Modelling the Determinants of
Domestic Egg Demand
A Report for the Rural Industries Research
and Development Corporation
by
Dr Edward Oczkowski
and
Mr Tom Murphy
Regional Economics Research Unit
Charles Sturt University
© 1998 Rural Industries Research and Development Corporation.
All rights reserved.
ISBN
ISSN
" Modelling the Determinants of Domestic Egg Demand"
The views expressed and the conclusions reached in this publication
are those of the author/s and not necessarily those of persons
consulted or the Rural Industries Research and Development
Corporation. RIRDC shall not be responsible in any way whatsoever
to any person who relies in whole, or in part, on the contents of this
report unless authorised in writing by the Managing Director of
RIRDC.
This publication is copyright. Apart from any fair dealing for the
purposes of research, study, criticism or review as permitted under
the Copyright Act 1968, no part may be reproduced in any form,
stored in a retrieval system or transmitted without the prior written
permission from the Rural Industries Research and Development
Corporation. Requests and inquiries concerning reproduction should
be directed to the Managing Director.
Researcher Contact Details
Dr Edward Oczkowski
School of Management and Regional Economics Research Unit
Charles Sturt University
PO Box 588
WAGGA WAGGA NSW 2678
Phone:
Fax:
email:
02 69332377
02 69332930
eoczkowski@csu.edu.au
RIRDC Contact Details
Rural Industries Research and Development Corporation
Level 1, AMA House
42 Macquarie Street
BARTON ACT 2600
PO Box 4776
KINGSTON ACT 2604
Phone:
Fax:
email:
Internet:
ii
02 6272 4539
02 6272 5877
rirdc@netinfo.com.au
http://www.dpie.gov.au/rirdc
Foreword
The Australian egg industry is well established with over 900 producers and a
gross production value of $280 million p.a. Even given the maturity of the industry, it
is surprising that very little research exists on what influences egg demand in
Australia. This report seeks to fill this void.
A comprehensive analysis of egg demand will help egg producers and
government policy makers make better informed marketing, planning and policy
decisions. This is particularly important given long-term declining trends in egg
consumption in Australia.
This project makes use of analytical tools from statistics and economics to
examine the determinants of egg demand for the five mainland states of Australia.
New insights into the main determinants of egg consumption are provided.
The report establishes quantitative measures of the impact of factors such as
egg prices, income, prices of related products, demographic variables and health
concerns on egg demand. The strategic and future implications of these measures are
described.
This report is part of RIRDC’s established industries program which seeks to
maximise the contribution of research and development to the profitability and
sustainability of established rural industries.
Peter Core
Managing Director
Rural Industries Research and Development Corporation
iii
Acknowledgments
We wish to acknowledge the assistance provided by the following people and
organisations for this project. Mr Graeme Bell (Corporate Services Manager) and Mr
John Bache (Managing Director) of Australian Quality Egg Farms. Mr Kevin Gent
(Manager-Regional Development) of Primary Industries & Resources South
Australia. Mr Harold Preston (General Manager-Finance & Administration) and Mr
Phillip Steel (General Manager) of Golden Egg Farms. Ms Christine Hoffman
(Personal Assistant to Managing Director) and Mr Phillip Osborne (Chief Executive
Officer) of The Egg Industry Cooperative Ltd. Mr Ian Peden (General Manager) of
the NSW Egg Producers Co-operative. Dr Jeff Davis of RIRDC. The NSW
Department of Agriculture. Mr Hugh McMaster of the Australian Egg Industry
Association supported this project from the outset and provided many important
insights into the industry. Hugh also made available to us his technical expertise
when requested, for this we are extremely grateful. Ms Julia Kirk from the Australian
Bureau of Statistics facilitated the timely collation of ABS secondary data. Ms Karen
Bell very efficiently provided research assistance in constructing the Cholesterol
publication index used in this report.
iv
Contents
Foreword
iii
Acknowledgments
iv
About the Authors
ix
Abbreviations
x
Executive Summary
xi
1.
Introduction
1
2.
Literature Review
3
2.1
Demand estimation
3
2.2
Demand for eggs
4
2.2.1
Price of eggs
4
2.2.2
Household income
5
2.2.3
Price of substitutes and complements for eggs
6
2.2.4
Demographic variables
6
2.2.5
Advertising expenditure
7
2.2.6
Cholesterol and changing consumer taste variables
8
3.
Methodology and Data
9
3.1
Interviews with egg industry representatives
9
3.2
Data and measurement issues
10
3.2.1
Egg production and sales
11
3.2.2
Prices of eggs and related products
13
3.2.3
Egg demand determinants
28
3.3
v
Regression modelling strategy
34
4.
5.
6.
Demand Estimates for Shell Egg Sales
37
4.1
New South Wales egg demand estimates
37
4.2
Victoria egg demand estimates
38
4.3
Queensland egg demand estimates
39
4.4
South Australia egg demand estimates
41
4.5
Western Australia egg demand estimates
42
Economic Implications of Egg Demand Estimates
45
5.1
Important egg demand factors: a statewise comparison
45
5.2
Cholesterol impact of egg demand
47
5.3
Advertising expenditure and egg demand
48
Conclusion
53
6.1
Main results and implications
53
6.2
Limitations of the study
55
Appendices
57
Appendix A: Data definitions and construction
57
A1:
Data definitions and sources
57
A2:
Data construction for shell egg sales
60
Appendix B: Regression test statistics
63
Glossary
65
References
67
Figures
3.1
Domestic Shell Egg Sales Per Capita
3.2
Real Egg Prices
3.3
New South Wales: Real Prices, Eggs and Substitutes
3.4
New South Wales: Real Prices, Eggs and Complements
3.5
Victoria: Real Prices, Eggs and Substitutes
3.6
Victoria: Real Prices, Eggs and Complements
3.7
Queensland: Real Prices, Eggs and Substitutes
12
14
15
16
18
19
21
vi
Figures
3.8
Queensland: Real Prices, Eggs and Complements
3.9
South Australia: Real Prices, Eggs and Substitutes
3.10 South Australia: Real Prices, Eggs and Complements
3.11 Western Australia: Real Prices, Eggs and Substitutes
3.12 Western Australia: Real Prices, Eggs and Complements
3.13 Worldwide Cholesterol Index
5.1
Victoria: Estimated Impact of State Advertising Expenditure
on Egg Sales
5.2
Victoria: Estimated Impact of State Advertising Expenditure
on Total Revenue
Tables
2.1
Own Price Elasticities of Demand for Eggs
2.2
Income Elasticities of Demand for Eggs
3.1
New South Wales: Correlations between Prices for Eggs and
Related Products
3.2
Victoria: Correlations between Prices for Eggs and
Related Products
3.3
Queensland: Correlations between Prices for Eggs and
Related Products
3.4
South Australia: Correlations between Prices for Eggs and
Related Products
3.5
Western Australia: Correlations between Prices for Eggs and
Related Products
3.6
New South Wales: Summary Data Statistics (1962/3-1995/6)
3.7
Victoria: Summary Data Statistics (1962/3-1992/3)
3.8
Queensland: Summary Data Statistics (1962/3-1995/6)
3.9
South Australia: Summary Data Statistics (1962/3-1995/6)
3.10 Western Australia: Summary Data Statistics (1962/3-1995/6)
4.1
New South Wales: Regression Estimates for Shell Eggs Sales
Per Capita
4.2
New South Wales: Estimated Elasticities for Shell Egg Sales
Per Capita
4.3
Victoria: Regression Estimates for Shell Eggs Sales Per Capita
4.4
Victoria: Estimated Elasticities for Shell Egg Sales Per Capita
4.5
Queensland: Regression Estimates for Shell Eggs Sales
Per Capita
4.6
Queensland: Estimated Elasticities for Shell Egg Sales
Per Capita
4.7
South Australia: Regression Estimates for Shell Eggs Sales
Per Capita
4.8
South Australia: Estimated Elasticities for Shell Egg Sales
Per Capita
4.9
Western Australia: Regression Estimates for Shell Eggs Sales
Per Capita
4.10 Western Australia: Estimated Elasticities for Shell Egg Sales
Per Capita
5.1
Statistically Important Elasticities of Shell Egg Sales
5.2
Cholesterol Impact on Shell Egg Sales Per Capita
vii
22
23
24
26
27
33
50
51
5
5
17
17
20
25
25
28
29
29
30
31
37
38
38
39
40
41
41
42
42
43
45
47
Tables
A.1
A.2
A.3
B.1
B.2
B.3
B.4
B.5
viii
ABS Total Egg Production Data
Estimated Domestic Egg Product Sales
Estimated Domestic Shell Egg Sales
New South Wales: Regression Test Statistics for Shell
Egg Sales Per Capita
Victoria: Regression Test Statistics for Shell Egg Sales
Per Capita
Queensland: Regression Test Statistics for Shell
Egg Sales Per Capita
South Australia: Regression Test Statistics for Shell
Egg Sales Per Capita
Western Australia: Regression Test Statistics for Shell
Egg Sales Per Capita
61
62
62
63
63
63
64
64
About the Authors
Edward Oczkowski, B.Ec. (LaT) M.Ec. (ANU) Ph.D. (LaT).
Eddie is a senior lecturer at Charles Sturt University and a deputy director of the
Regional Economics Research Unit. Eddie has extensive experience in the
econometric modelling of agricultural and resource markets, and has published
research in international agricultural economics journals on the following Australian
markets: tobacco leaf, premium table wines, coking coal and raw wool. Studies of
household demand behaviour have been carried out for Malaysia and PNG.
Numerous consultancies for businesses and government agencies have also been
completed.
Tom Murphy, B.Ec (Hons) (UNE) MSc (Lancaster).
Tom is the Director of the Regional Economics Research Unit at Charles Sturt
University. He has considerable experience in poultry industry research working with
Mr Terry Larkin and Dr Selwyn Heilbron on the RIRDC Cage Density Study (1993).
Tom is currently working on the Chicken Meat Bench Marking and the Integration of
the Egg and Processed Food and Food Service Industry studies. Tom has also
completed numerous other consultancy reports and published extensively in
economics journals.
ix
Abbreviations
ABARE
Australian Bureau of Agricultural and Resource Economics
ABS
Australian Bureau of Statistics
AEB
Australian Egg Board
AMLC
Australian Meat and Livestock Corporation
BAE
Bureau of Agricultural Economics
BS
Brown and Schrader
C.Qld
Central Queensland
CPI
Consumer price index
gm
grams
kg
kilograms
NSW
New South Wales
Qld
Queensland
RIRDC
Rural Industries Research and Development Corporation
S.Qld
Southern Queensland
SA
South Australia
UK
United Kingdom
US
United States
Vic
Victoria
WA
Western Australia
x
Executive summary
There is a clear lack of econometric research on egg demand in Australia. The
most recent comprehensive study is by Hickman (1979), this study was narrowly
price and income focused. A comprehensive modelling of the demand for eggs will
help us more clearly understand the markets for eggs and explicitly quantify how
changes in various factors influence the demand for eggs. The objectives of this
research are to provide quantitative measures of how changes in factors such as:
prices, incomes, population structure and dietary concerns; impact upon domestic egg
demand. The research strategy involves the use of secondary data and analytical
techniques from econometrics (i.e., combining economics and statistics), mainly
regression analysis.
Chapter two of this report reviews the relevant econometric literature on egg
demand. In reviewing the egg demand literature six classifications for the variables
seeking to explain demand for eggs are employed: 1) price of eggs; 2) household
income; 3) price of substitutes and complements for eggs; 4) demographic variables;
5) advertising expenditure; and 6) cholesterol and changing consumer taste variables.
Reported Australian egg price elasticities of demand range from -0.009 to -0.398
reflecting a low own price elasticity of demand for eggs in Australia. The estimates
do point to some differences in elasticities between states. For countries overseas egg
price elasticities appear to be generally more elastic than Australian estimates.
Reported Australian income elasticities of demand range from 0.00 to 1.43. In
general, low but positive income elasticities appear to be most common for the
Australian states. The main features of overseas estimates are that all income
elasticities are positive with the majority of overseas estimates being less than 0.6,
indicating a low income elasticity, which is broadly consistent with the Australian
estimates. In previous research only sausages have been found to be an important
substitute for egg demand in Australia. While in overseas research red meat prices
have been shown to have important substitute effects on U.S. egg demand.
Our literature review did not find any Australian studies assessing issues
relating to demographic, advertising or consumer taste variables. As a consequence
only results from overseas studies are cited. The review of literature identifies five
factors in overseas studies which may be important demographic determinants of egg
consumption: 1) women in the paid labour force; 2) age (older people may consume
fewer eggs); 3) income (high income families may consume less); 4) education (more
highly educated families may consume less); and 5) location (more urbanisation less
egg consumption). In assessing the impact of advertising expenditure on egg demand
Hallam (1986) alludes to U.K. evidence which found no relation between advertising
expenditure and egg consumption during the 1970s. However, Chyc and Goddard
(1994) for Canada and Schmit, Reberte and Kaiser (1997) for California find some
statistically important advertising elasticities for egg demand. In examining changing
consumer tastes, Brown and Schrader (1990) found for U.S. that both a declining time
trend and a cholesterol publication index to measure health concerns, to be important.
xi
In chapter three the methods and data to be employed in analysing egg
demand in Australia are discussed. Our discussions with various egg industry
representatives suggest that egg complements are mainly those products used in
conjunction with eggs when cooking, while substitutes are foods high in protein.
Numerous long-term demand factors were identified, including: vegetarianism, the
growing importance of fast-foods and health concerns about cholesterol. Finally,
industry representatives expressed concerns about the accuracy of ABS egg
production data, suggesting ABS figures maybe significant underestimates.
The availability of data dictates that statistical data analysis can only be
meaningfully performed for the five mainland states. Unfortunately, due data
unavailability an analysis of egg demand which incorporates state advertising
expenditure can only be performed for Victoria and Western Australia. For most
states, the analysis of data covers the period 1962/3-1995/6. For the four deregulated
mainland states and recent years (from the late 1980s) egg sales data does not directly
exist, as a consequence a disposal series mainly based on ABS agricultural census
data was employed. Based on these estimates and earlier data, domestic shell egg
sales per capita for most states appears to follow a flat inverted U shaped pattern. In
general, sales increased up until the mid-1970s and have generally fallen ever since.
Data on the demand determinants is described for the previously cited six
broad classifications. In general, real retail egg prices have declined significantly
overtime. In terms of annual growth rates, the percentage growth decline varies from
about 2% for Qld to 3% for NSW. Comparing 1962/3 to 95/6 the real price falls are:
NSW (-50%), Vic (-46%), Qld (-37%), SA (-44%) and WA (-48%). In assessing the
egg demand impact of related products our coverage is more extensive than previous
studies. For substitutes we consider breakfast cereal, potatoes and sausages, and for
complements bacon, flour and sugar. For substitutes, prices have generally risen in
real terms, this compares to the substantial egg price falls. For complements prices
have tended to fall like egg prices. In most cases these falls have not matched egg
price falls. Confirming these trends, all the large (greater than 0.7) correlations with
egg prices are favourable for egg demand, that is, the substitute prices are negatively
correlated with egg prices and the complement prices are positively correlated with
egg prices.
The other non-price demand determinants exhibit the following historical
trends. Real household disposable income per capita has consistently grown for all
states with annual growth rates varying from 0.6% for WA to 1.6% for NSW. The
percentage of employed females to the female working population for each state will
be employed to capture the ‘busier’ lifestyles of households over our time period.
This variable has consistently grown over-time with annual growth rates varying from
0.8% for Vic to 1.5% for Qld. As a measure of the ageing population we employ the
median age, this variable has annual growth rates varying from 0.56% for NSW and
Vic to 0.81% for SA. For a measure of urbanisation we make use of the capital city
percentage of a state’s population. Growth in this variable has been negligible except
for WA with an annual growth rate of 0.45%. State egg advertising expenditure data
is assessed for Vic and WA only. However, some national egg advertising
expenditure carried out the late 1980s is assessed for it’s impact on all sates, as is
advertising expenditure committed on the rival red meat products as pursued by the
AMLC. For assessing the impact of health concerns on egg demand we constructed
xii
two cholesterol publication indexes. The worldwide cholesterol index exhibits an
annual growth rate of 22%, however, from the late 1980s the index’s growth has
tapered off somewhat. The Australian (place of publication) cholesterol index’s
growth rate is 18%, with the first article appearing in 1974/5 and the number of
articles summing to 58 in 1995/6.
Chapter three concludes with an outline of the regression modelling strategy to
be employed in the study. We follow the regression modelling approach attributed to
Hendry (1995), but with modifications. Rather than let the entire modelling process
be driven by the nature of the data alone we impose some of the theoretical
expectations on the choice of the preferred model specifications. The modelling
emphasis is on diagnostic testing. By confronting the preferred specification to a
wide variety of tests we are trying to ensure that the results obtained and their
economic implications are valid and reliable. The modelling strategy leads to
parsimonious regression models, which omit statistically unimportant demand
determinants from the preferred demand model.
Chapter four presents the preferred regression estimates and demand
elasticities for the five mainland states. For NSW demand estimates suggest that egg
prices are unimportant. Income and the female workforce have strong positive
influences. The result for the female workforce variable reflects the increased
demand for take-away-foods which use eggs. The ageing of the population appears to
have some minor negative impact. The cholesterol index points to substantial health
concerns in reducing egg demand. A structural change in this variable indicates
however, that from 1990/1 the marginal impact of the cholesterol index has fallen
significantly. Interestingly, none of the prices of related products proved to be
important for NSW egg demand.
For Vic demand estimates suggest that egg prices are unimportant. Income
and state advertising have strong positive influences. Sausages appear to be mildly
important substitutes, while flour appears to be an important complement. The
cholesterol index points to substantial health concerns in reducing egg demand.
Unlike NSW and Vic, for Qld demand estimates suggest that egg prices are
important. For Qld, income, female workforce and national egg advertising have
strong positive influences. The price of flour indicates significant complementary
with egg demand. The cholesterol index again points to substantial health concerns in
reducing egg demand.
Similar to only Qld, SA demand estimates suggest that egg prices are
important. Income and female workforce have strong positive influences. The price
of sausages indicates significant substitutability with egg demand. The cholesterol
index points to substantial health concerns in reducing egg demand. Again, a
structural change in this variable indicates however, that from 1990/1 the marginal
impact of the cholesterol index has fallen significantly. Finally, WA demand
estimates suggest that egg prices are unimportant. Income and female workforce have
strong positive influences. The price of sugar indicates significant complementary
with egg demand. The cholesterol index points to substantial health concerns in
reducing egg demand. The structural change in this variable indicates however, that
from 1990/1 the marginal impact of the cholesterol index has fallen somewhat.
xiii
For all states, when the simple Hickman
specification is applied to the egg demand data,
emerge and estimated elasticities make little sense
elasticities are estimated). All this clearly points
important significant demand determinants.
(1979) price and income only
significant regression problems
(many significant positive price
to the fallacy of ignoring other
Chapter five examines the regression results in more detail and in particular
looks at their economic implications. Initially, a statewise comparison of elasticities
is made. This is followed by a detailed analysis of cholesterol and advertising
impacts on egg demand. The price elasticity of egg demand is effectively zero for
NSW, Vic and WA. For the other two states statistically important egg price impacts
result but estimates are still highly inelastic: Qld (-0.16) and SA (-0.24). Thus despite
substantial (up to 50%) real price falls over our 34 year data analysis time-period, in
general, changes in the egg price have had only a small overall influence on egg
demand.
Household disposable income was strongly positively statistically
significant for all states. For all states income elasticity appears to be increasing overtime. At the sample means of all data, all estimates are inelastic. The state estimates
can be broadly grouped into low elasticity (NSW 0.29, SA 0.40 and WA 0.32) and
high elasticity (Vic 0.80 and Qld 0.91).
In terms of the price of related products, of the six prices considered only three
prices proved to be important and not consistently for all states. Only one substitute
proved to be important, the price of sausages was important for Vic and SA. Two
complements were identified to be important. The price of flour was important for
both Vic and Qld, while the price of sugar proved to be important for WA only. It is
interesting to note that our results arise even given the identified price trends, where
prices of complements tended to fall with egg prices, while prices of substitutes
tended to rise when egg prices fell.
The female workforce variable was strongly significant and positive for all
states except Vic. The positive findings suggest that as more women go into paid
employment more take-away foods are consumed which use eggs and this outweighs
any negative effect due to women having less time for home egg cooking and baking.
This positive impact appears to be increasing over-time for all states and the elasticity
is greatest for WA. The median age variable proved to be important for NSW only.
The fact that the age variable was only important for NSW suggests that it was ‘overpowered’ by the highly correlated cholesterol index. Essentially, for most states the
cholesterol index appears to be also capturing the fact that the ageing of the
population is leading to fewer egg sales. The capital city population variable was
clearly unimportant, a cross-section study maybe more useful for capturing this
urbanisation effect.
One of the most important identified demand determinants is the impact of
health concerns on egg demand. Based on data covering 1975/6-95/6 the estimated
impact of the worldwide cholesterol publication index on egg sales differs
significantly between the states with three meaningful groupings: 1) SA has the
largest impact (-27.4% of sales), 2) Qld (-19.3%), 3) NSW (-11.5%), Vic (-12.7%)
and WA (-14.4%) have similar and the smallest impacts. In absolute terms the
percentage impacts appear to be large but they are also reasonably consistent with
results from the U.S.
xiv
State egg advertising expenditure was examined for only Vic and WA, and
was found to be important for Vic only. The three years of national egg advertising
expenditure was examined for all states but found to be important only for Qld.
Advertising expenditure on rival meat products pursued by the AMLC proved to have
no significant impact on egg demand. The average annual impact of national
advertising on Qld egg sales was 2.9% of sales or 0.28 dozen eggs per capita. In
terms of total gross retail revenue this implies an average annual revenue of 62.4 cents
per capita in 1989/90 dollars, and compares to the average advertising expenditure of
4.6 cents per capita. More generally, the national advertising Qld results imply that
every extra cent of expenditure per capita, generates 0.060 dozen sales per capita or
13 cents of total gross retail revenue per capita.
The average annual impact of state advertising on Vic egg sales was 15.9% of
sales or 1.498 dozen eggs per capita. In terms of total gross retail revenue this implies
an average annual revenue of $4.42 per capita in 1989/90 dollars, and compares to the
average advertising expenditure of 45 cents per capita. More generally, the state
advertising Vic results imply that every extra cent of expenditure per capita, generates
0.033 dozen sales per capita or 9.7 cents of total gross retail revenue per capita. In
terms of the advertising impact on the percentage of egg sales, results indicate a
general increasing trend in the importance of advertising over-time. The impact starts
at about 15% of sales in the early 1960s and finishes by contributing to about 30% of
sales in the early 1990s. The trend in the advertising impact on total retail revenue is
less obvious with the estimates exhibiting significant variability. The extremes are a
low of about $2 per capita in 1972/3 and a high of about $8 per capita in 1982/3.
Chapter six concludes with a summary of the main results with some possible
strategy and future implications outlined. The study’s limitations are also described.
The strategic implication of the general finding of highly inelastic egg price
elasticities, is that future price changes of the magnitude experienced over the last
thirty years will not significantly alter egg demand. In particular, price rises will lead
to total revenue gains, while price falls to total revenue losses. Likely future rises in
egg sales per capita p.a. due to rising household incomes are: NSW 0.57%, Vic
1.72%, Qld 1.00%, SA 0.65% and WA 0.79%. Likely future rises in egg sales per
capita p.a. due to female workforce changes are: NSW 0.98%, Qld 0.84%, SA 1.33%
and WA 2.37%. Likely future falls in egg sales per capita p.a. due to future medical
publications on cholesterol are: NSW 2.5%, Vic 3.6%, Qld 4.8%, SA 5.3% and WA
3.1%. The mixed results on state advertising expenditure (Vic important, WA
unimportant) do not suggest that advertising expenditure will always have a positive
effect on egg sales. However, the results do also imply that advertising can have a
significant influence on sales and this is particularly important given the health
concerns the population appears to have about egg consumption in general. The main
limitations of this study relate to the quality of data on egg sales, the lack of
comprehensive advertising expenditure data and the employed regression modelling
strategy.
xv
xvi
1.
Introduction
There is a clear lack of econometric research on egg demand in Australia. The
most recent comprehensive study is by Hickman (1979), this study was narrowly
price and income focused. A comprehensive modelling of the demand for eggs will
help us more clearly understand the markets for eggs and explicitly quantify how
changes in various factors influence the demand for eggs. These demand measures
would be relevant to market decisions by producers in the industry, government
policy and as key input to other egg industry studies. The industry could use this
information to accentuate the positive factors, mitigate the negative factors and ignore
the unimportant factors in an effort to improve the sales of eggs. The objectives of this
research are to provide quantitative measures of how changes in factors such as:
prices, incomes, population structure and dietary concerns; impact upon domestic egg
demand. The research strategy involves the use of secondary data and analytical
techniques from econometrics (i.e., combining economics and statistics).
The attained information from research into modelling egg demand can be an
important input into egg industry strategic planning. The estimates could prove
valuable for addressing various issues such as: should the industry 'induce' and
encourage price reductions and what impact would such reductions have on industry
revenue? If the identified changes in egg demand are found to be principally due to
price and income factors should the industry use resources on advertising/promotion
in an effort to alter tastes/preferences? Moreover, the output of the research in the
form of elasticity measures is often a key component of research into the industry
(e.g. The RIRDC project: Economic Impact of Proposed Revised Cage Densities on
the Australian Egg Industry 1993). The beneficiaries of the research will be the egg
industry which currently consists of 900 producers and has a gross production value
of $280 million p.a. For an illuminating description of the egg industry, see
Australian Egg Marketing Council (1989).
The plan of the report is as follows. In chapter two we review the relevant
econometric literature on egg demand. Both Australian and international studies are
surveyed. This review will help us isolate what data should be collected for the
demand determinants in modelling egg demand.
Chapter three outlines the methodology and data to be employed in the
analysis. Initially, the chapter summarises our discussions with egg industry
representatives and their perceptions of data quality and egg demand determinants.
The data collected from industry and the Australian Bureau of Statistics (ABS), on
these demand determinants are described in detail in section 3.2. Given the data
description, chapter three concludes with a discussion of the regression modelling
strategy to be followed in deriving the preferred egg demand regression equations for
each state.
Chapter four presents the preferred regression egg demand equations for each
state. Regression coefficient estimates, their statistical significance, elasticities and
associated diagnostic test statistics are presented and compared to previous studies.
Chapter five discusses some of the economic implications of the regression
results in more detail. Initially, a comparison of the statistically important elasticities
1
across the states is provided. The interpretation of regression coefficients is also
discussed. In section 5.2, we focus upon the results specific to health concerns and
cholesterol and how they impact on egg demand. Finally, section 5.3 examines the
impact of advertising expenditure on egg demand in detail.
In chapter six we conclude with an outline of the main results and their
strategic and future implications for the industry. The limitations of the study are also
provided. Note, a glossary defining some technical terms used in describing
regression results is provided at the end of this report.
2
2.
Literature Review
In this chapter we review the relevant literature. In section 2.1 a general
discussion of demand estimation is provided. In section 2.2 the literature specific to
egg demand available in both Australia and internationally is reviewed. This review
establishes the framework for our estimation of egg demand in Australia.
2.1
Demand estimation
One of the main areas of empirical research in economics is that of demand
estimation. There are two general approaches to demand estimation: market research
and econometric. The market research approach is a direct demand estimation
procedure and involves consumer interviews and market experiments. These methods
provide extensive current and realistic guides to consumer behaviour and allow
researchers to gain rich demographic information on consumers’ tastes and
preferences. However, market research approaches are very resource intensive and
expensive, and ignore changes in uncontrollable and long-term demand factors. In
contrast, the econometric approach typically makes use of historical data and employs
statistical regression analysis to measure the relationship between demand and its
determinants. The indirect econometric approach lacks the currency and richness of
direct procedures but is inexpensive, focuses on longer term issues and factors for
which market experiments cannot control. For more detail on these two approaches,
see Hirschey and Pappas (1996, ch. 6). In this report our focus is on econometric
demand studies.
Econometric demand estimation involves the use of regression analysis using
time-series and/or cross-section data to measure the relationship between the demand
for a product and the factors which are hypothesised to influence that demand.
Economic theory is used to guide the choice of independent variables (factors) which
are assumed to influence demand, see Hirschey and Pappas (1996, ch. 5) for a useful
discussion of economic demand theory. These demand factors include: price of the
product, household income, prices of substitutes and complements, demographics,
changes in consumer tastes and advertising expenditure.
Key outcomes from an estimated demand equation are unit free measures of
the responsiveness of demand to the independent factors, i.e., elasticities. That is, for
a given percentage change in a demand factor, the demand elasticity will measure
how much quantity demanded is expected to change in percentage terms (assuming all
other factors do not change). If an estimated elasticity is less than unity the demand
response is said to be inelastic, that is, a percentage change in the independent
demand factor results in a smaller percentage change in the demand for the product.
Alternatively, if the elasticity exceeds unity the demand response is said to be elastic,
that is, a percentage change in the independent factor results in a larger percentage
change in the demand for the product. An illustrative example of the usefulness of
elasticities relates to the price elasticity of demand and changes in producers’ total
revenue, see Hirschey and Pappas (1996, pp186-7). If price elasticity is elastic then a
price increase results in a total revenue decrease. Alternatively, if price elasticity is
inelastic then a price increase results in a total revenue increase. Elasticities are
important for policy development, planning and forecasting purposes.
3
In general there are two main approaches to regression demand estimation:
single demand equations and systems of demand equations, see Intriligator, Bodkin
and Hsiao (1996, ch.7) for a discussion of the differences between the approaches.
Single demand equations focus on the demand for one product in isolation and tend to
specify important individual factors such as demographics and advertising in
substantial detail. Demand systems estimate equations for a whole series of products
simultaneously and employ economic theory to recognise the linkages which exist
theoretically between the estimated equations. Demand systems tend to focus on
price and income variables only and place more emphasis on the relationships
between products rather than on individual products. Numerous demand studies have
been conducted for various foodstuffs in Australia (e.g., bread, fish), for a survey see
MacAulay, Niksic and Wright (1990). Demand systems have been estimated for
various countries and commodity types, for a general recent survey see Clements,
Selvanathan and Selvanathan (1996).
2.2
Demand for eggs
In this section we review some of the literature which has reported results on
the econometric demand for eggs in particular. Australian and non-Australian studies
are examined. The reviewed results come from a mixture of methodological
approaches including both, demand for eggs in isolation and egg demand estimates
from demand systems. The reported studies employ both time-series and crosssection data. We will categorise results in terms of the individual demand
determinants (factors) used in the various demand studies. Six classifications for the
variables seeking to explain demand for eggs are employed: 1) price of eggs; 2)
household income; 3) price of substitutes and complements for eggs; 4) demographic
variables; 5) advertising expenditure; and 6) cholesterol and changing consumer taste
variables.
2.2.1 Price of eggs
Economic theory predicts an inverse relationship between the price of a
product and its own demand. In other words, as the price of eggs increases we expect
a fall in the quantity of eggs demanded, if all other factors are held constant. The
theoretical expectation is that the price elasticity of demand for eggs is very small and
inelastic (less than unity) given that eggs are considered to be a basic foodstuff.
Reported Australian own price elasticities of demand are presented in table
2.1. In summary, estimates range from -0.009 to -0.398 confirming the low own price
elasticity of demand for eggs in Australia. Moreover, these estimates do point to
some differences in elasticities between states.
Interestingly, this general pattern of low price demand elasticities for eggs
does not exist throughout the world. Examples of non-Australian egg price
elasticities are: Chavas and Johnson (1981) U.S. -0.34; Burney and Akmal (1991)
Pakistan, urban (range -0.17 to -0.49), rural (range -0.15 to -0.96); Wu, Li and Samuel
(1995) urban China -0.47; Brown and Schrader (1990) U.S. -0.17; Chyc and Goddard
(1994) Canada (range -0.85 to -0.89); and Schmit, Reberte and Kaiser (1997)
4
California -1.7. The Californian elasticity of -1.7 is unusually large and there appear
to be unique responsible factors, i.e., egg prices were 40% higher than national prices
during the estimation time period. In general, the main features of these results are
that all elasticities are negative but wide ranging going from -0.15 to -1.7. Leaving
out the Californian -1.7, all estimates however are inelastic. Even so, these overseas
estimates appear to be generally more elastic than Australian estimates.
Study
Time Period
AUST
NSW
VIC
QLD
SA
WA
TABLE: 2.1
Own Price Elasticities of Demand for Eggs
Banks et.al
Gruen et. al.
Hickman (1979) Collard et.al.
(1966)
(1967)
(1982)
53/4-62/3
52/3-63/4
66(3)-77(4)
73(Jul)-79(Dec)
-0.013
-0.009
-0.306
-0.27
-0.295
-0.068
-0.32
-0.398
2.2.2 Household income
Economic theory classifies products into two types according to the income
elasticity of demand. Products with negative income elasticity are inferior goods.
Very simple foods such as beans and potatoes are often classified as inferior.
Products with positive income elasticities are normal goods. The majority of products
are normal. Being a basic food stuff (rather than a luxury) the expectation is for a low
but positive income elasticity for eggs.
Reported Australian income elasticities of demand are presented in table 2.2.
In summary, estimates range from 0.00 to 1.43. The Banks et.al. (1966) 1.43 estimate
appears to be unusually high and at variance with the other studies. Collard et.al.
(1982) failed to report an estimate for Vic as the income variable was found to be
statistically unimportant. In general, low but positive income elasticities appear to be
most common. Again however, these estimates do point to some differences in
elasticities between states.
Study
Time Period
AUST
NSW
VIC
QLD
SA
WA
5
TABLE: 2.2
Income Elasticities of Demand for Eggs
Banks et.al
Gruen et. al.
Hickman (1979) Collard et.al.
(1966)
(1967)
(1982)
53/4-62/3
52/3-63/4
66(3)-77(4)
73(Jul)-79(Dec)
0.2
0.212
0.065
0.00
0.621
0.606
1.43
0.491
Once again there appears to be greater variability in income elasticity
estimates in other countries. Examples of non-Australian egg income elasticities are:
Burney and Akmal (1991) Pakistan, urban (range 0.36 to 0.90), rural (range 0.24 to
2.31); Wu, Li and Samuel (1995) urban China 0.21; Brown and Schrader (1990) U.S.
0.35; Chyc and Goddard (1994) Canada (range 0.293 to 0.723); and Schmit, Reberte
and Kaiser (1997) California 0.07(income elasticity to price). The main features of
these results are that all elasticities are positive but wide ranging going from 0.07 to
2.31. In general the majority of overseas estimates are less than 0.6 indicating a low
income elasticity, which is broadly consistent with the Australian estimates.
2.2.3 Price of substitutes and complements for eggs
Economic theory suggests that the price of related products should appear in a
demand equation. Products which are substitutes have a positive cross price
elasticity, that is, as the price of the substitute increases the demand for eggs is
expected to increase also. By reverse argument products which are complementary to
eggs will have a negative cross price elasticity of demand.
It appears that the only Australian study to explicitly consider substitutes and
complements is Collard, Ryan and Alston (1982). For Victoria they found that bacon
had a -0.07 cross-price elasticity with eggs, this implies it is a complement. However,
the price of bacon variable was statistically unimportant in the estimated demand
equation. The price of sausages had a 0.09 cross-price elasticity which implies it is a
substitute. Sausage prices were statistically important in explaining egg demand.
Sausages were viewed to be a cheap red meat and hence an alternative cheap source
of protein.
There is only slightly more empirical evidence on egg complements and
substitutes in other countries. Chavas and Johnson (1981) for the U.S. found
important wholesale shell egg price elasticities with respect to beef prices (0.49) and
pork (0.13), implying some substitutability. Brown and Schrader (1990) for the U.S.
found a cross price elasticity of 0.102 for red meat implying significant
substitutability. Schmit, Reberte and Kaiser (1997) for California found a cross price
elasticity (to egg prices) of 0.20 for red meat again implying substitutability.
However, for California both the prices of cereals and bakery products were found to
be statistically unimportant related products.
2.2.4 Demographic variables
In many demand studies for various products different demographic factors
have been found to be important in explaining demand variations. There appears to
be no econometric demand study which has systematically included demographic
factors in an analysis of the demand for eggs for Australia. There are however some
overseas studies we can allude to.
A key demographic factor used in a time series context relates to the
percentage of women in the labour force. Two opposing arguments have led to its
consideration in demand for egg equations. First, as more women go into the labour
force they have less time for home egg cooking and baking and hence demand falls.
Second, as more women go into the labour force more take-away breakfasts are
6
consumed thereby increasing overall egg consumption. In their preferred demand
equation Brown and Schrader (1990) for the U.S., found the women in labour force
variable to be statistically unimportant but slightly positive, implying that the two
opposing effects may have cancelled each other out. Schmit, Reberte and Kaiser
(1997) for California found women in the labour force to have a statistically important
positive impact on demand indicating that a greater number of eat-away breakfasts
increases overall egg demand.
Other overseas demand studies which only in part, have examined egg
demand, focus on demographic differences and employ cross section data. Cortez and
Senauer (1996) examined U.S. consumer taste changes between two cross-section
surveys 1980 and 1990. Overall a negative taste change for eggs was identified. The
largest shift away from eggs was by households with high incomes, older household
heads and better educated spouses. In contrast, a positive preference change for eggs
was identified for lower income households with younger household heads and less
educated spouses. Yen, Jensen and Wang (1996) examining U.S. cross section
surveys between 1989-1991 found declining egg consumption due to higher education
levels, increasing urban population and an older population.
2.2.5 Advertising expenditure
There is an intuitive expectation that greater levels of advertising expenditure
on a product lead to higher levels of demand for that product. There appear to be no
econometric studies which have examined this link for Australia. Again however, we
can discuss some overseas studies which have analysed the link between advertising
expenditure and the demand for eggs.
Hallam (1986) comments on some previous U.K. evidence which found no
relation between advertising expenditure and egg consumption during the 1970s.
Hallam’s main criticism however, is about how the U.K. egg authority actually
pursued its advertising campaign. To this extent the U.K. evidence does not suggest
that advertising expenditure per se does not influence egg consumption but rather
authorities must spend advertising budgets thoughtfully and effectively to improve
egg demand.
Chyc and Goddard (1994) for Canada and the period 1974-1989 found
advertising elasticities for demand ranging from 0.00005 to 0.012. The advertising
variables were statistically important in the demand equations, but these elasticities
are low. The general finding of this Canadian study is that producers should invest
more in advertising than they currently do. Schmit, Reberte and Kaiser (1997) for
California (1985-1995) found a much greater advertising expenditure impact on egg
demand. The estimated long-run elasticity of advertising expenditure on price is 0.13.
In terms of producer profits, this translates to a marginal rate of return to advertising
of 6.9. In other words, each additional dollar spent on advertising generated $6.90 in
producer profits.
7
2.2.6 Cholesterol and changing consumer taste variables
There exists substantial evidence to suggest that egg demand per capita has
declined over time given the concerns of consumers that high egg consumption results
in high cholesterol levels, which in turn may have detrimental health effects. Survey
evidence in Australia exists to support this change in consumer tastes. Dobson et.al.
(1997) reports on the differences between two cross-section dietary surveys (1983 and
1994) carried out in the Hunter region of NSW. Results indicated that comparing
consumption in 1994 to 1983: 8% more consumers rarely consumed any eggs per
week, 18% more consumers consume 1-2 eggs per week, 18% fewer consumers
consume 3-5 eggs per week and 8% fewer consumers consume 5 or more eggs per
week. The reduced overall intake of cholesterol has also been well documented in
Australia. Crawford and Baghurst (1990) report on a survey undertaken in 1988
which suggests that 42% of women and 36% of men have reduced their intake of
cholesterol. In terms of the age profile of respondents, 55% of people 50 and over,
40% of people 35-49 and 30% of people 18-34 have reduced their cholesterol intake.
Finally, a survey study by Henson (1996) carried out in the U.K. for 1993 makes the
link between reduced egg consumption and cholesterol concerns. Of all respondents
42.1% decreased their egg consumption significantly in recent years. The main
reasons for this decreased consumption were reported as: 48.8% cholesterol content;
22.5% conditions of hens; and 19.7% food poisoning.
In terms of econometric demand for eggs equations only studies for overseas
countries exist. Using 1989 survey cross-section data for the U.S., Wang, Jensen and
Yen (1996) found that individuals who are aware of the associated cholesterol health
risks are less likely to consume eggs and those individuals who do consume eggs
consume fewer eggs. In a historical time-series data context two approaches have
been employed to econometrically cater for these changing tastes. The first approach
incorporates a time trend variable in the demand equation assuming the decline in
consumption follows some type of smooth pattern. The second approach employs a
cholesterol information index where the number of articles in medical journals
relating cholesterol to heart disease and arteriosclerosis is counted and summed
overtime to measure the public’s awareness of the health concerns about cholesterol
intake.
Brown and Schrader (1990) analysing U.S. egg consumption from 1955-1987,
used both a time trend and a cholesterol index to measure changing tastes. The
preferred regression equation found that both variables were statistically important.
The time trend variable implied a downward trend of 0.6% p.a. in consumption. The
cholesterol index implied a decrease in per capita consumption of 16% up to the first
quarter of 1987, if the entire period 1955-1987 is analysed. If the period 1966-1987 is
analysed a 25% decline in per capita consumption is estimated. In contrast, the study
by Schmit, Reberte and Kaiser (1997) for California covering 1985-1995 did not find
cholesterol indexes to be statistically important, but did find a declining logarithmic
trend to be important. The former result could be due to the short time period
analysed.
8
3. Methodology and Data
In this chapter the methods and data to be employed in analysing egg demand in
Australia are discussed. Initially, in section 3.1 we describe our discussion with
various egg industry representatives and make comment with regard to egg demand in
particular. Based on the literature review and industry discussions, section 3.2
describes in detail the data to be employed in the analysis. Section 3.2 looks at three
groups of variables: 3.2.1 examines egg sales data, 3.2.2 describes egg prices and
prices of related products, and 3.2.3 looks at the non-price egg demand determinants
data. Finally, section 3.3 outlines the regression modelling strategy to be employed in
this study to model egg demand.
3.1
Interviews with egg industry representatives
We interviewed key egg industry representatives in New South Wales,
Victoria, Queensland, South Australia and Tasmania in order to obtain the available
industry data on sales and advertising expenditure. Some of the interviewees were
also asked about their impressions of the main determinants of egg demand.
It was considered that the major complements of eggs are flour, sugar, bacon
and bread, reflecting the fact that more than half of all egg usage is in cooking. The
major substitutes for eggs are all proteins, such as meat, fish, poultry, dairy products
and pulses.
It was suggested that over time factors that have affected egg demand include: the
proportion of protein in the diet; vegetarianism (can have a positive or negative
effect); growing importance of fast food outlets; health concerns about cholesterol;
and the changing trends in the dietary habits of the Australian population, such as the
rise and fall in popularity of foods such as omelettes, quiche, pavlova etc.
Health concerns about the high level of cholesterol in eggs were given
considerable publicity in the 1970s. However the industry view is that by the mid1980s these concerns had declined. In the 1990s health professionals advise that a
moderate intake of eggs is a good health choice for most of the population.
According to an industry source only 13% of consumers still have concerns about
cholesterol in eggs.
Interviewees explained that egg demand has been subject to significant changes in
the meal cycle in recent decades. Breakfast has declined as a cooked meal, with
cereals becoming the primary breakfast food. As a consequence, only about 10% of
households now eat eggs for breakfast. Traditional main meal favourites such as steak
or chops and eggs have given way to pasta dishes and stir fries. Multiculturalism has
no doubt played a part in this, as new cuisines have been introduced and adopted
enthusiastically, particularly by younger consumers. There has also been a rising trend
to incorporate new styles of cuisine into the family meal cycle. Where once the
typical family meal cycle consisted of five or six meals in rotation, it is not unusual
now for the meal cycle to stretch to up to twenty different meals. The rapid
development of the fast food industry was regarded as having a major impact on egg
demand.
9
Finally, industry representatives expressed concern about the accuracy of
Australian Bureau of Statistics egg production data, considering it a significant
underestimate.
3.2
Data and measurement issues
This section describes the data to be employed for the analysis of egg demand.
Specific details about data definitions, sources and construction are provided in
appendix A. Based on the review of literature and discussions with egg industry
representatives data on numerous variables has been collected to facilitate the
econometric estimation of equations for the demand for eggs in Australia. The
availability of industry data on domestic shell egg sales and advertising expenditure,
and ABS (Australian Bureau of Statistics) data on other demand factors, determines
the level of aggregation and time-frame for the estimated equations.
We initially focussed our attention upon the six Australian states, leaving out
the territories given the smallness of their population base. However, Tasmania also
had to be excluded from the analysis since egg sales data appears not to have been
collected historically. The annual reports of the Australian egg board (AEB) and the
Bureau of Agricultural Economics (BAE) do not report historical egg sales figures for
Tasmania. For the remaining five mainland states obtaining reliable data on recent
egg sales figures and advertising expenditure posed most difficulties. Egg sales data
has not been systematically collected for all states since the late 1980s because of
deregulation in some states. As a consequence we were forced to construct our own
egg sales figures from available egg production, export and import data (see appendix
A.2 for details).
Unfortunately, state advertising expenditure data is available for long
historical time periods (more than ten years) for only Victoria (1962/3-92/3) and
Western Australia (1962/3-1995/6). As a consequence an analysis of the influence of
advertising expenditure on egg demand can only be performed for these two states.
Our data analysis does indicate that advertising expenditure did have an important
impact on egg demand in Victoria. As a consequence the time period 1962/3-92/3 (31
years) is employed for analysing egg demand behaviour for Victoria. For the
remaining four mainland states data covering 1962/3-1995/6 (34 years) is analysed. It
appears that for all the potentially important demand determinants only annual data is
available. Given our longer term focus this need not overly concern us and it also
avoids the need to address issues of seasonality in the data.
3.2.1 Egg production and sales
Our focus is upon egg demand within Australia. Given that egg export
demand is largely determined by different international factors, then exports are
ignored in our analysis. Using the most reliable available recent Australian data
ABARE (1989, covering 1980/1-86/7), total exports make up only about 7% of total
commercial egg Australian production. Further, given that egg product consumption
(liquid and dried) appears also to be driven by different factors to shell demand, we
also abstract from egg product demand. Based on recent available data (80/1-86/7)
domestic commercial product sales make up about 11% of total Australian
commercial egg production. Our focus therefore rests with domestic shell
10
consumption which accounts for approximately 82% of total Australian commercial
egg production during 1980/1-86/7. Note, imports of shell eggs for human
consumption are legally prohibited.
In focusing upon domestic shell egg consumption a distinction between
consumption from commercial producers and backyard (self-suppliers) producers
must be made. Unfortunately data from backyard production is far too unreliable and
aggregated to be used in this study. The ABS as part of their ‘apparent consumption
of foodstuffs’ publication (ABS Cat no. 4306.0) have made some estimates of
backyard production for Australia only and in recent years without great accuracy.
Annual data for backyard production exists for 1962/3-83/4, on average for this
period backyard production is estimated to be about 56% of commercial egg
production levels. However, in recent years, data is very sparse. Based on a single
survey conducted in 1992, for the years 1987/8 - 95/6 backyard production is assumed
to be 14% of commercial egg production. Given these obvious data inadequacies our
focus rests purely with domestic commercial egg sales. Clearly however, these sales
underestimate total consumption of shell eggs within Australia. The results which
follow should be interpreted with this in mind.
Consistent with all previous egg demand studies and with studies conducted
generally for food demand, per capita demand measures will be employed using
population levels from the various states. The use of shell egg sales per capita helps
avoids econometric problems such as excessive multicollineaity (as both population
and income would become independent variables) and heteroscedasticity (as the error
variance typically gets larger for higher absolute consumption levels). Further, the
use of per capita data allows for an easier and more meaningful comparison between
states. Finally, sales data reflects all shell egg sales, dis-aggregated data on sales of
different egg sizes are not available for significant time-periods for any of the states.
Figure 3.1 illustrates the historical time-series behaviour of shell egg sales per
capita for three of the five mainland sates. Data for Vic and WA is not published by
request. All states exhibit a similar flat inverted U shaped pattern. In general, sales
increased up until the mid-1970s and have generally fallen ever since. The peaks in
sales occur at reasonably similar times for all states : NSW (peak 1972/3 = 12.4 dozen
per capita), Qld (1973/4 = 10.7) and SA (1973/4 = 10.6). Comparing 1962/3 to
1995/6 long-term increases in sales have occurred in Qld (55%), with falls for NSW (3.2%) and SA (-4.4%). Qld exhibits the greatest relative variability in its sales. In
more recent times (90/1-95/6), NSW (9.9 dozen per capita) appears to have the largest
consumption level with the other two states having lower levels, Qld (8.5) and SA
(7.6).
11
12
3.2.2 Prices of eggs and related products
The ABS average retail price of eggs is employed for each state. Two
alternatives will be used in regressions. First, using the standard weight as defined by
the various ABS surveys, labelled (ABS). Second, standardising all prices to a 55gm
weight for all states and years, labelled (55gm). Even though the latter measure may
technically be more appropriate as it standardises across states and time, the former
measure may be useful as it may better reflect what was actually purchased at a given
point in time in a given state. It better reflects the standard as perceived by the
consumer. Annual prices are averages of the four quarters. To nett out inflation, real
egg prices are deflated by the respective states’ CPI (consumer price index).
Figure 3.2 illustrates the time-series behaviour of real (1989/90 dollars)
standardised retail egg prices for the states. In general, real egg prices have declined
significantly overtime. In terms of annual growth rates, the percentage growth decline
varies from about 2% for Qld to 3% for NSW. Comparing 1962/3 to 95/6 the real
price falls are: NSW (-50%), Vic (-46%), Qld (-37%), SA (-44%) and WA (-48%).
There appears to be some significant variability in prices with larger than normal
price falls in the late 1960s and price rises in more recent times, 1995/6. NSW has the
greatest relative variability in its prices. There are some substantial differences in egg
prices between states, for example in 1995/6 Queensland’s prices are 29% higher than
those in South Australia.
Even though only two prices of related products have been found to be
statistically important in previous studies, i.e., sausages and meat prices, we have
extended the collection of data on prices of related products based on our industry
discussions. The final choice of retail prices is also dependent upon the availability of
long-term consistent ABS historical data for the various products. For substitutes we
will consider: breakfast cereal, potatoes and sausages. For complements we will
consider: bacon, flour and sugar. Annual prices are quarterly averages and are
deflated by the CPI using 1989/90 dollars.
In figures 3.3 and 3.4, and table 3.1 graphical and correlation data is presented
on egg and related product real prices for New South Wales. The figures present data
in index form to facilitate the price comparisons. For substitutes figure 3.3 suggests
that substitutes generally have higher prices than 1962/3 levels. Potato prices are very
volatile. These price rises contrast to the price falls in eggs. For complements figure
3.4 suggests that all prices, except for bacon prices, have generally been lower than
their 1962/3 levels. Even given these falls, the fall in egg prices appears to be the
largest.
In terms of the correlation data in table 3.1, the two different egg price series
are highly correlated as expected, this is also the case for the other states. For
correlations and all states, we make comment only about large correlations, those
greater than 0.7 in absolute terms. The strongest correlations with egg prices are with
cereal (-0.79) and sugar (0.79), both implying favourable egg demand influences.
Ignoring the significance of correlations, egg prices are positively correlated with all
three complements and two substitutes, and negatively correlated with one substitute.
High correlations between the other prices occur for cereal and bacon (-0.73) and
bacon and sausages (0.73).
13
14
15
16
TABLE: 3.1
New South Wales: Correlations between Prices for Eggs and Related Products
Prices
Eggs (ABS)
Eggs (ABS)
Eggs (55 gm)
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
0.998
-0.764
0.118
0.369
0.502
0.272
0.809
Eggs
(55 gm)
1.000
-0.790
0.140
0.389
0.524
0.234
0.790
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
-0.237
-0.596
-0.731
0.305
-0.399
1.000
0.369
0.193
-0.265
0.045
1.000
0.733
-0.540
0.243
1.000
-0.470
0.188
1.000
0.517
1.000
Correlations based on real 1989/90 prices covering: 1962/3-95/6
In figures 3.5 and 3.6, and table 3.2 graphical and correlation data is presented
on egg and related product real prices for Victoria. A similar pattern emerges to
NSW. For substitutes figure 3.5 suggests that substitutes generally have higher prices
than 1962/3 levels. For complements figure 3.6 suggests that all prices, except for
bacon prices, have generally been lower than their 1962/3 levels. Again of all prices
eggs exhibit the largest falls.
In terms of the correlation data table 3.2, once again the strongest correlations
with egg prices are with cereal (-0.72) and sugar (0.76). Ignoring the significance of
correlations, egg prices are positively correlated with two complements and one
substitute, and negatively correlated with two substitutes and one complement. High
correlations between the other prices only occur for cereal and bacon (-0.83).
TABLE: 3.2
Victoria: Correlations between Prices for Eggs and Related Products
Prices
Eggs (ABS)
Eggs (ABS)
Eggs (55 gm)
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
0.999
-0.699
0.013
-0.117
0.471
-0.266
0.777
Eggs
(55 gm)
1.000
-0.718
0.017
-0.108
0.495
-0.287
0.756
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
0.007
-0.185
-0.831
0.514
-0.358
1.000
0.302
0.021
-0.337
0.060
1.000
0.447
-0.553
-0.039
1.000
-0.664
0.097
1.000
0.096
1.000
Correlations based on real 1989/90 prices covering: 1962/3-92/3
17
18
19
In figures 3.7 and 3.8, and table 3.3 graphical and correlation data is presented
on egg and related product real prices for Queensland. A slightly different pattern
emerges for Qld compared to NSW and Vic. For substitutes figure 3.7 suggests that
substitutes generally have higher prices than 1962/3 levels in line with NSW and Vic.
However, for complements figure 3.8 suggests a slightly different pattern, here all
prices, including bacon prices, have generally been lower than their 1962/3 levels.
Also sugar prices here appear to generally match the egg price falls.
In terms of the correlation data in table 3.3, the strongest correlations with egg
prices now occur with bacon (0.74) and sugar (0.76), both once again implying
favourable egg demand influences. Ignoring the significance of correlations, egg
prices are positively correlated with all three complements and one substitute, and
negatively correlated with two substitutes. High correlations between the other prices
only occur for flour and sugar (0.71).
TABLE: 3.3
Queensland: Correlations between Prices for Eggs and Related Products
Prices
Eggs (ABS)
Eggs (ABS)
Eggs (55 gm)
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
0.998
-0.668
0.161
-0.464
0.751
0.459
0.774
Eggs
(55 gm)
1.000
-0.648
0.174
-0.476
0.738
0.452
0.762
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
-0.236
-0.028
-0.647
0.115
-0.348
1.000
0.243
0.198
-0.358
-0.028
1.000
-0.086
-0.506
-0.305
1.000
0.139
0.573
1.000
0.714
1.000
Correlations based on real 1989/90 prices covering: 1962/3-95/6
In figures 3.9 and 3.10, and table 3.4 graphical and correlation data is
presented on egg and related product real prices for South Australia. For substitutes
figure 3.9 suggests that substitutes generally have higher prices than 1962/3 levels,
this contrasts to the lower egg prices. Here potato prices appear to be much higher
compared to other states. For complements figure 3.10 suggests that all prices,
excluding bacon prices, have generally been lower than their 1962/3 levels. Bacon
prices are relatively higher than most other states. In this case, sugar prices appear to
have generally fallen to a larger extent than egg prices.
In terms of the correlation data in table 3.4, three strong correlations with egg
prices now occur: cereal (-0.90), flour (0.81) and sugar (0.77); again all implying
favourable egg demand influences. Ignoring the significance of correlations, egg
prices are positively correlated with all three complements and one substitute, and
negatively correlated with two substitutes. High correlations between the other prices
occur for cereal and bacon (-0.73), and cereal and flour (-0.74).
20
21
22
23
24
TABLE: 3.4
South Australia: Correlations between Prices for Eggs and Related Products
Prices
Eggs (ABS)
Eggs (ABS)
Eggs (55 gm)
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
0.999
-0.881
-0.032
0.086
0.5392
0.809
0.789
Eggs
(55 gm)
1.000
-0.896
-0.005
0.115
0.569
0.808
0.766
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
-0.138
-0.321
-0.731
-0.741
-0.568
1.000
0.591
0.352
-0.070
-0.169
1.000
0.627
0.023
-0.084
1.000
0.348
0.075
1.000
0.689
1.000
Correlations based on real 1989/90 prices covering: 1962/3-95/6
In figures 3.11 and 3.12, and table 3.5 graphical and correlation data is
presented on egg and related product real prices for Western Australia. For
substitutes figure 3.11 suggests that substitutes generally have higher prices than
1962/3 levels, this contrasts to the lower egg prices. Here however, substitutes prices
are less volatile than other states. For complements figure 3.12 suggests that all
prices, excluding bacon prices, have generally been lower than their 1962/3 levels.
Similar only to SA bacon prices are relatively high here. Unlike SA, for WA egg
price falls generally appear to be the largest.
In terms of the correlation data in table 3.5, strong correlations with egg prices
occur with potatoes (-0.72) and flour (0.87); again implying favourable egg demand
influences. Ignoring the significance of correlations, egg prices are positively
correlated with two complements and negatively correlated with all three substitutes
and one complement. High correlations between the other prices occur for potatoes
and sugar (-0.77) only.
TABLE: 3.5
Western Australia: Correlations between Prices for Eggs and Related Products
Prices
Eggs (ABS)
Eggs (ABS)
Eggs (55 gm)
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
0.999
-0.468
-0.724
-0.450
-0.059
0.861
0.630
Eggs
(55 gm)
1.000
-0.454
-0.718
-0.459
-0.059
0.867
0.625
Cereal
Potatoes
Sausages
Bacon
Flour
Sugar
1.000
0.125
-0.299
-0.220
-0.452
0.133
1.000
0.517
0.273
-0.615
-0.766
1.000
0.543
-0.539
-0.455
1.000
-0.174
-0.266
1.000
0.415
1.000
Correlations based on real 1989/90 prices covering: 1962/3-95/6
In summary, many similarities between state prices exist, but differences also
prevail. In general, complements prices have tended to fall like egg prices. In most
cases these falls have not matched egg price falls. Bacon prices tend to be the
exception to this rule with bacon prices rising most strongly in SA and WA. For
substitutes, prices have generally risen in real terms, this compares to the substantial
egg price falls. The most significant feature here is the large volatility in potato
prices. Confirming these trends, all the large (greater than 0.7) correlations with
egg prices are favourable for egg demand, that is, the substitute prices are negatively
correlated with egg prices and the complement prices are positively correlated with
egg prices.
25
26
27
3.2.3 Egg demand determinants
Based on the literature review, discussion with industry representatives and
the availability of suitable data, other non-price information has been collated to help
model egg demand. These determinants fall into four major groups: 1) household
income, 2) demographic variables, 3) advertising expenditure, and 4) changing taste
variables. The summary data for all variables (including sales and prices) and all
states are presented in tables 3.6 thru 3.10. In addition to means and standard
deviation, correlations with egg sales and trend growth rates are also provided.
Household disposable income for each state will be employed. The measure
will be in per capita and real terms using population levels and the CPI. The income
variable has consistently grown for all states with real annual growth rates varying
from 0.6% for WA to 1.6% for NSW. Note, that with the exception of NSW all
pairwise correlations with egg sales are positive for income.
The review of literature identified five factors in overseas studies which may
be important demographic determinants of egg consumption: 1) women in the paid
labour force; 2) age (older people may consume fewer eggs); 3) income (high income
families may consume less); 4) education (more highly educated families may
consume less); and 5) location (more urbanisation less egg consumption). In our time
series context only changes in these demographic characteristics can be modelled. In
other words only changes in the proportion of the population with certain
characteristics can be employed to help explain any variations in egg consumption.
To this extent of use of demographic variables is restricted compared to the typical
cross-section data study.
TABLE: 3.6
New South Wales: Summary Data Statistics (1962/3-1995/6)*
Variable
Egg Sales
Egg Price (ABS)
Egg Price (55gm)
Income
Cereal Price
Potatoes Price
Sausages Price
Bacon Price
Flour Price
Sugar Price
Female Workforce
Age
Capital City Population
State Advertising
National Advertising*
AMLC Advertising
Cholesterol Index
Australian Cholesterol
Mean
11.041
281.44
276.76
12.547
217.85
92.463
348.14
295.53
244.38
208.04
40.851
30.62
62.10
na
0.047
0.460
1035.0
12.382
Standard
Deviation
0.86
85.8
82.7
2.06
31.9
18.2
48.7
36.5
22.5
38.1
3.89
1.88
1.56
na
0.05
0.47
1335.7
2.79
Correlation Trend Growth
with Egg Sales
% p.a.
1.00
-0.42
0.42
-3.01
0.46
-3.01
-0.15
1.62
-0.74
1.12
0.23
-0.20
0.43
-0.50
0.42
-0.64
-0.33
-0.16
0.09
-1.30
-0.50
0.90
-0.81
0.56
0.29
0.13
na
na
-0.94
85.75
-0.67
13.80
-0.76
22.39
-0.75
18.21
* National advertising summary data is based on three years (86/7-88/9) of non-zero data only. na – not available.
28
ABS data on the percentage of employed females to the female working
population for each state will be employed to capture the changing role of women in
society over our time period. This is a better measure than labour force rates as nonlabour force participation does not preclude home baking. In other words, our
measure is a better one of females’ availability to use eggs in the home. The
summary data suggest that this variable has consistently grown over-time with annual
growth rates varying from 0.8% for Vic to 1.5% for Qld.
TABLE: 3.7
Victoria: Summary Data Statistics (1962/3-1992/3)*
Variable
Egg Sales
Egg Price (ABS)
Egg Price (55gm)
Income
Cereal Price
Potatoes Price
Sausages Price
Bacon Price
Flour Price
Sugar Price
Female Workforce
Age
Capital City Population
State Advertising
National Advertising*
AMLC Advertising
Cholesterol Index
Australian Cholesterol
Mean
9.455
294.83
290.95
12.42
215.60
89.08
393.71
327.22
256.54
203.74
42.12
29.71
70.65
0.453
0.047
0.392
740.13
8.612
Standard
Deviation
1.17
79.5
75.4
1.78
23.2
21.1
44.7
40.0
17.3
34.2
3.56
1.73
1.22
0.23
0.05
0.43
966.4
11.01
Correlation Trend Growth
with Egg Sales
% p.a.
1.00
np
-0.47
-3.00
-0.45
-2.89
0.72
1.45
-0.08
0.79
-0.03
0.03
0.61
0.25
0.39
-0.61
-0.41
0.15
-0.54
-1.42
0.40
0.84
0.17
0.56
0.76
0.16
0.44
np
-0.33
85.75
-0.04
15.12
0.125
23.86
0.165
19.52
* National advertising summary data is based on three years (86/7-88/9) of non-zero data only. np – not for publication.
TABLE: 3.8
Queensland: Summary Data Statistics (1962/3-1995/6)*
Variable
Egg Sales
Egg Price (ABS)
Egg Price (55gm)
Income
Cereal Price
Potatoes Price
Sausages Price
Bacon Price
Flour Price
Sugar Price
Female Workforce
Age
Capital City Population
State Advertising
National Advertising*
AMLC Advertising
Cholesterol Index
Australian Cholesterol
Mean
8.987
278.94
278.86
11.093
213.56
87.06
394.55
292.01
210.33
196.23
39.12
29.19
46.82
na
0.047
0.460
1035.0
12.382
Standard
Deviation
1.46
67.22
59.62
1.43
30.20
19.25
51.41
29.56
16.00
34.90
6.01
2.04
0.96
na
0.05
0.47
1335.7
2.79
Correlation Trend Growth
with Egg Sales
% p.a.
1.00
0.73
-0.46
-2.32
-0.47
-2.02
0.71
1.04
-0.17
0.98
0.05
-0.16
0.42
0.56
-0.15
-0.78
-0.77
-0.32
-0.56
-1.33
0.28
1.52
-0.05
0.64
0.76
-0.04
na
na
0.79
85.75
-0.09
13.80
-0.06
22.39
-0.07
18.21
* National advertising summary data is based on three years (86/7-88/9) of non-zero data only. na – not available.
29
There has been a significant ageing of the Australian population in recent
years and this can be captured by using the median age of the population in each state.
Again there is consistent growth in this variable with annual growth rates varying
from 0.56% for NSW and Vic to 0.81% for SA.
Reliable and consistent historical data is difficult to obtain for the Australian
states on education and relative income levels. Given the time-period under
consideration large variations in the education and relative income levels may not
exist and therefore may not in any event impact significantly on egg consumption.
TABLE: 3.9
South Australia: Summary Data Statistics (1962/3-1995/6)*
Variable
Egg Sales
Egg Price (ABS)
Egg Price (55gm)
Income
Cereal Price
Potatoes Price
Sausages Price
Bacon Price
Flour Price
Sugar Price
Female Workforce
Age
Capital City Population
State Advertising
National Advertising*
AMLC Advertising
Cholesterol Index
Australian Cholesterol
Mean
8.482
287.60
284.35
11.85
211.30
88.00
363.82
290.61
205.99
199.30
41.37
30.43
72.01
na
0.047
0.460
1035.0
12.382
Standard
Deviation
1.11
72.98
69.20
1.28
24.82
22.99
50.32
31.15
17.05
35.32
4.17
2.62
1.49
na
0.05
0.47
1335.7
2.79
Correlation Trend Growth
with Egg Sales
% p.a.
1.00
-0.08
0.11
-2.61
0.14
-2.49
0.38
0.77
-0.45
0.96
0.31
0.41
0.52
0.06
0.65
-0.45
0.03
-0.68
-0.20
-1.43
0.10
0.93
-0.36
0.81
0.31
0.19
na
na
0.32
85.75
-0.33
13.80
-0.40
22.39
-0.39
18.21
* National advertising summary data is based on three years (86/7-88/9) of non-zero data only. na – not available.
We are able to obtain a measure of urbanisation. We will employ the
percentage of people in the capital city of a state to the total state population. To
some extent this measure may capture some education and relative income changes
overtime. Growth rates with the capital city percentage have been negligible except
for WA with an annual growth rate of 0.45%. Another outstanding feature is Qld’s
significantly lower average capital city percentage of 47%, which compares to the
60% and 70%s of the other states.
The only other significant change in the structure of the Australian population
in recent years, has been the increasing proportion of people who were born in East
Asia. There appears to be evidence to suggest that Asian immigrants may have
different dietary habits to traditional white Australians (see MacAulay, Niksic and
Wright, 1990, pp281-2). However, data at a state level on birthplace appears to be
only available at five-yearly census dates. Further there appears to be evidence to
suggest that Asian egg consumption patterns are not significantly different from nonAsian consumption patterns (Department of Community Services and Health, 1983).
As a consequence this study will not examine the influence of an increasing Asian
birthplace population on egg consumption.
30
TABLE: 3.10
Western Australia: Summary Data Statistics (1962/3-1995/6)*
Variable
Egg Sales
Egg Price (ABS)
Egg Price (55gm)
Income
Cereal Price
Potatoes Price
Sausages Price
Bacon Price
Flour Price
Sugar Price
Female Workforce
Age
Capital City Population
State Advertising
National Advertising*
AMLC Advertising
Cholesterol Index
Australian Cholesterol
Mean
10.255
284.00
283.06
12.34
257.32
95.34
349.67
279.07
225.74
204.91
42.15
28.70
70.13
0.197
0.047
0.460
1035.0
12.382
Standard
Deviation
0.92
82.68
74.98
1.20
28.03
12.12
34.86
14.59
28.62
29.34
5.91
2.27
3.27
0.15
0.05
0.47
1335.7
2.79
Correlation Trend Growth
with Egg Sales
% p.a.
1.00
np
-0.43
-2.95
-0.43
-2.65
0.58
0.61
-0.19
0.54
0.56
0.94
0.29
0.44
0.09
0.05
-0.32
-1.09
-0.67
-0.81
0.52
1.41
0.07
0.74
0.61
0.45
0.37
np
-0.75
85.75
0.14
13.80
0.03
22.39
0.03
18.21
* National advertising summary data is based on three years (86/7-88/9) of non-zero data only. np – not for publication..
The literature review does indicate that advertising expenditure may be an
important determinant of egg consumption. As previously stated, only data from
Victoria and Western Australia is available at a state level for analysis. Beyond state
egg advertising expenditure two other related variables are employed in the analysis.
During a three year period (1986/7-88/9) the Australian Egg Marketing Council
undertook some national egg advertising and promotion, this national advertising will
be employed for all states in the regression analysis. Further, as with related product
prices the impact of substitute products through advertising should also be examined.
The market-place is competitive and to totally ignore rival products and their
advertising efforts may be erroneous. To this extent domestic advertising expenditure
on red meat products (previous research has found red meat to be an important egg
substitute) committed by the Australian Meat and Livestock Corporation (AMLC)
will also be employed in the data analysis. All advertising expenditure variables will
be converted to a per capita basis (using the relevant population) and deflated to real
terms using the relevant CPI. State advertising variables for both Vic and WA have
mild positive correlations with egg sales. Advertising expenditure committed by the
AMLC has grown at an annual rate of 13.8%.
Clearly, measuring changing consumer tastes is of fundamental importance to
this study. The previously employed approaches to incorporating taste changes will
be investigated. First, various time trends and structural breaks in these trends will be
examined for their statistical importance in the estimated equations.
Second, following Brown and Schrader (BS) (1990) we have employed the
Medline medical database to construct a cholesterol information index. We have both
updated and modified the BS index. The keyword search used cholesterol and heart
disease or arteriosclerosis. Like BS we summed cumulatively over-time the number
of articles which had these keywords in either the title or abstract. However, unlike
31
BS we were unable to distinguish between articles supporting the link between
cholesterol and heart disease and those questioning the link. It is not clear to us how
this can be done consistently and reliably. In some cases abstracts were ambiguous
and/or truncated so as to make no logical sense. In any event the proportion of
articles questioning the link is probably so small (BS suggest 4% of all U.S. articles
up to 1987) that not differentiating between articles will not cause serious
measurement error problems. We have constructed two different indexes. Unlike BS
who only used U.S. articles, in one index we employ all articles in the database, this
assumes that Australian doctors and other health professionals may access literature
from all over the world. The second index is of Australian published articles only.
To account for the typical delay in the uptake of information, like BS a six month lag
is employed in constructing the index.
Figure 3.13 illustrates the worldwide cholesterol index. The estimated annual
growth rate in this index is 22%, however, the figure also illustrates that from the late
1980s the index’s growth has tapered off somewhat. For the worldwide index the first
relevant article appeared in 1965/6 and the index tops 4475 articles in 1995/6. For
Australian articles the growth rate is 18%, with the first article appearing in 1974/5
and the number of articles summing to 58 in 1995/6. Interestingly, only for NSW and
SA are these two indexes strongly pairwise negatively correlated with egg sales.
Finally in this outline of the data, to discuss issues of excessive
multicollinearity which may impact on the regression results, we make some
comment on the pattern of high correlations between the demand determinants. First,
the worldwide and Australian cholesterol indexes are nearly perfectly correlated (r =
0.995), and so only one index is needed for the analysis. Second, for all sates high
correlations appear to group the following variables: age, cholesterol index, female
workforce, cereal prices, egg prices and AMLC advertising. The highest correlations
occur for: age and cholesterol (over 0.95), age and AMLC advertising (over 0.95),
cholesterol and AMLC advertising (0.92), female workforce and egg prices (over –
0.87). Of all these variables the greatest concern is with is the age variable, we
suspect that the regression analysis may not be able to accurately distinguish the
individual impact of age on egg sales. The final comment about correlations is that,
Western Australia stands out slightly from the other states, as having fewer strong
correlations between its demand determinants.
32
33
3.3
Regression modelling strategy
Econometric modelling using regression techniques is clearly an art. Different
researchers use different modelling philosophies to arrive at a preferred regression
model given a data set, for a discussion see Gujarati (1995, chs 13-14). This comment
about the lack of a universally accepted procedure for deriving a preferred regression
also applies to estimating a demand function. The results from this report should be
interpreted with this caution in mind.
For numerous reasons we follow the regression modelling approach attributed
to Hendry (1995), but with modifications. Modelling from the general-to-specific is
pursued. That is, all the independent variables hypothesised to be theoretically
important are initially included in the model and then the model is reduced using tests
of significance, diagnostic checks, model selection statistics and checks with theory
consistency. This process leads to a more parsimonious model to be chosen as the
preferred specification. In choosing between models which appear to be equally
attractive, the eight model selection statistics discussed in Ramanathan (1998, pp 1645) are applied, to aid the ultimate final model choice. These statistics, which include
the adjusted R 2 , provide different measures of goodness of fit which account for
model complexity using different principles of optimality. When all model selection
statistics are employed a comprehensive indication of the relative performance of
models is provided.
The preferred model is then subject to a battery of diagnostic tests (see
appendix B) to ensure that the preferred equation adequately conforms to the
assumptions which underpin regression analysis. As a further check of the preferred
model we will attempt to re-include the previously omitted variables to investigate
whether these omitted variables significantly alter any conclusions reached.
Rather than let the entire modelling process be driven by the nature of the data
alone we impose some of the theoretical expectations developed in section 3.2
explicitly on the modelling process. Irrespective of their statistical significance in the
regression equations, the egg price, household income and cholesterol index variables
will always be retained in all specifications. Standard consumer demand theory
presents an overwhelming case for retaining the former two variables and the
literature on egg consumption patterns worldwide establishes a compelling case for
always retaining the cholesterol index. It appears that no other variables can mount
similarly strong cases for unconditional inclusion in the preferred specification. With
regard to the other hypothesised demand factors, these are only included if they
contribute importantly to the explanation of variations in egg demand according to
theoretical expectations. In other words, statistically significant results which are
counter to theoretical expectations are ignored and discounted as due to data
measurement, specification error or spurious correlation causes. In general our
modelling approach therefore consists of both theoretical and empirically driven
considerations.
The emphasis on diagnostic checking in the Hendry approach is particularly
appealing. By confronting the preferred specification to a wide variety of checks we
are trying to ensure that the results obtained and their economic implications are
reliable. Effectively only good strong robust models will pass these series of checks
34
and hence may be of some use to practitioners. We concur with Beggs’ (1988, p 83)
view of diagnostic testing, ‘the more [diagnostic] testing performed the greater the
chance of finding cause to reject a model, and hence the more difficult it is to present
results which purport to offer an adequate quantitative representation of economic
reality to policy makers.’ The diagnostic testing of models also overcomes some of
the criticism of the type of process researchers use to reach their preferred model.
The Hendry argument is that the way you discover or arrive at the final model in no
way impinges upon the value of the model selected. It is argued, that it matters not
whether you thought of your model ‘in your bath’ or via a complex set of procedures,
the specific discovery process does not affect the intrinsic validity of the preferred
specification. The validity of the preferred model can be checked via the diagnostic
tests. These diagnostic tests and the tests for coefficient significance should be
interpreted as conditional tests, conditional on the preferred model estimated and not
conditional on all models estimated.
35
36
4. Demand Estimates for Shell Egg Sales
This chapter presents the regression estimates and elasticities for the five
mainland states in sections 4.1 thru 4.5. Only estimates for the final preferred models
are presented, these preferred models are derived according to the strategy outlined in
section 3.3. The diagnostic test statistics which accompany these regressions appear
in appendix B. Three general comments which apply to all states can be made. First,
the preliminary modelling clearly points to the better performance of the worldwide
cholesterol index over the Australian cholesterol index. As a consequence the
worldwide index is used and retained throughout for all states. Second, for the
changing consumer taste variables, time trends and structural changes in these time
trends proved to be theoretically and empirically unsuccessful. As a consequence
these time trend variables are omitted from all equations. Third, structural change
however, is reflected via a change in the cholesterol impact on egg sales. Three
structural breaks were considered, at 1980/1, 1985/6 and 1990/1. Only the latter
structural break in cholesterol’s impact on egg demand proved to be important.
4.1
New South Wales egg demand estimates
Table 4.1 presents the regression egg demand estimates for New South Wales.
The accompanying diagnostic test statistics are presented in table B.1 (appendix B).
When interpreting these results recall that data on state advertising expenditure is not
available for NSW. In terms of the diagnostic tests no statistics are significant at the
5% level and thus the model appears to be relatively free of non-normality,
heteroscedasticity, specification error and auto-correlation problems. The model
explains 93% of the total variation in shell egg sales.
The model with 55gm egg prices outperformed the model with ABS egg
prices in terms of model selection statistics, 8 vs 0. Even though the age variable may
appear to be mildly insignificant (p value = 0.163), it is retained because of its better
performance in model selection statistics compared to a model with its exclusion.
None of the variables omitted from this model were remotely significant when reentered individually in the preferred model, the price of potatoes had the highest tratio for inclusion of –1.11.
TABLE: 4.1
New South Wales: Regression Estimates for Shell Egg Sales Per Capita
Variable
Coefficient*
T- Ratio
P-Value
10.332
1.53
0.138
Constant
0.0018
0.80
0.430
Egg Price (55gm)
0.257*
3.73
0.001
Income
0.124*
2.28
0.030
Female Workforce
-0.239
-1.44
0.163
Age
-0.00089*
-2.47
0.020
Cholesterol Index
0.00023
1.81
0.081
Cholesterol*(90/1-95/6)
* Denotes significant at a 5% level of significance. Results based on 1962/3–95/6, N = 34.
Demand estimates suggest that egg prices are unimportant. Income and the
female workforce have strong positive influences. The result for the female
workforce variable reflects the increased demand for take-away-foods which use
eggs. The ageing of the population appears to have some minor negative impact. The
37
cholesterol index points to substantial health concerns in reducing egg demand. The
structural change in this variable indicates however, that from 1990/1 the marginal
impact of the cholesterol index has fallen significantly. Interestingly, none of the
prices of related products proved to be important for NSW egg demand.
The resulting elasticity estimates from the preferred model are presented in
table 4.2. Elasticities are presented for average data for four time periods: pre 1980,
the 1980s, post 1990 and the whole sample. Clearly, price elasticity is effectively
zero, this finding is similar to Hickman (1979). Income elasticity is inelastic but
increasing over-time. The 0.25 income elasticity estimate for the pre - 1980 period is
similar to Hickman’s (1979) estimate of 0.21 for a reasonably similar time period,
however our most recent estimate is a much higher 0.38. The female workforce and
age elasticities are all inelastic but increasing in magnitude over-time. The
importance of the cholesterol index also increases significantly over-time.
TABLE: 4.2
New South Wales: Estimated Elasticities for Shell Egg Sales Per Capita
Variable
Egg Price (55gm)
Income
Female Workforce
Age
Cholesterol Index
1962/3-79/80
0.053
0.248
0.411
-0.606
-0.007
1980/1-89/90
0.038
0.323
0.485
-0.695
-0.103
1990/1-95/6
0.031
0.382
0.591
-0.817
-0.235
1962/3-95/6
0.046
0.292
0.460
-0.663
-0.076
Finally, we note that when the simple Hickman (1979) price and income only
specification is applied to the NSW data significant auto-correlation problems emerge
with the Durbin Watson statistic being 0.69. The goodness of fit falls to R 2 0.51,
and the price and income elasticities become 0.42 and 0.61 respectively. All this
clearly points to the fallacy of ignoring other important significant demand
determinants.
4.2
Victoria egg demand estimates
Table 4.3 presents the regression egg demand estimates for Victoria. The
accompanying diagnostic test statistics are presented in table B.2 (appendix B).
Recall for Victoria, state advertising expenditure is available but for a slightly shorter
time period, 1962/3-1992/3. In terms of the diagnostic tests no statistics are
significant at the 5% level and thus the model appears to be relatively free of nonnormality, heteroscedasticity, specification error and auto-correlation problems. The
model explains 93% of the total variation in shell egg sales.
TABLE: 4.3
Victoria: Regression Estimates for Shell Egg Sales Per Capita
Variable
Coefficient*
T- Ratio
P-Value
2.0488
0.83
0..414
Constant
0.0014
0.51
0.612
Egg Price (ABS)
0.608*
7.78
0.000
Income
0.0029
1.47
0.154
Sausages Price
-0.0095
-1.86
0.075
Flour Price
3.306*
6.31
0.000
State Advertising
-0.00101*
-7.01
0.000
Cholesterol Index
* Denotes significant at a 5% level of significance. Results based on 1962/3-92/3, N = 31.
38
In contrast to NSW, the model with ABS egg prices outperformed the model
with 55gm egg prices in terms of model selection statistics, 8 vs 0. Even though the
prices of flour (p value = 0.075) and sausages (p value = 0.154) appear to be mildly
insignificant their exclusion worsened model selection statistics and diagnostic tests,
for example, specification error becomes a problem. As a consequence both variables
are retained. Of the variables omitted from this model some were individually
significant when re-entered as single variables, but all these variables had signs
inconsistent with theoretical expectations and hence were excluded. Significant tratios existed for: potato prices –2.74, bacon prices 2.28, and the capital city
percentage 2.52. Further, the AMLC and national egg advertising expenditure
variables either individually or jointly proved to be unimportant in the model.
Demand estimates suggest that egg prices are unimportant. Income and state
advertising have strong positive influences. Sausages appear to be mildly important
substitutes, while flour appears to be an important complement. The cholesterol index
points to substantial health concerns in reducing egg demand.
The resulting elasticity estimates from the preferred model are presented in
table 4.4. Clearly, once again price elasticity is effectively zero, this finding is not
similar to Hickman (1979) –0.31 and Collard et.al. (1982) –0.27. Income elasticity is
inelastic but increasing over-time. The 0.8 income elasticity estimate for the entire
sample is substantially larger that the zero estimates produced by Hickman (1979) and
Collard et.al. (1982). The cross-price elasticity of sausages is about 0.12 and constant
over time, this is very similar to the Collard et.al. (1982) 0.09 estimate. The crossprice elasticity of flour is a relatively constant and important –0.26. The importance
of both advertising expenditure and the cholesterol index also increases significantly
over-time.
TABLE: 4.4
Victoria: Estimated Elasticities for Shell Egg Sales Per Capita
Variable
Egg Price (ABS)
Income
Sausages Price
Flour Price
State Advertising
Cholesterol Index
1962/3-79/80
0.054
0.773
0.124
-0.272
0.102
-0.009
1980/1-89/90
0.031
0.799
0.114
-0.238
0.216
-0.124
1990/1-92/3
0.026
0.971
0.123
-0.293
0.284
-0.336
1962/3-92/3
0.042
0.799
0.120
-0.258
0.159
-0.079
Finally, we note that when the simple Hickman (1979) price and income only
specification is applied to the Vic data significant specification error and autocorrelation problems emerge with the RESET(2) test producing F(1,27) = 8.32. The
goodness of fit falls to R 2 0.57, and the price and income elasticities become 0.20
and 0.94 respectively. Once again, all this clearly points to the fallacy of ignoring
other important significant demand determinants.
4.3
Queensland egg demand estimates
Table 4.5 presents the regression egg demand estimates for Queensland. The
accompanying diagnostic test statistics are presented in table B.3 (appendix B). When
interpreting these results recall that data on state advertising expenditure is not
available for Qld. In terms of the diagnostic tests the lack of state advertising data
39
appears to have profound effects. No matter what combination of variables were
employed in modelling Qld egg demand, significant specification error problems were
encountered with resulting highly significant RESET test statistics. The fact that the
national advertising variable proved to be important in the preferred specification
implies that maybe the omission of a state advertising variable is the cause of the
specification error problems for Qld. As a consequence all the results for Qld should
be interpreted with some degree of caution. Even though all three RESET test
statistics are significant at the 5% level, the preferred model appears to be relatively
free of the other problems of non-normality, heteroscedasticity and auto-correlation.
The model explains 96% of the total variation in shell egg sales.
TABLE: 4.5
Queensland: Regression Estimates for Shell Egg Sales Per Capita
Variable
Coefficient*
T- Ratio
P-Value
4.476
1.50
0.146
Constant
-0.0051
-1.93
0.064
Egg Price (55gm)
0.739*
11.25
0.000
Income
-0.022*
-4.39
0.000
Flour Price
0.087
1.85
0.075
Female Workforce
5.965
1.93
0.064
National Advertising
-0.0011*
-7.59
0.000
Cholesterol Index
* Denotes significant at a 5% level of significance. Results based on 1962/3-95/6, N = 34.
The model with 55gm egg prices outperformed the model with ABS egg
prices in terms of model selection statistics, 8 vs 0. Even though the female
workforce (p value = 0.075) and national advertising (p value = 0.064) variables may
appear to be mildly insignificant, they are retained because of their better performance
in model selection statistics. Only one of the variables omitted from this model was
significant when individually re-entered, the price of potatoes t = -3.12 had a
theoretically meaningless sign and hence was excluded.
Unlike NSW and Vic, for Qld demand estimates suggest that egg prices are
important. Income, female workforce and national egg advertising have strong
positive influences. Again the result for the female workforce variable reflects the
increased demand for take-away-foods which use eggs. The price of flour indicates
significant complementary with egg demand. The cholesterol index points to
substantial health concerns in reducing egg demand.
The resulting elasticity estimates from the preferred model are presented in
table 4.6. The own price elasticity is inelastic but significant at a sample mean of –
0.16, the elasticity however appears to be falling overtime. Our estimate is somewhat
smaller than Hickman’s (1979) –0.30 price elasticity estimate. Income elasticity
increases over-time and turns from the inelastic range to the elastic range during the
1990s. The 0.89 income elasticity estimate for the pre- 1980s period is larger than
Hickman’s (1979) estimate of 0.62 for a reasonably similar time period. The female
workforce elasticity is inelastic but increasing in magnitude over-time. The crossprice elasticity of flour appears to be reasonably constant overtime and about –0.52.
The national advertising variable is significant and positive. Once again, the
importance of the cholesterol index also increases significantly over-time.
40
TABLE: 4.6
Queensland: Estimated Elasticities for Shell Egg Sales Per Capita
Variable
Egg Price (55gm)
Income
Flour Price
Female Workforce
National Advertising
Cholesterol Index
1962/3-79/80
-0.205
0.894
-0.572
0.355
0
-0.010
1980/1-89/90
-0.128
0.872
-0.456
0.379
0.009
-0.143
1990/1-95/6
-0.126
1.098
-0.544
0.497
0
-0.457
1962/3-95/6
-0.159
0.912
-0.516
0.379
0.003
-0.125
Finally, we note that when the simple Hickman (1979) price and income only
specification is applied to the Qld data even more significant RESET problems
emerge (for example RESET(2) is F(1,30) = 27.6) and autocorrelation problems
emerge as well. The goodness of fit falls to R 2 0.53, and the price and income
elasticities become 0.18 and 1.13 respectively. This representation is clearly inferior
to our preferred specification which however, possesses its own problems which
appear to be due to the absence of state advertising data.
4.4
South Australia egg demand estimates
Table 4.7 presents the regression egg demand estimates for South Australia.
The accompanying diagnostic test statistics are presented in table B.4 (appendix B).
When interpreting these results recall that data on state advertising expenditure is not
available for SA. In terms of the diagnostic tests the lack of state advertising data
appears not to have any serious impact on the reliability of regression results. The
preferred model appears to be relatively free from the problems of non-normality,
specification error, heteroscedasticity and auto-correlation. The model explains 79%
of the total variation in shell egg sales, this is lower than the other states, and in part is
due to an unusual shell eggs sales figure for the year 1991/2.
TABLE: 4.7
South Australia: Regression Estimates for Shell Egg Sales Per Capita
Variable
Coefficient*
T- Ratio
P-Value
-0.508
-0.17
0.870
Constant
-0.0070
-1.73
0.095
Egg Price (ABS)
0.287*
2.59
0.015
Income
0.0068*
3.18
0.004
Sausages Price
0.157*
2.88
0.008
Female Workforce
-0.0017*
-6.44
0.000
Cholesterol Index
0.00058*
3.47
0.002
Cholesterol*(90/1-95/6)
* Denotes significant at a 5% level of significance. Results based on 1962/3–95/6, N = 34.
The model with ABS egg prices outperformed the model with 55gm egg
prices in terms of model selection statistics, 8 vs 0. Only one of the variables omitted
from this model was significant when individually re-entered, the price of cereals t =
-3.11 had a theoretically meaningless sign and hence was excluded.
Similar to only Qld, SA demand estimates suggest that egg prices are
important. Income and female workforce have strong positive influences. Again the
result for the female workforce variable reflects the increased demand for take-awayfoods which use eggs. The price of sausages indicates significant substitutability with
41
egg demand. The cholesterol index points to substantial health concerns in reducing
egg demand. The structural change in this variable indicates however, that from
1990/1 the marginal impact of the cholesterol index has fallen significantly.
The resulting elasticity estimates from the preferred model are presented in
table 4.8. The own price elasticity is inelastic but significant at a sample mean of –
0.24, the elasticity however appears to be falling overtime. Our estimate is somewhat
larger than Hickman’s (1979) –0.07 price elasticity estimate. Income elasticity
increases over-time but is always inelastic. The 0.37 income elasticity estimate for
the pre - 1980 period is smaller than Hickman’s (1979) estimate of 0.61 for a
reasonably similar time period. The female workforce elasticity is inelastic but
increasing in magnitude over-time. The cross-price elasticity of sausages appears to
be reasonably constant overtime and about 0.29. Once again, the importance of the
cholesterol index also increases significantly over-time.
TABLE: 4.8
South Australia: Estimated Elasticities for Shell Egg Sales Per Capita
Variable
Egg Price (ABS)
Income
Sausages Price
Female Workforce
Cholesterol Index
1962/3-79/80
-0.280
0.371
0.278
0.702
-0.016
1980/1-89/90
-0.214
0.415
0.325
0.792
-0.251
1990/1-95/6
-0.161
0.488
0.290
0.971
-0.504
1962/3-95/6
-0.236
0.400
0.292
0.764
-0.171
Finally, we note that when the simple Hickman (1979) price and income only
specification is applied to the SA data, autocorrelation problems emerge for the first
four lags. The goodness of fit falls to R 2 0.35, and the price and income elasticities
become 0.30 and 0.91 respectively. Once again, this representation is clearly inferior
to our preferred specification.
4.5
Western Australia egg demand estimates
Table 4.9 presents the regression egg demand estimates for Western Australia.
The accompanying diagnostic test statistics are presented in table B.5 (appendix B).
Recall, state advertising expenditure is available for WA for our entire period of
analysis 1962/3 to 1995/6. In terms of the diagnostic tests no statistics are significant
at the 5% level and thus the model appears to be relatively free of non-normality,
heteroscedasticity, specification error and auto-correlation problems. The model
explains 93% of the total variation in shell egg sales.
TABLE: 4.9
Western Australia: Regression Estimates for Shell Egg Sales Per Capita
Variable
Coefficient*
T- Ratio
P-Value
-0.612
-0.32
0.754
Constant
0.0021
0.75
0.460
Egg Price (ABS)
0.265*
4.24
0.000
Income
-0.0067*
-2.12
0.044
Sugar Price
0.222*
5.64
0.000
Female Workforce
-0.0010*
-4.69
0.000
Cholesterol Index
0.0002
1.57
0.127
Cholesterol*(90/1-95/6)
* Denotes significant at a 5% level of significance. Results based on 1962/3–95/6, N = 34.
42
The model with ABS egg prices outperformed the model with 55gm egg
prices in terms of model selection statistics, 8 vs 0. Even though the cholesterol index
structural change variable may appear to be mildly insignificant (p value = 0.127), it
is retained because of its better performance in model selection statistics compared to
a model with its exclusion. None of the variables omitted from this model were
remotely significant when re-entered individually in the preferred model, the price of
sausages had the highest t-ratio for inclusion of 0.92. Interestingly, none of the three
advertising variables were significant when either included individually or jointly.
For example, the state advertising variable when re-entered into the preferred model
had a t ratio of –0.83, which if anything implies a very weak negative influence on
egg sales.
Demand estimates suggest that egg prices are unimportant. Income and
female workforce have strong positive influences. Again the result for the female
workforce variable reflects the increased demand for take-away-foods which use
eggs. The price of sugar indicates significant complementary with egg demand. The
cholesterol index points to substantial health concerns in reducing egg demand. The
structural change in this variable indicates however, that from 1990/1 the marginal
impact of the cholesterol index has fallen somewhat.
The resulting elasticity estimates from the preferred model are presented in
table 4.10. The own price elasticity is effectively zero for all time periods. This
contrasts to both Hickman’s (1979) –0.40 and Banks et. al –0.32 price elasticity
estimates determined using data spanning the 1950s to 1970s. Income elasticity is
reasonably constant over-time and inelastic. The average 0.32 income elasticity
estimate is smaller than Hickman’s (1979) estimate of 0.49 for 1966-77 and Banks et.
al (1962) estimate of 1.43 for 1952/3-62/3. The female workforce elasticity increases
over-time from being inelastic to elastic in the 1990s. The cross-price elasticity of
sugar appears to be reasonably constant overtime and about -0.13. Once again, the
importance of the cholesterol index also increases significantly over-time.
TABLE: 4.10
Western Australia: Estimated Elasticities for Shell Egg Sales Per Capita
Variable
Egg Price (ABS)
Income
Sugar Price
Female Workforce
Cholesterol Index
1962/3-79/80
0.075
0.312
-0.148
0.832
-0.009
1980/1-89/90
0.045
0.307
-0.121
0.947
-0.123
1990/1-95/6
0.038
0.354
-0.129
1.091
-0.291
1962/3-95/6
0.057
0.319
-0.134
0.910
-0.092
Finally, we note that when the simple Hickman (1979) price and income only
specification is applied to the WA data, RESET specification error problems
(RESET(2) is F(1,30) = 36.9) and autocorrelation problems emerge. The goodness of
fit falls to R 2 0.36, and the price and income elasticities become –0.05 and 0.46
respectively. Model inadequacy is again apparent. Note also, that when state
advertising is added to this simple price and income model all the previous problems
still exist and state advertising remains highly insignificant.
43
44
5.
Economic Implications of Egg Demand Estimates
This chapter examines the regression results in more detail and in particular
looks at their economic implications. Section 5.1 presents an overview of all the
important results focusing upon both the similarities and differences identified
between the states. In the remainder of the chapter we focus upon the two most
important issues. Section 5.2 examines the impact health concerns have had on egg
demand through the use of the developed cholesterol publication index. Section 5.3
looks at the impact of advertising expenditure on egg demand, here the focus rests
principally with results from Victoria.
5.1
Important egg demand factors: a statewise comparison
Table 5.1 summarises the main results for all states in terms of statistically
important variables and their implied estimated elasticities. At the outset it is
important to reiterate that these results may be greatly influenced by the availability of
state advertising expenditure data for only Vic and WA. Since we found that state
advertising may (Vic) or may not (WA) have an important influence on egg demand,
the omission of this data for the other states may or may not seriously affect results.
Even though the diagnostic test statistics suggest that results for NSW and SA appear
not to be seriously affected by this data omission, Qld results indicate that
specification error had probably resulted from this omission. To this extent, results
for NSW, SA and in particular Qld, should be treated with some degree of caution.
TABLE: 5.1
Statistically Important Elasticities of Shell Egg Sales
Variable
NSW
VIC
QLD
SA
WA
0.046
0.042
-0.159
-0.236
0.057
Price Elasticity
0.292
0.799
0.912
0.400
0.319
Income Elasticity
0.120
0.292
Sausages Price
-0.258
-0.506
Flour Price
-0.134
Sugar Price
0.460
0.379
0.764
0.910
Female Workforce
-0.663
Age
-0.076
-0.079
-0.125
-0.171
-0.092
Cholesterol Index
0.159 (state) 0.029 (national)
Advertising
Elasticities based on sample averages of all data except national advertising, which is based
on three years of non-zero data only.
Recall for theoretical reasons, we included the egg price variable in all models
irrespective of its statistical importance. The price elasticity of egg demand is
effectively zero for NSW, Vic and WA. For the other two states statistically
important egg price impacts result but estimates are still highly inelastic: Qld (-0.16)
and SA (-0.24). Thus despite substantial (up to 50%) real price falls over our 34 year
data analysis time-period, in general, changes in the egg price have had only a small
overall influence on egg demand. This result is broadly consistent with previous
Australian studies (see table 2.1) and consistent with the view discussed in subsection 2.2.1 that Australian price elasticities may be lower than estimates for
countries overseas. Interestingly, the most appropriate egg price variable (ABS vs
55gm) for modelling differed between the states: for Vic, SA and WA the ABS price
was preferred; while for NSW and Qld the standardised 55gm price was preferred.
45
Finally, if we average across all five states, a 10 cent increase in real 1989/90 egg
prices is predicted to decrease egg sales per capita by 0.014 dozen or by 0.15% of
average sales.
Even though our regression modelling strategy was always to include the
household income variable for theoretical reasons, its inclusion in all states was also
guaranteed for statistical reasons. Income was strongly positively statistically
significant for all states. For all states income elasticity appears to be increasing overtime. At the sample means of all data, all estimates are inelastic. The state estimates
can be broadly grouped into low elasticity (NSW 0.29, SA 0.40 and WA 0.32) and
high elasticity (Vic 0.80 and Qld 0.91). These estimates differ somewhat from results
from previous studies (both Australian and overseas) where the expectation from subsection 2.2.2 was for low elasticities no greater than 0.6. If we average across all five
states, a $100 increase in real (1989/90 prices) household disposable income is
predicted to increase egg sales per capita by 0.043 dozen or by 0.45% of average
sales.
In terms of the price of related products, of the six prices considered only three
prices proved to be important and not consistently for all states. Only one substitute
proved to be important, the price of sausages was important for Vic and SA. The
result for Vic is consistent with the Collard et. Al. (1982) study. Two complements
were identified to be important. The price of flour was important for both Vic and
Qld, while the price of sugar proved to be important for WA only. For NSW, prices
of related products were always insignificant. Vic exhibited the greatest relationship
with related products with two important variables. In general, these results imply
that there appears to be only a minor degree of substitutability and complementary
between egg and related products. Once again, these general results appear to be
consistent with other studies, see sub-section 2.2.3. It is interesting to note that our
results arise even given the identified price trends of sub-section 3.2.2, where prices
of complements tended to fall with egg prices, while prices of substitutes tended to
rise when egg prices fell. As an example of the interpretation of regression
coefficients, for Vic, if the real price of sausages increased by 10 cents (1989/90
dollars) then egg sales per capita is predicted to increase by 0.029 dozen or by 0.31%
of average sales.
The female workforce variable was strongly significant and positive for all
states except Vic. The positive findings suggest that as more women go into paid
employment more take-away foods are consumed which use eggs and this outweighs
any negative effect due to women having less time for home egg cooking and baking.
The positive impact is consistent with some findings from the U.S., see sub-section
2.2.4. This positive impact appears to be increasing over-time for all states and the
elasticity is greatest for WA. The insignificant result for Vic is interesting, and we
point out that Vic has the slowest growth rate amongst all states for the female
workforce percentage. If we average across the four states which find female
workforce important, a 1% percentage point increase in the percentage of paid female
workers to the female working population, is predicted to increase egg sales per capita
by 0.148 dozen or by 1.52% of average sales.
46
The median age variable proved to be important for NSW only. It is not
obviously clear why this variable should be important only for NSW. It could reflect
the multicollinearity problems (sub-section 3.2.3) with age, as age was found to be
highly correlated with other variables, especially the important cholesterol index. In
any event it does point to the fact that an ageing of the general population may lead to
falling per capita shell egg sales. Predictions suggest for NSW, that a one year
increase in the median population age will reduce egg sales per capita by 0.239 dozen
or by 2.16% of average sales. The remaining cholesterol and advertising variables in
table 5.1, are discussed in the following sub-sections.
5.2
Cholesterol impact on egg demand
One of the most important identified demand determinants is the impact of
health concerns on egg demand. These health concerns have been proxied by a
cholesterol publication index. The estimated impact of these concerns on egg sales
has been quantified for three time periods: 1) the entire sample, 2) since 1975/6 when
the publication index started to grow significantly, and 3) over the last ten years. The
results are presented in table 5.2. These estimates measure the impact of the
cholesterol index on shell egg sales per capita in percentage terms, assuming all other
variables do not change.
TABLE: 5.2
Cholesterol Impact on Shell Egg Sales Per Capita (%)
Variable
1962/3-95/6
1975/6-95/6
1986/7-95/6
-7.6%
-11.5%
-20.0%
New South Wales
Victoria (upto 92/3 only)
-7.9%
-12.7%
-24.5%
-12.5%
-19.3%
-34.9%
Queensland
-17.1%
-27.4%
-45.2%
South Australia
-9.2%
-14.4%
-24.4%
Western Australia
* Cholesterol impact on shell egg sales per capita as a percentage of shell egg sales.
There are substantial differences between the states with three meaningful
state groupings: 1) SA clearly has the greatest impact, 2) Qld falls in the middle, 3)
NSW, Vic and WA have similar and the smallest impacts. The differences are
substantial, for example health concerns in SA have more than double the impact on
egg demand than they have in NSW. In absolute terms the percentage impacts appear
to be large. However, they are also reasonably consistent with results from the U.S.
which estimate -16% for 1955-1987 and -25% for 1966-1987, see sub-section 2.2.6.
Our estimated health concerns impacts increase substantially when we
consider the more recent time periods only. For example, for 1962/3-95/6 the NSW
impact is –7.6% of sales, but for the last ten years 1976/7-95/6 it increases to –20% of
sales. Even though the estimated marginal regression impact of the cholesterol index
falls for three states from 1990/91, the ever rising cholesterol publication index still
ensures that the percentage impact on sales increases further in recent years. Finally,
if we average across all five states, then for the period upto and including 1989/90,
predictions suggest that an extra 100 medical publications on cholesterol will reduce
egg sales per capita by 0.114 dozen or by 1.18% of average sales. For the period from
1990/1-95/6 an extra 100 publications is predicted to reduce egg sales per capita by
0.094 dozen or by 0.97% of average sales.
47
5.3
Advertising expenditure and egg demand
In assessing the impact of advertising expenditure on egg demand, the national
egg advertising variable (covering three years of expenditure) and the substitute
AMLC expenditure variable were considered for all states. Both variables proved to
be unimportant for all sates except for national egg advertising in Qld. State
advertising was examined for only Vic and WA, and was found to be important for
Vic only. As a consequence the results discussed below relate only to national egg
advertising expenditure in Qld and state advertising expenditure in Vic. These
presented estimates measure the impact of advertising on shell egg sales, assuming all
other variables do not change.
For national advertising in Qld we present results for the annual average
impact based on the three years of expenditure over 1986/7-88/9. Given the statistical
nature of the egg demand equation, 95% confidence interval statements can also be
generated for these findings to provide a fuller analysis of the advertising issue.
These 95% interval limits are presented in parentheses next to the average estimated
impacts. In words, we are 95% confident that the impact of advertising falls between
the two presented limits.
The average annual impact of national advertising on Qld egg sales was 2.9%
of sales (95% interval: -0.2% to 6.0% of sales) or 0.28 dozen eggs per capita (95%
interval: -0.02 to 0.57 dozen). In terms of total gross retail revenue (retail price *
sales) this implies an average annual revenue of 62.4 cents per capita in 1989/90
dollars (95% interval: -$0.04 to $1.29), and compares to the average advertising
expenditure of 4.6 cents per capita. More generally, the national advertising Qld
results imply that every extra cent of expenditure per capita, generates 0.060 dozen
sales per capita (95% interval: -0.004 to 0.123 dozen) or 13 cents of total gross retail
revenue per capita (95% interval: -0.93 cents to 27 cents).
We now turn to the state advertising impact on egg sales for Victoria.
Initially, we present point and 95% interval estimates for the average data based on
the entire analysed time–period 1962/3-92/3. The average annual impact of state
advertising on Vic egg sales was 15.9% of sales (95% interval: 4.4% to 27.4% of
sales) or 1.498 dozen eggs per capita (95% interval: 0.416 to 2.580 dozen). In terms
of total gross retail revenue this implies an average annual revenue of $4.42 per capita
in 1989/90 dollars (95% interval: $2.47 to $7.61), and compares to the average
advertising expenditure of 45 cents per capita. More generally, the state advertising
Vic results imply that every extra cent of expenditure per capita, generates 0.033
dozen sales per capita (95% interval: 0.022 to 0.044 dozen) or 9.7 cents of total gross
retail revenue per capita (95% interval: 6.6 cents to 12.9 cents).
Interestingly, the marginal impact of advertising expenditure is larger with
national advertising for Qld (0.06 dozen for each cent) compared to state advertising
for Vic (0.03 dozen for each cent). However, because the average annual expenditure
was far greater for Vic (45 cents) than for Qld (5 cents), then the actual Vic
advertising impact (15.9%) far exceeds that for Qld (2.9%).
48
Figures 5.1 and 5.2, present the Vic advertising impact point and interval
estimates for each individual year. In terms of the advertising impact on the
percentage of egg sales, figure 5.1 clearly indicates a general increasing trend in the
importance of advertising over-time. The impact starts at about 15% of sales in the
early 1960s and finishes by contributing to about 30% of sales in the early 1990s.
Figure 5.2 presents the year-by-year advertising impact on total retail revenue
in per capita terms and 1989/90 dollars. The trend in total revenue is less obvious
with the graph exhibiting significant variability. The extremes are a low of about $2
per capita in 1972/3 and a high of about $8 per capita in 1982/3.
49
50
51
52
6.
Conclusion
In this chapter we make our conclusions. Initially the main results from our
data analysis are summarised and some possible strategy and future implications
outlined. We conclude in section 6.2 by describing some of the study’s limitations.
6.1
Main results and implications
In general, the important egg demand determinants can be categorised into six
groups: 1) price of eggs, 2) household income, 3) prices of related products, 4)
demographic factors, 5) health concerns and cholesterol, and 6) advertising
expenditure. We will summarise the results and implications for each group in turn.
Egg prices represent an important factor for only Qld and SA, and even for
these states the price elasticities are highly inelastic, not exceeding –0.24. The
strategic implication of this finding, is that future price changes of the magnitude
experienced over the last thirty years will not significantly alter egg demand. In
particular, price rises will lead to total revenue gains, while price falls to total revenue
losses. Attempts to maintain or reduce real egg prices in the hope of increasing
demand and total revenue significantly are probably misguided.
Household disposable income is an important egg demand determinant for all
states, but particularly for Vic and Qld where income elasticities are 0.8 or higher.
The implication of these findings is that strong economic growth in states which leads
to higher real incomes can only encourage increases in egg demand. To gain an
insight into likely future trends in egg sales due to rising income we have calculated
the following annual growth rates based on the last five years of data (1991/2-95/6)
for real household disposable income: NSW 1.5%, Vic 1.8%, Qld 0.9%, SA 1.3% and
WA 2.2%. Based on these crude estimates of future growth and the recent estimated
elasticities, the following annual egg sales increases are predicted: NSW 0.57%, Vic
1.72%, Qld 1.00%, SA 0.65% and WA 0.79%. It would appear that Vic is likely to
gain most from future income movements, with NSW gaining least.
Consistent with previous studies, the prices of related products tend to have
only a minor overall and spasmodic influence on egg demand. The only identified
substitute are sausages for Vic and Qld. Sausages are probably viewed as a cheap red
meat and an alternative rich source of protein. Flour for Vic and Qld, and sugar for
WA were identified as complements, these products are typically used in home
cooking and baking in conjunction with eggs. All these related products appear to
have only a relatively minor impact of future egg sales. Of all products the most
noticeable recent trend occurs with the price of sausages in Vic, which has declined
by 5.3% p.a. in the last five years. Based on the recent elasticity estimates for Vic egg
sales, if this decline in the price of sausages continues then egg sales will decline by
0.65% p.a.
Of the demographic factors two variables were found to be important, the
female workforce for all states except Vic and the age variable only for NSW. As
suggested the female workforce variable most likely picks up the increased demand
for take-away foods which employ eggs. This possibly reflects the ‘busier’ lives of
households generally. The age variable probably reflects the fact that older people
53
tend to have greater health concerns about egg consumption than younger people. To
gain an insight into likely future trends in egg sales due to female workforce changes,
we have calculated the following annual growth rates based on the last five years of
data (1991/2-95/6) for female workforce: NSW 1.7%, Qld 1.7%, SA 1.4% and WA
2.2%. Based on these crude estimates of future growth and the recent estimated
elasticities, the following annual egg sales increases are predicted: NSW 0.98%, Qld
0.84%, SA 1.33% and WA 2.37%. Clearly WA is expected to gain the most from this
demographic trend. For the age variable and NSW, the expected increase in median
age is 0.89% p.a. which translates to an expected annual fall in egg sales of 0.73%.
The worldwide cholesterol index appears to capture the health concerns the
population has about cholesterol and egg consumption. This variable proved to be
very important for all states. In terms of future projections, the most recent five year
growth estimate from this index is 10.6% p.a. Based on recent elasticities this
translates to future possible falls in egg sales of: NSW 2.5%, Vic 3.6%, Qld 4.8%, SA
5.3% and WA 3.1% per annum. The greatest impact appears to be in SA and the
smallest in NSW.
State advertising was analysed only for Vic and WA. It was important for egg
sales in Vic with predictions that in the future a 1% increase in advertising
expenditure leads to 0.28% increase in egg sales. Reasons why the advertising
variable in the WA regressions proved to be unimportant are unclear. In any event
taken together our results do not suggest that universally, advertising expenditure will
always have a positive effect on egg sales. As argued by the U.K. study of Hallam
(1986) it may be the case that how an advertising campaign is pursued may
significantly influence its effectiveness on sales. On the other hand, our results do
also imply that advertising can have a significant influence on sales and this is
particularly important given the health concerns the population appears to have about
egg consumption in general.
Interestingly, some of the potentially important demand determinants proved
to be unimportant in the final analysis. The egg substitutes of cereal and potatoes
were unimportant, as was the egg complement bacon. The age variable was only
important for NSW, this though probably reflects the fact that it was ‘over-powered’
by the highly correlated cholesterol index. Essentially, for most states the cholesterol
index appears to be also capturing the fact that the ageing of the population is leading
to fewer egg sales. The capital city population variable was clearly unimportant, there
is probably too little time series variation in this variable to have an important
influence. A cross-section study maybe more useful for capturing this urbanisation
effect. Advertising expenditure for the egg substitute of red meat also proved to be
statistically insignificant for all states. This result may however be influenced by the
lack of contrasting state egg advertising data in three states. Finally, the Australian
cholesterol index was clearly outperformed and swamped by the worldwide
cholesterol index.
This study has shown the relative importance of the demand determinants of
eggs. This has implications for the types of strategies that should be adopted by the
egg industry. Further research could establish the dimensions of optimal strategies
and those already in use in the egg industry to determine the degree of congruence
54
between the two. In turn, transition approaches to bring the industry closer to the
ideal could also be researched.
6.2
Limitations of the study
The main limitations of this study are: 1) the quality of data on egg sales; 2)
the lack of advertising expenditure data; 3) the regression modelling strategy.
For modelling egg demand we employed commercial egg sales. Data from
WA was directly available for the entire data analysis time period. However, for the
remaining states and recent years (from the late 1980s) a disposal series based on
ABS agricultural census data was employed. Various egg industry representatives
have questioned the reliability of this data. Beyond this issue, we have not employed
any data on ‘backyard production’ and therefore our demand estimates for state egg
consumption are purely based on commercial sales.
Advertising expenditure data was only available for Vic and WA. For the
remaining states this implies that results may be unreliable given this data omission
and the positive advertising results for Vic. However, our RESET diagnostic test
results indicate that estimates for NSW and SA are reasonably valid. The results for
Qld may be less valid given these RESET results and the fact that national advertising
expenditure was found to be important for Qld.
As stated previously, regression modelling is an art and therefore results are
subject to the researchers particular use of these techniques. We have employed a
well established strategy emphasising the constant testing of the preferred regression
equation. Moreover, we have clearly illustrated the superiority of our estimates over
previous econometric studies for egg demand in Australia.
Finally, the interpretation of results presented in chapters 4, 5 and 6 is based
on the ceteris paribus assumption. That is, when measuring the individual impact of a
demand determinant, it is assumed that all other factors remain constant. This is the
standard interpretation of regression and related estimates.
55
56
Appendices
Appendix A: Data definitions and construction
A1:
Data definitions and sources.
Egg Sales:
Shell egg sales in Australia by commercial producers.
Dozen per head of population.
Sources:
Egg sales data: (for constructed data see appendix A2)
NSW 62/3-71/2 BAE (1982), 72/3-78/9 constructed,
79/80-87/8 NSW Egg Corporation (1990),
88/9-95/6 constructed.
Vic
62/3-71/2 BAE (1982),
72/3-88/9 Vic Egg Marketing Board (1993),
89/90-95/6 constructed.
Qld
62/3-71/2 BAE (1982), 87/8-95/6 constructed,
S.Qld 72/3-83/4 Sunny Queen Egg Farms (1993)
84/5-86/7 Australian Egg Marketing Council
(1988).
C.Qld 72/3-81/2 constructed
82/3-86/7 Australian Egg Marketing Council
(1988).
SA
62/3-71/2 BAE (1982)
72/3-88/9 South Australian Egg Board (1989)
89/90-95/6 constructed.
WA
62/3-71/2 BAE (1982),
72/3-95/6 Personal communication from WA
Egg Marketing Board.
State population figures from: ABS (Cat no. 3101.0)
Egg Price (ABS)
Average retail price of the ABS standard dozen eggs.
Cents per dozen, 1989/90 constant CPI deflated prices.
Sources:
Egg prices: ABS (Cat no. 6403.0)
CPI: ABS (Cat no. 6401.0)
Egg Price (55gm)
Average retail price of a 55gm (size) dozen eggs.
Cents per dozen, 1989/90 constant CPI deflated prices.
Sources:
Egg prices: ABS (Cat no. 6403.0)
CPI: ABS (Cat no. 6401.0)
57
Income
Household disposable income per head of population,
$1,000s in 1989/90 constant CPI deflated prices.
1962/3-74/5 constructed by extrapolating backwards the
exponential trend based on the succeeding five years
(75/6-79/80).
Sources:
Disposable Income ABS (Cat. no. 5220.0)
Population ABS (Cat no. 3101.0)
CPI: ABS (Cat no. 6401.0)
Cereal Price
Average retail price of breakfast cereal, corn based.
Cents per dozen, 1989/90 constant CPI deflated prices.
Figures for 62/3-73/4 not collected, assumed no change
in real prices.
Sources:
Cereal price: ABS (Cat no. 6403.0)
CPI: ABS (Cat no. 6401.0)
Potatoes Price
Average retail price of 1kg of potatoes.
Cents per dozen, 1989/90 constant CPI deflated prices.
Sources:
Potatoes price: ABS (Cat no. 6403.0)
CPI: ABS (Cat no. 6401.0)
Sausages Price:
Average retail price of 1kg of sausages.
Cents per dozen, 1989/90 constant CPI deflated prices.
Sources:
Sausages price: ABS (Cat no. 6403.0)
CPI: ABS (Cat no. 6401.0)
Bacon Price
Average retail price of a 250 gm packet of bacon middle
rashers.
Cents per dozen, 1989/90 constant CPI deflated prices.
Sources:
Bacon price: ABS (Cat no. 6403.0)
CPI: ABS (Cat no. 6401.0)
Flour Price
Average retail price of 2kgs of self-raising flour.
Cents per dozen, 1989/90 constant CPI deflated prices.
Sources:
Flour price: ABS (Cat no. 6403.0)
CPI: ABS (Cat no. 6401.0)
Sugar Price
Average retail price of 2kgs of sugar.
Cents per dozen, 1989/90 constant CPI deflated prices.
Sources:
Sugar price: ABS (Cat no. 6403.0)
CPI: ABS (Cat no. 6401.0)
58
Female Workforce
Females in paid employment as a percentage of the
civilian female population over 15 years of age.
Sources:
Female employment and civilian population : ABS (Cat.
no. 6202.0)
Age
Median age of all persons.
Sources:
ABS (Cat. no. 3201.0)
Capital City Population
Percentage of the state’s population located in the
state’s capital city.
Sources:
Capital city population: ABS (Cat. no. 3102.0)
State population ABS (Cat no. 3101.0)
State Advertising
Vic: Sales promotion and marketing costs. Cents per
head of population, 1989/90 constant CPI deflated
prices.
Sources:
Advertising expenditure: Vic Egg Marketing Board
(1993)
CPI: ABS (Cat no. 6401.0)
Population: ABS (Cat no. 3101.0)
WA: Advertising and promotion expenditure. Cents
per head of population, 1989/90 constant CPI deflated
prices.
Sources:
Advertising expenditure: Personal communication from
WA Egg Marketing Board.
CPI: ABS (Cat no. 6401.0)
Population: ABS (Cat no. 3101.0)
National Advertising
Advertising expenditure and promotion expenses
committed by the Australian Egg Marketing Council for
the years 1986/7-88/9. Cents per head of the Australian
population, 1989/90 constant CPI deflated prices.
Sources:
Advertising expenditure: Australian Egg Marketing
Council (1988)
CPI: ABS (Cat no. 6401.0)
Population: ABS (Cat no. 3101.0)
AMLC Advertising
Advertising expenditure and promotion expenses
committed by the Australian Meat and Livestock
Corporation for Australian markets. Cents per head of
the Australian population, 1989/90 constant CPI
deflated prices.
59
Sources:
Advertising expenditure: Australian Meat and Livestock
Corporation (1997).
CPI: ABS (Cat no. 6401.0)
Population: ABS (Cat no. 3101.0)
Cholesterol Index
The cumulative number of articles in the Medline
medical database which have the words cholesterol and
heart disease or arteriosclerosis in either the title or
abstract. Lagged six months.
Sources:
Constructed by a research assistant.
Australian Cholesterol
The cumulative number of articles in the Medline
medical database which have the words cholesterol and
heart disease or arteriosclerosis in either the title or
abstract. Articles only with an Australian place of
publication location. Lagged six months.
Sources:
Constructed by a research assistant.
A2:
Data construction for shell egg sales
First, we describe the necessary data construction for years before 1982/3. For
this period gaps existed only for NSW 72/3-78/9 and Central Qld 72/3-81/2. For C.
Qld shell egg sales figures are small and about 2 million dozen. Based on available
shell egg sales and total egg production data covering 82/3-86/7, to derive shell sales
(for 72/3-81/2) we assumed that 95% of C. Qld total egg production results in
domestic shell egg sales. Total production figures for C. Qld come from the
Australian Egg Board (1985).
As a consequence of these calculations and other data sources, for the period
72/3-78/9 we have shell egg sales for all states except NSW. Australian shell sales
data for this period are available from ABARE (1987) and this permits us to derive
NSW shell egg sales as the difference between Australian sales and the sum of the
non-NSW states, for the period 72/3-78/9. Note, Tasmania is excluded from the
Australian ABARE and AEB data.
Secondly, we describe the necessary data construction for the years on or after
1987/8. This construction is less reliable than the earlier period as it relies on far
more assumptions and on data for egg production from the ABS agricultural census.
The basic relationship employed to derive domestic shell egg sales is:
Domestic shell egg sales =
less
less
less
60
total egg production
domestic egg product sales
exports of shell eggs
exports of egg product
This relationship ignores movements in stocks about which data is unavailable. Egg
product includes all dried and liquid egg forms. ABS data is available on three of the
four categories which appear on the right hand side of this expression. Data is not
available on domestic egg product sales and this had to be predicted using data from
previous historical trends.
ABS data on total egg production comes from one of two different sources
depending on the year and state for which figures are required. For some figures, the
ABS source is the relevant state marketing board, while for other figures it is the ABS
agricultural census. However, some of the ABS provided data sourced from the state
marketing boards are clear overestimates which appear to have doubled counted egg
products in total egg production calculations. Data available directly from the state
boards confirmed these errors. Necessary adjustments are therefore made to this data.
The total production data employed and their source are provided in the table A.1.
Note, in this table there appears to be one unusual data point for SA and 1991/2, this
could pose problems for deriving reliable SA demand estimates.
TABLE: A.1
ABS Total Egg Production Data (Millions of dozen equivalent)
Year
NSW
Vic
Qld
SA
1987/8
29.504^
1988/9
80.300#
30.379^
1989/90
74.956*
47.416^
29.667^
13.496^
1990/1
80.118*
48.181^
29.832^
13.117^
1991/2
70.429*
45.348*
28.021^
11.573*
1992/3
68.312*
42.794*
29.568^
14.053*
1993/4
67.802*
39.493*
30.456^
12.705*
1994/5
64.033*
40.121*
28.501^
13.137*
1995/6
64.965*
40.131*
28.510*
12.217*
* ABS agricultural census. # ABS provided data attributed to the state egg marketing board. ^ ABS
provided data attributed to the state egg marketing board, but modified to avoid double counting of egg
product sales.
To gain predictions for domestic egg product sales, regressions were
performed on earlier available data and then the estimated equations (together with
available recent data) used to predict recent domestic egg product sales. That is, we
regressed domestic product sales against total egg production, exports of egg products
and exports of shell eggs using the following available data: NSW (79/80-87/8), Vic
(83/4-88/9), Qld (82/3-86/7) and SA (83/4-88/9). The data comes from the previously
cited annual reports of the state egg boards and the Australian Egg Board. The
resulting estimated equations were then employed with available data on these three
independent variables to generate domestic egg product sales for the more recent
missing data. These predictions however, may be overestimates because recently
imports of egg products have increased and these can be viewed as direct substitutes
for domestically produced egg products. The derived data are presented in table A.2.
The regression predictions (‘regress’ in table A.2) are modified by deducting imported
egg product sales (‘import modified’ in table A.2).
61
TABLE: A.2
Estimated Domestic Egg Product Sales (Millions of dozen equivalent)
Year
NSW
Vic
Qld
SA
1987/8
1988/9
1989/90
1990/1
1991/2
1992/3
1993/4
1994/5
1995/6
Regress
Import
Modified
Regress
Import
Modified
Regress
Import
Modified
Regress
Import
Modified
13.769
11.719
15.677
10.603
9.720
9.644
7.477
7.867
13.752
11.696
15.442
10.542
9.619
9.163
6.886
4.104
6.089
6.272
6.321
6.372
6.432
6.421
6.421
6.000
5.447
5.809
5.806
6.266
5.803
4.560
3.187
3.398
3.273
3.264
2.677
3.192
3.445
2.929
2.515
3.187
3.397
3.092
2.942
1.632
2.489
2.602
2.298
2.110
1.642
1.700
1.770
1.668
1.712
1.698
1.728
1.641
1.699
1.770
1.665
1.712
1.698
1.726
Finally, export data on product sales and shell eggs are gained from the ABS
via personal communication. Export and import data for egg products were converted
to shell equivalent using conversion factors provided by Mr Hugh McMaster of the
Australian Egg Industry Association.
In summary, our estimated domestic shell egg sales data uses table A.1 less
table A.2 (import modified) less export data on shell eggs and egg products from the
ABS. The ‘constructed’ data is presented in table A.3. This data is employed in the
regression analysis of egg demand.
TABLE: A.3
Estimated Domestic Shell Egg Sales (Millions of dozen)
Year
NSW
Vic
Qld
1987/8
26.100
1988/9
62.007
26.709
1989/90
60.103
41.319
26.521
1990/1
63.457
42.565
26.758
1991/2
58.930
39.068
25.789
1992/3
58.166
36.775
26.938
1993/4
58.416
32.864
27.741
1994/5
56.733
33.995
26.088
1995/6
60.262
35.295
25.494
62
SA
11.796
11.418
9.789
12.388
10.993
11.439
10.491
Appendix B: Regression test statistics
The diagnostic regression test statistics employed are described in Beggs
(1988). The presented statistics and tests are: R 2 the coefficient of determination;
Durbin-Watson test for 1st order auto-correlation; normality is the Jarque-Bera test
for non-normality; BPG-Hetero is the Breusch, Pagan and Godfrey test for
heteroscedasticity; RESET is Ramsey’s test for specification error; the residual
correlogram tests for auto-correlation of orders from 1 thru 6. Definitions of these
terms are provided in the glossary. A statistically significant test statistic (identified
by * in the tables) indicates the presence of a problem.
TABLE: B.1
New South Wales: Regression Test Statistics for Shell Egg Sales Per Capita
R2
Durbin-Watson
Normality = 2 (2)
0.931
2.349
0.002
Residual Correlogram
Lag: 1
Lag: 2
4.207
BPG-Hetero = 2 (6)
RESET(2) = F(1,26)
0.284
RESET(3) = F(2,25)
0.237
RESET(4) = F(3,24)
0.224
*Denotes significant at a 5% level of significance.
T- statistics
-1.15
-0.78
Lag: 3
-1.69
Lag: 4
Lag: 5
Lag: 6
0.16
-0.09
0.12
TABLE: B.2
Victoria: Regression Test Statistics for Shell Egg Sales Per Capita
R2
Durbin-Watson
Normality = 2 (2)
0.933
1.899
3.557
Residual Correlogram
Lag: 1
Lag: 2
5.005
BPG-Hetero = 2 (6)
RESET(2) = F(1,23)
3.700
RESET(3) = F(2,22)
1.955
RESET(4) = F(3,21)
2.504
*Denotes significant at a 5% level of significance.
T- statistics
0.25
-1.68
Lag: 3
-0.44
Lag: 4
Lag: 5
Lag: 6
-0.64
0.63
-1.07
TABLE: B.3
Queensland: Regression Test Statistics for Shell Egg Sales Per Capita
R2
Durbin-Watson
Normality = 2 (2)
0.957
2.088
1.925
Residual Correlogram
Lag: 1
Lag: 2
4.259
BPG-Hetero = 2 (6)
RESET(2) = F(1,26)
4.297*
RESET(3) = F(2,25)
11.227*
RESET(4) = F(3,24)
8.568*
*Denotes significant at a 5% level of significance.
63
T- statistics
-0.28
-0.91
Lag: 3
0.23
Lag: 4
Lag: 5
Lag: 6
-1.93
-0.34
-0.68
TABLE: B.4
South Australia: Regression Test Statistics for Shell Egg Sales Per Capita
R2
Durbin-Watson
Normality = 2 (2)
0.786
1.686
3.121
Residual Correlogram
Lag: 1
Lag: 2
8.390
BPG-Hetero = 2 (6)
RESET(2) = F(1,26)
0.034
RESET(3) = F(2,25)
0.330
RESET(4) = F(3,24)
0.390
*Denotes significant at a 5% level of significance.
T- statistics
0.69
-0.85
Lag: 3
-0.09
Lag: 4
Lag: 5
Lag: 6
-0.86
0.18
0.67
TABLE: B.5
Western Australia: Regression Test Statistics for Shell Egg Sales Per Capita
R2
Durbin-Watson
Normality = 2 (2)
0.929
1.884
1.257
Residual Correlogram
Lag: 1
Lag: 2
6.675
BPG-Hetero = 2 (6)
RESET(2) = F(1,26)
1.999
RESET(3) = F(2,25)
1.733
RESET(4) = F(3,24)
2.902
*Denotes significant at a 5% level of significance.
64
T- statistics
0.33
-1.36
Lag: 3
-1.07
Lag: 4
Lag: 5
Lag: 6
-0.48
-0.71
-0.28
Glossary
R 2 Coefficient
of determination: measures the goodness of fit for a regression
model. In percentage terms it measures how much variation in the dependent
variable (egg sales) is explained by the independent variables (demand
determinants).
Auto-correlation: this is the violation of a regression assumption, it relates to
correlation over time among the regression error terms. Autocorrelation
results in biased tests of statistical significance for regression coefficients.
The problem often arises if relevant and important independent variables are
omitted from the model.
The Durbin-Watson test and the residual
correlogram, test for the presence of auto-correlation.
Coefficient: the regression coefficient measures the impact of an independent
variable (demand determinant) on the dependent variable (egg sales).
Specifically, for a single unit increase in a demand determinant the coefficient
measures by how many units egg sales will change on average, assuming other
factors remain constant.
Correlation: measures how strongly associated two variables are. Correlation must
fall between –1 and +1. Negative values imply an inverse relation between the
variables, positive values imply a direct relation. Values close to zero imply
no association.
Ceteris paribus: assume that all other factors remain fixed or do not change.
Diagnostic tests: regression analysis rests upon various assumptions holding true. If
these assumptions are violated then results are biased and unreliable.
Diagnostic testing explicitly tests for the violation of these assumptions.
Elasticity: in percentage terms it measures how much a dependent variable (egg
sales) will change given a 1% change in an independent variable (demand
determinant).
Heteroscedasticity: this is the violation of a regression assumption, it relates a nonconstant error variance in the regression model. The problem results in biased
tests of statistical significance for regression coefficients. The problem often
arises often if the ‘size’ effect is important in a regression model. The BPGHetero test, checks for the presence of heteroscedasticity.
Mean: the arithmetic average of the data.
Model selection statistics: these are eight statistics which compare the goodness of
fit performance between various models by making allowances for model
complexity.
Multicollinearity: relates to a high degree of correlation among the independent
variables (demand determinants) of a model. It does not violate a regression
assumption but does make it difficult to estimate precisely the individual
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effect of each demand determinant. Essentially two demand determinants
which are highly correlated are competing against each other in trying to
explain variations in egg sales.
Non-normality: this is the violation of a regression assumption, it relates to the
regression error terms not following a standard normal distribution. It results
in biased tests of statistical significance for regression coefficients. The
Jarque-Bera normality test checks for the presence of non-normality.
P-Value: measures the actual statistical significance of a regression coefficient. For
example, if p-value = 0.075, then the associated regression coefficient is said
to be significantly different from zero at a 7.5% level. Put simply there is a
7.5% chance that the regression coefficient is zero, or a 92.5% chance that it is
not zero. Researchers tend to use a 5% level of significance to check for
strong or high statistical importance. That is, p-value < 0.05 is said to be
highly statistically significant, and a p-value > 0.05 is said to be statistically
insignificant.
Regression analysis: measures the statistical relationship between a dependent
variable (egg sales) and independent variables (demand determinants, prices,
income, etc.). The independent variables are hypothesised to explain changes
in the dependent variable. The resulting regression coefficients measure the
influence of each independent variable on the dependent variable. The
statistical importance of these coefficients is measured by t-ratios and pvalues. The usefulness of regression analysis depends on the validity
regression assumptions. These assumptions mainly relate to the regression’s
error terms which pick up the changes in the dependent variable not explained
by the independent variables. These assumptions are checked via diagnostic
tests.
Specification error: this is the violation of a regression assumption, it relates the
average of the error terms not being zero. The problem results in biased
regression coefficients. The problem often arises if an important independent
variable has been inappropriately excluded from the regression model. The
RESET test, checks for the presence of specification error.
Standard deviation: measures the degree of variability or spread in a data set.
Statistically significant: refers to the importance of a variable after allowing for
chance events. Regression analysis recognises the existence of random and
chance factors influencing results. A statistically significant variable implies
that even after allowing for these chance factors, that variable is an important
factor in explaining the dependent variable.
T-Ratio: provides a measure of the statistical significance of a variable. In general, a
t-ratio greater than 2 in absolute value terms is a highly significant variable.
The information contained in p-values and t-ratios is the same.
66
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Australian Bureau of Statistics (ABS) publications employed:
Cat. No.
Title
3101.0
Australian Demographic Statistics.
3102.0
Australian Demographic Trends.
3201.0
Estimated Resident Population by Sex/Age.
4306.0
Apparent Consumption of Foodstuffs and Nutrients Australia.
5220.0
Australian National Accounts: State Accounts.
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The Labour Force, Australia, Preliminary.
6401.0
Consumer Price Index.
6403.0
Average Retail Prices of Selected Items
Note, for some data series the earlier discontinued relevant ABS publication was
employed.
69