Abstract

Soon after independence in 1963, many firms including those in productive public and private sectors were set up in Kenya to produce goods and services for consumption in Kenya and beyond. Some public and private firms were set up in Kenya’s coastal counties while some were set up in other parts of Kenya. As at June 1990 however, most of these firms were either collapsed and liquidated or were ailing. Few were performing fairly. By 2015, there were 194 public firms which were in operations. Many of these however were formed after 1990. 46.4% of these had poor financial performance as measured by accounting and market ratios (ROE and ROA). About 35 of these firms, among them sugar firms and local authorities had more operating expenses than revenue. Postal Corporation of Kenya for example made KES 2.6b in revenues. Operating expenses however were KES 4.16b. National Oil Corporation of Kenya (NOCK) made KES 24.76b in revenues. Cost of sales excluding operating expenses however were KES 22.95b (Kenya’s treasury department, statement, 2015). In the small enterprise segment, about 300,000 SME’s were set up in 2010. About 350,000 SME’s were set up in 2012. However 2.2m SME’s closed down in six years ending 2015. About 35% of these closed down in 2015. Overall, about 96% of the firms which are set up closed down by the end of their first year in operations. (World Bank Report, 2010). The questions were ‘why did firms underperform and why did these firms fail?’ Published annual financial reports of firms, studies by Kenya National Bureau of Statistic (KNBS) and others attributed the poor financial performance and failure of the firms to many factors; high cost of energy, intense competition, high cost of raw materials, obsolete equipment, poor management, poor technical skills, high cost of finance and other bank charges, inadequate finance, family feuds, lack of succession plan etc. Some empirical studies attributed poor financial performance and failure of firms to financing; the capital structure. None however attributed this to capital structure gearing levels. This constituted a research gap to be filled by this study to add to the body of knowledge and literature. The capital structure gearing level is the proportion of external finance used in financing a firm. This proportion (gearing) may vary between ›0 to 100% (Brealey & Myers, 1991). Some firms however have a proportion ranging between ›0 and <30% (LG), 30%-‹35% (MG1) ≥35%-‹40% (MG2) ≥40%-≤60% (MG3) and ›60% (HG). The external finance may be inform of short term and long term debt and equity finance. Debt carries a fixed slice of earnings. The gearing levels therefore debt levels will carry a proportionate fixed slice of earnings. High gearing (HG) will magnify the effect on earnings and hasten the process of insolvency (Brealey & Myers, 1991). Poor financial performance and failure therefore maybe the result of inappropriate gearing level. Gearing level therefore was the problem. This study sought to do the following:

1. Assess the capital structure of public and the private sector firms in Kenya’s coastal counties.
2. Assess the capital structure gearing levels of public and private sector firms in Kenya’s coastal counties.
3. Determine the effect of the capital structure gearing levels on financial performance of public and private sector firms in Kenya’s coastal counties.

This involved a target population of 500 productive firms in Kenya’s Coastal Counties. Using the Cochran’s sample size formula, 50% proportion of the productive public and private sector firms randomly selected, the sample was 139 firms. They were observed for a period of 2003 to 2015. Questionnaires and structured interviews were used as instruments for collecting primary data from finance officers or finance managers or their equivalent of the firms. Secondary data was obtained from financial statements (income statement and the balance sheet). Control variables were; size, tangibility and growth. The basic framework for regression was of the form below;

Y= f (gearing levels+ tangibility+ size+ growth)

Where, Y= return on assets/return on equity

ROA/ROE=f (gearing levels+ tangibility+ size+ growth)

Data analysis was done using both descriptive statistics and inferential statistics (regression).

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