Attribute selection

A number of important attributes have been considered in terms of their influence on demographic changes of individuals and populations. These attributes have been discussed one by one here.

Age Sex Socio-economic status Family/Household structure Marital status Ethnicity

Lifestyle (smoking, alcohol, drug use, diet, exercise) Environmental factors

Age: Analysis of the age composition of populations is essential in

demographic investigations. The intensity of each of the demographic processes varies significantly by age but in different ways; mortality mainly affects older people; fertility involves women in the age range 13 to 50; migration is most intense at ages from 18 to the mid 30s; general health steadily deteriorates with age (Rees, 2009). At the aggregate level, demographers are interested in the age compositions of populations and the ages when people engage in certain behaviours. Previous studies also find that the ages of individuals when they move house, get married, have babies, use certain products or services are vital to explaining social trends, targeting markets and planning for the future. Mortality, fertility, and migration processes vary profoundly and in different ways by age. Indeed, the age composition of the population is central to understanding the nature and functioning of our society (Rowland 2003).

Sex: Due to the biological factors, behaviour and wellbeing differences

(women are generally better at looking after themselves if alone), males and females demonstrate considerable variation in their behaviours even when they are of the same age. Also the general fact that more boys are born than girls in the first place (although suffer greater mortality than girls later in different life stages) means that there is a need to model the sex dimension of the studied population from the beginning. Age and Sex are the two fundamental attributes and they play indispensible roles in all the six important demographic transitions. In fact, Age and sex composition are so important to the nature and functioning of societies that all traditional models are based on it.

Socio-economic status: It is obvious that socio-economic statuses of

individuals have an important impact on the living standard. Its impact on individual life style, education and occupation then in turn affect demographic changes in various aspects such as health, marriage and migration. There is also a strong relationship between socio-economic status and mortality risk: people in deprivation tend to have higher risk of mortality (Marmot, 2005; Seeman et al., 2004). However, Socio-Economic

Status (SES) is a general attributes which combines various attributes such as education, occupation and income in particular, which are measured in censuses or general surveys, together with standing or regard that are measured in specialist surveys.

The first two elements of SES, education and occupation, are measured in the 2001 Census. The difficulty is that they do not cover the whole population in the same way. Children may have completed only part of their education. Housewives may not have a paid occupation. So a method would need to be developed to compute an SES to each household member in the Census and in the survey. Also this attribute is more useful for working age population, therefore only covers about 75% of the population. Also different microdata often use different categories for this factor. Due to such complications, socio-economic status is not used as a general factor that applies to simulations of all demographic processes.

Family/Household structure: Family/household structure plays an

important role in decisions on marriage, migration and fertility. For example, we are all familiar with the migrations driven by housing stress due to growth (new arrivals or kids growing up) within current households. Family structure also becomes increasingly important in people’s later life. For example, there have been evidences that men living with their wives have much lower morbidity/mortality risks than single men living alone.

Marital status: The current marital status affects the decisions about

marriage and fertility. More births are found within marriage than outside marriage. Increasingly it has become a more important risk factor in mortality, especially in people’s later stage of life. Elderly people within marriage are at less risk of mortality than those whose who are not married, due to the support between them and their spouses. The cohabitating population is not separated from the singles in this model. Averaged probabilities of the cohabitated and singles are used in the demographic transitions such as fertility for women outside marriage. As decisions are based on whether an individual is married or not, the impact of not differentiating the cohabitated population is considered to be acceptable.

Ethnicity: Potentially ethnicity can reflect many aspects of demographic

characteristics. Ethnicity has a known impact on life style, migration, fertility and household structures. For instance, studies on ethnicity and fertility reveal that South Asians tend to have bigger families than other populations in UK and poorer groups such as Bangladeshis and Pakistanis tend to have poorer health records that may relate to their dietary/lifestyle (Dubuc and Haskey, 2010; Dubuc, 2012).

Lifestyle attributes: Lifestyle attributes such as smoking, drinking or diet is

important in terms of health, fertility and mortality. Because of their importance for being underlying many other demographic changes, these are currently being estimated for small areas by various research teams. However these characteristics would need to be imputed for the microsimulated populations.

Environmental factors: This model intends to introduce the environmental

impact from a geographical view. Lots of demographic and socio-economic processes (probabilities, rates and transitions etc.) vary by geographical location. The model also attempts to explain such variations by including in the description of the individuals in the MSM e.g. age, sex, socio-economic position, occupation and ethnicity and it tries to include environmental influences eg: land pollution and contaminated water. However, often some of these individual or household variables cannot be measured very well and there are lots of factors that remain unobserved. For example, the high mortality risk of people in Glasgow can be explained by their socio- economic deprivation and the high smoking and drinking rates, but still not all of the mortality can be accounted for. The mortality in Glasgow is represented by the influence of the other factors which might include cultures (gangs, violence and drugs etc.) or genetics (natural selection which results in high cardiovascular mortality etc.) However, it is known facts that such demographic patterns persist in certain geographical locations (Shaw et

al. 2006). Therefore geographical location is used in this study as a proxy

for all such unobserved factors, as well as to provide a local context. Geographic codes are used as the surrogates for unobserved variables 81

(features for wherever good microdata are not available).

After careful consideration, age, sex, geography factors and marital status have been used as the base attributes in all demographic process simulations. As described above, age and sex are the fundamental underlying factors of any demographic changes. Geography provides useful substitute for many factors that may contribute to the demographic changes, but difficult to capture in the model. Marital status is selected because of its important role in modelling Marriage and Fertility. It is necessary to simulate the Marriage process on the basis of the current marital status and the birth patterns within and outside marriages have been found to be quite different. Therefore the four attributes are selected as the base attributes for modelling all demographic processes. However, if necessary, different attributes/combinations can be used in the specific demographic processes, according to their roles on the processes. For instance, in the Fertility process, as this model uses a female-led approach to model the fertility process, sex is not relevant and the probabilities are only applied to female populations.

Due to the limitation of computing resources, appropriate microdata and the time scale for the PhD program, other attributes are excluded.