Assessment of Airport Productivity and efficiency in Nigeria.
NWAOGBE, OBIOMA REUBEN
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This study assessed the productivity and efficiency of airports in Nigeria employing the method of Data Envelopment Analysis (DEA).Two variants of DEA models were estimated and their outputs compared in order to enhance the robustness of the study. These models include the one based on Constant Returns to Scale (CRS) to characterise the input-output combinations and that based on Variable Returns to Scale (VRS). It is sometimes argued that DEA models generally tend to overestimate efficiencies of decision making units. Bootstrapping of the DEA results was therefore carried out to adjust the efficiency scores thereby improving considerably the robustness of the estimates and their status in terms of reliability. Another feature of the regular DEA model is that it is non-parametric. To reflect some parametric arguments a censured regression model was estimated to capture the impact of variables, such as hub status, type, size and nearness to seaports, which, in the context of airport operations, affect productivity and efficiency but are not evident in DEA. The data used in the DEA estimations were time series data covering the period 2003-2013 on the inputs and outputs of 30 airports in the industry. These data were sourced from the records of airport terminal operations kept by FAAN and NCAA. Primary data were used for the censored regression estimate. These were obtained from responses to structured questionnaires distributed to samples of airport users and airport operators. On the wholeCRS estimates show that 3 of the airports were efficient with the efficiency score of 100%, whereas the result of the VRS shows that 8 of the airports were operating efficiently with the efficiency score of 100%. The results also show that 7 Nigerian airports were operating under increasing returns to scale whilst 2 of the airports were operating under decreasing returns to scale. The least efficient of the airports scored 1.23% and 1.35% respectively in the CRS and VRS estimates. A decomposition of the average productive efficiency scores into Technical Efficiency (TE), Pure Technical Efficiency (PTE), and Scale Efficiency (SE) revealed the following values- 0.4223 (42%), 0.5989 (60%), and 0.7469 (75%) respectively, suggesting that Scale Efficiency was the main driver of productivity growth in the Nigerian airports during the study period. The result of the censured regression estimates showed that the recorded growth in productivity could be attributed to hub ports and privately owned ports. The study therefore concludes that significant insights have been gained in unraveling the status of productive efficiency in the airport industry, using a robust DEA model. The results are useful in terms of policy and provide the benchmarks which the government could use in the privatisation program currently canvassed in the airport industry.