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Estimating Small-Area Income Deprivation: An Iterative Proportional Fitting Approach

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Spatial Microsimulation: A Reference Guide for Users

Part of the book series: Understanding Population Trends and Processes ((UPTA,volume 6))

Abstract

Small-area estimation, in particular the estimation of small-area income deprivation, has potential value in the development of new or alternative components of multiple deprivation indices. These new approaches enable the development of income distribution threshold-based measures of income deprivation as opposed to benefit count-based measures of income deprivation and so enable the alignment of regional and national measures such as the Households Below Average Income with small-area measures. This chapter briefly reviews a number of approaches to small-area estimation before describing in some detail an iterative proportional fitting based spatial microsimulation approach. This approach is then applied to the estimation of small-area HBAI rates at the small-area level in Wales in 2003–2005. This chapter discusses the results of this approach, contrasts them with contemporary ‘official’ income deprivation measures for the same areas and describes a range of ways to assess the robustness of the results.

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Notes

  1. 1.

    See http://www.statistics.gov.uk/geography/gor.asp, mean size c 2 million households.

  2. 2.

    The second level of census aggregation (containing multiple census ‘output areas’) containing on average around 600 households.

  3. 3.

    Administrative areas that are nested within local authorities and which are exact aggregates of, on average, five LSOAs.

  4. 4.

    Modified OECD scale  =  1  +  0.5  ×  number of adults  +  0.2  ×  number of dependent children  <  14; HBAI scale (BHC)  =  0.67  ×  1 adult  +  0.33  ×  number of further adults  +  0.2  ×  number of children aged  <  14; HBAI scale (AHC)  =  0.58  ×  1 adult  +  0.42  ×  number of further adults  +  0.2  ×  number of children aged  <  14.

  5. 5.

    Somewhat counter intuitively, this means their WIMD 2005 income score would be low (i.e. not deprived).

References

  • Anderson, B. (2007, July). Cash in, cash out: Spatially microsimulating household income and expenditure at small area levels. Paper presented at the Royal Statistical Society Conference 2007, University of York, York, UK.

    Google Scholar 

  • Anderson, B. (2008). Time to play: Combining time-use surveys and census data to estimate small area distributions of potentially ICT mediated leisure. Paper presented at the AoIR 8, October 17, 2007, Simon Fraser University, Vancouver, BC, Canada.

    Google Scholar 

  • Anderson, B. (2009). Welsh small area estimates of income deprivation. Colchester: University of Essex.

    Google Scholar 

  • Anderson, B., De Agostini, P., Laidoudi, S., Weston, A., & Zong, P. (2009a). Time and money in space: Estimating household expenditure and time use at the small area level in Great Britain. In A. Zaidi, A. Harding, & P. Williamson (Eds.), New frontiers in microsimulation modelling: Public policy and social welfare Vol. 36. Aldershot: Ashgate.

    Google Scholar 

  • Anderson, B., De Agostini, P., & Lawson, T. (2009b, July). Estimating income, expenditure and time-use within small areas. Paper presented at the ESRC Microsimulation Seminar Series Workshop III ‘Moving beyond tax-benefit and demographic modelling’, University of Leeds, Leeds, UK.

    Google Scholar 

  • Ballas, D. (2004). Simulating trends in poverty and income inequality on the basis of 1991 and 2001 Census data: A tale of two cities. Area, 36(2), 146–163.

    Article  Google Scholar 

  • Ballas, D., & Clarke, G. (2001). Modelling the local impacts of national social policies: A spatial microsimulation approach. Environment and Planning C: Government and Policy, 19, 587–606.

    Article  Google Scholar 

  • Ballas, D., Clarke, G., & Turton, I. (1999, July). Exploring microsimulation methodologies for the estimation of household attributes. Paper presented at the 4th International Conference on GeoComputation, Mary Washington College, Fredericksburg, VA.

    Google Scholar 

  • Ballas, D., Clarke, G., Dorling, D., Eyre, H., Thomas, B., & Rossiter, D. (2005a). SimBritain: A spatial microsimulation approach to population dynamics. Population, Space and Place, 11, 13–34.

    Article  Google Scholar 

  • Ballas, D., Clarke, G., Dorling, D., Rigby, J., & Wheeler, B. (2005b, September). Using geographical information systems and spatial microsimulation for the analysis of health inequalities. Paper presented at the 10th International Symposium on Health Information Management Research – iSHIMR 2005, CITY Liberal Studies, Thessaloniki, Greece.

    Google Scholar 

  • Ballas, D., Dorling, D., Anderson, B., & Stoneman, P. (2006). Assessing the feasibility of producing small area income estimates: Phase I project report. Sheffield: Department of Geography, University of Sheffield.

    Google Scholar 

  • Bates, A. (2006). Methodology used for producing ONS’s small area population estimates. Population Trends, 125, 30–36.

    Google Scholar 

  • Birkin, M., & Clarke, M. (1988). SYNTHESIS – A synthetic spatial information system for urban and regional analysis: Methods and examples. Environment and Planning A, 20, 1645–1671.

    Article  Google Scholar 

  • Birkin, M., & Clarke, G. (1989). The generation of individual and household incomes at the small area level using synthesis. Regional Studies, 23(6), 535–548.

    Article  Google Scholar 

  • Birkin, M., & Clarke, M. (2011). Spatial microsimulation models: A review and a glimpse into the future. In J. Stillwell & M. Clarke (Eds.), Population dynamics and projection methods. London: Springer.

    Google Scholar 

  • Chin, S. F., & Harding, A. (2006). Regional dimensions: Creating synthetic small-area microdata and spatial microsimulation models (Technical Paper 33). National Centre for Social and Economic Modelling, Canberra: University of Canberra.

    Google Scholar 

  • Druckman, A., & Jackson, T. (2008). Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model. Energy Policy, 36(8), 3177–3192.

    Article  Google Scholar 

  • DWP. (2007). Households below average income (HBAI) 1994/95–2005/06. London: Department of Work and Pensions.

    Google Scholar 

  • Edwards, K. L., & Clarke, G. P. (2009). The design and validation of a spatial microsimulation model of obesogenic environments for children in Leeds, UK: SimObesity. Social Science & Medicine, 69(7), 1127–1134.

    Article  Google Scholar 

  • Eurostat. (2007). Inequality of income distribution (S80/S20 income quintile share ratio), at-risk-of-poverty rate and at-persistent-risk-of-poverty rate. Key indicators on EU policy – Structural indicators – Social Cohesion – Living conditions. Retrieved July 4, 2008, from http://europa.eu.int/estatref/info/sdds/en/strind/socohe_di_base.htm

  • Gong, C., McNamara, J., Vidyattama, Y., Miranti, R., Tanton, R., Harding, A., & Kendig, H. (2011). Developing spatial microsimulation estimates of small area advantage and disadvantage among older Australians. Population, Space and Place. doi:10.1002/psp.692.

  • Gordon, D., & Townsend, P. (2000). Breadline Europe: The measurement of poverty. Bristol: Policy Press.

    Google Scholar 

  • Gosh, M., & Rao, J. K. (1994). Small area estimation: An appraisal. Statistical Science, 9(1), 55–76.

    Article  Google Scholar 

  • Harding, A., Vidyattama, Y., & Tanton, R. (2011). Demographic change and the needs-based planning of government services: Projecting small area populations using spatial microsimulation. The Journal of Population Research, 28(2–3), 203–224.

    Article  Google Scholar 

  • Heady, P., Clarke, P., Brown, G., Ellis, K., Heasman, D., Hennell, S., et al. (2003). Model-based small area estimation Series No. 2. London: Office for National Statistics.

    Google Scholar 

  • Marsh, C. (1993). Privacy, confidentiality and anonymity in the 1991 Census. In A. Dale & C. Marsh (Eds.), The 1991 census user’s guide (pp. 111–128). London: Her Majesty’s Stationary Office.

    Google Scholar 

  • McLoone, P. (2002). Commercial income data: Associations with health and census measures (Occasional paper No 7). Glasgow: MRC Social & Public Health Sciences Unit.

    Google Scholar 

  • Mohana, J., Twigg, L., Barnard, S., & Jones, K. (2005). Social capital, geography and health: A small-area analysis for England. Social Science & Medicine, 60, 1267–1283.

    Article  Google Scholar 

  • Morrissey, K., Clarke, G., Ballas, D., Hynes, S., & O’Donoghue, C. (2008). Examining access to GP services in rural Ireland using microsimulation analysis. Area, 40(3), 354–364.

    Article  Google Scholar 

  • Noble, M., Wright, G., Dibben, C., Smith, G., McLennan, D., Anttila, C., et al. (2004). Indices of deprivation 2004. London: Office of the Deputy Prime Minister.

    Google Scholar 

  • Noble, M., Wright, G., Smith, G., & Dibben, C. (2006). Measuring multiple deprivation at the small-area level. Environment and Planning A, 38(1), 169–185.

    Article  Google Scholar 

  • Noble, M., McLennan, D., Wilkinson, K., Whitworth, A., Barnes, H., & Dibben, C. (2008). Indices of deprivation 2007. London: Communities and Local Government.

    Google Scholar 

  • Rao, J. K. (2003). Small area estimation. London: Wiley.

    Book  Google Scholar 

  • Simpson, L., & Tranmer, M. (2005). Combining sample and census data in small area estimates: Iterative proportional fitting with standard software. The Professional Geographer, 57(2), 222–234.

    Article  Google Scholar 

  • Smith, D. M., Harland, K., & Clarke, G. (2007). SimHealth: Estimating small area populations using deterministic spatial microsimulation in Leeds and Bradford. Leeds: University of Leeds.

    Google Scholar 

  • Smith, D., Clarke, G., & Harland, K. (2009). Improving the synthetic data generation process in spatial microsimulation models. Environment and Planning A, 41, 1251–1268.

    Article  Google Scholar 

  • Tanton, R., Mcnamara, J., Harding, A., & Morrison, T. (2009a). Small area poverty estimates for Australia’s eastern seaboard in 2006. In A. Zaidi, A. Harding, & P. Williamson (Eds.), New frontiers in microsimulation modelling: Public policy and social welfare Vol. 36. Aldershot: Ashgate.

    Google Scholar 

  • Tanton, R., Vidyattama, Y., McNamara, J., Vu, Q., & Harding, A. (2009b). Old, single and poor: Using microsimulation and microdata to analyse poverty and the impact of policy change among older Australians. Economic Papers, 28(2), 102–120.

    Article  Google Scholar 

  • Tanton, R., Vidyattama, Y., Nepal, B., & McNamara, J. (2011). Small area estimation using a reweighting algorithm. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(4), 931–951.

    Article  Google Scholar 

  • Vidyattama, Y., & Tanton, R. (2010). Projecting small area statistics with Australian spatial microsimulation model (SpatialMSM). Australian Journal of Regional Studies, 16(1), 99–126.

    Google Scholar 

  • Vidyattama, Y., Cassells, R., Harding, A., & McNamara, J. (2011). Rich or poor in retirement? A small area analysis of Australian private superannuation savings in 2006 using spatial microsimulation. Regional Studies. doi:10.1080/00343404.2011.589829.

  • Webber, R. (2004). The relative power of geodemographics vis a vis person and household level demographic variables as discriminators of consumer behaviour. London: UCL.

    Google Scholar 

  • Williamson, P. (2001). An applied microsimulation model: Exploring alternative domestic water consumption scenarios. In G. Clarke & M. Madden (Eds.), Regional science in business. London: Springer.

    Google Scholar 

  • Williamson, P. (2005). Income imputation for small areas. Liverpool: University of Liverpool.

    Google Scholar 

  • Williamson, P., & Voas, D. (2000). Income estimates for small areas: Lessons from the census rehearsal. BURISA, 146, 2–10.

    Google Scholar 

  • Williamson, P., Birkin, M., & Rees, P. (1998). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A, 30, 785–816.

    Article  Google Scholar 

  • Wong, D. (1992). The reliability of using the iterative proportional fitting procedure. Professional Geographer, 44(3), 340–348.

    Article  Google Scholar 

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Acknowledgements

This work is based on data provided through EDINA UKBORDERS with the support of the ESRC and JISC and uses boundary material which is copyright of the crown and the post office.

Census data was originally created and funded by the Office for National Statistics and was distributed by the Census Dissemination Unit, MIMAS (University of Manchester). Census output is crown copyright and is reproduced with the permission of the controller of HMSO.

The FRS is collected and sponsored by the Department for Work and Pensions and is distributed by the UK Data Archive, University of Essex, Colchester. FRS data is copyright and is reproduced with the permission of the Controller of HMSO and the Queen’s Printer for Scotland.

The WIMD 2004 was constructed by the Social Disadvantage Research Centre at the Department of Social Policy and Social Research at the University of Oxford and distributed by the Welsh Assembly Government.

We would also like to thank Professor Holly Sutherland (ISER, University of Essex) for advice on the treatment of negative income and housing costs.

This work was sponsored by the Welsh Assembly Government.

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Anderson, B. (2012). Estimating Small-Area Income Deprivation: An Iterative Proportional Fitting Approach. 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_4

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