Which of the three education indicators is the most meaningful and best measure of education to be used in the construction of a composite index of development? Table 2-4 shows zero-order correlations of the three education indicators with selected other indicators for 1980, 1970, and 1960. The proportion of persons with at least a high school education shows the highest correlations with all the selected indicators of urbanization, economy, communications, and health, while the percentage with elementary education shows the lowest correlations in all reference years in question. The greater variability between provinces with the percentage with high school education than with the percentage with elementary education (see Appendix 2.4, Table A2.4.1) may be the main reason for this pattern.
The correlations of LITERACY with all the other selected indicators of urbanization, economy, communications and health are not that far below those of HSCHOOL. This suggests that both indicators are more or less on a par in serving to compare different provinces at the same time in 1960, 1970 and 1980. Being direct measures of education, they are valid, objective, sensitive and specific indicators. LITERACY closely corresponds to WHO’s global indicator 11 and non-global social and economic indicator related to health (B2e) of Table 2-2. While it cannot be denied that literacy is very important for health because ‘... it enables people to understand their health problems and ways of solving them, and facilitates their active involvement in community health activities’ (WHO, 198la:21), the importance of taking into account the number of years of educational attainment of the population in question should not be overlooked especially in highly literate societies like the Philippine society. The higher the educational attainment, the better the approaches to attain better health.
As to which is the better measure of education to be used in the generation of a composite index of development, both LITERACY and HSCHOOL are potential candidates. In this thesis however, the higher correlation of HSCHOOL than LITERACY with most of the other variables is a statistical reason to take the proportion of persons with at least a high school education as the education indicator in the composite index construction in this chapter.
Table 2-4: Zero-order correlations of the three education indicators with selected indicators in various years
Other
selected
indicators
Education indicator/ Year
LITERACY HSCHOOL ELEM
1980 1970 1960 1980 1970 1960 1980 1970 1960 URBAN 0.50 0.56 0.30 0.60 0.63 0.49 -0.18 -0.08 -0.16 INCOME 0.30 - - 0.58 - - -0.39 - - AGRICL - 0.31 0.29 - 0.36 0.38 - -0.01 0.11 ELECT 0.43 0.59 0.40 0.52 0.73 0.68 -0.23 1 o o 00 0.01 ROAD 0.33 0.35 0.31 0.43 0.43 0.35 -0.31 -0.12 -0.09 WATER 0.63 0.59 0.54 0.66 0.68 0.68 -0.01 0.08 0.40 TOILET 0.56 0.65 0.39 0.58 0.65 0.63 0.07 0.17 0.11 MOTOR 0.55 0.57 0.40 0.69 0.80 0.60 -0.18 -0.10 -0.01
Note: Original variables are LITERACY and WATER (1980 and 1970),
ELEM (all years) and TOILET (1980); the rest are logarithmic transformations.
Key: URBAN= % urban; INCOME=average annual family income; AGRICL= agricultural output per worker; ELECT= % of households with electricity; ROAD=road density; WATER= % of households with drinking water supply; TOILET= % of households with sanitary toilets; MOTOR=number of registered motor vehicles per 10,000
population; LITERACY= % literate; HSCHOOL= % with high school education; ELEM= % with elementary education.
23.3. Intercorrelation among and further appraisal of the other socioeconomic and health-related indicators
Theoretically, measures of urbanization, economy, communication and education should be highly associated with each other. It is generally expected that the more modernized the area the greater the concentration of major income-generating activities, and the better the means of transport, communication and education. Provinces that are more urban, more economically advanced and exposed to more adequate means of communication are likely to have more secondary schools (public and private) than rural, less economically advantaged provinces. The more secondary schools available to meet the demand for training more students, the larger the proportion of population with at least a high school education.
The main concerns then in this subsection are to measure the degree to which variation in one variable is related to variation in another and to assess validity, objectivity, sensitivity and specificity for the remaining variables under consideration. If the expected direction of association is evident in these indicators and if they comply with at least one of the above WHO four criteria of ideal indicators, then it is
possible to have confidence in their usefulness in the analysis of Philippine mortality. For example, it is expected that the per cent urban (URBAN), average family income (INCOME), per cent using electricity for light (ELECT), road density (ROAD), number of motor vehicles per 10,000 population (MOTOR), per cent with at least high school education (HSCHOOL), per cent with safe drinking water supply (WATER), and per cent with sanitary toilet (TOILET) are highly and positively intercorrelated with one another. The higher the values for these indicators, the higher the degree of Philippine development. Furthermore, the higher their degree of positive intercorrelation, the greater their strength in indicating the unified process of Philippine development If this is met, then at least one, if not all, of the four WHO criteria for ideal indicators is also complied with.
Moreover, a strong negative relationship is expected between some of the socioeconomic indicators and some of the health-related indicators, such as the level of child malnutrition (MAL), population per hospital bed (HOSPBED), population per rural health unit (RHU), population per physician (DOCTOR), population per nurse (NURSE) and population per midwife (MIDWIFE). For instance, the higher the level of urbanization, the lower the level of child malnutrition, and the smaller the population per hospital bed, physician, nurse and midwife. Again, if this is satisfied, at least the variables are valid, or objective, or sensitive, or specific.
A close examination of the Pearson product-moment correlation (r) in Tables 2-5 to 2-7 reveals encouraging results: most of the above expectations are borne out by the data8. There is evidence of a high degree of relationship among the available Philippine indicators of urbanization, economy, communication and education over time.
While URBAN, ELECT, ROAD and MOTOR are indirectly related to the WHO social and economic indicators, their high intercorrelations with each other and most of the other variables suggest that at least they comply with most of the WHO criteria of ideal indicators. For example, URBAN is a proxy of WHO indicator, rate of population increase, with rural-urban migration as a major component, but within this limitation, it is a valid, objective and sensitive indicator. It is not specific, however, in relation to any particular development or health action, since its increase can result from a large number of factors related to socioeconomic, demographic and health changes.
8Values of r derived from both the untransformed and log-transformed variables were similar and for consistency of presentation, those results with log-transformed variables are presented.
Table 2-5: Zero-order correlations of the 1980 proxy indicators of urbanization, economy, education, communication, and health
Indi- H H c I S T 0 a U N E C M W O S t R C L H R O A I P o B 0 E 0 0 T T L B M r A M C 0 A 0 E E E A N E T L D R R T D L URBAN 1.00 INCOME .52 1.00 ELECT 61 .55 1.00 HSCHOOL .60 .58 .52 1.00 ROAD .32 .27 .42 .43 1.00 MOTOR . 66 .68 .68 .69 .53 1.00 WATER .46 .43 .47 .66 .42 .56 1.00 TOILET .36 .32 .26 .58 .41 .46 .66 1.00 HOSPBED- .26 -.28 -.26 -.57 -.34 -.43 -.42 -.45 1.00 MAL .21 -.16 -.06 -.21 -.10 -.23 -.13 -.27 .26
Key: URBAN» % urban; INCOME» average annual family income; ELECT» % of households with electricity; HSCHOOL=% of persons € years old and over with high school education;
ROAD» road density; MOTORS number of registered motor vehicles per 10,000 population; WATER» % of households with safe drinking supply; TOILET» % of households with sanitary toilets;
HOSPBED» population per hospital bed; IAH» index of accessibility to hospital services; MAL» level of child malnutrition.
Table 2-6: Zero-order correlations of the 1970 proxy indicators of urbanization, economy, education, communication, and health
Indi- H H M c A S T O D I a U G E C M W 0 S O N D t R R L H R 0 A I P C U W o B I E O 0 T T L B R T R I r A C C O A 0 E E E H O S F N L T L D R R T D U R E E URBAN 1.00 AGRICL .31 1.00 ELECT .68 .48 1.00 HSCHOOL .63 .36 .73 1.00 ROAD 28 .03 .41 .43 1.00 MOTOR . 62 .51 .85 .80 .46 1. 00 WATER .40 .26 .59 .68 .49 .57 1.00 TOILET .56 .15 . 65 . 65 .41 . 65 .57 1.00 HOSPBED- .25 .01 -.33 -.45 -.42 - .41 -.31 -.30 1..00 RHU .37 .22 .45 .05 .13 .46 -.06 .11 .17 1.00 DOCTOR - .05 .08 -.00 -.31 -.25 - . 01 -.48 -.35 .38 .59 1.00 NURSE .14 .19 .08 -.23 -.38 .03 -.45 -.16 .37 .54 .75 1.00 MIDWIFE .18 -.02 .03 -.20 -.18 .08 -.41 -.05 .44 .59 .72 .72 1.00
Key: AGRICL= agricultural output per worker; RHU= population per rural health unit; DOCTOR^ population per physician; NURSE= population per nurse; MIDWIFE= population per midwife. For the other codes, see footnote of Table 2-5.
Table 2-7: Zero-order correlations of the 1960 proxy indicators of urbanization, economy, education, communication, and health
Indi
cator URBAN AGRICL ELECT HSCHOOL ROAD MOTOR WATER TOILET HOSPBED
URBAN 1.00 AGRICL .39 1.00 ELECT . 68 .47 1.00 HSCHOOL .49 .38 .69 1.00 ROAD .30 -.07 .47 .35 1.00 MOTOR .50 .44 . 64 . 60 .33 1.00 WATER .39 .33 .64 .68 .47 .49 1.00 TOILET .56 .32 . 91 .63 .51 .54 .61 1.00 HOSPBED -.47 -.08 -.61 -.65 -.40 -.36 -.36 -.52 1.00
Note: See footnotes of Tables 2-5 and 2-6 for the meaning of codes.
Specifically in 1980, all of the socioeconomic indicators show highly significant correlations with each other. The Pearson product-moment correlation is greater than 0.5 for most of the variables. The high correlations suggest that they may be valid and objective, although most may not be sensitive and specific,
like URBAN as noted earlier.
Of the health-related indicators, as expected, WATER and TOILET, which closely correspond to WHO’s global indicator 7a and non-global indicator 3a (Table 2-2) are highly correlated with each of the other variables in question, suggesting that they comply with most if not all of WHO’s four criteria of ideal indicators. Population per hospital bed and level of child malnutrition, do not show the same uniformly significant relation with each of the other variables, indicating that they may not be as robust as the other variables. However, they indicate some associations with the other health-related indicators and with the education indicator.
According to WHO (1981b:20), the ratio of hospital beds to population is a health policy indicator of the degree of equity of distribution of health facilities between areas within the nation, region or the whole universe. It indicates the extent to which such areas vary in the provision of health facilities or of health care (Verhasselt and Mansourian, 1989:81). Although HOSPBED turns a weak measure in the present analysis, it may still be sensible to consider it in the context of equity between provinces. In 1973, the World Bank (1976: 280) noted that on average, health care is more available in the Philippines than in many developing countries but the ratio of hospital beds to population is just one-fifth of that typically
found in developed countries. In 1981, the hospital bed ratio was 1.8 beds per 1,000 population, which is on a par with ratios for Indonesia, Korea, Malaysia and Thailand but is somewhat lower than those for Singapore and Hongkong (World Bank, 1984: 49).
While MAL closely corresponds to WHO’s global indicator 8a and non-global indicator 4a (Table 2-2), its weak correlations with all the other variables is contrary to what is expected and therefore does not comply with the four WHO criteria of ideal indicators. This finding may be more a reflection of the poor quality of collection of this variable as discussed in Subsection 2.2.2 than of reality.
The same pattern of strong association among most of the proxy indicators of the five dimensions of socioeconomic development exists in both the 1970 and 1960 reference dates. The exception among the socioeconomic variables is agricultural output per worker, which shows no meaningful relation with road density nor with any of the health related indicators except water in both reference dates. Some explanations are put forward.
First, the City of Manila as one of the units of observation does not have this indicator, as agriculture does not exist as an industry here. It is 100 per cent urban and all economic activities refer to industries other than agriculture.
Second, while it is expected that a province with a lot of roads is much more efficient in agricultural production than a province with few roads, there may be other mechanisms operating. Agricultural output per worker may be measuring, not the provincial economic standing, but the socioeconomic standing of the minority, who are the elite and own most of the agricultural lands and for whom the poor majority of agricultural workers are working. These landowners generally reside in metropolitan areas and very few live in the provinces. Hence, agricultural output per worker may not be an appropriate measure of the economic standing of a given province. Furthermore, a province can still produce agricultural products even if there are no modem roads. Also, expansion of roads takes place even when agriculture as an industry is absent, as in the case of Manila City or the whole of Metro-Manila.
Third, some provinces have other industries apart from agriculture as their major means of livelihood. Hence, for such provinces, agricultural output per worker may not be as robust as average family income in indicating their economic status.
However, some association of agricultural output per worker is expected with most of our indicators: per cent urban, per cent with electricity, number of motor vehicles per 10,000 population, per cent with at least high school education, per cent with safe drinking water supply and per cent with sanitary toilet. The higher the agricultural output per worker, the better the means of acquiring electricity for the household, of purchasing a motor vehicle of some sort, of sending most household members to higher education, and of having a safe drinking water supply and a sanitary toilet in the household.
Examination of the relevant correlation coefficients affirms such expectations. While it is indirectly related to WHO’s non-global socioeconomic indicator of income distribution, satisfying the criterion of objectivity, AGRICL is a poor measure when it comes to the other criteria: validity and sensitivity as implied in the above discussion. Since Manila as one of the units of observation is non-agricultural, the comparison of all areas in question is jeopardized; thus AGRICL is not a valid indicator. However, it may be objective and specific in relation to agricultural development action although comparison is again confined to areas engaged in agriculture. Therefore, while the agricultural output per worker may be useful, it may not be as useful as one of the other variables in providing some insights as to provincial variations.
The exceptions among the 1960 and 1970 health-related indicators in showing a uniform strong association with all the other variables are the 1970 RHU, DOCTOR, NURSE and MIDWIFE. RHU shows a poor association with HSCHOOL, ROAD, WATER, TOILET and HOSPBED; the remaining variables show weak association with most of the socioeconomic indicators. However, all are similar to WHO’s global indicator 7c and d and non-global indicator 3a (Table 2-2) and are highly associated with each other, indicating that they comply with WHO’s criteria of evaluating indicators. These findings suggest that while they may be useful, they may not be as robust as WATER and TOILET as health indicators.
2 3.4. Composite index of development
Given their insignificant correlations with some variables, population per hospital bed and level of child malnutrition are excluded in the derivation of the composite index of development (ID) for the 1980 reference year used in the differential analysis by province. The resulting mean intercorrelation of the remaining eight indicators used in the index construction is 0.56; Cronbach’s coefficient alpha is 0.91. These values indicate robustness and internal consistency of the composite index of development (ID). These high values also provide further evidence of the usefulness of the indicators that went into the construction.