Academic journal article Asian Social Science

Population Distribution Pattern of 76 Provinces in Thailand: Application of Factor Analysis

Academic journal article Asian Social Science

Population Distribution Pattern of 76 Provinces in Thailand: Application of Factor Analysis

Article excerpt

Abstract

Thailand is in the demographic transition phase. The shape of population pyramid is shifting from stationary to contracting pattern. Age-sex distribution may vary by province. This study explores and describes the population distribution pattern of 76 provinces in Thailand using data from 2000 Thai population census. Factor analysis, a multivariate statistical method, was used to cluster provinces, based on pattern of age-sex distribution of the population. The study found three distinct patterns of population distribution in Thailand. Twenty-seven southern and northeastern region provinces, mainly bordering Myanmar, Cambodia or Malaysia, share the classical pattern of population distribution. The majority of central region provinces, and also Phuket from the south share a similar population distribution pattern which peaks at the young age group. So too, most of the northern region provinces share another pattern that dips at the young age group. In conclusion, this study found that population distribution is not symmetrical across Thailand. The factor model approximated well this variation and clustered the provinces in three patterns. The method applied in this study is straightforward and can be used in future demographic studies.

Keywords: factor analysis, multivariate, pattern, population distribution, province, Thailand

1. Introduction

Population is the function of key demographic variables that are fertility, mortality and migration. Population Reference Bureau defines population distribution as the patterns of settlement and dispersal of a population. It is an undeniable fact that developed and developing countries have different types of population distribution patterns (Cohen, 2003). Developing countries mainly have the classical pattern in which the number of children is high and the skew is towards young ages, whereas in developed countries the skew tends to be towards older ages (Abbasi-Shavazi, 2011). African countries, affected from HIV/AIDS epidemic, have dip among young age group (Zimmer, 2009). According to world population data sheet (2012), such differences may exist within a country. Even in United States, population change and age-sex structure varies widely within states.

In Thailand, fertility was high until 1970. Then it moved into a decline phase. After 1990 it was in a low fertility phase for 6-7 years. Now it is in the phase of 'below replacement' (Prasartkul, Patama, & Varachai, 2011). Mortality is also in a decreasing trend; infant mortality is decreasing at a slow pace. According to the CIA the World Factbook 2012, the current net migration rate is zero but the internal migration within the country, from rural province to urban and industrial province, always remained substantial (Thailand Migration Report, 2011; Guest et al., 1994). Similarly, the HIV epidemic in Thailand is yet another important event that affects the population distribution. It was estimated that the number of deaths from AIDS before the year 2000 was 550 000 (Surasiengsunk et al., 1998). AIDS was the leading cause of male deaths and the second leading cause of female deaths amounting to 16.5% and 6.3 % respectively, of total deaths in 1999 (Porapakkham et al., 2010). The provinces in Northern Thailand, adjacent to Myanmar and Laos, were greatly affected by this epidemic (Surasiengsunk et al., 1998).

As already mentioned in the first paragraph, the age - sex structure may vary within the country. There are 5 different regions and 76 provinces in Thailand. Each province is geographically different. Also, types of people, their religion, culture belief and health related practices are different. One can predict many differences in the pattern of population distribution in the different provinces in Thailand. One also can predict that some of these provinces may follow the same pattern. In order to find the evidence of the above predictions, this study applied a statistical method called "factor analysis". …

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