Chapter 5: Participation in economic activities
5.4 Settings of the models
This section will introduce the dependent variables and the testing variables. It will explain the expectation of their signs. There are no controlled variable in this model since all of them are treated as testing variables.
5.4.1 Dependent variables
Dependent variables are working hours in seven economic sectors including homestay, core tourism, tourism-induced sector, agriculture, agricultural labor service, commerce and non-agricultural labor service.
The data of working hours are from the questionnaire which is used to construct the SAM (annex 1). Villagers need to specify their time that they allocate to economic activities as well as income from those activities. Sub-activities are combined into major activities. The details of the activities are listed in section 3.5.1. Financial income is not counted for the working hours since villager do not spend time in gaining the income. They will get the dividend when they are members of the cooperative. Manufacture and commerce are merged together because the size of manufacture is too small to be modelled separately. It may be reasonable for this merge such that the manufacture is unlikely to go along well with other kinds of works. It is not a labor service, tourism or agriculture. It shares some similarities with commerce such that it is an independent work. The operation relies heavily on the owner.
5.4.2 Testing variables
There are 11 testing variables. The explanations of the expected signs are described as follows:
1.) Education of head of household
This variable is the schooling year of the head of household. This variable represents the quality of the head of household. In the Thai context, household members should obey to the leader. Therefore, the educated leader may direct the household to participate in a profitable sector. In contrast, an uneducated leader may be reluctant to allow the members to join some sectors or even prevent them to join.
2.) Age in 2003
The variable is the average age of household members. Simple average method is used for the calculation.
It was not obvious whether households with lower average age would participate in tourism more than the aging households. Considering that tourism activities are lighter than working on farms, participants do not require strong physical conditions. Therefore, the sector may be favorable to elderly people. The study predicts the positive sign for this variable.
3.) Women in 2003
This variable is a dummy variable indicating whether a household have more women than men. The value of the variable is one when number of female members exceeds male.
Otherwise, it is zero.
Tourism is a lighter work than agriculture or construction. Women are the key labor force in the sector. For example, operating a homestay needs cookers and cleaners. The tasks are assumed to be women’s duties in the Thai society. Thus, households with more women may have advantage in participating in tourism sector.
4.) Distance 1 kilometer from village center
This variable is a dummy variable indicating that a household is located within 1 kilometer from village center. The village center is a meeting hall in the temple where villagers gather for official meetings. They also welcome tourists there.
The distance creates a geographical advantage. Tourists who come in group would like to stay closely to one another. They choose houses near the village center rather than ones that locate further. In core tourism, households near the center can respond to calls for jobs faster than outer households. In tourism-induced sector, villagers need to walk to the temple to produce souvenirs. Households in the outer cluster are around two kilometers from the center.
The outer households may be reluctant to participate frequently. Therefore, the study predicts the positive sign for the variable.
5.) Members in 2003
The variable counts every member in household regardless of their ages and genders.
This is the hypothesis of the labor surplus. Assumed that the village is not at the full employment, household with larger number of members in 2003 should have more free time to allocate to tourism sector than households with smaller number of members.
6.) Dependency ratio in 2003
The dependency ratio is calculated as a ratio of the number of household members aged lower than 15 years old and older than 70 years old to the total number of household members.
Households with higher dependency ratio need to allocate working time to take care of children and elderly people. Their free working hours are less than households with lower dependency ratio. Thus, they tend to participate less in tourism sector.
7.) Change of dependency ratio
This variable is calculated as the dependency ratio in 2007 less the ratio in 2003.
The higher the dependency ratio, the more working hours are needed to be allocated to take care of the dependents. It reduces the probability to join tourism sector.
8.) Change of population in household
The variable is the simple difference between number of household members in a household in 2007 and 2003.
Additional labors in household should raise the opportunity to participate in tourism sector.
However, it is not obvious such that the additional labors may choose between tourism and non-tourism jobs depending on the rates of return. According to the higher labor productivity in tourism sector (table 6.9 in chapter 6), it predicts that the labor would choose to participate in the sector. Therefore, the sign should be positive.
9.) Income 2003
Initial income in 2003 is calculated based on the survey in 2003. It follows the definition and measurement of income described earlier in section 3.5.1.
Households with higher initial income tend to participate in tourism sector more intensely than households with lower income. Untong, et al (2006) mentioned that tourism was brought into villagers in Thailand by rich households. They controlled the sector from the beginning and hardly left the sector. Goodwin (2009) also supported that the poor cannot participate much in the sector.
10.) Ratio of tourism income to total income in 2003
The variable is the ratio of household income from tourism to total household income.
Among all sources of income, the variable indicates how important tourism sector is. It should be noted that in 2003 there was only two kinds of tourism activities, i.e. homestay and core tourism. Tourism-induced sector emerged in 2006.
Once a household get tourism income, they may like it. When they realized that tourism jobs can generate substantial income and the jobs are lighter than those on farms, they may want to keep participating in the sector.
11.) Human capital of household in 2003
This variable is an interaction between the average schooling year in 2003 and the number of household members in the same year. It represents a pool of knowledge in a household.
While the average schooling year represents the quality of each member, this variable emphasizes the collaboration between educated persons in household. The higher number of educated members, the more rational discussions can be made. Then, the household is likely to have a collaborative agreement to participate in tourism sector which is more profitable than other sectors.
12.) Schooling in 2003
The variable is the average years of schooling in household. It is calculated by simple average method.
Households with higher average years of schooling are likely to be called by the head of village to contact tourists with several reasons. First, these people can speak English. Second, they have some knowledge about hospitalities. Third, they may be able to respond to tourists’
demand more efficiently. Fourth, they tend to be more responsible.
Thus, households with higher years of schooling tend to participate in tourism more than those with lower years of schooling. Studies of Untong, et al (2006), Prachvuthy (2006) and Oula (2006) supported this argument.
It should be noted that the issue of the call from the head of village can be viewed both as a problem of selection bias and not the problem. For the first dimension, it can be the selection bias since the villagers make decision to respond to the call by themselves. This is the self-selection. For the second dimension, it may not be a self-selection in the sense that villagers are assigned by the head of village to do the duty. To treat the possible selection bias, it may apply the Heckman selection model to investigate the determinants of working hours in tourism sector. However, the decision of participating in tourism is not independent from the decisions to participate in other economic sectors. A more suitable model to take care the possible correlations among the decisions is seemingly unrelated regression (SURE). SURE is better than Heckman selection model such that it is a system of equations that can take care of every sector at the same time while Heckman selection model is a single equation which takes care of the participation in each sector separately.
5.4.3 Descriptive statistics of the variables.
The descriptive statistics of variables used in the SURE model are shown in table 5.1.
Table 5.1: Descriptive statistics of the variables
Variables Number of
observations
Mean Standard deviation Dependent variables:
Working hours in homestay 104 57.08 128.40
Working hours in core tourism 104 92.28 241.47
Working hours in tourism-induced sector 104 241.23 701.33
Working hours in agriculture 104 2,320.69 1,789.30
Working hours in agricultural labor service 104 165.99 271.25
Working hours in commerce 104 364.76 1,192.03
Working hours in non-agricultural labor service 104 690.86 1,081.18 Independent variables:
Education of head of household in 2003 (years) 104 4.27 1.84 Average age of household members in 2003
(years old)
104 39.37 12.03
More women than men in household in 2003 (yes=1)
104 0.35 0.48
Distance 1 km from village center (yes=1) 104 0.65 0.47
Members in 2003 (persons) 104 3.08 1.05
Dependency ratio in 2003 104 0.2374 0.2117
Change of dependency ratio 104 -0.0507 0.1889
Change of population in household (persons) 104 -0.0576 1.0128
Income 2003 (Baht) 104 43,592 28,455
Ratio of tourism income to total income in 2003 (%)
104 1.79 4.48
Human capital in household in 2003 (persons-years)
104 18.07 8.31
Average schooling of members in 2003 (years) 104 5.84 1.93 Source: Calculation