Download PDF

Taxing Animal Products: Protein Demand under
Environmental Pressure and Social Impact in France
F. Caillavet, A. Fadhuile, V. Nichèle
INRA-UR1303, ALISS
Selected Paper prepared for presentation at the Agricultural Applied Economics
Associations 2014 AAEA Annual Meeting, Minneapolis, MN, July 27-29, 2014.
Copyright 2014 by F. Caillavet, A. Fadhuile, and V. Nichèle. All rights reserved. Readers
may make verbatim copies of this document for non-commercial purposes by any means, provided
that this copyright notice appears on all such copies.
Taxing Animal Products: Protein Demand under Environmental
Pressure and Social Impact in France
F. Caillavet1 , A. Fadhuile1 , V. Nichèle1
May 2014
Preliminary version. Please do not cite or quote without permission of the authors.
Abstract
Europe committed to reduce greenhouse gas emissions (GHG) by 40 % by 2030 from
1990 levels. Food emits about 30% of all GHG, the major toll arising from animal products
(half of food GHG). This urges the necessity of public actions to encourage sustainable diets. Food policy is now at the double stake of preserving environment and improving health.
To implement a public policy combining environmental and nutritional issues in a sociallyconscious framework, a food demand study and the potential substitutions between foods is
necessary. This article aims at offering a solid base for such policy decisions. Which are
the food groups more suitable for a price change? Where are the more disparities in price
responsiveness among income classes? Are own-price effects the only relevant? Do crossprice effects matter? To study food demand, we estimate an EASI demand system. It is based
on a pseudo panel of 8112 observations constructed from Kantar panel data (1998-2010). It
registers French households purchases for food-at-home. We add the nutritional content and
Greenhouse gas emission related to foods through Life Cycle Analysis. For 21 food groups,
built according to their environmental and nutritional characteristics, we run expenditure and
price elasticities. Based on these results, two taxation scenarios are implemented. For each
we increase by 20% the prices of food categories with most adverse effects on (1) environment only (ENV) and (2) both environment and health (ENV-NUT). The ENV scenario
induces more reductions in environment impact than the ENV-NUT scenario, in particular
for SO2 emissions. A greatest impact is observed for lower-average income households and
with a household head less than 30 years old. However, undesirable nutritional effects lead
to consider the necessity of a trade-off between environment and nutrition. Our conclusions
find that this trade-off is not so costly (-18% in terms of CO2 ). Moreover, our results give
new insights for targeting public policies toward the youngest households which we find
more sensitive to prices and which are at the beginning of the consumption life-cycle.
Keywords: EASI demand system, food purchases, socioeconomic inequalities, public policy.
JEL Classification: C35, D12, Q15.
1
INRA-UR1303, ALISS Ivry-sur-Seine. This work was supported in part by FERRERO Cie and by the French
National Research Agency under the OCAD project (Offer and consume a sustainable diet). Our acknowledgements
go to O. Allais for his comments and N. Guinet for his data assistance.
Corresponding author: [email protected]
1
1
Introduction
Food consumption is estimated to be responsible for 30% of Greenhouse gas emission (GHG)
in Europe. To fill the European commitment to reduce GHG emissions by 40% before 2030,
changes of diet seem unavoidable. In a global context of increasing pathologies related to nutrition, food policy is now at the double stake of preserving environment (hereafter sustainability)
and improving health (here addressed through nutritional objectives). Furthermore, nutrition and
health show a strong social gradient (Mackenbach et al. (2008)). Socioeconomic disparities in
the purchase basket lead also to differentiated environmental impacts (Boeglin, Bour, and David
(2012)). Encouraging an environment-friendly diet through public action must take into account
nutritional and social consequences, and food policies should be implemented with those three
combined objectives.
Can economic incentives drive environmental sustainability and healthier diets ? Literature
and some real life experiences of price policies for health purposes have already addressed some
key points: would taxing less healthy products or lowering market prices/subsidizing healthier products increase consumption of desired more healthful products ? Some examples of increasing VAT on unhealthy products (fat, junk food) have been implemented in some countries
(France, Allais, Bertail, and Nichèle (2010); Denmark, Smed, Jensen, and Denver (2007); UK,
Briggs et al. (2013)) with controversial results in terms of efficiency on the diet and population
targeted. In the environmental field, the question whether similar tools could be implemented to
encourage both environmental-friendly and healthy food choices raises at least two issues. First,
the food groups targeted for environmental reasons may be different than those targeted for health
reasons. Second, a price policy may have divergent rationale for implementation, due to price
formation: in the environmental framework, price increases result from scarcity due to rarefac1
tion of resources more than from a regulatory policy decision. Besides, a price intervention could
be more adequate at the level of producers in case of environment than in the health one (Capacci
et al. (2012)). However, the perspective of inducing a more favourable diet for both health and
environment through consumer prices is not irrelevant.
Regarding the first issue in the literature, i.e. designing sustainable food policies facing the
complexity environment/nutrition, most papers consider policy scenarios or simulate a change
in diet dealing with a reduction of meat consumption, since the major toll arises from animal
products (McMichael et al. (2007)). In particular, recent studies in UK evaluate the impact of
alternative diets lower in red meat and processed meat (Aston, Smith, and Powles (2012)) or different diet scenarios where the largest potential reduction in GHG emissions is achieved by eliminating meat from the diet (35% reduction), followed by changing from carbon-intensive lamb
and beef to less carbon-intensive pork and chicken (18% reduction) (Hoolohan et al. (2013)).
Scarborough et al. (2012) study the health perspectives of three dietary scenarios based on GHG
emissions and finds a potential for substantial improvements. However, less compatibility in objective is not always found. In the French case, Vieux et al. (2013) results are controversial. The
impact of different meat reduction scenarios is modest and they emphasize adverse interactions
between health and environment aims. Keeping calorie intake constant, when substituting fruit
and vegetables for meat in a healthier perspective, GHG emissions may even increase (Masset
et al. (2014)). A more global work at the European level considers the consequences of six alternative diets consisting of a 25% or 50% reduction in the consumption of beef, dairy, pork, poultry
and eggs, compensated by a higher intake of cereals (Westhoek et al. (2014)). Note that the evaluation of environmental impact is realized mostly through a single indicator (CO2 ), which is quite
restrictive. Similarly to health where composite indicators have been built such as the Healthy
Eating Index (Drewnowski et al. (2009)), some literature environmental index combining various
2
indicators.
These scenarios simulating changes in diets have methodological drawbacks: most of them
do not consider a full consumption framework taking into account substitution behaviours. In
the French case, both papers cited above modelled various a priori hypothesis of substitutions
between food groups, which were not based on the estimation of elasticities from a demand
system. One reason for this is that they do not deal with the issue of how to obtain this change of
diet. Indeed, when we come to the second issue in the literature, i.e. implementing a price policy
at the consumer level for lowering GHG emissions, works are rather scarce. The taxation of food
products with higher GHG emissions through higher value-added taxes (VAT) on meat and dairy
products, or CO2 emissions level taxes (Edjabou and Smed (2013)), evidences the ambiguity of
increasing the price of healthy foods such as low fat sources of animal proteins, for example
milk. While waiting for guidelines which would combine health and environmental objectives,
the compatibility issue can only be driven by trade-off insights.
A third issue deals with heterogeneity of consumption, meaning in particular different consumption patterns and price sensitivity according to socioeconomic characteristics. A recent
paper on Danish data points out that income and education gradients in lifestyle choices vary
with age (Ovrum, Gustavsen, and Rickertsen (2014)). Consumption of key products for health
consequences such as fruits and vegetables show a widening income gradient with age till 70
years. In the French context, it has been proven to show strong age and generation effects (Hébel
and Recours (2007)). Chancel (2014) points out the importance of the generational dimension
in consumption patterns and consequent environmental footprint. Some works emphasize that
strategies to change meat eating frequencies and meat portion sizes appeal to different segments
of consumers which should be addressed in terms of their own preferences. A point of great
3
interest remains in testing the existence of a higher price sensitivity for low income households
in order to evaluate the potential of food policies to reduce socioeconomic inequalities. When
considering a tax on food prices which involves by nature a regressive content, a specific issue
for low income households should take into account food security and the eventual need of a
program assistance or compensatory mechanism.
In the perspective of implementing an economic policy combining environmental and nutritional issues in a socially-conscious framework, a food demand study and the potential substitutions between foods is necessary. This article aims at offering a solid base for such policy
decisions, which could aim at reducing the carbon content of food purchases with nutritional
benefits. Which are the food groups more suitable for a price change ? Where are the more disparities in price responsiveness among income classes ? Are own-price effects the only relevant
? Do cross-price effects matter ? To study food demand, we estimate an Exact Affine Stone
Index (EASI) demand system developed by Lewbel and Pendakur (2009) and recently implemented by Zhen et al. (2014) for beverage and food demand. This specification is more flexible
than the popular Almost Ideal Demand System (AIDS) of Deaton and Muellbauer (1980) and
the subsequent literature. For 21 food groups built according to their environmental and nutritional characteristics, we run expenditure and price elasticities. We obtain these results on four
income classes and four age groups in order to assess socioeconomic and life-cycle inequalities
in demand. The data cover the period 1998-2010.
Our paper involves several contributions to the study of food demand in a perspective of
simulating health and environmental-friendly policies. First, we use the utility-theoretic EASI
demand system to characterize household purchases on food and beverage preferences. To our
knowledge, this is the first application on French data. Second, we develop this approach on
4
a large panel dataset, an estimation framework which was not addressed till now. Third, we
study the implications of social differentiation of food patterns on public policies by taking into
account income inequalities and life-cycle effects. It provides detailed results on the eventuality
of various patterns of diet substitution and price responsiveness.
This article is organized as follows. Section 2 describes the EASI demand system and section
3 the data and the methodology implemented. Section 4 presents the estimation results and
comments the implications for policies. Section 5 concludes.
2
Specification and econometric consumption model
We retain the EASI demand system developed by Lewbel and Pendakur (2009) to describe food
demand functions. This approach uses an utility-derived model and non linear Engel curves
thus giving more flexibility to the demand specification. It enables to measure socioeconomic
inequalities and life-cycle effects in food consumption. Following Zhen et al. (2014), we consider an incomplete demand system to model food-at-home purchases which implies a strong
assumption of weak separability. In particular, this implies that we use food expenditure instead
of income to design consumer demand (Blundell and Robin (1999)). The EASI demand system
share with the AIDS some desirable properties. In particular, it is linear and enables to aggregate
over consumer preferences. It also has several advantages because it defines implicit Marshallian
demand functions with flexible Engel curves. These demand functions are given by defining the
implicit utility as the log of food expenditure deflated by the log Stone price index. Therefore,
this specification uses an exact deflator, and not an approximated expenditure.
Here, the EASI demand system is based on cohort observations c1 . Each cohort budget1
This is justified by the structure of our data, see section 3.
5
shares for food group i(i = 1, ..., N ) is defined as the sum over households’ budget-shares:
wict =
1
Nct
P
wiht , for h (h = 1, ..., H) households, c = 1, ..., C cohorts and t(t = 1, ..., T )
time periods; wiht is the household h budget-share for food group i and Nct is the number of
households within cell c in t. The demand for each food group is defined as a function of prices,
food expenditure and socio-demographic characteristics:
(1)
wict =
R
X
r
βir (yct ) +
r=1
L
X
l=0
αil Zl,ct +
N
X
γi ln pict + uict ,
i=1
where pict is the price for food group i, cohort c at period t ; yct is an implicit utility level at
the cohort level and for each time period t. Its polynomial degree r enables the flexibility of
Engel curves; Zl,ct include l(l = 1, ..., L) socio-demographic characteristics; β, α and γ are the
parameters to estimate; and uict is the residual. More precisely, the implicit utility level is given
by:
(2)
yct = ln(xct ) −
N
X
i=1
N
1X
wict ln pict +
γij ln pict ln pjct .
2 i,j=1
Plugging Equation (2) into (1) gives a demand system which is not restricted by Gorman rank
conditions. It can be see that Engel curves depend on each food group through β parameters,
which illustrate the shape of the Engel curve. The polynomial degree r is empirically chosen to
fit the data thus giving flexibility to the demand system. This specification enables to exploit the
unobserved heterogeneity through the error term. Note that because w appears on both sides of
the demand equations, controlling for endogeneity enables to obtain efficient results.
6
3
Data
3.1
Data description
We use Kantar Worldpanel data from 1998 to 2010. Each annual survey contains weekly food
acquisition data for an average of 15,000 households, with an annual rotation of one third of the
participants. The households are selected by stratification according to several socioeconomic
variables, and remain in the survey for a mean period of four years. All participating households
register the grocery purchases through the use of bar codes. From 1998 to 2008, to register
grocery purchases without a bar code, each household is assigned to one of two groups to alleviate
its workload. Each group (half of the survey) is requested to register its purchases for a restricted
set of products: meat, fish, and wine for the first group, and fresh fruits and vegetables for the
second group. For 2009 and 2010, the two sub-groups are associated into a single group. Hence,
the survey gives the food purchases for more than 8,000 households for 169 periods of four weeks
spanning over 1998 to 2010. We grouped food items into 21 categories taking into account the
environmental emissions and the nutritional contents of the products (according to Masset et al.
(2014) results), consumer preferences and consumer willingness to substitute products within
categories of foods, see Table 6.
[Insert Table 6 here.]
Nutrients characteristics are those of Vieux et al. (2013), and concern more than 500 food
products. For each product, the selected nutrients presented here are energy intake (measured
in food calories), and proteins, plant-based proteins, animal proteins, saturated fats, cholesterol,
vitamin B12, vitamin D, iron and sodium.
Environmental data2 are collected by Greenext, an environment consultancy, to assign the
2
Version of the data set 06/11/2013.
7
environmental impact of products through Life Cycle Analysis. The data set delivers, for 311
products, the environmental impact of producing these products. They are illustrated by the
following three variables : (1) CO2 relates to the impact on climate change (in gram of CO2 per
100g); (2) SO2 relates to air acidification (in gram of SO2 per 100g); (3) N relates to Nitrates
outputs (in gram of N per 100g).
3.2
Cohort Construction
We define 48 cohorts to capture both income effects, life-cycle effects, and regional heterogeneity. They are constructed on the following variables:
1. Four income classes, based on family income corrected by consumption units according
to OECD scale (Gardes et al. (2005); Allais, Bertail, and Nichèle (2010)): modest, lower
average, upper average, well-off;
2. Four age classes based on the age of household head: under 30 years old, 31-45, 46-60,
over 61;
3. Three regions with significant differences over food groups consumptions and expenditure:
Paris and its suburbs; the North and East; the South and the West3 .
Hence household data are aggregated to obtain a pseudo panel and recover the total foodat-home expenditure (Allais, Bertail, and Nichèle (2010)). Therefore, our data do not include
infrequency of purchase issue, contrary to Tiffin and Arnoult (2010). Here we consider that
the absence of purchase during a time period is a true zero corresponding to non consumption.
Hence, this dataset is made by 48 cohorts and 169 time periods, i.e. 8112 observations. The
descriptive statistics of this sample are presented in Table 7.
3
Variation between regions is very low (see Allen (2010)) hence with three regions we capture the main differences on food consumption.
8
[Insert Table 7 here.]
At the cohort level, and for each time period, we compute for the food purchases: the total
emissions (in terms of SO2 , CO2 and N) and the nutrient intakes, see column (a) of Table 8.
Based on the food purchase basket, these values enable to compute the contribution of each food
group to the full emissions (resp. nutrient intakes).
[Insert Table 8 here.]
3.3
Descriptive Statistics
The set of sociodemographic variables available in our data allows to characterize the modest
households (lowest income group) as those living with income per unit of consumption inferior
to 1500e/month, whose reference person has an education level inferior or equal to baccalaureate, mainly blue collars or retired. The majority of these households include children under 16
years. The level of equipment is inferior compared to other income groups: on average, these
households have at home less computers, or dishwashers. These characteristics induce budget
and food purchases disparities, which can be translated in terms of nutritional content and environmental impact.
Socioeconomic disparities in food purchases, nutritional content and environmental impact
At the global level, we observe a structure of purchases where food products including animal
content represent the major part of the budget (55.6%). This structure varies with age and income classes. Among youngest households (with a head of less than 30 years), animal products
represent a lesser share in modest (49.6%) than in well-off households (52.6%)4 . Concerning the
nutritional content, note that those disparities vanish: the share of animal source food products
4
Detailed tables per income and age classes are available upon request.
9
in total energy remains around 45.5%. Similarly, the share of animal proteins in total proteins
does not vary with income class, around 76.5%. Among the 21 food and beverage groups, plantbased fats and yogurts are the two largest sources of energy. This remains true for the two lowest
income classes, while cheese overpasses yogurts in both higher income classes (well-off and
upper-average households).
Concerning the environmental impact of purchases, our computation amounts to 1.43 tons
CO2 eq. or 3.9kg per household. Figure 1 shows that animal products account only for half of
these emissions, near the half for nitrates but 3/4 of SO2 impact. Highest CO2 emissions come
from modest households (3.9kg) compared to well-off ones (3.4kg). Combining with age, we
observe higher emissions with age 45-60, the least among upper-average and well-off households
of less than 30 years. Quite similarly, highest SO2 emissions come from modest households
with age 30-45 and 45-60 and the least among upper-average and well-off households of less
than 30 years. For nitrate emissions, the results are only slightly different: highest values are
observed for modest households with age 30-45 and 45-60, and the least for well-off households
less than 30 and 30-45 years. In comparison for CO2 emissions, Chancel (2014)’s work based
on French overall consumption found significant life-cycle effects, since older cohorts were the
more emitting. Income effects showed that richer were emitting more than poorer.
This is in tune with the range of disparities observed in the calorie and protein contents.
The larger environmental emissions and calories content found in modest households purchases
correspond to different food patterns, in particular between food-at-home and food-away-fromhome. In a previous analysis of French budget data, Caillavet, Lecogne, and Nichèle (2009)
found that the budget share for food consumed at home is higher for modest households. Moreover, the demographic composition of the households may differ between the different income
10
and age classes.
4
Estimations and Results
Before to describe the estimation of the demand system defined in Section 2, this section presents
the prices construction.
4.1
Recovering prices from unit values
Given we do not observe the price paid by households within the data, they are approximated by
using food groups expenditures and quantities purchased. This gives unit values instead of real
prices. Indeed, demand estimates are very sensitive to measurement units (Moschini (1995)),
therefore we follow Crawford, Laisney, and Preston (2003) to compute them from a first stage
estimation. There are several reasons for this. First, the structure of our data does not enable
to follow Cox and Wohlgenant (1986) and Park and Capps (1997). Indeed, their methodology
imposes to observe households total food-at-home expenditure and to regress in a first step unit
values on a total food-at-home expenditure and some socio-demographics variables. Then their
estimation results are used to control for the unobserved heterogeneity among prices. In particular, regressors introduce the sum of the intercept and the residual of the first stage regression.
Second, without observations on the price paid by households, Deaton (1988) suggests to approximate the actual prices by unit values. He used clustered observations thus controlling for
the unobserved geographical heterogeneity among observations.
Here, we construct unit values from the total food at-home expenditure and quantities of
11
food group at the cohort level by:
ln ψict = ηc0 +
(3)
K
X
ηik Z∗k,ct + ζict ln qict + δict ln xct + ict ,
k=1
where ψict is the unit value for food group i, cohort c and period t; Z∗k,ct stands for the sociodemographic variable k; qict is the quantity of i purchased; xct is the total food expenditure
of cohort c in t; η, ζ, and δ are the unit value equation parameters to be estimated; ict is the
residual. Adjusted unit values (lnd
ψict ) from this equation are used as proxy for prices (ln pict )
into the demand system. Accordingly, we have a vector of c prices per period corresponding to
one price per cohort with each unit values corrected from quality effects (Beatty (2010)).
Unit value regressions are made for each food group at the cohort level by regressing unit
values on the log-food-at-home expenditure (ln(x)), log-quantities (ln(q)), income classes, time
period dummies5 , and three variables illustrating households durable ownership. These latter
variables are fryer, dish washer, and freezer6 . As for the demand system, they are constructed
by cohort and indicate the proportion of each household owning these equipments. Estimation
results are presented in Table 9. We use a Fixed Effects (FE) estimator, which produce unbiased
and consistent parameters estimates with T = 169. We find that quantity parameters are all negative and significant at the level of 5%7 . For example, a 1 % increase of quantity of juices reduces
their demand by 0.10%. Food expenditure coefficients are always positive and significant. For
juices, alcohol, bottled water, beef, other meats and cheese, our estimates indicates that a 1%
increase of food-at-home expenditure increases the unit value of this food group by more than
1%, ceteris paribus.
[Insert Table 9 here.]
5
We have 169 time periods, which are constructed from 13 periods per years of 4-weeks purchases. Therefore,
we introduce 13 years and 12 periods of 4-weeks dummies.
6
Note that these variables are not included into the demand system.
7
This level is retained for all the comments in this article.
12
4.2
Demand estimates and elasticities
The demand system includes each food group unit values, food-at-home expenditure, and the
socio-demographic variables presented in Table 7. The demand system is estimated without
the last equation (for prepared desserts) whose parameters are recovered by using the theoretical
restrictions (symmetry, homogeneity and adding-up).We demeaned variables to estimate a SURE
demand system. For each value of r (starting from 1 to 6), we run 2SLS estimator. Based on
Wald tests, we retain r = 4. Hence, 72% of the polynomial degree coefficients are significant.
Lewbel and Pendakur (2009) and Zhen et al. (2014) retained r = 5 based on Wald test for the
last degree. Estimation results are available upon request, and are used to compute several kind
of elasticities.
Food Group Elasticities Expenditure, compensated own and cross-price elasticities of food
groups are presented in Table 1. They are computed for the sample median of each variable. To
measure socioeconomic inequalities, we compute them at the median value of each income and
life-cycle classes, see Tables 2 and 3.
[Insert Tables 1 to 3 here.]
To measure the impact of price variations on household purchases, first, we compute the
food expenditure elasticities (eEX
i ) as follows:
0
eEX
= (diag(W ))−1 [(IJ + BP )−1 B] + 1J ,
i
where W is a vector of budget shares, B =
PR
r=1
β̃ict,r y rct , P = ln pict .
The elasticities are conform with economic theory in the sense that we find positive food
expenditure elasticities. At the global level, we observe higher elasticities (slightly over 1) for
13
soft drinks, coffee and tea, and plant-based foods high in fats.
Disaggregated by income and age classes, food expenditure elasticities do not show much
variation, see Table 3. For the modest households, alcohol and meats are the more budget sensitive, so as for well-off households. There is more variability among food groups than among
income and age classes. Among drinks, soft drinks and bottled water elasticities show some variation with income and life-cycle classes. For example, an increasing budget means a higher increase of soft drinks for older households (reference person over 60 years) and richer ones. Juices
elasticities are higher for modest households, in particular over 60 years. Among other foods,
plant-based fats and fish elasticities are higher with increasing age. Conversely, starchy foods,
processed fruits and vegetables elasticities decrease for all age groups above 30 years.
Second, we compute two kinds of price elasticities. Indeed, the EASI is directly derived
from a cost function, therefore, it estimates Hicksian demand functions. These latter enables to
C
) (or Hicksian price elasticities). They are given
compute compensated price elasticities (eEP
ij
by:
C
= −δij +
eEP
ij
γij
+ wj , where δij = 1 if i = j.
wi
These elasticities give the impact of 1% price increase at a constant utility level. However, to
design policy issues, uncompensated price elasticities are commonly used because they enable to
consider a constant food expenditure in consumer choices. Hence these elasticities are computed
C
with eEX
and eEP
by:
i
ij
NC
C
eEP
= eEP
− wj eEX
ij
ij
i .
As expected, compensated own-price elasticities are negative and all significant, see Table
14
1. Their values are in the same range than Allais, Bertail, and Nichèle (2010)’s results, which
were obtained with an AIDS estimation.
Beverages show the highest price sensitivity, in particular soft drinks. Note that alcohol price
elasticity decreases with age and income, while soft drinks price elasticity increase strongly in
both dimensions (reaching -1.69 for well-off households whose head is over 60). Contrary to
other age groups, juices show a higher elasticity than alcohol above 45 years. Soft drinks and
juices are the products with more price sensitivity variations according to income classes at the
extreme age groups (younger and older households).
Excluding beverages, higher values are observed for products of animal origin: animal fats,
meats excluding beef (i.e.mainly poultry and pork), and yogurts. These foods groups are specially price sensitive. Cooked meats, fish and seafood, and beef price elasticities remain are
around 1.2. Yogurts elasticities vary with income : they are lower for modest households and
increasing over 45 years, in particular for well-off households. Animal fats and meats other than
beef have a price sensitivity driven by income more than by age of the household head. Controlled by age groups, we observe a varying price sensitivity to income for yogurts, animal fats,
and other meats (poultry, pork). Here, well-off households show higher elasticities compared to
modest households. Among plant-based products, processed fruits and vegetables and starchy
foods are the more price sensitive. Plant-based products show less varying elasticities: flat with
income, except starchy foods which are more price sensitive for well-off households in the 45-60
age group. Plant-based fats elasticities are higher for modest households under the age of 45, and
lower after 60. Note that fresh fruits and vegetables price sensitivity is the same among income
classes (but increasing with age of the household head till 60 years). Consequently, when restricting to own-price effects, food groups more suitable for intervention in terms of price sensitivity,
15
are firstly beverages: soft drinks, juices, and bottled water; then animal products: yogurts, animal fats and other meats; among plant-based products: processed fruits and vegetables, starchy
foods, and plant-based fats.
However overall food purchases, energy intake and environmental impacts also depend on
the signs and magnitude of cross-price elasticities between the 21 foods and beverages groups,
and in particular between animal and plant-based products. They are presented in Table 1. They
all have low values since 0.15 is the maximum. This result differs in particular from the high
values obtained by Zhen et al. (2014) in an EASI setting, but based on individual data and not
cohorts which have a smoothing effect due to loss of variability. In our estimation, compared to
own-price elasticities values, cross-price elasticities could in most cases be considered as negligible. We comment in the following the relationships reaching at least 0.1. Those relationships
are substitutions, since all complementarities have low values except in the case of mixed-origin
prepared dishes.
The cross-price elasticities observed (see Table 1) , when significant, show us both kinds of
substitutions: between animal and plant-based products, and within each same origin of products. They also implicate beverages with other food products, showing that liquid and solid part
of the diet interfere strongly. Indeed, focusing on animal products, we find that beef purchases
substitute mainly with cooked meats, but also with plant-based foods high in sugar and prepared
dishes. We observe the same relationships with cooked meats but with a lesser magnitude. Conversely, the group of other meats (mainly pork and poultry) substitute with plant-based foods:
fruits and vegetables products (fresh, processed, juices), starchy foods, mixed-origin prepared
dishes. Fish and seafoods seem to be a special case: they show higher values for substitutions:
with plant-based foods high in sugar (i.e. biscuits and confectionary) and with plant-based dishes.
16
They substitute also with several beverages: alcohol and coffee and tea. Animal fats do not show
substitutions nor complementarities with noticeable values. Concerning cheese purchases, we
find substitutions with mixed-origin prepared dishes and more unexpectedly with fresh fruits and
vegetables, bottled water and soft drinks. Note that Zhen et al. (2014) found also substitution
between cheese and soft drinks. Yogurts purchases substitute with plant-based fats and juices.
Finally, origin-mixed prepared dishes interact with a large number of other food groups. They
substitute mainly with plant-based foods high in sugar, fresh fruits and vegetables, cheese, soft
drinks, other meats.
Turning to income and life-cycle effects differences in price responsiveness, we find evidence of limited differentiation for some food groups. Beverages are driven by age more than
income (older households showing more price sensitivity to juices and lower for soft drinks, bottled water and alcohol). Conversely to previous estimations on French data (Bertail and Caillavet
(2008), Allais, Bertail, and Nichèle (2010)), fruits and vegetables -processed or fresh- have stable
price sensitivity according to the income class, but older households have a higher price sensitivity to processed ones. We find as well that disparities in animal or plant-based foods are more
driven by age than by income at this stage (for example animal fats and other meats while cooked
meats and beef are neutral). Several reasons may explain the weakness of these results. First, it
is known that the level of aggregation of data in food groups (21 in our case) does not allow to
detect differentiation of purchases through brands and quality within a same food group, which
is probably at this level the main strategy of low income households (Beatty (2010)). Second, the
aggregation of data through cohorts, implied by the structure of Kantar data, induces mechanically a loss of variability. Finally, we deal with purchases at the level of the household. Therefore
the variable of age of the household head becomes a proxy for household structure (determined
by the position in the life-cycle : number of members, presence of children) which is a major
17
determinant of the composition of food purchases. Indeed, the two medium age groups (30-45
and 46-60 years), corresponding to working-age adults, show very similar elasticities. The age
variable probably captures most part of the remaining variability left by the cohort structure of
the sample.
NC
, we compute environmental and
Nutrients and environmental elasticities Finally, with eEP
ij
nutrients elasticities (Huang (1996); Huang and Lin (2000); Allais, Bertail, and Nichèle (2010)).
For each indicator `, they are given by:
Ind` = S` × D` ,
(4)
where Ind` denotes the matrix of nutrient and environmental price elasticities, S` is a matrix
including the food share of indicator `, and D` is a matrix of price elasticities. Hence, we measure
the impact of 1% increase of prices on the amount of environmental emissions and nutrient
intakes. Such elasticities enable to compute the impact of a price increase on some targeted food
products on environmental emissions and nutrient intakes. Nutrient response to price changes are
presented in Table 4. They have very low values. This result confirms previous literature (Huang
and Lin (2000); Beatty and LaFrance (2005) ; Allais, Bertail, and Nichèle (2010)).
[Insert Table 4 here.]
Sociodemographic effects Education does not introduce different results (see Table 5): less
educated (less than baccalaureate) or more educated (more than baccalaureate) have the same
sign and range of coefficients for animal products. The presence of children under 15 years is
more relevant: it favours the budget share dedicated to beef (at the level of significativity of 10%8 )
8
For this section the level of 10% is retained because coefficients are not significant at the level of 5 %.
18
and other meats, prepared dishes of mixed origin, yogurts at the expenses of cooked meats and
animal fats. Farmers favour animal fats and cheese, and fish. Executives favour cooked meats,
animal fats, cheese, fish and yogurts.
[Insert Table 5 here.]
5
Impact of taxation : alternative scenarios
Finally, we implement two scenarios of tax, which are equivalent to a VAT increase:
• In the environment-friendly scenario, namely ENV, we increase by 20% the prices of all
products with animal contents. The tax concerns beef, other meats, cooked meats, fats
from animal production, cheese, fish and seafoods, yogurts, and prepared mixed meals.
These goods have a high environmental impact, see Masset et al. (2014).
• In the environment and nutrition-friendly scenario, namely ENV-NUT, we increase by 20%
the prices of food categories most adverse to both environment and health. The tax is
implemented on beef, cooked meats, fats from animal production, cheese, and prepared
mixed-origin dishes.
The results are presented in columns (b) of Table 8. To assess the attractiveness of our two
scenarios, our focus is primarily on the reductions obtained in the environmental impacts through
SO2 , CO2 and N emissions. Then we consider the induced changes in dietary health indicators,
such as the changes in total calories and in related animal sources of nutrients, which can be
positive or negative for health.
At the environmental level, columns (b) of Table 8, the scenario of taxing all animal products
with a 20% rate, ENV, has logically more impact than the mixed scenario, ENV-NUT. ENV
19
decreases emissions from 10% (CO2 and N) to 16.6% (SO2 ). Compared to ENV, ENV-NUT
displays inferior reductions in emissions, in particular for N (-30%). Concerning CO2 , total
yearly reduction would reach 117kg (ENV-NUT) to 142kg (ENV) CO2 equivalent per person9 .
This is comparable to the Danish estimates using consumer data which finds a 112kg to 277kg
reduction when taxing all foods according to their polluting potential (Edjabou and Smed (2013)).
No other literature were found to compare interventions on levels of SO2 and N emissions.
At the nutritional level, reductions in total energy content from purchases vary from -8.8%
in ENV to -5.4% in ENV-NUT. In ENV, interesting reductions concern animal proteins (-21.5%)
and total proteins (-17%). Vegetal proteins also decrease slightly due to the taxation of products mixing animal and vegetal contents (prepared mixed meals and prepared desserts). Health
favorable effects include decreases in saturated fats (-15.6%), cholesterol (-18.7%) or sodium
(-12.5%). However, we observe undesirable effects such as a strong decrease in key nutrients:
vitamin B12 (-20.6%), vitamin D (-18.8%) and iron (-6.4%). In ENV-NUT, total and animal proteins reductions are only half of those observed in ENV. This leads to lower effects on unhealthy
nutrients such as sodium or saturated fats, but also on losses of good nutrients.
In terms of environmental/nutritional compatibility, ENV-NUT appears more balanced: the
environmental impact on CO2 is not so different from ENV, saturated fats and sodium reductions
are still consistent, and good nutrients (here vitamins B12 and D, and iron) losses are more
limited than in ENV. We find that taking into account health constraints is not so costly since it
reduces CO2 by 17.7%.
Income and life-cycle effects For a given income class, we can observe life-cycle effects, see
Table 10. In both scenarios, regarding emissions, age introduces more variations in the well9
Computations are based on daily emissions of food purchases. Details are available upon request.
20
off income class, especially in N emissions, but this effect remains very moderate. From a
nutritional perspective, there are more differentiated effects: reductions in proteins are observed
mainly among the 30-45 years, while reductions in saturated fats are more important among the
60 years and over.
[Insert Table 10 here.]
For a given age group, we observe income class effects. Here, variations in emissions are
a little higher since the rate of change induced by taxation emissions decline from modest to
well-off households. However, the nutritional content of the food basket does not appear to vary
much with income class. Though moderate, these effects suggest that taxation would induce
more differentiated impacts on emissions according to income class, while nutritional changes
would induce more disparities among age groups.
Combining income and age effects, in both scenarios the highest rate of reduction in environmental impacts is observed for the young (less than 30 years) and lower-average income
households (in ENV, -17% for SO2 and -10% for CO2 and N). However in levels, higher total
yearly impacts are obtained for households which are in the middle of the life-cycle. Among
the various environmental indicators we use, the greatest impact is on SO2 emissions, which decrease (around -16%), corresponding to the highest reduction in animal proteins (approximately
-22%).
More precisely focusing on CO2 in the ENV scenario, the highest rate of CO2 reduction
is observed for modest or lower-average income households whose head is under 30. In that
case, yearly reductions amount to 160kg10 . However, with a smallest impact rate, total yearly
reductions reaches 179kg for the modest households which are in the middle of the life-cycle
10
Computations are based on daily emissions for each income and life-cycle classes of food purchases. Details
are available upon request.
21
(household head between 45 and 60 years). The smallest effect accounts for the young and welloff households which has a lower CO2 content of purchases and therefore reaches only 100kg
reduction. Similar impacts can be calculated concerning SO2 and N emissions. These disparities
are partly explained by different patterns of food-at-home consumption, probably more than
by variations in price responsiveness as shown by the elasticities. Trade-offs between the two
scenarios are however larger for the older households, be they well-off or modest (-18.1% loss in
CO2 reduction) than for the younger ones (-16.8%).
6
Conclusions
In a sustainability perspective, this article examined the impact of French food-at-home purchases on both environmental emissions and nutritional intakes. Furthermore, two alternative
price policy scenarios are explored in an attempt to combine environmental and health objectives. For this, we built 21 food groups distinguishing plant-based and animal-based products.
We computed food demand elasticities from the EASI demand system estimated on a sample
covering 13 years (1998-2010). This demand system allows for flexible Engel curves for each
food group. We found evidence of the relevance of this model since the best fit of our estimation
was obtained for a polynomial specification of range 4.
On this basis, we computed the mostly used CO2 emissions elasticities, and also SO2 and
nitrate ones. This is an attempt to widen the range of indicators to take into account different
aspects of pollution. To assess possible health effects, we ran as well nutrient elasticities. Finally,
we computed disaggregated elasticities over income and life cycle-effect classes in order to take
into account the social differentiation of food patterns and price responsiveness. At this point of
research, the price elasticities of emissions and nutrients do not induce very important income
22
disparities, but our results on life-cycle effects are noticeable. We are thus able to address key
issues in food policy design.
Are animal products sensitive to a price policy and possible to target ? Among food groups,
soft drinks, animal fats, yogurts and meats other than beef show the highest responsiveness to
prices. At the same time, we find high values of environmental and nutrient elasticities for
meats and soft drinks. Our results suggest that taxing these food groups may be a tool for an
environmental-friendly policy. This is supported by the fact that we find many interactions in
terms of substitutions between all types of foods, be they liquid, solid, plant-based or animalbased. However, their range remains quite limited compared to own-price values.
Is the environmental impact very sensitive to the use of different indicators ? Our results
evidence that the CO2 emissions, which is the indicator most used in the literature, does not show
the strongest results. We find that the price sensitivity of SO2 emissions is higher. Therefore,
widening the range of indicators studied could be crucial for the conclusions and is a useful input
of our study.
What would be the health implications of an environmental tax designed on animal products? Focusing on eight groups of animal products, which appeared the best candidates to tax on
environment grounds, we could appreciate that the nutritional impact is far from neutral. Strong
favourable effects on fats or cholesterol decreases coincide with undesirable ones, in particular
reduced intakes of key micronutrients such as vitamins and iron. A second scenario which takes
into account also health considerations and applies a price increase to five animal products groups
only, does not yield the same reduction in emissions but limits the unfavourable nutritional effects. Indeed, in the scenarios presented here, an improvement in health impact would lead to
18% loss of CO2 reduction.
23
In conclusion, our study illustrates in the French case the complexity of food demand through
the multiple relationships observed between animal and plant-based food groups. It also shows
the ambiguities of combining environment with nutrition objectives, through the choice of the
food groups on which a price increase should be applied. Obviously, a trade-off cannot be
avoided between both constraints. Our analysis shows that it may be not so costly. Of course,
more economic scenarios should be implemented. For this, a combined set of healthy and
environmental-friendly guidelines from nutritional experts is certainly one issue deserving other
developments in the future. Moreover, this perspective may be improved by computations of
Consumer Surplus to measure these different public policies.
24
Bibliography
Allais, O., P. Bertail, and V. Nichèle. 2010. “The effects of a fat tax on French households’ purchases: a nutritional
approach.” American Journal of Agricultural Economics 92:228–245.
Allen, T. 2010. “Impacts des variations de prix sur la qualité nutritionnelle du panier alimentaire des ménages
français.” PhD dissertation, Université Montpellier 1.
Aston, L.M., J.N. Smith, and J.W. Powles. 2012. “Impact of a reduced red and processed meat dietary pattern on
disease risks and greenhouse gas emissions in the UK: a modelling study.” BMJ open 2.
Beatty, T.K. 2010. “Do the poor pay more for food? Evidence from the United Kingdom.” American Journal of
Agricultural Economics 92:608–621.
Beatty, T.K., and J.T. LaFrance. 2005. “United States demand for food and nutrition in the twentieth century.”
American Journal of Agricultural Economics 87:1159–1166.
Bertail, P., and F. Caillavet. 2008. “Fruit and vegetable consumption patterns: a segmentation approach.” American
Journal of Agricultural Economics 90:827–842.
Blundell, R., and J.M. Robin. 1999. “Estimation in Large and Dissaggregated Demand Systems: An Estimator for
Conditionally Linear Systems.” Journal of Applied Econometrics 14:209–232.
Boeglin, N., C. Bour, and M. David. 2012. “Le contenu carbone du panier de consommation courante.” CGDDSOeS, Observation et Statistiques Environnement 121.
Briggs, A.D., A. Kehlbacher, R. Tiffin, T. Garnett, M. Rayner, and P. Scarborough. 2013. “Assessing the impact on
chronic disease of incorporating the societal cost of greenhouse gases into the price of food: an econometric and
comparative risk assessment modelling study.” BMJ open 3:e003543.
Caillavet, F., C. Lecogne, and V. Nichèle. 2009. “La fracture alimentaire : des inégalités persistantes mais qui se
réduisent.” La Consommation, INSEE Références, pp. 49–62.
Capacci, S., M. Mazzocchi, B. Shankar, J. Brambila Macias, W. Verbeke, F.J. Pérez-Cueto, A. Kozioł-Kozakowska,
B. Piórecka, B. Niedzwiedzka, D. D’Addesa, et al. 2012. “Policies to promote healthy eating in Europe: a
structured review of policies and their effectiveness.” Nutrition reviews 70:188–200.
Chancel, L. 2014. “Are younger generations higher carbon emitters than their elders?: Inequalities, generations and
CO2 emissions in France and in the USA.” Ecological Economics 100:195 – 207.
Cox, T.L., and M.K. Wohlgenant. 1986. “Prices and quality effects in cross-sectional demand analysis.” American
Journal of Agricultural Economics 68:908–919.
Crawford, I., F. Laisney, and I. Preston. 2003. “Estimation of household demand systems with theoretically compatible Engel curves and unit value specifications.” Journal of Econometrics 114:221–241.
Deaton, A. 1988. “Quality, quantity, and spatial variation of price.” The American Economic Review, pp. 418–430.
Deaton, A., and J. Muellbauer. 1980. “An almost ideal demand system.” The American Economic Review 70:312–
326.
Drewnowski, A., E.C. Fiddler, L. Dauchet, P. Galan, and S. Hercberg. 2009. “Diet quality measures and cardiovascular risk factors in France: applying the Healthy Eating Index to the SU. VI. MAX study.” Journal of the
American College of Nutrition 28:22–29.
Edjabou, L.D., and S. Smed. 2013. “The effect of using consumption taxes on foods to promote climate friendly
diets–The case of Denmark.” Food Policy 39:84–96.
25
Gardes, F., G.J. Duncan, P. Gaubert, M. Gurgand, and C. Starzec. 2005. “Panel and pseudo-panel estimation of
cross-sectional and time series elasticities of food consumption: the case of US and polish data.” Journal of
Business & Economic Statistics 23:242–253.
Hébel, P., and F. Recours. 2007. “Effets d’âge et de génération: transformation du modèle alimentaire.” Cahiers de
nutrition et de diététique 42:297–303.
Hoolohan, C., M. Berners-Lee, J. McKinstry-West, and C. Hewitt. 2013. “Mitigating the greenhouse gas emissions
embodied in food through realistic consumer choices.” Energy Policy 63:1065–1074.
Huang, K.S. 1996. “Nutrient elasticities in a complete food demand system.” American Journal of Agricultural
Economics 78:21–29.
Huang, K.S., and B.H. Lin. 2000. Estimation of food demand and nutrient elasticities from household survey data.
1887, US Department of Agriculture, Economic Research Service.
Lewbel, A., and K. Pendakur. 2009. “Tricks with Hicks: The EASI demand system.” The American Economic
Review, pp. 827–863.
Mackenbach, J.P., I. Stirbu, A.J.R. Roskam, M.M. Schaap, G. Menvielle, M. Leinsalu, and A.E. Kunst. 2008. “Socioeconomic inequalities in health in 22 European countries.” New England Journal of Medicine 358:2468–2481.
Masset, G., L.G. Soler, F. Vieux, and N. Darmon. 2014. “Identifying Sustainable Foods: The Relationship between
Environmental Impact, Nutritional Quality, and Prices of Foods Representative of the French Diet.” Journal of
the Academy of Nutrition and Dietetics, pp. .
McMichael, A.J., J.W. Powles, C.D. Butler, and R. Uauy. 2007. “Food, livestock production, energy, climate change,
and health.” The Lancet 370:1253–1263.
Moschini, G. 1995. “Units of measurement and the Stone index in demand system estimation.” American Journal of
Agricultural Economics 77:63–68.
Ovrum, A., G.W. Gustavsen, and K. Rickertsen. 2014. “Age and socioeconomic inequalities in health: Examining
the role of lifestyle choices.” Advances in Life Course Research 19:1–13.
Park, J.L., and O. Capps. 1997. “Demand for prepared meals by US households.” American Journal of Agricultural
Economics 79:814–824.
Scarborough, P., S. Allender, D. Clarke, K. Wickramasinghe, and M. Rayner. 2012. “Modelling the health impact of
environmentally sustainable dietary scenarios in the UK.” European journal of clinical nutrition 66:710–715.
Smed, S., J.D. Jensen, and S. Denver. 2007. “Socio-economic characteristics and the effect of taxation as a health
policy instrument.” Food Policy 32:624–639.
Tiffin, R., and M. Arnoult. 2010. “The demand for a healthy diet: estimating the almost ideal demand system with
infrequency of purchase.” European Review of Agricultural Economics 37:501–521.
Vieux, F., L.G. Soler, D. Touazi, and N. Darmon. 2013. “High nutritional quality is not associated with low greenhouse gas emissions in self-selected diets of French adults.” The American journal of clinical nutrition 97:569–
583.
Westhoek, H., J.P. Lesschen, T. Rood, S. Wagner, A. De Marco, D. Murphy-Bokern, A. Leip, H. van Grinsven, M.A.
Sutton, and O. Oenema. 2014. “Food choices, health and environment: effects of cutting Europe’s meat and dairy
intake.” Global Environmental Change, pp. .
Zhen, C., E.A. Finkelstein, J.M. Nonnemaker, S.A. Karns, and J.E. Todd. 2014. “Predicting the Effects of SugarSweetened Beverage Taxes on Food and Beverage Demand in a Large Demand System.” American Journal of
Agricultural Economics 96:1–25.
26
27
Juic
Alc
Soda
Wat
Cof
FF&V
Grains
VHF
VD
VHS
Starch
PF&V
Beef
OM
CM
AHF
Cheese
Fish
Yogurt
PrepM
Food X
Juic
-1.295
0.091
0.229
0.056
0.098
0.011
0.093
0.185
0.067
-0.002
0.068
0.087
0.091
0.110
0.093
0.161
-0.102
0.020
0.119
0.005
0.988
Alc
0.176
-1.206
0.300
0.072
0.149
0.025
0.104
0.116
0.060
0.274
0.081
0.166
0.050
0.114
0.146
0.120
0.156
0.221
0.116
0.102
0.957
Soda
0.173
0.117
-1.518
0.029
0.080
0.026
0.026
0.045
-0.054
0.013
0.031
0.038
0.033
0.038
0.035
0.073
0.126
0.005
0.056
0.084
1.050
Wat
0.061
0.041
0.041
-1.389
0.030
0.151
0.027
0.073
0.087
0.017
0.146
0.073
0.078
0.015
0.105
0.117
0.227
0.012
0.108
0.092
0.918
Cof
0.090
0.071
0.098
0.025
-0.942
-0.040
0.079
0.039
0.036
0.027
0.023
0.030
0.042
0.017
0.039
0.091
0.003
0.098
0.073
0.054
1.014
FF&V
0.006
0.007
0.018
0.071
-0.022
-1.086
0.011
-0.012
0.021
0.000
0.081
0.031
0.033
0.080
0.041
0.088
0.063
-0.027
0.011
0.070
0.944
Grains
0.028
0.016
0.010
0.007
0.025
0.006
-0.999
0.017
0.013
0.078
-0.004
0.012
0.023
0.025
0.018
-0.006
-0.010
0.028
0.021
-0.030
0.959
VHF
0.099
0.032
0.032
0.036
0.023
-0.013
0.031
-1.171
0.048
0.029
0.124
0.132
0.034
0.040
0.038
0.053
-0.088
0.018
0.095
0.001
1.008
VD
0.051
0.024
-0.055
0.060
0.030
0.032
0.033
0.068
-1.018
0.048
0.044
0.055
0.077
0.033
0.060
0.054
-0.077
0.134
0.062
0.080
0.837
VHS
-0.001
0.102
0.013
0.011
0.021
0.001
0.189
0.039
0.045
-1.078
0.049
0.024
0.079
0.043
-0.033
-0.004
-0.035
0.176
-0.018
0.103
0.988
Table 1: Food Expenditure, Compensated Own and Cross-Price Elasticities
Starch
0.032
0.020
0.020
0.062
0.012
0.074
-0.006
0.108
0.027
0.032
-1.234
0.064
0.026
0.063
0.053
-0.007
-0.012
-0.004
0.039
0.003
0.848
PF&V
0.039
0.039
0.023
0.030
0.014
0.027
0.018
0.111
0.032
0.015
0.061
-1.242
0.026
0.045
0.040
0.026
-0.025
0.040
0.036
0.001
0.853
Beef
0.155
0.044
0.076
0.122
0.078
0.111
0.131
0.109
0.173
0.188
0.095
0.099
-1.113
0.145
0.183
0.082
0.018
0.108
0.143
0.025
0.960
OM
0.129
0.069
0.059
0.016
0.022
0.182
0.099
0.089
0.051
0.070
0.157
0.118
0.099
-1.340
0.070
0.105
0.088
0.090
0.071
0.123
0.957
CM
0.087
0.071
0.043
0.090
0.039
0.075
0.056
0.066
0.073
-0.042
0.106
0.083
0.100
0.056
-1.199
0.123
0.024
0.001
0.076
0.039
0.976
AHF
0.085
0.033
0.051
0.056
0.052
0.091
-0.010
0.052
0.037
-0.003
-0.008
0.031
0.025
0.047
0.069
-1.402
0.007
0.032
0.025
0.014
0.900
Cheese
-0.153
0.122
0.252
0.313
0.005
0.186
-0.051
-0.249
-0.153
-0.074
-0.038
-0.083
0.016
0.114
0.039
0.019
-0.774
-0.007
0.036
0.169
0.374
Fish
0.022
0.124
0.007
0.012
0.116
-0.056
0.101
0.036
0.191
0.264
-0.010
0.097
0.069
0.083
0.001
0.067
-0.005
-1.148
0.085
0.058
0.862
Yogurt
0.141
0.071
0.087
0.117
0.094
0.025
0.083
0.211
0.097
-0.030
0.098
0.094
0.099
0.072
0.096
0.056
0.028
0.093
-1.347
0.035
0.958
PrepM
0.005
0.050
0.107
0.080
0.056
0.130
-0.097
0.002
0.100
0.135
0.007
0.002
0.014
0.100
0.040
0.025
0.106
0.051
0.028
-1.173
0.919
28
Juic
Alc
-1.237
-1.178
Well-off /-;30]
T
-20.596
-11.285
-1.239
-1.201
Well-off /]30;45]
T
-20.792
-12.190
-1.249
-1.204
Well-off /]45;60]
T
-21.465
-12.297
-1.270
-1.152
Well-off /]60;+
T
-22.937
-10.329
-1.334
-1.119
Upper-A. /-;30]
T
-27.449
-9.188
-1.239
-1.201
Upper-A ]30;45]
T
-20.800
-12.207
-1.262
-1.223
Upper-A /]45;60]
T
-22.385
-13.086
-1.243
-1.230
Upper-A /]60;+
T
-21.049
-13.395
-1.284
-1.190
Lower-A /-;30]
T
-23.949
-11.740
-1.360
-1.149
Lower-A /]30;45]
T
-29.194
-10.218
-1.252
-1.238
Lower-A /]45;60]
T
-21.667
-13.704
-1.298
-1.259
Lower-A /]60;+
T
-24.952
-14.650
-1.273
-1.271
Modest /-;30]
T
-23.168
-15.161
-1.304
-1.203
Modest /]30;45]
T
-25.366
-12.251
-1.379
-1.169
Modest /]45;60]
T
-30.498
-10.934
-1.271
-1.237
Modest /]60;+
T
-22.993
-13.680
T-stat are reported below elasticities.
Soda
-1.511
-37.540
-1.444
-32.413
-1.491
-36.040
-1.522
-38.376
-1.661
-48.514
-1.486
-35.593
-1.417
-30.365
-1.510
-37.485
-1.527
-38.755
-1.616
-45.318
-1.517
-37.982
-1.455
-33.243
-1.513
-37.676
-1.506
-37.185
-1.592
-43.599
-1.525
-38.633
Wat
-1.405
-26.063
-1.416
-26.817
-1.405
-26.077
-1.374
-24.061
-1.388
-24.930
-1.415
-26.743
-1.414
-26.649
-1.371
-23.875
-1.356
-22.895
-1.344
-22.129
-1.454
-29.346
-1.417
-26.844
-1.396
-25.453
-1.357
-22.971
-1.350
-22.485
-1.484
-31.421
Cof
-0.940
-18.954
-0.943
-21.098
-0.941
-19.418
-0.940
-19.127
-0.941
-19.653
-0.938
-17.989
-0.943
-21.179
-0.943
-20.745
-0.941
-19.446
-0.939
-18.591
-0.941
-19.474
-0.944
-21.724
-0.945
-22.088
-0.942
-20.078
-0.939
-18.427
-0.942
-20.303
FF&V
-1.096
-43.516
-1.081
-39.118
-1.082
-39.378
-1.086
-40.574
-1.078
-38.373
-1.093
-42.606
-1.084
-40.087
-1.091
-42.000
-1.087
-40.860
-1.075
-37.369
-1.096
-43.592
-1.091
-42.180
-1.095
-43.109
-1.087
-41.013
-1.078
-38.466
-1.093
-42.605
Grains
-0.992
-45.950
-0.993
-46.227
-0.994
-47.194
-0.998
-49.615
-0.999
-50.108
-0.995
-48.033
-0.994
-47.471
-0.997
-49.551
-1.000
-50.282
-1.000
-50.401
-0.998
-49.778
-0.997
-49.414
-1.001
-50.485
-1.001
-50.504
-1.001
-50.504
-1.000
-50.366
VHF
-1.139
-33.973
-1.158
-36.792
-1.166
-37.865
-1.178
-39.447
-1.168
-38.132
-1.139
-33.934
-1.170
-38.443
-1.180
-39.746
-1.175
-39.023
-1.154
-36.209
-1.148
-35.376
-1.182
-39.943
-1.193
-41.211
-1.178
-39.415
-1.149
-35.446
-1.146
-35.120
VD
-1.007
-23.344
-1.010
-24.188
-1.011
-24.328
-1.021
-27.233
-1.025
-28.761
-1.010
-24.122
-1.009
-23.955
-1.016
-25.896
-1.021
-27.336
-1.025
-28.583
-1.013
-24.913
-1.015
-25.591
-1.019
-26.708
-1.021
-27.502
-1.028
-29.568
-1.012
-24.651
VHS
-1.066
-25.336
-1.063
-24.818
-1.072
-26.225
-1.093
-29.098
-1.097
-29.575
-1.063
-24.830
-1.054
-23.389
-1.065
-25.119
-1.089
-28.685
-1.094
-29.298
-1.065
-25.158
-1.043
-21.725
-1.068
-25.619
-1.086
-28.283
-1.093
-29.118
-1.064
-25.081
Starch
-1.228
-48.539
-1.220
-47.249
-1.230
-48.903
-1.267
-55.388
-1.273
-56.481
-1.219
-46.989
-1.216
-46.517
-1.229
-48.785
-1.253
-53.080
-1.257
-53.641
-1.214
-46.074
-1.218
-46.790
-1.225
-48.014
-1.243
-51.350
-1.250
-52.457
-1.205
-44.392
Table 2: Compensated Own-price Elasticities by Income and Life-cycle Classes
PF&V
-1.214
-46.789
-1.207
-45.615
-1.222
-48.357
-1.260
-55.190
-1.261
-55.426
-1.214
-46.903
-1.211
-46.237
-1.227
-49.325
-1.261
-55.381
-1.262
-55.546
-1.217
-47.503
-1.210
-46.165
-1.236
-50.947
-1.260
-55.223
-1.254
-54.190
-1.216
-47.224
Beef
-1.114
-12.920
-1.151
-15.093
-1.122
-13.362
-1.109
-12.625
-1.089
-11.562
-1.120
-13.234
-1.146
-14.788
-1.132
-13.954
-1.109
-12.624
-1.093
-11.798
-1.110
-12.666
-1.137
-14.255
-1.123
-13.400
-1.108
-12.573
-1.086
-11.434
-1.110
-12.676
OM
-1.363
-23.970
-1.417
-27.530
-1.378
-24.900
-1.353
-23.344
-1.346
-22.859
-1.356
-23.534
-1.408
-26.919
-1.368
-24.278
-1.340
-22.495
-1.332
-21.973
-1.322
-21.361
-1.364
-24.040
-1.335
-22.199
-1.322
-21.338
-1.303
-20.207
-1.307
-20.419
CM
-1.217
-27.309
-1.197
-25.120
-1.205
-26.033
-1.216
-27.171
-1.235
-29.243
-1.202
-25.665
-1.194
-24.889
-1.200
-25.464
-1.202
-25.682
-1.221
-27.722
-1.193
-24.725
-1.187
-24.150
-1.182
-23.607
-1.190
-24.434
-1.209
-26.436
-1.182
-23.599
AHF
-1.442
-49.646
-1.466
-51.255
-1.465
-51.189
-1.466
-51.243
-1.439
-49.370
-1.400
-46.267
-1.423
-48.196
-1.421
-48.014
-1.411
-47.249
-1.399
-46.218
-1.372
-43.732
-1.389
-45.345
-1.375
-44.068
-1.374
-43.970
-1.345
-41.105
-1.358
-42.418
Cheese
-0.757
-11.540
-0.759
-11.401
-0.777
-9.677
-0.783
-8.743
-0.785
-8.425
-0.757
-11.517
-0.768
-10.671
-0.779
-9.442
-0.782
-8.998
-0.783
-8.856
-0.760
-11.327
-0.767
-10.726
-0.780
-9.386
-0.780
-9.366
-0.778
-9.612
-0.758
-11.486
Fish
-1.121
-18.297
-1.159
-21.903
-1.143
-20.385
-1.116
-17.921
-1.093
-15.968
-1.133
-19.466
-1.172
-23.119
-1.157
-21.651
-1.137
-19.806
-1.107
-17.090
-1.145
-20.518
-1.186
-24.601
-1.166
-22.540
-1.147
-20.758
-1.105
-16.933
-1.151
-21.147
Yogurt
-1.383
-24.454
-1.386
-24.684
-1.385
-24.606
-1.460
-29.401
-1.491
-31.372
-1.342
-21.924
-1.300
-19.452
-1.296
-19.232
-1.388
-24.777
-1.445
-28.392
-1.293
-19.066
-1.227
-15.414
-1.243
-16.279
-1.345
-22.135
-1.416
-26.552
-1.282
-18.405
PrepM
-1.169
-23.621
-1.164
-23.124
-1.168
-23.545
-1.174
-24.185
-1.177
-24.488
-1.171
-23.861
-1.154
-22.153
-1.169
-23.613
-1.174
-24.118
-1.201
-26.915
-1.176
-24.333
-1.158
-22.522
-1.167
-23.414
-1.178
-24.541
-1.207
-27.611
-1.171
-23.856
29
Juic
Alc
Soda
Wat
Cof
FF&V
Grains
VHF
VD
VHS
Starch
PF&V
Beef
OM
CM
AHF
Cheese
Fish
Yogurt
PrepM
LowerAverage
/-;30]
0.765
0.963
0.631
0.931
0.901
0.864
0.804
0.874
0.894
0.912
0.855
0.858
0.944
0.927
0.921
0.840
0.835
0.886
0.936
0.918
LowerAverage
/]30;45]
0.784
0.966
0.638
0.939
0.918
0.877
0.816
0.881
0.904
0.915
0.865
0.864
0.954
0.940
0.927
0.856
0.879
0.904
0.942
0.925
LowerAverage
/]45;60]
0.777
0.971
0.633
0.945
0.922
0.878
0.797
0.879
0.896
0.906
0.851
0.850
0.958
0.945
0.927
0.861
0.893
0.917
0.935
0.926
LowerAverage
/]60;+
0.734
0.973
0.540
0.942
0.918
0.883
0.785
0.882
0.889
0.902
0.846
0.848
0.960
0.945
0.921
0.867
0.897
0.924
0.930
0.924
0.777
0.965
0.687
0.938
0.912
0.876
0.815
0.880
0.905
0.926
0.872
0.871
0.951
0.936
0.930
0.870
0.864
0.895
0.952
0.929
Modest
/-;30]
0.807
0.967
0.664
0.948
0.921
0.882
0.811
0.885
0.907
0.927
0.878
0.875
0.957
0.946
0.935
0.881
0.892
0.909
0.957
0.932
Modest
/]30;45]
0.785
0.971
0.657
0.950
0.926
0.885
0.799
0.888
0.903
0.914
0.867
0.860
0.961
0.950
0.935
0.884
0.898
0.916
0.947
0.931
Modest
/]45;60]
0.738
0.973
0.600
0.950
0.928
0.894
0.790
0.897
0.897
0.910
0.864
0.857
0.963
0.950
0.930
0.885
0.899
0.925
0.940
0.924
Modest
/]60;+
UpperAverage
/-;30]
0.769
0.964
0.683
0.940
0.914
0.876
0.805
0.880
0.904
0.936
0.878
0.878
0.955
0.945
0.936
0.885
0.871
0.894
0.962
0.931
UpperAverage
/]30;45]
0.806
0.967
0.684
0.949
0.922
0.887
0.802
0.887
0.911
0.931
0.888
0.882
0.962
0.954
0.944
0.900
0.901
0.913
0.964
0.937
Table 3: Food Expenditure Elasticities by Income and Life-cycle Classes
UpperAverage
/]45;60]
0.782
0.970
0.676
0.952
0.926
0.889
0.791
0.890
0.906
0.919
0.876
0.865
0.962
0.953
0.940
0.897
0.898
0.915
0.953
0.932
UpperAverage
/]60;+
0.731
0.972
0.617
0.951
0.930
0.893
0.785
0.901
0.896
0.912
0.870
0.864
0.964
0.954
0.934
0.901
0.892
0.927
0.944
0.923
0.733
0.962
0.636
0.928
0.901
0.874
0.775
0.870
0.900
0.926
0.873
0.868
0.953
0.944
0.931
0.878
0.851
0.885
0.960
0.929
Well-off /;30]
0.773
0.966
0.674
0.945
0.913
0.882
0.760
0.883
0.907
0.927
0.885
0.874
0.961
0.955
0.943
0.902
0.885
0.904
0.966
0.934
Well-off
/]30;45]
0.752
0.968
0.664
0.945
0.916
0.882
0.749
0.882
0.896
0.912
0.873
0.854
0.960
0.952
0.936
0.899
0.882
0.902
0.954
0.927
Well-off
/]45;60]
0.655
0.967
0.597
0.945
0.916
0.885
0.741
0.890
0.880
0.903
0.852
0.845
0.959
0.950
0.927
0.891
0.871
0.916
0.942
0.917
Well-off
/]60;+
30
Alc
Nutrient Elasticities
Energy
Proteins
Animal Proteins
Vegetal Proteins
Saturated fats
Cholesterol
Vitamin B12
Vitamin D
Iron
Sodium
-0.057
-0.025
0.000
-0.115
0.000
0.000
0.000
0.000
-0.089
-0.005
-0.071
-0.009
0.000
-0.039
0.000
0.000
-0.021
0.000
-0.080
-0.006
Environmental Emissions Elasticities
CO2
-0.104
-0.126
-0.053
-0.066
SO2
N
-0.105
-0.074
Juic
-0.076
-0.000
0.000
-0.002
0.000
0.000
0.000
0.000
-0.006
-0.013
-0.065
-0.028
-0.050
Soda
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-0.008
-0.146
-0.046
-0.062
Wat
-0.004
-0.019
0.000
-0.086
-0.000
0.000
0.000
0.000
-0.021
-0.005
-0.004
-0.002
-0.004
Cof
-0.012
-0.009
-0.000
-0.038
-0.001
-0.001
-0.000
-0.001
-0.021
-0.004
-0.026
-0.012
-0.019
FF&V
-0.015
-0.015
0.000
-0.067
-0.004
0.000
0.000
0.000
-0.339
-0.016
-0.026
-0.014
-0.017
Grains
-0.187
-0.001
0.000
-0.006
-0.152
-0.000
0.000
0.000
0.000
-0.006
-0.029
-0.011
-0.161
VHF
-0.031
-0.014
-0.005
-0.041
-0.026
-0.021
-0.004
-0.019
-0.014
-0.098
-0.019
-0.007
-0.017
VD
-0.077
-0.026
-0.009
-0.073
-0.044
-0.012
-0.012
-0.050
-0.038
-0.018
-0.025
-0.024
-0.041
VHS
Table 4: Environmental Indicators and Nutrient Elasticities per Food Group
-0.073
-0.062
-0.002
-0.277
-0.006
-0.010
-0.002
-0.019
-0.057
-0.042
-0.031
-0.017
-0.027
Starch
-0.019
-0.013
-0.002
-0.052
-0.016
-0.012
-0.003
-0.010
-0.030
-0.076
-0.023
-0.009
-0.036
PF&V
-0.036
-0.149
-0.187
-0.028
-0.039
-0.082
-0.152
-0.002
-0.089
-0.015
-0.240
-0.437
-0.177
Beef
-0.047
-0.158
-0.208
-0.000
-0.051
-0.327
-0.176
-0.231
-0.076
-0.042
-0.067
-0.128
-0.144
OM
-0.029
-0.070
-0.090
-0.000
-0.043
-0.068
-0.105
-0.038
-0.038
-0.136
-0.053
-0.087
-0.101
CM
-0.099
-0.014
-0.019
-0.000
-0.332
-0.193
-0.004
-0.147
-0.008
-0.032
-0.088
-0.138
-0.057
AHF
-0.059
-0.117
-0.148
-0.000
-0.153
-0.093
-0.119
-0.051
-0.019
-0.125
-0.005
-0.008
-0.002
Cheese
-0.014
-0.074
-0.096
0.000
-0.005
-0.044
-0.169
-0.396
-0.023
-0.035
-0.021
-0.009
-0.006
Fish
-0.108
-0.208
-0.276
-0.002
-0.099
-0.073
-0.259
-0.043
-0.026
-0.103
-0.000
-0.000
-0.000
Yogurt
-0.051
-0.063
-0.051
-0.083
-0.058
-0.058
-0.045
-0.032
-0.041
-0.144
-0.025
-0.025
-0.029
PrepM
31
0 Child
T
Child<15
T
<Bac
T
Bac
T
>Bac
T
CSP1
T
CSP2
T
CSP3
T
CSP4
T
CSP5
T
CSP6
T
CSP7
T
HomeOw
T
Juic
-0.123
-3.913
0.027
1.297
0.124
7.569
0.057
5.530
0.059
4.936
0.000
-4.201
-0.019
-3.503
-0.037
-2.208
-0.110
-4.883
-0.099
-4.403
-0.093
-4.337
-0.109
-2.916
-0.009
-0.726
Alc
0.027
1.113
-0.032
-1.922
0.086
6.690
0.008
0.959
0.055
5.952
0.000
3.803
0.010
2.465
-0.047
-3.597
-0.041
-2.345
-0.037
-2.112
-0.072
-4.245
-0.037
-1.273
-0.079
-8.125
Soda
0.127
4.357
-0.055
-2.836
-0.002
-0.145
0.014
1.414
-0.028
-2.521
0.000
-2.350
-0.010
-2.059
-0.048
-3.086
-0.146
-6.945
-0.169
-7.930
-0.104
-5.146
-0.272
-7.817
0.067
5.805
Wat
-0.144
-9.244
-0.004
-0.376
-0.012
-1.520
0.001
0.210
-0.044
-7.345
0.000
-4.319
-0.015
-5.630
-0.012
-1.418
-0.016
-1.450
-0.015
-1.317
0.030
2.776
-0.023
-1.227
0.033
5.499
Table 5: Socio-economic elasticities
Cof
0.065
3.940
-0.056
-5.205
0.062
7.244
0.015
2.788
0.051
8.076
0.000
-1.836
-0.006
-2.237
-0.014
-1.577
-0.012
-1.055
-0.019
-1.565
-0.022
-1.981
0.041
2.119
-0.032
-5.042
FF&V
-0.009
-0.387
-0.088
-5.616
-0.068
-5.485
0.014
1.810
0.001
0.160
0.000
0.460
0.000
-0.001
-0.008
-0.607
0.017
0.978
0.011
0.633
0.085
5.238
0.005
0.172
0.047
5.048
Grains
-0.046
-0.798
-0.077
-1.982
0.000
-0.015
-0.041
-2.116
-0.030
-1.331
0.000
-1.637
-0.010
-1.030
-0.049
-1.622
-0.126
-3.068
-0.133
-3.214
-0.098
-2.487
-0.124
-1.824
0.047
2.024
VHF
-0.036
-1.832
-0.005
-0.407
0.007
0.692
0.002
0.405
-0.023
-3.083
0.000
-2.071
0.002
0.467
-0.052
-4.917
-0.065
-4.521
-0.027
-1.801
-0.062
-4.173
-0.119
-4.854
0.042
5.678
VD
0.192
7.318
0.096
5.458
-0.100
-7.283
-0.022
-2.557
-0.032
-3.267
0.000
-1.978
-0.010
-2.149
-0.002
-0.113
0.039
2.101
0.053
2.814
0.014
0.759
-0.078
-2.503
0.002
0.161
VHS
-0.111
-4.541
-0.062
-3.912
0.003
0.227
0.010
1.326
-0.040
-4.278
0.000
2.512
-0.007
-1.658
0.025
1.879
0.012
0.683
-0.033
-1.853
-0.015
-0.869
0.046
1.566
-0.011
-1.135
Starch
0.074
5.402
0.033
3.782
-0.063
-9.008
-0.021
-4.855
-0.030
-5.896
0.000
-0.002
0.003
1.469
0.021
2.805
0.033
3.362
0.025
2.393
0.029
2.862
0.042
2.518
0.018
3.375
PF&V
0.096
7.338
0.037
4.407
0.018
2.753
0.012
2.993
0.020
4.120
0.000
0.806
0.001
0.640
-0.001
-0.154
-0.019
-1.952
0.012
1.162
-0.015
-1.505
-0.037
-2.310
-0.026
-5.332
Beef
-0.010
-0.303
0.038
1.758
-0.100
-5.950
-0.052
-4.886
-0.081
-6.654
0.000
-0.841
-0.001
-0.178
-0.016
-1.028
-0.006
-0.286
0.023
1.082
-0.071
-3.419
-0.168
-4.631
0.070
5.466
OM
-0.004
-0.230
0.026
2.137
-0.049
-5.157
-0.007
-1.182
-0.075
-10.792
0.000
-1.149
0.000
-0.035
0.011
1.166
0.032
2.437
0.003
0.224
-0.016
-1.244
-0.059
-2.747
0.051
7.200
CM
-0.080
-6.496
-0.030
-3.794
-0.006
-0.946
0.009
2.297
0.017
3.571
0.000
0.030
0.007
3.548
0.024
3.686
0.078
8.742
0.049
5.340
0.053
6.024
0.121
8.185
-0.012
-2.433
AHF
-0.026
-0.857
-0.052
-2.560
0.021
1.337
0.010
0.952
0.034
2.887
0.000
4.419
0.005
0.906
0.030
1.830
0.039
1.750
0.036
1.634
0.078
3.649
0.103
2.825
-0.013
-1.108
Cheese
-0.030
-1.090
0.002
0.101
0.109
7.564
0.046
5.035
0.111
10.537
0.000
2.029
0.010
2.236
0.030
2.061
0.045
2.278
0.039
1.983
0.072
3.856
0.260
8.059
-0.101
-9.263
Fish
0.087
4.193
0.018
1.289
-0.046
-4.224
-0.056
-8.190
-0.042
-5.307
0.000
1.892
0.002
0.580
0.030
2.743
0.053
3.573
0.035
2.320
0.019
1.298
0.028
1.136
0.027
3.267
Yogurt
-0.034
-2.775
0.058
7.206
-0.027
-4.166
0.022
5.680
-0.020
-4.262
0.000
0.424
0.005
2.585
0.043
6.478
0.032
3.513
0.021
2.239
0.030
3.335
-0.005
-0.341
0.024
5.137
PrepM
0.163
8.542
0.097
7.741
-0.085
-8.571
-0.055
-8.920
0.018
2.458
0.000
0.680
0.004
1.231
0.005
0.490
0.013
0.980
0.051
3.607
0.127
9.546
0.191
8.425
0.004
0.572
(a) CO2
(b) SO2
(c) N
Figure 1: Contribution of Aggregated Food Groups to CO2 , SO2 and Nitrates Emissions
32
Table 6: Food Groups Sample Mean Budget-Shares and log-prices
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Food Groups
Labels
Juices
Alcohol
Soft drinks
Bottled water
Coffee and tea
Fresh fruits and vegetables
Grains and condiments
Plant-based foods high in fats
Plant-based dishes
Plant-based foods high in sugar
Starchy foods
Processed fruits and vegetables
Beef
Other meats (lamb, chicken, pork)
Cooked meats
Animal-based foods high in fats
Cheese
Fish and seafoods
Yogurts
Prepared mixed meals
Prepared desserts
Juic
Alc
Soda
Wat
Cof
FF&V
Grains
VHF
VD
VHS
Starch
PF&V
Beef
OM
CM
AHF
Cheese
Fish
Yogurt
PrepM
PrepD
Budget shares (wi )
Mean
Std. Dev
0.051
0.011
0.100
0.025
0.039
0.011
0.055
0.010
0.046
0.007
0.027
0.005
0.015
0.004
0.027
0.005
0.038
0.009
0.038
0.009
0.023
0.003
0.023
0.003
0.087
0.020
0.059
0.009
0.047
0.006
0.027
0.004
0.079
0.020
0.056
0.013
0.062
0.014
0.049
0.010
0.052
0.008
33
log-price (ln pi )
Mean Std. Dev.
-11.235
0.893
-10.752
1.134
0.949
-11.715
-12.814
0.918
-9.795
1.249
-12.432
1.230
-10.221
1.251
1.106
-10.63
1.090
-9.690
-11.213
1.072
-12.101
1.099
1.130
-11.992
-9.175
1.107
-10.670
1.020
-10.462
1.139
-10.834
1.091
-10.524
1.058
-9.831
1.032
0.961
-12.266
-10.897
1.112
-11.306
1.082
Table 7: Proportion of Households for Each Sociodemographic Variable
Sociodemographic Variables
Mean
Std. Dev.
0.501
0.338
0.417
0.153
0.235
0.527
0.333
0.308
0.167
0.084
0.204
0.246
0.012
0.025
0.127
0.185
0.174
0.176
0.261
0.023
0.023
0.149
0.137
0.115
0.159
0.364
0.085
0.205
0.249
0.175
0.285
0.162
0.213
0.175
0.153
0.313
0.720
0.535
0.492
0.130
0.166
0.155
Into the demand system
Without child
With at least one child (<15)
Low degree diploma
Level of baccalaureate
Baccalaureate and higher degree
Home owners
Socio-professional category
Farmers
Craftsmen
Executives
Intermediary profession
Employees
Workers
Retired
Into the unit value equations
Income ] ; 900[
Income [900; 1500[
Income [1500; 2300[
Income [2300; 3000[
Income [3000; [
Durable ownership
Fryer
Dish washer
Freezer
34
Table 8: Environmental Emissions, Nutrient Intakes and Percentage of Quantity Change
in Total Purchased with 20 % Targeted Taxes
Variable
Unit
Average Household Purchases
Daily Equivalent
Descriptive Statistics
(a)
Mean
Std. Dev.
Percentage of Quantity Change
Impact of Tax (%)
(b)
ENV
ENV-NUT
Environmental Emissions
CO2
g eq. CO2
SO2
g eq. SO2
N
g eq. N
3913.817
44.615
15.120
1313.980
14.790
4.769
-9.987
-16.621
-10.286
-8.224
-13.877
-7.297
Nutrient Intakes
Energy
Proteins
Vegetal Proteins
Animal Proteins
Saturated fats
Cholesterol
Vitamin B12
Vitamin D
Iron
Sodium
3081.496
102.641
22.680
78.178
58.885
494.838
7.152
2.498
18.500
3868.042
833.159
26.915
8.927
21.250
15.776
143.36
1.839
0.755
26.876
2304.655
-8.858
-17.034
-2.286
-21.502
-15.596
-18.767
-20.586
-18.767
-6.409
-12.515
-5.499
-8.283
-2.236
-9.941
-12.510
-9.924
-8.586
-5.432
-3.917
-8.956
kcal
g
g
g
g
mg
μg
μg
mg
mg
35
Table 9: Unit Value Equations per Food Group
Juic.
ln(q)
ln(x)
Income [900; 1500[
Income [1500; 2300[
Income [2300; 3000[
Income [3000; [
Fryer
Dish washer
Freezer
Cons
ln(q)
ln(x)
Income [900; 1500[
Income [1500; 2300[
Income [2300; 3000[
Income [3000; [
Fryer
Dish washer
Freezer
Cons
-0.732‡
(0.005)
0.966‡
(0.026)
-0.079‡
(0.028)
0.110‡
(0.033)
0.273‡
(0.044)
0.266‡
(0.048)
-0.043*
(0.026)
-0.085‡
(0.025)
-0.165‡
(0.024)
-3.410‡
(0.074)
Alc.
-0.803‡
(0.006)
1.106‡
(0.037)
-0.304‡
(0.039)
-0.075
(0.047)
-0.065
(0.062)
-0.089
(0.068)
-0.103‡
(0.036)
-0.096‡
(0.034)
-0.447‡
(0.033)
-1.485‡
(0.100)
Soda
Water
-0.701‡
(0.005)
0.905‡
(0.033)
0.202‡
(0.035)
0.070*
(0.041)
0.195‡
(0.054)
0.137†
(0.060)
-0.230‡
(0.032)
-0.219‡
(0.031)
-0.052*
(0.030)
-3.918‡
(0.084)
-0.780‡
(0.005)
0.955‡
(0.027)
-0.118‡
(0.028)
-0.138‡
(0.033)
-0.157‡
(0.044)
-0.219‡
(0.048)
0.086‡
(0.026)
0.139‡
(0.025)
0.003
(0.024)
-3.177‡
(0.085)
Cof.
-1.002‡
(0.005)
0.817‡
(0.026)
-0.151‡
(0.029)
-0.087†
(0.034)
-0.079*
(0.044)
-0.146‡
(0.049)
0.015
(0.026)
-0.019
(0.025)
-0.282‡
(0.024)
0.787‡
(0.071)
F.F&V.
Grains
V.H.F.
-0.909‡
(0.005)
0.768‡
(0.025)
-0.071‡
(0.027)
-0.131‡
(0.032)
-0.223‡
(0.042)
-0.280‡
(0.046)
0.198‡
(0.025)
-0.055†
(0.024)
0.011
(0.023)
-1.350‡
(0.075)
-0.981‡
(0.005)
0.931‡
(0.037)
-0.414‡
(0.040)
-0.735‡
(0.047)
-0.876‡
(0.062)
-0.899‡
(0.068)
-0.023
(0.037)
0.356‡
(0.035)
-0.041
(0.034)
-0.609‡
(0.086)
-0.896‡
(0.005)
0.637‡
(0.024)
-0.113‡
(0.026)
-0.205‡
(0.030)
-0.242‡
(0.040)
-0.289‡
(0.044)
0.079‡
(0.023)
0.104‡
(0.022)
-0.165‡
(0.022)
-0.965‡
(0.065)
P.F&V.
Beef
O.M.
C.M.
A.H.F.
Cheese
-0.971‡
(0.004)
0.674‡
(0.017)
-0.013
(0.019)
0.116‡
(0.022)
0.204‡
(0.029)
0.227‡
(0.032)
0.014
(0.017)
-0.096‡
(0.016)
-0.146‡
(0.016)
-0.407‡
(0.057)
-0.865‡
(0.006)
1.148‡
(0.037)
0.032
(0.039)
0.146‡
(0.046)
0.123†
(0.061)
0.151†
(0.067)
-0.125‡
(0.036)
0.110‡
(0.034)
-0.043
(0.033)
-1.373‡
(0.085)
-0.841‡
(0.005)
0.910‡
(0.026)
0.008
(0.027)
0.096‡
(0.032)
0.138‡
(0.042)
0.132‡
(0.047)
0.034
(0.025)
0.112‡
(0.024)
-0.049†
(0.023)
-1.812‡
(0.074)
-0.910‡
(0.004)
0.745‡
(0.019)
0.226‡
(0.021)
0.326‡
(0.024)
0.423‡
(0.032)
0.428‡
(0.035)
-0.086‡
(0.019)
-0.079‡
(0.018)
-0.036†
(0.018)
-0.868‡
(0.054)
-0.894‡
(0.004)
0.746‡
(0.018)
-0.047†
(0.019)
-0.046†
(0.023)
-0.030
(0.030)
0.005
(0.033)
0.053‡
(0.018)
0.052‡
(0.017)
-0.011
(0.017)
-1.387‡
(0.056)
-0.826‡
(0.006)
0.972‡
(0.023)
0.139‡
(0.025)
0.283‡
(0.029)
0.410‡
(0.039)
0.401‡
(0.043)
-0.005
(0.023)
0.061‡
(0.022)
-0.260‡
(0.021)
-2.125‡
(0.079)
Fish
-0.876‡
(0.006)
0.889‡
(0.028)
-0.124‡
(0.030)
-0.065*
(0.035)
-0.110†
(0.047)
-0.101†
(0.051)
0.029
(0.027)
0.095‡
(0.026)
-0.173‡
(0.025)
-0.840‡
(0.077)
Yogurt
-0.836‡
(0.004)
0.867‡
(0.019)
0.172‡
(0.020)
0.207‡
(0.024)
0.301‡
(0.032)
0.363‡
(0.035)
-0.002
(0.019)
0.089‡
(0.018)
0.092‡
(0.018)
-2.222‡
(0.064)
V. dishes
-0.892‡
(0.005)
0.933‡
(0.036)
-0.015
(0.039)
0.146‡
(0.046)
0.150†
(0.061)
0.052
(0.067)
0.066*
(0.036)
-0.105‡
(0.034)
-0.084†
(0.033)
-1.335‡
(0.085)
V.H.S.
Starch.
-0.886‡
(0.006)
0.694‡
(0.028)
-0.018
(0.030)
0.152‡
(0.035)
0.311‡
(0.047)
0.256‡
(0.051)
-0.265‡
(0.027)
0.071‡
(0.026)
-0.002
(0.026)
-0.748‡
(0.085)
-0.937‡
(0.004)
0.615‡
(0.017)
-0.013
(0.018)
0.026
(0.021)
0.073‡
(0.028)
0.059*
(0.030)
0.005
(0.016)
-0.019
(0.016)
-0.006
(0.015)
-0.821‡
(0.054)
Prep. meals
Prep. des.
-0.992‡
(0.006)
0.949‡
(0.029)
0.061†
(0.031)
-0.190‡
(0.037)
-0.353‡
(0.048)
-0.413‡
(0.053)
0.312‡
(0.028)
0.229‡
(0.027)
0.087‡
(0.026)
-0.124
(0.090)
-0.894‡
(0.004)
0.771‡
(0.018)
0.071‡
(0.019)
0.174‡
(0.023)
0.314‡
(0.030)
0.256‡
(0.033)
-0.019
(0.018)
0.007
(0.017)
-0.026
(0.017)
-1.111‡
(0.060)
Notes: Robust standard errors are bellow coefficients. Significance levels: ‡=1% ; †=5% ; *=10%.
Juic.=Juices; Alc.=Alcohol; Soda=Soft drinks; Water=Botteld water; Cof.=Coffee/tea; F. F&V=Fresh fruits and vegetables; V.H.F.=Plant-based foods high in fats; V.
dishes=Plant-based foods dishes; V.H.S.=Plant-based foods high in sugar; Starch.=Starchy foods; P. F&V=Processed fruits and vegetables; O. M.=Other meats, e.g.
Lamb poultry, pork, etc.; C. M.=Cooked meats, e.g. ham; A.H.F.=Animal-based foods with high fat content; Cheese=Cheese; Fish=Fish and seafoods; Yogurts; Prep.
meals=prepared meals; Prep. des=prepared desserts.
Year, four-week period dummies and year dummies are not reported in interests of space. They are available upon request.
36
Table 10: Percentage of Quantity Change in Total Purchased with 20 % Targeted Taxes
ENVNUT
Well-Off
ENV
CO2
SO2
N
Energy
Proteins
Vegetal Proteins
Animal Proteins
Saturated Fats
Cholesterol
Vitamin B12
Vitamin D
Iron
Sodium
-9.085
-15.791
-9.594
-8.365
-16.198
-2.075
-20.745
-14.536
-18.305
-19.582
-17.802
-6.492
-12.250
-7.565
-13.377
-6.970
-5.067
-7.884
-2.019
-9.509
-11.606
-8.979
-7.553
-4.452
-3.854
-8.576
CO2
SO2
N
Energy
Proteins
Vegetal Proteins
Animal Proteins
Saturated Fats
Cholesterol
Vitamin B12
Vitamin D
Iron
Sodium
-10.003
-16.608
-10.629
-9.035
-17.034
-2.039
-21.472
-15.463
-18.711
-20.751
-18.372
-6.557
-12.513
-8.256
-13.957
-7.648
-5.576
-8.528
-1.978
-10.227
-12.463
-9.741
-8.640
-4.938
-3.949
-8.948
CO2
SO2
N
Energy
Proteins
Vegetal Proteins
Animal Proteins
Saturated Fats
Cholesterol
Vitamin B12
Vitamin D
Iron
Sodium
-9.828
-16.429
-10.308
-8.793
-17.010
-2.034
-21.328
-15.772
-18.936
-20.702
-19.141
-6.195
-12.901
-7.976
-13.524
-7.310
-5.757
-8.980
-1.972
-10.822
-13.042
-10.479
-9.578
-5.630
-3.843
-9.732
CO2
SO2
N
Energy
Proteins
Vegetal Proteins
Animal Proteins
Saturated Fats
Cholesterol
Vitamin B12
Vitamin D
Iron
Sodium
-10.066
-16.579
-10.377
-8.978
-17.092
-2.055
-21.277
-16.424
-19.116
-20.905
-19.585
-5.793
-12.939
-8.242
-13.721
-7.531
-5.978
-9.166
-1.996
-10.991
-13.742
-11.489
-9.997
-6.410
-3.690
-9.890
ENVENVENV
NUT
NUT
Upper-Average
Lower-Average
Age less than 30
-9.848
-8.225
-10.318 -8.612
-16.532 -14.059 -16.959 -14.386
-10.173 -7.390
-10.470 -7.493
-9.142
-5.269
-9.506
-5.271
-17.710 -7.863
-18.074 -7.487
-2.346
-2.291
-2.488
-2.436
-22.127 -9.320
-22.490 -8.811
-15.054 -11.843 -15.374 -11.827
-18.532 -9.235
-18.802 -9.272
-21.233 -7.484
-21.530 -7.100
-18.357 -4.671
-18.553 -4.951
-6.802
-4.038
-7.023
-4.119
-13.073 -8.594
-13.822 -8.431
Age between 30 and 45
-10.001 -8.199
-10.290 -8.472
-16.670 -13.879 -16.952 -14.162
-10.496 -7.397
-10.601 -7.454
-9.325
-5.488
-9.489
-5.551
-17.530 -7.981
-17.788 -8.018
-2.135
-2.080
-2.327
-2.273
-22.005 -9.564
-22.283 -9.553
-15.467 -12.180 -15.762 -12.241
-18.602 -9.460
-18.764 -9.443
-21.178 -8.024
-21.482 -8.075
-18.227 -5.077
-18.440 -5.278
-6.627
-3.943
-6.796
-4.042
-13.269 -8.849
-13.542 -8.969
Age between 45 and 60
-9.885
-8.084
-9.804
-8.048
-16.550 -13.732 -16.524 -13.733
-10.364 -7.303
-10.197 -7.142
-8.918
-5.712
-8.889
-5.619
-17.196 -8.779
-17.316 -8.513
-2.190
-2.132
-2.241
-2.188
-21.669 -10.576 -21.949 -10.227
-15.779 -12.846 -16.109 -13.013
-18.776 -10.242 -19.091 -10.201
-20.914 -9.451
-20.990 -9.133
-18.873 -5.612
-19.009 -5.689
-6.350
-3.919
-6.317
-3.879
-13.132 -9.628
-13.324 -9.599
Age more than 60
-10.044 -8.215
-10.174 -8.399
-16.668 -13.795 -16.857 -14.061
-10.315 -7.355
-10.260 -7.327
-8.833
-5.810
-8.729
-5.712
-17.091 -8.923
-17.180 -8.765
-2.068
-2.011
-2.220
-2.171
-21.570 -10.744 -21.679 -10.535
-16.261 -13.528 -16.426 -13.636
-19.183 -11.207
-19.417 -11.524
37
-21.073 -9.963
-21.178 -10.079
-19.730 -6.315
-19.907 -6.605
-5.820
-3.684
-5.862
-3.743
-13.236 -9.928
-13.155 -9.716
ENV
ENV
ENVNUT
Modest
-10.316
-16.842
-10.276
-9.169
-17.646
-2.744
-22.135
-15.366
-19.002
-21.289
-18.386
-7.191
-13.299
-8.581
-14.231
-7.344
-5.220
-7.651
-2.701
-8.973
-11.633
-9.042
-7.202
-4.667
-4.271
-8.659
-10.308
-16.907
-10.391
-9.399
-17.938
-2.612
-22.470
-15.732
-19.062
-21.496
-18.678
-6.925
-13.573
-8.501
-14.101
-7.280
-5.405
-7.787
-2.565
-9.238
-11.950
-9.236
-7.735
-5.173
-4.101
-8.935
-9.713
-16.442
-9.812
-8.730
-17.286
-2.348
-22.043
-16.188
-19.135
-21.118
-18.905
-6.220
-13.006
-8.065
-13.795
-6.885
-5.485
-8.199
-2.306
-9.917
-12.969
-10.189
-8.746
-5.897
-3.821
-9.297
-10.059
-16.698
-10.053
-8.600
-17.285
-2.277
-21.811
-16.399
-19.345
-21.078
-19.784
-6.074
-13.114
-8.241
-13.857
-7.029
-5.487
-8.339
-2.236
-9.960
-13.374
-11.009
-9.353
-6.409
-3.777
-9.715