1. Using Microsimulation for Policy Evaluation
Cathal O’Donoghue
Teagasc Rural Economy and Development Programme (REDP)
President, International Microsimulation Association

2. Focus of Lecture
 Managing Complexity: Policy x Population
 Microsimulation Modelling
 Types of Models
 Implementation

3. Objectives of Policy Evaluation

4. Objectives of Policy Evaluation
 How much will it cost?
 Who is affected?
 Are different parts of the population affected in different ways?
 Distributional Impact?
 Who wins? Who loses?
 Where is the main impact?
 Is there a behavioural response?
 What are the impact on Policy Objectives
 Poverty
 Pollution
 Labour Supply
 Tax Revenue
 What is the impact of alternative policy designs?

5. Complexity

6. Sources of Complexity
Challenge: Understand policy impact in a complex world

7. Sources of Complexity – Other

8. Type of Analysis

9.  Ex Post Analysis
 Evaluate policy after program has been implemented
 Use of Treatment and Control Groups
 Randomised Control Experiment
 Randomly Select
 Quasi-experimental design
 E.g. Localities or groups randomly assigned
 Survey Before and After Program Implementation
 However
 Difficult to have randomised experiment in public policy
 Expensive as one needs to implement policy first
Ex Post Analysis

10.  Ex Ante Analysis
 Simulate impact on computer
 Collect Data on Population
 Model Policy in Computer
 Assess Policy Impact before implementing policy
 Less accurate  Structure of behaviour may change in
response to policy instrument
 Simpler as experiment on computer
 Cheaper  can be tested on computer before roll out
Ex Ante Analysis

11. Examples of Complexity

12. Unemployment Trap
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T VAT SIC IT CB UA FIS Earns
• No Incentive to Work
DisposableIncomeperWeek(£)
Hours Worked per Week
Means Tests Unemployment Assistance
Earnings
Child Benefit

13. Unemployment Trap – Introduce Family Income Supplement
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T VAT SIC IT CB UA FIS Earns
• Introduce In-Work Benefit to provide an incentive to work 20 hours
DisposableIncomeperWeek(£)
Hours Worked per Week
In-work Benefit (Family Income Supplement)

14. Combine Solutions  Poverty Trap
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T VAT SIC IT CB UA FIS Earns
• Combine
• FIS with 60% withdrawal rate
• Tax and Marginal Relief at 40% tax rate
• PRSI – c. 8%
•  108% Marginal Effective Tax Rate
•  Poverty Trap
DisposableIncomeperWeek(£)
Hours Worked per Week
Taxation

15. Irish M2K Budget Constraint 1994
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T VAT SIC IT CB UA FIS Earns
• Solution
• Make FIS dependent upon after tax income
• Exemption Limits and Marginal Relief eventually abolished
DisposableIncomeperWeek(£)
Hours Worked per Week

16. Is there an Average Household?
 Single Earner Couple with Children at Average Wage?
 Couple with Children – 33.5%
 Single Earner Couple with Children – 13.1%
 Single Earner Couple with Children at Average Wage –
1.7%
 Conclusion  Almost no one at “average”
 Population Highly Complex

17. Micro Targets and Policy Objectives

18. Targets and Policy Levers
 Increased focus on outcomes, targets and policy levers
 Millennium (Sustainable) Development Goals
 National Anti-Poverty Targets
 National Action Plans on Social Inclusion (EU)
 Labour Supply Targets
 Pensions Policy Reform  Multidimensional
 Financial sustainability of system
 Early retirement incentives
 Poverty risk
 Coverage of membership
 Policy Modelling used to design and evaluate policy

19. Microsimulation Overview

20. What Microsimulation Models Are
 Micro – Household Units
 Simulation – Policy, Social or Economic Change
 Therefore: Powerful tools for
 Running scenarios to
 Understand the impact of economic, social or policy processes
 On the distributional characteristics of the population
 Study Impact of Public Policy
 Effectiveness of Existing Policy
 Evaluate potential reform
 Those who use models and their results want more
 Significant Impact
 Increasing spread and use is an example

21. What Microsimulation Models are not
 They are not forecasting models
 Dynamic Microsimulation Modelling largely died off in Europe in the late 1980’s
 Perception of failure of earlier models to predict the future
 However
 Expectations too high
 Predictive Capacity of Models  weak
 Models should be used for scenario analysis!

22. Types of Models

23. Model Types
 Policy Only
 Hypothetical
 Policy and Population
 Static
 Policy, Population and Behaviour
 Labour Supply
 Consumption
 Policy, Population and Behaviour and Inter-temporal
 Pension Reform
 Policy, Population and Behaviour and Place
 Spatial Policy
 Policy, Population and Cross Country
 Pension Reform

24. Abstracting from Population Complexity – Hypothetical
Models
 Simplest type of model Hypothetical Model
 Ignores Behaviour and Population Variability
 Focuses solely on one dimension of complexity: Policy
 Use
 Communicating policy reforms
 OECD Jobs Study Comparative analysis of Work Incentives
 Issues
 Easier to Build
 Easier to Communicate
 No Average Household

25. Hypothetical Model
Hypothetical Family
Model Framework
Analytical Routines
Policy Routine
Output Routine

26. Results
• Distribution of Costs of Child Care including fee, rebates, benefits, tax
reductions to produce net cost for two earner couple, each with 67% of
Average Wage
• Net Costs and decomposition for lone parents
• Useful for policy learning
Can Parents Afford to Work - Childcare Costs Tax-Benefit Policies and Work Incentives ? (Immervoll and
Barber)

27. Introducing Population Complexity – Static Models
 Ignores Behaviour Variability  Day after effect of Policy Reform
 Two dimensions of complexity: Policy and Population
 Contains Fine Detail of Legislative Complexity
 Use
 Costings
 Winners and Losers
 Poverty Effectiveness of Policy Reform
 Distributional Incidence
 Widespread use
 Issues
 Can capture complex policy and population interactions
 Building block of more complicated models

28. Results
Benefit Changes
Welfare to Work Changes
• Children shift up income distribution
• Reduction in Poverty due to employment at (i) PT, (ii) FT, (iii) PT/FT at
(i) + (ii) MWl (iii) AW
• Poverty effect of Employment change under 2 TB - systems
How Effective is the British Government’s Attempt to Reduce Child Poverty? (Piachaud and Sutherland)

29. Introducing Behaviour
 Models Behavioural Impact of Policy Reform. E.g.:
 Labour Supply, Retirement, Child Labour
 Consumption
 Policy Participation (Benefit Take-Up, Tax Evasion, Education)
 Links Static Microsimulation Model
 Contains Fine Detail of Legislative Complexity
 Issues
 Change in Behaviour as a result of a policy reform
 Second – round impact on cost/distribution
 Often response small

30. Results
WORK INCENTIVES AND ‘IN-WORK’ BENEFIT REFORMS: A REVIEW
(Blundell)
• New budget constraint
• Increased pressure to participate
• Increased pressure to move to 30 hours
• Change from Family Credit to Working Families Tax Credit
• Increased Generosity
• Reduced Taper
• Child Care Component
• Change in simulated employment status before and after reform

31. Distributional Impact of a Carbon Tax
• Inequality increased by carbon tax
Carbon Taxation Prices and Inequality in Australia Cornwell and Creedy

32. Introducing Behaviour – Macro Shocks
 Large Interest in micro level impact of a macro-economic change
 Trade Agreement
 Macro-Shock
 Financial Crisis/Deregulation
 Links Macro-CGE to Microsimulation Model

33. Introducing Time: Dynamic Models
 Many Policies have a time dimension
 Pensions Policy
 Higher Education Loans
 Retirement
 Long-term care
 Dynamic Microsimulation Model
 Takes Population
 Simulates transition of population over time
 Issues
 Can use statistical based models or
 Behavioural models where behaviour like retirement choice responds to
policy

34. Poverty Impacts of Alternative Pension Proposals
• Different Indexation
• Reduction in poverty rate due to elimination of coverage gap
Poverty Impact of State Pension Reform on the Elderly: an Analysis of Reform Proposals in the 2007 Irish Green
Paper (Baroni and O’Donoghue)

35. Introducing Place: Spatial Models
 Models Locational Impact of Policy Reform. E.g.:
 Spatial Incidence of Policy
 Spatial Incidence of Outcomes like Poverty
 Behavioural impacts  Commuting
 Requires
 Spatially references micro-data
 However typically not available
 Developed data enhancement tools to link different sources

36. Results
• Travel to Work Area
• Location of Economically active with low skills in Leeds, UK
• At different levels of spatial aggregation
GIS and microsimulation for local labour market analysis (Ballas and Clarke)

37. Learning from Other Countries
 Policy experiments
 may be politically difficult or expensive
 However comparative analysis or modelling
 Can help to learn lessons
 Increasing comparative research
 EUROMOD
 Latin America
 Africa
 Issues
 Comparability of Data
 Comparability of Policy

38. Different Units of Analysis: Farm Level Models
 Models Behavioural Impact of Policy Reform. E.g.:
 Labour Supply, Retirement, Child Labour
 Consumption
 Policy Participation (Benefit Take-Up, Tax Evasion, Education)
 Links Static Microsimulation Model
 Contains Fine Detail of Legislative Complexity
 Issues
 Change in Behaviour as a result of a policy reform
 Second – round impact on cost/distribution
 Often response small

39. Winners and Losers Analysis from post 2014 CAP analysis
WinnersLosers
Winners in peripheral areas and in North East
Losers in the East and South East
However overlaps

40. Microsimulation in Practice – UK Department of Work and
Pensions

41. Context – Objectives of DWP
 PSA Targets
 PSA 1 Reduce the number of children in low-income households
 PSA 3 Demonstrate progress on increasing the employment rate
 PSA 4 & 7 Increase employment rates for disadvantaged areas and
groups (lone parents; ethnic minorities; people aged 50 and over;
lowest qualifications; poorest local authority districts; disabled )
 PSA 5 Reduce the proportion of children in households with no one
in work
 Other:
 Change in the ratio of spending on pensions by the State to
spending on pensions by the private sector from around 60:40 to
50:50 by 2025

42. Existing Models
 Treasury Sets Targets
 DWP uses microsimulation models to design policies to achieve targets
within the budget allowed
 Microsimulation Models on desktop of policy analysts ~ used like
EXCEL
 Models used:
 PSM – Tax-Benefit Analysis
 EHM – Labour Supply Model & Employment and Hours Model
 Pensions – GENESI/Pensim2 Dynamic Microsimulation Model
 Benefit Forecasting – Other Genesis Based Models
 Family Change Model

43. Don’t Re-invent the wheel

44. Learn from Others
www.microsimulation.org
New Handbook

45. Learn from Others
Journal

46. Model Frameworks
 Microsimulation Model Framework – Model Engine
 Expensive to create – 1 to 2 person years (or more)
 Because of cost, more effort spend on computing
environment than policy question
 Specific not general, so models die after initial use
 General Modelling Frameworks
 LIAM(2) – Dynamic and Spatial Microsimulation (C++)
 URBANSIM – Land Use and Transport Policy (planned link
to LIAM)
 EUROMOD – Cross-country microsimulation (C++)
 XLSIM – Easy to use EXCEL based framework for national
models

47. Thank You