Advances and Applications in Statistics
Volume 17, Issue 2, Pages 105 - 126
(August 2010)
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FACTORS AFFECTING THE SURVIVAL AND SOLVENCY OF SMALL INDUSTRIAL BUSINESSES IN EGYPT
Amr I. A. Elatraby
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Abstract: Small Industrial Businesses (SIB) do face many difficulties, some of these businesses survive the difficulties and are considered “solvent” businesses, and some do not survive the difficulties and are considered “non-solvent” businesses. This paper tries to identify factors affecting the “status” of these small industries, whether they are “non-solvent” or “solvent” businesses. Proportional Hazard Model, known as Cox regression is used to analyze event history, and to estimate the survival time for “non-solvent” businesses. Also, the paper tries to estimate the probability of “solvency” of SIB given some financial ratios.
Data used is primary source data that has been collected and compiled by the researcher. Data used in the analysis includes thirty-eight variables related to small industrial businesses. 230 Small Industrial Businesses in five industries, are randomly selected from SIB’s in Dakhalia, Kaluobia and Fayoum covering the period from January 1, 2005 to June 30, 2009.
The main objectives of this study are to reach a statistical model that classifies small business into “non-solvent” and “solvent” based on some covariates, and to find out a set of explanatory variables that significantly affect the SIB performance.
Factor analysis is applied to reduce the number of variables to only nine significant factors. These factors represent the obstacles or difficulties facing the SIB including: financial, tax, location cost, basic cost, labor, technical, marketing, lack of information, and feasibility study obstacles.
Cluster analysis is used to “create” the binary response variable that takes a value of zero when SIB is “solvent” and a value of one when the SIB is “non-solvent”. Nine factors are used as covariates in the analysis. Using Cox regression model, results show that the labor obstacle is the most important covariate affecting the “solvent” or the “non-solvent” SIB’s, followed by: cost of SIB’s location, then by the technical, managerial obstacles and finally the importance of the SIB’s feasibility study. The model proved significant at the 0.05 level. Kaplan-Meier function is used to reach the SIB survival time and it shows that the average survival life of SIB in Egypt is between 6 and 7 years and after the 7th year, the SIB owner is encouraged to reevaluate his/her policies regarding the SIB improvement.
Probit analysis is also used to identify some financial ratios that have the greater effect on the SIB being “solvent”. The analysis has proved that financial ratios that affect “solvency” differ according to the type of the industry. The food industry suffers from rent cost, annual social insurance cost and transportation cost. The spinning and weaving industry suffers from rent cost, wages and transportation cost. The Chemical industry and the Wood industry suffer from the cost of raw materials, and the Metal industry suffers from transportation cost. |
Keywords and phrases: small industrial businesses (SIB), cluster analysis, factor analysis, survival analysis, proportional hazards regression model, Probit analysis, Kaplan-Meier function. |
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