Dynamic efficiency of Australia's innovation systems: A regional and state analysis
Innovation systems present spatial heterogeneity across regions within states or provinces, posing a need for regional and state efficiency analysis. Yet existing efficiency models focus only on national and sub-national levels, inhibiting the analysis of resource misallocation across regions within...
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| Vydáno v: | Technological forecasting & social change Ročník 205; s. 123470 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.08.2024
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| Témata: | |
| ISSN: | 0040-1625, 1873-5509 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Innovation systems present spatial heterogeneity across regions within states or provinces, posing a need for regional and state efficiency analysis. Yet existing efficiency models focus only on national and sub-national levels, inhibiting the analysis of resource misallocation across regions within each state. This paper aggregated region-level efficiency into state-level efficiency measures. We constructed a new measure of state-level overall efficiency and decomposed it into technical and allocative efficiency. We implemented these aggregation and decomposition strategies using a new slack-based dynamic network data envelopment analysis model. Using data from 316 regions across Australia from 2012 to 2018, our empirical application showed that the misallocation of resources across regions within states was one significant factor explaining low state-level overall efficiency. Using Spatial Durbin Models, we found evidence for spatial agglomeration effects and the crowding out effects on regional technical efficiency (RTE) in relation to population and business density. Net business entry is one important driver of RTE. Regions having a smaller proportion of the manufacturing sector and a bigger proportion of the service sector are found to have lower RTE, reflecting the shift towards service industries in the Australian economy during the period 2012–2018.
•We construct state-level efficiency measures and decomposes them into technical and allocative efficiency components.•We use a slack-based dynamic network model in aggregation and decomposition strategies.•We provide the first empirical application to analyse regional innovation efficiency across Australian regions. |
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| ISSN: | 0040-1625 1873-5509 |
| DOI: | 10.1016/j.techfore.2024.123470 |