Spatially Microsimulating Consumption
•
0 likes•227 views
This document discusses using spatial microsimulation to estimate household characteristics at small area levels. It describes using survey data constrained by census data to model characteristics like time use, income, expenditures, and internet access at local area unit (LAU) levels. Examples are provided showing the method estimated characteristics like income deprivation, telephone bills, and water expenditures at LAU levels with correlations to other data sources from 0.77 to 0.84. Future work discussed includes modeling health behaviors, broadband access, and relative deprivation over time at LAU levels.
![Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
More Related Content
Viewers also liked
Viewers also liked (20)
Similar to Spatially Microsimulating Consumption
Similar to Spatially Microsimulating Consumption (20)
More from Sociology@Essex
More from Sociology@Essex (13)
Recently uploaded
Recently uploaded (20)
Spatially Microsimulating Consumption
- 1. Spatially Micro-simulating Consumption (and other things) Ben Anderson
- 13. The result…
Editor's Notes
- 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