The relationship between food consumptionand socio-economic status:

evidence among the British population

Paola De Agostini∗†

Abstract

This paper investigates the relationship between nutrition andsocio-economic status among British youths. It describes the dynam-ics of consumption over age and time using data from the BritishNational Food Survey (NFS) covering the period 1975-2000. Dailyintakes-age and food-age relationships for men and women are esti-mated by solving a non-linear least square model with a roughnesspenalty function approach. Focusing on young age groups, trends ofconsumption over the 25-year period of study and the cohorts effecthave been explored across three classes of age. Finally, an explorationof specific trend variations in eating habits has been implemented con-trolling for income distribution, region of residence, household struc-ture and the presence of children.

∗Institute for Social and Economic Research (ISER), University of Essex - UK; email:pdeago@essex.ac.uk

†Department of Economics, University of Verona - Italy.This paper is an extract of my PhD thesis. It would not have been possible without

the outstanding supervision which I have been receiving from Marco Francesconi. Thanksare also due to Stephen P. Jenkins, Federico Perali, Andrew Chesher and the participantsat the EEA Summer School in Microeconometrics and at the 97th EAAE Seminar forhelpful suggestions. I am grateful to the data depositors of the NFS (Department forEnvironment, Food and Rural Affairs), and to the UK Data Archive, University of Essex,for providing access to the data. Finally, I would like to thank Chimera, Institute forSocio-Technical Innovation and Research at the University of Essex for funding. Theauthor alone is responsible for errors and opinions.

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1 Introduction

In a growing number of industrialized Countries obesity has become a phe-

nomena of unprecedented proportions. In 2000, 300 million adults were obese

and 700 million were classified as overweight around the world (OECD, 2003).

There are however notable differences in obesity rates across countries. In

the United Kingdom, the obesity rate among adults has tripled over the last

twenty years to stand at 22% in 2001. This is higher than in nearly all other

OECD countries, but lower than in the United States (31% in 1999) and

Mexico (24% in 2000), and comparable to Australia (21% in 1999). Accord-

ing with the OECD data, if the average rate of increase in the prevalence of

obesity between 1980 and 1998 continues, over one fifth of men and about a

quarter of women in England will be obese by 2005, and over a quarter of

all adults by 2010. This would bring levels of obesity in England up to those

experienced now in the United States.

Obesity and overweight are not only adults issues. On the opposite, they

are increasing also among children and adolescents. In 1995, 18 million under

five and 155 million children between five and seventeen years old in 2000

were classified as overweight around the world. Over the past two decades the

number of overweight children and teens nearly double. The Surgeon General

Report for the United States reports that in 1999 13% of children aged 6 to

11 years and 14% of adolescents aged 12 to 19 years were overweight. This

prevalence has nearly tripled for adolescents in the past two decades. Even if

European and British indicators are not as high as in the US, the proportion

of children classed as overweight or obese increased between the mid-80s and

mid-90s. Studies looking at boys and girls in England and Scotland, aged

between 4 and 11, show that approximately 5% of British children in 1984

were overweight. A decade later, 9% were overweight.

The primary concern of overweight and obesity is one of health and not

appearance. Overweight and obese people are at risk for a number of health

problems including heart disease, diabetes, high blood pressure, and some

forms of cancer. Overweight adolescents have a 70% chance of becoming

overweight or obese adults then their non overweight counterparts. This

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increases to 80% if one or more parent is overweight or obese (genetic predis-

position). Overweight children and adolescents compared to children with a

healthy weight are at higher risk of developing such diseases listed above. On

the other hand, the most immediate consequence of overweight as perceived

by the children themselves is social discrimination. This is associated with

poor self-esteem and depression.

The second concern of overweight and obesity is one of health service

costs. As reported by the Summary of Intelligence on Obesity (2004) the

cost of obesity in UK is estimated at 3.7 billion per year and 7.4 billion when

adding the cost of overweight. Moreover, considering the time lag between

the onset of obesity and related chronic diseases, researchers suggest that

the rise in obesity that has been occurring in the last 20 years, will have

substantial implications on future costs.

The main causes of obesity have been identified on excessive consump-

tion (unbalance diet), lack of physical activities, genetic predisposition and

disorders that affect the normal bodily functions as metabolism and growth.

Leaving genetic predisposition aside, it remains unclear the exact relative

responsibility of an unbalance diet and reduce exercise.

Food choices depend on many social factors such as history, culture, and

environment, as well as on energy and nutrient needs.

The modern sedentary life, the growing number of fast food and restau-

rant, technological changes and women participation in the labor market

are often popular justifications of the growing calories consumption and the

reduce physical activity.

Lakdawalla and Philipson (2002) analyze the energy equation from the

energy expenditure point of view. They concluded that a sedentary worker

will be heavier than someone in a highly active job. Further, they estimated

that about 60% of the total growth in weight in the United States may be due

to demand factors, such as a decrease in physical activity, and about 40% is

due to expansion in calories, potentially through increased food abundance

due to agricultural innovations.

Increasing availability of fast food and ready meals have changed the rela-

tive costs of meals preparation and consumption increasing the consumption

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of some nutrient intakes as saturated fats, sugars and calories also because

of the bigger portion size.

Women labor participation increases the cost of time, reducing the time

spent cooking healthy home-meals and contributes at increasing number of

meals eaten out and consumption of ready meals.

Although few studies analyze the implication of economic and technolog-

ical changes as possible reasons of the change of food choices (Cutler et al.,

2003; Chou et al., 2001) finding the expected positive relationship between

obesity and the number of fast food per capita, there is little evidence on

the relation between the increasing number of women at work and the rising

in demand for eating out. Chou, Grossman and Suffer (2001) argue that

expanding labor market opportunity for women have resulted in significant

increases in families’ command of real resources and higher standards. Also

Cutler, Claser and Shapiro reject the theory for which the increasing number

of women at work have increased the demand for eating out, pointing out that

the main reason for increasing calorie, saturated fat and sugar consumption

is mainly consumption of snacks outside the main meals.

Another important question that has been addressed is whether differ-

ences in nutrition depend from differences in income. Are poorer eating

worst than wealthier? In fact, family choices associated with health and

the processes of biological programming are strongly mediated by their so-

cial context. Of course, the most powerful aspect of social context associated

with health is poverty because it is often associated with poor diet and conse-

quent poor likelihood of growth and development (Baeker 1998), with raised

risk of infection. But also low level of education are associated with poor

health behavior, for example in terms of diet and exercise, and in raised of

overweight and obesity.

Curry and Bhattacharya (2000) use data on Americans youth to deter-

mine the causes of poor nutritional outcomes. Their finding suggest that poor

nutrition is a problem for American youth neither entirely related to a lack

of household resources nor to family background. They measure information

about the relationship between nutrition and health through education and

age of head of household and the content of television programming. They

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argue that information and technology matter. TV viewing has a negative

effect on nutrition outcome both because of the content of the programming

and because it reduces physical activities. Similar results are found also by

Chou, Grossman and Suffer (2001), who argue that wealthier and more edu-

cated people are less likely to become obese or overweight, whereas Hispanic

and black individuals are more likely to suffer from obesity.

Cutler, Glaeser and Shapiro (2003) find that obesity and income are neg-

atively related, mainly because incomes were not increasing greatly at the

bottom of the income distribution in United States in the period they con-

sidered.

Case, Lubotsky and Paxson (2002) point out the importance for future

research of explaining the mechanisms that underlie the relationship between

income, nutrition and children’s health outcomes. Their findings show that

the robust relationship between children’s health status and family income

may be due to differences in parent’s and child’s health-related behaviors at

different levels of income. Choices made concerning how often a child sees a

doctor or about his eating habits, may have both short-term and long-term

health implications. Many of these behaviors are correlated with socioeco-

nomic status, and so may potentially explain at least part of the association

between children’s health and household income. In their results, inclusion of

these health-related behaviors reduces the observed income gradient among

Americans, but only slightly. Healthful diets help children grow, develop, and

do well in school. Food choices also can help to reduce the risk of chronic

diseases, such as heart disease, certain cancers, diabetes, stroke, and osteo-

porosis, that are leading causes of death and disability among Americans.

Good diets can reduce major risk factors for chronic diseases factors such as

obesity, high blood pressure, and high blood cholesterol.

The UK Department of Health recognizes that promoting diet changes

and physical activities increase would help, but it is difficult to be done be-

cause it would imply changes in preferences and consumer behaviors. They

suggest that preventing obesity and overweight in childhood is perhaps a

more effective approach in the long term. Child health is of the greatest

importance for the future health of a nation, not only because today’s chil-

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dren grow up to become the next generation of parents and workers, but also

because recent research in child health shows that early life health is, for

each child, the basis of health in adulthood and nutrition is one of its basic

determinants. As the UK Food Standard Agency Report points out:

A healthy balance of foods provides the energy and nourishment

everyone needs to survive and to enjoy life. Eating too little food

soon leads to illness, but eating too much or the wrong balance

of foods can lead to problems in the long term. So it is important

to get the balance right both in the amount and in the types of

foods eaten. A healthy and balance diet in childhood can reduce

the risk in anaemia and dental decay. In the longer term, it can

help to prevent ill health later in life. For example, it can reduce

the risk of heart diseases, obesity, stroke and some cancers.

National governments and international organizations, such as the World

Health Organization (WHO) and the Food and Agriculture Organization

(FAO), have been working on nutritional guidelines extension, in particular

recommending a reduction in total fat and sugar consumption.

But many questions regarding the reasons why people are becoming obese

remain unanswered. Have diet or physical activity changed over time? Do

we eat more? How has our diet changed across time? Who has been affected

more by this changes? (why them? and why was that?) Do we eat ”better”

today than in the past? This paper carries on a first exploration of eating

habit variation across age and time among the British population. Moreover,

it presents some evidence on the relationship between nutrition and socio-

economic status in Britain using cross sectional data from the National Food

Survey covering the period 1975-2000. In doing so, we use Chesher’s method-

ology for providing a through decomposition of the National Food Survey

data and identify original regularities for basic demographic subgroups.

In his paper from 1997, Chesher decomposes intakes household supply

into individuals consumption using a non parametric model applied to the

National Food Survey data pooled by three years from 1974 to 1994. He

estimates the age profile of nutrient intakes such as calories, fat, calcium

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and vitamin C controlling also for the potential effect of eating out and the

presence of visitors. Effects of household characteristics such as region of

residence and family income, are also considered.

The aim of this paper is to extend Chesher’s work using data from 1975

to 2000 considering both intakes and food groups consumption. It describes

how eating habits have changed by gender and age, by gender and time for

all age groups and particularly for children aged 0-17, and by generations in

the United Kingdom. Moreover, it will try to shed a light on the importance

of social and economic environmental to nutrition changes considering the

effect of income separately. There might have been many forces that have

affected people’s (especially children’s) food habits. This paper will simply

describe patterns without testing one explanation against another. We will

consider some hypotheses in a later work.

This work is organized as follows. Section 2 describes the data. Section 3

introduces the consumption specification using nonparametric techniques for

the estimation of average daily nutrition intakes and food group consump-

tion within a household model that adopts a roughness penalty function

and controls for income distribution, eating out, presence of visitors, family

structure, region of residence and presence of children. Section 4 presents

the results. Conclusions and extensions for future research are summarized

in Section 5.

2 Data

The data used in this study come from the National Food Survey (NFS). This

is a cross-sectional survey started in 1940, and it has run continuously since

1942. Its initial aim was to monitor the diet of the urban ”working class”

during the war years. In 1950 it was extended to the whole population in

Britain to collect data on food consumption and expenditures. Since 1992 the

NFS collects information also about confectionary, alcohol and soft drinks;

and since 1996 it has been extended to Northern Ireland.

The NFS collects weekly data over one year on household food acquisition

for a large nationally representative sample of British adults and children.

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It collects information from roughly 7,000 household in the UK every year

(corresponding to a response rate of 65 percent). It contains year and month

specific information about all food entering into the household. After a short

interview, the household’s member who does the most of the shopping is

asked to keep a diary where reporting expenditures in British pence as well

as physical quantities of food purchased among more than 200 food items

listed. Each of the other members, age 11 and over, are requested to col-

lect information on personal expenditure on snack, meals, sweets, and drinks

consumed outside the home. The data also record the number and type of

meals (breakfast, lunch or dinner) offered to guests. In addition, the survey

records some demographic characteristics, for example age and sex of each

member of the family, number of male and female working, household char-

acteristics, region of residence, and socio-economic variables, such as income

and occupation of head of household.

The time period considered in this paper covers 26 years from 1975 to

2000, in which 201,032 households and 521,000 individuals were observed.

2.1 Sample Characteristics

Descriptive statistics for the main sample are reported in Table 1. After

controlling for missing values, dropping households from North Ireland1 and

dropping people over 91 years because their number was not enough to pro-

duce significant figures, the final sample ends up containing 130,789 house-

holds and 353,989 individuals.

The individual average age in the sample is 35 years. Head of house-

hold on average are 49 years old and their wives are just one year younger.

Children are on average 8 years old and the sample seems roughly equal

distributed over age groups.

Information on eating out are summarized from the net balance variable.

This variable varies for each person from 0 and 100. It takes value zero

1Data on North Ireland have been collected only from 1996. In order to include thissample into the analysis, it is necessary to weight the data.

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if the person eats always out; it takes value 100 if the person eats every

meal at home. When a person eats outside the household, his net balance is

diminished of a certain amount depending on which of the main three meals

he did not took from the household. In particular, if a person has breakfast

outside his net balance will diminish of 3 points, if he has lunch outside, his

net balance will decrease of 4 points, and if he eats dinner outside, the net

balance will decrease of 7 points.

Table 1: Descriptive Statistics (1975-2000) - Household obs. 130,789 - Indi-viduals obs. 353,989

Descriptive StatisticsObs. Mean Std. Dev. Min Max

Individual Characteristicsage 353989 34.87747 22.73049 0 91age of wife of hoh 130789 47.68864 17.7052 16 92age of children 101001 8.15483 5.043785 0 17

Head of Household Characteristicsage 130759 49.34549 17.52024 16 91age if male 97252 47.27158 16.27708 16 91age if female 33507 55.36488 19.49869 16 91

Family Characteristicsnumber of members 130789 2.610946 1.366265 1 13number adult male 130789 .8797376 .5462758 0 6number adult female 130789 .9941738 .4376216 0 7number children 130789 .7370345 1.081004 0 10number adult aged greater than 64 130789 .3526214 .6372804 0 4number person aged 0000 130789 .0383518 .1935124 0 3number person aged 0104 130789 .177232 .4594763 0 5number person aged 0507 130789 .131953 .3800701 0 3number person aged 0811 130789 .1681028 .4514887 0 5number person aged 1215 130789 .1563052 .4449344 0 6number person aged 1617 130789 .0650972 .2599484 0 3

Eating Outnet balance per person 353989 87.47551 14.93684 0 100total net balance per household 130789 238.7557 127.2163 0 1247

In the sample considered here, the average individual records a net bal-

ance per week of 87.47 points. In other words, in average in a week a person

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has almost 13 percent of his net balance from outside the household. This

corresponds almost to one full day eating out (one breakfast, one lunch and

one dinner per week).

In average there are about 2 or 3 members per household, with a maxi-

mum of 13 members. Approximately 9 percent of the individuals lives alone,

59 percent lives in household without children, while 41 percent lives in

household with only adults (Table 2).

The sample is roughly 5 percent from Wales, 9 percent from Scotland, 7

percent from the Northern, 9 percent from York and Humberside, 11 percent

from the North West, 7 percent from East Midland, 10 percent from West

Midland, 8 percent from South West, 3 percent from East Anglia and 30

percent from South East.

The set of economic variables available from the data set includes net

family income, total expenditure on food, specific food expenditures on par-

ticular items and quantity of food purchased during the period of study.

The dependent variables used in this paper are quantities of major food

group and nutrient intakes consumed, whose allocations among different fam-

ily members have been compute according to the methodology described in

the following section.

Following the World Health Organization and Government Guidelines,

food items have been summarized into 14 key food groups: diary products,

meat, fish, eggs, oils and fats, sugar and preservatives, vegetable, fruit, cere-

als, beverage, miscellaneous, soft drinks, confectionary and alcoholic drinks.

Moreover, following standard grouping used by food analysts (e.g. Food

Standard Agency, US Department of Agriculture, etc.) meat and fish have

been clustered together into a single group2.

The number of observation on soft drinks, confectionaries, alcohol, mis-

cellaneous and beverage are not enough to produce significant estimations,

therefore the analysis will proceed distinguish the first six standard main

food categories used by nutritionists and others.

2Meat, fish and eggs belong to the food group providing proteins, therefore they areusually classified together. In this case, eggs are measured in number of eggs purchasedby the household, while meat and fish are measured in grams. Therefore in the rest of thepaper I will consider only the category meat and fish.

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Table 2: Region of residence, Number of family members and OccupationCode - Household obs. 130,387 - Individuals obs. 338,060

Variables Households IndividualsFreq. Percent Freq. Percent

Hhld composition1 adult only 29,737 22.74 30,634 8.651 adult and 1 or more children 5,510 4.21 15,642 4.422 adult only 41,354 31.62 85,491 24.152 adults, 1 child 12,987 9.93 40,655 11.482 adults, 2 children 18,237 13.94 76,102 21.502 adults, 3 children 6,158 4.71 32,360 9.142 adults, 4 or more children 2,026 1.55 13,710 3.873 adults 6,693 5.12 20,450 5.784 or more adults 2,057 1.57 8,521 2.413 or more adults, 1 or 2 children 5,145 3.93 24,053 6.793 or more adults, 3 or more children 885 0.68 6,371 1.80

Hhld with children 50,948 38.95 208,893 59.01Hhld without children 79,841 61.05 145,096 40.99

Region of ResidenceWales 6,819 5.21 18,298 5.17Scotland 11,726 8.97 32,234 9.11Northern 8,932 6.83 23,837 6.73York and Humberside 12,279 9.39 33,066 9.34North West 14,938 11.42 41,224 11.65East Midland 9,145 6.99 25,314 7.15West Midland 12,577 9.62 35,523 10.04South West 11,082 8.47 29,032 8.20East Anglia 4,528 3.46 11,565 3.27South East 38,763 29.64 103,896 29.35Total 130,789 100.00 353,989 100.00

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Table 3 reports descriptive statistics on net family income, total expen-

diture and quantities (in grams) of food purchased on average per household

in a week period on each food groups.

On average households spend 29 pounds each week on food, and earn

almost 150 pounds per week. In particular the average household spends 4

pounds in diary products, 8 pounds in meat, 2 pounds in fats and sugars,

6.50 pounds in vegetables and fruit, 4 pounds in cereals and pasta, 2 pounds

in soft drinks and 10 pounds in alcohol3.

Over the time period, the average household (of 2 or 3 people) buys in

one week almost 6 liters of diary products, 3 kg of meat, 12 eggs, almost 1

kg of fats and oils, 1.4 kg of sugars and preservatives, 6 kg of vegetables, 2.7

kg of fruit, 4 kg of cereals, pasta and rise, 4 liters of soft drinks and 3 liters

of alcohol.

2.2 Derived Variables

As many nutritionist point out, in order to have a healthy diet is important

to have the right balance of nutrients needed to be healthy. Therefore, it

is obviously important to study both consumption of nutrient intakes and

consumption of major food groups. A number of balance diets are based on

either combination of intakes or combination of food types or both.

Therefore, this paper considers also a second set of dependent variables:

nutrient intake quantities consumed. They are computed from the basic data

using the conversion factor tables from the Department for Environmental

Food and Rural Affairs (DEFRA, 1999). The full detail of reported food

purchased is used, with weights converted to intakes using the intake content

factors. DEFRA table reports 47 nutrient intakes on it. This paper considers

13 of them: calories, proteins, fat, carbohydrates, calcium, iron, vitamin C,

D, E, B6 and B12, potassium, magnesium. However, conversion factor for

potassium, magnesium, vitamin B6, vitamin B12 and vitamin E are available

only from 1992 and they do not produce significant estimation. Therefore the

analysis will proceed focusing on calories, fat intake, proteins, carbohydrates,

3Data on soft drinks, alcohol, miscellaneous and beverage are available from 1992.

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Table 3: [Continue] Descriptive Statistics (1975-2000) - Household obs.130,789 - Individuals obs. 353,989

Descriptive StatisticsObs. Mean Std. Dev. Min Max

Family Income and Expendituretotal household food expenditure 130789 29.15207 23.38368 0 604.96net family income 130789 150.1823 156.1296 10 2978.00

Family Food Expenditure (GBP)diary products 128332 3.841969 3.04438 0 64.63meat 121361 8.152233 7.689984 0 280.26fish 79799 2.225025 2.567056 0 75.40eggs 77267 .8009915 .5522406 0 29.43fats and oils 94085 1.211639 1.019039 0 27.12sugar and preservatives 66282 .8645352 .7003412 0 17.21vegetable 125033 3.991886 3.706174 0 59.50fruit 106087 2.514873 2.841234 0 69.89cereals 127236 4.808307 4.435264 0 101.42soft drinks 28251 2.347129 2.250002 0 46.83confectionaries 19082 1.979311 2.235051 0 40.90alcohol 15179 9.815807 13.10797 0 512.06miscellaneous 91765 1.797628 2.079407 0 149.65beverage 72702 1.70735 1.53138 0 47.98

Quantity of Food purchased (g)diary products 128332 6635.383 4463.997 42.00071 66640.75meat 121361 2846.82 2757.956 25.00042 140219.1fish 79799 592.8705 567.0425 28.00047 20723.85eggs (N. of eggs) 77267 12.18848 8.105958 0 162fats and oils 94085 962.3533 877.0756 11.00019 40161.2sugar and preservatives 66282 1397.647 1078.614 14.00023 62511.75vegetable 125033 6159.51 5846.392 20.00034 117907.7fruit 106087 2783.287 2588.278 9.977799 81364.5cereals 127236 4159.729 3274.509 19.00032 303111.1soft drinks 28251 4034.825 4035.488 74.8335 85050.78confectionaries 19082 437.463 455.752 7.370999 8500.323alcohol 15179 3188.451 3958.576 10.47816 123138.2miscellaneous 91765 1250.973 1584.85 0 61696.84beverage 72702 344.5001 268.0583 9.00015 10000.17

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calcium, iron and vitamin C. However, given that in the White Paper ”The

Health of the Nation” the Government has specifically set targets for the

proportion of energy from fats to be no more than 35% by 2005, I will report

the main results on fats also in terms of proportion of energy from fats

(PEF)4.

Table 4 shows household consumption of nutrient intakes in a week period.

In average a household purchases 37,414 calories per week and almost 1.5 kg

of fats, that means that almost half of the total calories purchased derives

from fats.

Table 4: [Continue] Descriptive Statistics (1975-2000) - Household obs.130,789 - Individuals obs. 353,989

Descriptive StatisticsObs. Mean Std. Dev. Min Max

Quantity of Intakes purchasedcalories (Kcal) 130789 37414.1 25811.94 0 1260780Proteins (g) 130789 769.6949 551.5648 0 22950.1Fat intake (g) 130789 1687.037 1278.57 0 39593.91Proportion of energy from fats (Kcal) 130789 15183.33 11507.13 0 356345.2Carbohydrates (g) 130789 4559.545 3475.143 0 263892.5Calcium (mg) 130789 16269.43 10108.62 0 228228.3Iron (mg) 130789 195.7957 134.4513 0 2731.77Vitamin C (mg) 130789 1030.547 921.3632 0 24667.22Vitamin D (mg) 130789 54.94992 55.28951 0 1568.759Potassium (mg) 52279 45858.38 31445.96 0 627095.6Magnesium (mg) 52279 4103.089 2821.844 0 75137.1Vitamin B6 (mg) 52279 35.06806 27.61754 0 1131.76Vitamin B12 (ug) 52279 93.21448 84.2328 0 3088.253Vitamin E (mg) 52279 4055.283 3122.384 0 57431.36

4Total amount of energy from fat is obtained multiplying total amount of fats by 9.00.The proportion of energy from fats is the ratio between total amount of calories from fatand total amount of calories.

14

3 Methods

Expected consumption of individual p is assumed to be function of individual

characteristics xp (e.g. sex and age) and household characteristics z. The

theoretical model follows Chesher (1997).:

E[cp|x, z] = f(xp, z)

Thus average household consumption is:

E[c|x, z] =∑P

p=1 f(xp, z)

where P denotes total household members. The National Food Survey

(NFS) collects information about total food acquisition and expenditure per

household. There is no information on individual consumption. In order to

take into account consumption variation with respect to age and sex of dif-

ferent household members this model relates food acquisition with household

composition.

If a household consumes qci , quantity of food i, and therefore the amount

of nutrient contained in each unit of food i, νi, the total quantity of nutri-

ent consumed by the household can be expressed as c =∑

νiqci . The total

quantity of nutrient entering the household is the total amount of nutrient

contained into total food purchased5, y =∑

νiqi. In the long term it is prob-

ably reasonable to assume that total amount of food entering the household

is equal to the total amount of food consumed by each family. Therefore the

expected value of total food acquisition is assumed to be equal to expected

value of total food consumed:

E[y|x, z] = E[c|x, z] =∑P

p=1 f(xp, z)

Where y represents the total quantity of food entering into the household

and c the total quantity of food consumed.

Assuming that f is a separable function with respect to xp and z allows

f to be written as product of two functions:

5The NFS records amount of food i entering the household h: qci 6= qi.

15

f(xp, z) = g(xp) · u(z)

The amount of food consumed by a person p still depends from his/her

own characteristics, such as age and sex, and from the characteristics of the

family. However, if we consider two persons of the same age and sex, living

in two different households, their ratio of consumptions will be the same. In

other words, the model proposed here estimate the average consumption of

food/nutrient for a person p of age a and sex s. Identical individuals in dif-

ferent households consume the same amount of food/nutrient independently

from their family structure uz. This assumption also implies that diet of chil-

dren and adults are not affected by family structure. In order to take that

into account, household composition and presence of children are introduced

in z.

It is known that consumption differs over age among male and female,

even in early ages. So, let gS(ap) be the function representing the relationship

between consumption and age for each sex S = M, F . The total distribution

of consumption over age can be express as follows:

g(xp) = g(age, sex) = spgM(ap) + (1− sp)gF (ap)

where gS(ap) are complex and non-linear functions and sp is a dummy

variable taking value 1 if the individual observed is male, and 0 otherwise.

3.1 Demand for food and nutrients

The demand for food and nutrient changes through lifetime with level of

activity and preferences. Following Chesher (1997), I use a non-parametric

approach in defining gS(ap) and add household characteristics in parametric

form, as follows:

u(z) = exp(z′γ).

The function gS(ap) represents the relationship between food consumption

and age for each gender S. For each individual p, let wp = [wp,0, wp,1, wp,2, . . . , wp,99]

16

be a vector of dummy variables allocating the value 1 to the dummy corre-

sponding to the class of age to which the individual belongs, that is:

wp,a =

1 if a ≤ ap ≤ a + 1

0 otherwise

So, for example, if family 1 holds 3 members, aged 50, 2 and 0 years old

respectively, the matrix of vectors wp where p = 1, 2, 3 identifies person p

(second column) in household h (first column), will look as follows:

X =

1 1 0 0 0 . . . 1 . . . 0

1 2 0 0 1 . . . 0 . . . 0

1 3 1 0 0 . . . 0 . . . 0...

......

......

......

......

Given the assumptions above, the relationship between age and intake

for males and females can be approximated by the discrete form:

gS(ap) = w′pβ

S =

wp,0

wp,1

wp,2

...

wp,99

(βS

0 βS1 βS

2 . . . βS99

)

Where βSa are the coefficients estimated at each age for S = M, F . They

represent the amount of nutrient consumed by a person p of age a and sex

S.

At this point we can formalize the expected value of household consump-

tion6 as follows:

E[y|x, z] =P∑

p=1

[sp · gM(ap) + (1− sp) · gF (ap)] · exp(z′γ)

6Therefore food acquisition in the long term - see first assumption above.

17

E[y|x, z] =P∑

p=1

[spw′pβ

M + (1− sp)w′pβ

F ] · exp(z′γ)

It should be noticed that∑

p spw′p represents the number of males of age

a living in the household and∑

p(1− sp)w′p represents the number of females

of age a living in the household. Thus, for each household, the expected

nutrient consumption is going to be:

E[y|x, z] =P∑

p=1

A∑

a=0

[nMpaβ

Ma + nF

paβFa ] · exp(z

′γ)

where A is the maximum value taken by the variable age and βSa represents

the amount of nutrient consumed by any individual of age a and gender S.

3.2 Penalized least square regression

In its simplest form the roughness penalty approach is a method for relaxing

the model assumptions in classical linear regression in a slightly different

way from polynomial regression (Green and Silverman, 1995). In order to

estimate βM , βF and γ, and given the discontinuity of age, I use non-linear

least squares with a roughness penalty function methodology and minimize

the following object:

minβMβF γ

[H∑

h=1

(yh −(β0 +

99∑

a=0

(nMhaβ

Ma + nF

haβFa )

)exp(z

′hγ))

]2

+

+λ2M

99∑

a=0

(βMa − 2βM

a+1 + βMa+2)

2 + λ2F

99∑

a=0

(βFa − 2βF

a+1 + βFa+2)

2

where β0 is included to capture flows of nutrients into households that are

unrelated to the number of household members (e.g. food for pets) and the

last term is the discrete version of the roughness penalty function capturing

the smoothness of the relationship between age and consumption. The same

18

object is representable in matrix form as follows:

min(Y −Xβ)′(Y −Xβ) + λ2β

′W

′Wβ

where λ > 0.

Using matrix the data structure can be summarized as follows. Let

D =

yh i NM NF Z

0 0 λ · A 0 0

0 0 0 λ · A 0

=

Y X Z

0 λ ·W 0

where:[i, NM , NF

]= X, and

0 λ · A 0

0 0 λ · A

= λ ·W

The final sum of squared model without considering Z is7:

minS = (Y −Xβ)′(Y −Xβ) + λ2β

′W

′Wβ

so the β estimator turns out to be biased and the bias depends on λ:

β(λ) = (X′X + λ2W

′W )−1X

′Y

with expected value and variance given by:

E[β(λ)

]= (X

′X + λ2W

′W )−1X

′E(Y |X)

= (X′X + λ2W

′W )−1X

′(Xβ + ε)

= (X′X + λ2W

′W )−1X

′Xβ + 0

=⇒ E[β(λ)

]= (X

′X + λ2W

′W )−1X

′Xβ

and

V[β(λ)

]= σ2(X

′X + λ2W

′W )−1X

′X(X

′X + λ2W

′W )−1

7The vector Z will be introduced later on in this chapter.

19

3.3 How to choose the degree of smoothness λ

As indicated by Green and Silverman, the most common method used to

identify λ is Cross Validation (CV). This methodology requires to omit ar-

bitrarily an observation i and estimate the curve from the remaining data.

This new object, denoted g−i(tj, λ), is the minimizer of:

∑

j 6=i

{yj − g (tj)}2 + λ∫ (

g′′)2

The value of λ derives from minimizing the sum of square differences be-

tween observed and estimated values, this time considering also observation

i omitted before:

minCV (λ) = n−1n∑

k=1

{yk − g−i(tk, λ)}2

Following Chesher (1997), this paper considers three possible values of

the degree of smoothness: no smoothness (λ = 0), λ = 57.3, that it is the

value that minimizes Wahba’s (1975) generalized cross-validation criterion,

and λ = 100.

Calories distribution over age for men and women using data from 1975

are shown in Figure 1 for each value of λ specified above. The same model

has been run for every year of the NFS considered.

For each year, estimate using λ = 0 show high variability across ages,

and all the models show that the trend of consumption of calories increases

during early ages and decreasing after 60, with two main local maximum at

age 15 and 50. Considering that the main aim of this work is to describe

variations of eating habits over age, and that the differences between using

λ = 57.3 or λ = 100 are not very big, all the estimation results that follow

will use λ = 100.

20

Figure 1: Estimated energy-age curves for male and female using data from1975 with roughness penalty λ=0, λ=57.3 and λ=100.

080

016

0024

0030

00K

cal

0 10 20 30 40 50 60 70 80 90age

(a) λ=0.

080

016

0024

0030

00K

cal

0 10 20 30 40 50 60 70 80 90age

(b) λ=57.3.

080

016

0024

0030

00K

cal

0 10 20 30 40 50 60 70 80 90age

(c) λ=100.

21

3.4 Introducing information on eating out and visitors

The National Food Survey provides some information about food eaten out

and visitors. Although it does not record the amount of food obtained from

no household supplies, for each person a measure of the number of meals

taken from the household during the survey week is available. This measure

is known as ”net balance” and it varies from 0 to 100. It is equal to 100

when the person obtain all his meals from household supply. It has value 0

when all the meals are eaten out. For each meal eaten out, the net balance is

reduced of 3, 4 or 7 depending from whether the missed meal was breakfast,

lunch or dinner respectively.

In the following estimation the model controls for eating out interpreting

the net balance as the proportion of food coming from household supplies for

each person (bp). If βMa and βF

a are interpreted as total food supplies from

the household and from outside the home, then the total amount of food

coming from the household is bpβSa and the initial model can be written as

follows:

E[y|x, z, b] = β0 +P∑

p=1

A∑

a=0

[nMpabpβ

Ma + nF

pabpβFa ] · exp(z

′γ)

= β0 +P∑

p=1

A∑

a=0

[b′MβM

a + b′F βF

a ] · exp(z′γ)

where for each household bS is a vector containing the net balance for

each individual at each year of age.

The net balance information is available also for each visitor. Using this

information, the model takes into account each visitor as an additional mem-

ber of the household, by age and sex, who takes from the household the

proportion of food indicated by his net balance.

22

4 Results

With the aim here of providing a through decomposition of the NFS data and

identify original regularities for basic demographic subgroups, this section de-

scribes the estimates of the Roughness Penalty Function Model obtained from

non-linear ordinary least squares (NL-OLS) method to account for function

smoothness. The paper investigates nutrition curves - using nutrient intakes

and major food groups - with the objective to see how they have changed by

gender and age over the recent time period and by gender and time for all

age groups and, particularly, for children aged 0-17. We will also consider

the effect of income and other household characteristics separately.

4.1 AGE

The relationship between nutrient intake (major-food-group) and age have

been estimated separately by each sample year in two stages: in a first stage,

the paper deals only with the results from the non parametric analysis (nu-

trient intake/food in relation to gender and age), while in a second stage,

we control for other household characteristics such as income, region of resi-

dence, household composition, presence of children.

4.1.1 Age curves estimation

At the first stage, we estimate nutrient and food consumption only in relation

to household members characteristics (i.e. age and gender). Coefficients

estimated separately for each year have been averaged up over the whole

sample period 1975-2000. The findings for each nutrient intake and food

group for males and females separately, by each completed year of age from

0 to 91, are reported graphically in Figures 2 and 3 respectively.

Both for male and female the distribution of consumption over the life

cycle show an inverse U shape, increasing rapidly until age 14 for girls and 16

for boys, then it declines until around age 25, and it increases again showing

a peak at the age 55 for females and 60 for males. After that there is a steady

decline.

23

The estimates show that on average males consume more then females at

any age. This picture is quite similar along all the period for all nutrients

and foods considered, with some exceptions such as calcium and vitamin

C. Figure 2 panels g) and h) show that on average females consume more

calcium than males after 40 years old, and more vitamin C along all the

life cycle. The reason of that is probably the higher consumption of food

estimated for females, like milk (after age 35) and fruits, shown from Figure

3 panels a) and f).

The peak at puberty is consistent with consolidation of body height and

weight during the adolescence period. The peak occurs 2 years earlier in

girls than in boys, as puberty itself does. Similarly, the fall in consump-

tion after middle age can be explained by the fact that elderly people lose

weight and spend less energy. It is also important to note the steady rise in

calories/nutrients consumption after 30. This usually coincides with a pe-

riod in life when people exercise less and increase weight, but these are not

necessarily the only explanations.

The age patterns of fats intake, carbohydrates and iron (Figure 2, panel

b), e) and f) respectively) are very similar to those of calories intake. For both

men and women, they increase during childhood, slightly decrease between

age 15 and 30, and then increase again, but more rapidly for women than for

men.

Proteins consumption distribution shows less differences among genders

((Figure 2, panel d)), showing the biggest difference between age 10 and 40.

Fat intakes converts to calories at the rate of 9 kcal per gram. Therefore,

multiplying estimate fat consumption by 9 and dividing it by total calories at

each age we obtain the distribution of proportion of energy from fat (PEF)

shown in Figure 2, panel c). Over the life cycle, in average PEF results

between 30 and 40 percent up to age 25, and over 40 afterwards. WHO

recommends proportion of energy from fat and saturated fats to be reduced

by 2005 to 35%. It will, therefore, be interesting to see in the following

sections how it has changed over the period of study and how its trend

moves over time.

Figure 3 shows the estimated food-age curves using a linear model not

24

Figure 2: Estimated intake-age curves using linear model with roughnesspenalty λ =100 - weighted average over 1975-2000.

30

08

00

13

00

18

00

23

00

28

00

Kca

l/day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Calories

(a) Calories.

02

04

06

08

01

00

12

01

40

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Fat intakes

(b) Fat Intake.

20

25

30

35

40

45

%K

cal/d

ay/

capita

fro

m fat

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Proportion of energy from fat

(c) Proportion of energy from fat.

02

04

06

08

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Protein

(d) Proteins.

90

19

02

90

39

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Carbohydrate

(e) Carbohydrate.

05

10

15

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Iron

(f) Iron.

20

04

00

60

08

00

10

00

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Calcium

(g) Calcium.

02

04

06

08

01

00

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Vitamin C

(h) Vitamin C.25

accounting for income, eating out, visitors, region of residence, etc.

Here we consider six main groups of food8, namely: 1) milk products, 2)

meat and fish products, 3) fat, oils, sugar and preservatives, 4) cereal, pasta,

rise and bread, 5) vegetable and 6) fruit.

Also in this case, the estimated curves show an inverse U shape, with

males consuming more than females, but milk products and fruit for which,

as we said above, women consume more milk products than men after 40,

and more fruit in average.

It is interesting to notice the stable increase in consumption of fat, oils,

sweets and preservatives from childhood up to age 70 for males and 55 for

females.

4.1.2 Estimate Non-linear model controlling for income, eating

out and visitors

This section presents the estimated intake/food-age curves obtained intro-

ducing other variables in the model previously estimated. Controlling for

other household characteristics such as income, region of residence, house-

hold composition, presence of children makes the model non-linear. Here we

compare the new nutritional-age curves with those obtained without taking

into account eating out and presence of visitors. Later in the paper we will

present estimates of coefficients on the other variables listed.

The model has been estimated separately for each year and for each nutri-

ent intake and food group considered. Figures 4 and 5 represent graphically

estimated coefficients using roughness penalty λ = 100 averaged up for the

period 1975-2000.

Introducing information about eating out, we would expect that the es-

timates produced would be higher for almost all ages. While controlling for

presence of visitors, we assume that some of the food bought from the house-

hold is consumed by visitors not by household members, causing a decrease

8Other foods have been analyzed using the same methodology, as for example, beverage,miscellaneous, soft drink, confectionaries and alcohol, but the NFS data are collected onlyfrom 1992 onwards. Consumption distributions over age for these food groups appear veryunstable and irregular with numerous peaks, so they have been left out from the rest ofthe analysis.

26

Figure 3: Estimated food groups-age curves using linear model with rough-ness penalty λ =100 - weighted average over 1975-2000.

01

00

20

03

00

40

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Milk

(a) Diary Products.0

50

10

01

50

20

02

50

30

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Meat and fish

(b) Meat and Fish.

04

08

01

20

16

02

00

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Oils and fats, sugar and preservatives

(c) Fat, oils, sugar and preserva-tives.

05

01

00

15

02

00

25

03

00

35

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Cereals, rice and pasta

(d) Cereal, pasta, rise and bread.

50

15

02

50

35

04

50

55

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Vegetables

(e) Vegetable.

05

01

00

15

02

00

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Fruit

(f) Fruit.

27

in the individual consumption.

At this point it is difficult to say which of the two effects is driving

the individual consumption estimation. Although showing a similar shape

to Figures 2 and 3 and confirming that men consume in general more than

women, the coefficients estimated from the non-linear model result lower than

the previous ones and the curves represented in Figures 4 and 5 are shifted

downward and look flatter. The biggest effect is that on fruit consumption

that becomes almost null. It is also to notice that part of the steep rise showed

before after age 30 disappears. This might be the effect of the presence of

visitors. If people after age 30 receive visitors in their home and invite them

to eat with them, then the age dependence relation estimated here will take it

into account assigning a lower amount of food and nutrients from household

supply to each individual.

The decrement in quantity consumed at home, might be also caused by

the use of net balance information to take into account eating out. If a

member of the family with a net balance of 86 eats out one day per week.

For that day what before attributed to his consumption given age and gender,

it will be now redistributed among the other household members with the

effect of increasing their consumption. The higher decrement from previous

estimates is for people age after 30. If people at this age range tend to eat

outside rather than take food from household supply, part of the change in

the shape of the curve might be due to the incidence of eating out.

Controlling for eating out and presence of visitors has also an effect on

proportion of calories from fats, that now varies between 25 and 35 percent

for both men and women.

28

Figure 4: Estimated intake-age curves using non-linear model with roughnesspenalty λ =100 - weighted average over 1975-2000.

30

08

00

13

00

18

00

23

00

28

00

Kca

l/day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Calories

(a) Calories.

02

04

06

08

01

00

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Fat intakes

(b) Fat Intake.

20

25

30

35

40

%K

cal/d

ay/

capita

0 10 20 30 40 50 60 70 80 90age

male female

Proportion of energy from fat

(c) Proportion of energy from fat.

01

02

03

04

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Protein

(d) Proteins.

90

19

02

90

39

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Carbohydrate

(e) Carbohydrate.

05

10

15

mg/d

ay/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Iron

(f) Iron.

10

02

00

30

04

00

50

06

00

mg/d

ay/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Calcium

(g) Calcium.

03

69

12

mg/d

ay/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Vitamin C

(h) Vitamin C.29

Figure 5: Estimated food-age curves using non-linear model with roughnesspenalty λ =100 - weighted average over 1975-2000.

05

01

00

15

02

00

25

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Milk

(a) Diary Products.

03

06

09

01

20

15

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Meat and Fish

(b) Meat and Fish.

05

01

00

15

02

00

25

03

00

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Oils and fats, sugar and preservatives

(c) Fats, oils, sugar and preserva-tives.

01

00

20

03

00

40

05

00

gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Cereals

(d) Cereals, pasta, rise and bread.

01

00

20

03

00

40

05

00

60

0gr/

day/

capita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Vegetables

(e) Vegetable.

−1

00

10

20

30

gr/

da

y/ca

pita

0 10 20 30 40 50 60 70 80 90age

male female 95% confidence interval

Fruit

(f) Fruit.

30

4.2 TIME

The focus of this section is on changes in food consumption and nutrient

intakes among younger people (age 0-17). Nutritional trends have been an-

alyzed for both the whole population and, in particular for all youth dis-

tinguishing three age classes (0-6, 7-12, 13-17) and gender. The aim of

this section is chart patterns over time. There might be many forces that

have affected people’s (especially children’s) food habits. Examples of such

drivers are, home technology improvements, changes of parental costs of time,

parental preferences and information about children: food and diet which

could affect intra-household resource allocations (Cutler et al., 2003).

In this section we simply describe patterns without testing one expla-

nation against another. We consider some of these hypotheses in a later

chapter.

4.2.1 Nutrient Intakes

Figures 6, 7, 8, 9, 10 and 11 aim to illustrate how eating habits have changed

over the last 26 years. In particular looking at age groups under 17, they

report trends of grams of nutrients consumed by the youngest three age

groups.

Figure 6 shows distribution of nutrient intakes over the time period 1975-

2000.

Calories and iron consumption (Figure 6 panel a) and f)) remain quite

stable over time for both men and women. Trend of calories as well as

carbohydrates show a steep rise from 1994 to 1995 that is probably due

to changes in the NFS data collection, as for example the introduction of

alcohol, soft drinks and confectionaries.

Amount of fats intakes consumed by people at the beginning of the time

period is instead very different from those consumed at the end. Panel b) in

Figure 6 shows a steady increase of consumption that almost double by the

end of the 90s. Similar finding are presented in panel d) for proteins.

The increasing incidence of fat on production of calories is also shown in

panel c). Proportion of energy from fat show a clear increasing trend up to

31

1990, that becomes flat and stable around 35 percent in the last decade.

As for the last two panels in Figure 6 they represent consumption of

calcium and vitamin C, respectively. Trend of calcium shows an inverse U

shape along the period. While the consumption of vitamin C results very

low. Given the steep rise shown from 1995 onwards, the model estimation

for this nutrient will require further study.

The trends of nutrient intakes consumption for younger people are shown

in Figure 7 and 8 separately for boys and girls by age groups. The first

column of graphs shows average daily intakes consumption for males, while

the second column shows estimates for females. In general younger children

consume less than older children. The variation of consumption of different

age groups results from the distance between the curves and respects the

different age distribution in consumption shown above.

Trends of consumptions do not show huge differences among the time

period under analysis. In particular calories and carbohydrates trends are

quite flat (Figure 7, panels a) and b), and Figure 8 panels a) and b)). Like

for the whole population, the trend of consumption of calories for older boys

show a jump upward at the beginning of the 90s when soft drinks, alcohol

and confectionaries were introduced in the survey. For girls the effect is

smaller. If this discontinuity in the trend captures the effect of soft drinks

and confectionaries among children, than it is interesting to notice that the

effect is bigger for boys than for girls (they show bigger differences between

the curves) and it affects their diet very early in life (there is no effect on the

youngest group 0-6, but from 7 onwards).

Proportion of energy from fat has increased along the time period (Figure

7, panels e) and f)). Even if it does not change a lot across age groups, it is

higher at the end of the period than in the 70s. It results slightly higher for

girls than for boys and it is quite flat around 35 percent from the 90s.

Fats intakes and proteins trends (Figure 7, panels c), d), g) and h)) for

both genders at all ages tend to increase slightly from 1978 up to 2000. The

difference between ages is higher for boys in fats and it tends to increase

more from the 90s onwards especially for proteins.

The others intakes show a trend very similar to the whole population.

32

Figure 6: Estimated intakes-year curves using non-linear model with rough-ness penalty λ =100 - weighted average over age (male and female).

05

00

10

00

15

00

20

00

25

00

30

00

Kca

l/day/

capita

1975 1980 1985 1990 1995 2000time

(a) Calories.

02

55

07

51

00

12

5gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(b) Fat Intake.

01

02

03

04

05

0%

Kca

l/day/

capita

fro

m fat

1975 1980 1985 1990 1995 2000time

(c) Proportion of energy from fat.

01

02

03

04

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(d) Proteins.

01

50

30

04

50

60

07

50

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(e) Carbohydrate.

02

46

81

0m

g/d

ay/

capita

1975 1980 1985 1990 1995 2000time

(f) Iron.

01

00

20

03

00

40

05

00

60

07

00

mg/d

ay/

capita

1975 1980 1985 1990 1995 2000time

(g) Calcium.

02

46

81

0m

g/d

ay/

capita

1975 1980 1985 1990 1995 2000time

(h) Vitamin C.33

Figure 7: Estimated intakes-year curves for children using non-linear modelwith roughness penalty λ =100 - weighted average by class of age (boys andgirls).

30

08

00

13

00

18

00

23

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(a) Calories - boys.3

00

80

01

30

01

80

02

30

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(b) Calories - girls.

02

04

06

08

01

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(c) Fat Intake - boys.

02

04

06

08

01

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(d) Fat Intake - girls.

10

20

30

40

50

%K

cal/d

ay/

cap

ita f

rom

fa

t

1975 1980 1985 1990 1995 2000time

male 0−6 male 7−12 male 13−18

Proportion of energy from fat

(e) Proportion of energy from fat -boys.

10

20

30

40

50

%K

cal/d

ay/

cap

ita f

rom

fa

t

1975 1980 1985 1990 1995 2000time

female 0−6 female 7−12 female 13−18

Proportion of energy from fat

(f) Proportion of energy from fat -girls.

01

02

03

04

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(g) Proteins - boys.

01

02

03

04

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(h) Proteins - girls.

34

Figure 8: [Cont.]Estimated intakes-year curves for children using non-linearmodel with roughness penalty λ =100 - weighted average by class of age(boys and girls).

01

00

20

03

00

40

05

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(a) Carbohydrates - boys.0

10

02

00

30

04

00

50

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(b) Carbohydrates - girls.

02

46

81

0m

g/d

ay/

capita

1975 1980 1985 1990 1995 2000time

(c) Iron - boys.

02

46

81

0m

g/d

ay/

capita

1975 1980 1985 1990 1995 2000time

(d) Iron - girls.

01

00

20

03

00

40

05

00

60

0m

g/d

ay/

capita

1975 1980 1985 1990 1995 2000time

(e) Calcium - boys.

01

00

20

03

00

40

05

00

60

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(f) Calcium - girls.

02

46

81

0m

g/d

ay/

capita

1975 1980 1985 1990 1995 2000time

(g) Vitamin C - boys.

02

46

81

0m

g/d

ay/

capita

1975 1980 1985 1990 1995 2000time

(h) Vitamin C - girls.

35

4.2.2 Major Food Groups

The same analysis has been conducted also on major food groups as milk

products, meat and fish products, fats from oils and sugars, cereals, vegetable

and fruit and results are presented in Figure 9.

In this case we notice that in general average daily consumption of food

tends to increase in the last decade considered. Consumption of meat and

fish increases during the 1990s as well as consumptions of fats and fruit for

both genders9 (Figure 9 panel b), c) and f)).

Bread, cereal, pasta and rice consumption trend shows a steep jump from

1992 to 1994. As for calories and carbohydrate it is probably link to the

introduction of new food items in the survey.

Also quantity of vegetables (Figure 9, panel e))consumed results mainly

constant over the whole period of study, with two local peaks between 1982

and 1987 and between 1991 and 1994 both for men and women, and declines

afterwards.

Figures 10 and 11 show trends for boys and girls by three age groups.

Trends of consumption follow in general the trend of the whole population.

9The very low estimates shown at the beginning of the time period have required furtherinvestigation of the data. However we did not find anything that can explain such lowvalues.

36

Figure 9: Estimated food-year curves using non-linear model with roughnesspenalty λ =100 - weighted average over age (male and female).

05

01

00

15

02

00

25

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(a) Diary Products.

05

01

00

15

02

00

25

03

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(b) Meat and Fish.

05

01

00

15

02

00

25

03

00

35

04

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(c) Fats, oils, sugar and preserva-tives.

01

50

30

04

50

60

07

50

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(d) Cereals, pasta, rise and bread.

03

00

60

09

00

12

00

15

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(e) Vegetable.

05

10

15

20

25

30

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(f) Fruit.

37

Figure 10: Estimated food groups-year curves for children using non-linearmodel with roughness penalty λ =100 - weighted average by class of age(boys and girls).

05

01

00

15

02

00

25

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(a) Diary Products - boys.

05

01

00

15

02

00

25

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(b) Diary Products - girls.

05

01

00

15

02

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(c) Meat and fish - boys.

05

01

00

15

02

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(d) Meat and fish - girls.

05

01

00

15

02

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(e) Fats, oils, sugar and preserva-tives - boys.

05

01

00

15

02

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(f) Fats, oils, sugar and preserva-tives - girls.

38

Figure 11: [Cont.]Estimated food groups-year curves for children using non-linear model with roughness penalty λ =100 - weighted average by class ofage (boys and girls).

01

00

20

03

00

40

05

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(a) Cereals, pasta, rise and bread -boys.

01

00

20

03

00

40

05

00

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(b) Cereals, pasta, rice and bread -girls.

02

50

50

07

50

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(c) Vegetable - boys.

02

50

50

07

50

gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(d) Vegetable - girls.

02

46

81

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(e) Fruit - boys.

02

46

81

0gr/

day/

capita

1975 1980 1985 1990 1995 2000time

(f) Fruit - girls.

39

4.3 COHORTS

The focus of this section is on differences in nutrient intakes and food con-

sumption among generations. The NFS is a series of household cross sectional

data and, thus, it does not follow the same individuals over time. In order

to see whether there exists some generation’s effect on differences between

people born at different times, we consider ten cohorts (1945, 1950, 1955,

1960, 1965, 1970, 1975, 1980, 1985 and 1990).

Cohorts are constructed by date of birth of each individual. For each

survey we average estimate nutrient intakes and food consumption by age

and then track the sample from the same cohort one year older in the next

survey. We do not distinguish here by gender. For example, people who

were born in 1945 are observed from age 30 (in 1975) to age 55 (in 2001),

while cohort 1975 is observed from age 0 to age 25. The last three cohorts

(those born in 1980, 1985 and 1990) are the youngest cohorts in the sample

who were born after the beginning of the survey, therefore they are observed

only for a short period of time: twenty, fifteen and ten years, respectively.

Results on nutrient intakes and food groups are shown in Figures 12 and 13

respectively.

Figure 12 shows the cohort intakes consumption curves beginning with

those born in 1990. In panel a) of Figure 12, the first line segment connects

the average consumption of calories of those who were zero years old in 1975

to the average consumption of calories of 1 year old in 1976, until the last

observation of the cohort in 2000, when they were 10 years old. The second

line segment repeats the exercise for those who were five years older until the

last cohort considered in this graph of those born in 1945.

There is a visible life-cycle pattern rising with age as we saw from the

previous sections. With few exceptions at older ages, the lines for the younger

cohorts are very often but not always above the lines for the older cohorts,

even when they are observed at the same age, that is when the cohorts

overlap.

Comparing nutritional habits of different generations at the same age,

calories consumption is slightly different for different cohorts at different

40

ages. Between age 0 and 10 younger generation consumed less than older

ones, while between age 10 and 18 they consume slightly more calories than

their older counterparts.

Figure 12 panel b) plots fats intake patterns. It is interesting to notice

that younger generations consume higher amount of fats intakes at all ages.

In particular looking at children between 0 and 10 years old we compare

cohorts from 1975, 1980, 1985 and 1990. Children born in 1990 eat more fats

than those born earlier since age 4. Consumption of fats intakes maintains

the same structure, with younger generation eating more fats than older

ones, at all ages. Similar patterns are shown for consumption of proteins

(Figure 12, panel d)). For all generations consumption of proteins sharply

increases with age, with younger generations consuming more proteins that

older generations at the same age.

Similar conclusion can be drawn from Figure 13 representing consumption

of food by cohort where again we notice that the younger cohorts tend to

have a higher average consumption than the older but this is not always the

case because of the within cohort movements. Each cohort seems to follow

the time effect studied in the previous section. A deeper analysis of these

figures would require us to distinguish the three effects: age, cohort and years

effects. This would require to estimate the decomposition of effects regressing

cohort averages of consumption against dummy variables for all three sets of

effects (Deaton and Paxson, 1994). We do not present this analysis here, but

it can be the object of future studies.

41

Figure 12: Estimated intakes-cohort curves using non-liner model with rough-ness penalty λ =100.

50

01

00

01

50

02

00

02

50

03

00

0K

cal/d

ay/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Calories

(a) Calories.0

25

50

75

10

01

25

gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Fat Intakes

(b) Fat Intake.

10

20

30

40

50

%K

cal/d

ay/

capita

fro

m fat

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Proportion of energy from fat

(c) Proportion of energy from fats.

01

02

03

04

0gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Proteins

(d) Proteins.

01

00

20

03

00

40

05

00

gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Carbohydrates

(e) Carbohydrates.

05

10

15

mg/d

ay/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Iron

(f) Iron.

02

00

40

06

00

80

0m

g/d

ay/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Calcium

(g) Calcium.

03

69

12

15

mg/d

ay/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Vitamin C

(h) Vitamin C.

42

Figure 13: Estimated food-cohort curves using non-liner model with rough-ness penalty λ =100.

01

00

20

03

00

gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Milk

(a) Diary products.

05

01

00

15

02

00

25

03

00

gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Meat and fish

(b) Meat and fish.

01

00

20

03

00

40

0gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Oil and fats, sugar and preservative

(c) Fats, oils, sugar and preserva-tives.

01

50

30

04

50

60

07

50

gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Cereals, Pasta and Rice

(d) Cereals, pasta, rise and bread.

02

00

40

06

00

80

01

00

0gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Vegetables

(e) Vegetable.

01

02

03

0gr/

day/

capita

0 10 20 30 40 50 60age

cohort 1945 cohort 1950 cohort 1955 cohort 1960 cohort 1965

cohort 1970 cohort 1975 cohort 1980 cohort 1985 cohort 1990

Fruits

(f) Fruit.

43

4.4 INCOME

The previous sections demonstrated that in general nutrition varies over age.

It has also been shown that children consumption have changed over time.

In particular, the findings show a common change among all the aspects an-

alyzed around 1993 and some other little and single changes over the last

26 years. We turn now to examine whether the accumulation of household

income has played a role in the way people eat. Poorer people may be more

likely to malnutrition that leads to poorer health status. In addition, their

families may be less able to provide the investment necessary to maintain

good diet in the presence of low income. In doing so I explore the relation-

ship between children’s consumption and per capita family income10, and I

analyze time trends of such a relationship.

To do this we use the estimates on the log of net family income per capita

from the non-linear least square model with Roughness Penalty Function of

nutrient intakes and food consumed by the whole household in one week

period. The estimated coefficients on log family income per capita which

represent elasticities of consumption with respect to income for each nutri-

ent intake and food group, are reported in Tables 5 to 6. This provides

alternative evidence on the health-income gradient discussed by a number of

analysts (e.g., Case et. al., 2002).

The income elasticity reported here measure the proportionate rate of

change in quantity of a nutrient or food consumed from household supply

due to a unit proportionate change in household income per capita, other

individual and household characteristics held constant.

Table 5 and 6 show the estimated income elasticities for each year ob-

tained using NFS data together with estimated standard errors. In all cases

the results indicate that nutrient intakes and food groups, with some excep-

tion in some years for fats from oils, sugar and preservatives, cereals and

vegetables, are ”normal” goods, quantity purchased increases as income rises

10Per capita family income derived from net family income divided by number of mem-bers of the household. The model also controls for family composition. Therefore, we donot use equivalence scales to compute income per capita.

44

at a slower rate (elasticity less than 1) than the rate at which income in-

creases.

For example, the first column in Table 5 reports income elasticity of

calories consumption for each year of the sample (γ) and panel a) in Figure

14 describes its trend over the whole period of study graphically. Elasticity

of calories with respect to income varies in a range between -0.029 and 0.10,

being negative only in 1996. Therefore, apart in 1996, an increase in family

income would augmented daily calories consumption.

Nutrient intakes show relatively low income elasticities. In fact, most of

the elasticities are close to zero (i.e. calories, carbohydrates, iron, calcium).

Elasticity of vitamin C results a bit higher than other intakes, but it does not

exceed 0.5. Fats intakes and proteins show to have been more sensitive to

income variation than other nutrients in the past. However their sensitiveness

to income changes becomes lower with time.

Looking at elasticity of food groups estimates show a general positive

relationship between quantity of food consumed and increment of per capita

family income (γ resulting positive). There are, however, some exceptions.

Elasticity of vegetable consumption (Table 6) being negative or zero from

1980 as well as income elasticity of cereal, pasta, rice and bread. Thus, in this

case, the effect of a rise in per capita family income is a decrease in quantity

of vegetables consumed. Cereals elasticity of consumption has floated around

zero along most of the period considered (panel d of Figures 15) revealing a

general insensitiveness of cereals consumption to income variations.

Different trends are observable for meat and fish products and for fruit.

Here income elasticity is positive, slightly higher than 0.5 at the beginning

of the time period. Both food groups show a trend downwards starting from

1985 (Figure 15, panel b) and f)).

Income elasticities for all milk products and fats from oils, sugars and

preservatives are in average of similar orders of magnitude, between 0 and

0.25. In particular elasticity trends of oils and sugars has been stable since

middle 70s until the beginning of 90s varying between 0.03 and 0.17. Between

1992 and 1998 income elasticities of those products show bigger variability

(range -0.07 to 0.42).

45

Tab

le5:

Ela

stic

ity

ofin

take

consu

mpti

onre

spec

tto

fam

ily

inco

me

per

capit

a[γ

from

NL-O

LS].

Inta

kes

γco

effici

ents

.C

alo

rie

s.e.

fat

inta

ke

s.e.

pro

tein

ss.

e.ca

rbohydra

tes

s.e.

Calc

ium

s.e.

Iron

s.e.

Vit

am

inC

s.e.

1975

.0521827

.013635

.1167741

.0167911

.1315306

.0192628

.0044775

.0152588

.0761469

.0106096

.0676739

.0144421

.433347

.0197529

1976

.0194216

.0146524

.0776474

.01728

.2034976

.0193733

-.0242167

.0170687

.0560549

.0116148

.033079

.0164354

.4520648

.019529

1977

.0223059

.0153302

.0928694

.0177008

.2389275

.0185802

-.0500879

.0178905

.0680019

.0127519

.0582841

.0168321

.4770511

.0201174

1978

.1005065

.0167508

.1643151

.019016

.2342044

.020263

.0533032

.0206522

.104681

.0132066

.1249448

.017267

.5038663

.020817

1979

.0652827

.0158257

.1333762

.0192426

.5644395

.014227

.0104416

.0176412

.0957686

.0132073

.083899

.0169489

.5598125

.0216536

1980

.0396437

.0170043

.1163824

.0201602

.222104

.021684

-.0279415

.0190227

.092391

.0125377

.0931727

.0173877

.5059993

.0184271

1981

.0534028

.0167465

.1280234

.0195514

.1698825

.0189892

-.0038197

.0188858

.0798878

.0138231

.103069

.0168843

.5054457

.0197932

1982

.0638088

.0165285

.1246046

.0192073

.1746992

.0201141

.0122623

.0184933

.1016478

.0126084

.098097

.0160846

.4910612

.0185739

1983

.0536164

.0159645

.094478

.0182227

.1601869

.0161979

.0208739

.019004

.1083945

.0138056

.1002752

.0163094

.528724

.0214616

1984

.0364573

.0173916

.0936876

.0196823

.1225217

.0188115

-.0173819

.0200418

.0893111

.0136746

.0984656

.0170675

.5084264

.0214614

1985

.0202297

.015328

.0773886

.0180477

.1507015

.0180142

-.0408154

.0170538

.1016032

.0132153

.0833701

.0159827

.5252997

.0204438

1986

.0534933

.0167879

.1236182

.0192564

.1654341

.0183505

-.0044793

.0187338

.1080226

.0139527

.1086249

.0165982

.515379

.0223539

1987

.0607489

.0156137

.1116535

.0182843

.2174186

.0173792

.0117148

.0173355

.1035324

.0133749

.1252697

.0160744

.5169719

.0214016

1988

.0559557

.0148751

.0686199

.0178326

.0973729

.0171512

.0484176

.0162578

.1085295

.0125052

.1115609

.0153175

.5027014

.0218625

1989

.0201637

.0157693

.0325466

.0190589

.074367

.0161255

.0156426

.0172759

.0899822

.0124514

.0749165

.0153058

.4983603

.0181812

1990

.024642

.015773

.0599366

.0186001

.11897

.0177573

-.0105793

.0173192

.0988949

.013101

.0895318

.0172017

.4626124

.0197981

1991

.0370192

.01399

.0637211

.0166486

.0979982

.014205

.0124217

.0163904

.0751826

.0124917

.0796878

.0146764

.4228986

.0176878

1992

.0467337

.0143042

.06801

.0169676

.0945982

.0142942

.0220627

.0161621

.0781438

.0125673

.0943081

.0150117

.4356766

.0154821

1993

.0655788

.0145824

.0944741

.0170285

.1084111

.0143728

.0316449

.0161369

.098801

.0121917

.1251528

.0146895

.4635633

.0155525

1994

.0316565

.0131103

.0221296

.0159683

.0841295

.0133948

.0153433

.0141781

.0731581

.0113561

.1031358

.0136138

.434798

.0150316

1995

.0101366

.0133681

.0148092

.0157804

.0876294

.0134178

-.027448

.0145453

.0612391

.0109298

.0927613

.0133341

.3949915

.0155369

1996

-.029737

.0148042

.0158789

.0152261

.043583

.0134069

-.1145085

.0182407

.0834132

.0113091

.0805311

.013016

.375404

.0147514

1997

.0286663

.0146283

.0334735

.0183954

.0729259

.0149547

.0095784

.0155846

.0710729

.0122255

.1218994

.0145097

.4197164

.0152957

1998

.0557527

.0143096

.0558842

.0182315

.0749743

.0155672

.0323474

.01523

.0480176

.0129171

.1215937

.0151048

.3726525

.0203701

1999

.028998

.016552

.0022434

.0190215

.0635345

.0154713

.0245117

.0190138

.0634992

.0124109

.1060496

.0151578

.3363529

.0190049

2000

.0121733

.0139528

-.0088689

.0165324

.054557

.014712

.0020739

.0156034

.033195

.0119333

.092326

.0152234

.3156308

.0192067

46

Finally, there is some evidence of changes through time in income elastic-

ities both for nutrient intakes and food groups. Effects of variation in income

are slightly stronger on food than on nutrients consumption. This may re-

flect the fact that consumers, at different income level, substitute between

food groups in a way that substitution within nutrients results very little

(Subramanian and Deaton, 1996). Possible drivers of such effects might be

sought in changes through time in the nature of food and in the way they are

presented to households, changes in the technology available for preparing

foods, changes in household circumstances including increased labor market

participation and cost of time, and so forth.

Although during the period of study increments of income have implied

little positive changes in quantity of intakes consumed, at this point it is not

possible to say whether consuming more nutrient intakes implies a better

diet and therefore a better health status.

47

Figure 14: Estimated nutrient intakes elasticity trend, λ =100.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

elasticity 95% confidence interval

Calories

(a) Calories.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(b) Fat Intakes.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(c) Proteins.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(d) Carbohydrates.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(e) Iron.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(f) Calcium.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(g) Vitamin C.

48

Tab

le6:

Ela

stic

ity

offo

od

grou

ps

consu

mpti

onre

spec

tto

fam

ily

inco

me

per

capit

a[γ

from

NL-O

LS].

Food

Gro

ups

γco

effici

ents

milk

s.e.

mea

tgr

oup

s.e.

fats

grou

ps.

e.ce

real

ss.

e.ve

geta

bles

s.e.

frui

ts.

e.

1975

.094

9065

.012

1076

.455

0134

.024

3509

.141

6234

.019

671

.000

9174

.016

4525

.065

4628

.026

8947

.529

3232

.024

9877

1976

.089

37.0

1296

65.5

4075

28.0

2094

89.1

1421

48.0

2311

96-.03

0950

3.0

1812

35.0

0362

69.0

2594

19.5

9246

39.0

2447

6519

77.1

2675

29.0

1303

25.6

5036

39.0

2612

43.1

2385

48.0

2274

65-.07

3628

1.0

1810

47.0

4293

42.0

2977

93.5

8628

6.0

2401

5119

78.1

1321

64.0

1377

06.5

1399

15.0

2389

06.1

7564

54.0

2841

9.0

5342

14.0

2031

03.1

7688

61.0

3160

38.5

7985

46.0

2737

1979

.110

7348

.013

9747

.529

6338

.023

1536

.077

1633

.023

7504

-.01

6206

5.0

1832

06.1

7917

45.0

2826

97.6

9127

37.0

2756

619

80.1

1266

02.0

1211

93.5

5441

05.0

2379

27.0

3368

21.0

2915

32-.02

0917

4.0

1926

12.1

5326

51.0

2692

6.5

6981

92.0

2405

0219

81.1

0298

12.0

1319

37.5

0428

61.0

2297

46.1

0578

4.0

2561

48.0

0993

73.0

1995

51-.02

2562

7.0

2850

73.5

7591

12.0

2335

919

82.1

1842

27.0

1218

26.4

8538

79.0

2087

36.1

4935

77.0

2236

09.0

1805

6.0

1970

89.0

2325

88.0

2546

21.5

9386

89.0

2526

6819

83.1

1804

85.0

1461

46.4

5380

04.0

1903

71.0

9883

58.0

2443

78.0

2595

78.0

1875

8-.01

5696

5.0

2823

15.5

5643

52.0

2582

6519

84.1

1216

57.0

1425

08.4

7777

24.0

2101

26.1

2703

7.0

2472

05.0

1490

42.0

1931

76-.02

1243

7.0

3019

91.6

1357

7.0

2806

1119

85.1

3200

23.0

1428

54.4

4364

15.0

1968

17.0

5356

89.0

2378

26-.02

0432

2.0

1714

49-.10

1822

9.0

2828

31.6

4237

65.0

2829

3119

86.1

3033

73.0

1480

44.1

9073

9.0

2705

22.1

4329

23.0

2383

13.0

4742

89.0

1843

64-.05

0159

9.0

2822

88.5

9510

9.0

2523

2519

87.1

0725

05.0

1493

22.5

0992

55.0

1884

6.0

8998

62.0

2141

47-.00

8804

7.0

1832

35.0

5162

96.0

2417

78.5

6892

.025

8693

1988

.124

4639

.013

6427

.137

8042

.021

8584

.111

4309

.022

8924

.026

037

.016

0657

.003

298

.024

8569

.524

5033

.023

5646

1989

.097

3338

.013

5209

.408

8185

.017

7708

.153

3527

.022

854

.015

3771

.017

3801

-.01

9797

9.0

2498

08.5

8483

76.0

2382

1119

90.1

3685

58.0

1447

18.4

4620

07.0

1889

27.1

4365

42.0

2507

98.0

1460

31.0

1684

89-.01

9562

1.0

2705

15.5

2351

9.0

2391

1519

91.0

7423

19.0

1426

69.1

2276

74.0

2113

98.0

7855

66.0

2248

57.0

3887

97.0

1590

96-.05

1530

8.0

2438

98.4

8939

45.0

2009

5419

92.0

8605

6.0

1428

91.1

3030

84.0

2126

88.0

5421

48.0

2581

32.0

1822

65.0

1675

32-.04

3096

5.0

2220

2.5

0515

59.0

1975

319

93.1

1177

82.0

1295

3.0

7181

59.0

2355

9.4

2392

69.0

1772

33.0

0703

8.0

1682

09-.03

0240

8.0

2353

16.4

9032

18.0

1983

5119

94.0

8540

39.0

1264

86.0

6962

73.0

1891

5-.02

7651

7.0

2677

65.0

1563

58.0

1482

17.0

0462

47.0

2075

15.4

8955

26.0

1848

7919

95.0

7059

17.0

1230

53.0

8611

06.0

1992

19-.14

1324

3.0

3096

51-.02

7134

9.0

1515

33-.03

4977

8.0

1999

98.4

4136

2.0

1814

8519

96.0

9216

48.0

1256

22-.06

5591

2.0

2058

96-.07

3020

9.0

2554

22-.13

4876

2.0

2104

73.0

2554

26.0

1833

25.4

0572

24.0

1619

1819

97.0

5080

53.0

1363

12.0

3679

01.0

2188

65.3

8450

1.0

2180

99.0

3075

9.0

1593

64.0

1638

36.0

2125

05.4

6248

88.0

1721

3719

98-.00

1461

2.0

1509

23.0

4434

7.0

2565

21-.03

8087

8.0

3114

21.0

4502

69.0

1627

45.0

4804

98.0

2137

87.4

2579

53.0

2399

5619

99.0

2650

9.0

1464

39.0

7562

69.0

2646

06-.03

0935

7.0

3391

91.0

4478

54.0

2135

93.0

2881

59.0

2260

49.4

1014

32.0

2156

1120

00.0

3484

87.0

1463

69.0

6979

74.0

2383

11-.02

8336

6.0

3040

83-.00

2115

2.0

1756

18.0

5608

45.0

1986

89.3

3971

32.0

2255

79

49

Figure 15: Estimated food groups income elasticity trend, λ =100.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(a) Diary Products.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(b) Meat and fish.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(c) Fats, oils, sugar and preserva-tives.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(d) Cereals, pasta, rise and bread.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(e) Vegetable.

−1

−.7

5−

.5−

.25

0.2

5.5

.75

1e

last

icity

1975 1980 1985 1990 1995 2000time

(f) Fruit.

50

4.5 Household Characteristics: household composition,

region of residence, presence of children

The models employed consider also some household characteristics, such as

per capita family income, household composition, region of residence and

presence of children. Summary statistics for the explanatory variables were

given in Table 2.

As shown by Chesher (1997) the effects of alternative amounts of smooth-

ing on estimates of the coefficients on household characteristics are negligible.

Tables 7 and 8 show coefficients estimated for regions, household compo-

sition and presence of children in 2000 using λ = 100 employed earlier11 on

nutrient intakes and food consumption respectively.

Estimates coefficients on nutrient intakes consumption for Scotland are

uniformly negative and significantly different from zero for most of the mod-

els employed (calories, fat intakes, carbohydrates, iron and vitamin C) with

exception of calcium, meaning that Scotish household consumption of intakes

is lower than in London and South East of England. Estimates for Northern,

Central, South West of England and Wales result significantly different from

zero only on calcium and vitamin C models. Consumption of calcium results

between 3 and 4 percent higher in the rest of England than in London and

the South East, whilst coefficients estimated on vitamin C are uniformly neg-

ative for the three region considered, meaning that they consume in average

less than people in London and South East (respectively 20 percent less in

Scotland, 12 percent in Northern England and 5 percent less in the Central,

South West and Wales).

Differences in intakes consumption might be due to different diets across

regions. In fact, as shown in Table 8, there is a significant regional effect on

consumption of milk, fat from oils and sugar, vegetable and fruit. Although

most of the coefficients across region result significantly different from zero,

consumption of meat and fish, and cereal, pasta, bread and rice do not differ

by region.

11The coefficients estimated for each year of survey considered are reported in Appendixat the end of this chapter

51

In particular, Scotish households consume less fruit and vegetable than

londoners and south-eastern, respectively 21 and 17 percent less. Northern

England consume 7 percent more milk, but 9 percent less fruit as well as

Central, South West of England and Wales. The latter consumes also more

vegetable (6 percent) and less fat from oils and sugar.

The analysis carried on in this paper assumes that expected consumption

of people of a given age and sex is independent from the presence, demo-

graphic characteristics of other household members. Of course this is an

approximation, in the sense that the coefficients estimated for each age and

gender represent average consumption across household composition types.

Therefore, when introducing household characteristics, we control also for

household composition introducing a dummy variable by type of household12

and an indicator of presence of children.

The coefficients estimates on presence of children are always positive and

significant at different critical levels for all nutrient intakes and food groups.

This clearly means that the presence of children is a main explanation of dif-

ferences in household consumption. In particular the magnitude of nutrient

intakes variation is in average around 10 percent higher respect a household

without children. This is mainly due to variation arising in food consump-

tion, as for example the increase in consumption of fat from oils and sugar of

about 13 percent as well as a 10 percent higher consumption of cereals and

vegetable.

In general, the effect of household composition is not largely significant,

and the presence of children remains the main explanation of differences

in household consumption. However it is worthy to highlight some results.

Coefficients estimated on the effect of different household composition on

consumption of fat from oils and sugar result uniformly negative (with ex-

ception of household with a single adult) and significantly different from zero.

The base category here is a household with two adults. It is interesting to

notice that a person living alone consume in general 33 percent more fats

121) one adult; 2) one adult and 1 or more children; 3) 2 adults; 4) 2 adults and 1 child;5) 2 adults and 2 children; 6) 2 adults and 3 children; 7) 2 adults and 4 or more children;8) 3 adults; 9) 4 or more adults; 10) 3 or more adults and 1 or 2 children; 11) 3 or moreadults and 3 or more children

52

Tab

le7:

Model

sfo

rnutr

ient

inta

kes:

esti

mat

edco

effici

ents

for

hou

sehol

dch

arac

teri

stic

s(t

valu

esin

bra

cket

s)-

2000

.

Nut

rien

tIn

take

sC

alor

ies

Fat

Inta

kes

Pro

tein

sC

arbo

hydr

ate

Cal

cium

Iron

Vit

amin

CSc

otla

ndvs

Lon

don

and

SEE

ngla

nd-0

.079

-0.0

79-0

.026

-0.0

870.

000

-0.0

93-0

.195

(-2.

810)

(-2.

360)

(-0.

870)

(-2.

730)

(0.0

20)

(-2.

930)

(-4.

660)

Nor

ther

nE

ngla

nd-0

.022

-0.0

380.

028

-0.0

240.

034

-0.0

03-0

.118

(-1.

170)

(-1.

700)

(1.4

30)

(-1.

160)

(2.0

70)

(-0.

130)

(-4.

370)

Cen

tral

,So

uth-

Wes

tan

dW

ales

0.00

90.

006

0.00

70.

012

0.03

90.

014

-0.0

55(0

.520

)(0

.310

)(0

.370

)(0

.590

)(2

.560

)(0

.720

)(-

2.25

0)1

adul

ton

lyvs

2ad

ults

0.03

20.

022

-0.0

320.

065

0.02

6-0

.013

-0.1

31(0

.710

)(0

.420

)(-

0.65

0)(1

.260

)(0

.650

)(-

0.24

0)(-

1.94

0)1

adul

tan

d1

orm

ore

child

ren

0.08

80.

065

0.12

00.

099

0.16

30.

150

0.34

6(1

.410

)(0

.860

)(1

.750

)(1

.450

)(2

.950

)(2

.130

)(3

.700

)2

adul

ts,1

child

-0.0

12-0

.029

-0.0

080.

001

0.05

00.

050

0.14

2(-

0.29

0)(-

0.60

0)(-

0.20

0)(0

.020

)(1

.400

)(1

.110

)(2

.440

)2

adul

ts,2

child

ren

0.01

70.

034

0.02

80.

006

0.05

90.

056

0.30

8(0

.370

)(0

.620

)(0

.570

)(0

.110

)(1

.420

)(1

.040

)(4

.310

)2

adul

ts,3

child

ren

-0.0

69-0

.082

-0.0

49-0

.060

0.02

5-0

.007

0.36

8(-

1.17

0)(-

1.18

0)(-

0.76

0)(-

0.93

0)(0

.470

)(-

0.10

0)(3

.960

)2

adul

ts,4

orm

ore

child

ren

-0.0

33-0

.030

0.09

2-0

.048

0.06

50.

099

0.50

1(-

0.45

0)(-

0.34

0)(1

.140

)(-

0.60

0)(1

.020

)(1

.190

)(4

.160

)3

adul

ts-0

.021

-0.0

310.

027

-0.0

140.

010

-0.0

160.

054

(-0.

670)

(-0.

870)

(0.8

40)

(-0.

390)

(0.3

70)

(-0.

450)

(1.1

709

4or

mor

ead

ults

-0.0

94-0

.097

-0.0

05-0

.095

-0.0

73-0

.041

-0.0

45(-

2.10

0)(-

1.86

0)(-

0.12

0)(-

1.87

0)(-

1.79

0)(-

0.79

0)(-

0.61

0)3

orm

ore

adul

ts,1

or2

child

ren

-0.0

27-0

.040

-0.0

79-0

.007

0.00

00.

019

0.18

8(-

0.61

0)(-

0.77

0)(-

1.62

0)(-

0.15

0)(0

.010

)(0

.360

)(2

.710

)3

orm

ore

adul

ts,3

orm

ore

child

ren

-0.1

77-0

.214

-0.1

87-0

.143

-0.0

53-0

.125

0.11

8(-

2.47

0)(-

2.48

0)(-

2.27

0)(-

1.87

0)(-

0.85

0)(-

1.49

0)(0

.970

)P

rese

nce

ofch

ildre

n0.

117

0.11

50.

102

0.13

70.

076

0.08

20.

113

(4.5

60)

(3.8

30)

(3.8

80)

(4.6

70)

(3.3

20)

(2.8

50)

(3.0

60)

53

Tab

le8:

Model

sfo

rfo

od

grou

ps:

esti

mat

edco

effici

ents

for

hou

sehol

dch

arac

teri

stic

s(t

valu

esin

bra

cket

s)-

2000

.

Food

grou

psM

ilkM

eat

and

Fis

hFa

tsan

dsu

gars

Cer

eals

Veg

etab

leFr

uit

Scot

land

vsLon

don

and

SEE

ngla

nd0.

044

0.00

4-0

.021

-0.0

40-0

.172

-0.2

11(1

.490

)(0

.070

)(-

0.35

0)(-

1.12

0)(-

3.85

0)(-

3.99

0)N

orth

ern

Eng

land

0.07

40.

042

-0.0

450.

014

0.00

1-0

.091

(3.6

10)

(1.2

80)

(-1.

060)

(0.5

80)

(0.0

30)

(-2.

790)

Cen

tral

,So

uth-

Wes

tan

dW

ales

0.05

00.

023

-0.0

820.

022

0.06

3-0

.089

(2.5

70)

(0.7

00)

(-2.

060)

(0.9

50)

(2.5

30)

(-3.

010)

1ad

ult

only

vs2

adul

ts0.

026

-0.0

340.

333

0.11

5-0

.070

-0.1

19(0

.500

)(-

0.44

0)(4

.960

)(1

.970

)(-

1.04

0)(-

1.77

0)1

adul

tan

d1

orm

ore

child

ren

0.24

60.

076

-0.0

890.

079

0.10

90.

363

(3.4

20)

(0.6

70)

(-0.

600)

(1.0

20)

(1.2

00)

(3.3

70)

2ad

ults

,1

child

0.14

7-0

.065

-0.2

510.

000

0.02

20.

184

(3.1

10)

(-0.

900)

(-2.

800)

(0.0

10)

(0.3

90)

(2.6

30)

2ad

ults

,2

child

ren

0.14

9-0

.062

-0.2

06-0

.038

0.07

90.

428

(2.6

90)

(-0.

730)

(-2.

100)

(-0.

640)

(1.1

90)

(5.1

00)

2ad

ults

,3

child

ren

0.12

7-0

.033

-0.3

62-0

.116

0.15

30.

558

(1.8

80)

(-0.

300)

(-2.

850)

(-1.

590)

(1.7

80)

(4.8

30)

2ad

ults

,4

orm

ore

child

ren

0.26

80.

081

-0.4

85-0

.100

0.38

20.

711

(3.3

20)

(0.6

10)

(-2.

820)

(-1.

120)

(3.5

60)

(4.3

10)

3ad

ults

0.00

9-0

.014

-0.3

76-0

.029

0.04

70.

035

(0.2

40)

(-0.

260)

(-5.

650)

(-0.

710)

(1.1

20)

(0.6

40)

4or

mor

ead

ults

0.03

90.

026

-0.5

47-0

.042

-0.1

30-0

.054

(0.7

20)

(0.3

30)

(-4.

930)

(-0.

750)

(-1.

950)

(-0.

560)

3or

mor

ead

ults

,1

or2

child

ren

0.04

0-0

.090

-0.4

170.

000

-0.0

030.

311

(0.7

30)

(-1.

050)

(-4.

560)

(0.0

00)

(-0.

040)

(3.6

00)

3or

mor

ead

ults

,3

orm

ore

child

ren

0.13

9-0

.156

-0.4

67-0

.145

-0.0

750.

340

(1.7

50)

(-1.

060)

(-2.

960)

(-1.

710)

(-0.

690)

(2.0

90)

Pre

senc

eof

child

ren

0.03

80.

076

0.13

50.

105

0.10

80.

045

(1.2

50)

(1.7

40)

(2.7

50)

(3.1

10)

(3.0

70)

(1.0

30)

54

than two adults living together, while increasing the number of adults in the

household (3 or 4 people) consumption of fats decreases up to respectively

38 and 55 percent less. There might be many factors driving this results.

People living alone might prefer to buy more ready meals or eat outside the

home because they do not have time to cook or because they do not enjoy it.

On the other side, adults sharing accommodation with other adults might

be single looking for partner and therefore caring more about their physical

aspect and their diet, they might also find in the time spent cooking a mo-

ment for socializing, and sharing rent might allowed them to have a easier

access to healthy but more expensive food like vegetable and fruit. The last

point does not however arise from the estimates of vegetable and fruit where

the coefficients result negative and not significantly different from zero for

household of adults.

It is also interesting to see that when children arrive, the couple decreases

significantly the amount of fats and sugar consumed and increases amount

of fruit and vegetable13.

There is not way to know here whether the presence of children or living

with others improve the quality of diet. The negative signs associated with

household composition in the fats and sugar model and the positive signs

shown in the fruit consumption model give a first hint on this direction.

Therefore this might be a point to be developed in future studies.

5 Discussion and Outline for future work

This paper has started to explore how eating habits of people in Britain have

changed over the last twenty-five years of the twentieth century. Using data

from 1975-2000 from the National Food Survey this paper reports an exten-

sive descriptive analysis that investigates the relationship between average

nutrient intake and key food groups consumption across ages and over time.

In doing so we estimates a Roughness Penalty Function Model obtained from

ordinary least squares method to account for function smoothness (Chesher,

13Coefficients estimated on consumption of vegetable are positive but not significantlydifferent from zero.

55

1997). I investigate nutrition curves - using nutrient intakes and major food

groups - with the objective to see how they have changed by gender and age

over time and by gender and time for all age groups and, in particular, for

children 0-17.

We stress five main results. First findings demonstrated that in general

nutrition varies over age and by gender. In general males consume more food

and nutrient intakes than females. Nutrients consumption strongly increase

during childhood until puberty, decrease at the beginning of adulthood age

and increase later on, decreasing again when people get older. Age distribu-

tion of consumption by major food groups show a general increase up to age

50 and decline afterwards.

The second finding focuses on changes in food consumption and nutrient

intakes over time and in particular among British youth by three age groups.

The results show a change in trend for some nutrient intakes such as fat

intake and proteins that increase along all the time period of study and for

calcium from the 90s, and a tendency to decrease in consumption of iron.

In particular the proportion of energy from fat increased at the end of the

80s to 35 percent and it is stable from there since. The variations emerged

in nutrient intakes might be due to variation in food consumption and to

some variation of the data collection process. In particular the data show an

increases in milk products, meat and fish products, fat from oils and sugars

and cereals since the beginning of the 90s.

The third finding focuses on cohort analysis in order to see whether dif-

ferent generations eat differently. We compare ten birth-cohorts and present

results both for nutrients intakes and food groups. The most interest find-

ings regard calories and fat intakes. While calories consumed do not change

a lot across generations, younger generations consume higher quantity of fats

intakes and proteins. The consequences of this can be seen in the proportion

of energy from fats, that for younger generation results much higher than

older generation at the same age. Younger generation consume also more

milk and fat from oil and sugars and less vegetable than their parents at the

same age.

56

In the fourth part we consider the effect of income on eating habits.

Therefore, focusing on the relation between eating habits and income distri-

bution, trends of elasticity of intakes and food consumption with respect to

income have been computed. The findings highlight that changes among nu-

trient intakes and food consumption due to income variations are relatively

low (all less than 1) and in general positive, meaning that as income rises

consumption rices as well but less than proportionally. In general the sensi-

tiveness of consumption to income variation becomes smaller with most of the

trends tending to zero. Finally, there some evidence of changes through time

in income elasticities both for nutrient intakes and food groups. However,

the effect of family income variation is much higher on food groups than on

intake nutrients consumption. This means that as households become richer,

the substitution between foods is much quicker than the variation of diet

through substitution within nutrient intakes consumption. In other words,

for people is much easier to change food quantity consumed than quantity of

intakes. However, at this moment it is not possible to say whether a positive

variation of family income improves nutrition and therefore health status.

The fifth finding reports on the effect of region of residence, household

composition and presence of children. The effects on nutrient intakes are not

very large, on the contrary the major findings appear in the food estimates.

Region mainly differ in consumption of vegetable and fruit. The presence of

children results the main explanation of differences in household consumption

for all nutrients and foods. The presence of children increases the amount of

fruit and reduces the amount of fat from oils and sugar consumed.

In considering these results, some extension should be considered for fu-

ture research. Many might be the causes of eating habits changes resulting

from the analysis carried on in this paper. For example technical change, in-

come growth, lifestyle changes, mass media and advertising, and changes in

relative prices. In fact, technical changes have provided food supply system

with mechanisms that increase productivity and improve food conservation

and distribution system. Moreover, development of supermarkets has greatly

changed supply chains system. Today, supermarkets make many new prod-

ucts available wherever in the world - either from other countries and out of

57

season - while small and local shops are increasingly less present.

In the next chapter we will study a demand system for some food groups

(like for example fats from oils and sugar, and vegetable and fruit) in order

to explore the effect of prices on household food demand.

58

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Chesher A. (1997), D iet Revealed?: Semiparametric Estimation of Nutrient

Intake-Age Relationships, Journal of Royal Statistical Society A, n.160. part

3, pp. 202-203

Chesher A. (1998), Individual demands form household aggregates: time and age

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Currie J. & Stabile M. 2002, Socioeconomic Status and Health: why is the rela-

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Deaton A. (1997). The Analysis of Household Survey, The John Hopkins Uni-

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Marmot M. & Wilkinson R.G. (1999). Social Determinants of Health, Oxford

University Press, Oxford.

Maxwell S. & Slater R. (2003). Food Policy Old and New, Development Policy

Review, 21 (5-6):531-553.

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Ministry of Agriculture, Fisheries and Food (1999). National Food Survey, Lon-

don: Her’s Majesty’s Stationery Office.

OECD Health Policy Unit (2003). H ealth at a glance - OECD indicators. Briefing

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Schmidhuber J. (2003). The outlook for long-term changes in food consumption

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Subramanian S., Deaton A. (1996). The demand for food and calories, Journal

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60

6 Appendix

61

Tab

le9:

Effec

tof

pre

sence

ofch

ildre

non

nutr

ient

inta

kes

consu

mpti

on.

Coeffi

cien

tsass

oci

ate

dw

ith

pre

sence

ofch

ildre

nby

Inta

kes

.C

alo

rie

s.e.

fat

inta

ke

s.e.

pro

tein

ss.

e.ca

rbohydra

tes

s.e.

Calc

ium

s.e.

Iron

s.e.

Vit

am

inC

s.e.

1975

0.0

88

0.0

22

0.1

02

0.0

26

0.0

91

0.0

30

0.0

78

0.0

25

0.0

87

0.0

18

0.0

59

0.0

24

0.1

59

0.0

37

1976

0.0

86

0.0

22

0.1

14

0.0

26

0.0

48

0.0

34

0.0

73

0.0

26

0.0

70

0.0

18

0.0

38

0.0

25

0.1

11

0.0

38

1977

0.0

92

0.0

23

0.1

00

0.0

26

0.1

12

0.0

28

0.0

88

0.0

28

0.0

92

0.0

21

0.0

69

0.0

26

0.0

66

0.0

40

1978

0.0

94

0.0

27

0.0

74

0.0

31

0.0

43

0.0

34

0.1

22

0.0

33

0.0

74

0.0

23

0.0

74

0.0

29

0.1

71

0.0

41

1979

0.1

54

0.0

23

0.1

83

0.0

27

0.2

68

0.0

38

0.1

23

0.0

27

0.0

93

0.0

20

0.1

58

0.0

24

0.1

27

0.0

39

1980

0.0

76

0.0

28

0.0

93

0.0

33

0.0

44

0.0

37

0.0

66

0.0

32

0.0

72

0.0

23

0.0

36

0.0

30

0.1

58

0.0

42

1981

0.0

56

0.0

26

0.0

74

0.0

31

0.0

50

0.0

29

0.0

48

0.0

31

0.0

31

0.0

23

0.0

34

0.0

28

0.0

52

0.0

42

1982

0.1

04

0.0

26

0.1

13

0.0

30

0.0

42

0.0

32

0.1

08

0.0

29

0.0

95

0.0

21

0.1

09

0.0

25

0.1

46

0.0

38

1983

0.0

52

0.0

26

0.0

91

0.0

30

0.0

67

0.0

26

0.0

22

0.0

33

0.0

45

0.0

23

0.0

40

0.0

27

0.0

92

0.0

45

1984

0.0

93

0.0

29

0.0

97

0.0

32

0.1

04

0.0

30

0.0

83

0.0

34

0.0

93

0.0

24

0.0

96

0.0

29

0.0

72

0.0

47

1985

0.0

94

0.0

25

0.1

10

0.0

29

0.0

98

0.0

28

0.0

86

0.0

29

0.0

60

0.0

22

0.0

76

0.0

27

0.1

37

0.0

41

1986

0.0

79

0.0

26

0.0

64

0.0

31

0.0

52

0.0

29

0.1

04

0.0

30

0.0

42

0.0

22

0.0

45

0.0

27

0.0

80

0.0

44

1987

0.1

03

0.0

26

0.1

00

0.0

31

0.0

80

0.0

30

0.1

16

0.0

29

0.0

87

0.0

24

0.0

98

0.0

28

0.1

89

0.0

48

1988

0.0

79

0.0

25

0.0

57

0.0

30

0.0

64

0.0

30

0.1

03

0.0

28

0.1

18

0.0

22

0.0

69

0.0

27

0.1

41

0.0

46

1989

0.1

34

0.0

25

0.1

77

0.0

30

0.1

76

0.0

25

0.0

94

0.0

30

0.0

73

0.0

22

0.0

93

0.0

26

-0.0

05

0.0

41

1990

0.1

48

0.0

25

0.1

62

0.0

30

0.1

52

0.0

29

0.1

40

0.0

28

0.1

08

0.0

23

0.1

15

0.0

29

0.1

92

0.0

39

1991

0.1

52

0.0

26

0.1

56

0.0

31

0.1

43

0.0

25

0.1

52

0.0

31

0.1

25

0.0

25

0.1

08

0.0

28

0.0

91

0.0

43

1992

0.1

02

0.0

26

0.1

28

0.0

32

0.1

26

0.0

26

0.0

93

0.0

30

0.0

89

0.0

24

0.0

91

0.0

28

0.1

71

0.0

40

1993

0.0

44

0.0

28

0.0

61

0.0

33

0.0

87

0.0

25

0.0

18

0.0

32

0.0

75

0.0

23

0.0

46

0.0

28

0.1

08

0.0

42

1994

0.1

67

0.0

24

0.1

85

0.0

29

0.1

44

0.0

24

0.1

64

0.0

27

0.1

38

0.0

22

0.1

30

0.0

25

0.1

71

0.0

35

1995

0.1

03

0.0

24

0.1

32

0.0

28

0.0

96

0.0

23

0.0

88

0.0

28

0.0

68

0.0

21

0.0

61

0.0

25

0.1

75

0.0

35

1996

0.1

34

0.0

28

0.1

22

0.0

29

0.0

95

0.0

24

0.1

63

0.0

37

0.1

22

0.0

21

0.0

93

0.0

24

0.0

97

0.0

32

1997

0.0

98

0.0

27

0.1

24

0.0

34

0.0

99

0.0

27

0.0

96

0.0

31

0.0

74

0.0

23

0.0

46

0.0

28

0.1

49

0.0

35

1998

0.1

55

0.0

25

0.1

89

0.0

31

0.1

27

0.0

26

0.1

41

0.0

28

0.1

05

0.0

24

0.1

03

0.0

27

0.0

68

0.0

37

1999

0.1

18

0.0

31

0.1

50

0.0

35

0.1

67

0.0

27

0.0

88

0.0

37

0.0

97

0.0

24

0.0

72

0.0

29

0.1

05

0.0

38

2000

0.1

17

0.0

26

0.1

15

0.0

30

0.1

02

0.0

26

0.1

37

0.0

29

0.0

76

0.0

23

0.0

82

0.0

29

0.1

13

0.0

37

62

Tab

le10

:E

ffec

tof

pre

sence

ofch

ildre

non

food

grou

ps

consu

mpti

on[fro

mN

L-O

LS].

Coe

ffici

ents

asso

ciat

edw

ith

pres

ence

ofch

ildre

nby

Food

Gro

ups

milk

s.e.

mea

tgr

oup

s.e.

fats

grou

ps.

e.ce

real

ss.

e.ve

geta

bles

s.e.

frui

ts.

e.

1975

0.09

40.

024

0.10

80.

050

0.06

70.

035

0.07

20.

027

0.09

60.

046

0.10

80.

046

1976

0.07

90.

024

0.07

50.

048

0.04

40.

036

0.08

00.

028

0.11

30.

040

-0.0

370.

050

1977

0.09

10.

024

0.16

90.

046

0.05

70.

037

0.09

40.

029

0.09

60.

048

0.02

80.

048

1978

0.03

80.

027

0.07

00.

058

0.04

60.

050

0.08

80.

034

0.21

80.

055

0.15

10.

050

1979

0.04

90.

024

0.13

10.

050

0.09

50.

036

0.16

10.

028

0.19

00.

046

0.02

50.

053

1980

0.09

50.

025

0.12

70.

062

0.06

90.

048

0.06

70.

034

0.14

50.

052

0.18

10.

051

1981

0.03

30.

025

0.10

30.

050

-0.0

030.

043

0.07

10.

035

0.03

00.

044

-0.0

490.

050

1982

0.04

30.

024

0.10

90.

044

0.05

50.

040

0.14

00.

031

0.12

00.

042

0.09

60.

048

1983

0.05

00.

028

0.06

90.

042

0.07

30.

046

0.00

30.

034

0.07

10.

046

0.07

40.

051

1984

0.06

60.

029

0.12

60.

056

0.06

40.

049

0.10

80.

035

0.02

00.

051

0.06

90.

057

1985

0.07

50.

026

0.15

10.

045

0.02

90.

043

0.07

30.

030

0.15

00.

048

0.06

60.

051

1986

0.00

70.

027

0.06

00.

045

0.11

40.

041

0.09

80.

032

0.08

30.

047

-0.0

120.

052

1987

0.05

10.

030

-0.0

100.

048

0.04

90.

043

0.09

40.

033

0.21

90.

042

0.09

60.

057

1988

0.11

00.

027

0.03

50.

042

0.00

10.

042

0.10

80.

028

0.17

40.

043

0.18

80.

047

1989

0.05

20.

027

0.40

40.

042

0.06

50.

043

0.08

90.

031

0.12

40.

041

-0.0

470.

049

1990

0.05

60.

029

0.20

10.

048

0.07

30.

046

0.12

00.

030

0.22

90.

040

0.14

00.

046

1991

0.05

90.

033

0.13

90.

039

0.09

30.

048

0.17

20.

031

0.01

60.

047

-0.0

020.

050

1992

0.07

50.

030

0.17

10.

039

-0.0

250.

054

0.08

90.

032

0.14

60.

040

0.06

40.

047

1993

0.07

70.

027

0.07

80.

044

0.07

50.

054

-0.0

420.

034

0.06

10.

042

0.11

40.

051

1994

0.09

10.

027

0.14

60.

036

0.11

20.

047

0.13

40.

030

0.15

60.

037

0.11

20.

042

1995

0.05

50.

026

0.06

70.

035

0.06

10.

052

0.11

00.

030

0.06

10.

036

0.17

90.

041

1996

0.09

90.

027

0.10

60.

038

-0.0

700.

043

0.15

40.

047

0.14

80.

033

0.05

60.

038

1997

0.07

50.

029

0.11

00.

045

0.19

30.

074

0.08

50.

033

0.16

00.

036

0.02

90.

042

1998

0.05

40.

030

0.14

10.

043

0.11

30.

054

0.11

90.

031

0.09

80.

036

0.04

70.

045

1999

0.09

70.

030

0.23

00.

047

0.05

70.

060

0.07

80.

042

0.07

50.

040

0.03

90.

045

2000

0.03

80.

031

0.07

60.

044

0.13

50.

049

0.10

50.

034

0.10

80.

035

0.04

50.

044

63