Redman, Barbara J.

English

Research Papers in Economics

SOUTHERN  JOURNAL  OF  AGRICULTURAL  ECONOMICS  DECEMBER,  1980
HOUSEHOLD  EXPENDITURE  ON  MEAT  VERSUS  NONMEAT
SOURCES  OF PROTEIN  IN THE  UNITED  STATES
Barbara J. Redman
In  the  last  decade,  vegetarianism  has  be-  have strong concerns about society tend to ex-
come  more  prevalent  in  the  United  States,  tend them in varying degrees to their own pri-
although by no  means  dominant (Adrian  and  vate behavior (Webster); therefore it is reason-
Daniel).  Even  households  which  have not  be-  able  to  suppose  that social  consciousness  on
come  vegetarian  have  considered  decreasing  food-related  issues should be reflected  in food
their consumption of meat. This trend has been  consumption patterns.
due to several factors. First, meat is relatively  Social  consciousness  in itself  is difficult  to
more expensive  than other sources  of protein,  measure.  Factors found to affect it (Berkowitz
Second,  inflation  has  diminished  consumer  and Lutterman) include education  (positive ef-
purchasing power. Third, recent health reports  fect),  age (negative effect),  and community  in-
have  focused  on carcinogenic  agents  in meat  volvement (positive effect).  Income is also posi-
and  the  dangers  of  too  much  cholesterol.  tively  associated  with  social  consciousness,
Fourth,  concern  with  ecology  and world  food  not  entirely  because  of  its  association  with
supplies has increased.  Meat  is thought  to be  education  (Kinnear,  Taylor, and Ahmed).  Con-
less efficient in feeding the masses than grains  trary  to the  usual  patterns  of  consumer  be-
and other nonmeat protein sources.  havior,  socially conscious behavior is predicted
Some  research  (Webster)  has examined  the  better  by personality  variables  such as domi-
relationship of social consciousness (particular-  nance (leadership) and tolerance than by demo-
ly ecological  consciousness) to behavior.  Until  graphic  and  socioeconomic  variables
now,  however,  the  socially  conscious  dimem-  (Webster).  Still, if these demographic variables
sion  of  meat/nonmeat  consumption  has  re-  relate to social consciousness  they should also
ceived  little attention.  Any shifts in consumer  relate to nonmeat protein consumption  as op-
preferences  for  meat versus  nonmeat  protein  posed to meat consumption.
sources  would affect the industries producing  Income.  In  addition  to  the  indirect  effect
those foods,  some of which  (such as soybeans  through  social  consciousness,  income  should
and  poultry)  are  concentrated  in  the  South.  have  a direct  economic  effect  on protein  con-
The  purpose  of  this  article  is  to  provide  a  sumption.  Although both meat and  nommeat
knowledge  of the factors affecting expenditures  protein  sources  should  be  normal  goods
for meat versus nonmeat protein  sources,  which  (Adrian  and  Daniel;  Brandow),  previous  re-
would benefit the industries and indirectly the  search (George and King) indicates a higher ex-
Southern region as a whole.  penditure  elasticity  for  meats  than  for  other
foods.  On these grounds,  then, income  should
negatively affect nonmeat protein expenditure
HYPOTHESES  in relation to meat protein expenditure.
Family  size  and  composition.  Family  size
Four  major  types  of  factors  besides  price  should  positively  affect  the  household  pur-
may  affect  a  household's  purchase  of  meat  chase of all forms of protein, as should the ages
versus  nonmeat  protein  sources:  social  con-  of the children (Adrian and Daniel).  In particu-
sciousness,  income,  family  size  and  composi-  lar, with progressively  older children meat pro-
tion, and cultural preferences.  tein  consumption  (and  thus  expenditures)
Social  consciousness.  A  lesser  consumption  should  increase  faster  than  nonmeat  protein
of meat protein in relation to nonmeat protein  consumption.
may  be associated  with social  consciousness.  Cultural preferences.  Lifestyle and  culture,
In  the  limit,  this  consumption  pattern  will  and  the  preferences  reflected  therein,  should
approach  complete  vegetarianism.  Relevant  also determine  the form of protein consumed.
social  concerns  include health and ecology,  as  Race,  region,  and rural/urban/metropolitan  lo-
well  as  philosophical  objections  to  animal  cation  should  all  reflect  subcultural
slaughter  and  waste.  In  general,  people  who  differences.
Barbara J. Redman is Assistant Professor, Department of Agricultural Economics, The University of Georgia College of Agriculture.
The study was supported by state and Hatch funds allocated to the Georgia Agricultural Experiment Stations.
51DATA  AND  METHODOLOGY  study  fish  was  not  treated  as  meat,  though
many vegetarians do consider it to be meat.)
Data  from  the  Bureau  of  Labor  Statistics
1972-73  and  1973-74  Consumer  Expenditure
Surveys provided the 9392  observations  used  RESULTS
in  multiple  regression  analysis.  For each  de-
pendent  variable,  two regression  models  were  Meat Protein
developed,' one to test the effect  of family  size
and the other to test the effect of family comp-  The  regression  equations  for  meat,  with
osition. If family size in the one regression was  standard deviations  in parentheses,  were esti-
significant,  the  other regression  served  to  lo-  mated as follows.
cate the source  of this observed  effect as well
as  to  test  the  effect  of  progressively  older  Meat = 2.00282a + 0.00044a(INC)  +
children.  (.87020)
Family income (INC),  family size (SIZE), re-
gion  (NE,  NC,  SO  for  the  Northeast,l  North  2.17090a(EMP)+  231122a(PSC)+
Central,'  and  South,'  respectively),  age  of  (.45756)  (.33746)
women (AGE),  and black racial identity'  (BL)
were obtained directly from the BLS tapes. For  2.99682a(ESC) + 4.99775a(HSC) -
hypotheses that referred to personal character-  (.29476)  (.32319)
istics, such as age and education,  those of the
women were used.  In general,  women  are still  1.63601a(EDUC)  + 3.03348a(NE) +
responsible  for  most  of  the  food  purchasing  (.50292)  (.61748)
and preparation decisions despite changing sex
roles. College education (EDUC) of the women'  0.36228(NC) + 1.05030(SO) +
meant that the women had completed at least  (.57804)  (.58570)
some college. No direct measure of community
involvement  was  available;  employment  of  a  0.07341a(AGE)  + 3.40039a(BL) +
wife outside the home  (EMP) was  the closest  (.00905)  (.65410)
available proxy and could also reflect an aspect
of lifestyle (Adrian and Daniel).  If the head of  1.24654(MET)  - 0.52710(URB)  -
the  household  was  married  and  a  positive  (.62531)  (.78901)
income  of  spouse  was reported,  the wife  was
presumed  to be employed  outside the  home.'  0.74136(RUR)---R 2 = 0.104265a
The  Western  region,  Caucasion  and  other  (.75716)
races,  non-college-educated  women,  and  non-
employed wives were the bases for the respec-  Meat = -2.85136a  + 0.00033a(INC)  +
tive dummy variables.  (.89089)  (.00003)
From  the  ages  of  children  given,  variables
were  constructed  for  number  of  preschool  1.01110a(EMP)  + 3.56933a(SIZE)  -
children  (PSC,  age  under  6),  number  of  ele-  (.45753)  (.14522)
mentary  school children  (ESC,  age  6-12),  and
number  of high school children  (HSC, age  13-  1.29277a(EDUC) + 2.69333a(NE) +
18). Metropolitan areas (MET)' were defined as  (.49693)  (.61213)
SMSAs  of  400,000-plus  population;  urban
areas outside SMSAs (URB)l and rural (RUR)l  0.35503(NC) + 0.91321(SO) +
areas were as defined  by the BLS.  SMSAs  of  (.57281)  (.58033)
population  less  than  400,000  served  as  the
base for these dummy variables.  0.06486a(AGE)  + 3.17207a(BL) +
The  dependent  variables  consisted  of  re-  (.00867)  (.64840)
ported  household  expenditures.  Therefore  in-
come elasticities could not be obtained and the  1.39901a(MET)  - 0.41334(URB)  -
region and location variables  served the added  (.61980)  (.78199)
function  of  controlling  for  geographic  price
variations.  The  coefficients  thus represent the  1.07648(RUR)---R2 = 0.119972a
dollar  influence  of  the various  characteristics  (.75058)
on sales of the categories of protein. Meat pro-  aSignificantly different from zero at a = .05 level.
tein included beef and veal, pork, poultry,  and
canned meats.  Nonmeat protein included  fish,  Meat expenditures  comprised  three  fourths
eggs, cheese, beans, and peanut butter. (In this  of  all protein  expenditures.  For  meat  protein
'Zero-one dummy variable.
52considered  alone,  income,  employment  of wife  0.01526a(AGE)  + 0.01898(BL) +
(which interacts  somewhat  with family income),  (.00205)  (.15304)
and family size graduated  with the ages of the
children  had  strong  positive  effects,  as  one  0.49480a(MET) + 0.10317(URB)+
would predict.  College education of the women  (.14629)  (.18457)
had a negative effect on meat protein expendi-
ture,  in accordance  with  the social conscious-  0.15168(RUR)---R2 = 0.16593a
ness  hypothesis;  although  age  of  the woman  (.17716)
had  a  positive effect  on  meat expenditure  as  aSignificantly different from zero at a = .05 level.
hypothesized,  it also  positively  affected  non-
meat  protein  expenditure  so  no  conclusions  As  nonmeat  protein  is also  a  normal  good
could  be  drawn.  Blacks  appeared  to  have  a  and expected  to increase  with size and age of
stronger  preference  than whites  for meat,  and  family,  the  coefficients  of  income  and  family
residents of the Northeast spent more on meat  size were positive. However, these variables do
than did residents of the rest of the country.  not affect expenditures for nonmeat protein to
the same magnitude  that they affect expendi-
tures  for meat protein.  An  increase in income
Nonmeat Protein  will  more  greatly  increase  meat  protein
expenditures  than  nonmeat  protein  expendi-
The  regression  equations  for  nonmeat  pro-  tures.  The  hypotheses  on  income  as  an  eco-
tein, with standard  deviations in parentheses,  nomic  variable seem to hold,  not surprisingly,
were estimated as follows.  at the expense of hypotheses  on income  as an
indicator  of social  consciousness.  As children
Nonmeat=  1.40813a+  0.00012a(INC)+  grow  older  they  consume  more  meat  at  the
(.20726)  (.00001)  expense  of  other  protein  sources  and  thus
cause  the household  to spend  more for  meat.
0.13833(EMP) + 0.70032a(PSC)  +  Again,  Northeasterners  spent more  than resi-
(.10898)  (.08038)  dents  of  other  regions,  perhaps  because  of
higher food  prices  in the Northeast.  The  fact
1.05118a(ESC)+  1.12015a(HSC)+  that  residents  of  metropolitan  areas  spent
(.07020)  (.07697)  more on nonmeat protein than did other people
could reflect higher prices and/or a measure of
0.14876(EDUC) + 1.08588a(NE)  - sophistication which tends to go with social in-
(.11978)  (.14707)  volvement and social consciousness.  These re-
sults  diverge  slightly  from  Adrian  and
0.62305a(NC)-  0.04836(SO)+  Daniel's;  the  differences  may be due  to their
(.13768)  (.13950)  use  of  an  earlier  time  period  and  quantity
rather than  expenditure  data,  as  well  as  the
0.01896a(AGE)  + 0.10672(BL) +  different construction of their variables.
(.00216)  (.15579)
0.44155a(MET)  + 0.06095(URB)  +  CONCLUSIONS
(.14893)  (.18792)
Measures  of all  four  types  of  hypothesized
0.25267(RUR) .... R 2 = 0.135508a  factors  have  significant  effects  on  relative
(.18034)  expenditure  for  meat  and  nonmeat  protein.
Education  of the woman,  family composition,
Nonmeat = 0.02194 + 0.00008a(INC)  - and race have the most marked  effects.  How-
(.21028)  (.00001)  ever, on all equations the R2 was very low. This
may be due partly to the cross-sectional nature
0.21762a(EMP)  + 1.05136a(SIZE)+  of the  data,  but  probably  due  more  to  Web-
(.10800)  (.03428)  ster's finding that personality  variables  carry
more weight.  Though these  data can only in-
0.27177a(EDUC)  + 0.94470a(NE) - directly measure preferences,  the findings pro-
(.11729)  (.14448)  vide  some useful information for manufacturers
of meat and nonmeat protein products and sug-
0.61767a(NC)  - 0.07528(SO) +  gest that the social consciousness  dimension of
(.13520)  (.13697)  food expenditure should be explored further.
53REFERENCES
Adrian, John and Raymond Daniel. "Specification  of the Impact of Socioeconomic Factors on Con-
sumption of the Animal and Vegetable Sources  of Protein."  S. J. Agr. Econ. (Dec.  1975):141-7.
Berkowitz, Leonard and Kenneth G. Lutterman. "The Traditional Social Responsible Personality."
Public  Opinion Quart.  32 (Summer 1968):169-85.
Brandow, G. E. "Interrelations Among Demands for Farm Products and Implications for Control
of Market Supply." Penn. Agr. Exp. Sta. Bul. 680,1961.
George, P. S. and G. A. King. "Consumer Demand for Food Commodities in the United States With
Projections for 1980." Giannini Found. Mono. No. 26, March 1971.
Kinnear, Thomas C., James R. Taylor and Sadrudin A. Ahmed.  "Ecologically Concerned Consumers:
Who Are They?" J. Marketing.  38 (April 1974):20-34.
Webster,  Frederick  E.  "Determining  the Characteristics  of the  Socially  Conscious  Consumer."
J. ConsumerRes. 2(Dec. 1975):188-96.
54

HOUSEHOLD EXPENDITURE ON MEAT VERSUS NONMEAT SOURCES OF PROTEIN IN THE UNITED STATES

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