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Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
Spatially Microsimulating Consumption
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Spatially Microsimulating Consumption

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Centre for Research in Economic Sociology and Innovation Seminar by Dr Ben Anderson, February 2009.

Centre for Research in Economic Sociology and Innovation Seminar by Dr Ben Anderson, February 2009.

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  • There are a range of statistical methods Multilevel and hierarchical modelling etc But we’re not using them We’re creating a synthetic ‘Income Census’ We fill each ‘area’ (LSOA)… with ALL households from the relevant region Then give them fractional weights so that key constraint variables in each area match known Census distributions
  • (and must be in the Census or Census-like data) Relatively few candidates in the Census
  • There are a range of statistical methods Multilevel and hierarchical modelling etc But we’re not using them We’re creating a synthetic ‘Income Census’ We fill each ‘area’ (LSOA)… with ALL households from the relevant region Then give them fractional weights so that key constraint variables in each area match known Census distributions
  • Validation against the census is excellent!
  • Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • OECD scale : 1 (first adult) + 0.5 for extra adults & children except 0.3 for child < 14
  • No external validation available (yet) for expenditure
  • No external validation available (yet) for expenditure
  • ,408 = Ghana, Turkmenistan & United States .268 = Bosnia, Hungary, Finland http://en.wikipedia.org/wiki/List_of_countries_by_income_equality
  • No external validation available (yet) for expenditure
  • No external validation available (yet) for expenditure
  • So are we picking up a different pattern of poverty in rural areas? Why? Pensioners? Can’t be students
  • Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • No external validation available (yet) for expenditure
  • No external validation available (yet) for expenditure
  • Have to be careful - many areas with large mean spend change are sparsely populated so small overall total spend change Need to account for n households
  • Transcript

    • 1. Spatially Micro-simulating Consumption (and other things) Ben Anderson
    • 2. Contents <ul><li>Why bother? </li></ul><ul><li>Method(ological) Summary </li></ul><ul><li>Time </li></ul><ul><li>Income </li></ul><ul><li>Expenditure </li></ul><ul><li>‘ Digital exclusion’ </li></ul><ul><li>Where next? </li></ul>
    • 3. Why bother? <ul><li>‘ Applied’ reasons </li></ul><ul><ul><ul><li>Public &amp; private resource allocation </li></ul></ul></ul><ul><ul><ul><li>Public &amp; private service delivery planning </li></ul></ul></ul><ul><li>‘ Research’ reasons </li></ul><ul><ul><ul><li>‘ small area’ level effects and trends </li></ul></ul></ul><ul><ul><ul><li>Deprivation indices and correlates </li></ul></ul></ul><ul><ul><ul><li>Environmental/ecological correlates </li></ul></ul></ul><ul><ul><ul><li>Local area inequalities and social justice (Runciman) </li></ul></ul></ul><ul><ul><ul><li>Intervention analysis </li></ul></ul></ul><ul><ul><ul><li>Sampling frameworks </li></ul></ul></ul><ul><ul><ul><li>Spatially embedded patterns of consumption </li></ul></ul></ul><ul><ul><ul><li>… ? </li></ul></ul></ul>
    • 4. What are ‘small areas’? <ul><li>Base map </li></ul><ul><li>Parliamentary Constituencies </li></ul><ul><li>Local/Unitary Authorities </li></ul><ul><li>Wards </li></ul><ul><li>Lower Layer Super Output Areas (LSOAs) </li></ul><ul><li>Output Areas (OAs) </li></ul><ul><li>[Postcodes] </li></ul>
    • 5. BUT! <ul><li>Spatial data on people are rare </li></ul><ul><ul><ul><li>Census - down to OA level </li></ul></ul></ul><ul><ul><ul><li>Large scale surveys - e.g LFS - district level but restricted access </li></ul></ul></ul><ul><ul><ul><li>Administrative - postcode but restricted access </li></ul></ul></ul><ul><ul><ul><li>Private/commercial - postcode but restricted access </li></ul></ul></ul><ul><li>And the data captured are restricted </li></ul><ul><ul><ul><li>Broad but shallow </li></ul></ul></ul><ul><ul><ul><li>Rarely much on consumption </li></ul></ul></ul><ul><ul><ul><li>And of sometimes uncertain quality </li></ul></ul></ul>
    • 6. The requirement… <ul><li>A Synthetic Consumption Census </li></ul><ul><ul><ul><li>Small area estimates </li></ul></ul></ul><ul><ul><ul><li>Sociologically interesting phenomena </li></ul></ul></ul><ul><ul><ul><li>Validated where possible </li></ul></ul></ul><ul><li>And what have we got? </li></ul><ul><ul><ul><li>Broad but shallow </li></ul></ul></ul><ul><ul><ul><li>Rarely much on consumption </li></ul></ul></ul><ul><ul><ul><li>And of sometimes uncertain quality </li></ul></ul></ul>
    • 7. Contents <ul><li>Why bother? </li></ul><ul><li>Method(ological) Summary </li></ul><ul><li>Time </li></ul><ul><li>Income </li></ul><ul><li>Expenditure </li></ul><ul><li>‘ Digital Exclusion’ </li></ul><ul><li>Where next? </li></ul>
    • 8. How does it work? <ul><li>Survey data cases </li></ul>Area 1 in Region X Area 2 in Region X If region = X Census ‘constraint’ tables Weights Iterative proportional fitting Ballas et al (2005)
    • 9. Identifying constraints <ul><li>Two criteria: </li></ul><ul><ul><li>Exist in Census and Survey </li></ul></ul><ul><ul><li>Good predictors of X in survey </li></ul></ul><ul><li>We need to know </li></ul><ul><ul><ul><li>Which ones make the most difference? </li></ul></ul></ul><ul><ul><ul><li>Which ones co-vary? </li></ul></ul></ul><ul><ul><ul><li>What is the smallest effective set? </li></ul></ul></ul><ul><li>Take microdata (survey) </li></ul><ul><ul><ul><li>Stepwise regression of all possible constraints </li></ul></ul></ul><ul><ul><ul><li>Which ones are best indicators? </li></ul></ul></ul>
    • 10. Example I: Time Use <ul><li>ONS 2000 Time Use Survey, EEDA region only </li></ul><ul><li>Stepwise linear regression </li></ul><ul><li>Values are standardised coefficients </li></ul><ul><li>Mean household minutes per weekday (adults) </li></ul>
    • 11. Selecting the constraints… <ul><li>These might work OK at the household level </li></ul><ul><li>These won’t </li></ul><ul><ul><li>Probably need an individual level model </li></ul></ul>
    • 12. Run the model… <ul><li>Survey data cases </li></ul>Area 1 in Region X Area 2 in Region X If region = X Census ‘constraint’ tables Weights Iterative proportional fitting Ballas et al (2005)
    • 13. The result…
    • 14. Contents <ul><li>Why bother? </li></ul><ul><li>Method(ological) Summary </li></ul><ul><li>Time </li></ul><ul><li>Income </li></ul><ul><li>Expenditure </li></ul><ul><li>‘ Digital Exclusion’ </li></ul><ul><li>Where next? </li></ul>
    • 15. Time in Space: Work <ul><li>Simulated ‘work time’ (2001) </li></ul><ul><li>East of England,LSOA </li></ul><ul><li>Validation: </li></ul><ul><ul><li>(Spearman rho = 0.8404, p &lt; 0.001) </li></ul></ul><ul><ul><li>Strong correlation with Census 2001 ‘work time’ </li></ul></ul>
    • 16. Time in Space - Mass media <ul><li>Simulated total ‘mass media’ time (2001) - TV, radio, DVD, video </li></ul><ul><li>East of England, LSOA </li></ul>
    • 17. Contents <ul><li>Why bother? </li></ul><ul><li>Method(ological) Summary </li></ul><ul><li>Time </li></ul><ul><li>Income </li></ul><ul><li>Expenditure </li></ul><ul><li>‘ Digital Exclusion’ </li></ul><ul><li>Where next? </li></ul>
    • 18. Example: Income Deprivation <ul><li>% households in each LSOA </li></ul><ul><ul><ul><li>below 60% English median income </li></ul></ul></ul><ul><li>Gross income before housing costs </li></ul><ul><ul><ul><li>No deductions (unlike DWP HHBAI) </li></ul></ul></ul><ul><ul><ul><li>Family Resources Survey (FRS) </li></ul></ul></ul><ul><li>Income is equivalised </li></ul><ul><ul><ul><li>Modified OECD scale </li></ul></ul></ul><ul><ul><ul><li>Controls for large households </li></ul></ul></ul>
    • 19. HHBMI Results 2005 <ul><li>Spatial microsimulation: ONS FRS, Census 2001, LSOA level </li></ul><ul><li>The poorest places are still urban </li></ul><ul><ul><li>as measured by income </li></ul></ul><ul><li>Rushmoor (Camberley) still up there from 2001 </li></ul><ul><li>Birmingham still at the bottom from 2001 </li></ul>
    • 20. HHBMI Results 2005 <ul><li>Spatial microsimulation: ONS FRS, Census 2001, LSOA level </li></ul><ul><li>The poorest places are urban </li></ul><ul><ul><li>as measured by income </li></ul></ul><ul><ul><li>Urban heterogeneity </li></ul></ul>
    • 21. Local income inequality - gini <ul><li>FRS 2005 </li></ul><ul><li>East of England, LSOA </li></ul><ul><li>LSOA gini: </li></ul><ul><ul><li>Mean = .35 (UK = .36 [UN]) </li></ul></ul><ul><li>Rural zones </li></ul><ul><li>Max = Central Cambridge </li></ul><ul><ul><li>(gini = .408, % HHBMI = 22%) </li></ul></ul><ul><li>Min = South East Ipswich </li></ul><ul><ul><li>(gini = .268, %HHBMI = 31.8%) </li></ul></ul>
    • 22. Local income inequality - results <ul><li>Equivalised household income </li></ul><ul><li>Gini </li></ul>
    • 23. External Validation Results <ul><li>Spatial microsimulation: ONS FRS, Census 2001, LSOA level </li></ul><ul><li>IMD 2004 income domain score (2001 data) </li></ul><ul><li>Table gives rank order correlation coefficients </li></ul><ul><li>Outliers where IMD income domain score low but % HHBMI high </li></ul><ul><ul><li>areas with many student HRPs </li></ul></ul><ul><ul><li>areas with many pensioner HRPs </li></ul></ul>
    • 24. But… <ul><li>Spatial microsimulation: ONS FRS, Census 2001, LSOA level </li></ul><ul><li>IMD 2004 income domain score (2001 data) </li></ul><ul><li>Spearman rank correlation cut by ONS rural/urban classification </li></ul>
    • 25. Contents <ul><li>Why bother? </li></ul><ul><li>Method(ological) Summary </li></ul><ul><li>Time </li></ul><ul><li>Income </li></ul><ul><li>Expenditure </li></ul><ul><li>‘ Digital Exclusion’ </li></ul><ul><li>Where next? </li></ul>
    • 26. Telephony: 2005/6 <ul><li>R sq = only 10.9% </li></ul><ul><li>Simulated household weekly telephone bill (landlines) (EFS 2005/6) </li></ul><ul><li>EEDA, LSOA level </li></ul><ul><li>Ward level comparison with BT billing data (EEDA, Ward level) </li></ul><ul><li>Spearman rho = 0.7796, p &lt; 0.001 </li></ul>
    • 27. Mobile Telephony 2005/6 <ul><li>R sq = 26.4% </li></ul><ul><li>Gini - measure of within area inequality </li></ul>
    • 28. Mobile Telephony 2005/6 <ul><li>R sq = 26.4% </li></ul><ul><li>Gini - measure of within area inequality </li></ul>
    • 29. Water 2005/6 <ul><li>R sq = 21% </li></ul><ul><li>Simulated household weekly water (EFS 2005/6) </li></ul><ul><li>EEDA, LSOA level </li></ul>
    • 30. Water 2005/6 <ul><li>R sq = 21% </li></ul><ul><li>Simulated household weekly water (EFS 2005/6) </li></ul><ul><li>Colchester, OA level </li></ul>
    • 31. Water Poverty 2005/6 <ul><li>R sq = 21% </li></ul><ul><li>% Household who spend &gt; 3% of income on water (EFS 2005/6) </li></ul><ul><li>AHC = After Housing Costs income </li></ul><ul><li>EEDA, LSOA level </li></ul><ul><li>Probably needs equivalised income </li></ul><ul><ul><li>to highlight low income / high demand </li></ul></ul>
    • 32. Contents <ul><li>Why bother? </li></ul><ul><li>Method(ological) Summary </li></ul><ul><li>Time </li></ul><ul><li>Income </li></ul><ul><li>Expenditure </li></ul><ul><li>‘ Digital Exclusion’ </li></ul><ul><li>Where next? </li></ul>
    • 33. Household Internet access <ul><li>Simulated % households with internet access (2001-2) </li></ul><ul><li>East of England,LSOA </li></ul>
    • 34. Change over time <ul><li>Simulated % households with internet access (2001/2 and 2005/6) </li></ul><ul><li>East of England,LSOA </li></ul>
    • 35. Contents <ul><li>Why bother? </li></ul><ul><li>Method(ological) Summary </li></ul><ul><li>Time </li></ul><ul><li>Income </li></ul><ul><li>Expenditure </li></ul><ul><li>‘ Digital Exclusion’ </li></ul><ul><li>Scenario (what if?) analysis </li></ul><ul><li>Where next? </li></ul>
    • 36. What if there was…? <ul><li>100% internet uptake in 2006 </li></ul><ul><li>Change in mean expenditure on fixed line telephony </li></ul><ul><li>Change in total expenditure on fixed line telephony </li></ul>
    • 37. Contents <ul><li>Why bother? </li></ul><ul><li>Method(ological) Summary </li></ul><ul><li>Time </li></ul><ul><li>Income </li></ul><ul><li>Expenditure </li></ul><ul><li>‘ Digital Exclusion’ </li></ul><ul><li>Where next? </li></ul>
    • 38. <ul><li>Work in progress: </li></ul><ul><ul><ul><li>DCLG </li></ul></ul></ul><ul><ul><ul><ul><li>Income Deprivation </li></ul></ul></ul></ul><ul><ul><ul><li>WAG </li></ul></ul></ul><ul><ul><ul><ul><li>Income deprivation &amp; child poverty </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Digital inclusion ‘indicators’ </li></ul></ul></ul></ul><ul><ul><ul><li>BT </li></ul></ul></ul><ul><ul><ul><ul><li>LSOA level census projection to 2021 </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Household expenditure projections </li></ul></ul></ul></ul><ul><ul><ul><li>DoH </li></ul></ul></ul><ul><ul><ul><ul><li>‘ Healthy Lifestyles’ </li></ul></ul></ul></ul><ul><li>Writing it up! </li></ul>Current Work
    • 39. Future Work <ul><li>Methodological </li></ul><ul><ul><ul><li>Efficiency/scalability of method </li></ul></ul></ul><ul><ul><ul><li>Indicators of ‘error’ or ‘confidence’ </li></ul></ul></ul><ul><ul><ul><li>Contemporary spatial data? </li></ul></ul></ul><ul><li>Substantive </li></ul><ul><ul><ul><li>Intervention baselines </li></ul></ul></ul><ul><ul><ul><ul><li>Next generation broadband and e-commerce? </li></ul></ul></ul></ul><ul><ul><ul><ul><li>UK Online centres? </li></ul></ul></ul></ul><ul><ul><ul><li>Health behaviours </li></ul></ul></ul><ul><ul><ul><li>Water, water, water! </li></ul></ul></ul><ul><ul><ul><ul><li>(and energy) </li></ul></ul></ul></ul><ul><ul><ul><li>Relative deprivation models </li></ul></ul></ul><ul><li>More writing!! </li></ul>
    • 40. Thank you <ul><li>I have an unfilled WAG/ESRC CASE Studentship! </li></ul><ul><ul><li>Oct 09 start, topped up stipend! </li></ul></ul><ul><li>Contact: </li></ul><ul><ul><li>[email_address] </li></ul></ul><ul><li>Further details: </li></ul><ul><ul><li>http://istr.essex.ac.uk/tasc/getPubsByTag.php?tag=spatial microsimulation </li></ul></ul>

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