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10 Micro and Macro Effects of Child Mortality on Fertility: The Case of India P.N. Mari Bhat The assumption that a secular fall in mortality would eventually lead to a fertility reduction is central to the propositions of demographic transition theory. But when mortality began to decline dramatically in most of the developing world in the 1950s and the 1960s, the lack of a quick fertility response puzzled many pundits and policy makers. Financial and technical assistance flowed in unprecedented quantities to help deal with the rapid growth of population in the world's poorest regions. Now that many countries have experienced declines in fertility, it is possible to investigate the degree to which improvements in survival chances, especially those of children, are responsible for the current declines in fertility. In this chapter I attempt such an exercise for India, the second-most populous country in the world, which contributes one-fifth of the global popula- tion increase. Perhaps because of the increasing availability of survey data on individual couples, much of the recent analyses of the relationship between infant mortality and fertility has focused on the estimation of the replacement rate and on refining techniques to measure it. As the estimated replacement rates are generally sig- nificantly below unity, an increasing body of literature has concluded that de- clines in child mortality tend to accelerate population growth both in the short- and the long-run. But why would a large majority of couples choose a strategy that generally tends to undercompensate child loss in societies where children are valued for their economic contribution and for their support at old age? Because it is unlikely that the majority of couples would use a strategy that usually fails, the mean replacement rate of significantly below unity probably suggests infre- quent use of the strategy rather than its inefficiency. An alternative to replacement is hoarding, which is a response to perceived 339

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340 MICRO AND MACRO EFFECTS: THE CASE OF INDIA mortality risk not necessarily learned from one's own experience. Consequently, true understanding of the influence of child mortality on fertility is impossible without a thorough analysis at both the micro and the macro levels (Preston, 1978~. In India I find that the macro relationship between child mortality and fertility is changing from a weak association to a strong bond. This shift appears to be occurring as a function of a growing preference for the replacement mecha- nism, increased access to family planning, and lags in the perception of mortality decline. As a consequence, it is contended that more than compensatory declines in fertility could result at certain phases of the mortality transition. In this chapter I focus on three aspects of the mortality fertility relationship with respect to India. First, I investigate the changing relationship between child mortality and fertility in the context of large regional variations in fertility and mortality levels observed in India (Bhat, 1996~. Second, I investigate the degree to which the fertility response is specific to the sex of the dead child. This has been a subject of several simulation studies in the past (May and Heer, 1968; Venkatacharya,1978~. Third, I investigate the implications for population policy of a family planning environment that emphasizes sterilization over reversible methods. The empirical results presented in this chapter are obtained using household and macro-level data, aggregated for various levels of administrative divisions. Period-specific indicators from census and registration systems are employed, as well as cohort measures from sample surveys. Table 10-1 gives a brief descrip- tion of the types of data employed, substantive themes addressed, and statistical methods employed at various levels of the analysis. THEORETICAL FRAMEWORK My conceptual framework rests on the useful distinction between hoarding and replacement effects of child mortality made by Ben-Porath (1978) and in- cludes some of the elements of the family-building model discussed by Lloyd and Ivanov (1988~. I assume hoarding, a family-building strategy based on the as- sumption that some children will not survive, is the typical form of behavior in high-mortality, high-fertility populations, whereas child replacement is the pre- dominant characteristic of family building in low-mortality, low-fertility settings. Such a switch in strategy is consistent with the notion of the demographic transi- tion as a process whereby individuals and households gain greater control over their vital events (see also Heer and Smith, 1968, and O'Hara, 1972~. Figure 10-1 portrays the expected relationship between total number of chil- dren ever born (B) and total child deaths (D) in cohorts that have completed their family-building process. Child deaths are represented on the horizontal axis and are assumed to reflect only the changes in child mortality (q) and not of live births. As child deaths fall, the number of births responds in a curvilinear fash- ion, as shown by the RH (replacement/hoarding) curve. The shape of the RH

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342 B b1 b2 be MICRO AND MACRO EFFECTS: THE CASE OF INDIA .* / l 1~ I / ~1 / Replacement Hoardinq / M ~ / ~ / / / / / / Bd H d3 d2 d1 D FIGURE 10-1 A macro model of the relationship between children ever born and child deaths in cohorts. curve is determined by the rate of substitution between the hoarding and the replacement strategies. At high levels of mortality, the RH curve is inelastic and the rate of substitu- tion low for several reasons. Because hoarding is associated with expected mortality rather than actual experience, the substitution rate is determined partly by perceived changes in mortality risks. Because there may be significant lags in the perception of mortality decline (see Montgomery, in this volume), large re- ductions in mortality may be necessary before they are perceived and acted upon. Furthermore, if mortality declines are not accompanied by an increase in the cost of children, there would not be a strong motive to avoid unwanted births. An extreme example of this is the decline in child mortality until point A in the graph which will not elicit any fertility response. Note that at extremely high mortality levels a price effect may be operating to suppress fertility levels (Schultz, 1976~. If few children survive, some may consider childbearing is not worth the effort.

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P.N. MARI BHAT 343 Consequently, as mortality declines from this high level, some parents may revise upward their family size desires, and fertility levels may rise instead of falling. However, because the cost of contraception is typically high in these settings, it is doubtful that a significant price effect of mortality was actually present in preindustrial societies (see also Lloyd and Ivanov, 1988~. At low levels of mortality the RH curve is highly elastic. To see why this is so, Figure 10-1 includes a trend line for the desired number of births. This line (By) intersects the RH curve at points A and E. Beyond A and E, there is an unmet need for children, whereas unwanted fertility prevails between the region A and E. As mortality declines from point A in the RH curve, unwanted fertility (given by the vertical distance between the RH curve and the line B`3) begins to accumulate since desired family size falls, but substitution of replacement for hoarding does not occur at the desired speed. Unwanted fertility reaches its maximum at point M in the RH curve. But, as unwanted fertility rises, the cost of births also rises (implied by the fall in desired family size), and thus the total cost of inefficiency mounts in the hoarding regime. If now the cost of contraception also falls, more and more couples would consider switching to a replacement strategy. Consequently, unwanted fertility begins to decline and may reach point E where it totally disappears. This conceptual framework informs the empirical analysis to follow. In high-mortality settings, say above point M in the graph, I would expect a rela- tively minor fertility response to child mortality variations, even though couples in those populations may be employing an insurance strategy that generally over- compensates actual child loss. On the other hand, for populations in the region under M, or who fall between the points A and E, I would expect a relatively larger impact of child mortality on fertility even though the majority of couples may be practicing the replacement strategy, which may undercompensate child deaths. This anomaly is due to the fact that the slope of the RH curve that measures the fertility response to child mortality is influenced by the changing rate of substitution between the two strategies, indicating the need to pay careful attention to the functional form posited for the relationship between fertility and child mortality in the empirical analysis. TECHNICAL ISSUES IN ESTIMATION In the analysis below, period measures of fertility and mortality, taken from censuses and registration systems for large geographic regions, are employed, as well as cohort measures available from sample surveys for individuals and for larger aggregates. The object of the analysis is to quantify, as far as possible, the effect of a reduction in child deaths (D) on children ever born (B) using multiple regression techniques. A number of limitations of the regression approach have been raisedin the literature (see, for example, Brass and Barrett, 1978; Williams, 1977; Olsen, 1980~. However, if the objective is to quantify both replacement

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344 MICRO AND MACRO EFFECTS: THE CASE OF INDIA and hoarding effects, the options available are quite limited. Therefore, I have pursued the regression approach, giving careful attention to the following ques- tions: What are the appropriate indicators of fertility and mortality? What is the appropriate functional form? What can be done about the problem of endogeneity of both births and deaths? And, what additional controls should be employed in the regression? For the cohort analysis, the information on the required variables (i.e., births and deaths) is directly available for cohorts at the end of childbearing. The problem is only one of defining an appropriate age cutoff. To have a sufficiently large sample, I use information on women aged 35 and older. For the analysis of period measures, the total fertility rate (TFR) is the appropriate choice for fertility because it is a surrogate measure of children ever born. For child mortality, the choice of an appropriate variable is less obvious because the relevant age interval changes as parents shift from a hoarding to a replacement strategy. Fortunately, because mortality levels at different intervals are strongly correlated, it should be sufficient to employ an age range that includes the majority of child deaths. I have used the under-5 mortality rate (q5), given that there is significant mortality beyond infancy in India. In the cohort analysis, the coefficient of child deaths in a linear regression of births and deaths (plus other appropriate controls) would give an estimate of the fertility response rate to a child death. To measure hoarding effects, one must add community-level measures of mortality as additional regressors. However, when cohort data refer to an aggregate, then the coefficient on the child death variable would capture a mixture of replacement and hoarding rates, and hereafter it is referred to as the RH rate, or simply as r. The RH rate may not be a constant, and is likely to increase as mortality declines. To model this relationship, I have regressed children ever born on the logarithm of child deaths. This functional form carries the implicit assumption that the effect of a child death on fertility is inversely proportional to total child deaths (8B = p8DID, where ~ is the regres- sion parameter). I refer to this functional form as the variable-rate form and the linear function as the constant-rate form. In the analysis of period measures, it is useful to employ a functional form that provides a direct estimate of the RH rate as a regression parameter. Because the regressor here is the mortality rate rather than the number of deaths, a regres- sion of logarithm of the TFR on under-5 mortality rate would give a direct estimate of the RH rate (8TFR/TFR = paq, or TAR = p8D). This is the constant- rate function. The variable-rate form in this case involves a regression of the logarithm of the TFR on the logarithm of the under-5 mortality rate. This func- tional form assumes that the effect of a reduction in child death on fertility is inversely proportional to child mortality rate (8TFR = pTFR8q/q = p8DIq). While regressing children ever born on child deaths, however, a serious problem of simultaneity arises because child deaths may be higher for a woman simply because she bore many children, even though her children's mortality rate

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P.N. MARI BHAT 345 was the same as that of others. This problem can be remedied with a two-stage least-squares (TSLS) approach, using the proportion of children dead as an in- strument for child deaths (Olsen, 1980~. However, if fertility also influences the child mortality rate, this estimator can also be inconsistent. In the case of period measures, the problem of reverse or joint causation can also be addressed by the instrumental variable method, but appropriate instru- ments for child mortality are hard to find. As a partial remedy, I have used the child mortality variable lagged by several years. In this case, the lag structure was estimated using a distributed-lag model applied to annual time series data on fertility and mortality rates for the country as a whole. Because of my failure to fully address the problem of endogeneity of both births and deaths, my estimates of replacement and hoarding effects are subject to a potential upward bias. However, this bias is likely to be small because the effect of fertility on child survival is smaller than often claimed (Bongaarts, 1987~. Moreover, my regressions suggest that the existence of a strong correla- tion between the two is of recent origin, and there is no reason to expect the effect of fertility on child mortality to increase with time. Therefore, I am confident that the measured effects of child mortality on fertility are largely genuine. Because fertility levels are influenced by family size desires in addition to child mortality, it is essential to control for changes in the former so as to obtain an unbiased estimator of the effect of the latter on fertility. A question arises as to whether factors influencing unwanted fertility should also be used as covariates in the regression. An important insight on this can be obtained by examining the following identity for a cohort: B = D + C C4+ C- Cat =D+ Cat+ Cu. (1) where C is the number of surviving children, Ca! is the desired number of children, and Cu is the number of unwanted children. Essentially, the above equation states that children ever born is the sum of child deaths, surviving children desired, and surviving children unwanted. Note that the coefficient of all the terms on the right-hand side of the equation are equal to 1. Because of singularity, the model cannot be estimated from a regression analysis if all three variables are present. Suppose I drop unwanted fertility and impose the following model on the data: B=~+rD+pC~+u, (2) where u is the stochastic disturbance term, and or is a constant representing the

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346 MICRO AND MACRO EFFECTS: THE CASE OF INDIA average unwanted births. By subtracting equation (1) from equation (2) and rearranging terms, I obtain Cu = or + (r- 1)D + (p - l)Ca! + u . (3) From equation (3) it is obvious that unwanted fertility is a direct conse- quence of r end p being different from 1. When the two parameters are less than 1, child deaths and desired family size are inversely related to unwanted fertility. When r is more than 1 (in the region below M in the RH curve in Figure 10-1), child deaths and unwanted children will be positively correlated. It is, however, difficult to visualize a situation in which p is larger than 1, which would imply that desired family size and unwanted fertility are positively related. Note that, as the nonstochastic variation in unwanted fertility is implicit in the parameters of child mortality and desired family size variables, unwanted fertility, or its prox- ies, should not be used as controls in the regression. Factors that influence desired family size such as female literacy could also be influencing unwanted fertility. Hence, the desired family size variable is used directly in the regressions wherever possible. This eliminates indirect effects of child mortality on fertility through changes in reported desired family size. Be- cause these effects are expected to have a net positive value, I could be underes- timating the total effect of child mortality on fertility. This should act to partly suppress the upward bias resulting from not fully accounting for the endogeneity of fertility and mortality. The above arguments are equally applicable to the analysis of period mea- sures. I would have preferred to use period measures of desired family size or wanted fertility in these regressions (see Bongaarts, 1990 and references therein). But data on these measures are hard to obtain, and I have been forced to use proxies such as female literacy to control for variations in desired family size. Consequently, I am unable to interpret unambiguously the child mortality coeffi- cients from these regressions as unconditional estimates of the RH rate. EMPIRICAL RESULTS National Trends Child mortality in India began to fall around 1921 and accelerated downward in the 1950s. The evidence for this, however, is largely indirect and based mainly on data from the decennial censuses (Bhat, 1989~. Fertility began its downward course in the 1960s, especially in certain pockets such as Kerala and Punjab (Bhat et al., 1984~. By the beginning of the 1970s, when the Sample Registration System (SRS) began to track annual trends in vital rates, the total fertility rate had fallen to fewer than six births per woman, and the infant and child (under-5) mortality rates were below 140 and 230 per 1,000 live births, respectively.

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P.N. MARI BHAT Total FertiIity Rate Under-5 Mortality Rate 5.5 a) Cd .~ LL 5 a'45 cat 4 3.5 347 - 250 '\ 1 1 1970 1975 1 1 1980 1985 1990 Year a) - 200 ~ .~ - o ~1 - 150 _ - 100 FIGURE 10-2 Trends in the total fertility rate and the under-5 mortality rate, 3-year moving averages, all India, 1971-1991. Figure 10-2 shows the annual trends in fertility and child mortality (q5) since 1971 as recorded by the SRS, plotted as 3-year moving averages. The figure shows that, by the time the SRS began to keep records, fertility had already begun to fall rapidly. The TFR declined from 5.3 births per woman around 1971 to 4.4 in 1978, whereupon the decline came to a sudden halt and remained at that level until the downward trend resumed around 1983. Since then, the TFR has fallen continuously to reach 3.7 births per woman by 1991. Meanwhile, under-5 mor- tality fell slowly initially, but the pace picked up after 1976 to reach a level of 170 in 1982. After remaining at this level until 1985, it began to fall again rapidly to reach a level of 120 by 1991.i iThere is evidence to suggest that, because the SRS was just taking root in many areas at the beginning of 1970s, it was probably underestimating vital rates, especially fertility levels. After examining all available evidence, a Panel on India constituted by the Committee on Population and Demography of the U.S. National Academy of Sciences, put the TFR at 5.6 in 1971-1972 instead of 5.3 births per woman recorded by the SRS (Bhat et al., 1984). On the other hand, the panel con- cluded that SRS death rates, including child mortality, did not require any corrections at the national level. The logistical problems that cause greater underenumeration of births than deaths has, how

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348 MICRO AND MACRO EFFECTS: THE CASE OF INDIA To what extent are these two trends related and what do they imply for the lag between changes in mortality and fertility? These issues are explored below by fitting a distributed-lag model to national-level data on fertility and child mortality using figures supplied by the SRS for the period 1970-1993. Table 10- 2 shows the results obtained when a geometric-lag function (also known as a Koyck lag) is employed. This infinite lag function assumes that the effect of child mortality on a given period of fertility decays geometrically with time, a reasonable approximation of the delayed fertility response to reductions in child mortality. The distributed-lag form of the variable-rate function can be written as t in TFR' = (x + ,B(1 - A) in q5,t + p(1 - it)/ in q5,t_1 + + ut, (4) = oc+,B(l-~)>,/ 1nqst i +Ut, i=o where us is the random disturbance term. However, the model has been estimated in its simpler autoregressive form, wherein the fertility level of a given year is regressed against its level in the previous year and the level of child mortality in the same year.2 in AFRO = oc(1 - \) + ~ in TFR~_~ + p(1 - \) in q5 ~ + Vie, where vie = us- Mu_. (5) When the model is estimated in this form, the coefficient of lagged fertility provides an estimate of the parameter X, from which the implied mean lag of fertility response to child mortality can be computed as \/~1 - hi. The coefficient of the mortality rate gives information required for the computation of the RH rate. In the first specification, the under-5 mortality rate is employed as the only regressor apart from the lagged fertility variable. This specification suggests an RH rate of 2.8 births for the death of a child under-5 and a mean lag in the fertility response of 3.8 years (see Table 10-2~. This estimate of r may be high due to the ever, remained unclear. It could have arisen from the Indian custom of the mother returning to her natal home for delivery, if such births were not being properly recorded by the SRS enumerators in the early years of the SRS operation. The SRS performance appears to have improved markedly after the SRS sample units were replaced from those drawn using the 1961 census frame to those based on the 1981 census results. As such, fertility in India may have declined more rapidly than Figure 10-2 suggests (see Bhat, 1996). 2An attempt was also made to fit directly the distributed-lag form of the model through maximum likelihood procedures. However, it yielded less meaningful results, possibly because of autocorre- lation in residuals.

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349 o EM a' 3 _4 o .~ a' a' o a' o be ~ o o a' ~ ^ be ~ ~ ~ o o Cq ~ a, Cq ~ a' cq a' o ~ o sol ~ a' ~ .= Cq ~ a' VO Cq ~ ~ o O ~ a' . ~ ~ .~ ~ Em ~ o C) ~ C) V, o C) ~ C) V, o C) ~ C) V, o .0 4= 1 4= C) ~ o v o .0 4= 1 4= C) ~ o v o .0 4= 1 4= C) ~ o v ~ ;^ so o 4= x oo o ~ ~ cMoo ~ M cM o ~cM cM . . . . .. . ~ o ~ ~ ~ o ~ o o o o ~ . .. o o~ ~ ~ ~ o M . . . ~ o o ~ o . . . o o ~ ~ ~ ~ cM ~ cM ~ ocM ~ ~ ~ ~ cM ~ o o ~ cM . . . . . . . . . o o o 1 o c~ o ~ o 1 1 ~ ~oo ~ ~ I~ ~, ~ o . . . . . o oo ~ oo . . . . . . . . o o 1 1 o ~ o ~ o ~ ~ . . ca ;^ Cd ~ ~ 4.-, ~ ~ i~, o .e .e o E~ E~ V ~ ;^ ;^ X ;^ c,;, ~ o 4= ~ 4= ~ ~ ~ '~ ~ o ~ ca s~ ;^

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P.N. MARI BHAT 373 rate would rise by 16, 20, and 9 percent at parities 2, 3, and 4, respectively. However, the combined result of these changes is expected to cause an increase of only 4 percent in the level of contraceptive practice of all married women (5 percent from the elimination of a male child death and 3 percent from a female child). This is because a large percentage of child deaths in the population is accounted for by women with five or more births who respond only weakly to their own child mortality experience, possibly because they are self-selected for higher family size desires or for greater reliance on an insurance strategy. A rough estimate of the mean replacement rate can be derived by multiplying the expected increase in the contraceptive practice by the total fertility rate and dividing by the proportion of nonusers of contraception. As the TFR was about 3.0 and about 50 percent of the married women (including childless women) were not using contraception in Karnataka, the average increase of 4 percent in the contraceptive prevalence rate implies a volitional replacement rate of 0.24 births per child death (0.30 for boys and 0.16 for girls). In addition, if a biological component of 0.3 is associated with prolonged breastfeeding, the implied total replacement rate would be 0.54, almost identical to the estimate derived from the cohort analysis using desired family size as a control (0.56~. However, the cohort analysis indicates a smaller sex differential in the response rate than suggested here. Because an overwhelming majority of women (87 percent in my sample) who use contraceptives use sterilization, a nonreversible method, children's sur- vival after acceptance of contraception could be a real concern. To test for its possible effects, age of the first surviving child at the time of the survey was used as an additional covariate. The results obtained in the case of all women show that the age of the first child and its square are strongly significant in the steriliza- tion equation and marginally significant in the case of reversible methods (see Table 10-12~. The older the child is, the more likely that his or her parents would accept sterilization. As one would expect, the effect of child's age is nonlinear; the estimated age function implies that prevalence of sterilization peaks when the first surviving child is 24 years old. Because the mean interval between steriliza- tion acceptance and the survey was about 8 years, the result suggests that accep- tance of contraception rises until the child is 16 years old. Thus, parents appeared to be concerned about child survival to ages much beyond infancy, and it is in such conditions that hoarding behavior flourishes. Although the coefficient of age of the first child is marginally significant in the case of women who use a reversible method, a Wald test reveals that the estimated effect is substantially smaller than that on sterilization acceptance (sig- nificant at the 0.001 level). Thus, my empirical results support the contention that sterilization tends to promote hoarding behavior. By demonstrating the importance of age of the child on contraceptive accep- tance I can demonstrate the existence of an insurance motive, but it is difficult to quantify a hoarding rate. The use of contextual child mortality as an additional

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374 _4 o a' o o = a' o o a' Cq o Cq a' o o Cq Cq bC o o Cq o ~ C~ ~ 1 o ~ o ~ a' ~ E~ ~. o m v o S~ z ;^ o ;^ o ca o ~ S~ O 4.;, ~o V, ~ S~ ;^ 4= x * * * * * * * * * * 0 ~00 ~00CM 00 0 ~0 ~CM 000 ~CM00 ~0 CM 0 ~00 ~0 ~00 ~0CM . . . ... .. ..... ... . O Ol O Ol 11 o 0 Ol Ol Ol OlO O O OlO Ol * * * * * * * * cM ~ ~ oo ~ c~ ~ c ~ ~ ~ ~ ~ c~ ~ oo o ~ c ~ ~ ~ o cM cM ~ o o o~ o c c~ o ~ o ~ cM ~ o o ~ c~o o o oo ~ . . . . . . . . . . .. . . .. . O Ol O O1 11 Ol O Ol I Ol OO Ol O OlO O ~ * * * * * * * * * * * * * * * * * * * * * M oo ~ ~ ~ ~ ooo ~ ~ o ~ ~ ~ oo ~ ~ c~c~ o ~ o~ ~c cM . . . . . . . . . . .. . . .. . O Ol ~ O1 1 11 Ol O Ol Ol Ol O O O O Ol Ol O ~ * * * * * * * * * * * * oo ~ ~ o oo ~ oo ~ ~ cM cM ~ ~ ~ cM ~ ~ ~ o o oo c~ ~ ~ o ~ ~ ~oo o ~ o ~ oo c~ o oo o oo ~ ~ o oo ~ ~o o o o o ~ . . . . . . . . . . . . . . . . . O Ol O O1 1 1 Ol Ol O Ol Ol Ol Ol O Ol O O O O * * * * ~ oo ~ ~ oo o ~o cM ~ o o ~ ~ . . . . . O Ol O1 1 1 1 1 ~1 1 * * * * M ~ ~ ~ c~ o ~ o ~ ~ ~ c~ o o o o o ~ o o o ~ o . . . . . . . . . Ol Ol Ol Ol O O O O Ol ~ ~ ~ ~ o ~ ~ c~ oc~ o oo o oo ~ ~o o o o . . .. . . . Ol Ol OlO O O O * M cM ~ c~ ~ ~ c~ o ~ o o ~ ~ o o o . . .. . . . O Ol OlO O O Ol * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ** M ~ o cM ~ ~ ~ ~ c~ cM o c~ ~ ~c~ o ~ o cM o c~ cM c~ o c~ o c~ o o cM oo o o o . . . . . . . . . . . . .. . . . o o ~ o o o o o o o ~ o oo o o o 1 1 ' 1 1 1 1 1 1 1 ~ ~c . . Ol O * oo ~ . . Ol Ol 0 R ~ o o ~ = ~ ~ ~ 0 0 C) C) ,~ ~ ~ ~.; ~ ~ ~ ~ ~ ~ ~ ~ ~ ~S ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ R ~ o<, c,) c,) cd cd c,) cd ~ t.4 ~ C) C) 4= ^~D ^~D ,r~ ,r ~ ~ O O 2 2 ~ ~ 2 ~ ~ ~o ~ 2

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375 * * ~ ~ ooCM ~ o ~ ~CM ~CM oo CM o ~ o~ ~o o CM CM ~o ~oo o ~ o~ ~o o o ~o ~ . . .. . . . . ... . ... O O OlO Ol O Ol O OOlOl Ol I ~ O oo 1 ca * * * * ~ * * ~ ~ ~ ~o ~ CM o o o~ o ~ o ~ ~o o CM o o oo ~o o o ~ o o o o o o o o o o o ~CM oo ~ o s~ 1 1 1 1 1 1 1 ~ ~ ~cd CM ~ ~ * E~ * ~ ~ o oo ~CM o oo o o ~o oo ~;^ o ~ o ~ ~ o CM ~CM oo ~oo o o o o ~o o o ~o o ~oo . . O O Ol Ol O O Ol Ol O o O Ol I ~ o ~ O V, 1 ~ * * * * * ~ ~ CM ~ ~ o ~ oo ~oo ~o ;^ o ~ o ~ oo CM o CM o o o o ~o o o ~CM ~oo ~ oo . . . . . . . . . . . . . . . . ~ o o o o o o o o o o o o oo ~ oo ~ o ~ I I I I I I ~ ~ ~r ~ ;~6 o . 00 c' O s~ O 0~ c'

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376 MICRO AND MACRO EFFECTS: THE CASE OF INDIA regressor provides a means by which one can not only detect hoarding behavior but can also quantify the fertility response. As in the case of cohort analysis presented above, the level of contextual mortality was measured at the district level. As seen in Table 10-12, this variable was strongly significant in both sterilization and reversible method equations. Interestingly, the effect is larger in the case of reversible method use; the Wald test confirmed this at a 5 percent level. The greater sensitivity of reversible method use to variations in contextual mortality is in line with my expectation that mortality decline induces a switch from a predominantly hoarding to a predominantly replacement regime. Clearly, as more couples adopt a replacement strategy, use of reversible methods should increase. I also find an increase in the use of sterilization associated with declines in community-level mortality because it reduces the number of children required for insurance. The results of parity-specific analysis also conform to my a priori expecta- tions. The district-level child mortality variable is significant at most parities, but its effect is nearly three times larger at parity four than at any other parity (see Table 10-12~. Because the average desired family size in Karnataka is about three children, many have a fourth child primarily for insurance. Thus, contex- tual mortality has greater bearing on contraceptive acceptance at this parity. By multiplying by the contraceptive prevalence rate and dividing by the mean child mortality rate, the coefficient of contextual mortality can be con- verted to an estimate of the hoarding response to a change in community-level mortality. The weighted average of method-specific effects implies a hoarding response of 2.1 births per child death, whereas the weighted average of parity- specific coefficients implies a response rate of 2.0 births. If I add to this the replacement effect of 0.54, estimated earlier, I obtain a total RH rate of 2.6 children per child death, which is significantly higher than the estimate of 1.5 derived from the cohort analysis of women aged 35-49. The difference in the estimates obtained from period and cohort analyses suggests a rising trend in the RH rate, an indication consistent with the hypothesis of a structural shift. The almost exclusive reliance on sterilization in the Indian family planning program tends to promote hoarding behavior. This point becomes even clearer when the timing of sterilization is examined. Due to certain social as well as technical reasons, most women find it advantageous to accept sterilization imme- diately after delivery. The data for Karnataka imply that the interval between the last live birth and the acceptance of sterilization is less than a month in 58 percent of cases and 1 month in another 14 percent of the cases (Table 10-13~. This obviously raises questions as to the parents' concern about the survival of the last child. The question becomes even more intriguing when one observes that the sex ratio of the last live birth is highly skewed with 144 male births for 100 female births among sterilized couples. Thus, there is a clear indication that couples waited to have a son before accepting a nonreversible method. Most likely, couples do not accept sterilization as soon as the desired family size is

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P.N. MARI BHAT 377 TABLE 10-13 Percentage Distribution of Sterilized Couples by Interval Between Birth of Last Child and Date of Sterilization and According to Sex of Last Child, Karnataka, 1992-1993 Sex of Last Child Interval in Months Male Female Either Sex 0 59.0 56.6 58.0 1 14.6 14.1 14.4 2-3 7.0 7.6 7.2 4-6 2.6 3.1 2.8 7-11 1.6 2.8 2.1 12+ 9.8 9.9 9.8 Inconsistent/not reported 5.4 5.9 5.6 Sterilized couples 1,020 710 1,730 SOURCE: National Family Health Survey, Karnataka. reached but they wait until after the next birth to decide whether to discontinue childbearing. If the next child is a boy, the survival safeguard is considered complete and couples accept sterilization immediately after the child is born. Perhaps parents would have accepted contraception after the preceding birth if reversible methods were easily accessible. The strong influence of the sex com- bination of children on sterilization acceptance seen in Table 10-12 partly reflects this sex-specific hoarding behavior. In short, the analysis of contraceptive acceptance patterns shows that the prevalence rates for sterilization, a method used by a overwhelming majority of couples in India to control their fertility, is lower because of their experience of child loss and their concerns about the survival of their living children. The estimated reduction in sterilization prevalence because of child loss implies a volitional response of about 0.25 for a child death in Karnataka. The strong presence of hoarding is indicated both by the significance of first child's age to contraceptive acceptance and by the timing of sterilization, which suggests that the last child born is a hoarded child and in many cases is insurance against the death of a son. The analysis unambiguously shows that the almost exclusive reliance on sterilization, and its provision mainly as a postpartum service, has accentuated the hoarding response, increased inefficiency (in the form of un- wanted fertility), and enhanced the effect of son preference on fertility. CONCLUDING REMARKS The analysis presented in this chapter is based on the premise that as indi- viduals gain increasing control over their fertility and mortality risks, they switch

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378 MICRO AND MACRO EFFECTS: THE CASE OF INDIA from a hoarding to a replacement strategy to cope with the uncertainties of child survival. The shift occurs because over the transition mortality risks beyond infancy reduce substantially, the amount and cost of unwanted fertility increase, and improved access to family planning methods make it more feasible to use a replacement strategy. However, at initial stages of the transition, the substitution Of replacement for hoarding occurs at a sluggish pace because of lags in indi- vidual perception of community-wide mortality declines, the low cost of un- wanted fertility, and the lack of access to family planning services. Conse- quently, the relationship between fertility and child mortality is generally weak at this stage. As conditions change, couples increasingly switch to a replacement strategy. Because replacement can be less than fully compensatory, and hoarding often results in excess fertility, the switch tends to accelerate the fertility decline. Even though replacement is incomplete at the family level, at the macro level, the mortality-fertility relationship is characterized by increasing returns to scale. My data for India show that the replacement rate is substantially below 1, about 0.5-0.6, of which about one-half is a volitional component. I interpret the low rates of volitional replacement as evidence that the majority of Indian women have still not adopted a replacement strategy. This is supported by the signifi- cance of variations in community-level mortality as demonstrated in the multi- variate analysis of household-level data and the analysis of macro-level data that suggest that declines in child mortality currently bring more than compensatory changes in fertility. This is a different situation than two decades ago when the magnitude of the fertility response indicated deceasing returns to scale. Thus, the strategy shift appears to have begun. The implication of this result to health policy is fairly obvious. Whatever the fertility response in the past, structural changes have occurred, and current investments in child survival programs would trigger more than compensatory effects on fertility and thus contribute signifi- cantly in reducing global population growth. To maximize this effect, it is essential to strengthen fertility control programs, focusing mainly on reversible methods. As my analysis of Indian data shows, almost exclusive reliance on sterilization has favored the insurance strategy and delayed the adoption of a pure replacement mechanism.

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P.N. MARI BHAT APPENDIX Appendix tables begin on following page. 379

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380 a' a' Cq Cq a' .~ o x o Cq _4 VO a' a' ~ a' _4 0 o ~ ~ 0 ~ ~ o ~ o ~ a' VO ~ ^ a c., .= = ~4 o Cq Cq X C Cq a' bC a' ~ ~ o X ~ + -- 1 + 1 +1 + + -- 1 o oo o o ~CM ~C . ~ C~ o ~o~ o ~ .0 o o o o CM o~ ~ o o o V, o ~oo o C ~ oo o oo o~ C ~C ~C CM o ~CM~ o o ~o o o o ~o~ o o o o ~ ~ oo ~o ca 8 c ~ ~ ~ A ~ ~ - ~t . ~o ~ o ~ o ~ o ~ ~o ~ ~ ~ ~ o o ~ o ~ ~ .~= ~ ~ =0 ~ aO s =0 ~ ~o ,~, ~ ~ e ~ .~.<,` e 0= e ~ m ~>~m ~m 0 4= ~C) ~~ C) Ct ~ C ~ ~ ~ ' ~' ~ ~ 5

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381 +- . ~CM o~ .. oo 1 11 1 1+1+ CM ~oo CM ~CM~ o ~o ... . ... o o o ~ ~ooo ooo ~CM oo oo ~CMoo .... ..... oooo ooo ~ooo ca v, ~ca00 ca ~00 ca R c,,~ c7I R ~, c a ~ U ~ c ~e e ~a ~ _ ~o ~_ ~, _ . ~ ~, ~9 _ E~ o .0 4= C~ . C) oc . ~ ~ y~e ~' ~ 4= ca .= C) ca s~ s~ ~ 4= ca ca s~ ca 4= C) .S ca ca ~ ca ;^ C~ o .0 ca s~ .~ ;^ o 4= .= ~ m V, V, . . v o V,

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382 MICRO AND MACRO EFFECTS: THE CASE OF INDIA REFERENCES Ben-Porath, Y. 1978 Fertility response to child mortality: Microdata from Israel. Pp. 161-180 in S.H. Preston, ea., The Effects of Infant and Child Mortality on Fertility. New York: Academic Press. Bhat, M. 1989 Mortality and fertility in India, 1881-1961: A reassessment. In T. Dyson, ea., India's Historical Demography: Studies in Famine, Disease and Society. London: Curzon Press. 1996 Contours of fertility decline in India: A district-level study based on the 1991 census. Pp. 96-177 in K. Srinivasan, ea., Population Policy and Reproductive Health. New Delhi: Hindustan. 1997 Micro and Macro Effects of Child Mortality on Fertility: The Case of India. Population Research Center Report no. 94. Dharawad, India: JSS Institute of Economic Research. Bhat, M., S.H. Preston, and T. Dyson 1984 Vital Rates in India. Report no. 24, Committee on Population and Demography. Wash- ington, D.C.: National Academy Press. Bongaarts, J. 1987 Does family planning reduce infant mortality rates? Population and Development Re- view 13(2):323-334. 1990 The measurement of wanted fertility. Population and Development Review 16(3):487- 506. Brass, W., and J.C. Barrett 1978 Measurement problems in the analysis of linkages between fertility and child mortality. Pp. 209-233 in S.H. Preston, ea., The Effects of Infant and Child Mortality on Fertility. New York: Academic Press. Heer, D.M., and D.O. Smith 1968 Mortality level, desired family size and population increase. Demography 5(1):104-121. International Institute for Population Sciences (IIPS) 1995 National Family Health Survey, India, 1992-93. Bombay: IIPS. Lloyd, C.B., and S. Ivanov 1988 The effects of improved child survival on family planning practice and fertility. Studies in Family Planning 19(3): 141-161. May, D.A., and D.M. Heer 1968 Son survivorship motivation and family size in India: A computer simulation. Population Studies 22(2):199-210. O'Hara, D.J. 1972 Mortality risks, sequential decisions on births, and population growth. Demography 9(3):485-498. Olsen, R. 1980 Estimating the effect of child mortality on the number of births. Demography 17(4):429 443. Operations Research Group 1970 Family Planning Practices in India. Baroda, India: Operations Research Group. Preston, S.H. 1978 Introduction. Pp. 1-18 in S.H. Preston, ea., The Effects of Infant and Child Mortality on Fertility. New York: Academic Press. Schultz, T.P. 1976 Interrelationship between mortality and fertility. Pp. 239-289 in R.G. Ridker, ea., Popu- lation and Development: The Search for Selective Interventions. Baltimore, Md.: Johns Hopkins University Press.

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P.N. MARI BHAT 383 Srinivasan, K., S.J. Jeejeebhoy, R.A. Easterlin, and E.M. Crimmins 1984 Factors affecting fertility control in India: A cross-sectional study. Population and Development Review 10(2):273-296. Trussell, J., and R. Olsen 1983 Evaluation of the Olsen technique for estimating the fertility response to child mortality. Demography 20(3):391-405. Venkatacharya, K. 1978 Influence of variations in child mortality on fertility: A simulation model study. Pp. 235- 257 in S.H. Preston, ea., The Effects of Infant and Child Mortality on Fertility. New York: Academic Press. Williams, A.D. 1977 Measuring the impact of child mortality on fertility: A methodological note. Demogra- phy 14(4):581-590.