Introduction

This chapter introduces the purpose of the book. When a researcher needs to perform microsimulation for population projections, building its own model with a common statistical software such as SAS might a good option, because this software is widely used among scholars and is taught in most social sciences departments. We define what is microsimulation: a modelling based on individual-level data rather than aggregated level data, in which transitions between the states are determined stochastically with a random experiment. We finally provide some examples of microsimulation models used by social scientists.

Building Alternative Scenarios

New dimensions added in the projection models in Chap. 10.1007/978-3-030-79111-7_4, the labour force participation and the sector of activity, are modelled using regression parameters. In this chapter, we show how building alternative scenarios with this type of inputs. In the first example, we test an assumption in which women with young children at home have the same participation rate as other women. The second example, we test a scenario in which labour force participation rates of women gradually increase and reach those of men by 2060. The code to implement those alternative scenarios is explained, and some results are presented.

Getting Started

This chapter sets the stage before building the microsimulation model. First, we describe proprieties of the microsimulation model that will be built. The model is time-based, discrete-time and stochastic. We then describe properties of a multistate model that will be converted into a microsimulation model and we show how building a synthetic base population that consists of the individuals that will be projected. We finally explain how to set up the workspace in SAS.

Conclusion

In this book, we have shown how to build a microsimulation model in the SAS language.

Adding New Dimensions

This chapter shows how implementing new dimensions into the microsimulation model. As examples, we add two dimensions that can hardly be implemented in a classical projection model: the labour force participation and the sector of activity (formal/informal). Those modules are implemented through statistical modeling using regression parameters. They use as predictors individual characteristics, such as age, sex, region, education, and for women, a binary variable indicating if she gave birth to a child within the last five years. Those two new variables are thus dynamically implemented, as assumptions on fertility have a direct impact on their outcomes.

Extending and Adapting the Model

This chapter shows different ways of extending or adapting the model built in previous chapters for other contexts. We show how adding and removing modules, how changing the country, how implementing a deterministic approach for some events and how calibrating projection outcomes on other projections.

Converting a Cohort Component Model into a Microsimulation Model

In this chapter, we show and explain the code that reproduces the multistate projection of India described in Chap. 10.1007/978-3-030-79111-7_2 into a microsimulation model. The microsimulation code is divided into modules for each demographic event, namely the mortality, the education, the fertility, the domestic migration, and the reclassification of rural to urban areas. Section by section, we explain the code for the simulation and the production of outputs. We also a basic validation of the mode. The code file “Chapter 3—Replicating multistate.sas” contains the final complete code that generates the simulation for 2010–2060, including the setting up of the workspace (see Chap. 10.1007/978-3-030-79111-7_2).