An Economic Analysis of Community-Based Tourism in Thailand
Using Village Computable General Equilibrium Model
Komsan Suriya
ZEF b
Center for Development Research (ZEF)
University of Bonn, Germany
1. Rationale
Tourism is an important source of foreign currencies for Thailand. During 2003 –
2007, the average earning from tourism was US$11,292 Million per year (Bank of Thailand,
2008a and 2008b). Tourism income was around half (51.53 percent) of total service income
and around 10 percent of total exports. Moreover, it surpassed foreign direct investment, 1.65
times on average. Besides, inbound-tourism income was around 5.91 percent of GDP on
average during 2003 - 2006 (Table 1).
Table 1: Tourism income and balance of payment of Thailand
Unit: Millions of US Dollars
Average
Items
2003
2004
2005
2006
2007
(2003 –
2007)
Tourism income
7,855 10,057
9,576
13,400 15,573
11,292
Service income
15,801 19,050 20,165
24,833 29,008
21,771
Exports
78,105 94,941 109,362 127,941 151,147
112,299
Foreign Direct Investment
4,614
5,786
7,545
7,978
8,284
6,841
GDP at current price
142,647 161,338 176,405 206,705
171,774
Tourism-service ratio (%)
49.71
52.79
47.49
53.96
53.69
51.53
Tourism-exports ratio (%)
10.06
10.59
8.76
10.47
10.30
10.04
Tourism-FDI ratio (%)
170.24 173.82 126.92
167.96 187.99
165.39
Tourism-GDP ratio (%)
5.51
6.23
5.43
6.48
5.91
Source: Bank of Thailand, 2008a and 2008b.
Both non-agricultural sectors and agricultural sectors benefits from tourism. In case of
Thailand, Wattanakuljarus (2006) used Computable General Equilibrium (CGE) model to
simulate a scenario of 10 percent of inbound tourist expansion at the national level. The
results showed that the non-agricultural wage income increased by 3.72 percent while the
agricultural wage income also increased by 2.53 percent. Non-agricultural capital income
increased 3.88 percent whereas agricultural capital income also increased by 2.50 percent.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
2
However, tourism income was heavily distributed to the rich rather than the poor.
From the same CGE simulation, Wattanakuljarus (2006) also found that tourism corporations
shared the largest portion of tourism income (38.87 percent). The richest 20% in nonagricultural sectors shared 34.02 percent. The richest 20% in agricultural sectors shared 2.89
percent. The poorest 80% in non-agricultural sectors gained 16 percent while the poorest 80%
in agricultural sectors gained only 8.21 percent. The studies of Kaosa-ard (2006), Untong et
al (2006), Prachvuthy (2006) and Oula (2006) also showed that the distributions of tourism
income were heavily biased to the rich.
Community-based tourism (CBT) was thought to be an alternative tool to carry more
tourism benefits to the poor. CBT arose after the Earth Summit in 1992 at Rio de Janeiro1 in
accordance with the Agenda 21 (Phayakvichien, 2005). There are several definitions of CBT.
The accordance among definitions given by World Bank (2000), UN-ESCAP (2001), REST
(2003), and Ashley, Roe and Goodwin (2001) can be compiled as follow. Community-based
Tourism is tourism that emphasized the ownership, management and involvement of
communities’ members in the tourism activities. CBT is not just an ecotourism. While
ecotourism focuses on ecological friendliness, community-based tourism focuses on the
participations of villagers in the tourism activities and the sharing of tourism benefits among
villagers.
International organizations concerned CBT as a mean for development. The United
Nations declared the year 2002 as the International Year of Ecotourism (UNEP, 2002). The
World Bank arranged a work shop on CBT (World Bank, 2000). The United Nations carried
out a study of the effect of CBT to poverty reduction (UNDESA, 1999). CBT was also
included in national development strategies of several countries such as South Africa
(Brennan and Allen, 2001) and Namibia (Ministry of Environment and Tourism of Namibia,
1995).
Community-based tourism in Thailand was heavily expanded during the Amazing
Thailand years, 1998 -1999 (Homestay Thailand, 2007). Before that period, the tourism in
1
The United Nations Conference on Environment and Development (UNCED), Rio de Janeiro, 3 – 14
June 1992. The informal name was the Earth Summit. Number of participants at level of heads of
state of government was 172,108. Number of representatives of NGOs was 2,400. The conference
gave a result of Agenda 21, the Rio declaration on environment and development, the statement of
forest principles, the United Nations framework convention on climate change and the United Nations
convention on biological diversity. (UN, 1992)
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
3
rural villages can be dated back to 1960. It was limited to the provision of accommodations
for trekkers, social activists and rural developers which was called “homestay”. Extending
from accommodations service, villagers offered ecotourism and soft adventures for visitors.
Then CBT was gradually developed in villages along with the growing participations of
village members. The expectation for CBT to the rural village economies encourages
government agencies to get involved to the activity. Tourism Authority of Thailand (TAT)
started in 2007 to give the so called “The Most Outstanding Community-Based Tourism
Award” to 62 villages out of 183 candidates (TAT, 2007).
While CBT is heavily promoted in Thailand, its viability and sustainability are
dubious. There are four problems associated to CBT. The first problem is the low income
generation. CBT takes time to deliver benefits to villagers. Therefore, in the beginning
period, income generation is low (Lynn, 2003). It is also added by Strasdas (2005) that
communities need at least 5 years before they generate substantial income for the villages.
Although most of villages want CBT for additional income, they confronted with the
opportunity cost obtained from agricultural productions Rozemeijer (2001). In this case, they
might not be patient enough to wait for the growing stage of CBT.
The second problem is the uneven tourism income distribution. Tourism income
distribution is more uneven than agricultural income (Kao-saard, 2006; Untong et al, 2006;
Oula, 2006; Prachvuthy, 2006). Tourism activities were controlled by village’s leaders and
the richest group of villagers who occupied more capitals. The situation violates the ideology
of CBT which aims for the sharing benefits among villagers.
The third problem is the fluctuation of income due to the tourism seasonal effect.
During the tourism high season, around 4 months from November to February in Thailand,
villagers abandoned their lands to participate in tourism. Therefore, the agricultural lands
were degraded because of less effort for the maintenance (Lynn, 2003). However, during the
tourism low season, people have to switch back to agricultural production because of less
tourism activity. They may find the decrease in agricultural outputs due to less fertility of
soil. There may be some households who totally switched to tourism sector may have to
switch to other non-agricultural sectors. The whole economic structure of a village may be
totally different between the high season and low season.
The fourth problem is the ignorance of the failures of CBT. Only a few studies
admitted the failure of CBT. Strasdas (2005) brought an argument that most CBT projects,
especially in South America, were failed. Brennan and Allen (2001) also insisted that a CBT
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
4
in South Africa was failed even after 7 years of operations. The ignorance of the failure of
CBT is thus dangerous for communities who adopted CBT without any concern and any
preparation for the tourism collapse.
To address these four problems, there are rooms for a quantitative study to investigate
the impact of CBT at the village level. In the issue of income generation, there are two points.
The first point is the underestimation of tourism income. Low income generation may be
underestimated due to only direct effect was evaluated. Including the indirect effect may find
more income generation for CBT to a village. Mitchell and Ashley (2007) argued that the
indirect effect may share 50 – 90 percent of the impact of tourism. However, the study was
still at the national level not at the village level. The second point is the sensitivity of tourist
quantity and tourism price to the effects of income generation. How many tourists needed for
shifting the average household’s income in each household group to be above the poverty
line? How much of the tourism price that will yield the same result? How much is the
trading-off between the number of tourists (the tourism quantity) and the tourism price (the
tourism quality)? These questions remained unanswered.
The issue of income distribution was clearly revealed by the quantitative studies of
Kao-saard, (2006), Untong et al, (2006), Oula, (2006) and Prachvuthy, (2006). However, only
the income distribution within tourism sector, the direct effect, was investigated. The
distribution of income from tourism sector to other sectors was not included. It is
hypothesized by Untong et al (2006) that if the indirect effect was included, the tourism
income distribution may be more even. This hypothesis was strongly supported by Mitchell
and Ashley (2007) who emphasized the significance of the indirect income distribution
because tourism requires a range of supply chains that can extend deep into the host
economy.
The seasonal effect is an annual shock due to the difference of seasonal weather
conditions. The issue were mentioned by several literatures such as but not quantitatively
quantified (Alvord et al, 2007; Wilton and Wirjanto, 1998). Only time series analysis takes
the seasonal effect into account (Shariff and McAleer, 2004). There is still no quantitatively
comparative statistic analysis between the tourism high and low seasons at the village level.
Besides, a qualitative question how villagers prepare for the fluctuation in tourism income in
a CBT project was not answered by literatures.
For the issue of tourism collapse, there is no quantitative study investigating in case of
CBT. However, at the national level, the effect of tourism collapse, the suddenly negative
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
5
shock of tourism demand, was quantitatively evaluated by Wattanakuljarus (2006) with the
same CGE model. The study found that the ability of assets to mobile across sectors
determined the size of the effect. In contrast with the income distribution problem, the richest
20% were at the most risky situation whereas the low-income households were least affected
due to the occupation of fixed assets in tourism sectors. However, this study was at the
national level. It is interesting to answer some crucial questions related to these shocks. What
will be happened if CBT is failed? How much income will be reduced? Can villagers who
participate in tourism switch back to agricultural production?
A crucial mechanism for the quantitative analysis of the four problems is the
switching effect between tourism sector and non-tourism sectors. The switching effect will
indicate how many people will switch from non-tourism sectors to tourism sector in response
of the changes of economic variables. It will also indicate the backward switching from
tourism sector to non-tourism sectors while the economic situation in the tourism sector gets
worse. Questions related to the switching effect will be found out before other parts of the
analysis. Some questions are such as when a farmer will switch to tourism? After the
switching, what they do to their lands? Are there anybody else come to take over or rent the
land for the agricultural production? Will they convert their lands into a campsite or a resort?
How do they allocate their time for both tourism activities and agricultural production? Will
the drops of agricultural production during the tourism high season induce the import of
agricultural products from outside the village? Will agricultural productions drop due to the
less fertility of soil? How villagers switch back to agricultural production or switch to othernon-agricultural sectors during the tourism off-season? How much is the fluctuated income
between the high season and off-season?
With these rooms for the quantitative analysis, it is interesting to construct a CGE
model at the village level, VCGE, to provide a concrete economic impact of communitybased tourism to a village economy. The model will take into account both direct and
indirect effect of income generation and income distribution. The switching effect between
non-tourism activities and tourism activities can be investigated. It will be able to simulate
scenarios such as changes in number of tourists and changes of tourism price. The simulation
will reveal the seasonal effect between the tourism high season and low season which may
significantly change the economic structure of a village. The scenario of tourism collapse can
be also simulated.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
6
The outputs obtained from this study will enable stakeholders to understand more on
the effects of CBT to a village economy. They may suggest donors and government agencies
to strategic issues which enhance tourism benefits to the poor. They will reveal the seasonal
effect to communities in order to carry appropriate preparations for the fluctuation of tourism
income. They may also deliver a warning to all partners about the effects of the tourism
collapse before they choose to start a CBT project.
2. Objectives
2.1 Investigate the income generation and income distribution caused by
community-based tourism in a village by taking into account both direct and
indirect effect
2.2 Investigate the seasonal effect of community-based tourism in a village
2.3 Investigate the effect of community-based tourism collapse in a village
2.4 Investigate the switching effect of households between non-tourism sectors and
tourism sector in a village
3. Conceptual framework
This section will discuss five issues related to CBT. Income generation and income
distribution will begin the section following by the seasonal effect, the tourism collapse, and
the switching effect. Apart of the literature review upon the issues, ideas how to model the
issue quantitatively will be discussed.
3.1 Income generation and income distribution
Goals and practices of CBT regarding to income generation and income distribution
are mismatched. Communities expect for high income generation but it is not an ultimate
goal of CBT. Income distribution which is the goal was ignored and not achievable. The
following section will discuss these topics. The section will begin with income generation.
Then the issue of income distribution will be followed.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
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a) Income generation
CBT is not initially aimed for high income generation. It is just a tool for villagers to
seek for additional income (Strasdas, 2005). Although CBT gets involved in business
activities, it is not a fully commercial project. The practice of villagers is also not as
professional as in tourism industry in cities. The carrying capacity is also limited.
The average income per household from CBT did not exceed the average income
from agricultural production according to Untong et al (2006), Oula, (2006), and Prachvuthy
(2006). In case of Mae Kam Pong village in Thailand, the average tourism income in 2003
was US$175 while the average non-tourism income was US$750. In case of Nammat Mai
village in Lao PDR, the average tourism income was US$28 whereas non-tourism income
was US$38. In case of Chambok village in Cambodia, tourism activities yielded only US$26
per household per year while agricultural activities yielded US$158. Most villagers
participating in tourism sectors concerned the activities as part-time jobs.
Raising CBT income can be possible by working together between communities and
professional tourism operators (Goodwin, 2006). It was also shown that a joint-venture
between communities and professional tourism operators yielded better outcome for the
income generation than either letting private enterprises take over the whole tourism activities
in a village or letting the community operate the CBT alone (Ashley and Garland, 1994).
Apart of increasing in tourist quantity, raising tourism standard and providing high
quality tourism service will be able to boost up the tourism income via higher tourism price.
Phayakvichien (2005) stated that the Thai government can still find rooms to improve
tourism products for communities such as upgrading services, finding better selling points,
and investing in human resource. Following his speech, the award of outstanding CBT
initiated by Tourism Authority of Thailand (TAT) in 2007 did not only emphasize on the
potential of income generation but also the adoption of the sufficiency economy, the
preservation of the local way of life, the offer of a diverse-range of tourism related activities,
the meaningful exchange between the hosts and the guests and the effectiveness of local
resource management.
Tourism income generation is not limited to only direct payment from tourists to
villagers. There is indirect effect which is the expenditures from tourism sector to other nontourism sectors. Mitchell and Ashley (2007) emphasized the significance of this indirect
effect. They calculated that around 50 – 90 percent of the impact of tourism came from the
indirect effect. The expenditures include spending of tourism staffs on food and non-food
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
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consumption, purchasing of intermediate goods for tourism activities, and money transfer
from tourism sectors to other public institutions such as temples and schools. Kaosa-ard
(2006), Untong et al (2006), Oula, (2006), and Prachvuthy (2006) studied the income
generated by CBT but the studies did not take into account of the indirect effect and the
general equilibrium.
Both the direct and indirect income generation can be captured by using Input-Output
(I-O) table and Social Accounting Matrix (SAM). Village Computable General Equilibrium
(VCGE) model will enable the simulation of the change of number of tourists (the issue of
tourism quantity) and the tourism price (the issue of tourism quality) to the income
generation. It can answer some key questions. How many tourists or how much of the
tourism price can lift the average household income above the poverty line? The details of the
methods will be discussed in the methodologies section.
Another critical question regarding to the income generation is the trading-off
between the target of poverty alleviation and the loss in other dimensions. Even though
government agencies as well as villagers would like to see higher figures of tourism income,
it should not be a goal for encouraging CBT to host much more tourists without limit.
Phayakvichien (2005) warned that concentration just on the rapid growth of tourists will
create only more loss in the long term and will negatively affect the community’s economy
and society. The limit of carrying capacity will cause the unbalance between supply and
demand. The degradation of natural resources and the insecurity can also be problems if the
tourism sector expands too much. Thus, it is interesting to find out what will happen if a
village expands its carrying capacity to the level that poverty will be alleviated? Will there be
significant loses in other sectors? These questions can also be analyzed by the VCGE model.
b) Income distribution
A major goal of CBT is to share the income to village members as wide as possible.
However, the goal was not achieved to Kaosa-ard (2006), Untong et al (2006), Oula (2006),
and Prachvuthy (2006).
Villagers could not share the tourism income if they did not participate in CBT. There
are several hypotheses why villagers did not participate in tourism activities. Firstly, the
relative return in non-tourism sectors was higher than in tourism sector. Prachvuthy (2006)
reported that 56 percent of villagers in Chambok village in Cambodia did not participate in
tourism activities. More than half of them revealed that they would like to grow vegetables
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
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and fruits instead. The return from industrial and commercial activities (US$203 per
household per year) and agricultural activities (US$158) were apparently higher than tourism
activities (US$26). Untong et al (2006) also reported that only 30 percent of villagers in Mae
Kam Pong village in Thailand participated in tourism. Households gained average income
from non-tourism and tourism activities around US$750 and US$175 per year respectively.
In contrast, it was not a case in Nammat Mai village inLao PDR where a narrower gap of the
relative returns was observed. Oula (2006) reported that almost every villager participated in
tourism. The average non-tourism income was around US$38 per household per year whereas
tourism income yielded around US$28.
Secondly, the poor might be interested in tourism but could not enter the sector. They
might lack of necessary capitals and skills necessary for tourism activities according to
Untong, et al (2006), Prachvuthy (2006) and Oula (2006). Villagers who could not provide
standard service to tourists were not selected by village’s leaders to participate in tourism. In
contrast, in a village where there was no need for advanced physical capitals and advanced
skills, Oula (2006) found that most of villagers participated in tourism activities. CBT in
Nammat Kao and Nammat Mai villages in Lao PDR were heavily related to primary life style
of villagers.
Although the studies of Kaosa-ard (2006), Untong et al (2006), Oula (2006), and
Prachvuthy (2006) provided a perfectly clear picture of uneven tourism income distribution,
they were limited to only direct effect which villagers received income directly from visitors.
They did not follow the expenditure spent by the tourism sector to other sectors in the village
which is the indirect effect. They also hypothesized in their own literatures that when the
indirect effect is included, the result may be changed in a better way. It is therefore
interesting how much of the improvement of the tourism income distribution if the indirect
effect is also calculated. This answer required a SAM for the investigation of both direct and
indirect effects.
Another point is there was no literature exactly answered whether the tourism income
distribution will be improved when more villagers are enhanced to participate in tourism. It
may be possible that after every villager participated in tourism, the income distribution will
be still uneven. There is no mechanism to ensure the relationship between number of
participants and the equality of income distribution. The answer to this question will clarify if
a low participatory ratio caused the uneven tourism income or CBT by itself cannot provide
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
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the equally distributed tourism income. The simulation in a VCGE model will be able to find
out the answer. The methods will be discussed in the methodologies section.
3.2 Seasonal effect
Tourism cannot avoid the seasonal effect (Alvord et al, 2007; Shariff and McAleer,
2004; Wilton and Wirjanto, 1998). Seasonal effect is the variation of tourism demand due to
the change of seasonal weather. The high season of tourism in Northern Thailand starts from
November and lasts until February. During this period, the rain stops and the weather gets
colder especially in mountainous areas. For tourism in the South where sun, sand and sea are
the selling points, the high season extends to the middle of April before the first monsoon of a
year comes.
Low season is a hard time for seeking income from tourism. Workers in tourism
industry actually manage to adapt themselves for the survival during the period. Some taxi
drivers switch to agricultural productions (Suriya, 2004). Transportation operators switch
their target groups from tourists to agencies or companies which arrange incentive travels for
employees.
CBT is also affected by the seasonal effect. There may be absolutely no tourism
demand in some villages during the rainy season. The inaccessibility by cars to the villages
may be impossible because of the inappropriate road condition. At Huay Hi village in
Northern Thailand whose CBT got the best outstanding award from the Tourism Authority of
Thailand in 2007, even villagers have to walk 20 kilometers downhill on a muddy and
slippery road from the village to downtown without any possibility of motorcycles or cars
(Suriya, Srichoochart and Pruekruedee, 2007).
The whole economic structure of a village may be totally different between the high
and low seasons. The structural change can be modelled by two SAMs. The first SAM will
represent the high season and the second one will be for the low season. Comparative static
analysis will be applied for the comparisons of the structural change. These two SAMs will
also allow the VCGE models to provide different results of income generation and income
distribution between the high and low seasons. The detail of the construction of both VCGE
models will be discussed in the methodologies section.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
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3.3 Tourism collapse
Tourism collapse can be viewed as a suddenly negative shock to tourism demand due
to insecurities such as terrorism, violence, political unrest, disease outbreak, and disaster.
(Gardner,2001; Steinmueller,2005; Wattanakuljarus,2006). It can also be viewed as a fading
of tourists over years due to failure in tourism management and marketing (LA Times, 2008;
Sokwanele, 2004). Most of CBT projects in Africa were failed due to unprofessional
management and marketing (Strasdas, 2005). Provisions of tourism facilities in many CBT
projects were not appropriate for generating income (Goodwin, 2006). Failing to construct a
joint-venture between communities and professional touring agencies may also fail the CBT
projects in the long term. (Ashley and Garland, 1994).
An interesting point is the relationship between size of the tourism sector and size of
the effect. While the effect should be proportional to the size of tourism sector, it is curious
whether villagers who participate in CBT have prepared something for their securities in case
of the tourism collapse or not. Does the prosperity in tourism sector give them opportunity to
save more or to invest in other sectors or just encourage them to consume more? Will they
have enough savings to survive without working for years? Can they switch to other sectors
where they have invested? Will the collapse destroy everything in their lives? Will the
collapse force them back to agricultural sector without any advantages over other farmers
who initially did not participate in tourism?
The tourism collapse can be simulated in the VCGE model. Starting with a village
economy with tourism sector, a simulation can raise the tourism income to a certain level.
Then drop the tourism income down. The result will reveal what will happen if a CBT is
collapsed from that particular level. Varieties of tourism income levels can be set. Then the
sizes of effect at different sizes of tourism sector will be observed. However, some questions
mentioned in the last paragraph may need additional qualitative analysis for the answers. The
detail will be discussed in the methodologies section.
3.4 Switching effect
There is still no quantitative study investigating the switching effect from non-tourism
sector to tourism sector at a village level. Therefore, this study will construct the the
conceptual framework of the switching effect by a modification from other related concepts.
One of the closest concepts to the switching effect is migration. In a Computable
General Equilibrium (CGE) model for regional analysis conducted by Vargas et al (1999),
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
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labors migrate from one region to another region because of the difference between the
relative wage rate. Equation (1), modified from Vargas et al (1999), stated that migration
depends on the relative wage rate.
 PW 
LMG  LRO    log 

 PR 
…..(1)
LMG = Number of migrated labors from a region to other regions
LRO = Initial number of labor in a region

= Labor migration elasticity
PW = Wage rate in other regions
PR
= Regional wage rate
In equation (1), if the regional wage rate and the wage rate in other regions are equal,
the migration will be zero. When there is difference between both wage rates, there will be
migrations.
A problem from this concept is that the threshold of the switching is not captured. As
long as the relative price changes, the switching effect plays its role immediately. Practically,
there may be a threshold where the switching effect starts to work, not immediately after the
change of the relative price. To integrate the threshold into the switching effect, a
modification of equation (1) was presented below.
 PW   
LMG  LRO    log 

 PR 
…..(2)
 = Threshold of migration
In equation (2), if the threshold of migration (  ) is positive, then the wage rate in
other regions must be higher than the sum of the regional region and the threshold to make
labors migrate from this region to other regions.
A problem still appears from this equation. It contains a negative value, the migration
from other regions to the region, when the wage rate in other regions is not higher than the
sum of the regional region and the threshold. The relationship between the relative wage rate
and the migration was shown below.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model

 0


LMG   0

 0


13
if PW  PR  
if PW  PR  
if PW  PR  
To overcome the problem of negative migration, forcing equation (2) not to be
negative can be set.
 0

LMG  
 0

if
if
PW  PR  
PW  PR  
This study apply these ideas to the switching from non-tourism sector to tourism
sector. Equation (3) showed the modification of the relationship. A person will switch from
non-tourism sectors to tourism sectors when the return in tourism sector exceeds the sum of
return in non-tourism sectors plus the threshold.
 RT   
SWT  LNT    log 

 RNT 
…..(3)
SWT = Number of switching labors from non-tourism sectors to tourism sector
LNT = Initial number of labor in non-tourism sectors

= Forward switching effect

= Threshold of switching from non-tourism sectors to tourism sector
RT
= Return in tourism sector
RNT = Return in non-tourism sectors
The problem of negative migration will be restricted again as followed.
 0

SWT  
 0

if RT  RNT  
if RT  RNT  
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
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The switching back from tourism sector to non-tourism sectors will be captured by
another equation instead, equation (4). The elasticity and threshold in this equation are
different from equation (3).
 RNT   
SWB  LTO    log 

 RT 
…..(4)
SWB = Number of labors switching back from tourism sector to non-tourism sectors
LTO = Initial number of labor in tourism sector

= Backward switching elasticity
RNT = Return in non-tourism sectors

= Switching threshold from tourism sector to non-tourism sectors
RT
= Return in tourism sector
The problem of negative migration will be restricted again as followed.
 0

SWB  
 0

if RNT  RT  
if RNT  RT  
In Vargas (1999), the elasticity of labor migration was obtained from other literatures.
However, there is no other sources providing those elasticities and thresholds needed by this
study. Therefore, it is necessary to estimate them by econometric models. The estimation
method can be the logistic regression. To derive the elasticity directly for the logistic
regression, equation (3) should be modified to be equation (5).
1


SWT  LNT  
  x  
1 e

where
N 1
 RT   
x     log 
     i H i ,
 RNT 
i 1
………(5)
……....(6)
For the switching back effect, equation (4) should be modified to be equation (7).
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
1


SWB  LTO  
  g   
1 e

N 1
 RNT   



 iHi
where g     log 


 RT 
i 1
15
…….…(7)
……….(8)
The set of explanatory variable was stated in equation (6) and (8). Dummy variables
indicating the household groups, Hi, are added into the set of explanatory variables to avoid
the regression through origin. Besides, it will also differentiate the switching effect in
different household groups.
The switching effect derived from equation (5) and (7) will be always positive.
Therefore, the problem of inverse migration can be avoided without setting any restriction.
However, the switching effect cannot be zero. This is true for equation (5) because someone
in the village has to take care of tourism activities right after the introduction of CBT to the
village. It is also true in equation (7) that there’s always switching back effect from tourism
sector to non-tourism sectors. This is because people just concern tourism as a part-time job.
No one gets fully involved in tourism activities. The detail of the estimation strategy will be
discussed in the methodologies section.
4. Methodologies
This section will discuss the methodologies in detail. The simulation and estimation
methods will be revealed. The section will begin with the Village Computable General
Equilibrium (VCGE) model following by an econometric model and the qualitative analysis.
4.1 Village Computable General Equilibrium (VCGE)
In this section, firstly the VCGE will be introduced about its history, advantages and
disadvantages. Secondly, the specification of the VCGE which will be used in this study will
be shown. The specifications will include an Input-Output (I-O) table, a Social Accounting
Matrix (SAM) and the VCGE model. Lastly, how to apply the model to answer key questions
in this study will be clarified.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
16
a) Introduction to VCGE
Computable General Equilibrium was firstly applied to village economies in Tylor
and Adelman (1996). The model was based on Social Accounting Matrix (SAM). The
authors were also pioneers in using SAM for village economic analysis (Adelman, Taylor and
Vogel, 1988).
The VCGE model is a combination between a neoclassical household-farm model
(Barnum and Squire, 1979; Singh, Squire, and Strauss, 1986) and SAM. On the other hand,
SAM is a combination of an Input-output (I-O) analysis and an expenditure system (Davis et
al, 2002)
Davis et al (2002) mentioned the advantage of VCGE over neoclassical householdfarm (HF) model as listed below.

VCGE captures the production and expenditure linkages among
households while the HF model does not capture this issue.

VCGE introduces the general equilibrium feedback effects while the HF
model does not take the feedback effect into account.
Advantages of VCGE over SAM were also mentioned below.

VCGE captures the price effect while SAM is the fixed-price model.

VCGE allows non-linearity in household-farm responses to policy changes
while SAM assumes production utilizes linear and fixed-proportion
technologies.

VCGE relaxes the assumption of perfectly elastic supply used in SAM by
applying the family resources constraints on production.

VCGE used data base from SAM, therefore the model captures the detail
of income and expenditure patterns of household groups and institutions
listed in SAM.
There are limitations of VCGE mentioned as listed below.

The most important criticism upon VCGE is related to data and parameter
values. There are many problems related to consistency, reality and
adequacy of data in a village economy.

VCGE cannot model in modelling long-run process of development and
change because it cannot predict inter-temporal structural change.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
17
b) Specification of VCGE
The VCGE is based on SAM while SAM is based on an I-O table. Therefore, this
section will begin from the specification of I-O table. Then the specification of SAM will be
shown. After that the construction of the VCGE will be briefed.
b.1) Specification of Input-Output table
An I-O table contains data of transactions among production sectors in an economy.
The sectors will play roles of buyers and sellers. A sector buys inputs from other sectors as
well as itself. It also acquires labors, capitals and lands. When the buyer need more inputs
than the availability in the economy, an import may be possible. Moreover, it may receive
money transfer from government. On the other hand, a sector sells its output. It sells to other
sectors as well as itself. It also sells to consumers which are concerned as the final demand. It
may sell to the government. The excess supply can be possible to be exported as well. The
principle of I-O table is the sum of buying value will be equal to the selling value.
In this study, according to the target village stated in section 5, there will be ….
Sectors of production. Government sector as well as external trade will be included. The I-O
table will be like table 2.
Table 2 The I-O table in this study
Industries
Sectors
Sector … Sector
1
Agricultural
1. Fermented tee
2. Coffee
3. Other crops
4. Livestock
Light
5. Furniture
industries
6. Bamboo basket
7. Hat
8. Pillow
9. Other handicrafts
22
Final
demand
Govern Export Total
ment
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
Industries
Sectors
Sector … Sector
1
22
Final
demand
18
Govern Export Total
ment
Commercial 10. Restaurant
11. Communication
12. Gas station
13. Trading service
14. Medical care
15. Other
commercial services
Tourism
16. Management
17. Accommodation
18. Food catering
19. Transportation
20. Cultural shows
21. Trekking
22. Other tourism
services
Primary
Family labors
inputs
Hired labors
Capitals
Land
Government Government transfer
External
Import
trade
Total
Source: Modified from Siripoon (2003)
A uniqueness of I-O table at village level is the imperfect substitution between family
labors and hired labors (Taylor and Adelman, 1996). While hire labors are concerned as
tradable factors, family labors are not tradable. Therefore, in this study, family labors and
hired labors will be separately presented in the I-O table.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
19
To address the seasonal effect, only one I-O table covering activities in the whole year
is not enough. The economic structure in high and low seasons are hypothesized to be
different from each other. Therefore, two more I-O tables should be constructed. The first one
is for the high season and the second one is for the low season. These additional two I-O
tables will produce two more SAMs for the different seasons respectively. These SAMs will
consequently allow the simulation in VCGE model to produce different results for the high
and low seasons.
b.2) Specification of SAM
SAM is an extension of I-O model by adding the expenditure system into the I-O
table. It will show the detail of who are payers and receivers of money and how much they
pay or get. Households can be grouped according to economic status. Institutions can be
included such as cooperative, temple and school. Government sector will be also included.
The import and export will be classified in details such as nearby villages, nearby cities,
regional market, national market, and the rest of the world.
In the target village, there are a lot of special government projects, a micro-power
plant and a royal project for agricultural (Teen Tok royal project). They will be displayed
separately from other government activities.
Moreover, there are tourism destinations nearby the village such as a hot spring and a
cave. The destinations share a significant amount of income from the village because they are
included in touring programs. Therefore, they will be presented separately as well.
The specification of SAM in this study was modified from Subramanian (1996)
presented in table 3.
Table 3 : The specification of SAM in this study
Sellers (Receivers)
Buyers (Payers)
PA
CM
FT
Total
HH
IN
CS
MT
Stock
EX
Exports
Commodity
Production
supplies
activities
(PA)
Commodities
(CM)
Factors (FT)
Households
The richest 20%
(HH)
The second
richest 20%
The middle 20%
The second
poorest 20%
The poorest 20%
Intermediate
Composite
Household
Institutional
Investment
Maintenance
Stock
demands
commodities
consumption
consumption
demand
Expenditures
changes
Expenditures
Expenditures
Wages,
Household
Institutional
Payment
Payment to
Factors
salaries,
interest paid
interest paid
to labor
labor for
earnings
interest, rents
on
on
for
maintenance
from
consumption
consumption
investment
outside
Profit to
Factor
Transfer
Transfer to
Transfer
households
payments
between
households
from
to
households
households
outside to
households
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
Sellers (Receivers)
Buyers (Payers)
PA
Institutions
Cooperatives
21
Taxes
(IN)
Temple
CM
FT
HH
IN
Transfer
Transfer
Transfer
between
between
from
households
institutions
outside to
and
School
Enterprises
Government
Household
Institutional
saving
saving
change
change
Maintenance
Maintenance
Maintenance
expenses
of household
of
(depreciation)
durable
institutional
goods
durable
stocks (CS)
Maintenance
(MT)
CS
MT
Stock
EX
institutions
institutions
Capital
Total
goods
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
Sellers (Receivers)
22
Buyers (Payers)
Total
PA
CM
FT
HH
IN
CS
External
Energy (Micro
Tax to the
Imports
Factor
Household
Institutional
Capital
trade (EX)
hydro-power
rest of the
from the rest
payments
transfers to
transfers to
outflow
world
of the world
outside
the rest of
the rest of the
the world
world
plant)
Nearby tourism
destinations (hot
spring and cave)
Nearby villages
Nearby cities
Regional market
National market
Rest of Thailand
Total
Source: Modified from Subramanian (1996)
MT
Stock
EX
b.3) Specification of VCGE
To construct a VCGE, Davis et al (2002) introduced the structure of five blocks of
equations. They were production block, income block, expenditure block, general equilibrium
closure equations and price block. Therefore the construction of a VCGE model in this study
will follow the literature with some modifications.
However, the principle of VCGE was mentioned by Taylor and Adelman (1996)
which should be taken into consideration. The principle shows utility maximization with four
constraints. The detail of the principle were presented below.
Household are assumed to maximize utility obtained by consumption of goods and
leisure.
U  f (Xi , Xl )
i = 1,…,I goods
.……(9)
where Xi denotes household demand for good I and Xl is the demand for leisure.
The four constraints are a cash-income constraint, production technologies, a family
time constraint, and remittance functions. They were listed below.
A cash-income constraint:
PX
i
i
    * REMITS  PV VS  Y
Production technologies: Q  q( FLi ,Vi , K i )
…….(10)
……..(11)
I
A family time constraint: X l   FLi  MIG  LS  T
..……(12)
Remittance functions: REMITd =  d ( MIG d )
…..…(13)
i 1
where
Pi = the village price of good i
 = net income from household farm production
 = exchange rate to convert remittance obtained from abroad
REMIT = remittance obtained from migration to other regions
PV = local price of tradable input V
VS = household supply of input V
Y
= exogenous income
Qi = output of good i
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
24
FLi = family labor input into activity i
Vi = vector of tradable inputs (including hired labor)
Ki = capital inputs
MIG = time spent for migration
LS = labor supply to be hired
T
= family’s total time endowment
MIGd = family migration to destination d
The VCGE model incorporates household-farms into a local general equilibrium
framework. Four sets of conditions ensure that the decisions of households yield an
equilibrium solution to the village model. They are the material-balance condition, the
equilibrium in factor markets, the equilibrium in the local capital market, and the balance of
payment. The detail of these conditions were listed below.
The material-balance condition: Qi  Ci  Gi  I i  MS i
The equilibrium in factor markets:
H
I
h 1
i 1
………(14)
VS h  VM  Vi
………(15)
I
H
i 1
h 1
The equilibrium in the local capital market: I   I i   S h Yh 
I
The balance of payment:
i 1
where
H
D
 Pi MS i   REMIT h,d  0
………(16)
………(17)
h 1 d 1
Qi = output of good i
Ci = consumption demand for good i
Gi = government demand for good i
Ii
= investment demand for good i
MSi = net marketed surplus of good i
VSh = factor supplies
VM = imported factors
Vi
= factor demands
Sh(Yh) = household saving levels
Pi = price of good i
REMITh,d = remittance obtained by household h from destination d
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
25
Although these are principle of VCGE, they are flexible to meet specific requirements
in a particular study. For example, the balance of payment can be relaxed when villagers
spend their remittance within the economy or save for consumption in next periods (Vargas,
1999). Therefore, this study will take consideration of these principles and modify some
conditions for the suitability of the study.
c) Simulations in VCGE model
After the construction of VCGE model, simulations will be possible to answer key
questions. However, some questions can be answered just by calculations from SAM. The
questions and how to answer them were listed in table 4.
Table 4 : Simulations in the Village Computable General Equilibrium Model
No.
1
Issues
Income
Questions
How much of the income
generation
generation after taking into
How to simulate in VCGE
Direct calculation from SAM.
account both direct and indirect
effect?
How many tourists needed for
Vary the number of tourists in the
shifting the average
VCGE model until the average
household’s income in each
household income in each
household group to be above
household group be above the
the poverty line?
poverty line. Do it again until
incomes of all villagers are above
the poverty line.
How much of the tourism price
Vary the tourism price in the
that shifts the average
VCGE model until the average
household income in each
household income in each
household group above the
household group be above the
poverty line?
poverty line. Do it again until
incomes of all villagers are above
the poverty line.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
No.
Issues
26
Questions
How much is the trading off
How to simulate in VCGE
Shock to the VCGE model with a
between tourism quantity and
decrease in number of tourist,
quality?
then increase the tourism price
until it raises the income to the
same level before the drop of
tourists.
2
What will happen if a village
Raise the tourism demand until
expands its carrying capacity to
income of all villagers are above
the level that poverty will be
the poverty line. Observe the
alleviated? Will there be
number of villagers left in non-
significant loses in other
tourism sectors. Also observe the
sectors?
outputs in non-tourism sectors.
Income
How will be the income
Direct calculation from SAM.
distribution
distribution after taking into
account of both direct and
indirect effect?
What will happen to the income Shock to the VCGE model with
distribution in cases of the
the increase in tourists and the
increase of tourists or the
increase in tourism price and look
increase of tourism price?
at the income distribution.
Which one between raising the
Compare the income distribution
tourism quantity or quality is a
obtained from VCGE model
better strategy for the income
between cases of increasing
distribution?
tourists and tourism price,
measuring at the point that the
two variables generate the same
income level.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
No.
Issues
27
Questions
How to simulate in VCGE
What will happen to the income Raise the tourism demand to a
distribution when more
certain level of participation of
villagers participate in tourism
villagers. Then observe the
activities? Is the uneven income income distribution. Vary the
distribution caused by low
demand to reach different levels
participatory rate or by CBT
of participations. Compare the
itself?
income distribution between
different participatory levels.
3
Seasonal effect
How much is the fluctuation of
Comparative static between two
tourism income between high
SAMs which constructed in the
and low season?
high and low seasons
respectively.
When applying the same shock,
Shock both VCGE model for
how different of income
high and low season respectively
generation and income
with the same shock on tourism
distribution between high and
demand and tourism price. Then
low seasons?
observe the difference in the
results between the two models.
4
Tourism
How much income will be
Raise the tourism income to a
collapse
reduced from different levels of
certain level. Then drop the
tourism size? Is the effect
tourism income down to zero.
proportional to the size of
Vary tourism income levels.
tourism sector?
Then drop the income again.
Compare the effects among
different levels of initial tourism
income.
4.2 Econometric models
A critical mechanism for VCGE model is the elasticities and thresholds of switching
decision. It cannot obtain these parameters from other literatures because there was no
literature conducting the same issue. Therefore, econometric models are needed for
estimation of the switching effect. The first model will capture the forward switching from
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
28
non-tourism sectors to tourism sectors. The second model will capture the backward
switching. Logistic regression will be applied in these cases.
a) Forward switching
There are only 2 choices for a villager, working in tourism sector or not. According to
equation (5) and (6), the probability of the switching from non-tourism sectors to tourism
sector can be written as in equation (18) and (19).
Pr( switiching ) 
1
1  e  x
Pr( not _ switiching ) 
e  x
1  e  x
………(18)
………(19)
The logarithm of the odds ratio between the probability of switching and not
switching can be written as a linear equation of explanatory variables as followed.
 Pr switching  
  x 
ln 
 Pr( not _ switching ) 
……….(20)
.
Explanatory variables, shown in equation (6), consist of the relative return and the
dummy variables indicating the household groups.
For the data collection strategy, the head of each household will be given a scenario of
a relative return between tourism sector and non-tourism sectors. He or she will be asked
whether the household will switch to tourism sector or not. If the answer is yes, it will be
noted as Y equals to 1. In contrast, if the answer is no, it will be noted as Y equals to zero.
There are 118 household in the target village. Each household will be asked once. So, there
will be 118 observations in the regression. This number of observation will be enough for the
estimation of equation (20).
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
29
b) Backward switching
For the backward switching from tourism sector to non-tourism sectors as stated in
equation (7) and (8), only 30 households who have already participated in tourism activities
can provide the information. Repeating the same procedure, a scenario of a relative return
will be shown to the head of household. The question is whether his or her household will
switch back from tourism sector to non-tourism sectors or not. Due to the rare observations,
each household will be given two scenarios. Then a household will provide two answers.
Therefore, 60 observations will be obtained. It is expected to be enough for the estimation of
equation (21).
 Pr switching _ back  
  g 
ln 
 Pr( not _ switching _ back ) 
……….(21)
4.3 Qualitative analysis
Personal interviews with village’s leaders and committees are necessary to draw the
whole picture of CBT supply chain. Participants in each part of the chain can also be
identified. The interview may be conducted individually or in group of few persons.
The interview will be crucial for construction of network in the villages which will be
beneficial for coming major surveys. It will be a signal to villages’ leaders that the research is
aimed to help understanding what is going on in CBT and help enhancing the strength of the
CBT. The leaders should be convinced that it will not be related to taxes. Moreover, the
village will gain more reputations when the knowledge spills over international community of
CBT. With this understanding, it is hopeful that they will allow the researcher to approach
their villagers in very details.
List of questions which need qualitative analysis were listed in table 5.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
30
Table 5: Qualitative issues and the related questions for the interview
No.
1
Issues
Switching effect
Questions
When will a villager switch to tourism?
After the switching, what they do to their agricultural lands?
Are there anybody else come to take over or rent the land for
the agricultural production?
Will they convert their lands into a camp site or a resort?
How do they allocate their time for both tourism activities
and agricultural production?
2
Seasonal effect
How villagers switch back to agricultural production or
switch to other-non-agricultural sectors during the tourism
off-season?
How do they prepare for the fluctuation in tourism income?
3
Tourism collapse
Does the prosperity in tourism sector give them opportunity
to save more or to invest in other sectors or just encourage
them to consume more?
Will they have enough savings to survive without working
for years?
Can they switch to other sectors where they have invested?
Can villagers who participate in tourism switch back to
agricultural production?
In case of switching back to agricultural production, will
they have some advantages over other farmers who initially
did not participate in tourism?
How do they prepare for the tourism collapse?
4.4 Steps in the quantitative model construction
Quantitative models in this study consists of SAM, VCGE and econometric model.
An output from a model may be needed to be an input for other models. Therefore, it should
be clarified about the steps in the quantitative model construction. The steps were listed
below.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
31
Step 1: Construct the Social Accounting Matrix (SAM)
Step 2: Estimation of the logistic regression to obtain the switching effect
Step 3: Construct the VCGE model for low season using SAM in step 1 as
the data base and the switching effect in step 2 as switching
elasticities between tourism and non-tourism sectors
Step 4: Repeat step 1 to 3 for the high season
5. Data collection
5.1 Target village
There are at least 183 villages establishing their community based tourism in Thailand
(TAT, 2007). However, villages are not suitable for this study. Some of them are production
sites not aiming for tourism but export instead such as Ban Tawai in Chiang Mai. Some
villages are too big to investigate all households such as Plai Phong Phang Sub-district where
8 villages are bounded with at least 1,000 households. The village also turned into total
business model where outsiders from Bangkok have heavily invested in the areas. Some
villagers are too small such as Ban Huay Hi in Mae Hong Sorn where only 4 households
provide homestay just because the village is located on the adventurous trekking way. CBT
villages in the Southern Thailand have been already developed into shopping destinations
such as Ko Yo village in Songkla. Visitors are reluctant to stay overnight in Southern villages
because of the insecurity due to terrorism. In Northern villages especially in mountainous
areas, villagers cannot speak Thai. It will be an obstacle for a construction of massive data
base for I-O table, SAM and VCGE with good quality.
There is a village outstanding and suitable for this study. Mae Kam Pong village in
Chiang Mai, Thailand has experienced the CBT for 7 years. Currently, the CBT in the
village is growing, viable and self-reliant. The size of the village is not too big and not too
small providing sufficient observations for the survey. Moreover, villagers speak Thai.
Therefore, high quality of data collection can be expected. In the next section, the detail Mae
Kam Pong village will be shown.
5.1.1 Detail of Mae Kam Pong village
Mae Kam Pong village is located to the East of Chiang Mai province, around 50
kilometers from downtown. It consists of 118 households with around 500 villagers. There
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
32
are around 30 households participating in CBT, approximately 25 percent of all households.
The villagers are local Northern Thai. They speak official Thai and Northern Thai language.
The village is well organized, receiving the best CBT award two tomes in 2004 and 2006.
The CBT is still run by villagers. Its primary aim is to generate supplementary income to
villagers.
Information of Mae Kam Pong village was listed in table 4.
Table 6 : Information of Mae Kam Pong village
Items
Information
Location
 Mae On sub-district, Chiang Mai province, Thailand
 48 kilometer Eastern from Chiang Mai downtown
 1,300 meter above the sea level
People
 118 households
 Approximately 500 villagers
 Local Northern native
 Speaking Northern Thai and Official Thai
 Head of village: Mr. Prommin Puangmala
Tourism products
 18 Homestays (January 2008) with 3 – 5 visitors per
homestay
 Welcoming ceremony called “Bai Sri”
 Traditional dancing show
 Northern breakfast from organic vegetables
 A temple with old Northern decoration
 A 7 cascading waterfall with multiple streams over the
waterfall
 Rocky landscape with highland cool weather
 Visiting tea and coffee plantations
 Trekking programs
 A resort with 3 huts established by a foreigner “John’s
house”
Tourism related
productions
 Baskets and furniture made from bamboo
 Handmade local hats
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
Items
33
Information
 Herbal products
 Pillows with tea inside
Agricultural productions
 Fermented tea called “Miang”
 Arabica coffee
 Livestock
Other facilities and
 Micro hydro-power plant producing 40kW of electricity
tourism destinations
 Concrete roads connecting the village and downtown
nearby the village
 Teen Tok Royal project which assists and encourages the
villagers to plant recommended agricultural products.
The project also buy the harvests from the village.
 San Kam Pang hot spring
 Muang-On cave
Access to the village
 Highway route 118 from Chiang Mai to Chiang Rai
 Highway route 1317 and 1006 from Chiang Mai
downtown via Mae On sub-district
Sources: Untong et al (2006) and http://www.siamleisure.com, http://www.travelblog.org,
http://johnhousethailand.com
Mae Kom Pong village earned around US$5,300 from tourism in 2003. It was around
4 percent of total income. Sources of income of Mae Kam Pong village in 2003 compiled by
Untong et al (2006) were shown in table 7.
Table 7 : Source of income of Mae Kam Pong village in 2003
Source of income
Amount
Average
% of total
(US$)
(US$/Household)
income
86,897
776
63
82,672
749
60
75,012
695
54
- Coffee bean
7,431
105
5
- Other crops
229
115
<1
4,225
282
3
1. Agricultural income (112 households)
1.1 Crops
- Fermented tea
1.2 Livestock
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
Source of income
34
Amount
Average
% of total
(US$)
(US$/Household)
income
46,242
514
33
2.1 Commercial
13,962
1,074
10
2.2 Others
32,280
427
23
5,388
180
4
138,528
1,184
100
2. Non-agricultural income (90 households)
3. Tourism (30 households)
Total income (118 households)
Source: Untong et al (2006): Survey during 26 – 28 April, 2004.
5.1.2 Reasons why Mae Kam Pong village is suitable for conducting the
research
There are several advantages for conducting a study at Mae Kam Pong village.
The advantages were listed below.

The growing stage of CBT life cycle.
The village has
established its CBT project since 2000. Annual tourism income grew up from around
US$5,300 in 2003 (Untong, et al) to US$34,000 in 2006 (Suriya, Srichoochart and
Pruekruedee, 2007). The average income growth was around 85 percent per year. This high
growth rate was classified by Kotler (2000) as a growing stage in the product life cycle
model. The CBT in the village has already passed that introductory stage while many other
villages are still stuck in the introductory stage.

The economic linkages. Varieties of economic linkages can be
found in the village. Agricultural productions as well as light-industrial productions are
linked to tourism sector. The micro hydro-power plant represents the energy sector in the
village. Tourism income also distributed to the hot spring and the cave nearby the village.
The presence of the royal project links to the agricultural sector. There are also touring
operators joining in marketing activities. A temple and a school are additional institutions to
share the benefit from tourism.

The community’s power in tourism control. According to the
principle of CBT, the ownership and management of tourism activities should be controlled
by the community. Villagers occupy almost total tourism assets and control almost tourism
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
35
activities. Even though there is a resort established by a foreigner, it consists of only 3 huts
comparing to 18 homestays operating by local villagers.

The participation of villagers. Not only adults participating in
tourism sector, but also children show traditional dancing to visitors. Farmers also let visitors
to visit their plantations. There are senses of hospitality to tourists all around the village.

The size of the village. Composing from 118 households, the
village is not too big and not too small. Two rounds of data collection, in high and low
seasons, can be possible in a year.

The language. Villagers speak Northern Thai. There is no need
for a translator because the researcher understands the language. It is good for asking deeper
questions and getting the answers clearly. It is easier to make friends with villagers who
speak the same language and share the same customs. It is also good for access to deeper
information from the head of village and village committee’s through interviews.

The accessibility. The village is linked by concrete roads from
downtown. There will be no problem getting access to the village in the rainy season.

The interest of policy makers. Mae Kam Pong village won the
best CBT village from the Thai government two times in 2004 and 2006. The village is a case
study for many other villages in CBT. Policy makers also learn from the village for the
promotion of CBT around the country. Therefore, the research conducted in this village will
gain more interests from policy makers.
5.2 Timing of data collection
There will be two rounds of data collection. The first round will be held in the
tourism low season (March – October). The second round will be in the tourism high season
(November – February). Starting with the introductory period in May, the researcher has to
ask for permission from the head of village for conducting a research. During this first month,
the research will have to present the structure and detail of the research to the village’s
committees to make them understand the purpose and procedure of the research. Besides,
informal interviews with some of the village’s committees will pave the way for the major
data collection. After the introductory period, data collection in the low season can be
possible around June to October while the data collection for the high season can be
conducted during November to February.
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
36
6. Research plan
The schedule of the field research was shown in table 8 below. There will be 2 main
procedures, the data collection (indicated by thicker arrows) and the data base and model
construction (indicated by thinner arrows).
Table 8 : The schedule of the field research
No
1
2
3
4
5
6
7
2008
Activities
May
Jun
Jul
Aug
Sep
2009
Oct
Nov
Dec
Introduction of the
project to the head of
village and village’s
committees. Asking for
the permission for the
data collection.
Data collection in the
low-season
Construction of data
base for low-season
(I-O table and SAM)
Data collection in the
high-season
Construction of data
base for high-season
(I-O table and SAM)
Construct a primary
VCGE model
Wrap up the data and
error correction
Data collection procedure
Data base and model construction procedure
Jan
Feb
Mar
Apr
An Economic Analysis of Community-Based Tourism in Thailand Using Village CGE Model
37
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