A survey of dynamic microsimulation models: Uses, model structure and methodology

A dynamic microsimulation model is a model that simulates the behaviour of micro-units over time. Orcutt et al. (1961) described the first dynamic microsimulation model following the inspiration of Orcutt’s (1957) article. Most dynamic microsimulation models that have developed in following decades trace a direct or indirect link back to this model. Urban planners use microsimulation techniques to estimate traffic flows, while in the field of economics and social science, microsimulation is often used to analyse the social economic policies. In this paper we shall review how dynamic microsimulation in social science has developed, with main focuses on the economic models developed in the past decade.

Micro level data, such as data obtained from a household survey, is often chosen as the basis for social economic research. In order to evaluate certain impacts of public policies, e.g. the redistributive impact over the course of a lifetime, it is necessary to utilise a long panel dataset. In general, such datasets are not available, either because the analysis relates to the future, as in the case of pension forecasts, or because collected datasets do not cover sufficiently long time periods; therefore, analysts use dynamic microsimulation models to assist in their analysis, a concept which was initially suggested by Orcutt in 1957. Essentially, microsimulation is a tool to generate synthetic micro-unit based data, which can then be used to answer many “what-if” questions that, otherwise, cannot be answered.

Microsimulation models, as in the field of policy modelling, are usually categorised as “static” or “dynamic.” Static models, e.g. EUROMOD (Sutherland, 2007), are often used to evaluate the immediate distributional impact upon individuals/households of possible policy changes without reference to the time dimension and extensive behavioural adjustment. Some newer static models, e.g. IZAΨMOD (Peichl et al., 2010), improved the traditional model by incorporating certain behaviour responses assuming the market adjusts to the new steady state overnight. Dynamic models, e.g. DESTINIE, PENSIM, and SESIM (Bardaji et al., 2003; Curry, 1996; Flood, 2007), extend the static model by allowing individuals to change their characteristics due to endogenous factors within the model (O’Donoghue, 2001a) and let individual units to progress over time. Because of the integrated long-term projections and time dependent behaviour simulations, dynamic microsimulation models could offer further insights in theory.

10 years ago O’Donoghue (2001a) surveyed the dynamic microsimulation models that had been developed up to that point. However, many of the barriers that existed for model development back in 2001 have been overcome. Data collection projects such as the European Community Household Panel and the increased availability of longitudinal administrative data such as the Lifetime Labour Market Database in the UK have loosened the data constraints. A number of new models were developed in the past decade, for instance Pensim2 (Emmerson et al., 2004), IFS Model (Brewer et al., 2007) and SAGE (Zaidi & Rake, 2001) models in UK, APPSIM in Australia (Harding, 2007a) and DESTINIE2 (Blanchet et al., 2009) in France etc. Meanwhile, a few generic software programmes have emerged, such as ModGen (Wolfson & Rowe, 1998), UMDBS (Sauerbier, 2002), GENESIS (Edwards, 2004) and LIAM (O’Donoghue et al., 2009), eliminating the need to create a model from scratch. It has allowed an internationalisation of the models with developments in Belgium (Dekkers & Belloni, 2009), Italy (Dekkers et al., 2010), Canada (Spielauer, 2009), UK (Emmerson et al., 2004) etc. Nevertheless, the decade has seen the demise of a number of models such as DYNACAN in Canada, CORSIM in U.S., NEDYMAS (Dekkers et al., 1993) in the Netherlands, the Belgian model (Joyeux et al., 1996) and MIDAS in New Zealand etc. The micro-econometric and micro-economic understandings of the processes that make up a dynamic microsimulation model have also greatly improved over this period. It is therefore worth in considering the progress made by the discipline over the past decade.

In this paper we shall describe the models developed, their uses and discuss some of the methodological choices faced. We then review the progress made by the discipline since the earliest models and suggest some directions for future development.

Dynamic microsimulation models can have many uses and this section provides an overview of the principle uses. Table 1 summarises many of the existing dynamic microsimulation models in terms of their main purposes, which covers projection, evaluating/designing public policies, inter-temporal behaviour studies, etc. Given that the most accessible micro datasets for social scientists are household or individual level information, most models do not incorporate information on business establishments, with a few exceptions for models like (Sweden) MOSES (Eliasson, 1977), NEDYMAS model, where business behaviours are incorporated through market equilibriums in the models. There are only a small number of firm-level microsimulation models, e.g. DIECOFIS (Parisi, 2003), and they are mostly static.

Following the introduction of the time dimension into dynamic microsimulation, these models are able to provide useful projections for the trend of socio-economic development under current policies. DYNASIM2/3 (Wertheimer et al., 1986; Favreault & Smith, 2004), APPSIM (Harding, 2007a), the SfB3 population model (Galler & Wagner, 1986), DYNAMITE (Ando et al., 2000), SADNAP (Van Sonsbeek, 2010) and DESTINIE1/2 (Bonnet & Mahieu, 2000; Blanchet et al., 2009), have all been used for these purposes. In some cases, dynamic microsimulation models have been used as an input for macro models such as in the case of the MOSART (Andreassen et al., 1994; Fredriksen, 1998, 2003), DYNASIM2 and DARMSTADT (Heike et al., 1987) models.

Dynamic microsimulation models can also be used to evaluate the future performance of various long-term programmes such as pensions, educational financing, and health and long-term care, by analysing simulated future cross-sectional data. The governmental models such as DYNCAN (Morrison, 2000), POLISIM (McKay, 2003), PENSIM2 (Emmerson, 2004), the Sfb3 models (Galler & Wagner, 1986), MOSART (Andreassen et al., 1996), PENMOD (Shiraishi, 2008) and SESIM (Ericson, & Hussenius, 1999; Klevmarken & Lindgren, 2008) have been extensively used for this purpose. The existence of baseline projections allows the design of a new public policy by simulating the effect of potential reforms. Models such as LIAM (O’Donoghue et al., 2009), PRISM (Kennell & Sheils, 1990), the Belgian dynamic model (Joyeaux et al., 1996), the SfB3 population model (Galler & Wagner, 1986), LIFEMOD (Falkingham & Johnson, 1993), SESIM (Flood, 2007; Klevmarken, 2010) and Belgium MIDAS (Dekkers et al., 2010; Dekkers & Belloni, 2009), have all been used to look at pension reform. A number of models such as DYNAMOD, the SfB3 cohort model (Hain & Hellberger, 1986), LIFEMOD (Harding, 1993), and SAGE (Zaidi & Scott, 2001) have been used to examine changes to education finance, whereby education costs are to be paid for over an individual’s lifetime. Fölster (2001) used a microsimulation model to examine reforms to social insurance utilising personal savings accounts.

With the longitudinal information created from dynamic microsimulation models, researchers can study the inter-temporal processes and behaviours at both the aggregate and individual levels. For example, CORSIM (Keister, 2000), DYNAMOD (Baekgaard, 1998), and the New Zealand MIDAS model (Stroombergen et al., 1995) have all been used to look at wealth accumulation. Creedy and van de Ven (2001), Nelissen (1998) and others have used dynamic microsimulation models to explore lifetime earning redistributions. Models such as DESTINIE1/2, LIAM, LifePaths, and IFSIM have been used to examine intergenerational transfers (Rowe & Wolfson, 2000; Bonnet & Mahieu, 2000; Blanchet et al., 2009; Baroni et al., 2009; O’Donoghue et al., 2009), whilst FAMSIM (Lutz, 1997) has been used to study the demographic behaviour of women, and MICROHUS (Klevmarken & Olovsson, 1996) examined the impact of a tax-benefit system on labour market mobility. Models that simulate these processes can be used to design policies to combat these problems, for example DYNASIM was used to study the effect of teenage childbearing, while CORSIM has been used to look at dental health within the US population (Brown et al., 1995). The FEM and POHEM models were designed to evaluate the evolution of the population’s health status and its budget implications for the US and Canada (Zucchelli et al., 2010; Will et al., 2001), whist the LifePaths modelling framework has been used in Canada to examine time use issues (Wolfson & Rowe, 1998).

By combining spatial information with dynamic microsimulation models, the model can also be used to predict the geographical trend of certain social economic activities. This type of model is usually referred to as a dynamic spatial microsimulation model, e.g. MOSES (Wu et al., 2008) from U.K., there are a number of models that attempt to analyse policy changes at the national level. For instance, the SVERIGE model simulates a number of demographic processes for policy analysis in Sweden (Vencatasawmy et al., 1999; Holm et al., 2003), whilst the SMILE model (Ballas et al., 2005a; O’Donoghue et al., 2011) analyses the impact of policy change and economic development on rural areas in Ireland. In addition to modelling economic policy, SimBritain (Ballas et al., 2005b) looks at the evolution of health at the national level while models such as SustainCity (Morand et al., 2010) focus on the housing market with a dynamic setting.

Since dynamic microsimulation models typically project samples of the population over time, one can use the model to examine future income distributions under different economic and demographic scenarios. DYNASIM2/3 (Wertheimer et al., 1986; Favreault & Smith, 2004), APPSIM (Harding, 2007a), the SfB3 population model (Galler & Wagner, 1986), DYNAMITE (Ando et al., 2000), SESIM (Klevmarken & Lindgren, 2008), SADNAP (Van Sonsbeek, 2010) and DESTINIE1/2 (Bonnet & Mahieu, 2000; Blanchet et.al, 2009) have been used for these purposes. These models typically utilise macro-models or forecasts to align their own projections. However, occasionally the opposite has occurred, where dynamic microsimulation models have been used as input into macro models as in the case of MOSART (Andreassen and Solli, 2000), DYNASIM (Wertheimer et al., 1986; Favreault & Smith, 2004) and the DARMSTADT models.

Although Table 1 tries to cover as many known models as possible, it is nearly impossible to give a complete list as new models are being developed every year. In addition, the list focuses more on the dynamic microsimulation models that are mainly used for social economic analyses. Certain regional dynamic spatial models and transportation models are not included.

One can also track the development of models through a number of lineages. The original Orcutt Socio-economic System (Orcutt et al., 1961) led to DYNASIM described above, which in turn led to CORSIM which in turn led to POLISIM, DYNACAN and SVERIGE models. In parallel, large modelling developments in the 1970’s took place in Sweden and Germany with current antecedents, while the LSE welfare state programme of the 1980’s have spawned the LIFEMOD, PENSIM, PENSIM2 and SAGEMOD models in the UK as well as the HARDING model in Australia and LIAM model in Ireland. Subsequently the HARDIING model led within the creation of NATSEM to a range of models in Australia, while the LIAM model has influenced a number of European models including the LIAM2 modelling framework. Separately to these largely related developments, Statistics Canada has developed a series of Lifepath/MODGEN based models based upon the original DEMOGEN.

All these powerful dynamic microsimulation models come with the cost of high complexity. Compared with static microsimulation, dynamic microsimulation is much more costly to develop and also has more issues with the methodologies used. This paper intends to discuss some of the methodological issues related to the construction of a dynamic microsimulation model, surveying current practice in the field around the world.

Microsimulation models are usually built for specific purposes and are thus custom developed, although there are a few packages that are often used in the development of dynamic microsimulation model. These packages can be grouped in to three main categories according to their development environments, each with their own advantages and disadvantages:

• General purpose programming language tool (C/C++/C#/Java etc.)

• General purpose statistical package (Stata/SAS/R/MatLab/Mathematica etc.)

• Simulation modelling package (Modgen, LIAM2, GENESIS etc.)

Development using a general purpose programming language clearly enjoys the highest degree of flexibility and possibly speed advantage. FORTRAN was popular among some earlier models, e.g. MICROHUS, PRISM, and DYNASIM2 while C language family seems to be a popular choice for later models. SAGE, DYNAMOD, LIAM, DYNACAN and a few others models were all developed using C++, whilst POLISIM used a mixture of C and C++, and APPSIM and MOSART were programmed in C#. Models have also been developed in Java (e.g. IFSIM), Visual Basic (e.g. SESIM) and a number of other languages. Evaluation based on the prototype microsimulation models by Percival (2007) suggested that there is a substantial speed advantage when the prototype model is coded using C++. However, the cost of development is much higher when compared with other approaches, as all the potential numeric calculations and related data management need to be programmed. In practice, it is likely that policy modellers are not adequately proficient in programming, while professional programmers may not fully understand the economic theories and scientific principles that are to be implemented.

The second approach is to develop microsimulation models based on existing statistical or mathematics packages, such as GAMEO, DYNASIM, and PENSIM2 which were built on SAS. Modern statistical packages are capable of executing computation commands in batch and the code of a model is commonly referred to as a “script”, “do-file” or “syntax file” etc. The main advantage of this approach is short development time and easy access to the statistical power of the package. However, the speed of the model might be lower and the data structure may not be optimised for simulation purpose.

Another way to develop microsimulation models is to use modelling packages developed by the third parties. Software in this category ranges from generic purpose modelling software, e.g. AnyLogic etc. to microsimulation specific packages. Whilst agent based simulation modellers use more generic purpose packages, microsimulation modellers tend to use more specific software.

The most notable dynamic microsimulation modelling tools include

• Modgen, developed by Statistics Canada (Wolfson & Rowe, 1998)

• LIAM2, developed by the Belgium Federal Planning Bureau (Bryon et al., 2011)

• UMDBS, developed by Sauerbier (2002)

• GENESIS, developed by UK Department for Work and Pensions (Edwards, 2004)

• JAMSIM, developed by Centre of Methods and Policy Application in the Social Science (COMPASS), University of Auckland (Mannion et al., 2012)

• LIAM, developed by O’Donoghue et al. (2009)

Modgen provides a C++ library for developers to incorporate required common actions and modules. It is often used to develop continuous microsimulation models, such as LifePath. In contrast, LIAM2 is a microsimulation scripting engine which is capable of reading its own syntax. The engine itself is coded in Python with extensive references to libraries written in C. Microsimulation packages such as this one offer the great benefit of rapid development. There are also a few dynamic microsimulation models that were built with generic deployment in mind. For example, LIAM avoids hardcoded parameters and variable names during the development, which greatly reduce the repetitive work load of a new modeller. GENESIS offers a platform to create a SAS based microsimulation model by reading the model specification from an Excel Sheet. JAMSIM combines several opens source packages, e.g. R, Ascape, to create a user friendly interface to model and execute microsimulation models. However, these models still require end users to understand the internal mechanism and make changes at source code level when building larger models.

Most models today are based on a statistical package or a generic purpose programming language. However, it is not uncommon to see mixed combinations of environments in order to utilise the advantages of different software, especially for pre- or post-simulation analysis, e.g. DYNACAN, LIAM.

This paper has discussed some of main issues involved in constructing a dynamic microsimulation model and described some of the choices made by different models in use worldwide. The main issues discussed have covered some of the general model development issues, such as base dataset selection, cohort or population based model structure, programming environment, and model validation. The paper has also discussed some of the technical choices made in model implementation, such as whether the model should be open or closed, whether alignment algorithms should be used, whether the model should incorporate behavioural response to policy changes, and links to the agent based models etc.

Over the past decades, microsimulation models have been applied to many different policy areas and a comparison of models as given in Table 1 illustrates the scope of application of dynamic microsimulation models. Most dynamic microsimulation models listed can be categorised as discrete models using dynamic ageing approach. For newer models, alignment has become a standard component allowing interactions with macro aggregates and more recently, simulation packages that are dedicated solely to microsimulation have become a viable option in model development. These packages, together with increased co-operation through meetings and code sharing (e.g. LIAM, Modgen model series), could significantly increase the development process.

The increasing use of microsimulation models has raised many technical challenges to meet the needs of more complex and accurate policy analyses. For instance, there is a growing interest in integrating CGE into microsimulation models, although the actual implementations of CGE-microsimulation are at this stage restricted due to data and technical limitations. Behavioural responses in microsimulation could also be further improved and one should consider more life-cycle models when simulating inter-temporal choices. Microsimulation models could potentially implement some elements from ABM to allow dynamic behaviour interactions and adaptations. In addition, considering different unit of analysis, budget and political constraints, may also broaden the field of microsimulation applications. Furthermore, certain practices within the simulations, such as alignment for complex models and error term simulation, should be more thoroughly studied.

Besides the technical challenges, there are also some general issues in the field. The lack of documentations often forces new modellers to reinvent the wheel; closed sourced models which slow down the knowledge transmission. The unrealistically high expectation in long run simulation may challenge the creditability of the model and make applying for funding more difficult in long run. Future modellers may help to address these issues by publishing model details in academic journals and be more open on the algorithm implementations. Newer modelling platforms like LIAM2 attempt to be more open and transparent in the software source code, which would potentially benefit the field development and knowledge transmission. In addition, the field can also explore topics other than taxations and standard government policies. Topics like the impact of climate change, the social consequence of ageing, for instance, could also potentially gain benefits from microsimulation techniques.

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Author details

1. Jinjing Li

   The National Centre for Social and Economic Modelling (NATSEM), University of Canberra, Australia

   For correspondence

   Jinjing.Li@natsem.canberra.edu.au

2. Cathal O’Donoghue

   Rural Economy and Development Programme, Teagasc Teagasc Rural Economy Research Centre, Ireland

   For correspondence

   Cathal.ODonoghue@teagasc.ie