Measuring the Economic Impact of Pension Reform with Microsimulation: an Introduction

1
Measuring the Economic Impact of Pension Reform
with Microsimulation an Introduction

• Elisa Baroni
• National University Ireland, Galway
• Institute for Futures Studies, Stockholm
• Sept. 20, 2006

2
About me.

• MSc Economics (2003) from LSE, PhD Economics
  (ongoing).
• Since 2004, Micro-simulation Modelling of
  tax/benefit policy pension systems. Team leader
  of DWP policy micro-simulation model (PSM).
• Currently at IFS, Stockholm. Developing new
  simulation model for Sweden.
• Research Focus developing micro-simulation model
  for analysing the impact of pension reforms on
  poverty and inequality (Ireland Sweden)

3
Premise

• Pension Systems redistribute income between
• workers and retirees
• birth cohorts
• individuals life periods
• Redistributive Systems change
• households incomes and poverty levels
• households positions in the income distribution
  and inequality levels
• Pension Systems and Pension Reforms affect
  Poverty and Inequality levels (e.g. pre- and
  post-retirement incomes)

4
Our Questions

• What is the Redistributive outcome of a Pension
  System or Pension Reform ?
• What will their Redistributive outcome be in the
  future ?
• Who pays what and who gains what under a given
  Pension System ?
• What incentives or disincentives are created?
• What are the costs of Pension Reform ?

5
Our Answers

• We can measure (redistributive) outcomes of
  Pension Reform, e.g.
• Winners and Losers
• Change in Poverty Inequality indicators
• Poverty Reduction Efficiency measures
• Related Distortionary Impact measures (e.g.
  effective marginal tax rates)
• Redistributive outcome does not depend only on
  the Pension System / Reform alone ? need to study
  interactions with the whole Tax and Benefit
  System Demographic Labour Market trends

6
Our Tool Micro-Simulation Modelling

• MSM are micro-based statistical tools used by
  many governments to inform social policy
  decisions, including Pension reforms.
• MSM simulate how heterogeneous individuals are
  affected by policy changes (incomes
  behaviours)
• MSM allow to compare costs redistributive
  outcomes of alternative reforms ? choose the
  policy which fits better governments aims

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Our Aims.

1. To Introduce MSM
2. To understand MSM key features advantages
   limitations
3. To understand MSM use for measuring effects of
   Pension Systems and Pension Reforms
4. To show examples of models used for pension
   reform analysis in OECD (EU, UK, Ireland, Sweden)
   
5. To discuss applicability of MSM to Developing
   Economies
6. To summarise how to build a MSM

8
An (incomplete) map of MSM
DYNACAM LIFEPATHS ,
MOZART
SESIM SVERIGE MICROHUS ,
POLIMOD, PSM, PENSIM II, IGOTM, TAXBEN
LIAM / SMILE
EUROMOD
NEDYMAS
ESPASIM ,
DYNAMITE ,
DESTINIE ,
DYNASIM CORSIM
BRAHMS ,
NATSEM STINMOD ,
9

• The Department for Work and Pensions (DWP)
  has for the past few years been building and
  validating a dynamic micro-simulation model
  called Pensim2.
• This is a highly sophisticated model which
  attempts to mimic the evolution of both private
  and state pension accumulation and decumulation
  between now and 2050. We have used Pensim2 to
  help inform our recommendations for the UK
  private and state pension systems. In particular
  we use Pensim2 to estimate the cost of state
  pension reforms, the number of individuals on
  Pension Credit and the impact of the proposed
  National Pension Savings Scheme on private
  pension incomes.
• 2nd UK Pension Commission Report, November
  2005

10
Uses of MSM

• Projections
• Evaluation of Public Policy e.g. effects on
  Inequality and Redistribution
• Designing new Policy Reforms
• International comparisons of Policy Reforms
• Studies of inter-temporal processes / behaviours

11
MSM Structure
12
Types of MSM

• Static MSM simulates effects of Policy Change on
  net incomes without demographic, economic or
  behavioural changes ? not suitable if major
  population changes expected
• Dynamic MSM simulates effects of Policy Change
  in conjunction with demographic, labour market
  and behavioural changes ? suitable if e.g.
  population aging expected

13
Input Data

• Without data, no MSM !
• Cross-Sectional or Panel Data
• Variables include e.g. demographic,
  socio-economic, health, housing information
• Often used data sources
• Household Surveys
• Administrative Data (!)
• Census Data
• Synthetic Data

14
Cross-Section Panel Data Set
Time Individual Unit 1 2 3 4 5 6 7 8 9
I Xi, 1 Xi,2 etc
J Xj, 1 Xj,2 etc
K Xk,1 Xk2 etc
L XL,1 XL2 etc
X Age
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IT Infrastructure

• Large memory needed (1 Gigabyte)
• Programming software e.g.
• Excel
• SAS
• Stata
• Object-Oriented Java languages e.g. C
• Existing modelling platforms e.g. Genesis, LIAM,
  JAS, SIMULA
• Average running time
• Static 2 minutes of CPU time for 300,000
  individuals
• Dynamic 2 hours for 300,000 individuals

16
Static MSM

• Uses cross-sectional data as inputs
• Evaluates the short term effects of policy
  change first round impact
• Accounting tool
• Static Aging
• future population is aged to look like current
  population.
• Monetary variables are up-rated using exogenous
  assumptions e.g. inflation ? no dynamics

17
Example I PSM (UK)
P
FRS
FRS
2001/02
2001/02
S
(selected variables)
M
PSM INITIAL PREPROGRAMS
PSM INITIAL PREPROGRAMS
Read in Data

Merge Outside Data

EXTRA
EXTRA
P
DATA
DATA
Derive new Variables

e.g. Rent Officer statistics,
Create Flags and Store FRS values
R
Calibration
E
PSM
PSM
P
INTERIM
INTERIM
PREPROGRAM
PREPROGRAM
DATASET
DATASET
R
PSM OUTPUT 2005/06
PSM UPRATING PREPROGRAMS
PSM UPRATING PREPROGRAMS
O
Static Aging
Tabulations Outputs
G
Uprate Benefit Values
EXTRA DATA
PSM YEAR
SPECIFIC
R
Adjust Benefits Caseloads
PSM OUTPUT 2006 / 07
Population Grossing

A
M
PSM INPUT
PSM INPUT
S
PSM Rules 2005/06
2004/05
2004/05
PSM INPUT
PSM INPUT
PSM OUTPUT 2007 / 08
2005/06
2005/06
SIMULATION USER CHANGES RULES
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Static MSM Assumptions

• Key assumptions made
• No Tax Evasion ? individuals report all their
  incomes
• Full Benefit Take up ? individuals cash in all
  the benefits they are entitled to
• Policy changes do not change individual
  propensity e.g. to evade tax or claim benefits

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Example I PSM Output






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Example II BRAHMS (Brazil)
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Static MSM Limitations

• MSM outputs are as good as microdata ? compare
  them with other analysis or admin data
• Model benefit entitlement only, given rules ? not
  actual take-up
• Non-Behavioural only first round policy effects
  are estimated, no responses
• Non-Dynamic no evolution in sampled population
• Non-Longitudinal no past information ?
  time-series data required for pension modelling

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Dynamic MSM

• Use panel data past information to simulate
  future life histories ? Time Behavioural
  Dynamics are introduced.
• Dynamic Aging
• Individual transitions predicted over the life
  cycle ? synthetic future panel data set from
  input data.
• Transitions estimated under different policy
  scenarios ? compare inter-temporal effects on
  life income path behaviours
• Better suited for Pension analysis

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Example SESIM (Swe)Simulated Transitions
LINDA data
(Lennart Flood, Ministry of Finance and
Gothenburg University)
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Transitions

• Individual Probability of a status change
• Can be estimated by

Behavioural Methods Behavioural
Responses are included to generate new aggregate
patterns
Statistical Methods Future
individual events Made to reproduce
existing aggregate patterns and behavioural
preferences, through Stochastic methods
Deterministic Methods (certain events)
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From Transitions Probability

• Discrete Choice Estimation Probability that a
  certain event, e.g. death, happens, at time t 1
  is a function of input variables at time t
• Pr (si, t 1 1) f (xi, t , ß P )
• Pr (si, t 1 0) 1 - f (xi, t, ß P )
  )
• If no micro data available, conditional
  probability based on observed flows (Transition
  Matrices or Markov Chains)
• Pr (si, t 1 1 si, t ) 0.02 if male,
  0.01 if female

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To Event Simulation

• From individual probabilities ? must simulate for
  whom event actually happens ? stock
• Random Simulation (Cramer, 1991)
• Pr (si, t 1 1) Pr (ui lt pi )
• Alignment (O Donoghue)
• Pr (si, t 1 1) Pr (ui - pi lt Z )

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To Behavioural Simulation

• Behavioural responses to redistributive policy
  can be estimated
• Stuctural Models panel data used to infer
  preference functions i.e. the behavioural rules
  (unobservable) F(y, ß, P)
• Estimated parameters (ß) used to simulate
  (future) responses to policy reform e.g.
• ?(Labor Supply) F(y, ß, P) - F(y, ß, Palt)

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Dynamic MSM Limitations

• inputs
• Limited Panel Data availability
• Greater resources (e.g. IT infrastructure) needed
  to build maintain model ? high costs
• (ii) types of processes simulated
• Sufficient knowledge of micro-behaviours?
• Generally productive or financial sector not
  modelled (growth)
• No Macro feedbacks (attempts ongoing)
• (iii) types of policies modelled
• Not possible to include policies which depend on
  non-financial criteria
• But ..MSM provides an organising
  framework (Burtless,1996)

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MSM Pension Analysis

• 3 AIMS
• to simulate income distribution under pension
  system P (static MSM)
• to simulate future public private pension
  accumulation and de-cumulation over the life
  cycle, under pension system P, given demographic
  behavioural changes (dynamic MSM)
• to simulate effects of reforms to P on
  (life-cycle) incomes distribution costs (both
  Static and Dynamic MSM).

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1. Static MSM and Pensions

• Can be used to simulate short-term effects of
  policy (change) on current pensioners current
  income distribution
• Can be used to make comparisons between pension
  systems ? pure redistributive impact of each
  system can be isolated
• Cannot track pension entitlement accumulation
  through life cycle or simulate who will benefit
  from what pension in the future

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1. Example Pensions Outcomes by Gender for UK,
Fr, D
UK Old Womens mean weekly pension as of mens
Total Pension Income 42.5
Fr
Total Pension income 28.5
D
Total Pension Income Source MAPS K. Rake (2002) 29.2 Data 1988 British Retirement and Retirement Plans Survey
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2. Dynamic MSM and Pensions

• Given P or Palt, DMSM needed to estimate future
• Pensions participation (for workers)
• Pensions coverage (for retirees)
• (Difference between) work and retirement incomes
  (RR)
• (Difference in) n. of poor pensioners
• (Difference between) incomes from each pension
  pillar
• (Difference in) Pension Wealth ( lifetime return
  to system)
• (Difference) in future income inequality between
  groups
• Levels of Analysis
• Population (cross-sector differences between
  groups)
• Intra-Cohort (life-time differences)
• Multi-Cohort (birth-cohort differences ?
  intergenerational equity)

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2. Pension Modelling in DMSM

• For each individual in sample, Pension Module
  simulates over time
• Public Pension Coverage Benefit Income
• Occupational Pension Coverage Benefit Income
• Private Pension Coverage Benefit Income
• Retirement behaviour

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2. Statistical Modelling of Pensions

• For working age individuals ? must simulate
  pension coverage
• Is person accumulating State Pension rights ?
• Is person member covered by an Occupational
  Pension plan? If so which are plan
  characteristics?
• Is person saving in a Personal Pension ? How
  much?
• For retirees ? must simulate pension
  participation and retirement income
• Is retiree receiving what type pensions ? How
  much from each type? Replacement rate ?
• For deceased ? must simulate pension inheritance
  by spouse / dependents

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2. Behavioural Modelling of Retirement

• For working age individual, what age to retire
  given financial incentives built in pension
  system?
• Ex. Option Value Model (Stock and Wise, 1990)
  in any given year, the individual will retire if
  the expected gain from retiring is greater than
  that of waiting, given pension system P
• Useful to measure pension incentives for early
  retirement

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3. Why Pension Reform Aging

• Aging lower fertility mortality rates
  higher life expectancy
• Doubled Dependency Ratios expected by 2050
• More pressure for redistribution from shrinking
  active population to growing inactive population
• Growing pressure on future performance of
  existing (public) pension systems
• Higher pension / health care costs ?
• Higher taxes ? Lower Savings ? Lower Private
  Transfers ?
• Lower Output Growth ?
• Higher pensioner poverty ?
• Higher inequality ?

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3. Aging in Latin AmericaSources Calculations
using the United Nations data base (1999).
1950 1990 2025
60 6.1 7.2 12.9
Mean Pop. Age 24.7 26.6 33.6
Growth Rate 60 0.11 0.13 0.21
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3. Life Expectancy at 60 Latin America
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3. MSM and Pension Reform

• Future pension outcomes will depend on complex
  interactions between Demography Labour Macro
  Pension System.
• Aging can affect Pension System outcomes but
  Pension Reform can curb the effects of aging e.g.
  by introducing incentives for later retirement
• What Pension System is better to cope with aging
  problem, for a given context?
• MSM can help to simulate these interactions under
  different assumptions ? sensitivity analysis

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3. MSM Pension Reforms

• Parametric Reforms
• Changes to retirement age, replacement ratio,
  contribution rate, indexing, or taxation of
  pensions
• Systemic Reforms
• Changes to system structure or financing of the
  system
• Moving from PAYG to Funding
• Making benefit more actuarial (DC)

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3. Ex From DB to DC Systems Pension Outcome by
Gender
Chile Average Married Womans expected change in lifetime pension income at age 65, (normalized to top educated married man)
New System (DC Minimum Pension) / Old System (DB) 1.1
Argentina
New System (DC Minimum Pension) / Old System (DB) 1.8
Mexico
New System (DC Minimum Pension) / Old System (DB) Source Estelle James (2003), Method Cell-based micro simulation 1.4 Data Representative men and women from CASEN 94, ENGH96-97 and ENE-97, simulates life histories
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3. STATIC ex EUROMOD (EU)

• Common Pension Reform Package simulated for Dk,
  D, IT , UK (Working Paper EM5/05)
• Lowering replacement rates of contributory
  earnings-related pensions between 5 10 ?
  lower cost
• Introducing Minimum Pension 40 average
  earnings ? reduce poverty inequality
• Increasing Contribution rates between 1 -3 ?
  revenue neutrality
• Effects on current pensioners incomes, poverty,
  inequality ? How large are the differences ?

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3. STATIC ex EUROMOD (EU)

• 4 Systems main differences
• It / D more earnings related pensions
• Dk/UK more flat pensions
• Common Reform Package lowers poverty and
  inequality in all 4 countries, but...
• Size of distributional impact and beneficiaries
  vary depending on initial conditions
• Conclusion different systems across EU must
  follow different pathways to reach common
  redistributive goal.

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EUROMOD Distributional Effects of Reform Package
45
EUROMOD Distributional Effects of Reform Package
46
3. Dynamic ex SESIM (SW)

• By 2030, number of 85 to double in Sweden
• Public Pension Reform in 1999
• Notional DC PAYG system (16 of pension base) ?
  income pension
• Advanced-Funded DC System (2.5 of pension base)
  ? premium pension
• Fully covers only those born gt 1953 ? different
  birth cohorts subject to different pension
  systems
• Flood, L. (2003) Uses SESIM to simulate incomes
  for multiple cohorts for 1999 2041
• Compare HH incomes before and after retirement
  (Replacement Rate) by birth Cohorts
• Decompose income by pension pillars, by Cohorts
• Evaluate importance of private (real financial)
  wealth

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SESIM Simulation

• LINDA panel data used
• Assumptions retirement age is 65, 2 yearly
  inflation, 3 real growth, variable return on
  financial assets 3-7
• Results The 1999 Reform is less generous.
  Younger cohorts (born after 1953) enjoy lower
  replacement rate, unless retirement age is
  delayed to 67, and returns on savings are high

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SESIM Output
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MSM for Developing Economies

• Redistribution increasingly recognised as
  essential for economic growth and development
• Static MSM well spread among developing countries
• World Bank
• UN Wider Dart (Russia), 5 MSM for Africa online
• NUIG (BRAHMS, XLSIM)
• Dynamic MSM not spread among developing countries

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MSM for Developing Economies

• Challenges due to
• Weak Administrative Structures ? gap between
  policy and reality ?
• Private, in-kind redistribution might be larger
  than public transfers ?
• Behavioral responses to policy changes might be
  different ?
• MSM must be modified accordingly !

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Correcting for Tax Evasion

• Survey income data generally more truthful than
  income reported to tax authorities
• Bank of Italy infers undeclared gross net
  incomes from income reported in survey data
• EV Ysurvey - Ydeclared
• Ydeclared - Tax Ydisp(declared)
• Ydisp(declared) EV Ydisp(real)
• Estimates evasion behaviour from household
  characteristics (e.g. level and type of income)
• EV f (X, Ydisp(declared), Ysurvey ,Ytype
  etc)
• Effective incomes and taxes can be calculated

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Administrative weakness

• Difference between law and reality (e.g. tax
  evasion or benefit abuse) can be inferred from
  household survey data
• Survey income information can be compared with
  model output administrative data
• Household expenditure data can be used to infer
  income under-reporting, tax evasion or benefit
  fraude
• Merging with outside data can also help

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Public Transfers Effectiveness

• IncomeTax Benefit system has smaller
  redistributive power than Indirect Tax System,
  cash benefits or subsidised services (health and
  education).
• ? Include Indirect tax model e.g. (EUROMOD
  Working Paper EM7/01)
• ? Include imputed social expenditures per capita
  or per household (e.g. Old age care and
  assistance). See Pettersson and Pettersson, SESIM
• ? Include imputed in-kind benefits from Household
  Information
• Pension Benefits usually targeted to better-off
  or public sector employees
• ? need to model pension take up by social or
  employment group

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Conclusions on Building a MSM

• Why building a model ? Identify Government
  objectives first
• Understand Micro Data Available ? they will
  determine what can be modelled
• Select what data should be included
• Identify adjustments needed to data and policy
  rules
• Scope for dynamics ?
• Validation and Limitations

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Data requirements

• Individual or household as basic unit ?
• Income Data sources, time period (yearly?
  monthly ?), quality ? tax and benefit liabilities
  rely on accuracy of income information
• Coverage and Representativeness of Population
• Sample size
• Non-response
• Grossing
• Early high quality data collection recommended

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Validation

• Validation plan fundamental how reliable
  robust are results ?
• No definite guidelines
• Do input data overlap with other available sample
  or real admin data ?
• How close are simulated stocks to past
  distributions, by sub-groups ?
• How close are simulated amounts close to admin
  data ?
• How much behavioural responses count in
  explaining differences from aggregate reality ?
  E.g.
• Low Benefit Take up by those entitled in the
  model
• Tax evasion

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Recommendations

• Formulate Policy proposals simply so as to be
  coded
• Choose most reliable unit of analysis, and
  possibly include multi-level data
• Be cautious about dynamic MSM
• Collect new data designed for the purpose of
  building a MSM
• The effects of assumptions made should be tested
  by changing them ? sensitivity analysis
  external validation essential.

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References

• O Donoghue, C (2001) Dynamic Microsimulation a
  Methodological Survey
• Harding, A. ed.(1996) Microsimulation and Public
  Policy
• Flood, L. (2003), Can we afford the future? An
  evaluation of the new Swedish Pension System.
  (www.sesim.org)
• Mantovani, Papadoglou, Sutherland, Tsakloglou
  (2005), Pension Incomes in the European Union
  Policy Reform Strategies in Comparative
  Perspective, IZA paper 1357
• UK Pension Commission Reports, 2005
• Sutherland, H. (1991) Constructing a Tax-Benefit
  Model what advice can one give ?
• Atkinson, Bourguignon (1991), Tax Benefits Models
  for Developing Countries Lessons from Developed
  Countries
• ODonoghue, C, Baldini, M, Mantovani, D(2004)
  Modelling the redistributive impact of indirect
  taxes in Europe a EUROMOD application. EUROMOD
  WP EM7/01
• Lifetime Redistribution through Taxes, Transfer
  and Non-Cash Benefit, Pettersson and Pettersson,
  Natsem Conference, 2003.
• Information on DART model for Russia can be found
  on www.WIDER.unu.edu
• You can download some of this reference material
  from my own website
• www.elisabaroni.com

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Annexes

• (not to be presented)

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Dynamic MSM LIAM (Ireland)Simulated
Transitions
Output Data, t 1
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LIAM Public Pension Model
PUBLIC PENSION SYSTEM P
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LIAM Occupational Pension Model
LABOR MARKET Module
Firms Survey Data
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LIAM Private Pension Model
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Simulation of Pension Reform in LIAM

• O Donoghue, 2003 LIAM used to estimate
  distributional effects of raising retirement age
  to 70 (before LIAM pension model was built)
• No changes to future pension entitlements
  modelled in this paper
• No occupational or private pensions modelled in
  this paper

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LIAM Effects on Poverty and Inequality
Poverty HeadCount(Pop.) 2000 0.0 2030 -4.1 2050 -2.4
Poverty HeadCount (65) 0.0 -6.5 -2.6
Gini 0.0 0.0 -0.8
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UK Pensions Challenges

• UK Population Aging
• 78 increase in number of 65 by 2050
• Dependency ratio expected to increase from 27 to
  48 by 2050
• Total Pension costs projected from 9.9 to 17.5
  of GDP
• Voluntary private pension saving is declining
• of pensioners with inadequate pension will
  increase

67
UK Pension Commission Strategy

• Reduce Pension Costs by
• abolishing state earnings-related pension (S2P)
  ? stimulate private savings instead
• reducing means tested pension (Pension Credit)
  introduce universal, flat-rate, earnings-indexed
  basic pension
• increasing pension age
• Reduce Old Age poverty by
• introducing a compulsory National Savings scheme
• Indexing to wages

68
PENSIM II Effects on Poor Pensioners