3
The Proximate Determinants of Fertility

Carole L.Jolly and James N.Gribble

INTRODUCTION

Fertility levels in sub-Saharan Africa are among the highest in the world. As a result, recent fertility declines in a few countries have gained the attention of researchers and policy makers, and have renewed interest in the factors affecting fertility. As first outlined by Davis and Blake (1956), the factors affecting fertility can be classified into two groups: background variables and intermediate or proximate variables. The former includes cultural, psychological, economic, social, health, and environmental factors. The proximate determinants are those factors that have a direct effect on fertility. The background factors operate through the proximate determinants to influence fertility; they do not influence fertility directly.

Drawing on data from the Demographic and Health Surveys (DHS) and World Fertility Surveys (WFS), this chapter examines the relative effects of four proximate determinants on fertility: marriage patterns, contraceptive use, postpartum infecundability, and primary sterility. Using the Bongaarts model of proximate determinants of fertility, we examine how these four factors influence the levels of fertility and illustrate different effects of each

Carole L.Jolly and James N.Gribble are program officers for the Committee on Population, National Research Council. They thank Kenneth Hill for his assistance in estimating the measure of the degree of childbearing outside marriage and are also grateful for his help in the computations.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 68
Demographic Change in Sub-Saharan Africa 3 The Proximate Determinants of Fertility Carole L.Jolly and James N.Gribble INTRODUCTION Fertility levels in sub-Saharan Africa are among the highest in the world. As a result, recent fertility declines in a few countries have gained the attention of researchers and policy makers, and have renewed interest in the factors affecting fertility. As first outlined by Davis and Blake (1956), the factors affecting fertility can be classified into two groups: background variables and intermediate or proximate variables. The former includes cultural, psychological, economic, social, health, and environmental factors. The proximate determinants are those factors that have a direct effect on fertility. The background factors operate through the proximate determinants to influence fertility; they do not influence fertility directly. Drawing on data from the Demographic and Health Surveys (DHS) and World Fertility Surveys (WFS), this chapter examines the relative effects of four proximate determinants on fertility: marriage patterns, contraceptive use, postpartum infecundability, and primary sterility. Using the Bongaarts model of proximate determinants of fertility, we examine how these four factors influence the levels of fertility and illustrate different effects of each Carole L.Jolly and James N.Gribble are program officers for the Committee on Population, National Research Council. They thank Kenneth Hill for his assistance in estimating the measure of the degree of childbearing outside marriage and are also grateful for his help in the computations.

OCR for page 68
Demographic Change in Sub-Saharan Africa factor with country examples. We also examine differentials across countries for each of the determinants and compare the changes over time by comparing results from the WFS and the DHS for those countries that conducted surveys under the auspices of both programs. DATA The data sources for this analysis are the 12 DHS of women conducted in sub-Saharan Africa during the 1980s and the WFS conducted during the 1970s in Ghana, Kenya, Senegal, and northern Sudan. Only four WFS countries are examined—those for which there was a subsequent DHS. The DHS core instrument gathered data on the socioeconomic status and reproductive history of women and the health of their children, as well as their experiences in using health services. The WFS also systematically gathered comparable data on fertility and mortality in nine sub-Saharan African countries, including northern Sudan.1 Although Sudan is included in our analysis, it is important to note that the survey was conducted in northern Sudan, a region that is primarily Arab/Muslim and quite distinct from the black African/Christian or animist south, which is more similar to the rest of sub-Saharan Africa. Table 3–1 provides information on the sample sizes, criteria for being included in the sample, and dates of fieldwork for the surveys. FRAMEWORK Bongaarts et al. (1984) enumerate nine major proximate determinants of fertility at the societal level: marriage or union patterns, contraception, lactational amenorrhea, postpartum abstinence, pathological sterility, induced abortion, frequency of sexual intercourse, spontaneous intrauterine mortality, and natural sterility. 1   Although this analysis generally used data from the core questionnaires of the DHS and the WFS, in some cases a variation of a core question was asked or a core question was eliminated. In these cases, it was necessary to obtain the information from another question or to use an imputation procedure. See Technical Notes at the end of this chapter for details.

OCR for page 68
Demographic Change in Sub-Saharan Africa TABLE 3–1 Data Sets Used in the Analysis Country Abbreviation Time of Fieldwork Respondents Sample Size Demographic and Health Surveys   Botswana BWA August-December 1988 All women 15–49 4,368 Burundi BDI April-July 1987 All women 15–49 3,970 Ghana GHA February-May 1988 All women 15–49 4,488 Kenya KEN December-May 1988–1989 All women 15–49 7,150 Liberia LBR February-July 1986 All women 15–49 5,239 Mali MLI March-August 1987 All women 15–49 3,200 Ondo State, Nigeria   September-January 1986–1987 All women 15–49 4,213 Senegal SEN April-July 1986 All women 15–49 4,415 Sudana SDN November-May 1989–1990 Ever-married women 15–49 5,860 Togo TGO June-November 1988 All women 15–49 3,360 Uganda UGA September-February 1988–1989 All women 15–49 4,730 Zimbabwe ZWE September-January 1988–1989 All women 15–49 4,201 World Fertility Surveys   Ghana GHA February-March 1979–1980 All women 15–49 6,125 Kenya KEN August-May 1987–1988 All women 15–50 8,100 Senegal SEN May-October 1978 All women 15–49 3,985 Sudana SDN December-April 1978–1979 Ever-married women age 50 or under 3,115 aWFS and DHS data for Sudan refer only to northern Sudan.

OCR for page 68
Demographic Change in Sub-Saharan Africa Bongaarts and Potter (1983) developed a model to quantify the effects of the six proximate determinants that in their analysis had the most important influences on fertility levels: union patterns, contraception, lactational amenorrhea and postpartum abstinence, pathological sterility, and abortion (Bongaarts, 1982; Bongaarts et al., 1984). The analysis in this chapter is based on the Bongaarts and Potter model, but does not include abortion because reliable and comparable estimates are not available for sub-Saharan Africa. The model relates total fertility to total potential fertility reduced by a series of indices, each of which reflects the fertility-reducing effect of a proximate determinant. An index, which has a range between 0 and 1 for most of the proximate determinants, is estimated (lactational amenorrhea and postpartum abstinence are joined into one index). An index value of 0 has the strongest effect of reducing fertility (fertility equals zero); a value of 1 has the weakest effect on fertility (the proximate determinant has no fertility-limiting effect). The lower the index, the more influential the proximate determinant is in reducing the total fecundity rate (TF), the level of fertility that would occur in the absence of all of the proximate determinants. Thus, the proximate determinants can be thought of as inhibitors of fertility. For example, delayed entry into marriage, use of family planning methods, and prolonged breastfeeding or postpartum abstinence are factors that reduce fertility to levels lower than those that would occur in the absence of these proximate determinants. Below is a description of the proximate determinants used in this analysis, the way these factors influence fertility through inhibiting TF, and the computational procedure used to estimate the indices. (Equations for deriving these indices are given in the appendix to this chapter.) Marriage or Union Patterns2 Because entry into marriage is a process and not a single event in many parts of sub-Saharan Africa (see Chapter 4), this analysis looks at the effect of the proportions of women in sexual union, rather than marriage per se, on fertility. The proportion of women in a sexual union in a society indicates the degree to which women of reproductive age are exposed to the risk of becoming pregnant (if one assumes that all sexual intercourse occurs within union). In populations where women marry early and there is little divorce or separation, exposure to pregnancy is very high. In many parts of sub- 2   In the context of this analysis, the term “marriage” refers to being married or living in a fairly stable union.

OCR for page 68
Demographic Change in Sub-Saharan Africa Saharan Africa, women marry young; median age at first union among women ages 25–49 at the time of the DHS surveys ranges from 15.7 in Mali to 19.7 in Ondo State, Nigeria. Although union dissolution is relatively common, most women remarry quickly, which results in a large proportion of ever-married women who are actually in a union (Mhloyi, 1988). Such behavior can be expected to result in high levels of fertility. In the Bongaarts model, the index of the proportion in marriage or union, Cm, is intended to measure the effect on fertility of the proportion of women in a sexual union. The effect of marriage or union patterns on fertility is captured as the ratio of the average number of children a woman bears throughout her life (total fertility rate or TFR) to the number she would bear if she first entered a union at age 15 and stayed in that union until age 50 (total marital fertility rate or TMFR). Cm has the value of 1 when all women of reproductive age are in union and is equal to 0 when none are in union. This formulation assumes that all fertility occurs within marriage or union. This assumption does not hold in many parts of sub-Saharan Africa, where substantial proportions of births are reported by women who describe themselves as single or never married, which may result in the calculated Cm being greater than 1. Anthropological studies indicate that the Western concept of marriage is not necessarily the appropriate paradigm to be applied to all of sub-Saharan Africa. Union formation may be an extended process, and births do occur outside of union (see Chapter 4; and Working Group on the Social Dynamics of Adolescent Fertility, 1993). The fact that nonmarital births occur raises a problem for the Bongaarts model (which Bongaarts recognized). If births to unmarried women are excluded from the analysis, the TFR is underestimated, but the TMFR is estimated accurately. If, on the other hand, these births are included in both, the TFR is calculated accurately, but the estimated TMFR is inflated, giving the impression that marriage patterns reduce fertility by a much greater fraction than is actually the case. To circumvent this problem and to maintain a consistent definition for other variables in the Bongaarts’ model using women currently in union only, we have added a variable to the model. This variable, Mo, captures the effect on total fertility of births outside union. Mo relates total fertility calculated by using all births to total fertility from using births only to women in union. C′m, a modified version of Cm, captures the effect on total fertility of the specific observed union pattern, under the assumption that no births occur outside unions. The product of Mo and C′m is Cm, the usual definition of the effects of marriage patterns on fertility used in the Bongaarts model. To summarize, in our model, Mo can be thought of as the effect of births outside union on total fertility (thus a value of Mo of 1.43 indicates

OCR for page 68
Demographic Change in Sub-Saharan Africa that the TFR is approximately 43 percent higher than it would have been if all fertility occurred in unions). C′m can be thought of as the effect of reported union patterns on fertility if births occur only in unions, and Cm is the combined result of the fertility-inhibiting effect of union pattern and the fertility-promoting effect of sexual relations outside union. The two new indices are related because, if all women were in unions from age 15 to age 50, there would be no births to women not married, and Mo would be equal to 1 (i.e., there would be no effect on fertility) and C′m would equal Cm. It is important to note that Mo is not a fertility-reducing parameter of the model, but rather a device to maintain comparability across cultures in the interpretation of other parameters of the model. Cm, C′m, and Mo are reported in the tables; only Cm is shown in the figures. Contraception The proportion of women using contraception to space or limit births and the effectiveness of the contraception they use directly affect a society’s fertility level. In sub-Saharan Africa, contraceptive prevalence rates are generally low in comparison with other regions of the world (Rutenberg et al., 1991). There is also substantial use of traditional methods, which are not as effective in preventing pregnancy as modern methods. The index of contraception, Cc, measures the effect of actions intentionally taken to reduce the risk of conception. Cc equals 1 if no form of contraception is used and 0 if all fecund exposed women use modern methods that are 100 percent effective. Postpartum Infecundability There are several practices women can follow after the birth of a child that delay a subsequent pregnancy. A woman is unable to conceive after a pregnancy until her normal pattern of ovulation returns. When she is breastfeeding, the length of lactational amenorrhea is determined primarily by the duration, intensity, and pattern of breastfeeding. Moreover, in a number of societies, sexual relations are not permitted while women breastfeed their newborn children, which further reduces the chances of conception. In much of sub-Saharan Africa, women breastfeed for long periods and refrain from sexual relations after the birth of a child. Both of these practices are seen as necessary to preserve the health of the child and mother (van de Walle and van de Walle, 1988). In most of the sub-Saharan African countries analyzed here, the duration of breastfeeding was much longer than postpartum abstinence (see Table 3–2). However, substantial variation in both practices exists within the region. The index of postpartum infecundability, Ci, estimates the effect of

OCR for page 68
Demographic Change in Sub-Saharan Africa TABLE 3–2 Mean Duration (months) of Postpartum Variables for Women Currently Married Country Breastfeeding Amenorrheic Abstaining Nonsusceptiblea Weighted No. of Births Botswana 19.2 11.7 8.9 13.3 932 Burundi 23.9 19.4 2.4 19.6 2,306 Ghana 20.9 14.6 12.9 17.7 2,314 Kenya 20.1 11.2 3.9 11.7 3,667 Liberia 17.5 11.7 13.1 15.5 2,554 Mali 21.5 15.7 7.0 17.0 2,101 Ondo State 18.8 14.2 22.7 23.9 1,847 Senegal 19.2 15.8 6.8 17.6 2,433 Sudanb 19.7 14.1 4.6 14.9 3,885 Togo 23.0 14.6 17.2 20.1 1,804 Uganda 19.1 13.1 3.0 13.4 2,654 Zimbabwe 18.2 11.4 4.1 11.9 1,760 NOTE: Data are national-level DHS. aSee Technical Notes (at end of this chapter) on derivation of indices for a discusssion of nonsusceptible period. bDHS data for Sudan refer only to northern Sudan.

OCR for page 68
Demographic Change in Sub-Saharan Africa postpartum amenorrhea and abstinence on fertility. When there is no lactation or postpartum abstinence, Ci equals 1; when infecundability is permanent, Ci equals 0. Pathological or Primary Sterility Several studies of infecundity in sub-Saharan Africa have indicated relatively high levels, particularly in Central Africa (Frank, 1983a; Bongaarts et al., 1984; Farley and Besley, 1988). Bongaarts et al. (1984) found that at least 20 percent of women in much of Central Africa are childless at the end of their reproductive years. In parts of Central and East Africa, between 12 and 20 percent of women ages 45 to 49 are childless. Lower levels generally exist in West Africa. Clearly, such high levels of infecundity inhibit the level of fertility achieved in many African societies. Although infertility increases naturally as a woman ages (natural sterility), much of the primary sterility (inability to have any children at all) in sub-Saharan Africa is caused by sexually transmitted diseases (STDs) (Caldwell and Caldwell, 1983; Frank, 1983a). It is generally thought that gonorrhea is the most prevalent STD affecting African populations. Ip, the index of sterility, takes into account only primary sterility and not secondary sterility, which is the inability to bear a second or subsequent child. Calculation of Ip is based on a 3 percent standard rate of childlessness in developing countries (Frank, 1983a; Bongaarts et al., 1984). If the rate of childlessness exceeds 3 percent, Ip will have a value less than 1, indicating that it reduces fertility. However, if less than 3 percent of women aged 40 to 49 are childless, then Ip has a value greater than 1, which indicates that levels of primary sterility are lower than would be expected in a developing country. It is difficult to interpret such a result in the context of a proximate determinants analysis because it suggests that low levels of primary infecundity increase fertility. When calculating Ip with the data used here, most of the indices were greater than 1. As a result, the index was omitted from many of the figures (see further discussion below). Summary of Model Each index outlined above (except Mo) acts as an inhibitor to fertility. The observed fertility rate (TFR) is equal to total fecundity rate (TF) multiplied (generally reduced) by each index: TFR=TF•Cm•Cc•Ci•Ip.

OCR for page 68
Demographic Change in Sub-Saharan Africa FIGURE 3–1 Relationship between the fertility-inhibiting effects of the proximate determinants and various measures of fertility.   Proximate Determinant Indices Cm: index of marriage Mo: effect of births outside unions on total fertility C′m: adjusted index of marriage Cc: index of contraception Ci: index of postpartum infecundability Ip: index of sterility The model can also be shown graphically, as in Figure 3–1. The column, which represents TF, is divided into five segments. The solid base at the bottom indicates the observed total fertility rate based on the reported number of births occurring in the four years prior to the survey. Moving upward, the height of the next segment indicates the level fertility would be if all women were in a union during the whole of their reproductive years (the TMFR). If no women in union practiced contraception, observed fertility would rise to the top of the next segment. This height represents the total natural marital fertility rate (TNMF). The top two segments of the

OCR for page 68
Demographic Change in Sub-Saharan Africa column indicate the effects of postpartum infecundability and sterility. The height of the column indicates the fertility level one would observe if none of the proximate determinants was exerting a fertility-reducing effect (i.e., if all the indices were equal to 1). EMPIRICAL RESULTS National-Level Results for DHS Countries The national-level results of the proximate determinants analysis for the DHS countries are illustrated in Figure 3–2. The index of primary sterility is not included in the graph because many of the values are greater than 1. (The actual numbers used in the figure are reported in Table 3–3.) The height of the columns estimates the total fecundity rate (TF) of the national population of each country. The columns vary in height, but fall within the range of 12.9 to 16.5, basically within the theoretical range of 13 to 17 suggested by Bongaarts and Potter (1983). FIGURE 3–2 Relationship between the fertility-inhibiting effects of the proximate determinants and various measures of fertility, by country (for country abbreviations, see Table 3–1). NOTE: PPI: Postpartum infecundability; DHS data for Sudan refer to only northern Sudan.

OCR for page 68
Demographic Change in Sub-Saharan Africa TABLE 3–3 Proximate Determinants of Fertility Country Index of Marriage, Cm Adjusted Index of Marriage, C′m Measure of Births Outside Marriage, Mo Index of Contraception, Cc Index of Postpartum Infecundability, Ci Index of Sterility, Ip Model Estimate of Total Fecundity Rate, TF Observed TFR Botswana 0.87 0.46 1.89 0.70 0.63 1.00 13.0 5.0 Burundi 0.80 0.76 1.06 0.97 0.53 1.03 16.5 6.9 Ghana 0.85 0.77 1.11 0.93 0.55 1.02 14.3 6.4 Kenya 0.86 0.73 1.17 0.80 0.66 1.01 14.4 6.6 Liberia 0.93 0.75 1.24 0.94 0.59 1.00 12.9 6.7 Mali 0.98 0.95 1.02 0.98 0.56 0.99 13.1 7.0 Ondo State 0.83 0.80 1.04 0.96 0.47 1.03 15.8 6.1 Senegal 0.90 0.84 1.07 0.97 0.55 0.98 14.0 6.6 Sudana 0.68 0.66 1.03 0.94 0.60 0.99 12.9 4.9 Togo 0.87 0.82 1.06 0.94 0.52 1.02 15.3 6.6 Uganda 0.92 0.77 1.19 0.97 0.63 0.97 13.6 7.4 Zimbabwe 0.81 0.73 1.12 0.63 0.66 1.01 16.3 5.5 NOTE: Data are national-level DHS. aDHS data for Sudan refer only to northern Sudan.

OCR for page 68
Demographic Change in Sub-Saharan Africa Effects of Indices To express the effects of each index in births per woman, the following calculations are used (Bongaarts, 1982). The effect of marriage patterns equals TMFR—TFR, where TMFR equals TFR/Cm. The effect of contraception equals TNMF—TMFR, where TNMF equals TFR/(Cm•Cc). The effect of postpartum infecundability and primary sterility equals TF—TNMF, where TF equals TFR/(Cm•Cc•Ci•Ip). The effect of postpartum infecundability alone equals TFR/(Cm•Cc•Ci) —TNMF. The effect of primary sterility alone equals TF—TFR/(Cm•Cc•Ci). When Ip is greater than 1, the effect of postpartum infecundability is estimated as TF—TNMF; that is, Ip is set equal to 1, because a number greater than 1 is not interpretable in the proximate determinants framework. In these cases, the effect of postpartum infecundability is slightly underestimated. Care should be taken in interpreting these effects expressed in births per woman, because the number of births estimated depends on the order in which they are calculated. For example, by using the formulas outlined above, the effects of the proximate determinants for Botswana would be as follows (see Table 3–2 and Figure 3–1) in terms of number of births: Proximate determinant Number of births Marriage patterns 0.74 Contraception 2.45 Postpartum infecundability 4.84 Primary sterility 0 If the order in which each variable is calculated is reversed, the results would be as follows (number of births): Primary sterility (TFR/Ip—TFR) 0 Postpartum infecundability (TFR/(Ip•Ci) —TFR/Ip) 2.92 Contraception (TFR/(Ip•Ci•Cc) —TFR/(Ip•Ci)) 3.38 Marriage patterns (TFR/(Ip•Ci•Cc•Cm) —TFR/(Ip•Ci•Cc)) 1.68 Therefore, the order of estimation matters a great deal. However, because Bongaarts et al. (1984) used the first-outlined approach in their work, we have done the same for consistency.

OCR for page 68
Demographic Change in Sub-Saharan Africa The reduction in average number of births per woman can also be expressed in terms of percentages. In Ondo State, Ci=0.47, which indicates that Ci reduces fertility to 47 percent of what it would otherwise have been in the absence of postpartum breastfeeding and abstinence. APPENDIX The tables that follow present the proximate determinants of fertility by age (Table 3–A.1), by urban and rural residence (Table 3–A.2), and by level of education (Table 3–A.3). WFS and DHS data are presented.

OCR for page 68
Demographic Change in Sub-Saharan Africa TABLE 3–A.1 Proximate Determinants of Fertility by Age and by Survey Country and Survey Index of Marriage, Cm Adjusted Index of Marriage, C′m Measure of Births Outside Marriage, Mo Index of Contraception, Cc Index of Postpartum Infecundability, Ci Index of Sterility, Ip Observed TFR Ghana WFS   National 0.88 0.81 1.08 0.95 0.56 1.01 6.4 15–24 0.77 0.68 1.14 0.96 0.55 1.01 2.0 25–34 0.96 0.91 1.06 0.93 0.58 1.01 2.6 35–49 0.92 0.86 1.06 0.96 0.55 1.01 1.8 DHS   National 0.85 0.77 1.11 0.93 0.55 1.02 6.4 15–24 0.71 0.60 1.20 0.95 0.53 1.02 1.9 25–34 0.95 0.89 1.07 0.93 0.57 1.02 2.6 35–49 0.90 0.84 1.07 0.91 0.54 1.02 1.8 Kenya WFS   National 0.91 0.81 1.12 0.96 0.64 1.00 8.2 15–24 0.81 0.66 1.23 0.97 0.64 1.00 2.6 25–34 0.97 0.91 1.07 0.95 0.65 1.00 3.2 35–49 0.95 0.88 1.08 0.95 0.62 1.00 2.3 DHS   National 0.86 0.73 1.17 0.80 0.66 1.01 6.6 15–24 0.75 0.56 1.35 0.87 0.66 1.01 2.3 25–34 0.94 0.85 1.11 0.79 0.68 1.01 2.7 35–49 0.92 0.86 1.08 0.76 0.64 1.01 1.6

OCR for page 68
Demographic Change in Sub-Saharan Africa Senegal WFS   National 0.94 0.89 1.05 0.99 0.65 0.99 7.2 15–24 0.87 0.79 1.09 0.99 0.63 0.99 2.5 25–34 0.98 0.95 1.03 0.99 0.66 0.99 3.0 35–49 0.97 0.94 1.03 1.00 0.66 0.99 1.7 DHS   National 0.90 0.84 1.07 0.97 0.64 0.98 6.6 15–24 0.81 0.71 1.14 0.99 0.63 0.98 2.2 25–34 0.95 0.91 1.04 0.96 0.65 0.98 2.7 35–49 0.95 0.92 1.03 0.97 0.63 0.98 1.7 Sudan WFS   Northern 0.80 0.78 1.03 0.96 0.63 0.93 6.0 15–24 0.61 0.60 1.03 0.97 0.64 0.93 1.9 25–34 0.92 0.89 1.02 0.95 0.62 0.93 2.7 35–49 0.93 0.91 1.02 0.97 0.65 0.93 1.5 DHS   Northern 0.68 0.66 1.03 0.94 0.60 0.99 4.9 15–24 0.44 0.43 1.03 0.96 0.61 0.99 1.2 25–34 0.81 0.79 1.03 0.93 0.60 0.99 2.4 35–49 0.87 0.85 1.02 0.93 0.59 0.99 1.3 NOTE: Data are national-level WFS and DHS.

OCR for page 68
Demographic Change in Sub-Saharan Africa TABLE 3–A.2 Proximate Determinants of Fertility by Urban and Rural Residence and by Survey Country and Survey Index of Marriage, Cm Adjusted Index of Marriage, C′m Measure of Births Outside Marriage, Mo Index of Contraception, Cc Index of Postpartum Infecundability, Ci Index of Sterility, Ip Observed TFR Ghana   WFS Urban 0.86 0.78 1.10 0.91 0.62 1.00 5.7 Rural 0.95 0.88 1.08 0.96 0.54 1.01 6.8 DHS   Urban 0.78 0.70 1.11 0.89 0.60 1.01 5.3 Rural 0.90 0.83 1.08 0.95 0.54 1.03 6.9 Kenya   WFS Urban 0.84 0.73 1.17 0.90 0.69 0.90 6.1 Rural 0.92 0.82 1.11 0.96 0.64 1.00 8.4 DHS   Urban 0.82 0.66 1.24 0.74 0.70 0.96 4.7 Rural 0.87 0.75 1.17 0.81 0.66 1.01 7.0 Senegal   WFS Urban 0.86 0.79 1.08 0.98 0.67 0.99 6.6 Rural 0.98 0.94 1.04 1.00 0.64 0.99 7.5 DHS   Urban 0.79 0.71 1.12 0.92 0.69 0.96 5.6 Rural 0.98 0.93 1.04 0.99 0.62 0.99 7.3

OCR for page 68
Demographic Change in Sub-Saharan Africa Sudana   WFS Urban 0.72 0.69 1.03 0.90 0.70 0.95 5.1 Rural 0.83 0.81 1.03 0.99 0.61 0.93 6.4 DHS   Urban 0.55 0.53 1.03 0.87 0.65 0.98 3.7 Rural 0.71 0.70 1.01 0.98 0.58 1.00 5.2 NOTE: Data are national-level WFS and DHS. aWFS and DHS data for Sudan refer only to northern Sudan.

OCR for page 68
Demographic Change in Sub-Saharan Africa TABLE 3–A.3 Proximate Determinants of Fertility by Level of Education and by Survey Country and Survey Index of Marriage, Cm Adjusted Index of Marriage, C′m Measure of Births Outside Marriage, Mo Index of Contraception, Cc Index of Postpartum Infecundability, Ci Index of Sterility, Ip Observed TFR Ghana   WFS None 0.93 0.88 1.06 0.98 0.54 1.01 6.7 1–4 years 0.90 0.80 1.13 0.95 0.58 1.01 6.9 5–7 years 0.88 0.78 1.13 0.93 0.62 1.05 7.1 8+years 0.83 0.76 1.09 0.88 0.60 0.99 5.3 DHS   None 0.88 0.84 1.05 0.95 0.51 1.02 6.8 1–4 years 0.87 0.78 1.11 0.93 0.54 1.00 6.6 5–7 years 0.88 0.79 1.11 0.92 0.58 1.05 6.0 8+years 0.79 0.70 1.13 0.89 0.61 1.03 5.5 Kenya   WFS None 0.96 0.87 1.10 0.98 0.61 0.99 8.2 1–4 years 0.93 0.84 1.11 0.96 0.64 1.03 9.0 5–7 years 0.90 0.79 1.13 0.94 0.69 1.05 7.9 8+years 0.83 0.75 1.11 0.83 0.70 1.03 7.0 DHS   None 0.91 0.85 1.07 0.88 0.61 1.00 7.2 1–4 years 0.94 0.80 1.18 0.80 0.65 1.02 7.7 5–7 years 0.88 0.75 1.18 0.78 0.69 1.04 7.2 8+years 0.82 0.66 1.24 0.70 0.71 1.00 5.0

OCR for page 68
Demographic Change in Sub-Saharan Africa Senegal   WFS None 0.96 0.92 1.04 1.00 0.64 0.99 7.4 Primary 0.98 0.89 1.10 0.96 0.67 1.05 7.1 Secondary+ 0.56 0.53 1.06 0.88 0.71 1.05 3.0 DHS   None 0.95 0.91 1.05 0.99 0.63 0.97 7.0 Primary 0.83 0.69 1.20 0.93 0.67 1.05 5.7 Secondary+ 0.59 0.52 1.14 0.75 0.74 1.05 3.6 Sudana   WFS None 0.84 NA NA 0.99 0.61 0.93 6.3 Primary incomplete 0.95 NA NA 0.90 0.69 0.96 7.6 Primary complete+ 0.88 NA NA 0.73 0.79 0.88 6.0 DHS   None 0.78 0.76 1.03 0.98 0.57 0.99 5.8 Primary 0.69 0.67 1.03 0.91 0.62 0.96 4.9 Secondary+ 0.36 0.35 1.03 0.83 0.69 1.05 3.3 NOTE: Data are national-level WFS and DHS. aWFS and DHS data for Sudan refer only to northern Sudan.

OCR for page 68
Demographic Change in Sub-Saharan Africa REFERENCES Acsadi, G.T.F., and G.Johnson-Acsadi 1990 Demand for children and for childspacing. Pp. 155–185 in G.T.F.Acsadi, G. Johnson-Acsadi, and R.Bulatao, eds., Population Growth and Reproduction in Sub-Saharan Africa: Technical Analyses of Fertility and Its Consequences. Washington, D.C.: The World Bank. Adegbola, O., H.J.Page, and R.Lesthaeghe 1977 Breast-feeding and postpartum abstinence in metropolitan Lagos. Paper presented at the meeting of the Population Association of America, St. Louis, April 21–23. Bongaarts, J. 1982 The fertility-inhibiting effects of the intermediate fertility variables. Studies in Family Planning 13(6/7):179–189. Bongaarts, J., and R.G.Potter 1983 Fertility, Biology, and Behavior: An Analysis of the Proximate Determinants. New York: Academic Press. Bongaarts, J., O.Frank, and R.Lesthaeghe 1984 The proximate determinants of fertility in sub-Saharan Africa. Population and Development Review 10(3):511–537. Caldwell, J.C., and P.Caldwell 1983 The demographic evidence for the incidence and cause of abnormally low fertility in tropical Africa. World Health Statistics Quarterly 36(1):2–34. Cochrane, S.H. 1979 Fertility and Education: What Do We Really Know? Baltimore, Md.: Johns Hopkins University Press. 1983 Effects of education and urbanization on fertility. Pp. 587–625 in R.A.Bulatao and R.D.Lee, eds., Determinants of Fertility in Developing Countries, Vol. 2. New York: Academic Press. Coeytaux, F.M. 1988 Induced abortion in sub-Saharan Africa: What we do and do not know. Studies in Family Planning 19(3):186–190. Davis, K., and J.Blake 1956 Social structure and fertility: An analytic framework. Economic Development and Cultural Change 4(4):211–235. Farley, T.M.M., and E.M.Besley 1988 The prevalence and aetiology of infertility. Pp. 15–30 in African Population Conference, Dakar 1988, Vol. 1. Liège: International Union for the Scientific Study of Population. Frank, O. 1983a Infertility in sub-Saharan Africa: Estimates and implications. Population and Development Review 9(1):137–144. 1983b Infertility in Sub-Saharan Africa. Working Paper of the Center for Policy Studies, No. 97. New York: The Population Council. Goldman, N., S.O.Rutstein, and S.Singh 1985 Assessment of the Quality of Data in 41 WFS Surveys: A Comparative Approach. WFS Comparative Studies, No. 44. Voorburg, Netherlands: International Statistical Institute. Institute for Resource Development 1990 An Assessment of DHS-I Data Quality. Demographic and Health Surveys Methodological Reports No. 1. Columbia, Md.: Institute for Resource Development/ Macro Systems, Inc.

OCR for page 68
Demographic Change in Sub-Saharan Africa Laing, J. 1978 Estimating the effects of contraceptive use on fertility. Studies in Family Planning 9(6):150–175. Larsen, U. 1989 A comparative study of the levels and the differentials of sterility in Cameroon, Kenya, and Sudan. Pp. 167–211 in R.Lesthaeghe, ed., Reproduction and Social Organization in Sub-Saharan Africa. Berkeley: University of California Press. Menken, J. 1984 Estimating proximate determinants: A discussion of three methods proposed by Bongaarts, Hobcraft and Little, and Gaslonde and Carrasco. Paper prepared for the IUSSP Seminar on Integrating Proximate Determinants into Analysis of Fertility Levels and Trends. International Union for the Scientific Study of Population and World Fertility Survey, London. Mhloyi, M.M. 1988 The determinants of fertility in Africa under modernization. Pp. 2.3.1–2.3.22 in African Population Conference, Dakar 1988, Vol. 1. Liege: International Union for the Scientific Study of Population. Ndiaye, S., I.Sarr, and M.Ayad 1988 Enquête Demographique et de Santé au Senegal 1986. Dakar: Ministère de l’Economie et des Finances, Direction de la Statistique, Division des Enquêtes et de la Demographic; Columbia, Md.: Institute for Resource Development/Westinghouse. Page, H.J., and A.J.Coale 1972 Fertility and child mortality south of the Sahara. Pp. 51–67 in S.H.Ominde and C. Ejiogu, eds., Population Growth and Economic Development in Africa. London: Heinemann. Reinis, K.I. 1992 The impact of the proximate determinants of fertility: Evaluating Bongaarts’s and Hobcraft and Little’s methods of estimation. Population Studies 46:309–326. Romaniuk, A. 1980 Increases in natural fertility during the early stages of modernization—Evidence from an African case study: Zaire. Population Studies 2(34):293–310. Rutenberg, N., M.Ayad, L.H.Ochoa, and M.Wilkinson 1991 Knowledge and Use of Contraception. DHS Comparative Studies, No. 6. Columbia, Md.: Institute for Resource Development. Sudan 1982 The Sudan Fertility Survey 1979, Principal Report, Vol. 1. Khartoum: Department of Statistics of the Sudanese Ministry of National Planning. 1991 Sudan Demographic and Health Survey 1989/1990. Khartoum: Ministry of Economic and National Planning; Columbia, Md.: Institute for Resource Development/Macro International, Inc. Working Group on the Social Dynamics of Adolescent Fertility 1993 Social Dynamics of Adolescent Fertility in Sub-Saharan Africa. C.H.Bledsoe and B.Cohen, eds. Panel on the Population Dynamics of Sub-Saharan Africa, Committee on Population, National Research Council. Washington, D.C.: National Academy Press. van de Walle, F., and K.Omideyi 1988 The cultural roots of African fertility regimes. Pp. 2.2.35–2.2.52 in African Population Conference, Dakar 1988, Vol. 1. Liege: International Union for the Scientific Study of Population.

OCR for page 68
Demographic Change in Sub-Saharan Africa van de Walle, E., and F.van de Walle 1988 Postpartum sexual abstinence in tropical Africa. Paper presented at the International Union for the Scientific Study of Population Seminar on the Biomedical and Demographic Determinants of Human Reproduction. Johns Hopkins University, Baltimore, Md. Zeidenstein, S. 1979 Learning about rural women. Studies in Family Planning 10(11/12):309–312.