A survey of dynamic microsimulation models: Uses, model structure and methodology
- University of Canberra, Australia
- Teagasc Teagasc Rural Economy Research Centre, Ireland
Cite this article
as: J. Li, C. O’Donoghue; 2013; A survey of dynamic microsimulation models: Uses, model structure and methodology; International Journal of Microsimulation; 6(2); 3-55.
doi: 10.34196/ijm.00082
Tables
Table 1
Uses of dynamic microsimulation models.
| Model | Country | Uses |
|---|---|---|
| APPSIM | Australia | Designed to provide answers regarding the future distributional impact of policy change and other issues associated with policy responses to population ageing (Harding, 2007a; Kelly and Percival, 2009) |
| BRALAMMO | Brazil | Models the Brazilian labour market for pension welfare analysis (Zylberstajn et al., 2011) |
| CAPP_DYN | Italy | Analyses the long term redistributive effects of social policies (Mazzaferro & Morciano, 2008) |
| CBOLT | US | Analyses potential reforms to federal entitlement programmes and quantifies the US nation’s long-term fiscal challenges (Oharra et al., 2004) |
| CORSIM | US | Models changes occurring within kinship networks, wealth accumulation, patterns of intergenerational mobility, the progressivity and the life course of the current social security system, as well as potential reforms, household wealth accumulation, health status, interstate migration, time and income allocation, and international collaborations (Caldwell et al., 1996; Caldwell et al., 1997) |
| Czech Republic Model | Czech Republic | Designed to analyse public pension system and potential reforms in Czech republic (Fialka et al., 2011) |
| DEMOGEN | Canada | Models distributional and financial impact of proposals to include homemakers in the Canadian pension plan (Wolfson 1989) |
| DESTINIE I/II | France | Models public pensions and intergenerational transfers (Blanchet et al., 2009; Bonnet and Mahieu, 2000; Bonnet et al.,1999) |
| DYNACAN | Canada | Models the Canada Pension Plan and its impact on the Canadian population (Morrison, 2000; Osberg & Lethbridge, 1996) |
| DYNAMITE | Italy | Models microeconomic issues and the impact of macroeconomic/institutional changes on the distribution of income (Ando and Nicoletti-Altimari, 1999; Ando et al., 2000) |
| DYNAMOD I & II | Australia | Models life course policies such as superannuation, age, pensions and education, long-term issues within the labour market, health, aged care and housing policy, future characteristics of the population and the projected impact of policy changes (Antcliff 1993; Antcliff et al., 1996; King et al., 1999a; King et al., 1999b) |
| DYNASIM I & II | US | Forecasts the population up to 2030 by employing different assumptions regarding demographic and economic scenarios, and analyses the cost of teenage childbearing to the public sector under alternative policy scenarios, also includes a link to a macro model (Citro & Hanushek, 1991a; Citro & Hanushek, 1991b) |
| DYNASIM III | US | Designed to analyse the long-term distributional consequences of retirement and ageing issues (Favreault & Smith, 2004) |
| DYPENSI (SIPEMM) | Slovenia | A Slovenia Dynamic Microsimulation Model with the focus on pension system simulation (Majcen, 2011) |
| FAMSIM | Austria | Models the demographic behaviour of young women (Lutz, 1997) |
| FEM | US | A demographic and economic simulation model designed to predict the future costs and health status of the elderly and to explore what current trends or future shifts might imply for policy, developed by RAND (Goldman et al., 2006; Goldman et al., 2009) |
| GAMEO | France | Analyses and assesses the consequences of various higher education policies (Courtioux et al., 2008) |
| HARDING | Australia | Analysis of lifetime tax-transfer analysis, for analysis of policy concerning the Higher Education Contribution Scheme and redistributive impact of government health outlays over the lifetime of an individual (Harding, 1993) |
| HealthPaths | Canada | Simulate the trajectory of health and life expectancies, and analyses the relative importance of determinants of health-adjusted life expectancy (Wolfson & Rowe, 2013) |
| IFS Model | UK | Studies pensioner poverty under a variety of alternative tax and benefit policies (Brewer et al., 2007) |
| IFSIM | Sweden | Studies intergenerational transfers and the interdependence between demography and the economy (Baroni et al., 2009) |
| INAHSIM | Japan | Simulates demographic and social evolution, able to simulate kinship relationships in detail (Inagaki, 2010) |
| INFORM | UK | Developed for forecasting of benefit caseloads and combinations of receipt, designed to incorporate significant benefit reforms planned over the coming years, based entirely on administrative data (Gault, 2009) |
| Italian Cohort | Italy | Analyses lifetime income distribution issues (Baldini, 2001) |
| Japanese Cohort | Japan | Looks at the impact on household savings of changes in demographic structure (Ando & Moro, 1995; Ando, 1996) |
| LABORsim | Italy | Simulates the evolution of the labour force over future decades in Italy (Leombruni, 2006) |
| LIAM 0 | Ireland | Models inter-temporal issues relating to the degree of redistribution within the tax-benefit system (O’Donoghue, 2001a; O’Donoghue, 2001b) |
| LIAM 1 | Ireland | Evaluates potential reforms to the Irish pensions system in terms of changes to life-cycle incomes (O’Donoghue et al., 2009) |
| LIFEMOD | UK | Models the lifetime impact of a welfare state (Falkingham & Lessof, 1992) |
| LifePaths | Canada | Models health care treatments, student loans, time-use, public pensions and generational accounts (Rowe & Wolfson, 2000) |
| Long Term Care Model | UK | Models long term care reform options (Hancock, 2000) |
| Melbourne Cohort | Australia | Analyses income inequality in a lifetime context (Van de Ven, 1998) |
| MICROHUS | Sweden | Models dynamic effects of changes to the tax-benefit system on the income distribution and economic-demographic effects of immigration (Klevemarken, 1991; Klevmarken & Olovsson, 1996) |
| MICSIM | Germany | Analyses German pension and tax reform (Merz et al., 2002) |
| MiMESIS | Sweden | Evaluates Swedish Pension Reform (Mikula et al., 2003) |
| MIDAS | Multi | Analyses pension system and social security adequacy (Dekkers & Belloni, 2009) |
| MIDAS | New Zealand | Models wealth accumulation and distribution (Stroombergen et al., 1995) |
| MIND | Italy | Simulates the economic impact resulting from alternative values of the income growth rate and real interest rate (Vagliasindi et al., 2004) |
| MINT | US | Forecasts the distribution of income for the 1931–1960 birth cohorts in retirement, MINT5 extends to the 1926–2018 birth cohorts ((Panis & Lillard, 1999; Smith et al., 2007; Toder et al., 2002) |
| MOSART 1/2/3 | Norway | Models the future cost of pensions, undertakes micro level projections of population, education, labour supply and public pensions, incorporates overlapping-generations, models within a dynamic microsimulation framework (Andersson et al., 1992; Fredriksen, 1998) |
| MOSES | Sweden | Investigates the micro basis for inflation and the interactions between inflation, economic growth, and profitability (Eliasson, 1977) |
| NEDYMAS | Netherlands | Models intergenerational equity and pension reform, the redistributive impact of social security schemes in a lifetime framework (Nelissen, 1996; Nelissen, 1998) |
| PENMOD | Japan | Public pension system analysis (Shiraishi, 2008) |
| PENSIM | UK | Models the treatment of pensioners by the social security system across the income distribution (Hancock et al. 1992; Curry, 1996) |
| PENSIM | US | Analyses lifetime coverage and adequacy issues related to employer-sponsored pension plans in the US (Holmer et al., 2001) |
| PENSIM2 | UK | Estimates the future distribution of pensioner incomes to analyse the distributional effects of proposed changes to pension policy (Emmerson et al., 2004) |
| Pensions Model | Belgium | Analyses and forecasts the medium term impact of a change to pension regulations (Joyeaux et al., 1996) |
| POHEM | Canada | A longitudinal microsimulation model of health and disease, it is used to compare competing health intervention alternatives within a framework that captures the effects of disease interactions (Will, 2001) |
| POLISIM | US | Demographic-economic and social security projection for US social security administration (Holmer, 2009; McKay, 2003) |
| PRISM | US | Evaluates public and private pensions (Citro & Hanushek, 1991a; Citro & Hanushek, 1991b) |
| SADNAP | Netherlands | Evaluates the financial and economic implications of the problem of ageing (Van Sonsbeek, 2009) |
| SAGE | UK | Dynamic demographic/tax model for the UK (Zaidi & Rake, 2001; Zaidi & Scott, 2001) |
| SESIM | Sweden | Analyses the consequences of population ageing and models budget and distributional impact of inter-temporal policy issues such as student grants, labour supply, savings decisions and pensions (Ericson & Hussenius, 1998; Ericson & Hussenius, 1999; Klevmarken & Lindgren, 2008; Klevmarken, 2010; Pylkkänen, 2001) |
| Sfb3 | Germany | Analyses pension reforms, the effect of shortening worker hours, distributional effects of education transfers (Galler & Wagner, 1986; Hain & Hellberger, 1986) |
| SimBritain | UK | Simulates urban and regional populations within the UK (Ballas et al., 2005a; Ballas et al., 2005b); |
| SMILE | Ireland | Population projections with spatial details for Ireland (Ballas et al., 2005a; O’Donoghue et al., 2011) |
| SustainCity | Multi | A dynamic model with a focus on land use simulations (Morand et al., 2010) |
| SVERIGE | Sweden | Models human eco-dynamics, e.g. the impact of human cultural and economic systems on the environment (Vencatasawmy et al., 1999) |
| Swedish Cohort | Sweden | Models the replacement of social insurance by personal savings accounts and the distribution of lifetime marginal effective tax rates (Fölster, 2001) |
| Tdymm | Italy | Analyses the Italian labour market and pension system, with a focus on pension adequacy and related distributional effects (Tedeschi, 2011) |
| XEcon | Canada | A model intended for theoretical exploration rather than practical empirical application (Wolfson, 1995) |
Table 2
Base dataset selection of dynamic microsimulation models.
| Model | Country | Base Dataset | Observation |
|---|---|---|---|
| APPSIM | Australia | 1% census sample drawn from the 2001 Census | 188,013 individuals |
| CAPP_DYN | Italy | Survey of Households’ Income and Wealth (SHIW), 2002 | 21,148 individuals and 8,011 households |
| CBOLT | US | Continuous Work History | |
| CORSIM | US | 0.1% sample drawn from the 1,960 census | 180,000 individuals |
| Czech Republic Model | Czech Republic | Synthetic age 0 cohort, distribution parameters obtained from multiple sources | 119,914 individuals |
| DEMOGEN | Canada | Synthetic age 0 cohort | 1,000–5,000 individuals |
| DESTINIE I & II | France | Financial Assets Survey,1991 | 37,000 individuals |
| DYNACAN | Canada | 1% sample drawn from 1971 census, public use file | 212,000 individuals |
| DYNAMIC TUSCAN | Italy | EU-SILC 2003 wave | |
| DYNAMITE | Italy | Household Income and Wealth, 1993 | 67,000 households |
| DYNAMOD I and II | Australia | 1% sample drawn from the 1986 census | 150,000 individuals |
| DYNASIM I | US | 1960 Census 1–10,000 Public Use Sample 1970 Census 1–10,000 Public Use Sample |
4000 individuals 10000 individuals |
| DYNASIM II | US | CPS 1973 matched to Social Security Administration (SSA) data | |
| DYNASIM III | US | SIPP panels 1990 to 1993 | 100,000 individuals and 44,000 households |
| DYPENSI (SIPEMM) | Slovenia | Administrative dataset by Slovenia Statistical Office (SORS), 2007/2010 | 115,000 individuals / 40,000 households |
| FAMSIM | Austria | Family and Fertility Survey (Austria), 1995–96 | 4,500 women |
| FEM | US | Individual records drawn from the Medicare Current Beneficiary Survey (MCBS), 1992–1998 | 10,000 individuals |
| GAMEO | France | French Labour Force Survey (FLFS), 2003–2005 | |
| HARDING | Australia | Synthetic cohort aged 0 | 4,000 individuals |
| IFS Model | UK | English Longitudinal Study of Ageing (ELSA), 2002–2003 | 12,100 individuals |
| IFSIM | Sweden | Swedish Household Panel Survey (HUS), 1996 | 3,000 individuals |
| INAHSIM rev1/2/3 | Japan | Rev1: 1974 Comprehensive Survey of the Living Conditions of People on Health and Welfare (CSLC) with private household only
Rev2: 2001 CSLC (private household only) Rev3: 2004 CSLC, aligned with population census |
Rev1:32,000 individuals and 10,000 households Rev2: 126,000 individuals and 46,000 households Rev3: 128,000 individuals and 49,000 households |
| INFORM | UK | 1% sample drawn from Department for Work and Pensions (DWP) administrative data | 110,000 individuals |
| Italian Cohort Model | Italy | Synthetic cohort aged 0 | 4,000 individuals |
| Japanese Cohort Model | Japan | Synthetic multiple cohorts (single representative of each cohort type) | 4,000 individuals |
| LABORsim | Italy | 2003 Rilevazione Trimestrale delle Forze Lavoro (RTFL) | 50,000 individuals |
| LIAM 0 | Ireland | LII survey, 1994 (Pop.), synthetic cohort aged 0 (Cohort) | Around 4,500 households |
| LIAM 1 | Ireland | LII survey, 1994–2001 | 15,000 individuals |
| LIFEMOD | UK | Synthetic cohort aged 0 | 4,000 individuals |
| LifePaths | Canada | Synthetic cross-section | Varies |
| Long Term Care Model | UK | Family Expenditure Surveys, 1993–1996 | 1,770 individuals |
| Melbourne Cohort Model | Australia | Synthetic sample of 20 year olds in 1970 | 50,000 males and families |
| MICROHUS | Sweden | The Swedish Household Panel Survey (HUS), 1984 | |
| MIDAS | Multi | PSBH dataset for Belgium, 2002, GSOEP dataset for Germany,2002, ECHP dataset for Italy, 2001 | |
| MIDAS | New Zealand | Synthetic cross-section based on 1991 Census | 10,000 individuals |
| MIND | Italy | ISTATA, IRP and SHIW Data, 1995 | |
| MINT | US | SIPP, 1990–93, matched to SSA data, SIPP, 1990–96, matched to SSA data for MINT5 | 85,000 individuals, expanded in later versions |
| MOSART 1/2/3 | Norway | 1% sample drawn from administrative data, 1989, version 3 used a 12% sample drawn from administrative data, 1993 | 40,000 individuals, 500,000 observations in version 3 |
| NEDYMAS | Netherlands | Synthetic cross-section based on 1947 census | 10,000 individuals |
| PENMOD | Japan | Synthetic dataset based on the official aggregate statistics | |
| PENSIM | UK | Retirement Survey, 1988, Social Change and Economic Life Initiative Survey, 1986 and Family Expenditure Survey, 1988 | 5,000 benefit units |
| PENSIM | US | Synthetic cohort aged 0
Family Resource Survey, British |
|
| PENSIM2 | UK | Household Panel Survey and Lifelong Labour Market Database, 1999–2001 | |
| Pensions Model | Belgium | Synthetic cross-section based on survey data | |
| POHEM | Canada | Administrative data | |
| POLISIM | US | A subset (1–10%) of the 1960 US Census Bureau Public use Microdata Sample (PUMS) | |
| PRISM | US | CPS, March 1978, March and May 1979, matched to SSA data | 28,000 adults |
| PSG | US | Mixed | 100,000 individuals |
| SADNAP | Netherlands | Administrative data from Statistics Netherlands (CBS)
10% sample drawn from the |
|
| SAGE | UK | Individual/Household, 1991 anonymised records combined with several survey datasets | 54,000 individuals |
| SESIM | Sweden | Longitudinal Individual Data Base (LINDA), 1999 | 100,000 individuals |
| Sfb3 Cohort | Germany | Integrated Micro Data File, 1969 (Pop.), synthetic cohort aged 0 (Cohort) | 69,000 households / 7,300 individuals |
| Sfb3 Population | Germany | Integrated Micro Data File, 1969 (Pop.), synthetic cohort aged 0 (Cohort) | 69,000 households / 7,300 individuals |
| SimBritain | UK | UK Census and BHPS, 1991 | |
| SMILE | Ireland | Census of Population of Ireland | |
| SustainCity | Multi | Multiple data sources, including survey datasets and administrative datasets | Depends on the end user, 120,000 individuals for the Paris demography module, |
| Swedish Cohort | Sweden | Synthetic cohort aged 20 | 1,000 individuals |
-
Source: see Table 1.
Table 3
An overview of the technical choices made by dynamic microsimulation models.
| Model | Country | Base Pop Cross | Type of Time Modelling | Open or Closed Model | Use of Alignment Algorithms | Use of Behavioural Equations |
|---|---|---|---|---|---|---|
| APPSIM | Australia | D | C | Y | N | N |
| CAPP_DYN | Italy | Cross | D | C | Y | N |
| CORSIM | US | Cross | D | C | Y | N |
| DEMOGEN | Canada | Cohort | D | O | N | N |
| DESTINIE I & II | France | Cross | D | C | Y | N |
| DYNACAN | Canada | Cross | D | C | Y | N |
| DYNAMITE | Italy | Cross | D | C | Y | N |
| DYNAMOD I & II | Australia | Cross | C/D | C | Y | N |
| DYNASIM I & II | US | Cross | C/D | C | Y | N |
| DYNASIM III | US | Cross | D | C | Y | Y |
| FAMSIM | Austria | Cross | D | C | N | N |
| FEM | US | Cross | D | N | N | |
| GAMEO | France | Cross | D | Y | ||
| HARDING | Australia | Cohort | D | C | N | N |
| IFS Model | UK | Partial Cross | D | C | Y | Y |
| IFSIM | Sweden | Cross | D | C | Partial CGE | |
| INAHSIM | Japan | Cross | D | C | Y | N |
| INFORM | UK | Cross | D | Y | ||
| Italian Cohort Model | Italy | Cohort | D | C | N | N |
| Japanese Cohort Model | Japan | Cohort | D | C | Y | Y |
| LABORsim | Italy | Cohort | C | C | Y | N |
| LIAM 0 | Ireland | Cohort | D | C | Y | Y |
| LIAM 1 | Ireland | Cross | D | C | Y | Y |
| LIFEMOD | UK | Cohort | D | C | N | N |
| LifePaths | Canada | Cross | C | O | N | |
| Long Term Care Model | UK | Cross | D | C | Y | N |
| Melbourne Cohort Model | Australia | Cohort | D | O | N | |
| MICROHUS | Sweden | Cross | D | C | N | Y |
| MIDAS | Multi | Cross | D | C | Y | Y |
| MIDAS | New Zealand | Cross | D | C | N | |
| MIND | Italy | Cross | O | Y | ||
| MINT | US | Cross | C/D | O | Y | N |
| MOSART 1/2/3 | Norway | Cross | D | C | Y | N |
| NEDYMAS | Netherlands | Cross | D | C | Limited CGE | Y |
| PENSIM | UK | Cross | C | C | Y | N |
| PENSIM | US | Cohort | C/D | O | N | N |
| PENSIM2 | UK | Cross | D | C | Y | Y |
| Pensions Model | Belgium | Cross | D | C | N | |
| POHEM | Canada | Cohort | C | N | N | |
| POLISIM | US | Cross | D | C | Y | Y |
| PRISM | US | Cross | D | C | Y | Y |
| PSG | US | Cohort | C | O | N | N |
| SADNAP | Netherlands | Cross | D | C | Y | Y |
| SAGE | UK | Cross | D | C | Y | Y |
| SESIM | Sweden | Cross | D | C | Y | Y |
| Sfb3 Cohort | Germany | Cohort | D | O | N | N |
| Sfb3 Population | Germany | Cross | D | C | Y | N |
| SIPEMM | Slovenia | Cross | D | C | Y | Y |
| SustainCity | Switzerland | Cross | D | C | Y | N |
| SVERIGE | Sweden | Cross | D | C | Y | N |
| Swedish Cohort Model | Sweden | Cohort | D | C | N | N |
| Tdymm | Italy | Cross | D | C | Y | Y |
-
Source: see Table 1.
-
Key: Cross, cross-sectional, C, continuous, D, discrete, Y, yes, N, No.
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