Content uploaded by Laurie Ricciuto
Author content
All content in this area was uploaded by Laurie Ricciuto
Content may be subject to copyright.
ORIGINAL ARTICLE
Socio-demographic influences on food purchasing
among Canadian households
L Ricciuto
1
, V Tarasuk
1
and A Yatchew
2
1
Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada and
2
Department of Economics, University of
Toronto, Toronto, ON, Canada
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 ¼ 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 (Po0.0001), whereas households
with children purchased greater quantities of milk products (Po0.0001). Higher income was associated with purchasing more of
all food groups (Po0.0001), 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 (Po0.0001), 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.
European Journal of Clinical Nutrition (2006) 60, 778–790. doi:10.1038/sj.ejcn.1602382; published online 18 January 2006
Keywords: food; Canada; households; socio-economic factors; demographics
Introduction
In order to explore the role of diet in socio-economic
differentials in health and disease, relationships between
socio-economic status (SES) and different aspects of the diet
have been examined in several different countries (Smith
and Baghurst, 1992; Irala-Estevez et al., 2000; Dubois and
Girard, 2001; Groth et al., 2001; Giskes et al., 2002).
Associations between SES and diet have also been examined
along with various other factors (e.g., family status) in order
to gain a better understanding of food consumption patterns
in relation to dietary recommendations (Roos et al., 1998;
Perez, 2002). The measures of SES employed in these studies
vary, where a single variable, such as income, education,
occupation or a composite measure based on all three, is
used as an indicator of SES (Roos et al., 1998; Irala-Estevez
et al., 2000; Dubois and Girard, 2001; Giskes et al., 2002),
or two or three variables (e.g., income and education)
are included together in one model (Smith and Baghurst,
1992; Groth et al., 2001; Perez, 2002). A number of studies
conducted in different countries have shown income and
education to have similar effects on food consumption,
where higher income and higher education are associated
with greater consumption of vegetables and fruit (Billson
et al., 1999; Nayga et al., 1999; Irala-Estevez et al., 2000;
Groth et al., 2001; Giskes et al., 2002; Perez, 2002), and with
diets more in accord with dietary guidelines (Roos et al.,
1998; Mancino et al., 2004). However, a few studies have
Received 24 May 2005; revised 3 November 2005; accepted 15 November
2005; published online 18 January 2006
Correspondence: L Ricciuto, Department of Nutritional Sciences, University of
Toronto, Fitzgerald Building, 150 College St., Toronto, ON, Canada M5S 3E2.
E-mail: laurie.ricciuto@utoronto.ca
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.
European Journal of Clinical Nutrition (2006) 60, 778–790
&
2006 Nature Publishing Group All rights reserved 0954-3007/06 $
30.00
www.nature.com/ejcn
found income and education to have different effects on the
consumption of certain food groups (e.g., grains and milk
products) (Smith and Baghurst, 1992; Nayga et al., 1999).
Some studies indicate that marital and family status are
also important in explaining variations in food consumption
among adults (Roos et al., 1998; Billson et al., 1999; Perez,
2002; Mancino et al., 2004), and that accounting for these
factors can attenuate socio-economic differences in diet
(Martikainen et al., 2003). Among adults, being married has
been associated with the consumption of more vegetables
and fruit (Billson et al., 1999; Perez, 2002), and being married
or married with children has been associated with greater
compliance with dietary guidelines (Roos et al., 1998;
Mancino et al., 2004), although the strength of these
associations varies by gender.
Given that food choices are not solely the domain of the
individual, but are also influenced by the family context,
decisions around food at the level of the household are
important determinants of food consumption. Studies
conducted at the household level have shown that higher
income or higher education is associated with greater
purchasing of recommended foods (i.e., vegetables and fruit,
low-fat milk, and high-fibre foods) (James et al., 1997;
Trichopoulou et al., 2002; Kirkpatrick and Tarasuk, 2003),
consistent with studies of individual intakes. Examinations
of US household food expenditure surveys have shown that
household composition, household income and education
level of household head all influence food purchasing, albeit
in different ways (Paulin, 1998; Huang and Lin, 2000; Curry
Raper et al., 2002; Stewart et al., 2003), but inferences from
these studies are limited by their singular focus on food
spending (Paulin, 1998; Curry Raper et al., 2002), and by
methodological problems with one of the expenditure
surveys (Milans, 1991; Huang and Lin, 2000).
This study was undertaken to characterize the relation-
ships between different socio-demographic factors (i.e.,
household size and composition, household income and
education) and food selection among Canadian households.
Methods
Family Food Expenditure survey
In Canada, household food expenditures are monitored
through the Family Food Expenditure (FOODEX) survey,
which is conducted periodically by Statistics Canada.
The survey sample is selected from the Canadian Labour
Force Survey sampling frame through stratified multistage
sampling, and is representative of the non-institutionalized
Canadian population, excluding persons living on native
reserves (Statistics Canada, 1999). The sample is drawn for
the whole year and then divided into monthly subsamples to
allow for seasonal variation and other changes throughout
the year that may affect food expenditures. Socio-demo-
graphic data are collected through an interview with the
household reference person, the person mainly responsible
for the household’s financial maintenance. The reference
person maintains a diary of household food expenditures
over a 2-week period, recording the type and quantity of
food and beverages purchased from stores, price paid and the
type of store where food was purchased (e.g., supermarket,
convenience store, specialty store). Information on food
expenditures at restaurants locally and on day trips is
recorded on a weekly basis, as are the number of meals
served to guests and received free. Food expenditures at
restaurants and stores while away from home overnight or
longer are estimated for the previous month and converted
to a weekly expenditure. Details on the types of foods and
the composition of meals obtained in restaurants, received
free or served to guests are not recorded.
We used data from the 1996 FOODEX survey, as this was
the most recent survey containing detailed socio-demo-
graphic information on each household.
Analytical sample
A total of 10 924 households were sampled in the 1996
survey. Households were excluded if they did not report
income or if they reported zero income; if they did not report
education level; if they reported purchases for only 1 week;
or, if they reported zero food expenditures at stores. In total,
955 households were excluded (9% of the original sample),
resulting in a final analytical sample size of 9969 households.
Compared to included households, excluded households
were more likely to be one-person households, less likely
to be in the higher income quintiles, and the household
reference person was less likely to have post-secondary
education (data not shown). Food purchasing patterns
among excluded households who reported food purchases
were similar to those found among included households
(data not shown).
Measures
Food purchased in stores was categorized by Statistics
Canada using 200 different food codes (Statistics Canada,
1999). We classified 182 food codes into five food groups
(i.e., grain products, vegetables and fruit, milk products,
meat and alternatives, and ‘other’ foods), based on the food
categories in Canada’s Food Guide to Healthy Eating (CFGHE)
(Health Canada, 1992) (Table 1). The 18 remaining food
codes, which together comprised only 7% of food expendi-
tures at stores, were omitted from the analysis as they did
not fit into any one food group. Recognizing that there
are marked differences in nutritional and other qualitative
characteristics of foods within the CFGHE groups, we
subdivided the five food groups into 17 smaller food groups,
differentiating foods in terms of nutritional quality insofar as
the existing coding in FOODEX permitted such differentia-
tion (Table 1). However, as divisions of food groups become
more refined (i.e., dividing milk products into lower fat milk,
higher fat milk, cheese/yogourt and ice cream/other)
Socio-demographic influences on food purchasing
L Ricciuto et al
779
European Journal of Clinical Nutrition
estimates become less precise partly because of the occur-
rence of more zeroes in the food purchase measures (i.e.,
more households not purchasing particular foods over a
2-week period). Thus, we were limited in the extent to which
we could subdivide food groups for analyses.
Average weekly expenditures and quantities purchased
were calculated for each food group and subgroup. All
quantities were calculated as ‘edible quantities’ using con-
version factors obtained from Agriculture and Agri-Food
Canada, which account for trim and cooking losses (L
Robbins, 2001, personal communication). In order to
examine proportional allocations to different food groups,
food expenditure shares were calculated as the ratio of
average weekly expenditures multiplied by 52 to total annual
household income (before tax).
Statistical analysis
All statistical analyses were performed using SAS/PC Version
8.02 (SAS Institute, Cary, NC, USA) or S-Plus Version 6.2
(Insightful Corporation). Each household in the FOODEX
survey was assigned a weight by Statistics Canada to account
for unequal probabilities of selection, non-response bias and
population demographics (Statistics Canada, 1999). Descrip-
tive statistics for our analytical sample were calculated using
analytic weights, obtained by dividing the originally as-
signed weight by the average of the original weights for those
households included in our sample. The weighted sample is
designed to be nationally representative.
Multiple regression analysis was used to estimate the
relationships between household socio-demographic char-
acteristics and food purchasing. Quantities of food pur-
chased and expenditure shares were the two dependent
variables modelled for the five food groups, but only
quantity was examined in the analyses of the smaller
subgroups. Quantities of food were log-transformed to
improve model fit. Because in some cases, zero quantities
were purchased, a constant (one) was added to avoid taking
the logarithm of zero. The explanatory variables were
household size and composition, per capita income (i.e.,
household income/household size), and education level of
the reference person. Both the household size and per capita
income variables were log-transformed. See Appendix A for
details. Preliminary analyses testing different types of
models, including linear and polynomial regression models,
indicated that a model based on the above-mentioned log-
transformations provided the best fit for the data. Household
composition was represented by two variables: the propor-
tion of household members less than 15 years of age and the
proportion of household members over 65 years of age.
Dummy variables were entered for education, representing
four increasing levels of education, with the lowest level
being omitted as the reference group. Households with food
expenditure shares greater than one were omitted as
Table 1 Description of food groups used in analyses
Food group and food group subdivisions
a
Types of foods included
Grain products (9) Breads, pasta, rice, grains, breakfast cereals
Grains (8) Breads, pasta, rice, grains
Breakfast cereals (1) Ready-to-eat breakfast cereals
Vegetables and fruit (59) Fresh, frozen, and canned, including juices
‘ABC-rich’ (31) Containing amounts of vitamin A, C and folate at or above the 75th percentile level
‘Other’ (28) Containing amounts of vitamin A, C and folate below the 75th percentile level
Milk products (18) Milk, cheese, yogourt, ice cream
Lower fat milk (3) Skim and 1% fluid milks
Higher fat milk (4) 2 and 3.2% fluid milks
Cheese/yogourt (7) Regular and processed cheeses, yogourt
Ice cream/other (4) Ice cream and ice milk novelties
Meat and alternatives (58) Beef, pork, poultry, fish, eggs, beans, nuts
Lower fat meat (18) Fat content less than or equal to the 50th percentile level
Higher fat meat (15) Fat content more than the 50th percentile level
Poultry/fish (17) Chicken, turkey, fish
Eggs (1)
Nuts/beans (7) Variety of nuts, peanut butter, legumes
‘Other’ foods (38) Fats and oils, sugars/sweeteners, desserts and savory snacks, non-alcoholic beverages
Oils (6) Butter, margarine, cooking/salad oils
Sugars (5) Sugars, syrups, jams/jellies, candies
Desserts/snacks (19) Pies, cakes, cookies, chips
Non-alcoholic beverages (8) Coffee, tea, fruit drinks, carbonated beverages
a
Numbers in parentheses denote the number of food codes included in that food group.
Socio-demographic influences on food purchasing
L Ricciuto et al
780
European Journal of Clinical Nutrition
implausible. These constituted less than 0.5% of the
analytical sample.
The amounts and types of foods purchased in stores could
be influenced by what food is obtained in other ways
by household members (i.e., in restaurants, workplaces).
A variable ‘meals out’ was constructed, representing the
average weekly number of meals received free and purchased
from restaurants (e.g., table service, fast food, cafeterias)
locally and while away from home overnight or longer. Once
the final regression model had been developed, this ‘meals
out’ variable was added to adjust for the effects of eating out
and was found to have no impact on the relationships
between socio-demographic characteristics and food pur-
chasing.
Two additional analyses were conducted to further explore
the impacts of income and education on food purchasing.
There is some evidence indicating that the effects of income
on food purchasing diminish as income increases (Horton
and Campbell, 1991). As there is a limit to the amount of
food that can be consumed, we expected income effects to
level off at a certain point. For these reasons, income/
quantity relationships were examined further using a partial
linear model in order to capture any nonlinearities in the
effects of income. See Appendix A for details.
In our main analyses, the education level of the household
head (i.e., reference person) was used as the indicator for
household education; however, given the evidence that an
individual’s dietary choices can be influenced by the spouse’s
social position (Martikainen et al., 2003), additional analyses
were conducted among households with a married couple to
determine whose education level had the strongest effect on
food purchasing. We used multiple regression analysis,
similar to that described above, where the education levels
of both the male and female were included in the model,
rather than the reference person’s education level. As spousal
education levels were correlated (r ¼ 0.58, Po0.0001), differ-
ential education effects may be difficult to detect.
Results
Socio-demographic characteristics and purchasing patterns of the
analytical sample
The mean annual household income before taxes was
$46 960 (s.d. ¼ $37 083, median ¼ $39 600), with income
ranging from $300 to $848 000. The majority of households
were composed of at least two people age 25–64 years
(Table 2), and in 41% of the households either the reference
person, spouse, or both had completed post-secondary
education (Table 3). A comparison of our weighted sample
to the Canadian population, based on 1996 census data
(Statistics Canada, 1997), indicated our sample was unbiased
with regards to household size, income and education
(Table 3), and therefore retained its national representative-
ness, even after exclusions.
Food expenditures in stores comprised 76% of total food
expenditures (s.d. ¼ 22%, median ¼ 80%), with the remain-
ing 24% being spent at restaurants (s.d. ¼ 22%, median ¼
20%). On average, 13% of household income was spent on
food in stores (s.d. ¼ 25%, median ¼ 9.6%). The largest
average share of income was allocated to the meat and
alternatives food group (3.7%), which may be because of the
high cost of these foods, as suggested by the high level of
expenditure and low quantity purchased, relative to the rest
of the food groups (Table 4).
Associations between food purchasing and household
socio-demographic characteristics
Household size, composition, income and education to-
gether explained a substantial portion of the variation in
food purchasing (21–29%) (Tables 5 and 6). The variables
with the greatest statistical significance were household size
and (per-capita) income. An increase in household size was
associated with purchasing more from all food groups
(Table 5), although declines in expenditure shares indicated
Table 2 Household composition characteristics of the analytical sample
Age category (years) % Households (n ¼ 9969)
0 persons 1 person X2 persons
X65 77 15 8
25–64 19 26 55
15–24 75 15 10
o15 70 13 17
Table 3 Comparison of selected socio-demographic characteristics of
the analytical sample with those of the Canadian population
Analytical
sample
(n ¼ 9969
households)
Canadian
population
a
(n ¼ 10 820 055
households)
Household size (%)
12324
23232
31617
41817
5 or more 10 10
Household income (mean, $)
Households with 1 person 23 545 24 549
Households with 2 or more persons 54 110 54 949
Education level (%)
Post-secondary graduates 41
b
40
c
a
(Statistics Canada, 1997).
b
Percentage of households in which either the reference person, spouse or
both are post-secondary graduates.
c
Percentage of population 15 years and over who are post-secondary
graduates.
Socio-demographic influences on food purchasing
L Ricciuto et al
781
European Journal of Clinical Nutrition
‘economies of scale’ (i.e., larger households spend less per
person than smaller ones, holding per capita income
constant) (Table 6).
Households with the same number of people, but with
more older adults (over 65 years) than younger adults (age
15–64 years) purchased significantly more from all food
Table 4 Reported purchase, average weekly quantities, expenditures and expenditure shares for the major food groups and food subgroups, n ¼ 9969
Food group Percent households reporting purchase Weekly quantity
a
Weekly expenditure Expenditure share
b
Mean7s.d. (median) (kg) Mean7s.d. (median) ($) Mean7s.d. (median) (%)
Grain products 95.7 3.6173.74 (2.61) 8.7877.29 (7.07) 1.4372.36 (0.96)
Grains 94.8 3.3073.56 (2.31) 6.9375.89 (5.59) 1.1572.18 (0.75)
Breakfast cereals 46.3 0.3270.56 (0) 1.8672.94 (0) 0.2870.56 (0)
Vegetables and fruit 96.5 8.4977.53 (6.77) 16.0372.79 (13.35) 2.6376.59 (1.72)
‘ABC-rich’ 92.6 3.6573.78 (2.73) 7.7577.15 (5.98) 1.2672.96 (0.78)
‘Other’ 94.4 4.8474.98 (3.65) 8.2777.05 (6.83) 1.3773.88 (0.88)
Milk products 96.8 7.2977.47 (5.17) 11.2678.96 (9.20) 1.8273.42 (1.22)
Lower fat milk 35.6 1.5773.71 (0) 1.3572.69 (0) 0.1970.61 (0)
Higher fat milk 73.6 4.2576.27 (2.39) 3.5974.42 (2.29) 0.6471.34 (0.32)
Cheese/yogourt 75.0 0.6870.88 (0.43) 4.6675.22 (3.23) 0.7271.54 (0.41)
Ice cream/other 53.8 0.7971.32 (0.15) 1.6772.49 (0.75) 0.2771.28 (0.07)
Meat and alernatives 95.5 3.0673.19 (2.35) 22.99721.98 (17.93) 3.6977.96 (2.34)
Lower fat meat 60.4 0.6171.50 (0.23) 5.86710.64 (2.58) 0.9272.55 (0.32)
Higher fat meat 84.4 1.1771.41 (0.79) 8.2978.78 (6.09) 1.3673.29 (0.79)
Poultry/fish 70.8 0.6171.10 (0.30) 6.70710.41 (3.58) 1.0572.47 (0.48)
Eggs 60.5 0.4070.54 (0.35) 1.0671.28 (0.92) 0.2071.21 (0.08)
Nuts/beans 41.2 0.2870.63 (0) 1.0872.09 (0) 0.1770.47 (0)
‘Other’ foods 95.7 7.7079.33 (4.73) 16.79714.26 (13.43) 2.7375.93 (1.81)
Oils 62.2 0.6671.01 (0.39) 2.5073.72 (1.68) 0.4371.46 (0.20)
Sugars 40.1 0.5271.17 (0) 0.9971.85 (0) 0.1770.52 (0)
Desserts/snacks 86.3 1.2871.59 (0.82) 7.6778.24 (5.25) 1.1772.39 (0.72)
Beverages 80.1 5.2678.23 (2.37) 5.8276.28 (4.07) 0.9973.32 (0.53)
a
All quantities have been converted to edible quantities to account for losses because of trimming and cooking.
b
Expenditure Share ¼ ((Average weekly expenditure on food group 52)100)/(Total annual household income).
Table 5 Associations between household socio-demographic characteristics and quantity of food purchased from each of the five major food groups,
n ¼ 9921
a
Log (quantity purchased)
Beta
b
(standard error),P-value
Grain products Vegetables and fruit Milk products Meat and alternatives ‘Other’ foods
Log (household size)
c
0.658 (0.014)**** 0.775 (0.017)**** 0.776 (0.017)**** 0.645 (0.013)**** 0.842 (0.020)****
Proportion less than 15 years
d
0.116 (0.039)*** 0.213 (0.047)**** 0.303 (0.046)**** 0.307 (0.036)**** 0.302 (0.054)****
Proportion over 65 years
d
0.075 (0.019)**** 0.259 (0.023)**** 0.148 (0.022)**** 0.043 (0.018)* 0.130 (0.026)****
Log (per-capita income)
e
0.038 (0.010)**** 0.164 (0.013)**** 0.083 (0.012)**** 0.068 (0.010)**** 0.081 (0.014)****
Education
f
(% households)
o9 years (ref) (13) — — — — —
Secondary (40) 0.010 (0.020) 0.011 (0.025) 0.035 (0.024) 0.040 (0.019)* 0.010 (0.028)
Some post-secondary (14) 0.007 (0.025) 0.015 (0.030) 0.007 (0.029) 0.090 (0.023)**** 0.056 (0.034)
Post-sec non-university (19) 0.007 (0.023) 0.060 (0.028)* 0.064 (0.028)* 0.073 (0.022)** 0.009 (0.033)
University degree (15) 0.030 (0.026) 0.142 (0.031)**** 0.092 (0.030)** 0.128 (0.024)**** 0.137 (0.036)****
Adjusted R
2
0.238 0.207 0.279 0.229 0.221
a
Households with expenditure shares greater than one (n ¼ 48) were omitted from analytical sample.
b
Obtained from multiple regression models including all socio-demographic variables.
c
Beta 100 is the percent change in quantity purchased when household size doubles, controlling for household composition, income and education effects.
d
Beta (change in proportion) is the corresponding percent change in quantity purchased, controlling for household size, income and education effects.
e
Beta 10 is the percent change in quantity purchased when income increases by 10%, controlling for household size, composition and education effects.
f
Beta 100 is the percent change in quantity purchased relative to the reference group, controlling for household size, composition and income effects.
****Po0.0001, ***Po0.001, **Po0.01, *Po0.05.
Socio-demographic influences on food purchasing
L Ricciuto et al
782
European Journal of Clinical Nutrition
groups, except ‘other’ foods, from which they purchased less
(Table 5). Moreover, the share of income spent on vegetables
and fruit was larger in these households (Table 6). Further
examination of selections made within the food groups
indicated that the negative relationship found for ‘other’
foods was primarily because of purchasing lower quantities
of beverages (Table 7). These types of households were also
found to purchase less higher fat meat. A greater proportion
of children in the household (less than 15 years old) was
associated with lower quantities purchased in all food
groups, with the exception of milk products, where greater
quantities were purchased (Table 5). Differences in milk
product purchasing could be attributed to the greater
purchasing of higher fat milk among these types of house-
holds (Table 7). Although purchasing from grain products
and ‘other’ foods declined as the proportion of children in
the household increased, within these food groups, purcha-
sing of breakfast cereals and desserts/snacks increased
(Table 7).
Higher income was associated with purchasing more from
all food groups (Table 5), although the share of income
devoted to each food group declined strongly with income,
with the largest declines for meat and alternatives and
‘other’ foods, regardless of household size, composition and
education level (Table 6). The purchase of grain products was
the least responsive to income – a 10% increase in per capita
income resulted in only a 0.38% increase in the quantity of
grain products purchased (Table 5). At the other extreme, the
purchase of vegetables and fruit was the most responsive to
income – a 10% increase in per capita income increased
purchasing by 1.6%.
The weak relationship found for grain products was driven
mainly by the purchase of greater quantities of breakfast
cereals with higher income (Table 7). Higher income was
associated with purchasing more from all food subgroups,
with the exception of higher fat milk, eggs and sugars, where
lower quantities were purchased.
Further examination of income/quantity relationships
revealed that the quantities of vegetables and fruit purchased
increased steadily with income, consistent with the results
from the log-linear model, whereas purchasing of the rest of
the food groups increased only up to a certain level of per
capita income (approximately $10 000–$15 000) (Figure 1).
Furthermore, at higher income levels, purchasing of ‘other’
foods appeared to show a slight downward trend. Purchasing
patterns within the food groups were similar (i.e., steady
increases in purchasing from both subgroups within the
vegetables and fruit group) (data not shown), with the
exception of the milk products and meat and alternatives
groups. The threshold effect for milk products could be
attributed to declines in fluid milk purchasing below a
certain level of income (Figure 2). Although purchasing of
milk levelled off above a certain level of income, there was a
tendency towards choosing lower fat milk over higher fat
milk with greater income. Within the meat and alternatives
group, there was a very slight increase in the purchase of
lower fat meat and poultry/fish with higher income (data
not shown).
Higher education was associated with purchasing greater
quantities of vegetables and fruit and milk products, and
smaller quantities of meat and alternatives and ‘other’
foods (Table 5), regardless of income, household size and
Table 6 Associations between household socio-demographic characteristics and expenditure shares allocated to each of the five major food groups,
n ¼ 9921
a
Expenditure shares
Beta
b
(standard error),P-value
Grain products Vegetables and fruit Milk products Meat and alternatives ‘Other’ foods
Log (household size)
c
0.294 (0.031)**** 0.543 (0.052)**** 0.429 (0.038)**** 0.404 (0.081)**** 0.424 (0.057)****
Proportion less than 15 years
d
0.133 (0.084) 0.781 (0.140)**** 0.054 (0.102) 1.824 (0.218)**** 0.722 (0.155)****
Proportion over 65 years
d
0.029 (0.041) 0.632 (0.068)**** 0.012 (0.050) 0.063 (0.106) 0.003 (0.075)
Log (per-capita income)
e
1.251 (0.022)**** 1.905 (0.037)**** 1.595 (0.027)**** 2.813 (0.058)**** 2.210 (0.041)****
Education
f
(% households)
o9 years (ref) (13) — — — — —
Secondary (40) 0.057 (0.044) 0.026 (0.074) 0.151 (0.054)** 0.347 (0.001)** 0.198 (0.082)*
Some post-secondary (14) 0.125 (0.054)* 0.165 (0.090) 0.235 (0.065)**** 0.399 (0.001)** 0.176 (0.099)
Post-sec non-university (19) 0.136 (0.051)** 0.314 (0.085)**** 0.230 (0.062)**** 0.307 (0.001)* 0.301 (0.094)**
University degree (15) 0.345 (0.056)**** 0.823 (0.092)**** 0.527 (0.067)**** 0.040 (0.144) 0.403 (0.102)****
Adjusted R
2
0.269 0.244 0.29 0.222 0.253
a
Households with expenditure shares greater than one (n ¼ 48) were omitted from analytical sample.
b
Obtained from multiple regression models including all socio-demographic variables.
c
Beta 100 is the change in expenditure share when household size doubles, controlling for household composition, income and education effects.
d
Beta (change in proportion) is the corresponding change in expenditure share, controlling for household size, income and education effects.
e
Beta 10 is the change in expenditure share when income increases by 10%, controlling for household size, composition, and education effects.
f
Change in expenditure share relative to the reference group, controlling for household size, composition and income effects.
****Po0.0001, **Po0.01, *Po0.05.
Socio-demographic influences on food purchasing
L Ricciuto et al
783
European Journal of Clinical Nutrition
Table 7 Associations between household socio-demographic characteristics and quantity of food purchased from each of the food subgroups, n ¼ 9921
a
Log (quantity purchased)
Beta
b
(standard error), P-value
Grains Breakfast cereals ‘ABC-rich’ veg and fruit ‘Other’ veg and fruit Lower fat milk Higher fat milk Cheese/Yogourt Ice cream/other
Log (household size)
c
0.625 (0.014)**** 0.176 (0.007)**** 0.587 (0.016)**** 0.678 (0.017)**** 0.292 (0.019)**** 0.559 (0.022)**** 0.254 (0.009)**** 0.302 (0.012)****
Proportion less than 15 years
d
0.161 (0.039)**** 0.099 (0.019)**** 0.316 (0.042)**** 0.070 (0.045) 0.062 (0.051) 0.435 (0.058)**** 0.003 (0.024) 0.047 (0.034)
Proportion over 65 years
d
0.034 (0.019) 0.093 (0.009)**** 0.237 (0.021)**** 0.186 (0.022)**** 0.058 (0.025)* 0.135 (0.028)**** 0.001 (0.012) 0.101 (0.016)****
Log (Per-capita income)
e
0.019 (0.011) 0.023 (0.005)**** 0.160 (0.012)**** 0.112 (0.012)**** 0.162 (0.014)**** 0.083 (0.016)**** 0.079 (0.006)**** 0.049 (0.009)****
Education
f
(% households)
o 9 years (ref) (13) — — — — — — — —
Secondary (40) 0.022 (0.020) 0.022 (0.010)* 0.000 (0.022) 0.027 (0.024) 0.091 (0.027)** 0.066 (0.030)* 0.015 (0.012) 0.021 (0.018)
Some post-secondary (14) 0.022 (0.025) 0.044 (0.012)**** 0.059 (0.027)* 0.028 (0.029) 0.193 (0.033) **** 0.194 (0.037)**** 0.049 (0.015)** 0.045 (0.021)*
Post-sec non-university (19) 0.013 (0.024) 0.049) (0.012)**** 0.082 (0.026)** 0.018 (0.027) 0.194 (0.031)**** 0.123 (0.035)**** 0.053 (0.014)**** 0.054 (0.020)**
University degree (15) 0.003 (0.026) 0.089 (0.013)**** 0.230 (0.028)**** 0.051 (0.030) 0.301 (0.034)**** 0.215 (0.038)**** 0.109 (0.016)**** 0.083 (0.022)****
Lower fat meat Higher fat meat Poultry/fish Eggs Nuts/beans Oils Sugars Desserts/snacks Beverages
Log (household size)
c
0.251 (0.010)**** 0.401 (0.011)**** 0.273 (0.009)**** 0.177 (0.007)**** 0.131 (0.007)**** 0.282 (0.009)**** 0.234 (0.011)**** 0.429 (0.011)**** 0.693 (0.023)****
Proportion less than 15 years
d
0.206 (0.027)**** 0.100 (0.030)** 0.176 (0.025)**** 0.099 (0.019)**** 0.073 (0.019) **** 0.202 (0.025)**** 0.171 (0.029)**** 0.159 (0.031)**** 0.335 (0.063)****
Proportion over 65 years
d
0.028 (0.013)* 0.035 (0.015)* 0.051 (0.012)**** 0.022 (0.009)* 0.007 (0.009) 0.066 (0.012)**** 0.110 (0.014)**** 0.081 (0.015)**** 0.377 (0.030)****
Log (per-capita income)
e
0.036 (0.007)**** 0.022 (0.008)** 0.035 (0.007)**** 0.012 (0.005)* 0.015 (0.005)** 0.004 (0.007) 0.031 (0.008)**** 0.082 (0.008)**** 0.059 (0.017)**
Education
f
(% households)
o 9 years (ref) (13) — — — — — — — — —
Secondary (40) 0.033 (0.014)* 0.038 (0.016)* 0.020 (0.013) 0.020 (0.010)* 0.002 (0.010) 0.012 (0.013) 0.012 (0.015) 0.021 (0.016) 0.014 (0.033)
Some post-secondary (14) 0.061 (0.017)**** 0.082 (0.019)**** 0.009 (0.016) 0.031 (0.012)** 0.006 (0.012) 0.047 (0.016)** 0.042 (0.015)* 0.044 (0.020)* 0.080 (0.040)*
Post-sec non-university (19) 0.057 (0.016)**** 0.075 (0.018)**** 0.008 (0.015) 0.038 (0.011)** 0.001 (0.011) 0.029 (0.015) 0.016 (0.018) 0.081 (0.019)**** 0.029 (0.038)
University degree (15) 0.085 (0.018)**** 0.162 (0.020)**** 0.028 (0.017) 0.038 (0.012)** 0.022 (0.013) 0.046 (0.017)** 0.002 (0.019) 0.047 (0.020)* 0.204 (0.041)****
a
Households with expenditure shares greater than one (n ¼ 48) were omitted from analytical sample.
b
Obtained from multiple regression models including all socio-demographic variables.
c
Beta 100 is the percent change in quantity purchased when household size doubles, controlling for household composition, income and education effects.
d
Beta (change in proportion) is the corresponding percent change in quantity purchased, controlling for household size, income and education effects.
e
Beta 10 is the percent change in quantity purchased when income increases by 10%, controlling for household size, composition and education effects.
f
Beta 100 is the percent change in quantity purchased relative to the reference group, controlling for household size, composition and income effects.
****Po0.0001, **Po0.01, *Po0.05.
Socio-demographic influences on food purchasing
L Ricciuto et al
784
European Journal of Clinical Nutrition
composition. Interestingly, post-secondary education, and in
particular a university degree, had by far the largest impacts
on purchasing patterns. For example, households where the
reference person had completed post-secondary education
purchased 6% more fruit and vegetables than those with less
than 9 years schooling. Households where the reference
person had a university degree purchased 14.2% more fruit
and vegetables. In contrast, having secondary or some post-
secondary education had no significant impact on quantities
of fruit and vegetables purchased. A similar pattern was
observed for the quantities of milk products. Quantities of
meat and alternatives generally declined with increasing
education.
Subsequent examination of purchasing within the various
food groups revealed that the positive relationship found
between education and vegetables and fruit purchasing
was due to greater purchasing of ‘ABC-rich’ vegetables and
fruit among more highly educated households (Table 7).
Although purchasing of most milk products increased with
higher education, purchasing of higher fat milk declined.
Furthermore, declines in meat and alternatives purchasing
could be attributed to the lower quantities of red meat and
eggs purchased at higher education levels. Positive relation-
ships were found for breakfast cereals and desserts/snacks,
where higher quantities were purchased at higher levels of
education.
When the education variables were omitted from the
model of purchasing for the five major food groups, the
effects of income on most food groups appeared slightly
stronger (data not shown). The exception was the meat and
alternatives group, where income effects appeared consider-
ably weaker. As income and education are positively
correlated, the income variable picks up some of the effects
of education when educational covariates are omitted from
the model. For most food groups, income and education
worked in similar directions, so that when education was
omitted from the model, the impact of income on food
purchasing appeared stronger; however, because income and
education had opposite effects on meat and alternatives
purchasing, the impact of income on purchasing of this food
group appeared weaker.
Effects of male and female education levels on food purchasing
among households with a married couple
Of the 9969 households in the analytical sample, 6348 (64%)
were households with a married couple. These households
showed similar purchasing patterns according to the educa-
tion level of the reference person as were seen in the full
sample (data not shown). Compared with the higher
education levels of the female spouse, post-secondary
education or a higher level among male subjects tended to
be significantly correlated with all outcomes, with the
exception of meat and alternatives purchasing (Table 8).
Discussion
Our analyses revealed that household socio-demographic
characteristics had a strong influence on food purchasing
patterns. The ages of household members were important to
food selections, reflecting the fact that particular life stages
(i.e., childhood, older adulthood) have specific food needs
and preferences, which are incorporated into household
purchase decisions. Income and education were also strong
determinants of food selection; however, they tended to
operate somewhat differently, suggesting that these variables
represent distinct dimensions of socio-economic position. By
using non-parametric statistical techniques, this study was
Per capita income ($)
Average weekly quantity purchased (kg)
0 20000 40000 60000 80000
0
2
4
6
8
10
Veg & Fruit
Milk
Other
Grain
Meat
Figure 1 Relationships between quantity purchased (kg) and per
capita income ($) for the five major food groups, based on partial
linear model and holding other variables constant (Equation (A.2),
Appendix A.).
Per capita income ($)
Average weekly quantity purchased (kg)
0 20000 40000 60000 80000
0
1
2
3
4
Lower Fat Milk
Higher Fat Milk
Ice Cream/Other
Cheese/Yogourt
Figure 2 Relationships between quantity purchased (kg) and per
capita income ($) for the milk product subgroups, based on partial
linear model and holding other variables constant (Equation (A.2),
Appendix A.).
Socio-demographic influences on food purchasing
L Ricciuto et al
785
European Journal of Clinical Nutrition
able to more accurately characterize the nature of the
relationship between income and food selection, in effect
distinguishing between a gradient effect for vegetables and
fruit purchasing and threshold effects for the purchase of
milk and ‘other’ foods. To our knowledge, no other study has
characterized income/food selection relationships to this
extent, and thus our results provide a novel contribution to
the literature. In addition, this study importantly adds to our
knowledge and understanding of Canadian food consump-
tion patterns, which is very limited, because of the lack of
nationally representative data on dietary intake.
Not surprisingly, household size was an important deter-
minant of expenditures on food and quantities purchased,
consistent with analyses of household food expenditures
in many countries (Horton and Campbell, 1990; Deaton
and Paxson, 1998). The economies of scale gained by larger
households likely result from the benefits of buying larger
quantities that often cost less per unit (Deaton and Paxson,
1998); however, lower food spending among larger house-
holds may also be explained by their tendency to substitute
less-expensive foods, regardless of income level (Horton and
Campbell, 1990), which may not be beneficial if these
substitutions come at the expense of nutritional quality.
The composition of the household was also important in
explaining variations in food purchasing. The emphasis
on purchasing vegetables and fruit in households where
older adults are present suggests health concerns may be a
significant driver of food selections in these households.
These purchasing patterns are in accord with individual
consumption patterns in Canada, indicating older adults
consume more vegetables and fruit than their younger
counterparts (Perez, 2002). Older adults are more likely than
younger adults to report health concerns as being very
influential to their food choices, where they will impose
restrictions on fat, salt and sugar intake and avoid certain
foods (National Institute of Nutrition, 2002), attitudes that
are consistent with an emphasis on vegetables and fruit and
a de-emphasis on higher fat meat. At the other end of the age
spectrum, where children are present in the household, the
emphasis on higher fat milk suggests food selections in these
households are influenced by the needs of children. These
results correspond with those from analyses of US food
expenditure data, indicating that households with propor-
tionally more young children spend less on vegetables and
fruit (Stewart et al., 2003), and spend more of their food
budgets on dairy products (Huang and Lin, 2000).
Income- and education-related differences in food pur-
chasing suggest higher education is associated with purcha-
sing less of particular foods (i.e., meat and alternatives and
‘other’ foods), whereas lower income is associated with
purchasing less of most foods, with some levelling off at per
capita income levels upwards of $15 000. These patterns are
consistent with those found in analyses of US expenditure
data, where higher income was associated with increased
spending on most foods (Huang and Lin, 2000; Curry Raper
et al., 2002; Stewart et al., 2003), and higher education was
associated with spending less of the food budget on some
types of meat and more on vegetables (Huang and Lin,
2000). The similarly positive effects of income and education
on the purchase of vegetables and fruit is consistent with
those found for the consumption of vegetables and fruit
among Canadian adults (Perez, 2002). However, the similar
Table 8 Effects of male versus female education level on food purchasing among households with a married couple, n ¼ 6328
a
Education level
c
(% households) Log (quantity purchased)
Beta
b
(standard error), P-value
Grain Products Vegetables and fruit Milk products Meat and alternatives Other foods
Male
d
o 9 years (ref) (11) — — — — —
Secondary (40) 0.061 (0.030)* 0.026 (0.035) 0.024 (0.034) 0.064 (0.028)* 0.030 (0.041)
Some post-secondary (13) 0.002 (0.036) 0.047 (0.042) 0.048 (0.042) 0.012 (0.034) 0.003 (0.049)
Post-sec non-university (19) 0.004 (0.034) 0.072 (0.040) 0.080 (0.040)* 0.056 (0.032) 0.005 (0.047)
University degree (16) 0.018 (0.038) 0.119 (0.044)** 0.105 (0.043)* 0.053 (0.035) 0.124 (0.051)*
Female
d
o9 years (ref) (9) — — — — —
Secondary (43) 0.019 (0.033) 0.003 (0.038) 0.003 (0.038) 0.016 (0.030) 0.035 (0.044)
Some post-secondary (13) 0.003 (0.039) 0.009 (0.045) 0 (0.045) 0.067 (0.036) 0.036 (0.053)
Post-sec non-university (21) 0.006 (0.037) 0.006 (0.043) 0.039 (0.043) 0.036 (0.034) 0.022 (0.050)
University degree (13) 0.017 (0.042) 0.056 (0.049) 0.003 (0.048) 0.121 (0.039)** 0.075 (0.057)
a
Households with expenditure shares greater than one (n ¼ 20) were omitted from married sub-sample.
b
Obtained from multiple regression models including all socio-demographic variables.
c
Education levels of male and female included as separate variables in multiple regression model.
d
Beta 100 is the percent change in quantity purchased relative to the reference group, and controlling for spouse’s education level, household size, composition
and income.
**Po0.01, *Po0.05.
Socio-demographic influences on food purchasing
L Ricciuto et al
786
European Journal of Clinical Nutrition
effects of income and education on the purchase of lower
fat milk runs contrary to those found in one US study
of individuals’ food consumption (Nayga et al., 1999). That
study showed that only education, not income, was
associated with the consumption of low-fat milk products.
Discrepancies in study results may be attributed to the
different dietary behaviours examined (i.e., individual-level
consumption versus household-level purchasing), the use of
various other explanatory variables in the model of US food
consumption, and to different contextual factors (e.g.,
cultural, relative food prices).
Education-related differences in food purchasing seem to
be more reflective of health concerns than those found for
income. Individuals with higher education are reportedly
more aware of diet–disease relationships than those with
lower education, and are more likely to believe that their
food choices can influence their health (National Institute of
Nutrition, 2002). These attitudes would explain the greater
purchasing of ‘ABC-rich’ vegetables and fruit and lower fat
milk in more highly educated households, and reduced
purchasing of higher fat milk, red meat, eggs, sugars and
beverages, foods that have gained an unhealthy reputation.
Although those with higher levels of education may simply
be more responsive to health messaging, it is also possible
that health messaging is more geared to this group than to
those with less education.
Male education seemed to have a stronger impact on
household food selections than female education, suggesting
that while women may be the primary food shoppers, their
selections are driven to a large extent by the preferences
of their husband. Other studies have shown that women’s
diets are influenced less by social position than are men’s
(Roos et al., 1998; Groth et al., 2001), and that other
factors, including spouse’s SES and family status, are
stronger determinants of women’s food behaviour than
men’s (Roos et al., 1998; Groth et al., 2001; Martikainen
et al., 2003).
In contrast to the negative association between higher
education and the purchase of particular foods, food
purchasing generally expanded with higher income, where
an income gradient was apparent for the purchase of
vegetables and fruit, and an income threshold was apparent
for the purchase of milk products and ‘other’ foods. These
patterns are consistent with the widely observed pheno-
menon that as incomes rise, households spend more on
changing the type, quality, and variety of foods rather than
increasing the quantity of calories consumed (Horton and
Campbell, 1991). From a sociological perspective, these
patterns may also reflect symbolic food selections, where
particular foods are chosen in order to demonstrate one’s
affluence (Crotty, 1999); hence, a move away from more
‘basic’ foods like higher fat milk to more ‘luxury’ foods like
vegetables and fruit with higher income. Those interpreta-
tions should not be taken to discount the role of health
concerns; indeed, the desirability of some foods and their
perceived higher quality (e.g., vegetables and fruit, lower fat
milk, poultry and fish) may be related to their ‘health-
enhancing’ properties.
It is important to recognize that, in this Canadian sample,
the impact of income on food selection was nonlinear, with
the strongest effects at lower income levels, suggesting severe
constraints on food purchasing in the context of low
income. These results are consistent with other Canadian
studies of food purchasing and food intake (Leaman and
Evers, 1997; Jacobs Starkey et al., 1999; Jacobs Starkey and
Kuhnlein, 2000; Tarasuk, 2001; Kirkpatrick and Tarasuk,
2003), showing compromised food selection among low-
income groups, with vegetables and fruit, and milk products
identified as being particularly vulnerable to situations of
low income. However, by using non-parametric statistical
techniques, we have been able to discern an income
threshold below which food purchasing appears to be
severely constrained. The compromises in food purchasing
we observed at per capita incomes below $15 000 are
consistent with population surveys documenting dramati-
cally increased odds of household food insecurity at incomes
below this level (Che and Chen, 2001; Vozoris and Tarasuk,
2003; Ledrou and Gervais, 2005). These results have
important implications for the design of income support
programmes and other targeted interventions for low-
income Canadians, insofar as they identify levels of income
where food purchasing is severely limited and supplementa-
tion may be required.
One point of concern when using household expenditure
data is the considerable error in estimating annual food
purchasing patterns, based on those observed in only a 2-
week time frame, because of inventory effects (i.e., food
already available in the household will influence what food
is purchased) and temporal effects (i.e., the season and weeks
of the month in which purchases are recorded). During any
2-week period, some households will spend disproportio-
nately large amounts on food, whereas others may spend
disproportionately small amounts for various reasons (i.e.,
stocking up or alternatively depleting existing food stocks, or
being away from home for an extended period during the 2-
week recording time). Although households with expendi-
ture shares greater than one were omitted, it is possible that
there remained some households with disproportionately
large and some with disproportionately small expenditure
shares. Fortunately, this averages out over a large sample,
resulting in an accurate depiction of average purchasing
patterns.
Our analyses were restricted to foods purchased in stores,
which represented the most substantial portion of total food
spending. Foods purchased in restaurants and other eating
establishments could not be included in our analyses, as
there was no detailed information available on the types and
quantities of these foods. However, as food spending in
restaurants declined with decreases in income, it is unlikely
that lower levels of purchasing among lower income house-
holds would be compensated for by greater consumption
of foods outside the home. Regarding education-related
Socio-demographic influences on food purchasing
L Ricciuto et al
787
European Journal of Clinical Nutrition
differences in purchasing, it is possible that lower levels of
purchasing of some foods (i.e., meat, ‘other’ foods) among
highly educated households could be compensated for by
greater consumption outside the home, as food spending in
restaurants was higher among more educated households.
But, this is highly unlikely, given the small amount of food
purchased in restaurants relative to that purchased in stores.
(Among the highest educated households, on average 33% of
total spending was allocated to food in restaurants, but this
represents a much smaller proportion when considering
food quantity, as a large portion of expenditures in restau-
rants is attributed to the cost of service.) In addition, if
health concerns have a strong influence on food selections,
it is likely that this is the case both at home and elsewhere.
Unfortunately, as the consumption of food outside the
home and the distribution of food among household
members were not measured directly, it is not possible to
fully evaluate the nutritional adequacy of individual food
intakes. Therefore, conclusions about whether Canadians are
meeting dietary recommendations are somewhat limited.
However, this was not our aim; rather the intent of this study
was to document relative differences in purchasing accord-
ing to socio-demographic characteristics in order to further
our understanding of food selection determinants. Never-
theless, comparisons to other studies, which have examined
socio-economic differentials in diet quality, are limited. In
addition, because of the nature of food coding in this data
set, it was not possible to ascertain socio-demographic
differences in the purchase of some specific classes of foods
considered important to health (e.g., whole grain or lower fat
products), limiting comparisons to other studies in this
regard.
In conclusion, household socio-demographic characteris-
tics have a strong influence on food purchasing. Of
particular concern are the constraints lower income places
on the purchase of foods generally recommended for health
(i.e., vegetables and fruit and milk), highlighting the need
for targeted interventions among particularly disadvantaged
groups. Further exploration of the income gradient for
vegetables and fruit purchasing is warranted, in order to
understand what underlies this gradient. One possible
explanation is that vegetables and fruit are higher in price
relative to other types of foods, and price has less of
an influence on purchase decisions at higher incomes
(Drewnowski and Barratt-Fornell, 2004; Drewnowski et al.,
2004; Drewnowski and Specter, 2004). More research is needed
in this area, particularly given the proposed application of taxes
and subsidies on some foods as a means to address problems
of overweight and obesity. Regarding current public educa-
tion programmes aimed at improving dietary behaviours, it
appears that these are more relevant to those individuals
with the greatest amount of resources at their disposal (i.e.,
knowledge, prestige, social connections). However, in order
to achieve dietary improvements where they are most
needed, alternative approaches, which address the specific
needs and situations of those with the least resources, need
to be embraced. These will likely fall outside the scope of
food and nutrition policy, and traverse areas of social and
economic policy.
Food policy in Canada, as elsewhere, is increasingly
focused on marketplace interventions designed to influence
food choice, in the competition for consumer dollars (Lang
and Heasman, 2004). In Canada, new food labelling regula-
tions were recently implemented (Health Canada, 2003), and
significant changes to the regulations governing the addi-
tion of vitamins and minerals to foods are now being
introduced (Health Canada, 2005). These policies are
designed to alter dietary practices through changes in the
marketplace, providing consumers with a greater array of
foods from which to choose and helping them to make more
informed choices. However, given the profound influence of
income and education on food selections, as documented in
our study, it is an open question how much of these effects
can be overridden by improved product labelling and the
availability of more product options. It will be critical to
monitor the impact on different population subgroups of
such market-based interventions in order to guide decision-
making around the effective use of public resources.
Acknowledgements
We are grateful for the constructive suggestions made by the
anonymous referees, many of which were incorporated
directly into our manuscript.
References
Billson H, Pryer J, Nichols R (1999). Variation in fruit and vegetable
consumption among adults in Britain. An analysis from the
dietary and nutritional survey of British adults. Eur J Clin Nutr 53,
946–952.
Che J, Chen J (2001). Food insecurity in Canadian households.
Health Rep 12, 11–22.
Crotty P (1999). Food and class. In: Germov J and Williams L (eds). A
Sociology of Food and Nutrition: The Social Appetite. Oxford
University Press: New York, pp 135–148.
Curry Raper K, Namakhoye Wanzala M, Nayga Jr R (2002). Food
expenditures and household demographic composition in the US:
a demand systems approach. Appl Econ 34, 981–992.
Deaton A (1997). The Analysis of Household Surveys: A Microeconomic
Approach to Development Policy. Johns Hopkins University Press:
Baltimore.
Deaton A, Paxson C (1998). Economics of scale, household size, and
the demand for food. J Political Econ 106, 897–930.
Drewnowski A, Barratt-Fornell A (2004). Do healthier diets cost
more? Nutr Today 39, 161–168.
Drewnowski A, Darmon N, Briend A (2004). Replacing fats and
sweets with vegetables and fruits – a question of cost. Am J Pub
Health 94, 1555–1559.
Drewnowski A, Specter SE (2004). Poverty and obesity: the role of
energy density and energy costs. Am J Clin Nutr 79, 6–16.
Dubois L, Girard M (2001). Social position and nutrition: a gradient
relationship in Canada and the USA. Eur J Clin Nutr 55, 366–373.
Engel E (1895). Die Lebenskosten Belgischer Arbeiter-Familien Fru
¨
her
and Jetzt. Int Stat Bull 9, 1–124.
Socio-demographic influences on food purchasing
L Ricciuto et al
788
European Journal of Clinical Nutrition
Giskes K, Turrell G, Patterson C, Newman B (2002). Socioeconomic
differences among Australian adults in consumption of fruit and
vegetables and intakes of vitamins A, C and folate. J Hum Nutr
Dietet 15, 375–385.
Groth M, Fagt S, Brondsted L (2001). Social determinants of dietary
habits in Denmark. Eur J Clin Nutr 55, 959–966.
Health Canada (1992). Food Guide Facts Background for Educators and
Communicators. Minister of Supply and Services Canada: Ottawa.
Health Canada (2003). Nutrition Labelling http://www.hc-sc.gc.ca/
hpfb-dgpsa/onpp-bppn/labelling-etiquetage/index_e.html.Health
Canada.
Health Canada (2005). Addition of Vitamins and Minerals to Foods.
Health Canada: Ottawa.
Horton S, Campbell C (1990). Do the poor pay more for food? Food
Market Commentary 11, 33–39.
Horton S, Campbell C (1991). Wife’s employment, food expendi-
tures, and apparent nutrient intake: evidence from Canada. Am J
Agric Econ 73, 784–794.
Huang K, Lin B (2000). Estimation of food demand and nutrient
elasticities from household survey data. Economic Research Service,
USDA: Washington, DC. Technical Bulletin no.1887.
Irala-Estevez J, Groth M, Johansson L, Oltersdorf U, Prattala R,
Martinez-Gonzalez M (2000). A Systematic review of socio-
economic differences in food habits in Europe. Eur J Clin Nutr
54, 706–714.
Jacobs Starkey L, Gray-Donald K, Kuhnlein HV (1999). Nutrient
intake of food bank users is related to frequency of food bank use,
household size, smoking, education and country of birth. J Nutr
129, 883–889.
Jacobs Starkey L, Kuhnlein HV (2000). Montreal food bank users’
intakes. Can J Diet Pract Res 61, 73–75.
James W, Nelson M, Ralph A, Leather S (1997). The contribution of
nutrition to inequalities in health. BMJ 314, 1545–1549.
Kirkpatrick S, Tarasuk V (2003). The relationship between low
income and household food expenditure patterns in Canada.
Pub Health Nutr 6, 589–597.
Lang T, Heasman M (2004). Food Wars: The Global Battle for Mouths,
Minds and Markets. Earthscan: London.
Leaman M, Evers S (1997). Dietary intake by food groups of preschool
children in low-income communities in Ontario. J Can Diet Assoc
58, 184–191.
Ledrou I, Gervais J (2005). Food insecurity. Health Rep 16, 47–50.
Mancino L, Biing-Hwan L, Ballenger N (2004). The role of economics in
eating choices and weight outcomes. Economic Research Service,
USDA: Washington, DC. Agriculture Information Bulletin no.791.
Martikainen P, Brunner E, Marmot M (2003). Socioeconomic
differences in dietary patterns among middle-aged men and
women. Soc Sci Med 56, 1397–1410.
Milans F (1991). Mismanagement of Nationwide Food Consumption
Survey GAO/T-RCED-92-7. United States General Accounting
Office, Department of Agriculture: Washington, DC.
National Institute of Nutrition (2002). Tracking Nutrition Trends IV: An
Update on Canadians’ Nutrition-Related Attitudes, Knowledge and
Actions. National Institute of Nutrition: Ottawa, ON.
Nayga R, Tepper B, Rosenzweig L (1999). Assessing the importance of
health and nutrition related factors on food demand: a variable
preference investigation. Appl Econ 31, 1541–1549.
Paulin G (1998). The changing food-at-home budget. Monthly Labor
Rev 121, 3–32.
Perez C (2002). Fruit and Vegetable Consumption. Statistics Canada:
Ottawa, ON. Health Reports 13 (3).
Roos E, Lahelma E, Virtanen M, Prattala R, Pietinen P (1998). Gender,
socioeconomic status and family status as determinants of food
behaviour. Soc Sci Med 46, 1519–1529.
Smith A, Baghurst K (1992). Public health implications of dietary
differences between social status and occupational category
groups. J Epidemiol Commun Health 46, 409–416.
Statistics Canada (1997). 1996 Census Nation Tables www.statcan.ca/
english/census96/nation.htm Statistics Canada: Ottawa, ON.
Statistics Canada (1999). 1996 Food Expenditure Survey Public-use
Microdata Files, Version 1. Statistics Canada: Ottawa.
Stewart H, Blisard N, Jolliffe D (2003). Do income constraints inhibit
spending on fruits and vegetables among low-income households.
J Agric Resource Econ 28, 465–480.
Tarasuk V (2001). Household food insecurity with hunger is
associated with women’s food intakes, health, and household
circumstances. J Nutr 131, 2670–2676.
Trichopoulou A, Naska A, Costacou T (2002). Disparities in food
habits across Europe. Proc Nutr Soc 61, 553–558.
Vozoris N, Tarasuk V (2003). Household food insufficiency is
associated with poorer health. J Nutr 133, 120–126.
Yatchew A (2003). Semiparametric Regression for the Applied Econome-
trician, 213 pages, Themes in Modern Econometrics. Cambridge
University Press: Cambridge.
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):
y ¼ b
0
þ b
1
log hhsize þ b
2
log pcinc
þ
X
q
j¼1
g
j
prop
j
þ zd þ e ðA:1Þ
where hhsize is the household size; pcinc is the per capita
income, that is, household income divided by household
size; prop
j
is the proportion of the household consisting of
members of type ‘j’, for example the proportion of indivi-
duals 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 correspond-
ing coefficients. For example, suppose initially household
spending on fruits and vegetables is 1.7% of household
income. If the estimate of b
1
is 0.54 then increasing
household size by say 50% while holding per capita income
constant decreases the share of income spent to approxi-
mately 1.43% ¼ (1.70.54 0.5)%. If the estimate of b
2
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% ( ¼ 1.71.9 0.1)%. Finally,
Socio-demographic influences on food purchasing
L Ricciuto et al
789
European Journal of Clinical Nutrition
coefficients of dummy variables may also be used directly to
calculate impacts. For example, suppose that z
5
is a dummy
variable which equals 1 if the head of household has a
university degree with coefficient estimate
^
d
5
¼ 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 b
1
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% ¼ (0.78 0.5) 100%. If the estimate of b
2
is 0.16, then increasing per capita household income by 10%
increases the quantity of fruits and vegetables purchased by
1.6% ( ¼ 0.16 0.1) 100%. Finally, suppose that z
5
is a
dummy variable which equals 1 if the head of household has
a university degree with coefficient estimate
^
d
5
¼ 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 equa-
tions 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:
y ¼ f log pcincðÞþb log hhsize þ
X
q
j¼1
g
j
prop
j
þ zd þ e ðA:2Þ
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.
Socio-demographic influences on food purchasing
L Ricciuto et al
790
European Journal of Clinical Nutrition
