A novel composite index for regional innovation assessment with an application to Egyptian governorates
Innovation can be classified based on the type of its outcome which includes knowledge and technology, creative and cultural outcomes in addition to intangible assets. Innovation composite index is generally designed with the purpose of estimating the innovation capabilities and competencies of different governorates or regions. In this work, a governorate innovation composite index (GICI) is constructed and applied to the Egyptian governorates to evaluate the efficiency and effectiveness of adopted innovation measures and policies in these regions along with the assessment of the societal impact. Considering standard type of innovation composite indicators, the Egyptian index proposed in this work is conceptually broken down into a set of innovation inputs and outputs composing its production function. Inputs are divided into factors which are used to produce innovation output while considering specific enabling factors. The application of the innovation governorate index to the Egyptian context has delineated general as well as specific results. First, Innovation performance of governorates measured by the value of the composite index, shows a moderate attitude, whereas input sub-pillars for governorates are however on the low side. In light of this finding, Egypt’s government needs to consolidate efforts towards enhancing the capacity of innovation inputs. Second, the mean value of the governorates output pillar ranged from 53 to 99 percent. Based on this finding, the Egyptian government needs to adopt an integrated policy package to achieve the balance between input and output parameters of innovation. Finally, this paper suggests that the difference in innovation performance between regions should be considered as an important part in developing national innovation strategies.
innovation production function, composite index, regional innovation, benchmarking, innovation metrics
O31 , O32 , O38
FundingThis work received support from TED-PPP project, an EU funded project with agreement number ENI 2017/394-139
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