Educational Finance in Thailand - UNESCO Bangkok

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4.1 General Pattern of Income and Expenditure on Education 4.1.1. As can be expected the average household incomes of residents of the Bangkok Metropolitan Region (BMR) is the highest in the country. Table 9 shows that income inequality is still pervasive, with household incomes in the Northeast remaining virtually unchanged in relative terms between 1994 and 1996 at a level of 34 percent the average household income in the BMR. There is also evidence of a widening gap between the BMR and the rest of the country, since the disparity indices have either worsened or remained unchanged for all the regions in Thailand. 4.1.2. For rural Thailand, the relative position vis-à-vis the BMR has in general worsened, with disparity indices falling by as much as 4 percentage points for the rural South, and 3 percentage points for rural households in the Central region. Table 9: Average Annual Household Income in 1994 and 1996 Average household income: 1994 Average household income: 1996 % change in average household income Disparity Index: (as % of BMR income) 1994 1996 Region and Area Whole Kingdom 99,144 129,348 30.5 - - BMR 197,016 263,364 33.7 100 100 Central 104,688 130,884 25.0 53 50 North 75,120 99,972 33.1 38 38 Northeast 67,188 88,656 32.0 34 34 South 96,168 118,152 22.9 49 45 Municipal District Central 153,312 189,096 23.3 78 72 North 154,044 205,656 33.5 78 78 Northeast 156,828 206,736 31.8 80 78 South 149,724 184,500 23.2 76 70 Sanitary District Central 126,396 158,628 25.5 64 60 North 85,584 113,232 32.3 43 43 Northeast 98,100 118,104 20.4 50 45 South 113,016 159,108 40.8 57 60 Rural Central 91,860 115,068 25.3 47 44 North 64,944 87,912 35.4 33 33 Northeast 56,712 76,848 35.5 29 29 South 84,528 103,308 22.2 43 39 Source: Socio-Economic Surveys, 1994 and 1996 4.1.3.In terms of expenditures, data indicates that expenditures on education rank third in terms of importance after food and housing. 3 Expenditure on education as a percentage of total household expenditures varies within a narrow range across regions. Data shows that BMR households spend on average a little over 6 per cent of their total household expenditures on education, whereas in the Northeast, only 3.58 per cent of household expenditures is devoted to education and supplies. 3 Only the 1996 data are shown here, since the 1994 results were similar. 170
4.1.4. The percentages calculated from the SES data are much lower than that obtained from the Parents Survey conducted by ONEC. This is probably due to the fact that the ONEC survey was a purposive survey of some 6,000 respondents and the questions were focussed specifically on educational expenditures, whereas the nationwide SES typically has a sample size of over 30,000 households, with comprehensive and detailed questions on every component of income and expenditure. As such interviewer attention was not directed specifically at education expenditures. The difference is likely to lie in the reported figures for contributions to schools, but enumerators at the NSO believe that only an insignificant number of households make sizeable enough contributions for this to affect overall national averages and stand by their figures. 4.1.5. When expenditures on education are calculated as a proportion of household income, the expected Engel pattern is seen. Table 10 divides households in each region into income quintiles, and shows that the proportion of household income spent on education varies inversely with the level of income. This indicates that even though high income families spend more on education in absolute terms, these expenditures remain a smaller proportion of their income than in the case of poorer families. This can be taken to indicate the “burden” of educational expenditures that fall disproportionately on the lower income groups. 4.1.6. This pattern re-affirms the need to target subsidies in favor of low-income groups. Identifying individuals or households that are “poor” may be an intractable task, given the data requirements, the prevalence of subsistence activities, underreported incomes, and the tendency for some groups to pass themselves off as “poor”. But other targeting methods may be used, including (i) geographical demarcation, whereby poor areas are defined for coverage, (ii) self-targeting, using variations in the cost and quality of services to induce self-selection, and (iii) targeting based on socio-demographic characteristics such as age, sex, occupation and ethnicity if financial vulnerability are believed to be based on these characteristics. For example, it is clear from the regression analyses that, for the same level of income and other characteristics, a female household head tends to spend more on the household’s education than a male household head. Thus families in poor geographical areas with females at the head of the household would be more suitable for subsidy. The first step would therefore be to ensure that an up-to-date information system exists. Table 10: Average expenditure on education as a percentage of average income, 1994 and 1996 Bottom Quintile Next Quintile Next Quintile Next Quintile Top Quintile 1994 1996 1994 1996 1994 1996 1994 1996 1994 1996 BMR 6.77 4.11 4.36 5.74 3.72 4.32 5.17 5.24 5.20 4.73 Central 6.09 5.77 5.12 5.11 4.67 4.82 4.26 4.09 2.73 2.72 North 5.72 5.10 5.10 5.29 4.51 4.59 3.98 4.42 2.64 2.72 Northeast 5.43 6.61 4.92 5.07 3.94 4.19 3.86 3.40 2.42 2.20 South 8.09 7.99 6.02 6.80 5.55 5.39 5.07 4.08 2.86 2.52 Whole Kingdom 6.01 6.21 5.16 5.43 4.52 4.67 4.42 4.18 3.35 3.05 171
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- Volume II/3 - Educational Finance
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Table of Contents A. Executive Summ
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Table of Tables Table 1 Adjustments
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mission and operations. The term
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The information requirements for fi
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Page 131 and 132: Description and assessment of compu
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Page 135 and 136: Table 3: Continued Current existing
Page 137 and 138: Description of flows of financial i
Page 139 and 140: (teachers, facilities, etc) if data
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Page 143 and 144: Table 5: Assessment of information
Page 145 and 146: An assessment of information system
Page 147 and 148: general administration, these syste
Page 149 and 150: Recommendation for IS Most involved
Page 151 and 152: 6. The nation wide implementation.
Page 153 and 154: References Office of the permanent
Page 155 and 156: Office of private education commiss
Page 157 and 158: Interviews 10 September 1998, 9:00-
Page 159 and 160: Appendix 2 Projections of Costs of
Page 161 and 162: Projected Cost Increases for Expand
Page 163 and 164: Identification of Schools for Sampl
Page 165 and 166: 8. Please place an X in a box below
Page 167 and 168: enrollment as a whole growing at an
Page 169 and 170: Figure 1: Thailand’s Development
Page 171 and 172: Table 5: Crude transition rates of
Page 173 and 174: and level of education, 1993 and 19
Page 175 and 176: 2.31 Questionable quality of educat
Page 177: income inequality between agricultu
Page 181 and 182: Table 11: Public and Private sector
Page 183 and 184: 4.3.8. As would be expected, unempl
Page 185 and 186: 5.7 The dilemma is to balance the s
Page 187 and 188: Table 14: Unemployment and Underemp
Page 189 and 190: there any activities that would be
Page 191 and 192: References Chalongphob Sussangkarn
Page 193 and 194: Appendix 6 BMA Voucher Policy A. Lo
Page 195 and 196: Appendix 7 Finance Allocation Metho
Page 197 and 198: Appendix 8 Program Structure of the
Page 199 and 200: Not all possible flows are show, si
Page 201 and 202: Appendix 11 Results of Regression A
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Educational Finance in Thailand - UNESCO Bangkok

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