The Australian Journal of
Journal of the Australian
Agricultural and Resource
Economics Society
The Australian Journal of Agricultural and Resource Economics, 54, pp. 477–490
The income elasticity of meat: a meta-analysis
Craig A. Gallet†
The demand for meat has been estimated by many studies utilizing various data and
estimation methods. In this study, we perform a meta-analysis of the income elasticity
of meat that involves regressing 3357 estimated income elasticities, collected from 393
studies, on variables that control for study characteristics. Across several meta-regression specifications, we find significant differences in income elasticities tied to the type
of meat being studied, as well as a few functional forms, data aggregations, publication characteristics, and locations of demand. However, many study characteristics do
not significantly influence reported income elasticities. Less concern should be given to
such characteristics when choosing an income elasticity from the literature.
Key words: income elasticity, meat demand, meta-analysis.
1. Introduction
Numerous studies estimate the demand for meat using various data and estimation methods, which several qualitative literature reviews (e.g., Kuznets
1953; Reeves and Hayman 1975; Richardson 1976; Tomek 1977; Raunikar
and Huang 1987; Smallwood et al. 1989; Alston and Chalfant 1991; Moschini
and Moro 1996; Griffith et al. 2001; Asche et al. 2007) suggest contribute to
differences in reported outcomes. However, because qualitative literature
reviews can be sensitive to the subjective decision of the reviewer to emphasize certain study attributes over others, meta-analysis is an increasingly
popular method used to quantitatively survey literature. A typical metaanalysis involves regressing a parameter commonly estimated in the literature
on variables that control for study characteristics. By doing so, the subjective
decision of the reviewer is replaced by statistical tests, the results of which
shed light on the relative statistical importance of study characteristics to
influence the parameter estimate.
With respect to the demand for meat, Gallet (2010) reports the results of a
meta-analysis of the price elasticity of meat. Regressing 4120 observations of
the price elasticity of meat, collected from 419 studies, on a series of study
characteristic variables, he finds the price elasticity is particularly sensitive to
the type of meat being studied and the estimation methodology.
Yet the income elasticity also plays an important role in the literature. For
example, a meat producer might use the income elasticity to gauge the growth
of demand as incomes rise. Also, in light of recent attention given to the pros†
Craig A. Gallet (email: [email protected]) is at the Department of Economics, California
State University at Sacramento, 6000 J Street, Sacramento, CA 95819-6082, USA.
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AJARE Ó 2010 Australian Agricultural and Resource Economics Society Inc. and Blackwell Publishing Asia Pty Ltd
doi: 10.1111/j.1467-8489.2010.00505.x
478
C.A. Gallet
pect of using food price policies to improve health outcomes (e.g., Kuchler
et al. 2005; Chouinard et al. 2007), arguments in favor or against such policies can be bolstered by knowledge of the income elasticity. For example,
consider a policymaker contemplating a tax (subsidy) on red meat (white
meat) in an effort to shift consumption away from red meat towards white
meat. If the income elasticity of white meat exceeds that of red meat, then in
the presence of rising incomes, there is less need to use the tax and subsidy to
coerce a shift in budget shares towards white meat, because consumers will
do this on their own, ceteris paribus.
Accordingly, this paper complements Gallet (2010) by reporting the results
of a meta-analysis of the income elasticity of meat. Specific questions
addressed in this meta-analysis are the following: (i) Does the income elasticity differ across meat products? (ii) Is the income elasticity sensitive to the
specification of demand? (iii) Does the type of data used to estimate meat
demand influence the income elasticity? (iv) Is the income elasticity sensitive
to the method used to estimate demand? (v) Do the quality of the publication
outlet and the year of publication influence the income elasticity?, and (vi)
Are there regional differences in the income elasticity? By answering such
questions, we gain better insight into the tendencies in the literature to sway
the income elasticity one way or the other.
The paper proceeds as follows. In Section 2, we discuss the data and metaregression model, which is followed in Section 3 with a discussion of the estimation results. The paper concludes with a summary in Section 4.
2. Data and meta-regression model
2.1 Data
An initial search of the literature was conducted using EconLit, AgEcon
Search, and Google Scholar, as well as several qualitative literature reviews
(i.e., Kuznets 1953; Reeves and Hayman 1975; Richardson 1976; Tomek
1977; Raunikar and Huang 1987; Smallwood et al. 1989; Alston and Chalfant 1991; Moschini and Moro 1996; Griffith et al. 2001; Asche et al. 2007),
to identify candidate studies that estimate the income elasticity of meat. Subsequent to surveying the reference sections of all studies identified, 393 studies
(see Table 1) reporting 3357 income elasticity estimates were included in the
meta-data set.1 These 3357 income elasticity estimates become observations
of the dependent variable in a meta-regression model.
Similar to other meta-analyses of the income elasticity (e.g., Espey 1998;
Dalhuisen et al. 2003; Gallet and List 2003; Gallet 2007), in addition to the
1
Initially, 3363 income elasticity estimates were retrieved from the 393 studies. However, six
observations were two or more standard deviations from the mean, and so similar to that stated by Gallet (2010), these observations were dropped to reduce the influence of outliers. Nonetheless, the results presented in Section 3 change little whether or not these six outliers are
included.
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Table 1 Studies included in meta-analysis
Abdulai et al. (1999)
Abdulai and Aubert (2004)
Abdullah (1994)
Abdullah et al. (1999)
Ackah and Appleton (2003)
Agbola (2003)
Agbola et al. (2003)
Ahmed and Shams (1994)
Akbay et al. (2007)
Alfonzo and Peterson (2006)
Ali (2002)
Allais and Nichele
(2004, 2007)
Alston and Chalfant
(1987, 1991)
Alston et al. (1995, 2002)
Andrikopoulos et al. (1987)
Angulo and Gil (2006)
Apaza et al. (2002)
Armagan and Akbay (2007)
Arzac and Wilkinson (1979)
Asche (1996, 1997)
Asche et al. (1997)
Asche et al. (1998)
Atkins et al. (1989)
Azzam et al. (2004)
Babula (1997)
Babula and Corey (2004)
Bacchi and Spolador (2002)
Balcombe (2004)
Balcombe and Davis (1996)
Ball and Dewbre (1989)
Barten (1964)
Beatty and LaFrance (2001)
Benson et al. (2002)
Bergstrom (1955)
Bewley and Young (1987)
Bhati (1987)
Bielik and Kunova (2007)
Bjorndahl et al. (1992)
Bjorndahl et al. (1994)
Blackorby et al. (1978)
Blanciforti and Green (1983)
Blanciforti et al. (1986)
Boetel and Liu (2003)
Boutwell and
Simmons (1968)
Fousekis and
Pantzios (2000)
Fousekis and Revell (2000,
2003, 2004, 2005)
Fox (1951)
Fraser and Moosa (2002)
Freebairn and
Rausser (1975)
Freebairn and
Gruen (1977)
French (1952)
Fulponi (1989)
Funk et al. (1977)
Gao and Shonkwiler (1993)
Gao et al. (1996, 1996)
Menkhaus et al. (1985)
Mergos and Donatos (1989)
Miljkovic et al. (2002)
Millan and Aldaz (2005)
Miran and Akgungor (2005)
Mittelhammer et al. (1996)
Garcia (2004)
Molina (1994)
Moro and Sckokai (2000)
Morrison et al. (2003)
Moschini (1998, 2001)
Moschini and Meilke
(1984, 1989)
Moschini and Vissa (1993)
Garcia et al. (2005)
Moschini et al. (1994)
Gibson (1998)
Goddard and
Cozzarin (1992)
Golan et al. (2001)
Goodwin (1992)
Goodwin and
Phaneuf (2001)
Goodwin and Sheffrin (1982)
Gould (2002)
Murray (1984)
Mutondo and Henneberry
(2007, 2007)
Nayga (1995)
Nayga and Capps (1994)
Nerlove and Addison (1958)
Gould and Villarreal (2006)
Gould et al. (2002)
Gracia et al. (1998)
Greenfield (1974)
Hahn (1988, 1994)
Hahn et al. (2003)
Halbrendt et al. (1994)
Hancock et al. (1984)
Hannah (1970)
Hanrahan (2002)
Hassan et al. (2001)
Hassan and Johnson (1979)
Hassan and Katz (1975)
Hayes et al. (1990)
Hayes et al. (1991)
Heien (1982)
Heien and Pompelli (1988)
Heien and Wessells (1990)
Henneberry and
Mutondo (2007)
Herrmann and Lin (1988)
Herrmann et al. (1992, 1993)
Herrmann et al. (2002)
Hossain and Jensen (2000)
Houston and Ermita (1992)
Nyankori and Miller (1982)
Ogunyinka and Marsh
(2002, 2006)
Omezzine et al. (2003)
O’Neill and Buttimer (1973)
Pantzios and Fousekis (1999)
Park et al. (1996)
Peeters et al. (1997)
Peng et al. (2004)
Peterson and Chen (2005)
Piggott et al. (1996)
Piggott and Marsh (2004)
Piggott et al. (2007)
Pitt (1983)
Pope et al. (1980)
Price and Gislason (2001)
Pudney (1981)
Purcell and Raunikar (1971)
Quagrainie (2003)
Raper et al. (2002)
Reed et al. (2003)
Reed et al. (2005)
Regorsek and Erjavec (2007)
Reynolds and Goddard (1991)
Rickertsen (1996, 1997, 1998)
Rickertsen and Vale (1996)
Rickertsen and
Cramon-Taubadel (2000)
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Table 1 (Continued)
Boyle (1996)
Brester (1996)
Brester and
Schroeder (1995)
Brester and
Wohlgenant (1993)
Bureau of
Econ Analysis (1967)
Burney and Akmal (1991)
Burton (1992)
Burton and Young
(1992, 1996, 1997)
Byrne et al. (1993)
Byrne et al. (1995)
Byron (1970, 1970)
Cai et al. (1998)
Capps (1989)
Capps and Havlicek
(1984, 1987)
Capps and Pearson (1986)
Capps and Schmitz (1991)
Capps et al. (1994)
Cashin (1991)
Chalfant (1987)
Chalfant et al. (1991)
Chang (1977, 1980)
Chang and Green
(1989, 1992)
Chavas (1983)
Chen (1996)
Chen and Veeman (1991)
Cheney et al. (2001)
Cheng and Capps (1988)
Chern et al. (2003)
Chesher and Rees (1987)
Choi and Sosin (1990)
Christensen and Manser (1977)
Chung (1994)
Coulibaly and Brorsen (1999)
Court (1967)
Cowan and Herlihy (1982)
Cramer (1973)
Cranfield and
Goddard (1995)
Crutchfield (1985, 1985)
Davis et al. (2004)
Davis et al. (2007)
DeVoretz (1982)
Hsu (2000)
Huang (1979)
Huang and Raunikar
(1978, 1986)
Huang and Rozelle (1998)
Rickertsen et al. (2003)
Roy et al. (1994)
Salfyurtlu et al. (1986)
Sahn (1988)
Huang and Bouis (2001)
Saleh and Sisler (1977)
Huang (1994)
Huang and Haidacher
(1983, 1989)
Hudson and Vertin (1985)
Salvanes and DeVoretz (1997)
Sam and Zheng (2007)
Hutasuhut et al. (2002)
Hyde and Perloff (1998)
Jabarin (2005)
Jan et al. (2002)
Jensen and Manrique (1998)
Jiang and Davis (2007)
Sasaki (1993)
Sasaki and Fukagawa (1987)
Savadogo and Brandt (1988)
Schroeder et al. (2000)
Schroeder et al. (2001)
Schroeter and Foster (2004)
Johnson et al. (1998)
Johnson (1978)
Jones and Yen (2000)
Jung and Koo (2000, 2002)
Kaabia et al. (2001)
Kaabia and Gil (2001)
Karagiannis and
Velentzas (1997)
Karagiannis et al.
(1996, 2000)
Kastens and Brester (1996)
Schroeter (1988)
Schultz (1935)
Shahid and Gempesaw (2002)
Shonkwiler and Taylor (1984)
Soe et al. (1994)
Soshnin et al. (1999)
Steen and Salvanes (1999)
Sarmiento (2005)
Stone (1951)
Stroppiana and
Riethmuller (2000)
Su and Yen (1996)
Katchova and
Chern (2004)
Keller and Driel (1985)
Kennes (1983)
Kim and Gould (1998)
Kinnucan and
Thomas (1997)
Kinnucan et al. (1997)
Kinnucan and Miao (1999)
Klonaris (2001)
Klonaris and Hallam (2003)
Kokoski (1986)
Kouka (1995)
Kounker (1977)
Kreinin (1973)
Kulshreshtha (1979)
Teisl et al. (2002)
Teklu and Johnson (1988)
Thompson (2004)
Throsby (1974)
Thurman (1986, 1987, 1989)
Tintner (1950, 1952)
Tomek and Cochrane (1962)
Tonsor and Marsh (2007)
Traesupap et al. (1999)
Kulshreshtha and
Wilson (1972)
Kusumastanto and
Jolly (1997)
Ladd and Tedford (1959)
Lambert et al. (2006)
Trierweiler and
Hassler (1971)
Tryfos and
Tryphonopoulos (1973)
Tsoa et al. (1982)
Unnevhr and Khoju (1991)
Sulgham and Zapata (2006)
Taljaard et al. (2004, 2006)
Talukder (1993)
Tambi (1996, 1998)
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Table 1 (Continued)
DeVoretz and
Salvanes (1993)
Dey (2000)
Dey and Garcia (2007)
Dhehibi and Laajimi (2004)
Dhehibi et al. (2005)
Doll (1972)
Dong et al. (1998)
Dong et al. (2004)
Dong and Fuller (2004, 2006)
Dono and
Thompson (2002)
Duffy (1999)
Duffy and Goddard (1995)
Durbin (1953)
Eales (1996)
Eales and Unnevehr
(1988, 1993)
Eales et al. (1997)
Eales et al. (1998)
Eales and Wessells (1999)
Lanfranco et al. (2002)
Vale (1996)
Langemeier and
Thompson (1967)
Lazaridis (2003)
Le et al. (1998)
Lechene (2000)
Lee et al. (1992)
Lee and Seaver (1971)
Lerdau (1954)
Leuthold and
Nwagbo (1977)
Lin et al. (1989)
Van Der Meulen (1961)
Liu and Chern (2004)
Liu and Sun (2005)
Ma et al. (2004)
Main et al. (1976)
Mainland (1998)
Maki (1957)
Manrique and
Jensen (2001)
Manser (1976)
Edgerton (1996, 1997)
Effiong and Njoku (2001)
Fabiosa (2000)
Fabiosa and Ukhova (2000)
Fan and Chern (1997)
Fan et al. (1994)
Fan et al. (1995)
Fanelli and
Mazzocchi (2002)
Fayyad et al. (1995)
Marceau (1967)
Marsh et al. (2004)
Martin (1967)
Martin and Porter (1985)
Mazany et al. (1996)
Mazzocchi (2003, 2006)
Mazzocchi et al. (2004)
Mazzocchi and Lobb (2005)
Felixson et al. (1987)
Fidan (2005)
Fisher (1979)
Mbala (1992)
McGuirk et al. (1995)
McNulty and
Huffman (1992)
Mdafri and Brorsen (1993)
Meinken et al. (1956)
Flake and Patterson (1999)
Fofana and Clayton (2003)
Mazzocchi et al. (2006)
Veeman et al. (2004)
Verbeke and Ward (2001)
Vere and Griffith (1988)
Vickner et al. (2006)
Wahby (1952)
Wahl and Hayes (1990)
Wahl et al. (1991)
Wang et al. (1998)
Wellman (1992)
Wessells and Wilen
(1993, 1994)
Wessells et al. (1995)
Wilkie and Godoy (2001)
Wilkie et al. (2005)
Wilson and Marsh (2005)
Wohlgenant
(1985, 1986, 1989)
Wohlgenant and
Hahn (1982)
Working (1952)
Wu et al. (1995)
Xi et al. (2003, 2004)
Xu and Veeman (1996)
Yanagida and Tyson (1984)
Yandle (1968)
Yang and Koo (1994)
Yeboah and
Maynard (2004)
Yen and Huang
(1996, 2002)
Yen et al. (2003)
Yen et al. (2004)
Zhuang and Abbott (2007)
Zidack et al. (1992, 1993)
Zwick (1957)
Note: Complete references of the 393 studies to be posted online.
reported income elasticity estimates, several characteristics of the 393 meat
demand studies were noted. First, it is common to estimate the income elasticity for a variety of meats, including beef, pork, lamb, poultry, fish, and a
composite category consisting of several meats. Second, concerning the specification of demand, in addition to the commonly adopted linear and doublelog functional forms, many studies estimate the demand for meat using theoretically consistent functional forms, such as the linear-approximate almost
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C.A. Gallet
ideal demand system (AIDS-Linear), which relies on a price index to linearize
Deaton and Muellbauer’s (1980) AIDS specification, the traditional nonlinear AIDS form (AIDS-Nonlinear), the quadratic AIDS form (AIDS-Quadratic) of Banks et al. (1997), and the generalized AIDS form (AIDSGeneral) of Bollino (1990). Studies have also estimated the demand for meat
using a variety of other functional forms (i.e., semi-log, Rotterdam, CBS,
translog, S-Branch, Box–Cox, the generalized addilog, and the quadratic
expenditure forms).
Third, continuing with demand specification, several income elasticity
estimates come from specifications that include other meats as substitutes.
Also, some studies estimate dynamic specifications of demand by including lag terms on the right side of the demand equation, while others estimate a two-step model, in which meat demand is modeled as (i) the
choice of whether or not to consume meat followed by (ii) the decision
of how much to consume.
Fourth, we also note several characteristics of the data and estimation
methods used by the 393 meat demand studies. Specifically, in addition
to cross-sectional, time-series, and panel data, studies utilize data that are
temporally aggregated to the annual, quarterly, and less than quarterly
(i.e., monthly and weekly) levels, as well as spatially aggregated to the
multiple countries, country, region of country (i.e., multiple states or
provinces), state or province, city, firm, and individual consumer levels.
Also, in addition to ordinary least squares (OLS), studies have estimated
meat demand using two-stage least squares (2SLS), three-stage least
squares (3SLS), full information maximum likelihood (FIML), singleequation maximum likelihood (MLE), seemingly unrelated regression
(SUR), generalized method of moments (GMM), generalized least squares
(GLS), and although sparingly, the minimum distance estimator and maximum entropy.
Fifth, information on the publication outlet in which each of the 393 studies appeared was also collected. In particular, we note the year in which the
study was published, as well as whether or not the study was published in a
premium journal, such as a top-36 economics journal (as identified by Scott
and Mitias (1996)) or the American Journal of Agricultural Economics
(AJAE), and whether or not the study was published in a book.
Lastly, the demand for meat has been estimated throughout the world.
Accordingly, using the Nations Online Project, we note the location of
demand across 13 different regions (i.e., Australia, North America, South
America, North Europe, West Europe, South Europe, East Europe, East
Asia, South East Asia, South Central Asia, Middle East, South Africa, and
other parts of Africa).2
2
See Gallet (2010) for the frequency of each study characteristic. For example, in the literature, it is most common to adopt the AIDS-Linear specification of meat demand, which is estimated with country-level time-series data using SUR.
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2.2 Meta-regression model
Observations of the income elasticity of meat collected from the literature
serve as the dependent variable in a series of meta-regressions. Specifically, as
studies typically report multiple income elasticity estimates, similar to other
meta-analyses (e.g., Rosenberger and Loomis 2000; Gallet and List 2003;
Johnston et al. 2006; Gallet 2010), we consider the following unbalanced
panel data meta-regression model:
Eij ¼ ai þ bXij þ eij ;
ð1Þ
where Eij is the study i’s jth income elasticity estimate, ai is the ‘random
researcher’ effect, which controls for unobserved study-specific effects that
might influence the income elasticity, b is the vector of coefficients, and Xij
accounts for the study characteristics mentioned previously. Specifically,
included in Xij are the year the study was published, as well as a series of
dummy variables controlling for each of the study characteristics mentioned
(i.e., variable equals 1 if the respective study characteristic holds, 0 if not).3
Finally, eij is an iid error term with zero mean and variance r2e .
There are several issues concerning the estimation of Equation (1) that
need to be addressed. First, to avoid perfect multicollinearity, dummy variables for several of the study characteristics must be dropped from the metaregressions. These variables comprise the baseline upon which results are
compared.4 Second, because many of the study characteristics in Xij do not
vary within studies, this prevents using a fixed effects estimator. Instead, in
addition to using OLS as a point of comparison, we estimate Equation (1)
using a random effects estimator. Third, White’s (1980) test rejected the null
of no heteroskedasticity in each meta-regression, and so similar to other
meta-analyses of the income elasticity (e.g., Espey 1998; Dalhuisen et al.
2003; Gallet 2007), heteroskedasticity-consistent standard errors are used to
construct t-statistics. Fourth, we explore the impact of different meta-regression specifications by comparing the results with all study characteristics
included as regressors (labeled the full model) to those that exclude study
characteristics that are jointly insignificant in the full model (labeled the
3
Because they are adopted infrequently in the literature, the generalized addilog and quadratic expenditure functional forms are collectively accounted for by the dummy variable
labeled ‘Other Form’, while the minimum distance and maximum entropy estimators are collectively accounted for by the dummy variable labeled ‘Other Method’.
4
For instance, similar to Gallet (2010), the dummy variable corresponding to the composite
meat category is dropped from each meta-regression, and so results are interpreted relative to
this baseline meat. The baseline further corresponds to one obtained from a linear version of
meat demand (absent substitute meats, dynamic considerations, and a two-step treatment) that
is estimated with panel data (aggregated to the annual individual consumer level) using OLS.
Also, the baseline income elasticity is not published in a top-36 economics journal, the AJAE,
or a book. Finally, the baseline income elasticity is not specific to a particular region of the
world.
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C.A. Gallet
restricted model). Fifth, across all 3357 observations, the mean income elasticity equals 0.90, and so a positive (negative) meta-regression coefficient is
interpreted as that particular study characteristic inflating (deflating) the
income elasticity.
3. Estimation results
Table 2 presents the results for the full and restricted models. Across nine
major categories of variables, each restricted model was determined by
eliminating those categories for which the corresponding coefficients were
jointly insignificant in the full model. Accordingly, based on the F-test
values at the bottom of Table 2, the restricted model corresponding to
the OLS meta-regression eliminates the variables controlling for the nature of data and temporal aggregation, while the restricted model corresponding to the random effects meta-regression eliminates the variables
controlling for specification issues, nature of data, and spatial aggregation. As provided at the bottom of Table 2, LaGrange multiplier tests
reject the null hypothesis of homogeneous researcher effects, thus favoring
the random effect results over the OLS results. Nonetheless, a perusal of
the coefficients in Table 2 indicates similarities in their sign and significance across the meta-regressions, and so rather than discussing the
results of each meta-regression separately, we focus on the pattern of the
coefficients across all four meta-regressions.
There are several noteworthy results concerning the individual coefficients. First, compared to the baseline composite meat category, the
income elasticity is significantly lower for pork, lamb, and poultry.5 Second, concerning the specification of meat demand, although the income
elasticity tends to be deflated (inflated) when a semi-log or CBS (translog
or S-branch) functional form is adopted, for the majority of the functional forms the meta-regression coefficients are insignificantly different
from zero. Consequently, compared to the linear baseline form, theoretically consistent functional forms, such as the various AIDS forms and
the Rotterdam form, have little statistical influence on the estimated
income elasticity.6 Also, with the exception of including substitute meats
in the OLS meta-regressions, specification issues matter little in determining the income elasticity.
5
To put these differences into perspective, using the random effects results for the full
model, similar to that followed by Gallet (2010), the predicted income elasticities for each meat
are calculated at the mean of each study characteristic (with the exception of the dummy variables corresponding to each other meat, which are set to zero). At these values, the rank order
of income elasticities (provided in parentheses) are as follows: beef (1.00), composite meat
(0.97), fish (0.90), poultry (0.82), pork (0.80), and lamb (0.74). Hence, the income elasticity of
lamb is nearly 25 per cent lower than that of beef, ceteris paribus.
6
Although we might expect theory-based functional forms to yield estimates closer to the
true demand and thus contribute to differences in income elasticity estimates across functional
forms, the meta-regression results do not provide appreciable evidence of this.
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Table 2 Meta-regression results
Category
Product
Variable
Beef
Pork
Lamb
Poultry
Fish
Functional
form
Double-Log
Semi-Log
AIDS-Nonlinear
AIDS-Linear
AIDS-Quadratic
AIDS-General
Rotterdam
CBS
Translog
S-Branch
Box–Cox
Other form
Specification
issues
Substitute meats
Two-step
Dynamic
Nature of data
Time-series
Cross-sectional
Temporal
aggregation
Quarterly
Less than quarterly
Full model
Restricted model
OLS
Random
effects
OLS
Random
effects
0.008
(0.202)
)0.175***
(4.773)
)0.146*
(1.686)
)0.125**
(2.438)
)0.005
(0.068)
0.081
(0.710)
)0.190**
(2.251)
)0.067
(0.659)
)0.025
(0.296)
0.005
(0.047)
0.023
(0.159)
)0.029
(0.320)
)0.310***
(2.824)
0.335***
(3.285)
0.615***
(5.935)
)0.053
(0.442)
0.010
(0.125)
)0.077**
(2.554)
0.021
(0.517)
0.032
(0.641)
)0.214
(1.397)
)0.031
(0.429)
0.043
(0.876)
0.169*
(1.710)
0.0285
(0.651)
)0.174***
(4.117)
)0.227***
(2.684)
)0.156***
(2.961)
)0.075
(1.141)
0.031
(0.196)
)0.221*
(1.930)
)0.034
(0.229)
)0.150
(1.118)
)0.040
(0.247)
)0.061
(0.146)
)0.098
(0.803)
)0.329*
(1.944)
0.131
(0.633)
0.405*
(1.820)
0.003
(0.031)
)0.159
(1.183)
)0.006
(0.081)
)0.042
(0.593)
)0.061
(0.774)
)0.104
(0.356)
0.038
(0.615)
0.209***
(3.089)
0.239**
(2.032)
0.009
(0.228)
)0.172***
(4.454)
)0.140*
(1.645)
)0.123**
(2.536)
0.015
(0.263)
0.078
(0.703)
)0.210***
(2.761)
)0.066
(0.655)
)0.027
(0.322)
0.009
(0.089)
0.021
(0.137)
)0.035
(0.381)
)0.317***
(2.975)
0.318***
(3.189)
0.620***
(6.219)
)0.060
(0.505)
)0.006
(0.071)
)0.091***
(3.035)
)0.006
(0.160)
0.045
(1.010)
0.032
(0.724)
)0.171***
(3.990)
)0.222**
(2.491)
)0.148***
(2.599)
)0.070
(0.989)
0.017
(0.111)
)0.222**
(1.974)
)0.049
(0.336)
)0.153
(1.170)
)0.112
(0.631)
)0.064
(0.154)
)0.123
(0.773)
)0.373**
(1.971)
0.110
(0.573)
0.287
(1.325)
)0.014
(0.138)
)0.194
(1.476)
0.262***
(4.880)
0.338***
(3.088)
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Table 2 (Continued)
Category
Spatial
aggregation
Variable
Multiple countries
Country
Region of country
State/province
City
Firm
Estimation
method
2SLS
3SLS
FIML
MLE
SUR
GMM
GLS
Other method
Publication
Top-36 journal
AJAE
Book
Year published
Region
Australia
North America
South America
North Europe
West Europe
South Europe
East Europe
East Asia
Full model
Restricted model
OLS
Random
effects
OLS
0.484
(1.248)
0.417**
(2.425)
0.952***
(3.042)
0.143
(1.449)
0.300
(1.368)
0.527***
(2.588)
0.744***
(4.694)
0.247***
(3.419)
0.202***
(6.519)
)0.025
(0.834)
0.114***
(2.759)
0.101
(0.958)
0.032
(0.266)
)0.201*
(1.710)
)0.091**
(2.362)
)0.006
(0.199)
0.182***
(3.389)
0.009***
(4.807)
)0.416***
(3.184)
)0.100
(0.853)
)0.287
(1.544)
)0.143
(1.412)
0.146
(1.304)
0.121
(1.105)
)0.040
(0.240)
0.062
0.307
(0.461)
0.303
(0.865)
1.802**
(2.344)
)0.021
(0.114)
0.419
(1.178)
0.415
(1.104)
0.356**
(2.450)
0.008
(0.064)
0.062
(1.065)
)0.045
(1.131)
)0.044
(0.751)
)0.108
(0.487)
)0.100
(0.753)
)0.231
(1.213)
0.014
(0.199)
0.026
(0.429)
0.196*
(1.677)
0.011***
(3.648)
)0.480**
(2.286)
)0.117
(0.581)
)0.425
(1.446)
)0.010
(0.045)
0.184
(0.800)
0.187
(0.897)
)0.059
(0.280)
0.077
0.329
(1.249)
0.244***
(4.165)
0.839***
(2.734)
0.122**
(2.012)
0.284
(1.339)
0.492***
(3.631)
0.753***
(4.761)
0.253***
(3.456)
0.182***
(6.063)
)0.036
(1.209)
0.102**
(2.289)
0.039
(0.344)
0.068
(0.575)
)0.247**
(2.198)
)0.068*
(1.766)
)0.022
(0.858)
0.148***
(3.294)
0.010***
(7.013)
)0.421***
(4.030)
)0.109
(1.036)
)0.336*
(1.881)
)0.154*
(1.699)
0.118
(1.239)
0.112
(1.175)
)0.045
(0.305)
0.052
Random
effects
0.382***
(2.974)
0.110
(0.839)
0.091
(1.618)
)0.041
(0.918)
)0.013
(0.197)
0.039
(0.162)
)0.082
(0.507)
)0.175
(0.882)
)0.025
(0.367)
0.005
(0.093)
0.267**
(2.565)
0.008***
(2.889)
)0.553***
(3.823)
)0.136
(0.913)
)0.511**
(2.319)
)0.051
(0.285)
0.196
(1.214)
0.139
(1.237)
)0.158
(1.624)
0.058
Ó 2010 The Author
AJARE Ó 2010 Australian Agricultural and Resource Economics Society Inc. and Blackwell Publishing Asia Pty Ltd
Income elasticity of meat
487
Table 2 (Continued)
Category
Variable
Full model
OLS
South East Asia
South
Central Asia
Middle East
South Africa
Other Africa
F (Product)†
F (Functional Form)
F (Specification Issues)
F (Nature of Data)
F (Temporal Aggregation)
F (Spatial Aggregation)
F (Estimation Method)
F (Publication)
F (Region)
Adjusted R2
v2 (1 df)
N
Random
effects
Restricted model
OLS
Random
effects
(0.619)
(0.407)
(0.592)
(0.323)
0.168
0.105
0.128
0.059
(1.283)
(0.430)
(0.980)
(0.309)
0.361***
0.356*
0.349***
0.267
(2.960)
(1.770)
(2.909)
(1.514)
0.558***
0.864***
0.520***
0.929**
(6.082)
(3.457)
(5.451)
(2.039)
)0.185
)0.133
)0.234
)0.138
(0.930)
(0.433)
(1.153)
(0.499)
)0.239*
)0.117
)0.283**
)0.165
(1.735)
(0.510)
(2.229)
(0.800)
9.845
10.080
10.055
9.736
57.367
8.164
58.802
6.021
2.469
0.233
4.155
–
0.986
0.493
–
–
1.606
5.011
–
15.734
3.831
1.312
11.072
–
14.018
2.974
15.404
2.965
48.734
4.809
57.173
4.745
65.363
20.325
73.194
11.954
0.12
–
0.12
–
–
334.92
–
448.56
3357
3357
3357
3357
Note: t-statistics (in absolute value) provided in parentheses. Levels of significance: *10%, **5%, and
***1%. †F-tests of the joint significance of coefficients associated with respective category. For example, F
(product) refers to an F-test of the significance of the five coefficients of the meat product dummy variables.
SUR, seemingly unrelated regression; OLS, ordinary least squares; MLE, single-equation maximum likelihood; GMM, generalized method of moments; GLS, generalized least squares; FIML, full information
maximum likelihood; 2SLS, two-stage least squares; 3SLS, three-stage least squares.
Third, given that many of the coefficients associated with data issues are
jointly insignificant, data issues overall appear to have little influence on the
income elasticity. Yet there are a number of individually significant coefficients associated with temporal and spatial aggregation of data that do affect
the income elasticity. In particular, compared to the baseline use of annual
data from individual consumers, the use of quarterly and less than quarterly
data, as well as data aggregated to the country, region of country, and firmlevel tend to inflate the income elasticity.7
Fourth, there is a noticeable difference between the OLS and random
effects results concerning the influence of estimation methods on the income
elasticity of meat. In particular, compared to the baseline OLS estimator, the
use of 2SLS, 3SLS, FIML, and SUR inflates the income elasticity in the OLS
7
Such results are consistent with a number of studies (i.e., Blundell et al. 1993; Denton and
Mountain 2001) that find evidence of aggregation bias in the estimation demand.
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C.A. Gallet
meta-regressions, while the use of other methods (i.e., minimum distance and
maximum entropy) deflates the income elasticity. With the exception of
2SLS, though, each of the estimation methods fails to significantly affect the
income elasticity in the random effects meta-regressions.
Fifth, similar to Gallet (2010), we find certain publication characteristics
influence the income elasticity of meat. Specifically, across all four metaregressions, not only is the income elasticity higher when published in a book,
but more recent studies report higher income elasticities compared to older
studies.8 Nonetheless, publishing in the AJAE or a top-36 economics journal
(with the exception of the OLS results) does not appreciably influence the
reported income elasticity.
Lastly, although the coefficients of many of the region dummy variables
are insignificantly different from zero, which suggests the income elasticity
differs little across locations, there are a few notable regions. In particular,
across the majority of meta-regressions, we find the income elasticity is lower
in Australia and higher in South Central Asia and the Middle East, which is
consistent with the preferences for meat differing in these regions.
4. Concluding comments
Based on the meta-regression results, we find several patterns concerning estimates of the income elasticity of meat in the literature. For instance, the
income elasticities of lamb, pork, and poultry tend to be lower than those of
other meats. Furthermore, the income elasticity is sensitive to a few functional forms, data aggregation, publication, and regional characteristics.
Nonetheless, it is interesting that a number of factors commonly employed in
the literature (e.g., AIDS and Rotterdam functional forms, other specification issues, whether or not time-series or cross-section data is used, and many
estimation methods) do not significantly affect the reported income elasticity;
and so less concern needs to be given to such factors when choosing an
income elasticity from the literature.
Having a more clear understanding of tendencies in the literature to sway
the income elasticity one way or the other is beneficial to policymakers and
academics alike. For instance, based on our results, increasing income will
shift a greater (lesser) budget share towards beef and fish (lamb, pork, and
poultry). Not only is this of interest to those teaching courses in consumer
theory, but such a finding suggests that policymakers wishing to alter meat
consumption (e.g., shift consumption away from certain meats towards
others) should develop policies that are meat specific. Furthermore, our
results suggest avenues for future research to uncover why such tendencies
are observed in the literature.
8
This positive trend in the income elasticity could be the result of (i) changes in consumer
preferences over time or (ii) later studies extending the results of earlier studies, thereby refining
income elasticity estimates.
Ó 2010 The Author
AJARE Ó 2010 Australian Agricultural and Resource Economics Society Inc. and Blackwell Publishing Asia Pty Ltd
Income elasticity of meat
489
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