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\title{Analysis of Financial Distress and its Determinants in Selected SMEs in Wolaita Zone}
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             \author[1]{Ephrem G.  Selassie}

             \author[2]{Ephrem G.  Selassie}

             \affil[1]{  Wolaita Sodo University}

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\date{\small \em Received: 8 December 2015 Accepted: 5 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


The development of SMEs is considered as one of vital determinants of the growth of Ethiopian economy, and for secure equitable distribution of the benefits of the economic growth. However, SMEs in the country are leveled as not performing well and falling short of yielding the much anticipated contribution for the growth of the economy as they are expected. This study is conducted to analyze financial distress level of SMEs in Wolaita Zone and indentify those factors affecting their financial health. In this study 30 firms form three sectors are selected as samples selecting ten samples from each of manufacturing, service and trade sector using purposive sampling method. Accordingly, the results of Altman?s Zeta Score Model analysis indicate that three of the ten selected firms in the service sectors are found to be financially distressed, but none of the sampled SMEs in the sector are below the bankruptcy point. In manufacturing sector, one of the ten selected SMEs is found with the Zeta score of below the bankruptcy line and all of the rest of the sampled SMEs are found to be under financial distress though their Zeta score is above the bankruptcy point.

\end{abstract}


\keywords{manufacturing, service and trade sector.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
thiopian government is giving due attention for the growth and development of Small and Medium Enterprises both in quantity and quality as they are believed to play very important role in accelerating the country's much anticipated Growth and Transformation Plan by boosting national income and wealth and promoting private sector development. This is also well articulated in the Growth and Transformation Plan which prioritizes and identifies the development of micro and small businesses as catalyst for promoting industrial development. SMEs are believed to be main derives of economic growth in the countries urban areas. They create employment and income generation opportunity for women and youth in urban areas by accommodating large number of young and women population. This in turn makes the economic growth not only sustainable but promotes fair distribution of wealth among the citizens by accommodating the most venerable group of the population such as the youth and the women at large. Thus studies on SMEs are important because SMEs are viewed as the backbone of the economy of many countries all over the world since they are the incubators of employment, innovation and growth (Craig, Jackson and Thomson 2004).\par
The growth of Small and medium Enterprises however has been challenged by various problems of which lack of fund is found to the significant one \hyperref[b0]{(Alemayehu 2007}). Small and Medium-sized Enterprises (SMEs), particularly small firms, have historically faced significant difficulties in accessing funding due to the lack of credible information available to potential providers of funds (Ang, 1991). These enterprises are usually found to be informational y more obscure the bigger business organizations since they often have no certified audited financial statements to provide credible financial information on a regular basis(IBD). Difficulties in accessing funding are especially relevant for small firms as they have low assets with which to secure funding. Furthermore it has been difficult for creditors to measure the credit worthiness of these firms, as they have no concrete financial information. As a result studies in the area of financial distress conditions of SMEs are very much rare due to unavailability of financial data. Yet it is very important and reasonable to measure the financial distress conditions for SMEs as they are found to be the key divers of the nation's economic growth. 
\section[{a) Objectives of the study}]{a) Objectives of the study}\par
Therefore the main objective of the study is to measure the financial distress conditions, which is the financial healthiness of Small and Medium Enterprises in Wolaita Zone. 
\section[{b) Significance of the study}]{b) Significance of the study}\par
Currently, there is an information vacuum on the financial health status of SMES in the study area. The study will strengthen information need in the sector on financial health status and provide a foundation for monitoring and assessing the level of intervention needed in this era of the Nation's much anticipated transformation of the sector. 
\section[{II.}]{II.} 
\section[{Methodology}]{Methodology}\par
were collected for a year 2015. A sample of 30 small scale businesses was purposively selected for the study. The sampled firms were randomly selected from the list of businesses incorporated as private limited liability companies with the Trade and Industry Department of the Zone. The sample of ten firms was taken from each of three sectors such as service sector, trade and manufacturing. The choice of private limited liability businesses was to ease the problem of data collection since this category of enterprises are required by law to prepare annual financial reports for the purpose of rendering annual returns. Thus data were derived from the financial statements (Balance sheets and income statements) of sampled business enterprises. The financial data extracted include working capital, sales, total assets, earnings before interest and tax, market value of equity and book value of total liability (debts). In order to use Altman's Z-score model in predicting financial health, Z-scores' were computed for each sampled firms for the year. Based on the computed Z-scores and using Altman's criterion, the businesses were then classified into financially healthy, unhealthy or cannot say (Grey). 
\section[{III.}]{III.} 
\section[{Model Specification}]{Model Specification}\par
In this study the well known Altman's Z-score model, based on five financial ratios and a bankruptcy predictor model developed by \hyperref[b19]{(Teti et. al 2012)} used firstly exclusively for small and medium-sized enterprise in Wolaita for the year of 2015 is applied the specified analytical technique for this study is the Altman's discriminant function model and is as follows:Z=0.012X1+0.014X2+0.033X3+0.006X4+0.999X5 ??\textbf{(1)}\par
Where: XI = Working capital/Total assets X 2 = Retained Earnings/Total assets X 3 = Earnings /Total assets X 4 = Equity/Book value of total debt X 5 = Sales/Total assets Z = Overall Index\par
Working Capital/Total Assets (X1): The Working capital/Total assets ratio is a measure of the net liquid assets of the SMEs relative to the total capitalization. Working capital is defined as the difference between current assets and current liabilities (Sulphey, M. M., \& Nisa, S. 2013). However for the SMEs under the study cash will be used as a proxy for working capital as they have only cash in their current assets section, and do not have current liabilities Ordinarily, an SME experiencing consistent operating losses will have shrinking current assets in relation to total assets. 
\section[{Retained Earnings/Total Assets (X2):}]{Retained Earnings/Total Assets (X2):}\par
This is a measure of cumulative profitability over time. The age of a firm is implicitly considered in this ratio. For example, a relatively young firm will probably show a low RE/TA ratio because it has not had time to build up its cumulative profits (Aremu, M. A., \& Adeyemi, S. L. 2011). Therefore, it may be argued that the young firm is somewhat discriminated against in this analysis, and its chance of being classified as bankrupt is relatively higher than another, older firm, ceteris paribus. The incidence of failure is much higher in a firm's earlier years. 
\section[{Operating Earnings /Total Assets (X3):}]{Operating Earnings /Total Assets (X3):}\par
This ratio is calculated by dividing the total assets of a firm into its earnings before interest and tax reductions. It is a measure of the true productivity of the firm's assets, abstracting from any tax or leverage factors. Since a firm's ultimate existence is based on the earning power of its assets, this ratio appears to be particularly appropriate for studies dealing with corporate insolvency/failure. Furthermore, insolvency in a bankruptcy sense occurs when the total liabilities exceed a fair valuation of the firm's assets with value determined by the earning power of the assets \hyperref[b19]{(Teti et. al (2012)}). 
\section[{Book Value of Equity/Book Value of Total Debt (X4):}]{Book Value of Equity/Book Value of Total Debt (X4):}\par
The measure shows how much the firm's assets can decline in value before the liabilities exceed the assets and the farm firm becomes insolvent \hyperref[b19]{(Teti et. al (2012)}. Sales/Total Assets (X5): The capital-turnover ratio is a standard financial ratio illustrating the sales generating ability of the firm's assets. It is one measure of management's capability in dealing with competitive conditions (Sulphey, M. M., \& Nisa, S. 2013).\par
In this model Altman stated Z' Score which is less than 1.21 as financially distressed zone (Zone I) and is Z' score is greater than 2.90 it is called Zone II which is financially not distressed zone. The result of the Z' Score which is in between 1.23 to 2.90 is categorized as a "gray area". A "gray area" as defined by (ALTMAN E. 1983) is an area where there is no clear line between bankruptcy and non-bankruptcy. It is in deeded undesirable condition of financial health of the firms. It is characterized by distress than healthiness.\par
IV. 
\section[{Data Analysis}]{Data Analysis}\par
In this section the results of the analysis made to determine the financial health position of selected SMEs is presented. To achieve the objective of the study, which is to evaluate financial distress (health) condition of selected SMEs, the data collected are first analyzed with the use of five accounting ratios which are part of the Z -score analysis. These accounting ratios are then combined into a single measure of Altman's Zscore with the help of Multiple Discriminate Analysis (MDA). The result of the analysis for each sector under study is presented as follows. 
\section[{i. SMEs in Service sector}]{i. SMEs in Service sector}\par
The following table indicates the Z-score of selected SMEs Wolaita zone in service sector in which is calculated based on the (Altman1991) Model. As it can be observed form the above table, the only three of the selected ten small businesses in service scoter are found to be in "gray" of financial health condition indicator. The gray area is undesirable area as it amounts to financial distress. The rest of the firms under study are financially healthy. The highest zeta score is 3.45 and the lowest is 2.35. None of the selected firms is found under the bankruptcy zone. However there are three firms under gray area, which is between the Zeta scores of 1.21 and 2.90, which in turn indicates that those firms are under finical distress.  The manufacturing sector in Ethiopia recently is given a due attention as the government is working to bring structural transformation in the economy from agrarian based to manufacturing based. The strategic pillars of the GTP II (Growth and Transformation Plan) related to manufacturing include (1) developing light and small manufacturing enterprises that are globally competent and leading in Africa (2) establishing a foundation for further growth of the strategic heavy industries which ultimately enable Ethiopia to become an industrialized country by 2025 (source: GTP II, PP 38). This is, however, seems an over ambitious plan as reports are indicating that despite the sector level growth, the much needed structural transformation has never even showed a sign of change. The industrial base of the country has remained low contributing only 12-14\% to GDP of which the medium and large factories as well as the light and small manufacturing shared respectively 4\% and 1.2\% throughout the past decade. In light of the above national strategy the performance and growth of SMEs which are have been expected to be graduated and joined the medium and large scale being hampered by various factors. That is what the financial health conditions analysis of some selected manufacturing firms on table below shows.\par
The following table indicates the Z-score of selected SMEs Wolaita zone in manufacturing sector in which is calculated based on the (Altman1991) Model. As it can be observed from the above table one of the sampled firms is already below the bankruptcy threshold with the Zeta score of 1.13 which indicates that it is soon going to be bankrupt as the Altman model indicates. It is also least Z-score of all sampled firms. The maximum Z-score is 1.93. This indicates that the remaining firms, though they are above the bankruptcy threshold, all of them are within the area identified as "gray" area in which one cannot exactly determine the exact nature of financial health of the firm. This is not recommendable status for the firms. Therefore, they are not yet free from bankruptcy threat. This indicates that they are financially distressed. The above figure indicates that factors affecting financial health of the firms are observed to be fluctuating among sampled firms. Sales to total assets ratio is the one which significantly affects the financial healthiness of the firms. Particularly for the firm which has the lowest Zeta score, sales to assets ratio is the lowest as it can be observed from the table above. Retained earnings to total assets ration appears to look not changing in the case of most of the selected manufacturing firms. Therefore the firms need to work on increasing sales volumes in ordered to get out out of financial distress.\par
iii. 
\section[{SMEs in Trade Sector}]{SMEs in Trade Sector}\par
Trade is one of the prominent sectors in the economy of Ethiopia (Tesfayenesh, 2016). The result of the analysis indicates that SMEs in the sector are highly performing. The following table indicates the Altman Zeta score of selected ten SMEs in Wolaita Zone. The above data indicates that all the sampled firms are way above the bankruptcy threshold, which is 1.23. The individual Zeta score of the firms is also very high. The maximum score is 3.68 whereas the minimum score is 2.59. However, there are still firms that are found in gray area. Four of the ten firms are found in the gray area. Firms which are found gray area need to work hard to join the healthy area which is indicated by the Zeta score of above 2.9. It can be understood from this fact that all the selected firms are financially healthy and way far from bankruptcy threat. The figure above indicates how the variables are affecting the Zeta scores of the firms. Sales ratio, working capital and EBIT are observed to be playing greater role in the financial healthiness of the selected firms in service sector. Accordingly, those forms which are under financial distress need to work on increasing Sales ratio, working capital and EBIT ratios.\par
V. 
\section[{Conclusion}]{Conclusion}\par
In this study 30 firms form three sectors are selected as samples selecting ten samples from each of manufacturing, service and trade sector using purposive sampling method. The purpose of the study was to determine the financial healthiness of the SMEs and to determine those firms under financial distress. Accordingly, the results of Altman's Zeta Score Model analysis indicate that three of the ten selected firms in the service sectors are found to be financially distressed, but none of the sampled SMEs in the sector are below the bankruptcy point. In manufacturing sector, one of the ten selected SMEs is found with the Zeta score of below the bankruptcy line and all of the rest of the sampled SMEs are found to be under financial distress though their Zeta score is above the bankruptcy point. On the other hand SMEs in the trade sector has shown preferably good results with high Zeta scores. However the three of the ten samples are found in the distress zone among the selected SMEs in the trade sector as well. Sales ratio, working capital and EBIT are found to be the major variables among the other variables in the model that affects the financial health of the SMEs. Therefore the SMEs under the financial distress are needed to work particularly on increasing their Sales ratio, working capital and EBIT ratios.    \begin{figure}[htbp]
\noindent\textbf{01} \par 
\begin{longtable}{}
\end{longtable} \par
  {\small\itshape [Note: (Source: author competition)]} 
\caption{\label{tab_0}Table 01 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.12072463768115942\textwidth}P{0.08992753623188406\textwidth}P{0.002463768115942029\textwidth}P{0.08992753623188406\textwidth}P{0.002463768115942029\textwidth}P{0.09115942028985506\textwidth}P{0.002463768115942029\textwidth}P{0.01108695652173913\textwidth}P{0.08376811594202899\textwidth}P{0.09239130434782608\textwidth}P{0.1539855072463768\textwidth}P{0.10963768115942028\textwidth}}
firms, according\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Ratio/ Firms\tabcellsep WC/TA\tabcellsep \tabcellsep RE/TA\tabcellsep \tabcellsep EQT/TD\tabcellsep \tabcellsep \multicolumn{2}{l}{EBIT/TA}\tabcellsep SAL/TA\tabcellsep Zeta Score\\
F1\tabcellsep 1.2687\tabcellsep \tabcellsep 1.0286\tabcellsep \tabcellsep 1.3852\tabcellsep \tabcellsep \tabcellsep 1.1999\tabcellsep 2.2740\tabcellsep 2.3543\tabcellsep 2016\\
F2 F3\tabcellsep 1.3079 1.3795\tabcellsep \tabcellsep 1.4487 1.0532\tabcellsep \tabcellsep 1.5636 1.4210\tabcellsep \tabcellsep \tabcellsep 1.2080 1.3429\tabcellsep 2.9046 3.3705\tabcellsep 2.9965 3.4534\tabcellsep Year\\
F4\tabcellsep 1.3692\tabcellsep \tabcellsep 0.0920\tabcellsep \tabcellsep 2.7751\tabcellsep \tabcellsep \tabcellsep 1.0163\tabcellsep 2.3581\tabcellsep 2.4711\\
F5 F6 F7 F8 F9 F10\tabcellsep 1.4449 1.4569 1.4627 1.4613 1.4268 1.3582\tabcellsep \tabcellsep 0.0388 0.0799 1.0956 1.0713 1.0578 0.0960\tabcellsep \tabcellsep 1.3061 2.0358 3.6042 2.9745 1.5895 0.4739\tabcellsep \tabcellsep \tabcellsep 1.1404 1.2301 1.2042 1.2908 1.2114 1.2452\tabcellsep 3.1854 2.9236 3.2577 2.8727 2.9847 2.3311\tabcellsep 3.2500 3.0139 3.4135 3.0082 3.0734 2.3695\tabcellsep Volume XVI Issue VIII Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( ) C\\
1.5000 2.0000 2.5000 3.0000 3.5000 4.0000\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep WC/TA RE/TA EQT/TD EBIT/TA SAL/TA zeta score\tabcellsep Global Journal of Management and Business Research\\
f1\tabcellsep f2\tabcellsep f3\tabcellsep f4\tabcellsep f5\tabcellsep f6\tabcellsep f7\tabcellsep f8\tabcellsep f9\tabcellsep f10\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{02} \par 
\begin{longtable}{P{0.053942307692307685\textwidth}P{0.12259615384615383\textwidth}P{0.12259615384615383\textwidth}P{0.12423076923076923\textwidth}P{0.1258653846153846\textwidth}P{0.17\textwidth}P{0.13076923076923078\textwidth}}
Ratio/ Firms\tabcellsep WC/TA\tabcellsep RE/TA\tabcellsep EQT/TD\tabcellsep EBIT/TA\tabcellsep SAL/TA\tabcellsep Zeta Score\\
F1\tabcellsep 0.28090\tabcellsep 0.03828\tabcellsep 0.56534\tabcellsep 1.30587\tabcellsep 1.84093\tabcellsep 1.86948\\
F2\tabcellsep 0.29050\tabcellsep 0.03352\tabcellsep 0.82653\tabcellsep 0.31788\tabcellsep 1.85028\tabcellsep 1.88157\\
F3\tabcellsep 0.30194\tabcellsep 0.05251\tabcellsep 0.56145\tabcellsep 0.20399\tabcellsep 1.10747\tabcellsep 1.13047\\
F4\tabcellsep 0.23846\tabcellsep 0.02527\tabcellsep 0.58532\tabcellsep 0.25692\tabcellsep 1.54405\tabcellsep 1.56658\\
F5\tabcellsep 0.32052\tabcellsep 0.06055\tabcellsep 1.04120\tabcellsep 0.12947\tabcellsep 1.34615\tabcellsep 1.38463\\
F6\tabcellsep 0.33556\tabcellsep 0.03712\tabcellsep 1.12840\tabcellsep 0.08022\tabcellsep 1.56990\tabcellsep 1.61059\\
F7\tabcellsep 0.26087\tabcellsep 0.05646\tabcellsep 0.81479\tabcellsep 0.18019\tabcellsep 1.60894\tabcellsep 1.63922\\
F8\tabcellsep 0.46741\tabcellsep 0.04005\tabcellsep 0.50673\tabcellsep 0.18112\tabcellsep 1.76555\tabcellsep 1.78777\\
F9\tabcellsep 1.37222\tabcellsep 1.06121\tabcellsep 0.59780\tabcellsep 1.71014\tabcellsep 1.87620\tabcellsep 1.93564\\
F10\tabcellsep 0.39854\tabcellsep 0.49633\tabcellsep 0.65452\tabcellsep 0.57630\tabcellsep 1.80587\tabcellsep 1.84086\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{(Source: author competition)}\end{longtable} \par
 
\caption{\label{tab_2}Table 02 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{03} \par 
\begin{longtable}{P{0.06097826086956522\textwidth}P{0.1201086956521739\textwidth}P{0.1201086956521739\textwidth}P{0.12195652173913044\textwidth}P{0.12380434782608694\textwidth}P{0.17369565217391306\textwidth}P{0.12934782608695652\textwidth}}
Ratio/ Firms\tabcellsep WC/TA\tabcellsep RE/TA\tabcellsep EQT/TD\tabcellsep EBIT/TA\tabcellsep SAL/TA\tabcellsep Zeta Score\\
F1\tabcellsep 1.3364\tabcellsep 0.2697\tabcellsep 1.5007\tabcellsep 1.3299\tabcellsep 2.5640\tabcellsep 2.6387\\
F2\tabcellsep 1.4001\tabcellsep 0.2330\tabcellsep 1.3836\tabcellsep 1.1556\tabcellsep 2.7073\tabcellsep 2.7772\\
F3\tabcellsep 1.4736\tabcellsep 0.1276\tabcellsep 3.3093\tabcellsep 0.7792\tabcellsep 2.9308\tabcellsep 3.0612\\
F4\tabcellsep 1.4814\tabcellsep 0.0857\tabcellsep 1.8014\tabcellsep 0.5804\tabcellsep 2.5937\tabcellsep 2.6730\\
F5\tabcellsep 1.4844\tabcellsep 1.0742\tabcellsep 1.1274\tabcellsep 1.4003\tabcellsep 2.8599\tabcellsep 2.9355\\
F6\tabcellsep 1.4885\tabcellsep 0.0907\tabcellsep 1.4066\tabcellsep 1.5778\tabcellsep 3.6125\tabcellsep 3.6839\\
F7\tabcellsep 1.4926\tabcellsep 1.0189\tabcellsep 5.0766\tabcellsep 0.2816\tabcellsep 2.9234\tabcellsep 3.1219\\
F8\tabcellsep 1.4941\tabcellsep 1.1540\tabcellsep 4.5129\tabcellsep 0.5880\tabcellsep 3.5773\tabcellsep 3.7602\\
F9\tabcellsep 1.4848\tabcellsep 0.1172\tabcellsep 4.1788\tabcellsep 0.3978\tabcellsep 3.3860\tabcellsep 3.5423\\
F10\tabcellsep 1.4763\tabcellsep 0.1158\tabcellsep 2.7817\tabcellsep 0.4988\tabcellsep 2.4818\tabcellsep 2.5935\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{(Source: author competition)}\end{longtable} \par
 
\caption{\label{tab_3}Table 03 :}\end{figure}
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