教育的達成における不平等 : 測定及び決定要因

The main objective of this dissertation is to analyze an inequality in educational attainment. The author hopes to shed the light on the following questions: 1) what factors determine educational attainment and its inequality? 2) How does educational attainment and its inequality affect economic outcome? 3) Are there any existences of intergenerational transmission of educational attainment and its inequality? The dissertation covers national, provincial, and individual analyses. For national analysis (chapters four and five), the author uses the data of educational attainment from Barro-Lee and Cohen- Soto. For provincial and individual analyses (chapters six and seven), the cross-sectional data from the Household Socioeconomic Survey (SES) which was conducted in 2011 by Thailand's National Statistics Office was obtained for estimations. After the introductory discussion in Chapter one, Chapter two provides theoretical discussion. Definition of the key concept, inequality in education is identified in comparison with similar terms while its measurement is argued. In addition, theoretical approaches concerned such as the human capital approach, the intergenerational persistence in educational choices, and the wage regression are introduced. Next, more specific review on the empirical literature is conducted, followed by introducing the research methodology and the overall conceptual framework of this dissertation.……広島大学(Hiroshima University)博士(学術)Philosophydoctora

Assessing Inequalities in Thai Education

Using data from Thailand's Household Socioeconomic Survey, this paper measures the inequalities of Thai education in 2011. We utilize the Gini coefficients to estimate Thai educational inequalities from cumulative years of educational attainment which are between zero (no schooling) to twenty-one (doctoral level) years. The education Gini coefficient of the whole country is 0.349. At the provincial level, the Gini coefficients are in a range between 0.272 (Nonthaburi) and 0.521 (Mae hong son). The provinces located near the Bangkok metropolis have greater equality in education, except for Samut Sakhon, while the provinces in the northern part of Thailand have severe inequality in education, especially the border provinces. As for the effect of schooling on educational inequality, we found that at the regional level, average years of schooling was significantly and negatively associated with the educational inequality, except in the northern part of Thailand. The magnitudes of coefficients of average years of schooling in the northern and southern parts are twice that of the central part of Thailand. The policy implication of this paper is that the Thai government should pay attention to two points in adjusting the scope of distribution: reduce the number of people without schooling and extend the educational attainment of people with primary education to secondary education. At the regional level, the policy of education expansion for reducing educational inequality is workable only in central Thailand, the north, and the south. Governments should utilize different policies in each region. In addition, the Thai government should pay more attention to solving the social problems which contribute to the issue of educational inequality.訂正のため本文ファイル差し替え(2013年5月7日

On the Determinants of Inequality in Education

The main objective of this paper has been to investigate macroeconomic factors influencing the schooling inequality across 69 countries during the period of 1975 to 2005 with five-year intervals period. Relying on 201 observations, we found that education expansion, ratio of capital to GDP, and ratio of female to male primary enrollment statistically significantly play as powerful equalizers in education distribution .Previous-year educational inequality, per capita real income, and growth rate of rural population play as significant disequalizers of educational inequality. There are two major findings. Firstly we found that factors directly involved to schooling like enrolment rate or education expenditures are not significant while the factors indirectly involved to schooling have significant impacts on educational inequality. Secondly, we found quadratic (U-shape) relationship between rural growth rate and educational inequality. So the higherrural population growth rate brings increasing or decreasing of educational inequality with turning point at rate -1.39

The Ranking of Inequality in Human Capital : Evidence from Asian Countries

The objective of this paper is to investigate that the international cross-sectional comparison of inequalities in human capital and education among 16 Asian countries. More specifically we employed the order-ranking of Gini coefficients that is workable in empirical studies as well as that of Lorenz curves sequenced from basic pairwise Lorenz dominance comparisons of 240 cases. The latter is provided as an alternative measure of education and human capital distribution in comparison with the former measure. Our major finding is rank correlation coefficients between both measures of both inequalities are high and significant but not unity. At least in this data set, the rankings of inequalities in education and human capital from two measures are able to apply in empirics. Gini index of both inequalities were calculated from Cohen & Soto's educational attainment data-set during 1960-2010; ten-year interval period. Data obtained from these Asian countries is computed to confirm the relationship education, human capital, and their inequalities. We found the negative linear relationship between average years of schooling and its Gini while the relationship between stock of human capital and its Gini becomes inverted-U shape curve

The prevalence and economic burden of treatment-resistant depression in Thailand

Abstract Background The objectives of this study were to investigate the proportion of treatment-resistant depression (TRD) among patients with diagnosed major depressive disorder (MDD) and undergoing antidepressant treatment, to estimate the economic cost of MDD, TRD, and non-treatment-resistant depression (non-TRD), and to examine the differences between TRD and non-TRD MDD in a Thai public tertiary hospital. Methods This was a combined study between retrospective review of medical records and a cross-sectional survey. The sample size was 500 dyads of antidepressant-treated MDD patients and their unpaid caregivers. MDD patients’ medical records, the concept of healthcare resource utilization, the Work Productivity and Activity Impairment Questionnaire: depression and mood & mental state versions (WPAI: D, MM), the Class Impairment Questionnaire (CIQ), and the Family Experiences Interview Schedule (FEIS) were applied as the tools of the study. Pearson Chi’s square, Fisher’s Exact test, and independent T-test were employed for statistical analysis. Results The proportion of TRD was 19.6% among antidepressant-treated MDD patients in a Thai tertiary public hospital. The results of the study indicated that several factors showed a statistically significant association with TRD criteria. These factors included younger age of MDD patients, a younger age of onset of MDD, lower body mass index (BMI), a history of suicide attempts and self-harm, as well as frequent smoking behavior. The annualized economic cost of TRD was 276,059.97 baht per person ($7,668.33), which was significantly higher than that of cost of non-TRD (173,487.04 baht or$4,819.08). The aggregated economic costs of MDD were 96.8 million baht annually ($2.69 M) if calculated from 500 MDD patients and unpaid caregivers. This contributed to the economic cost of TRD 27.05 million baht (98 respondents) and the economic cost of non-TRD 69.74 million baht (402 respondents). Conclusions The economic burden associated with TRD was significantly higher compared to non-TRD among antidepressant-treated MDD patients. Specifically, both direct medical costs and indirect costs were notably elevated in the TRD group