household food consumption: the influence of household characteristics
TRANSCRIPT
41
HOUSEHOLD FOOD CONSUMPTION: THE INFLUENCE OF HOUSEHOLD CHARACTERISTICS
P. J. Lund and B. J. Derry* Ministry of Agriculture, Fisheries and Food 7
This paper reports an attempt to identify the effects of various household characteristics on household food consumption. The analysis was based on National Food Survey (NFS) data for Grent Britain for the calendar year 1982. Consumption (per person per week) of various food items was related to each of the main household characteristics on which information is collected in the course of the Survey-location (both country/region and type of area), income (generally of the head of the household), household composition, age of housewife, housing tenure and freezer ownership. The results indicated that all these variables are important in the explanation of households ’ food consumption pat terns.
Introduction The National Food Survey is a continuous sampling enquiry into the domestic food consumption and expenditure of private households in Great Britain. Its results are reported in various forms, most comprehensively in the annual reports of the National Food Survey Committee. These include an appendix giving a detailed description of the structure of the survey and provide the most complete tabulation of the results, which are mainly expressed in terms of average expenditure or consumption per person per week. Results are presented for about 200 different food groups, with separate averages being given both for ‘all households’ and for households classified, in turn, according to each of seven different factors. However, in all but one case these factors are considered only in isolation, the exception being in the tables showing cross-classifications by household composition and income group (Tables 17 and 18 of the 1982 report). While this form of descriptive classification is valuable, it clearly does not identify the partial relationships between food consumption and each of the several characteristics. This paper seeks to do this through the application of multiple regression analysis to individual household data (expressed on a per person basis) obtained in calendar year 1982.
The authors acknowledge the assistance and comments of various colleagues in MAFF, particularly Miss S. E. Middleton. Mrs F. C. Snoswell. and M. Ullyett who arranged the extensive computations. The authors also acknowledge the permission of the Ministry to publish this paper though they are solely responsible for its content.
Ministry of Agriculture. Fisheries and Food, Whitehall Place, London SWI 2HH.
42 P. J . LUND AND B. J . D E R R Y
Methodological Issues For each food+ the dependent variable chosen for analysis was ‘food obtained for consumption’. This variable is defined as purchases from all sources (including purchases in bulk) made by households during their week of participation in the Survey, and intended for human consumption either during that week or later, plus supplies obtained free of payment from an employer, as well as any garden and allotment produce actually consumed during the survey week. This variable clearly gives a better (though obviously imperfect) guide to physical consumption of each food than either the volume of purchases or the value of expenditure and, unlike the latter, does not reflect variations in unit price.
Given that the NFS covers only household food consumption, one determinant of a household’s consumption of food is likely to be the extent to which its members eat out. This has been allowed for by including, as an explanatory variable, a numerical indicator-the household’s average ‘net balance’ 7-of the extent to which the household’s members and visitors relied on its food supplies during the survey week. Second, since each household participating in the Survey does so for only one week, its recorded food consumption is also likely to reflect the time of year of its participation. Some allowance has been made for this factor by including quarterly seasonal dummy variables in the regression analysis.
Of the seven classificatory factors, four are of a qualitative nature and can only be allowed for in multiple regression analysis through the inclusion of sets of dummy variables. These are country/region (9 categories), type of area (6), housing tenure (6) and deep freezer ownership (2). The other three factors-age of housewife (7). househoid composition (1 1) and income @)-are treated in a similar way in the standard NFS tabular analyses and this procedure has been followed in this study. Table 1 lists the separate categories into which each household is classified and shows the percentage distribution of households between them: it also introduces the notation used in presenting the results in Table 2.
The reasons for adopting a dummy variable treatment of these factors include the difficulty of specifying simple functional forms for the relationships between them and food consumption. This problem clearly affects the age variable but is also relevant to income (not least because of the physical constraint on a person’s total food consumption) and to household composition, with its separate numbers of people and age/sex attributes. The dummy variable approach avoids the choice of functional forms at a cost, in terms of degrees of freedom, which can be tolerated in a cross-sectional study with so many observations (7,945). Another factor influencing the choice was the lack of numerical information on the incomes of all households; numerical data (on net household income) is available for only about 60% of the households whereas income-group data based on the head of the household (or the principal earner) is available for the full sample. Finally the adopted procedure permits a more ready comparison with the standard tabular analysis and hence whatever further analysis may be conducted it would appear to be a useful first-stage in its development.
However, some implications of the adopted dummy variable procedure, and of the specification of the income variable in particular, should not be
For NFS purposes food is defined to exclude certain items which individual family members often buy for themselves such as chocolates and sugar confectionery. Soft-drinks, ice-cream. fish and chips and other take-away meals are included if they are brought home to cat. though soft-drinks are currently excluded from the main analyses and from ‘total food expenditure’.
t For details of the construction of net balances, see Appendix A of the annual reports of the NFS Committee.
Tab
le 1
C
ompo
siti
on o
f th
e 19
82 N
FS S
ampl
e of
7,9
45 H
ouse
hold
s B
etw
een
[he
Seve
ral C
ateg
orie
s of
eac
h C
lass
ifica
tory
Var
iabl
e
Clo
zrifi
calo
ry
1 V
orio
Me
Fu
ll D
escr
iptio
n
Sco
tland
w
ales
E
ngla
nd-N
orth
--
Yor
kshi
rc
and
Hum
bers
idc
-Nor
th
Wes
t -E
ast
Mid
land
s - We
st M
idla
nds
--Sou
th
Wes
t --
Sou
th
Eas
t/Eas
t A
nglia
Gre
ater
Lon
don
Met
ropo
litan
dis
trict
s an
d th
e C
entra
l
Non
-met
ropo
litan
dis
trict
s w
ith
Cly
desi
de c
onur
batio
n
elec
tora
te p
er a
cre
of
7 or
mor
e 3
but l
ess
than
7
0. 5
but
less
than
3
less
than
0.5
Hou
seho
lds)
co
ntai
ning
I
or m
ore
earn
ers
l24
0 (g
ross
per
wee
k) o
r m
ore
1127
but
less
tha
n 12
40
171
but
less
than
[I27
Less
than
ill
Hou
seho
lds
Ell
or
mor
e w
ithou
t an
ear
ner
less
than
~7
7
Pen
sion
er h
ouse
hold
s (as
defin
ed in
NFS
)
Abb
revi
nlio
n us
ed in
Tn
ble
2
Sco
t. W
ales
N
Y
H
NW
E
M
WM
sw
S
E/E
A
GL
C
Met
ro.
7+
3-
7 0.
5-3
<0
.5
A B
C D El
EZ
OA
P
Per
cen r
age
Dis
rrib
urio
n of
H
ouse
hold
s
1.2
4
.9
1.4
9
.8
10.3
7.
8 9
.8
9.9
3
1 0
12.6
21
.4
18.7
16
.3
14 6
15
.3
6.4
29
.6
26.7
9
.2
2.1
11
.3
14.3
Clo
ss~
ico
rory
V
onob
le
~
Hou
seho
ld
Com
posi
tion
(NB
A
n
adul
t is
a
pers
on a
ged
8 ye
ars
or
mor
e.
thou
gh 'h
ead
of h
ouse
hold
' an
d 'h
ouse
wil
arc
alw
ays
clas
sed
as
adul
ts)
Age
of
Hou
scui
lc
Hou
sing
Te
nure
Free
zer
Cor
enor
ies
Ful
l Lk
scri
prio
n
Adu
lts
Chi
ldre
n I
0 I
I or
mor
e 1
0 2
I 2
2 2
3 2
4 or
mor
e 3
0 3
or m
ore
3 o
r m
ore
4 o
r m
ore
0
I or
2 3
or m
ore
Und
er 2
5 ye
ars
25-3
4 ye
ars
35-44 y
ears
45
-54
year
s 55
-61
year
s 65
-74
year
s 75
and
ove
r
Unl
urni
shcd
: cou
ncil
Unf
urni
shed
: ot
her
rent
ed
Furn
ishe
d. re
nted
R
enl l
ice
Ow
ned:
out
right
O
wne
d: w
ith m
ortg
age
Ow
ning
a d
eep
freez
er
Not
ow
ning
a d
eep
freez
er
A b
brev
rori
on
used
in
Tobl
e 2
IA
IA I
+C
2A
2A
IC
2A
2C
2A
3C
2A
4+
C
3A
3i
A I-
ZC
3+
A 3
+C
4
+ A
< 25
25
-34
35-44
45-5
4 55
-64
65-7
4 75
+
Cou
ncil
Oth
er r
cntc
d R
ent.
[urn
. R
ent
lice
Ow
n: o
utrig
ht
Ow
n: m
ortg
ag,
With
W
ithou
t
Dis
rrib
urio
n P
erce
nro8
r
of H
ouse
hold
s
17.5
3.
1 30
.7
9.8
14
.8
5. I
I .5
7.4
6.5 I .o
2.1
6.3
20
.2
19.5
16
.5
16.2
13
.7
7.5
31.8
7.3
I .2
I .
3 24
.6
33.8
54.5
45
.5
P. J . LUND AND B. J . DERRY 44
overlooked. First, the income of the head of the household may reflect social class factors rather than total household income*. Second, total household income is likely to be higher relative to that of the head of the household, or the principal earner, the greater is the number of adults (basically persons aged 18 and over). On the other hand the greater is the number of persons in the household, the lower, for given total household income, is the real income of the household after taking account of its greater needs (which vary between adults and children). Recognition of this latter point sometimes leads to the construction, or estimation, of equivalent-adult scales, both for total expenditure and for each particular good or range of goods. Adoption of this approach would naturally lead to the specification of relationships accounting for variations in total household consumption or consumption per equivalent- adult (perhaps measured differently for each item) rather than of consumption per person. It must therefore be accepted that the effects of household composition estimated from analyses of consumption per person represent a complex mixture of separate effects which more sophisticated forms of analysis would seek to disentangle. On the other hand the adopted dummy variable representation of household composition may provide a better indication of the effects of incremental changes in household size than equivalent-adult scales usually permit 7.
Although a more rigorous analysis of the effects of household income and composition might have been possible this would-given constraints on time and computing resources-have detracted from the analysis of the effects of the other variables (region, type of area, age of housewife, housing tenure and deep-freezer ownership). Variables such as these are typically overlooked, or less fully covered, in econometric analyses of household expenditures and it seemed desirable to remedy this by including them within the multiple regression rinalysis. This has been done, though the resulting number of dummy variables did impose constraints on the extent of regression experimentation which could be undertaken. Perhaps the most important of these was that the effects of each variable were presumed to be independent and additive; thus location in a particular region was allowed to increase or reduce consumption (per person) of each food by a set amount rather than by one which might vary according to, say, the household's income or composition. This is contrary to recommended best econometric practice which would allow all factors represented by dummy variables to affect both the intercept and slope coefficients. In this study the large absolute number of dummy variables makes this less restrictive approach computationally impractical, if not impossible T.
The regression analysis was conducted with two main objectives: to assess the relative importance, or significance, of each of the several factors in the explanation of inter-household variations in food consumption, and to identify the patterns of variation within each characteristic. However, the
' It is of note that the Manchester study of 1965 NFS data, reported by Thomas ef al. (1972). which defined income in terms of family net income, identified significant social class effects for total food expenditure and expenditures on most of the individual items considered (milk, particular beverages, various meats).
t For a convenient discussion of equivalent-adult scales the reader is referred to Cramer (1971), pp.161-170. Basically, equivalent adult scales may either be imposed, on the basis of prior judgements, or estimated from the data: the latter approach is however subject to identification problems on which Deaton and Muellbauer (1980) provide more recent references.
f Moreover it raises the more basic question as to why this form of specification is now considered almost obligatory when the effects of a relatively small number of separate classificatory factors are a n a l y d alongside some quantitatively measured ones. but not so when the effects of only quantitatively measured variables are being examined.
HOUSEHOLD FOOD CONSUMPTION: THE INFLUENCE OF HOUSEHOLD CHARACTERISTICS 45
fulfilment of the first of these objectives is not straightforward since it presumes, first, the choice of a particular regression specification and, second, the emphasis to be given to a variable’s apparent contribution to overall explanation and its statistical significance in the conventional sense. For a number of reasons it was decided to include all the considered variables in the finally chosen regression equation; this provided the base for the assessments of both the statistical significance of each classificatory variable and the patterns of variations within them. These reasons were partly general ones which, in principle, apply to all similar regression studies (the arbitrariness of significance cut-offs and the presentational advantage of having comparable equations for all items covered) but also included some specific to this study. These were the cost of rigorously selecting ‘best’ equations even amongst the sets of classificatory variables and the illogicality of excluding a whole set of dummy variables for one or more classificatory variables but not excluding (i.e., condensing) dummy variables within each classificatory set (another possibility excluded by computational limitations).
Given this choice, the statistical significance of each classificatory variable (represented by a set of dummy variables) was examined by conducting an F- test on the difference in overall explanatory power provided by equations respectively including or excluding it (all other variables being included). This procedure was adopted because the more usual I-test of statistical significance relates to individual regression coefficients which, in this case, are those of the dummy variables representing the separate categories within each classificatory variable*. However, this test, like the I-test, is a very strict one since it only allows for the marginal contributions to the overall explanation provided by each variable, considered in turn, on the assumption that the effects of all the others have been allowed for. The consequence is that the sum of the explained variations accounted for in these tests is less than the total explained by the regression equation. An alternative examination-more strictly of ‘contribution to overdl explanation’-was therefore conducted by including the classificatory variables in successive regression equations according to a predetermined order. This was: (i) controlling factors (net balance, calendar quarter), (ii) main variables (household composition, income and region-ordered on the basis of preliminary regression experimentation) and (iii) other variables (type of area, age of housewife, housing tenure and freezer ownership-in that order).
This regression analysis was applied to data on the consumption (per person per week) of each of 40 different though partly overlapping food items and to total household expenditure on food similarly expressed. Between them the 40 food items covered 96% of total recorded food expenditure. The main exclusions from the analysis were the miscellaneous foods for which aggregation of consumption quantities does not make much sense. Expenditure (per person per week) on the 40 items analysed ranged from 0 . 7 9 ~ in the case of oatmeal and oat products to € 2 . 5 5 ~ in the case of total meat. In drawing up the specification of the items, account was taken both of the
meaningfulness of aggregating individual food codes and of the percentage of households making purchases of the items as specified during their week of participation. This latter consideration is relevant because of technical econometric problems which arise when equations are estimated in which the dependent variable regularly assumes zero values, the recording of negative
Moreover, the magnitudes of these individual r-statistics reflect the arbitrarily chosen base and arc not therefore reported. Readers may, however, be interested to note that the mythical base household was one consisting of a single high-earning young adult sharing unfurnished council accommodation in the GLC area with a deepfreezer.
46 P. J . LUND AND B. J . DERRY
values being constrained by the nature of the variable being investigated*. The literature on this topic is extensive but while the problem in this particular case may be different from that normally examined (being more due to length of recording interval than non-purchase as such) it seems advisable to reduce it by grouping food codes so that the incidence of non-purchase is relatively low. For this reason the number of housholds recording purchases of each of the 40 analysed food items in shown in the table of results. The problem of zero observations clearly does not affect the other analysis undertaken-that in respect of total expenditure (per person per week) on household food.
Results The results of the regression analysis are summarised in Table 2. This shows the following for each of the 40 food items and total household expenditure on food (per person per week):
the statistical significance of each of the classificatory variables as assessed (a) in conventional terms-by excluding it alone and (b) in terms of its contribution to overall fit in the sequential ordering of the variables. the apparent order of statistical significance in (b) of household composition, income of the head of the household and region as indicated by the initial regression experimentation involving these variables. the category within each of the classificatory variables for which consumption (total expenditure) was highest or lowest, after taking account of all other considered factors. the percentage of the variation (R2) in consumption (total expenditure) explained. in a statistical sense, by the equations.
The most notable feature of Table 2 is perhaps the marked contrast between the statistical significance of the various variables (however assessed) and their overall explanatory power. Four of the variables (net balance, composition, region, age) are significant for nearly all foods and a other three (income, area, tenure) for well over half of them yet the highest R values were 0.16 for fresh green vegetables and 0.18 for total food. It must be stressed that these results are not inconsistent and certainly do not invalidate each other. Essentially, they mean that while there is a lot of unexplained variation in the consumption patterns of individual households, and also in their acquisitions between weeks, there is clear evidence that a number of factors have an important role to play in explaining the recorded differences between households. Indeed all the overall fits are clearly significant at the 99% level 7.
?
This type of problem was first explored by Tobin (1958) who proposed a hybrid of probit analysis and multiple regression analysis which was subsequently developed by Amemiya (1973). Dcaton and Irish (1982) have recently considered the question in the specific context of household budget surveys in which the zero observations may be due either to people never buying the commodity in question or simply not doing so in the survey period. Their analysis and evidence (partly based on the UK Family Expenditure Survey) while suggesting that ‘tobit’ is not an appropriate model for analysing Engel curves when zeros are present, does not indicate which alternative would be better (p.23).
t Cramer (1971) remarked that ‘the decision-making process of the individual consumer defies complete description’ (p. 146). He also showed how the apparent ability, as indicated by R’, of particular forms of Engel curves to explain household data on expenditure on various foodstuffs varied considerably according to whether or not the data were grouped (and the extent of grou ing) without much affecting the estimated income elasticities. It should also be
particular, since household sire clearly has a larger part to play in the explanation of total household consumption than consumption per person, the R2 obtained in the present study cannot be compared directly with those obtained in the earlier study of NFS data by Thomas er al. (1972).
noted that R ? will be affected by the precise specification of the dependent variable. In
Tab
le 2
Sum
mar
y of
Reg
ress
ion
Ana
lyse
s of
the
Con
sum
ptio
n of
Indi
vidu
al F
oods
and
Tot
al F
ood
Exp
endi
ture
(Hou
seho
ld F
ood,
Per
Per
son
Per
Wee
k)
Stat
istic
al S
igni
fica
nce
asse
ssed
: (a
) in
con
vent
iona
l ter
ms.
by
excl
udin
g on
ly t
he r
elev
ant
vari
able
fro
m t
he r
egre
ssio
n.
(b)
acco
rdin
g to
con
trib
utio
n to
ove
rall
fit
in l
arge
ly p
rede
term
ined
ord
erin
g-w
ith
appa
rent
ord
er o
f im
port
ance
of
hous
ehol
d co
mpo
sitio
n,
regi
on a
nd i
ncom
e gr
oup
indi
cate
d.
Sign
ific
ance
Lev
els
indi
cate
d as
: **
, 99%
; +,
97.5
%;
*, 95
%;
+ , 9
0%;
NS.
Not
sig
nifi
cant
at 90%
leve
l. B
road
Pat
tern
of
Eff
ects
indi
cate
d by
sho
win
g ca
tego
ries
with
hig
hest
and
low
est c
onsu
mpt
ion/
expe
nditu
re.
all o
ther
var
iabl
es b
eing
hel
d co
nsta
nt
Tota
l WUIC
m
eat (
73)
Cod
er 3
1. 3
6. 4
1
Food
ifem
. (as
reris
ks
Rel
atin
g ro
Tab
le 3)
. PL
of
all h
ouse
hold
s pu
rcha
sing
item
dur
ing
(hei
r su
rvey
wee
k.
NFS
Foo
d C
odes
.. .. S
ign.
(a)
NS
S
ign.
(b)
N
S H
igh
QI
Low
Q2
Fear
ure of
Res
ulrs
Bal
ance
.. .*(I) 2A
3+
A
3+
C
*. S
ign
(a)
*+
Liqu
id m
ilk'
(97)
Si
gn (b
) C
odes
4-6
+ .. ..
.. NS
N
S(3
) *
W
NS
sw
G
LC
65
-74
Sco
t. O
AP
7
+
< 25
I S
ign.
Si
sn.
(a)
Ib\
\:
I:
:
Tot
al m
ilk &
cre
am (9
9)
Cod
es 4
-17
Hig
h .-,
I Low
Che
ese.
(6
9)
Cod
er 2
1, 2
3
.. S
ign.
(a)
'+
Sig
n. (
b)
.. S
ign.
(a)
NS
S
ign.
(b)
B
ecf &
vea
l. (5
6)
Cod
e 31
H
igh
I Low
.. ..
Sig
n. (a
) S
ign.
(b)
M
utto
n &
lam
b.
(28)
C
ode
36
Hig
h I Low
Por
k. (
33)
Cod
e 41
.. .. S
ign.
(a)
N
S S
ign.
(b)
N
S
Cln
rriji
caro
ry
Van
able
r
Hou
seho
ld
Inco
me
Are
a A
ge
oj
Com
posi
tion
Gro
up
Op
e
Hou
sew
fi
.. ..
.. ..
IA
Wal
es
3+
A 3
+C
G
LC
<
25
.. I
.. I
.. I
.+
I
..
+i3)
*+
(a
NS
.. "(I
)
.,+::+c
I Ei
I OAAP
I "7
:' I :::
:
Hou
sing
Te
nure
.. .. R
ent
frcc
Ren
t. lu
rn.
.. .. R
ent
free
Ren
t. fu
rn.
.. .. R
ent.
furn
. C
ounc
il
NS
N
S
Oth
er rented
Ren
t fre
e
NS
NS
Oth
er re
nted
R
ent f
rcc
NS
NS
O
ther
ren
ted
Ren
t. fu
rn.
NS
NS
Oth
er r
ente
d R
ent f
rcc
I
Free
zer
Rz
With
W
it ho
u t
+ W
ith
With
out
With
W
ithou
t
6t
h
I '02
With
out
With
W
ithou
t
With
W
ithou
t .. With
W
ithou
t I
8 5 P rn I
0 B U 8 5 5 c z -I I
rn
Z
r
C
rn
Z c: rn
I
0 - 7 % z, n I 3 r
0
c: I
P 21 5 rn E i
v? Q P 4
T8b
lc 2
(co
ntd.
)
Food
item
. (o
sm
d
trhtiw
to
Tab
k 3)
,
prr
rhp
dru
item
&tin
# t
he
u~
y
mk
.
NFS
Foo
d C
odu
&fM
&h
un
un
cook
ed.
(60)
co
dc 5
5
Ic o
fdl
Lonr
eLdd
c
Poul
try,
uncooked. (
35)
coda
73.
77
Oth
er m
ut
and
mea
t W
oduC
II.
(89)
c
h
6.5
1, 5
8-71
7a~a
PI
Tot
al mu:
(96)
co
da
31-9
4
Fish
' (6
5)
Co
da
1001
27
tw
(72)
co
de 1
29
But
ter'
(47)
C
ode
135
Mar
gari
ne'
(48)
co
de 1
38
TO
I~
1.11
(so)
CD
du
135
-148
Cla
stili
cato
ry V
aria
bles
R2
F
ealu
no
f R
anlu
N
et
Hd
dd
In
com
e A
rm
Age
of
Ho
uin
; F
rm
~
scor
on
Wa
n-
Cor
npar
ition
G
roup
T
yw
Hou
sewi
fe
Tenu
re
+ ..
NS
NS
t
.. Ns
Ns
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pe
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54
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251-
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Low
Q
l
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(45)
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Cou
ncil
Rcn
l. lu
rn.
Tabl
e 2
(coo
ld.)
Food
item
. (a
sIer
tsk
rel~
iin
g 10
Tabl
e 3)
, 41 of
nll h
ouse
hold
s pu
rcha
sing
ilem
dur
ing
thei
r su
rvey
wee
k.
NFS
Foo
d C
odes
Bre
akfa
st c
ncal
r* (
41)
Cod
e 26
2
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er c
erea
ls.
(56)
co
des
28s-
301
Tot
al c
erea
ls (9
8)
Cod
er 2
51-3
01
Tea
. (S
O)
Cod
e 304
Cof
fee.
(3
1)
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es 3
01.3
09
Tot
al b
evcr
ager
(64)
Cod
es 3
04-3
13
Tot
al h
ouse
hold
food
ex
pend
iture
Cla
sslJ
ical
ory
Varia
bles
RZ
F
miu
re of
Res
ulis
N
et
Hou
seho
ld
Inco
me
Are
0 A
ge o
f H
ousi
ng
free
rer
Bal
ance
C
ompo
siri
on
Reg
ion
Gro
up
Type
H
ouse
wife
Te
nure
+ N
S
NS
N
S
NS
N
Y3)
"(
2)
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) N
S
NS
.. .. N
S .0
1 ..
*+
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n. (
b)
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C
EM
A
0.
5-3
35-4
4 R
ent.
furn
. W
ithou
t S
ign.
(a)
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h 0
3
3A
scot
. O
AP
G
LC
< 2
5 C
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il W
ith
Low
QI
+ +
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N
S
.. .. ..
NS
N
S
-02
*+
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Sco
t. A
G
LC
.. S
ign.
(a)
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S
Hig
h Q
s Lo
w
Q2
Sig
n. (
a)
NS
Sig
n. (
b)
NS
H
igh
Q3
LO
W
QI
Sig
n. (
b)
NS
*+
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3)
**(I
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(2)
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n: M
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age
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out
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AP
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n re
nted
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ith *. ..
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S
NS
.. ..
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S
NS
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2)
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etro
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-54
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t fr
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ent
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ith
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S
10
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ign.
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S
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n. (b)
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2 Lo
w
Q3
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D
2A
4
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ot.
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ht
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out
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n. (
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igh
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ala
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AP
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ncil
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t fre
e W
ith
.. ..
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+
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ign.
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ther
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ted
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-54
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er r
ente
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ith
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. (a)
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l+C
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AP
<
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ncil
With
out
Hig
h Q
2 Lo
w
QI
52 P. J. LUND AND B. J . DERRY
Table 3 summarises the statistical significances of the classificatory variables, under both assessments, in the explanation of the differences in per capita consumption of the 30 non-overlapping food items (totals excluded) and in per capita expenditure on household food. The difference between the two methods of assessing significance is greatest in the case of income, which appears much more significant under the sequential inclusion approach (b) than in the more conventional test (a). This difference probably reflects a particularly strong relationship between this variable and the others in the set.
The exact attribution of effect to particular factors will, to some extent, be influenced by the measurement and specification problems which have already been discussed. These are particularly likely to affect the apparent effects of household composition (because of the differing per capita needs of adults and children), income (of the head of the household/principal earner only) and age of housewife (likely to be related to both the ages and hence needs of children and household income). Thus, it is perhaps not surprising that per capita expenditure and consumption tended to be highest (other things being equal) in single adult households and lowest in the large households with several children. The notable exceptions were for carcase meat, poultry and fresh green vegetables (all highest in adults-only households), and margarine, potatoes and breakfast cereals (lowest in adults-only households). However, the household composition effect may largely explain the marked difference between the position of pensioner households* as shown by this multiple regression analysis and as recorded in the conventional classifications. Thus, although pensioner households typically record above average household food expenditure they are shown by this multiple regression analysis to have (other things being equal) the lowest per capira total expenditure and consumption of many foods including meat, fish, eggs, potatoes, fresh and frozen vegetables and fruit. While this does not mean that the actual consumption of old-age pensioners is below the national average-or below their nutritional requirements-it does confirm the view that the relatively high per capita household food expenditure of pensioner households reflects their typically small size, the paucity of children within them and their greater than average reliance on household food supplies (i.e., fewer meals out).
Not surprisingly, the highest income group (A) had the highest per capila expenditure on food and consumption of many individual foods-with the notable exceptions of margarine, sugar (and preserves), potatoes, white bread and tea. Another marked set of differences is to be observed in the case of income group D (low earning families) which had high consumption of margarine, sugar, ‘other’ processed vegetables, white bread and tea and low consumption of butter and fresh green vegetables. These differences may have implications for forecasting as also may the differences according to age of housewife, especially if these reflect vintage effects rather than age (and associated family circumstances) as such. For most food items the general pattern was for per capita consumption to rise with the age of the housewife. often peaking (as for total expenditure) at the 45-54 years age group, before declining again. To a large extent this pattern may reflect the associated differences between the numbers of adults/children and the equivalent-adult ways of measuring household size and between total household income and that of the head/principal earner. The main interest therefore lies in the notable departures from this general pattern. Younger housewives seem to
* For the purpose of the NFS, pensioner households are defined as ones in which at least three-quarters of total income is derived from National Insurance retirement or similar pensions and/or supplementary pensions or allowances paid in supplementation or instead of such pensions.
HOUSEHOLD FOOD CONSUMPTION: THE INFLUENCE OF HOUSEHOLD CHARACTERISTICS 33
Table 3 Summary of Statistical Significance of lhe Classificatory Factors in the Regressions for 30 Separate Foods (Asterisked in Table 2) and for Total Food Expenditure
Season
Net Balance (a) (b)
Household (a) Composition (b)
Region
Freezer
Statistic01 Significonce
In the 30 separate individual food
consumption equations
99% - 8 9
29 25
21 25
21 2a
13 26
IS 13
21 24
15 15
8 a -
17!4ll - 3 I
- 1
1 -
3 -
1 2
3 5
1 I
2 3
2 2 -
_.
NS - 16 13
I 3
s 3
2 I
7 I
8 7
6 3
I2 12
16 16 -
In total food expenditure equation
99% 99%
99% 99%
99% 99%
NS 99%
99% 99%
99% 99%
99% 99%
NS NS
91.5% 97.5%
have a marked preference for processed and convenience foods such as ‘other’ meat, frozen and other processed vegetables and ‘other’ cereals (which includes rice and pasta) while older housewives consume more milk, carcase meats (particularly lamb), butter and tea.
Three factors-housing tenure, type of area and freezer ownership-were each significant for relatively limited groups of foods while season was generally insignificant. Leaving aside the relatively small tenure groups, households living in unfurnished council accommodation seem to have more clearly identifiable food consumption patterns than do owner-occupiers, both with and without a mortgage. In particular, they consume more ‘other’ meats, processed vegetables, white bread and tea and less cheese, fresh fruit and vegetables, brown bread, breakfast cereals and coffee. The type of area comparisons were also noteworthy in some cases, with the consumption of milk and fresh green vegetables increasing with rurality and that of ‘other’ meats and frozen and other processed vegetables declining according to the same characteristic. In part this pattern reflects the greater abundance of free or cheap supplies, particularly of milk and fresh vegetables, available to rural
54 P. J. LUND AND B. J . DERRY
households, an availability which is reflected in their lower total food expenditure. Freezer ownership seems associated with high levels of consumption of carcase meat, poultry, fish, fresh and frozen vegetables and fruit, but a low consumption of white bread and cakes. This result may however simply be a reflection of this characteristic’s close relationship with household income.
The final set of differences are those between Wales, Scotland and the (standard statistical) regions of England. These differences, which often occasion some interest, are set out for selected foods in Table 4. This table provides three different estimates of the percentage difference between the consumption of each food in a particular region and the average GB level of consumption. The estimates labelled (a) are based on the results of this multiple regression analysis while those labelled (c) are based on the standard NFS classificatory analysis. The comparison between these is not however straightforward since the form of averaging used in the derivation of (c) is different from that which would be consistent with (a). To overcome this problem the averages (b) have been constructed so that comparisons between (a) and (b) reflect the influence of other factors in the multiple regression analysis while those between (b) and (c) reflect different forms of averaging*. In fact it will be seen that all three sets of figures are very similar, indicating that the allowance for the effects of other factors (and the precise form of averaging) is not very crucial in the case of regional comparisons. In particular it means that the practitioner’s interest in the variation in consumption patterns between regions largely coincides with the theorist’s interest in the specific influence of region.
The regional patterns which are shown in Table 4 could provide the basis for endless comparisons of regional characteristics though they may also reflect the precise locations-within each region-surveyed in 1982. They may also provide the basis for some speculation about future developments. Thus, while the Scots consume less lamb, pork, bacon and ham, poultry, yellow fats, fruit and vegetables, brown bread, flour, breakfast cereals and tea (but more beef, ‘other’ meats, eggs and oats) than their fellow Britons, it may be the relative similarity of their milk consumption which is of greater interest.
Conclusions-The Scope for Further Analysis The study reported in this paper is perhaps best viewed as a multivariate equivalent of the simple tabular classificatory analyses published each year in the annual reports of the NFS Committee. The value of this form of analysis is perhaps best indicated by the case of pensioner households; the study has indicated that their relatively high (per capita) household food consumption and expenditure can be explained by their associated characteristics (small household size, few children, few meals out). The study has also indicated that variables often excluded from conventional demand analyses may be significant, at least for some foods. These factors include housing tenure, type of area and age of housewife, though account should be taken of the relationship between at least the latter and other relevant factors (e.g., the ages of children and numbers of earners within the household). Differences
* Denoting consumption of a commodity by the ith household in quarterj by eij and the number of people in that household by nu where i = I ... h, a n d j = I ... 4, where h, is the number of households in the survey in quarter j .
Tab
le 4
D
etai
led
Res
ults
for
Ind
ivid
ual F
ood
Item
s an
d St
anda
rd S
tatis
tical
Reg
ions
IPer
cent
age
diff
eren
ces
in p
er c
apita
con
sum
ptio
n (t
otal
exp
endi
ture
) be
twee
n re
gion
and
Gre
at B
rita
in a
s as
sess
ed:
(a)
on t
he b
asis
of
the
cond
ucfe
d m
ultiv
aria
te a
naly
sts.
(b
) on
the
bas
is o
f th
e av
erag
e pe
r cu
pira
con
sum
ptio
n of
eac
h ho
useh
old
surv
eyed
dur
ing
the
year
-the
aver
age
mos
t co
mpa
rabl
e w
ith (
a).
(c)
on t
he b
asis
of
the
aver
age
of t
he s
epar
ate
four
qua
rter
ave
rage
s of
per
cup
ilu c
onsu
mpt
ion
of p
erso
ns c
over
ed b
y th
e Su
rvey
-the
co
nven
tiona
l N
FS
anal
yses
.
I- vo
d
Mil
k
Che
ese
Bee
f and
vea
l
Mu
ito
n a
nd l
amb
Por
k
Bac
on a
nd h
am.
unco
oked
Po
ult
ry,
unco
okcd
Oth
er m
eat a
nd
mea
l pr
oduc
ts
Fish
""IP
S + 10
+ 14
+
12
~ I2
-
7
- I2
- I5
-
13
- 10
+ 21
+II
t 10
- 17
- I
5 -
1Y
+ 27
+ 32
t 32
1 In
+
17
t II
-8
-
14
- 1
2
- 2
4 - 2
4 -
26
c,
I I
+I
-
I
+z
+I
-
7
-7
+ 25
+
18
+ II
- 54
-
54
- 50
- 53
- 56
-
53
- 13
-
IV
-in
-H
-
I2
-in
+ II
+
I6
+ IY
-5
-
7
-8
- I4
-
I3
- I2
- 22
- 2s
- 23
+s
-
I
+I
- 40
-
40
- 1
7
-7
-
13
- II
+ I3
+
I5
t 15
-I
-
4
-5
+ IY
+
25
+ 27
+ 26
+ 27
+ 1
2
-7
-
6
-7
- I4
-
I4
-1s
+ IY
+
I6
+ 16
- 15
-?
I
- 27
+5
+
2
+ I2
+ IY
I
IY
I IY
7)
-- -
25
- 24
+6
+7
+ 14
+ 21
+ 25
+n
-5
-
5
-7
-7
-
7
-7
-6
- I
?
-0
+ IY
+ 2
2 +
30
- 23
- 27
- 25
+ 29
4
ZY
I 30
II
-
1
+I
+ I3
+
16
+ I5
-0
+
I
-1
,011
tori
! l!d
/fl/
ldS
+J
t
6
+5
+f
i
tY
+
I3
-1
-4
-
3
6 -?
I
23
+m
i
3
tY
3 4 7 n I0
-7
3 --
5 4
- It)
-
I2
.- Y
+.I
t5
+
.l
+5
+
s
+I
- 17
-2
1 ~
25
tn
+
n
+ I3
+ 17
+
I4
+ 12
+ 13
t I
3
17
18
+
7
+ 10
-2
+
I
-3
-2
-
2
-5
.So1
,1h
I4 P
si
+3
+
.I
+5
-1
+
3
+2
+ II
+ I3
+
I1
+ 22
+
I2
tn
t 4f
i + 4
7 +
34
- In
-
IS
17
7
--
9
-0
-n
-
10
- 10
-12
- II
- 10
+?
+
I
+I
I 10
+
10
+II
6 +
I1 *
L
iY
In
t I6
il
t
7
+7
- I6
-
17
- I6
t5
I 10
+
I0
-7
-I
1
- II
-I
-
I
-1
Tnb
k 4
(con
ld.)
r
Tol
d fr
uii
Whi
te b
rud
Form
of
CW
IlpaliS
OA
W
ak
+7
+ 1
2 +
I3
+ 49
+%
+ 5
1
- 18
-
7
- 17
+ II
+a
t I9
+o
-
0
+3
-1
+I
I +
6
t4
+
8
+8
-I
-
II
- II
-9
-22
- 19
-4
+o
-
3
+I8
+ I5
+ 1
3
+7
+
8
+II
-8
-
13
- I3
-I1
- 12
-9
+4
+
I
+2
-4
+7
+
8
- 54
-61
- 61
-9
- 19
- 25
-6
-
10
- 14
- I9
-
3
-I
-7
- 2
3 - 2
5 -
I
+ I4
+ I4
No
rth
+m
+ 21
+2
0
+ 13
+ 14
+I
-4
-5
-8
- 10
-1
1 -
10
+ II
+ 1
7 + 2
1
- 14
- I5
- 10
i8
+
4
+6
- 34
-40
- 32
i 25
+ 36
+ 36
-I1
- 19
- 19
-2
+
7
t9
Sta
York
s an
d H
um
knid
e
+5
+
6
+l
- I
8 -
I7
- I9
+ 22
+ 1s
+ 2
8
+I
+
2
+3
+2
+
J
*s
-
6
-5
-
3
-0
-2
-
I
- I4
- 20
- 2
1
+ I2
+I5
+ I6
-4
-
9
- 1
0
-7
-
4
-I
ard
Sta
tistic
al R
Nor
th
wes
t
-I
+
o
+o
-
7
-6
-7
+M
+ 26
+ 19
+s
-
I
+I
+ 10
+ 17.
+ I
2
- 23
- 22
-22 - 10
-11 - 10
-9
- 1
6 - I2
+1
+ I
4 + 1
4
- II
- I3
-1
s
+ I3
+ I8
+ 20
'on
Ep
rr
Mid
land
s
-4
-5
-4
-4
-5
-6
-5
+
o
+2
-3
+
I
+7
-8
-
7
-3
+ II
+IS
+ I5
- 12
- 12
-9
-1
-4
tl
+ I5
+ 12
+ II
-6
-9
-
9
+I
+
I
t2
wrs
r M
idla
nds
-4
-2
-
2
+l
+
6
+6
-3
+
2
+6
+ 23
+ 24
+ 23
+8
+ I4
+ 10
+I
+
3
i4
-3
-
3
-2
+3
+
2
+o
+
5
+ I4
+ 10
-7
-1
1 -
12
+ 16
+ J2
+ 29
Sout
h w
est
-9
-
8
-6
-9
-8
-3
- 21
- I5
- 16
- 1
3 -
1
-6
+3
+
5
+7
+ 24
+3I
+ 2
9
+8
+ I
I + 1
2
+3
+
4
t6
-8
-
17
- 19
+4
+
6
t6
- 1
7 - 2
0 -22
Sour
h E
pn
/ E
ast A
nglia
-3
-
6
-8
+I
+
o
+3
-I
-
7
-6
-5
-
1
-9
-7
- I4
- 1
7
+ I4
+ 10
+ II
+4
+
6
+6
+ I4
+ 23
+ 21
-7
-I
S - 1
4
+ II
+
20
+ 13
-7
-I
S -
I7
HOUSEHOLD FOOD CONSUMPTION: THE INFLUENCE OF HOUSEHOLD CHARACTERISTICS 57
m - n = O D = - a t ~ 2 % - - a O P O - c n - o n + + + I l l I I 1 I I I 4 I + I I I + + + + + + - -
- * a - - t N - N 3-n T - N c c n n n n n t - I l l + i I I + + I l l I I + i l l I + + I l l -
eas= a = - - 0 . 0 - - - = N O z c m O N C c l - o
+ + + I + I + + + + I 1 + + + + + + I l l + + + - -
o m 0 C - D D ~ m n - s n m - - o m e - - o - N - o
( 1 1 + + + + + + I I I + + + + + + + + + + + + n n n - -
58 P. J. LUND AND B. J. DERRY
between regions also appear to be highly significant and in this case the analysis has served to confirm the patterns portrayed by the conventional classificatory analyses of NFS data.
It must, however, be acknowledged that in a number of respects this study could be improved upon and extended. The most obvious directions for improvement and development are perhaps:
the utilisation of the other NFS data: on net household income (which, although reducing the usable sample of Survey households by about 40070, may provide a better guide to pure income effects); on the age (particularly of children) and sex of the household members; on the occupation and industry of earners; on price movements during the calendar year; and on the extent of free supplies available to the household;
(ii) the utilisation of data for other years, whether to increase the sample size, check the consistency of patterns between years or examine trends in them;
(iii) the adoption of particular functional forms with respect to the potential numerical variables (income, household composition), whether or not derived from some underlying theory of consumer demand. Linked to this might be an allowance for specific forms of interaction between variables-e.g., do the same regional differences apply at all income levels?
While it is unlikely that all of these possible lines of development (and there must be more) could be tackled in any one study it seems clear that NFS data provides considerable scope for detailed analysis of household food consumption patterns and trends. It will therefore be of interest that the MAFF is making the data tapes for individual calendar years available, for research purposes, through the ESRC Data Archive as well as selling up-to- date data, at various levels of aggregation and classification*.
(i)
References Amemiya. T. (1973). Regression Analysis when the Dependent Variable is Truncated Normal,
Cramer. J . S. (1971). Empirical Econometrics. Amsterdam: North-Holland Publishing Company. Deaton. A. and Irish, M. (1982). A Statistical Model for Zero Expenditures in Houehold Budgets,
University of Bristol Discussion Paper No. 821128. Deaton. A. and Muellbauer, J . (1980). Economics and Consumer Behaviour. Cambridge:
Cambridge University Press. National Food Survey Committee (annual reports). Household Food Consumpion and
Expenditure. London: HMSO. Thomas, W. J. et al. (Currie, J . M., Hamid Miah, M. A.. Moore, S. A., Rayner, A. J . .
and Stewart, 1.) (1972). The Demand for Food: An Exercise in Household Budget Analysis. Manchester: Manchester University Press.
Tobin, J . (1958). Estimation of Relationships for Limited Dependent Variables, Econometrica,
Econometrica, 41, 997 - 1022.
26, 24 - 36.
* Further details, including a brief note outlining the range and cost of information available, may be obtained from: National Food Survey Branch, Room 419. Whitehall Place (West), London SWI A 2HH. (Tel. 01-233 5088).