Abstract
This chapter details issues and specific measures that need to be taken into account when preparing and harmonising sample survey and census data to build a spatial microsimulation model. Transforming and manipulating these data sources, so that they are as compatible as possible, will ensure that the spatial microsimulation technique being used is optimised and the output gained from the model will be as accurate as possible. Several processes and issues are discussed in this chapter, including data requirements and compatibility and data imputation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A nationally representative survey is one that has been designed and conducted in a way so that it captures the characteristics of all persons and households. Households and persons within the survey are then assigned a ‘weight’, which, when summed, will equal the entire population of that nation.
References
Brick, J. M., & Kalton, G. (1996). Handling missing data in survey research. Statistical methods in medical research, 5, 215–238. Sage publications, United Kingdom.
Cassells, R., Harding, A., Miranti, R., Tanton, R., & McNamara, J. (2010). Spatial microsimulation: Preparation of sample survey and census data for SpatialMSM/08 and SpatialMSM/09 (NATSEM Technical Paper 36). https://guard.canberra.edu.au/natsem/index.php?mode=download&file_id=1065
Harding, A. (Ed.). (1996). Microsimulation and public policy (Contributions to economic analysis series). Amsterdam: North Holland.
Harding, A., Vu, N. Q., Payne, A., & Percival, R. (2009). Trends in effective marginal tax rates in Australia from 1996–97 to 2006–07. Economic Record, 85(271), 449–461.
Lloyd, R. (2007). STINMOD: Use of a static microsimulation model in the policy process in Australia. In A. Harding & A. Gupta (Eds.), Modelling our future: Population ageing, social security and taxation (International symposia in economic theory and econometrics, Vol. 16). Amsterdam: North Holland.
Percival, R., Abello, A., & Vu, Q. (2007). STINMOD (Static income model). In A. Gupta & A. Harding (Eds.), Modelling our future: Population ageing, health and aged care (International symposia in economic theory and econometrics, Vol. 16, pp. 477–482). Amsterdam: North Holland.
Tanton, R., Vidyattama, Y., McNamara, J., Vu, Q. N., & Harding, A. (2009). Old, single and poor: Using microsimulation and microdata to analyse poverty and the impact of policy change among older Australians. Economic Papers: A Journal of Applied Economics and Policy, 28(2), 102–120.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media Dordrecht.
About this chapter
Cite this chapter
Cassells, R., Miranti, R., Harding, A. (2012). Building a Static Spatial Microsimulation Model: Data Preparation. In: Tanton, R., Edwards, K. (eds) Spatial Microsimulation: A Reference Guide for Users. Understanding Population Trends and Processes, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4623-7_2
Download citation
DOI: https://doi.org/10.1007/978-94-007-4623-7_2
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-4622-0
Online ISBN: 978-94-007-4623-7
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)