5
Proximate Determinants of Fertility

From the analysis in Chapter 4, it is clear that fertility declined substantially between the times of the Kenya Fertility Survey (KFS, 1977–1978) and Kenya Demographic and Health Survey (KDHS, 1998–1989). This chapter takes a closer look at the proximate determinants that contributed to this decline by using the Bongaarts framework to quantify the effects on fertility of marriage patterns, contraception, postpartum infecundability, primary sterility, and abortion.

FRAMEWORK

Bongaarts et al. (1984) enumerated nine proximate determinants of fertility:

  1. percentage of women in sexual union,

  2. frequency of sexual intercourse,

  3. postpartum abstinence,

  4. lactational amenorrhea,

  5. contraceptive use,

  6. induced abortion,

  7. spontaneous intrauterine mortality,

  8. natural sterility, and

  9. pathological sterility.

These factors are the behavioral and biological factors that influence fertility directly. Cultural, psychological, economic, social, health, and environ-



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Population Dynamics of Kenya 5 Proximate Determinants of Fertility From the analysis in Chapter 4, it is clear that fertility declined substantially between the times of the Kenya Fertility Survey (KFS, 1977–1978) and Kenya Demographic and Health Survey (KDHS, 1998–1989). This chapter takes a closer look at the proximate determinants that contributed to this decline by using the Bongaarts framework to quantify the effects on fertility of marriage patterns, contraception, postpartum infecundability, primary sterility, and abortion. FRAMEWORK Bongaarts et al. (1984) enumerated nine proximate determinants of fertility: percentage of women in sexual union, frequency of sexual intercourse, postpartum abstinence, lactational amenorrhea, contraceptive use, induced abortion, spontaneous intrauterine mortality, natural sterility, and pathological sterility. These factors are the behavioral and biological factors that influence fertility directly. Cultural, psychological, economic, social, health, and environ-

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Population Dynamics of Kenya mental factors affect fertility indirectly through these proximate determinants. Bongaarts and Potter (1983) quantified the effects of six of the nine proximate determinants of fertility that were shown to have the greatest effect on fertility in 41 populations: percentage of women in sexual union, postpartum abstinence and lactational amenorrhea (taken together), contraceptive use, abortion, and pathological sterility. They summarized the effect of each determinant on fertility in an index, which generally ranges from 0 to 1, with 0 having the greatest inhibiting effect on fertility and I having the least inhibiting effect (i.e., the lower the index, the more it reduces fertility). Each index (not equal to 1) reduces the total fecundity rate (TF), which is the level of fertility expected in the absence of any of the nine proximate determinants outlined above. Of course, no one knows what TF really is, but Bongaarts and Potter (1983) estimated that it ranges from 13 to 17, with an average of approximately 15, Below is a description of each of the proximate determinants used in this analysis and how they affect fertility. The computational procedures used to estimate each index are described in the appendix to this chapter. Percentage of Women in Sexual Union It is assumed that the number of women of reproductive age married or living with someone determines the proportion of women in a society exposed to the risk of becoming pregnant. The greater the number of women exposed, the higher is the resulting fertility. In sub-Saharan Africa, entry into union1 has generally occurred at an early age, and although union dissolution is frequent in many regions, remarriage occurs rapidly (Cochrane and Farid, 1989). Kenya has been no exception to this general pattern. Table 5-1 shows the median age at first union for women 20 to 49 years. In 1977–1978 the median age at first union at the national level was 17.5 years. At the province level, Coast Province had the lowest age at first union, 16.4; followed closely by Nyanza, 16.5; and Western, 16.8. As expected, Nairobi had the highest age at first union, 19.1; with Central, 18.8, and Eastern, 18.7, not far behind. Results from the KDHS show that age at first marriage has risen across all provinces, ranging from an increase of 0.4 year for Nyanza to 1.1 years for Nairobi, Central, and Western provinces. At the national level, age at 1   In this report, the terms marriage and union are used interchangeably. Because entry into marriage may be a process not just a single event, and because a woman may live with a man without being formally married, this analysis looks at the effect of the proportions of women in sexual union, rather than marriage per se, on fertility.

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Population Dynamics of Kenya TABLE 5-1 Median Age at First Marriagea Among Women Age 20–49 by Subgroup, 1977–1978 KFS and 1988–1989 KDHS (years)   KFS KDHS-Age at Time of Survey KDHS Subgroup Total 20–24 25–29 30–34 35–39 40–44 45–49 Total National 17.5 19.8 18.6 17.9 17.9 17.3 18.5 18.5 Residence                 Rural 17.4 19.7 18.3 17.7 17.8 17.3 18.4 18.3 Urban 18.1 20.3 19.9 19.6 18.7 18.7 19.5 19.8 Province                 Nairobi 19.1 20.5 20.1 19.9 19.5 19.4 22.6 20.2 Central 18.8 21.9 20.1 19.3 19.3 18.2 19.1 19.9 Coast 16.4 19.5 17.1 16.3 16.2 15.1 16.3 17.0 Eastern 18.7 22.5 19.2 20.0 19.1 18.3 18.9 19.5 Nyanza 16.5 17.7 17.1 16.4 16.6 16.4 17.1 16.9 Rift Valley 17.5 19.3 17.6 17.2 18.3 17.3 20.4 18.1 Western 16.8 19.0 18.5 17.7 17.1 16.9 15.4 17.9 a The age by which 50 percent of women have entered their first union. first union increased by one year to 18.5. Among other sub-Saharan African populations, where the Demographic and Health Surveys (DHS) were conducted, age at first union ranges from 15.7 in Mali to 19.7 in Ondo State, Nigeria, placing Kenya toward the upper end of these two extremes. Furthermore, there are other indications that age at first union is increasing in Kenya; younger women are marrying at older ages. As indicated in Table 5-1, for women age 20 to 24 the median age at first union in 1989 was 19.8 years. The index measuring the effect of marriage patterns on fertility is denoted as Cm. It takes the value of 1 when all women of reproductive age are in union and 0 when none are in union. Contraception Use of contraception to delay or limit the number of children born clearly affects a society's fertility level. Historically, contraceptive use in sub-Saharan Africa, including Kenya, has been very low. However, substantial increases in the use of contraception have been identified in Kenya, Botswana, and Zimbabwe on the basis of data from the DHS (Jolly and Gribble, 1993; Working Group on Factors Affecting Contraceptive Use, 1993). Tables 5-2 and 5-3 show contraceptive prevalence rates by subgroup and specific method used for Kenya at the times of the KFS and the KDHS, respectively. In 1977–1978, contraceptive use was very low, with only 5.6

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Population Dynamics of Kenya TABLE 5-2 Women Currently in Union Using Contraception by Subgroup, 1977–1978 KFS (percent) Subgroup Any Method Pill IUD Injection Vaginal Method Condom Female Sterilization Periodic Abstinence Withdrawal Other National 5.6 2.0 0.7 0.6 0.0 0.1 0.9 1.1 1.1 0.0 15–24 3.9 2.0 0.2 0.1 0.0 0.2 0.0 1.2 0.1 0.1 25–34 6.6 2.6 0.9 0.7 0.0 0.1 0.9 1.2 0.1 0.1 35–49 5.9 1.4 0.8 O.8 0.1 0.1 1.4 0.9 0.2 0.2 Residence                     Rural 4.7 1.6 0.5 0.5 0.0 0.1 0.8 1.1 0.1 0.0 Urban 11.6 5.1 1.9 1.4 0.0 0.2 1.4 1.3 0.2 0.1 Education                     None 3.2 0.7 0.4 0.3 0.0 0.1 0.7 0.8 0.2 0.0 1–4 years 5.4 1.3 0.8 0.9 0.1 0.1 0.7 1.5 0.0 0.0 5–7 years 7.5 3.4 0.7 0.6 0.0 0.2 0.8 1.2 0.3 0.3 8+ years 18.4 9.6 2.6 1.4 0.0 0.5 2.5 1.8 0.0 0.0 Parity                     0 1.1 0.1 0.0 0.0 0.0 0.0 0.5 0.3 0.0 0.2 1 2.4 1.2 0.1 0.0 0.0 0.2 0.0 0.8 0.0 0.1 2 4.7 2.5 0.4 0.0 0.0 0.3 0.5 0.9 0.0 0.1 3 5.6 2.4 0.3 0.2 0.0 0.0 0.6 1.6 0.3 0.2 4+ 7.0 2.2 1.1 1.0 0.0 0.1 1.2 1.2 0.2 0.0 Province                     Nairobi 15.9 7.3 2.6 2.1 0.0 0.1 2.0 1.5 0.0 0.3 Central 9.0 3.3 2.1 1.4 0.0 0.2 0.7 0.9 0.4 0.0 Coast 4.4 1.6 0.3 1.0 0.0 0.0 1.0 0.4 0.1 0.0 Eastern 6.8 2.3 1.2 0.1 0.1 0.6 1.3 0.8 0.4 0.0 Nyanza 3.1 0.9 0.1 0.3 0.0 0.0 0.3 1.5 0.0 0.0 Rift Valley 5.5 1.5 0.1 0.5 0.0 0.0 1.3 1.9 0.2 0.0 Western 2.9 1.5 0.1 0.0 0.0 0.0 0.5 0.1 0.0 0.7

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Population Dynamics of Kenya TABLE 5-3 Women Currently in Union Using Contraception by Subgroup, 1988–1989 KDHS (percent) Subgroup Any Method Pill IUD Injection Vaginal Methods Condom Female Sterilization Periodic Abstinence Withdrawal Other National 26.8 5.2 3.7 3.3 0.4 0.5 4.7 7.5 0.2 1.3 15–24 18.4 6.3 2.0 0.9 0.1 0.8 0.5 6.8 0.2 0.8 25–34 28.4 6.7 4.1 4.2 0.4 0.5 3.3 7.4 0.3 1.5 35–49 30.7 2.9 4.4 3.9 0.7 0.2 9.0 8.0 0.2 1.4 Residence                     Rural 26.0 4.3 2.9 3.4 0.4 0.4 4.9 8.1 0.2 1.4 Urban 30.5 9.8 8.0 2.8 0.5 0.8 3.6 4.0 0.4 0.6 Education                     None 18.3 2.1 1.3 2.2 0.1 0.3 3.7 6.9 0.0 1.7 1–4 years 25.5 4.3 2.7 4.3 0.6 0.1 6.0 6.6 0.0 0.9 5–7 years 29.4 6.2 3.5 4.7 0.3 0.3 4.9 7.7 0.4 1.4 8+ years 37.9 9.2 8.8 2.2 1.1 1.4 4.8 9.0 0.6 0.8 Parity                     0 4.6 0.6 0.0 0.2 0.0 0.0 0.0 3.4 0.4 0.0 1 16.8 5.2 1.8 0.7 0.0 0.5 0.3 8.1 0.0 0.2 2 24.2 6.7 4.4 1.6 0.2 1.2 1.9 7.2 0.1 0.9 3 28.4 9.3 3.3 2.4 0.3 0.5 2.7 8.4 0.9 0.6 4+ 31.4 4.3 4.4 4.8 0.7 0.4 7.1 7.7 0.1 1.9

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Population Dynamics of Kenya Subgroup Any Method Pill IUD Injection Vaginal Methods Condom Female Sterilization Periodic Abstinence Withdrawal Other Province                     Nairobi 33.6 11.8 7.9 2.3 1.2 0.4 4.4 4.0 0.8 0.8 Central 39.7 8.1 10.0 3.6 0.3 1.3 7.7 7.1 0.3 1.3 Coast 18.1 5.5 1.7 3.6 0.1 0.3 3.6 3.0 0.3 0.0 Eastern 40.1 5.9 4.7 3.5 0.4 0.4 4.5 17.9 0.3 2.5 Nyanza 13.7 2.7 0.8 2.5 0.0 0.3 3.9 3.0 0.0 0.5 Rift Valley 29.6 3.6 2.3 5.3 1.0 0.5 5.5 9.0 0.3 2.1 Western 13.7 3.8 1.6 1.6 0.2 0.2 2.6 3.0 0.0 0.7 District                     Bungoma 9.3 2.7 1.5 0.5 0.0 0.3 1.3 1.1 0.0 1.9 Kakamega 14.9 3.1 1.1 2.3 0.3 0.3 3.8 3.7 0.0 0.3 Kericho 23.1 3.4 0.8 5.3 0.4 0.0 5.3 6.8 0.0 1.1 Kilifi 10.8 4.6 0.6 2.5 0.3 0.3 0.8 1.4 0.3 0.0 Kirinyaga 52.2 12.4 18.6 8.0 0.4 0.9 4.0 7.1 0.4 0.4 Kisii 21.5 2.5 2.0 5.7 0.0 0.4 6.4 4.1 0.0 0.4 Kisumu 17.9 4.8 1.0 3.0 0.0 0.0 5.3 3.5 0.0 0.3 Machakos 40.4 5.3 1.4 1.1 0.0 0.7 3.5 24.5 0.7 3.2 Meru 36.3 12.4 8.3 5.7 1.6 0.5 5.7 2.1 0.0 0.0 Mombasa 24.4 8.8 5.4 2.0 0.0 0.7 4.1 2.7 0.7 0.0 Muranga 33.9 3.8 10.5 3.0 0.4 1.3 7.5 6.6 0.4 0.4 Nakuru 47.1 1.7 5.4 4.9 2.0 1.2 12.7 14.7 0.0 4.5 Nyeri 40.7 9.2 9.1 2.5 0.4 0.8 12.6 5.3 0.4 0.4 Siaya 8.4 0.6 0.0 1.2 0.0 1.2 2.4 2.4 0.0 0.6 South Nyanza 6.1 2.0 0.0 0.0 0.0 0.0 1.4 2.0 0.0 0.7 Uasin Gishu 13.4 3.8 0.5 3.8 0.0 0.0 1.0 3.3 0.5 0.5

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Population Dynamics of Kenya percent of all women in a union currently using any method.2 About one-fourth of the methods used were traditional (periodic abstinence or rhythm, withdrawal, and other). As in many other regions of the world, contraceptive use was higher for women who were living in urban areas and were well educated. Contraceptive use was also greater among women of higher parity. By 1988–1989, use of any method had increased substantially among women in union to 26.8 percent, with about one-third of these women using traditional methods. Contraceptive prevalence remained higher for women who were living in urban areas (30.5 percent), who were well educated (37.9 percent for women with more than 8 years of schooling), and who had given birth to more children (31.4 percent of women with four or more births). Nairobi, Central, and Eastern provinces had the highest prevalence rates of the provinces at both times. Western and Nyanza had the lowest rates. Among the districts in 1988–1989, contraceptive use was highest in Kirinyaga (52 percent), followed by Nakuru (47.1 percent), Nyeri (40.7 percent), and Machakos (40.4 percent). Siaya and South Nyanza had very low prevalence rates, 8.4 and 6.1 percent, respectively. The type of contraceptive used also varied by province. The pill was the most commonly used contraceptive in Nairobi, Coast, and Western provinces; sterilization in Nyanza Province; the IUD in Central Province; and periodic abstinence or rhythm in Eastern and Rift Valley provinces. Cc, the index of contraception, measures the effect on fertility of the proportion of women using contraception, as well as the effectiveness of the methods used: Cc equals 1 if no contraception is used and 0 if all fecund women use modern methods that are 100 percent effective. Postpartum Infecundability The practices of breastfeeding and sexual abstinence after the birth of a child reduce a woman's exposure to becoming pregnant. Breastfeeding of long duration and on demand delays the return of a woman's normal pattern of ovulation. Cultural norms often prescribe limiting sexual relations after birth. In sub-Saharan Africa, both of these practices are utilized and are seen as necessary to protect the health of the child and the mother (van de Walle and van de Walle, 1988). Tables 5-4 and 5-5 show the mean number of months of breastfeeding and postpartum amenorrhea, abstinence, and insusceptibility for currently married women by subgroup in Kenya at the times of the KFS and KDHS. In 1977–1978, the average duration of breastfeeding was 17.3 months, and the average duration of sexual abstinence was 3.9 months. In 1988–1989, 2   Abstinence is excluded as a method (see the appendix to this chapter for justification).

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Population Dynamics of Kenya TABLE 5-4 Mean Number of Months of Breastfeeding, Postpartum Amenorrhea, Postpartum Abstinence, and Postpartum Insusceptibility for Currently Married Women by Subgroup, 1977–1978 KFS Subgroup Months Weighted No. of Births Breastfeeding Amenorrheic Abstaining Insusceptiblea National 17.3 12.0 3.9 12.7 4,963 15–24 18.8 12.2 4.0 12.7 1,429 25–34 16.3 11.6 3.4 12.1 2,368 35–49 17.3 12.5 4.3 13.8 1,152 Residence           Rural 17.6 12.3 3.8 12.9 4,426 Urban 14.7 10.0 4.2 10.5 537 Education           None 18.7 13.6 4.6 14.2 2,361 1–4 years 16.7 12.1 3.7 12.8 1,016 5–7 years 16.2 9.8 2.8 10.4 1,193 8+ years 13.6 9.1 3.6 9.9 397 Province           Nairobi 14.8 10.3 4.3 10.6 225 15–24 17.1 11.9 5.7 12.7 101 25–34 13.1 9.0 3.4 9.0 106 35–49 12.1 8.3 1.4 8.3 18 Central 14.5 10.5 2.7 11.0 749 15–24 16.5 9.4 2.7 10.1 154 25–34 13.5 10.6 2.4 10.9 367 35–49 14.7 11.1 3.0 11.9 228 Coast 18.1 13.8 3.2 14.0 383 15–24 21.9 14.0 3.7 14.0 129 25–34 16.4 11.7 2.7 12.2 180 35–49 15.8 18.5 3.5 18.7 74 Eastern 18.5 12.0 4.3 12.9 766 15–24 18.6 10.9 3.4 11.8 152 25–34 17.3 11.4 3.5 12.1 376 35–49 20.4 12.9 5.3 14.3 238 Nyanza 17.7 12.3 2.9 12.9 1,125 15–24 18.6 13.4 3.1 13.6 357 25–34 17.5 11.9 2.6 12.2 490 35–49 17.2 11.8 3.2 13.3 274 Rift Valley 17.6 13.1 6.6 14.0 988 15–24 19.7 13.8 6.4 14.2 300 25–34 16.8 12.4 6.0 13.3 486 35–49 16.5 13.8 8.4 15.9 193 Western 17.9 11.3 2.8 11.8 702 15–24 18.3 10.2 3.2 10.6 231 25–34 16.9 12.2 2.6 12.5 345 35–49 19.3 10.7 1.6 11.6 125 a Estimated as the mean number of months of postpartum amenorrhea or abstinence, whichever is longer.

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Population Dynamics of Kenya TABLE 5-5 Mean Number of Months of Breastfeeding, Postpartum Amenorrhea, Postpartum Abstinence, and Postpartum Insusceptibility for Currently Married Women by Subgroup, 1988–1989 KDHS Subgroup Months Weighted No. of Births Breastfeeding Amenorrheic Abstaining Insusceptiblea National 20.1 11.2 3.9 11.7 3,667 15–24 20.9 11.4 4.5 11.9 1,016 25–34 19.5 10.7 3.4 11.1 1,787 35–49 20.3 11.8 4.4 12.6 864 Residence           Rural 20.0 11.4 4.0 11.9 3,174 Urban 20.3 9.7 3.4 10.1 493 Education           None 21.0 13.3 5.7 14.2 1,046 1–4 years 19.8 12.0 4.1 12.4 598 5–7 years 19.5 10.2 2.9 10.5 1,314 8+ years 20.0 9.3 3.2 9.6 705 Province and District         Nairobi 21.7 9.7 4.2 10.3 208 15–24 22.3 10.7 4.5 11.6 98 25–34 20.9 8.8 3.8 9.1 92 35–49 23.1 9.0 5.2 9.0 18 Central 19.2 11.1 4.2 11.6 464 15–24 21.2 11.8 6.2 12.0 119 25–34 19.0 10.8 3.4 11.2 231 35–49 17.5 10.9 4.0 12.0 114 Kirinyaga 19.3 8.9 5.3 10.9 54 Muranga 21.7 11.4 3.0 11.9 108 Nyeri 16.5 9.0 3.9 9.6 141 Coast 19.1 10.0 2.1 10.1 223 15–24 17.0 10.7 3.0 10.9 59 25–34 20.7 9.8 2.3 9.8 112 35–49 18.2 9.9 0.7 9.9 52 Kilifi 19.5 9.9 1.7 10.1 88 Mombasa 17.8 9.7 3.8 9.7 55 Eastern 21.7 9.6 4.3 10.2 594 15–24 24.4 11.4 5.2 11.8 114 25–34 20.2 8.8 3.1 9.0 286 35–49 22.4 9.8 5.5 11.2 195 Machakos 24.0 10.6 3.5 11.2 261 Meru 21.6 10.0 4.9 10.5 146 Nyanza 19.3 11.5 2.2 11.9 677 15–24 19.7 10.8 2.1 11.3 207 25–34 18.3 11.3 2.1 11.7 322 35–49 20.8 12.8 2.5 13.2 149 Kisii 17.5 11.8 3.2 12.8 181

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Population Dynamics of Kenya Subgroup Months Weighted No. of Births Breastfeeding Amenorrheic Abstaining Insusceptiblea Nyanza—         Kisumu 21.0 9.1 2.8 9.3 208 Siaya 19.7 13.2 1.6 13.5 94 South Nyanza 19.0 12.9 0.8 13.1 193 Rift Valley 19.5 12.1 6.3 12.9 850 15–24 20.0 12.8 7.4 13.3 235 25–34 19.4 11.1 6.1 12.0 411 35–49 19.1 13.2 5.2 14.1 203 Kericho 20.6 11.2 4.9 12.3 179 Nakuru 17.1 10.4 3.3 10.4 113 Uasin Gishu 20.2 10.8 4.4 11.1 64 Western 20.4 12.1 2.8 12.3 651 15–24 21.2 10.8 2.3 11.2 184 25–34 19.5 12.2 2.1 12.2 333 35–49 21.6 13.6 5.0 14.0 134 Bungoma 20.3 11.4 2.1 11.5 183 Kakamega 19.3 10.8 2.6 11.0 361 a Estimated as the mean number of months of postpartum amenorrhea or abstinence, whichever is longer. breastfeeding had increased to 20.1 months, which is surprising given that many populations experience reductions in length of breastfeeding as they develop economically. Because amenorrhea, which is determined by the length of breastfeeding, decreased by almost 1 month over the same period, this increase in breastfeeding may be more an artifact of the data than a reflection of what actually occurred.3 The length of sexual abstinence did not change over time. By 1988–1989, the length of breastfeeding was approximately the same by type of residence and did not vary substantially across educational group. Length of amenorrhea and abstinence was longer in rural areas and among the least educated women. The effect of postpartum amenorrhea and abstinence on fertility is measured by Ci, the index of postpartum infecundability.4 When there is no 3   The working group is not aware of any studies assessing the validity of this increase in breastfeeding or linking it to possible causal factors, such as breastfeeding campaigns or the price of food for children. 4   Because Ci is calculated from the mean length of postpartum amenorrhea or abstinence, whichever is longer (see Appendix 5-1), the possible errors in the reported length of breastfeeding do not directly affect the estimate of C1. However, if the increase in breastfeeding is real and is not reflected in an increase in the length of amenorrhea (due to misreporting), Ci will be overestimated.

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Population Dynamics of Kenya lactation or postpartum abstinence, C1 equals 1; when infecundability is permanent, Ci equals 0. Pathological or Primary Sterility Primary sterility, or the inability of a woman to bear a child for biological reasons, has historically been high in sub-Saharan Africa, particularly in Central Africa (Frank, 1983; Bongaarts et al., 1984; Farley and Besley, 1988). In societies that value large families, such levels of sterility prevent some women who would like to bear children from doing so and lower the average level of fertility. In this analysis, primary sterility is measured by the percentage of ever-married women age 40 to 49 who are childless. Table 5-6 gives the percentages for the KFS and KDHS. In 1977–1978, 3.1 percent of women were childless. Bongaarts et al. (1984) estimated that the standard rate of childlessness in developing countries is about 3 percent, indicating that Kenya was close to the standard and little excess sterility existed. However, there were substantial differentials among subgroups of the population. Urban areas demonstrated a very high level of primary sterility, with 9.7 percent childless. Primary sterility was higher among those women with no education than among those with some education. Nairobi and Coast provinces demonstrated fairly high levels of sterility, 8.2 and 7.2 percent, respectively. TABLE 5-6 Women Aged 40–49 Who Are Childless by Subgroup, 1977–1978 and 1988–1989 (percent) Subgroup 1977–1978 1988–1989 National 3.1 2.4 Residence     Rural 2.7 2.1 Urban 9.7 5.6 Education     None 3.8 2.9 1–4 years 1.5 1.9 5–7 years 0.0 0.5 8+ years 2.9 3.1 Province     Nairobi 8.2 4.4 Central 1.3 1.9 Coast 7.2 2.1 Eastern 2.1 3.8 Nyanza 4.3 2.6 Rift 1.6 1.7 Western 3.3 1.5

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Population Dynamics of Kenya Primary Sterility Primary sterility generally had little effect on fertility across subgroups in Kenya from 1977–1978 to 1988–1989. However, it did have an effect on the fertility of urban women, particularly in the KFS. Nairobi and Coast showed an effect in 1977–1978, but this effect was eliminated by 1988–1989, reflecting either a drop in rates of primary sterility due to improved medical care or sample sizes that were too small to yield reliable estimates. Summary In looking at the national-level indices from the 1988-1989 KDHS, the most important fertility-suppressing index is postpartum infecundability, followed by contraception, and then marriage. Abortion and primary sterility had limited effects. Results from the 1977–1978 KFS also indicate that postpartum infecundability was the most important fertility-inhibiting variable at the national level. Marriage patterns (Cm) followed in significance in the earlier period, with contraception having a relatively minor effect. What is most notable is the substantial change between the two surveys in contraceptive use patterns, which replaced marriage as the second most important fertility-inhibiting factor at the later date. This decline in Cc is due to increasing contraceptive prevalence, since the method use-effectiveness mix has changed very little. Cm also declined between the two surveys, although not as steeply as Cc. The effects of infecundability and primary sterility (Ci and Ip) changed little. The indices by subgroup generally follow the national pattern, with postpartum infecundability as the most important fertility-suppressing variable, followed by contraception and marriage for the KDHS and by marriage and contraception for the KFS. There are a few notable exceptions. For all of the seven provinces included in the surveys, postpartum infecundability had the greatest fertility-suppressing effect of the proximate determinants in 1977–1978. In 1988–1989, it had the largest effect for only four of the seven provinces: Coast, Nyanza, Rift Valley, and Western. For the other provinces—Nairobi, Central, and Eastern—contraceptive practices had the greatest impact on fertility. However, for all the provinces, the effect of contraception in inhibiting fertility increased over time, due to substantial increases in contraceptive use, as shown in Figure 5-4. Among educational groups, contraceptive practices surpassed postpartum infecundability in suppressing fertility only in the most-educated group. However, contraceptive use increased dramatically for all subgroups between the two surveys, as shown in Figure 5-5.

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Population Dynamics of Kenya Figure 5-4 Current contraceptive use by province for women in union—KFS and KDHS. Figure 5-5 Current contraceptive use by subgroup for women in union—KFS and KDHS.

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Population Dynamics of Kenya RELATION BETWEEN CHANGES IN PROXIMATE DETERMINANTS AND FERTILITY The proximate determinant indices of the Bongaarts framework have been estimated from the KFS and KDHS data by identical methods. They can be taken as applicable to the few years preceding the surveys, although the exact time specification varies from index to index. For practical purposes, the changes between the two surveys can be set against the corresponding changes in fertility as measured by the total fertility rates calculated for the previous 5 years. KFS and KDHS fertility estimates are available by provinces, by urban and rural residence, and by the education of the mother. The KFS sample sizes by districts were too small for usable indices of proximate determinants to be derived. Table 5-9 shows levels based on data from the KDHS as a percentage of the KFS values. The index Ca was omitted because it was taken as constant throughout and makes no contribution to the calculation of change. The estimated total fertilities in the 5 years before the KDHS as a percentage of the corresponding measures from the KFS are presented in parallel. By using the Bongaarts model, if the estimates are reliable, the change in the product (Ip · Cm · Cc · Ci · Ca) should equal the change in the total fertility. For Kenya as a whole, the agreement is excellent, with a 21 percent reduction in the proximate determinants effect compared to the 19 percent decline in fertility. For the subpopulations, such good correspondence is not TABLE 5-9 Proximate Determinant and Fertility Indices from the KDHS as Percentages of KFS Measures Subgroup Ip Cm Cc Ci Product TFR National 101 95 80 103 79 81 Provinces             Nairobi 107 101 80 100 86 74 Central 99 90 68 97 59 69 Coast 107 89 87 113 94 73 Eastern 98 105 70 109 79 84 Nyanza 103 95 91 103 92 87 Rift Valley 100 94 78 103 76 80 Western 102 97 91 98 88 96 Urban/Rural             Urban 107 98 80 101 85 77 Rural 101 95 80 103 79 83 Education             No schooling 101 95 87 100 84 86 1-4 years 100 101 81 101 83 85 5+ years 99 98 79 100 77 83

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Population Dynamics of Kenya to be expected. Sample errors could be quite substantial with the comparatively small numbers, particularly for the component indices Ip and Cm. In allowing for this possibility, the agreement for the education subgroups is satisfactory, which confirms the small variation in fertility decline by education. The changes in urban and rural proximate determinants indicate a reversal from the reductions in total fertility rates, that is, a greater decline in rural areas. The discrepancy is not large, however, and seems to arise from the Ip measure, which is particularly vulnerable. For the provinces, given the sample errors, the correspondence between changes in fertility and the proximate determinants product is reasonable for Central, Nyanza, Rift Valley, and Eastern. The problems of estimating valid measures for Nairobi have been noted in the chapter on fertility. The implication of the determinants for Western is that the estimated fertility decline of 4 percent is too small; the earlier breakdown of this decline by age of women had also raised doubts about reliability. The major discrepancy, however, is for the Coast, where a 27 percent decline in fertility is associated with only a 6 percent decline in the combined proximate determinants. The small overall decline arises from substantial reductions in Cm and Cc, offset by large rises in Ip and Ci. The latter are puzzling. At the time of the KFS, the Coast had the lowest Ip index of all the provinces except Nairobi (i.e., the highest reported childlessness among ever-married women aged 40–49 years) and the lowest Ci value (the longest insusceptible period following a birth). At the time of the KDHS the ranking of the Coast was largely reversed, with little childlessness reported by 40-to 49-year-old ever-married women and the shortest insusceptible period among the provinces (4 months less than that obtained from the KFS). The national changes in these measures between the two surveys were small. Such an extreme alteration in these biosocial parameters for the Coast is very implausible, and data errors must be suspected. If the Ip and Ci changes for the Coast are ignored and only the product Cm · Cc is considered, the KDHS value is 77 percent of the KFS level, which implies a fertility decline of 23 percent in rather good agreement with the directly estimated 27 percent. The use of the Cm · Cc product only to assess the effects of changes in the proximate determinants gives, on average, slightly better agreement for the other provinces also. Apart from the measures for the Coast, which are of doubtful validity, the Ip and Ci indices showed little change between the KFS and KDHS for the individual provinces. The movements in Cm were also rather small, except possibly in Central and Coast provinces. It can be noted that the large decline in the fertility of young women in the Coast also suggests an effect of later marriage. The reductions in the proximate determinant product from the KFS to the KDHS are thus, in general, dominated by the increase in contraceptive

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Population Dynamics of Kenya use as gauged by the Cc index. The reduction in this index is reflected almost exactly in the total fertility rate decline for Kenya as a whole, very closely for Central and Rift Valley provinces, and with reasonable agreement for Nairobi and Nyanza. The Coast comparison suggests that in this region, later marriage may also have made a substantial contribution to the fertility decline. The comparison for Eastern Province is less convincing because the increase in contraceptive use would have been expected to produce a greater fertility decline than that recorded. A similar examination at the district level is precluded by the small sample sizes of the KFS. An attempt at a rough assessment is presented in Table 5-10. It has been assumed in the calculations that the proximate determinant indices for districts at the KFS can be taken to be the same as for the provinces that contain them at the same time. The crudeness of this assumption is obvious. It may be satisfactory for the more homogeneous provinces such as Central and Western but is highly suspect for the diverse Rift Valley. However, the Cc indices at the KFS were all rather close to 1 because of low contraceptive use in all provinces except Nairobi (.85) and Central (.92). The scope for error due to the assumption is thus small here. There is a broad association between the percentage reductions in fertil TABLE 5-10 Reductions in Fertility and Proximate Determinants, KFS to KDHS (percent) District Cumulated Fertilitya Product Cm · Cc · Ci · Ip Cc Kirinyaga 36 50 43 Siaya 27 8 4 Uasin Gishu 26 0 6 Nyeri 24 38 34 Nakuru 23 35 39 Kericho 22 13 16 Meru 22 32 30 Muranga 21 30 26 Mombasa 20 4 20 Kilifi 19 -9 6 Kisii 19 22 16 Machakos 12 21 29 Kakamega 10 9 10 Bungoma 8 2 5 Kisumu 7 8 14 South Nyanza 7 -6 2 NOTE: Proximate determinants indices for the districts at the KFS are taken to be the same as for the provinces that contain them. a From KDHS, 1974–1978 to 1984–1988, women under 40 years.

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Population Dynamics of Kenya ity and in the proximate determinants. Thus, Kirinyaga, Nyeri, and Nakuru had the largest reductions in Cc and the combined product: They are near the top of the ranking for fertility declines. The districts with the four smallest fertility declines (Kakamega, Bungoma, Kisumu, and South Nyanza) showed only modest changes in the proximate determinants. But there are striking inconsistencies, notably for Siaya and Uasin Gishu, which recorded the second and third highest fertility declines but negligible alterations in Cc and the product index. The doubts about the fertility measure for Siaya are discussed in Chapter 4. Plots of the fertility declines against the changes in proximate determinants are shown in Figures 5-6A (combined product) and 5-6B (Cc index). The latter relation is closer, particularly if the regression line is constrained to pass through the origin. To a large extent, the closer relation is due to the improved agreement for Kilifi and Mombasa in the Coast Province, where the Ip and Ci changes are suspect. As has been pointed out, this examination is subject to considerable uncertainty because of data limitations and errors, chance fluctuations due to small numbers, and crudeness of assumptions. Despite these caveats, the general agreement of the estimates of fertility decline with the changes in proximate determinants gives strong support to the belief that the findings of our analysis of the proximate determinants are broadly reliable. Figure 5-6A Declines in fertility and proximate determinant indices.

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Population Dynamics of Kenya Figure 5-6B Declines in fertility and Cc (index of contraception). COMPARISONS OF THE PROXIMATE DETERMINANTS OF KENYA WITH OTHER SUB-SAHARAN POPULATIONS Table 5-11 gives the indices for the proximate determinants at the aggregate level for all the populations in sub-Saharan Africa for which a DHS had been conducted and a standard recode data tape made available as of 1992. The results for Botswana and Zimbabwe, two countries that have also experienced a notable drop in fertility, show that contraception was an important inhibitor of fertility at the time of the surveys, in contrast to the findings for the other sub-Saharan African populations shown.9 Sudan, which may also have experienced a fertility decline (Sudan Department of Statistics and Institute for Resource Development, 1991), showed a fairly weak Cc, but an exceptionally strong effect of marriage patterns (see Jolly and Gribble, 1993, for a fuller discussion). Like Kenya in 1977–1978, most of sub-Saharan African fertility at the time of the DHS was inhibited primarily by postpartum infecundability, followed in most cases by marriage patterns. The question remains whether these countries will follow the pattern of Botswana, Kenya, and Zimbabwe of increased contraceptive use and lower fertility. 9   Unfortunately, data from the nationally representative survey of Nigeria were not available at the time this table was constructed.

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Population Dynamics of Kenya TABLE 5-11 Proximate Determinants of Fertility, Other DHS Sample Populations in Sub-Saharan Africa Country Observed TFR Index of Marriage, Cm Index of Contraception, Cc Index of Postpartum Infecundability, Ci Index of Sterility, Ip Model Estimate of Total Fecundity Rate, TF Botswana 1988 4.97 .873 .692 .629 .998 13.1 Burundi 1987 6.92 .801 .948 .525 1.029 16.9 Ghana 1988 6.35 .850 .894 .552 1.021 14.8 Kenya 1988–1989 6.62 .860 .761 .662 1.009 15.1 Liberia 1986 6.69 .932 .939 .588 1.000 13.0 Mali 1987 7.04 .976 .973 .563 .994 13.2 Ondo State, Nigeria 1986–1987 6.09 .826 .949 .472 1.033 15.9 Senegal 1986 6.57 .898 .959 .554 .976 14.1 Sudan 1989–1990 4.87 .680 .925 .599 .989 13.1 Togo 1988 6.59 .865 .905 .518 1.021 15.9 Uganda 1988–1989 7.35 .918 .958 .627 .967 13.8 Zimbabwe 1988–1989 5.49 .812 .597 .658 1.005 17.1   SOURCE: Jolly and Gribble (1993).

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Population Dynamics of Kenya SUMMARY Examination of the changes in the proximate determinants of fertility in Kenya from the late 1970s to the late 1980s reveals the primary importance of increasing contraceptive use in the fertility decline over this same period. Although postpartum infecundability continued to have the strongest fertility-inhibiting effect of all the proximate determinants, contraceptive use replaced marriage patterns as the second most important fertility-inhibiting factor. Comparing the changes in the proximate determinants with the declines in fertility shows almost equal reductions in both, indicating that the results of the analysis are generally credible.

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Population Dynamics of Kenya APPENDIX COMPUTATIONAL PROCEDURES TO ESTIMATE THE INDICES OF THE PROXIMATE DETERMINANTS Marriage Patterns where TFR = average total number of births a woman would have in her lifetime at current age-specific fertility rates (ASFRs), and TMFR = average total number of births a woman in union throughout her reproductive years would have in her lifetime at current age-specific marital fertility rates. Both rates were estimated for the four years prior to the survey. The TMFR was estimated for women currently in union. Contraception Cc = 1-1.08ue, where u = current contraceptive use prevalence rate among women in sexual union, and e = average use-effectiveness of contraception. Abstinence is excluded as a method because it was listed as a potential response only on the KFS questionnaire and not on the DHS, and because many of the women who reported using abstinence as a contraceptive method were practicing postpartum abstinence, which is captured in the Ci index. Periodic abstinence or rhythm method, however, is included as a method. The average use-effectiveness of a method is calculated as the weighted average of the method-specific use-effectiveness levels, with the weights equal to the proportion of women using a given method. The levels used were adapted by Bongaarts and Potter (1983) from a study by Laing (1978) in the Philippines. They are Pill 0.90 IUD 0.95 Sterilization 1.00 Other methods 0.70 Postpartum Infecundability Ci = 20/(18.5 + i), where i = mean number of months of postpartum infecundability (estimated as mean number of months of postpartum amenorrhea or abstinence, which

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Population Dynamics of Kenya ever is longer) for women in union. The mean number of months of postpartum infecundability is estimated by using the prevalence/incidence method. In this analysis, Primary Sterility Ip = (7.63-.11s)/7.3, where s = proportion of married women between ages 40 and 49 who have never had a child. Bongaarts et al. (1984) used the percentage of women age 45–49 who were childless. In this analysis, the percentage of childless women age 40–49 is used to increase the number of women in each subgroup and reduce the standard error in estimating s. It is assumed that all women had their first child by age 40 in Kenya. Abortion where A = 0.4(1 + u) TA, u = contraceptive prevalence rate, and TA = number of abortions per female during her reproductive years. Total Natural Fecundity Rate TF = TFR/(Cm · Cc · Ci · Ip · Ca ) (Bongaarts and Potter, 1983; Bongaarts et al., 1984).