The study sought to know the extent to which various factors influenced access to credit facilities for micro and small enterprises in Wolaita Sodo town. The extent to which the factors influenced access to credit was measured on a Likert scale of 1-5 where 1-highly disagree, 2-disagree, 3-neutral, 4-agree and 5-highly agree.\par
The study found out that the extent of major obstacles influence of access to credit facilities to a moderate extent (M = 3.52, SD= 0.731) while source of initial capital influenced access to financing to a moderate extent also (M= 3.5205, SD= 0.618). The influence of working capital financed by firm size was to a moderate extent (M= 2.41, SD= 0.552) and so did purchase of fixed assets finance such as internal fund, owners contribution (M= 2.83, SD= 0.639). Availability of information like limited information on financial management and limited training /experience had the least influence but still it was to a moderate extent (M= 3.8, SD= 0.625). The standard deviations indicated the extent to which the responses were dispersed from the mean. Overall, measure obstacles influenced access to credit facilities only to a moderate extent (M= 3.52, SD= 0.731. This implies that access to credit was highly limited by measure obstacles such as lack of skilled manpower, lack of good telecommunication services, high interest rate and group guarantee. The results are as shown in In accordance with the above table, it can be inferred that the researchers rejects the H1 and H2 as there is an indication that a strong and significant relationship exists between sources of capital X2 and major obstacles faced by SMEs X1, as well as the obstacles faced by SMEs X1 is not due to availability of information to access finance X5. The scores p = 0.147 and p = 0.6 reveal their significant association and hence we fail to reject the hypothesis. Further, working capital finance X3 and purchasing of fixed assets X4 having p values less than 0.05 shows no significant association with major obstacle of SMEs X1 and hence alternate hypothesis gets accepted by rejecting the null. 
\section[{XIII. conclusion and recomendation a) Conclusions}]{XIII. conclusion and recomendation a) Conclusions}\par
The study sought to establish the extent to which major obstacles of SMEs, sources of initial capital, working capital financed by firm size, availability of information on accessibility of credit facilities by MSEs in Wolaita soddo town. From the study findings, it can infer that, sources of initial capital influences accessibility to credit facilities to a great extent followed by purchase of fixed assets financed by and major obstacles of SMEs which influenced access to credit facilities to a moderate extent and then availability of information on finance which has an influence in accessing credit facilities for micro and small enterprises only to a small extent. The study also concludes further that the respondents preferred to get their start-up capital from personal savings, relatives and friends because the collateral requirements and high repayment costs by financial institutions. 
\section[{i. Recommendations}]{i. Recommendations}\par
The study findings yielded the following recommendations in view of the challenges faced by MSEs when accessing credit facilities for the expansion of their businesses. ? The management of banks should stretch further to more business people so as to be able to serve their banking needs. This will serve in increasing their chance of securing credit facilities. ? Credit provider institutions should come up with products for MSEs where collateral requirements are lenient. ? The credit giving institutions come up with programs of educating the MSEs on how they can go about obtaining credit facilities. The study therefore recommends that the management of these businesses should put in place proper accounting practices and adequate internal control systems, which will counter problems of information asymmetry that make MSEs risky for credit giving institutions. ? The management of the institutions should increase their product portfolios they offered to MSEs. ? The marketing departments should endeavor to research on the clients' desired options and be able to include the probable and realistic ones onto the list. This will help to reach out to the MSEs that cannot access to credit facilities.\par
XIV. 
\section[{Areas for Further Research}]{Areas for Further Research}\par
The study recommends further research in the following areas; the effect of initial capital to start up and on the profitability of micro and small enterprises; the effect of finance to acquire fixed assets on MSEs access to credit facilities in Wolaita Sodo town. In future, a similar research should be done covering other towns as this research only covered micro and small enterprises in Wolaita Soddo town.   \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.5042372881355932\textwidth}P{0.17288135593220338\textwidth}P{0.17288135593220338\textwidth}}
Type of business\tabcellsep Population\tabcellsep Sample size(n)\\
Manufacturing\tabcellsep 135\tabcellsep 40\\
Construction\tabcellsep 230\tabcellsep 68\\
Trade\tabcellsep 223\tabcellsep 65\\
Service\tabcellsep 295\tabcellsep 87\\
Urban Agriculture\tabcellsep 74\tabcellsep 22\end{longtable} \par
 
\caption{\label{tab_1}Table 3 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
2017\\
Year\\
Volume XVII Issue VII Version I\\
( )\\
Global Journal of Management and Business Research\end{longtable} \par
 
\caption{\label{tab_2}C}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.6390510948905109\textwidth}P{0.19233576642335767\textwidth}P{0.018613138686131386\textwidth}}
\multicolumn{3}{l}{1: Results of the Pilot Study Reliability Statistics}\\
\multicolumn{3}{l}{Cronbach's Alpha Cronbach's Alpha No. of Items}\\
0.761\tabcellsep 0.780\tabcellsep 280\\
\tabcellsep \multicolumn{2}{l}{Source: Field Survey, 2017}\end{longtable} \par
 
\caption{\label{tab_3}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.11590909090909089\textwidth}P{0.28977272727272724\textwidth}P{0.38636363636363635\textwidth}P{0.057954545454545446\textwidth}}
\tabcellsep \tabcellsep 1: Sex of Respondents\tabcellsep \\
Sex\tabcellsep \multicolumn{3}{l}{Frequency Percent Cumulative Percent}\\
Female\tabcellsep 105\tabcellsep 37.5\tabcellsep 37.5\\
Male\tabcellsep 175\tabcellsep 62.5\tabcellsep 100.0\\
Total\tabcellsep 280\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Field survey, 2017}\end{longtable} \par
 
\caption{\label{tab_4}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.34404761904761905\textwidth}P{0.22261904761904763\textwidth}P{0.21755952380952379\textwidth}P{0.06577380952380953\textwidth}}
\tabcellsep \multicolumn{3}{l}{2: Education Level of Respondents}\\
\multicolumn{4}{l}{Qualification Frequency Percent Cumulative Percent}\\
BA\tabcellsep 38\tabcellsep 13.6\tabcellsep 13.6\\
Diploma\tabcellsep 105\tabcellsep 37.5\tabcellsep 51.1\\
TVET\tabcellsep 137\tabcellsep 48.9\tabcellsep 100.0\\
Total\tabcellsep 280\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Field survey, 2017}\end{longtable} \par
 
\caption{\label{tab_5}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.12570422535211268\textwidth}P{0.2693661971830986\textwidth}P{0.377112676056338\textwidth}P{0.07781690140845071\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{3: Age of Respondents}\\
Age\tabcellsep \multicolumn{3}{l}{Frequency Percent Cumulative Percent}\\
<25\tabcellsep 87\tabcellsep 31.1\tabcellsep 31.1\\
25-35\tabcellsep 186\tabcellsep 66.4\tabcellsep 97.5\\
35-45\tabcellsep 7\tabcellsep 2.5\tabcellsep 100.0\\
Total\tabcellsep 280\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Field Data of 2017}\end{longtable} \par
 
\caption{\label{tab_6}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.30146666666666666\textwidth}P{0.2652\textwidth}P{0.20626666666666665\textwidth}P{0.07706666666666667\textwidth}}
\tabcellsep \multicolumn{2}{l}{4: Age of the Company}\tabcellsep \\
\tabcellsep \multicolumn{3}{l}{Frequency Percent Cumulative Percent}\\
2-5 years\tabcellsep 122\tabcellsep 43.6\tabcellsep 43.6\\
6-10 years\tabcellsep 20\tabcellsep 7.1\tabcellsep 50.7\\
Below 2 Years\tabcellsep 138\tabcellsep 49.3\tabcellsep 100.0\\
Total\tabcellsep 280\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Field Survey 2017}\\
\multicolumn{4}{l}{Table 4.5: Nature of the Organization}\\
\tabcellsep \multicolumn{3}{l}{Frequency Percent Cumulative Percent}\\
Construction\tabcellsep 67\tabcellsep 23.9\tabcellsep 23.9\\
Manufacturing\tabcellsep 40\tabcellsep 14.3\tabcellsep 38.2\\
Service\tabcellsep 83\tabcellsep 29.6\tabcellsep 67.9\\
Trade\tabcellsep 66\tabcellsep 23.6\tabcellsep 91.4\\
Urban Agriculture\tabcellsep 24\tabcellsep 8.6\tabcellsep 100.0\\
Total\tabcellsep 280\tabcellsep \multicolumn{2}{l}{100.0}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Field Survey, 2017}\end{longtable} \par
 
\caption{\label{tab_7}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
2017\\
Year\\
Volume XVII Issue VII Version I\\
( ) C\\
Global Journal of Management and Business Research\end{longtable} \par
 
\caption{\label{tab_8}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.5508259212198221\textwidth}P{0.1144853875476493\textwidth}P{0.1414866581956798\textwidth}P{0.04320203303684879\textwidth}}
\tabcellsep \multicolumn{3}{l}{6: Current position of respondents}\\
\tabcellsep \multicolumn{3}{l}{Frequency Percent Cumulative Percent}\\
Accountant\tabcellsep 6\tabcellsep 2.1\tabcellsep 2.1\\
Cashier\tabcellsep 38\tabcellsep 13.6\tabcellsep 15.7\\
Manger\tabcellsep 208\tabcellsep 74.3\tabcellsep 90.0\\
Secretary\tabcellsep 28\tabcellsep 10.0\tabcellsep 100.0\\
Total\tabcellsep 280\tabcellsep 100.0\tabcellsep \\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Field Survey, 2017}\\
\multicolumn{3}{l}{c) Descriptive Analysis of access to finance of Small}\tabcellsep \\
and Micro Enterprise\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{This part explains the descriptive statistics}\tabcellsep \\
\multicolumn{3}{l}{based on the factors that affect the access to finance of}\tabcellsep \\
\multicolumn{3}{l}{SME's. The results for measures of central tendency and}\tabcellsep \\
\multicolumn{3}{l}{dispersion were obtained from the sample of}\tabcellsep \\
\multicolumn{3}{l}{respondents of manufacturing, construction; trade,}\tabcellsep \\
\multicolumn{3}{l}{service and urban agriculture are shown in the}\tabcellsep \\
following tables.\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{Table 4.7: Access to Finance SME's}\\
N\tabcellsep Mean\tabcellsep \multicolumn{2}{l}{Std. Deviation Variance}\\
Y 280\tabcellsep 2.088\tabcellsep .7310\tabcellsep .534\\
X1 280\tabcellsep 3.5205\tabcellsep .61876\tabcellsep .383\\
\multicolumn{2}{l}{X2 280 2.41652}\tabcellsep .552472\tabcellsep .305\\
\multicolumn{2}{l}{X3 280 2.833333}\tabcellsep .6392200\tabcellsep .409\\
X4 280\tabcellsep 3.436\tabcellsep .9595\tabcellsep .921\\
X5 280\tabcellsep 3.800\tabcellsep .6259\tabcellsep .392\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Field Survey, 2017}\end{longtable} \par
 
\caption{\label{tab_9}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.15454545454545454\textwidth}P{0.6954545454545454\textwidth}}
XII.\tabcellsep Econometric Result\end{longtable} \par
 
\caption{\label{tab_10}Table 4 .}\end{figure}
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