Vol.4, No.10, 549-557 (2013) Agricultural Sciences
Validating the demand for goat meat in the US meat
Xuanli Liu1*, Mack Nelson2, Erika Styles2
1Agricultural Research Station, Fort Valley State University, Fort Valley, USA; *Corresponding Author: liux@fvsu.edu
2Department of Economics, Fort Valley State University, Fort Valley, USA
Received 8 May 2013; revised 9 June 2013; accepted 10 July 2013
Copyright © 2013 Xuanli Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
There is a large body of literature in economics
examining the US meat market, but few studies
have focused on the US goat meat market. This
study, as a catch-up effort, provides an estimate
of the demand for goat meat and assesses the
impact of driving factors in the US goat meat
market. The data for this study were collected in
11 southern states and specifically elicit the de-
mand and consumer preferences in this non-
conventional market. Four econometric models
are fitted to examine the multiple layers of the
demand, including the current, the potential, the
latent, and the seasonal demand. Findings indi-
cate a substantial demand for goat meat with
great growth potential, driven by demographic
factors and food safety concerns. Ethnic groups
and the aged comprise the current niches for
goat meat, and the preferences for healthy and
safe meat will define the market in the future.
Keywords: Market; Demand; Consumer
Preferences; Ethnics; Goat Meat
The past decades have witnessed substantial changes
in the US meat market. The declining demand for beef,
the growing demand for poultry, and the leveling off of
demand for pork characterize the well-known consump-
tion shift from red to white meat [1]. Driving the shift is
consumers’ increasing demand for healthy meat and meat
products [2]. In fact, the attributes related to meat safety
have become a critical component in motivating con-
sumers to make food selection [3,4]. A study from the
USDA ranked health concerns as the number one factor
influencing consumer demand for meat products [1], and
choice experiment studies confirmed consumers’ willing-
ness to pay (WTP) for products with a USDA certified
organic label [5]. Discussion in a broader spectrum re-
garding meat markets and consumer preferences could be
referenced to many other studies [2,6-9].
A result of interest from the preference shift of con-
sumers is that the US goat meat market has been stimu-
lated, which, for a long time, has been a stagnant niche.
It is now recognized by researchers and consumers that
there is a good match between goat meat and their pre-
ferences for lower risk and more healthy products [10-
12]. This acknowledgement led to a continual growth in
the demand for goat meat in the United States. Thus, re-
searchers are becoming attentive to issues of demand,
supply, marketing channels, and influential factors in the
goat meat market [12-15]. Yet, in contrast to many stu-
dies on beef, pork and poultry products, the investiga-
tion in the goat meat market is notably inadequate, and
some crucial issues such as the market demand and con-
sumer preferences have not been studied in the depth
needed for reshaping the marketing strategies of the in-
dustry or changing the status quo of the industry. As a
matter of fact, a literature review revealed only a few
journal publications and the extreme paucity of analyses
supported by solid data. While the call for more studies
is widespread, the appreciation of effective data gather-
ing and quantitative investigations is especially warrant-
This study is an attempt to fill in some of the gaps in
quantitative analyses and estimates of the goat meat de-
mand and the corresponding driving factors. To accom-
plish the task, the study uses data from a survey in eleven
southern states (Alabama, Arkansas, Florida, Georgia, Mis-
sissippi, North Carolina, Oklahoma, Louisiana, South Ca-
rolina, Tennessee, and Texas) commonly known as the
goat meat consumption and production region. Four eco-
nometric models were fitted to examine the multiple lay-
ers of demand, including the current demand, the poten-
tial demand, the latent demand, and the seasonal demand.
As a result of the support of the robust data, this study is
Copyright © 2013 SciRes. OPEN A CCESS
X. Liu et al. / Agricultural Sciences 4 (20 13) 549-557
capable of providing an in-depth investigation into the
promising US goat meat market.
The data used in this study were from a telephone sur-
vey conducted by the Survey Research Center of the
University of Georgia. By using a random sampling pro-
cedure [16], the survey collected 11 subsamples, each with
a size of 237 to 257 respondents from a state, and amoun-
ted to 2751 households.
The survey questionnaire consists of 23 simple and 25
multiple-layer questions. These questions can be grouped
into six categories: 1) the status quo of goat meat con-
sumption, including current consumption, willingness to
consume more, and willingness to try if not consumed at
the time of the survey; 2) consumer preferences for va-
rious goat meat cuts, different search, experience, and
credence attributes; 3) cooking methods, such as soup,
barbeque, roasting, meat sauce, chili, and meat loaf; 4)
consumption of other meats, including beef, pork, chic-
ken, turkey, lamb, and fish; 5) demographics, such as gen-
der, age, ethnic affiliation, household size, and family
structure; 6) socioeconomic characteristics of respon-
dents, including education and household income.
Authors were solicitous in the selection of levels for
categorical variables. Ethnic affiliation, matched the US
census, is comprised of Whites, African Americans,
Black non African Americans, Hispanics, Asians, and
multi-racial races. Data were also collected on the origin
of the ethnic groups and the length of time away from
their original culture. To measure impacts of age, respon-
dents were placed into ten age groups, with a narrower
interval for the elder group given their larger likelihood
of consuming goat meat products. For household income,
nine levels were used with a narrower interval for the
lower range of income, reflecting the early findings that
the low- and middle-income households constitute the
greater part of demand in the goat meat market. In view
of spatial discrepancy in consumption, data from the
eleven states were merged into four levels, based on the
preliminary analysis. Education was initially measured at
eight levels in an effort to gauge its impacts with suffi-
cient accuracy, but the three levels as reported in Table 1
were used as suggested by the analyses. Other variables
and their levels were also elucidated in Table 1. As a rule
of thumb, for variables with six levels or more, trade-off
between the accurate measurement of the variable and
the loss of the degree of the freedom was made in favor
of the degree of the freedom.
The logistic model is used as the major analytical tool
in this study. As a matured method for categorical
analysis, the logistic model has been extensively used in
many areas of social investigations when the dependent
variable of interest is categorical [17-19]. Similar approa-
ches in the model-building paradigm of categorical data
analysis also include the Linear Probability model (LPM)
and the Probit model. While the LPM is seldom used in
recent years for its inherent disadvantages in estimator,
variance dependence, and outbound range of response
prediction, the other two approaches, especially the lo-
gistic model, have found extensive use for its handling of
odds ratios via logit parameters.
A detailed elaboration of the logistic model is availa-
ble in many seminal studies [20,21], thereby will not be
addressed here. Here a discussion of the model is limited
to the level necessary for facilitating the understanding
of our analyses. In general, the logistic model assumes
that yij is the number of occurrence of the response j for
ith observation in Ni repetitions (if Ni = 1 then yij = 0 or 1)
and Pij is defined as the probability that alternative j
occurs for the ith observation, then the multinomial pro-
bability density function is:
121 2
!! !
ii iJ
iiiJi iiJ
ii iJ
yyyP PP
yy y
 
 (1)
In most cases, Ni = 1 for all observation units i = 1,
2, ... , n, and yij becomes 1 because only one of the J
alternatives can occur for any observation unit i. In such
case, the log likelihood function is:
ij ij
 p (2)
Enforcing the restriction of
the probability of response j will be:
112 2
112 2
112 2
1for 1
exp for 2
jijikjik i
jijikjik i
ij J
jijikjik i
Bx BxBx e
Bx BxBx e
Bx BxBx e
 
 
For nominal response variables, all non-reference cate-
gories are compared with a reference category and the
logit of the level j, with the reference category k, is:
log1, ,
pBx forjJ
For ordinal response variables, the logistic model uses
the cumulative logit function and the reference category
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X. Liu et al. / Agricultural Sciences 4 (20 13) 549-557
Copyright © 2013 SciRes.
Table 1. Variable definitions and summary statistics (cont.).
Variable Definition & code Statistics (means%)
0 1 2 3 4 5
Q28x1 Rankings of food page advertisement: 0 (important);
1 (neutral); 2 (not important) 30.4 39.4 29.8
Q29 Rankings of store display: 0 (important);
1 (neutral); 2 (not important) 46.9 31.7 21.4
Q30 Rankings of chevon price specials: 0 (important);
1 (neutral); 2 (not important) 54.5 18.9 26.5
Q30x1 Rankings of other meat price specials: 0 (important);
1 (neutral); 2 (not important) 77.9 13.4 8.8
Q32 Rankings of USDA inspection: 0 (important);
1 (neutral); 2 (not important) 78.7 7.8 13.5
Q34 Rankings of chevon fat content: 0 (important);
1 (neutral); 2 (not important) 62.8 8.0 29.2
Q35x1 Ranking of the cholesterol content: 0 (important);
1 (neutral); 2 (not important) 73.7 7.2 19.1
Q47 Family Size: number of person in the household
Q49 Race: 0 for White; 1 for African American; 2 for non-African
American; 3 for Hispanic, 4 for Asian, 5 for multi-racial 77.2 13.9 1.4 2.8 0.5 2.0
Q54 Age: 0 for < 34; 1 for 35 - 54; 2 for 55 - 64; 3 for > 65 22.1 38.7 16.8 26.2
Q55 Education: 0 for high school diploma or less; 1 for associate
degree or some college; 2 for college or higher 34.5 29.2 34.0
Q56 Gender: 0 for female; 1 for male 68.8 31.0
Q57 Household income: 0 (less than 19,999); 1 (20,000 - 34,999);
2 (35,000 - 49,999); 3 (50,000 - 74,999); 4 (75,000 or more)11.0 13.7 12.0 15.3 16.1
Geographic location: 0 (TN);
1 (AL, AR, GA, MS, MC, OK, LA , SC)
2 (FL); 3(TX)
8.9 73.5 8.6 9.0
4. MODEL SELECTION varies with the level of the response variable under con-
sideration. The logit is defined as: Four logit models were fitted to estimate the current
demand, the potential demand, the latent demand, and
the seasonal demand. Starting with a similar variable set,
alternative models were screened for the best-fitting ones
via multiple diagnostic statistics [22,23]. Given the large
data set, we were able to make the selection of variables
from a much broader spectrum, which partially insured
the efficacy of the models retained.
log 1,
Bx forjJ
Parameters in a logistic model measure the logit
change of the response variable for a unit change in an
explanatory variable. If the explanatory variable is cate-
gorical, the parameter measures the impact of a specific
level of the variable on the probability of event, given the
selection of the reference level and no changes in all
other variables.
The initial explanatory variable set used in modeling
includes most variables in the survey; such as meat
prices, rankings of the cholesterol content, rankings of
fat content, other meat consumption, real income, age,
education, ethnic groups, household structure, gender,
and geographic locations. To identify and retain the best
models, multiple statistics, including R2, AIC, LR, P-
value, and Hosmer and Lemeshow test, weighted in the
model selection [20,24]. A candidate model under prior
consideration requires a significant LR, a lower AIC, a
significant Wald statistic, and a non-significant Hosmer
and Lemeshow test. Variables, lack of appropriateness
The SAS software package provides a good platform
for implementing the model. All data management, cod-
ing, and modeling processes have been done in the SAS
environment. One thing worthy to being highlighted is
that we selected reference coding for nominal variables,
and used ordinal coding for ordinal variables in the mo-
X. Liu et al. / Agricultural Sciences 4 (20 13) 549-557
and power, were not retained for the sake of the degree
of freedom. For the variables retained, the levels of in-
significance were normally merged if there are more than
five originally.
4.1. The Current Demand Model
The model examines the existing demand and focuses
on consumers who eat goat meat at the time of the survey.
The dependent variable comes from the question “have
you or any member of your immediate family member
eaten goat meat recently?” The model starts with the
consumption of goat meat substitutes (beef, pork, and
chicken), demographics (household size, ethnic groups,
age, and race), food safety, and socioeconomic factors
(education and household real income). Fitted with a
general logit function, we first tested whether the model
as a whole explains the variability of the current demand,
and then examined each variable and removed those of
insignificance for the sake of the degree of the freedom.
There were 2675 observations effective for fitting the
model. The best model selected suggests an intriguing re-
lationship between the current demand and a set of ex-
planatory variables.
4.2. The Potential Demand Model
The potential demand model is to catch the potential
increase in goat meat consumption due to extra con-
sumption of the existing consumers. Differing from other
meat markets, the goat meat market is featured with
consumers of low per capita consumption, an average of
about 4 pounds annually. A question to be raised is: whe-
ther the consumers have the willingness to purchase
more? And if yes, under what conditions? The question
asked in our survey is “would your family eat more goat
meat if it was available in your local grocery stores?”
Responses to the question suggest the existence of a
large potential of demand. The model identified meets
the needs of a robust model and provides some insightful
marketing tips for the industry.
4.3. The Latent Demand Model
The latent demand model examines the potential in-
crease in goat meat consumption due to the influx of new
consumers. About 20% of consumers, not eating goat
meat at the time of the survey, were willing to make
purchase if available locally. The latent demand fitted
would help to get a full view of the future expansion of
the US goat meat market. The response variable is de-
rived from the survey question “Do you think you will
try goat meat if it is available in your area food stores?”
Following the procedures in model (1) and (2), the latent
demand model was identified.
4.4. The Seasonal Demand Model
The seasonal demand largely characterizes the status
quo of the existing US goat meat market. Based on the
survey data, the seasonal and occasional consumers ac-
counted for 73% of the goat meat market consumption.
To have a broad understanding of the seasonality, we in-
tended to have a multinomial logit model which sepa-
rates spring, winter, summer, winter demand as well as
the social and demographic factors. However, the issue
of missing values in observations impeded our efforts. As
an alternative, we fitted a simple logit model with a de-
pendent variable of two levels: seasonal or non-seasonal
consumption. We identified a seasonal demand model,
but found it less robust in statistics and lack of explana-
tory power in both theory and practice.
The SAS results confirm the existence of explanatory
variable sets of appropriateness and significance for each
demand model. The four models fitted provide a high-
dimension view of consumer demand and preferences in
the US goat meat market.
5.1. Current Demand
The results of the current demand model are reported
in Table 2. Those variables retained in the model shed
light on the understanding of the existing US goat meat
market. First, the consumption of other meats influenced
the consumption of goat meat. While chicken turns out to
be a complementary diet component to goat meat consu-
mers, pork consumption appears to reduce the likelihood
of goat meat consumption or is a substitute. No signi-
ficant relationships between the consumption of goat meat
and other types of meat were identified.
As expected, demographics take a role in goat meat
demand. There are remarkable differences in consump-
tion between ethnic groups. Taking Whites as the refe-
rence group, the odds ratios for both non-African Ame-
rican Blacks and Hispanics is as high as 3.42, indicating
a much higher propensity to consume. However, an
unexpected result is that African Americans and Asians
show a similar consumption pattern as Whites in goat
meat consumption. Age is another influential demogra-
phic factor; the odds ratios of the groups (55 - 64; 65 and
over) are twice as high as the odds ratio of young age
group (0 - 34). This result highlights the propensity of
the elder to consume, specifically for the group aged 55 -
64. Reflecting the effect of gender, male consumers have
a smaller odds ratio than females, suggesting men are
less likely to consumer goat meat.
Socioeconomic factors are relevant, but the impacts
defy the conventional wisdom with a negative relation-
ship between household income and goat meat consump-
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X. Liu et al. / Agricultural Sciences 4 (20 13) 549-557
Copyright © 2013 SciRes. OPEN A CCESS
Table 2. Existing demand and variables associated.
Va riab l e Level Estimate Wald Statistics P-Value OR
Intercept*** 1 1.63 13.99 0.00 0.20
Chicken consumption** 1 0.33 6.39 0.01 1.39
Chicken consumption 2 0.42 1.50 0.22 0.66
Chicken consumption* 3 0.96 3.04 0.08 0.38
Pork consumption 1 0.36 7.78 0.00 0.70
Pork consumption** 2 0.43 6.23 0.01 0.65
Pork consumption 3 0.33 2.15 0.14 0.72
Family size 0 0.05 2.09 0.14 1.05
Race 1 0.24 2.07 0.14 1.27
Race*** 2 1.23 9.98 0.00 3.42
Race*** 3 1.23 9.39 0.00 3.42
Race 4 0.34 0.25 0.61 1.40
Race** 5 1.07 7.41 0.00 2.92
Age*** 1 0.49 9.35 0.00 1.63
Age*** 2 0.73 7.00 0.00 2.08
Age*** 3 0.65 6.31 0.00 1.92
Gender*** 1 0.77 4.86 0.00 0.46
Income* 0 0.38 3.30 0.06 1.46
Income 1 0.13 0.46 0.49 1.14
Income 2 0.20 0.91 0.33 0.82
Income 4 0.18 0.86 0.35 0.84
State 1 0.24 1.72 0.19 1.27
State 2 0.34 2.48 0.11 0.71
State*** 3 0.59 6.36 0.00 1.80
Note: ***significant at the 0.01 level; **significant at the 0.05 level; *significant at the 0.10 level.
tion. The households with $20,000 income or less are
more likely to eat goat meat than the households with
higher income. This result is likely due to the influence
of the conventional belief in the inferior quality of goat
meat. There is no complex spatial pattern in goat meat
consumption. Most states are alike in sharing the propen-
sity to consume goat meat except Texas and Florida.
Texan tops other states in the likelihood of goat meat
consumption while Florida is at the bottoms.
5.2. Potential Demand
The results of potential increase in per capita con-
sumption are reported in Table 3. Starting with a similar
exploratory variable set, this model ends with a number
of quite different variables featuring preference variables,
which are largely absent in the first model.
The consumption of other meats impacts on the con-
sumption of goat meat and the influence is more closely
related to the frequency of other meat consumption. Con-
sumers, who purchase beef and chicken regularly, show a
large odds ratio of being willing to purchase more goat
meat, suggesting a positive relationship between other
meat consumption and goat meat purchase.
The impacts of demographics remain, but are much
weaker. The influence of age is discernible, featured by
the willingness of the age group 55 - 64 to purchase more
than all other age groups. Also evident is the impact of
gender; males are less likely to increase their purchase
than females. Nevertheless, ethnic affiliations are out of
the model, implying that goat meat as a regular diet is
not taken for granted even by ethnic groups.
While demographics take less weight and socioeco-
nomic factors largely missed in influencing the potential
demand, consumer preferences carry more in explaining
the willingness of purchasing more goat meat. Three pre-
ference variables (price specials, safety assurance, and
fat contents) were retained in the model, suggesting that
lower goat meat price, better safety assurance, and lower
fat content labeling are attractive to consumers who tend
to consumer more goat meat. This finding signals possi-
ble attributes of a marketing strategy to expand the goat
X. Liu et al. / Agricultural Sciences 4 (20 13) 549-557
Table 3. Potential demand and variables associated.
Va riab l e Level Estimate Wald Statistics P-Value OR
Intercept 1 0.31 2.16 0.14 1.36
Price special*** 0 1.04 7.95 0.00 0.35
Price special*** 1 1.58 9.95 0.00 0.21
USDA inspection 0 0.42 1.46 0.22 1.52
USDA Inspection** 1 0.48 3.83 0.05 0.62
Fat content* 0 0.49 3.22 0.07 0.61
Fat content 1 0.26 2.30 0.12 0.77
Beef consumption** 1 0.28 4.95 0.02 0.76
Beef consumption* 2 0.42 2.79 0.09 0.66
Beef consumption 3 0.08 0.04 0.82 1.08
Chicken consumption 1 0.23 2.41 0.12 0.79
Chicken consumption 2 0.44 1.46 0.22 0.64
Chicken consumption ** 3 1.48 4.80 0.02 0.23
Age 1 0.14 0.93 0.33 1.15
Age* 2 0.33 3.28 0.07 1.39
Age 3 0.26 2.13 0.14 1.30
Gender*** 1 0.91 5.53 0.00 0.40
State 1 0.19 1.01 0.31 1.21
State** 2 0.46 6.11 0.01 1.58
State 3 0.05 0.07 0.78 0.95
Note: ***significant at the 0.01 level; **significant at the 0.05 level; *significant at the 0.10 level.
5.3. Latent Demand
The results of potential increase in goat meat demand
from the influx of new consumers who have not pre-
viously eaten goat meat are reported in Ta b le 4. The re-
sults show sizeable potential entrants with distinguish-
able demographic and socioeconomic characteristics.
Again, demographics are linked with willingness of
trying goat meat. With a four-fold larger odds ratio than
Whites, African Americans and multiple racial house-
holds are more likely to be the new entrant. Age factor
again shows its influence, evidenced by the fact that the
elder is more likely to try goat meat than the young (0 -
34), this is especially true for the group aged 55 or over.
The impact of gender is insignificant, though it points
toward more flexibility of females than males in trying
goat meat.
Both socioeconomic factors, education and household
income, demonstrate a U-shaped impact on consumers’
willingness to enter the goat meat market. Taking the
educational level of high school or less as the reference,
consumers with an associate degree or some college
education are less willing to try goat meat, but those with
full college or higher education are more likely to trying.
Likewise, the middle income households are more hesi-
tated to try goat meat than their counterparts at the lower
and higher spectrum of incomes.
Once again, consumer preferences hold the edge in in-
fluential factors related to latent demand. Preferences for
cooking instruction, cooking methods (broiling and bar-
beque), store display, price specials, and safety assurance
were retained in the model. The odds ratios of those con-
sumer preferences demonstrate that consumer’ preferenc-
es for cooking guidance, price specials, and food safety
may stimulate an influx of new consumers into the goat
meat market.
5.4. Seasonal Demand
The results of the seasonal demand model are reported
in Table 5. Few variables have been retained and less in-
sightful suggestions are offered in this model.
Limited information conveyed in this model suggests
that chicken consumers tend to have seasonal consump-
tion of goat meat and eating other meat seems to have no
significant impact on goat meat consumption. Male con-
sumers and those sensitive to price specials are more
likely to change their consumption in different seasons.
In addition, consumers favoring less cholesterol comprise
the large portion of seasonal purchasers of goat meat.
Beyond the results observed above, it is an insurmoun-
table challenge to further clarify the profile of regular
Copyright © 2013 SciRes. OPEN A CCESS
X. Liu et al. / Agricultural Sciences 4 (20 13) 549-557 555
Table 4. Latent demand from new consumers and variables associated.
Va riab l e Level Estimate Wald Statistics P-Value OR
Intercept** 1 1.52 5.22 0.02 4.57
Cooking instruction*** 0 1.89 7.89 0.00 0.15
Cooking instruction 1 0.45 1.57 0.20 0.64
Broiling goat meat** 0 1.14 4.16 0.04 3.13
Broiling goat meat 1 0.28 0.37 0.54 0.76
Barbeque 0 0.29 0.52 0.46 0.75
Barbeque*** 1 1.62 7.31 0.00 0.20
Store display 0 0.10 0.03 0.85 1.11
Store display** 1 0.87 6.08 0.01 0.42
Price special** 0 2.45 5.76 0.01 0.09
Price special 1 0.64 2.09 0.14 0.53
USDA inspection*** 0 3.00 6.60 0.01 20.09
USDA inspection** 1 1.57 4.69 0.03 0.21
Race*** 1 1.42 8.00 0.00 4.14
Race 2 0.88 0.91 0.33 2.41
Race 3 0.15 0.08 0.76 0.86
Race 4 1.51 0.72 0.39 0.22
Race* 5 1.57 2.85 0.09 4.81
Age 1 0.45 1.11 0.29 1.57
Age** 2 1.16 5.38 0.02 3.19
Age* 3 0.88 3.16 0.07 2.41
Gender 1 0.47 2.21 0.13 0.63
Education* 1 0.64 2.99 0.08 0.53
Education 2 0.61 2.47 0.11 1.84
Income 1 0.52 0.94 0.33 0.59
Income** 2 1.22 4.06 0.04 0.30
Income 3 0.25 0.23 0.62 1.28
Income 4 0.03 0.00 0.95 1.03
State 1 0.26 0.28 0.59 1.30
State 2 0.28 0.21 0.64 1.32
State 3 0.65 2.28 0.13 0.52
Note: ***significant at the 0.01 level; **significant at the 0.05 level; *significant at the 0.10 level.
goat meat consumers against those of sporadic and sea-
sonal consumers even with the support of a large survey
data set. The ill-fitted model reflects the empirical reality
of the fuzzy division across regular and seasonal con-
sumers in the goat meat market. No practical ways are
readily available in the fitted model for transferring the
seasonal demand to a year round demand in the US goat
meat market. Possibly, there is no such one-size-fits-all
way of reshaping the seasonality in goat meat consump-
tion. More investigations are obviously needed in this di-
Demand for goat meat and its potential growth are
evidenced in this study. A promising US goat meat mar-
ket mirrors itself in four layers of demand model: current
demand, potential demand increase of current consumers,
latent demand of new consumers, and seasonal demand.
The US goat meat market isn’t trivial because seven-
teen percent of consumers currently purchase goat meat.
The lower income, the elder, and the ethnic groups com-
prise the current goat meat niche market. The niche is
expected to grow with more demographically homoge-
nous immigrants from developing countries and the com-
ing retirement of the “baby boom” generation.
The incremental demand of current consumers and la-
tent demand of consumers who never eat goat meat are
likely to stimulate the expansion of the US goat meat
market. Therefore, the growth potential is not driven just
by the size of ethnic groups or the elder, but also by
consumer preferences for healthy and safer attributes of
goat meat. The provision of goat meat products with the
attributes may define the fuure of the US goat meat t
Copyright © 2013 SciRes. OPEN A CCESS
X. Liu et al. / Agricultural Sciences 4 (20 13) 549-557
Table 5. Seasonal demand and variables associated.
Va riab l e Level Estimate Wald Statistics P-value OR
Intercept 1 1.33 2.50 0.11 3.78
Price special* 1 0.59 3.22 0.07 1.80
Price special** 2 1.25 4.09 0.04 0.29
Cholesterol level** 1 -0.61 5.18 0.02 0.54
Cholesterol level 2 0.22 0.23 0.62 0.80
Chicken consumption** 1 1.56 4.67 0.03 0.21
Chicken consumption* 2 1.35 3.38 0.06 0.26
Chicken consumption** 4 3.39 5.69 0.01 0.03
Lamb consumption 1 0.93 0.56 0.45 0.39
Lamb consumption 2 0.41 0.56 0.45 0.66
Lamb consumption 4 0.34 2.31 0.12 1.40
Seafood consumption** 1 0.13 0.17 0.67 0.88
Seafood consumption** 2 0.67 4.97 0.02 0.51
Seafood consumption 4 0.54 0.62 0.42 0.58
Gender*** 2 0.88 6.16 0.00 0.41
Note: ***significant at the 0.01 level; **significant at the 0.05 level; *significant at the 0.10 level.
The seasonal demand is likely to keep its dominance
in the US goat meat market for some time into the future.
So far as the results of our study and findings of others, it
is not readily available to have one-size-fits-all identi-
fying the shift of the US goat meat market from a market
of seasonal demand dominance into a market with a sta-
ble year round demand.
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