@journal { 2017constructi, title = {Constructing a Database for Computable General Equilibrium Modeling of Sydney, Australia, Transport Network}, address = {}, booktitle = {Transportation Research Record: Journal of the Transportation Research Board}, chapter = {}, edition = {}, editor = {}, eprint = {}, howpublished = {}, institution = {}, journal = {}, key = {}, location = {}, month = {}, note = {}, number = {}, organization = {}, pages = {54-62}, publisher = {}, series = {}, school = {}, url = {http://trrjournalonline.trb.org/doi/abs/10.3141/2606-07}, volume = {2606}, year = {2017}, isbn = {}, doi = {10.3141/2606-07}, language = {}, accession_number = {}, short_title = {Constructing a Database for Computable General Equilibrium Modeling of Sydney, Australia, Transport Network}, author_address = {}, keywords = {}, abstract = {In the search for benefits to justify transport projects, economic appraisals have increasingly incorporated the valuation of impacts to the wider economy. Computable general equilibrium (CGE) models provide a framework to estimate these impacts by simulating the interactions of urban economies and transport networks. In CGE models, households and firms are represented by microeconomic behavioral functions, and markets adjust according to prices. As markets both inside and outside the transport network are taken into account, a wide variety of measures that can assist in economic appraisals can be extracted. However, urban CGE models are computationally burdensome and require detailed, spatially disaggregate data. This paper discusses the methodology used to develop a database, including an input–output table, for the calibration of an urban CGE model for Sydney, Australia. Official and publicly available data sources were manipulated by using a number of mathematical and statistical techniques to compile a table for 249 regions and 20 sectors across Sydney. Issues, such as determining the appropriate level of aggregation, generating incomplete data, and managing conflicting data, that other input–output table developers may encounter when constructing multiregional tables were addressed in the study. The table entries themselves were mapped and explored, as they provide a useful study of the spatial economy of Sydney. Future work will focus on streamlining the construction of input–output tables and incorporating new data sources.}, call_number = {}, label = {IELab}, research_notes = {}, author = {Robson , Edward and Dixit , Vinayak V.} }