Proximate components
To estimate the share of the proximate determinants of fertility, the Bongaarts method is
proximate determinants through which socioeconomic and cultural factors affect fertility (Bongaarts and Potter 1983). Bongaarts and Potter further demonstrate that most of the variations in fertility are mainly due to the differential impact of the first four of these variables. The survey data enable us to apply the Bongaarts model of proximate determinants of fertility. The model formulates the TFR that is determined by total fecundity (TF),2 a hypothetical potential of fecundity that a woman would have in her lifetime, being inhibited by the indices of non-marriage (Cm), contraception (Cc), induced abortion (Ca), and lactational infecundability (Ci). The model can be quantified through the following equation:
m c a i
TFR=C ×C ×C × ×C TF (3)
Based on studies of historical populations with the highest recorded fertility, Bongaarts recommends using 15.3 as the maximum number of births, the total fecundity rate. The index of marriage, Cm, measures the effects on fertility of the proportion of women in a sexual union. It is calculated as the weighted average of age-specific proportions married and age-specific marital fertility rates. The index of marriage equals one when all women of reproductive age are in a union and zero when no women are in a union. Implicit in the use of the index is the assumption that only women in unions are exposed to the risk of childbirth. This assumption holds in Egypt. The index of contraception, Cc, equals one if no form of contraception is used and zero if all fecund women use modern and effective methods. Owing to the unavailability of information on the sensitive issue of induced abortion, we have assumed that the overall total induced abortion rate is zero. Finally, the index of postpartum infecundability, Ci, equals one in the absence of breastfeeding and zero when infecundability is permanent.
It is calculated as the average birth interval in the absence of breastfeeding, divided by the average length of the interval when breastfeeding takes place.
Theoretically, the value of each index ranges from 0 to 1. The complement of each index represents the proportionate reduction in fertility attributed to each determinant of fertility; the smaller the index value, the greater the fertility-reducing effect of the variable. Multiplying all of the indices together by the total fecundity rate of 15.3 produces the predicted TFR for the population. The predicted TFR will typically differ from the observed TFR because of the underreporting of births, underreporting of any of the behaviors measured by the indices, or the omission of proximate determinants that are influential in determining fertility levels, such as induced abortion.
The summary measures that are needed for the application of the model are presented in the first panel of Table 7. The second panel presents the estimated values of the indices of the three principal proximate determinants of fertility. In 2000, the marriage pattern reduces the actual fertility level by almost 29 percent (Cm=0.712).
Contraception has the strongest effect on fertility, accounting for a reduction of about 50 percent (Cc=0.502) in the total fecundity rate. Postpartum infecundability has the lowest fertility-reducing impact, reducing the total fecundity rate by almost 22 percent (Ci=0.784).
Table 7. Estimates of selected reproductive measures and derived indices of proximate determinants.
1992 1995 2000 Percent change
Notes: Cm = sum of age-specific proportion currently married times age-specific marital fertility, divided by age-specific marital fertility. Cc = 1 - proportion using modern contraceptive techniques. Ci = 20/(18.5 + average duration of postpartum amenhorrhea).
Ca = 1.
To document the changes among the indices over the period 1992-2000, estimates for three time points are compared in Table 7. The results indicate that during the 9-year period, an appreciable amount of change has occurred only for contraceptive use. During the period 1992-2000, the index of marriage declined by just over 2 percent and the index of contraception declined by more than 14 percent, but the index of lactational infecundability increased by almost 5 percent. Thus, the decline in TFR based on observed single-year age data (from 4.36 to 3.54) between 1992 and 1995 was caused primarily by the fertility-reducing effect of contraception. The fertility-reducing effect of marriage pattern was offset by a reduction in duration of lactational infecundability. The combined fertility-limiting effect of the three proximate determinants (Cm, Cc, Ci) was 0.319 in 1992, 0.312 in 1995, and 0.280 in 2000, indicating a decline of almost 13 percent in fertility during the period 1992-2000.
Table 8 exhibits the magnitude of the total fertility-inhibiting effect being accounted for by each proximate fertility determinant at three points of time, 1992, 1995, and 2000. The difference between the total fecundity (TF), taken as 15.3, and the estimated TFR is attributed to the result of the inhibitory effect of each determinant. The total fertility-inhibiting effect is pro-rated by the proportion of the logarithm of each index to the sum of the logarithm of all indices (Wang et al. 1987). For example, the fertility-inhibiting effect of the marriage variable is obtained as:
3.1 births (29.2 percent) are due to the effect of the marriage variable, 5.1 birth (49.2 percent) is due to contraception, and 2.8 births (26.5 percent) are due to lactational infecundability. Similarly, in 2000, the three proximate variables (marriage, contraception, and lactational infecundability) which inhibited 11 births, are distributed as 3.1 births (28.5 percent), 6.4 births (57.9 percent), and 2.3 births (20.4 percent), respectively.
Table 8. Magnitude of the total inhibiting effect accounted for by each proximate fertility determinant.
Births per woman Percent
1992 1995 2000 1992 1995 2000
Marriage 3.05 3.10 3.14 29.24 29.43 28.51
Contraception 5.13 5.31 6.37 49.18 50.39 57.85 Lactational
infecundability 2.76 2.88 2.25 26.51 27.31 20.43 Total: 15.3-TFR
(estimated) 10.43 10.53 11.01 100.00 100.00 100.00 From the foregoing analysis, it may be noted that contraception has the highest fertility-reducing effect; this may be considered as the single most important determinant of fertility-reduction in Egypt. The proportion of women non-married was the second most important fertility-reducing factor in all three surveys. Although the impact of the non-marriage component has increased, the rate of change is very slow.
However, the fertility-reducing effect of lactational infecundability is gradually decreasing from 1992 to 2000. It should be noted that the effect of marriage did not change much during the 8-year period, 1992-2000. The prevailing cultural and social norm in Egypt is unlikely to permit a change in the proportion non-married beyond a certain limit and the prospect for an immediate rise in age at marriage for females beyond 20 years does not seem to be very bright. This leads to the conclusion that future reductions in fertility in Egypt will depend largely on increased use of effective contraception and decreased lactational infecundability.
To quantify the contribution made by each of the proximate determinants of fertility to an observed change in fertility between two points in times (in this analysis, 1992 and 2000), Bongaarts and Potter (1983) turned the Bongaarts (1978) model into a decomposition equation. The equation states simply that a given proportional change in the TFR between two points of time equals the sum of the proportional fertility changes due to the different proximate determinants plus an interaction term.
The decomposition results are presented in Table 9. It indicates that TFR declined during the whole period of observation by almost 19 percent (or in absolute terms by 0.82 births per woman), from 4.36 births in 1992 to 3.54 in 2000. This total
determinants, is in no way negligible. In the second column of Table 9 the decomposition results are standardized to add up to 100 percent. It is evident that contraception played the largest role in the reduction of fertility during the period 1992-2000.
Separate decompositions of the changes in TFR between 1992 and 1995 as well as between 1995 and 2000 are displayed in the second and third panels of Table 9. The results indicate strong differences in the components of fertility decline for these two periods. While the total decline in TFR was much stronger in the second period than in the first (0.34 births and 0.48 births), the main driving forces seem to be other proximate determinants in the first period (including natural fecundability, spontaneous intrauterine mortality, and permanent sterility), while contraceptive practice is the overwhelming determinant of fertility decline between 1995 and 2000.
Table 9. Decomposition of the change in total fertility between 1992 and 2000.
Percentage
Proportion of married women -2.06 10.97 -0.09
Contraceptive practice -14.19 75.44 -0.62
Duration of lactational
infecundability 4.67 -24.85 0.20
Other proximate determinants -7.70 40.96 -0.34
Interaction 0.47 -2.52 0.02
Total -18.81 100.00 -0.82
1992-1995:
Proportion of married women -0.14 1.76 -0.01
Contraceptive practice -1.20 15.34 -0.05
Duration of lactational
infecundability -0.80 10.27 -0.03
Other proximate determinants -5.80 74.36 -0.25
Interaction 0.14 -1.74 0.01
Total -7.80 100.00 -0.34
1995-2000:
Proportion of married women -1.93 16.15 -0.08
Contraceptive practice -13.15 110.12 -0.53
Duration of lactational
infecundability 5.52 -46.21 0.22
Other proximate determinants -2.02 16.93 -0.08
Interaction -0.36 3.01 -0.01
Total -11.94 100.00 -0.48
Individual components
Additional insight into the decline of fertility can be obtained by analyzing the individual components of the change, including direct (or proximate) and indirect (or socioeconomic) determinants. Many analyses have consisted of individual-level linear regressions with children ever born as the dependent variable, in the tradition of Easterlin and Crimmins (1985). The form of the data suggests that event-history analysis would have advantages over the other two methodologies. However, due to unreliable reporting of the exact times of events, we refrain from this method. Instead, we follow the suggestion by Retherford and Thapa (2004) and concentrate on the number of births per woman in the five years preceding the survey. This variable has the advantage of being closer to the TFR with regard to content.
In the applied method, however, we differ from Retherford and Thapa (2004).
When analyzing the determinants of the number of births in a multivariate model, one has to take care of the nature of the dependent variable, which is typical count data. In principle, we could analyze these data using the standard multiple linear regression. But the predominance of zeros and the small values indicate that the dependent variable is clearly of a discrete nature. The Poisson regression model accounts for these characteristics and has been widely used to study such data. A problem with the standard Poisson model is often that the equidispersion assumption (E(Y|X)=V(Y|X)) is violated, i.e., the conditional mean does not equal the conditional variance. To solve this problem, different approaches have been proposed, including the generalized event count model and the generalized Poisson model to account for overdispersion (E(Y|X)<V(Y|X)) and underdispersion (E(Y|X)>V(Y|X)) (Winkelmann 2003).
Descriptive statistics for the EDHS data indicate strong evidence of underdispersion, i.e., the conditional mean exceeds the conditional variance in the full sample. Therefore, we estimated a zero-inflated Poisson model which accounts for underdispersion and for the prevalence of zero counts in the data (Winkelmann 2003).
In the zero-inflated Poisson model, zero outcomes can arise from one of two regimes. In one regime, the outcome is always zero (e.g., the infertile). In the other, the usual Poisson process is at work, which can produce a zero outcome or some other. Let c denote a binary indicator for regime 1 (ci =1) or regime 2 (ci =0), and let yi∗ denote the outcome of the Poisson process in regime 2. Then the observed count yi is given by:
0 if 1
probability of a zero outcome when following the Poisson process ( exp{−λi}). The probability of outcomes greater than zero is the probability of belonging to regime 2 times the probability from the Poisson process. Hence, the standard Poisson model is nested in the zero-inflated Poisson model for ω =0.
Systematic variation can be introduced in the parameter λi, as in a log-linear model:
exp(λi = xiβ). (7)
The coefficients in this model cannot be interpreted directly; only the sign of a coefficient indicates the direction of an effect. However, the expected number of events is given by:
2
( )
(1 )
i i i
i i
E y x x
λ β λ
∂ | = .
∂ + (8)
In our analysis, we calculated this marginal effect at the means of the independent variables. The effect can be interpreted as the percent increase in the number of events accompanying a one-unit increase of the independent variable.
Table 10 shows the means of the direct and indirect determinants of the number of births five years preceding the survey. Most interestingly, the mean number of births during the five years before the survey declined from 0.89 in 1992 to 0.73 in 2000. At the same time, the age at marriage decreased. The number of women who never used any contraceptive method declined from 35.4 percent in 1992 to 24.9 percent in 2000.
The percentage of women using only folkloric or traditional methods remained stable while the share of modern-method users increased considerably from 62.9 percent to 73.4 percent. Wives’ and husbands’ average education decreased during our observation time. Finally, the number of living children at the start of the five-year period declined slightly.
Table 10. Means of variables used in the regressions.
1992 1995 2000 Births during the five years before the survey 0.89 0.78 0.73 Direct determinants:
Only folkloric/traditional methods 1.7 1.8 1.8
Modern methods 62.9 66.7 73.4 Primary education 25.8 24.8 18.3 Secondary education 21.1 25.1 30.3
Higher education 4.8 6.4 8.2
Husband’s education:
No education 34.1 30.5 30.1 Primary education 28.2 27.4 21.8 Secondary education 25.2 27.6 31.6 Higher education 12.2 14.5 16.5 Wife worked before marriage:
Yes 25.1 21.1 15.8
No 74.9 78.9 84.2
Number of living children at start of 5-year period 2.45 2.43 2.40
Table 11. Effects of direct determinants on the number of births five years preceding the survey, zero-inflated Poisson regression (marginal effects).
1992 1995 2000
Duration of marriage: 10-14 0.140*** 0.076*** -0.020 Duration of marriage: 15-19 -0.099*** -0.169*** -0.237***
Duration of marriage: 20-24 -0.352*** -0.353*** -0.407***
Duration of marriage: 25+ -0.689*** -0.697*** -0.708***
N 9864 14779 15573
BIC -2022.371 -3671.119 -4497.142 Model 3:
BIC -2413.260 -4110.909 -4983.737 Model 4:
Contraceptive use: Only folkloric/traditional methods
0.509*** 0.435*** 0.824***
Contraceptive use: Modern methods 0.085*** 0.096*** 0.233***
N 9864 14779 15573
BIC -27.173 -47.247 -260.053
Model 5:
The marginal effects of direct determinants on the number of births five years preceding the survey from the zero-inflated Poisson model are listed in Table 11. The overall goodness-of-fit can be evaluated using the Bayesian Information Criterion (BIC). The more negative the BIC, the better the model fit. Generally, the model fits increased from survey to survey. For all tested indirect determinants, the best model fit is obtained with the wife’s age (model 3). Thus, the wife’s age at the time of the survey seems to be the best predictor of the number of births during the last five years compared to the other direct determinants. Compared over the three years of investigation, the effect of the wife’s age on the number of births five years preceding the survey decreased for the age group 20 to 39. Moreover, in each of the three surveys the effect of the wife’s age decreased over the life course. Compared to women aged 15-19, the number of births for the age group 20-24 is about 48.5 percent higher in 2000, compared to 62.5 percent in 1992 and 54.2 percent in 1995. The number of births decreased for all age groups, except for women aged 45-49. Interestingly, the model fit for age at marriage is much worse (model 1). Concerning contraceptive use, at first glance our results are counterintuitive (model 4): women who have ever used modern contraceptive methods have a 23.3 percent increase in the number of births during the last five years compared to women who never used any contraceptive method. This effect, however, is most likely a result of the way of questioning: “ever use” of contraceptive methods seems to be a rather bad indicator for our purposes.
The marginal effects of the indirect determinants on the number of births during the previous five years are listed in Table 12. Compared to the direct determinants, the overall model fit is worse (model 6 in Table 12 and model 5 in Table 11). The best indirect determinant is clearly the number of living children at the start of the five-year period (model 4). With each additional child the number of births during the five years preceding the survey declined by 11.2 percent in 1992, 12.9 percent in 1995, and 14.6 percent in 2000. Concerning place of residence (model 1), urban residency clearly lowers the number of births compared to rural residency, though to a decreasing extent (from –32.8 percent in 1992 to –17.1 percent in 2000). The effects of female education (model 2) are as expected in 1992 and 1995, however not so in 2000. In the first two surveys, we observe negative effects of primary, secondary and higher education compared to no education at all. In 2000, women with secondary and higher education have an 18.1 percent and 5.4 percent increase in the number of births compared to women with no education at all. Additional calculations (not shown here) yield that this effect is due to the increase in education for younger cohorts, which also have an increased number of births in the five years preceding the survey.
Table 12. Effects of indirect determinants on the number of births five years preceding the survey, zero-inflated Poisson regression (marginal effects).
1992 1995 2000
Model 1:
Urban-rural residence: Urban -0.328*** -0.248*** -0.171***
N 9864 14779 15573
BIC -279.734 -271.643 -140.036
Model 2:
Wife’s education: Primary education -0.143**** -0.155*** -0.067**
Wife’s education: Secondary education -0.071** 0.027 0.181***
Wife’s education: Higher education -0.127** -0.172*** 0.054+
N 9864 14779 15573
BIC -14.775 -91.419 -146.230
Model 3:
Husband’s education: Primary education 0.053* 0.071** 0.111***
Husband’s education: Secondary education -0.008 0.086*** 0.266***
Husband’s education: Higher education -0.063+ -0.003 0.152***
N 9839 14745 15553
BIC 14.164 1.643 -170.802
Model 4:
Number of living children at start of 5-year period
-0.112*** -0.129*** -0.146***
N 9864 14779 15573
BIC -623.539 -1439.637 -2059.577
Model 5:
Wife worked before marriage 0.040+ -0.002 -0.028
N 9863 14779 15573
BIC 5.971 9.593 7.455
Model 6:
Urban-rural residence: Urban -0.305*** -0.212*** -0.172***
Wife’s education: Primary education -0.067** -0.106*** -0.090***
Wife’s education: Secondary education -0.089** -0.078*** -0.040*
Wife’s education: Higher education -0.097+ -0.179*** -0.077**
Husband’s education: Primary education 0.095*** 0.058** 0.080***
Husband’s education: Secondary education 0.000 0.001 0.085***
Husband’s education: Higher education 0.017 0.005 0.064*
Number of living children at start of 5-year period
-0.117*** -0.133*** -0.144***
Wife worked before marriage 0.014 -0.006 -0.051**
N 9838 14745 15553
BIC -932.424 -1800.946 -2263.773
Notes: + p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
Conclusions
The objectives of this paper are, first, to provide improved estimates of recent fertility levels and trends in Egypt that account for chances in the timing of first and subsequent births, and second, to analyze the components of fertility change. The analysis is based on data from the Egyptian Demographic and Health Surveys 1992, 1995 and 2000.
Total fertility rates are derived by the birth-history method.
The analysis in this study shows that fertility trends estimated from the three waves of the EDHS suffer from various distortions due to errors in the data. These distortions have much to do with the quality of age reporting, both for women and for children. In all three surveys, we find a misreporting of women’s ages at the time of the survey and, thus, a misreporting of the age at first and subsequent births. Moreover, there is a systematic underreporting of children under age 5. As a consequence, estimates of the decline in the total fertility rate are most likely downward biased.
As a consequence of the evident age heaping and birth displacement, we must be very cautious in interpreting the tempo-adjusted estimations of the total fertility rate.
However, bearing this caution in mind, and given the observed changes in the timing of births, the Bongaarts-Feeney adjusted fertility rates suggest that there was little fertility decline from 1992 to 1996, but a resumed fall from 1997 to1999.We agree with Bongaarts (1999: 288): “Countries where substantial tempo effects are present today are the most vulnerable to stalls in their fertility transition.” The proof of tempo effects, however, is nontrivial in the case of developing countries where data quality often suffer from poor age reporting as in Egypt.
To estimate the share of the proximate determinates of fertility decline, we employed Bongaart’s aggregate level decompositions. Our results indicate that the main driving force in the change in fertility was the increase in contraceptive use. However, contraceptive use began to decrease fertility mainly around 1995.
On the individual level, we decompose the decline in fertility in its direct and indirect determinants by applying a zero-inflated Poisson model to the number of births per woman in the five years preceding the survey. Our results show that the number of children is decreasingly determined by the wife’s age over time. Moreover, with each additional child the number of births during the five years preceding the survey declined from survey to survey. This result is a hint that fertility decrease is underway in Egypt.
A final assessment of the actual level of fertility in Egypt and its determinants using individual-level data remains difficult because of misreporting of women’s ages and births of children. However, given that the extent and the way of misreporting
A final assessment of the actual level of fertility in Egypt and its determinants using individual-level data remains difficult because of misreporting of women’s ages and births of children. However, given that the extent and the way of misreporting