AWA: Academic Writing at Auckland

Aims

In this research proposal, I hope to ascertain whether or not the need for a physiological approach to the ‘demographic transition,’ is academically justified. The ‘demographic transition’ refers to a recent life history shift in modern industrialised human populations. A theory of ‘parental investment’ based on economic concepts has developed out of the literature in this area. This theory states that parents with fewer children are able to invest more in them, raising their chances of future success in a modern economy (Kaplan et al., 1995). I hope in my research to fill a gap in the literature by focusing on the physiological implications of the demographic transition. My main goals are to assess, through a study of body mass and composition measurements, whether or not the ‘demographic transition’ and the parental investment theory are evident physiologically. I plan to do this by comparing various body mass and composition measures of people from families of varying offspring number. Familial resources and parental backgrounds will also be assessed as proxy variables.

Critical Literature Review

The demographic transition is seen as something of an evolutionary paradox. Evolution by natural selection broadly specifies that competition between individuals over resources is the single greatest determinant of reproductive success. Assuming the applicability of the theory to modern societies with raised standards of living, theoretically fertility should rise. Instead, as living standards and access to resources rise, fertility tends to decline (Kaplan et. al, 1995: 253, Becker et al 1990, S12). Much of the research on the demographic transition has approached this paradox from evolutionary and economic perspectives. This is understandable given the need to develop a model for this evolutionary ‘paradox’  (Kaplan et al 1995:327) and the transition’s strong relation to the emergence of the modern economy (Galor 2005:494). However, it is worth noting that there is some debate over whether evolutionary assumptions are applicable to human demographics. Making evolutionary assumptions about human demographics is indeed the reason that the transition is considered a ‘paradox’ in the first place. Mueller and Short (1983:508-9) in particular argue that the raised incomes in modernised economies do not have any straightforward correlation with offspring number, and any positive correlations are offset by other factors such as the raised cost of children. Nonetheless, the more popular theories in the literature do have explanatory worth regardless of the debate over assumptions at the base of them. They tend to offer a short term model of why the transition has happened, rather than extending long term evolutionary assumptions to argue that this behaviour represents some new reproductive ‘strategy’.

Mulder (1998:267-8) outlines the three most popular areas of literature regarding the causes of the demographic transition. One interesting theory from Boyd and Richerson (1984:432) states that in a modern society, people have no or fewer children are more likely to be successful professionals. These individuals are likely to become cultural models for success, emulated by other people’s choice to have fewer children. This seems to me to be an extension of the economic approach, in that these ‘professionals’ are seen as successful in modern society due to higher income and/or education. These are criteria that Kaplan (1994, 1995) focuses on as indications of higher parental investment in his economic model.

The second area of scholarly argument on the demographic transition focuses on the idea that the shift is simply maladaptive, which even Kaplan admits is a possibility (1995:354). There is, admittedly, no evidence that limiting fertility for education or other benefits is evolutionarily adaptive in the long run (Mace 2000:277). However, when considering the possible reasons behind the demographic transition, arguing maladaptivity is unhelpful. This approach is simply applying evolutionary theory to evidence without considering the cognitive processes behind it. In any case, in terms of the cognitive processes that go into family planning, the evolutionary adaptiveness of an individual’s choice would hardly be their main concern. Additionally, this idea seems to apply evolutionary theory to the ‘effect’ (fertility decline) without considering the cause, and is therefore not mutually exclusive to the cause-focused literature.

The third area focuses on economics, chiefly on the idea that lower fertility in modern economic societies can be considered optimal due to higher parental investment. This theory is particularly supported by Kaplan (1994, 1995) and Becker (1988, 1990, 1992). Kaplan et al’s fertility study of New Mexican men was based around the theory of a trade-off between the quantity and quality of children (1995:327). The study reported that more educated parents tended to have fewer children, who also tended to be more educated (1995:351). However, this may be more of an association than a strict cause and effect relationship (Becker 1992:89). The primacy of technology and higher learning in modern industrial economies has been hypothesised as a major factor in the ‘quantity vs. quality’ shift (Draper and Harbending, 1987:226). In this type of economy, Becker et al (1990) argue that there is a higher demand for human ‘capital,’ which results from increased demand for ‘investment’ in education and skills. This is also echoed by other authors (Galor, 2005:499; Kaplan et al 1995:351). Families with fewer children tend to spend more money on their children’s education, which is viewed through the economic approach as a form of higher investment (Becker 1992:90). The cost of children in developed societies also increases with the increased investment expected in education and other skills (Draper and Harbending 1987:227; Willis 1982:207). This could certainly affect the cognitive processes of family planning to encourage a shift toward fewer children, as could perceptions of the job market and the economy.

The focus and interest on explaining the causes of the demographic transition stem from the view that the shift is an evolutionary paradox.  Two modern behaviours deviate from the predictions that the principles of natural selection would dictate. Firstly, that higher parental fertility is associated with lower education levels and lower economic status. Secondly, the modal fertility of economically developed societies is 2 children (Kaplan and Lancaster 2000:283). This is even lower in developed areas such as Western Europe and Eastern Asia (World Development Indicators 2010, The World Bank). This is despite evidence that number of grandchildren is highest amongst those who have had the most children (Kaplan et al 1995:345). Therefore, while children from larger families receive lower levels of investment, this does not affect their fertility (Mulder 1998:266). Perhaps the key to explaining this is in accepting that natural selection could favour different strategies for optimal fertility in humans as another means of competition (Mulder 1998:266; Draper and Harbending 1987:228). Alternatively, analysing fertility as an evolutionary ‘strategy’ may be the less relevant approach. Our understanding of the demographic transition could be better served by analysing it as an interesting shift in its own right regardless of whether or not it is an evolutionary ‘paradox’ or ‘strategy’.

Becker (1988:2) notes that in his economy-based approach to fertility, he heavily relies on the assumption that parents are ‘altruistic’ towards their children. This implies that the ‘parental investment’ theory is largely based on the idea of altruistic investment: to invest more in the education and skills of your child than in your own consumption. Once again, this is too simplistic a view. Although people living in a modern economy more or less share a common impetus towards education and skills, this does not necessarily mean that this becomes a common ‘culture’ in which the success or failure of raising children is based solely on these criteria. This ignores the many different cultural values and ideas on the education and rearing of children and the differential access of parents across economic strata to opportunities and resources. It also runs dangerously close to assuming that parents who are do not invest largely in a child’s state education are ‘selfish’ (Becker 1988:5). In reality there are many different types of behaviours which may be considered ‘investment’ or ‘involvement’ (Lee and Bowen 2006:198). This presents an opportunity to extend criteria to include physiological data in this debate. This would give a more holistic view of the variety that may or may not be present in adults as a result of their upbringing. This data would also be useful in analysing whether physiological differences can be predicted from types of upbringing and differential ‘investment’ in offspring. 

The literature on the demographic transition therefore focuses overwhelmingly on models of its causes, leading to the parental investment theory as an economic explanation. While these are crucial to understanding the principles of the shift, the literature is lacking in empirical data to support the parental investment theory in terms of biological and physiological effects, though there are some interesting studies that can be incorporated. One study by Neill (2007) notes that higher-earning women in Indo-Fijian households tend to buy more processed, less nutritious food for their children. As a result the children have higher BMIs than rural or poorer children. This indicates a trade-off between income and food choice. On the other hand, higher BMI has been noted as a physiological characteristic of lower socio-economic strata (Wake et al 2007, Evans et al 2000). Mclanahan (2004:608) states that mothers in modern economies are moving down increasingly different trajectories based on their income and education.  This manifests itself in increasingly disparate access to resources such as higher education for their children, and potentially in their body mass and/or composition as well.

Quinlan’s study (2003) on the factors that affect age at weaning also provides an insight into the complex factors behind cognitive processes and their physiological effects. Only one of the associations with age at weaning predicted from the parental investment theory was seen: that father absence was correlated with early weaning age. Quinlan suggests that the termination of breastfeeding was influenced more by modern desires for offspring success and embodied capital. This indicates a trade-off between prolonged breast feeding and enhanced social learning time (Quinlan 2003:12). These studies indicate just how complex and minute the factors that count as ‘parental investment’ can be.

I have come across one study that has measured BMI in relation to family size and found no correlation (Koziel and Leipowitz, 2009). However, this study did not focus exclusively on BMI and its correlation with family size; it also measured height and compared these measurements to the education level of parents. Crucially, the study also focused on adolescent girls between the ages of 13 and 15. The maturation stage of humans when measuring BMI is important to consider in these types of studies (Daniels et al 1996). Taking a section of girls between the ages of 13 and 15 may not give an accurate idea of completed BMI in relation to family size, given that some girls in that age group will have finished puberty and some will not have started. Indeed, the BMI maximum velocity is reached during the age period measured in this study. Some girls measured may have been at the end of the process of achieving near adult BMI, while others may not have begun (Guo et al, 2000).

The various theories for the ‘causes’ of the demographic shift are likely to be a continual matter of debate. There is, however, a lack of empirical data based on the parental investment theory. Analysing the differential physiologies of families with varying offspring number would provide another perspective to the debate. The literature has largely used non-physiological criteria for demonstrating differential parental investment (e.g. education).  Nonetheless, it would be very interesting to see whether the parental investment theory manifests itself as physical differences in humans. If body mass and composition is already known to be affected by ethnicity, socioeconomic strata, familial correlation and education, it is plausible to suggest that there may also be a correlation between family size and physiology (Tavani et al 1994, Duncan et al 2004). Even if there is no correlation, which personally I believe is likely, it would likely still contribute valuable data to the literature on the demographic transition and the factors behind human variance.

Participants and Methods

This study, first and foremost, shall be a cross-sectional study of New Zealand adults of Anglo descent. Surveying adults (over 21 years old) will ensure completed or near completed adolescence; measuring children or adolescents in a cross-sectional study of measures such as BMI will not give reliable data (see above). Due to the impracticality of attempting to survey both parents and all offspring, just the two parents and their first child will be surveyed. Also due to the impracticality of assessing families at the same time the sample size will likely be a few hundred families optimistically. The measures taken from both parents and the first-born child (assuming participant permission) will include Body Mass Index, wrist breadth, sitting and standing heights, and bioelectrical impedance. These measures will give a more reliable picture of body mass and composition, as BMI is of questionable use when used on its own (Duncan et al 2004). In addition to the measurements, one-on-one interviews will be conducted to survey several proxy variables as outlined below.
 

Issues for data collection and analysis

Variables must be taken into account for both data collection and subsequent analysis. The main data being assessed are the physiological measures. Care will be taken to ensure that, where possible, family measurements of both parents and the first child are taken at roughly the same time (due to seasonal fluctuations in body mass) (Van Stavaren et al 1986). Age will also be noted as an important factor in determining body mass and composition, the first born children will ideally fall within the age range of 21-35, as body mass increases especially past middle age (Lyratzopoulos et al 2005:32). The most important variable assessed is of course family size. However, when assessing this the definition of ‘family’ for the purposes of this study also needs to be narrowed, for practical reasons, to a nuclear family of fully biological children each raised in the same household. This excludes stepchildren, adopted or foster children, and children who left home before completing puberty and adolescence (crudely put here at the age of 16). Divorced parents will not be excluded as long as the first born child was raised by one or both parents until the age of 16.

Interview questions for the parents shall include: Age, birthplace, age of each parent at the birth of their first child (with each other), number of children the parents have had with each other (and raised in the same household until at least the age of 16), interbirth interval, income (assessed within $5000 dollar increments), education, and self-stated family planning practices (if any). Questions will also be asked regarding home environment, for example home ownership status, stability and rural or urban environment. Lastly, an attempt will be made to survey the allocation of care (self-reported) in regards to the child being assessed but also other offspring generally- for example, were one or both parents able to care for their children full or part time, or were the children cared for by a nanny, relative or professional day-care service?

Upon assessment of these factors, attempts will be made to compute some kind of composite ‘embodied cultural capital’ score for ease of analysis in the discussion of results. The embodied capital is distinguished as ‘cultural’ in this study since embodied capital can also refer to every type of body measurement, brain size, and other variables not assessed in this study (Ball 2004:20). By computing a crude score, the study will be able to compare the physiological measurements taken and the non-physiological factors usually stated in the literature to represent differential ‘parental investment’ (e.g education). This comparison will of course be crude and each variable will be subject to finer individual analysis outside of this. This score will be based on ranking systems of various criteria attained from the range of data given in the surveys. income, home environment, and degree of care given to the first-born child and other siblings For example, if full time care of a parent was available to the children of both one and two child families, the score will still be different based on the division of time for each child. Embodied cultural capital will also include scores based on factors such as income, education, and home environment.

Interview questions for the first born child will include: Age, age upon leaving home (if applicable), income, and education (completed or in progress). An embodied capital score will also be computed but only for the basis of crude comparison with parental scores.

Data analysis and testing

The data will be checked for quality ideally as early as possible. Income and education data will be checked against the 2006 New Zealand Census statistics to ascertain how representative the sample is. If the data is highly skewed, attempts will be made to widen the sample size and distribution. Data will also be assessed for extremes and missing values. The data will be assessed for normality and appropriate transformations will be applied if the distribution differs from normal.

The physiological measurements and family size distribution will first be represented in histograms as a graphical representation of distribution. Bivariate data will likely be represented in a graphical form such as scatter plots. Different graphs will be computed to show data between individuals, family averages, and between parents and first-borns (for example, a scatter plot showing family size on the y axis and first-born BMI on the x axis).  In this form we can assess for statistically significant positive or negative associations (if any). Alternatively, hierarchal loglinear analysis can be performed to assess the relationship between more than two variables (for instance family size, BMI of each parent, and the BMI of the first-born) (Spicer 2005:204-5).

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