Estimating income, expenditure and time-use within small areas

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Presentation at 'Moving beyond tax-benefit and demographic modelling', University of Leeds on July 2nd 2009. One of a series co-funded by the ESRC and the BSPS on ‘Microsimulation modelling in the UK: bridging the gaps’.

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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
  • OECD scale : 1 (first adult) + 0.5 for extra adults & children except 0.3 for child < 14
  • No external validation available (yet) for expenditure
  • Uses Stephen Jenkins’ stata ineqdec0 command to include 0 values
  • 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
  • R sq = 21%
  • Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • R sq = only 10.9% Total minutes per day used as sums total behaviour - e.g. what if per minute charging for service? Also indicator of broadband bandwidth demand.
  • Can’t show demand system modelling results (yet) but here a fictitious example of what we’ve been doing
  • 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.
  • Traditional static microsimulation
  • Would be very interesting to look again at this as broadband changes the way TV is delivered
  • Demand system: ML method, as gets more complex (e.g. more demographics) gets even slower
  • Estimating income, expenditure and time-use within small areas

    1. 1. Estimating income, expenditure and time-use within small areas Ben Anderson, Paola De Agostini & Tony Lawson ESRC/BSPS Microsimulation Seminar Series University of Leeds July 2nd 2009
    2. 2. A story in five parts… <ul><li>What’s the problem? </li></ul><ul><ul><ul><li>And what can we do about it? </li></ul></ul></ul><ul><li>Income </li></ul><ul><ul><ul><li>Small area income inequality </li></ul></ul></ul><ul><li>Expenditure: </li></ul><ul><ul><ul><li>Small area spending </li></ul></ul></ul><ul><li>Time-use: </li></ul><ul><ul><ul><li>A spatialised demand system model </li></ul></ul></ul><ul><li>Current activities </li></ul><ul><ul><ul><li>Census projection </li></ul></ul></ul><ul><ul><ul><li>Agent-Based Microsimulation </li></ul></ul></ul>
    3. 3. 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>
    4. 4. Why small area data? <ul><li>‘ Applied’ reasons </li></ul><ul><ul><ul><li>Public & private resource allocation </li></ul></ul></ul><ul><ul><ul><li>Public & 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>
    5. 5. BUT! <ul><li>Small area data on people are rare </li></ul><ul><ul><ul><li>Census - down to OA level </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 or time-use </li></ul></ul></ul><ul><ul><ul><li>And of sometimes uncertain quality </li></ul></ul></ul>
    6. 6. The requirement… <ul><li>Synthetic Census(es) </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 also </li></ul><ul><ul><ul><li>What if? (tomorrow) </li></ul></ul></ul><ul><ul><ul><li>What if? (in 5 years time) </li></ul></ul></ul><ul><ul><ul><li>What if? (by 2021) </li></ul></ul></ul>
    7. 7. How to achieve it? <ul><li>Survey data cases </li></ul>Area 1 in Region X Area 2 in Region X If region = X Census ‘constraint’ tables Weights e.g. Iterative proportional fitting, combinatorial optimization
    8. 8. 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>
    9. 9. Example: Income deprivation <ul><li>FRS 2004-6 (pooled) </li></ul><ul><li>p(income < 60% UK median) </li></ul><ul><li>Net income </li></ul><ul><li>Equivalised </li></ul><ul><li>Before housing costs </li></ul><ul><li>Stepwise regression to select constraints </li></ul>
    10. 10. How to achieve it? <ul><li>Survey data cases </li></ul>Area 1 in Region X Area 2 in Region X If region = X Census ‘constraint’ tables Weights e.g. Iterative proportional fitting, combinatorial optimization
    11. 11. The result…
    12. 12. A story in five parts… <ul><li>What’s the problem? </li></ul><ul><ul><ul><li>And what can we do about it? </li></ul></ul></ul><ul><li>Income </li></ul><ul><ul><ul><li>Small area income inequality </li></ul></ul></ul><ul><li>Expenditure: </li></ul><ul><ul><ul><li>Small area spending </li></ul></ul></ul><ul><li>Time-use: </li></ul><ul><ul><ul><li>A spatialised demand system model </li></ul></ul></ul><ul><li>Current activities </li></ul><ul><ul><ul><li>Agent-Based Microsimulation </li></ul></ul></ul><ul><ul><ul><li>Census projection </li></ul></ul></ul>
    13. 13. Income Deprivation <ul><li>For each (L)SOA: </li></ul><ul><ul><ul><li>% households below 60% UK median income </li></ul></ul></ul><ul><ul><ul><li>% individuals in households below 60% UK median income </li></ul></ul></ul><ul><ul><ul><li>% children … </li></ul></ul></ul><ul><ul><ul><li>% working age adults </li></ul></ul></ul><ul><ul><ul><li>% pensioners </li></ul></ul></ul><ul><li>Why? </li></ul><ul><ul><ul><li>Potential use in future IMD (CLG funded) </li></ul></ul></ul><ul><ul><ul><li>Analyses of deprivation patterns (CRC, NISRA, WAG funded) </li></ul></ul></ul><ul><li>Data </li></ul><ul><ul><ul><li>Income: Family Resources Survey (FRS) 2004-6 </li></ul></ul></ul><ul><ul><ul><li>Space: Census 2001 (LSOAs) and LFS 2004-6 (LA/UAs) </li></ul></ul></ul><ul><ul><ul><li>Net equivalised income BHC/AHC </li></ul></ul></ul>
    14. 14. Results: Households <ul><li>BHC </li></ul>AHC
    15. 15. Results: Pensioners <ul><li>BHC </li></ul>AHC
    16. 16. Validation <ul><li>Spearman correlation results at English LSOA level for household poverty rates and IMD 2007 income domain score </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>
    17. 17. <ul><li>We have an income distribution… </li></ul>Local income inequality <ul><li>So we can calculate a gini coefficient for each zone… </li></ul><ul><ul><li>Income inequality within small areas </li></ul></ul>
    18. 18. Local income inequality - results <ul><li>Mean equivalised household income </li></ul><ul><li>Equivalised household income gini </li></ul>
    19. 19. 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>
    20. 20. A story in five parts… <ul><li>What’s the problem? </li></ul><ul><ul><ul><li>And what can we do about it? </li></ul></ul></ul><ul><li>Income </li></ul><ul><ul><ul><li>Small area income inequality </li></ul></ul></ul><ul><li>Expenditure: </li></ul><ul><ul><ul><li>Small area spending </li></ul></ul></ul><ul><li>Time-use: </li></ul><ul><ul><ul><li>A spatialised demand system model </li></ul></ul></ul><ul><li>Current activities </li></ul><ul><ul><ul><li>Agent-Based Microsimulation </li></ul></ul></ul><ul><ul><ul><li>Census projection </li></ul></ul></ul>
    21. 21. Expenditure Modelling <ul><li>Approach </li></ul><ul><ul><ul><li>Census 2001 </li></ul></ul></ul><ul><ul><ul><li>Expenditure & Food Surveys </li></ul></ul></ul><ul><ul><ul><li>Demand system models </li></ul></ul></ul><ul><ul><ul><li>Spatial microsimulation </li></ul></ul></ul><ul><li>Why? </li></ul><ul><ul><ul><li>Resource/consumption ‘poverty’ and inequality modelling </li></ul></ul></ul><ul><ul><ul><li>‘ Share of wallet’ & revenue modelling for BT </li></ul></ul></ul>
    22. 22. Water 2005/6 <ul><li>Simulated household weekly water expenditure (EFS 2005/6) </li></ul><ul><li>EEDA, LSOA level </li></ul><ul><li>Water gini </li></ul>
    23. 23. Water Poverty 2005/6 <ul><li>% Household who spend > 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>
    24. 24. Telephony: 2005/6 <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 < 0.001 </li></ul>
    25. 25. Broadband tax…Noddy’s view <ul><li>Simple ROI model </li></ul><ul><ul><li>£500 cost per household </li></ul></ul><ul><ul><li>20% increase in household level spend on internet subscription </li></ul></ul><ul><ul><li>50% of additional income to repay investment </li></ul></ul><ul><ul><li>No spatial cross-subsidisation </li></ul></ul><ul><li>How long to pay back? </li></ul>
    26. 26. A story in five parts… <ul><li>What’s the problem? </li></ul><ul><ul><ul><li>And what can we do about it? </li></ul></ul></ul><ul><li>Income </li></ul><ul><ul><ul><li>Small area income inequality </li></ul></ul></ul><ul><li>Expenditure: </li></ul><ul><ul><ul><li>Small area spending </li></ul></ul></ul><ul><li>Time-use: </li></ul><ul><ul><ul><li>A spatialised demand system model </li></ul></ul></ul><ul><li>Current activities </li></ul><ul><ul><ul><li>Agent-Based Microsimulation </li></ul></ul></ul><ul><ul><ul><li>Census projection </li></ul></ul></ul>
    27. 27. Time in Space: Work <ul><li>Simulated ‘work time’ </li></ul><ul><li>ONS Time-Use Survey (2001) and Census 2001 </li></ul><ul><li>East of England, LSOA </li></ul><ul><li>Validation: </li></ul><ul><ul><li>(Spearman rho = 0.8404, p < 0.001) </li></ul></ul><ul><ul><li>Strong correlation with Census 2001 ‘work time’ </li></ul></ul>
    28. 28. Time in Space - Mass media <ul><li>Simulated total ‘mass media’ time (2001) - TV, radio, DVD, video </li></ul><ul><li>ONS Time-Use Survey 2001 & Census 2001 </li></ul><ul><li>East of England, LSOA </li></ul>
    29. 29. What if? <ul><li>Estimate base model </li></ul><ul><ul><ul><ul><li>time(v 1 ) = a(have_internet) + b[socio-demographics] </li></ul></ul></ul></ul><ul><ul><ul><ul><li>… . </li></ul></ul></ul></ul><ul><ul><ul><ul><li>time(v n ) = a(have_internet) + b[socio-demographics] </li></ul></ul></ul></ul><ul><ul><ul><ul><li>demand system ( Deaton & Muellbaeur, Blundell…) </li></ul></ul></ul></ul><ul><li>Change a parameter </li></ul><ul><ul><ul><ul><li>e.g: make internet access = 100% </li></ul></ul></ul></ul><ul><li>Predict new values of time(v 1-n ) </li></ul><ul><ul><ul><li>for each household </li></ul></ul></ul><ul><li>Re-run spatial microsimulation </li></ul>
    30. 30. An example - Internet & TV <ul><li>We model ‘what if 100% internet access’ on TV time in 2001? </li></ul><ul><li>Internet access has no main effect </li></ul><ul><ul><li>But has interaction effects with constraints (demographics) </li></ul></ul><ul><li>Which is why TV time increases for some but not others </li></ul>Survey effects (microsimulation) Spatial effects (spatial microsimulation)
    31. 31. But it’s not that simple <ul><li>The demand system eats time </li></ul><ul><ul><ul><li>Only converges slowly, sometimes never </li></ul></ul></ul><ul><li>‘ Instant’ change is unlikely </li></ul><ul><ul><ul><li>Need a forecasting time frame </li></ul></ul></ul><ul><li>And ‘internet effects’ are marginal </li></ul><ul><ul><ul><li>Certainly in 2000 data (but maybe not now?) </li></ul></ul></ul><ul><ul><ul><li>Other changes have more effect on time-use </li></ul></ul></ul><ul><li>=> Work in Progress </li></ul>
    32. 32. A story in five parts… <ul><li>What’s the problem? </li></ul><ul><ul><ul><li>And what can we do about it? </li></ul></ul></ul><ul><li>Income </li></ul><ul><ul><ul><li>Small area income inequality </li></ul></ul></ul><ul><li>Expenditure: </li></ul><ul><ul><ul><li>Small area spending </li></ul></ul></ul><ul><li>Time-use: </li></ul><ul><ul><ul><li>A spatialised demand system model </li></ul></ul></ul><ul><li>Current activities </li></ul><ul><ul><ul><li>Agent-Based Microsimulation </li></ul></ul></ul><ul><ul><ul><li>Census projection </li></ul></ul></ul>
    33. 33. What if? (by 2021) <ul><li>Projected census </li></ul><ul><ul><ul><li>Fixing geography over time </li></ul></ul></ul><ul><ul><ul><ul><li>EDs (71,81,91) -> LSOAs (2001) </li></ul></ul></ul></ul><ul><ul><ul><li>Projecting census counts/distributions </li></ul></ul></ul><ul><li>Projected survey population </li></ul><ul><ul><ul><li>Dynamic agent-based microsimulation model </li></ul></ul></ul><ul><ul><ul><li>Individuals within households </li></ul></ul></ul><ul><ul><ul><li>Demographic change and consumption </li></ul></ul></ul><ul><li>Combine results: 2006,2011,2016,2021 </li></ul>
    34. 34. <ul><li>Tony Lawson’s PhD (ESRC/BT) </li></ul><ul><li>Implementation </li></ul><ul><ul><ul><li>Netlogo </li></ul></ul></ul><ul><ul><ul><li>BHPS for transition probabilities </li></ul></ul></ul><ul><ul><ul><li>Actuarial tables for mortality </li></ul></ul></ul><ul><ul><ul><li>ONS and EFS for prices </li></ul></ul></ul><ul><li>Validation </li></ul><ul><ul><ul><li>BHPS 1991 seed, run to 2005 and compare </li></ul></ul></ul>The dynamic model…
    35. 35. The dynamic model… <ul><li>3 examples on web </li></ul><ul><ul><ul><li>Model 1 – dynamic demographic microsimulation model </li></ul></ul></ul><ul><ul><ul><li>Model 2 – simplified model 1 with food/fuel and housing expenditure model </li></ul></ul></ul><ul><ul><ul><li>Model 3 – full model </li></ul></ul></ul><ul><li>Ongoing work </li></ul><ul><ul><ul><li>Work location, broadband and fuel consumption </li></ul></ul></ul>
    36. 36. Thank you <ul><li>benander@essex.ac.uk </li></ul><ul><li>[email_address] </li></ul><ul><li>[email_address] </li></ul><ul><li>http://cresi.essex.ac.uk </li></ul>

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