metadata: authors: 0: author: Cochrane, Susan H.
docna: 0: docna: The effects of education, health, and social
            security on fertility in developing countries
docty: Policy Research Working Paper
subtopic: Reproductive Health,Early Child and Children's Health,Educational Sciences,Family Planning Research,Law and Justice Institutions
teratopic: Gender
abstracts: cdata!: This paper analyzes the effects of
            education, health and social security on fertility in
            developing countries. Spending on education does not
            necessarily reduce fertility. For example, with small
            amounts of education fertility tends to increase before
            falling off at the level of completed primary school; its
            specific effect can only be measured by working at a
            particular population group. Health care services which
            reduce child mortality also reduce fertility when life
            expectancy reaches a level which anticipates the need for
            fewer children. Generally, this is a more costly way to
            reduce fertility than family planning. Lastly, even though
            the general effect of social security systems on fertility
            is more difficult to study and quantify, a preliminary
            estimate shows it to be the most costly method, compared
            with education, health care and family planning.
repnme: repnme: The effects of education, health, and
            social security on fertility in developing countries
display_title: The effects of education, health,
            and social security on fertility in developing countries
listing_relative_url: /research/1988/09/700641/effects-education-health-social-security-fertility-developing-countries
topicv3: Infant and Child Mortality m1326544 1446,Education m1327048 470,Family Planning m1327501 1452,Population, Aging and Health m1327736 640,Mortality m1504365 3661
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Gender
origu: Policy Plan & Prog Review Dept
no_of_pages: 46
model_extractions: Polay nning, and Rewsrch Populbtlon, HeaM, and Nutrition Spending on education does not automaticaNly reduce fertility.

Fertility tends to increase, for example, with small amounts of education before falling off at the level of completed primary school. Fertility responds more strongly to female than to male education, and more strongly to education in urban than in rural areas. The specific effect can be measured only by looking at a particular population group.

Health care services that reduce child mortality also reduce fertility when life expectancy reaches a level where couples anticipate the need for fewer children. This differs from society to society and, in general, is a more costly way to reduce fertility than family planning.

The general effect of social security systems on fertility is m-re difficult to study and to quantify. A preliminary estimate, however, shows it to be the most costly method, compared with education, health care, and family planning.

The cost of averting births, for most countries, appears to be lowest when policy emphasizes family planning. The mix of other programs depends on the country.

This paper is a product of the Population, Health, and Nutrition Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Sonia Ainsworth, room S6-065, extension 31091.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Copyright e 1988 by the Intemational Bank for Reconstruction and Deve:lopmenVthe World Bank
The Effects of Education, Health, and Social Security on Fertility in Developing Countries

# by

# Susan H. Cochrane

|The Nature of the Evidence**..............................**<br>**1**|
|---|
|Education**.........................**<br>2|
|Mortality**..........**<br>**7**|
|Social Security**.........................................**<br>11|
|Policy Relevance of the Findings**........................**<br>17|
|Bibliography**............................................**<br>22|
|Tables**..................................................**<br>25|
|Figures**.................................................**<br>27|
# THE NATURE OF THE EVIDENCE

The effects of various types of social programs on fertility is of interest, not only to help in understanding the demographic transition, but also to enable social engineering to accelerate fertility decline. Our understanding of these relationships differs greatly, in part because of the nature of the evidence needed to test various hypotheses. The effect of education on fertility is the easiest to study for two reasons: (1) it is primarily the effect of one individual's education on his or her own fertility that is most important and not the effect of the community's education, and (2) the individual's education is established prior to the fertility decision. This is not to say that the community's level of education has no effect beyond that of the individual or that the decision on one's own fertility is not made to some degree simultaneously with the decision on how much schooling to get, but these effects are of a secondary order of magnitude compared to the direct and recursive decisions. The effect of infa&kt and child mortality on fertility is harder to study because, fertility effects mortality as well as the converse and as will be discussed later part of the effect of mortality on fertility operates at the community level. The effect of social security on fertility is the most complex factor to study, because (1) it is difficult to assess the motivations of individuals in general, (2) the time lags between fertility decisions and old-age income are very long, (3) it is the community's social security system that are believed to affect individual fertility, and (4) aggregate fertility can affect the institution of social security programs as well as the converse.
2

# EDUCATION

Until the mid-1970's it was generally believed that fertility fell uniformly with education. This belief was the result of the fact that the model of demographic transition hypothesized fairly uniform falls in fertility with development and the data on the relationship between education and fertility came from developed countries or from the more developed underdeveloped countries of Latin America. The effect of education on fertility acts through three channels : the family's desired number of children, the natural fertility the family would have if no contraception were used and the use of contraception. The development of both tl.: economic model of fertility incorporating these channels (Easterlin, 197t the demographic model of the proximate determinants of fertility covet,rq natural fertility and the impact of contraceptive use (Bongaarts, 1976) have provided a rich structure for studying the channels through which education affects fertility. In addition, the data from the World Fertility Survey has complemented the many often small scale, not always comparable, data sets of earlier periods in establishing these channels empirically. We have thus learned an enormous amount about this relationship in the past 10 years. In 1979, I published a book on the relationship between educatior. and fertility based on the Easterlin model and the precursor to the proximate fertility model perfected by Bongaarts and reviewing a wide range of findings from the studies preceding, for the most part, the WFS surveys. That study found that the relationship between education and fertility was not always monotonically inverse in the poorest, most illiterate societies and in rural areas. In those circumstances, a small amount of education was
3 often associated with an increase in fertility. Only with education of completed primary school was fertility likely to fall with increased education. The reasons for these effects were primarily the sometimes positive effects that education had on fertility through reductions in breastfeeding and possibly improved maternal health and fecundity. The 1979 study also documented that female education was much more likely to have a negative effect on fertility than male education. Male education was less lik-ly to have negative effect on fertility than female education in part because it was more likely to have positive effects on the ability to afford children while having less of a negative effect through postponing marriage.

Since 1979 the WFS surveys have allowed these findings to be tested against a larger, more comparable set of data from a large number of developing countries. A number of studies have been done using the WFS data. (Jain, 1981, Cochrane, 1983 and U.N., 1987). The 1983 study using data from 20 countries confirmed the facts that (1) in the poorest, most rural areas fertility was likely to increase with small amounts of education before falling with higher amounts, (2) female education had stronger negative effects on fertility than male education, and (3) education's effects are more likely to be negative in urban than rural areas. A much more exhaustive study of data from 37 WFS studies confirmed these facts as well. (U.N., 1987). Table 1 and Figure 1 illustrate these relationships for individual countries and for aggregates of thes.e 37 countries respectively.

The 1987 U.N. study also tracked the channels through which education affected fertility and looked at regional, as well as development level differentials in the effect of education on fertility. Work by Cochrane and
4 Farid, 1986 also examined regional differentials. The U.N. paper is particularly useful for updating the relationship between desired family size and education. Desired family size varies greatly between countries and across education groups. Desired family size is more likely to fall uniformly with education than is actual fertility. Only in the Cameroons and the Dominican Republic was desired fertility higher for those with some schooling than for those with no education. (See Table 2 and Figure 2). The differentials across education groups tended to be higher in the countries with higher base levels among the least educated. (Primarily in Sub Saharan Africa.) The percentage of women 40 to 49 who have more living children than their desired family size is substantial everywhere except in Sub Saharan Africa. In the most developed developing countries this percentage falls with education while in the poorest countries it increases with education. In the intermediate countries the pattern is irregular. This pattern probably arises from the difficulty of obtaining contraceptives even among the most educated in the poorest countries despite the reduced desired family size of this group. (The study did not provide data on the educational differentials in unwanted fertility classified by the availability of family planning.) While changes in desired family sizes across educational groups are clearly important, the behavior of women with respect to the "Proximate Determinants of Fertility "is also important because fertility preferences only affect actual fertility if breastfeeding, marriage, contraception or abortion behavior change. The WFS data have also contributed substantially to our understanding of how education affects these intermediate determinants of fertility. Singh, Casterline and Cleland have analyzed the educational differentials in three proximate determinants of fertility.(1985). Table 3
5 from this study shows the effect of educat'Ln. For example, Ghanaian women with no schooling had fertility 87% of what it would have been if th- had been married through out their reproductive life. Regional averages for this value show that age of marriage has a greater effect on reducing fertility in Latin America and the Caribb- (.73) than in Africa (.8'' and Asia (.78). The educational differentials in tnese factors can be show.. by looking at the marriage index for women of different education groups relative to that for the uneducated. Fcr example, in Ghana women with 1-3 years of school have a marriage index 11% lower than women with no school (i.e. 89% as high).

In Africa the marriage index for those with all levels of schooling is always lower than that of the unschooled, but Latin America and Asia there are several countries where those with some school marry earlier than those with no school (Jamaica, Haiti, Costa Rica, Philippines, Korea and Jordan). Those with 7 or more years of schooling have reduced fertility due to marriage behavior relative to those with no school in every country except Jamaica. On average those with 7 or more years of schooling have fertility only 74% of that of those with no school because of their tendencies to marry later. In Africa, however, education has a somewhat lesser effect than elsewhere. (Here, however, only 5 African countries are used. In other studies where more African countries are used the education effect on marriage is shown to be larger in Africa than elsewhere. U.N., 1987 and Cochrane and Farid, 1985).

The indices for contraceptive use show much wider variations than do those for marriage. Fertility is reduced by only 2% among the least educated in Africa (.98) compared with 21% (.79) among ther least educated in Latin
6 America. The relationship between the amount that fertility is reduced by contraceptive use and education is more uniform than for age of marriage. Only in Panama and Senegal are there cases where more educated women are slightly less likely to have been using contraception. The most educated women on average have contraceptive use patterns that imply that their fertility is 67% of what it would be if they used no contraception compared to 86% for the uneducated. These effects vary by region with the education effects through contraceptive use being the lowest in Africa and highest in Latin America and intermediate in Asia.

The factor that is both most important for suppressing the fertility of the uneducated, infecundity due to breastfeeding, is also the one with the most atypical pattern. For all counties combined the fertility of the least educated is only 70% of what it would be in the absence of breastfeeding, but the fertility of women with more education is less suppressed. Thus women with 1-3 years of schooling have fertility 3% higher than the less educated because of reduced breastfeeding, holding all other things constant. Women with 7 or more years of schooling would have fertility 15% higher than the uneducated because of less breastfeeding. In addition, this breastfeeding pattern is fairly similar across countries.

Decomposing the differences in fertility across educational groups by the proximate determinants underscores the complexity of the relationship between education and fertility and the various roles that education plays in changing behavior. Table 4 from Singh et al shows the role various proximate determinants have in explaining the difference in fertility between education groups. The proximate fertility model fails to capture other factor such as coital frequency, abortion, and other health influences
7 on fecundity. The measure of the importance of these other factors is captured in value of phi. This leads Singh et al to conclude more work is needed on these other factors. Despite the complexity of these channels, the effect of education on fertility is trackable at the individual level because of maternal education's more or less exogenous nature and the importance of education's effect through individual behavior.

# MORTALITY

Whether mortality decline is necessary or sufficieat for fertility decline and the thresholds for bringing mortality decline about have received a considerable amount of attention. (See Bulatao and Elwan, 1985 for a review.) The consensus is that life expectancies of 50 years are necessary, but not sufficient conditions for fertility decline. The magnitude of the effect of preventing a death on fertility has been studied extensively. The general consensus as suummarized after the 1975 Bangkok Lonference was "...on average, an additional child death in the family, ceteris paribus leads to far less than one additional birth." (Preston, 1978, 11) In an earlier study it had been estimated that the effect varied by level of socio-economic development, but it was less than half in all the countries studied.(Preston, 1975). The World Fertility Survey again provides the opportunity to study infant and child mortality as a determinant of fertility with a broader set of comparable data.

The effect of infant and child mortality is believed to affect fertility through three channels. First, because of the importance of breastfeeding in suppressing fertility in most developing countries as illustrated above, the death of an infant will shorten the period of infecundity and the woman will
8 conceive much sooner if no contraception is used than if the child had survived. This is the biological chinnel. The second channel is that through family's decision to replace a child that has died in order to achieve their desired family size. The biological and behavioral channels operate at the individual level and probably have their effects over the short term. The third channel operates at a more abstract level. Couples are not always able to replace a child after it has died because the death occurs too late in the reproductive cycle. Therefore families will insure against such losses by havir-) more children than they would ideally desire. The number o' children that a couple has for insurance purpose depends, not only on their own mortalPty experience, hut what they perceive the community level of mortality to be. Thus this effect is difficult to measure without having data on what individuals perceive the community level of mortality to be and such data are rarely collected. Thus the effect of mortality reductions through the first two channels which is more or less measurable, probably underestimates the total effect. Even estimating the effect of the first two channels, however, is complicated by the fact that not only does mortality effect fertility, but ferti'ity affects mortality as shown by evidence of the effect of short spacing of births on the survival of subsequent births. (See Hobcraft et al, 1985).

In a paper prepared for a background expert meeting for the 1984 World Population Conference, several different techniques were used to measure the household effects of infar4- and child mortality on fertility controlling, to the extent possible, for the reverse directions of causation. First the effect of infant and child mortality on fertility through the biological channel was tested by comparing the intervals following the birth
9 of a child depending on whether that child died in the first month of life, died later in the first year cf life or survived 12 months. The median intervals were examined and regressions were used controlling for maternal age and education, husband's occupation, wife's work status and rural residence. (See Cochrane and Zachariah, 1983). These effects were estimated separately for all women and also only those that had never used contrdceptives and thus for whom the biological effect could not be compounded by the behavioral effect of child loss. In 122 of 143 comparisons the median birth intervals were longer, the longer the child lived. In the exceptional cases the sample sizes were often very small. In the regression equations, 88 of the 141 regression coefficients were significantly negative controlling for other factors that might effect birth intervals. (115 of 141 medians were as expected when samples were restricted to women who had never used contraception. 86 of 141 regression coefficients were significant for this group.) The magnitude of the effects are shown by averages in Table 5 taken from Cochrane and Zachariah. The effects range from a low of 2 months to a high of 7.8. The effects for individual countries seem to be consistent with what was found in several Latin American countries in earlier studies. (Rutstein and Medica, 1978). In general the effects estimated by the regression-coefficients are substantially larger than the differences between medians. These birth interval effects can be converted into the number of births averted per death averted by dividing the interval effect by the average length of a birth interval. Table 6 taken from Cochrane and Zachar'ah shows that these effects range from less than a tenth of a child to .35 of a child.(See last two columns of the table.) While it would be expected that these effects would be greater the greater the effect of
10

# breastfeeding in the country these relationships are generally not

significant.

The behavioral effect of child loss can be estimated in several ways. The simplest technique is to compare the subsequent number of children born to couples of a given parity who have and have not experienced the death of the child of that parity or a previous parity. This, however, is not a pure behavioral effect since it will incrporate `-he biological effect as well. Since it measures the subsequent fertility it is to a large degree independent of the effect of fertility on the mortality of children. There is, however, the possibility that people who have poor survival of children in general will have both more deaths in the preceding and subsequent period and because of more subsequent deaths will also have more subsequent births because of these deaths through the biological mechanism. While it is impossible to completely control for these factors, they can be partially controlled for by introducing statistical controls for those factors affecting chi41 mortality, maternal age, education and work status, rural residence and husband's occupation. The results are shown in Table 6. 108 of 150 coefficients are statistically significant. There are substantial differentials between the size of the effects in various countries, but the effects tend to be substantially larger than those observed in earlier studies. The effects range from a low of .14 in Sri Lanka to a high of .81 in Syria and average .48. This would mean that on average if one infant death were prevented about one half of a birth would be prevented. The size of the effects do not seem to vary by socio-economic development as was found with earlier data. (Prest_n, 1975).
11

# SOCIA:. SECURITY

The effect of education and infant and child mortality on fertility have been well established with household level data. While debate exists over the exact form of the relationship and the magnitude of the effects in various circumstances, the relationships are not in doubt. The effect of social security systems, however, is not well established. There are four kinds of data that have been brought to bear on this issue: (1) cross country data, (2) anthropological analysis of fertility and social security in certain countries, (3) survey data on people's stated motives for having children and (4) experimental data on savings programs designed to provide incentives for reduced fertility. These data sources vary enormously in there richness and their findings. In addition to the empirical work, there are a number of theoretical arguments on the probable effect of the old age security motive for having children. These are reviewed by Nugent and Gillaspy, (1983).

The anthropological data is difficult to review thoroughly in a paper of this length. Vlassoff and Vlassoff provide an excellent piece of work on the old age security arrangements and attitudes among men in rural India. The theoretical and anthropological studies raise several questions (1) granted that children may be an important source of old age support, how many children are needed to ensure adequate support? (2) given that men tend te have very short periods of inactivity prior to death in many poor countries and women tend to be considerable younger than their husbands and are longer lived in most areas, are women more likely than men
12 to put a premium on the old age security motive for children?, and (3) how much of the need for old age support is financial and how much is emotional and thus the desire to live with children in old age may not be a financial motive? Each of these questions are important for designing policy. Some survey data can provide insight into these factors, but relatively little work has been done.

The most thorough examination of the motives for child bearing in developing countries is the Value of Children Study. Table 7 summarizes some data from that study and Figure 3 traces through the salience of various motives for childbearing at different levels of fertility. This data seem to indicate that husbands and wives express the same relative importance to the need for children for old age support and this declines in about the same way as national fertility declines. Another interesting finding of this study is shown in Table 7. In those countt-xes where there is not a strong preference for sons (the Philippines and Thailand), the extent to which people expect old age support from sons and daughters are about equal. In countries such as Korea and Taiwan where sons are valued much more than daughters, there is a wide difference between the extent to which husbands and wives rely on sons rather than daughters for old age support. These data are extremely valuable, but cannot address the issue of causation.

There are several interesting questions that need to be addressed with such data:(1) what are the determinants of such attitudes towards the utility of children?, (2) whether the desire for old-age support affects family size preferences?, and (3) whether the desire for old age support affects behavior?
13 The Value of Children Survey did not provide analysis separately for old age support but combined it with data on financial assistance from children. The study found significant negative correlations between economic motives for child bearing and parental education and income in all six of the countries in their sample at that time. (Arnold et al, 1975, p.109). A recent study of husbands and wives in Pakistan showed educational difi3rentials in the amount of support expected from children. (Sotoudeh-Zand, 1987). A study of Egyptian husbands and wives showed strong negative relationships between extent of old age support expected from children and parental education in both urban and rural areas. Differences across income and expenditures groups were also sharp, but not always monotonic. (Hallouda et al, 1983). The effect of reliance on old age support on desired family size is not well established. The Value of Children Study found that among individuals in Korea, Taiwan, Hawaii and the Philippines these was a significant positive association between the economic motivation for childbearing (including old age support) and ideal family size. No significant relationship was found in Japan or Thailand. A study using the Egyptian data mentioned above found a significant effect of expectation to live with children in old age and desired family size among women in urban and rural areas. There was no significant effect for men. Nor was the extent of financial support expected from children significant for men or women. (Cochrane, Khan and Osheba, 1988).

There is little data on whether the old age motive for childbearing causes a couple to use contraception earlier or more frequently as would be expected if it had important effects on desire family size. Bulatao in analyzing the Value of Children data compared the attitudes of those who were high parity
14 non contraceptive users with low parity users. There was some relationship between the expectations of old age support and contraceptive use, but in some cases the differences were small and in several cases were reversed. (Bulatao, 1979).

The major cross country studies that have attempted to estimate the effects of pension programs on fertility are that by Hohm in 1975, reanalysis of his data by Kelly et al in 1976, reanalysis by Hohm himself in 1976 and a more recent effort by Entwisle and Winegarden in 1984. Hohm attempted to test two hyp- aeses; (1) The coverage of a country's old age or retirement, invalidity, and survivorship programs will vary inversely with that country's subsequent fertility, and (2) The benefit level of old age, nvalidity and survivorship programs will vary inversely with subsequent levels of fertility. Data (partly generated) from 67 countries showed strong zero order correlations between coverage and benefit levels and total fertility rates. Multiple regressions controlling for infant mortality, newspaper circulation and per capita domestic product, showed only marginally significant coefficients for coverage and no significance for level of benefits. In smaller random subsamples the coverage and benefits measure were significant in one of 5 cases. Despite these rather weak findings, the author stated that since the other determinants of fertility were even weaker, social security programs should be considered to reduce fertility.

Kelly et al took issue with Hohm's findings and showed that if a modernization index composed of 5 variables was used, it eliminated the significance of the coverage and benefit level variables. An expanded analysis by Hohm in a reply used a simpler measure of old-age support, the percentage of GNP going to social security, and found that this measure was
15 significantly related to fertility when controlling for level of development either as measured originally by him or with Kelly's modernization index. The regression portion of Kelly's analysis was perhaps less interesting, however, than was their presentation of the dating of the onset of fertility decline and old age legislation in developed countries. (See Table 8 from Kelly). Only in Ireland did legislation significantly predate fertility decline. This data raises two important points: (1) pension legislation is certainly not a prerequiEite for fertility decline. As Hohm's points out this does not mean, howevei, that it might not be a cost effective means of stimulating decline. (2) it is possible that fertility decline can stimulate pressure for social security legislation as families feel the need to supplement the support provided by children. This particular direction of causation is one that was tested in a recursive model developed by Entwisle and Winegarden.

Entwisle and Winegarden developed a recursive model in which the level of government expenditures on pensions as a percentage of GDP in 1970 was a determinant of total fertility rates in 1975 controlling for life expectancy, literacy, percentage of population in agriculture, the log of GDP per worker, the index of government family planning effort and the presence of a government family planning program a decade before 1975. Government pension expenditures were in turn a function of the old age dependence ratio, percentage of GDP from agriculture, life expectancy, life expectancy squared, and the log of GDP per worker and the total fertility rate in 1965. This model allows fertility 10 years in the past to affect pension spending in 1970 which in turn affects fertility in 1975. This model had excellent predictive power, especially for the TPR in 1975 which was significantly
16 dependent on pensions and all other controls except GDP per worker. However, only 61% of pension expenditures compared with 82% of TFRs was explained by the model. These findings proved robust with alternative samples, lags and prior levels of variables introduced. It should be noted that the measure of pensions here is the same one that proved more robust in Hohm's reply to the Kelly comment. It should be noted, however, that there was a very large difference between the ordinary least squares (.21) and the reduced form (1.14) coefficients of the effect of pensions on total fertility rates.

This macro evidence has clear policy implications as pointed out by Hohm, Entwi. e and Winegarden. It is less clear, however, how polices would be introduced and how cost effective pension programs would be relative to other methods to reduce fertility. Ridker has provided some insight by reviewing an experiment with a no-birth bonus scheme in southern India tea plantations. That relatively modest program did appear to have effects on reducing fertility. (Ridker, 1980). Unfortunately a measure of the cost per birth averted from the program is not available to compare this type of program with the larger, national pension schemes studied in the cross country analysis. There is, however, another analysis by Ridker that gives an illustrative analysis of the cost of a birth averted through a similar scheme in Malaysia. Very rough guestimates indicate the costs would be between $430 and $1725 depending on the rate of discount. (Ridker, 1973).
17

# POLICY RELEVANCE OF THE FINDINGS

The above findings differ in their specificity to particular countries and the extent to which they probably cover both the individual and community (external) effects of the three dimensions of development. To examine the policy proscriptions implied by the findings, I have calculated the costs of births averted directly through family planning programs in a variety of countries and through improved female education, improved infant mortality and a national pension program. Such simplistic measures of impact have obvious limitation. (See Cochrane and Zachariah, 1983 for an outline of the methodology and its limitations). In particular they represent average rather than marginal costs in most cases and do not allow for expected changes in these costs over time. More importantly perhaps they do not show how various interventions to reduce fertility might best be used in combination. This objective would require further research on interaction effects of the various policies. Despite these limitations, it is useful to compare the alternative programs to understand the feasibility in the first instance of various indirect policies to reduce fertility.

The most direct policy to reduce fertility is to support family planning programs. Using data on costs of programs, contraceptive usage rates and age specific fertility rates for various countries around 1980, the costs of a birth averted through family planning were calculated for 16 developing countries. Two different estimate of births averted per user were made depending on the assumption about age specific fertility in the absence of family planning. The difference between rates are small in the low usage
18 countries but high elsewhere. The alternativ **e** stimates are shown in Table 9 taken from Cochrane and Zachariah.

The effect of education on fertility is probably the most frequently suggested alternative method of reducing fertility. Estimates of the cost of averting a birth through five years of primary school for girls are shown in Table 10. Where the fertility of those with 4 to 6 years of school are lower than the unschooled the calculation of these costs are fairly straight forward and the costs of 5 years of schooling (capital and recurrent) are divided by the reduction in the total fertility rate that accompanies that increase in schooling. Table 10 summaries these costs. In cases where those with 4-6 years of schooling have higher fertility than those with no schooilng, the costs per birth averted is infinite. In those cases and in cases where 4-6 years of schooling has only small effects I have also calculated the costs of a birth averted by examining the fertility of those with 7 or more years of schooling and assuming the costs of this additional schooling to grade 8 or 9 on average is approximately equal to the first five years of school. These costs are shown in the last column of Table 10. Comparing the costs of births averted through family planning and education, one finds that in only one case, Nepal, is the cost of a birth averted through education ( 8 to 9 years of school) less than the cost through the family planning program. In the other country with very high costs of averting a birth through family planning, Kenya, the costs through education are even higher because of the small effects of education on fertility. There are two countervailing consideration that apply here. First the costs of births averted through education may be overestimated because the external effect through community level of education are not included. It
19 was found earlier that the positive effects of education on fertility are reduced as the overall literacy rate rises, but it is not possible to immediately quantify these effects. Second, the costs of birth averted in Table 10 are underestimated relative to those for family planning programs because the family planning program has fairly immediate effects while the effects of education on fertility take 10 to 20 years to materialize and thus the births averted by education should probably be discounted. Nonetheless, the primary message is that the effectiveness of different policies and their costs differ greatly from country to country and the policy recommendations need to differ also.

The recommendation to bring about fertility decline through mortality decline is also frequently mentioned as an alternative to family planning. Table 11 contains estimates of the costs of averting a birth by averting an infant or child death. These estimates are based on the effect of reducing a death shown in Table 6 and costs of $250 per death averted from Walsh and Warren (1979) in addition to some country specific estimates of mortality reduction catalogued in Cochrane and Zachariah. The high and low estimates differ depending on whether the highest or lowest estimate of mortality's effect on fertility is used.

Comparing the costs in Table 11 with those in Table 9, one finds that family planning is more cost effective than mortality reduction in every case where the $250 per death averted is used. The costs of deaths averted through measles immunization in Kenya, however, is so low and the costs per birth averted through family planning is so high in Kenya that measles immunization is a more cost effective way to reduce fertility in Kenya than is family planning. In no other country is that true. If one compares costs
20 through mortality reduction with costs through education in Table 10, one finds that education is more cost effective in Bangladesh, Colombia, Dominican Republic, Mexico, Nepal, Pakistan, Peru, Philippines, and Sri Lanka. (In some cases this is plus 7 education, however.) It should again be borne in mind that education's effect probably takes somewhat longer to occur than effects through mortality reduction. On the other hand, education has an additional effect not included here through its own effect on mortality. (Cochrane, O'Hara and Leslie, 1980, and Caldwell and Caldwell, 1985).

The effect of pension programs or; fertility can not be estimated in the same way as mortality's and education's effect on fertility were estimated, because of the lack of country specific estimates on effect size. Using Entwisle and Winegarden's estimates, however, have the benefit of including the community effects of the programs. To estimate the costs per birth averted, it was first necessary to get costs. An expenditure of 1% of GNP on a pension program was used as the numerator. (This is a completely arbitrary level, but this is without consequence as long as the denominator is appropriately selected and the relationship is linear.) The denominator was selected by using the two estimates of the effect of a 1% expenditure by Entwisle and Winegarden: 0.21 and 1.14. The number of births averted was set equal to the coefficient times the size of one age cohort of women (one fifth of the women aged 25-29). The high and low estimates of the costs per birth averted are quite far apart which appropriately reflects our lack of knowledge. In general the costs per births averted through pensions are quite high. In only one case is the lowest estimate of the costs of births averted through pensions less than the costs of a birth averted through
21 family planning, Nepal. In no case is the higher estimate of costs below family planning. The low estimate for pensions is below the costs of a birth averted through education in Bangladesh, Indonesia, Jordan, Nepal and Sri Lanka. In no case is the higher estimate of costs from pensions below that for education.

The implications of these findings are that in most countries family planning is the most cost effective means of reducing fertility. In a few countries that have very costly programs per effect, education, infant mortality reduction, or pension schemes may be more cost effective. The optimal program ignoring interactions would be designed by estimating the benefits per birth averted and then using programs until their marginal costs exceeded the benefits.

These results also emphasize the country specific nature of the preferred policy. It is recommended that for policy analysis in each country, updated and refined estimates of the costs of births averted be calculated. For comparative analysis, it is necessary to :(1) develop better understanding of the externalities or the community level effects of mortality reduction and education on fertility, and (2) better understand the interaction between different policies. For example,(a) how does increased access to family planning affect educational differentials?, (b) how does improved family planning access alter the relationship between mortality and fertility?, and (c) how does improved mortality and education reduce the pension motive's effect on fertility?

Acknowledgements: I would like to thank Susan Stout, Dirk Prevoo and Ella Hornsby of the World Bank for their assistance at various stages in the preparation of this paper.