Development of a funding model for allocating capital grants to the federal universities in Nigeria
The Nigerian University system enjoys the block grant funding policy, for both recurrent and capital funds. Unfortunately, the allocation of capital grants was never based on scientific parameters right from inception thereby necessitating the research work. The main aim and objectives of the research were to develop a more equitable model for the allocation using acceptable parameters. Condition surveys were conducted to obtain information on: ages of selected buildings, floor areas, years of commissioning and years of last renovation. Eleven out of the twenty-one Federal universities selected on stratified sampling basis, were used to assemble the primary data as at 1997. The main assumptions imposed on the data on the buildings were: no remarkable differences in: (i) height and shape, (ii) location and climatic factors, (iii) labour and management policy. Multiple Regression Analysis was used to develop a linear allocation model which enabled some predictive transparent variables to be developed and was used to estimate the cost of repairs for the existing building stocks. A predictive linear model with goodness of fit value (R2) of 81.55% was developed which provided a basis for allocating available capital funds among the Universities. It was recommended that, for practical purpose, the method of capital funds allocation should have the combination of formula and non-formula driven components. Modified forms of the model can be used in policy issues on building maintenance allocations for other tertiary institutions in Nigeria, and in other African countries. The research work points out the need for: (a) the establishment of modem maintenance information system in all the Universities with a central database at the National Universities Commission, Abuja; (b) the introduction of independent contractor/consultant-controlled Facilities Management outfits for each University; (c) educating the Universities on the new fiscal allocation strategies; and, (d) developing appropriate linkages with maintenance research establishments.
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