Simulation is a particular type of modelling that simplifies a structure or system and that generates outputs when the system is run, aiming to predict future trends and gaining a better understanding of some features in the social world (Gilbert and Troitzsch, 1999b). Gilbert and Troitzsch (1999a) discussed a number of simulation models, for example, queing models, multilevel simulation models and microanalytical simulation (microsimulation) models.
These simulation models are best conducted with a computer to allow the simulation of complex calculations, and different types of model are designed for different purposes or
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research questions. For example, some simulation methods can be used to project future social and economic outcomes, and are not only used to project the future outcome based on certain characteristics, but also the impact of policy change. In this study, the focus is on employing a simulation model to project the impact of social policy. Whatever the simulation method chosen, the main aim of a microsimulation model is to analyse the possible impact of policy change upon persons (or household, or firms, or other micro-units) (Harding and Gupta, 2007). Therefore, a simulation model relevant to the purpose of this study is one designed to answer the research questions presented in Chapter 1, whose objective is to analyse the impact or effects of disruptions, and flexible parameters (i.e.
retirement age, contribution rates and pre-retirement withdrawals) on current pension policy in Malaysia and to examine the effectiveness of giving pension credit contributions to women with disruptions throughout their unemployment years.
There are four major types of microsimulation model that have been used in various countries in the design of social policy: hypothetical models, static models, dynamic population models, and dynamic cohort models (Falkingham and Johnson, 1993; Rake et al., 2000;
Sutherland, 2001; Zaidi and Rake, 2001). Different simulation models are used for different purposes. Simulations are used to illustrate and analyse the behaviour of a system using
‘what if’ questions relating to the real system and the output can be used to assist the design of the actual system (Banks, 1999). This means that identifying the research questions in the beginning is the most important part of developing a simulation model as the model later on will be used for answering the ‘what if’ research questions.
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With the aim of predicting the effects of social and financial policy on individuals and groups, the microsimulation approach has been developed (Gilbert and Troitzsch, 1999a).
Microsimulation models are important policy tools in analysing data that involves social and economic units (e.g. persons, households or firms) (Zaidi and Rake, 2001) as demonstrated in previous international studies which have used microsimulation models for analytical purposes. For example, the MOSART model was used in the process of reforming the Norwegian public pension system, and a few versions of the model have been developed that take account of demographic events, educational level and labour force composition as well as household sources of income, taxation, savings and wealth (Fredriksen and Stolen, 2007).
The DYNAMOD model, which has been used to analyse social policy in Australia, simulates events that occur in the lives of citizens, including demographic and labour force transitions, household wealth, superannuation and taxation (Kelly, 2007) and SAGE model was constructed to project the development of social policy in Britain for the twenty-first century, with the focus on pension, health and long-term care needs (Evandrou et al., 2007).
A microsimulation model that includes demographic characteristics, labour supply, and a detailed description of the pension system seems to be the most appropriate tool to obtain estimates of the direct effects of individual benefits, Government expenditures and the future pension burden (Fredriksen and Stolen, 2007). Public and Government concerns about income earnings and the future of pensions have led to further research building and the use of more simulation models to project earnings and income or pensions. For example, the LIFEPATHS model that has been used to simulate public and private components of Canada’s retirement income system has also been used for various studies focusing on intergenerational equity, pension privatisation, lifetime use accounting and rates of return on education (Rowe and Gribble, 2007). The PENSIM model has been used to study the
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influences of policy change on the income distribution of pensioners in the UK up to 40 years in the future (Hancock et al., 1992) and the PHYLIS model, which can compare six national sets of pension entitlements was developed to address issues related to income and pension systems (Evans and Falkingham, 1997).
Each simulation model has its own strengths and limitations, and has its own aims to achieve.
Therefore, microsimulation models are not likely to answer all the questions and issues raised by pension reforms and different tools may be required to capture the complexity of a pension system (Blanchet and Minez, 2009).
Lagergren (2007) contends that using a microsimulation model makes it possible to easily calculate the results under different assumptions, while the effect of an argument can be explored, and new data can be introduced when it becomes available. Additionally, the aim of a simulation is to measure possible change and difference from implementing a policy and to assist Government policy makers to further improve and perfect the policy settings and rules (Linping et al., 2007). Thus, microsimulation models can be used to address the impacts of detailed and complex changes in pension rules (Blanchet and Minez, 2009).
4.2.1 Hypothetical Models
A model that produces a simulation based on individuals and their different characteristics can be categorised as a hypothetical simulation model. This type of simulation model is normally used to explore and examine the output of certain characteristics for different individuals. Joshi et al. (1996) stated that if we are enquiring about what will happen to someone over their lifetime, some artificial time needs to be created in which their hypothetical lifetime unrolls.
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In cases where complete data is not available, it is an advantage to use this type of simulation model as it does not require a complete life history data to obtain outcomes for each individual. The characteristics are set for each hypothetical individual based on the objective of the model. According to Evans and Falkingham (1997), each hypothetical individual can have any characteristic set as the parameter to calculate the outcome required. Due to the lack of complete data, a hypothetical simulation model was the most suitable model for use in this thesis to answer to the research questions set in Chapter 1. However, this type of simulation has its weaknesses. Although the characteristics are set to symbolise an individual’s life characteristics, they may not show the individual’s real life background and outcome in the real world (Joshi et al., 1996; Evans and Falkingham, 1997). Extant literature on hypothetical simulation models is reviewed later in this chapter (refer to Section 4.3).
4.2.2 Static Models
Static models use simulation based on simple snapshots of current circumstances of a sample of the population, and provide an overall picture of a certain scenario that is happening at the time of the simulation. Such models are appropriate for analysing the immediate impact of policy changes (Hancock and Sutherland, 1992). This type of simulation model is commonly used in the United Kingdom and has been used to study the distributional aspects of a range of policy options (Pudney and Sutherland, 1994).
Examples of those who have used static simulation models include Parker and Sutherland (1991) who examined different approaches to child support; Webb and Wilcox (1992) who investigated the need for mortgage benefit; Sutherland (1991) who analysed the effects of introducing a national minimum wage; and Hills (1988) who focused on the 1979 tax-benefit system (Hancock and Sutherland, 1992). STINMOD and EUROMOD are also examples of
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static simulation models. STINMOD has been used to estimate the impact of the system (payment of personal income taxes, the receipt of social security, and family payment cash transfers) on Australian families and on the Government budget (Lloyd, 2007). On the other hand, EUROMOD has been used as a tax-benefit simulation model covering 15 Member States of the European Union to calculate the impact on household incomes of changes in policy parameters (Atkinson, 2005).
Since static models are created based on simple snapshots of current circumstances, they are useful for analysing the immediate effects of policies i.e. ‘the morning after’ (Sutherland, 2001, Atkinson, 2005; Vanags and Chandler, 2006). However, it is argued that static simulation models also have disadvantages. They are not suitable for estimating behavioural responses and for policies that require the effects of their impacts in the long term (Sutherland, 2001). Moreover, static models cannot simulate outcomes prospectively, unlike dynamic simulation models.
4.2.3 Dynamic Population Models
In contrast to static microsimulation models, dynamic population simulations are linked to the ‘ageing’ procedure, which is operated prospectively. Each micro-unit is aged individually based on the survivor probabilities, which tends to change the characteristics of the sample (Merz, 1991). Hancock et al. (1992) designed PENSIM, a dynamic population model used to project the distribution of pensioners’ incomes for the next 40 years into the future. This simulation model simulates individuals’ future retirement income which is estimated from the first employment year until retirement based on the assumptions used throughout the simulation.
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The DESTINIE and SAGE Models are also examples of Dynamic Population Models. The SAGE Model was developed to generate projections of the likely future socio-economic characteristics of the older population and to inform the development of alternative policy options within pensions and long-term care (Evandrou et al., 2007), whereas the DESTINIE model has been used to simulate the distribution of pensioners’ incomes until 2050 (Afsa and Buffeteau, 2007). These models ‘age’ each individual of the sample and create a profile of life histories based on a longitudinal data survey.
Dynamic population simulation is best conducted if the effects of policy changes in the future depend on individuals’ histories, for example changes in the contribution conditions for contributory social security benefits (Hancock and Sutherland, 1992). However, this type of simulation model does not project from birth until death, which results in a greater degree of uncertainty in the results relating respectively to the distant future or the later part of individual life cycles (Hancock et al., 1992).
Having complete life history sources for each individual does not necessarily address all the issues that are essential in the simulation. Davies et al. (2000) pointed out that, by their nature, those sources may be retrospective and data on the early years of people now in their sixties and above will relate only to the circumstances of thirty or more years ago. This is agreed by An (2004) as there will be a problem in simulating prospective purposes, for example, simulating the income prospects of today’s younger generations based on older generation histories.
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The last microsimulation model is the dynamic cohort model which applies the same ageing procedure as dynamic population simulation models. However, the dynamic cohort model can be differentiated from the dynamic population simulation model as it creates ‘synthetic’
micro-units and each micro-unit is then projected from birth to death (whole life-cycle) (Merz, 1991).
The availability of a complete individual life-cycle history for each member in the cohort is an advantage for this type of microsimulation model, since it is suitable for exploring issues over a life-cycle. For example, LIFEMOD model were used to simulate life histories of a cohort of 2,000 males and 2,000 females born in 1985 in the United Kingdom and Australia, respectively (Falkingham and Lessof, 1992; Zaidi and Rake, 2001). Another dynamic cohort model is LIFEPATHS, which was developed in Canada and used to simulate public and private components of Canada’s retirement income system. This model is able to generate a full life history of individuals with a synthetic initial database which is created using a range of overlapping cohorts (Zaidi and Rake, 2001).
This type of model is suitable for applying simulations that are concerned with the lifecycles of individuals which static and dynamic population models are unable to do. However, the lack of complete data needed from birth is the major constraint for dynamic cohort models.