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Human Ecology Review, Vol. 10, No. 1, 2003 1
© Society for Human Ecology
Social Structural Influences on Meat Consumption
Marcia Hill Gossard
Department of Sociology
Washington State University
Pullman, WA 99164-4020
USA1
Richard York
Department of Sociology
University of Oregon
Eugene, OR 97403-1291
USA2
Abstract
Meat production is a major hidden cause of many critical
environmental problems, indicating that individual dietary
habits are a form of environmentally significant consumption
(ESC). We build upon the growing literature on ESC by ana-
lyzing the effects of social structural factors on the total meat
and beef consumption of individuals. Our purpose here is to
further our understanding of the factors that contribute to
individual consumption patterns of environmentally signifi-
cant commodities. Gender, race, ethnicity, location of resi-
dence (region and urban vs. non-urban), and social class all
appear to affect dietary habits even when controlling for phys-
iological variables such as body weight and age. We argue
that social structural factors in combination with macro-eco-
nomic structure and psychological factors provide a rich
explanation of the consumption patterns of individuals.
Keywords: vegetarian diet, environmentally significant
consumption
Introduction
“Patterns of food production and consumption are at the
core of all human ecology” (Dietz, Kalof and Frisch 1996,
181). Dietary habits and the food production processes that
support them clearly have dramatic consequences for the
global environment and economy (Goodland 1997). Nearly
37% of the land surface of the Earth is used for agricultural
production, including both cropland and grazing land
(Harrison and Pearce 2000). Due to the environmental impli-
cations of food production and consumption, it is important
to understand both the factors that influence the human diet
and the aspects of food production that are most harmful to
the environment. Here we focus on meat consumption
because of its particularly serious effects on the global envi-
ronment. There is a substantial and growing social science
literature that examines meat consumption and the vegetarian
diet (Dietz, Kalof and Frisch 1996). However, this literature
lacks cohesion, and much of it does not address the environ-
mental and social consequences of the current human diet
(Dietz, Kalof and Frisch 1996).
Largely independent of the literature examining the
human diet is an emerging body of research that examines
environmentally significant consumption (ESC), a broad term
used to encompass consumption practices that have particu-
larly serious environmental consequences (for examples of
this research see Stern et al. 1997a). As Stern et al. (1997b)
argue, it is important to identify and empirically analyze
human activities that have substantial effects on the environ-
ment. Stern (1997, 20) notes that “[consumption] is not sole-
ly a social or economic activity but a human-environment
transaction. Its causes (driving forces) are largely economic
and social, at least in advanced societies, but its effects are
biophysical.”
Here we draw on both the human diet literature and the
ESC literature to analyze the meat consumption habits of
individuals. As Stern et al. (1997b) argue, consumption can
only be properly understood through the analysis of multiple
factors: social, economic, technological, political, and psy-
chological. While researchers have studied the influence of
social psychological factors on meat consumption (e.g., Dietz
et al. 1995; Kalof et al. 1999), social structural factors have
not been as extensively examined. Here we provide an
exploratory analysis of the effect of social structural factors
on meat consumption. We expand the existing body of
research by examining meat-eating behavior — the quantity
of meat people consume — rather than focusing on whether
people identify as vegetarian. We discuss the environmental
significance of meat production and the social significance of
Research in Human Ecology
2Human Ecology Review, Vol. 10, No. 1, 2003
meat consumption. Our purpose here is to explore the social
structural factors that influence individual consumption
patterns of environmentally significant commodities in gen-
eral and meat-eating behavior in specific. The relationship
between social structural factors and consumption practices
has not been fully developed in the ESC literature nor has the
topic received adequate treatment by those who study meat
consumption and vegetarianism.
The Environmental Significance of Meat Production
and Consumption
The environmental literature identifies industrial meat
production as a leading cause of many ecological problems
(Durning and Brough 1991; Ehrlich, Ehrlich and Daily 1995;
Goodland 1997; Pimentel and Pimentel 1996; Rifkin 1992;
Subak 1999). Modern, intensive meat production places a
burden on ecosystems since it requires the use of large quan-
tities of natural resources — particularly land, energy, and
water used to produce feed grain (Durning and Brough 1991;
Dutilh and Kramer 2000; Fiddes 1991). Relative to the pro-
duction of grain and other vegetable matter for human con-
sumption, meat production is extremely resource inefficient
— several times more people can subsist on a vegetarian diet
than can on a meat centered diet (Durning and Brough 1991;
Dutilh and Kramer 2000; Ehrlich, Ehrlich and Daily 1995;
Lappé 1991; Rifkin 1992).
Beef production is particularly resource intensive, hav-
ing an even greater impact on the environment than is sug-
gested by the amount of grain — and the resources that go
into producing grain — that it requires (Subak 1999).
Livestock grazing contributes to many environmental prob-
lems including soil erosion, desertification, water pollution,
and loss of biological diversity (Durning and Brough 1991;
Ehrlich, Ehrlich and Daily 1995; Pimentel and Pimentel
1996; Rifkin 1992). For example, millions of acres of tropi-
cal forest in Latin America have been cleared for cattle graz-
ing (Durning and Brough 1991; Harrison and Pearce 2000;
Myers 1981). Additionally, due to their digestive physiology,
cattle also emit a large quantity of methane, a greenhouse
gas, and their manure expels gaseous ammonia into the air,
contributing to acid rain (Durning and Brough 1991;
Harrison and Pearce 2000; Subak 1999).
The Social Significance of Meat Consumption
Although vegetarianism is on the rise in Western soci-
eties (Amato and Partridge 1989; Beardsworth and Keil
1997; Dietz et al. 1995), meat consumption is still a central
part of the U.S. diet (Beardsworth and Keil 1997). Yet there
is substantial evidence that meat is not only unnecessary for
a healthy diet, it is a leading contributor to many health prob-
lems (ADA 1999; Amato and Partridge 1989; Lappé 1991;
Marcus 1998; Melina et al. 1995; Robbins 1987). Given that
widespread meat-eating behavior in affluent societies cannot
be readily explained by biological necessity, other factors
must play a major role in determining individual dietary
habits.
A critical macro-level approach suggests that the pro-
duction of meat cannot simply be explained as a direct
response to consumer demand, since production is affected
by government subsidies and industry groups, such as the
beef and pork councils. Political economists argue that the
economic elite control consumer preferences through means
of social, psychological, and cultural manipulation — for
example, by the use of advertising (Schnaiberg 1980;
Schnaiberg and Gould 1994). Therefore, production may
generate consumption because producers, processors, and
marketers have cultural hegemony, that is, control over the
values and beliefs of a culture. Consequently, from this per-
spective, the structural power of the meat industry is expect-
ed to be a major determinant of levels of meat consumption.
Cronon’s (1991) analysis of how the U.S. meat industry grew
throughout the 19th Century by transforming American agri-
culture provides clear support for the argument that consumer
habits are greatly influenced by powerful corporate interests.
However, although this perspective may explain aggregate
levels of production and consumption in a society, it does not
explain variation of consumer behavior among individuals
within a shared political economic context.
A micro-level approach to understanding consumer pat-
terns focuses on the social psychological factors that lead to
meat consumption. Dietz et al. (1995) and Kalof et al. (1999)
argue that social psychological factors, such as values and
beliefs, have a substantial influence on consumer demand for
various food types. The results of their analyses suggest that
values and beliefs have a greater influence on the choice of a
vegetarian diet than do demographic factors. Consistent with
these results, other researchers have found that social psy-
chological factors have a greater influence on consumer
demand than do demographic and economic factors
(Breidenstein 1988; Guseman et al. 1987; Sapp and Harrod
1989). However, social structural factors form the context in
which psychological factors operate. Social structural posi-
tion (for example race, class, and gender) likely plays an
important role in shaping each individual’s socialization, life
experiences, and psychological attributes. Recognizing the
intertwined importance of social structure and psychology is
necessary to understand behavior.
Both the critical macro-level perspective and the social
psychological perspective have made important contributions
to our understanding of ESC in general and meat consump-
Gossard and York
Human Ecology Review, Vol. 10, No. 1, 2003 3
tion in particular. We argue that social structural factors play
an important role in mediating between macro-structural fac-
tors (e.g., political economic system) and psychological con-
ditions, having the potential to contribute to a fuller explana-
tion of consumer behavior. As Stern et al. (1997b) argue, in
order to properly understand ESC it is necessary to identify
which types of individuals are engaging in particular activi-
ties. Therefore, connecting the social structural position of
individuals with their behavior is essential for a deeper
understanding of ESC. Social structural factors are likely to
be important for explaining meat consumption. According to
McCracken (1988), the creation of social distinctions, such as
class, race, and occupation, is supported and authenticated
through material objects. Therefore, variation in consump-
tive patterns may be expected among individuals in different
social categories. Differences in food consumption patterns
may distinguish one social group from another - e.g., the
upper class may eat grilled portabello mushrooms on a bed of
arugula while the lower class eats pot roast and potatoes - and
these consumption patterns may reproduce social differentia-
tion (Bourdieu 1984).
Our study adds to the existing literature on meat con-
sumption in two ways. First, we provide an analysis of the
influence of social structural factors on dietary habits based
on a large and representative sample of individuals. Dietz et
al. (1995) note that demographic factors (which are social
structural) have not been carefully studied in relation to their
influence on diet, and the studies that have been done are typ-
ically based on non-representative samples. Just as Dietz et
al. (1995) and Kalof et al. (1999) have begun to explore the
importance of social psychological factors for understanding
meat consumption, we provide an exploratory analysis of the
importance of social structural factors. We recognize, how-
ever, that since we do not take into account psychological
factors in our analysis (they are not available in our dataset),
associations between structural factors and meat consump-
tion may be due in part to variation in psychological condi-
tions between social groups.
Secondly, we use as our dependent variables the quanti-
ty of meat (both beef and total meat) individuals consume,
instead of a binary dependent variable representing whether
or not individuals identify as vegetarian (as used in the analy-
ses of Dietz et al. 1995 and Kalof et al. 1999). Those who
identify as vegetarian may eat some meat and some of those
who do not identify as vegetarian may only eat small quanti-
ties of meat (Beardsworth and Keil 1991, 1992, 1993).
Therefore, vegetarian identity is only a rough measure of
meat consumption. The actual quantity of meat consumed is
the relevant factor to the environment. This study is a neces-
sary step toward a more complete understanding of the influ-
ence of social structural factors on meat consumption.
Data and Methods
We use data from the 1996 Continuing Survey of Food
Intakes by Individuals (CSFII) conducted by the U.S.
Department of Agriculture, Agricultural Research Services
(1998). A stratified, multistage area probability sample was
collected of United States residents, using estimates of the
U.S. population in 1990 as the sampling frame. The data for
the CSFII were collected over two-nonconsecutive days
through in-person interviews with individuals using 24-hour
recall of the previous day’s food intake. Day-1 and day-2
intake questionnaires included three passes by the interview-
er to assist in recall, and the interviewer obtained detailed
descriptions of each item. Thus, the data provide a reason-
able estimate of how much meat each respondent consumed
in a typical day. The response rate for the day-1 intake sur-
vey was 80% and for the day-2 intake survey 76%, both con-
sistent with conventional standards (Dillman 2000).
A total of 15,028 people provided the necessary infor-
mation about their food consumption. Our sample includes
all cases for which there is necessary data for all variables of
interest. This subset of the total survey includes 8,876
respondents. We have examined cases excluded from the
analysis due to missing data to assess whether they differ
from included cases on variables for which data are available.
We have not found a substantive difference between exclud-
ed and included cases and assume that data are missing effec-
tively at random.
We use ordinary-least-squares (OLS) regression to
assess the effects of social structural factors on meat con-
sumption. We use two dependent variables. First, we exam-
ine the total amount of meat — including beef, pork, poultry,
seafood, and processed meats — individuals consumed.
Second, we specifically examine the amount of beef con-
sumed, since beef production has a particularly large impact
on the environment. Due to the structure of the data set, this
measure of beef does not include beef that is in highly
processed meat products, such as lunchmeat and sausages,
nor beef that was consumed in a meat mixture. We use the
average of the quantity of meat consumed by individual
respondents, measured in grams, during the two days for
which data were collected. The mean amount of total meat
consumption per day is 215 grams, with a standard deviation
of 198. The mean amount of beef consumption per day is 26
grams, with a standard deviation of 60.
Gossard and York
4Human Ecology Review, Vol. 10, No. 1, 2003
A complete description of each independent variable and
its respective coding is presented in Tables 1, 2, and 3.
Income, education, and occupation are used as basic indica-
tors of social class position. We use the natural logarithm of
income because we expect that any effects income has on
meat consumption will diminish as income rises. The educa-
tion variable is the number of years of education in excess of
twelve, since we do not anticipate substantial effects from
increasing education on meat consumption until a minimum
threshold is reached.3Occupation is divided into four
dummy coded categories: professional, service worker, labor-
er, and not working.4
Gender, race and ethnicity (Hispanic/non-Hispanic)
variables have been included in the analysis, since they are
frequently recognized as important factors for understanding
a variety of behaviors, including those that relate to the envi-
ronment (Dietz et al. 1995; Kalof et al. 1999; Dietz, Kalof
and Stern 2002). Since there is the potential that gender may
interact with race and ethnicity, we present additional models
that include interaction terms. Following standard practice,
the interaction terms are the gender variable multiplied by
each of the dummy-coded race variables and the ethnicity
variable.
Variables for region of residence (dummy coded into
four categories: Northeast, South, Midwest, and West) and
urban vs. non-urban residence are included to control for cul-
tural and resource availability differences. To control for
physiological differences we include measures of body
weight (pounds) and age.5For age, we include both age in
years and the quadratic of age in years (centered by subtract-
ing the mean before squaring to reduce problems with
collinearity) to allow for a non-linear relationship between
age and meat/beef consumption. For the beef analysis alone
we include the variable of total meat consumption minus beef
consumption to control for tradeoffs among types of meat.
Table 1. Description of independent variables.
Independent Variable Description
Income (logged) Annual household income in dollars (in original
units a value of 100,000 indicates 100,000+)
Education Education in years > 12 (a value of 5 indicates
5+)
Occupation/Work status Set of dummy variables (labor is the omitted
category)
Professional occupation Professional and technical; manager, officer or
proprietor
Service occupation Clerical or sales worker; service worker or other
similar job
Laborer occupation** Farmer; craftsman or foreman; operative
Not working Not working
Age Two variables: age in years and age in years,
centered by subtracting the mean, squared
Race Dummy variables (white,** black, Asian, Native
American, other)
Hispanic Dummy variable (1 = Hispanic, 0 = non-
Hispanic)
Sex Dummy variable (1 = female, 0 = male)
Urban Dummy variable (1 = urban, 0 = non-urban)
Region Set of dummy variables (Midwest is the omitted
category)
Northeast Connecticut, Maine, Massachusetts, New
Hampshire, New Jersey, New York,
Pennsylvania, Rhode Island, Vermont
Midwest** Illinois, Indiana, Iowa, Kansas, Michigan,
Minnesota, Missouri, Nebraska, North Dakota,
Ohio, South Dakota, Wisconsin
South Alabama, Arkansas, Delaware, District of
Columbia, Florida, Georgia, Kentucky,
Louisiana, Maryland, Mississippi, North
Carolina, Oklahoma, South Carolina, Tennessee,
Texas, Virginia, West Virginia
West Alaska, Arizona, California, Colorado, Hawaii,
Idaho, Montana, Nevada, New Mexico, Oregon,
Utah, Washington, Wyoming
Weight Weight of respondent in pounds
**Indicates reference category in the regression models.
Table 2. Summary statistics for continuous independent variables
(n=8876).
Variable Mean Standard Deviation Minimum Maximum
Income (in dollars)* 40,300 28,200 0 100,000
Education (years >12) 1.5 1.9 0 5
Age 47 17 18 90
Weight 169 39 86 350
* Note that the summary statistics for income are for the variable in original
units, not the transformation used in the regression model. Note also that a
value of 100,000 for income indicates an annual income of $100,000 or more
and a value of 5 for education indicates 5 or more years of education beyond
high school.
Table 3. Summary statistics for categorical independent variables
(n=8876).
Variable Frequencies
Occupation/
Work Status Professional Service Laborer Not working
27.4% 19.9% 14.6% 38.1%
Region Northeast Midwest South West
17.7% 24.1% 35.8% 22.4%
Race White Black Asian Indian Other
80.0% 11.9% 2.6% 0.6% 5.0%
Hispanic Hispanic Non-Hispanic
8.5% 91.5%
Sex Male Female
52.7% 47.3%
Urban Urban Non-urban
73.8% 26.2%
Gossard and York
Human Ecology Review, Vol. 10, No. 1, 2003 5
Results and Discussion
An initial finding of interest is that people in our sample
who identify as vegetarian may sometimes eat meat. Self-
identified vegetarians, who make up 2.3% of our sample, eat
an average of 83.2 grams of total meat and 3.4 grams of beef
per day compared to 217.8 and 26.6 grams respectively for
non-vegetarians. The fact that some self-identified vegetari-
ans eat meat is not entirely surprising. Beardsworth and Keil
(1991, 1992, 1993) have noted that there are different forms
of vegetarianism, and definitions of vegetarianism vary
among individuals. This finding demonstrates the impor-
tance of analyzing the quantity of meat individuals consume,
rather than singularly focusing on vegetarian identity.
The OLS regression results are presented in Table 4.6
Models 1 (total meat) and 3 (beef) do not include the terms
for an interaction effect between gender and race and ethnic-
ity. The interaction effect terms are included in models 2
(total meat) and 4 (beef). Each model clearly explains a sta-
tistically significant portion of the variation in the dependent
variable.7
Focusing first on models 1 and 3, gender, race, and eth-
nicity all appear to influence dietary habits. Since weight and
age have been controlled for in these analyses, it appears
unlikely that differences in levels of meat consumption along
the lines of gender, race, and ethnicity can be explained sim-
Table 4. OLS regression coefficients and standard errors (in parentheses) for both the total meat consumption and beef consumption mod-
els (the dependent variables are measured in grams per day).
Independent Variable Model 1: Model 2: Model 3: Model 4:
Total Meat Total Meat Beef Beef
Education - 3.683** (1.231) - 3.618** (1.231) - .911* (.375) - .912* (.374)
Income (logged) .379 (2.164) .350 (2.165) 1.753** (.658) 1.784** (.658)
Urban 4.969 (4.826) 5.179 (4.830) - 3.568* (1.468) - 3.508* (1.469)
Weight .438*** (.059) .436*** (.059) .066*** (.018) .063*** (.018)
Age - .977*** (.133) - .960*** (.133) - .235*** (.040) - .235*** (.041)
(Age-mean)
2
- .003 (.007) - .004 (.007) - .004* (.002) -.004* (.002)
Female - 74.299*** (4.707) - 74.821*** (5.274) - 17.405*** (1.445) - 19.057*** (1.614)
Hispanic 7.237 (9.771) - 9.169 (13.842) 9.392** (2.972) 11.556** (4.209)
Race
a
Black 41.088*** (6.589) 33.818*** (9.634) 6.202** (2.008) .443 (2.931)
Asian 56.550*** (13.090) 81.585*** (18.176) - 1.158 (3.986) - .042 (5.532)
Native American 24.267 (27.651) 44.554 (35.123) 4.041 (8.411) - 18.747 (10.680)
Other - 3.767 (12.245) 18.414 (17.454) - 5.451 (3.725) - 9.346 (5.307)
Interaction effects
Black*Female 12.824 (12.749) 10.436** (3.876)
Asian*Female - 50.787* (25.588) - 2.281 (7.781)
Native Am*Female - 49.164 (56.034) 58.378*** (17.038)
Other*Female - 42.511 (24.306) 7.397 (7.391)
Hispanic*Female 31.207 (19.081) - 3.531 (5.802)
Region of residence
b
Northeast - 19.739** (6.442) - 19.507** (6.442) - 7.395*** (1.960) - 7.516*** (1.959)
South - 20.722*** (5.419) - 20.452*** (5.420) - 4.395** (1.649) - 4.478** (1.649)
West - 12.271* (6.230) - 12.155 (6.233) - 6.989*** (1.895) - 7.216*** (1.895)
Occupation
c
Professional - 36.448*** (7.277) - 37.243*** (7.290) - 10.813*** (2.215) - 10.851*** (2.218)
Service - 29.340*** (7.359) - 29.488*** (7.359) - 7.970*** (2.240) - 7.935*** (2.238)
Not working - 36.055*** (7.097) - 36.099*** (7.127) - 7.502*** (2.161) - 7.370*** (2.169)
Total meat minus beef - .059*** (.003) - .059*** (.003)
Constant 257.145*** (26.433) 257.393*** (26.455) 43.272*** (8.073) 44.262*** (8.075)
R
2
.084 .085 .068 .070
Adjusted R
2
.082 .083 .067 .068
n 8876 8876 8876 8876
Mean VIF, Highest VIF 1.57, 2.92 2.09, 3.65 1.54, 2.92 2.05, 3.65
* p < 0.05, ** p < 0.01, *** p < 0.001
a
White is the omitted category
b
Midwest is the omitted category
c
Laborer is the omitted category
Gossard and York
6Human Ecology Review, Vol. 10, No. 1, 2003
ply by physiological differences. Gender has a particularly
strong influence on meat consumption. We are aware of no
physiological reason, other than the average differences in
weight, for which we have controlled, that men would require
more meat than women. Not only do women consume sub-
stantially less total meat than do men (74 grams a day less),
they also consume less beef (almost 17 grams a day less) —
considered a “powerful” and masculine food (Adams 1990;
Bourdieu 1984). Dietz et al. (1995) found that gender differ-
ences in vegetarianism could in part be explained by differ-
ences between the values of men and women, suggesting that
our finding of a substantial gender effect on meat consump-
tion may indicate the effect of gender-related values.
Blacks and Asians eat more total meat than whites, and
blacks also eat more beef than whites. The finding that
Asians eat more total meat than whites is surprising given
that the traditional diet in most Asian nations is not as meat-
centric as the Western diet. This may suggest that the cultur-
al meaning and value of meat is influenced by social context,
although our data do not allow for an unambiguous conclu-
sion on this matter. The finding that Hispanics eat more beef
than non-Hispanics suggests that ethnicity also matters.
These findings for race and ethnicity suggest that it is also
possible that meat acts as a status marker for groups that have
historically been marginalized in U.S. society.
An examination of models 2 and 4, where the gender and
race/ethnicity interaction terms are included, does not sub-
stantively alter these interpretations, but does suggest further
subtlety in how gender and race affect dietary habits. The
gender/ethnicity interaction term is not statistically signifi-
cant, indicating that the effect of gender on meat consump-
tion is not affected by ethnicity — and, conversely, the effect
of ethnicity on meat consumption is not affected by gender.
However, the effect of race on meat consumption does appear
to depend on gender (and vice versa). Since the inclusion of
interaction terms makes the interpretation of results more
complex, we present in table 5 the estimated differences in
meat consumption between racial and gender groups.
In the total meat model with interaction terms (model 2)
the Asian*Female interaction term is significant and nega-
tive. This indicates that the difference in meat consumption
between Asian males and females is significantly greater than
the difference between white males and females — stated one
way, Asian females eat less meat than would be expected
based on the independent effects of race and gender alone.
None of the other gender/race interaction terms are signifi-
cant, indicating that gender has a similar effect on meat con-
sumption among racial groups other than Asians.
In the total beef model with interaction terms (model 4),
the Black*Female and Native American*Female interaction
terms are significant and positive. This indicates that the
effect of gender on meat consumption in both black and
Native American groups is different from that in whites.
Whereas white women eat 19.1 grams less beef a day than
white men, black women eat only 8.6 grams less beef a day
than black men (see table 5). The most striking finding is
that, unlike among all other racial groups, among Native
Americans, women eat more beef than men.
These findings on the effects of race and gender on meat
and beef consumption suggest that there is considerable sub-
tlety in how social position affects dietary habits.
Furthermore, the results suggest that the social construction
of gender differences varies between racial groups.
Subsequent research would be required to tease out the spe-
cific nature of these differences and how they relate to diet.
However, these initial findings are provocative.
The inclusion of the interaction terms in the models does
not substantively alter the estimated effects of other factors
on meat and beef consumption — i.e., the coefficients for
factors other than gender, race, and ethnicity do not vary sub-
stantially between model 1 and model 2 or between model 3
and model 4. Therefore, whether one focuses on either the
models with interaction terms (models 2 and 4) or the mod-
els without interaction terms (models 1 and 3) will not sub-
stantively alter the interpretation of the effects of factors
other than gender, race, and ethnicity on meat and beef con-
sumption.
Social class appears to have a substantial influence on
meat consumption. Those in laborer occupations eat both
more beef and total meat than those in either service or pro-
fessional occupations.8Furthermore, education is inversely
related to beef and total meat consumption (i.e., people with
more education eat less beef and total meat).9Interestingly,
income does not influence total meat consumption.10 Beef
consumption, however, does appear to rise with income,
which may possibly be explained by the price of beef relative
to other types of food. Taken together, these findings support
the argument that eating habits reflect an individual’s class
position (see Bourdieu 1984).
Table 5. The interactive effects of gender and race on total meat
and beef consumption (white male is the reference category; all
values are in grams per day).
Meat Beef
Race Female Male Female Male
White - 74.821 0 - 19.057 0
Black - 28.179 33.818 - 8.178 .443
Asian - 44.023 81.585 - 21.38 - .042
Native American - 79.431 44.554 20.574 - 18.747
Other - 98.918 18.414 - 21.006 - 9.346
Gossard and York
Human Ecology Review, Vol. 10, No. 1, 2003 7
The location of residence also appears to have a sub-
stantial influence on the meat consumption habits of individ-
uals. Midwesterners eat considerably more beef and total
meat than people in other regions,11 and urbanites eat less
beef (but not total meat) than non-urban residents. These dif-
ferences in meat consumption could simply be explained by
the availability and price of meat in different locations, or
they could reflect regional cultural differences.
We included age in the models primarily to control for
changes in physiology, and therefore dietary requirements, as
people age. The coefficients indicate that people eat both less
total meat and beef as they grow older. It is possible, how-
ever, that this effect is not only, or even primarily, due to
physiological changes, but, rather, due to differences in the
dietary norms of people from different age cohorts.
Finally, in the beef analysis, the total of other meats con-
sumed has a significant negative effect on beef consumption.
This finding suggests that tradeoffs are made among different
types of meat — i.e., reducing the consumption of one type
of meat may increase the consumption of another type. Since
different types of meat require different quantities of
resources for their production, the tradeoffs between types of
meat may have substantial environmental implications.
Overall, it appears that social structural factors clearly
influence meat consumption habits. Therefore, meat con-
sumption is clearly not the outcome of biological necessity,
but a practice embedded within a complex of social forces.
Meat consumption cannot be largely explained by the argu-
ment that material affluence leads individuals to select “supe-
rior” food since they can afford it. These results taken as a
whole clearly indicate that an individual’s social status has a
substantial influence on her/his eating habits.
Conclusion
The environmental literature identifies meat production
as an ecologically detrimental practice. A substantial reduc-
tion in the scale of meat production and consumption would
reduce the human impact on the natural environment and may
increase global food security. This verity highlights the eco-
logical significance of the dietary habits of individuals. The
environmental literature does not, however, adequately
address the social factors that elevate meat to a central role in
the U.S. diet, nor does it address the persistence of a meat-
based diet despite clear alternatives.
We find that the social structural position of an individ-
ual affects meat consumption. Specifically, gender, race, eth-
nicity, location of residence (region and urban vs. non-
urban), and social class all appear to affect dietary habits
even when controlling for physiological variables such as
body weight and age. Those who argue that meat consump-
tion should be reduced because it is burdensome to the envi-
ronment must recognize the social context in which this basic
practice takes place, as meanings, customs, and traditions
may shape or constrain consumer patterns. While beyond the
scope of this study, ESC research could be furthered by
including cultural factors, such as the social and cultural sig-
nificance of various foods to different social groups (see
Douglas and Isherwood 1979; Fiddes 1991; Mauer and Sobal
1995). The addition of cultural factors could further illumi-
nate the centrality of meat in the U.S. diet.
Endnotes
1. E-mail: mgossard@wsu.edu
2. Author to whom correspondence should be directed.
Fax: 541-346-5026
E-mail: rfyork@darkwing.uoregon.edu
3. To assess the effects of education on meat consumption we have
experimented with different transformations of the education variable
in our analysis. We note the results below.
4. The structure of the data does not allow for theoretically meaningful
disaggregation of the “not working” category. We cannot tell, for
example, if the person is a housewife/househusband. We can tell if a
person is retired, but this does not give us theoretically meaningful
information since we do not know the occupation he/she retired from
and, therefore, do not have an indication of social class. The “not
working” category, therefore, serves as an undifferentiated group for
which we cannot tell social class.
5. We recognize that in addition to indicating physiological conditions,
age also indicates the effects of characteristics particular to genera-
tional cohorts.
6. Tests for statistical problems associated with multicollinearity sug-
gest that no such problems exist. The highest VIF for any variable
out of all four models is 3.65 (equivalent to a tolerance of .27), a
value well within conventional standards (Greene 2000). Using a
robust regression procedure does not dramatically alter coefficient
estimates compared with OLS results, suggesting that our results are
not overly influenced by outliers in the residuals. To allow for the
potential of a non-linear relationship between the independent vari-
ables (in the forms used in the models) and meat/beef consumption,
we have also run alternative models. First, we have used the loga-
rithm of the dependent variables (adding a value of 1.0 to eliminate
zeros) to estimate the same models presented here. Using this trans-
formation does not dramatically alter our substantive findings, but the
models do not fit as well as using the dependent variables in original
units, suggesting that the linear specification is appropriate. We have
also run the models using multinomial logistic regression, where
each dependent variable is divided into three categories (less than one
standard deviation below the mean, more than one standard deviation
above the mean, and within one standard deviation of the mean). The
results from these analyses do not suggest a substantively different
interpretation than presented here. However, since such a procedure
is less powerful than OLS (since information is “lost” by categoriz-
ing the continuous dependent variable), some independent variables
Gossard and York
8Human Ecology Review, Vol. 10, No. 1, 2003
that are significant with OLS are not significant with the logistic
model, although the direction of effects for such variables is the same
in both OLS and multinomial logistic models.
7. Note that the relatively modest R2values are to be expected. As
Greene (2000, 241) notes, it is important to realize that what consti-
tutes a “high” R2value depends on the type of data used and the type
of analysis performed. We expect social structural factors to explain
a portion of the variance among individuals in general meat con-
sumption habits (i.e., long-term average behavior), but not the day-
to-day variance in meat consumption for any specific individual.
Since the estimates of meat consumption for individual cases are
based on data for only two days, we have an unbiased (the estimate
should be unbiased if we assume that the sample days were selected
randomly), but relatively inefficient estimate of long-term average
meat consumption (general meat consumption habits). Since the data
is for only two days, the variance of the dependent variables includes
not only average long-term differences between individuals (which
we expect the independent variables to partially explain), but also day
to day variability of meat consumption (which we do not expect the
independent variables to explain). For this reason the variance of the
dependent variable is inflated and the R2is fairly low.
8. The differences in total meat and beef consumption among the other
occupational categories are not statistically significant.
9. We also estimated the models using two other transformations of
education: education in total years (not years after 12 as presented
here), and education in years squared. Neither of these alter the coef-
ficients for other factors in the model substantively. However, neither
of these transformations of education has a statistically significant
coefficient. This suggests that the effects of education are best rep-
resented by the specification of education we present here.
10. Obviously, this finding is context dependent. Although income does
not appear to have a significant effect on meat consumption in the
U.S., an affluent nation, we would expect that, to the extent meat
costs more than other foods, the very poor, particularly in less devel-
oped nations, would be unable to afford meat.
11. The coefficients for all region variables in all models are statistically
significant, except for “West” in model 2. Note, however, that the
value of the coefficient for West in model 2 is nearly identical to that
in model 1.
Acknowledgments
We thank Thomas Dietz, Loren Lutzenhiser, Eugene A. Rosa,
Thomas Rotolo, and the four anonymous reviewers for HER for their valu-
able comments, which helped improve this manuscript.
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