Disciplina y autoridad
Ejemplo 4. El color de los ojos (edad: 4, 2) Informe de una madre sobre una conversación con su hija
Ri the residual effect o f unmeasured variables 2.
It should be stressed that the aim of this section is to detect the direct or net influences of one variable on another represented by one-way straight arrows. Hence, there is no analysis of the correlations between the exogenous variables shown in two-headed arrows. The net influence of the predictors on the dependent variables resulting from these three equations is displayed in the path diagram in Figure 5.2 below. These direct effects are the partial regression coefficients in standard form, or the beta
\
coefficients, of the multiple regression equations (Kendall and O’Muircheartaigh, 1977: 15; Hermalin, 1979: 103).
Table 5.10 shows that the nine predictors of fertility change included in the model explain 66 per cent of the variance. Nevertheless, only two out of these independent variables are significant, namely infant mortality rate and percentage using contraception. The rest of the independent variables are insignificant even at the 10 per cent level of significance.
The table shows that, controlling for the other variables, a decrease of one standard deviation in the infant mortality rate, which is one of the indicators of social and economic condition of an area, would be associated with a 0.30 standard deviation increase in fertility change. The beta coefficient for the infant mortality rate with proportion using contraception as the dependent variable consistently shows a negative figure. This means that, taking other variables into account, reducing the infant mortality rate will help to increase the adoption of family planning in the area. These findings support the government’s program to reduce the infant mortality rate in order to hasten fertility decline.
2
It is assumed that the residual effects are uncorrelated with each other and with the exogenous factors in the m odel (Hermalin, 1979: 103).
(.064) (-.014) (-.213) (.009; -.079) (-.086) (-.303) (.208; -.220) SINGLE (Y2) CU80 (Y3) CLINIC DOCTOR TFRDIFF IRRFLD NONCLF FELMINT NONFWKR FLPR (Xd) URBAN DEPTIO (Xa) IMR Sources: Heimalin, 1979:102.
Notes: Numbers between parantheses are direct effects.
Furthermore, as expected, the standardized partial regression coefficient of the proportion using contraception is positive. This means that a rise of one standard deviation in the percentage of current users would be associated with a standard deviation increase in fertility change, even after taking into account other development aspects in the district. Thus the present study supports the government’s expectation that the family planning program will help to lower fertility, even after taking into account other development programs.
Table 5.10: Direct effect/ path coefficients {betas) and coefficient of determinations (R2) for specified combinations of variables
Independent variables Direct effect/ path
coefficient {betas)
Dependent variable: fertility change
Proportion urban residents -.086
Infant mortality rate -.303c
Proportion irrigated paddy fields .009
Proportion household with non-nuclear -.079
Proportion females finished primary school -.058
Female labour force participation rate .002
Proportion females non-family workers .099
Proportion females 20-24 who are single . 145
Proportion using contraception .490°
R2 .659
Dependent variable: Percentage females 20-24 who are single
Proportion urban residents .064
Dependency ratio -.014
Infant mortality rate -.213a
Proportion irrigated paddy fields .076
Proportion household with non-nuclear -.050
Proportion females finished primary school .163
Female labour force participation rate .328
Proportion females non-family workers .14lb
R2 .400
Dependent variables: Proportion using contraception.
Proportion urban residents -.177b
Dependency ratio -.190b
Infant mortality rate -.269c
Proportion irrigated paddy fields .286c
Proportion households with non-nuclear .179b
Proportion females finished primary school .168b
Female labour force participation rate ,378c
Proportion females non-family workers -.270b
Number doctors per 10,000 married women 15-44 -.220° Number clinics per 10,000 married women 15-44 .208c
R2 .734
Notes:
a Significant at 10 per cent level of significance b Significant at 5 per cent level of significance c Significant at 1 percent level of significance
In contrast to other studies of fertility determinants, the proportion of women aged 20- 24 years remaining single in the present study is not statistically significant in influencing the fertility change, although the coefficient is quite large {beta-0.\5). As mentioned before, the chapter focuses on the year 1978, to which the estimation of fertility level refers. At that time the family planning program had been implemented for less than ten years, and the government had been concentrating its efforts on extending the program to the villages in Java in the case of the responsibility for administration and supply of contraceptive devices. Other efforts beyond family planning , such as increasing the age at marriage and income-generating programs for the contraceptive acceptors, were given attention by the government afterwards (Suyono, 1979: 59). This might be one of the reasons why the proportion of women single is statistically insignificant, whereas the proportion using contraception is highly significant.
The picture for districts with low5 infant mortality rate is presented in Table 5.11. Unlike the findings for the whole districts, in this group of districts where the people’s age at marriage is relatively higher than in high-mortality areas, the proportion of women aged 20-24 who remain single is one of the significant predictors of fertility change in the area. The percentage of current users, with a large magnitude of beta coefficient (0.59), is another significant variable. The table for the districts with ‘high’ infant mortality rate shows that the proportion of women single is not a significant predictor (Table 5.12). Consistent with the findings in the ‘low’ infant mortality districts, the proportion using contraception in these districts is a significant predictor, although it has a slightly lower beta coefficient.
These findings show that, even after taking into account other development programs, besides infant mortality rate, family planning practice is a significant factor in
^ The ‘lo w ’ infant mortality rate districts in this section mean those which have less than 100 infant deaths per 1000 births.
influencing the fertility decline in ‘low’ as well as ‘high’ infant mortality districts, although the effect is slightly stronger in the low infant mortality areas.
The percentage of urban residents has a different effect on fertility change in those two groups of districts. Table 5.11 shows that in the ‘low’ infant mortality districts, a high proportion of persons residing in urban areas will lead to a high percentage of fertility decline in the area. On the other hand, it will have the opposite effect in the other group of districts. The more people in high mortality districts who reside in urban areas, the less the pressure people will get from the key persons to practise birth control. Furthermore, the higher proportion of urban residents in the ‘high’ infant mortality districts, is a significant factor in slowing the fertility decline. Contrary to population belief, the study confirms Freedman et al.’s findings that having more people reside in urban areas does not always cause the region to succeed in lowering the fertility level.
The last part of Table 5.12 provides standardized partial regression coefficients of nine predictors of family planning practice. Besides the proportion of irrigated paddy fields, the women’s economic activity variables are also found to be significant predictors of family planning acceptance in high infant mortality districts. In low infant mortality districts, furthermore, seven predictors, including the three variables just mentioned, are statistically significant (Table 5.12). It is interesting that when other variables are taken into account, the number of doctors is shown to be a significant predictor of family planning practice in the low infant mortality districts but not in the other group of districts. Although the difference is not great, the tables also show that for the low infant mortality areas, the model explained 75 per cent of the variation of fertility change in kabupaten in Java, whereas in the other districts the R.2 is 67 per cent (Tables 5.11 and 5.12). The fever variables significantly associated with contraceptive practice and the slightly higher R.2 in high mortality districts than in the other group of districts shows that there are other factors in these regions,
family formation, such as key persons and community organizations, these are unmeasured residuals.
5.5 Summary
In confirmation of similar studies done before, the infant mortality rate is significantly related to the decline of fertility in Icabupaten in Java. The direct effect of infant mortality rate, estimated from the 1980 Census, to fertility change between 1967-1970 and 1976-1979 was 0.30, which means that reducing one standard deviation of one of the indicators of social and economic condition would be associated with one third of standard deviation of the fertility change. The proportion of women aged 20-24 who are single significantly influence the decline of fertility only in low-fertility areas, where the average age at marriage is relatively higher than that in high infant mortality regions.
The effort of the government family planning program has had a significant effect on fertility decline in Icabupaten in Java during that period, even after taking into account other development programs in the area. The beta coefficient of the contraceptive use (0.49), furthermore, is greater than that for the infant mortality rate. The direct effect of contraceptive practice on the fertility decline is slightly stronger in low infant mortality areas (0.58) than in high infant mortality areas (0.51).