| dc.description.abstract | The first part of this study applies a Markov chain model to investigate patterns of labor mobility across occupations, economic sectors, and geographical areas. These patterns of mobility are then compared between in peak and the off agricultural seasons. The second part investigates factors affecting single type of mobility and combination of various types of mobility using data from two levels as explanatory variables: individual and contextual. / Data for the study of mobility patterns is based on rounds 1 (off agricultural season) and 3 (peak agricultural season) of the 1993 Labor Force Survey which was collected and complied by the National Statistics Office; however, only round 3 of the 1993 Labor Force Survey is selected for the analysis of factors affecting various types of labor mobility. The respondents are members of the labor force aged between 13-59 who were employed during the time of the survey. There are 41,546 and 42,846 respondents in rounds 1 and 3, respectively. / For the whole Kingdom, the results show that the change in occupational classes is less among professionals and skilled labors than semi-skilled and unskilled classes. In addition, there are no differences in mobility patterns across occupations between the peak and off agricultural seasons. But there are some seasonal differences in mobility patterns in the Northeast and the North. In the latter, the upward labor mobility from unskilled to semi-skilled classes are 17.9 and 22.5 percent during the off and peak seasons, respectively. / The pattern of mobility across economic sectors varies with seasons. There is a tendency for some agricultural labors to work temporarily in other sectors during summer. Of those who change, they overwhelmingly become factory workers while the remaining work in the commerce and service sectors. Consequently, it is reasonable to expect that these labors from industry, commerce and service would return back to agriculture during the peak season. This is quite evident from the results of Markov analysis. / Regarding the pattern of geographical mobility, it is found that Bangkok annd its vicinity has an ongoing movement (i.e., it shows no seasonal pattern), on the other hand the movement of labors in the Northeast is seasonal. However, labors in the South and North do not show much variation. During the off season, the Northeast has higher migration rate than other regions. Most labors from other regions outside Bangkok and its vicinity, if they move, are more likely to migrate to Bangkok and its vicinity and tend to return to their original regions when the agricultural season begins. / Concerning factors affecting labor mobility, the findings show that individual characteristics, contextual factors and their interactions together explain the dependent variable in various analytic models by having values of (pseudo) R2 ranging from 68.4 to 84.9 percent. / In the analysis of occupational mobility by regions, the findings indicate that the key factors are individual characteristics, except in the North that the contextual factors show some significance. Age is the only explanatory variable that has a positive significant effect on occupational mobility for all regions. In fact, about 1.3 percent of workers change their occupations within one year. However, the South has the higher rate of occupation change which is about 4.7 percent. For the analysis of factors determining mobility across economic sectors, it is found that in Bangkok and its vicinity, the Northeast, and the South, only individual characteristics affect the probability of changing economic sectors; while, in the Central and the North, both individual characteristics and contextual factors are important. The individual factors that are statistically significant are age, income, and previous residency; while, for the contextual factors, they are population density, GPP and unemployment rate. There are about 3 percent of workers changing economic sectors within a year. Regarding geographical mobility, the findings show that individual characteristics as well as contextual factors influence the decision of labors at the regional level. These factors are previous residency, age, sex, GPP, and unemployment rate. The findings show that the migration rate is 7.6 percent for the whole kingdom and 9.1 percent in the North. / Regarding the joint mobility, the findings, as a whole, show that both individual characteristics and contextual factors are important. / In joint mobility across occupations and economic sectors, the findings show that individual characteristics (age, income, previous residency) and contextual factors (GPP and unemployment rate) affect the probability of changes in various categories of mobility related to such combination. But these factors have different direction and intensity on the dependent variable across regions. For all regions except the South, age has a negatively significant effect on the probability of changing both occupations and economic sectors. Except Bangkok and its vicinity, income inserts a negative influence on the probability of changing occupations without changing economic sectors. Age, income, GPP, and unemployment rate are significant factors determining the probability of changing economic sectors without changing occupations. For joint mobility across occupations and geographical areas, the findings show that previous residency, age, income, household size, sex, marital status, unemployment rate affect occupation-geographical mobility. Previous residency is statistically significant in explaining the probability of changing both occupations and geographical areas; while, age, previous residency, and income are primary factors that influence the probability of changing occupations without changing geographical areas. In addition, factors determining the probability of changing geographical areas without changing occupations are age, previous residency, sex, marital status, household size, unemployment rate and GPP. / In joint mobility across economic sectors and geographical areas, the findings suggest that age, previous residency, household size, and income affect the probability of changing both economic sectors and geographical areas. Age, income, and unemployment rate tend to determine the probability of changing economic sectors without changing geographical areas. In addition, age, previous residency, sex, marital status, unemployment rate, and GPP are statistically significant in determining the probability of changing geographical areas without changing economic sectors. / In sum, findings from the analysis of social mobility (changes associated with occupations or economic sectors) and spatial mobility (changes associated with geographical areas) indicate that education is not an important determinant of the labor mobility as stated in mobility theories and other researches. The present study finds that it is statistically significant in some types of mobility and in some regions especially Bangkok and its vicinity. It can be partially explained by the fact that more than 80 percent of workers are semi-skilled and unskilled labors and most of changes occur in these two classes of occupations. Therefore, there is not so much variation in education among labors. In addition, the majority of changes come from agricultural sector which are temporal mobility. | th |
