metadata: authors: 0: author: Subbarao, Kalanidhi
1: author: Raney, Laura
docna: 0: docna: Social gains from female education : a
            cross-national study
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Reproductive Health,Early Child and Children's Health,Secondary Education,Educational Sciences,Family Planning Research
teratopic: Education
abstracts: cdata!: The authors explore the strength of
            female secondary education relative to, and in combination
            with, family planning and health programs in reducing
            fertility and infant mortality.  They find that family
            planning and health programs do influence fertility and
            mortality, but that the impact of expanding female secondary
            enrollments appears to be much greater, especially in
            countries with low female secondary enrollment.  Fertility
            and infant mortality are more elastic with respect to female
            secondary education than to family planning and health
            programs.  Their simulations suggest the following. Doubling
            female secondary school enrollment (from 19 to 38 percent)
            in 1975 would have reduced the total fertility rate in 1985
            from 5.3 to 3.9.  Doubling the family planning service score
            (from 25 to 50 percent) in 1982 would have reduced the total
            fertility rate only from 5.5 to 5.0.  Doubling female
            secondary school enrollment in 1975 from 19 to 38 would have
            reduced the infant mortality rate from 81 to 38.  Halving
            the ratio of population per physician would have reduced the
            infant mortality rate only from 85 to 81. Doubling per
            capita GDP from $650 to $1,300 would have reduced the infant
            mortality rate only from 98 to 92. Doubling female secondary
            school enrollment (from 19 to 38) in 1975 would have lowered
            the number of births by 29 percent of the 1985 number.
            Doubling family planning services would have reduced it by
            3.5 percent.  Doubling female secondary school enrollment
            would have reduced infant deaths by 64 percent.  Halving the
            ratio of population per physician would have reduced it only
            by 2.5 percent.  Doubling per capita GDP has no effect on
            reducing infant deaths, all other factors being constant.
            Female education affects desired family size by raising the
            opportunity cost of a woman's time in economic
            activities, increasing demand for family planning, and
            promoting more effective contraceptive use.
repnme: repnme: Social gains from female education : a
            cross-national study
display_title: Social gains from female education :
            a cross-national study
listing_relative_url: /research/1992/11/699505/social-gains-female-education-cross-national-study
topicv3: Infant and Child Mortality m1326544 1446,Education m1327048 470,Secondary Education m1327058 481,Family Planning m1327501 1452,Health m1504661 3480
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Education
origu: Pop & Human Resources Department
no_of_pages: 61
model_extractions: * t . v *

<!-- Start of picture text --> ~~~~~~~~Policy Researclj<br><!-- End of picture text -->

**Women In Development** WPS 1045

This paper is a product of the Women in Development Divis )n, Population and Human Resources DepartmenL Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Abundo, room S2-270, extension 36820 (November 1992, 50 pages).

Subbarao and Raney explore the strength of female secondary education relative to, and in combination witi' family planning and health programs in reducing fertility and infant mortality. They find that family planning and health programs do influence fertility and mortality, but that the impact of expanding female secondary enrollments appears to be much greater, especially in countries with low female secondary enrollment. Fertility and infant mortality are more elastic with respect to female secondary education than to family planning and health programs. Their simulations suggest: Doubling female secondary school enrollment (from 19 to 38 percent) in 1975 would have reduced the total fertility rate in 1985 from _5.3_ to 3.9. Doubling the "family planning service score" (from 25 to 50 percent) in 1982 would have reduced the total fertility rate only from 5.5 to 5.0.

* Doubling female secondary school enrollment in 1975 from 19 to 38 percent would have reduced the infant mortality rate from 81 to 38.

Halving the ratio of population per physician would have reduced the infant mortality rate only from 85 to 81. Doubling per capita GDP from $650 to $1,300 would have reduced the infant mortality rate only from 98 to 92.

> * Doubling female secondary school enrollment (from 19 to 38 percent) in 1975 would have lowered the number of births by 29 percent of the 1985 number. Doubling family planning services would have reduced it by 3.5 percent.

* Doubling female secondary school enrollment would have reduced infant deaths by 64 percent. Halving the ratio of population per physician would have reduced it only by _2.5_ percent. Doubling per capita GDP has no effect on reducing infant deaths, all other factors being constant.

Female education affects desired family size by raising the opportunity cost of a woman's time in economic activities, increasing demand for family planning, and promoting more effective contraceptive use.

The Policy ResearchWorkimg PaperSeries disseminates the findings of work **under** way in theBanlc Anobjectiveof 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 Bank policy.

Produced by the Policy Research Dissemnination Center
# SOCIAL GAINS FROM FEMALE EDUCATION:

# A CROSS-NATIONAL STUDY

## K. Subbarao and Laura Raney

PHRWD
# Table of Contents

|Summary<br>_.............._|_i_|
|---|---|
|1.<br>Introduction.|1|
|2.<br>Analytical Framework and the Reduced Form Equations.|_2_|
|A. Analytical Framework.<br>|2<br>|
|B. Reduced Form Equations and the Estimation Procedures.|_5_|
|3.<br>Definitions and Data Sources .......<br>..............<br>.|8|
|4.<br>Results and Discussion<br>....................<br>...................|10|
|A. Education, Family Planning Services and Fertility ...................|10|
|B. Female Education, Desired Family Size<br>||
|and Contraceptive Prevalence .................................|19|
|<br>C. Female Education, Access to Medical Care, and Child Health ......|......<br>26|
|_5._<br>Quantifying the Gains in Fertility and Mortality Reduction:<br>Some Simulations .33||
|6.<br>Implications for Policy and Concluding Remarks .37||
|References_..........................................._|_....._<br>39|
|Tables||
|Table 1.<br>Female Education, Family Planning Services<br>and Total Fertility Rate .12||
|<br>Table 2.<br>Female Education, GDP per capita and Desired<br>||
|Family Size .23||
|<br>Table 3.<br>Female Education, GDP per capita and Contraceptive<br>||
|Prevalence .23||
|Table 4.<br>Infant Mortality (1985), Female Education and<br>||
|Program Variables .28||
|<br>Table 5.<br>Gains in Births Averted through Female Education and||
|<br>Family Planning Program Services: Simulation Results .34||
|<br>Table 6.<br>Gains in Infant Deaths Averted through Female Education<br>versus Health Services and GDP per capita change:||
|<br>Simulation Results .36||
|Appendices||
|Table Al.<br>List of Sample Countries .42||
|<br>Table A2.<br>Variables and Definitions .43||
|Table_A3._<br>TFR Equations - Means of Standard Deviations.44||
|Table A4.<br>IMR Equations - Means of Standard Deviations .44||
|Table A5.|Education, Family Planning Services, and Total||
|---|---|---|
||Fertility Rate .................<br>......................|45|
|Table A6.|Elasticities of TFR with respect to Female Secondary<br>Enrollment and Family Planning Services at<br>Different Levels of Enrollments, and Service .46||
|Table A7.|Female Education, GDP per capita, Desired||
||Family Size, and Contraceptive Prevalence .47||
|Table A8|Elasticities of Contraceptive Prevalence, with respect to||
||Female Secondary School Enrollment Rate, Access to Family<br>Planning Services, and GDP per capita, at Different|47<br>|
||Levels of Enrollment, Access and Income .........................|48|
|Table A9|Elasticities of IMR with respect to Female||
||Secondary Enrollments, Population per Physician, and<br>GDP at Different Levels of Enrollmeat, 1975 .48||
|Table AlO|Education, Program Variables and Infant Mortality .49||
|Table All|Infant Mortality (1985), Female Education and Program<br>Variables, including Immunization ..........<br>............|_50_|
# SUMMARY

Female education increases the value of women's time in economic activities by raising labor productivity and wages (with a consequential rise in household incomes and a reduction in poverty). Female education also produces social gains, by improving health (the woman's own health and the health of her children), increasing child schooling, and reducing fertility. This paper is concerned with the estimation of these social gains from female education. The paper examines the role of female educatior. (measured by gross enrollment rates at the secondary level) relative to, and or/in combination with, some health and family planning services that influence fertility and infant mortality. It uses reduced-form estimation of the total fertility rate (CMR) and infant mortality rate (IMR). The paper presents crosscountry regressions based on data drawn from 72 developing countries. The results from these crosscountry regressions should be interpreted cautiously as they may not be representative of a particular country's experience and reflect the usual problems of relying on national averages. Moreover, the aggregate measures of education, health, and family planning available to us provide little information on the specific kinds or quality of services. However, the results are broadly consistent with evidence using household data sets (see e.g. an overview by Schultz 1989, an overview by Cochrane et al 1980, and studies by Gertler and Molineaux 1972; Rosenzweig and Schultz 1987; and Schultz 1988, 1990).

ii. The analysis in this paper generally shows that female secondary education, family planning, and health programs all affect fertility and mortality, and the effect of female secondary education appears to be very strong. Moreover, our results suggest that family planning will reduce fertility more when combined with female education, especially in countries that now have low female secondary enrollment levels. The elasticities of fertility and infant mortality with respect to female education substantially exceed those with respect to family planning and health programs when the elasticities are estimated with appropriate controls. Simulations in the paper were based on our equation using gross enrollment rates for secondary education from 1975, which we consider a more plausible
- ii -

scenario than with 1970 enrollment rates. In this equation, family planning is significant when interacted with female secondary education. Thus, the effect of family planning should be evaluated with reference to specific education levels. The simulations suggest that a doubling of female secondary enrollments (from the mean of 19 percent to 38 percent) in 1975 would have reduced the total fertility rate (IAFR) in 1985 from 5.3 to 3.9, whereas a doubling of the "family planning service score" (from 25 percent to 50 percent) in 1982 would have reduced the TFR in 1985 from 5.5 to 5.0. The implications of female secondary education for reducing the infant mortality rate (IMR) are also striking. A doubling of female secondary enrollments in 1975 from the mean of 19 percent to 38 percent would have reduced the IMR from 81 to 38, whereas halving the ratio of population per physician would have reduced the IMR from _85_ to 81. A doubling of GDP per capita from $650 to the median level of $1300 would have reduced IMR from 98 to 92.

iii. Simulations were also done to compare the differences in the quantitative magnitudes of the gains (in terms of births and deaths averted) from female secondary education with the gains from expansion of family planning and some health program efforts to reduce fertility and infant mortality. Doubling female secondary school enrollments from 19 percent to 38 percent in 1975, keeping all other variables (including family planning services) constant at their mean values, would have lowered the number of births by 29 percent compared to the actual number in 1985. By contrast, doubling family planning services, holding female secondary enrollment constant at the mean, would have reduced the number of births by 3.5 percent. Simulations show that the gains from female education are also striking in terms of deaths averted. While doubling secondary female education reduces infant deaths by 64 percent, halving the ratio of population per physician reduces the number of infant deaths by _2.5_ percent, whereas a doubling of GDP per capita from $ 650 to the median level of $ 1300 would have no effect on reducing infant deaths, keeping all other factors constant.
iv. Female education influences fertility and mortality through many pathways. For example, education affects desired family size by enhancing the opportunity cost of a woman's time in economic activities relative to child-bearing and by changing her -3pirations, thus building demand for family planning services. Maternal education promotes child health which in turn affects desired family size. Education also promotes more effective contraceptive use and more willingness to use modern contraceptive methods - which is, we believe, why we find that education and family planning interact in our TFR equation. Exploring these pathways is important from a policy perspective. Yet the limitations of data and estimation methods prevent us from attempting a detailed analysis of the relative importance of the various pathways. A limited attempt was made to examine the determinants of desired family size for a small sample of 37 countries for which data were available. After controlling for other key factors (such as the level of economic development of a country), female secondary education retains its high statistical significance. However, "desired fanily size" is a subjective concept, so most researchers rely more on estimates of TFRs observed directly. Among the determinants of contraceptive use, female secondary education and the family planning service score are each highly significant.

v. The research findings suggest that it would be valuable to study further the costs and benefits associated with education, health, and family planning programs. Data limitations prevented us from doing so, but any comparison of the benefits of female education and health and family planning services should take cognizance of the muldple benefits from both. Timing should also be considered. The female education route to reduce fertility or infant mortality needs 10-15 years to bring tangible results. While family planning can in principle have a shorter run impact, expanding either health or family planning programs in countries with poor administrative structures is difficult and also takes time. As of 1982, only two countries, China and Indonesia, had been able to reach a family planning service score of 78, whereas as many as 14 developing countries reached female secondary enrollment levels of
- iv -

40 percent in 1975, and this number rose to 28 in 1988. Notwithstanding massive investment, India took over 40 years to build a good family planning delivery system in 5 _o"_ 11 states.

vi. Future research could usefully ask how the marginal gains from female education, family planning, and health programs at different stages of development compare with marginal costs, once cost data become available for female secondary schooling and for health and family planning program interventions for different countries. Research should then address appropriate phasing of different combinations of education, health, and family planning programs. In so doing, much more information should be developed on different specific kinds of education, health and family planning services that might be made available, so that more precise program strategies can be developed.
# SOCIAL GAINS FROM FEMALE EDUCATION: A CROSS-NATIONAL STUDY

# K. Subbarao and Laura Raney'

# 1. Introduction

1.1 Female education increases the value of women's time in economic activities by raising labor productivity and wages (with a consequential rise in household incomes and a reduction in poverty). Female education also produces social gains, by improving health (the woman's own health and the health of her children), increasing child schooling, and reducing fertility. This paper is concerned with the estimation of these social gains from secondary female education (measured as gross enrollment rates). That female education contributes to lower fertility and infant and child mortality is well-known. However, the extent to which female education interacts with health **And** family planning programs **and policies** is less well-known. This paper examines the role of secondary female education relative to, and/or in combination with, health and family planning programs and policies that reduce fertility and infant mortality. To the extent the data permit, the paper also explores some of the routes through which female education lowers fertility and mortality, for instance by reducing desired family size or by promoting more effective use of available services for contraception. The paper is based on cross-country data from 72 developing countries (accounting for over 95 percent of the population of developing countries), drawn from the World Bank and other data sources. The period of investigation is 1970-85. The analysis generally shows that female secondary education, family planning, and health programs all affect fertility and mortality, and the effect of female secondary education appears to be very strong. Moreover, our results suggest that family planning will reduce fertility more when combined with female

I Acknowledgements: For excellent advice and contributions at **all stages** in the **preparation** of this study, we are grateful to Barbama Herz. Comments from participants of a PHR workshop where **preliminy** results of this study were presented **were** very helpful. We **also** wish to thank Randy Bulto, Susan Cochrane, **Dennis de Tray, Elizabeth** King, **Fmances Plunkett,** Lamt **Pritchett,** Agnes Quisumbing, Martin Ravallion, **Guilhenne** Sedlacek, and Roger Slade, for comments and suggestions. Of **course** responsibility for the **present** paper is ours.
-2 -

education, especially in countries that now have low female secondary<sup>enrollment levels. The elasticities</sup> of fertility and infant mortality with respect to female education<sup>substantially exceed those with respect</sup> to family planning and health programs when the elasticities are<sup>estimated with appropriate controls. The</sup> results from these cross-country regressions should be interpreted cautiously<sup>as they may not be</sup> representative of a particular country's experience and reflect the usual<sup>problems of relying on national</sup> averages. Although endogeneity of services may be less of a problem in<sup>cross-country data, using cross-</sup> country data imposes a common structure on the econometric reladonship<sup>between the variables.</sup> However, the results are broadly similar to a number of micro studies (e.g.<sup>see Schultz 1989 for a</sup> review) and with another cross-country study, Mauldin and Ross (1991) which<sup>found that the direct effect</sup> of family planning effort is less than the total (direct and indirect effects. -,f<sup>socio-econonlic factors</sup> (including education).

1.2 The paper is organized as follows. The basic analytical framework and<sup>the models to be</sup> tested are described in Section 2. Definitions, data sources, and<sup>limitations are discussed briefly in</sup> Section 3. The results from cross-country equations are discussed in<sup>Section 4. Some policy simulations</sup> are attempted in Section 5. Last, the concluding section draws some inferences for policy.

**2. AnalytIcal** Framework **and the Reduced Form Equations**

A. Analytical Eram=yrk 2.1 For most couples, achieving a pregnancy does not require a deliberate decision,<sup>but</sup> avoiding a pregnancy does. Following Hen (1984), this section outlines<sup>a structual amework focusing</sup> on the factors determining the number of children parents want and<sup>their ability to limit births to that</sup>
3 number, with particular reference to the role of female education in influencing parental decisions and abilities.

2.2 The total fertility rate (TFR) - the number of chi!dren that would be born to a woman if she lives to the end of her childbearing years and bears children at each age in accordance with the prevailing age-specific fertility rates - comprises the number of children parents want (desired family size, or DFS) plus unintended births (which depend on parents' ability to achieve desired family size). In economic terms, DFS thus represents the demand for children by parents. DFS depends mainly on female education, male education, the insurance value of children, and the demand for female labor in the wage labor market, which may be reflected in the country's level of development (and proxied by the per capita GDP). DFS also reflects the chances for child survival.

2.3 Child survival depends in turn on the mother's education and on other factors such as access to safe drinking water, access to health services including immunization and curative care for common childhood diseases, and access to family planning (which promotes healthier spacing of births).

2.4 Of course, parents may have more or fewer children than they desire. This paper focuses on the more usual case where couples could have more children than they desire if they took no steps to limit pregnancy. The ability to achieve desired family size depends on the availability of family planning services, but also on other factors such as the age at marriage or union, duration of breastfeeding, and the scope for abortion.

2.5 These structural relationships suggest many pathways through which female education and family planning services affect fertility. Educadon, especially of the mother, works through wage and
-4.

productivity effects on the opportunity cost of the mother's time and thus affects desired family size. It also improves the chances of child survival and so indirectly affects desired family size. Finally, education builds ability as well as willingness to use the available modern methods of contraception effectively and thus helps people achieve their desired family size. Family planning services enable couples more readily to achieve (lower) desired family size and over time may influence desired family size itself by affecting infant deaths (and perhaps also social norms).

2.6 From a policy perspective, it would be valuable to know more about the relative importance of the many pathways through which female education and family planning program services influence fertility. But such a structural model is extremely difficult to estimate because of data and econometric limitations. First and most important, DFS caanot be observed objectively. It is essentially an opinion, and people's responses may be influenced by the number of children they already have. Many a couple may be reluctant to say they did not want the children they already have. Second, country-level data sources are often not suitable for comparison as the type of canvassing and questions asked of the respondents do not always match. Therefore, most analysts feel much more comfortable banking on objectively observed fertility. Moreover, the task of estimating a structural model with crossnational data is even more difficult because the (admittedly unsatisfactory) data on DFS is available only for a small set of 37 countries which have had World Fertility Surveys. Finally, even if more satisfactory data on DFS were available for a wider sample of countries, it is difficult to find identifying exogenous variables, especially in cross-national data sets, that permit distinguishing the influences on desired family size, child survival, and ability to achieve desired family size. Female education clearly affects all three. Many analysts therefore rely on reduced form estimation of the TFR, as do we. Thus, we can determine the net impact of female secondary education and other exogenous variables but not precise pathways.
2.7 The conceptual underpinnings for the analysis of infant mortality (IMR) are more straightforward. Infant mortality is influenced by female education, male education, and access to services that affect mortality such as immunization; safe drinking water; trained birth attendants, nurses or physicians; and family planning services that promote better spacing of births. As in the case of the TFR, female education influences infant mortality through better use of available facilities and the parents', especially the mother's, knowledge of hygiene, sanitation, and health care. But as in the case of the TFR, we rely on a reduced form that shows the net impact of female secondary education and other exogenous variables without specifying pathways.

# B. Reduced Form Equations and the Estimation Procedures

2.8 Our reduced form equations derive their logic from the structural relationships outlined in the preceding paragraphs. Following Schultz (1990), the structural relationships can be expressed so that the TFR and IMR depend only on exogenous explanatory variables including education, health services, and family planning programs as well as personal and household characteristics that affect fertility and mortality. In specifying our reduced forms, we have tried to be carefil to use as explanatory variables only those that are exogenous in a fairly strict sense, i.e. they affect, but are not the result of household decisions.

2.9 It is possible to estimate the determinants of fertility or mortality without bias with the OLS method when the function relating the (country-level) explanatory variables and dependent variable is correctly known, the explanatory variables are independent of each other, the variables are observed without error, and the error term is normally distributed with zero mean, has a constant variance, and is uncorrelated with the explanatory variables. But Schultz (1990) and Behrman (1990) discuss potendal
_-6-_ endogeneity between family planning or health programs and socio-economic variables, especially education levels, in countries where family planning or health program efforts may<sup>be focused on</sup> states/districts with higher levels of socio-economic development. Such endogeneity may be a less serious problem in cross-country analysis than in studies y_ithin a country. To test<sup>for endogeneity between</sup> socio-economic development and program effort at the cross-country level, we regressed an index of socio-economic development (WFS and UN 1987) on family planning services across countries,<sup>and</sup> found the correlation to be weak (R = .38). Such correlations with other socio-economic and human development indices were found to be even weaker.<sup>2</sup> In this paper, therefore,<sup>the possible endogeneity</sup> between program supply and socio-economic development is ignored.

2.10 Female Education and T,otal Fertilit: The first equation presented in this study is intended to explain the total fertility rate CM'7R) - the number of children that would be born to a woman if she lives to the end of her childbearing years and beare children at<sup>each age in accordance with</sup> prevailing age-specific fertility rates. The hypothesis is that the TFR depends<sup>in a non-linear way on</sup> female and male secondary education (FED and MED) and in a linear way<sup>on per capita GDP (GDP),</sup> the rate of urbanization (URB), population per physician (PHYS), and the level of<sup>a family planning</sup> services score (SERV), and potendal interaction between education<sup>and family planning services:</sup> TFR = ao + alFED + a2FED^2 + a3MED + a4MEDA2 +a5GDP+ a6URB + a7SERV + a8(FED*SERV) + a9PHYS + alODAFR + allDAS + al2DLAC + ei (6)

> 2 **Mauldin** and Ros **(1991)** found **sdmilar resuk.**
where AFR, AS, and LAC stand for regional dummies for Africa, Asia and Latin America and the Caribbean. 3 The model does not separate the exact route through which education is influencing fertility (delay of marriage, contraceptive use, abortion, or breastfeeding or a reduction in child mortality), but summarizes the effects as they work themselves through an underlying structural model. The estimated equation is thus a reduced form equation with controls and interactions.' 2.11 Female Education and Infant Mortality: Numerou **s** tudies have examined the role of female education in determining mortality outcomes using cross-country data.<sup>5</sup> Much of the earlier literature using cross-country data did not consider any specific health program variable, though socioeconomic variables have been thoroughly investigated and modelled. King and Hill (1991) did consider May program variables but did not treat interactions. In the present study, the IMR in 1985 is hypothesized to be inversely related to lagged female and male secondary enrollments (FED and MED), the rate of urbanization (URB), per capita GDP, and family planning services (SERV), and positively to population per physician (PHYS). Regional dummies were added to capture region-specific intercepts. The estimated model is as follows: IMR = ao + alFED + a2FED2 + a3MED + a4MED2 + aSPHYS + a6URB + a7SERV + aSGDP + a9DUMAFR + alODUMAS + allDUMLAC + ei (7) 'See the next sction for definitions and exact spocification of the variables.

'The under-five morality rate aso affects the TPR but is endogenous, since better educated women are morc likely to have knowledge of preventive healthcre and to se medicalattention for their children. Under-five morlity theefore does not enter this equation dircy, but both female education and population per physician probably aca in pat through under-five mortality.

See, for example, Pregton (1980, 1985); Wheeer (1980); Mensch, Lcerand Prton (1986), and King and Hill (1991). Por thoughtful reviews of tudies, see Cochrane, O'Hara and Lesie (1980, 1982), Bebrman (1990) and Shultz (1992).
- 8 -

One program variable, "population per physician" (PHYS)", was considered." The model captures the influence of the proximate determinants as well as female (and male) education without informing us about the precise route through which female education influences infant mortality outcomes; so it is again a reduced form equation.

2.12 To explore the routes through which female education affects fertility, data for a limited sample of countries where World Fertility Surveys were conducted were used to analyze the determinants of desired family size (DFS), and contraceptive prevalence (CP).

# 3. Definitions and Data Sources

3.1 The analysis is based on cross-country data for 72 developing countries compiled from various secondary sources. The sample consists of low and middle-income developing economies, according to the World Bank classification by income. We have deliberately excluded Eastern European countries for which data is scarce, Cuba and high-income countries such as Hong Kong, Saudi Arabia, Singapore and the United Arab Emirates as they are not strictly considered developing economies. The total sample of 72 countries represents the maximum number of countries for which data were available for the variables used in our analysis. Not all countries are included in all the equations and simulations owing to data gaps on certain specific variables. The approach we have taken is to maximize the number of observations in each equation; therefore the sample size varies across the equations. The countries, 6 Our measure of health services is thus obviously inadequate. We wish we had had exogenous measures of the availability of other kinds of health carm. Another health variable was available, namely percent of infants immunized. But it is potentially eadogenous. It refects the intemation of demand as well as availability since better educated women are more Ulkely to bring their children to health centers to be immunized. We therefore did not include it in our main equations but did include it in an equation shown in Appendix Table 11. It would have been most interesting had we had data on access to immunization as distnct from use, sine access would have been exogenous and thus could have shown the impact of making immunization available.
-9 -

along with a summary of the variables and definitions, means and standard deviations,<sup>are listed in</sup> Appendix Tables 1 - 4. The total fertility rate is for 1985 (TFR) and represents the number<sup>of children</sup> that would be born to a woman if she lives to the end of her childbearing years and<sup>bears children at each</sup> age in accordance with prevailing age-specific fertility rates. The infant mortality rate<sup>(IMR), 1985, is</sup> defined as the number of infants who die before reaching one year of age per 1000 live births.<sup>Both the</sup> TFR and the IMR are taken from the World Bank's World Development Report, (WDR)<sup>1987. Desired</sup> family size (DFS) is taken from the World Fertility Survey data from 1983 to 1988.<sup>It is based on the</sup> conventional method of measuring "preferred" family size and is available only for 37 countries<sup>from</sup> (Lightbourne, 1987). Contraceptive prevalence (CP) represents contraceptive use among<sup>currently</sup> married women for the latest available year ranging from 1982 to 1989, as assessed in 1988<sup>from the</sup> United Nations in Levels and Trends of Contraceptive Use and the Popu!ation Division's 1989<sup>database</sup> on contraceptive use found in the United Nations "Women's Indicators and Statistics<sup>Spreadsheet</sup> Database for Microcomputers" (WISTAT).

3.2 Gross secondary enrollment rates for males and females for 1970 and 1975 (MED, FED) are taken from UNESCO in the World Bank's Economic and Social Database (BESD). Gross<sup>Domestic</sup> Production (GDP) per capita, 1985 is corrected for purchasing power parity and is expressed<sup>in 1985</sup> international prices from Summers and Heston (1991). The family planning services score (SERV),<sup>1982</sup> is based on part of a broader index developed originally by Mauldin and Lapham (1982) and<sup>updated by</sup> Mauldin and Ross (1991). The score used In this paper assesses the availability and strength<sup>of service</sup> and service-related family planning programs in developing countries (Ross et al. 1992).<sup>The index</sup> reflects 13 separate measures of service and service-related activities, including commnunity-based 7 **This** index **is** one of the four components which make up the family planning program effort<sup>score in 1982 of Mauldin</sup> **and** Ross (1991). **We** have chosen this component to isolate a non-endogenou **s** upply variable.
- 10-

distribution, administrative structure and mass media for IE & C. Service (SERV) is the percent<sup>of the</sup> maximum score for each country. The actual values range<sup>from 0 to 78. Population per physician</sup> (PHYS), 1984 and the urban population as percent of total<sup>population, (URB) 1985 are from the _DR</sup> 1987.

3.3 In the simulations, the population of women of childbearing age in<sup>1985 is taken from</sup> WISTAT. This measure, women aged 15-44, omits<sup>women in the 45-49 bracket because the data as</sup> presently organized and published (which is for ages 15-19, 20-24,<sup>25-44 and 45-59) does not enable us</sup> to compute the number of women in this older cohort. The number<sup>of births in this age cohort, however,</sup> is quite small, and as such may not significantly understate<sup>the births and deaths simulated in section 5.</sup>

# 4. Results and Discussion

# A. Education. Family Planning Services, and Fertility

4.1 In the literature, several alternative specifications of education variables have been used. In the present study, female and male gross secondary<sup>school enrollment rates, lagged by approximately</sup> 10 and 15 years, were used, the inference being that the higher the<sup>enrollment rates in the lagged) years,</sup> the higher the proportion of mothers (fathers) with secondary schooling<sup>in the year 1985, in each of the</sup> countries for which data on all the variables are available. Data<sup>limitations prevented looking at age</sup> cohorts separately. Considering tk 'high proportion of births (i.e.<sup>a high proportion of the life-time,</sup> realized total fertility rate) occurs before a woman reaches the age<sup>of 25-29, 15 and 10 year-lagged school</sup> I A beter approach which was not **used** in this paper for **lack**<sup>of data would be to</sup><sup>**use years**of</sup><sup>**schooling**completd (or</sup> estimate of same) which would take **into** account **primary enrollment**<sup>more</sup><sup>**effectively**(see Schultz 1989).</sup>
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enrollment (i.e. 1970 and 1975 enrollment levels) appeared the most appropriate; the results reported belong to these years.

4.2 We estimated the equations with both female and male secondary enrollments so as to capture the effect of female enrollments, controlling for male enrollments. We recognize that male and female education tend to be correlated and therefore tried female education separately (reported in Appendix Table **_5)._** But we felt that relying only on female education might overstate its effect. This is, we recognize, a difficult judgement. We could see no strong theoretical case for simply relying on male education. We also estimated the equations with and without interaction between female education and family planning services. In each equation, non-linearity in the linkages between enro!!inents and fertility was tested with quadratic terms. The descriptive statistics are given in Appendix l able 3, and the results are presented in Table 1.

4.3 Irrespective of the model specification, female secondary school enrollment, lagged by either 10 or 15 years, is inversely related to fertility rate, and the coefficient is relatively large and highly significant. We evaluated the positive quadratic term and found that the turning point occurs at about 60 percent of gross secondary school enrollment in 1970 and 80 percent in 1975 - a level which none of the countries in our sample had reached in either year. Male enrollment is not statistically significant. Family planning services is statistically significant by itself odv in the equations wi1 the interaction terms. Once interacted with female education, it is no longer independentdy statistically significant. Equation 4 in Table 1 (using 1975 education data) suggests that family planning works in conjunction with education (the interaction term is significant). This implies that the effect of family planning can only be evaluated with reference to specific levels of female secondary enrollment, as it differs depending on the enrollment level. It is well known that education promotes more willingness to use contraception and
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Table 1. **Female** Education, Family **Planning** Sevices, and Total FertlUty Rate

|Indepandent Variables|_________||Dprnden|Variable, TF|R. 198S||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Witbut hioat<br>Sec.Sob. Enr<br>1970|ion.<br>ollmmts<br>|(2)<br>With**interci**<br>See. Sob. Enr<br>1970|**on**<br>ollment<br>|(3)<br>Witdout inte<br>Sec. Sob.Er<br>19|mdon<br>olumet<br>75|(4)<br>With Intacti<br>Sec. ScbEnr<br>1975|on<br>ollmnso|
|________________|CoefficienT|<br>twdo|Coefficient|t.rato|Coofficient|:at-ido|Coeflicint|t.ratio|
|Co| t__|7.322|15.801"e0|7.199|14.35***|7.137|14.453*0|6.77|12.453*"0|
|F|emale<br>Seconday<br>Gross Eaollmet, 1970|.0.187|*3.723*|.0.177|-3.4S0_|||||
|Fenmie Secoadary<br>GrosaEnrollnent squard|0.002|2.1660|0.002|2.3250||_|||
|Male Secondary<br>GrossEnrollmn, 1970|0.038|0.996|0.029|0.709|||||
||MaleSecondaq<br>Grou Enamentsquared|0.0004|0.756|0.001|0.914||,|||
|Femule Secondacy<br>osEnrollent,197S|||||-0.116|-3.64400|0.100|-3.079*0|
|FemaleSeconday<br>Gross Enrolmnsquared|||||0.001|1.496|0.001|1.939*|
|Male Secondey<br>Gross<br>Eanollm,<br>1975|||||0.027|1.08|0.010|0.272|
|MaleSecondary<br>**Grou**Enaollment||||_|0.003|0.775|0.001|_1.39S_|
|GDP per capita, 1985|40.0001|*1.361|4.0002|-1.490|4.0001|4.653|4.0001|4.726|
|Faemilyplaig<br>servces,<br>19S2|<br>4.021|-3.900w*|4.014|-1.251|4.024|-4.6030'|4.010|4.695|
|Fem. Sec.saroil_en and<br>FP service|||4.0004|4.723|||4.001|-1.853*|
|Urbanpopulation<br>u<br>Softo<br>populadon, 1985|40.010|-1.040|4.100|4.871|4.009|4.963|4.006|4.613|
|Population per physiia,_1985_|-0.000004|4.576|4.000004|.0.497|4.000001|4.101|0.000001|0.07|
|AfricaRegional Dumy|0.229|0.741|0.282|0.835|0.302|0.931|0.515|1392|
|AsiRegional Dummy|49|**-**.<br>1.249|0.460|-1.125|4.561|-1.361|4.401|4.965|
|LACRegional Dummy|0.879|2.244"|0821|2.1470-|033|1.19S|0.2S2|0.886|
|R'|.8|2|.8|2|.8|3|.|84|
|a|6|4|6|4|6|5|6|5|

Absolute value of t-tios, wheo, *, and * indicate aspficanc lvels of 10,5, and I perent respectively.
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more effective use. On the other hand, equation 2 in Table I (using 1970 education data) fails to confirm the interaction. What we can say definitely is that family planning does matter and may well interact with education, and the magnitude of the effect of female education remains large, even controlling for its interaction with family planning services. Controlling for education and family planning services, the GDP variable is significant in only one of the equations. In two equations the regional dummy for LAC is significant. On the whole, the results suggest that the total effect on fertility of female education combined with family planning services is substantial.

4.4 To get a better idea of the magnitude of the effects of female education and family planning services on total fertility, elasticities were computed. Figure 1 shows the values of elasticities of the TFR (1985) with respect to female secondary enrollments (1970) (controlling for male enrollments), and with respect to family planning services (1982) at different levels of female secondary enrollments and family planning service scores. These were computed from equation 1, Table 1. The elasticity of TFR with respect to female education has a "U" shape, suggesting that the effect of female secondary enrollment on fertility is high initially until it reaches a threshold level of and then tapers off. The elasticity of fertility with respect to female education is very high for female enrollments from 0 to 40 percent - a range in which all but 4 of the sample countries fall. Female secondary enrollment has the greatest negative effect on fertility at around 30 percent enrollment; the mean of female secondary enrollment in the 1970 sample is 13 percent. Family planning services undoubtedly reduce the fertility rate,9 but the magnitudes of the elasticities of the TFR with respect to family planning services are small.

9 In this equation, since it does not have an intemcetion **term,** we can estinate the independent **effect** of family planning **services.**
<!-- Start of picture text --> Figure 1: Elasticities of TFR 1985, with respect to Female<br>Secondary Enrollments, 1970 and FP Services, 1982<br>Elasticities of TFR<br>0.6<br>-0- Fem Ed.(indep.) o FP Service (indep.)<br>0.4 -<br>0.2<br>0I<br>-0.2<br>-0.4<br>-0.6 0 10 L . 20 30 I i 40 i _ 50 60 70 I 80 I 90 I X 100<br><!-- End of picture text --> Levels of female sec. enrollment, 1970 Note. Elasticities computed from eq.1 Table 1, holding all other variables<sup>constant at their mean value.</sup> I... (Mean.s: Female Sec. Enroll. (1970). 13%, Family Planning Service Score (1982),<sup>24%, TFR (1985), 5.4).</sup>
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Only at the maximum possible score of the index (100) does the elasticity of fertility with respect to family planning services reach that of female education at 30 percent enrollment rate.10 Yet the highest score of family planning service any country in 1982 was 78.

_4.5_ We use the equation with female secondary enrollments for 1975 In subsequent simulations of the effects of increasing female education and family planning services on TFR". When female secondary enrollments lagged by 10 years (1975 enrollments) were interacted with family planning services, the interaction term is significant (see equation 4, Table 1). From this equation, the elasticities of the TFR with respect to the tgS effect of female secondary education family planning services were computed at different levels of enrollments and service scores. These are shown in Figure 2. At 20 percent enrollment level, the elasticity of the TFR with respect to the total effect of female education is three times higher than that of family planning services. Clearly, in countries with currently low female secondary enrollments, an expansion of female education, in combination with even a low family planning service score, can bring fertility down substantially.

4.6 Simulations show that a doubling of female secondary enrollments (from the mean of 19 percent to 38 percent) in 1975 holding all other variables constant at their mean values, would have reduced the TFR in 1985 from 5.3 to 3.9, whereas a doubling of family planning service score<sup>12</sup> (from the mean of 25 percent to 50 percent), again holding other variables constant, would have reduced the TFR in 1985 from _5.5_ to 5.0. These iilustrative scenarios are suggestive of the powerful influence of

# **'°** See **Appendix Table** 6 for actual values of **the elasticities.**

11 We found **the** 1970 **equation** to yield unrealistic declines in **TFR as a resut** of increasing female seconday enrollments by one **percent,** e.g. a reduction of **lose** to **0.2** or a **fifth** of a **chd,** in comparson to a **tnth** of a **child** in the 1975 equation.

12 **The independent** effect of family planning **sevces** cannot **be** estimated from this equation as the **variable** is non- **significant. The** effect of family planning services can only be **evaluated** with **reference** to specific **levels** of **female enrollment.**
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female education in fertility reduction.

4.7 The extent to which female education, interacting with the mean level of family planning services, reduces fertility can also be seen from the predicted values of total fertility rate, also derived from equation 4, Table 1. Figure 3 presents the predicted values of total fertility rate in 1985 for varying levels of female enrollments in 1975 and family planning services in 1982. The fertility rate falls steadily with increases in female secondary enrollments. The rate of decline in fertility appears steep initially, but tapers off after female enrollment levels reach 40 percent. Family planning services also reduce fertility but by a slower rate. Even with the maximum family planning service score of 100 (quadruple the mean of 25 percent in 1982), fertility does not reach the same level as with 40 percent female secondary enrollment. It would appear that mere expansion of family planning services by itself affects fertility much less than if it were accompanied by expansion of female secondary enrollment." _1"_ Several micro-level studies based in househol **d** ata sets have reached similar conclusions. For example, one recent cameful study based on Indonesian household-level data set confirms the small contribution of family planning program effort to fefility decline (P. Gcrtler and J. Molineaux, 1992). In this study, the authors show that over the period 1982-87, the combined educational and economic impacts, workdng though increases in contraceptive use, accounted for 87 per cent of fertlity decline over the period. By contrast, measures of family planning program inputs were rsponsible for only 4 to 8 per cent of the decline. Of course, this study uses a fixed effects model which relates changsg during 1982-86 in family planning inputs and socio-economic variables to chanIzes in contracepive use and ferility. Since family planning was already quite well established in 1982, these results may reflec **t** his context.
<!-- Start of picture text --> Figure 2: Elasticities of TFR with respect to Female<br>Secondary Education, 1975 and FP Services, 1982<br>Elasticities of TFR<br>0.6<br>-|- Fem ed.(tot.effect) -<br>FP service (tot.eff)<br>0.4 -<br>0.2<br>-0.2<br>-0.4 -<br>-0.6 0 10 , 20 . 30 . 40 . 50 . 60 70 80 90 100<br>Levels of Female Secondary Enroll. 1975 & FP Services, 1982<br>Note: Elasticities computed from eq.4 Table 1. holding all other variables constant at their mean value.<br>(Means; Female Sec. Enroll. (1975). 19% Family Planning Service Score (1982), 25% TFR 5.3)<br><!-- End of picture text -->
# Figure 3: Predicted TFR, 1985 for Different Levels of Female Education & FP Services

## TFR

87

| _ TFR with Fem. Ed. g TFR with FP Services

65- 4- 3- 20 0 10 20 30 40 50 60 70 80 90 100 Secondary Enrollment Rate, 1975 & levels of FP Service, 1982 Note: calculated from Table 1 holding all other variables constant at their mean value. (Means: Female Sec. Enroll. (1975, 19.1%, Family Planning Service Score (1982). 25%, TFR (1985) 5.3).
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# B. Female Education. Desired Family Size and Contraceptive Prevalence

4.8 Female education affects fertility in several ways, but we consider two ways:<sup>it reduces</sup> the desired family size, and it promotes contraceptive use. There are important<sup>empirical problems in</sup> mapping out these effects. For example, desired fertility could fall for<sup>a variety of reasons of which</sup> female education is one. Similarly, contraceptive prevalence could increase due to many factors. There are (unknown) time lags in each effect. There are, of course, serious<sup>data and measurement problems,</sup> as discussed in para 2.6 above. For example, desired family size is available from WFS<sup>surveys only</sup> for 37 countries, may not be exactly comparable across countries, and reflects subjective<sup>judgments.</sup> These limitations must be taken into consideration when interpreting<sup>the results.</sup> 4.9 Data from World Fertility Survey **s** hown in Figure 4 depict the total (actual) fertility<sup>and</sup> desired fertility for a sample of 37 countries in 1988, along with the country's<sup>female secondary</sup> enrollment rate in 1975. Above a female secondary enrollment of about 40<sup>percent, actual and desired</sup> fertility rates begin to converge, indicating that women with more education are better able to<sup>reach their</sup> desired fertility. In Figure **_5_** contraceptive prevalence, total fertility and female secondary<sup>school</sup> enrollments are plotted. As expcted. contraceptive prevalence increases and total fertility decreases with higher levels of female education.

4.10 The results on DFS and CP were obtained from two regressions with desired family size (DFS) and with contraceptive prevalence (CP) as the dependent variables; the estimated equations are presented in Tables 2 and 3 and show the role of female secondary education relative to other factors.
Figure<sup>4: Total and Desired Fertility</sup> <!-- Start of picture text --> Total Fertility, 1988<br>10<br>--TFR -GDFS<br>8<br>6<br>4 -(<br>2-<br>70%<br>60%<br>0 40% 60%<br>30%<br>20%<br>10%<br>0%<br>Female Secondary Enrollment, 1976<br><!-- End of picture text -->
# Figure 5: Female Secondary Enrollment, Contraceptive Prevalence and TFR, 1988

<!-- Start of picture text --> CODtracepUve Prevalence, 1988<br>80<br>60<br>0<br>20 4<br>a<br>S<br>2* aI<br>0<br>6x 1% 2% 3%40 0 0 0<br>T a~~~eal eodr nrlmn.17<br><!-- End of picture text -->
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The independent variables include male and female secondary enrollments, GDP per capita, and regional dummies, and family planning services in the CP equation. The equations were also estimated allowing female secondary enrollments to enter separately. In both DFS and CP equations, the coefficient of female secondary school enrollments is highly significant with the expected signs for the linear term and the quadratic term; the turning point occurs only at a high (40 percent) level of female secondary enrollment."1 While the male education variable is not significant, GDP per capita is highly significant in both DFS and CP equations. Ceteris paribus, a rise in GDP per capita leads to a higher desired family size up to a threshold level reflecting the positive income effect on fertility, but beyond a threshold level increases in GDP lead to a fall in desired family size; the quadratic terms for GDP are highly significant with a negative sign. The impact of an increase of GDP per capita on contraceptive prevalence (CP) is also non-linear; it leads to a rise in CP initially, but after a threshold income level, it has a negative impact on CP. Family planning services are highly significant and has positive impact on CP.

4.11 The elasticities of contraceptive prevalence with respect to secondary school enrollment, GDP per capita, and access to family planning services are shown in Figure 6. The elasticity with respect to education has an inverted **'UU** shape, peaking again at 30 percent enrollment with an elasticity of 0.33. However, it is worth stressing that the family planning service score is an extremely important deterninant of contraceptive prevalence. The service component has a strong positive upward slope, and at a score level of 20, reaches the same contraceptive prevalence level as for 10 percent female enrollment.'<sup>5</sup> 1 **When female secondary enrollments** were entered without male enrollments (see Appendix Table **7)** female **secondary enrolmena still** retain their high stistical significance.

" The actual values of **these** elasticities **am** given in Appendix Table **8.**
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Table 2. Female Education, GDP per capita and Desired Family Size

|Independent Variables|Dependent Variable, DFS,|1988|
|---|---|---|
|Constant|3.404|3.817***|
|Female Secondary Gross Enrollment, 1975|-0.109|-2.781***|
|Female Secondary Gross Enrollment2|0.001|2.579***|
|Male Secondary Gross EnroUlment, 1975|0.018|0.320|
|Male Secondary Gross Enrollment2|0.00001|0.125|
|GDP per capita, 1988|0.001|2.381**|
|GDP per capita2|-0.0000001|-2.162**|
|Africa Regional Dummy|3.181|7.301***|
|Asia Regional Dummy|-0.147|-0.361|
|LAC Regional Dummy|0.392|0.865|
|Adjusted R<sup>2</sup>|.74||
|nm|~~~|~~~~~~~~~~~~~~32|
|4 solute values of t-ratios, whem,<br>,and<br>dicate|sigificance levels of 10, 5, and 1 per|cent respectively.|

Table 3. Female Education, GDP per capita and Contraceptive Prevalence

||Independent Variables|Dependent|Variable, CP, 1988|
|---|---|---|
||_<br>_<br>_<br>_<br>__|_ _Coefficient<br>t-ratio|
|Constant|-8.166|-I-.572|
|Fanale<br>Secondary Gross Enrollrnent,<br>1975|1.437|2.683***|
|Female Secondary Gross Enrolmnent2|-0.012|-1.924*|
|Male Secondary Gross Enrollment, 1975|-0.040|-0.093|
|Male Secondary Gross EnroilmenC^|-0.005|-0.970|
|GDP per capita 1988|4.007|-3.1078**|
|GDP per capitas|-0.000001|-2.655***|
|Family planning services 1982|0.526|7.7018**|
|Africa Regional Dummy|-0.525|4.150|
|Asia Regional Dummy|-5.351|-1.451|
|LAC Regional Dummny|-3.738 _4.8|17|
|Rz||.83|
|n||61|

Absolute values of t-ratios, where **8, 88,** and ****** indicate significance levels of 10, 5, and I percent respecdvely.
<!-- Start of picture text --> Figure 6: Elasticities of Contraceptive Prevalence with<br>respect to Female Enrollments, 1975, FP Services & GDP pc<br>Elasticity with respect to CP<br>2<br>_- |O Female Sec. Enroll.<br>Access to FP Service<br>1.5 *<br>GDP per capita .. . .<br>0L<br>0 10 20 30 40 50 60 70 80 90 100<br>300 600 900 1200 1500 1800 2100 2400 2700 3000<br>Note: Elasticitle computed from eq. Levels of Enroll., Service & GDP pc ($) 1 Table S. holding all other variables constant at their mean values<br>(Moan.s: Fer. So.. Enroll., (1976) 20.4%. Access to ServIoos (1982) 28%, GOP per capita (1988) $2324. CP 30.5)<br><!-- End of picture text -->
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4.12 Controlling for female education, the elasticity of CP with respect to GDP per capita is smaller. It could be argued that progress in contraceptive prevalence may occur quickly (without timelags) as GDP per capita grows, whereas the female education route would require "waiting". But this argument may be more hypothetical than real. Consider a typical, poor country with per capita GDP under $ 300 and a female secondary enrollment rate of 10 percent. If this country wants to promote contraceptive prevalence by I percent in 1985, it would involve an increment of female secondary school enrollment of 2.4 percent in 1975 and a wait of 10 years. That country might in theory raise GDP per capita and achieve the same objective more quickly, say in one year, without any time lag. However, to do this, the country would need annual GDP per capita growth of 14 percent, which is not usually feasible. A per capita GDP growth of this magnitude was achieved by few countries during the entir decade of the 1980s, especially in Sub Saharan Africa."<sup>6</sup> Thus, while "waiting time" may seem a disadvantage in the female education route to fertility reduction, the income route may also not be quick. Building a sound institutional and administrative framework for effective family planning programs also needs time.

4.13 Does provision of family planning services appear more attractive in financial terms, relative to female education? Unfortunately due to the absence of precise estimates of costs of<sup>secondary</sup> education across the world, we are unable to compare the cost of education alternatives.<sup>The differences</sup> in unit costs of education as well as family planning program services can be expected<sup>to vary</sup> substantially across continents. Family planning program services probably generally cost considerably less per capita than secondary female education does. However, benefitlcost comparisons, once<sup>cost data</sup> become available, must be done with great care to ensure that the multiple benefits from both<sup>female</sup> _16_ **Sc.** World Bank (1989), Sustainable Growth **with** Equity: Sub-Saharan **Africa.**
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education and family planning are duly accounted for. Future research should focus on careful comparisons of costs and benefits of alternatives, including the time needed to place the required administrative apparatus to achieve tangible results.

# C. Female Education. Access to Medical Care. and Child Health

4.14 The results relating to the determinants of infant mortality are presented in Table 4. One public program variable is considered: population per physician. Of course, this measure of health programs affecting the IMR is highly inadequate (as we acknowledge in footnote 6). We considered using additional variables, namely access to safe drinking water and the percentage of infants immunized, but were unable to use the former due to lack of reliable data, and the later due to its endogeneity and the lack of a good instrumenting variable to measure access rather than use.

4.15 Female secondary enrollments again are highly significant. The quadratic terms are positive but not statistically significant. Male education is not statistically significant for either 1970 or 7 1975 enrollments". The health program effort variable, population per physician, is highly statistically significant in all equations. GDP per capita is also significant with the expected negative sign. Family planning services is negative and statistically significant only in the equation with 1975 enrollments, while the urban variable is positive and significant in the 1970 equation only. The LAC regional dummy is significant in the 1975 equation. Thus, even after controlling for income and the program variable, female secondary education is clearly a prime factor in reducing the IMR.

17 Appendix Table 10 presents **the** results when **female** education alone is entered into **the** equation.
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4.16 The elasticities of the IMR with respect to female enrollments in 1975, population per physician and GDP per capita have been computed from Table 4 and are shown in Figures 7a and 7b. The direction of the results was similar for the 1970 equations, thus only the results with 1975 enrollments are presented here. The elasticity of IMR with respect to female secondary enrollment sharply rises to -0.92 at an enrollment level of 40 percent; whereas the elasticity of IMR with respec **t** o GDP per capita is low at -0.21 even at a per capita GDP level of $1800.'8 The elasticity of IMR with respect to population per physician is positive and decreases as the ratio decreases. It reaches 0.06 percent when the ratio of population per physician is halved (from the mean). For a typical, poor country with a GDP per capita of $300, a female secondary enrollment rate of 10 percent, and physician for every 15,000 people, let us consider alternative approaches to reducing infant deaths. A doubling of female enrollments (from 10 percent to 20 percent) would bring down the IMR from 105 to 78, whereas a doubling of GDP per capita would reduce IMR from 102 to 99. Halving the ratio of population per physician would reduce the IMR from 85 to 81. These illustrative scenarios suggest the powerful influence of mother's education in the prevention of infant deaths.

**1ThM actual** values of these elasticities are given in Appendix Table 9.
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Table 4. Infant Mortality (1985), Female Education and Program Variables

|Indqendent Variables|(1)<br>Enrollme|Dependent<br>nts 1970|Variable, IMR 19<br>(2)<br>Enrollment|85<br>s 1975|
|---|---|---|---|---|
||Coefficient|t-ratio|Coefficient|t-ratio|
|Constant|85.246|7.474***|98.849|7.561'**|
|Female Secondary<br>Gross Enrollment Rate, 1970|-3.309|-2.373**|||
|Female Secondary<br>Gross Enrollment Rate squared|0.020|0.946|||
|Male Secondary|||||
|<br>Gross Enrollment Rate, 1970|1.609|1.718|.||
|Male Secondary<br>Gross Enrollment Rate squared|-0.01|-0.494|||
|Female Secondary<br>Gross Enrllmcnt Rate, 1975|||_-3.055_|-3.720***|
|Female Secondary<br>GrossEnrollmentRate squared|.___._||0.014|1.237|
|Male Secondary|||||
|<br>Gross Enrollment Rate, 1975|||0.710|1.113|
|Male Secondary<br>Gross Enrollment Rate squared|||0.01|0.910|
|Family planning services|1.049|1.047|-0.187|-1.682*|
|Population per physician, 1984|0.001|2.433**|0.001|3.795'St|
|Urban population as % of total population|0.660|1.681w|0.389|1.571|
|GDP per capita, 1985|-0.018|-2.466**|-0.010|-3.872***|
|Africa Regional Dummy|8.540|0.604|10.470|1.532|
|Asia Regional Dummy|3.252|0.229|9.299|1.118|
|LAC Regional Dummy|20.611|1.080|19.094|2.315**|
|Adjusted R<sup>2</sup>||_.75_|.7|3|
|n=||64|6|5|

Absolute values of t-ratios, where m, *, and * indicate significance levels of 10, 5, and I percent respectively.
Figure 7a: Elasticities **of IMR.** 1986 with respect to Female Secondary Enrollment, 1976 & GDP, 1985 <!-- Start of picture text --> Elasticities of IMR<br>1 .<br>Ferm. Secondary Educ. GOP por capita<br>0.5 -<br>0~~~~~~~~~~~~~~~<br>-0.5 - - - - - - -- - -<br>.. . -. __§}| **-1-<br>0 10 20 90 40 50 00 70 80 90 100<br>900 600 1300 1000 2800 2800 3600 9800 4S00 4500 00(<br>L.#a;i. M earmilment. tmunltlon and GOP per capit<br>1101., Eleatalell. .m" ,...1.wa I lee *1. Tab le* 41. bdsia lI. we4eeea.<br>S4.ef.o e t b* t *1**1 yt:q".<br>IMese Vale. 300. WOOeL (1O0). 100. SOPar0 *.pim e1b08S .* 1.0. INA 100). so.<br><!-- End of picture text -->

<!-- Start of picture text --> Figure 7b: Elasticities of IMR, 1985 with respect to<br>Population per Physician, 1984<br><!-- End of picture text --> <!-- Start of picture text --> Elasticities of IMR<br>~~~~~~~~~~~~~~~~~~~~~~~1<br>Pop- per Phycian<br>0.5<br>1 4 t00<br>2O 000O9000oo003000i00007000 4000 1000<br>Levels of Population per Physician<br>l0t. I . . 4.b all other 14l0,1*0t<br>e.aaest as me.. Vale.<br>Wtoe*e. tpoelalma Par Pp"alelem 6114). tIsS. INN Irea6) so.<br><!-- End of picture text -->
. 30 4.17 To examine the above scenarios more clearly, the response of the IMR to female education relative to population per physician and GDP per capita is shown in Figures 8a and 8b, which give the predicted values of the IMR for different levels of female secondary enrollments, holding all other variables constant at their mean level. The IMR falls steadily until female secondary enrollment reaches 50 percent. Also as expected, a rise in GDP per capita leads to a fall in IMR, ceteris paribus, a with a slightly steeper slope than for population per physician. However, a GDP level of $4800 - clearly implausible proposition for many developing countries even within a decade - would be needed to reach the level of IMR obtained with 30 percent female secondary enrollments. The IMR falls after halving the ratio of population per physician (again keeping all other variables, including female secondary enrollments constant at th^ir mean level), but at a much lower pace compared to female enrollments. Thus, for developing countries, female education appears to be a powerful way to reduce infant deaths.

4.18 The combined effect of fertility reduction and improvement in infant survival from increasing female enrollments can be seen from the predicted values of each plotted in Figure 9."' The reduction in fertility and improvement in infant survival are striking. The simulation shows that with increasing levels of female secondary enrollment in 1985, not only does the number of births per woman fall, but the proportion of infants who survive rises. Doubling the mean level of female education from 19 percent to 38 percent, holding all other variables constant at their mean values, would reduce the TFR from 5.3 to 3.9. Similarly, the proportion of infants surviving increases as the IMR falls from 81 to 38.

**19 Infant** survival rate computed as **1-(IMRWIOOO)** multiplied by TFR.
Figure Oa: Predicted<sup>IMR, 1986 tor different levels of</sup> Female Secondary<sup>Enrollment, 1976, 8 GDP pc, 1985</sup> <!-- Start of picture text --> IMR<br>140<br>IMR with Fer. Ed.<br>120 IMR. wth GDPo p<br>100 - - - ~~~~~ - - - - -~~~~~ -<br>100~~~~~~~~~~~~~6<br>80~~~~~~~~~~~~~~6<br>--<br>4 00 800 1900 tB 0029 028009 009800 900480<br>20 e - - t --<br>0 a 10 - 20 - s0 40 so so T0 so t0 H 100<br>300 800 1300 1800 28002800830089004300 4000<br><!-- End of picture text -->

**NoW.s e.g..I.d .. Meoa....Oahi&AoOemy gtem *S.** I **Table atimoaltu**<sup>**4. beMidimof**</sup> Wish **Big elk vs,l.bi..** II)o **gor** aigoita **OaStebi c Ewoelo. our boi61** 04 **TColm .l**

Figure 8b: Predicted<sup>IMR, 1985 for different levels of</sup> Population per Physician,<sup>1984</sup> <!-- Start of picture text --> 10IMR<br><!-- End of picture text --> <!-- Start of picture text --> go IMR with Pop/Phym.<br>0 8 0 6 20 tO 004<br>20 X t<br>0<br>80aS0@8 500@602000900aeo0oa00CO0oo000o 4000 1000<br>MOte,- 0.i061.t.d 1101 04. S feb10 4. beldiftg all 0th.t<br>.-. aga P01041Bt@a pot 111iaIisea (10491. aoSol. 1mbf isOSei<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 9: Predicted TFR and Infant Survival Rates at<br>different levels of Female Secondary Enrollment in 1975<br>8<br>TFR<br>6 Surviving<br>2 l.3<br>0<br>0 10 20 30 40 50<br>Secondary Enrollment Rate, 1975 (%)<br>Note: Simulated from equations 4 & 2 in Tables 1 & 4<br>(holding all other variables constant at their mean value.)<br><!-- End of picture text -->
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# **_5._** Quantifying the Gains in Fertility and Mortality Reduction: Some Simulations

5.1 The econometric estimates obtained from cross-country analysis can be used to simulate what the impact in the 1980s would have been had the female secondary school enrollment rate been double its actual its average in 1975, keeping all other variables (including health and family planning services) constant at their mean values. The impact of doubling (and trebling) the family planning services index was also simulated. The results (shown in Table 5) are striking. For a sample of 65 low and middle income developing countries (including China) which account for 93 percent of the population of developing countries, doubling female secondary enrollment from the mean of 19 percent to 38 percent in 1975, keeping all other variables (including family planning services) constant at their mean values, would have reduced the number of births by 29 percent compared to the actual in 1985X. By contrast, doubling the family planning services score, without raising female enrollments, would have reduced the number of births by only 3.5 percent. Results comparable to a doubling of female secondary school enrollments cannot be obtained even by a trebling of the family planning services score (see column 9 and 10, Table 5). Of the developing countries, only two have been able to reach a service level of 75 (China and Indonesia have scores of 78), whereas as many as 14 developing countries, some of which with relatively low per capita GDP such as Sri Lanka and Indonesia, reached 40 percent female secondary enrollment level in 1975, and this number rose to 28 in 1988. The highest gains (in terms of births averted as percent of actual) from female education occur in the countries of South Asia and Sub Saharan Africa.

# 5.1

I Calculated using the predicted values of TFR and actual female population of childbearing age (15-44)<sup>in the sample</sup> countries for 1985.
Table S. Gains In Births Averted through Female Education and Family Planning Program Services: Simulation Result4 Births per year, 1985 (millions)

||RaiingFe|mal Secondary Enrolment||||Rasing_Fami_|_ly_Pbanning Sei|c|||
|---|---|---|---|---|---|---|---|---|---|---|
|Countriesp|Actual|Rmarks|Simulated|Bilbw<br>Averted|Col._5_as<br>% of col. 2|Remarks|Simulated|Biths<br>Avrted|CoL 9<br>as% of<br>l 2||
|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|co.<br>(10)||
|_LAw Nad_Middle Income<br>(a60|102.5|Scenario 1: if female<br>gross enollmet atesin<br>1975 wene doubled to<br>|72.9|29.6|29|Scenaro 1W<br>Scenario 2Y|98.9<br>93.4|3.6<br>9.1|_3.5_<br>8.9||
|Low Income (n=31W|81.2|38% from 19% (actual).?<br>.|_55.6_|25.6|32|Scenario_1e_<br>Scenario<br>2|79.6<br>77.1|1.6<br>4.1|2.0<br>_5.0_||
|Sub-Saaran Afica (n-26)P|17.4||11.3|6.1|35|Scenario<br>lI<br>Scenario 2Y|16.2<br>15.5|1.2<br>1.9|6.9<br>11.0|w4S.|
|SouthAsia(n=S)P|33.9||17.7|16.2|48|Scenario 1l<br>Scnario 2'|33.7<br>31.9|0.2<br>2.0|0.6<br>5.9||

1/ These simulations are based on equation 4 in Tabk 1. 2/ According to World Bank claification. 11 Thes 65 countries represent 76% of the world's population and 93 of the LDC population, including China. _4i_ Keeping all other variables constant at their mean values.

_I These 31 countries represent 61% of the world's populaticoa **n** d 97% of the low-income LDC population, including China. __i_ These 26 countries are: Benin, Botswana, Buriira Faso, Burundi, Central African Republic, Cot d'Ivoire, Ethiopia, Ghana, Guinea, Kenya, Lesotho, Libeia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Niger, Nigera, Rwanda, Sierra Leone, Somalia, Sudan, Tanzania, Togo, Uganda, Zaire, Zambia, and Zinbabwe. They represent 8.7% of the world's population nd 94% of SSA's population. 2/TheseS counries ae: Bagladesb, Nepal, India, Pakistan, and Sri Lanka. They repreet 23.1% of the world's population and 96% of South Asia's popubtion.

_jI_ Scenario 1: if family planning service index score in 1982 were doubled to 50%, from 25% (actual) (see footnote 4). kI Scenrio 2: if family planning service index score in 1982 were tripled to 75%, fom 25% (acua (see footnote 4).
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5.2 Table 6 shows similar simulations for gains in the reduction of infant deaths. Doubling female secondary school enrollments has a dramatic impact on infant deaths averted, reducing<sup>them by</sup> 64 percent for the sample of developing countries as a whole. The gains in terms of deaths averted<sup>are</sup> again highest in South Asia and Sub Saharan Africa. Reduction of the ratio of population per physician results in prevention of infant deaths, but to a much smaller extent (2.5 percent), while an increase<sup>in</sup> GDP per capita from $650 to the median level in the sample of $1300 has no effect on infant deaths.

_5.3_ The simulation **s** how the virtue of expanding education as well as health and family planning services to reduce fertility and promote child health. Just expanding female<sup>education itself</sup> would have a significant impact. However, combining female education with health and family<sup>planning</sup> services would have the greatest impact on the reduction of births and infant deaths.<sup>At the same time,</sup> the simulation **s** uggest that exclusive reliance on health and family planning program<sup>efforts, without</sup> expanding female education, will not affect the TFR and IMR substantially,<sup>especially in very poor</sup> countries with a low female secondary education base. Female education is thus a powerful force for health and family planning in the developing world.
Table 6. Gains in Infant Deaths Averted **through** Female Education **versus** Health Servces and **GDP** per capita change: Simulation Results<sup>1</sup> ' Infant Deatss per year, 1985 (millions)

|CountrueP<br>(1)<br>_ _{(2)|Actual|Remnarks<br>(3)|Simulated<br>(4)|Averted<br>(5)|Col. S**asS **<br>ofCol.2<br>(6)|Remarks<br>(7)|Simulbted<br>(8)|Averted<br>t9)|Col. 9 u%<br>of Col. 2<br>(10)|Remarks<br>(II)|Simulated<br>(12)|Averted<br>(13)|Col. 3as%<br>of Col. 2<br>(14)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Low&middle income<br>(n=65)F|8.0|Thescettio<br>if<br>felmale<br>gros<br> <br>|2.9|5.1|64|Scenrio<br>halvingthe<br> <br>|7.8|0.2|2.5|Scenario<br>raising<br>GDP|8.0|0|0|
|Lowincome (n=31)Y|6.8|enollment<br>in<br>1975 were<br>dbld f|2.3|4.5|66|ratio<br>of<br>ppop<sup>t</sup>jti-o<br>pe<br>|6.6|0.2|2.9|per capita<br>level<br>from<br>|6.8|0|0|
|Sub,Ssamn Africa<br>(n=260|2.1<br>|oue tron<br>19% (actual) to<br>38%.1'<br>|0.4|1.7|81|physician in<br>1984 from<br>11,851(mean)<br> <br>'|1.9<br>|0.2<br>|9.5|S650 in 1985<br>to the median<br>ofS13C0.'|2.0|0.1|4.8|
|Soutb Asfit (n=5)P|South Asia (n =<br>3.2|)11|0.7|2.5|78|~~~~~~~~<br>5926.|~~~~~~<br>3.2|~~to<br>0|0||3.2|0|0|

It The2e simulations ar based on equation 2 in Table 4.

21 According to World Bank classifgeation.

2_ These 65 countries eresent 76% of the worlds population and 93 or the LDC population, induding Chins.

fI Keeping all other variables constant at their mean values.

_I These 31 countrics represent 61% of the worad'soppulation and 97% of the low-income LDC population, including China. 61 These 26 countries **are:** Benin, Botswana, Burkina Faso, Burundi. Central African Republic, Cote d'lvoire, Ethiopia. Ghana, Guinea, Kenya. Lesoth, Liberia, Madagascar, Malawi, Mali. Maurnius. Mozambique, Niger, Nigeria, Rwsnda, Simfma Leone, Somalia. Sudan, Tanzania, Togo. Uganda. Zaire, Zambia. and Zimbabwe. Thcy represent 8.7% o' .ae world's popumation and 94% of SSA's population. 7/ Thse S countries are: Bangladesh, Nepal. India, Pakistan, and Sri Lanka. They represent 23.1 % of the world's population and 96.3% of South Asia's population.

0
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# 6. **Implications for Policy and Concluding Remarks**

6.1 This paper explored the strength of female secondary enrollment relative to, and/or<sup>in</sup> combination with some health and family planning program efforts to reduce fertility and<sup>infant mortality</sup> in the developing world. The relative strength of these strategies seems to vary at<sup>different levels of</sup> socio-economic development, suggesting the need for careful consideration of priorities especially<sup>in</sup> countries with low female secondary enrollments. The equation with 1975 enrollment<sup>rates suggests that</sup> effect of family planning services should be evaluated with reference to specific levels<sup>of female</sup> enrollments. The elasticity of the total fertility rate with respect to female secondary enrollments<sup>is</sup> consistently higher than for family planning services, ceteris paribus. Thus expansion in female<sup>education</sup> reduces fertility substantially and even more when family planning services accompany it. For<sup>example,</sup> a doubling of female secondary enrollments in 1975 from the mean of 19 percent to 38<sup>percent would</sup> have reduced the fertility rate from 5.3 to 3.9, whereas a doubling of family planning services<sup>in 1982</sup> from the mean of 25 to 50 would have reduced the total fertility rate from _5.5_ to 5.0. In countries where the female secondary education base is low, this research suggests that the expansion of female<sup>secondary</sup> education may be the best single policy lever for achieving substantial reductions in fertility. However, couples who desire smaller families certainly ought to have good family planning options at hand, and improvements in family planning may encourage more couples to opt for contraception.

6.2 With respect to infant mortality, public programs with reduction in the ratio of population per physician coverage do reduce infant deaths independently of female education. However, the elasticities of the IMR with respect to female secondary enrollments are always greater than with reduction in the ratio of population per physician coverage or GDP per capita. Our simulations show that the reduction in IMR from expansion of female enrollments is striking. For example, in a typical poor country with a GDP per capita of $300, female secondary enrollment of 10 percent, and a physician for
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every 12,000 people, a doubling of female education in 1975 would have reduced the IMR in 1985 from 105 to 78; in comparison, a halving of the ratio of population per physician would have lowered the IMR from 85 to 81, and a doubling of GDP per capita would have lowered the IMR from 102 to 99. When the results are translated into deaths averted, the magnitude of reductions in deaths obtained from small increments in female education swamp the reduction in IMR obtained from even massive direct programs; for example, while doubling female enrollment rate reduces infant deaths by 64 percent, halving the ratio of population per physician reduces infant deaths by 2.5 percent. Of course, our measure of health programs is very imperfect and doubtless understates the impact of health care. But it is reasonably clear that expanding female education is a powerful option for many developing countries.

6.3 The simulations attempted in this paper show that the gains from female education in tems of births averted are also striking. For example, doubling female secondary school enrollments from their mean level in 1975, keeping all other variables (including family planning services) constant **at** their mean values, would have lowered births by 29 percent compared to the actual number in 1985. Doubling the family planning service score, keeping all other variables constant at their mean values, wo. Id have reduced births by 3.5 percent. The gains from expansion of female secondary schooling are largest in South Asia and Sub-Saharan Africa where the female enrollment rates were among the lowest in the world _in_ 1975. Since then there has been progress; yet even as of 1989, 25 out of 27 countries with female enrollment rates of less than 20 percent are in Sub Saharan Africa and South Asia.

6.4 The broad conclusion of this study is that family planning and health programs do reduce fertility and mortality, but the impact of expanding female secondary enrollments appears to be even greater, especially in countries with low female secondary enrollment; the gains are, of course, greatest when female education is combined with health and family planning programs.