Educational Finance in Thailand - UNESCO Bangkok
Educational Finance in Thailand - UNESCO Bangkok
Educational Finance in Thailand - UNESCO Bangkok
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4.1.4. The percentages calculated from the SES data are much lower than that obta<strong>in</strong>ed<br />
from the Parents Survey conducted by ONEC. This is probably due to the fact that the<br />
ONEC survey was a purposive survey of some 6,000 respondents and the questions were<br />
focussed specifically on educational expenditures, whereas the nationwide SES typically<br />
has a sample size of over 30,000 households, with comprehensive and detailed questions<br />
on every component of <strong>in</strong>come and expenditure. As such <strong>in</strong>terviewer attention was not<br />
directed specifically at education expenditures. The difference is likely to lie <strong>in</strong> the<br />
reported figures for contributions to schools, but enumerators at the NSO believe that<br />
only an <strong>in</strong>significant number of households make sizeable enough contributions for this<br />
to affect overall national averages and stand by their figures.<br />
4.1.5. When expenditures on education are calculated as a proportion of household<br />
<strong>in</strong>come, the expected Engel pattern is seen. Table 10 divides households <strong>in</strong> each region<br />
<strong>in</strong>to <strong>in</strong>come qu<strong>in</strong>tiles, and shows that the proportion of household <strong>in</strong>come spent on<br />
education varies <strong>in</strong>versely with the level of <strong>in</strong>come. This <strong>in</strong>dicates that even though high<br />
<strong>in</strong>come families spend more on education <strong>in</strong> absolute terms, these expenditures rema<strong>in</strong> a<br />
smaller proportion of their <strong>in</strong>come than <strong>in</strong> the case of poorer families. This can be taken<br />
to <strong>in</strong>dicate the “burden” of educational expenditures that fall disproportionately on the<br />
lower <strong>in</strong>come groups.<br />
4.1.6. This pattern re-affirms the need to target subsidies <strong>in</strong> favor of low-<strong>in</strong>come groups.<br />
Identify<strong>in</strong>g <strong>in</strong>dividuals or households that are “poor” may be an <strong>in</strong>tractable task, given<br />
the data requirements, the prevalence of subsistence activities, underreported <strong>in</strong>comes,<br />
and the tendency for some groups to pass themselves off as “poor”. But other target<strong>in</strong>g<br />
methods may be used, <strong>in</strong>clud<strong>in</strong>g (i) geographical demarcation, whereby poor areas are<br />
def<strong>in</strong>ed for coverage, (ii) self-target<strong>in</strong>g, us<strong>in</strong>g variations <strong>in</strong> the cost and quality of<br />
services to <strong>in</strong>duce self-selection, and (iii) target<strong>in</strong>g based on socio-demographic<br />
characteristics such as age, sex, occupation and ethnicity if f<strong>in</strong>ancial vulnerability are<br />
believed to be based on these characteristics. For example, it is clear from the regression<br />
analyses that, for the same level of <strong>in</strong>come and other characteristics, a female household<br />
head tends to spend more on the household’s education than a male household head. Thus<br />
families <strong>in</strong> poor geographical areas with females at the head of the household would be<br />
more suitable for subsidy. The first step would therefore be to ensure that an up-to-date<br />
<strong>in</strong>formation system exists.<br />
Table 10: Average expenditure on education as a percentage of average <strong>in</strong>come,<br />
1994 and 1996<br />
Bottom Qu<strong>in</strong>tile Next Qu<strong>in</strong>tile Next Qu<strong>in</strong>tile Next Qu<strong>in</strong>tile Top Qu<strong>in</strong>tile<br />
1994 1996 1994 1996 1994 1996 1994 1996 1994 1996<br />
BMR 6.77 4.11 4.36 5.74 3.72 4.32 5.17 5.24 5.20 4.73<br />
Central 6.09 5.77 5.12 5.11 4.67 4.82 4.26 4.09 2.73 2.72<br />
North 5.72 5.10 5.10 5.29 4.51 4.59 3.98 4.42 2.64 2.72<br />
Northeast 5.43 6.61 4.92 5.07 3.94 4.19 3.86 3.40 2.42 2.20<br />
South 8.09 7.99 6.02 6.80 5.55 5.39 5.07 4.08 2.86 2.52<br />
Whole K<strong>in</strong>gdom 6.01 6.21 5.16 5.43 4.52 4.67 4.42 4.18 3.35 3.05<br />
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