Abstract
Objective:
To characterize the relationships between selected socio-demographic factors and food selection among Canadian households.
Design:
A secondary analysis of data from the 1996 Family Food Expenditure survey was conducted (n=10 924). Household food purchases were classified into one of the five food groups from Canada's Food Guide to Healthy Eating. Parametric and non-parametric modelling techniques were employed to analyse the effects of household size, composition, income and education on the proportion of income spent on each food group and the quantity purchased from each food group.
Results:
Household size, composition, income and education together explained 21–29% of the variation in food purchasing. Households with older adults spent a greater share of their income on vegetables and fruit (P<0.0001), whereas households with children purchased greater quantities of milk products (P<0.0001). Higher income was associated with purchasing more of all food groups (P<0.0001), but the associations were nonlinear, with the strongest effects at lower income levels. Households where the reference person had a university degree purchased significantly more vegetables and fruit, and less meat and alternatives and ‘other’ foods (P<0.0001), relative to households with the lowest education level.
Conclusions:
Household socio-demographic characteristics have a strong influence on food purchasing, with the purchase of vegetables and fruit being particularly sensitive. Results reinforce concerns about constraints on food purchasing among lower income households. Furthermore, the differential effects of income and education on food choice need to be considered in the design of public health interventions aimed at altering dietary behaviour.
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Acknowledgements
We are grateful for the constructive suggestions made by the anonymous referees, many of which were incorporated directly into our manuscript.
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Guarantor: L Ricciuto.
Contributors: LR and VT conceived the idea for the paper. LR conducted preliminary data analyses and led the writing process. AY provided statistical guidance, conducted statistical analyses and assisted in data interpretation. All authors contributed to the final version of the manuscript.
Appendix A
Appendix A
Statistical analysis of household survey data is often conducted using equations of the particular form (see Deaton 1990, p. 231, Equation 4.14):
where hhsize is the household size; pcinc is the per capita income, that is, household income divided by household size; propj is the proportion of the household consisting of members of type ‘j’, for example the proportion of individuals that are children up to age 15 years, or the proportion of individuals who are over the age of 65 years; and, z is a vector of additional variables, such as education which may help to explain household expenditures.
Alternative dependent variables may be selected for the specification in (A.1). If y is the share of household income spent on a commodity or class of commodities, then the equations are called Engel curves after a 19th century economist who first studied the statistical relationship between income and expenditure on food and other goods (Engel, 1895; Deaton, 1997).
In these models, it is common to log-transform certain variables. This affords a direct interpretation to corresponding coefficients. For example, suppose initially household spending on fruits and vegetables is 1.7% of household income. If the estimate of β1 is −0.54 then increasing household size by say 50% while holding per capita income constant decreases the share of income spent to approximately 1.43%=(1.7−0.54 × 0.5)%. If the estimate of β2 is −1.9, then increasing per capita household income by 10% decreases the share of income spent on the given commodity to approximately 1.51% (=1.7−1.9 × 0.1)%. Finally, coefficients of dummy variables may also be used directly to calculate impacts. For example, suppose that z5 is a dummy variable which equals 1 if the head of household has a university degree with coefficient estimate δ̂5=0.823. Then the expected impact of a university degree, relative to households with the lowest level of schooling (less than 9 years, see Table 5) will be to increase the share of income spent on fruits and vegetables by 0.823%.
By setting the dependent variable y to be the (log of the) quantity purchased, specification (A.1) may also be used to estimate the relationship between quantities and household characteristics. Once again coefficients of log-transformed variables admit a direct interpretation. If the estimate of β1 is 0.78 in the ‘fruits and vegetables’ equation, then increasing household size by say 50% while holding per capita income constant increases the quantity of fruits and vegetables purchased by 39%=(0.78 × 0.5) × 100%. If the estimate of β2 is 0.16, then increasing per capita household income by 10% increases the quantity of fruits and vegetables purchased by 1.6% (=0.16 × 0.1) × 100%. Finally, suppose that z5 is a dummy variable which equals 1 if the head of household has a university degree with coefficient estimate δ̂5=0.14. Then the expected impact of a university degree, relative to households with the lowest level of schooling is to increase the quantity of fruits and vegetables by 14%.
One of the disadvantages of the specification in Equation (A.1) is that the impact of log (per capita income) on the dependent variable is linear. For commodities such as foods, this may not be appropriate. For example, consider equations where the dependent variable is the quantity of grain products consumed. One would expect that at low-income levels, quantities increase as income increases. However, at some point quantities purchased are likely to level off. To model nonlinearity of the income effect it is convenient to modify (A.1) as follows:
where f(log pcinc) is a smooth non-parametric function of its argument and all other variables appear in the same format as they did before. Equation (A.2) is called a ‘partial linear model’ and techniques for its estimation are well-known (Yatchew, 2003). By graphing the non-parametric estimate of f, one can better appreciate nonlinearities and saturation effects.
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Ricciuto, L., Tarasuk, V. & Yatchew, A. Socio-demographic influences on food purchasing among Canadian households. Eur J Clin Nutr 60, 778–790 (2006). https://doi.org/10.1038/sj.ejcn.1602382
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DOI: https://doi.org/10.1038/sj.ejcn.1602382
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