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CGE or SAM? Ensuring quality information for decision-making.

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African Journal of Hospitality, Tourism and Leisure Vol. 4 (Special edition) - (2015) ISSN:
2223- 814X Copyright: © 2014 AJHTL - Open Access- Online @ http//: www.ajhtl.com
1
CGE or SAM? Ensuring quality information
for decision-making
L.J.M. Van Wyk
Student -NWU
&
Professor M. Saayman,*
Melville.Saayman@nwu.ac.za
North-West University,
Potchefstroom, South Africa.
&
Professor R. Rossouw
North-West University,
Potchefstroom, South Africa.
Corresponding author*
Abstract
The purpose of this paper is to conduct an economic assessment of the Aardklop National Arts
Festival (hereafter referred to as Aardklop) by means of applying both the SAM and CGE methods,
in order to ensure quality information for decision-taking. Data from a visitor survey conducted at
Aardklop during 2010 was used for the analyses, which were executed using two regional (or place-
specific) models (i.e. SAM and CGE models) constructed for South Africas North West Province.
The results confirm that when these two economic impact assessment methods are applied to the
same data-set of an event, the reported economic impact results differ significantly. This finding
serves as a further warning to assessors that economic impact results can be misleading (if the
underlying method and assumptions are not clearly stated and explained) and therefore their
application should be handled with the utmost care as the results can readily be misinterpreted by
stakeholders.
Keywords: Event tourism; Quality information, Economic impact; Aardklop National Arts Festival;
Regional CGE modelling; Social accounting; Multiplier analysis; Potchefstroom.
Source:
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Introduction
Special events, such as arts festivals and
sport competitions, date back several
centuries (see e.g. Quinn, 2005; Jago &
Dwyer 2006). Since then there has been
a considerable increase in, specifically,
the number of arts and cultural festivals
that occur on a global scale. Schoombie
(2003) highlights that by 2003, more than
1.2 million international arts and cultural
festivals were listed on the Internet.
Festivals that create platforms for
celebrating the diverse preferences of
visitors, such as different language and
cultural opportunities, have therefore
grown in popularity. This notion is most
observable in the frequent media
announcements of new festivals, some
brand-named, in order to mandate
recognition of the hosting region,
community, or the main sponsor. The
financing of such arts and cultural
festivals is, however, a contentious issue
in view of the encouraging community
support, socio-economic impact and spin-
offs that are generated through such
events. In this regard, Saayman and
Saayman (2012) state that few events
would be able to continue in the absence
of sponsorships.
However, sponsors also want to know
how their money benefited communities.
In fact, sponsors as well as event
organisers, more than often have to
demonstrate this impact. Hence quality
information for decision-taking is
important, and quality information is not
always rated that important in the
literature, since researchers have long
been focused on the customer and staff,
whilst very few studies attempt to
address a level of quality assurance
when it comes to making decisions. The
ever growing demand on government
budgets also make it difficult for
government to continue to fund arts
festivals and in the cases where they do,
they are obligated to show how
communities benefited from the support.
For this reason, reliable impact
assessments that provide quality
information needs to be conducted (Van
Wyk, 2011).
In this regard, Bowdin and Williams
(2007) argue that event evaluation by
means of conducting quantifiable
economic impact studies have become a
valuable tool to demonstrate the success
and achievements of festivals. In recent
years, several methods to conduct these
economic impact assessments have
been developed and applied in numerous
international studies. Of these, the most
prevalent methods utilised in surveys
include the Input-Output (I-O), Social
Accounting Matrix (SAM) and
Computable General Equilibrium (CGE)
methodologies.This paper provides an
overview of two of the aforementioned
methodologies, namely the SAM and
CGE approaches, and demonstrates the
differences which may be obtained in
their results. Both methodologies are
applied to a set of data collected from the
Aardklop National Arts Festival (hereafter
referred to as Aardklop) in order to
compare the resulting assessments of
the economic impact of an arts festival.
This enables us to study a key question,
namely: How do economic impact results
compare when applying SAM and CGE
to Aardklop data, and which model would
offer organisers quality information for
decision-taking?
Literature review
Given the heterogeneous nature of arts
festivals, estimation of the value of
visitors to a local area or region is
considered to be time consuming,
methodologically complex, and for some,
such as Frechtling (1994), “arcane.”
Therefore, the methods and models that
are used to assess the economic impact
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of festivals may have significant
consequences. And so, it is imperative
that serious consideration be given to the
choice of method, as festivals can serve
as a vehicle to achieve socio-economic
objectives. Such goals may include
creating infrastructure, providing jobs,
generating revenue, attracting
investment, growing the arts, promoting a
region and building a better image
(Gursoy, Kim & Uysal, 2004; Saayman &
Rossouw, 2010).
A common approach in studies relating to
the impact of arts or culture is the use of
the sales (or transactions) multipliers
where the original, direct festival attendee
expenditure is linked to the final total
business revenue in the economy (e.g.
Myerscough, 1991). However, criticisms
of the broad, aggregate approach of
multipliers have led to the development of
increasingly sophisticated models. I-O
models, for instance, are based on
dividing the economy of the area under
study into sectors and the construction of
a matrix (Bond, 2008). Each sector of the
economy is shown in each column as a
purchaser of goods and services from
other sectors in the economy and in each
row as a seller of output to each of the
other sectors (Bond, 2008). Whilst I-O
models provide a means of estimating
the effect of additional exogenous
expenditure on every sector of the
economy, the initial extensive, additional
data requirements are seen as prohibitive
by some commentators. I-O models are
criticised for the assumptions that
underpin their utilisation. In particular, the
models are based on a perfectly elastic
supply of inputs and constant prices. In
response, CGE models have been
developed where production functions
and prices are allowed to vary.
Furthermore, whilst I-O analysis only
provides an aggregated estimate of the
additional income accruing to study area
households, SAMs have been developed.
Both CGE and SAM models are,
however, more complex and necessitate
more extensive data requirements than I-
O models. Traditionally, they have been
seen as more appropriate for the study of
national economies or larger regions,
rather than estimating the local effects of
festivals. Economic impact assessment
methods such as SAM have been applied
by several researchers, for example,
Wagner (1997), Edmiston and Thomas
(2004), McIntyre (2004), Saayman,
Rossouw and Saayman (2008), Rivera,
Hara and Kock (2008), Saayman and
Rossouw (2010), Kruger, Saayman,
Saayman and Rossouw (2011), as well
as Van Wyk (2011). The application of
CGE for assessment purposes is further
evident in studies done by various
researchers, such as Adams and
Parmenter (1995), Narayan (2004), URS
Finance and Economics (2004), Blake
(2005), PricewaterhouseCoopers (2005),
Dwyer, Forsyth and Spurr (2006a and
2006b), Bohlmann and Van Heerden
(2008), Saayman and Rossouw (2008)
and Rossouw and Saayman (2011).
When reviewing these studies, most
researchers acknowledge that each
model, that measures economic impact,
has both advantages and disadvantages.
The tendency in recent studies is to apply
a combination of models, rather than
favouring a specific one, is noted as a
way of improving the acceptability and
quality of the results and of reducing
some of the shortcomings acknowledged
(Van Wyk, 2011).
Most CGE models typically describe
relatively large geographical regions or
countries and are therefore not able to
capture the uniqueness of the cities and
towns that comprise the region.
Examples of such studies include Seung
and Kraybill (2001) who examine the
impact of public investment in the Ohio
economy, Hoffmann, Robinson and
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Subramanian (1996) investigate the role
of Defence cuts in California, and finally
Jones and Whalley (1989) construct a
CGE covering most of Canada. In their
comprehensive survey of CGE models,
Partridge and Rickman (1998) mention
that the slow start of regional CGE
models is mainly due to the scarcity of
regional data. However, nowadays high
quality regional data exists and region-
specific CGE models can be constructed
for small areas. Unfortunately, authors
such as Crompton (1999) and Saayman
and Rossouw (2008) have found
evidence that although many economic
impact assessments are completed with
integrity, many are not. This is evidenced
by several examples of assessment
studies where researchers and
consultants adopted inappropriate
procedures and assumptions in order to
generate high economic impact results.
In addition, erroneous assumptions were
made during data collection practices that
had a substantial impact on the results
that subsequently led to stakeholders
being misinformed. Consequently,
criticism against the integrity of analyses
and of the outcomes of economic impact
studies is increasing. Studies that
focused on assessing the economic
impact of events after 2000 primarily
used I-O and CGE models. The SAM
model is generally viewed as an
improvement of the I-O model. The aim
of an I-O model is to analyse the
interdependence of industries in an
economy. Being regarded as a broader
model, SAM models include both social
and economic data of an economy and
so present a way for the logical
arrangement of statistical information in
as far as income flows in a countrys
economy within a set time-frame, usually
a period of 12 months, are concerned
(Cameron, 2003). I-Os and SAMs serve
as building blocks to develop CGE
models. Positioning a SAM model within
the conceptual framework of a CGE
model (that contains behavioural and
technical relationships between variables
among sets of accounts) may prove to be
very functional when evaluating
economy-wide effects of event policy
changes or other economic phenomena
(White & Patriquin, 2003). Factors
affecting the choice of approach or
methodology include the size of the
festival being assessed, the scope of the
analysis, the duration of the festival, and
the specific concerns of relevant decision
makers. Ultimately, however, it is argued
that accuracy and information are related
to the budget available for the study,
which is true of almost any study that
is, such research involves a trade-off
between accuracy and cost. In summary,
methodological approaches to the
estimation of the value of visitors to
festivals vary. For some, the analytical
advantages of I-O or SAM models are
seen to be vast and appear to be the
most appropriate for most analyses of the
economic impact of festivals or events.
Furthermore, they appear to be the most
widely used techniques reported in recent
literature.
Methodology
During 1998, the town of Potchefstroom,
situated in the North West Province,
South Africa, hosted the first Aardklop
National Arts Festival. The mission of the
original organisers was to offer a cultural
experience to all visitors, while
simultaneously providing an opportunity
for economic growth in the local
community. Not only did the festival
provide a platform for displaying the
creativity and talent of South African
artists, but it also initiated additional
investment, spending and job
opportunities in the local economic arena
(Potchefstroom City Council, 2007).
Since the initial festival in 1998, when a
mere 25 000 tickets were sold for 45
productions (Kruger, Saayman, Saayman
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& Oberholzer, 2009), ticket sales during
the 2010 event reached almost 70 000
(Botha, Saayman, Saayman &
Oberholzer, 2010). Sales for the 2007
festival peaked at a record 90 000 tickets
sold. The questionnaires for this survey
were developed in line with those
previously used at various arts festivals in
South Africa (see Botha et al., 2010). The
scope of 26 questions ranged from
seeking demographic details of
respondents, their behaviour during the
festival, the duration of their stay at the
festival and expenses incurred. Although
550 questionnaires were distributed
during the festival (30 September to 4
October 2010), only the data of 516 could
be used. The data was collected by
trained fieldworkers who interviewed
visitors and completed questionnaires
using the recall method. The respondents
were asked to indicate their spending
during the festival. A destination-based
survey was utilised and allowed for
interviews to be conducted during the
event. Different venues and sites were
targeted to conduct interviews during the
event in order to ensure that responses
represented the diversity of visitors and
their opinions.
SAM and CGE model comparison
The models applied are similar impact-
type models, i.e. single-region multi-
sectoral models. The purpose of
describing them is to highlight the relative
strengths and weaknesses of such
models rather than to determine which
models are “the best”. Put simply, the
best model is the smallest, simplest and
most transparent model that sheds light
on the link between the variables of most
concern to the modellers (Denniss,
2012). Table 1 provides a summary of
the main characteristics of each model.
Table 1: Comparison of characteristics of SAM and CGE models
Model
Level of effects on
a local economy
Shocks that can be
analysed
Results
Strengths
Boundaries
SAM
Indirect and induced
effects on output,
income and
employment; by
disaggregated
households, firms
and other
institutions,
products, types of
demand and other
elements
Changes in
consumption by
product or industry;
changes in policy:
tax rates,
government
spending, price
inflation,
Regional output,
income,
employment,
production; product
prices, wage rates;
broken down by
type of household,
labour and capital
source
Disaggregates
households, firms
and other
institutions,
products, types of
demand and other
elements of the
economy according
to analytical needs
and data resources
No standard
methodology or
presentation; same
boundaries as I-O
model
CGE
Indirect and induced
effects on output,
income and
employment; prices
and wage rates by
industry
Changes in
consumption by
product or industry;
changes in policy:
tax rates,
government
spending, price
inflation,
Regional output,
income,
employment,
production; product
prices, wage rates;
broken down by
type of household,
labour and capital
source
Allows factor of
production prices to
vary; effects of
resource constraints
covered; all markets
clear
No standard
methodology or
presentation;
posited relationship
equations,
parameters,
elasticities seldom
made public; heavily
dependent on
assumptions
requires massive
input data that is
seldom current;
require validation
against the actual
economy
Note: Characteristics are not necessarily mutually exclusive or exhaustive
Source: Adapted from Frechtling (2011:13)
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It is important to note that one cannot
simply compare SAM and CGE models
as if they are inherently different and that
it is an either or situation. That is wrong.
CGEs are more advanced (later
generation) SAM-based models as they
use SAMs as their database. SAM-based
models only focus on the linear
relationships based on the Leontief
inverse, while CGEs do so as well, but
expand it to also include constant-
elasticity-of-substitution (CES)-based and
non-linear relationships. The application
of SAM-Leontief models and that of
CGEs are, however, exactly the same.
CGEs, as mentioned above, are,
however, more advanced and therefore
provide a more sophisticated analysis.
CGEs can also be dynamic taking into
account time, whereas SAM-Leontief and
static CGEs do not.
Figure 1: Simplified relationship between an I-O, SAM and CGE model
Source: Adopted from Cameron (2003:2)
Figure 1 illustrates the simplified
relationship between an I-O table, SAM
and CGE model.
One can therefore summarise the
relationship and context between SAMs
and CGE models as follows (Cameron,
2003):
a) A SAM comes from I-O tables,
national income statistics, and
household income and expenditure
statistics. Therefore, a SAM is
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broader than an I-O table and typical
national accounts, showing more
detail about all kinds of transactions
within an economy.
b) A CGE model comes from a SAM,
coupled with a conceptual
framework that contains the
behavioural and technical
relationships among variables within
and among sets of accounts. The
aim of CGE modelling is to convert
the abstract representation of an
economy into realistic, solvable
models of actual economies. In brief
one can therefore state that a CGE
model has the benefit that it can
therefore be used for a more
detailed and realistic evaluation of
the economy wide effects of policy
changes or other economic impacts
than either an I-O analysis or SAM.
SAM model
A SAM is a data system, including both
social and economic data for an
economy. The data sources for a SAM
come from I-O tables, national income
statistics, and household income and
expenditure statistics (Cameron, 2003).
Therefore, a SAM is broader than an I-O
table and typical national accounts,
showing more detail about all kinds of
transactions within an economy.
However, an I-O table records economic
transactions irrespective of the social
background of the transactors. A SAM,
contrary to national accounts ...attempts
to classify various institutions to their
socio-economic backgrounds instead of
their economic or functional activities
(Chowdhury and Kirkpatrick, 1994).
A SAM is a way of logical arrangement of
statistical information, concerning income
flows in a countrys economy within a
particular time period (usually a year). It
can provide a conceptual basis to
analyse both distributional and growth
issues within a single framework
(Statistics South Africa, 1998). For
instance, a SAM shows the distribution of
factor incomes of both domestic and
foreign origin, over institutional classes
and re-distribution of income over these
classes. In addition, it shows the
expenditure of these classes on
consumption, investment and savings
made by them. King (2003) points out
that a SAM has two main objectives: first,
organising information about the
economic and social structure of a
country over a period of time and second,
providing statistical basis for the creation
of a plausible model capable of
presenting a static image of the economy
along with simulating the effects of policy
interventions in the economy or other
economic impacts. For the current
analysis a SAM for the North West
Province, as developed by Conningarth
Economists (2006), was used. This
model makes use of a consistent and
comprehensive data set in terms of all
manual transactions among productive
and institutional sectors of the provinces
economy. Using 2006 prices as a base, it
distinguishes 46 sectors, 12 household
types and 4 ethnic groups. With the
application of multipliers according to the
SAM for the North West province, the
direct spending of visitors at Aardklop is
converted into the linked increases in
production, income and jobs in the
region, represented by the indirect and
induced impacts.
Finally, a SAM coupled with a conceptual
framework that contains the behavioural
and technical relationships among
variables within and among sets of
accounts, can be used for the evaluation
of the economy wide effects of policy
changes or other economic impacts
rather than only for purely diagnostic
purposes (Pyatt, 1988). The conceptual
framework is supplied in the form of a
CGE model.
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CGE model
The aim of CGE modelling is to convert
the abstract representation of an
economy into realistic, solvable models of
actual economies. In the CGE framework
the main focus of analysis is quantitative
and is based on the empirical data from a
particular country being investigated. One
of the major features of CGE modelling is
its attempt to combine theory and policy
in such a way that the analytic
foundations of policy evaluation work are
improved (Cameron, 2003). A CGE
model can accordingly briefly be
described as an economy-wide model
that includes feedback between demand,
income and production structure, and
where all prices adjust until decisions
made in production are consistent with
decisions made in demand (Dervis, De
Melo & Robinson, 1982:132).
The main equations of these models are
derived from the constrained optimisation
of neoclassical production and utility
functions. Producers choose inputs to
minimise costs of a given output subject
to non-increasing returns to scale
industry functions. Consumers are
assumed to choose their purchases to
maximise utility functions subject to
budget constraints. Production factors
are paid according to their marginal
productivity. The government sector is
included and imperfect competition can
be introduced via price fixing, rationing
and quantitative restrictions. At the
equilibrium level these models solutions
provide a set of prices that clear all
commodity and factor markets and make
all individual agents optimisations
feasible and mutually consistent
(Cameron, 2003). Unlike the SAM model,
the CGE model is an optimisation model,
i.e. it provides the optimal solution mix of
endogenous variables in response to an
exogenous shock. Also, the CGE model
contains explicit supply constraints,
usually embedded in a neoclassical
framework. Finally, unlike the SAM
model, which achieves equilibrium in
supply and demand quantities only, the
solution to the CGE model is given
through both quantities and prices
(Dervis et al., 1982). The CGE model
used in this study is elaborated by the
Centre of Policy Studies at Monash
University in Australia (see TPMH0060:
http://www.monash.edu.au/policy/archive
p.htm). It is a static model developed for
use with a regional SAM and data. This
basic model was taken and adapted with
data from the North West Province SAM.
The resulting model is a conventional
Johansen-type model (see, for example,
Dixon, Parameter, Powell & Wilcoxen,
1992) with Keynesian-type closure.
Cobb-Douglas-type functions are used.
While the import levels are endogenously
determined, the import and export prices
are assumed as given (i.e. the small
country assumption). Other final demand
(excluding household consumption)
quantities are exogenously set. The
specification is designed to fit as closely
as possible to the SAM model, with the
only real difference being the forced
market clearing closure mechanism and
the form of the production functions.
SAM and CGE model empirical
comparison
The next step is to commence with an
empirical comparison of the SAM and
CGE models. First, multipliers are
derived and compared and then a study
of the impact of visitor expenditures at
Aardklop on the local and regional
economy is described. Please note that
the following results should not be
regarded as showing definitive
differences between the SAM and CGE
models but, instead, are indicative of the
general differences which may be
observed. Of course, different model
structures and assumptions, and different
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applications would produce a different set
of results.
Multipliers
The value-added, income (wages and
salaries) and employment multipliers for
each model are presented in Table 2.
These multipliers represent the change in
value-added, income (wages and
salaries) and employment per million-
rand increase in final demand
expenditure of the sector in question. An
index is calculated for each multiplier
category with the SAM average
multipliers as base. The multipliers (see
Table 2) are derived from two models
which include: (a) a SAM (type III) model;
and (b) a CGE model under a short-term
closure scenario (the closure scenario for
the CGE model holds capital supply fixed
which represents a standard short-run
assumption.
Table 2: Value-added, income (wages and salaries) and employment (per million ZAR) multipliers
Category
SAM
CGE
Type III
Short-term
Value-
added
Income
Employmen
t
Value-
added
Income
Employmen
t
Agriculture
0.737 (6)
0.267
(9)
23.462 (1)
0.213 (7)
0.200
(7)
1.590 (4)
Mining
0.913 (2)
0.473
(2)
7.452 (6)
0.717 (1)
0.065
(8)
2.640 (3)
Manufacturing
0.616 (9)
0.295
(8)
7.962 (5)
0.567 (4)
0.722
(3)
6.652 (1)
Electricity and water
0.729 (7)
0.310
(7)
4.664 (7)
0.203 (8)
1.830
(1)
0.880 (9)
Construction
0.644 (8)
0.314
(6)
9.807 (2)
0.601 (2)
0.700
(4)
0.955 (7)
Trade and accomm.
0.822 (4)
0.387
(4)
8.662 (3)
0.467 (5)
0.060
(9)
3.310 (2)
Transport and comms.
0.756 (5)
0.326
(5)
3.060 (9)
0.199 (9)
0.585
(5)
0.890 (8)
Fin. and business
services
0.903 (3)
0.390
(3)
4.323 (8)
0.250 (6)
0.400
(6)
1.400 (6)
Community services
0.953 (1)
0.613
(1)
8.281 (4)
0.573 (3)
0.900
(2)
1.560 (5)
Mean
0.786
0.375
8.630
0.455
0.607
2.209
Index
1.000
1.000
1.000
0.579
1.618
0.256
Coefficient variation
0.014
0.012
36.079
0.049
0.301
3.459
Notes: Numbers in parentheses denote the rank.
With regards to the value-added
multipliers in Table 2, the SAM model
produces the largest multipliers, with an
average value of 0.786, because of the
additional induced demographic effects.
Similarly, the short-term CGE model
produces the smallest multipliers, with an
average value of 0.455 (or 57.9% of the
average SAM multiplier), as a result of
the constraints on capital supply. One
would also expect the SAM model to
produce smaller multipliers than the
short-term CGE model, because of the
marginal rather than average household-
induced relationships, and because
supply restrictions are relaxed. When
observing the multipliers in Table 2 these
expected differences are observed.
However, there are also some significant
differences in the distributions of the
multipliers for each model. For example,
the largest SAM multiplier is 0.953 in
community services, whereas the largest
CGE multiplier occurs in mining, with a
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value of 0.717 in the short-term. The
overall spread of values from the CGE
model is also greater, as a result of the
additional limited resource factor. Sectors
which have limited access to capital will
experience additional dampening effects,
while sectors which can easily draw
capital from other sectors will show
relatively larger multiplier effects.
In terms of relative sizes of the income
and employment multipliers, the same
general conclusions can be reached as
for the production multipliers (also shown
in Table 2), except for greater variation in
the SAM model. The CGE model gives
marginally greater relative multiplier
values, as a result of the Keynesian-type
closure. While the multipliers form one
basis for a comparison between these
models, they can be misleading in some
ways, if taken as a general guide to the
relative differences in any given
application.
The reason is that impact situations are
usually more complex, involving multiple
changes across a range of sectors. In the
following section, a case study is used to
highlight further the differences between
the models. These results should be
viewed in the context of the festival under
review. In view of the above
methodological and multiplier exposition
on SAM and CGE models, Aardklop
revealed the following results.
Model results
Aardklop comparative results
The impact scenario chosen for this study
is the impact of expenditures by visitors
who attended Aardklop in Potchefstroom
in 2010 on the North West economy. This
application presents as near as possible
a valid comparison of the two models,
since visitor expenditures can be
classified as final demand (final
consumption expenditure of visitors) in all
the models. As proposed by Stynes and
White (2006), a segmentation strategy
was followed where the expenditure data
were split according to the origin of the
visitors. Three groups were identified,
namely (i) visitors from the North West in
which the festival is held, (ii) visitors from
the rest of South Africa, and (iii) foreign
visitors. By splitting the respondents into
various groups, a more accurate value of
spending can be determined. It is often
argued that spending by locals (in this
instance, visitors from North West)
should be excluded, since it only
represents a shift in expenditure patterns
and not new money that flows into the
region. However, Crompton (2006)
indicates that there are two
circumstances when local spending can
be included; (i) when the existence of the
festival caused the residents to stay at
home rather than take a trip elsewhere,
referred to as the “deflected impact”, and
(ii) when a study of the significance of the
festival is made, i.e. the size and nature
of the influence that the festival has on
local economic activity. Since visitors
travel within the province to visit the
festival, it implies that they would travel to
another province if the festival took place
elsewhere. Therefore there is a strong
case that option (i) mentioned above is
true and the spending by visitors from the
North West is therefore included in the
analysis. The contribution is, however,
always listed separately in the analysis to
allow economic impact estimation with
and without locals spending. The
questionnaire is used to gather
expenditure information from visitors, but
some visitors travel with fellow visitors
(i.e. in groups).
The spending per group thus includes
spending by visitors and fellow visitors.
To determine the spending per visitor,
spending on entrance fees was used.
Given the amount spent on entrance fees
to the festival, North West visitors travel
African Journal of Hospitality, Tourism and Leisure Vol. 4 (Special edition) - (2015) ISSN:
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11
in visitor groups of 2.10, other South
African visitors in visitor groups of 2.06
and foreign visitors in visitor groups of
2.88. The magnitude of spending for
each category was therefore divided by
the number of visitors in the group, in
order to derive the value of spending per
visitor. Table 3 indicates this spending
per visitor based on the survey results
(columns 2, 4, 6 and 8) as well as the
visitors per group and indicates how the
classification by commodity was mapped
to the SAM and CGE sectors. Note that
this spending includes the accompanying
persons for whom the visitor is financially
responsible. Table 3 also shows the total
visitor expenditure per origin in the
festival area (columns 3, 5, 7 and 9),
derived from the total visitor numbers.
Table 3: Estimated per visitor and total visitor spending by visitor origin (in ZAR) and
mapping/classification of expenditure by commodity to SAM and CGE sectors
Spending item
Foreig
n
Total
Foreign
North
West
Total
North
West
Rest
of SA
Total
Rest of
SA
Total
per
visitor
spendin
g
Total
visitors
spending
Entrance fees
47
62,213
46
1,036,222
51
1,940,655
145
3,039,090
Accommodation
628
824,754
49
1,105,570
238
8,987,535
916
10,917,85
9
Food and
Restaurants
144
188,512
96
2,156,413
177
6,672,708
580
9,017,632
Liquor
53
69,348
101
2,261,652
150
5,659,410
304
7,990,411
Soft drinks
87
114,153
59
1,314,641
70
2,658,505
216
4,087,298
Performances
58
76,197
21
460,119
33
1,228,912
741
1,765,228
Retailers
91
119,860
58
1,306,650
91
3,424,356
240
4,850,866
Curios &
Memorabilia
36
47,259
8
182,291
14
512,475
962
742,025
Transport to
Aardklop
287
376,704
25
557,808
107
4,020,833
837
4,955,345
Transport at
Aardklop
49
64,496
11
251,193
52
1,976,841
113
2,292,531
Parking
16
20,833
10
215,007
21
780,263
46
1,016,103
Other
-
-
35
785,371
66
2,489,558
101
3,274,930
Number of
visitors (#)
1,313
1,313
22,41
4
22,414
37,74
2
37,742
61,469
61,469
Total (in ZAR)
1,496
1,964,33
0
519
11,632,93
6
1,069
40,352,05
1
5,200
53,949,31
7
Mapping/classification of expenditure by commodity to SAM and CGE sectors
SAM/CGE
sectors
Trade
-
453,917
-
6,184,834
-
16,985,38
6
-
23,624,13
7
Accommodation
-
886,967
-
2,141,792
-
10,928,19
0
-
13,956,94
9
Transport
services
-
441,201
-
809,001
-
5,997,674
-
7,247,875
Business
activities
-
161,412
-
1,496,931
-
3,170,980
-
4,829,323
Activities/service
s
-
20,833
-
1,000,378
-
3,269,821
-
4,291,032
Total (in ZAR)
-
1,964,33
0
-
11,632,93
6
-
40,352,05
1
-
53,949,31
7
Source of data: authors own calculations based on survey results
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Table 3 presents a breakdown of the
activity sectors where expenditure was
incurred. The total direct spending that
takes place in the North West economy
amounts to R83.9 million, of which R11.6
million is contributed by local visitors and
R40.4 and R2.0 million by visitors from
the rest of South Africa and abroad
respectively. The estimated spending
allows adjustments to exclude direct
spending that took place outside the
North West province. Such exclusions
encompassed the remuneration paid to
the majority of artists residing outside
Potchefstroom, Production Tax paid to
SARS in Pretoria, and commissions
payable to Computicket in Johannesburg.
The total spending by visitors from
different origins was allocated to the
categories of the North West SAM. Since
a multiplier approach is followed, distinct
multipliers for each expenditure-related
economic activity are applied. The
subsequent change in commodity
demand is therefore translated into a
change in economic activity by using the
SAM calculated multipliers the so-
called “corrected” direct impact of the
festival. The multipliers then convert the
spending into the associated increase in
production, income and employment
opportunities due to the circulation of the
additional spending through the local
economy. The expenditure data by
visitors have been deflated to 2006
values, allocated to industry sectors and
converted to producers values, as shown
in Table 3, to be compatible with the SAM
and CGE data. All results are expressed
in 2006 values. The implementation of
the impact analyses in all the models is
similar, in that the visitor expenditures are
incorporated into the models as final
demand shocks. The results pertaining to
the impact scenario on value-added,
income and employment are given in
Tables 4, 5 and 6 respectively. They
show the value impact of visitor
expenditure over the industrial sectors of
the total impact on the North West
economy. Across industries and in
contrast with a priori expectations, the
total impacts derived from the type III
SAM model are greater than those from
the short-term static CGE model, and
those obtained from the CGE model are
also the smallest. It can be expected that
the impacts from the SAM model will be
the largest, because of the additional
induced demographic effects.
Table 4: Distribution of value-added impacts (in ZAR millions, 2006 prices)
Category
SAM
CGE
Type III
Short-term
Foreign
North
West
Rest of
SA
Total
Foreign
North
West
Rest of
SA
Total
Agriculture
0.124 (7)
0.533 (7)
0.036 (7)
0.693 (7)
-0.003
(10)
0.139
(10)
-0.003
(10)
0.142
(10)
Mining
0.065 (9)
0.234
(10)
0.012
(10)
0.310
(10)
0.290 (8)
1.071 (8)
0.063 (8)
1.437 (8)
Manufacturing
1.073 (6)
4.220 (6)
0.246 (5)
5.539 (6)
2.073 (6)
7.645 (6)
0.418 (6)
10.179
(6)
Electricity and
water
0.061
(10)
0.237 (9)
0.014 (9)
0.311 (9)
0.263 (9)
1.053 (9)
0.055 (9)
1.380 (9)
Construction
0.115 (8)
0.363 (8)
0.016 (8)
0.494 (8)
0.814 (7)
2.911 (7)
0.160 (7)
3.901 (7)
Trade and
accomm.
16.754
(2)
55.621
(2)
2.592 (2)
74.967
(2)
3.554 (4)
14.166
(4)
0.890 (4)
18.668
(4)
Transport and
comms.
3.517 (4)
16.900
(3)
1.015 (3)
21.432
(3)
5.347 (3)
20.147
(3)
1.121 (3)
26.708
(3)
Fin. and business
4.403 (3)
11.964
0.569 (4)
16.935
6.181 (2)
20.684
1.125 (2)
28.098
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13
serv.
(4)
(4)
(2)
(2)
Community
services
2.968 (5)
9.855 (5)
0.219 (6)
13.042
(5)
2.754 (5)
10.012
(5)
0.610 (5)
13.365
(5)
Total (in ZAR
million)
29.080
99.927
4.719
133.723
21.274
77.830
4.440
103.878
Notes: Numbers in parentheses denote the rank.
In terms of the aggregate impacts, the
estimated value-added (Table 4) from the
SAM model is R133.72 million, while the
short-term CGE model produces the
lowest estimate of R103.88 million, or
only 78% of the SAM models value. The
corresponding multipliers are 0.403 and
0.519 respectively.
Table 5: Distribution of household income impacts (in ZAR millions, 2006 prices)
Category
SAM
CGE
Type III
Short-term
Foreign
North
West
Rest of
SA
Total
Foreign
North
West
Rest of
SA
Total
Agriculture
0.009
(7)
0.033
(8)
0.142
(7)
0.185
(7)
0.020
(9)
0.101
(9)
0.362
(9)
0.485
(9)
Mining
0.004
(10)
0.024
(9)
0.087
(9)
0.115
(9)
0.532
(2)
2.662
(2)
9.596
(2)
12.836
(2)
Manufacturing
0.069
(6)
0.302
(6)
1.187
(6)
1.558
(6)
0.104
(7)
0.518
(7)
1.867
(7)
2.498
(7)
Electricity and
water
0.005
(9)
0.021
(10)
0.084
(10)
0.110
(10)
0.014
(10)
0.072
(10)
0.258
(10)
0.345
(10)
Construction
0.005
(8)
0.036
(7)
0.112
(8)
0.153
(8)
0.038
(8)
0.188
(8)
0.678
(8)
0.907
(8)
Trade and
accomm.
1.059
(2)
6.848
(2)
22.735
(2)
30.643
(2)
0.188
(5)
0.939
(5)
3.386
(5)
4.529
(5)
Transport and
comms.
0.332
(3)
1.150
(5)
5.525
(4)
7.007
(4)
0.192
(4)
0.962
(4)
3.466
(4)
4.637
(4)
Fin. and
business serv.
0.232
(4)
1.798
(4)
4.885
(5)
6.915
(5)
0.157
(6)
0.786
(6)
2.832
(6)
3.788
(6)
Community
services
0.137
(5)
1.860
(3)
6.174
(3)
8.171
(3)
0.281
(3)
1.403
(3)
5.058
(3)
6.766
(3)
Total (in ZAR
million)
1.935
11.929
40.992
54.856
1.526
7.631
27.504
36.791
Notes: Numbers in parentheses denote the rank.
Table 6: Distribution of employment impacts (# employed)
Category
SAM
CGE
Type III
Short-term
Foreign
North
West
Rest of
SA
Total
Foreign
North
West
Rest of
SA
Total
Agriculture
3 (7)
11 (7)
1 (7)
14 (7)
-3 (9)
-13 (9)
-44 (9)
-59 (9)
Mining
0 (9)
1 (9)
0 (9)
1 (9)
-6 (10)
-30 (10)
-108
(10)
-144
(10)
Manufacturing
4 (6)
14 (6)
1 (6)
18 (6)
0 (7)
-2 (7)
-6 (7)
-8 (7)
Electricity and
water
0 (10)
1 (10)
0 (10)
1 (10)
0 (6)
0 (6)
12 (6)
14 (6)
Construction
1 (8)
2 (8)
0 (8)
3 (8)
-1 (8)
-3 (8)
-11 (8)
-15 (8)
Trade and
accomm.
95 (2)
314 (2)
15 (2)
424 (2)
9 (3)
18 (4)
107 (3)
134 (3)
Transport and
comms.
5 (5)
25 (4)
1 (3)
31 (5)
2 (5)
8 (5)
36 (5)
46 (5)
African Journal of Hospitality, Tourism and Leisure Vol. 4 (Special edition) - (2015) ISSN:
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14
Fin. and
business serv.
9 (4)
23 (5)
1 (5)
33 (4)
5 (4)
28 (2)
86 (4)
120 (4)
Community
services
15 (3)
51 (3)
1 (4)
68 (3)
17 (2)
27 (3)
120 (2)
152 (2)
Total (in ZAR
million)
131
442
20
593
24
33
193
238
Notes: Numbers in parentheses denote the rank.
This last point is brought out in Tables 5
and 6, which show that the income and
employment flow-ons for the CGE are
much smaller relative to the SAM model
than those for value-added, indicating the
greater role played by marginal labour
productivity changes. For example, the
short-term CGE model produces only
67.1% of the household income impact
and 40.1% of the employment impact of
the SAM model (see e.g. Sun and Wong
(2010) for a more detailed exposition of
the overestimation of employment effects
of short-run events). The length of the
festival should also be kept in mind when
measuring economy-wide impacts of
such events. In this case Aardklop takes
place over a period of 5 days. Therefore,
even if 70,000 tickets were sold in 2010,
the festival is estimated to generate an
extra 593 and 238 jobs (according to the
SAM and CGE respectively). The SAM
model is an annual snapshot of the
economy. There may be an extra 593
and 238 employed for 5 days but
averaged over the year, the employment
impact is negligible. Further, other
research among business owners
suggests that at festival time, they do
employ more people but may extend the
hours of existing employees or work the
existing employees harder. In fact only
19% of businesses in the survey state
they employed more staff during the
event (5 days).
Findings and implications
This article explored the possible
variance of results, in order to improve
the quality of information for decision-
taking, when the SAM and CGE
methodologies were employed to
measure the economic impact of
Aardklop. The following emerged from
this research:
Firstly, this article confirms previous
findings that when different measuring
tools are applied to the same data (of the
same event, in this instance Aardklop), it
is likely that very different results will be
obtained (Van Wyk, 2011). It is therefore
critically important for economic
assessors to pay serious attention to the
purpose, scope and characteristics of
models that measure economic impact
results before interpreting them. Ignoring
the purpose and intention of each model
applied may lead to misinterpretation and
inaccurate conclusions that can mislead
stakeholders, which can also lead to bad
decisions. Therefore the more accurate
the model, the better quality of
information and results are available.
Secondly, based on the modelling
results, the general distributions of the
impacts across the industrial sectors
agree more or less with expectations.
The largest effects occur in those sectors
directly affected by visitor expenditure,
i.e. trade and accommodation, and
transport and communication. With
income (wages and salaries) and
employment, the rankings differ
marginally but, overall, the distributions
are much the same. Obviously, labour
has a greater impact on labour-intensive
industries (such as service industries)
and less impact on manufacturing and
other more capital-intensive industries.
Generally speaking, the SAM model
produces relatively larger impacts in the
African Journal of Hospitality, Tourism and Leisure Vol. 4 (Special edition) - (2015) ISSN:
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15
manufacturing sectors and smaller
impacts in the service sectors,
particularly with respect to wages and
employment. In other words, the service-
type industries are better able to support
the increase in tourist activity largely
within existing resources, whereas
manufacturing-type industries, which
have more rigid value-added structures,
respond in a manner closer to that of the
Leontief value-added system. Yet, the
CGE model results in a much larger
redistribution of resources among all the
sectors in the economy; in particular,
from agriculture, mining, metal products,
manufacturing and construction (which all
experience negative flow-on effects) to
the sectors most affected by the boost in
tourist activity, i.e. trade and
accommodation, and transport and
communication, financial and business
services, and community services. This
occurs as capital is drawn away from
those sectors with more abundant and
less efficient usage, going to those
sectors in greater need in the short term.
In terms of additional job opportunities
resulting from Aardklop, the SAM model
indicates a much more optimistic amount
of additional positions created, recording
593 compared to the 238 jobs measured
by the CGE model.
Thirdly, a previous study conducted on
the economic impact assessment of the
Klein Karoo National Arts Festival in
South Africa, suggests that the local
community supports the festival more
than foreign visitors and visitors from the
rest of South Africa do. Fifty-eight percent
(58%) of visitors who attended the
festival originated from the local Western
Cape Province (Erasmus, Slabbert,
Saayman,, Saayman & Oberholzer,
2010). However, by contrast, in this study
of the economic impact assessment of
Aardklop, visitors from the rest of South
Africa support the festival significantly
more than local visitors do. This may be
ascribed to the geographical location of
the hosting community. The fact that
Aardklop (Potchefstroom) is located
closely to the densely populated Gauteng
Province may be a reason why the
festival is considerably better supported
by visitors from the rest of South Africa,
than it is by locals. Botha et al. (2010)
report that 62% of visitors who attended
the 2010 Aardklop were from provinces
other than North West. Gauteng visitors
were estimated at 39% of the total
visitors. The obvious positive impact that
the geographical location of an event
may have, provides an opportunity for
organisers to explore expansion
opportunities, such as possible
commuting facilities and packages, to
further increase visitor attendance.
Fourthly, this article, confirms that the
difference in measured economic impact,
when applying various assessment tools
to the same event, may be ascribed to
the characteristics of the specific model
used and therefore careful consideration
of the conditions, context and main aim
for conducting an economic assessment
must be made, since this will provide
better quality results and information.
SAM models, based on I-O models, are
regarded as fairly simple, quick, reliable,
effective, efficient and flexible, making
use of readily available data. In contrast,
CGE models are known for making use
of detailed and informative economic
modelling techniques. Such models are
normally utilised to address specific what-
if economy-wide scenarios used in
surveys where a large shock is to be
applied to a complex economy. Perhaps
because of their accuracy (quality) and
flexibility, CGE models seem to be the
preferred tools to measure economic
impact as they may overcome many of
the limitations experienced with SAM
models, including supply constraints and
price movements. Consequently, these
models are often applied during
African Journal of Hospitality, Tourism and Leisure Vol. 4 (Special edition) - (2015) ISSN:
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16
economic impact studies at national level,
but have limited use during lower level
surveys.
The fifth finding indicates that the
methodological application of
assessment models is not without
limitations. Despite the development of
SAM models (based on I-O models) with
multiplier effects, certain methodological
problems may persist, such as outdated
data that are used in order to publish
tables, published tables that are not
applicable to the level or region they are
being applied to, trusting in
recommendations made by
inexperienced analysts, etc.
Finally, this article confirms that the
interpretation of economic impact results
obtained from applying various
measuring tools such as SAM and CGE
models may have unintended
consequences for the various
stakeholders involved, such as event
organisers, visitors, the hosting
community and academic scholars. The
results obtained in this study confirm that
the utmost caution should be taken when
decisions are to be made based on the
results, that quality information is
paramount. Not only may the
(inappropriate) results have an adverse
effect on all stakeholders, but they may
even jeopardise the existence of the
event itself.
Conclusions
The aim of this article was to interrogate
and illustrate the findings of previous
studies that applied different measuring
tools to an event in order to assess the
resulting economic impact, in order to
produce quality information for decision-
taking. The discussion in this article
therefore articulates the assessment of
the economic impact of Aardklop when
applying SAM and CGE models.
This article confirms the finding of
previous studies indicating the variance
in measured economic impact results.
This is emphasised by an even larger
difference in results when Aardklop data
were assessed. Due to the variance in
the measured results that different
models produce, very serious and
deliberate consideration should be given
to the preferred model that is utilised. A
hasty approach to the choice,
interpretation and application of
assessment models must be avoided as
inappropriate result information may
adversely influence decision-taking and
have serious consequences for all
stakeholders depending on the
sustainability of an event. For this reason
quality management is not always about
the level of service one renders or the
quality of products one uses, but also
quality information for decision-taking.
The literature study and an even larger
difference in the economic impact as
measured by SAM and CGE for Aardklop
confirms that CGE assesses economic
impact more conservatively.
This article provides an important
contribution to the discussion of which
assessment tools should be chosen as
the tool to measure the economic impact
of a specific event, in order to make a
contribution to improving the quality of
decision-taking. To date, only limited
studies have been conducted within the
South African context where different
models that measure economic impact
have been applied to a chosen event.
Furthermore, this article affirms that,
regardless of the assessment method or
measuring tool that is applied, popular
national events will, doubtless, have a
variable impact on the economy. Further
research will have to be conducted as
only two models, namely SAM and CGE,
were applied to measure the economic
African Journal of Hospitality, Tourism and Leisure Vol. 4 (Special edition) - (2015) ISSN:
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17
impact of Aardklop. The remainder of the
three most popular models, an I-O model,
was excluded from this study. Literature
studies show that I-O models to measure
economic impact of event are frequently
applied, especially to evaluate the impact
of smaller events. When a regional town
hosts a local event, the event will attract
visitors from surrounding areas bringing
new expenditure to the town, although
perhaps very little to the province as a
whole. Therefore, a significant economic
impact may be measured by the town but
the impact on the economy of the
province may be hardly noticeable.
Regional models should aim to measure
the money flow and impact on the local
economy due to hosting an event, and
therefore results should be more
accurate and relevant than when
applying models that were developed for
provincial or national levels. An economic
assessment that includes an I-O model
may provide an even broader platform to
assess the economic impact of events.
For future research, it is suggested that
an economic assessment should be
conducted on the same set of Aardklop
data, but applying an I-O model. The
outcome thereof may confirm or
contradict the assumption that various
models of economic assessments
produce different outcomes.
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... Traditionally, one prevailing approach for conducting economic analyses in tourism is the input-output (IO) methodology (Hara, 2008). Although it is especially important to take into consideration the limitations of this methodology (Van Wyk et al., 2015), a common issue observed in most tourism IO studies is that the base of analysis is given by either the most recent available IO model for one particular (calendar) year (Ü nlü ö nen et al., 2011; or by a comparison of two (calendar) years separated by a gap of several years (Surugiu, 2009;Sun and Wong, 2014). Researchers usually argue that underlying IO tables are infrequently published, e.g. ...
... Social accounting matrices (SAMs), as an extension of the IO framework, incorporate a comprehensive view on the economy by considering the detailed roles of (disaggregated) households, factors of production and social institutions (Miller and Blair, 2009). Nevertheless, the IO/SAM framework is often criticized for its underlying assumptions and limitations that might lead to false conclusions if wrongly interpreted (Van Wyk et al., 2015). More precisely, assumptions behind IO/SAM imply that industries consist of linear input structures; produce one representative good or service; exhibit constant return to scale; and are capable to provide unlimited labour and capital at fixed prices (Miller and Blair, 2009). ...
... Despite their well-documented limitations (Dwyer et al., 2004), literature argues that IO models are a reasonable compromise (especially regarding its time and data efficiency), if a study's purposes are well-considered and underlying methodological assumptions and limitations are clearly interpreted (Hara, 2008;Martínez-Roget et al., 2013;Robison, 2009;Van Wyk et al., 2015). IO models are particularly suggested as an appropriate tool if the purpose of the study is to measure the gross change in economic activity associated with tourism activities (Watson et al., 2007, p. 142), such as an ex post perspective of tourism's current or past economic contribution to a regional economy. ...
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