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Population Dynamics of Kenya (1993)

Chapter: 4 Fertility Trends

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Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

4
Fertility Trends

Although reports of births in recent periods preceding the census and surveys were collected, they are subject to biases of coverage and time location errors. Adjustments can be made but not with any certainty. It is simpler to examine the trends in fertility through the ratios of children ever born to women in age groups. These measures are shown in Table 4-1 for the three censuses (1962, 1969, 1979), the Kenya Fertility Survey (KFS), the Kenya Contraceptive Prevalence Survey (KCPS), and the Kenya Demographic and Health Survey (KDHS). Values are given separately for the first and third rounds of the National Demographic Survey (NDS) (1977 and 1983); the data from the second round have not been closely analyzed. Panel A of the table gives the mean parities, that is, the total children born divided by the total women in the age group; panel B shows the percentage of women in each age group reporting no children; panel C presents mean births to mothers (instead of all women). On a first inspection of panel A, the pattern of change over time is clear enough. The mean parities of the younger women show little change up to 1984. The levels for the older women increase steadily until the late 1970s. However, the large-scale operations—the NDS and the 1979 census—give lower mean parities for women over 40 years of age in comparison with the KFS and KCPS, which were close to them in time. These differences suggest that there was underreporting of births at the census and the NDS of 1983. It seems likely that there was at least as much underreporting at the earlier censuses, and this conclusion is supported by the configuration. The mean parity of 5.07 for women aged 35–39 in 1962 rises by about 1.5 children for the same

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Table 4-1 Birth Measures from Censuses and Surveys

Age Group of Women

1962 Census

1969 Census

1977 NDS

1978 KFS

1979 Census

1983 NDS

1994 KCPS

1989 KDHS

A. Mean Parities per Woman

15–19

0.36

0.35

0.33

0.35

0.32

0.29

0.35

0.28

20–24

1.65

1.88

1.83

1.84

1.85

1.75

1.96

1.58

25–29

3.01

3.65

3.72

3.76

3.65

3.56

3.96

3.47

30–34

4.20

5.11

5.55

5.55

5.38

5.36

5.70

5.01

35–39

5.07

6.00

6.67

6.82

6.47

6.66

7.04

6.48

40–44

5.61

6.44

7.25

7.59

7.02

7.43

7.84

7.36

45–49

5.90

6.69

7.46

7.88

7.17

7.65

8.15

7.63

B. Percentage of Childless Women

15–19

79.1

75.5

77.3

73.9

78.4

77.8

73.2

78.6

20–24

36.8

24.7

24.2

19.1

26.6

22.9

19.7

21.5

25–29

22.3

11.1

7.6

5.4

10.9

7.3

4.9

5.3

30–34

17.8

8.2

4.6

3.3

7.9

3.9

4.2

2.9

35–39

15.3

7.6

5.1

1.6

7.0

3.6

2.6

2.2

40–44

14.4

7.8

4.1

3.4

7.5

2.9

3.1

2.3

45–49

13.7

8.0

4.6

2.8

7.6

2.8

3.3

2.8

C. Mean Births per Mother

15–19

1.71

1.45

1.45

1.34

1.48

1.32

1.31

1.31

20–24

2.61

2.50

2.41

2.27

2.52

2.27

2.44

2.01

25–29

3.87

4.11

4.02

3.98

4.10

3.84

4.16

3.66

30–34

5.11

5.56

5.82

5.74

5.85

5.58

5.95

5.16

35–39

5.99

6.50

7.03

6.93

6.96

6.90

7.23

6.63

40–44

6.55

6.99

7.56

7.86

7.59

7.65

8.09

7.53

45–49

6.84

7.26

7.82

8.11

7.76

7.87

8.43

7.85

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

cohort by 1969, too large an increase to be accounted for by additional births at these late ages. Part of the discrepancy can be attributed to the inclusion of women who did not report numbers of children with childless women at the censuses and NDS. The lower percentages of childless women in the 1977 and 1978 surveys at ages beyond which first births rarely occur (panel B), compared with the measures in 1969 for the same cohorts, must be due to error. This source of bias is eliminated in panel C in the measures of mean births per mother because women who did not report children are not included in the measure. However, the trends remain similar to those of panel A, although they are reduced in magnitude. There remains uncertainty about whether the apparent increase in fertility in the 1960s and 1970s was real or due entirely to improved birth reporting. On balance, the evidence suggests that there was an increase in fertility. The relatively lower estimates from the 1948 census analysis were supported by some small surveys, and the mean births per mother by age group in 1962, 1969, and 1979 are consistent with a gradual rise in fertility for cohorts born in the 1930s. A similar conclusion was reached from the detailed analysis of the KFS birth histories (Henin et al., 1982).

More recent attempts to examine fertility trends in Kenya use the parity progression ratio (PPR; the proportion of women going from an nth to an (n + 1)st birth). This index is a sensitive measure of fertility change but is very robust to data errors, for example, in time location of births and confusion between childlessness and failure to report. These ratios can be calculated directly for women past the ages of childbearing. In practice, the end of childbearing can be taken as 40 years because births after that age have a negligible effect on the PPRs except at very high birth orders. From the Kenya censuses, PPRs for age groups of women were calculated by Feeney (1988), up to 70–74 years of age for 1962 and 1979, and up to 60–64 years for 1969. He plotted the PPRs of each order on a time scale of year of birth of the woman so that the measures for the same cohorts at the three censuses were at coincident points on the horizontal axis. Blacker, in an unpublished note, extended Feeney's work by adding PPRs from 1969 and 1979 reports of births to women still in the childbearing period. The extrapolation to the end of reproduction was based on the fertility rates by birth order calculated from the births reported for the 12 months before the censuses. The method was developed by Brass (1985). Blacker also added measures from older women (more than 46 years of age) at the 1948 census and from those aged 45–49 years at the KFS and the KCPS. His graphs are reproduced in Figure 4-1.

The picture that emerges from the investigation of these measures is a coherent one. The comparable PPRs are higher for each successive census but only slightly for 1979 versus 1969. The 1962 values vary rather erratically over time, but the 1969 and 1979 trends are more regular. At every

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Figure 4-1 Kenya parity progression ratios.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×
Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×
Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

birth order there is a steady decline in the PPRs that extends from the 1970s back as far as the records go, that is, to women born around the beginning of the century. The pattern of the PPRs confirms that there was improved reporting in the censuses. It is possible that better reporting by younger women than by older contributed to the apparent rise in the PPRs over time. On the other hand, the closeness of the 1969 measures to the 1979 values for the same cohorts reported when 10 years older makes this possibility implausible. The extra PPRs derived by Blacker from the reports by younger women at the 1969 and 1979 censuses refer to more recent time periods. They suggest a cessation of the rise in the 1970s, and even possibly a decrease, but the synthetic nature of the calculations, projected by the use of current fertility reports, must signal caution. Blacker pointed out that the steepness of the rise in the PPRs between 1920 and 1970 increases with parity. This pattern casts doubts on the conclusion that the fertility increases were due mainly to decreases in pathological sterility. Blacker suggested that the main causes may have been biosocial factors, such as shorter birth intervals due to declines in breastfeeding and postpartum abstinence, along with lengthening reproductive lives. However, there are virtually no data on the proximate determinants of fertility prior to the late 1970s.

The surveys between 1977 and 1984 provide mean parities by age group of women that fluctuate but reveal no clear trend (Table 4-1, panel A). The sample errors, particularly for the older women, are appreciable, and the variations can be accounted for by chance and by small systematic biases in coverage and age reporting. The deduction is that the tendency for fertility to increase had ended. Of course, women at the end of their reproductive periods around 1980 had their peak childbearing in the 1960s. The substantial rise in fertility was probably a product of factors in the 1940s and 1950s, a finding that agrees with the analysis of the KFS birth histories (Henin et al., 1982) and an unpublished analysis in 1981 by the Panel on Tropical Africa of the Committee on Population and Demography.

In contrast to the surveys bracketing 1980, the 1989 KDHS gives mean parities that are distinctively lower. The striking reductions are for the younger age groups of women. Thus, the 2.01 and 3.66 mean births per mother at the KDHS for age groups 20–24 and 25–29 years, respectively (Table 4-1, panel C), are below all the other levels in the series from 1962 onward. The implication is that a sharp decline in fertility occurred in the 1980s. The 1983 round of the NDS is consistent with this conclusion, with slightly lower mean parities than the 1977 first round and the 1978 KFS for women age 20–34 years. The measures from the 1984 KCPS are contradictory, however, since they are the highest of the whole series. Nevertheless, the authors of the KCPS report discerned a slight decrease in current fertility from the KFS level on the basis of recent births. The evidence of

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

fertility reduction from the KFS and KDHS comparison is examined in detail below.

EVIDENCE OF DECLINES IN FERTILITY FROM RECENT SURVEYS

There have been many analyses of the KFS data. The main aim here is to establish a base for the study of subsequent fertility change in the 1980s. This brief exposition follows closely the approach in the report by Henin et al. (1982) published by the World Fertility Survey program. However, for the convenience of later comparisons, the birth measures are arranged by cohorts of women in time periods rather than in time periods by age groups of women. In the latter form, the age-specific rates are for the conventional five-year groups; in the former, for the intervals in which a cohort of women moves from one age group to the next (e.g., from 20–24 to 25–29 years). Table 4-2A shows the age-specific birth rates for cohorts of women, denoted by their ages at the time of the KFS, in five-year time intervals before the survey. The cumulated values of these rates from the beginning of childbearing up to five yearly time points are given in Table 4-2B. These represent what the mean parities would have been for the same women reporting in the same way at surveys 5 years, 10 years, and so on previously.

Summation of the rates within time periods gives the cumulated fertilities up to the highest age groups of women reporting; these are increasingly truncated as the past recedes. The measures for the preceding four time periods are shown in Table 4-2C. Comparison of the cumulated fertilities at equivalent ages along the top diagonal suggests that there had been a quite remarkable trend, with a sharp increase at 10–14 years before the survey followed by a substantial reduction thereafter.

Simple, approximate estimates of the fertility up to the 45–49 age group (very close to the total fertility) are obtained by assuming that the age-specific rates removed by the truncation are the same as for the women aged 45–49 years at the time of the survey. If, in fact, a decline had occurred, these extrapolations would lead to an under-rather than an over-estimation. The outcome is a total fertility of 9 births per woman in the 10–14 years before the survey, falling to 8.3 in the previous 5 years. The level indicated for 1963–1967 (10 to 14 years before KFS) is well above that deduced from census data near that time; the apparent trend is highly implausible and hard to explain in terms of changes in nuptiality, fertility control, or biological factors.

Examination of the age-specific birth rates in Tables 4-2A and 4-2B reveals that the distributions change over cohorts. The cumulated totals to equivalent ages rise over time for cohorts up to 30–34 years but decrease for

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Table 4-2A Births per 1,000 Women by Age Groups and Time Period, KFS 1978

 

Time Preceding Survey (years)

Age at KFS

0–4

5–9

10–14

15–19

20–24

25–29

30–34

35+

Total Births

15–19

334

11

 

 

 

 

 

 

345

20–24

1,439

383

21

 

 

 

 

 

1,843

25–29

1,804

1,463

449

25

 

 

 

 

3,741

30–34

1,641

1,852

1,570

479

34

 

 

 

5,576

35–39

1,402

1,711

1,850

1,379

437

49

 

 

6,828

40–44

1,029

1,530

1,787

1,614

1,209

372

10

 

7,551

45–49

615

1,138

1,582

1,577

1,557

1,112

292

25

7,898

Total

8,264

8,088

7,259

5,074

3,237

1,533

302

25

 

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Table 4-2B Cumulated Births per 1,000 Women by Age Group, KFS 1978

 

Time Preceding Survey (years)

Age at KFS

0

5

10

15

20

25

30

15–19

345

 

 

 

 

 

 

20–24

1,843

404

 

 

 

 

 

25–29

3,741

1,937

474

 

 

 

 

30–34

5,576

3,935

2,083

513

 

 

 

35–39

6,828

5,426

3,715

1,865

486

 

 

40–44

7,551

6,522

4,992

3,205

1,591

382

 

45–49

7,898

7,283

6,145

4,563

2,986

1,429

317

Table 4-2C Cumulated Births per 1,000 Women by Time Period, KFS 1978

 

Time Preceding Survey (years)

Age at KFS

0–4a

5–9

10–14

15–19

30–34

5,218

 

 

 

35–39

6,620

5,420

 

 

40–44

7,649

6,950

5,677

 

45–49

8,264

8,088

7,259

5,074

Extrapolated to

 

 

 

 

45–49

8,264

8,703

9,012

8,409

Adjusted

7,910

8,053

8,120

7,836

a Small undercount because no births recorded for women currently 10–14.

Table 4-2D Births per 1,000 Women by Age Group and Time Period: Observed and Model

 

Time Preceding Survey (years)

Age at KFS

Source

0–4

5–9

10–14

15–19

Total Births

30–34

Observed

1,641

1,852

1,570

479

5,576

 

Model

1,693

1,823

1,551

499

 

35–39

Observed

1,402

1,711

1,850

1,379

6,828

 

Model

1,387

1,653

1,778

1,513

 

40–44

Observed

1,029

1,530

1,787

1,614

7,551

 

Model

946

1,342

1,598

1,721

 

45–49

Observed

615

1,138

1,582

1,577

7,898

 

Model

344

947

1,342

1,599

 

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

older cohorts (when comparing along the diagonals in Table 4-2B). The variation is relatively slight up to the 35–39 age cohort, but sharp for the two oldest groups of women. Thus, for fertility up to age 30–34, 37 percent (2,083 divided by 5,576) was reported as before age 20–24 for the 30–34 age cohort, but only 31 percent (1,429 divided by 4,563) for the 45–49 age cohort. This difference might occur because of a move to earlier childbearing, but it seems unlikely and there is no evidence for it. A more convincing explanation (particularly since the same feature has occurred in several birth history surveys of populations with a low literacy level) is recording error in which the births are located too near the present time. Corrections are made by fitting the observations for cohorts aged 30–34 and 35–39 years by the relational Gompertz model (Brass, 1981). The two parameters of the model distribution are taken as rounded averages over the two cohorts. The observed age-specific rates for the two oldest cohorts are then replaced by the values from the model distribution, with their sums constrained to equal the observed cohort totals. As can be seen from Table 4-2D, the model values for the 30–34 and 35–39 cohorts are fairly close to the observed birth rates. Therefore, only the distributions for the two oldest cohorts have been adjusted. The total fertilities (to age 45–49 years), extrapolated from the adjusted measures in the same way as for the observed measures, are also given in Table 4-2C. The fluctuation in total fertility over the 20 years 1958–1977 has now largely disappeared, leaving a roughly constant level of about 8 children per woman for that period. Similar exercises allowing for systematic changes in the distribution of fertility by age of woman have been carried out. The estimates differ in detail, but the general conclusions are the same.

An alternative explanation of the time displacement of births in the reports of older women is age error. Perhaps, these women were on average younger than the recorded age span, and hence the cumulated fertilities were to earlier points of the reproductive period than specified. However, the bias would have to be very large, with women in the 45–49 years age group really about 3 years younger than reported relative to the 30–34 age group. Moving the average ages to bring the locations of the cohort time-period birth distributions into agreement would not bring them into near coincidence because those for the cohorts under age 40 have a more peaked shape than those for the older women. Nor, of course, would the time-period total fertilities be much changed, leaving the trend anomalies unexplained. Thus, the time-period shift seems to be much the most plausible explanation although there may easily be a contribution from systematic age biases.

Tables 4-3A, 4-3B, and 4-3C present the fertility measures from the KDHS maternity histories in the same form as Tables 4-2A to 4-2D for the KFS. Thus, the age-specific birth rates for cohorts and time periods are in

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Table 4-3A Births per 1,000 Women by Age Group and Time Period, KDHS 1989

 

Time Preceding Survey (years)

Age at KDHS

0–4

5–9

10–14

15–19

20–24

25–29

30–34

35+

Total Births

15–19

272

9

 

 

 

 

 

 

281

20–24

1,269

300

11

 

 

 

 

 

1,580

25–29

1,558

1,415

470

29

 

 

 

 

3,472

30–34

1,359

1,665

1,445

498

48

 

 

 

5,015

35–39

1,190

1,550

1,780

1,477

448

29

 

 

6,474

40–44

728

1,360

1,663

1,647

1,433

501

30

 

7,362

45–49

326

1,011

1,488

1,526

1,672

1,256

326

20

7,625

Total

6,702

7,310

6,857

5,177

3,601

1,786

356

20

 

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Table 4-3B Cumulated Births per 1,000 Women by Age Group, KDHS 1978

 

Time Preceding Survey (years)

Age at KDHS

0

5

10

15

20

25

30

15–19

281

 

 

 

 

 

 

20–24

1,580

311

 

 

 

 

 

25–29

3,472

1,914

499

 

 

 

 

30–34

5,015

3,656

1,991

346

 

 

 

35–39

6,474

5,284

3,734

1,954

477

 

 

40–44

7,362

6,634

5,274

3,611

1,964

531

 

45–49

7,625

7,299

6,288

4,800

3,274

1,602

346

Table 4-3C Cumulated Births per 1,000 Women by Time Period, KDHS 1978

 

Time Preceding Survey (years)

Age at KDHS

0–4a

5–9

10–14

15–19

30–34

4,458

 

 

 

35–39

5,648

4,939

 

 

40–44

6,376

6,299

5,369

 

45–49

6,702

7,310

6,857

5,177

Extrapolated

6,702

7,636

8,194

8,002

Adjusted

6,714

7,269

7,723

7,891

a Small undercount because no births recorded for women currently 10–14.

Table 4-3D Births per 1,000 Women by Age Group and Time Period: Observed and Model

 

Time Preceding Survey (years)

Age at KDHS

Source

0–4

5–9

10–14

15–19

Total Births

30–34

Observed

1,359

1,665

1,445

498

5,015

 

Model

1,404

1,623

1,465

494

 

35–39

Observed

1,190

1,550

1,780

1,477

6,474

 

Model

1,190

1,501

1,709

1,544

 

40–44

Observed

728

1,360

1,663

1,647

7,362

 

Model

798

1,207

1,521

1,733

 

45–49

Observed

326

1,011

1,488

1,526

7,625

 

Model

268

797

1,206

1,521

 

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Table 4-3A; the cumulated fertilities from the start of childbearing for cohorts in Table 4-3B, and the summation over the rates in time periods in Table 4-3C. As with the KFS sample, approximate estimates of fertilities up to the age group 45–49 years in earlier periods are derived by substituting the age-specific rates removed by the truncation.

The signs of displacement in the time location of births are much less evident in the KDHS than in the KFS maternity histories. The crudely extrapolated total fertilities up to ages 45–49 in Table 4-3C give only a slight indication of peaking 10–14 years before the survey, and the birth distributions by age for cohorts are very similar up to the group 40–44 years of age. Only for the 45–49 cohort does a configuration consistent with the distortions for the KFS maternity histories occur. This distortion is illustrated by the calculation shown in Table 4-3D. As with the KFS data, the observed birth rates over ages for the cohorts aged 30–34 and 35–39 years were fitted by the relational Gompertz model. The averaged parameters defined a distribution pattern that was taken to hold for the two oldest cohorts also giving the model birth rates in Table 4-3D. The observed rates were replaced by the model rates for the two oldest cohorts, and the total fertilities to age 45–49 years were estimated by extrapolation. Because of the apparent trends, the truncated upper tails of the birth histories were completed from the model measures of the KFS. Thus the rates from the KFS at 0–4 and 5–9 years before the survey were taken to represent the missing upper value of the KDHS at 10–14 and 15–19 years earlier for the appropriate cohorts. The small discrepancy due to the interval between the surveys being slightly greater than 11 years rather than 10 is negligible at the level of approximation.

The differences between the adjusted and crudely extrapolated total fertilities are much smaller for the KDHS than for the KFS data, and indeed it is possible that the adjustment has resulted in an overcorrection. The assumption that the fertility pattern by age for cohorts had remained constant is much more plausible for the KFS maternity histories than for the KDHS ones because of the near stability of rates in the former case. If, in fact, the pattern for the two oldest cohorts at the KDHS is taken to be that estimated from KFS observations, the resulting adjusted total fertilities to ages 45–49 are close to the values extrapolated without adjustment. The most direct method for the determination of fertility changes in Kenya is comparing the birth rates for the period just before the KDHS with the corresponding measures derived from the KFS. The examination in the preceding paragraphs suggests that there will be little overall bias in such a comparison. The fertility rates in the past 5 years seem to have been reported with little time location distortion in both surveys. There is more uncertainty about the detailed trends over the 1970s and 1980s.

A direct assessment of the levels of reporting at the KDHS and KFS is

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Table 4-4 Births per 1,000 Women Reported in KDHS and KFS for Approximately the Same Periods and Age Ranges of Women

 

KFS

KDHS

KDHS Adjusteda

Age of Women at KDHS

25–29

346

499

492

30–34

1,844

1,990

2,020

35–39

3,741

3,735

3,881

40–44

5,582

5,274

5,533

45–49

6,829

6,288

6,686

Time preceding KDHS in years

10–14

6,623

6,845

6,973

15–19

5,422

5,178

5,448

20–24

3,892

3,601

3,881

25–29

1,885

1,786

1,915

30+

500

376

445

a Including estimates for omitted dead children.

obtained from births recorded at the two surveys for the same cohorts and time periods. If the intersurvey interval had been exactly 10 years, the organization of the check would have been very simple with the five-yearly grouping of ages and time. The actual interval of slightly more than 11 years requires awkward modification of tabulations or interpolation. Such calculations have been made, but the adjustments are so small compared with other factors (e.g., the sample error) that they can be ignored for the present purpose. In effect, the KDHS sample of women is back-dated 10 years, and the reporting of births up to then is compared with the KFS data as if there was a coincidence of time. The results are shown in Table 4–4.

In the first part of the table, the lifetime births up to the same ages are shown for the cohorts of women. The second part presents the reported births in given time periods. Chapter 3 shows that there was a substantial omission of dead children in the KDHS relative to the KFS. Measures with approximate adjustments for this omission are also presented in the table.1 Agreement in the reporting of births in the two sources is remarkably close, particularly after the correction for omitted dead children. Of course, identical levels in the individual cells are not to be expected in the presence of sample variation, and of age and time location errors. There is no indication, however, that the completeness of reporting of surviving children dif-

1  

Adjustments for omitted dead children are not presented elsewhere in this chapter because their inclusion does not significantly alter the analyses. Adjustments are presented in Table 4-4 because the effect on comparisons of more distant KDHS reports with recent KFS ones is appreciable.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

fered materially in the two surveys. It should be noted, in particular, that the births per 1,000 women 10–14 years before the KDHS (and 0–4 years before the KFS) are a little higher for the former. This difference may be a residual time location distortion of the typical form. There is no indication either that the KFS births in the 5 years before the survey have been appreciably overstated, or that there is consequently a significant bias in their use as a base for measuring fertility change.

Checks of the kind made in Table 4-4 can be applied for subgroups of the population, but conclusions can be only tentative because of the smaller numbers and larger sample variability. Comparisons for provinces, residence, and educational levels are shown in Tables 4-5A, 4-5B, and 4-5C.

Table 4-5A Births per Woman Reported in KDHS and KFS by Age Group and Province in Approximately the Same Time Periods

Age of Women at KDHS

KFS

KDHSa

KFS

KDHSa

 

Kenya

Nairobi

25–29

0.35

0.45

0.39

0.35

30–34

1.84

2.02

1.31

1.54

35–39

3.74

3.88

3.43

3.12

40–44

5.58

5.53

4.98

423

45–49

6.83

6.69

6.96

5.46

 

Central

Coast

25–29

0.19

0.36

0.51

0.41

30–34

1.62

1.99

1.98

1.77

35–39

3.75

3.76

3.58

3.85

40–44

5.58

5.56

5.21

5.70

45–49

6.84

6.27

6.22

6.06

 

Nyanza

Rift Valley

25–29

0.40

0.57

0.44

0.64

30–34

2.01

2.35

1.94

2.09

35–39

3.78

4.19

3.81

3.95

40–44

5.58

5.65

5.75

5.82

45–49

7.21

7.67

6.78

5.70

 

Western

Eastern

25–29

0.34

0.44

0.24

0.42

30–34

2.15

2.16

1.67

1.66

35–39

3.97

3.96

3.61

3.59

40–44

5.95

5.69

5.48

5.07

45–49

7.11

7.55

6.62

6.41

a Adjusted.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-5B Births per Woman Reported in KDHS and KFS by Age Group and Residence in Approximately the Same Time Periods

Age of Women at KDHS

KFS

KDHSa

KFS

KDHSa

 

Urban

Rural

25–29

0.41

0.33

0.34

0.53

30–34

1.57

1.55

1.91

2.12

35–39

3.41

3.26

3.80

3.97

40–44

4.77

4.04

5.67

5.78

45–49

6.25

5.24

6.88

6.82

a Adjusted.

TABLE 4-5C Births per Woman Reported in KDHS and KFS by Age Group and Education in Approximately the Same Time Periods

Age of Women at KDHS

KFS

KDHSa

KFS

KDHSa

 

No Schooling

1–4 Years

25–29

0.75

0.80

0.32

0.75

30–34

2.07

2.25

2.09

2.37

35–39

3.71

4.03

3.94

4.07

40–44

5.54

5.46

6.03

5.82

45–49

6.57

6.53

7.50

7.47

 

5–8 Years

9+ Years

25–29

0.27

0.43

0.19

0.21

30–34

1.94

2.05

1.06

1.10

35–39

3.92

3.90

2.89

2.75

40–44

5.27

5.77

4.78b

3.32b

45–49

6.99

6.40

c

c

a Adjusted.

b 32 and 30 women for KFS and KDHS, respectively

c 10 women or less.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

For nearly all the subgroups there is no consistent tendency for the fertility reported over the same period to be significantly higher or lower from the KDHS or the KFS. The exceptions are Nairobi Province and urban residence (nearly half from Nairobi), where the KDHS births per woman are substantially lower than the KFS for the cohorts aged 40–44 and 45–49 years at the former survey. It is difficult to interpret these differences with confidence because of the large migration to and from Nairobi and the heterogeneous nature of the population. It would not be surprising if reporting by the Nairobi women of births that had occurred some time before, often in a different location, suffered from omissions. It does not necessarily follow that the reporting of recent births was similarly biased.

There is, of course, no external check on the accuracy of the number of births in the two 5-year intervals preceding the KDHS, but the generally good agreement overall and in most of the subgroups for the more distant periods inspires confidence. It is concluded that the total fertility was close to 8.2 births per woman in 1973–1977 and 6.7 in 1984–1988. The evidence is convincing that there was little, if any, fertility reduction before the mid-1970s but an appreciable decline in the later 1970s and the 1980s. The detailed trend cannot be determined with certainty, but the time schedule of births from the KDHS seems reliable. If this is accepted, the rate of decrease accelerated in the later 1980s.

DIFFERENTIALS IN THE DECLINES IN FERTILITY

The pattern of the decline at different stages of reproduction for Kenya and subgroups of the population is analyzed in Tables 4-6A, 4-6B, and 4-6C. The reductions in births per woman in the 5 years before the KDHS from the corresponding measures for the 5 years before the KFS are shown for three age ranges. Early reproduction refers to births to women at ages up to 20–24 years or up to approximately 22.5 years because the births were over the preceding 5 years; the middle stage refers to 25–29 and 30–34 years (fertility at approximately 22.5 to 32.5 years); later reproduction refers to ages over 35 (fertility after approximately 32.5 years). The purpose of grouping by stages is to reduce sample variability but to preserve biosocial differentiation. The measures calculated are of the reductions in births overall and for each reproduction stage, and the percentage contribution of each stage to the overall decline.

For Kenya as a whole, all age groups contributed to the overall fertility decline of 19 percent, but the reduction at the late stage was more than double those at earlier ages. Because more births occur at the middle stage, however, it contributed slightly more than the late stage to the total decline. The subpopulations (province, urban/rural, education) show appreciable variations in the fertility decreases and their composition by ages of women. Because

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-6A Reduction in Total Fertility at Different Stages of Reproduction 1973–1977 to 1984–1988, by Province

Stage of Reproductiona

Fertility 1973–1977

Reduction

% Fall

% of Total

Fertility 1973–1977

Reduction

% Fall

% of Total

 

Kenya

Nairobi

E

1.77

.23

13

15

1.43

.11

8

7

M

4.85

.74

15

47

4.26

1.57

37

98

L

1.64

.59

36

38

.46

-.07

-15

-4

T

8.26

1.56

19

100

6.15

1.61

26

100

 

Central

Coast

E

1.58

.20

13

7

1.92

.71

37

35

M

5.19

1.55

30

57

4.14

.85

21

42

L

1.93

.95

49

35

1.31

.45

34

22

T

8.70

2.70

31

100

7.38

2.02

27

100

 

Nyanza

Rift Valley

E

1.86

.04

2

4

2.00

.37

19

21

M

4.75

.50

11

47

5.07

.91

18

53

L

1.50

.53

35

50

1.73

.44

25

25

T

8.11

1.07

13

100

8.80

1.73

20

100

 

Western

Eastern

E

2.02

.34

12

 

1.54

.13

8

10

M

4.92

-.35

-7

 

4.87

.49

10

36

L

150

.43

29

 

1.86

.74

40

54

T

8.44

.42

5

 

8.27

1.36

16

100

a E = early, up to 20–24 years; M = middle, 25–29 to 35–39 years; L = late, 40–44 years and over; T = total.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-6B Reduction in Total Fertility at Different Stages of Reproduction, 1973–1977 to 1984–1988, by Residence

Stage of Reproductiona

Fertility 1973–1977

Reduction

% Fall

% of Total

Fertility 1973–1977

Reduction

% Fall

% of Total

 

Urban

Rural

E

1.59

0.28

18

19

1.82

0.21

12

15

M

3.83

0.83

22

58

4.96

0.63

13

44

L

0.75

0.33

44

33

1.70

0.59

35

41

T

6.17

1.44

23

100

8.48

1.43

17

100

a E = early, up to 20–24 years; M = middle, 25–29 to 35–39 years; L = late, 40–44 years and over; T = total.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-6C Reduction in Total Fertility at Different Stages of Reproduction, 1973–1977 to 1984–1988, by Education

Stage of Reproductiona

Fertility l973–1977

Reduction

% Fall

% of Total

Fertility 1973–1977

Reduction

% Fall

% of Total

 

No Schooling

1–4 Years

E

2.18

0.18

8

16

1.96

-0.27

-14

-21

M

4.62

0.40

9

35

5.18

0.78

15

60

L

1.63

0.57

35

50

1.86

0.81

44

62

T

8.43

1.15

14

100

9.00

1.31

15

100

 

5+ Years

 

E

1.59

0.14

9

11

 

 

 

 

M

4.87

1.01

21

77

 

 

 

 

L

1.22

0.17

14

13

 

 

 

 

T

7.68

1.32

17

100

 

 

 

 

a E = early, up to 20–24 years; M = middle, 25–29 to 35–39 years; L = late, 40–44 years and over: T = total.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

some of the sample sizes are quite small and there are signs of distortion from age and other misstatements, a part of the variability must be discounted as due to error. In the interpretation, only the most notable deviations can be confidently accepted as valid. Perhaps the major finding is the near universality of the fertility reductions in subgroups. With the exception of Western Province (see below), there were moderate to substantial declines in every category. The rural decline was only marginally smaller than the urban (17 versus 23 percent), with quite similar changes in the corresponding reproductive stages. Among the provinces, Central stands out for the high overall reduction (31 percent) with only a small contribution from the youngest women. The fertility decline in the Coast (27 percent) was also great, but was attained by a particularly large effect in the early reproductive stage, the drop at later ages being about average. The pattern for Nairobi is anomalous, with virtually no change at early or late ages but a heavy (37 percent) reduction in the middle reproductive stage. However, the Nairobi sample sizes were comparatively small, and as noted above, there are doubts about the stability of the estimates from the KFS to KDHS. The oddest results are for Western Province, where average fertility reductions at early and late ages were offset by a rise for the middle stage of reproduction to give an overall inconsequential decrease of 5 percent. It is difficult to find a sensible explanation for such a pattern of change. Alteration in forms of age error is possible but not plausible. The suggestion that the rise in the middle reproductive stage is an artifact due to improvement in reporting cannot be dismissed. If this were so, the true fall in fertility would be closer to the average for Kenya.

The striking feature of the results by level of education is the near equality of the fertility declines in the three categories: no schooling (14 percent), 1–4 years of schooling (15 percent), and 5 years or more of school (17 percent), despite appreciably different initial fertility levels. There was a rapid increase in the educational attainment of women in the interval between the two surveys, and the movement into higher groups leads to the larger reduction (19 percent) for Kenya than for any of the component groups. Changes in the composition of the education categories by other socio-economic characteristics may have modified the size of the reductions but not by much. The women with no schooling at the time of the KDHS were a residual group considerably smaller in number than at the time of the KFS, particularly at the younger ages. In that context, the fertility reduction of 14 percent is even more notable.

PATTERNS OF FERTILITY DECLINE

Fertility decline comes mainly through two mechanisms: (1) reduction in the proportion of women at risk through changes in the pattern of mating

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

(e.g., later age at marriage), and (2) decreased propensity for additions to families because of alterations in the factors of reproduction, most commonly, the use of contraceptive measures. The extent to which the decreases in Kenya occurred in the middle and late reproductive stages suggests that the second mechanism was dominant. It is difficult in African societies to isolate the effects of mating changes because of the complexity of behavior (see van de Walle, 1993), but a direct guide to family limitation can be obtained from the study of parity progression ratios, that is, the proportion of women in an age cohort who, having attained an nth birth, go on to higher orders. For cohorts who have completed their fertility, the calculations are direct and elementary. When the data available are birth histories of women still capable of bearing further children, as from the KDHS, the analysis technique is more complicated. For each birth progression, a life table form of calculation is required that allows for the removal of women from risk during the reproductive period. For each age group of women, the rates of movement from the nth to the (n + 1)st birth are computed by intervals (normally monthly) from the earlier birth. As the gap lengthens, the number of women whose experience extends that far is reduced, ultimately becoming too few for the calculations to be effective. However, most births occur within 5 years of the previous one. Accordingly, instead of the PPR (which can never be measured when fertility is incomplete) we investigate B60 (the proportion of women moving on to the next birth within 5 years). Estimates of B60s can be made up satisfactorily to high birth orders for women over 30, and low to medium orders for the 25–29 and 20–24 cohorts. The specific definitions of high and medium depend on sample sizes, as well as levels and age patterns of fertility. However, the estimates of B60 are biased, slightly but significantly, by the truncation of birth histories. The women who will ultimately go to the (n + 1)st birth, but at a slower pace than average, will tend to be excluded from the calculation to a greater extent than those who bear children more rapidly, and the estimated B60s are too high (Hobcraft and Rodriguez, 1980). The bias increases with the truncation and thus distorts the trend over cohorts. A simple but effective adjustment has been devised (Brass and Juarez, 1983; Juarez, 1983). The procedure is to compare the B60s for adjacent cohorts with the preceding 5 years of births omitted for the older. Thus the B60s for the group 20–24 years are set against the corresponding measures for the 25–29 cohort when these women were 20–24 years old. The ratios of the B60s for the comparably truncated birth histories give the unbiased trends. It is assumed that the ratios of the equally biased B60s are the same as those for the true B60s, but over most of the range the adjustments are small. There is most doubt for the marginal estimates, that is, the highest birth orders for each cohort where the sample errors are also the largest.

Appendix Table 4A-1 shows the calculation of the basic B60s for Kenya

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

as a whole, the comparatively truncated values for the adjacent cohorts and their ratios, and the adjusted B60s that measure the trends. In countries where nearly all conceptions occur within marriage, the B60s from marriage to first birth provides useful information, but in Kenya, as is common in sub-Saharan Africa, the configurations are more complicated. There are too many records in which the reported date of marriage is later than the first birth for this index to be meaningful. The B60s are presented for the parity progressions from the first to second births up to the ninth to the tenth in tabular form and also in a series of graphs (see Figure 4A-1). The numbers of women in many of the individual birth order-cohort cells are relatively small. Sample and bias errors can be appreciable. Interpretation of the trends must be based on the overall pattern rather than specific indices. Nevertheless, the results are quite clear. The B60 level is close to 90 percent at low birth orders for the oldest cohort (45–49 years), falling to 80 percent for progressions from five to eight children with a further decrease thereafter. There are distinct, although erratic, declines for younger cohorts in nearly all the progressions, the most doubtful being the fifth to the sixth and the seventh to the eighth. Perhaps the most notable finding is the decrease for the younger cohorts in the B60s at low birth order, even for the movement from first to second. Because most of the women had already attained these births by the time of the survey, the scope for estimation error is small.

To exhibit the trends more effectively, Table 4-7 has been constructed. Here B60s for adjacent parity progressions have been combined to reduce the effects of erratic errors. The slightly anomalous feature of the trend pattern is the apparent modesty of the reductions at the fifth to the seventh births compared to those at higher and lower birth orders by age. This pattern may be an aberrant finding, but it is not necessarily so. The behavioral changes producing the declines in parity progressions are influenced by cohort, time period, and family size factors that may not combine to give strict regularity of trends. Calculations of the ages of mothers at which the

TABLE 4-7 Cumulated B60s from KDHS by Age Group

 

Parity Progression

Age Group

1–3

3–5

5–7

7–9

20–24

.70372

.4325

25–29

.7611

.6751

.6261

30–34

.7713

.7339

.5471

.4080

35–39

7941

.7589

.6161

.5036

40–44

.7969

.7721

.6521

.5526

45–49

.8133

.8108

.6498

.5955

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

births of different orders took place show that the time period is displaced roughly 5 years later for each column of the table. Thus, the measures along downward diagonals are approximately for the same time periods, for example, progression one to three for the cohort aged 30–34 years, three to five for 35–39 years, five to seven for 40–44 years, and seven to nine for 45–49 years. The progressions are consistent with the previous conclusion that there was little change in fertility up to some 10 to 15 years before the KDHS but a strong reduction thereafter. Furthermore, the reduction was considerable at all birth orders, although the measures are not sufficiently precise for the exact pattern to be determined.

COMPARISON OF THE PATTERN OF FERTILITY DECLINE IN KENYA WITH OTHER POPULATIONS

Examination of the pattern of change of the parity progression characteristics of Kenya becomes more significant when comparisons are made with other populations. Juarez (1983, 1987) has presented calculations of B60s from KFS data for several Latin American countries. Brass and Juarez (1983) provided similar results for four Southeast Asian countries. The same methods were applied to the Demographic and Health Survey data for 10 countries of sub-Saharan Africa (other than Kenya) for the present study (Botswana, Burundi, Ghana, Liberia, Mali, Nigeria, Senegal, Togo, Uganda, and Zimbabwe). The interest here is in the trends of parity progression at different orders. To illustrate these as clearly as possible, the progressions have again been combined in adjacent pairs, as in Table 4-7 for Kenya, and expressed relative to the level for the cohort of women aged 45–49 Years. The measures for Burundi, Ghana, Liberia, Mali, and Uganda do not suggest that these populations experienced a significant, systematic reduction in fertility in the period prior to the survey. Only the calculations for Burundi are shown as an example, along with the trends for the countries where evidence of change is impressive. The relative progressions for these seven sets of data are given in Table 4-8A.

The downward trends in the parity progressions for Zimbabwe are very similar to those for Kenya but are even more pronounced at the lower birth orders. Equivalent remarks can be made about the Nigerian measures, but the reductions are just as large at the higher birth orders. The Botswana fertility declines are larger still, but it should be noted that the sample sizes for this country were very small. For example, only 87 women aged 45–49 years recorded a second birth, and 33 a seventh, for Botswana compared to 343 and 241, respectively, in the Kenya survey. The very low measures for the relative parity progressions in Botswana for the first to third and third to fifth births in the youngest age groups of women must be treated with great caution. Nevertheless the evidence is convincing that in Botswana, Nigeria,

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-8A Cumulated B60s (relative to 1,000) from KDHS by Age Group for Cohort Aged 45–49: Africa

 

Parity Progression

Parity Progression

AgeGroup

1–3

3–5

5–7

7–9

1–3

3–5

5–7

7–9

 

Botswana, 1988

Senegal, 1986

20–24

478

 

 

 

932

 

 

 

25–29

756

507

 

 

998

811

 

 

30–34

938

726

797

 

1,044

967

864

 

35–39

1,012

894

765

703

1,022

945

911

867

40–44

1,054

929

970

767

992

949

923

985

45–49

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

 

Burundi, 1987

Togo, 1988

20–24

1,047

 

 

 

851

 

 

 

25–29

1,109

952

 

 

870

962

 

 

30–34

1,046

1,080

1,013

 

940

889

760

 

35–39

1,074

956

1,014

1,093

938

978

792

1,060

40–44

1,005

967

986

851

946

981

870

1,011

45–49

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

 

Kenya, 1988–1989

Zimbabwe, 1988–1989

20–24

866

 

 

 

813

 

 

 

25–29

936

832

 

 

868

704

 

 

30–34

948

904

841

 

892

843

857

 

35–39

987

935

949

845

970

939

909

974

40–44

979

952

1,003

928

958

972

946

1,080

45–49

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

 

Nigeria, 1990

 

20–24

781

 

 

 

 

 

 

 

25–29

881

821

 

 

 

 

 

 

30–34

941

859

792

 

 

 

 

 

35–39

964

898

905

793

 

 

 

 

40–44

976

944

929

768

 

 

 

 

45–49

1,000

1,000

1,000

1,000

 

 

 

 

and Zimbabwe, as in Kenya, the reductions in fertility were spread over all parities in the period 10–15 years before the surveys. The findings for Senegal and Togo are less clear. There are strong signs of decreases in the parity progressions over cohorts for both countries, but the features are erratic and inconsistent. The declines in Senegal hardly extended to the lower birth orders and are more irregular than for Kenya, Zimbabwe, and Nigeria. The measures for Togo are even more uneven, with little reduction at middle (three to five births) and high (seven to nine) orders. Although

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

the conclusion that there have been declines in parity-dependent fertility seems justified, there must be doubts about the mechanisms of change.

Table 4-8B presents comparable measures derived from World Fertility Survey (WFS) data for Latin American and Southeast Asian populations (Juarez 1983, 1997; Brass and Juarez, 1983). For Peru, Panama, and Paraguay the B60s are calculated only up to the sixth birth. The fifth to seventh progression is, therefore, replaced in the table by the fourth to sixth to retain a two-birth interval only slightly displaced from the majority classification. In interpreting the trends, it should be remembered that birth histories are snapshots of the fertility expression at points of time and do not in

TABLE 4-8B Cumulated B60s (relative to 1,000) from KFS by Age Group for Cohort Aged 45–49: Latin American and Asia

 

Parity Progression

Parity Progression

Age Group

1–3

3–5

5–7

7–9

1–3

3–5

5–7

7–9

 

Colombia, 1976

Panama, 1975a

25–29

812

591

 

 

915

 

 

 

30–34

949

731

627

 

984

714

686

 

35–39

980

847

773

676

1,047

956

916

 

40–44

967

983

933

794

1,067

988

837

 

45–49

1,000

1,000

1,000

1,000

1,000

1,000

1,000

 

 

Costa Rica, 1976

Paraguay, 1979a

25–29

630

520

 

 

745

 

 

 

30–34

801

723

513

 

876

871

781

 

35–39

900

856

741

765

892

813

790

 

40–44

966

1,003

898

952

997

910

924

 

45–49

1,000

1,000

1,000

1,000

1,000

1,000

1,000

 

 

Mexico, 1976–1977

Peru, 1977–1978a

25–29

985

813

 

 

968

 

 

 

30–34

998

899

895

834

975

828

908

 

35–39

1,054

973

954

850

1,040

859

949

 

40–44

1,009

1,040

1,022

892

1,024

903

970

 

45–49

1,000

1,000

1,000

1,000

1,000

1,000

1,000

 

 

Korea, 1974

Sri Lanka, 1975

25–29

1,076

 

 

 

898

671

 

 

30–34

1,089

542

 

 

938

740

741

 

35–39

1,091

648

535

 

1,005

837

928

821

40–44

1,041

911

738

654

993

926

928

897

45–49

1,000

1,000

1,000

1,000

1,000

1,000

1,000

1,000

a Fourth to sixth births.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

general correspond to the same stage of development in each case. For the present purpose of comparison with the African changes as recorded in the late 1990s, it is desirable that the initiation of fertility declines should have been some 10 to 15 years before the survey. It is fortuitous, but convenient, that this criterion is roughly met by most of the WFS data sets for Latin America and Asia that are the basis of Table 4-8B. The parity progressions from the WFS for the Dominican Republic in 1975, Pakistan in 1975, and Nepal in 1976 are not shown, because there was no evidence of decline.

The typical trend pattern displayed in Table 4-8B is of initial movements downward at moderate parity progressions (three to four, and four to five). Reductions at higher parities are generally somewhat later and smaller. The extension to lower birth orders (one to two, and two to three) occurs more slowly and may lag considerably as, for example, in Mexico, Peru, and Korea where little, if any, reduction was achieved in the progression from the first to the third birth by the time of the WFS. Nearly all the countries conform well with this typical pattern, although there are uncertainties due to sample fluctuations and the truncation at higher parities, particularly for Paraguay. The most notable exception is Costa Rica where the declines in the parity progressions at the third to the fifth births were matched by the declines at higher and lower orders, such as occurred in Kenya, Zimbabwe, and Nigeria. However, the reductions were much larger in Costa Rica, and the fertility transition was probably at a more advanced stage. The configurations of change in the African countries during the 1980s are not demonstrably unique but are certainly unusual in comparison with the Latin American and Asian experience.

DIFFERENTIALS IN THE PATTERNS OF FERTILITY DECLINE

The B60s for provinces, residence, and educational groups are displayed in Tables 4-9A, 4-9B, and 4-9C. Because of the relatively small numbers, the parities are amalgamated further to show progressions only from the first to the fourth and the fourth to the seventh births. For Kenya as a whole, the downward trend over cohorts for the first to the fourth progression is strongly established, but for the fourth to the seventh it is rather modest, although apparent. This pattern is in conformity with the comments made earlier made about the fifth to the seventh birth progression. The measures for the subpopulations confirm that the fertility reductions contain a strong component of declines in family building and are not due primarily to changes in populations at risk.

The variations among subpopulations cannot be traced with any precision because of the erratic consequences of the small numbers, and reference will be made only to the most notable. The steep trend downward in Central Province for both low and moderate birth orders is evident. The

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-9A Cumulated B60s from KDHS by Age Group—by Province for Parity Progressions One to Four and Four to Seven

Age

Kenya

Nairobi

Central

Coast

Group

1–4

4–7

1–4

4–7

1–4

4–7

1–4

4–7

20–24

.5022

.4617

.5553

.6743

25–29

.6152

.5230

.2836

.5190

.2356

.5434

.3602

30–34

.6631

.4670

.4015

.1780

.7214

.2736

.6424

.5089

35–39

.6935

.5354

4534

.2806

.6712

4849

.6538

.4912

40–44

.7187

.5596

5244

.2326

.7539

.5786

.7708

.6713

45–49

.7326

.5849

.6000

.2084

.6817

.4694

.5663

.5892

Nyanza

Rift

Western

Eastern

1–4

4–7

1–4

4–7

1–4

4–7

1–4

4–7

20–24

.6631

.4757

.8207

.4321

25–29

.7123

.4729

.5790

.8192

.6824

30–34

.7199

.5783

.5434

.3410

.8670

.7002

.6882

.5933

35–39

.7415

.6039

.6867

.4820

.7930

.6274

.6980

.6106

40–44

.7019

.6307

.7064

.4968

.7172

.5986

.7432

.5600

45–49

.8500

.7163

.7112

.4906

.7949

.6596

.7253

.6633

TABLE 4-9B Cumulated B60s from KDHS by Age Group—by Residence for Parity Progressions One to Four and Four to Seven

Urban

Rural

Age Group

1–4

4–7

1–4

4–7

20–24

.4382

.5373

25–29

.3305

.6775

.5693

30–34

.4158

.2506

.7133

.4934

35–39

.4788

.3437

.7235

.5900

40–44

.5146

.2626

.7394

.5811

45–49

.6300

.3248

.7412

.6038

only province for which there are not well-established reductions in parity progressions is Western. In contrast, there are indications of increases for younger cohorts, which add further doubts about the reliability of the data. Of course, there are determinants that could have altered to produce the configuration. The most obvious possibilities are reductions in secondary sterility and shorter periods of lactation. Neither appears very plausible in

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-9C Cumulated B60s from KDHS by Age Group—by Education for Parity Progressions One to Four and Four to Seven

 

No Schooling

1–4 Years

Age Group

1–4

4–7

1–4

4–7

20–24

.6018

.5176

25–29

.6691

.4304

.6492

30–34

.6671

.5536

.7465

.5008

35–39

.7148

.5721

.7530

.5938

40–44

.6750

.5903

.7993

.5749

45–49

.6966

.5965

.8511

.6240

 

5–8 Years

9+ Years

 

1–4

4–7

1–4

4–7

20–24

.5383

.3352

25–29

.5513

.4204

30–34

.7200

.4314

.4406

.1862

35–39

.6207

.4967

.3957

.4408

40–44

.7979

.5043

.4590

.4363

45–49

.7795

.4669

.4243

.3918

such a high-fertility subpopulation, since the magnitude of the biological changes would have to be large to produce more than a marginal effect.

In both Nairobi and the urban areas as a whole, there appears to be little if any downward trend at moderate parities, but the cautions already given about interpretation of the fertility measures for these subpopulations apply. The parity progressions for educational categories are again in line with expectations based on the comparison between the birth rates in the 5 years preceding the KDHS and the KFS. The downward trends are very similar for the subgroups. Again the distinct declines for the residual category of women with no schooling are particularly noteworthy. The picture that emerges from the examination of the B60s for Kenya and its subpopulations is in close agreement with results from the comparison of measures of fertility by subgroups in 1973–1977 and 1984–1988. As pointed out, the former analysis is focused on changes in family building that are little influenced by alterations in the populations at risk. Nor are biological proximate determinants significant factors in parity progression changes, except possibly for secondary sterility. The near universality of the trends over subpopulations suggests that change in sterility is an unlikely explanation. The most plausible reason for the parity progression reduction is the

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

increased use of contraception.2 It is also important to bear in mind that these calculations are based on birth records for cohorts collected at one survey, the KDHS, and are thus insensitive to data errors due to possible lack of comparability in the procedures and efficiency of the two surveys, the KFS and KDHS. The consistency of the findings from the two approaches is a cogent argument for their essential validity.

FERTILITY DECLINES BY DISTRICT

The declines in fertility show distinct regional differences, but the provinces are not sufficiently homogeneous to be accepted as the most effective aggregates for the study of patterns. Unfortunately, the information for analysis at the district level is limited by the sample sizes of the KFS and the KDHS. When tabulations from the 1989 census of women by age and the number of children born to them are available by district, it will be possible to compare the measures with the corresponding data from the 1979 census. Although problems from the underreporting of births in the censuses by the older women will still remain, it is probable that satisfactory allowances can be made and the trends estimated. The direct measures of current fertility based on the census reports of births in the preceding year are subject to substantial error and cannot be used without adjustments. The fertility declines in Kenya between the two censuses should be large enough for the effects to be evident in the mean parities, although the magnitudes of these cohort changes will not be the same as for the time-period movements.

In the KDHS there was some oversampling to provide larger numbers of households in selected districts. Tabulations of births by age group of women and 5-year calendar periods have been made for the 16 districts with the largest sample sizes. The smallest group included is 167 women for Nakuru; the largest excluded, 111 for Kiambu. Nairobi has already been included among the provinces in earlier analysis. Our evaluation of birth reporting at the KDHS concluded that the fertility calculated for the period 10 to 14 years before the survey was in close agreement with the corresponding measure from the 5 years preceding the KFS. Accordingly, the change at the district level is estimated from the comparison of the cumulated rates for women up to age 40 in the 5 years preceding the KDHS with those from 10–14 years before. The upper limit of 40 years is necessary because of the truncation of maternity histories with increasing time in the past.

2  

The role of contraceptive use as a proximate determinant of Kenyan fertility is examined more thoroughly in the next chapter.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Table 4-10 presents the percentage declines in fertility as measured for selected districts and also for provinces. For the latter, the reductions found previously from the comparison of KDHS and KFS estimates (Tables 4-6A to 4-6C) are given in the last column and can be compared to KDHS estimates in the first column. The bases of the two sets of province indices differ in the age ranges of women (less than 40 and up to 50 years), as well as the source and calendar period of the earlier fertility measure (1974–1978 from the KDHS and 1973–1977 from the KFS). Nevertheless the agreement is rather satisfactory except for Nairobi. The discrepancy for the capital is probably due to the less stable nature of the population, leading to both erratic change and reporting errors. In Central, Coast, Eastern, and Western provinces, the fertility declines calculated solely from KDHS data are a few percentage points lower than the corresponding values from the KDHS-KFS comparison, and for Nyanza and Rift Valley slightly above.

The agreement at the province level confirms that the measures of district change based on KDHS alone are valid, but the problem of sample variability remains. To provide some check, the changes in fertility from 5–9 years prior to the KDHS to 0–4 years before for women under 45 were also calculated and are shown in the middle column of the table. The estimates from the two series are not, of course, independent but provide a useful guide to the possible existence of major anomalies. The results are, generally, satisfyingly consistent. On average, the fertility declines from 1979–1983 to 1984–1988 were about two-thirds the corresponding percentages of 1974–1978 to 1984–1988. The largest discrepancies are for Kirinyaga, Kisii, and Uasin Gishu, where the second series suggests that the already large fertility decreases may have been underestimated, and for Bungoma and Kisumu where increases are indicated rather than small reductions. Thus the relative order of the changes is little affected. The one exception is Mombasa where a moderately large fertility decline in the first series becomes a small one in the second. The correlation coefficient between the rankings of the two series (Kendall's) is .68, which indicates a strong association.

The selected districts in the Coast and Rift Valley record fertility declines that are close to the overall measures for their provinces. The district variations in Central Province are greater, although all the reductions are substantial; the 36.5 percent reduction for Kirinyaga is the largest of all the declines. In Nyanza, the fertility decrease of 13.5 percent conceals two small reductions in Kisumu and South Nyanza, as well as two large ones in Kisii and Siaya. The differential for Kisii is not particularly surprising because the district has several characteristics, including ethnic composition, that distinguish it from the rest of Nyanza; the result for Siaya is, however, unexpected. The two selected districts of Western Province have

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-10 Declines in Cumulated Fertility by Province and District

 

Decline (%)

Province and District

1974–1978 to 1984–1988 Women Under 40 Yearsa

1979–1983 to 1984–1988 Women Under 45 Yearsa

1973–1977 to 1984–1988 Women Under 50 Yearsb

Central

26.7

21.4

31

Kirinyaga

36.5

33.5

 

Muranga

20.6

12.4

 

Nyeri

24.2

18.6

 

Coast

21.0

22.6

27

Kilifi

19.3

15.7

 

Mombasa

19.8

5.2

 

Eastern

13.3

12.1

16

Machakos

11.8

6.9

 

Meru

21.7

15.1

 

Nyanza

13.5

8.4

13

Kisii

19.3

18.9

 

Kisumu

6.6

-1.2

 

Siaya

27.4

18.5

 

South Nyanza

6.6

6.3

 

Rift Valley

22.8

16.2

20

Kericho

22.3

11.4

 

Nakuru

23.3

18.7

 

Uasin Gishu

25.8

22.6

 

Western

1.3

-1.8

5

Bungoma

8.1

-4.8

 

Kakamega

10.2

4.8

 

Nairobi

14.09.1

26

a Estimated from births in time periods reported in the KDHS.

b Estimated from births reported in the five years prior to the KDHS and KFS.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

small fertility declines, and the province measure is even lower, implying that such is the case for the one district excluded, Busia.

The selected districts comprise 17 of the 41 total but include more than two-thirds of the Kenyan population. Many of the excluded areas are geographically remote from the central area and are thinly populated, but those characteristics do not make their fertility changes less interesting—rather, the contrary is true. In particular, the results for Coast Province demonstrate that the declines for the districts not selected must on average have been a little greater than the overall provincial measure of 21 percent to balance the 19.3 percent for Kilifi and 19.8 percent for Mombasa. Of the remaining districts, three (Kwale, Lamu, and Tana River) have typical Coast Province features of low educational level and high child mortality with little improvement; the other, Taita, does not conform to these characteristics but includes only one-quarter of the population of the selected districts and slightly more than one-tenth of the province as a whole.

A further check was applied by comparing the mean parities by age groups of women for the selected districts at the 1988–1989 KDHS with corresponding measures from the 1979 census. There is clear evidence of the underreporting of births by older women in 1979, but the mean parities for younger women are close on average to measures from the 1977–1978 KFS. Another reason for omitting the older women in the comparisons is the fact that a considerable proportion of their births took place before the fertility decline was clearly established. After investigation it was decided that only the first four 5-year age groups (i.e., 15–34 years) should be retained to provide a measure of fertility as a sum of the mean parities. Various alternative systems of weighing the mean parities were examined, but the resulting changes in the estimates of fertility declines from the 1979 census to the 1988–1989 KDHS were too small to justify the added complication.

Table 4-11 gives the ratios of the fertility index from the 1988–1989 KDHS to the 1979 census for provinces and the selected districts. These are generally slightly lower than the measures presented earlier, probably because of relative underreporting of births at the census, but in Nyanza the discrepancy is in the opposite direction. The possible effects of age misstatements and sample errors should not be forgotten. It is arguable whether adjustments for the presumed birth reporting errors at the 1979 census should be made for districts, but on balance, their use seems to improve the measures of change. The adjustment made assumes that the proportional discrepancy between the 1979 census and the 1977–1978 KFS for a province also applies to districts in the province. The adjusted ratios are then translated into percentage declines in the fertility index. The last column repeats for comparison the district fertility reduction between 1974–1978 and 1984–1988 calculated from the KDHS birth histories. The province divergences

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

TABLE 4-11 Declines in Fertility Indices by Province and District

 

Mean Parity Index Ratios 1998–1999 KDHS to 1979 Census (per thousand)

 

Province and District

Reported

Adjusted

Reduction Based on Adjusted %

Cumulated Fertility Reduction 1974–1978 to 1984–1988 from KDHS (%)

Central

889

879

12.1

26.7

Kirinyaga

815

806

19.4

36.5

Muranga

848

839

16.1

20.6

Nyeri

901

891

10.9

24.2

Coast

889

847

15.3

21.0

Kilifi

890

848

15.2

19.3

Mombasa

882

840

16.0

19.8

Eastern

904

898

10.2

13.3

Machakos

881

875

12.5

11.8

Meru

920

914

8.6

21.7

Nyanza

911

959

4.1

13.5

Kisii

830

874

12.6

19.3

Kisumu

854

899

10.1

6.6

Siaya

1,035

1,090

-9.0

27.4

S. Nyanza

916

964

3.6

6.6

Rift Valley

925

893

10.7

22.8

Kericho

980

946

5.4

22.3

Nakuru

666

643

35.7

23.3

Uasin Gishu

908

877

12.3

25.8

Western

981

951

4.9

1.3

Bungoma

969

939

6.1

8.1

Kakamega

985

955

4.5

10.2

Nairobi

915

759

24.1

14.0

(reported to adjusted) are only around 1 percent in Central and Eastern provinces, and 3 percent in Rift Valley and Western, but rise to 5 percent in Coast and Nyanza. The disagreement for Nairobi is much larger, but there are particular problems of obtaining accurate reports from this fluid population.

As already noted, the declines in the mean parity index are not measuring the same effects as the direct time-period reductions. There should, however, be a reasonably consistent relationship. If Nairobi is excluded, the decreases in the mean parity index are overall roughly three-fifths of the change in the direct time-period fertility measures. For individual provinces there is a moderate fluctuation about this relation but no striking anomaly. The adjusted mean parity decline for Nairobi is well above the expected value, but the unadjusted is considerably below. Discrepancies at the district level are greater than at the province level in accord with expec-

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

tation, but the general order of the fertility declines is largely preserved. Thus, Nakuru and Kisumu record substantially greater fertility reductions in the mean parity ratios than in the time-period measures, but in the former case the shift is from high to the highest and in the latter from the lowest to moderately low. The divergences for Kericho and Machakos are of more concern because the former ranks well up for fertility reduction by the direct time-period measurement but near the lowest for the mean parity ratio, and the shift for the latter district is the reverse. The most striking anomaly of all is for Siaya, which shows the second highest decline in the cumulated fertility measure but a rise in mean parities (whether adjusted or not). It is clear that care has to be exercised in drawing conclusions about fertility change for these districts in which the evidence is contradictory.

SUMMARY

The evidence from censuses and surveys indicates that fertility fell in the late 1970s and the 1980s, probably with a more rapid rate of decline in the late 1980s. Total fertility decreased from approximately 8.2 births per woman in 1973–1977 to 6.7 births per woman in 1984–1988. The decline was notable in that it occurred in almost every subgroup. All age groups contributed to the decline, with the middle and later reproductive ages contributing the most. Fertility declined 23 percent in urban areas, followed closely by a decrease of 17 percent in rural areas. The greatest fertility reduction among the provinces occurred in Central Province (31 percent). The fertility decline in Coast Province, noted for its substantial Muslim population, was also high (27 percent). Western Province was an anomaly, with a small decline of only 5 percent, which may be more a result of the data than a reflection of what actually occurred. Fertility reductions in percentage terms were very similar regardless of women's educational level.

Fertility declines occurred at all birth orders, a pattern that is very different from that observed in Latin America and Asia, where fertility declines started in the middle parities and moved successively to the higher and then lower birth orders (see Caldwell et al., 1992, for related discussion on the differences of fertility declines in Africa). An analysis of the patterns of decline among other sub-Saharan African countries revealed a pattern similar to Kenya in those countries experiencing fertility change.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

APPENDIX

TABLE 4A-1 The Proportion of Women Progressing to the Next Birth Within 5 Years, KDHS, All Women

 

Parity Progression

 

Ist–2nd

2nd–3rd

3rd–4th

4th–5th

B60 unadjusted

 

 

 

 

Cohort age

 

 

 

 

15–19

.9246

1.0000

.5625

.0000

20–24

.8744

.8832

.8122

.5952

25–29

.8926

.8975

.8638

.8697

30–34

.8747

.9081

.8907

.8893

35–39

.9056

.8934

.8877

.8818

40–44

.8966

.8961

.9049

.8598

45–49

.9121

.8917

.9008

.9001

Cohort age (5 years later)

 

 

 

 

20–24a

.8808

.5362

.2779

.0000

25–29a

.9080

.9198

.9199

.8203

30–34a

.8762

.9264

.9188

.8889

35–39a

.9082

.9005

.9048

.9052

40–44a

.8974

.9047

.9167

.8688

45–49a

.9137

.8975

.9038

.9039

Indices of relative change

 

 

 

 

20–24/25–29a

0.9630

0.9602

 

 

25–29/30–34a

1.0187

0.9687

0.9402

.9783

30–34/35–39a

0.9631

1.0085

0.9844

.9824

35–39/40–44a

1.0091

0.9875

0.9684

1.0149

40–44/45–49a

0.9813

0.9985

1.0012

.9512

B60 adjusted

 

 

 

 

20–24

.8533

.8247

 

 

25–29

.8861

.8589

.8083

.8352

30–34

.8699

.8866

.8597

.8537

35–39

.9032

.8792

.8733

.8690

40–44

.8951

.8903

.9018

.8562

45–49

.9121

.8917

.9008

.9001

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

 

Parity Progression

 

5th–6th

6th–7th

7th–8th

8th–9th

9th–10th

B60 unadjusted

 

 

 

 

 

Cohort age

 

 

 

 

 

15–19

.0000

.0000

.0000

.0000

.0000

20–24

1.0000

.0000

.0000

.0000

.0000

25–29

.9180

.9093

.8549

.3388

.0000

30–34

.8620

.8076

.8980

.7195

.6169

35–39

.8321

.8557

.8266

.8086

.8342

40–44

.8777

.7966

.7955

.7874

.7094

45–49

.8174

.7950

.8018

.7427

.6763

Cohort age (5 years later)

 

 

 

 

 

20–24a

.0000

.0000

.0000

.0000

.0000

25–29a

.4302

.1417

.0000

.0000

.0000

30–34a

.8713

.8370

1.0000

.8769

.4020

35–39a

.8484

.9241

.8362

.9540

.7319

40–44a

.9005

.8369

.8771

.8361

.8164

45–49a

.8303

.8391

.8202

.8231

.7891

Indices of relative change

 

 

 

 

 

20–24/25–29a

 

 

 

 

 

25–29/30–34a

1.0536

1.0864

 

 

 

30–34/35–39a

1.0160

0.8739

1.0740

0.7542

0.8428

35–39/40–44a

0.9240

1.0224

0.9424

0.9670

1.0218

40–44/45–49a

1.0570

0.9494

0.9699

0.9567

0.8989

B60 adjusted

 

 

 

 

 

15–19

 

 

 

 

 

20–24

 

 

 

 

 

25–29

.8546

.7326

 

 

 

30–34

.8112

.6744

.7871

.5183

.5236

35–39

.7984

.7717

.7329

.6871

.6213

40–44

.8640

.7548

.7777

.7106

.6080

45–49

.8174

.7950

.8018

.7427

.6763

a Truncated to give the same age range of births as for the next lower age group.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Figure 4A-1 Unadjusted and adjusted B60s for the parity progressions.

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Fourth to Fifth Birth

Fifth to Sixth Birth

Sixth to Seventh Birth

Suggested Citation:"4 Fertility Trends." National Research Council. 1993. Population Dynamics of Kenya. Washington, DC: The National Academies Press. doi: 10.17226/2210.
×

Seventh to Eighth Birth

Eighth to Ninth Birth

Ninth to Tenth Birth

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This detailed examination of recent trends in fertility and mortality considers the links between those trends and the socioeconomic changes occuring during the same period.

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