Patrick Sabourin

The mechanics of the baby boom: Unveiling the role of the epidemiologic transition

Recent research on the baby boom and its causes has shown that common explanations, such as the recuperation of births following the Great Depression or Second World War, are not sufficient to account for the phenomenon. However, that research has stressed the role of increasing nuptiality. In this paper, we argue that the increase in survivorship of children and young people that resulted from the epidemiologic transition accounted for a large portion of the increased number of births during the baby boom. We use a microsimulation model to assess the respective roles of mortality, nuptiality, fertility, and immigration on the size and dynamics of the boom in Quebec, Canada. Results show that decreasing mortality contributed significantly to the baby boom, along with immigration and nuptiality changes, while fertility rates attenuated the phenomenon. These results substantiate the hypothesis that the epidemiologic transition was an important cause of the baby boom.

The Life Table

With the help of the Modgen wizard, we have developed and successfully run our first microsimulation program. This simple model was able to calculate life expectancy for a cohort whose risk of death remains constant throughout the simulation. For demographers, and probably for most users, this unrealistic mortality hypothesis is unsatisfactory. As a minimum, even in a simple cohort model, mortality should vary with age and sex, thus enabling us to calculate all the components of a conventional life table.

The Base Population

This fifth chapter will deal with the integration of a base population into a Modgen microsimulation model, and as such will be more technical than conceptual or demographic. Up to now, the simulated population has been synthetic; all the actors and their characteristics have been generated by the model itself. A synthetic population requires statistical distributions to be created, describing the distribution of characteristics in the population. An example of this is the distribution of births by province which we included in Chap. 3. However, it may be more practical to use microdata taken directly from a real population as a starting point for the simulation. Rather than generating random characteristics for the actors, such as birth province or sex, we will be using information contained in a database representing an actual population for our simulation. In this chapter, data from the National Household Survey (NHS) will be used to make a projection of the population of Canada starting from 2011 (the year of the survey). We will start by giving a brief description of how to prepare and format the data file to be used for the base population. Next, we will see how to incorporate an external class into the Modgen project in order to read the datafile and import its content. Finally, we will modify the main file and the Start function so as to incorporate the import functions into the model code and initialize actor states using the imported data.

Creating a Basic Cohort Model

In this chapter our main aim is to introduce you to the Modgen software and to the Visual Studio programming software, guiding you through the creation of a first basic model. This model will be based on one single type of event: death. Although this will yield only a small number of results, such as life expectancy at birth, it will allow us to demonstrate and to practise the use of several of the basic ideas in Modgen. We will move on to the calculation and presentation of other elements of the life table in the next chapter.

The Multiple Increment-Decrement Life Table

In the mid 1970s, Professor Andrei Rogers (1975) proposed adapting the life table methodology for regional analysis. The classic demographic approach often abstracted migration, which was considered to be a disrupting phenomenon. Rogers had the idea that migration could be taken into account explicitly by using a multi-regional table which would make it possible to calculate, for example, the share of life expectancy spent in a specific region (or country). The multi-regional model he developed is now an integral part of the demographer’s toolkit.