Does global meat consumption follow an environmental Kuznets curve?

Abstract

In this article, we use data on meat consumption, per capita income, and other socioeconomic variables for 150 countries to determine whether data support the hypothesis that per capita meat consumption follows a Kuznets-style inverted U-curve. In other words, as nations increase their real per capita incomes, while individuals at first consume more meat, ultimately, over time and with increased income, do they moderate their consumption? Our results signal that although there is evidence of a Kuznets relationship, the income at which our data suggests a deceleration of meat is large enough that for many countries this deceleration will not be reached in the foreseeable future. In a cross-section sample of low-income countries, we find no evidence of a Kuznets relationship. In a cross-section sample of high-income countries, we do find a potential Kuznets relationship and a deceleration of meat consumption at a per capita income of US49,848.Inthefullpanel−datasamplecombininghigh−andlow−incomecountries,includingdataonlandareaandurbanization,ourresultssuggestaninflectionpointinmeatconsumptionatanincomeofUS49,848. In the full panel-data sample combining high-and low-income countries, including data on land area and urbanization, our results suggest an inflection point in meat consumption at an income of US36,375, still quite high for any realistic impact. Thus, our results highlight that effectively decelerating the global demand for meat may require aggressive and potentially controversial policy interventions, which, while leaving individuals with less choice, would address the otherwise devastating environmental impacts of increasing meat consumption .

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ARTICLE
Does global meat consumption follow an environmental Kuznets
curve?
Jennifer Rivers Cole1 & Suzanne McCoskey2
1Department of Earth & Planetary Sciences, Harvard University, Hoffman Laboratory 103, 20 Oxford Street, Cambridge, MA 02138
USA (email: jcole01@fas.harvard.edu)
2Department of Economics, Frostburg University, 101 Braddock Road, Frostburg, MD 21532 USA (email:
skmccoskey@frostburg.edu)
In this article, we use data on meat consumption, per capita income, and other socioeconomic variables for 150
countries to determine whether data support the hypothesis that per capita meat consumption follows a Kuznets-style
inverted U-curve. In other words, as nations increase their real per capita incomes, while individuals at first consume
more meat, ultimately, over time and with increased income, do they moderate their consumption? Our results signal
that although there is evidence of a Kuznets relationship, the income at which our data suggests a deceleration of
meat is large enough that for many countries this deceleration will not be reached in the foreseeable future. In a
cross-section sample of low-income countries, we find no evidence of a Kuznets relationship. In a cross-section sam-
ple of high-income countries, we do find a potential Kuznets relationship and a deceleration of meat consumption at a
per capita income of US$49,848. In the full panel-data sample combining high- and low-income countries, including
data on land area and urbanization, our results suggest an inflection point in meat consumption at an income of
US$36,375, still quite high for any realistic impact. Thus, our results highlight that effectively decelerating the global
demand for meat may require aggressive and potentially controversial policy interventions, which, while leaving indi-
viduals with less choice, would address the otherwise devastating environmental impacts of increasing meat con-
sumption.
KEYWORDS: income, socioeconomic aspects, environmental impact, meat production, food consumption
Introduction
Approximately three billion people are currently
chronically malnourished while our agricultural sys-
tems are concurrently degrading land, water, and bio-
diversity, and altering climate on a global scale
(WHO 2000; Foley et al. 2011). Despite malnour-
ishment at the individual level, overall, increasing
population and consumption are placing unprece-
dented demands on agriculture and natural resources
(Pelletier & Tyedmers, 2010). Meat, the most unsus-
tainable form of food that humans husband and con-
sume in particular places a heavy strain on global
resources. Factory-based farming has overtaken
transportation as the largest contributor to global cli-
mate change, and the impacts on water, air, and soil
are without parallel. In many developing countries,
these natural resources are already compromised,
turning the increased consumption of meat into a
potential environmental disaster. It is clear, forecast-
ing into the future, that global population growth will
further stress global resources.
In this article, we emphasize another source of
increasing demand for meat: per capita income
growth, most significantly in developing countries
such as China and India. We further investigate
whether data from higher income countries reveals a
projected deceleration of meat demand once a thresh-
old income is reached. This deceleration could offer
some hope of a natural moderation to an otherwise
serious growth pattern in demand. The modeling of
this pattern of deceleration takes on the potential
form of a Kuznets curve.
In his seminal paper from 1955, economist
Simon Kuznets first discussed the nonlinear relation-
ship between a country’s stage of development,
measured through income, and the income distribu-
tion within the country (Kuznets, 1955). According to
Kuznets, as a country moves along its path of growth,
it first experiences an increase in income inequality
as wealth increases; however, later, with changing
social preferences and the development of strong
institutions empowered to redistribute wealth, income
inequality decreases. Thus, graphing a measure of
income inequality, for example the Gini coefficient,
over time would result in a bell-shaped, quadratic
curve showing an ultimate inflection point.1
1 The Gini coefficient is used to compare income inequality across
countries and can take on values between 0 (perfect equality) to 1

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This approach, that certain characteristics of
economic growth are nonlinear, has been applied in
other areas as well, most significantly in the study of
the link between economic growth and pollution. The
World Health Organization (WHO) established the
Global Environmental Monitoring System (GEMS)
and collected data on both water and air pollution to
determine whether pollution in a developing country
would have a similar inverted U-shape, called an en-
vironmental Kuznets curve (EKC). Later, Shafik &
Bandyopadhyay (1992) and Grossman & Krueger
(1993) both estimated cross-country relationships
between income and air and water pollution, defor-
estation, and waste output. The former study con-
cluded that airborne sulfur dioxide (SO2) and smoke
concentrations began to diminish after an income
level of US$3,000–US$4,000 (in 1992 dollars) per
capita was reached, and the latter found the same but
with a turning point of US$4,000–US$6,000. Several
other research studies have since verified this curve,
with inflection points differing only slightly.
Later research focused on the question of how
this shape consistently arises in individual countries
as they develop. Copeland & Taylor (2003) recapit-
ulate these studies, detailing four possible mecha-
nisms by which EKC emerges: control not being im-
plemented until pollution builds up to a discernible
amount; strong increasing returns to scale in pollution
abatement; reduced corruption in government en-
hancing abatement effort; and a natural origin, alt-
hough the peak could occur at virtually any income
level.
More recently, Deacon & Norman (2006) iden-
tify a gap in previous research: data had not been
analyzed for individual countries, thus these re-
searchers investigated time-series data by country for
criteria pollutants including SO2, smoke, and partic-
ulates to determine what impact development has on
in-country environmental quality. Further, Deacon &
Norman (2006) assert that EKC-consistent patterns
are most likely to emerge if using three points, as
then only four shapes are possible: monotone-
increasing, monotone-decreasing, single-peaked, and
single-troughed. The first three are potentially con-
sistent with the EKC hypothesis and the fourth is
EKC-inconsistent. Deacon & Norman (2006) found
that many factors complicate the data and that only
three of the 25 countries investigated followed this
EKC hypothesis.
Several authors have extended the hypothesis of
an EKC relationship into the general category of
animal welfare and the specific relationship between
(perfect inequality). Thus, if a country experiences a “Kuznets
curve,” graphing its Gini coefficient over time would result in a
downward-shaped bell curve. For a description of the computation
of the Gini coefficient, see, for example, Todaro & Smith (2009).
income and meat consumption. Vinnari et al. (2005)
considered the relationship between meat consump-
tion and income in the European Union (EU) and
found a potential apex at the per capita income of
US$15,000. Using the broader definition of animal
welfare, Frank (2008) considers the relationship be-
tween income and meat consumption as well as the
impact of income on a more general “concern for
animal welfare.” Frank considers a sample of high-
income countries in his study—focusing on the
United States—and his models seem more successful
in finding linear relationships. Rather than showing a
rigorous Kuznets curve, they provide more back-
ground as to why an apex in the relationship between
income and meat consumption might exist. For ex-
ample, Frank finds strong evidence that higher in-
comes result in better treatment of companion ani-
mals, implying a stronger emotional bond with and
concern about animals.
Past studies on the Kuznets relationship and the
environment highlight the complications of using
cross-section versus time-series data to investigate
what are supposed to be dynamic relationships. In
general, time-series data following one country can
be effective in understanding short-run deviations
from a long-run process or path while cross-section
data can be effective in understanding long-run stable
relationships (with the assumption that long-run rela-
tionships are robust to the cross-section dimension).
In our study, we use both cross-section and panel
(time-series) data. Further, to build on the evidence
suggested between income and meat consumption
(and animal welfare) in high-income countries, we
explicitly include developing countries in our data
set. The rapidly growing populations and incomes in
countries such as China and India, we believe, make
understanding a potential Kuznets in such countries
especially important. To begin our discussion, we
review the environmental impact of meat husbandry
and consumption.
Environmental Impact of Meat Consumption
Agriculture is a major force driving the environ-
ment beyond the boundaries of what the planet can
produce (Rockstrom et al. 2009). Of all activities
humans engage in on Earth, producing, distributing,
and consuming meat has the largest environmental
impact on scales ranging from local to global. The
industrial, concentrated animal-feeding operation
(CAFO) practices of raising animals for food in con-
fined quarters with lax restrictions on resultant pol-
lutants are responsible for unsustainable resource use
and significant air pollution and resultant climate
change, water overuse and pollution, land degrada-
tion, arable soil erosion, fossil-fuel use, climate

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change, and biodiversity loss. Similarly, these envi-
ronmental problems occur in developing nations, and
biodiversity loss is particularly troubling in Earth’s
rainforests. To meet the world’s future food security
and sustainability needs, food production must grow
substantially while, at the same time, agriculture’s
environmental footprint must shrink dramatically
(Foley et al. 2011).
Air Pollution and Climate Change
Estimates of greenhouse gases (GHG) emitted by
CAFOs vary: in its book Livestock’s Long Shadow:
Environmental Issues and Options, the Food and Ag-
riculture Organization of the United Nations (FAO)
(2006) estimates that the meat industry contributes
18% of all emissions of GHGs, although Koneswaran
& Nierenberg (2008) report a 51% minimum of total
emissions, which highlights the difficulty in con-
straining emissions totals. Even the conservative 18%
FAO estimate means livestock are responsible for
40% more than all the cars, trucks, planes, trains, and
ships in the world combined. In its 2006 report, the
United Nations (UN) estimated that 7.8 billion
tons/year of carbon dioxide (CO2) are produced in the
raising of meat animals. Globally, ruminant livestock
(cows) produce about 80 million metric tons of me-
thane annually, accounting for about 28% of global
methane (CH4) emissions from human-related activi-
ties (USEPA, 2006). This is in addition to the GHG
emissions used in producing the crops fed to live-
stock (Parry et al. 2007; Verge et al. 2007).
Animals raised for human consumption are also
responsible for nearly 70% of anthropogenic ammo-
nia emissions, which contribute significantly to acid
rain and acidification of terrestrial and aquatic eco-
systems. The livestock sector emits 37% of anthro-
pogenic CH4 (with 23 times the global warming po-
tential—or GWP—of CO2)...[and] emits 65% of an-
thropogenic nitrous oxide (with 296 times the GWP
of CO2) (Parry et al. 2007).
Water-Resource Use and Degradation
Seventy percent of global freshwater withdraw-
als (80–90% of consumptive uses) are devoted to
irrigation (Foley et al. 2011). Furthermore, rain-fed
agriculture is the world’s largest user of water
(Gordon et al. 2005). Livestock production also uses
significant amounts of water and generates large vol-
umes of water pollution. Sixty gallons of water are
necessary to produce a pound of potatoes, yet a
pound of beef requires over 12,000 gallons of water.
An estimated 240 trillion gallons per year of water,
equal to 7.5 million gallons per second of water, is
used for livestock production (UN, 2006). Indeed, the
UN states that “water used by the [livestock] sector
exceeds 8 percent of the global human water use”
(Parry et al. 2007).
Animal agriculture in the United States is re-
sponsible for 33% of overall water pollution, includ-
ing the aquatic pollutants nitrogen and phosphorous,
and half of its water pollution from antibiotics. In the
United States, 80% of surface-water bodies are pol-
luted due to livestock production, and nearly 17 bil-
lion tons per year (over a million pounds per second)
of chicken, hog, and cow waste are produced glob-
ally. The world’s seven billion human inhabitants
produce just 1/60th of this waste. This excrement
makes the livestock sector the largest source of water
pollution in the world, contributing to overfertiliza-
tion (eutrophication) of surface waters, which creates,
among other things, dead zones in coastal wetland
ecosystems, irreversible destruction of tropical coral
reefs, and human infectious diseases such as E. coli
(UN, 2006). Fertilizer for crops fed to cattle also cre-
ates nutrient excess, which are especially large in
China, Northern India, the United States, and West-
ern Europe (Vitousek, 2009).
Soil Erosion and Land Degradation
Agriculture occupies about 38% of Earth’s ter-
restrial surface—the largest use of land on the planet
(Ramankutty et al. 2008). Livestock in CAFOs are
fed on grain, a highly inefficient method of obtaining
calories, and estimates range from a 4:1 up to a 54:1
energy-input to protein-output ratio, meaning, for
example, that the United States could feed up to 800
million people with the grain currently fed to live-
stock. Therefore, producing animal-based food is
much less efficient than the harvesting of grains, veg-
etables, legumes, seeds, and fruits for direct human
consumption. Meat consumption thus has staggering
economic and environmental repercussions.
The production, distribution, and consumption of
meat, dairy, and eggs is responsible for over half of
the erosion that causes sedimentation of waterways,
or 40 billion tons per year of soil loss (6 tons per year
for every human on the planet). Of this outsized vol-
ume, 60% accumulates in surface waterways (rivers,
streams, and lakes), making these water bodies prone
to flooding and contamination from agrochemicals
(inorganic fertilizers and petroleum-based pesticides)
sorbed to sediment grains (Foley, et al. 2007). Fur-
ther, lost soil may become windborne, thus increasing
the volume of dust in the atmosphere. Finally, FAO
(2005) concludes that expanding livestock production
is one of the main drivers of the destruction of tropi-
cal rain forests in Latin America, which is causing
serious environmental degradation globally (Foley et
al. 2007; Gibbs et al. 2010). More food can be deliv-
ered on less land by changing our dietary preferences.
Simply put, we can increase food availability (in

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terms of calories, protein, and critical nutrients) by
shifting crop production away from livestock feed,
bioenergy crops, and other nonfood applications
(Foley et al. 2011).
Fossil-Fuel Use
Significant amounts of fossil fuels are used in the
production, distribution, and consumption (including
refrigeration) of meat. In fact, using current factory
farming methods, it takes more than ten times as
much fossil fuel to make one calorie of animal pro-
tein as it does to make one calorie of plant protein.
Fossil fuels are nonrenewable, finite resources that
are highly polluting, responsible for CH4, CO2, mer-
cury, arsenic, and radioactivity found in air and wa-
ter.
Land Use and Biodiversity Loss
Over ten billion acres of the terrestrial portion of
Earth is used to raise animals for food. According to
Livestock’s Long Shadow (2006):
[L]ivestock production accounts for 70 per-
cent of all agricultural land and 30 percent
of the land surface of the planet…70 percent
of previous forested land in the Amazon is
occupied by pastures, and feed crops cover a
large part of the remainder…about 20 per-
cent of the world’s pastures and rangelands,
with 73 percent of rangelands in dry areas,
have been degraded to some extent, mostly
through overgrazing, compaction and ero-
sion created by livestock action.
A total of 836 million tons per year of grain (in-
cluding corn) is grown to feed livestock. If this grain
were used directly for human consumption, the hu-
man agricultural impact on the environment would be
significantly lessened. Five million acres of Amazon
rainforest are obliterated each year, either to graze
livestock or to grow grains for their consumption.
Over 90% of Amazon deforestation is due to raising
animals for food. Over one thousand species go ex-
tinct every year due to animal-based agricultural ac-
tivities.
Further, the FAO (2006) concludes:
[T]he livestock sector may well be the
leading player in the reduction of biodiver-
sity...livestock now account for about 20
percent of the total terrestrial animal bio-
mass, and the 30 percent of the earth's land
surface that they now pre-empt was once
habitat for wildlife…An analysis of the
authoritative World Conservation Union
(IUCN) Red List of Threatened Species
shows that most of the world's threatened
species are suffering habitat loss where
livestock are a factor.
As developing nations tend toward both
Western-influenced, meat-rich diets and the agricul-
tural techniques involved in procuring meat, the lo-
cal, regional, and global consequences of this transi-
tion are sure to be felt. In short, we need better data
and decision-support tools to improve management
decisions (Zacks & Kucharik, 2011).
The Economic Decision to Consume Meat
For economists, the consumption decision at the
household level presumes that the household chooses
to spend its income in a manner that maximizes the
household’s satisfaction. This decision-making pro-
cess requires that a household use information on its
income, the prices of the goods it considers consum-
ing, and how tradeoffs between consumption goods
affect its overall satisfaction to reach the choice to
consume an optimal bundle of goods. Within this
process, economists define economic goods as having
certain properties relative to their own price and
overall household income, and income elasticity re-
spectively. Price elasticity measures the responsive-
ness of consumers, in the consumption decision, to
price changes of a given good, and income elasticity
measures the responsiveness of consumers, in the
consumption decision, to changes in income. In this
section, we discuss past empirical studies on the im-
pact of price and income on the decision to consume
meat, focusing on studies done in developing coun-
tries.
Price Elasticity of Meat
Gallet (2010) reviews past studies on meat-price
elasticities. In general, Gallet’s summary of previous
findings suggests that all meats have inelastic prices.
The median value for past studies on meat in general
is given as –0.710; the median estimate for beef is
reported as –0.869 and pork –0.780. (Unit elasticity
occurs when the percentage change in quantity de-
manded is equal to the percentage change in price, so
Ed = –1, and relative elasticity occurs when the per-
centage change in quantity demanded is greater than
the percentage change in price, so Ed < –1).
Price elasticity becomes especially important in
the case of a policy interest in moderating meat con-
sumption through a tax; price elasticities are critical
in evaluating the financial impact on consumers and
producers. To the extent that general growth in de-
mand for meat could in turn increase its price, a neg-
ative price elasticity suggests a potential source of
moderation in overall meat consumption. For exam-

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ple, a November 2011 news story from an online UK
financial journal suggested that growth in China’s
and India’s demand for meat caused a 25% increase
in the price of turkey in the UK (Elliott, 2011).
Income Elasticity of Meat
Of more direct interest to this study is the rela-
tionship between income and meat consumption. We
now investigate past evidence on that relationship.
York & Gossard (2003) conducted a cross-national
study of per capita meat consumption using economic
and ecological variables. The data include 132 coun-
tries categorized as being from one of four cultural
regions: the West, Africa, Asia, and the Middle East.
The authors confirm that income, as measured in
their study by per capita gross domestic product
(GDP) purchasing power parity (PPP),2 increases per
capita meat consumption by 2.67 kilograms (kgs) for
each increase in income of US$1,000. They also find
that the percent urbanization of a country’s popula-
tion has a positive impact on meat consumption as
well as land exploited per capita; the latter, they hy-
pothesize, is due to the ecological requirements of
animal husbandry. Consistent with a positive income-
growth and meat-demand relation-ship, Gehlar &
Coyle (2002) find that the composition of world agri-
cultural trade has substantially changed in the past
two decades; for developing countries, consumption
and trade are shifting from basic staples toward
higher value livestock products.
Other studies estimate income—or expenditures
used for consumption and not saving—elasticity.
Shono et al. (2000) measure expenditure elasticities
for food-consumption products, including meat, using
data from urban income groups in China, while Jiang
& Davis (2007) estimate elasticities for rural Chinese
households. The more aggregated urban data yield
estimates for income elasticity for meat that are both
positive and relatively inelastic. Jiang & Davis’s es-
timate for the income elasticity of pork is 0.462 and
beef 0.496. The only food products which have esti-
mates larger than one in their study—relatively re-
sponsive to changes in income—are fruits and dairy
products, which the authors acknowledge are viewed
as luxury goods in China. The authors’ overall con-
clusion about the change of demand for meat in
China is that while meat demand is increasing with
2 The use of “purchasing power party” and “constant 2005 dollars”
allows for two dimensions of the neutralization of price effects
across countries and across time to facilitate direct comparison.
“Purchasing power parity” adjustments in the data control for
different prices across countries, accommodating for the fact, for
example, that one could live much better on US$5,000 a year in
Botswana than the UK. Using “constant 2005 dollars” converts the
data from nominal values to real values, neutralizing the impact of
a basic upward trend in prices over time.
income, China is likely to follow a path closer to
other Asian countries, rather than Western countries,
which have higher consumption of seafood. In fact,
the authors find the income elasticity of carp (0.856)
and shrimp (0.762), the only seafood products in their
study, to be higher than meat.3 In their study on rural
meat consumption, Jiang & Davis (2007) use house-
hold-level data for 1,520 households in Jilin Prov-
ince. The authors model the household-consumption
decision in a first stage as allocating income across
food and nonfood consumption, and in a second stage
as taking the household-food budget and allocating it
across four categories: grains, vegetable products,
animal products, and other foods. In the final stage,
the household takes the budget for each category and
allocates purchasing decisions within the category.
Given the household-data available for China, only
the decision across animal products can be rigorously
estimated for the family. Their estimate for income
elasticity for meat is close to the urban estimates at
0.88. Interestingly, correcting for specific household
characteristics does not substantively change the es-
timate, lowering it only to 0.87.
Several other studies estimate household-income
elasticity for meat in other developing countries.
Hendriks & Lyne (2003) consider data from 99
households in two communities of Kwa-Zulu Natal,
South Africa. In their results, both overall food and
specific meat elasticities are close to unity: the food
elasticity for the community of Sawyimana (46
households in the study) is estimated to be 1.09 and
for meat 0.97; for Umzumbe (47 households) the
estimates were 0.98 and 1.04 respectively. The au-
thors recognize that preferences for increased meat
consumption with increases in income could actually
be higher than the estimates suggest, as consumption
could be mitigated by the lack of refrigeration for
these households. Another study, using data from
9,189 households, estimates income elasticities for
meat in Viet Nam as 1.068 for rural households and
0.692 for urban households (Le, 2008). This inter-
esting result on the rural/urban divide contrasts with
results from the cross-national study by York &
Gossard (2004). When the latter divided the house-
holds by income quintiles, they found that the income
elasticity of meat increased for the wealthier quin-
tiles, a result which certainly challenges the possibil-
ity of a Kuznets curve. The estimated elasticities for
quintiles (poor to wealthy) are 0.22, 1.12, 1.86, 2.07,
and 2.75. These estimates suggest that mean con-
sumption for the wealthy in Viet Nam is highly elas-
3 For comparison purposes, the authors quote earlier results on
urban areas in China (Huang & Bouis, 1996), which find overall
income elasticity for meat to be closer to one, 0.967, with pork at
0.916, beef and mutton at 0.788, and poultry at 1.222.

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tic and responsive to income, and further develop-
ment in the country would cause great increases in
the demand for meat.
The above studies should be seen as representa-
tive, not exhaustive, of the estimation of expenditure
elasticities. All of the studies reinforce the notion of a
positive response to meat demand as income in-
creases in developing countries. However, the mag-
nitude of the increase varies to some degree across
the studies. Simply put, all these results acknowledge
the first side of the hypothesized Kuznets relation-
ship. A mechanism for an ultimate downturn in meat
consumption, if anything, is challenging for these
countries. The studies also raise other issues, such as
a dominant role for rural versus urban location.
Background to a Meat Consumption EKC
In the justification of a Kuznets inverted curve
for both income distribution and pollution, an initial
burst of industrial activity is ultimately offset by a
societal interest in mitigating income inequality and
pollution primarily through strong, responsive, and
responsible institutions. Changes in meat consump-
tion, which would mitigate an upward trend, would
be very different, structurally, from the narrative of a
change in social values and desired intervention by
strong institutions to either redistribute income or
mitigate pollution. Rather, changes in the decision to
consume meat depend on an increased awareness of
the environment, animal rights, and human health,
striking directly at individual consumer preferences
and ideas that typically persist about what makes an
individual “happy” or “better off.” Further, while the
effects of pollution on society may be directly visi-
ble—such as smog-filled air or discolored water—the
environmental impact of meat may be more abstract
to individual consumers, making an argument predi-
cated on social need or economic negative externality
more difficult.
To fully follow a Kuznets pattern, the relation-
ship between income and meat consumption must
have a turning point, an income level after which the
demand for meat decelerates. As is the case with the
Kuznets curves described previously, this requires
some structural change in the underlying relationship
between income and the desire to consume meat. In a
study on the social influences of meat consumption in
the United States, Gossard & York (2003) discuss
factors that may influence consumer demand for meat
and cite the rise of vegetarianism in Western socie-
ties. Other factors such as the negative health effects
of meat consumption, government subsidies for meat-
producing industries, and cultural manipulation are
all discussed as playing a potential role in American
meat consumption. Due to data limitations, however,
the authors are only able to model the impact of spe-
cific demographic variables—age, gender, race,
weight, and region, as well as income, education, and
occupation—in their regression analysis of meat con-
sumption for a survey of 15,028 individuals con-
ducted by the United States Department of Agricul-
ture in 1996. Results from their study, which may add
to the interpretation of cross-national data, include
negative and significant relationships between age
and education and meat consumption and the signifi-
cant finding that women consume less meat than
men. Finally, they find that social class influences
meat consumption, a claim they substantiate with the
finding that workers in professional occupations con-
sume significantly less meat than those in laborer
occupations. To the extent that income can capture
education levels and class dynamics in the cross-
section, and if the dynamics are indeed similar cross-
nationally, it would allow potential for the nonlinear
relationship we are hoping to find.
As discussed previously, Frank (2008) also finds
that as incomes increase (in his set of developed
countries) there tends to be more interest in animal
welfare and empathy toward companion animals,
which could extend to an overall interest in animal
rights.
Data Analysis
In our study, we use data for 1980–2009 taken
from the FAO to compute the per capita consumption
of meat. Specifically, we use the data series for food-
supply quantity (kilograms per capita) for bovine
meat, pig meat, and poultry. Data for per capita GDP
(PPP in constant 2005 dollars), the percent urbaniza-
tion of a country’s population, and the percent of a
country’s land area used for agriculture are taken
from the World Bank Indicators of Development
(2013). The measures for land area and urbanization
are used to proxy the relative scarcity of agricultural
and natural-resource-based land use and resultant
relative high cost of animal husbandry within a do-
mestic economy, as well as the socioeconomic phe-
nomenon, dominant in most developing economies,
of rural to urban migration.
By using a traditional ln/ln regression specifica-
tion, using the natural log of meat consumption and
natural log of income, we can report a point estimate
for income elasticity, which provides an initial com-
parison of how our data perform with respect to pre-
vious studies. Specifically, the estimated slope coef-
ficient in a ln/ln regression model can be interpreted
as an elasticity. More importantly, to test our Kuznets
hypothesis, we use a polynomial specification for
income both to test the statistical significance of the

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Summer 2013 | Volume 9 | Issue 2
32
second-order polynomial and to solve for an esti-
mated turning point for income.4
An initial consideration of the data for the last
year in the data set, 2009, reveals the following about
meat consumption for the 150 countries: approxi-
mately 41% of the nations in the dataset consume less
than 25 kgs/capita/year; only 2% of the countries
consume more than 100 kgs/capita/year (See Table
1). The average annual meat consumption within the
data set is approximately 42.2 kgs per person; the
highest meat consumption per capita per year in the
data set is in the United States at 118.9 kgs and the
lowest is in Bangladesh at 2.6 kgs.
Notable primarily for their current population
and rapid economic growth are China, at 54.1
kgs/capita/year, and India, at 3.6 kgs/capita/year.
Table 2 reports an estimate of the annual growth rate
of per capita meat consumption for several countries,
including China and India. In terms of per capita con-
sumption, China clearly has an extremely large
growth rate, 5.1%. India’s growth in consumption is
much lower, less than 1%. Although our data do not
investigate this dynamic, the ethnic and religious
demographics (specifically, vegetarianism) in India
may play a role in keeping this growth low (at least to
date). Brazil, often mentioned alongside China and
India for its high economic growth, has a growth rate
of meat per capita consumption of 3.3%. The United
States has a growth rate of 0.62%, although Norway,
a country with a higher per capita income than the
United States in 2009, has a growth rate of 1.6%.
This modest sample of five countries shows no dis-
cernible pattern; for example, it is not true that the
4 The “turning point” (or “apex”) income is computed from the
polynomial regression output by solving for, and setting to zero,
the first derivative of estimated meat consumption with respect to
income. So that:
Estimated meat consumption = a + b1 (income) + b2 (income)2
d (est-meat)/d(income) = b1 + 2b2 (income*) = 0
income* = –b1/2b2.
wealthier countries have a slower growth rate in meat
consumption.
The Bivariate Relationship in the Cross-Section
(2002)
A scatter plot of meat consumption and per cap-
ita income for 149 countries in 2009, with a fitted
polynomial curve, reveals the possibility of a Kuznets
relationship (see Equation 1). While a downward
curve fits the data, it is clear that there is much varia-
tion about this curve. To rigorously consider the
Kuznets hypothesis, we estimate the following re-
gression in order to hypothesis test the significance of
the coefficient on the polynomial term, which would
distinguish the relationship from a basic linear form:
Meat = α + β1 Income + β2 Income2 + ε
(1)
Results from this regression for the high income
and full sample reveal a statistically significant coef-
ficient on income squared (see Table 3). Further, us-
ing these estimates we can solve for an apex or turn-
ing value for the full sample curve at approximately
Table 1 Frequency table for per capita meat consumption
(2009).
Kilograms Count Percent
0–25
60
37.50
25–50
36
22.50
50–75
30
18.75
75–100
22
13.75
100+
12
7.50
Total
160
100.00
Table 2 Growth in annual per capita meat consumption in pounds.
Year/Rate
China
India
USA
Japan
Denmark
Consumption 1961
3.8
3.7
89.2
7.6
56.7
Consumption 2002
52.4
5.2
124.8
43.9
145.9
Estimated Growth
Rate (%)
5.51
0.92
0.65
3.88
2.84
Note: Growth rates estimated from data using a simple growth
trend model, i.e., a ln/ln regression model.
Table 3 Dependent variable: per capita meat consumption.
Parameter
Full group
Low
Income
High
Income
Intercept
16.7198***
–1.4695
39.1747***
–2.7657
(5.9520)
–7.6116
Income
0.0042***
0.0188***
0.0023***
–0.0004
–0.0060
–0.0007
Income squared
(–0)***
(–0)**
(–0)
0
0
0
R-squared
0.59
0.22
0.30
N
160
79
81
Note: standard errors reported in parenthesis. “*”, “**”, and “***”
indicate p-values less than 0.10, 0.05, and 0.01 respectively.
The estimates for “income squared” need at least six decimal
places to appear non-zero.

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US$43,901, a value quite a bit higher than the
US$15,000 suggested by Vinnari et al. (2005) for EU
countries. In addition, with the full sample, the
maximum meat consumption—the predicted meat
consumption at the apex income of US$43,901—
would be 89 kgs (Table 4). Because, as the results
will discuss, the variables for urban population and
agricultural land use may be significant in a regres-
sion for meat consumption, to check the robustness of
the turning point estimate we re-estimated the equa-
tion, including those variables. With these variables
included—and evaluating the expected meat con-
sumption at the sample means for urbanization and
agricultural land use—we find that meat consumption
in the full sample decelerates at an income of
US$45,263 (and annual meat consumption of 84
kgs). Taken together, the results from these two mod-
els show that a country would not be predicted to
reach the Kuznets apex until it reached a per capita
income of more than US$40,000, a long journey for
many developing countries.
The clustering of observations at the low end of
the income scale, however, suggests that dividing the
dataset may allow for more insights (see Equations 2
and 3). Therefore, we re-estimate our regression
model after dividing the data between “high-income”
and “low-income” countries. We use US$5,000 per
capita as the dividing income. The average annual per
capita meat consumption within the low-income
countries is approximately18 kgs; within the high-
income group the yearly mean is approximately 59
kgs. Notably China, within the low-income group,
has an annual per capita meat consumption that is
more than twice the low-income group average. The
results of these split regressions are given in Table 3.
As we did with the full sample model, for the
split models we solve both for the income at which
the data suggests an inflection point (see Table 4), or
downturn in meat consumption, and for the maxi-
mum meat consumption at the inflection point. Ac-
cording to these results, the low-income countries
would not see a Kuznets curve relationship; in fact,
for these countries, neither income variable is signifi-
cant in the regression. For the high-income countries,
with only the income variables in the regression, a
turning point would be expected to occur at an in-
come of US$49,848, with a predicted annual meat
consumption of 88 kgs. When variables for urbaniza-
tion and agricultural land use are again added to the
model, meat consumption is expected to decelerate at
a smaller income of US$39,809 with a predicted
maximum meat consumption of 65 kgs per year.
To test the consistency of our results with previ-
ous findings in the literature, we further estimate in-
come elasticities with the ln/ln regression specifica-
tion:
Ln(Meat) = α + β1 Ln(Income) + ε
(2)
Our overall results are largely similar to the
literature-review values. In all cases, our estimates
suggest that meat consumption is inelastic (< 1) with
respect to income. The results for these elasticities
are given in Table 5. According to our results, for the
full set of countries, a 1% increase in income would
increase meat consumption by 0.557%. Our point
estimates suggest that income elasticity for high-
income countries, at 0.487, is actually lower than for
low-income countries, 0.525. As their confidence
intervals overlap, though, we cannot rigorously con-
clude that the income elasticity is actually different
across income groups.
Panel-Data Analysis (1980–2002)
In this section, we consider extending our re-
sults, both to a panel-data set for all our countries
with a maximum of 30 years for each country, as well
as to a broader regression specification. In this
broader specification we add two variables, measur-
ing overall land area and the percent of a country’s
population living in an urban area. In our dynamic
model, these variables fit our goal to model more
critically both land and the spatial use of land within
a country. By modeling the spatial use of land, we
hope to capture a proxy for the potential cost of re-
sources used for animal husbandry, land area, and,
with the data on urbanization, a measure of popula-
tion concentration and potential industrialization of
the domestic economy. Beck & Sieber (2010) further
discuss the importance of spatial land and its impact
Table 4 Income “inflection” point and maximum meat
consumption.
Parameter
Full
Group
Low
Income
High
Income
Inflection Income ($PPP)
$40,043
$3,566
$55,606
Maximum Meat Consumption
(pounds)
100
32
102
Note: In the case of “high income” a linear relationship, i.e. no
turning point in the data, cannot be excluded. The maximum
meat consumption is computed at the inflection point income.
Table 5 Estimated income elasticity.
Parameter
Full
group
Low
Income
High
Income
0.5974
0.6565
0.3745
Min
0.5348
0.4607
0.2504
Max
0.6601
0.8524
0.4986
Note: Max and min form 95% confidence interval.

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on human geography. We also add a time trend to
estimate an average increase and growth rate over
time. The panel-data approach allows us to consider
information on the actual growth paths of our coun-
tries. For our panel results, we estimate the base re-
gression for Model 1:
Meat = α + β
1
Income +
β
2
Income2 + β
3
Land + β4 Urban + β5 Time + ε
(3)
In Model 2, we estimate income elasticity by
using the natural log of meat for the dependent varia-
ble and replace the variables for income and income
squared with the natural log of income. Results are
given in Table 6.
In Model 1, again income is positive and signifi-
cant as well as the variables urban and land. The pos-
itive coefficient on land coincides with the notion
that more land-rich countries may find animal hus-
bandry less expensive. The positive coefficient on
urbanization may indicate further impacts of market
structures, transportation efficiencies, and industriali-
zation, beyond simply income growth. It may also
indicate a difference between tastes and preferences
of urban versus rural residents, consistent with York
& Gossard (2004)—but contrary to Le (2008)—in
which urban residents prefer more meat consumption.
The polynomial term is negative and significant,
again indicating an inflection point, and the magni-
tude is somewhat different from the cross section:
results from the panel regression suggest an inflection
point at an income of US$36,375. Again these re-
sults, and indicated incomes, differ dramatically from
the findings of Vinnari et al. (2005). For countries in
the low-income group (which showed no deceleration
in the tendency for meat consumption in the cross-
section) there are potentially many years before a per
capita income of over US$35,000 will be reached,
meaning, based on these results, there can be no ex-
pectation of moderation in meat consumption for
many countries in our sample. Interestingly, time is
not statistically significant, indicating there is no ro-
bust estimation of an increase in per capita meat con-
sumption.
In Model 2, our estimate for income elasticity,
0.577, is similar to the cross-section, still indicating
that meat consumption is inelastic with respect to
income. Using the ln (meat) model, the coefficient on
urban, 0.006, can be interpreted as indicating that a
one percentage point increase in a country’s popula-
tion living in an urban area would result in a 0.6%
increase in per capita meat consumption. Here the
time trend is significant and the point estimate of
0.004 indicates an average growth rate per year of
0.4% in meat consumption for this panel of countries.
Conclusion
When considering the possibility of a Kuznets
curve in cross-national meat consumption, our
strongest results are found in the cross-section full
group and high-income countries. In the full sample,
our cross-section results suggest an inflection in meat
consumption at US$45,263; however, our fuller sam-
ple results across time, including land area and ur-
banization, suggest a lower income of US$36,375,
still not encouraging for those concerned about the
resource cost of meat consumption. In our sample of
150 countries for 2009, only eight countries (or 5.4%
of the sample) actually had a per capita income
higher than US$36,375; only three countries had a
per capita income higher than US$45,263. It may be
true, however, that from a sustainability point of
view, increasing food production in non-tropical
zones might reduce pressures on tropical forests, as
economic drivers hold great sway over deforestation,
and ecologically friendly economic incentives could
play an important part in slowing forest loss (Lambin
& Meyfroidt, 2011).
These mixed results do not provide overall a
compelling argument that consumer demand for meat
experiences a structural change at an environmentally
advantageous level along a country’s income path. If
income growth brings with it higher education (and
literacy) levels and increased labor participation in
professional occupations, there is little evidence, in
this cross-national study, to suggest that this has
caused a change in behavior either due to a resultant
broad understanding of the health or environmental
consequences of meat consumption. Nor are we able
to pick up any deceleration that could be caused by
Table 6 Dependent variable: per capita meat consumption.
Parameter
Model 1
Model 2 (ln)
Intercept
2.7548*
–0.6246***
–1.6141
–0.1183
Income
0.0028***
–0.0001
Income squared
(0)***
0
ln (income)
0.4041***
–0.0149
Land
0.0002***
(0)***
0
0
Urban
0.2764***
0.0112***
–0.0345
–0.0008
N
3132
3132
Note: Standard errors reported in parenthesis. “*”, “**”, and
“***” indicate p-values less than 0.10, 0.05, and 0.01
respectively.

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the price impact of increased global demand. The
results on the impact of urbanization in our panel
study suggest even further that, in addition to income
growth, rural-urban migration fuels meat consump-
tion. This is especially concerning as, even stronger
than the trend of income growth in low-income
countries, has been the overwhelming urbanization in
the developing world. An important area of future
research would be to investigate whether age dy-
namics within developing countries influence meat
consumption, as developing countries tend to have
much younger populations than in higher income
countries; a negative relationship was found for the
United States by Gossard & York (2003). The 2003
study concluded higher meat consumption occurs in
youthful countries with growing economies.
The lack of what appears to be an encouraging
inflection point in meat consumption opens the door
to a broader public-policy debate. Free-market advo-
cates suggest that markets should take care of them-
selves: higher demand for meat should drive prices
for meat products higher and, as was found in earlier
studies, consumers do respond to price in their
choices to consume meat. As the environmental con-
sequences of meat on land, air, soil, and water re-
sources have far-reaching, global consequences,
however, policy makers may find it more compelling
to intervene in the formation of consumer demand
either through direct policies further targeting the
price of meat through taxes or through indirect poli-
cies of broad environmental education and health
awareness, as well as the elimination of subsidies for
the meat industries. Importantly, while many con-
cerns suggested above relate to income and urbaniza-
tion growth in developing countries, our results
strongly indicate no reason to neglect the patterns of
meat consumption in high-income countries either.
The social factors and preferences that may drive a
Kuznets curve in different measures of pollution
seem still absent in meat consumption in these coun-
tries. Reasons for this difference can be hypothe-
sized: perhaps air pollution is more visible and obvi-
ous than the environmental damage caused by animal
husbandry, or perhaps the issues of animal rights and
ideological vegetarianism obfuscates, for many, the
need to consider this issue beyond the individual
choice to eat meat.
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