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1
www.esri.ie
Working Paper No. 340
March 2010
An Estimate of the Number of Vegetarians in the World
Eimear Leahy
a
, Seán Lyons
a
and Richard S.J. Tol
a,b,c
Corresponding Author: Richard.Tol@esri.ie
a
Economic and Social Research Institute, Dublin, Ireland
b
Institute for Environmental Studies, Vrije Universiteit, Amsterdam, The Netherlands
c
Department of Spatial Economics, Vrije Universiteit, Amsterdam, The Netherlands
ESRI working papers represent un-refereed work-in-progress by members who are solely
responsible for the content and any views expressed therein. Any comments on these papers will
be welcome and should be sent to the author(s) by email. Papers may be downloaded for
personal use only.
2
An Estimate of the Number of Vegetarians in the World
Methane is the second-most important anthropogenic greenhouse gas. Ruminant
livestock are a major source of emissions. The expansion of pasture is one of the
main drivers of deforestation, one of the larger sources of carbon dioxide emissions.
An understanding of dietary choice is needed for scenario building and for assessing
policy options. This paper focuses on those who eat no meat whatsoever. We
estimate that there are one and half billion vegetarians. Only 75 million are
vegetarians of choice, a number that will gradually grow with increasing affluence
and education. The other 1,450 million are vegetarians of necessity. They will start
to eat meat as soon as they can afford it.
The importance of diet and particularly the amount of red meat in diet, for global
environmental change has long been acknowledged (1). Pasture land has expanded by
10% between 1961 and 2005 (2). Meat consumption has increased by 250% between
1960 and 2002 (2). The world population has doubled in that time (2), so that average
meat consumption per head has grown by three-quarters.
There has been intense study of diets (3-7). It is well-established that the very poor have a
limited intake of animal protein (8). Meat consumption, whether measured in calories (9)
or expenditures (10), initially goes up as people grow richer. Meat consumption levels off
at higher incomes, first when measured in calories and later when measured in
expenditures, as consumers become satiated. At middle and high income levels, excessive
meat consumption is a health concern (11-15). Given the importance of diet, it is
surprising that the global number of vegetarians has not been estimated. Research has
focussed on average diets and expenditures and on identifying those with unhealthy
eating habits, but no one has counted those that do not eat meat.
Anecdotally, vegetarianism is an increasingly popular life style choice for those
concerned about animal welfare, poverty, health, and the environment. There is limited
scope for reducing methane from ruminants by technical measures (16). With present
technologies, deep emission reduction cuts require a smaller size of the herd, and this
3
implies a change in diet. An understanding of the trends in the number of vegetarians
provides insight into the (in)feasibility of curbing methane emissions from livestock.
We count the number of vegetarians in the following manner. See Methods and Materials
for further detail. We use surveys of households’ budgets, expenditures, and living
standards for 29 countries, which together represent some 54% of the world population.
We have surveys covering more than one year for many of these countries, so that we
have a total of 139 samples. The average sample size is 5,000. Our database thus contains
almost 700,000 observations. The surveys typically record purchases, gifts and
subsistence production of food per item over a two week period. We excluded those
households that acquired an unusually small amount of food (compared to their peer
group) in the sample period. This is particularly prevalent in the USA, where many
households appear to buy groceries less than once per fortnight. Given this data, the
number of households that do not consume any meat is easily identified. We refer to
these as all-vegetarian households.
There are mixed households as well. Using the consumption patterns of one-person
households and the estimated economies-of-scale of food consumption, we conditionally
predict the share of meat in total food consumption for multi-person households given the
number of vegetarians. We then use the observed meat share to test the hypotheses that
there are one, two, … vegetarians in the household. We impute the number of vegetarians
from the first rejection. That is, if the hypothesis is rejected that there is (are) one (two,
three) vegetarian(s), we impute zero (one, two) vegetarians.
In sample, we find that 18% of people are vegetarian. This amounts to 680 million people
in the countries for which we have observations. See Table 1. Figure 1 displays the share
of vegetarians and all-vegetarian households against per capita income (corrected for
purchasing power). Figure 1 also shows the best quadratic fit. As expected, there are
more vegetarians in low income countries. More strikingly, there is enormous variation.
In Vietnam, more than 99% of the population eats meat, while in East Timor less than
half does. The fraction of vegetarians rapidly falls until average income reaches $15,000
per person per year.
4
There are no obvious patterns in the data for low income countries. For instance, more
than 80% of the people in India and Nepal are Hindu; 34% of Indians are vegetarian, and
7.4% of Nepalis. Local availability of meat, and the relative price of meat most likely
play a role in explaining the differences between countries, but reliable data is not readily
available.
At higher incomes, the differences between countries are less pronounced. The fraction of
vegetarians slightly increases with income. Figure 2 illustrates this for the United
Kingdom, the country for which data are best. In the 1960s, less than 0.5% of the
population was a vegetarian. Forty years later, more than 2.0% is.
Figure 3 “validates” our results. As noted above, this is the first estimate of the number of
vegetarians using a consistent methodology for a number of countries. However, there are
estimates of the number of vegetarians for individual countries. Figure 3 plots our results
against such estimates. The results presented here are consistent with earlier estimates,
but the match is not perfect.
As a further check on our data and methods, we computed the fraction of households that
do not consume any animal products ("vegans") and those that do not consume either fish
or meat ("strict vegetarians"). Figure 4 compares these numbers to the fraction of
households that do not consume meat ("vegetarians"). Figure 4 reveals the pattern one
would expect: There are fewer vegans than there are strict vegetarians. In turn, there are
fewer strict vegetarians than there are vegetarians. This is true in general and for every
single country/year in the sample.
Using the quadratic curve in Table 1, we tentatively extrapolate the estimate of the
number of vegetarians to the whole world. This rough method suggests that 22% of the
world population is a vegetarian. This amounts to one and a half billion people. Of these,
95% lie on the downward sloping part of the curve. We deem these to be vegetarians of
necessity. Only 5% are on the upward sloping part of the curve. These we call
vegetarians of choice.
The implications of these numbers are profound. As the current poor grow to middle
income levels, many more of them will start to eat meat. As the current rich grow richer
still, more will become vegetarian. The latter process is much slower, and starts from a
5
lower base. In the medium term, therefore, one should expect a dramatic drop in the
number of vegetarians. Methane emissions will continue to rise and forests will be
converted to pasture. Only in the longer term, when affluence becomes more widespread,
can we expect these trends to level off.
6
Table 1: Global number of vegetarians
Number of
countries
Number of people
(mln)
Number of vegetarians
(mln)
Share of
vegetarians
In sample 28 3,707 678 18.3%
Out of
sample
176 3,145 813 25.8%
Total 204 6,851 1,490 21.8%
7
Figure 1. Vegetarianism and income per capita (gross domestic product per person
per year in Geary-Khamis dollars)
0%
10%
20%
30%
40%
50%
60%
0 5000 10000 15000 20000 25000 30000 35000
PPP GDP Per Capita 1995
% vegetarian
households
% vegetarians
8
Figure 2. Vegetarianism over time in the United Kingdom
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
All-vegetarian households
Vegetarians
9
Figure 3. The fraction of vegetarians in the population as estimated here versus
earlier estimates
0.00
0.01
0.10
1.00
0.00 0.01 0.10 1.00
This study
Other estimates
10
Figure 4. Share of vegetarian, strict vegetarian and vegan households by country
0.001
0.01
0.1
1
UK 1969
UK 1968
UK 1963
UK 1973
UK 1971
Vietnam 2006
UK 1962
Ireland 1987
Bosnia 2001
Bulgaria 1995
Serbia 2007
UK 1978
UK 1980
Vietnam 1992
UK 1982
Guatemala 2000
Ireland 2004
UK 1985
Serbia 2002
Jamaica 1991
France 1995
Serbia 2003
Jamaica 1992
UK 1986
UK 1989
UK 1987
UK 1991
UK 1997
Jamaica 2000
UK 1993
UK 1994
France 2001
UK 2000
Germany 2003
USA 1981
France 2006
USA 1980
UK 2002-03
UK 2001
Jamaica 1999
UK 2003-04
Jamaica 2004
Brazil 1997
USA 1995
USA 1992
USA 1994
USA 2001
USA 2003
USA 1996
USA 2000
USA 1997
USA 2002
USA 1998
USA 1999
USA 2005
Nepal 2003/04
Jamaica 2007
South Africa 1993
Australia 2003/04
Ivory Coast 1985
China 1997
Tanzania 1993
Ivory Coast 1987
Azerbaijan 1995
Ivory Coast 1988
Kyrgyzstan 1993
Russia 1993/94
India 1998
Tajikistan 1999
Timor Leste 2001
fraction of households
Vegetarians
Strict Vegetarians
Vegans
11
Materials and Methods
Data
Household expenditure surveys provide a detailed account of all of the expenditures
incurred by a large sample of individual households over a specified time period.
Household expenditure surveys have a similar structure across countries. Often, these
datasets also provide information on household income and other socio-economic
variables. Most of the datasets used in this paper are from the Living Standard
Measurement Studies (LSMS), which are available from the World Bank website (17).
We used these data to estimate levels of vegetarianism in Albania, Azerbaijan, Bosnia
and Herzegovina, Brazil, Bulgaria, China, Guatemala, India, Ivory Coast, Kosovo,
Kyrgyzstan, Papua New Guinea, Peru, Serbia, South Africa, Tajikistan, Tanzania and
Timor Leste. The remaining data were obtained from statistical offices in individual
countries. Data for the U.S.A (18), Russia (19), Nepal (20), Ireland (21), Vietnam (22),
France (23), the UK (24, 25) and Jamaica (26) were obtained in this manner. We did not
have direct access to the microdata for Singapore (27), Germany (28) or Australia (29)
but the analyses we required (for households) were carried out by the relevant statistical
offices to our specifications. In the case of Singapore, analyses were carried out on
uncooked meat items only.
Where available, we used data on all meat consumed in the household, be it from
purchases, home production, gifts or in-kind payments. For other countries, only data on
meat expenditures were available. Table A1 indicates which measure was used in each
case. Where possible we used household disposable income. However, on some
occasions, net or gross income had to be used. Where available, the value of income
received in kind was included in the income variable. For some countries, income was
not available or was inconsistent with reported levels of expenditure. In such instances,
total household expenditure was used as a proxy for income. Table A1 specifies which
income measure was used. In almost all cases the total household food consumption
variable was composed of all food bought for home consumption by the household. Food
purchases which occurred while eating out, in cafes and restaurants for example, were
excluded because we were not able to determine what proportion of this consumption
12
related to meat products. Purchases of alcohol, cigarettes and tobacco were also excluded
but non-alcoholic beverages were included.
Infrequency of purchase
Expenditures are normally recorded in a diary for a specified period. For surveys with a
short diary period, individual households’ responses may not always reflect their
“normal” purchasing patterns with respect to individual goods or categories of goods. We
filter the data to exclude observations where infrequency of purchase is likely to have led
to an unrepresentative expenditure pattern in the period surveyed. That is, we exclude
from the analysis all those households that appeared to have not purchased enough food,
relative to income and household size in the defined period. We do this by estimating
food share F, which is the share of food consumption (or expenditure) as a percentage of
income (or total expenditure) for every household in the sample:
(1)
j
d
j
j
q
j
C
N
F
Y
=
where C denotes total food consumption of household j; N number of people in
household j (raised to the power d); and Y income of household j (raised to the power q).
We thus control for the number of people in the household as well as for economies of
scale in household consumption through the equivalization factor d. Income is also
equivalized using the elasticity
q. The income elasticity for food varies between 0.2 and
0.4 for the countries in the sample; small changes in
q have little impact on results. So,
we set q = 0.3. We then find the mean and the standard deviation of food share F for each
income decile in each country. Any household whose food share is less than the average
minus the standard deviation for the relevant income decile is omitted from the analysis.
There is another adjustment required for United States data. The Consumer Expenditure
Survey (CES), which is the microdata we use for the USA, is carried out on an annual
basis and asks respondents to list all food items purchased over a weekly period. The
majority of households stay in the sample for two weeks. We found that the number of
zero observations on food expenditures was much higher in the CES than was the case for
other countries. We have 19 years of cross sectional data and this pattern appeared
13
throughout. As a result, we applied another measure to identify those households that did
not shop frequently enough for us to include them in the analysis. One of the CES data
files had already amalgamated food products into different categories. We further
reduced the number of categories to leave nine food groups in total. These are cereal and
bakery products, meat products, fish products, eggs, milk and dairy products, processed
fruit and vegetables, fresh fruit and vegetables, sweets, non-alcoholic beverages and
miscellaneous food and oils. If households reported zero expenditures in six or more of
these food groups we omitted them. We also omitted those households that reported
expenditures for one week only. The remaining samples for the USA consisted of 5,122
households per annum on average.
Mixed households
Where there are two or more residents and the household reports some level of meat
consumption, we estimate the probabilities that there are different numbers of vegetarians
in that household. We refer to these as mixed households because they can contain both
vegetarians and meat eaters. Since the expenditure data we are using is recorded on a
household rather than on an individual basis, we derive expected meat and non-meat
consumption based on equivalised income and number of members for all possible
combinations of vegetarians and non-vegetarians in the household. The predicted share of
meat in total food consumption, conditional on the household structure, is then compared
to the observed meat share. We then sequentially test the hypotheses that there are 0, 1, 2,
… vegetarians; and impute the lowest number that is not rejected.
Divide total food expenditure
C
i
for person i into three components: consumption of non-
meat items if the person is a vegetarian
C
vv
, consumption of non-meat items if the person
is a meat-eater
C
vm
, and consumption of meat items if the person eats meat C
mm
:
(2)
vv vm mm
ii i i
CC C C≡++
Segment the population into two types, vegetarian (
v
i
=1) and non-vegetarian (v
i
=0).
Now assume that all persons of a given type (vegetarian or non-vegetarian) have
homogeneous demand for each component of food. Food demand is a fixed sum per
person scaled by the level of household income per capita, using an equivalisation factor
14
that accounts for economies of scale in household consumption. This specifies the
following:
(3a)
q
j
vv
ii
d
j
Y
CW ij
N
⎛⎞
=∈
⎜⎟
⎜⎟
⎝⎠
(3b)
r
j
vm
ii
d
j
Y
CV ij
N
⎛⎞
=∈
⎜⎟
⎜⎟
⎝⎠
(3c)
s
j
mm
ii
d
j
Y
CM ij
N
⎛⎞
=∈
⎜⎟
⎜⎟
⎝⎠
where
Y is household disposable income of household j and q, r and s are the elasticities
of demand with respect to equivalised income for the relevant food types.
N is the
number of persons in the household and 0<d1 is the equivalisation factor. W, V and M
are per capita expenditures on the relevant food types by those who consume them.
By restricting the sample of households examined, we can obtain regression equations
that allow us to recover the values of the structural parameters d, q, r, s, W, X and Y.
First consider single-person vegetarian households, which would allow one to estimate W
and q:
(4a) 1, 1
Tq
iij i j
CWY v N=∀==
Taking logs of both sides yields an equation that can be estimated with OLS regression:
(4b)
ln ln ln 1, 1
T
ii jij
CWqYvN=+ = =
One can get more general results of these parameters (plus an estimate of d) using data on
vegetarian households of all sizes:
(5a)
()
ln ln ln 1 ln 1, 1
q
j
Td T
iji i i j ji j
d
j
Y
CNW C WqYd qNv N
N
⎛⎞
=⇔=++=
⎜⎟
⎜⎟
⎝⎠
Equation (5a) is estimated as
(5b)
ˆ
ˆ
ˆ
ln ln ln ln ;
ˆ
1
T
ijji
b
CaqYbN Wad
q
=+ + = =
The standard deviation of
d is estimated by developing the first order Taylor
approximation around the estimated parameter
15
(6)
()
()
()
2
ˆˆ
1
ˆ
ˆ
ˆˆ
111
ˆ
1
bb b
dbbqq
qqq
q
=≈+ +
−−
and computing the variance of that
(7)
()
()
()
() () ()
2
2
2
,
2
22
243
ˆˆˆ
1
ˆ
ˆ
(, )dd
ˆˆ ˆ
11 1
ˆ
1
ˆˆ
12
ˆˆˆ
111
d
bq
bqbq
bbb
bb qq fbqbq
qq q
q
bb
qqq
σ
σσσ
⎡⎤
≈++
⎢⎥
−−
⎢⎥
⎣⎦
=++
−−−
∫∫
The estimates of
r, s, V and M are based on data on single person meat-eating households:
(8)
ln ln ln 0, 1
vm r vm
iij iijij
CVY C VrYv N=⇔ =+ = =
(9) ln ln ln 0, 1
mm s mm
iij i i ji j
CMY C MsYv N=⇔ =+ ==
Our goal is to estimate the number of vegetarians in a given household. For households
that do not buy meat, we declare all members to be vegetarian: v
i
= 1. For single-person
household, there is therefore no uncertainty. For multi-person households, we proceed as
follows. We predict the expected share of meat in total food consumption S, conditional
on the hypothesized number of vegetarians in the household:
(10a)
()
ˆ
E|
ˆˆ ˆ
Mmm
ji
V
jj
M
mm vm V vv
ji i ji
NC
SN
NC C NC
⎡⎤
=
⎣⎦
++
with
(10b)
:;:
VMV
jijjj
ij
NvNNN
==
Using the standard errors of the regressions (4), (8) and (9) and a second-order Taylor
approximation of (10), we find that
(11)
()
()
()
()
()
()
2
2
22
2
3
22 2
4
ˆˆˆ
Var |
ˆˆ ˆ
ˆ
ˆˆ ˆ
Mvm Vvv Mmm
ji ji ji
VM
jj jmm
Mmm vm Mvv
ji i ji
Mmm
ji
MV
jmmvm jvv
Mmm vm Vvv
ji i ji
NC NC NC
SN N
NC C NC
NC
NN
NC C NC
σ
σ
σσ
+−
⎡⎤
=+
⎣⎦
++
++
++
16
Assuming a lognormal distribution, we compute the relative probabilities of the
hypotheses N
V
=0, 1, …, N
j
-1. We then impute the number of vegetarians Ñ as the
smallest Ñ for which p(Ñ
V
> N
V
)0.95.
Aggregation
The number of households in the sample that report no meat consumption or purchase, is
readily estimated. Sample weights are used where available to estimate the fraction of all-
vegetarian households. We estimate the total number of vegetarians in a country as the
number of vegetarians in each household in our sample, again applying a weight for
representativeness where appropriate. For households that report no meat consumption,
the number of vegetarians is equal to the number of household members.
17
Table A1: Share of vegetarians and vegetarian households by country
Country Year
% vegetarian
households
Std. Error
vegetarian
households
Households Weight
%
vegetarians
Number
of people
Household
income measure
Consumption
measure
Data
Albania 2005 0.047 0.004 3275
0.078 13734 Net income expenditure LSMS
Azerbaijan 1995 0.218 0.010 1864
0.226 9281 Total declared
income
consumption LSMS
Brazil 1997 0.019 0.003 2838
0.036 11917 Net income expenditure LSMS
Bulgaria 2003 0.037 0.003 3012
0.031 8152 Total declared
income
consumption LSMS
Bulgaria 2001 0.039 0.004 2359
0.029 6618 Total expenditure consumption LSMS
Bulgaria 1995 0.007 0.002 2264
0.008 6448 Total expenditure consumption LSMS
France 2006 0.026 0.002 8970
0.019 21891 Net income expenditure EBF
France 2001 0.024 0.002 8956
0.015 22265 Gross income expenditure EBF
France 1995 0.016 0.001 9099
0.009 23462 Total expenditure consumption EBF
France 1985 0.017 0.001 9814
0.014 32251 Gross income expenditure EBF
France 1979 0.016 0.001 9406
0.010 28413 Total expenditure expenditure EBF
Guatemala 2000 0.011 0.001 6078
0.014 31155 Gross income expenditure LSMS
India 1998 0.333 0.012 1527
0.344 9903 Total declared
income
consumption LSMS
Ireland 2004 0.012 0.002 5266
0.006 15934 Disposable
income
consumption HBS
Ireland 1999 0.008 0.001 6700
0.004 20918 Disposable
income
consumption HBS
Ireland 1994 0.008 0.001 6958
0.005 22311 Disposable
income
consumption HBS
Ireland 1987 0.004 0.001 6909
0.003 24161 Disposable
income
consumption HBS
Ivory Coast 1988 0.248 0.011 1523
0.203 9266 Total declared
income
consumption LSMS
Ivory Coast 1987 0.210 0.010 1560
0.168 10666 Total declared
income
consumption LSMS
Ivory Coast 1986 0.170 0.010 1546
0.130 11803 Total declared
income
consumption LSMS
Ivory Coast 1985 0.139 0.009 1522
0.108 12124 Total declared consumption LSMS
18
Country Year
% vegetarian
households
Std. Error
vegetarian
households
Households Weight
%
vegetarians
Number
of people
Household
income measure
Consumption
measure
Data
income
Jamaica 2007 0.064 0.006 1783
0.057 5787 Total expenditure expenditure JSLC
Jamaica 2006 0.054 0.005 1678
0.035 5268 Total expenditure expenditure JSLC
Jamaica 2005 0.047 0.005 1698
0.025 5679 Total expenditure expenditure JSLC
Jamaica 2004 0.032 0.004 1755
0.021 6020 Total expenditure expenditure JSLC
Jamaica 2003 0.043 0.005 1781
0.037 5930 Total expenditure expenditure JSLC
Jamaica 2002 0.035 0.002 6165
0.021 20847 Total expenditure consumption JSLC
Jamaica 2001 0.027 0.004 1436
0.017 4727 Total expenditure consumption JSLC
Jamaica 2000 0.022 0.004 1570
0.016 5447 Total expenditure consumption JSLC
Jamaica 1999 0.029 0.004 1633
0.015 5480 Total expenditure consumption JSLC
Jamaica 1998 0.022 0.002 6461
0.014 22409 Total expenditure consumption JSLC
Jamaica 1997 0.020 0.003 1743
0.017 6086 Total expenditure consumption JSLC
Jamaica 1996 0.017 0.003 1608
0.016 5867 Total expenditure consumption JSLC
Jamaica 1995 0.016 0.003 1715
0.012 6191 Total expenditure consumption JSLC
Jamaica 1994 0.017 0.003 1708
0.017 5815 Total expenditure consumption JSLC
Jamaica 1993 0.014 0.003 1710
0.010 6058 Total expenditure consumption JSLC
Jamaica 1992 0.017 0.002 3843
0.016 13679 Total expenditure consumption JSLC
Jamaica 1991 0.016 0.003 1576
0.020 5813 Total expenditure consumption JSLC
Jamaica 1990 0.034 0.007 703
0.033 2468 Total expenditure consumption JSLC
Jamaica 1989 0.025 0.004 1256
0.027 5381 Total expenditure consumption JSLC
Jamaica 1988 0.044 0.005 1648
0.037 6225 Total expenditure consumption LSMS
Kosovo 2000 0.061 0.006 1373
0.062 8975 Total declared
income
consumption LSMS
Kyrgyzstan 1993 0.271 0.010 1894
0.398 9338 Total declared
income
consumption LSMS
Nepal 2003/04 0.056 0.004 3431
0.066 18174 Total expenditure expenditure NLSS
Nepal 1996 0.072 0.005 3014
0.081 17095 Total expenditure consumption NLSS
Peru 1985 0.436 0.007 4615
0.418 23470 Total expenditure consumption LSMS
Russia 2002 0.143 0.006 2962
0.126 10908 Gross income expenditure RLMS
Russia 2001 0.188 0.007 2950
0.173 10504 Gross income expenditure RLMS
R
ussia 2000 0.237 0.008 2843
0.223 9526 Gross income expenditure RLMS
Russia 1993/94 0.271 0.006 5224
0.254 13422 Gross income expenditure RLMS
19
Country Year
% vegetarian
households
Std. Error
vegetarian
households
Households Weight
%
vegetarians
Number
of people
Household
income measure
Consumption
measure
Data
Russia 1993 0.271 0.006 5388
0.247 14260 Gross income expenditure RLMS
Russia 1992/93 0.333 0.006 5296
0.292 14556 Gross income expenditure RLMS
Russia 1992 0.268 0.006 5946
0.222 15985 Gross income expenditure RLMS
Serbia 2007 0.008 0.001 4708
0.004 14657 Net income consumption LSMS
Serbia 2002 0.016 0.002 5956
0.010 17914 Net income consumption LSMS
South
Africa
1993 0.073 0.004 4776
0.059 20517 Net income consumption LSMS
Tajikistan 2003 0.467 0.008 3620
0.495 21915 Total declared
income
consumption LSMS
Tajikistan 1999 0.462 0.012 1818
0.480 12265 Total declared
income
expenditure LSMS
Tanzania 1993 0.180 0.006 4844
0.159 26705 Total declared
income
consumption LSMS
Timor Leste 2001 0.538 0.012 1596
0.491 7699 Total expenditure consumption LSMS
UK 2006 0.028 0.002 5713
0.021 13547 Disposable
income
expenditure EFS
UK 2005-06 0.028 0.002 5889
0.024 13859 Disposable
income
expenditure EFS
UK 2004-05 0.032 0.002 5759
0.032 13518 Disposable
income
expenditure EFS
UK 2003-04 0.030 0.002 6152
0.021 14628 Disposable
income
expenditure EFS
UK 2002-03 0.028 0.002 6037
0.021 14293 Disposable
income
expenditure EFS
UK 2001-02 0.029 0.002 6520
0.020 15661 Disposable
income
expenditure EFS
UK 2001 0.028 0.002 5537
0.020 12868 Disposable
income
expenditure FES
UK 2000 0.024 0.002 6225
0.016 14589 Disposable
income
expenditure FES
UK 1999 0.024 0.002 5469
0.016 12888 Disposable
income
expenditure FES
UK 1998 0.022 0.002 5315
0.017 12461 Disposable expenditure FES
20
Country Year
% vegetarian
households
Std. Error
vegetarian
households
Households Weight
%
vegetarians
Number
of people
Household
income measure
Consumption
measure
Data
income
UK 1997 0.021 0.002 5537
0.017 13515 Disposable
income
expenditure FES
UK 1996 0.020 0.002 5973
0.017 14560 Disposable
income
expenditure FES
UK 1995 0.023 0.002 6018
0.020 14529 Disposable
income
expenditure FES
UK 1994 0.023 0.002 5811
0.019 13881 Disposable
income
expenditure FES
UK 1993 0.023 0.002 6143
0.019 15180 Disposable
income
expenditure FES
UK 1992 0.027 0.002 6569
0.019 15916 Disposable
income
expenditure FES
UK 1991 0.020 0.002 6237
0.017 15041 Disposable
income
expenditure FES
UK 1990 0.021 0.002 6167
0.018 15226 Disposable
income
expenditure FES
UK 1989 0.019 0.002 6583
0.016 16399 Disposable
income
expenditure FES
UK 1988 0.016 0.002 6331
0.016 15897 Disposable
income
expenditure FES
UK 1987 0.020 0.002 6463
0.018 16420 Disposable
income
expenditure FES
UK 1986 0.017 0.002 6399
0.015 16348 Disposable
income
expenditure FES
UK 1985 0.013 0.001 6155
0.011 15853 Disposable
income
expenditure FES
UK 1984 0.012 0.001 6294
0.011 16385 Disposable
income
expenditure FES
UK 1983 0.011 0.001 6171
0.011 16414 Disposable
income
expenditure FES
UK 1982 0.010 0.001 6491
0.011 17435 Disposable
income
expenditure FES
UK 1981 0.007 0.001 6680
0.009 18172 Net income expenditure FES
21
Country Year
% vegetarian
households
Std. Error
vegetarian
households
Households Weight
%
vegetarians
Number
of people
Household
income measure
Consumption
measure
Data
UK 1980 0.008 0.001 6141
0.012 16706 Net income expenditure FES
UK 1979 0.009 0.001 6068
0.012 16494 Net income expenditure FES
UK 1978 0.008 0.001 6182
0.009 16886 Net income expenditure FES
UK 1977 0.004 0.001 6428
0.008 17890 Net income expenditure FES
UK 1976 0.003 0.001 6205
0.007 16909 Net income expenditure FES
UK 1975 0.004 0.001 6373
0.011 17936 Net income expenditure FES
UK 1974 0.005 0.001 5935
0.010 16827 Net income expenditure FES
UK 1973 0.003 0.001 6320
0.005 17806 Net income expenditure FES
UK 1972 0.003 0.001 6231
0.004 18034 Net income expenditure FES
UK 1971 0.003 0.001 6328
0.005 18109 Net income expenditure FES
UK 1970 0.002 0.001 5731
0.003 16642 Net income expenditure FES
UK 1969 0.001 0.000 5995
0.002 17019 Net income expenditure FES
UK 1968 0.002 0.000 6408
0.003 18746 Net income expenditure FES
UK 1963 0.003 0.001 2972
0.003 8710 Total expenditure expenditure FES
UK 1962 0.004 0.001 3114
0.003 9040 Total expenditure expenditure FES
UK 1961 0.004 0.001 3057
0.003 9027 Total expenditure expenditure FES
USA 2006 0.055 0.003 5098
0.045 14610 Net income expenditure CES
USA 2005 0.055 0.003 6151
0.050 17758 Net income expenditure CES
USA 2004 0.045 0.003 5941
0.038 17043 Net income expenditure CES
USA 2003 0.040 0.003 4152
0.035 11762 Net income expenditure CES
USA 2002 0.046 0.003 5424
0.038 15678 Net income expenditure CES
USA 2001 0.038 0.003 5496
0.033 15942 Net income expenditure CES
USA 2000 0.043 0.003 5357
0.039 15608 Net income expenditure CES
USA 1999 0.048 0.003 5260
0.043 15193 Net income expenditure CES
USA 1998 0.047 0.003 4155
0.039 11994 Net income expenditure CES
USA 1997 0.044 0.003 4100
0.038 11952 Net income expenditure CES
USA 1996 0.042 0.003 4061
0.035 11764 Net income expenditure CES
USA 1995 0.034 0.003 3875
0.026 11260 Net income expenditure CES
USA 1994 0.036 0.003 4251
0.
028 12374 Net income expenditure CES
USA 1993 0.041 0.003 4764
0.032 13738 Net income expenditure CES
USA 1992 0.035 0.003 4745
0.033 13814 Net income expenditure CES
22
Country Year
% vegetarian
households
Std. Error
vegetarian
households
Households Weight
%
vegetarians
Number
of people
Household
income measure
Consumption
measure
Data
USA 1991 0.036 0.003 5112
0.032 14969 Net income expenditure CES
USA 1990 0.031 0.002 4830
0.029 14159 Net income expenditure CES
USA 1981 0.025 0.003 3794
0.024 11687 Gross income expenditure CES
USA 1980 0.027 0.003 4002
0.024 12428 Gross income expenditure CES
Vietnam 2006 0.004 0.001 2620
0.008 34006 Total expenditure consumption VHLS
S
Vietnam 2004 0.008 0.001 8268
0.010 32180 Total expenditure consumption VHLS
S
Vietnam 2002 0.003 0.000 26589
0.003 113557 Total expenditure consumption VHLS
S
Vietnam 1998 0.001 0.000 4989
0.030 24192 Total expenditure consumption VHLS
S
Vietnam 1992 0.009 0.001 4331
0.015 20503 Total expenditure consumption VHLS
S
Australia 2003/04 0.106 0.001 6957
na na na expenditure AHES
Australia 1998/99 0.082 0.000 6893
na na na expenditure AHES
Australia 1993/94 0.109 na 8,389
na na na expenditure AHES
Bosnia 2004 0.010 0.002 2959
na na na consumption LSMS
Bosnia 2001 0.004 0.000 5335
na na na consumption LSMS
China 1997 0.155 0.013 787
na na na expenditure LSMS
Germany 2003 0.024 0.001 11831
na na na expenditure EVS
Germany 1998 0.024 0.001 12680
na na na expenditure EVS
Germany 1993 0.023 0.001 15825
na na na expenditure EVS
Papua New
Guinea
1996 0.024 0.004 1336
na na na consumption LSMS
Serbia 2003 0.016 0.002 2548
na na na consumption LSMS
Singapore 2003 0.210 na 6749
na na na expenditure SHES
23
Table A2. The share of vegetarians according to this and other studies.
Country Year This study Other studies Remarks and source
Brazil 1997 3.6% 5% (Brazilian Vegetarian Society, 2004)
France 2006 1.9% 1.7% 15-75 year olds (Alliance Végétarienne, 1996, 2002)
France 2001 1.5% 0.9% (International Vegetarian Union)
India 1998 34.4% 40% (Hindu -CNN-IBN State of the Nation Survey, 2006)
India 42% Households (National Sample Survey consumption data, 2005-06)
India 20-30% (United States Department of Agriculture, 2004)
Ireland 2004 0.6% 6% (Vegetarian Society of Ireland)
Ireland 1999 0.4% >2.5% (Irish Times, Oct 8 2004)
Ireland 1994 0.5% 1.6% (Foley in Hotel and Catering Review, 1998)
Ireland 1987 0.3% 2-3% (Corbett in The Irish Vegetarian, 1997)
Russia 2002 12.6% >10% (Euroasian Vegetarian Society, 2002)
UK 2006 2.1% 3% Adults (Food Standards Agency, 2009)
UK 2005-06 2.4% 2% Adults (Food Standards Agency, 2008)
UK 2004-05 3.2% 3%
(Defra survey of attitudes, knowledge and behaviour in relation to the
environment, 2007)
UK 2003-04 2.1% 3% Adults (Food Standards Agency, 2007)
UK 2002-03 2.1% 6% (Mintel, 2006)
UK 2001-02 2.0% 6.1% (International Vegetarian Union)
UK 2001 2.0% 7.6% >15 years of age (BMBR Access Panel Research, 2004)
UK 2000 1.6% 7% (Vegetarian Society)
UK 1999 1.6% 7% Adults (Food and Drink Federation, 2003)
UK 1998 1.7% 8% Students (JMA Marketing & Research Survey for Scolarest, 2003)
UK 1997 1.7% 5% >19 years of age (National Diet and Nutrition Survey, 2001)
UK 1996 1.7% 6.5% (TGI Annual Survey, 2001)
UK 1995 2.0% 5.7% (Mintel, 2001)
UK 1994 1.9% 5% Adults (Taylor Nelson poll for RSPCA, 2000)
UK 1993 1.9% 4.5% (Mintel, 1996)
USA 2006 4.5% 0.5% <17 years of age (Centre for Disease Control and Prevention, 2009)
USA 2005 5.0% 3.2% Adults (Harris Interactive Service Bureau on behalf of Vegetarian Times, 2008)
USA 2004 3.8% 2.3% >18 years (Vegetarian Resource Group, 2006)
24
Country Year This study Other studies Remarks and source
USA 2003 3.5% 6.7% >18 years (VRG, 2006)
USA 2002 3.8% 1.4% Vegan (VRG, 2006)
USA 2001 3.3% 2.8% >17 years (VRG, 2004)
USA 2000 3.9% <3% Adults (American Dietetic Association, 2003)
USA 1999 4.3% 2.5% >17 years (VRG, 2000)
USA 1998 3.9% 2% Adults (VRG, 1997)
USA 1997 3.8% 1.2% Adults (VRG, 1997)
25
Table A3. Share of vegetarian, strict vegetarian and vegan households by country
Country Year Households Weight Vegetarians
Std. Error
vegetarians
Strict
Vegetarians
Std. Error strict
vegetarians
Vegans
Std. Error
vegans
Albania 2005 3275
0.047 0.004 0.045 0.004 0.029 0.003
Azerbaijan 1995 1864
0.218 0.01 na na 0.110 0.007
Brazil 1997 2838
0.032 0.003 0.031 0.003 0.004 0.001
Bulgaria 2003 3012
0.037 0.003 0.025 0.003 0.008 0.002
Bulgaria 2001 2359
0.039 0.004 0.039 0.004 0.005 0.001
Bulgaria 1995 2264
0.007 0.002 0.000 0.000 0.000 0.000
France 2006 8970
0.026 0.002 0.016 0.001 0.002 0.001
France 2001 8956
0.024 0.002 0.012 0.001 0.001 0.000
France 1995 9099
0.016 0.001 0.007 0.001 0.002 0.000
France 1985 9814
0.017 0.001 0.013 0.001 0.006 0.001
France 1979 9406
0.016 0.001 0.012 0.001 0.006 0.001
Guatemala 2000 6078
0.011 0.001 0.004 0.001 0.000 0.000
India 1998 1527
0.333 0.012 0.333 0.012 0.115 0.008
Ireland 2004 5266
0.012 0.002 0.007 0.001 0.000 0.000
Ireland 1999 6700
0.008 0.001 0.006 0.001 0.000 0.000
Ireland 1994 6958
0.008 0.001 0.006 0.001 0.001 0.000
Ireland 1987 6909
0.004 0.001 0.003 0.001 0.000 0.000
Ivory Coast 1988 1523
0.248 0.011 0.037 0.005 0.027 0.004
Ivory Coast 1987 1560
0.210 0.010 0.035 0.005 0.030 0.004
Ivory Coast 1986 1546
0.170 0.010 0.024 0.004 0.017 0.003
Ivory Coast 1985 1522
0.139 0.009 0.019 0.003 0.014 0.003
Jamaica 2007 1783
0.064 0.006 0.028 0.004 0.016 0.003
Jamaica 2006 1678
0.054 0.005 0.017 0.003 0.007 0.002
Jamaica 2005 1698
0.047 0.005 0.013 0.003 0.003 0.001
Jamaica 2004 1755
0.032 0.004 0.006 0.002 0.002 0.001
Jamaica 2003 1781
0.043 0.005 0.010 0.002 0.004 0.001
Jamaica 2002 6165
0.035 0.002 0.010 0.001 0.002 0.001
Jamaica 2001 1436
0.027 0.004 0.006 0.002 0.000 0.001
Jamaica 2000 1570
0.022 0.004 0.006 0.002 0.005 0.002
Jamaica 1999 1633
0.029 0.004 0.007 0.002 0.001 0.001
Jamaica 1998 6461
0.
022 0.002 0.006 0.001 0.001 0.000
26
Country Year Households Weight Vegetarians
Std. Error
vegetarians
Strict
Vegetarians
Std. Error strict
vegetarians
Vegans
Std. Error
vegans
Jamaica 1997 1743
0.020 0.003 0.007 0.002 0.000 0.000
Jamaica 1996 1608
0.017 0.003 0.005 0.002 0.001 0.001
Jamaica 1995 1715
0.016 0.003 0.006 0.002 0.001 0.001
Jamaica 1994 1708
0.017 0.003 0.006 0.002 0.001 0.001
Jamaica 1993 1710
0.014 0.003 0.005 0.002 0.000 0.000
Jamaica 1992 3843
0.017 0.002 0.006 0.001 0.000 0.000
Jamaica 1991 1576
0.016 0.003 0.006 0.002 0.001 0.001
Jamaica 1990 703
0.034 0.007 0.013 0.004 0.003 0.002
Jamaica 1989 1256
0.025 0.004 0.019 0.004 0.002 0.001
Jamaica 1988 1648
0.044 0.005 0.006 0.002 0.001 0.001
Kosovo 2000 1373
0.061 0.006 0.011 0.003 0.003 0.002
Kyrgyzstan 1993 1894
0.271 0.010 0.045 0.005 0.000 0.000
Nepal 2003/04 3431
0.056 0.004 0.036 0.003 0.001 0.001
Nepal 1996 3014
0.072 0.005 0.056 0.004 0.011 0.002
Peru 1985 4615
0.436 0.007 0.207 0.005 0.160 0.005
Russia 2002 2962
0.143 0.006 0.109 0.006 0.050 0.004
Russia 2001 2950
0.188 0.007 0.144 0.006 0.061 0.004
Russia 2000 2843
0.237 0.008 0.188 0.007 0.086 0.005
Russia 1993/94 5224
0.271 0.006 0.214 0.006 0.107 0.004
Russia 1993 5388
0.271 0.006 0.211 0.006 0.090 0.004
Russia 1992/93 5296
0.333 0.006 0.268 0.006 0.128 0.005
Russia 1992 5946
0.268 0.006 0.228 0.005 0.092 0.004
Serbia 2007 4708
0.008 0.001 0.004 0.001 0.001 0.000
Serbia 2002 5956
0.016 0.002 0.000 0.000 0.000 0.000
South Africa 1993 4776
0.073 0.004 0.040 0.003 0.020 0.002
Tajikistan 2003 3620
0.467 0.008 0.463 0.008 0.183 0.006
Tajikistan 1999 1818
0.462 0.012 0.459 0.012 0.136 0.008
Tanzania 1993 4844
0.180 0.006 0.068 0.004 0.049 0.003
Timor Leste 2001 1596
0.538 0.012 0.412 0.012 0.304 0.012
UK 2006 5713
0.028 0.002 0.016 0.002 0.000 0.000
UK 2005-06 5889
0.
028 0.002 0.014 0.002 0.000 0.000
UK 2004-05 5759
0.032 0.002 0.019 0.002 0.003 0.001
UK 2003-04 6152
0.030 0.002 0.020 0.002 0.001 0.000
27
Country Year Households Weight Vegetarians
Std. Error
vegetarians
Strict
Vegetarians
Std. Error strict
vegetarians
Vegans
Std. Error
vegans
UK 2002-03 6037
0.028 0.002 0.013 0.001 0.000 0.000
UK 2001-02 6520
0.029 0.002 0.017 0.002 0.001 0.000
UK 2001 5537
0.028 0.002 0.014 0.002 0.000 0.000
UK 2000 6225
0.024 0.002 0.014 0.001 0.000 0.000
UK 1999 5469
0.024 0.002 0.014 0.002 0.000 0.000
UK 1998 5315
0.022 0.002 0.010 0.001 0.000 0.000
UK 1997 5537
0.021 0.002 0.012 0.001 0.000 0.000
UK 1996 5973
0.020 0.002 0.010 0.001 0.000 0.000
UK 1995 6018
0.023 0.002 0.013 0.001 0.001 0.000
UK 1994 5811
0.023 0.002 0.013 0.001 0.001 0.000
UK 1993 6143
0.023 0.002 0.013 0.001 0.000 0.000
UK 1992 6569
0.027 0.002 0.012 0.001 0.001 0.000
UK 1991 6237
0.020 0.002 0.011 0.001 0.000 0.000
UK 1990 6167
0.021 0.002 0.010 0.001 0.000 0.000
UK 1989 6583
0.019 0.002 0.009 0.001 0.000 0.000
UK 1988 6331
0.016 0.002 0.007 0.001 0.000 0.000
UK 1987 6463
0.020 0.002 0.008 0.001 0.001 0.000
UK 1986 6399
0.017 0.002 0.008 0.001 0.000 0.000
UK 1985 6155
0.013 0.001 0.006 0.001 0.000 0.000
UK 1984 6294
0.012 0.001 0.007 0.001 0.001 0.000
UK 1983 6171
0.011 0.001 0.005 0.001 0.000 0.000
UK 1982 6491
0.010 0.001 0.007 0.001 0.000 0.000
UK 1981 6680
0.007 0.001 0.004 0.001 0.000 0.000
UK 1980 6141
0.008 0.001 0.006 0.001 0.000 0.000
UK 1979 6068
0.009 0.001 0.006 0.001 0.000 0.000
UK 1978 6182
0.008 0.001 0.005 0.001 0.000 0.000
UK 1977 6428
0.004 0.001 0.002 0.001 0.000 0.000
UK 1976 6205
0.003 0.001 0.002 0.001 0.001 0.000
UK 1975 6373
0.004 0.001 0.003 0.001 0.000 0.000
UK 1974 5935
0.005 0.001 0.003 0.001 0.000 0.000
UK 1973 6320
0.
003 0.001 0.002 0.001 0.000 0.000
UK 1972 6231
0.003 0.001 0.002 0.001 0.000 0.000
UK 1971 6328
0.003 0.001 0.002 0.000 0.000 0.000
28
Country Year Households Weight Vegetarians
Std. Error
vegetarians
Strict
Vegetarians
Std. Error strict
vegetarians
Vegans
Std. Error
vegans
UK 1970 5731
0.002 0.001 0.002 0.001 0.000 0.000
UK 1969 5995
0.001 0.000 0.000 0.000 0.000 0.000
UK 1968 6408
0.002 0.000 0.000 0.000 0.000 0.000
UK 1963 2972
0.003 0.001 0.001 0.001 0.000 0.000
UK 1962 3114
0.004 0.001 0.002 0.001 0.000 0.000
UK 1961 3057
0.004 0.001 0.002 0.001 0.000 0.000
USA 2006 5098
0.055 0.003 0.030 0.002 0.000 0.000
USA 2005 6151
0.055 0.003 0.029 0.002 0.000 0.000
USA 2004 5941
0.045 0.003 0.025 0.002 0.000 0.000
USA 2003 4152
0.040 0.003 0.022 0.002 0.000 0.000
USA 2002 5424
0.046 0.003 0.024 0.002 0.000 0.000
USA 2001 5496
0.038 0.003 0.022 0.002 0.000 0.000
USA 2000 5357
0.043 0.003 0.024 0.002 0.000 0.000
USA 1999 5260
0.048 0.003 0.025 0.002 0.000 0.000
USA 1998 4155
0.047 0.003 0.027 0.003 0.000 0.000
USA 1997 4100
0.044 0.003 0.025 0.002 0.000 0.000
USA 1996 4061
0.042 0.003 0.023 0.002 0.000 0.000
USA 1995 3875
0.034 0.003 0.016 0.002 0.000 0.000
USA 1994 4251
0.036 0.003 0.020 0.002 0.000 0.000
USA 1993 4764
0.041 0.003 0.022 0.002 0.000 0.000
USA 1992 4745
0.035 0.003 0.020 0.002 0.000 0.000
USA 1991 5112
0.036 0.003 0.017 0.002 0.000 0.000
USA 1990 4830
0.031 0.002 0.013 0.002 0.000 0.000
USA 1981 3794
0.025 0.003 0.012 0.002 0.000 0.000
USA 1980 4002
0.027 0.003 0.012 0.002 0.000 0.000
Vietnam 2006 2620
0.004 0.001 0.001 0.001 0.001 0.001
Vietnam 2004 8268
0.008 0.001 0.004 0.001 0.003 0.001
Vietnam 2002 26589
0.003 0.001 0.001 0.000 0.001 0.000
Vietnam 1998 4989
0.001 0.001 0.001 0.000 0.001 0.000
Vietnam 1992 4331
0.009 0.001 0.004 0.001 0.003 0.001
Australia
2003/04 6957
0.106 na 0.085 na 0.029 na
Australia
1998/99 6893
0.082 na 0.072 na 0.020 na
29
Country Year Households Weight Vegetarians
Std. Error
vegetarians
Strict
Vegetarians
Std. Error strict
vegetarians
Vegans
Std. Error
vegans
Australia
1993/94 8,389
0.109 na 0.077 na 0.017 na
Bosnia
2004 2959
0.010 0.002 0.007 0.002 0.000 0.000
Bosnia
2001 5335
0.004 0.000 0.003 0.001 0.000 0.000
China
1997 787
0.155 0.013 0.130 0.012 0.078 0.010
Germany
2003 11831
0.024 0.001 0.016 0.001 0.001 0.000
Germany
1998 12680
0.024 0.001 0.016 0.001 0.001 0.000
Germany
1993 15825
0.023 0.001 0.015 0.001 0.001 0.000
Papua New
Guinea
1996 1336
0.024 0.004 0.012 0.003 0.009 0.003
Serbia
2003 2548
0.016 0.002 0.014 0.002 0.001 0.001
Singapore
2003 6749
0.210 na 0.170 na 0.090 na
30
Reference List
1. FAO, "Livestock's Long Shadow: Environmental issues and options" (Food and
Agriculture Organization, Rome, 2006).
2. WRI. EarthTrends. http://earthtrends.wri.org
. 2009. World Resources Institute.
3. W. S. Chern, K. Ishibashi, K. Taniguchi, Y. Tokoyama, "Analysis of the Food
Consumption of Japanese Households" (ESA Working Paper 02-06, Food and
Agricultural Organization, Rome, 2002).
4. C. Newman, M. Henchion, A. Matthews, Eur Rev Agric Econ
28, 393 (2001).
5. R. M. Nayga, Jr., Review of Agricultural Economics
17, 275 (1995).
6. M. Burton, M. Tomlinson, T. Young, Journal of Agricultural Economics
45, 202
(1994).
7. B. W. Gould, Y. Lee, D. Dong, H. J. Villareal, paper presented at the Annual
Meeting of the American Agricultural Economics Association, Long Beach, CA,
28-31 July 2002.
8. O. Mueller, M. Krawinkel, Canadian Medical Association Journal
173, 279 (2005).
9. B. M. Popkin, Asia Pacific Journal of Clinical Nutrition
10, (2001).
10. J. Reimer, T. W. Hertel, Economic Systems Research
16, 347 (2004).
11. E. Giovannucci et al., Cancer Research
54, 2390 (1994).
12. A. Drewnowski, S. E. Specter, American Journal of Clinical Nutrition
79, 6 (2004).
13. F. B. Hu et al., American Journal of Clinical Nutrition
72, 912 (2000).
14. D. P. Rose, A. P. Boyar, E. L. Wynder, Cancer
58, 2363 (1986).
15. W. P. T. James, M. Nelson, A. Ralph, S. Leather, British Medical Journal
314,
1545 (1997).
16. B. J. DeAngelo, F. C. de la Chesnaye, R. H. Beach, A. Sommer, B. C. Murray,
Energy Journal, 89 (2006).
Dataset Reference List
17. World Bank. Living Standards Measurement Study 1985-2007.
http://go.worldbank.org/PDHZFQZ6L0
18. U.S. Department of Labor, Bureau of Labor Statistics. Consumer Expenditure
Survey, Diary Survey. http://www.bls.gov/cex/
31
19. Carolina Population Centre. Russia Longitudinal Monitoring Survey.
http://www.cpc.unc.edu/rlms/
20. Central Bureau of Statistics. Nepal Living Standards Survey.
http://www.cbs.gov.np/Surveys/NLSSII/NLSS%20II%20Report%20Vol%201.pdf
21. Central Statistics Office Ireland. Household Budget Survey.
http://www.eirestat.cso.ie/surveysandm
ethodologies/surveys/housing_households/s
urvey_hbs.htm
22. General Statistics Office of Vietnam. Vietnam Household Living Standards Survey.
http://www.gso.gov.vn/default_en.aspx?tabid=515&idm
id=5&ItemID=8183
23. Institut National de la Statistique et des Études Économiques. Enquête budget de
famille. http://www.insee.fr/fr/publications-et-services/irweb.asp?id=BDF06
24. Office for National Statistics. Expenditure and Food Survey.
http://www.statistics.gov.uk/ssd/surveys/expenditure_food_survey.asp
25. Office for National Statistics. Family Expenditure Survey - UK.
http://www.statistics.gov.uk/StatBase/Source.asp?vlnk=1385&More=Y
26. Statistical Institute of Jamaica. Jamaica Survey of Living Conditions.
http://www.statinja.com/surveys.htm
27. Singapore Department of Statistics. Household Expenditure Survey.
http://www.singstat.gov.sg/stats/themes/people/house.html
28. Statistiches Budesamt Deutschland. Einkommens- und Verbrauchsstichprobe.
http://www.destatis.de/jetspeed/portal/cms/Sites/destatis/Internet/DE/Presse/abisz/E
inkommens__Verbrauchsstichprobe,templateId=renderPrint.psm
29. Australian Bureau of Statistics. Household Expenditure Survey, Australia.
http://www.abs.gov.au/Ausstats/ABS@.nsf/12ce1aabe68b47f3ca256982001cc5da/5
f1422f1af472d80ca256bd00026aee6!OpenDocument
32
Year Number
Title/Author(s)
ESRI Authors/Co-authors
Italicised
2010
339 International Migration in Ireland, 2009
Philip J O’Connell
and
Corona Joyce
338 The Euro Through the Looking-Glass:
Perceived Inflation Following the 2002 Currency
Changeover
Pete Lunn
and
David Duffy
337 Returning to the Question of a Wage Premium for
Returning Migrants
Alan Barrett and Jean Goggin
2009 336 What Determines the Location Choice of Multinational
Firms in the ICT Sector?
Iulia Siedschlag, Xiaoheng Zhang, Donal Smith
335 Cost-benefit analysis of the introduction of weight-
based charges for domestic waste – West Cork’s
experience
Sue Scott
and
Dorothy Watson
334 The Likely Economic Impact of Increasing Investment
in Wind on the Island of Ireland
Conor Devitt, Seán Diffney, John Fitz Gerald, Seán
Lyons and Laura Malaguzzi Valeri
333 Estimating Historical Landfill Quantities to Predict
Methane Emissions
Seán Lyons,
Liam Murphy
and
Richard S.J. Tol
332 International Climate Policy and Regional Welfare
Weights
Daiju Narita
, Richard S. J. Tol,
and
David Anthoff
331 A Hedonic Analysis of the Value of Parks and
Green Spaces in the Dublin Area
Karen Mayor, Seán Lyons, David Duffy
and
Richard
S.J. Tol
33
330 Measuring International Technology Spillovers and
Progress Towards the European Research Area
Iulia Siedschlag
329 Climate Policy and Corporate Behaviour
Nicola Commins,
Se
án Lyons,
Marc Schiffbauer,
and
Richard S.J. Tol
328 The Association Between Income Inequality and
Mental Health: Social Cohesion or Social
Infrastructure
Richard Layte
and
Bertrand Maître
327 A Computational Theory of Exchange:
Willingness to pay, willingness to accept and the
endowment effect
Pete Lunn
and Mary Lunn
326 Fiscal Policy for Recovery
John Fitz Gerald
325 The EU 20/20/2020 Targets: An Overview of the
EMF22 Assessment
Christoph Böhringer, Thomas F. Rutherford, and
Richard S.J. Tol
324 Counting Only the Hits? The Risk of Underestimating
the Costs of Stringent Climate Policy
Massimo Tavoni,
Richard S.J. Tol
323 International Cooperation on Climate Change
Adaptation from an Economic Perspective
Kelly C. de Bruin, Rob B. Dellink and
Richard S.J. Tol
322 What Role for Property Taxes in Ireland?
T. Callan, C. Keane
and
J.R. Walsh
321 The Public-Private Sector Pay Gap in Ireland: What
Lies Beneath?
Elish Kelly, Seamus McGuinness, Philip O’Connell
320 A Code of Practice for Grocery Goods Undertakings
and An Ombudsman: How to Do a Lot of Harm by
Trying to Do a Little Good
34
Paul K Gorecki
319 Negative Equity in the Irish Housing Market
David Duffy
318 Estimating the Impact of Immigration on Wages in
Ireland
Alan Barrett, Adele Bergin
and
Elish Kelly
317 Assessing the Impact of Wage Bargaining and Worker
Preferences on the Gender Pay Gap in Ireland Using
the National Employment Survey 2003
Seamus McGuinness, Elish Kelly, Philip O’Connell, Tim
Callan
316 Mismatch in the Graduate Labour Market Among
Immigrants and Second-Generation Ethnic Minority
Groups
Delma Byrne
and
Seamus McGuinness
315 Managing Housing Bubbles in Regional Economies
under
EMU: Ireland and Spain
Thomas Conefrey
and
John Fitz Gerald
314 Job Mismatches and Labour Market Outcomes
Kostas Mavromaras
, Seamus McGuinness,
Nigel
O’Leary, Peter Sloane and Yin King Fok
313 Immigrants and Employer-provided Training
Alan Barrett, Séamus McGuinness,
Martin O’Brien
and
Philip O’Connell
312 Did the Celtic Tiger Decrease Socio-Economic
Differentials in Perinatal Mortality in Ireland?
Richard Layte
and
Barbara Clyne
311 Exploring International Differences in Rates of Return
to Education: Evidence from EU SILC
Maria A. Davia,
Seamus McGuinness
and
Philip, J.
O’Connell
310 Car Ownership and Mode of Transport to Work in
Ireland
35
Nicola Commins
and
Anne Nolan
309 Recent Trends in the Caesarean Section Rate in
Ireland 1999-2006
Aoife Brick
and
Richard Layte
308 Price Inflation and Income Distribution
Anne Jennings, Seán Lyons
and
Richard S.J. Tol
307 Overskilling Dynamics and Education Pathways
Kostas Mavromaras,
Seamus McGuinness
, Yin King
Fok
306 What Determines the Attractiveness of the European
Union to the Location of R&D Multinational Firms?
Iulia Siedschlag, Donal Smith, Camelia Turcu,
Xiaoheng Zhang
305 Do Foreign Mergers and Acquisitions Boost Firm
Productivity?
Marc Schiffbauer, Iulia Siedschlag, Frances Ruane
304 Inclusion or Diversion in Higher Education in the
Republic of Ireland?
Delma Byrne
303 Welfare Regime and Social Class Variation in Poverty
and Economic Vulnerability in Europe: An Analysis of
EU-SILC
Christopher T. Whelan and
Bertrand Maître
302 Understanding the Socio-Economic Distribution and
Consequences of Patterns of Multiple Deprivation:
An Application of Self-Organising Maps
Christopher T. Whelan, Mario Lucchini, Maurizio Pisati
and
Bertrand Maître
301 Estimating the Impact of Metro North
Edgar Morgenroth
300 Explaining Structural Change in Cardiovascular
Mortality in Ireland 1995-2005: A Time Series Analysis
Richard Layte, Sinead O’Hara
and Kathleen Bennett
36
299 EU Climate Change Policy 2013-2020: Using the Clean
Development Mechanism More Effectively
Paul K Gorecki, Seán Lyons
and
Richard S.J. Tol
298 Irish Public Capital Spending in a Recession
Edgar Morgenroth
297 Exporting and Ownership Contributions to Irish
Manufacturing Productivity Growth
Anne Marie Gleeson,
Frances Ruane
296 Eligibility for Free Primary Care and Avoidable
Hospitalisations in Ireland
Anne Nolan
295 Managing Household Waste in Ireland:
Behavioural Parameters and Policy Options
John Curtis, Seán Lyons
and
Abigail O’Callaghan-Platt
294 Labour Market Mismatch Among UK Graduates;
An Analysis Using REFLEX Data
Seamus McGuinness
and
Peter J. Sloane
293 Towards Regional Environmental Accounts for Ireland
Richard S.J. Tol , Nicola Commins, Niamh Crilly, Sean
Lyons
and
Edgar Morgenroth
292 EU Climate Change Policy 2013-2020: Thoughts on
Property Rights and Market Choices
Paul K. Gorecki, Sean Lyons
and
Richard S.J. Tol
291 Measuring House Price Change
David Duffy
290 Intra-and Extra-Union Flexibility in Meeting the
European Union’s Emission Reduction Targets
Richard S.J. Tol
289 The Determinants and Effects of Training at Work:
Bringing the Workplace Back In
Philip J. O’Connell
and
Delma Byrne
288 Climate Feedbacks on the Terrestrial Biosphere and
the Economics of Climate Policy: An Application of
37
FUND
Richard S.J. Tol
287 The Behaviour of the Irish Economy: Insights from
the HERMES macro-economic model
Adele Bergin, Thomas Conefrey, John FitzGerald
and
Ide Kearney
286 Mapping Patterns of Multiple Deprivation Using
Self-Organising Maps: An Application to EU-SILC Data
for Ireland
Maurizio Pisati,
Christopher T. Whelan
, Mario Lucchini
and
Bertrand Maître
285 The Feasibility of Low Concentration Targets:
An Application of FUND
Richard S.J. Tol
284 Policy Options to Reduce Ireland’s GHG Emissions
Instrument choice: the pros and cons of alternative
policy instruments
Thomas Legge and
Sue Scott
283 Accounting for Taste: An Examination of
Socioeconomic Gradients in Attendance at Arts Events
Pete Lunn
and
Elish Kelly
282 The Economic Impact of Ocean Acidification on Coral
Reefs
Luke M. Brander, Katrin Rehdanz,
Richard S.J. Tol
,
and Pieter J.H. van Beukering
281 Assessing the impact of biodiversity on tourism flows:
A model for tourist behaviour and its policy
implications
Giulia Macagno, Maria Loureiro, Paulo A.L.D. Nunes
and
Richard S.J. Tol
280 Advertising to boost energy efficiency: the Power of
One campaign and natural gas consumption
Seán Diffney, Seán Lyons
and
Laura Malaguzzi Valeri
279 International Transmission of Business Cycles
Between Ireland and its Trading Partners
38
Jean Goggin
and
Iulia Siedschlag
278 Optimal Global Dynamic Carbon Taxation
David Anthoff
277 Energy Use and Appliance Ownership in Ireland
Eimear Leahy
and
Seán Lyons
276 Discounting for Climate Change
David Anthoff, Richard S.J. Tol
and Gary W. Yohe
275 Projecting the Future Numbers of Migrant Workers in
the Health and Social Care Sectors in Ireland
Alan Barrett
and Anna Rust
274 Economic Costs of Extratropical Storms under Climate
Change: An application of FUND
Daiju Narita
, Richard S.J. Tol, David Anthoff
273 The Macro-Economic Impact of Changing the Rate of
Corporation Tax
Thomas Conefrey
and
John D. Fitz Gerald
272 The Games We Used to Play
An Application of Survival Analysis to the Sporting
Life-course
Pete Lunn
2008
271 Exploring the Economic Geography of Ireland
Edgar Morgenroth
270 Benchmarking, Social Partnership and Higher
Remuneration: Wage Settling Institutions and the
Public-Private Sector Wage Gap in Ireland
Elish Kelly, Seamus McGuinness, Philip O’Connell
269 A Dynamic Analysis of Household Car Ownership in
Ireland
Anne Nolan
268 The Determinants of Mode of Transport to Work in
the Greater Dublin Area
Nicola Commins
and
Anne Nolan
39
267 Resonances from
Economic Development
for Current
Economic Policymaking
Frances Ruane
266 The Impact of Wage Bargaining Regime on Firm-Level
Competitiveness and Wage Inequality: The Case of
Ireland
Seamus McGuinness, Elish Kelly
and
Philip O’Connell
265 Poverty in Ireland in Comparative European
Perspective
Christopher T. Whelan
and
Bertrand Maître
264 A Hedonic Analysis of the Value of Rail Transport in
the Greater Dublin Area
Karen Mayor, Seán Lyons, David Duffy
and
Richard
S.J. Tol
263 Comparing Poverty Indicators in an Enlarged EU
Christopher T. Whelan
and
Bertrand Maître
262 Fuel Poverty in Ireland: Extent,
Affected Groups and Policy Issues
Sue Scott, Seán Lyons, Claire Keane,
Donal McCarthy
and
Richard S.J. Tol
261 The Misperception of Inflation by Irish Consumers
David Duffy
and
Pete Lunn
260 The Direct Impact of Climate Change on Regional
Labour Productivity
Tord Kjellstrom, R Sari Kovats, Simon J. Lloyd, Tom
Holt,
Richard S.J. Tol
259 Damage Costs of Climate Change through
Intensification of Tropical Cyclone Activities:
An Application of FUND
Daiju Narita,
Richard S. J. Tol
and
David Anthoff
258 Are Over-educated People Insiders or Outsiders?
A Case of Job Search Methods and Over-education in
UK
Aleksander Kucel,
Delma Byrne
40
257 Metrics for Aggregating the Climate Effect of Different
Emissions: A Unifying Framework
Richard S.J. Tol,
Terje K. Berntsen, Brian C. O’Neill,
Jan S. Fuglestvedt, Keith P. Shine, Yves Balkanski and
Laszlo Makra
256 Intra-Union Flexibility of Non-ETS Emission Reduction
Obligations in the European Union
Richard S.J. Tol
255 The Economic Impact of Climate Change
Richard S.J. Tol
254 Measuring International Inequity Aversion
Richard S.J. Tol
253 Using a Census to Assess the Reliability of a National
Household Survey for Migration Research: The Case
of Ireland
Alan Barrett
and
Elish Kelly
252 Risk Aversion, Time Preference, and the Social Cost of
Carbon
David Anthoff, Richard S.J. Tol
and
Gary W. Yohe
251 The Impact of a Carbon Tax on Economic Growth and
Carbon Dioxide Emissions in Ireland
Thomas Conefrey, John D. Fitz Gerald, Laura
Malaguzzi Valeri
and
Richard S.J. Tol
250 The Distributional Implications of a Carbon Tax in
Ireland
Tim Callan, Sean Lyons, Susan Scott, Richard S.J. Tol
and Stefano Verde
249 Measuring Material Deprivation in the Enlarged EU
Christopher T. Whelan, Brian Nolan
and
Bertrand
Maître
248 Marginal Abatement Costs on Carbon-Dioxide
Emissions: A Meta-Analysis
Onno Kuik, Luke Brander and
Richard S.J. Tol
41
247 Incorporating GHG Emission Costs in the Economic
Appraisal of Projects Supported by State Development
Agencies
Richard S.J. Tol
and
Seán Lyons
246 A Carton Tax for Ireland
Richard S.J. Tol, Tim Callan, Thomas Conefrey, John
D. Fitz Gerald, Seán Lyons, Laura Malaguzzi Valeri
and
Susan Scott
245 Non-cash Benefits and the Distribution of Economic
Welfare
Tim Callan
and
Claire Keane
244 Scenarios of Carbon Dioxide Emissions from Aviation
Karen Mayor
and
Richard S.J. Tol
243 The Effect of the Euro on Export Patterns: Empirical
Evidence from Industry Data
Gavin Murphy
and
Iulia Siedschlag
242 The Economic Returns to Field of Study and
Competencies Among Higher Education Graduates in
Ireland
Elish Kelly, Philip O’Connell
and
Emer Smyth
241 European Climate Policy and Aviation Emissions
Karen Mayor
and
Richard S.J. Tol
240 Aviation and the Environment in the Context of the
EU-US Open Skies Agreement
Karen Mayor
and
Richard S.J. Tol
239 Yuppie Kvetch? Work-life Conflict and Social Class in
Western Europe
Frances McGinnity
and
Emma Calvert
238 Immigrants and Welfare Programmes: Exploring the
Interactions between Immigrant Characteristics,
Immigrant Welfare Dependence and Welfare Policy
Alan Barrett
and Yvonne McCarthy
237 How Local is Hospital Treatment? An Exploratory
Analysis of Public/Private Variation in Location of
Treatment in Irish Acute Public Hospitals
42
Jacqueline O’Reilly
and
Miriam M. Wiley
236 The Immigrant Earnings Disadvantage Across the
Earnings and Skills Distributions: The Case of
Immigrants from the EU’s New Member States in
Ireland
Alan Barrett
,
Seamus McGuinness
and
Martin O’Brien
235
Europeanisation of Inequality and European
Reference Groups
Christopher T. Whelan
and
Bertrand Maître
234 Managing Capital Flows: Experiences from Central
and Eastern Europe
Jürgen von Hagen and
Iulia Siedschlag
233
ICT Diffusion, Innovation Systems, Globalisation and
Regional Economic Dynamics: Theory and Empirical
Evidence
Charlie Karlsson, Gunther Maier, Michaela Trippl,
Iulia
Siedschlag,
Robert Owen and
Gavin Murphy
232
Welfare and Competition Effects of Electricity
Interconnection between Great Britain and Ireland
Laura Malaguzzi Valeri
231 Is FDI into China Crowding Out the FDI into the
European Union?
Laura Resmini and
Iulia Siedschlag
230 Estimating the Economic Cost of Disability in Ireland
John Cullinan, Brenda Gannon and
Seán Lyons
229 Controlling the Cost of Controlling the Climate: The
Irish Government’s Climate Change Strategy
Colm McCarthy,
Sue Scott
228 The Impact of Climate Change on the Balanced-
Growth-Equivalent: An Application of
FUND
David Anthoff
,
Richard S.J. Tol
227 Changing Returns to Education During a Boom? The
Case of Ireland
Seamus McGuinness
,
Frances McGinnity, Philip
43
O’Connell
226 ‘New’ and ‘Old’ Social Risks: Life Cycle and Social
Class Perspectives on Social Exclusion in Ireland
Christopher T. Whelan
and
Bertrand Maître
225 The Climate Preferences of Irish Tourists by Purpose
of Travel
Seán Lyons, Karen Mayor
and
Richard S.J. Tol
224 A Hirsch Measure for the Quality of Research
Supervision, and an Illustration with Trade
Economists
Frances P. Ruane
and
Richard S.J. Tol
223 Environmental Accounts for the Republic of Ireland:
1990-2005
Seán Lyons, Karen Mayor
and
Richard S.J. Tol
2007 222 Assessing Vulnerability of Selected Sectors under
Environmental Tax Reform: The issue of pricing
power
J. Fitz Gerald
, M. Keeney and
S. Scott
221 Climate Policy Versus Development Aid
Richard S.J. Tol
220 Exports and Productivity – Comparable Evidence for
14 Countries
The International Study Group on Exports and
Productivity
219 Energy-Using Appliances and Energy-Saving Features:
Determinants of Ownership in Ireland
Joe O’Doherty,
Seán Lyons
and
Richard S.J. Tol
218 The Public/Private Mix in Irish Acute Public Hospitals:
Trends and Implications
Jacqueline O’Reilly
and
Miriam M. Wiley
217 Regret About the Timing of First Sexual Intercourse:
The Role of Age and Context
Richard Layte
, Hannah McGee
44
216 Determinants of Water Connection Type and
Ownership of Water-Using Appliances in Ireland
Joe O’Doherty,
Seán Lyons
and
Richard S.J. Tol
215 Unemployment – Stage or Stigma?
Being Unemployed During an Economic Boom
Emer Smyth
214 The Value of Lost Load
Richard S.J. Tol
213 Adolescents’ Educational Attainment and School
Experiences in Contemporary Ireland
Merike Darmody, Selina McCoy, Emer Smyth
212 Acting Up or Opting Out? Truancy in Irish Secondary
Schools
Merike Darmody, Emer Smyth
and
Selina McCoy
211 Where do MNEs Expand Production: Location Choices
of the Pharmaceutical Industry in Europe after 1992
Frances P. Ruane
, Xiaoheng Zhang
210 Holiday Destinations: Understanding the Travel
Choices of Irish Tourists
Seán Lyons, Karen Mayor
and
Richard S.J. Tol
209 The Effectiveness of Competition Policy and the Price-
Cost Margin: Evidence from Panel Data
Patrick McCloughan,
Seán Lyons
and William Batt
208 Tax Structure and Female Labour Market
Participation: Evidence from Ireland
Tim Callan
, A. Van Soest,
J.R. Walsh
... Moreover, there have long been those who have not eaten meat because of cultural, philosophical or religious reasons, such as the Pythagoreans in ancient Greek, or many Hindus in India. In present-day India, around 30% of the population report being vegetarians, according to an Indian government survey from 2014. 25 As Leahy et al. (2010) argue, those not eating meat out of religious reasons, for example, have generally not chosen to be vegetarians, but they have been born into vegetarianism. For example, in India, the principle of ahimsa, nonviolence, prohibits eating meat within much of Hinduism, Jainism and Buddhism, as harming animals makes a person spiritually impure (Zaraska, 2016a). ...
... The estimate in Leahy et al. (2010) is that 22%, or around 1.5 billion people worldwide, are vegetarians, mostly out of necessity. In contrast, they estimate that out-of-choice vegetarians would number globally only 75 million, or around 1% of the current global population. ...
... While the proportion of out-of-necessity vegetarians may have decreased in the last years since these estimates, (see Section 2.1.3 and the discussion on the protein transition), the proportion of out-of-choice vegetarians is likely to have increased somewhat, trends recognized by Leahy et al. (2010) as well. Interestingly, the Faunalytics study (Asher et al., 2014) indicates that there are five times as many former vegetarians and vegans in the United States as there are current ones. ...
... Moreover, there have long been those who have not eaten meat because of cultural, philosophical or religious reasons, such as the Pythagoreans in ancient Greek, or many Hindus in India. In present-day India, around 30% of the population report being vegetarians, according to an Indian government survey from 2014. 25 As Leahy et al. (2010) argue, those not eating meat out of religious reasons, for example, have generally not chosen to be vegetarians, but they have been born into vegetarianism. For example, in India, the principle of ahimsa, nonviolence, prohibits eating meat within much of Hinduism, Jainism and Buddhism, as harming animals makes a person spiritually impure (Zaraska, 2016a). ...
... The estimate in Leahy et al. (2010) is that 22%, or around 1.5 billion people worldwide, are vegetarians, mostly out of necessity. In contrast, they estimate that out-of-choice vegetarians would number globally only 75 million, or around 1% of the current global population. ...
... While the proportion of out-of-necessity vegetarians may have decreased in the last years since these estimates, (see Section 2.1.3 and the discussion on the protein transition), the proportion of out-of-choice vegetarians is likely to have increased somewhat, trends recognized by Leahy et al. (2010) as well. Interestingly, the Faunalytics study (Asher et al., 2014) indicates that there are five times as many former vegetarians and vegans in the United States as there are current ones. ...
Book
Social practice theories help challenge the often hidden paradigms, worldviews and values at the basis of many unsustainable practices. However, practice theoretical research can also struggle to provide useful results for policymaking. Connected to social practices, discourses and their boundaries define what is seen as possible, what the range of issues and their solutions are. By exploring the connections between practices and discourses - where paradigms, worldviews and values are represented through cognitive frames – this book develops, firstly, a conceptual approach to help enable purposive change in unsustainable social practices. This is done in an interdisciplinary manner integrating different literatures. Secondly, the book takes the current vastly unsustainable meat system as a central theme. Radical transformation towards new meatways, such as strong flexitarianism, is arguably necessary, yet complex psychological, ideological and power related mechanisms still inhibit change. Discourses around new solutions, such as cultivated meat, new generation plant-based meats, and insects, are explored for answers.
... Based on estimates of more than a billion world population, are vegetarians by means of affordability, and much smaller proportion of world population that are vegetarian by choice. Anecdotally, vegetarianism is lifestyle of those who are more concerned toward unnecessarily slaughtering of animals and also concerned about health and the environment (Leahy et al., 2010). On the other hand, increase in pasture land to meet the demand of world food has limitation in relation to the livestock growth. ...
... This can be mainly attributed to affordability in developing economy of Pakistan where the proportion of middle class population and the population below poverty line is considerably high. Meat consumption both in terms of calories and/ or expenditures found higher in rich people (Leahy et al., 2010). ...
Article
Full-text available
Meat is one of the major sources that fulfills protein and vitamin need of human body. Because of increased urbanization and change in human eating habits the global demand of meat has been increasing. At present, in Pakistan, poultry consumption is the key source of the general masses to get the mandatory protein and nutrients. The foremost impact of the poultry industry is to boost up nutrients value and provide an inexpensive and economical source of meat for the masses. Globally, the poultry industry has shown exponential growth and profitability in the sector is considerably higher. However, in the case of Pakistan both producers and consumers are unable to reap the benefits. Consumers and poultry farmers, the two extreme of value chain are in surplus loss because of no. of agents in the supply chain. Keeping in view the importance of poultry industry, the study has attempted to assess the economic viability of the poultry farmers in district Mardan, Khyber Pakhtunkhwa by evaluating profitability of various actors involved in the poultry industry. The total of 200 farmers, commission agents and consumers were surveyed using multistage sampling technique. Building on quantitative industry figures the study also highlighted various qualitative problems and factors which are responsible for the production, and profitability of the poultry industry in the district. The results of the study revealed that the unfair and unwarranted distribution of profits among producers, commission agent and retailers are the major impediment to the extension of the poultry industry in the district. The results of the study quantified that the commission agent obtain 37% of the abnormal profit which results in inflated consumer prices. At the same time none of the profit get invested back in the industry as producer keep struggling to meet financing requirements. Henceforth, to improve nutrient consumption of consumers steps are required to be taken to provide level playing field to all the agents involved and to provide easy market access to producers.
... Vegan, Pronounced as "Veegan", was coined in 1944 by a carpenter Donald Watson from a suggestion by early members Mr George A [1]. Henderson and his wife mark the beginning and end of vegetarianism [2]. Strict vegetarian and vegan households around the world are tabulated to acknowledge the importance of veganism diet for global environmental change [2]. ...
... Vegan, Pronounced as "Veegan", was coined in 1944 by a carpenter Donald Watson from a suggestion by early members Mr George A [1]. Henderson and his wife mark the beginning and end of vegetarianism [2]. Strict vegetarian and vegan households around the world are tabulated to acknowledge the importance of veganism diet for global environmental change [2]. There are more than 2.5% of U.S. adults that have adopted a strict vegan lifestyle in the year 2018. ...
Chapter
Full-text available
This report is suggesting the beneficial effect of clustering micro bloggers tweets from 60 hash tags relating to the issue of Veganism. Going Vegan is a wellknown effect on health. We aimed to analyze tweets coming from casual Twitter users and twitter accounts representing the veganism society and industry. We cluster the group of discourse that coming from 60 and more hashtags. These tags include tweets that have tagged with #plantbaseddiet, #vegan food, #vegetarian, etc. We collected n = 50,634 tweets and analyzed n = 25,639 processed tweets. The result shows that sampled tweets, which includes 1) concerns about animal welfare; 2) sustainability (environment) 3) ways to live a healthier lifestyle (Health), and 4) methods and options for Vegan (recipe). Although with 60 + hash tags, this grouping practice allows decision making processes more manageable. This work not only demonstrates the application of a clustering algorithm to collate micro blogs with different hash tags into groups of similar topics but also shown that it is possible to develop a platform for automatically assembling information on the same subject from a range of different micro blogs. The application can significantly assist others, including academic researchers, or businesses, to quickly and effectively find information and knowledge from these sources. This application is possible for society looking for a healthy life.
... The consumption of TVP in meat analogs in different regions of the world is based on cultural and religious reasons [7]. Vegetarian dishes including alternative proteins were frequently consumed in the Buddhist religion [70]. In 1960 the invention of TVP led to the modernization of meat analogs as TVP was used as a prime ingredient in the vegan version of meat alternatives [11,71]. ...
Article
Full-text available
Abstract: Meat analogs produced through extruded products, such as texture vegetable protein (TVP) with the addition of various plant-based ingredients are considered the products that have great potential for replacing real meat. This systematic review was conducted to summarize the evidence of the incorporation of TVP on the quality characteristics of meat analogs. Extensive literature exploration was conducted up to March 2022 for retrieving studies on the current topic in both PubMed and Scopus databases. A total of 28 articles published from 2001 to 2022 were included in the data set based on specific inclusion criteria. It appears that soy protein is by far the most used extender in meat analogs due to its low cost, availability, and several beneficial health aspects. In addition, the studies included in this review were mainly conducted in countries, such as Korea, the USA, and China. Regarding quality characteristics, textural parameters were the most assessed in the studies followed by physicochemical properties, and sensory and taste attributes. Other aspects, such as the development of TVP, the difference in quality characteristics of texturized proteins, and the usage of binding agents in various meat analogs formulations are also highlighted in detail. Keywords: meat analogs; textured vegetable protein; quality characteristics; systematic review
... This could occur if a guardian using a conventional or unconventional pet diet expected a better health outcome as a result, and if this expectation exerted an unconscious effect on their answers about pet health indicators. Our study included more vegans than reported in some other studies [53]. It is conceivable that vegans, or respondents following other dietary groups, such as omnivores, might have had greater subconscious expectations of good health, when animals were fed diets similar to their own. ...
Article
Full-text available
Alternative pet foods may offer benefits concerning environmental sustainability and the welfare of animals processed into pet foods. However, some worry these may compromise the welfare of pets. We asked 2,639 dog guardians about one dog living with them, for at least one year. Among 2,596 involved in pet diet decision-making, pet health was a key factor when choosing diets. 2,536 provided information relating to a single dog, fed a conventional meat (1,370 = 54%), raw meat (830 = 33%) or vegan (336 = 13%) diet for at least one year. We examined seven general indicators of ill health: unusual numbers of veterinary visits, medication use, progression onto a therapeutic diet after initial maintenance on a vegan or meat-based diet, guardian opinion and predicted veterinary opinion of health status, percentage of unwell dogs and number of health disorders per unwell dog. Dogs fed conventional diets appeared to fare worse than those fed either of the other two diets. Dogs fed raw meat appeared to fare marginally better than those fed vegan diets. However, there were statistically significant differences in average ages. Dogs fed raw meat were younger, which has been demonstrated to be associated with improved health outcomes. Additionally, non-health related factors may have improved apparent outcomes for dogs fed raw meat, for three of seven general health indicators. We also considered the prevalence of 22 specific health disorders, based on predicted veterinary assessments. Percentages of dogs in each dietary group considered to have suffered from health disorders were 49% (conventional meat), 43% (raw meat) and 36% (vegan). Significant evidence indicates that raw meat diets are often associated with dietary hazards, including nutritional deficiencies and imbalances, and pathogens. Accordingly, the pooled evidence to date indicates that the healthiest and least hazardous dietary choices for dogs, are nutritionally sound vegan diets.
... The reasons for choosing a vegetarian diet often go beyond health and well-being including economical, ecological and social concerns, which leads to another sphere of nutritional ecology concerned with sustainable life styles and human development (Leitzmann 2005); however, Hargreaves et al. (2021) have recently thoroughly reviewed domains affecting the quality of life that might go in favor or against adopting a vegetarian diet. Some estimates suggest that there is 1.5 billion vegetarians worldwide and 75 million of them are vegetarians by choice (Leahy, Lyons, and Tol 2010), while the number of vegans is growing more rapidly than the number of vegetarians (Leitzmann 2014). ...
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The health benefit of a vegetarian diet is still under debate as it may result in a higher intake of some beneficial micronutrients, while others may be reduced, thus influencing various metabolic pathways and health-related biomarkers. This scoping review discusses inflammatory, oxidative and DNA damage status in vegetarians and vegans compared to omnivores. Most of the reviewed studies indicated favorable effects of a vegetarian diet on oxidative status compared to omnivores but did not clearly associate particular dietary habits to genome damage. The evidence on the effect of vegetarian diet on the inflammatory and immunological biomarkers is poor, which could at least partly be explained by methodological constraints such as small sample size, short duration of vegetarianism and inconsistent definitions of the omnivorous diet. The only inflammatory biomarker that seems to be associated with the vegetarian diet was inflammatory mediator C-reactive protein, which in several studies showed lower values in vegetarians as compared to omnivores. There were very few studies on immunological markers and the results on the difference between vegetarians and omnivores were inconclusive. Although several biomarkers involved in oxidative stress and inflammation showed a beneficial association with the vegetarian diet, further research in well-defined and sufficiently sized cohorts is needed to provide more evidence.
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The 1978-1979 mortality rates for cancers of the breast, prostate, ovary, and colon in 26 to 30 countries were related to the average 1979-1981 food availability data published by the United Nations. The previously described relationship between breast cancer mortality rates and animal fat consumption continues to be evident, and applies also to the other three tumor types. The correlation with breast cancer was particularly strong in postmenopausal women. Since 1964, particularly notable increases in both breast cancer mortality rate and dietary fat intake have occurred in those countries with a relatively low breast cancer risk. The international comparisons support evidence from animal experiments that diets in which olive oil is a major source of fat are associated with reduced breast cancer risk. The excess in mortality rates for breast and ovarian cancer in Israel relative to the national animal fat consumption may be due to the mixed ethnic origin of the Israeli population. Positive correlations between foods and cancer mortality rates were particularly strong in the case of meats and milk for breast cancer, milk for prostate and ovarian cancer, and meats for colon cancer. All four tumor types showed a negative correlation with cereal intake, which was particularly strong in the case of prostate and ovarian cancer. Although, in general, there was a good positive correlation between prostate and breast cancer mortality rates and between prostate cancer and animal fat, discrepancies in national ranking indicate the operation of other etiologic factors that modify risk. The observed positive correlations between the four cancer mortality rates and caloric intake from animal sources, but negative correlations for vegetable-derived calories, suggest that, of the two, animal fat and not energy is the major dietary influence on cancer risk.
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Some evidence suggests that diets high in animal fat or red meat may increase the risk of colon cancer, whereas high intake of fiber or vegetables may be protective. Frequently, intake of red meat has been a stronger risk factor than total fat. Because data from prospective cohort studies are sparse, we examined fat, meat, fiber, and vegetable intake in relation to risk of colon cancer in a cohort of 47,949 U.S. male health professionals who were free of diagnosed cancer in 1986. At baseline, these men, 40 to 75 years of age, completed a validated food frequency questionnaire and provided detailed information on other lifestyle and health-related factors. Between 1986 and 1992, 205 new cases of colon cancer were diagnosed in these men. Intakes of total fat, saturated fat, and animal fat were not related to risk of colon cancer. However, an elevated risk of colon cancer was associated with red meat intake (relative risk, 1.71; 95% confidence interval, 1.15-2.55 between high and low quintiles; P = 0.005 for trend). Men who ate beef, pork, or lamb as a main dish five or more times per week had a relative risk of 3.57 (95% confidence interval, 1.58-8.06; P = 0.01 for trend) compared to men eating these foods less than once per month. The association with red meat was not confounded appreciably by other dietary factors, physical activity, body mass, alcohol intake, cigarette smoking, or aspirin use. Other sources of animal fat, including dairy products, poultry, and fish as well as vegetable fat, were slightly inversely related to risk of colon cancer. No clear association existed between fiber or vegetable intake and risk of colon cancer. These data support the hypothesis that intake of red meat is related to an elevated risk of colon cancer.
  • C Newman
  • M Henchion
  • A Matthews
C. Newman, M. Henchion, A. Matthews, Eur Rev Agric Econ 28, 393 (2001).
  • J Reimer
  • T W Hertel
J. Reimer, T. W. Hertel, Economic Systems Research 16, 347 (2004).
  • O Mueller
  • M Krawinkel
O. Mueller, M. Krawinkel, Canadian Medical Association Journal 173, 279 (2005).