Geography matters: estimating the local impacts of national social policies

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Geography matters estimating the local impacts
of national social policies
Employment Research Institute, Napier
University Edinburgh, Friday 23 January 2004

• Dimitris Ballas
• Department of Geography, University of Sheffield
• http//www.sheffield.ac.uk/geography/

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Outline

• The need for regional and local socio-economic
  impact assessment
• What is microsimulation?
• Conceptual issues spatial vs. aspatial
  microsimulation dynamic vs. static
  microsimulation the current state of
  geographical microsimulation
• Geographical approaches to analysing survey data

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Outline (cont.)

• Adding geography to national survey data
• Estimating and updating small area statistics
• Simulating small area microdata validation and
  policy relevance
• Using spatial microsimulation for the evaluation
  of national social policies
• Concluding remarks

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Traditional approaches to socio-economic impact
assessment and policy analysis

• Regional Keynesian multiplier analysis
• Input-output models
• Regional econometric Models
• Socio-economic indexes
• Qualitative research methodologies
• Descriptive survey-based studies
• Microsimulation

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What is microsimulation?

• A technique aiming at building large scale data
  sets
• Modelling at the microscale
• A means of modelling real life events by
  simulating the characteristics and actions of the
  individual units that make up the system where
  the events occur

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Microsimulation in Economics

• First conceptualised and developed by Orcutt
  (1957)
• Since then, very successful history
• Wide range of applications tax/benefit, budget
  analysis, measurement of poverty, policy impact
  assessment etc.
• Microsimulation is an established method in
  Economics

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Some examples of microsimulation applications in
Economics

• PENSIM. This was a microsimulation model for the
  simulation of pensioners incomes up to the year
  2030. Hancock et al. (1992)
• Sutherland and Piachaud (The Economic Journal,
  2001) developed and used a microsimulation
  methodology for the assessment of British
  government policies for the reduction of child
  poverty in the period 1997-2001. Results suggest
  that the number of children in poverty will be
  reduced by approximately one-third in the short
  term and that there is a trend towards further
  reductions

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Doesnt geography matter? Why didnt economists
incorporate space into their models?

• Lack of good quality geographical data there
  were very few sources of geographical
  socio-economic data. Even today there are no
  small area population microdata, which is the
  standard datasets used by economic
  microsimulation models
• Computational intensity the incorporation of
  geography into standard microsimulation models
  increases significantly the computational demand
• Concerns with simulation accuracy
• Belief that geography is not important
• Unfamiliarity with geographical data and methods

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Distribution of microsimulation academic studies
in the period 1967-2003 (source
http//www.sciencedirect.com/ Accessed 15
October 2003)
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Microsimulation in Geography and Regional Science

• First study by Hägerstrand (1967) spatial
  diffusion of innovation
• Foundations for spatial microsimulation of
  populations laid by Wilson and Pownall (1976)
  building small area microdata
• Clarke et al. (1979 onwards) extended the
  theoretical framework of Wilson and Pownall

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Spatial microsimulation procedures

• The construction of a micro-dataset from samples
  and surveys
• Static What-if simulations, in which the impacts
  of alternative policy scenarios on the population
  are estimated for instance if there had been no
  poll tax in 1991 which communities would have
  benefited most and which would have had to have
  paid more tax in other forms?
• Dynamic modelling, to update a basic
  micro-dataset and future-oriented what-if
  simulations for instance if the current
  government had raised income taxes in 1997 what
  would the redistributive effects have been
  between different socio-economic groups and
  between central cities and their suburbs by 2007?

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Static spatial microsimulation

• Reweighting probabilistic approaches, which
  typically reweight an existing national microdata
  set to fit a geographical area description on the
  basis of random sampling and optimisation
  techniques
• Reweighting deterministic approaches, which
  reweight a non geographical population microdata
  set to fit small area descriptions, but without
  the use of random sampling procedures
• Synthetic probabilistic reconstruction models,
  which involve the use of random sampling

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Reweighting approaches (1)
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Reweighting approaches (2)
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Probabilistic reconstruction approaches

• p(xi ,S,A,Q,EP,SEG)
• given a set of constraints or known
  probabilities
• p(xi ,S,A,EP)
• p(xi ,Q,S)
• p(xi ,SEG,EP)

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Static spatial microsimulation approaches -
Iterative Proportional Fitting (IPF)-based
microsimulation

• The IPF procedure can be seen at its simplest
  form as
• a method to adjust a two-dimensional matrix
  iteratively until
• the row sums and column sums equal some
  predefined values
• IPF can also be defined as a mathematical scaling
  procedure,
• which ensures that a two-dimensional table of
  data is adjusted
• so that its row and column totals agree with row
  and column totals
• from alternative sources

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Dynamic spatial microsimulation

• Probabilistic dynamic models, which use event
  probabilities to project each individual in the
  simulated database into the future (e.g. using
  event conditional probabilities).
• Implicitly dynamic models, which use independent
  small area projections and then apply the static
  simulation methodologies to create small area
  microdata statically

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SimBritain main data sources Census data and the
BHPS

• 1991 Census of UK population
• 100 coverage
• fine geographical detail
• Small area data available only in tabular format
  with limited variables to preserve
  confidentiality
• cross-sectional

• British Household Panel Survey
• sample size more than 5,000 households
• Annual surveys (waves) since 1991
• Coarse geography
• Household attrition

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SimBritain aims and objectives

• Reweight the first wave of the BHPS data to fit
  small areas
• Dynamically simulate this population for the
  years 2001, 1991, 2011, 2021 (groundhog day
  scenario)
• What-if dynamic simulations

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SimBritain modelling approach

• Establish a set of constraints
• Choose a spatially defined source population
• Repeatedly sample from source
• Adjust weightings to match first constraint
• Adjust weightings to match second constraint
• Adjust weightings to match final constraint
• Go back to step 4 and repeat loop until results
  converge
• Save weightings which define membership of
  SimBritain

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How do we make SimBritain dynamic?

• Original strategy model the ageing death and
  creation of households (from the panel nature of
  the BHPS) and the geographic movement of
  households (using migration data from the Census
  and other sources). This was abandoned when
  migration data proved to be of insufficient
  quality.
• Intermediate strategy extrapolate constraint
  values and re-populate each area anew at
  the-yearly intervals using the original samples
• Future strategy create synthetic household
  histories from the panel data. Methods are also
  being developed to allow for inflation of values
  over time (e.g. income, pc ownership etc) and for
  changing geographical composition (via projected
  constraint values)

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CONSTRAINT TABLES
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SimBritain spatial distribution of poor
households, 1991
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SimBritain spatial distribution of poor
households, 2001
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SimBritain spatial distribution of poor
households, 2011
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SimBritain spatial distribution of poor
households, 2021
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SimBritain spatial distribution of retired
households, 1991
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SimBritain spatial distribution of retired
households, 2001
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SimBritain spatial distribution of retired
households, 2011
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SimBritain spatial distribution of retired
households, 2021
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How do we know it makes sense?
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How do we know it makes sense?
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Comparing Census data to projected data for 1991
(projection based on data from the Censuses of
1961, 1971 and 1981)
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(No Transcript)
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Policy relevance some results from SimYork

• Background Seebohm Rowntrees study of poverty
  in York
• Primary poverty, which meant that the total
  family earnings are insufficient to obtain the
  minimum necessaries for the maintenance of merely
  physical efficiency (Rowntree, 2000 86)
• Secondary poverty, which meant that the family
  earnings would be sufficient for the maintenance
  of merely physical efficiency were it not that
  some portion of it is absorbed by other
  expenditure, either useful or wasteful
  (Rowntree, 2000 86-87)

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Defining and estimating poverty
The subsistence approach to the definition
of poverty is an absolute concept of poverty
it is dominated by the individuals requirements
for physiological efficiency. However, this is a
very limited conception of human needs,
especially when considering the roles men and
women play in society. People are not just
physical beings, they are social beings. They
have obligations as workers, parents, neighbours,
friends and citizens that they are expected to
meet and which they themselves want to meet.
(Gordon and Pantazis, 1997 9)
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SimBritain household classification

• Classifying households
• Very poor all households with income below 50
  of the median York income
• Poor all households with income more than 50 of
  the median but lower than 75 of the median
• Below-average all households living on incomes
  higher than 75 of the median but less than or
  equal to the median
• Above-average all households living on incomes
  higher than the median and lower than 125 of the
  median
• Affluent all households living on incomes above
  125 of the median

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SimBritain results in York
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SimBritain results, York children in households
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Living standards of very poor households
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Living standards of very poor households
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Causes of poverty
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Very poor households sources of income
An analysis of persons in the city who are below
the primary poverty line shows that more than
one half of these are members of families whose
wage-earner is in work but in receipt of
insufficient wages. Rowntree (2000 114)
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The potential for policy analysis
Source The Guardian, 22 March 2000
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Simulating the spatial impact of policy reforms

• Family Credit and Tax Credit
• Minimum Income Guarantee
• Minimum wage
• Winter Fuel Payment and Free TV licence for the
  elderly

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The estimated spatial impact in York
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The estimated spatial impact in Wales
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Social policy impacts at smaller area level an
example from Leeds
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Estimated spatial distribution of change in tax
paid under scenario 1
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Estimated spatial distribution of change in tax
paid under scenario 2
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Future challenges modelling income and
substitution effects

• A substitution effect making leisure more
  attractive than work
• An income effect, encouraging people to work more
  to make up the loss of income
• Different taxes have different effects, and
  affect people at different levels of income or in
  different household circumstances in different
  ways.
• (Hill and Bramley, 1986 85)

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Conclusions and future priorities

• Geography matters need to estimate the
  geographical as well as the social, temporal and
  economic impacts of policies
• In some instances, spatial impacts of social
  policies be compared with the respective impacts
  of area-based policies, as social policies can be
  seen as alternatives to area-based policies.
• New approach to measuring deprivation at the
  local level based on the measurement and analysis
  of income and wealth distribution
• Spatial microsimulation can be used for the
  design of pro-active geographically-oriented
  social policies

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Conclusions and future priorities

• SimBritain outputs there are trends of dramatic
  increases of socio-economic polarisation in
  Britain
• Limitations of SimBritain localised factors
  (e.g. large Universities)
• Refine SimBritain
• Policy spatial micro-modelling - income and
  substitution effect
• Include more regional subsystems (labour demand,
  schools, hospitals, etc.)
• Small area multiplier analysis
• What-if, what-will-happen-if and
  What-would-have-happened-if analysis