WDP-107 Public Disclosure Authorized

IiILI uI WorldBank Discussion Papers Kenya at the Public Disclosure Authorized Demograph'ic Turning Point?

Hypotheses and a Proposed

Public Disclosure Authorized Research Agenda

Allen C. Kelley Charles E. Nobbe Public Disclosure Authorized E w4 Recent World Bank Discussion Papers

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(Continued on the inside back cover.) 10 7~ZI World Bank Discussion Papers Kenya at the Demographic Turning Point?

Hypotheses and a Proposed Research Agenda

Allen C. Kelley Charles E. Nobbe

The World Bank Washington, D.C. Copyright ( 1990 The International Bank for Reconstruction and Development/THEWORLD BANK 1818 H Street, N.W. Washington, D.C. 20433, U.S.A.

All rights reserved Manufactured in the United States of America First printing November 1990

Discussion Papers present results of country analysisor research that is circulated to encourage discussion and comment within the development community. To present these resultswith the least possible delay, the typescript of this paper has not been prepared in accordance with the procedures appropriate to formal printed texts, and the World Bank accepts no responsibilityfor errors. The findings, interpretations, and conclusions expressedin this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, to its affiliatedorganizations, or to members of its Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibilitywhatsoever for any consequence of their use. Any maps that accompany the text have been prepared solely for the convenience of readers; the designationsand presentation of material in them do not imply the expression of any opinion whatsoever on the part of the World Bank, its affiliates,or its Board or member countries concerning the legal status of any country, territory, city, or area or of the authorities thereof or concerning the deiniitation of its boundaries or its national affiliation. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to Director, Publications Department, at the address shown in the copyright notice above. The World Bank encourages dissemination of its work and will normally give pemuissionpromptly and, when the reproduction is for noncommercial purposes, without asking a fee. Pernmissionto photocopy portions for classroomuse is not required, though notification of such use having been made will be appreciated. The complete backlist of publications from the World Bank is shown in the annual Index of Publications, which contains an alphabetical tide list (with fill ordering information) and indexes of subjects, authors, and countries and regions. The latest edition is availablefree of charge from the Publications SalesUnit, Department F, The World Bank, 1818 H Street, N.W., Washington, D.C. 20433, U.S.A., or from Publications, The World Bank, 66, avenue d'Iena, 75116 Paris, France.

ISSN: 0259-210X

Allen C. Kelley is James B. Duke professor of economics at Duke University; Charles E. Nobbe is a population and health specialistwith the Canadian International Development Agency; both are consultants to the World Bank.

Library of Congress Cataloging-in-Publication Data Kelley, Allen C. Kenya at the demographicturning point? : hypotheses and a proposed research agenda / Allen C. Kelley, Charles Nobbe. p. cm. -- (World Bank discussion papers ; 107) Includes bibliographicalreferences. ISBN 0-8213-1692-3 1. Kenya--Population. 2. Kenya--Populatlonpolicy. I. Nobbe, Charles, 1934- . II. InternationalBank for Reconstructionand Development. III. Title. IV. Series. HB3662.5.A3K44 1990 304.6'096762--dc2O 90-19533 CIP - iii -

Preface and Acknowledgments Research for this Sector Report was undertaken in Nairobi in November 1989. The Report was largely written in eight weeks and, as such, does not represent a completed piece of basic research, but rather an agenda of hypotheses that appear to be especially relevant to the analysis of fertility and population programming in Kenya in light of the results of the recent Demographic and Health Survey. Charles Nobbe, whose background includes experience in designing and assessing MCH and family planning programs, was responsible for the description and analysis of Kenyan population policies and programs in sections 4.0 and 5.52. Allen C. Kelley, an economic-demographer, drafted the rest of the paper. Overlaying this division of responsibilities was vigorous and constructive debate that conditioned all aspects of the study.

The thematic orientation of this Report, and its initiation and encouragement, was provided by Dr. V. Jagdish. His confidence in us by providing a free hand to develop our ideas and recommendations as we saw fit is especially appreciated. The facilities and support of the National Council for Population and Development in Nairobi, and in particular the cooperation and assistance of Simon W. Ndirangu, Director, and Walter Obungu and Paul M. Kizito, are gratefully acknowledged.

Ms. Ruth Kagia of the World Bank-Nairobi was especially helpful in providing data and ideas relating to Kenyan education trends and costs--a key theme in our analysis. Dr. Ann Way of the Institute for Resource Development provided us special statistical tabulations on the DHS, and Dr. Annie Cross of IRD was helpful in interpreting some aspects of the household survey.

Data on population programs have been generously compiled and supplied by Dr. David Oot and Ms. Laura Slobey, USAID/Nairobi; Mr. Linus Ettyang and Ms. Rose Mosongo, NCPD/Nairobi; and Ms. Caroline Blair, UNFPA/Nairobi. Ms. Irene Muranguri and Mr. Peter Nunda at the Family Health Divison, Ministry of Health, renedered valuable assistance in compiling financial and manpower statistics. Helpful feedback on various drafts was provided by Martha Ainsworth, Christine Allison, Jill Armstrong, Nick Barnett, Susan Cochran, Althea Hill, and Dennis Mahar.

ACK and CN 5-23-90

-v -

TABLE OF CONTENTS Page No.

Executive Summary ix

1.0 Introduction 1

2.0 Population Trends 4

3.0 Economic Trends 7

3.1 Overview and Comparative Analysis: The Long 7 Run

3.2 Variability and Vulnerability: The Short Run 10

4.0 Population Policies and Programs: 1965-1989 14

4.1 Introduction 14

4.2 Kenyan Commitments: Political and 14 Programmatic

4.3 Financial Resources: Foreign and Local 21 Expenditures for Population Activities

4.4 Human and Physical Resources: Trained 23 Personnel, Service Delivery Points, and Contraceptive Supplies

5.0 A "Turning Point" (?) in Kenyan Demographic 27 History: Documentation, Speculations and Issues

5.1 An Historical Perspective 27

5.2 An Analytical Perspective 28

5.3 The Demographic Transition in Kenya 31

5.4 The Turning Point (?) 33

5.41 Total Fertility Rate: Spread and 33 Variation by Province 5.42 Total Fertility: Correlates 35 5.43 Total Fertility: International 36 Comparisons 5.44 Total Fertility: Family Planning 37 5.45 Total Fertility: Trends and Timing 37

5.5 The Turning Point: Causes and Implications 41 - vi -

5.51 The Political Environment 41 5.52 The Provision of Family Planning 43 Services 5.53 The Drought 47 5.54 The Prevalence of AIDS 48 5.55 Infant and Child Mortality 49 5.56 The Costs of Education 50 5.57 Fertility Decline and the Turning 54 Point Revisited

5.6 The Turning Point: The Roles and 56 Determinants of Family Planning

5.61 The Roles 56 5.62 The Determinants 61

5.7 The Turning Point: Strategies and Policies 66

5.71 An Analytical Perspective: The Relative 66 Roles of Socioeconomic Change and Family Planning 5.72 Applications to Kenya 69

6.0 A Research Agenda 71

6.1 The State of Kenyan Population Research 71

6.2 Illustrative Research Themes 72

6.3 A Strategy and Proposal for Generating 76 Increased Momentum in NCPDs Population Research Program

7.0 Population Strategies, Policies and Programs: 79 Recommendations

7.1 The Perspective 79

7.2 Principles and Priority Areas of Focus 79

References 87

Appendix

A. Childhood Immunization by District, Two Points 94 in Time, Within Interval 1980-1989 - vii -

TABLES AND FIGURES

Page No.

Table 2.0 Population Size, Fertility and Infant 4 Mortality: 1948-1987

Table 2.1 Fertility, Child and Infant Mortality 6 Rates by Province: 1989

Table 3.0 Comparisons of Kenyan Production/- 9 Consumption/Accumulation Indicators

Table 4.0 Major Events in Population Policy- 15 making and Program/Planning in Kenya: 1965-1989

Table 4.1 Foreign and Local Expenditures for 22 Population Activities in Kenya and Sub-Saharan Africa: 1983 - 1988

Table 4.2 Comparative Data on Training of Health 24 Workers and Service Delivery Points in Kenya: 1981-1985 and 1985-88

Table 4.3 Contraceptive Supplies Provided by 26 Donors to Kenya's Population Program: 1984 - 1988

Table 5.0 Crude Birth and Death Rates, Selected 31 Regions and Kenya

Table 5.1 Total Fertility Rates by Province 33

Table 5.2 Correlations (r2's) Between TFR and 35 Selected Variables

Table 5.3 Trends in TFR for 10-14 and 0-4 Years 36 Prior to the WFS, by Level of Development and Strength of Family Planning Effort

Table 5.4 Age-Specific Fertility Rates, Family 39 Size Preferences, and Contraception in Kenya: 1977-1989

Table 5.5 Family Planning/Acceptors: 1984-1988 44

Table 5.6 Contraceptive Prevalence Rates by 57 Province - viii -

Table 5.7 Contraceptive Prevalence Rates of 58 Married Women of Reproductive Ages, Currently Using Each Method, at Early Stages of the Fertility Transition

Table 5.8 Shift form Traditional to Modern 59 Methods of Contraception

Table 5.9 Correlations (r2's) Between the TFR 60 and CPR by Province

Table 5.10 Selected Correlates of Family 62 Planning Use by Province: 1984, 1989

Table 5.11 Correlations (r2's) Between CPR and 64 Selected Variables

Table A Childhood Immunization by District, 95 Two Points in Time Within Interval 1980 - 1989

Figure 1 Growth of Kenyan Output: 1972-1988 12

Figure 2 Schematic Diagram of the Demographic 27 Transition

Figure 3 Easterlin's Model of Fertility 29 Determination

Figure 4 Path Analysis of the Effect of 68 Program Effort on Contraceptive Prevalence for 73 Developing Countries EXECUTIVE SUMMARY

An analysis of the determinants and consequences of rapid population growth in Kenya represents an encounter in contrast and paradox. On the one hand, no country in Africa has had a longer tradition of concern about rapid population growth. Yet, the apparent record of achievement in population programming has been disappointing--at least as measured by the high annual rate of population growth approaching 4 percent. On the other hand, in spite of concerns by Kenyan and world leaders about population growth that have sometimes reached alarmist proportions, this nation has been able to accommodate a high rate of population increase, although not without considerable effort and cost. This report examines these paradoxes not by providing yet another rendering of the impacts or potential challenges of accommodating rapid population change--such reports are readily available and would thus serve little useful purpose--but by analyzing the manner in which Kenya has confronted its unusual demographic situation. This analysis will speculate why, now a quarter of a century after Independence, Kenya's demography may be entering a new phase of declining growth rates.

Interestingly, this new phase may well be substantially caused by those very adverse impacts of rapid population growth highlighted by analysts and political leaders in the past. Indeed, "population pressures" may be naturally, and without fanfare, giving rise to a moderated pace of demographic change by reducing the benefits and raising the costs of children to the household.

It is plausible that the results of population programming in Kenya, while somewhat disappointing to date, may in fact represent a key to success in the future. For example, improved systems of health delivery and family planning, now in place, will predictably influence the pace at which population growth recedes in the coming decades. One might therefore characterize the record of population programming in the past as one of laying the groundwork with infrastructure that will reap increasing returns in the future. These programs will facilitate the powerful interactions between demographic and economic change to adjust to a new, sustainable balance between population numbers and the economy and environment. It is uncertain how fast this adjustment will take place. That depends fundamentally on the strength of those factors motivating parents to seek smaller families. And this, in turn, depends on the interactions of population growth and the society and economy, as well as population policy and programming. This -x - report, which raises more questions than it answers, attempts to provide a preliminary assessment of some of these interactions in a way that lays the basis for developing both a research agenda, and some insights useful for formulating a population strategy for Kenya over the next decade. To provide background for such an assessment, the record of population change in Kenya is examined in section 2.0. As an overlay to the demography, aspects of the way in which the Kenyan economy has performed, has accommodated, and has been influenced by rapid population growth is next presented in section 3.0. The interactions of population with the economy have been conditioned by population policies and programming. Both are summarized in section 4.0.

The development of population policies and programs requires identifying those factors giving rise to the "turning point" in fertility. Will the decline in fertility be rapid or slow? Will it be sustained, or will there be pauses of relatively constant rates of change? Unfortunately, population research on Kenya precludes the providing of even provisional answers to these questions. However, some empirical results are beginning to emerge from the recently released Demographic and Health Survey 1989 (DHS), and from an analysis of economic and social trends and events. Some of these results are presented in section 5.0. Given the limited number of observations across geographic areas (districts/provinces) in the published DHS, any conclusions based on geographic associations must be guarded, and indeed be considered as informed working hypotheses until the micro data can be examined in detail. The analysis does, however, permit the identification of some issues that require further research to arrive at a longer-run population strategy for Kenya--one that not only facilitates the pace of fertility decline in the short run, but more importantly, sustains that pace over time. (Components of a research agenda are provided in section 6.0.) Section 7.0 provides a set of recommendations. Population Trends Kenyan population growth has risen steadily from an annual rate of 2.5 percent in 1948 to around 3.8 percent in the 1980s. These trends derive from an increase in the total fertility rate (TFR) from 6 to almost 8 children over this period (the crude birth rate has been high and constant at around 50 per thousand), and a steady decline in the crude death rate from 25 to around 13 per thousand. Reductions in child and general mortality, and especially in infant mortality (from 184 per thousand in 1948 to 60-80 per thousand in the late 1980s) have increased life expectancy at birth from 35 to over 56 years. - xi -

A "turning point" in population growth may be in the making. Evidence from the recent DHS shows a substantial reduction in the TFR from 7.7 in 1984 to a current figure of 6.7. Government projections (based on the higher TFR) place the Kenyan population at 35 million by the turn of the century.

A striking feature of Kenyan demography is the substantial variation in fertility and mortality by geographic area. Total fertility rates vary from a low of 4.6 in Nairobi to a high of 8.1 in Western Province; infant mortality is lowest in Rift Valley and Central (34.6 and 37.4, respectively), and highest in Coast (107.3). These figures justify caution in generalizing national experience to individual areas, as well as implementing a common format of population programming to all regions of the country. They, in addition, offer clues on the determinants of the decline in total fertility.

Economic Trends

Kenya's overall economic and social development has been quite respectable since Independence, at least as compared with the experience of other developing countries. The record can be divided into early (1965-1980) and late (1980-1987) periods, corresponding to relatively normal growth, and a decade when most countries sustained poor performance. During the first period Kenyan per capita output grew by 2.8 percent per year, exceeding the average of Sub-Saharan Africa (2.4 percent) and South Asia (1.4 percent), although short of the impressive performance of Latin America and the Caribbean (3.5 percent). Agricultural expansion exceeded population growth by 1.3 percent; and industrial growth, while beginning from a small base, proceeded at a substantial pace (6.2 percent). Most countries performed badly in the 1980s and Kenya was no exception. While there was a slight reduction in output per capita, this retrenchment was less than that experienced in Sub- Saharan Africa and Latin America/Caribbean. The severe drought of 1984 exacerbated the adverse impacts of the second oil crisis and the world recession on Kenya. Presently the expansion of output per capita is moderate in major producing sectors. A driving force in Kenyan development has been high rates of accumulation, both in physical and human capital. Gross domestic investment as a share of GDP has risen from around 14 to 25 percent, and is presently one of the highest in the Third World. Most of this investment is financed from domestic saving which is around 20 percent of GDP--also high by world standards. Kenyans have invested heavily in human (capital) development as well. The increase in the primary school enrollment ratio from 54 to 94 percent is exceptional by world standards. While - xii -

Kenya's central government expenditures on education are substantial (5.8 percent of GNP), given the manner in which education is financed through increased and now considerable "cost sharing" at the local (household) level, the total share of national output devoted to education could well exceed 10 percent.

The ability to accommodate high rates of population growth in the future will be increasingly difficult given the mounting pressures on the land and the fragile ecology in what is still a dominantly agrarian-based economy. Moreover, demographic pressures, requiring heavy investments in education and health services, as well as in job creation as the baby boom enters working ages, will necessitate structural adjustments toward an economy less dependent on agriculture. In a sense demographic pressures are not only stimulating structural changes, but importantly these pressures (e.g., the financing of education) are beginning to provide a primary mechanism causing population growth itself to decline. Population Policies and Programs

To what extent can the recent reduction in the TFR be attributed to population policies and programs in Kenya? An answer to this question requires an assessment of the political and financial commitments, mainly to family planning. The evolution of political statements and programs directed toward population in Kenya has been path breaking as assessed by African historical experience. However, until recently Government's programs in population have been modest in scope and intensity--the record can be characterized as a deliberate, but not a particularly dynamic progression of Government commitment and activity.

In 1984 there was a modest break in the previous, slow-paced evolution, toward a somewhat expanded involvement in population matters, as signaled in a pair of speeches by Hon. Mwai Kibaki at the Second World Population Conference in Mexico City, and President Daniel Arap Moi in his address at the first National Population Conference held in Nairobi. Whether the recent increasingly strong political statements are now in fact translated into efficient and effective population programs remains to be seen. An examination of the financial and related resource commitments (e.g., family planning training and service delivery points) by Kenya and international donors to population programming, as well as measures of outcomes (e.g., family planning acceptors), reveals that serious government commitment to fertility reduction through active population programming began around 1987, and that the momentum is likely to be maintained for at least the next few years. - xiii -

A "Turning Point" (?) in Kenyan Demographic History: Documentation. Speculations and Issues

Three questions arise in assessing the "turning point" in Kenya. First, what is the nature and plausibility of the recent turning point in fertility; are its timing and magnitude of change believable? Second, what factors appear to account for the break in fertility? While answers to this question must be speculative, they not only provide elements of a research agenda, but also provide yet another basis for assessing the turning point. If quantitatively important factors causing a reduction in fertility cannot be identified and documented, confidence in the (magnitude of the) observed downturn in fertility must be qualified. Third, what have been the roles of family planning (FP) in accounting for the fertility decline?

Several empirical findings relating to the turning point are notable.

e The reductions in the TFR between 1984 and 1989 are pervasive geographically, occurring in most provinces. * The reductions are large by international standards.

* While increases in the contraceptive prevalence rate (CPR) per se account for a significant portion of the decline in the TFR, much of the decline is due to other factors (e.g., an unexplained increase in the effectiveness of contraception).

* The reductions do not appear to be as strongly correlated with changes in the "usual" socioeconomic determinants as might be expected. This suggests another major (given the size of the TFR change), common (given the pervasive spread of the TFR changes) factor or factors as possibly triggering the fertility decline. (As a group, the usual socio- economic factors are certainly antinatal in impact, serving to reinforce the emerging downward trend in fertility. A threshold model, or one positing substantial lags, may also be offered as alternative perspectives in accounting for the turning point.)

Six "unusual" factors occurring during the mid-1980s were assessed as possible candidates explaining the TFR decline. Five, while supportive, probably did not act as the primary underlying cause. They are:

* Changes in the political environment toward FP, the results of which did not translate into increasingly effective programming and manpower/financial commitment until very late in the period. - xiv -

* Changes in the provision of FP services, which did not expand notably until the late 1980s. However, returns on earlier FP infrastructure did indeed partially account for observed increases in the CPR, a trend motivated in large part by shifts in demand for children. * The drought of 1984, which is probably viewed as an "unusual" short-run phenomenon, and thus unlikely to have affected long-run fertility decisions.

* AIDS, whose potential impact on increasing the use of condoms for disease protection is not yet notable, at least in the period that could have influenced the TFR. * Changes in infant/child mortality (in part resulting from the expanded immunization program), which may well have been influential in accounting for the timing of the turning point, although the evidence is shaky. (Mortality changes are more likely to exert impacts on longer-run fertility trends.) An important factor accounting for the turning point in fertility was probably the substantial increased costs of education resulting from the (8-4-4) curriculum reform of 1985 and the financing of a major portion of those costs by parents in direct proportion to the number of their children in school. This format/distribution of financing is unusual by international standards, and may represent an emerging "African Model" (applicable to other public services as well) that could be important to influencing the pace and pattern of future fertility trends. Will the downward trend in fertility continue? Yes, since several of the antinatal fertility impacts will continue to intensify over time. However, the trend could be attenuated if FP services do not continue to improve in accessibility and effectiveness, unemployment of youth results in diminished parental investments in education, and infant/child mortality rates do not continue to decline.

Several empirical findings relating to FP in Kenya are notable. * The change in the overall rate from 17.0 in 1985 to 26.9 in 1989, while seemingly large, is not unusual at the turning point, as assessed by international experience.

* There has been some shift toward modern methods. - xv -

* The association of the TFR and the overall CPR appears to be weak, although it is fairly strong for modern methods, suggesting the relative efficiency of the latter.

* The CPR appears to be strongly associated with ideal family size and reductions in child mortality; and somewhat less with knowledge of methods, which is already quite high.

The reduction in the "demand" for children has been large, with "ideal family size" declining from 6.3 to 4.8 children over the five-year period. This is corroborated by the percent of mothers who state "they don't want more children," which increased from 31.5% in 1984 to almost 50% in 1989.

The reduction in demand has been widespread, even in areas that appear to be lagging in the fertility transition.

* Lagged infant/child mortality impacts on the CPR are empirically strong, as indirectly measured by the impact of immunization on the CPR. Integration of immunization and MCH with FP appears particularly promising in high-mortality areas.

Research Providing answers to the types of questions asked in this report--in particular, explaining the turning point in fertility, ascertaining whether recent fertility trends will be sustained, and identifying how specific population policies and programs will impact on these trends--requires a reasonably detailed empirical research base. Unfortunately, the volume of high- quality policy-oriented empirical research on fertility, mortality, and family planning is insufficient to meet current programmatic requirements for project formulation and evaluation. Kenya must move to close this "research gap." Population Strategies and Policies Based on the results of this report and the experience of other countries, eleven areas of population programming are singled out for attention. These can be grouped into five strategies in which it is argued that Kenya should: * Develop an IEC strategy and program that focuses not on lowering the "value" of children, but on lowering the social/psychological costs of family planning; and consider options for redirecting IEC efforts toward a major public-, or possibly private-sector program that eliminates unproductive duplication, and is sensitive to local conditions and needs. - xvi -

* Improve the effectiveness and efficiency of the existing service delivery facilities and system, even if this is at the expense of some replication and expansion.

* Intensify the research program to provide timely and useful inputs to population policy and programs.

* Formulate differentiated population programs and targets specific to relatively small geographic areas (i.e., districts).

* Position NGOs to undertake a relatively larger role in service delivery and a relatively smaller role in IEC, and the public sector to shift its responsibilities primarily to coordination, financing, and possibly IEC. 1.0 Introduction

An analysis of the determinants and consequences of rapid population growth in Kenya represents an encounter in contrast and paradox. On the one hand, no country in Africa has had a longer tradition of concern about rapid population growth. Yet, the apparent record of achievement in population programming has been disappointing--at least as measured by the high annual rate of population growth approaching 4 percent. On the other hand, in spite of concerns by Kenyan and world leaders about population growth that have sometimes reached alarmist proportions, this nation has been able to accommodate a high rate of population increase, although not without considerable effort and cost. While one can only speculate about what might have occurred had population growth rates been lower, the fact remains that since Independence, Kenya has amassed one of the better records of economic and social development on the African continent. Moreover, its overall performance compares favorably with other relatively low-income countries that began the process of modern economic growth during this period. This report will examine these paradoxes not by providing yet another rendering of the impacts or potential challenges of accommodating rapid population change--such reports are readily available1 and would thus serve little useful purpose--but by analyzing the manner in which Kenya has confronted its unusual demographic situation. This analysis will speculate why, now a quarter of a century after Independence, Kenya's demography may be entering a new phase of declining growth rates.

Interestingly, this new phase is substantially caused by those very adverse impacts of rapid population growth highlighted by analysts and political leaders in the past. Indeed, "population pressures" may be naturally, and without fanfare, giving rise to a moderated pace of demographic change by reducing the benefits and raising the costs of children to the household. This should come as no surprise; it is a result consistent with a large scholarly literature on the determinants of population change, as well as the experience of scores of nations, past and present. As such, the intriguing aspect of Kenya's demographic future is that it may be one of the first African countries to enter the declining-growth-rate phase of the "Demographic Transition." This constitutes uncharted waters for the continent, and an opportunity to gain insights into whether Kenya's experience represents an "African Model" from which other countries may benefit.

IFor example, World Bank (1988a), pp. 3-5; Republic of Kenya (1984a), pp. 9-13, and (1987), pp. 1-6; and Ominde (1980, 1984). 1 It is plausible that the results of population programming in Kenya, while somewhat disappointing to date, may in fact represent a key to success in the future. For example, improved systems of health delivery and family planning, now in place, will predictably influence the pace at which population growth recedes in the coming decades. One might therefore characterize the record of population programming in the past as one of laying the groundwork with infrastructure that will reap increasing returns in the future. These programs will facilitate the powerful interactions between demographic and economic change to adjust to a new, sustainable balance between population numbers and the economy and environment.

It is uncertain how fast this adjustment will take place. That depends fundamentally on the strength of those factors motivating parents to seek smaller families. And this, in turn, depends on the interactions of population growth and the society and economy, as well as population policy and programming. The present report will attempt to provide a preliminary assessment of these interactions in a way that lays the basis for developing a population strategy for Kenya over the next decade. To make such an assessment, it is first useful to examine briefly the record of population change in Kenya. Section 2.0 provides the background necessary to appraise the significance of recent changes in fertility, and the basis for formulating some revised views of Kenya's demographic future. As an overlay to the demography, aspects of the way in which the Kenyan economy has performed, and has accommodated and has been influenced by rapid population growth are presented in section 3.0.

The interactions of population with the economy have been conditioned by population policies and programming. Both are summarized in section 4.0. To develop population policies and programs, it is important to isolate those factors giving rise to the "turning point" in fertility. Will the decline in fertility be rapid or slow? Will it be sustained, or will there be pauses of relatively constant rates of change? Unfortunately, population research on Kenya precludes the providing of even provisional answers to these questions. However, some empirical results are beginning to emerge from the recently released Demographic and Health Survey 1989 (DHS), and from an analysis of economic and social trends and events. Some of these results are presented in section 5.0. Given the limited number of observations across geographic areas (e.g., provinces) in the published DHS, any conclusions based on geographic 2 correlations must be guarded, and indeed be considered at most as the basis for informed working hypotheses until the micro data can be examined in detail. Such analysis does, however, permit the identification of issues that require further research to arrive at a longer-run population strategy for Kenya--one that not only facilitates the pace of fertility decline in the short run, but more importantly, sustains that pace over time.

This will require knowledge about how to mesh programs of family planning with economic and social policies in mutually reinforcing ways. Dimensions of a research program that would be helpful in furthering this goal are discussed in section 6.0. Section 7.0 provides a set of recommendations. These are based on 1) an interpretation of the roles, successes and failures of population programming in Kenya, 2) an assessment of factors responsible for the "turning point" in fertility, and 3) speculations as to whether or not Kenya's programs and policies will and should emerge as a somewhat unique "African Model," or instead as an adaptation of the now well documented (although not fully understood) experiences elsewhere, particularly in Asia.

3 2.0 Population Trends Kenyan population growth has risen steadily from an annual rate of 2.5 percent in 1948 to around 3.8 percent in the 1980s. The recent pace may represent the highest in Africa (the Ivory Coast may be higher), and one of the highest in recorded history. By comparison, the current Sub-Saharan population growth rate averages 3.2 percent, while that in South Asia, Latin America and the Caribbean averages 2.3 percent. These trends derive from an increase in the total fertility rate (TFR) from 6 to almost 8 children over this period (the crude birth rate has been high and constant at around 50 per thousand), and a steady decline in the crude death rate from 25 to around 13 per thousand (see table 2.0) .2 Reductions in child and general mortality, and especially in infant mortality (from 184 per thousand in 1948 to 60-80 per thousand in the late 1980s) have increased life expectancy at birth from 35 to over 56 years. Improvements in living standards and health conditions have greatly extended the life span of the Kenyan people. Table 2.0

Population Size, Fertility & Infant Mortality: 1948-1987

1948 1962 1969 1979 1987

Population (Millions) 5.4 8.6 10.9 16.9 21.8 Total Fertility Rate 6.0 6.8 7.6 7.9 7.7

Crude Birth Rate (/1000) 50 50 50 52 50 Crude Death Rate (/1000) 25 20 17 14 13 Infant Mort. Rate (/1000) 184 na 118 104 80

Annual Growth Rate (%) 2.5 3.0 3.3 3.8 3.7 Life Expectancy (Years) 35 44 49 54 56

Source: Republic of Kenya (1987b), p. 1.

2 World Bank (1989c) places total population growth in 1987 at 4.1 percent. This corresponds to a CBR and CDR of 52 and 11, respectively. The current life expectancy at birth is estimated to be 58. The TFR in table 2.0 does not reflect the results of the DHS, which places the rate at 6.5 in 1988. Republic of Kenya (1989d).

4 A "turning point" in population growth may be in the making. Evidence from the recent DHS shows a substantial reduction in the TFR from 7.7 in 1984 (Kenya Contraceptive Prevalence Survey 1984, hereafter KCPS) to a current figure of 6.7.3 While there was some indication of a decline between 1979 and 1984, the change was small and well within the margin of statistical error. Moreover, the TFR for Western Province was understated in 1984 which, when corrected, would raise the Kenyan figure to around 7.9. Thus, the recent decline constitutes the first direct evidence of a major break in the pace of reproduction (the TFR) .4

Government projections, based on the assumptions of declining fertility and mortality, place the Kenyan population at 35 million by the turn of the century.5 This represents a more than threefold expansion of population size since Independence.

A striking feature of Kenyan demography is the substantial variation in fertility and mortality by geographic area. Table 2.1 presents total fertility, child, and infant mortality rates by Province and urban/rural areas. Total fertility varies from a low of 4.6 in Nairobi to a high of 8.1 in Western; infant mortality is lowest in Rift Valley and Central (34.6 and 37.4, respectively), and highest in Coast (107.3). And while the TFR is much lower in urban than in rural areas (4.8 versus 7.7), surprisingly the mortality differentials are negligible. These figures showing wide dispersion of vital rates justify caution in generalizing national experience to individual areas. They in addition offer clues, discussed below, on the determinants of the decline in total fertility.

3Even though this change is large for a five-year period, it is not without precedent. See section 5.43 below. A more definitive assessment of fertility will be available with the release of the 1989 Kenyan census results.

4See section 5.45 for qualifications of this assessment. 5The Government's declining-fertility/declining-mortality projections assume a TFR of 5.5, a life expectancy at birth of 58.5 for males and 61.5 for females, and an infant mortality rate of 60 per thousand in the year 2000. Extrapolating linearly over the period of projection (1980 - 2000), the implied TFR in 1989 is 6.75. This corresponds closely to the DHS figure of 6.7. Republic of Kenya (1983), pp. 2, 7.

5 Other notable attributes of Kenya's demography, discussed in detail elsewhere, are its exceptional youth (over 50 percent of the population is under 15), and its high rate of growth of first-time job seekers (the 15-19 cohort is growing at 4.2 percent per annum).

Table 2.1

Fertility, Child & Infant Mortality Rates by Province, 1989

Total Fertil- Infant Mortal- Child Mortal- Province ity Rate ity Rate(/1000) ity Rate (/1000)

Nairobi 4.6 46.3 35.7 Central 6.0 37.4 10.0 Coast 5.5 107.3 54.5 Eastern 7.0 43.1 22.2 Nyanza 7.1 94.2 60.0 Rift Valley 7.0 34.6 16.9 Western 8.1 74.6 62.9

Urban 4.8 56.8 34.2 Rural 7.7 58.9 34.3

Total 6.7 58.6 34.3

Source: Republic of Kenya (1989d), pp. 22, 57. Sampling errors for the TFR are presented in Table 5.1.

In short, Kenya may be entering the next phase of its "Demographic Transition" where high fertility, preceded by reductions in mortality, is beginning to decline. An assessment of how fast this decline will take place is uncertain and must await detailed research into the household- and individual-level results of the DHS and the yet-to-be released census.

6 3.0 Economic Trends

3.1 Overview and Comparative Analysis: The Long Run

Kenya's overall economic and social development has been quite respectable since Independence, at least as compared with the experience of other developing countries.

Table 3.0 divides the record into early (1965-1980) and late (1980-1987) periods, corresponding to relatively normal growth, and a decade when most countries sustained poor performance. During the first period Kenyan per capita output grew by 2.8 percent per year, exceeding the average of Sub-Saharan Africa (2.4 percent) and South Asia (1.4 percent), although short of the impressive performance of Latin America and the Caribbean (3.5 percent). Agricultural expansion exceeded population growth by 1.3 percent; and industrial growth, while beginning from a small base, proceeded at a substantial pace (6.2 percent).

Most countries performed badly in the 1980s and Kenya was no exception. While there was a slight reduction in output per capita, this retrenchment was less than that experienced elsewhere in Sub-Saharan Africa and Latin America/Caribbean. The severe drought of 1984 exacerbated the adverse impacts of the second oil crisis and the world recession on Kenya. Presently the expansion of output per capita is moderate in the major producing sectors. A driving force in Kenyan development has been high rates of accumulation, both in physical and human capital. Gross domestic investment as a share of GDP has risen from around 14 to 25 percent, and is presently one of the highest in the Third World.6 Most of this investment is financed from domestic saving which is around 20 percent of GDP--also high by world standards. The infusion of foreign capital has been important, although moderate. With the exception of the last two years, the foreign saving share has remained relatively constant at around seven percent of gross national product. This has financed around a quarter of gross investment. Increases in the investment share over time have therefore been largely financed by an expansion of

6 Political scientist Robert H. Bates contrasts Kenyan contemporary political origins with those of Uganda and Tanzania by observing that Kenya's rebellion resulted from the capture of power by the prosperous producers of cash crops. As a result, Kenya "...came to independence under a government that favored the accumulation rather than the redistribution of wealth." Bates (1989), p. 147. 7 domestic saving.7

Kenyans have invested heavily in human (capital) development as well. The increase in the primary school enrollment ratio from 54 to 94 percent is exceptional by world standards.8 Presently few if any countries in the developing world are devoting a larger share of national output to educating their children. While Kenya's central government expenditures on education are substantial (5.8 percent of GNP), given the manner in which education is financed through increased and now considerable "cost sharing" at the local and household levels, the total share of national output devoted to education could well exceed 10 percent.

Medical services, as measured by the per capita availability of health personnel, have also improved since 1965.9 The orientation toward primary health care with a relatively heavy concentration on nurses vis-a-vis physicians stands out in the comparative statistics. The impressive decline in the crude death rate from around 25 to 13 per thousand (there is considerable variation by Province) provides some evidence of the productivity of health investments over this period, although improvements in education and, to a lesser extent, nutrition, are important contributing factors as well.1 0

One area where Kenyan development appears to have lagged is food supply, although there is conflicting evidence on this issue. On the one hand, per capita calorie supply has diminished over time and in absolute terms is less than in most other countries and regions (see table 3.0). On the other hand, the Government of Kenya reports that the availability of major food items has kept pace with population growth, and acute

7 Republic of Kenya (1989c), p. 10. 8From 1960 to 1980 in LDCs with greater than ten million people, only Tanzania recorded a greater increase in combined primary and secondary school enrollments. Lapham and Mauldin (1984).

9Per capita availability of doctors has increased by 50 percent, and registered and enrolled nurses by 100 percent; there were even more impressive gains in dentists and public health officers. Republic of Kenya (1989d), p. 19. '0From 1960 to 1980 in LDCs with greater than ten million people, only five (China, Korea, Malasia, Vietnam, and Turkey) recorded a greater increase in life expectancy. Lapham and Mauldin (1984). An assessment of the contributions of various factors to explaining Kenya's mortality trends is provided in Ewbank, Henin and Kekovole (1986).

8 Table 3.0

Comparisons of Kenyan Production/Consumption/Accumulation Indicators

BeginninQ Year (Period) Ending Year (Period) Beginning/ Kenya Sub- South Latin Kenya Sub- South Latin Ending Year Saharan Asia Amer. Saharan Asia Amer. (Period) Africa Africa

Production

1965-80/1980-87

Population Growth 3.6 2.7 2.4 2.5 4.1 3.2 2.3 2.2 GDP Growth/N 2.8 2.4 1.4 3.5 -.3 -2.8 2.5 -.8 Agric. Growth 1.3 -1.0 .3 .7 -.7 -2.0 -.9 .0 Industry Growth/N 6.2 6.8 1.9 3.5 1.1 -4.4 4.9 -1.4

Inflation 7.3 12.3 8.4 29.3 10.3 15.2 7.8 109.1

Accumulation

1965 / 1987

Gross Dom. Invest. 14 14 18 20 25 16 22 18 Gross Dom. Saving 15 14 15 21 20 13 19 20

1965 / 1986 % Primary Enroll. 54 41 68 98 94 66 84 108 % Secondary Enroll. 4 4 24 19 20 16 32 48

1972 / 1987 Education GNP (%) 4.6 na na 2.3 5.8 na .6 na Health GNP (%) 1.7 na na 1.1 1.7 na .4 na

1965 / 1984 Pop./Physicians 13,280 33,840 6,220 2,370 10,100 23,760 3,570 1,230 Pop./Nurses 1,930 5,460 8,380 2,090 950 2,130 2,710 1,010 Calorie Supply/N 2,289 2,096 2,060 2,450 2,060 2,101 2,228 2,701

Source: World Bank (1989e). Regional averages are weighted by the country's population. India dominates South Asia. School enrollments relate enrolled pupils to customary ages for primary and secondary, respectively.

9 malnutrition, prevalent at Independence, is no longer a national problem. This is demonstrated with statistics on per capita availability (including imports) of sixteen major commodities showing ten to increase over the period 1965-70 to 1981-85.11 While a shift toward proteins and away from carbohydrates, and a reallocation of food to insure the elimination of malnutrition, can reconcile the two apparently conflicting trends, still it is clear that Kenya's successes with improvements in other areas of human development have not been matched by progress in the area of food provision.

Overall, economic and social conditions in Kenya have improved at a respectable pace. Kenya's lackluster performance in the 1980s was no worse than that of other comparable countries facing depressed economic conditions, especially when account is made for Kenya's severe drought. While from these trends alone it is not possible to offer conclusive statements about the connections between economic/social development and demographic change, it does appear that Kenya has been reasonably successful in accommodating its rapid pace of population growth. This has been due in part to unusually high rates of accumulation. The ability to accommodate high rates of population growth in the future will be increasingly difficult given the mounting pressures on the land and the fragile ecology in what is still a dominantly agrarian-based economy. Moreover, demographic pressures, requiring heavy investments in education and health services, as well as in job creation as the baby boom enters working ages, will necessitate structural adjustments toward an economy less dependent on agriculture. In a sense demographic pressures are not only stimulating structural changes, but importantly these pressures (e.g., the financing of education) are beginning to provide a primary mechanism causing population growth itself to decline, a theme that will be examined in some detail below. 3.2 Variability and Vulnerability: The Short Run

The impacts of demography on the economy are gradual and change little from year to year; they are long-run in nature, exerting their primary effects through slowly changing resource/population ratios. This perspective should guide an analysis of the determinants and consequences of population change in Kenya--an economy subject to substantial short-run variability in output growth--since it is easy (but misleading) to attribute short-run difficulties to inappropriate longer-run

11The commodities that increased include mutton, eggs, poultry, fish, maize, wheat, rice, sugar, fats/oils, and potatoes; those that decreased include milk, beef, pork, pulses, cassava, and sorghum/millet. Republic of Kenya (1989d), p. 22.

10 causes. While short- and long-run factors are interrelated, it is useful to isolate their individual roles over time. In the Kenyan case, the primary causes of short-run variability in output performance have been mainly exogenous--due to factors largely beyond Kenya's control--and are, as a result, only secondarily related to population pressures. However, the vulnerability of the economy to accommodate such exogenous shocks is indeed conditioned by the rapid pace of demographic change. The connection in agriculture is direct. Population pressures result in farming expanding into areas with less abundant and reliable moisture, and poorer soils. This increases the number of people at risk to the vagaries of bad weather. Exacerbating this trend is the shift from herding to settled agriculture as land becomes scarce. While herds can be moved during drought, settled agriculture cannot. In the absence of rates of technical change greater than population growth--and no country has experienced rates of technical change in agriculture exceeding 4 percent per year over substantial periods of time, rapid population growth is subjecting Kenyans to increasing vulnerability due to adverse weather conditions. These shocks can also 2result in multiplier effects amplified throughout the economy. The importance of short-term shocks on Kenyan development can be illustrated by accounting for the substantial swings in aggregate output growth over time. Figure 1 presents growth rates for GDP, agriculture, and manufacture over the period since 1972, when reasonably consistent data are available. Shocks, taking the form of exceptional weather or prices in the dominant agriculture sector, have resulted in agricultural growth rates varying from 11 to -4 percent. These swings have largely determined the timing of variations in GDP growth which have ranged from 8.2 percent in 1977/78 to .8 percent in 1984. (The agriculture booms and busts have sometimes been reinforced and sometimes partially offset by even wider variations in manufactures.) While this variability appears endemic to Kenya's development--a relatively open agricultural economy particularly sensitive to export prices and weather conditions, the vulnerability of the economy can be illustrated by comparing deviations of the output lines from a population-growth line superimposed on figure. Except for two years in the mid 1970s, the deviation of GDP growth was positive, but not large, and during 1983/84 it was strongly negative. Negative deviations of agriculture growth are more frequent (1974, 1979/80, 1985/85), and the positive deviations are larger. At any rate, it is clear that the impact of population change in accounting for specific years of prosperity and poverty is small. To account for these years, one must turn to short-run factors.

12For an analysis of ways in which Kenya has coped with drought, see Bates (1989) and Downing et al. (1989).

11 Figure 1 Growthof KenyanOutput: 1972-1988 17 16- 15- 14- 1 3 12 11

9-

7- 6- 5- I3 - C/T 4

2-

0-

-2- -3 -4

-5 D 1972 1 1974 j 1976 I 1978 1 1980 1982 ) 1984 1 1986 1 1973 1975 1977 1979 1981 1983 1985 1987 0 Agricuture + Mabmactu o GOP

------Population Growth "Shocks"

W = Adverse Weather C/T = Coffee/Tea Price Boom 0 = Oil Price R = World Recession D = Drought

The first major shock to the economy came with the 1973 oil crisis, resulting in increased import prices, a deterioration in the trade balance, reduced domestic demand, and indirectly, reduced demand for exports as the world adjusted to the transfer of real resources to the oil producing countries. This was compounded by bad weather in 1974. GDP growth was cut in half over the 1973-1976 period. (From 1965 to 1972, GDP growth averaged 6.5 percent per annum.)

Fortunate for Kenya, the adverse consequences of the collapse of the East African Community coincided with the highly favorable impacts of the coffee and tea price boom in 1976-78. Coffee

12 prices quadrupled and tea prices doubled in two years. The impact on the economy was pervasive and substantial.

The decade ended and the next began with a bout of bad luck--another episode of unfavorable weather (agriculture growth was negative in 1979), a second oil crisis, and a world recession as economies attempted to rein in inflation and complete the adjustment to higher yet unstable energy prices. Kenyan GDP growth again declined and, as the economy was beginning to recover, the severe drought of 1984 resulted in the largest economic setback since Independence. Cereal production declined by thirty to forty percent, and livestock herds were drastically reduced.

Since 1985 the economy has performed moderately well with GDP growth exceeding 5 percent although, with population growth approaching 4 percent, improvement in living standards has been slow, and there is little margin to cushion the downside shocks that appear to be endemic in Kenya's economic structure.

13 4.0 Population Policies and Programs: 1965-1989 4.1 Introduction

This section documents and analyzes the record of Kenya's population positions, policies, and programs since 1965. The goal is to provide the empirical basis for assessing the quantitative significance of these activities in explaining fertility trends. Section 4.2 reviews and assesses major Kenyan policymaking and programmatic events. Sections 4.3 and 4.4 review respectively GOKs, and foreign donor's, financial and other commitments in support of mortality and fertility interventions.

4.2 Kenyan Commitments: Political and Programmatic The evolution of political statements and programs directed toward population in Kenya has been path breaking as assessed by African historical experience. However, until recently the Government's programs in population have been modest in scope and intensity--the record can be characterized as a deliberate, but not a particularly dynamic progression of Government commitment and activity. Around 1984 there was a modest break in that slow- paced evolution toward a somewhat expanded involvement in population matters.

Table 3.0 summarizes the key policy initiatives and program/planning responses in the population arena since 1965. To reflect the changing pace of activity it is convenient to divide the period at 1984. 1965-1984

Concern by GOK about the adverse consequences of population growth dates from Independence. Possibly the earliest reflection is provided in the historic document African Socialism and Its Application to Planning in Kenya, published in 1965. It notes that ". ..the present high rate of population growth makes extensive and intensive provision of social services more expensive, the unemployment problem more intractable, and saving for development harder than need be--thus lowering the rate of economic growth" (p. 52); and it goes on to recommend that "...immediate steps should be taken toward family planning education" (p. 52).

14 Table 4.0

Major Events in Population Policymaking and Program/Planning in Kenya: 1965-1989

Policy Initiative Program/Planning Response

Period 1965-1984

Initial expression of GOK request to the Population Government concern about Council to advise on programs impacts of population growth to lower population growth. on development. African (Population Council Advisory Socialism and Its Application African Mission Report, 1966.) to Planning in Kenya, Paper No. 10, 1965.) Establishment of National Family Planning Program. Further recognition of the (1967) consequences of rapid population growth, and Creation of National Family statement of intent to expand Welfare Center in MOH. (1976) voluntary family planning programs on a more systematic Establishment of Population basis under MOH. (Kenya Studies Research Institute. Country Statement, World (1977) Population Conference, 1974.) Creation of National Council for Population and Develop- ment in Office of Vice President. (1982)

Detailed development of population policy goals to guide policy/programming/planning. (Population Policy Guidelines, Sessional Paper No. 4, 1984.)

Period 1984-1989

Strong presidential commitment to reduce pop- ulation growth as a necessary condition to achieve national devel- opment goals. (First National Population Conference, 1984.)

15 Table 4.0 (Continued)

Policy Initiative Program/Planning Response

Period 1984-1989 (Continued)

Regional African confer- ence to promote awareness for active population and development activities. (All-African Parliamentary Conference on Population & Development, Harare, Zimbabwe, May 1986.) NCPD decisions to: Development and implementation of district plans on a phased-in - decentralize population basis. (Guidelines for District program to district Population & Family Planning level. Committees, District Focus Circular No. 2/1986.) - expand/modify service delivery to wider Creation of community-based dis- audience, particularly tribution program to be implement- in rural areas. ed by NGOs. (Report of the National Workshop on Community - enable subsidized sale Based Distribution of Contracep- of family planning tives, June 1987.) commodities in urban areas. Development of a commercial marketing program on national basis. (Not yet implemented.)

The need to integrate a Articulation of the relationships wide range of population between population and develop- variables with development ment. (Sixth Five Year Plan: plans. (Economic Management 1989-1993.) for Renewed Growth, Sess. Paper No. 1, 1986; Second National Pop. Conference, 1989.)

16 This was followed in 1966 by an invitation to the Population Council of the United States to send an Advisory Mission to Kenya. The Mission's Report can in retrospect be viewed as a "blueprint" for Kenyan population activities.13 However, the recommended activities were slow in implementation, likely the result of only modest political commitment at senior levels of government.

Prompted partly by the Mission's report, a National Family Planning Program was launched in 1967. Family planning was integrated with Maternal and Child Health (MCH), and the Ministry of Health (MOH) was given the responsibility for implementation. Acceptance of family planning services was to be voluntary, and individual customs and values were to be respected. Emphasis was placed on the spacing of children and the limitation of family size. Due to the lack of an effective infrastructure and trained personnel in family planning, MOH relied heavily at this stage on the implementation capacities of the Family Planning Association of Kenya (FPAK) to fulfill the FP mandate.

The 1969 census results confirmed earlier findings about high fertility, leading Government to step up its focus on various dimensions of population policies and programs.

In 1974, the Country Statement presented at the first World Population Conference held in Bucharest was, in large part, a factual document providing descriptive details bearing on a wide range of demographic changes occurring in Kenya. It reiterated the general concerns about the adverse consequences of population growth. In a country suffering from high unemployment, a high population growth rate has only adverse economic effects. First, it will increase the proportion of total income that is consumed, thus diminishing the level of domestic savings available for investment. Second, the new population requires more capital, schools, houses, hospitals, roads, and machines. Only after the new population is provided for will there be any net increase in these amenities per person. Third, when the dependency ratio is increasing, more people will be employed simply providing for the new people, without increasing the real income per person. Fourth, the pressure of people on land and capital will reduce the productivity of labor (p. 15).'4

13 Population Council (1966). 4Republic of Kenya (1974).

17 Taking these considerations into account, Government signified its intentions to intensify efforts "to expand its voluntary family planning program"...by means of establishing "set targets"... that formed an "important part of its five-year develoPment plan (1974-78) and its ten-year rural health master plan". Three activities are notable.

First, in 1976 Government created a National Family Welfare Center in the Ministry of Health with the following objectives: 1) establish 400 MCH/FP daily-service delivery points (SDP) and part-time services rendered by 17 mobile teams at some 190 clinics; 2) provide in-service courses for nurses in family planning; 3) intensify Information/Education/Communication (IEC) activities through 817 Family Health Field Educators. Second, in 1977 Government created the Population Studies Research Institute (PSRI) at the University of Nairobi with a mandate to carry out basic/applied research on socioeconomic determinants of fertility, mortality, and migration as well as operations and evaluative research to address program issues and impact of family health interventions intended to create demand (IEC) and supply (delivery of MCH/FP services). Third, in 1982 Government established the National Council for Population and Development (NCPD) in the Office of the Vice President (1982) to coordinate all population activities implemented by Government Ministries, non-government organizations (NGOs), and foreign donors that relate to planning and programming. NCPDs first major activity was assistance in the preparation of a detailed set of population goals to guide policy, programming and planning, resulting in GOKs 1984 Sessional Paper No. 4, Population Policy Guidelines.

1984-1989 A watershed in visible political commitment to population activities occurred in 1984 with a pair of speeches.

In a stimulating address to the Plenary Session at the Second World Population Conference held in Mexico City (1984), the Vice President of Kenya, the Hon. Mwai Kibaki, conceded categorically that Government efforts in the past to lower fertility had proved a failure. He cited as the principal limitations: 1) over- reliance on the supply side of family planning services at the expense of ignoring the creation of demand for family planning; 2) disproportionately high priority by MOH on curative services with the result that potential family planning acceptors were

15 Republic of Kenya (1974).

18 largely ignored (particularly mothers who wanted no more children but were unable to obtain services); and 3) "perpetuation of the global argument on the national dangers of population growth rather than concentrating on the benefits of family planning to the couple" (p. 3). 16 Aware of these limitations, Kibaki proposed several remedial actions, all of them practical proposals designed to improve the implementation of the program. These included: 1) doubling of service delivery points to make contraceptives accessible to all clients; 2) using medical practitioners in the private sector to dispense contraceptives freely to suitable clients; 3) using rural and urban shops to sell certain types of contraceptives through a "social marketing" strategy; and 4) developing a community-based distribution system to extend the outreach beyond clinics. To further accelerate implementation he stressed the need for political commitment and leadership, and additional resources of money and manpower. These points were elaborated by Kenya's President Daniel Arap Moi in his address at the first National Population Conference held in Nairobi in 1984. At this Conference specific strategies were proposed and agreed to by Government; namely l)decentraliza- tion of the population program to the district level; 2) expansion/modification of service delivery programs to reach a wider audience, particularly in rural areas; and 3) subsidized sales of family planning commodities in urban areas through social marketing strategies.17

Another milestone achieved during the 1980 decade was the convening of the All-African Parliamentary Conference on Population and Development in Harare, Zimbabwe in May 1986. One of the major objectives was to interest and inspire policymakers and legislators to more vigorously promote an array of population "causes" (including family planning) in their respective constituencies as well as in Parliaments back home. Kenyans played an active role in the Conference.

In 1989 possibly the strongest manifestation yet of Kenya's political commitment to population matters was recorded at the Second National Population Conference. In his official opening address President Daniel Arap Moi minced no words about the need for balance between population growth and available resources:

16Republic of Kenya (1984b).

17Republic of Kenya (1986b & d).

19 The greatest challenge we face today is for us to influence the attitude of our people towards having smaller families... We should therefore continue to promote family planning as one of the most important means of reducing population growth to a level which our economy can sustain. 8 Professor George Saitoti, Vice-President and Minister for Finance, concurred, adding that 1) family planning is an inalienable human right of all Kenyans, 2) more effort is needed to create demand for family planning through IEC and increases in formal education and employment opportunities (particularly among women), and 3) local communities must be actively and directly involved in population education aimed at promoting small family norms.19

In terms of the political barometer, while at the beginning of the decade interest in population matters took their place with a large number of competing problems about which politicians were "concerned," at the end of the decade population issues were elevated, at least in political statements, to the status of first rank in the affairs of state.

Indicators of this heightened interest are numerous. For example, between 1986 and 1989 the interest of planners in the economic impacts of population appears to have increased. In 1986 GOKs Sessional Paper No. 1, Economic Management for Renewed Growth, gave but fleeting acknowledgement that rapid population growth posed a challenge to economic growth. In contrast, in 1989 GOKs Sixth Five Year Plan: 1989-1993 elevates population size and growth to one of the Plan's themes, stressing the need for a sustainable long-run balance with Kenya's national resource base, and for a broad, integrated perspective toward population, development, and planning.20

Whether the recent increasingly strong political statements by senior government officials, reflected by the reports of various Ministries, are now in fact translated into efficient and effective population programs remains to be seen. An examination of the financial and related resource commitments of Kenya, as well as international donors, to population programming, as well as measures of outcomes, is both necessary and instructive to placing the political dimensions into perspective.

18Daniel Arap Moi (1989), pp. 7-8. 19 George Saitoti (1989). 2 0Republic of Kenya (1989c).

20 4.3 Financial Resources: Foreign and Local Expenditures for Population Activities

In the context of population programming, and in generic terms, money, personnel, physical facilities and supplies/equipment constitute the essential program inputs (elements) in the absence of which outputs and impacts are unlikely to occur. With respect to Kenya's family planning program, this section examines various sets of input data over time, bearing on donor (loan) and recurrent expenditures. Disbursement figures provide insights about the program's capacity to absorb funds, and increases in expenditures over time indicate expanded coverage of both geographical space and population. Section 4.4 presents data on service delivery points (SDPs), training of paramedicals in MCH/FP, and availability of contraceptive supplies.

Recent interest in obtaining systematic and accurate expenditure data on population activities in Sub-Saharan Africa has heightened among bilateral and multilateral donor agencies. However, information on population expenditures in LDCs is not easy to collect, in part because finance cells within Ministries of Health and Population are typically not strong. Once collected, the data are often fraught with inconsistencies, incompleteness and inaccuracies. For these and other reasons, such information is usually not readily available or, if in existence, remains unpublished as "working papers" closeted in "project files" of aid (loan) agencies, as well as Ministries of Health and Population. Four sets of expenditure data are summarized and reported in table 4.1. Set 1 reports information compiled and published by UNFPA, and reflects disbursements through bilateral, UN and NGO channels. Figures in set 2a were obtained from major donor and local agencies involved in population activities in Kenya-- notably World Bank/IDA, USAID, UNFPA (including bilateral and multilateral contributions), SIDA, IPPF, and MOH. This series includes expenditures for contraceptive commodities which are also shown separately in set 2b. Set 3 represents an average of sets 1 and 2a and provides a more realistic picture of both levels and changes in disbursements. Set 4 represents only UNFPA disbursements in Sub-Saharan Africa.

21 Table 4.1

Foreign and Local Expenditures for Population Activities in Kenya and Sub-Saharan Africa: 1983-1988

Focus Baseline Period 1 Period 2 % Increase Avg 83/84 Avg 85/86 Avg 87/88 Period 1 Period 2

Kenya

Set 1 6.8 7.0 11.4 3 63

Set

2a 11.6 17.4 25.4 50 46

2b 1.2 1.4 2.8 17 100

Set 3 9.2 12.2 18.4 33 51

Sub-Sahara

Set 4 4.5 5.0 7.2 11 44

Sources and notes. Set 1 represents expenditures for donor agencies contributing funds through bilateral, UN (including bilateral and multilateral sources), and NGO channels as reported in UNFPA (1989), table 9, p. 37. Expenditures include data on contraceptive commodities. Set 2a includes many of the same donors with additional contributions from MOH and WB. All figures have been compiled, prepared, and approved by each of the agencies involved. Recurrent expenditures provided by MOH relate to promotive/preventive costs, annual contributions from NCPD, and indirect (mainly administrative) costs. Set 3 averages sets 1 and 2a. Set 4 represents only UNFPA disbursements in Sub- Saharan Africa, and is taken from the above report. All figures are in current $US 1,000s.

22 Several points emerge from table 4.1, and the underlying data upon which the aggregated data sets are based.

* There is a sizeable expansion in population expenditures in Kenya, especially in 1987-88. (See set 3.) Relative to 1985-86, disbursements for 1987- 88 increased by 51%. The corresponding change for contraceptive commodities (set 2b) was 100%.

* UNFPAs share of expenditures in Kenya compared with disbursements for Sub-Saharan Africa increased from 10% (1982-86) to 17% (1987-88).

* Kenya's proportionate share of expenditures through NGOs has not only been prominent throughout the past decade (averaging around 36% on an annual basis), but has increased over time: 28% (1982-84) to 39% (1985- 86) to 43% (1987-88).

4.4 Human and Physical Resources: Trained Personnel, Service Delivery Points, and Contraceptive Supplies Table 4.2 presents data on trained health workers and service delivery points over the period 1984-88. Interpretation of these series requires some background. Trained Personnel. The Ministry of Health has had a long- standing tradition of upgrading skills of nurses and paramedical workers in all facets of Primary Health Care except MCH/FP. This deficiency was recognized in the late 60s to early 70s, and efforts have been made to rectify the situation. Presently Registered Nurse Midwives and Enrolled Community Nurses undertake a seven-week course offered by the Family Health Division (FHD) of the Ministry of Health. Topics covered in this course include: 1) information about MCH/FP from the perspective of Family Life Education and Primary Health Care; 2) client management of family planning services--information about the human reproductive system (both sexes), contraceptive technology, how to undertake physical examinations, and interviewing and counselling of clients; 3) specific information related to child health care (growth and development, breast feeding, immunization and oral rehydration); and 4) management of service delivery points (SDPs) with emphasis given to record-keeping and reporting. The proportion of RNMs and ECNs trained in MCH/FP is 25 and 75 percent, respectively.

21 These figures overstate the increases in effective population programming since inflation at a rate of around four percent per year reduces the purchasing power of the dollars over time. 23 Table 4.2

Comparative Data on Training of Health Workers and Service Delivery Points in Kenya: 1981-1985 and 1985-88

Item Period 1 Period 2 Change within Period 1981-85 1985-88 1985-86 1987-88 N N Percent

New Health Workers Trained in MCH/FP 1027 26 2,170 43 43 57

New Service Delivery Points 87 na 465 na 39 61 Ratio of New Health Workers Trained in MCH/FP to New SDPs 12 5 5 4 Source: All data were obtained from the Family Health Division in MOH and relate to calendar year. Table 4.2 reveals a sizeable expansion in MCH/FP training of health workers, specifically by the FHD. Two out of every three health workers who had received such training between 1981 and 1988 obtained their instruction during period 2. This represents 43% of all health workers who potentially could have been trained during this period, a notable change from the 26% figure relating to the 1981-84 period. There was also upward change in the rate of workers trained within the 1985-88 period, particularly during 1987 and 1988. Though figures are not presented for 1989, this increasing trend continues unabated. The trends in training also appear to be mirrored by changes in the number of SDPs; the pattern, however, is more accentuated. Comparisons between periods 1 and 2 reveal that more than 8 in 10 SDPs were created between 1985-88, again with pronounced acceleration occurring during 1987 and 1988. These changes, considered in relation to each other, had the effect of reducing the ratio of health workers trained in MCH/FP to SDPs by two-thirds (from 12 to 5).

Service Delivery Points. SDPs were created originally to respond to the problem of insufficient attention given to family planning at health facilities under the responsibility of the MOH. This concept has subsequently been extended to apply to the private sector as well, in order to assure an adequate and reliable provision of supplies. An SDP in the public sector program is typically a place within an existing health installation such as a health center, dispensary, mobile clinic, or sub-district hospital. In the private sector, SDPs correspond 24 to physician clinics, city health clinics, NGO clinics and commercial dispensaries on tea estates and within factories.

All such SDPs, whether public or private, come into existence as a result of an agreement with MOH whereby contraceptive commodities are provided to these outlets conditional on their being officially registered and signifying their willingness to undergo training on record-keeping and reporting, and on use of computers to facilitate the process of data retrieval for management purposes. Additionally, personnel stationed at SDPs (primarily Enrolled Community Nurses) must be trained in MCH/FP. A review of the SDP concept with officials at FHD indicates that the criterion for submission for reports has been weakly enforced, with the result that many SDPs receiving supplies are not accounting for their distribution. In the absence of such accountability, one would understandably raise questions concerning the value of proliferating SDPs. Fortunately, the solution to this problem is now well in hand, given MOH insistence on submission of usage reports as a condition for resupply. At the district level (initially in 12 districts), relevant personnel at SDPs are being trained to record and enter consumption information for submission to FHD on a quarterly basis. One of the major accomplishments of this exercise will be the provision of data to calculate "couple-years of protection," an estimator of active users. By mid-1990 such data should be available for a 12-month period in 12 districts. This innovative logistics approach has captured the interest of field workers who have come to see the value of these data as a management tool.

25 Table 4.3 Contraceptive Supplies Provided by Donors to Kenya's Population Program: 1984 - 1988 Calendar Year Type of Method 1984 1985 1986 1987 1988 Total

Condoms (pieces-l,OOOs) 4.3 5.0 10.0 19.3

Oral pills (cycles-l,OOOs) 1.4 2.0 3.0 4.0 3.5 13.9

Injectables (1-cc vials-000s) 1.2 62.5 214.0 1073.0 1026.0 2376.2

IUDs (pieces-OOOs) 1.4 .1 2.0 80.0 83.5 167.0 Source and notes: Data on condoms and IUDs are obtained from USAID offices in Nairobi and Washington. Figures on oral pills are provided by SIDA/Stockholm and IPPF/London. Data on injectable supplies are obtained from WB/Washington and UNFPA/New York.

Contraceptive Supplies. Additional insight into the time pattern of family planning activities in Kenya can be obtained from data in table 4.3 on contraceptive supplies provided by donors over the period 1984-1988. On a method-specific basis, supply provision for the combined 1987 and 1988 years, expressed as a proportion of total supplies made available during this interval, was as follows: condoms: 77%, OCs: 54%, injectables: 88%, IUDs: 81%. The data in table 4.3 reflect the same picture when presented as rates of increase. The overall conclusion one obtains from examining this table, and indeed all the supporting materials in section 4, is that serious government commitment to fertility reduction through active population programming began primarily during the latter part of the 1984-88 period. This finding will be important in assessing the determinants of the possible fertility decline in Kenya, a topic examined in detail in section 5.

26 5.0 A "Turning Point" (?) in Kenyan Demographic History: Documentation, Speculations and Issues

5.1 An Historical Perspective

The historical record of economic and demographic development reveals systematic empirical "patterns" that evolve over the course of modernization. In terms of economic development, these include shifts of household consumption away from food and toward other goods and services as family incomes rise (Engel's Law); and shifts in the structure of production first from primary to secondary (mainly manufacturing), and then to tertiary (services) activities. These trends are grounded in well understood and widely accepted theoretical propositions about household behavior and production.22

In terms of demographic development the most widely investigated pattern, denoted as the "Demographic Transition," relates to systematic changes in birth and death rates. As seen in figure 2, countries evolve through several distinct stages: an early stage (I) when birth and death rates are

Figure 2 Schematic Diagram of the Demographic Transition Crude CBR Birth Rate CDR Crude\* \ Death Rate

Stage I . Stage II Stage III . Stage IV Modernization

Popula- tion . Growth Rate

Stage I .Stage II .Stage III .Stage IV

Modernization

22 Kuznets (1966); Chenery and Syrquin (1975).

27 both high; a second stage (II) when the death rate declines (usually the result of improved health conditions and modernization) while the birth rate remains high; a third stage (III) when the birth rate declines (resulting in a diminished rate of population growth); and a fourth stage (IV) when birth and death rates are both low (resulting, as in stage I, in a low rate of population growth). While this pattern has occurred in many countries, unlike the systematic changes in the economy, the fundamental causes of the Demographic Transition are less well understood. As a result, forecasts of change are less predictable. In some countries reductions in death rates did not precede the fertility decline; in some the decline in births, once begun, was rapid, and in others it was slow; and in some countries, the pace at which fertility declined was related to the level of fertility at the turning point.23 Demographers and sociologists have associated these patterns with multi-faceted aspects of "modernization," a complex and imprecise concept. An analysis of the resulting demographic patterns has therefore been speculative, and has relied on an examination of empirical patterns as forecasts of the future. These forecasts have sometimes been wide of the mark, largely because they lack analytical underpinnings, a shortcoming that has been addressed in recent studies.

5.2 An Analytical Perspective

From the vantage point of organizing the analysis for the present report, the most useful model of the Demographic Transition is provided by Richard A. Easterlin (1978) since it reveals explicitly a role for population policy and family planning. In particular, it confronts the issue of the appropriate emphasis of population policy as between the supplving of family planning services versus the creation of the demand for such services through socioeconomic programming and other efforts (e.g., IEC). It, in addition, incorporates both biological and behavioral determinants of fertility and mortality. Figure 3 depicts one possible evolution of trends accounting for surviving children per married woman as modernization takes place. Three major factors determine the surviving-children-per- married-woman outcome. The first is "natural fertility" less child deaths (Cn)--the number of surviving children the woman would bear in the absence of any conscious use of modern or traditional means of family size control. Natural fertility (less child deaths) rises over time as a result of improved health and economic conditions affecting fecundity and mortality,

23Kirk (1971); Coale and Watkins 1984.

28 and changes in breast feeding practices.

The second factor accounting for the number of surviving children is the parents' desire for children (Cd). This depends primarily on the monetary and non-monetary benefits and costs of children. As the relative benefits versus costs decline, so too does the desired number of children.

Finally, the attaining of an ideal family size is not costless. It involves conscious decision making and the adoption of family planning behavior. The latter has psychic, social, and monetary costs. Until these are low, parents will not attain their desired number of children (in figure 3 the desired fertility line to the right of the intersection with the Cn line), but rather have families exceeding this norm (the broken line). In the Easterlin model, a major impact of family planning is to reduce the costs of fertility control, and to close the gap between the number of children desired and the regime of natural fertility.

Figure 3 Easterlin's Model of Fertility Determination

NATURALFERTILITY a.EI ! LESSCHILD DEATHS (Cn) mwDESIREDFERTILITY (Cd) z LU Y_ I OPTIMUMI \ SURVIVING zI _ - -N\CHICDREN (C)

I ~ Nt{{l z

Source: R. A. Easterlin (1978), p. 106, panel f.

There are several phases in the model. In phase I, the desired number of children (Cd) exceeds the number of surviving children under a natural fertility regime (Cn). Population growth is constrained by natural fertility and infant/child mortality, and the optimum number of surviving children (C) equals Cn. (In other words, parents can't get enough children.) In phase II, the supply constraint is released (Cn exceeds Cd), but contraception is too costly (it may be unavailable, monetarily expensive, psychologically and/or physiologically undesired, or religiously and/or culturally taboo); C therefore

29 remains at Cn. In phase III contraception becomes increasingly pervasive as the gap between Cn and Cd widens and contraceptive costs decline. The Demographic Transition is completed in phase IV where contraceptive costs are low relative to the motivation for fertility control. Here C is largely explained by Cd.

Nationally, Kenya has recently entered phase III of the framework, although specific provinces (Western, Coast) may be in phase II, and others (Central, Nairobi) are further along into phase III.24

Several features of this model merit highlighting since they provide perspectives useful for organizing an analysis of the Kenyan Demographic Transition. First, the decline in the demand for children represents a powerful and fundamentally determining force in the (derived) demand for family planning. Population programs that focus on the provision of low-cost means of contraception are effectively constrained by family size norms. This is in contrast to somewhat broader programs that emphasize factors influencing the demand for children. For example, in some settings the demand for family planning may be more influenced by programs that reduce infant/child mortality than by efforts to increase access to and knowledge about modern contraception. These aspects of population programming will be reexamined below in light of recent Kenyan fertility and mortality experience.

Second, while the number of surviving children is independently influenced by the costs of family planning, these costs are not simply those of acquiring and using modern forms of birth control. They include psychological, psychological, and cultural costs as well.

Finally, population policies (especially in Kenya) should take into account the wide diversity of family size desires, child/infant mortality, and economic and social conditions. A uniform model of population programming that is applied to all areas (of Kenya) makes little sense if one accepts the basic organizing framework of the Easterlin model.25 This theme will be elaborated in section 7.0 where some of the policy implications of Kenya's Demographic Transition are examined.

24For a recent econometric analysis of the relationships that provide support for this interpretation, see Republic of Kenya (1989b), pp. 7-8. 25 Republic of Kenya (1989b), pp. 1-2, 7-8, 55-56.

30 5.3 The Demographic Transition in Kenya

Kenya's overall pattern of demographic development has evolved through the first two stages of the Demographic Transition. Population growth and fertility rates are both high, and mortality rates are continuing to decline. If Kenya's experience replicates that in other nations, it too will (may be) soon enter(ing) the third stage, with the "turning point" occurring when high fertility rates begin their downward course. A comparison of Kenya's demographic trends with those in Sub- Saharan Africa, East Asia (excluding China), and Latin America and the Caribbean is provided in table 5.0. Not only is Kenya (and Africa) late to enter stage III of the Demographic Transition, but Kenya's distinctive population profile of relatively high fertility and low mortality is clearly evident. Table 5.0

Crude Birth and Death Rates, Selected Regions and Kenya

Year Kenya Sub-Saharan East Asia Latin America Africa Excl. China & Caribbean

CBR CDR CBR CDR CBR CDR CBR CDR

1950 50.0 24.0 49.8 29.3 42.5 27.1 42.9 16.1 1965 50.0 18.0 49.4 22.8 42.1 16.3 39.2 17.7

1980 52.0 14.0 48.8 17.7 33.3 10.5 33.5 8.5 1988 50.0 12.0 46.0 16.0 22.0 7.0 29.0 7.0 Source: For 1950, 1965 and 1980, World Bank (1983). For 1988, World Bank (1989d). Kenyan data are from Republic of Kenya (1987b); for 1988, author's estimates based on World Bank (1989d).

The Kenyan situation is complex since the underlying demographic patterns vary significantly by province, with Coast and Nyanza recording infant mortality rates of around 100/1000, and Central and Rift Valley rates of 35/1000. Kenya's national Demographic Transition can therefore be represented as the aggregation of off-phased transitions occurring in different regions of the country. Ultimately, an understanding of Kenya's transition will require a detailed inquiry into these regional patterns of change. The present report will draw upon this fundamental dimension of Kenya's record and examine in some detail several systematic differences in demography and development by province.

Recently, with the release of the DHS, evidence has been provided to suggest that Kenya may be at its "turning point" in

31 fertility, and thereby entering stage III of the Demographic Transition. The TFR is estimated to be 6.7 children, a significant decline from the rate estimated to be 7.7 to 7.9 just five years before. This fertility reduction was largely unanticipated since African fertility has been characterized by many observers to be exceptionally pronatal, and relatively immutable to change, especially rapid change.26 One should not take lightly such characterizations of African family life, and as a result, caution is merited in examining the apparent break in Kenyan fertility. Three questions arise. First, what is the nature and plausibility of the recent turning point in fertility; are its timing and magnitude of. change believable? These issues are taken up in section 5.4 where the details of the turning point are examined to ascertain whether the change in fertility is associated with predictable changes in accompanying variables; and moreover, whether Kenya's experience corresponds to that found in other countries that have experienced turning points in their past.

Second, what factors appear to account for the downturn in fertility? While answers to this question, taken up in section 5.5, must be speculative, they not only provide elements of a research agenda, but also provide yet another basis for assessing the turning point. If quantitatively important factors causing a reduction in fertility cannot be identified and documented, confidence in the (magnitude of the) observed break in fertility must be qualified. Third, what have been the roles of family planning in accounting for the fertility decline? Family planning not only enables couples to meet their family size and child spacing goals, but it also provides a means to improve maternal and child health through child spacing. And it represents a widely used programmatic instrument of population policy. It is therefore appropriate to examine the roles of family planning in general, and the use of modern contraceptives in particular, in modifying the course of Kenya's fertility transition. From a public policy perspective, it is also important to identify some of the determinants of family planning use. These topics are examined in section 5.6. Section 5.7 provides a simple analytic model for evaluating alternative population strategies, and applies this framework to Kenya. Given the uncertainties about the nature and significance of Kenya's fertility transition, it is particularly appropriate to set out some researchable issues that merit priority attention, a

26Dow and Werner (1983), Frank (1987a, 1987b), Frank and McNicoll (1987c).

32 task undertaken in section 6.0. Demographic research useful for policymaking in Kenya is limited. If this country is indeed embarking upon a course of fertility decline, it will be important to have a clearer picture of the analytical and empirical dimensions of this event. Such knowledge will be necessary for the formulation of prudent policies and programs, and significantly, for identifying elements of the process of fertility decline that may be different from that experienced elsewhere--thus providing insights into a possible "African Model" of the Demographic Transition. 5.4 The Turning Point? 5.41 The Total Fertility Rate: Spread and Variation by Province Table 5.1 presents estimates of total fertility rates by province in 1977/78, 1984 and 1989. Several findings are relevant to the present assessment. Table 5.1

Total Fertility Rate by Province

Province 1977/78 1984 1989 Sampling Changes Between Error, 1989 1977/78- 1984- 1977/78- 1984 1989 1989

Nairobi 6.1 5.6 4.6 4.03-5.07 - .5 -1.0 -1.5 Central 8.6 7.8 6.0 5.60-6.40 - .8 -1.8 -2.6 Coast 7.2 6.7 5.5 5.00-5.88 - .5 -1.2 -1.7 Eastern 8.2 8.0 7.0 6.48-7.52 - .2 -1.0 -1.2 Nyanza 8.0 8.2 7.1 6.69-7.41 + .2 -1.1 - .9 Rift Valley 8.8 8.6 7.0 6.38-7.42 - .2 -1.6 -1.8 Western 8.2 6.3 8.1 7.59-8.45 -1.9 +1.8 - .1 Kenya 8.2 7.7 6.7 6.54-6.88 - .5 -1.0 -1.5 Source: For 1989, Republic of Kenya (1989d), p. 22; for 1977/78 and 1984, Republic of Kenya (1984a), p. 529. The 1977/78 and 1989 rates apply to five years before the survey; the 1984 rate applies to one year before the survey. First, the decline in the TFR is consistent across Kenya. Between 1984 and 1989, the decline is 1.0 or more children in 6 of 7 provinces; and between 1977/78 and 1989, the decline is 1.5 or more children in 4 of the provinces. The reduction of the

2 7While sampling errors are not available for the earlier two surveys (they were specially tabulated for this report for the DHS by the Institute for Resource Development/Macro Systems, Inc.), these errors are larger for 1984 than the other two

33 Kenyan TFR is therefore not a statistical artifact deriving from one or two provinces. It represents a pervasive decline that has spread over a wide geographic area characterized by considerable variability in socioeconomic conditions. Second, Western province shows an increase in the TFR of 1.8 children. This is implausible for such a short period of time, especially since there are no obvious factors specific to Western's demography or development that appear to account for such a change. The increase likely derives from an understatement of the 1984 rate.28 Third, the reduction in the TFR for two provinces, Central and Rift Valley, seem large--1.8 and 1.6 children, respectively. However, these two provinces began with high (possibly over- estimated?) TFRs; as a result, relatively large absolute declines are to be expected, other things equal. Moreover, Central and Rift Valley recorded the largest changes in "ideal family size" (-1.6 and -1.8 children, respectively), percent of women who want no more children (26.1 and 20.7, respectively), and the modern contraceptive prevalence rate (10.1 and 12.7, respectively) .29 Whether these changes are associated with special explanatory conditions is yet to be determined. The consistent pattern of change does lend some credibility both to the estimated reduction in fertility and the relatively large magnitude of the change. Finally, the overall reduction in the TFR of 1.0 is itself large for a four-year period, all the more so if the experience of Western province is omitted.30 The reduction in the TFR for the remaining provinces would be then 1.3 children. Alternatively, if we assume that the TFR in Western is accurate surveys. One must therefore be cautious in interpeting the statistical significance of period- to-period changes uisng the 1984 TFR, although between 1977-78 and 1989, the differences for Kenya, Nairobi, Central, Coast, and Rift Valley are almost certainly statistically significant, and possibly also for Eastern.

2 8 Examination of parity and related demographic statistics support this interpretation. Moreover, as will discussed below, desired family size decreased, and the overall contraceptive prevalence rate increased in Western Province between 1984 and 1989.

29 Central province recorded a relatively small increase in the overall contraceptive prevalence rate, a puzzle (including the shift from traditional to modern methods) that requires examination in some detail.

30The DHS rates apply to 0-4 years before the survey, centering on 1987; the KCPS rates are for 1983. The interval between these two sets of rates is around four years.

34 for 1989 and did not change over the period, the 1984 Kenyan TFR would then be 7.9. The resulting decline over 4 years would therefore be 1.2 children, a reduction of 15 percent.

5.42 Total Fertility: Correlates

In an attempt to appraise the cross-province variation in total fertility, province-level TFR rates have been correlated with several potential explanatory variables: education (the percent of women without education)31, ideal family size, and mortality (infant, child, and infant/child). The r2's have also been examined for pooled samples (1984 and 1989 combined), and for changes between 1984 and 1989. While the sample sizes are small and do not allow an assessment of statistical confidence, the pattern is one of low association.32 (See table 5.2.) This is somewhat surprising, especially since there are substantial cross-province differences in the variables. Possibly the unit of observation (the province) is too large and heterogenous, and/or the sample size too small, to adequately account for this heterogeneity. (District- and individual-level data are not yet available for analysis.) Or possibly there are one or more explanatory variables common to all provinces that strongly dominate the ones examined here. (This hypothesis will be examined in section 5.5 below.) At any rate, the low correlations found in table 5.2 provide some caution in interpreting total fertility variations across provinces and changes over time.

Table 5.2 Correlations (r2's) Between TFR and Selected Variables

Sample Education Ideal Mortality Rates (% without) Family Infant Child Infant/Child Size

1984 .07 .57 na na na 1989 .06 .11 .00 .03 .01 1984, 1989 .03 .31 Change .01 .03

31 This is the only definition of the education variable that is fully comparable between the KCPS and the DHS. 32In an attempt to assess the importance of possible outlier observations, the correlations have also been run on subsamples omitting Nairobi, or Western. The results in the text showing low correlations are found in the subsamples as well.

35 5.43 Total Fertility: International Comparisons

An assessment of changes in the Kenyan TFR can be made by examining the experience of other countries at the beginning of their fertility declines. To this end, it is instructive to examine estimates of changes over time in the TFR compiled from birth histories for nineteen countries judged by the UN to have data of good to acceptable quality from the World Fertility Surveys. These countries have been grouped by the UN into "high" and "middle-high" levels of development, and by strength of family planning program: strong, moderate, weak, and very weak. The results, presented in table 5.3, indicate that at the beginning of the fertility decline, countries with "weak" family planning programs experienced 7 percent declines in the TFR; and that in countries at middle-high levels of development, the decline was 9 percent. Since Kenya's development level is classified by the UN as solidly in the "middle-low" category, and the family planning effort in 1983 as "weak," an estimated decline in the TFR of around 7 percent would not be unreasonable. Kenya's decline (15 percent), twice this rate, can therefore be judged as being relatively large.33

Table 5.3 Trends in TFR for 10-14 and 0-4 Years Prior to the WFS, by Level of Development and Strength of Family Planning Effort

Total Fertility Rate Percentage Change in Periods Prior to 10-14 5-9 10-14 the Survey to to to 10-14 5-9 0-4 5-9 0-4 0-4 Years Years Years Years Years Years

Level of Devt. High 6.4 5.6 4.5 -13 -20 -30 Middle High 6.7 6.1 5.2 - 9 -15 -22 Strength of FP Prog. Effort Strong 6.6 5.6 4.5 -15 -20 -32 Moderate 6.1 5.2 4.3 -15 -17 -30 Weak 6.8 6.3 5.3 - 7 -16 -22 Very Weak 7.0 6.5 5.9 - 7 - 9 -16 Source: United Nations (1987), p. 31.

33The UN study fails to uncover a strong relationship between initial fertility levels and the amount of decline over the period, although countries with total fertility rates of seven children in the earlier period had somewhat larger absolute declines over the period. United Nations (1988a), pp. 28-29. 36 5.44 Total Fertility: Family Planning

The association between the TFR and the contraceptive prevalence rate (CPR) is quite close across countries. Estimates of this relationship by Mauldin and Segal (M/S) (1988) can be used to appraise the Kenyan fertility decline. They find that a 10 percentage point increase in the CPR (a change corresponding to the 1984-1989 period in Kenya) is typically associated with a .7 reduction in the TFR. This is well less than the 1.2 change that is estimated to have occurred in Kenya. On the other hand, the M/S estimates derive from a linear model in which changes are invariant to levels. For several reasons it is plausible that declines in fertility associated with a given change in the CPR would be (absolutely) larger at high than at low levels of the TFR. Since Kenya began from the highest TFR of any country in the M/S sample, one might speculate that the difference of Kenya's predicted TFR from the "normal" relationship is not likely to be as large if a more appropriate non-linear model were used. In an attempt to assess this hypothesis, the M/S model was re-estimated using several non-linear specifications, and the results were re-evaluated for Kenya.34 While some statistically significant non-linearity does in fact exist, it is quantitatively unimportant. It therefore appears that Kenya's change in the TFR is considerably larger than would be expected on the basis of the change in the CPR alone. 5.45 Total Fertility: Trends and Timing

While there is considerable evidence of a decline in Kenyan fertility, the actual timing of the turning point in the TFR is uncertain. On the one hand, as seen in table 5.4, four successive estimates of the TFR rate between 1977 and 1984 (the NDS, KFS, Census, and KCPS) yield high rates of around 7.9. (An adjustment for the experience of Western Province raises the 1984 KCPS rate to 7.9.) At face value, then, the 1989 DHS estimate of 6.5 for 1988 (or 6.7 for 1986-1988) appears to indicate a "break" in fertility taking place around the mid 1980s. On the other hand, there are several "indirect" indications that fertility may have been declining as early as the late 1970s and early 1980s. First, between 1977/78 (KFS) and 1984 (KCPS), and 1984 and 1989 (DHS), the absolute reduction in "ideal family size" was about the same, -1.5 and -1.4 children, respectively; the same pattern applied to the absolute increase in the percent of women who wanted no more children (+16.7 and +17.9, respectively), and the overall contraceptive prevalence rate (+10 in each period). This would suggest the presence of conditions favorable to an earlier

34For example, regression models were fitted on both the total and LDC samples in linear, quadratic, and cubic forms, and in double logarithms. The estimated impact of changes in 10 percentage points of the CPR on changes in the TFR varied from -.4 to -.8. For an update of the M/S results, see Westoff (1989). 37 decline in fertility--a decline that does not appear to show up in the 1984 KCPS estimate of the TFR. Second, the birth history data from the DHS3 5 provides an estimate of the TFR for the period 1983-1985 of 6.8. Finally, based on an analysis of birth history data from the KFS, Henin et al. (1982) conclude that the early stages of the fertility transition may have been in progress in the late 1970s; and based also on the KFS, Lesthaeghe et al. (1983) conclude that the educational conditions were set for an incipient decline in the TFR in Nairobi, Mombasa, and the Central and Eastern Provinces.

Detailed technical demographic research on the three surveys will be required to date the turning point in fertility and to reconcile the discrepancies and puzzles. This involves not only attempts to account for fertility changes by examining proximate determinants in each of the periods, and across time, but also rigorous statistical analysis focusing on issues of bias and sampling errors. 3 6 For example, while the birth-history data from the DHS would be expected to provide somewhat more reliable data than the summary questions asked in the KCPS, there is also an incentive by interviewers in the DHS to under count children five years before the survey since the existence of such children increases the number of questions that then must be asked on the health component of the survey. The figure for the TFR of 6.8 for the period 1983-1985 may thus be underestimated. An examination of age-specific changes in fertility on both sides of the KCPS shown in panel 1 of table 5.4 is also instructive of the difficulties in reconciling the estimates. Abstracting for the moment from issues of data reliability, it is seen that in the late 1980s the reduction in fertility was consistent across all age cohorts (excepting 15-19); this is not the case in the early 1980s, and moreover, during this period, the greatest reduction was in the early ages, a somewhat surprising result.

35An appraisal of these trends requires an examination of sampling errors around the various TFR estimates. At the time of this writing, such information was not available, although it is presently being compiled. 36 Much of this work has been done on the KFS, and comparable studies are required for the other two surveys. See, for example, Henin, Korten and Werner (1982) and Ferry and Page (1984).

38 Table 5.4

Age-Specific Total Fertility Rates, Family Size Preferences, and Contraception for Kenya: 1977-1989

Change between Periods Fertility Rates Early Late 1977 1977/78 1979 1984 1989 1977/78- 1979- 1984- NDS KFS Census KCPS KDHS 1984 1984 1989

Panel 1

15-19 135 177 179 143 152 -34 -36 +9 20-24 365 369 368 358 314 -11 -10 -44 25-29 361 356 372 338 303 -18 -34 -35 30-34 316 284 311 291 255 +7 -20 -36 35-39 231 216 226 233 183 +17 +7 -50 40-44 133 132 105 109 99 -23 +4 -10 45-49 56 51 14 66 35 +15 +52 -31

TFR 8.0 7.9 7.9 7.7 6.7 -. 2 -. 2 -1.0

Panel 2

Changes in Changes in Percent Want No More CPR-All Methods % Decline in TFR Age 77-84 84-89 77-89 77-74 85-89 77-84 77-84 84-89 77-89 (1) (2) (3) (4) (5) (6) (7) (8) (9)

15-19 1.8 5.5 7.3 2.1 7.4 9.5 -19.2 6.2 -14.1 20-24 6.7 7.6 14.3 4.3 7.9 12.7 - 3.0 -12.3 -15.0 25-29 11.4 15.9 27.3 9.6 8.5 18.1 - 5.1 -10.4 -14.9 30-34 26.0 11.0 37.0 8.1 10.3 18.4 2.5 -12.4 -10.2 35-39 28.7 13.3 42.0 12.6 12.9 25.5 7.8 -21.5 -15.3 40-44 26.6 11.8 38.4 6.6 10.5 17.1 -17.4 -19.2 -25.0 45-49 33.9 5.5 39.4 7.8 3.7 11.5 29.4 -47.0 -31.4

Notes and sources. The 1977 figures in panel 2 apply to the 1977/1978 Kenya Fertility Survey. Republic of Kenya (1980), pp. A-580 to A-581; Republic of Kenya (1986c), pp. 58, 88; Republic of Kenya (1989d), pp. 35, 52.

Panel 2 of table 5.4 presents age-specific chanqes in the "demand for children" (the percent of married women who want no more children), and the use of contraception (CPR-all methods). The figures on changes in the TFR in panel 2 also show the percentage decline to adjust for the level from which the decline occurred. In an attempt to assess the veracity of the 1984 TFR estimates which, if reliable, would suggest a "break" in fertility around the mid-1980s, the following questions are addressed: are the changes over the total period (excludina the

39 experience of 1984) plausible, and, based on this assessment, does the pattern of age-specific change inclusive of the 1984 data make sense?

The results are inconclusive. On the one hand, there is no statistically significant age-specific pattern of differential rates of fertility reduction (column 9) in the prime fertility ages (20-39), a somewhat surprising result. On the other hand, there is indeed a plausible age-specific pattern in changes in family size desires (column 3) and the CPR (column 6). However, the percentage decline in the TFR for the 20-24 and 35-39 cohorts is the same (around 15%), yet the increase in the CPR for the first cohort is half that of the second. While without accounting for sampling errors, and controlling for other relevant factors, one cannot draw strong conclusions from these comparisons, it does appear that the patterns of change in the TFR over the entire period present puzzles that require resolution. With this in mind, the chances of making much sense out of the two sub periods with the objective of appraising the 1984 TFRs is reduced. Untangling these complex puzzles represents high research priority, but is beyond the scope of this report. As a working hypothesis this report will attach a somewhat greater weight to the possibility of a "break" in fertility around the early-to-mid 1980s, a perspective that gives some justification for identifying major changes in fertility determinants in this period. 37 This hypothesis will serve more as an organizing device for examining sets of determining factors and exploring hypotheses for further test than as a judgment that the TFR estimates are reliable. Indeed, the possibility of a gradual decline in the TFR beginning as early as the late seventies (but more likely early 1980s) is also a reasonable outcome. The decision to lean toward the fertility-break hypothesis is determined in part by pragmatic research options: discriminating between the two hypotheses necessitates a fairly detailed and rigorous statistical analysis of the data at the micro level, a format that is presently constrained both by time and data availability. Thus, the approach in this report of accounting for a "break" in fertility in the early to mid 1980s, while the received working hypothesis, might also be characterized as one of answering the question: "if such a break did occur, what factors appear to offer themselves as candidates to explain the fertility decline." If few or no candidates can be found, then the earlier/gradual-change hypothesis obtains some support. At any rate, a primary objective of this report is to

37An alternative strategy to looking for major changes in determining factors to explain a major downturn in fertility would be to focus on "threshold" models and variables whose cumulative impacts are strong, or on models positing substantial lags.

40 identify key areas of research relevant to population studies and programming in Kenya, and the above strategy of "testing" one approach to explaining the fertility transition, while speculative, serves that objective well by uncovering fruitful hypotheses and areas for further exploration. (Some of these research topics are listed in section 6.0) 5.5 The Turning Point: Causes and Implications

Between 1984 and 1989, were there changes in the social/political/economic environment that could potentially account for the observed changes in fertility? Because the decline in fertility is estimated to be large, because it occurred in all regions of the country, and because it (possibly) took place after a long period of fairly stable rates, attention can be initially directed toward identifying major, nation-wide events, taking place around the middle of the 1980s, that could have significantly reduced the TFR.

There are several candidates, including changes in 1) the political environment favorable toward limiting family size, 2) the provision and scope of family planning services, 3) (lack of) prosperity of the agricultural sector deriving from the drought of 1984, 4) the prevalence of AIDS, 5) infant and child mortality, and 6) the costs of children (especially the costs of education). 5.51 The Political Environment While the adverse impacts of population growth on economic and social development represent a long-standing concern of Kenyan political leaders, from 1967 (with the Population Council's Advisory Mission to the MOH) to the early 1980s, Kenya's political leadership attached relatively low priority to population matters.38 Family planning was substantially implemented by the Family Planning Association of Kenya, and population programming through the MOH emphasized maternal and child health.

A notable change in political commitment took place in 1984 with Vice President Mwai Kibaki's landmark address at the World Population Conference in Mexico City, and with President Daniel Arap Moi's subsequent elaboration and endorsement at the first National Population Conference in Nairobi. These speeches represented a watershed that initiated an elevated political momentum directed toward population matters that has continued to this day. For example, a Sessional Paper relating to population has been issued (1984); Kenyans played a visible and active role at the Parliamentarian's Conference on Population and Development in Zimbabwe (1986); Government planning has increasingly

38A fuller elaboration of the political trends is provided in section 4.2.

41 incorporated population goals and consequences in the planning process (1989); and, most recently, President Daniel Arap Moi at the second National Population Conference stated that "The greatest challenge we face today is for us to influence the attitude of our people towards having smaller families..." (1989).

It is fair to say that the period from 1984 to 1989 represents a change in the political commitment toward population matters from one of concern--but relative passive attention--to one of increased prominence.39 However, the impact of this change on the recent decline in fertility is indirect and, as a result, difficult to assess. On the one hand, parents' decisions on family size are little influenced by political speeches in Mexico City, Harare, or Nairobi. On the other hand, if these statements are in fact translated into significant changes in specific programs that in turn exert quantitatively important impacts on the benefits and costs of children, and/or the capacity to control family size, then the indirect link between changes in political commitment and the break in fertility can be established.

Historical perspective is useful in assessing the current situation. In the past, political statements by Kenyan leaders on population have been translated into rather broad initiatives (sometimes taking the form of creating a poorly funded government entity) of little impact. While Government commitment has been seemingly positive, it has not been strong; funding has been inadequate, and management and implementation of population programs have been deficient. The results of Mauldin and Lapham's (1985) detailed quantitative assessment of Kenya's programs as of 1982 are instructive.

Kenya scores below 50 percent of the scale maximum on policy and stage-setting activities, less than one-third of the maximum score on record-keeping and evaluation, only about one-fourth of the maximum on service and service-related activities, and less than one-sixth of the maximum on availability and accessibility. (p. 117)

39 Lapham and Mauldin's (1984) cross-country assessment of "family planing program effort" in 100 countries over the period 1977-83 places Kenya solidly in the "weak" category. Kenya's aggregate score of 33.7 is around half that of programs assessed as being "moderately" strong.

42 They go on to observe that while Kenya

...scores the maximum of 4 on the items regarding government policies and statements by leaders...[it scores] ... only 2 on import regulations and permission to advertise contraceptives, only 1 on the level of leadership managing the program, less than 1 on the involvement of other ministries, and 0 on the provision of in-country funds for the program (which means that outside sources provide more than half of the family planning budget). (p. 117)

This assessment provides the basis for exercising caution in interpreting political statements, no matter how visible, as necessary reflecting a "commitment" translated into programs that explain recent changes in fertility. As a result, the political change that seems to have taken place since 1984, and that has coincided with the notable decline in fertility, can at most be considered as a potentially facilitating--a necessary, but not sufficient--condition in accounting for a portion of recent reduction in fertility.

5.52 The Provision of Family Planning Services What, then, has been the nature and effectiveness of Government's programming in population matters in recent years, and specifically in family planning? Has there been a marked expansion and improvement in the delivery of family planning services sufficient to account for the downturn in fertility? Probably not, although increased population programming was a part of the story, and its role will be increasingly important in the future in closing the widening gap between actual and desired family sizes. Table 5.5 presents estimates of acceptors of various forms of contraception for the years 1984-1988. These data are hard to interpret since their coverage is uneven in terms of new versus continuing acceptors, and the relative representation of private versus public FP outlets.4 0 Thus, the data can at most be used to gain broad impressions of FP program activity. With this objective in mind, it is seen that up through 1985, program activity was relatively stable, and that in 1986-87 it recorded a substantial increase. This impression is corroborated by data on

40For a detailed assessment of difficulties in using acceptor data, see Laing (1982) and Srikantan (1980).

43 Table 5.5

Family Planning Acceptors: 1984-1988

Year Pills IUD Injectables Sterilizations Total

1984 60,194 29,101 21,888 1,474 112,657 1985 50,921 22,949 33,391 2,610 109,862 1986 116,254 31,553 47,391 5,372 200,570 1987 181,553 48,396 81,693 8,405 320,047 1988 174,029 42,756 106,685 9,128 332,598

Sources. Data were obtained from MOH, USAID, and NGO contacts in Nairobi. Data for the public sector represent MOH clinics and relate only to new acceptors. No information was available on continuing acceptors; moreover, the reporting rate for MOH clinics is about 60%, according to USAID-Nairobi. For the private sector, data include FPAK clinics that report new and continuing acceptors. NGO CBD program data are unavailable by method, and are thus excluded. Estimates of CBD (new) acceptors would increase total acceptors in 1985-86 and 1987-88 by 13,618 and 50,934, respectively. Sterilization data, from USAID- Nairobi, cover FPAK, CHAK, and MOH.

contraceptive supplies provided by donors (table 4.3) showing injectables increasing dramatically in 1987-88, although acceptor trends for pills are somewhat at variance with pill supplies. It is unlikely that inventories acting as a buffer were sufficient to explain the discrepancy. Thus, errors in the acceptor and/or pill supply estimates must be considered. Even with this qualification, it is clear that there is a notable overall increase in activity, especially in the 1987-88 period.

Can the trends in FP programming explain the apparent turning point in the TFR? Again, probably not, and for several reasons. First, a significant portion of the change in FP activity represents the substitution of one efficient form of contraception for another (e.g., injection for IUD).41 Second,

41An assessment of IUD use is puzzling. An analysis of underlying data indicate its acceptance has increased in MOH clinics, and possibly declined in FPAK outlets. Clearly "unpopularity" of IUDs is too simple an explanation of its relative overall decline. Research is required to examine this issue since IUDs constitute a cost-effective technology, especially the newer generation (e.g., Copper T380) presently being provided by USAID to the program.

44 the major change that did occur transpired somewhat after the turning point in fertility. Third, the recorded change in contraceptive acceptors is well short of that required to account for the estimated reduction in fertility, especially when plausible adjustments are made for less-than-efficient use, and discontinuance characteristic of first-time family-planning acceptors. A reconciliation of the estimated large reduction of the TFR with the more modest estimated increase in the modern CPR must await a detailed analysis of the DHS.42

The data in table 5.4 refer to the quantitative dimensions of family planning. Is it possible that qualitative changes may have taken place to account for the turning point in fertility?

Probably not. The World Bank assessment of family planning service delivery in 1988 mirrors the "weak" assessment rating provided by Mauldin and Lapham (1985) for the year 1982. According to the World Bank (1988): Despite the substantial increase in service outlets there has been so far little emphasis by MOH on programmatic aspects such as development of a supervision system for FP services, retraining of health staff in FP, and education of the public properly coordinated with service availability. Management of the FP program is poor and funding is inadequate...

42Exemplary of one puzzle is the need to reconcile the estimates of the prevalence of sterilization as reported in the DHS with the service-program statistics. Over the period 1984- 1988, the number of reported sterilizations (almost totally tubal ligations) for the three major outlets (FPAK, CHAK, and MOH) was 27,326. This compares with around 50,000 that would be found by combining DHS prevalence data with estimates of married women by age derived from age-specific marital fertility rates, and age- sex population distributions. Are the two series reconciled by consideration of sampling errors in each? Are service-statistics underreported; DHS statistics overreported? Are sterilizations being unreported as a result of C-sections? (Informed observers lean toward the latter explanation, plus the fact that sterilizations at private clinics are not reported.) Apart from these statistical issues, it does appear that sterilization is increasingly accepted in Kenya, and that it has substantial potential if obstacles relating to knowledge, fear, and health- official reluctance are overcome. See Center for Disease Control (1987).

45 There is poor communication between the Division of Family Health, other MOH offices, NCPD, and the NGOs. There have also been difficulties in the procurement and distribution of contraceptives. (p. 12)

On the other hand, this assessment can be modestly qualified by recent evidence (table 4.2 above) showing a notable improvement in the training of MCH/FP workers during the 1985-88 period. However, this training represents only one component of quality, and while potentially important in impact, it must be combined with other elements--logistics, administration, coordination, etc.--to translate into an effective overall program. (There is also some indication that in 1989, qualitative improvements are being recorded in several additional dimensions of Kenya's FP program.43)

It appears that Government's commitment to population matters, while visibly strong, is only beginning to translate into an efficient and strong program of family planning. 44 As a working hypothesis, one might characterize the public-sector family planning program until quite recently as a "follower" in the fertility transition--lagging (and significantly relying on) the activities of the private/NGO sectors and, quite likely, the changing fertility behavior of millions of Kenyans. However, and importantly, a significant portion of the "infrastructure" for the family planning program is now in place, there is presently an expansion in the demand for family planning, and public-sector programmatic efforts of the past and present, while not path

43A recently completed assessment of the national FP program based on a stratified random sample of 100 SDPs reveals improvements with regard to the availability of contraceptive supplies, provider-client relations, a reduction in client waiting time, and staff retraining. Management and supervision continue to suffer, and client follow-up has been deficient. Gachara, Miller and Ndhlovu (1989). The World Bank's (1990) assessment arrives at similar indicators of improvement. For example, referring to the DFH of MOH, the World Bank staff observe that "The past year has seen a considerable improvement in the Division's status," largely in staffing, but also with "...considerable improvements in communication with other MOH units and the NCPD. Despite this promising trend much needs to be done..." (p. 7). 44A more detailed account of the role of Non-Government Organization (NGO) family planning delivery, important in Kenya, is required to complete the analysis. While this sector's contribution has been path breaking at every stage of Kenya's family planning effort, its specific role in explaining the turning point in fertility is not likely to alter the trends summarized above with respect to the public sector. 46 breaking, can in the future yield high returns. Moreover, on several fronts family planning programming is improving in Kenya (e.g., 15 District Population Officers are in place), and there are indicators that the volume and scope of this activity will increase over the foreseeable future. (See Appendix tables B1, B2 and B3.) Thus, while it is necessary to look beyond the family planning programs to explain the turning point in total fertility, future trends in fertility will predictably be increasingly influenced by family planning activities. 5.53 The Drought

Did the exceptionally severe Kenyan drought of 1984 play an important role in accounting for the decline in fertility?

Probably not.

In Kenya's case one can dismiss the direct impact of drought on fertility through the mechanism of famine. Government's management of food supplies was highly successful and the potential adverse impacts through hunger or malnourishment were minimized. One must therefore turn to indirect impacts through the drought's effects on the benefits and costs of children. These can be classified into short- and long-run effects. In the short run, during the drought, the productivity (benefits) of children declined precipitously while the costs of children remained little altered. For this reason one would have expected fertility to decline. However, to the extent that the drought was considered to represent a highly unusual phenomenon, the reduction in fertility would plausibly be short lived. The drought is more likely to have affected the timing than the number of births.45

The long-run effects of the drought on the value of children are more difficult to identify. Because decisions relating to family size are predominantly long-run in nature, the relevant issue is how short-run variations in the benefit-cost ratios of children affect their long-run value to parents. The net impacts are unclear. On the one hand, one might observe that large families, through extended family networks, provide a mechanism for insuring against variations in income, including the vagaries of weather. Large families can therefore enhance the capacity to cope with droughts. (This is especially true of localized droughts. The widespread scope of the 1984 drought lessened the

4 5 Economists make the distinction between "transitory" and "permanent" effects, corresponding loosely to short- and long-run determinants. Prominent cycles in birth rates corresponding to business cycle conditions have been widely observed in developed countries.

47 value of extended-family insurance mechanism.) However, to the extent that Government backstops the household with food during drought, the value of large families in providing income- maintenance insurance is diminished. In the Kenyan case Government played an active role in minimizing the adverse consequences of the drought. Other things equal, this would exert an antinatal impact. On the other hand, the converse may be the case. During a drought children, who are unproductive on the farm, simply represent more mouths to feed, and large numbers strain the household's financial and food reserves that are fixed in the short run. (Government assistance lessens this cost.)

In summary, while drought can have an adverse short-run impact on fertility, such an outcome of the Kenyan drought was muted given the success of Government in managing food supplies. And the long-run impacts of drought on family size are unclear, but probably not important in Kenya's case. 5.54 The Prevalence of AIDS

The rising prevalence of AIDS in Kenya introduces an additional, somewhat complex dimension into the analysis of fertility and mortality, as well as population policy and programming. Since AIDS affects mortality directly, and fertility indirectly through its impacts on mortality and on contraceptive use, an assessment of Kenya's fertility decline requires attention to the AIDS phenomenon. In particular, is the notable decline in marital fertility related to the rising incidence of AIDS? Probably not.

One somewhat imperfect measure of AIDS' current and future impacts on marital fertility is condom use. Limited evidence fails to reveal any significant shift toward preference for condoms on grounds of protection against AIDS. The absolute increase in condom use that has taken place can be attributed almost totally to its value as a contraceptive.

This conclusion is based on evidence relating to the changing composition of family planning mix over time. There appears to have been a shift away from condoms, the least preferred method of modern contraception (excepting male sterilization). In 1984 condoms were estimated to constitute 3.2% of modern family planning techniques used by currently married women (KCPS, table 7.10); this figure dropped to 2.8% in 1989 (DHS, table 4.7). These estimates, based on household survey data, are broadly consistent with World Bank estimates of contraceptive supply showing rates of increase in condoms, while sizeable in absolute terms, to be roughly the same as comparable rates of increase for the pill, female surgical procedures, and injectables. Only in the last year or so has the rate of 48 increase in condom use edged out comparable rates of increase of other forms of contraception.46 Given its timing, this result, while possibly related to AIDS, will have had little or no impact on fertility over the period 1984-1989.47 The data are therefore consistent with the conclusion that the increase in condom use is associated predominantly with the increase in family planning in general, and any impacts of AIDS have been quite recent. As a result, corresponding reductions in marital fertility are unlikely to have been caused by concerns about AIDS. This situation could well change in the future as information about AIDS and a public perception of its dangers and consequences increase. The interrelationships of AIDS and population demographics is complex analytically, and empirical information useful for untangling the more important linkages is scarce. These topics merit high priority on the population- research agenda, and constitute an area for creative rethinking of important dimensions of population policy.

5.55 Infant and Child Mortality Across countries, and over time, reductions in fertility have been strongly associated with reductions in infant and child mortality. And, as will be seen below, across Kenyan provinces in 1989, the correlation of family planning use and infant mortality is high (an r2 of .84). Can a turning point in the TFR in the 1980s be explained by a reduction in mortality? Maybe, but probably not; the evidence is shaky. While infant and child mortality rates have declined steadily over the last forty years, the issue is whether there was a major change around (or somewhat before) the mid-1980s that

4 6This is plausibly due in part to non-marital relationships, and is possibly related to concern about AIDS. 47World Bank (1989c) estimates show the use of injectable contraceptives increasing from 300,000 doses in 1986 to 1 million doses in 1989. Pill usage has increased from 1.2 million cycles in 1986 to 3.2 million cycles at present. Condom usage has more than doubled from 5 million pieces in 1987 to 11 million pieces in 1988. And surgical contraception procedures have risen from less than 100 in 1982 to over 12,000 in 1988. In recent times, an annual doubling of several forms of birth control appears to be commonplace, although in the last year this rate has been highest for condoms. 49 could have accounted for the decline in fertility.4 8 No such break seems to have occurred, although the evidence is difficult to interpret. On the one hand, according to recent estimates from the DHS, the under-five-years mortality rate decreased moderately from 93.1/1000 to 89.2/1000 between 1979-1983 to 1984- 1989, and the infant mortality rate actually increased from 57.6/1000 to 59.6/1000 over the same period.49 The reduction in the under-five rate appears to represent a continuation of a long-run trend. On the other hand, both the infant and under- five mortality rates declined substantially over the previous five-year period. Hill's (1987) estimates show the under-five rate declining by 22 percent between 1975 and 1980, and the DHS shows the rate declining by 11 percent between (approximately) 1976 and 1981. Even though there are differences between the mortality rate levels of these two estimates, the percentage reductions are both large. If there were a substantial lag (five to ten years) of fertility reduction behind mortality decline, then the earlier decline in mortality may have played a notable part in explaining the recent fertility rate change. 50 Unfortunately, estimates of infant and child mortality in Kenya possess considerable variance, and scattered small-scale studies are not particularly representative. Although one must be cautious in assessing the role of mortality change in accounting for the recent decline in fertility, the best guess is that it was not sufficient to have played a leading role; rather, it took its place with several other trend factors examined in this section as contributing (likely significantly) to long-run downward pressures on fertility. This preliminary judgment must be qualified on the basis of the yet-to-be-undertaken detailed analysis of the DHS, as well as the forthcoming results of the 1989 census.

5.56 The Costs of Education

In no area have the public and private costs of rapid population growth been greater than in education. In Kenya these cost increases have derived not only from increasing numbers of children, but equally (and possibly more) important, from increases in the scope and quality of education services. In

48 Estimates of childhood mortality rates for 1965, 1970, 1975 and 1980 are 188, 167, 149, 118, respectively. Government of Kenya and United Nations Children's Fund (1989), p. 54; A. Hill (1987).

49Republic of Kenya (1989d), table 6.1. 50Indeed, in section 5.62 below it is found that the proximate determinant of infant/child mortality, "full immunization," is strongly and importantly associated with the use of family planning (a proximate determinant of fertility).

50 particular, over the period 1965-86, primary and secondary enrollment rates have increased from 54 to 98, and 4 to 20 percent, respectively; universal primary education is a goal within attainment; and primary education is itself being expanded in both scope and quality as a result of the (8-4-4) curriculum reform of 1985. Thus, the "education cost problem" is not one of demographics alone; it derives substantially from the ambitious goals of Kenyans to upgrade investments in their youth. While some economizing has been necessary, for the most part--and to the credit of the Kenyan people--population pressures have been more than accommodated, albeit at substantial cost. The burden on Government's budget has been high. Since 1970, education's share of the recurrent budget has ranged 5a between 26 and 41 percent; it currently stands at 38 percent. This mainly represents teachers salaries, with the bulk of remaining costs assumed by local communities and parents. This "cost sharing," a well-established and increasing phenomenon in Kenya, places almost all capital costs at the local level, as well as recurrent expenditures for school supplies, books, and the like. The specific way in which these local costs are shouldered is critical to appraising the impact of economy-wide population pressures on parents' costs to finance their children's education. In particular, most of the (non-teacher) recurrent and capital costs are transferred directly to parents as visible expenditures, and substantially in proportion to the number of children attending school. Thus, while parents do not pay tuition fees to public schools per se, they do assume a share of capital costs (building "levies"); they pay for text books, supplementary readers, stationery and consumable materials; they purchase relatively expensive uniforms required for school admittance; and they participate in Harambees to support other school-related programs. Moreover, pre-school attendance, the high costs of which are substantially underwritten by parents directly, is increasingly used as a factor in admitting students to some of the preferred schools.

These individual parental costs are high, they have increased enormously over the last several years52, and they promise to increase rapidly in the foreseeable future. The costs vary by locale and are difficult to document on a systematic basis. One informal survey reveals costs ranging from around 800 shillings in low-cost, rural primary schools in

51World Bank (1989a), p. 3.

52One estimate indicates they have more than doubled in real terms. World Bank (1989a), p. iv.

51 Muranga and Embu to 2,800 shillings in a high-cost public school in Nairobi. 53 A median figure of 1,250 shillings would not be unreasonable. This conservatively represents around ten to fifteen percent of household annual cash income per child attending a rural school. Since much of rural household income is in-kind, and since most schooling costs must be paid in cash, the burden on the "discretionary" cash income of relatively poor households can be onerous. This is not a new phenomenon in Kenya. In the 1960s and 1970s school fees were commonplace, and households spent substantial portions of their incomes on education. Indeed, an econometric analysis of household expenditures in Nairobi, Mombassa, and Kisumu in 1969 reveals that families tended to substitute "financial" for "human" saving/investment so that total household accumulation was largely invariant to family size. 5 4 Children therefore represented a mechanism for accumulation. While such expenditures were more-or-less voluntary parents in fact sacrificed to invest in their children.5 This strong historical tradition of supporting education is critical to assessing the impact of skyrocketing education costs on the "price" of children in recent times.

In particular, the increase in the "effective price" of children, driven by rising education costs, is especially large for relatively poor households. This may at first glance appear contrary to the facts. After all, the absolute costs of education to parents (uniforms, books, capital contributions) in rural areas may not differ all that much by locale, rich or poor. On the other hand, the effective costs--the value of the foregone expenditures resulting from the costs of education assessed to individual households--certainly do. The "effective sacrifice"

5 3 Private schools are two to three times more costly. The breakdown of costs in an informal sample of rural Muranga and Embu schools is as follows: activity fees (41 shillings), books (182 shillings), uniforms (158 shillings), miscellaneous (4 shillings), PTA and development levies, including Harambee (432 shillings). World Bank (1989a). 5 4 Kelley (1980). See also Lillydahl (1976).

55"Owing to the high demand of Africans for education, the Kikuyu father will sell his land, or the Luo will sell his cattle, to educate his children." M. Forrester (1962), p. 139. "But an African has many people who can help him pay a school bill--wives, brothers, sisters, even cousins." A. C. Fisher (1969), p. 157. In Kenya, the underwriting of school costs constitutes a primary motivation for remitting income outside the household. G. E. Johnson and W. E. Whitelaw (1974).

52 (i.e., the opportunity-cost price) is greater for poor households. This represents a conclusion that has significant implications for assessing the present and future course of Kenyan fertility, and for formulating population policies and programs. For example, one might observe a reduction in family size with income (or prosperity of a locale). This is because the impact of the rising costs of education in reducing fertility could well be greatest in relatively poor areas, other things equal .56

From the perspective of population policy, such a result can be important. If in fact the rising education costs are a (the?) primary mechanism driving the current decline in fertility, these forces may well be greatest in areas previously considered least receptive to family planning. And moreover, even if the gap in unmet need for family planning may not be greatest in these areas (due, say, to high infant and child mortality), the welfare benefits of assisting households to close the widening gap resulting from the rising costs of children may be relatively high. There is an urgent need for research to untangle the empirical relationships between the changing costs of children, mortality, and family planning need and demand. The design of effective population policy could well hinge on the results of such research. At any rate, the costs of education will continue to increase, and possibly on an accelerated basis in the short to intermediate run, for two reasons. First, parents are presently spending substantially less on books than recommended by the Ministry of Education. There will be pressures (and desires) to close this gap. Second, and equally important, Kenya is in the midst of implementing the major (8-4-4) curriculum reform introduced in 1985. This new curriculum not only increases the commitment to primary education by expanding the years attended from 7 to 8, but increases capital and consumable supplies costs given a reorientation toward more practical and applied subjects.

56This may not be observed in cross-section data without statistical control for the "other things equal" since infant and child mortality, also correlated with income, may be offsetting. 57In Nairobi, the annual cost of one set of full uniform for a medium cost school is 1,000 shillings; in the typical rural school it is about 300 shillings. The Ministry of Education recomaended text books for primary schools (at the middle standard) averages 517 shillings. Expendable supplies, and heavy building levies, are in addition. World Bank (1989a). 53 In summary, the long-run trend of increasing education costs per pupil (deriving from demography and enrollment rates) has recently been adjusted upward with the revised curriculum. The accelerating costs, significantly shouldered (given the funding of education) by individual parents largely in proportion to the number of children in school, has significantly increased the already high per pupil costs of education, and has greatly raised the "price" of children. These costs will continue to increase at a rapid pace in the foreseeable future.

5.57 The Fertility Decline and the Turning Point Revisited

Several events occurring in 1980s can be offered as hypotheses accounting for the major shift in preferences for smaller families, and thus for the transition to lower fertility. Possibly the most conspicuous has been the rapidly rising costs of education. If true, then those very factors pointed out by concerned politicians and analysts in the past as adverse consequences of rapid population growth may now constitute the same factors that will automatically result in smaller families in the future. "Population pressures" on the land, and in the provision of educational services, may well represent primary forces accounting for the turning point in fertility. If so, the speed of the transition to small families depends fundamentally on the pace at which population-induced costs change over time, and importantly, on how they are distributed.

In Kenya's case, the mechanism for transmitting and distributing these population pressures to the household may have been (or ultimately may be) decisive in facilitating the fertility transition. That is, "cost sharing" of education budgets has been implemented such that substantial expenditures have been directly and conspicuously assumed by parents, and largely in proportion to the number of children in school. World-wide this is an unusual format, and it may represent a "model" for meshing national population policies with the goal of providing population-sensitive social services. A careful empirical assessment of the response of Kenyan families to mounting education costs, and the impacts of the Kenyan financing mechanism on the distribution of education services and costs, must rank high on the agenda of population research. In summary, over the last two decades many gradual changes have occurred to encourage parents to seek smaller families. These include increases in the roles and status of women, school enrollments, and urbanization, and decreases in infant and child mortality. In the early to mid 1980s, there may have been a break in this gradual trend with several identifiable and conspicuous events that can be offered as potential explanations of a possible break in the TFR. These include the increased political commitment to family size limitation, the expansion in

54 family planning, the drought, the prevalence of AIDS, an earlier reduction in mortality, and the notable increase in the costs of education shouldered by parents. By process of elimination the first five (possibly excepting child mortality changes), while relevant, can be assessed as less important to explaining the turning point in fertility than the sixth--the increasing costs of education and the "price" of children.

Whether the newly established trend is maintained is uncertain. On the one hand, the drought, and to a certain extent, the political statements, represent isolated events; unless repeated, their impacts will diminish over time. On the other hand, family planning programs are likely to expand in size and improve in effectiveness over time.

Possibly more important, the rising costs of education will continue to mount since families will attempt to close the gap between the recommended costs of the new curriculum and that which they are presently underwriting; and proportionally more children will advance to secondary and tertiary levels. Moreover, the now firmly established principle of "cost sharing" will expand not only in education, but in other areas as well. (This is already occurring in the provision of medical services.) Many of these changes will be antinatal in impact. While the direction of such impacts on fertility is predictable, an assessment of their quantitative importance requires empirical research. What does appear certain is that the antinatal impacts of development in general, and of government taxation/spending in particular, increase if child-related expenditures are directly associated with child rearing, and are also assessed to parents in rough proportion to the number of children in the family. To the extent that this form of "cost sharing" is maintained or increased in Kenya, it seems likely that the forces of fertility decline will be maintained for some time. Two major qualifications could well modify this conclusion. First, if future economic events lower the return to education (e.g., if job creation is insufficient to employ school leavers at perceived reasonable wages), and if such economic motives substantially explain Kenyan parent's strong preferences for education, then school participation rates (now "voluntary" but effectively "compulsory" on social grounds) could adjust downward. 58

58The general issue is the tradeoff Kenyans are likely to make between the quantity and quality (expenditures on education, food and clothing, medical services) of children in a environment of increasing cost-sharing.

55 Second, factors are emerging that could curtail the pace of mortality (and thus induced fertility) reduction in the future. Immunization programs, important to past reductions in mortality, will be difficult to sustain financially without substantial long-term donor support, the availability of which in uncertain. Moreover, AIDS and other public health problems will increasingly compete for health resources. Finally, public health budgets are already taxed, resulting in a recent movement toward cost sharing. Whether this cost sharing will have the same antinatal impact that has occurred in the area of educational expenditures depends on the form in which it occurs (i.e., whether it is assessed to parents in proportion to their number of children), and parental priorities toward investments in child health versus competing expenditures. Research is needed to assess the nature of these likely tradeoffs. Similarly, research is required to assess likely governmental responses as budget pressures in the MOH continue to mount. Will AIDS treatment expenditures, and increasing demands for infant- and child-health immunization and other costs, result in reduced MOH priorities toward family planning? Whether at some point the recent fertility decline plateaus, or is even reversed, will be determined in part by answers to these and related questions. Current population programming and strategies should be sensitive to these issues as well.

5.6 The Turning Point: The Roles and Determinants of Family Planning 5.61 The Roles Contraceptive Prevalence: The Record

Table 5.6 presents contraceptive prevalence rates for all and modern methods by province for 1984 and 1989, as well as changes over the five-year interval. While a change of around ten percentage points in the country-wide rates seems large, it is not. Table 5.7 assembles comparable data for other countries at early stages of their fertility transition. A change per year in the CPR of around two percentage points, corresponding to Kenya's experience, is commonplace. While it is true that there was considerable variance geographically in Kenya, even this pattern is consistent with that found elsewhere. For example, the degree of regional variation is similar to that found in Indonesia (not shown in the table). It appears, then, that Kenya's overall experience conforms to that found in a wide range of countries at the beginning of their fertility transition.

56 Table 5.6

Contraceptive Prevalence Rates by Province

Change Per Year All Methods Modern Methods All Modern 1984 1989 Chng 1984 1989 Chng Methods Methods

Nairobi 28.3 33.5 5.2 22.9 27.9 5.0 1.0 1.0

Central 34.1 39.5 5.4 20.7 30.8 10.1 1.1 2.0

Coast 10.5 18.1 7.6 6.8 14.8 8.0 1.5 1.6

Eastern 26.3 40.2 13.9 14.2 19.5 5.3 2.8 1.1

Nyanza 8.6 13.8 5.2 5.5 10.2 4.7 1.0 .9

Rift V. 15.1 29.6 14.5 5.4 18.1 12.7 2.9 2.5

Western 4.6 13.7 9.1 3.5 10.0 6.5 1.8 1.3

Kenya 17.0 26.9 9.9 9.6 17.9 8.3 1.9 1.7

Source: Republic of Kenya (1986a), table 7.13; Republic of Kenya (1989d), table 4.7.

57 Table 5.7

Contraceptive Prevalence Rates of Married Women of Reproductive Ages, Currently Using Each Method, at Early Stages of the Fertility Transition

All Modern Change Per Year Methods Methods All Methods Modern Methods

Kenya 1978 7 4 1984 17 10 1.7 1.0 1989 28 18 2.2 1.6

Botswana 1976 8 na 1984 29 18 2.6 na

Zimbabwe 1976 5 na 1979 14 na 3.0 na 1984 40 27 5.2 na

Guatemala 1978 19 13 1983 25 19 1.2 1.2

Honduras 1981 27 23 1984 35 29 2.7 2.0

Morocco 1980 20 17 1983 27 22 2.3 1.7

Java-Bali 1973 10 9 1976 28 22 6.0 1.3

R. of Korea 1966 20 12 1972 30 24 1.6 2.0

Source: Compiled from Ross (1988), table 26. Sample of countries with all- and modern-methods CPRs between 5 and 35, with two or more comparable definitions, and with surveys spanning at least 3 years. The Kenyan figures represent women aged 15-50 for 1978, and aged 15-49 for 1984 and 1989.

58 While there was a sizeable absolute increase in the use of both modern and traditional methods of contraception--evidence of a moderately strong increase in the overall demand for family planning, there was also some shift toward modern methods, representing a substitution of more for less efficient methods of fertility control. This can be seen in table 5.8 which shows the percentage of all users that employ traditional methods.

Table 5.8

Shift form Traditional to Modern Methods of Contraception

Percent Using 1989 Traditional-Methods Traditional Methods Contraceptive Prevalence of All Current Users Rate 1984 1989 Change Actual Rate if Change Rate No Shift

Nairobi 17.2 16.7 - .5 5.7 5.6 - .1

Central 46.2 22.0 -24.2 18.2 8.7 - 9.5

Coast 35.6 18.2 - 7.4 6.4 3.3 - 3.1

Eastern 51.4 51.7 .3 20.6 20.8 - .2

Nyanza 35.8 25.4 -10.4 4.9 3.5 - 1.4

Rift Valley 62.1 38.9 -23.2 18.4 11.5 - 6.9 Western 20.5 27.0 6.5 2.8 3.7 .9 Source: Derived from Republic of Kenya (1986a), table 7.13; Republic of Kenya (1989d), table 4.7.

The shift toward modern methods was greatest, in order, in Central, Rift Valley and Coast provinces. (There appears to be a relative shift away from modern methods in Western province, although this may derive from mismeasurement of the 1984 rates.) An alternative measure of the shift that takes into account the absolute CPR is provided in the last column. This represents the deviation of the 1989 traditional-methods CPR from the one that would have prevailed if the composition of the contraceptive mix had not changed between 1984 and 1989. While the rank ordering of the shifts is the same, the importance (the magnitude of the impacts) is not. For example, the shift is three times greater in Central than in Coast province--a result deriving from the lower prevalence rate in Coast province.

59 A shift in contraceptive mix toward modern methods is usually taken as an indication of increased efficiency of use. This interpretation might also be expanded by hypothesizing that such shifts represent a proxy for an overall improvement in family-planning effort, including the use of traditional methods. If so, one would expect the greatest reduction in the total fertility rate, per unit change in the overall and the modern prevalence rates, in provinces where the efficiency gains were greatest. This hypothesis will be explored below.

Contraceptive Prevalence: The Impacts The "bottom line" of population policy relates not simply to increasing the contraceptive prevalence rates, but to reducing the fertility rate. To what extent were reductions in the TFR associated with increases in the CPR?

In terms of the overall CPR, table 5.9 reveals the association to be weak or non-existent--surely a disquieting puzzle. However, the correlations are considerably stronger with modern methods, suggesting that family planning over this period not only expanded in coverage, but also likely improved in the quality of services--more efficient methods and, if the above interpretation of the "shift" from traditional to modern methods is correct, to improved delivery and use of modern methods as well. This hypothesis obtains some support from additional

Table 5.9 Correlations (r2's) Between the TFR and CPR by Province

Sample All Modern Methods Methods Obsvs.

1984 .01 .11 7 1989 .09 .40 7 Pooled 1985 .10 .37 14 Change Between 1984 and 1989 .01 .23 7

correlations between the TFR and the "shift" factors, with values ranging between .36 and .67 (not shown in the table). It appears that there is much to be learned from a closer examination of the 60 disaggregated (district and individual) data of changes in the mix in contraceptive use--both in terms of the interpretation of such changes, as well as their impacts.

5.62 The Determinants The analysis of the determinants of family planning involves complex modeling and statistical estimation that focuses on: 1) the supply of children (biological "proximate" determinants such as age of marriage or length of breast feeding, as well as infant/child mortality5); 2) the demand for children (socioeconomic determinants such as educational status and income, mainly associated with the costs and benefits of children); and 3) the costs of family planning (knowledge of and access to family planning, including monetary and psychic costs).60 While the individual- and district-level data from the DHS permit such an analysis, the published data, aggregated by province and examined below, can but provide only provisional and suggestive insights into the determinants of family planning. Table 5.9 presents several variables associated with family planning use: the demand for children (ideal family size, the percent of fecund women who don't want more children); the supply of surviving children (the complement of the infant, child and infant/child mortality rates); and the costs of family planning (knowledge of at least one method, and female approval). Education affects all three channels of causation.61 Attention has been focused on those variables available in both the 1984 and 1989 contraceptive surveys. Several features of this table merit emphasis. First, the reduction in the "demand" for children has been large. Over the five-year period, ideal family size has declined from 6.3 to 4.8, or by 1.5 children. Unfortunately, comparable time-dimensioned data of changes in ideal family size have not been assembled for a group of countries at the beginning of their fertility transition, so it is not possible to appraise the

59 Bongaarts (1978).

6 0Studies examining all three of these components in an analytically and statistically integrated framework are few in number. The Easterlin model outlined in section 5.2 offers promise as an organizing framework. See Easterlin and Crimmins (1985), Kelley and Schmidt (1988), and Boulier and Mankiw (1986b). For a study focusing on proximate determinants in Kenya, see Ferry and Page (1984); and on socioeconomic factors, Republic of Kenya (1989b). 61Cochrane (1979).

61 Table 5.10

Selected Correlates of Family Planning Use, by Province: 1984, 1989

Variable Nairobi Central Coast Eastern Nyanza Rift V. Western Kenya

Ideal Family Size 1984 4.3 5.4 6.1 5.4 5.9 6.5 6.0 5.8 1989 3.6 3.8 5.6 4.2 4.6 4.7 4.9 4.4 Change 1984-89 - .7 -1.6 - .5 -1.2 -1.3 -1.8 -1.1 -1.4

Don't Want More 1984 32.6 41.2 20.4 43.6 24.2 29.0 34.1 31.5 1989 43.7 67.3 28.0 59.7 41.7 49.7 43.2 49.4 Change 1984-89 11.1 26.1 7.6 16.1 17.5 20.7 9.1 17.9

Infant Mortality 1984 na na na na na na na na 1989 46.3 37.4 107.3 43.1 94.2 34.6 74.6 58.6

Child Mortality 1984 na na na na na na na na 1989 35.7 10.0 54.5 22.2 60.0 16.9 62.9 34.3

Infant/Child Mort. 1984 na na na na na na na na 1989 80.4 47.0 156.0 64.3 148.5 50.9 132.8 90.9

Knowledge of at Least One Method 1984 84.5 90.0 80.2 88.8 87.5 69.5 68.7 84.0 1989 94.8 95.8 92.3 92.7 93.3 84.6 90.6 90.0 Change 1984-89 10.3 5.8 12.1 3.9 5.8 15.1 21.9 6.0

Female Approval of Family Planning 1984 79.2 85.7 75.4 75.6 79.3 74.7 69.9 76.2 1989 92.7 92.2 74.4 92.0 91.7 87.1 83.0 88.2 Change 1984-89 13.5 6.5 - 1.0 16.4 12.4 12.4 13.1 12.0

No Education (%) 1984 14.3 20.6 55.7 32.2 34.6 43.1 34.2 34.8 1989 8.5 12.8 47.5 23.6 27.4 32.1 25.5 25.1 Change 1984-89 - 5.8 - 7.8 -8.2 - 8.6 - 7.2 -11.0 - 8.7 - 9.7

Source: Republic of Kenya (1986a) for 1984; Republic of Kenya (1989d) for 1989.

62 Kenyan experience with respect to international experience.62 However, the reduction in "demand" (by 24%) exceeds the reduction in the TFR (by 15%), suggesting that there may be an increase in unmet need for family planning. Second, the reduction in the demand for children has been widespread (all provinces), and is taking place even in those areas (Coast, Western) that appear to be lagging in the fertility transition. Third, the results showing a notable decrease in the demand for children as measured by changes in ideal family size are corroborated by evidence that the proportion of mothers who state "they don't want more children" has increased from 31.5% in 1984 to almost 50% in 1989. These proportions increased in every province. The fact that the responses to these questions are highly consistent in Kenya gives added weight to the interpretation of a significant reduction in the demand for children.63 Thus, the key necessary condition for the commencement of stage III of the Demographic Transition--a strong downturn in the demand for children--has apparently taken place in Kenya. This has already been translated into an increase in the demand for family planning. Fourth, while not shown in table 5.10, infant and child mortality continue to decline in Kenya. This will further reduce fertility. It should be recalled that the incidence of mortality varies substantially by province, and this plausibly accounts for a portion of provincial variations in the demand for children and family planning. Fifth, both knowledge and approval of family planning is high and widespread. This was generally true in 1984, although approval has increased over the last five years. Only Coast and Western provinces presently possess some resistance to family planning. Knowledge and acceptance, then, do not appear as

62 Caution must be exercised in interpreting results based on questionnaire responses to "ideal" or "desired" family size, although it is generally held that responses to questions on "don't want more children" are more reliable and reasonably interpretable. See Bongaarts (1989). 63For an examination of the methodological issues in interpreting the results, see Lightbourn (1985), United Nations (1986b), p. 36, Bongaarts (1989), and Westoff (1989). Westoff (1989) estimates a linear regression 2 TFR = 8.15 - .078 (Percent Want No More), r = .76, with survey data for 83 countries. Applying this statistical relationship to Kenya, the estimated TFRs in 1984 and 1989 would be 5.7 and 4.3, respectively. Clearly the TFR levels are greatly underestimated, although the estimated change of 1.4 is quite close to the change of 1.2 between the KCPS and the DHS. 63 barriers to the expansion of family planning over the coming years.

While the results in table 5.10 provide some clues of several factors accounting for increases in contraceptive prevalence rates, it would be instructive to assess the relative importance of these factors by examining the extent to which changes in these variables, across provinces and over time, are associated with variations in the prevalence of family planning. Table 5.11 presents the results of simple correlations between two measures of family planning--the CPRs for all, and modern methods--and the variables found in table 5.10.

Several results emerge.

First, the overall correlations are reasonably high, suggesting that the observed variations in the CPRs are systematically related to the same types of influences found to be relevant in other countries.

Table 5.11

Correlations (r2's) Between CPR and Selected Variables

Variable Contraceptive Prevalence Rates

1984 1989 1984-1989 Total Modern Total Modern Total Modern

Ideal Family Size .40 .81 .59 .60 .12 .82

Don't Want More .43 .21 .60 .36 .10 .47

Infant Mortality na na .70 .48

Child Mortality na na .84 .60

Infant/Child Mort. na na .81 .57

Knowledge of at Least One Method .38 .36 .07 .18 .04 .02

Female Approval of Family Planning .52 .38 .32 .46 .14 .07

No Education (%) .42 .62 .31 .44 .22 .07

64 Second, family planning use appears to be most strongly related to demand factors. (For example, the correlations for ideal family size and "don't want more" are higher than those for the more supply-oriented variables.) It is notable that the correlation of changes in ideal family size and the CPR (modern methods) is .82. Since variables measuring change incorporate relatively higher error, this level of correlation can be judged to be strong. Third, the importance of knowledge and approval of family planning does not appear to be as strong as the other measures. This is likely explained by the fact that both knowledge and approval are already high, and variance over time and across space is not great.

Finally, the mortality correlations (not available for 1984) are not only high--suggesting a potentially important role for this variable--but they appear to be consistently lower for the CPR using modern methods. One can only speculate why. Since mothers who use modern family planning methods have likely received the combined health-sector benefits of family planning and child/maternal health care, it is plausible that child mortality has a smaller influence on family planning for those mothers who use modern methods. This speculation supports a policy of combined emphasis on (but not necessarily a programmatic integration of) family planning and maternal and child care.

To gain further insights into the fertility-mortality-family planning linkages, regressions were run explaining CPR by 1) "full immunization" in the early, and in late 1980s, and 2) early childhood mortality (0-2) for 1979, using district-level data provided in the DHS, and Republic of Kenya (1988c). The above general findings at the provincial level of a strong association and better results using all-methods CPR are confirmed at the district level. The most interesting model was: CPR-ALL = -11 + .78 FULL-SHOTS 2 (.14) r = .74 where the late 1989 CPR is explained by the early (i.e., lagged) 1980s full-immunization rate. CPR-ALL is also (somewhat less strongly) related to later immunization. Both the lag (often attributed to delayed recognition of changes in child mortality on fertility and the size of the impacts are notable, especially given the enormous variation in FULL-SHOTS across districts in Kenya. (See Appendix A.) For example, a 40 percentage point improvement in the full-immunization rate (corresponding to the difference in the average of the three highest and three lowest rates in the sample) results in a rise in the CPR by 31 percentage points. The meaning of these and similar

65 relationships must await the availability of the micro data from the DHS for further articulation and explanation. These results showing the importance of immunization (a proxy for mortality in explaining family planning and indirectly fertility use) are examined in detail in Lloyd and Ivanov's (1988) recent study which shows that the response of fertility to reductions in mortality are related to the stage of the fertility decline, and the form and format of family planning. In Kenya, the population-increasing effects of mortality reduction (e.g., through child replacement) are likely to be minimized--even, and possibly especially, in provinces with high mortality--given the already sizeable reductions in the demand for children. In these specific areas the incipient pronatal impact of modernization through reduced breast feeding is likely to be relatively low. Indeed, in the Lloyd/Ivanov framework, this combination of circumstances--low modernization, still continuing popularity of breast feeding, a strongly reduced demand for children, and high mortality--represents by far the most favorable environment for targeted programs of combined MCH, health education, and family planning. The interactive effects of health4/education/family planning are likely to be strongly positive.

It should be emphasized that the above speculations are still based on a fragile and inadequate empirical base.6 5 However, as a totality, the correlations seem to reveal the same pattern of relationships found in other countries at the beginning of the fertility transition, and thus taken as a whole, provide some confirmation that this transition may be under way in Kenya. The results also point out the need to undertake detailed analyses of the underlying data to provide sufficiently robust results to inform the formation of population policies and programs.

5.7 The Turning Point: Strategies and Policies

5.71 An Analytical Perspective: The Relative Roles of Socioeconomic Change and Family Planning66

There has been considerable debate by policy analysts on the relative emphasis to place on two alternative population

64For a careful analysis of the potential for various health interventions on mortality in Kenya, see Ewbank, Henin and Kekovole (1986).

65When the micro data from the DHS data set becomes available for analysis, this deficiency can be reduced or eliminated.

66This section draws upon Kelley (1989).

66 strategies. One, the "development approach," emphasizes the upgrading of living standards (health, education, nutrition, work opportunities) with the expectation that, at some stage, population growth will automatically decline. After all, this is what happened in the developed countries during the Demographic Transition, and this occurred without the benefit of modern methods of birth control. Second, the "family planning approach," emphasizes the provision of information and training and the distribution of low-cost services for family size control.

Much of the debate has been cast in an either/or context, although a more balanced perspective is gaining support whereby the joint use--and even the integration--of these two approaches is advocated.67

A useful but simple analytic framework that attempts to measure the relative importance of development versus family planning is provided by W. P. Mauldin and R. J. Lapham (1985). Based on the experience of 73 developing countries (including Kenya) over the period 1977-1983, Mauldin and Lapham estimate the parameters of the path-analysis model depicted in figure 4. This framework hypothesizes that contraceptive prevalence is determined by socioeconomic conditions and family-planning "effort" and that the latter is also determined in part by socioeconomic change.

67The Population and Development Project designed and implemented by the Population and Family Planning Board in Egypt represents one possible prototype of an integrated response to population policymaking. Kelley, Khalifa and El-Khorazaty (1982), ch. 10. 68The index of socioeconomic change is based on adult literacy, primary and secondary school enrollment, life expectancy at birth, infant mortality rate, GNP per capita, proportion of males 15-64 employed in nonagriculture, and the proportion of total population living in cities of 100,000 or more. The index of program effort is a complex aggregation of 30 proxies for four program components: the sum of policies adopted and implemented; activities carried out to provide family planning knowledge, supplies and services; the availability and accessibility of fertility control methods; and the monitoring and evaluation of all of these.

67 Figure 4

Path Analysis of the Effect of Program Effort on Contraceptive Prevalence for 73 Developinq Countries

1.45

Socioeconomicc .53 W Use diof t Conditoisd lnetption ootwn

.78 Program .46 Effort

.63

cahe results suggest that the explained variance in contraceptive prevalence is mainly associated with socioeconomic change whose total impact is .89 versus the total impact of camily planning program effort alone (controlling for hocioeconomicchange) of .10. While the direct impacts of the two determinants are about the same (.53 versus .46), a considerable portion of program effort would not have taken place in Mauldin and Lapham's posited model without socioeconomic change. And even though a sizeable portion of the variation in contraceptive prevalence is unexplained (i.e., .45), the results are consistent with the relative importance of socioeconomic change in driving the use of family planning.69

69The direct and indirect impacts of socioeconomic change are .53 and .36 (.78 times .46), respectively. The direct impact of program effort is .46, but .36 of this is accounted for indirectly by socioeconomic change. (Mauldin and Lapham also present a second version of the model that yields comparable results.) The potential importance of an overestimate of the impact of family planning as a result of the omission of the impact of socioeconomic change was pointed out by Demeny (1979). He also noted that the impact of family planning may be overstated unless there were adequate control for previous experience with fertility reduction. Presumably countries on a sustained course of fertility reduction would continue this trend, with or without

68 This model, and these results, should not be interpreted as downplaying the importance of family planning. Quite to the contrary. They reveal the facilitating role of family planning in accounting for the total change in fertility that will take place over the Demographic Transition. Indeed, family planning programs represent an exceptionally cost-effective component of a broad-based population strategy. This is because the timing of the impact of family planning programs must be factored into the analysis. While changes in socioeconomic conditions that bring about an alteration in the motivation for family planning usually occur at a fairly slow pace--sometimes over a period of decades, the influence of family planning, given some motivation for its use, can take place over a short period--months, or a few years. Family planning, then, can be viewed as permitting a more rapid adjustment by households to their family size desires, and these impacts can be realized in the short run. Given the cumulative impact of compound population growth rates, short-run influences on these rates can have relatively great impacts on long-run population stocks. Comparisons of socioeconomic versus family planning population policies should take these timing issues into account. 5.72 Applications to Kenya The Mauldin and Lapham model provides several useful insights for assessing and formulating population policies and strategies in Kenya.

First, in most countries the impact of socioeconomic change on family planning programs is quantitatively important. (The path analysis coefficient is .78.) One way of interpreting this finding is that in most of the Third World, family planning effort has generally been targeted to those countries, and regions within countries, where it is most likely to be effective. Such "strategic targeting" might well represent a component in Kenya's approach to population policymaking. Second, the impact of family planning is greatest in its short-run facilitating role, and strategically, this role should be emphasized. The cost-effectiveness of family planning greatly

family planning, and failure to control for this possibility would overstate the effect of family planning. Boulier (1986a) finds this impact to be quantitatively large, explaining around one-third of fertility decline in a large group of developing countries over the period 1965-1975. However, he does not take the additional step of appropriately assessing the impact of socioeconomic change on prior fertility trends and thus his estimates of the impact of socioeconomic change are likely biased downward.

69 increases with the speed with which populations close the "unwanted fertility gap." Positioning programs to respond promptly and efficiently to reducing or eliminating such gaps represents an appropriate family-planning strategy. Interestingly, this argument may justify the seeming inefficiency of "building programs ahead of demand," and even of maintaining some excess capacity in family planning infrastructure. Put differently, evidence of some limited excess capacity does not imply that family-planning investments are less than efficient if evaluated over a longer time interval. The opposite may be the case if such facilities and programs, which may take years to develop, are located in areas where incipient changes in the demand for family planning are likely to be notable so that response for services can be rapid and effective. Given the fundamental demographics of compound growth rates, efficiencies can derive from closing the unmet need gap quickly ("up front benefits") in a situation of rapidly changing population dynamics.

Third, the feedbacks and interactions between family planning and socioeconomic change are important and pervasive. A successful population strategy will capitalize on those specific feedbacks that are operationally within the domain of population planning. One such feedback relates to infant/child mortality. Locating family planning programs in areas where infant/child mortality rates are low, and, simultaneously implementing active programs (elsewhere) to decrease these rates, represents a "package approach" that promises relatively high returns in the intermediate run. However, to the extent that there is a strong unmet demand for FP in areas of high mortality, and Government is actively engaged in programs to reduce mortality, then the adverse consequences of the latter on population growth rates will be minimized by an active family planning program in areas of hiph mortality. This theme will be developed in more detail in section 7.0.

70 6.0 A Research Agenda

Providing answers to the types of questions asked in this report--in particular, explaining the turning point in fertility, ascertaining whether recent fertility trends will be sustained, and identifying how specific population policies and programs will impact on these trends--requires a reasonably detailed empirical research base. Unfortunately that base does not presently exist in Kenya, although a useful beginning has been made largely through the efforts of the Population Studies Research Institute (PSRI) at the University of Nairobi. This section assesses aspects of population research in Kenya, offers observations on the current institutional arrangements for undertaking population research, and provides an illustrative list of topics that merit attention in the short run. It also proposes a strategy and specific activity for increasing the pace of research focused on current population programming needs. 6.1 The State of Kenyan Population Research

The volume of high-quality policy-oriented empirical research on fertility, mortality, and family planning is insufficient to meet current programmatic requirements for project formulation and evaluation. While a similar condition prevails in most African countries, Kenya's situation is different. Kenya appears to be entering the declining-growth-rate phase of the fertility transition, a period when information on specific factors causing the turning point in fertility, as well as accounting for its likely course over time, is critical to designing programs that meet Kenya's population goals. No major successful population program in the world has been without a substantial research capacity to buttress its programming needs during this period of the fertility transition. Kenya must move to close the "research gap" that presently exists. This is an attainable goal, although in the short- to intermediate-run, formidable capacity constraints must be overcome. On the positive side, the research division of NCPD, in its coordinating role, has both the mandate to coordinate and the resources sufficient to launch a fairly ambitious research program. With several demonstrated achievements, this program could garner and deploy additional resources to address most priority research issues.

A beginning has been made, although much remains. For example, the NCPD, in collaboration with the Institute for Resource Development/Macro Systems, Inc., has successfully completed the Demographic and Health Survey, issuing a report in record time. Needed now is a well articulated and focused research program, and a plan of action, to capitalize on the potential research contributions of the DHS. This data set could (and should) be used as a "target of opportunity" to assist NCPD 71 build its institutional structure and capability to effect an increasingly active population research program, as well as providing answers and/or insights relevant to some programmatic questions in the short run. (Section 6.3 addresses this proposed strategy in greater detail.)

Also contributing to Kenyan population-related research have been the activities of the Population Studies Research Institute (PSRI) and, to a lesser extent, the Institute for Development Studies at the University of Nairobi. Both groups undertake population research directly, and the PSRI has trained a number of Kenyan population specialists. However, while these units have made significant contributions over time, their size and the commitment of their staff to competing activities constrains their ability to make even a major dent in closing the gaps that currently exist in population research.

6.2 Illustrative Research Themes Presently the research program of NCPD is broad. While all of the items on the research agenda merit attention, given limited resources and the short-term need to formulate population policies and programs, there is merit in considering a more tightly focused program responsive to short-term programmatic requirements. This does not imply that "basic" research should be down played. Quite to the contrary, all or most programmatic research questions have as their foundation fundamental basic research themes. For example, the following unprioritized list of research topics represent areas for which information is needed to design and implement a population program in Kenya. Each broad theme (capitalized) represents a component of "basic applied research" (underlined) that has potentially generalizable results beyond Kenya, and for which there is typically useful information available from other country experience. Each basic topic, as formulated, also addresses specific operational questions (in italics), the answers to which are required for successful implementation of population programs in Kenya.

72 UNMET DEMAND FOR FAMILY PLANNING SERVICES

What determines the unmet demand for family planning services? ...If one were to "target" the quality/quantity of family planning services with the objectives of maximizing the contraceptive prevalence rate (CPR) or the reduction of fertility, to what geographic areas and groups of individuals should what types and quantities of services be directed?

MIX OF CONTRACEPTIVE SERVICES

What determines the relative demand for various types of family planning services? In particular, what determines the demand for iniectables?

... To what extent are injectables a substitute for other family planning techniques? Which techniques? What is the impact on the CPR (and fertility reduction) of this substitution effect? What change in the "relative price" of injectables (mainly taking the form of assess with no change in the low to zero monetary price) would minimize the "substitution effect" and maximize the favorable impact on reducing fertility?

CONDOMUSE AS AN AIDS DETERRENT What is the impact of condom use (to deter AIDS) on mortality and fertility?

... To what extent should family planning resources be deployed to emphasize a relatively unpopular form of contraception? Apart from public health benefits, what would be the impact of such a deployment on population growth, now and in the future?

IDEAL/DESIRED FAMILY SIZE What determines ideal/desired and actual family size? What has caused changes in ideal/desired and actual family size in the 1980s? What will cause it to change in the future? By how much?

.. .If one were to maximize reductions in desired family size, what specific areas of socioeconomic programming and family planning should be emphasized?

73 BREAST FEEDING What determines the length of breast feeding? What is the impact of modern contraceptive use on breast feeding. and what are the related consequences on child health and fertility? ...What programmatic procedures should be implemented to encourage an appropriate use and duration of breast feeding so as to maximize child health, as well as family- size goals? INFANT/CHILD MORTALITY What determines infant/child mortality?

...What specific area(s) of social programming should be emphasized to maximize the reduction in the overall infant/child mortality rate? What is the impact of infant/child mortality on the demand for children (and, for example, ideal/desired family size}? ...what is the impact of reductions in the infant/child mortality rate on fertility and the CPR?

CHILD SPACING What is the impact of child spacing on maternal and child health?

..What format of family planning delivery combines optimally the goals of child spacing, health, and family size?

BENEFITS AND COSTS OF CHILDREN What are the (mainly economic) benefits and costs of children? How have these chanaed over time?

... What socioeconomic policies or trends (e.g., education, retirement, and medical financing; inheritance laws; land use/densities) influence the benefits and costs of children, and how might changes in these policies and/or trends affect ideal/desired family size and the CPR?

74 MEDICAL SIDE-EFFECTS OF FAMILY PLANNING TECHNIQUES What are the medical side-effects of various family planningr techniaues? ...What programmatic procedures should be implemented to monitor and minimize potentially adverse medical side- effects of some family planning techniques?

PROXIMATE DETERMINANTS OF FERTILITY What is the relative importance of the various proximate determinants of fertility as estimated from the KFS. KCPS, and the DHS?'u How have they changed over time? What insights can be obtained from these results on the timing of the fertility transition as well as its likely course in the future? ...Based on the empirical results of a proximate determinants analysis of fertility (including a linkage of these determinants with socioeconomic variations and/or change), what IEC and socioeconomic policies are most likely to be complementary to population programming and goals?

HOUSEHOLD RESPONSES TO THE CHANGING BENEFITS & COSTS OF CHILDREN How are households likely to respond to the changing benefits and costs of children, and in particular, the changing costs of education and medical services?

..As "cost-sharing" of social services on children expands, will households economize by reducing the quantity (numbers) or the quality (e.g., reduced school enrollments) of children? How should IEC strategies relate to these household responses?

70The Ferry-Page (1984) proximate determinants analysis on the WFS needs replicated on, and the results compared with, the KCPS and DHS.

75 GOVERNMENT RESPONSES AND OPTIONS IN THE HEALTH SECTOR

What will be Government's response to the increasing costs of AIDS prevention/treatment and infant/child immunization?

... Will priorities toward family planning be reduced as the public health-sector responds to competing needs in the future and, if so, what programmatic strategies should be put in place now to anticipate such a possibility?

6.3 A Strategy and Proposal for Generating Increased Momentum in NCPD's Population Research Program

Addressing the "population research gap" in Kenya involves attention to three dimensions of the problem: resources (population analysts and data required to undertake the research are in limited supply), timing (there exists a substantial backlog of needed research), and institutional capacity (the research program at NCPD is just beginning and the institutional support mechanisms are not yet fully in place). This section addresses these dimensions by proposing a strategy--actually, a specific program activity--that simultaneously confronts each aspect of the problem. While not the only strategy or activity, the present proposal, at a maximum, offers promise in reducing the research-gap that presently exists in Kenya; and at a minimum, a basis for dialogue from which an expanded and/or modified set of activities might be developed. Resources. Because the availability of Kenyan researchers interested in and capable of undertaking population research is limited, and because their numbers located at institutions (mainly the University of Nairobi) approved for obtaining research support is even more limited, mechanisms should be considered 1) to attract these Kenyan researchers to specific research themes for which priority research is needed (i.e., refocusing existing research),71 2) to expand research support to a broader group of qualified contracting/consulting individuals and institutions in Kenya, and, in the short run, 3) to support research by expatriates in and outside of Kenya with priority given to projects involving Kenyan collaboration. Timing and Focus. Because the turning point in the fertility transition may be at hand, and because the determinants, course,

71PSRI has already moved in this direction. See PSRI (1988).

76 and pace of the fertility decline are highly uncertain, research on how to respond to these empirical realities through population policies/programs is urgently needed. Given the current rather slow pace of Kenyan population research outputs, a strong push to build momentum of focused research on priority outputs is appropriate.

Institutional Capacity. NCPD represents the major institution of national scope that has the potential for coordinating and managing a large-scale sponsored research program in population. Effort is required to build NCPD's capability in a way that is responsive both to the timing and the resource strategies noted above. This involves developing 1) a focused program that emphasizes programmatic needs yet one which is grounded on solid empirical research, 2) a capability to subcontract, monitor, and evaluate components of that program to successful completion, and 3) a means to incorporate research findings into the programmatic/policymaking process in a timely and effective manner.

A Proposal. These goals and strategies are easier to describe than effect, but they are nonetheless important to the success of Kenya's population activities. One specific way to move forward on all of the above goals simultaneously would be for NCPD:

1) to sponsor a series of Annual Population Research Seminars that bring together for presentation, evaluation, and dissemination the major on-going research projects on Kenyan population (focusing initially on fertility, mortality, and family planning);

2) to set out a five-year research program that is revised each year based on research needs revealed by the results of the seminar, is sensitive to the interests of sponsoring agencies, and is coordinated in timing with the annual seminar;

3) to develop and prioritize a five-year agenda of data collection and processing capabilities that is also revised at the time of the annual seminar.

The research seminar could constitute a (the?) primary focus of NCPD's research programming--bringing together at a single time a visible program for presentation, review and dissemination. Incorporated as a part of the seminar would not only be the presentation of major research papers, but also the presentation of papers on three commissioned topics: 1) "Kenyan Population Policies and Programs: Accomplishments and Requirements;" 2) "Kenyan Population Research: Accomplishments and Requirements;" and 3) "Kenyan Population Data: Accomplishments and Requirements." These three papers could form 77 the basis for NCPD's annual revision of its on-going research program.

For NCPD the above proposal provides a visible activity that enables (and possibly demands) a tight research focus, is institution building, encourages an integration of and active participation in all critical research activities (data collection, analysis, presentation, evaluation, dissemination), and importantly, incorporates a time schedule that provides necessary momentum, and accountability.

All of these elements also confront the current urgent need to significantly advance the pace, usefulness, and use of Kenyan population research.

78 7.0 Population Strategies, Policies and Programs: Recommendations

7.1 The Perspective

Recommendations relating to Kenya's population strategies, policies and programs are based on two sets of data: the specific results presented in this Report that pertain largely (but not exclusively) to Kenyan experience; and the results comparing Kenyan population programs with those in other countries that have traversed the "turning point" into Stage III of the Demographic Transition.

These recommendations are not in the form of specific programmatic packages or activities since the results of this report are not sufficient to discriminate amongst many formats for accomplishing needed programming. They instead represent principles and priority areas of focus that appear to be appropriate to guiding the formulation of Kenya's population programs.

The perspective guiding these recommendations is that of economics in which there is considerable emphasis on maximizing "outputs" per unit of "inputs." Such a perspective elevates resource scarcity to an important role in decision making. Thus, for example, while greatly expanding the number of family planning outlets will increase the CPR, recommending such an expansion is of limited value. The important issue is whether resources so used could have been more effectively deployed in alternative ways.

The primary output stressed in these recommendations is "fertility reduction," not necessarily CPR increase per se. Inputs include not only financial resources, but physical structures, supplies, and trained personnel. If there is, in fact, a limited capacity to substitute one input for another (e.g., personnel versus supplies versus structures), then a specific input in limited supply can be particularly important since it effectively "constrains" total output. Identifying and relaxing constraints can thus represent a reasonable population strategy.

7.2 Principles and Priority Areas of Focus

Each of the eleven principles/priority areas of focus (in italics) discussed below is followed by a short summary of supporting findings and arguments.

79 In brief, it is argued that Kenya should: * Develop an IEC strategy and program that focuses not on lowering the "value" of children, but on lowering the social/psychological costs of family planning; and consider options for redirecting IEC efforts toward a major public-, or possibly private-sector program that eliminates unproductive duplication, and is sensitive to local conditions and needs.

* Improve the effectiveness and efficiency of the existing service delivery facilities and system, even if this is at the expense of some replication and expansion.

* Intensify the research program to provide timely and useful inputs to population policy and programs.

* Formulate differentiated population programs and targets specific to relatively small geographic areas (i.e., districts).

* Position NGOs to undertake a relatively larger role in service delivery and a relatively smaller role in IEC, and the public sector to shift its responsibilities primarily to coordination, financing, and possibly IEC.

SERVICE DELIVERY: THE SHORT RUN

1. The Pace of Family Planning Activities

The priority short-run strategy should be to meet existing demand for FP as rapidly as feasible. This dictates a strategy of using the present family planning infrastructure (facilities, systems, personnel) to full capacity. It also dictates a strategy of upgrading the effectiveness of FP services, and the efficiency of service delivery. There is presently considerable unmet demand for family planning services. This unmet demand is widespread geographically. Given the cumulative nature of population growth on long-run population levels, the rate of return on responding rapidly to close the "unmet-need gap" in the short run is relatively high. A "blitz" strategy using the existing facilities more intensively and effectively should represent the prevailing modus operandi.

Since a substantial infrastructure is already in place, and administrative, management, and other factors are constraining full utilization of this infrastructure, emphasis should be placed on improving the

80 efficiency/effectiveness of service delivery. This contrasts to a strategy of replication and expansion, which given administrative constraints, could result in an overall deterioration of service quality. 2. The Composition of Family Planning Activities From amongst the many dimensions of a broad-based population program (IEC, service delivery, training, socioeconomic and health interventions), short-run priority should be accorded to service delivery. The present unmet demand for family planning services has been created by factors largely unrelated to population programs, including IEC. The short-run need is not to "create demand" but rather to accommodate existing demand as rapidly a feasible. Demand creation is likely to continue to outstrip service delivery., To the extent that resources (mainly personnel, administration, and commitment) are scarce, the short-run focus should be on qualitative improvements in service delivery.

3. The Targeting of Family Planning Service Delivery Service delivery should be targeted to areas where unmet need is highest. There is substantial variability of unmet need by locale. Targeting areas where that unmet need is highest will maximize the short- and long-run returns on family planning. (An important qualification to this strategy is noted in recommendation 7 below.) IEC: SHORT AND INTERMEDIATE RUN

4. The Role and Format of IEC: The Short Run IEC should focus less on "creating demand" for FP and more on facilitating those couples with actual and incipient excess demand to utilize existing FP services. Emphasis should be placed on lowering the psychological/cultural/social costs of using family planning. 72

Given the excess demand for FP services, the goal of IEC

72In the Easterlin model (figure 3), IEC should shift from influencing the "desired family size line" to closing the gap between that line and the "optimum surviving children line." This would represent a major reorientation of IEC in Kenya.

81 in the short run should not be to increase the unmet need gap, but to close it. This represents an efficient strategy in terms of utilizing the existing family planning personnel and infrastructure more fully, and most importantly, it maximizes welfare--it permits Kenyans to better meet their existing personal goals.

The IEC marketing perspective should not be to ask "why don't more parents wish to limit their family size?" (after all, unmet demand is high), but rather to ask "why do parents who wish to limit family size fail show up at FP clinics?" Emphasis of IEC on the latter question would reorient existing programming in a way that conforms to the empirical realities of the Kenyan setting, present and future.

5. The Format of IEC: Short and Intermediate Run IEC should shift away from the negative emphasis that portrays children as somewhat "unpleasant burdens" on parents, and toward a positive orientation of the great parental pleasures associated with providing high-quality education, health, and economic security for their children. A primary force presently creating a demand for family planning in Kenya is the struggle by parents to provide education and other amenities for their children. They are sacrificing substantially to meet this goal; they are both willing and indeed eager to do so; and in frustration, they are finding it both rational and necessary to reduce their family size goals to provide fully for their children. IEC should capitalize on, and emphasize, this empirical reality. For example, IEC could emphasize the high benefits of quality education, the costs of this education, and the benefits of spacing children widely so as to meet these costs in a programmed way, and to the benefit of each child. In short, IEC should promote the desirability of meeting the high costs of children. This is in contrast to portraying children as rather "unpleasant burdens"--a concept, given the perceptions and behavior of most Kenyans, that is alien, if not insulting. SERVICE DELIVERY: INTERMEDIATE TO LONG RUN

6. The Pace. Composition, and Targeting of Family PlanninQ Activities Plans should be made to expand family planning infrastructure to meet increased demand in the intermediate to long run. Such an expansion should be deliberate- (and not necessarily fast-) paced, and well planned, and it should consider alternative modes of service delivery. Emphasis should continue to be on quality service delivery. Targeting

82 should be to areas where unmet demand will be highest.

Those factors that have created the unmet-demand gap for family planning in the immediate past will likely continue into the intermediate future. Effort should be made to identify, through empirical research, the location-specific size and determinants of unmet demand so as to target and deploy FP services rationally and to greatest impact.

Expansion of FP outlets should be preceded by careful market research, which should begin immediately. Expansion should take second priority to a wide range of investments that would result in utilizing the existing private and public infrastructures more fully. Expansion should also consider a reorientation toward alternative modes of service delivery, including CBD.

MATERNAL AND CHILD HEALTH

7. The Role of MCH

In areas where infant/child and maternal mortality is high, the population program should jointly, and simultaneously, emphasize both health and family planninig. Sequencing health interventions as a prerequisite to effective family planning is unnecessary in the short run.

Even in areas where mortality rates are high, present unmet demand for family planning is substantial. This contrasts to other countries where mortality reduction often precedes fertility reduction. Thus, and especially from a welfare point of view, focusing on high mortality areas has merit--both in reducing deaths, and, importantly, in enabling relatively poor families to close the unmet-need gap quickly. Put differently, given the underlying forces driving the fertility transition (e.g., exogenous increases in real child costs which are regressive in incidence), the adverse impact of MCH programs on population growth will be minimized, a feature that enhances their attractiveness as a component of a broad-based population program.

Thus, unmet need should not represent the only criterion in targeting FP. It is possible that the intermediate rate of return on FP will be relatively high if, in the short run, FP is combined with MCH in areas of high infant/child and maternal mortality. This conclusion is specific to Kenya at the present time.

83 DISTRICT-LEVEL FOCUS 8. Goal Setting and Program Focus

FP/MCH programming and targets should have a district- level focus. While this strategy is consistent with government policy, it also follows directly from the empirical reality that Kenya has enormous location-specific variations in economic/social/cultural conditions. Implementing a "standard" Kenya-wide FP, and/or MCH/FP strategy/program makes little sense. Location-specific differences should be emphasized in setting goals, in designing programs, in developing emphases in contraceptive mix, in providing an orientation to IEC, and in formulating a format of service delivery. In areas where mortality rates are low and the excess demand for FP is high, emphasis should be placed on FP delivery; in high mortality areas, a combined MCH/FP delivery with an IEC emphasis on health is appropriate; and so forth. THE ROLES OF THE PUBLIC AND NGO SECTORS 9. Program Efficiency and Absorptive Capacity Emphasis should be placed on maximizing the return on population programming resources in the short to intermediate run. This may imply focusing attention on "breaking bottlenecks" and allocating financial resources to areas and sectors with relatively high returns, even if this is not as administratively and/or politically attractive. There are presently "absorptive capacity" constraints in population programming in Kenya's public sector. Increasing funding to this sector may yield disappointing results until constraints (manpower, administrative, etc.) are relaxed. In such an absorptive-capacity setting, emphasis on maximizing the short-run rate of return on existing programming efforts is therefore appropriate.

Simultaneously, an intermediate-run strategy could focus on relaxing constraints, and a paced program expansion within existing constraints. In the short run this may for example imply increasing the channelling of funding to NGOs until the less flexible and responsive public sector is competitive in terms of infrastructure and service delivery. The public sector's short-run roles would be one of coordination and resource generation while its infrastructure is further developed; the private sector would be mainly implementing. Or it may imply a division of implementing responsibility: the public sector may focus on high mortality districts where MCH and FP should combined, and the private sector in other

84 areas were where FP service delivery is the focus. Finally, irrespective of the issue of appropriate roles to be played, careful consideration should be given in both sectors to implementation of innovative management practices and design of information systems as a means of facilitating and enhancing program efficiency and absorptive capacity.

10. The Role of NGOs in the Long Run

A strategy of gradually shifting FP service-delivery toward the NGOs, and a shifting IEC away from NGOs toward either the public or private sectors, merits consideration.

Demands on public sector programming to maintain and expand immunization programs, to cope with the predictably high and exploding costs of AIDS treatment and prevention, and to respond to the general health needs of a rapidly growing population will continue to put increasing pressures on "family planning" in competition for scarce resources. To respond to this possibility, a strategy of NGO capacity- building in the area of service delivery appears both prudent and necessary. While NGO IEC activities in the past have been path breaking, the pace and needs of current IEC requires a major effort that is nationally organized yet responsive to district-level conditions and needs. Given the economies of scale of such an effort, plus the need for substantial coordination to minimize duplication whereby IEC is individualized to the district level, a single, coordinated effort most likely represents the best format at this stage in Kenya.

RESEARCH 11. Population Research

An intensified and focused research program to support population activities, short and long run, is needed. Research into the implications and interrelationships of AIDS and population programming should be elevated to high priority. Key issues of designing and targeting population programs depend on answers to empirical questions, and this requires applied research, usually based on micro data and moderate- to-complex statistical modeling. Presently the demand far exceeds the supply of such research. While in the past, rather slow-paced population programming environment, the supply of research has been sufficient, it is not at present, nor will it be in the future. Given the prospective speed of the present and incipient fertility transition, and given the argument made in 9 with respect to "absorptive capacity" on

85 the programming side, it is exceptionally important that population-programming resources be efficiently deployed in the short run. That will require a substantial expansion in the knowledge base that must be provided by population research.

AIDS is prevalent in Kenya and its incidence is rising. The interrelationships of this disease and many aspects of population programming, including family planning, are pervasive. Since timing in the control of any communicable disease is of the essence, priority should be accorded to effecting a research program that has a short time-line, and the mandate to provide recommendations on the implications of AIDS to population programming.

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Republic of Kenya. 1984b. Office of the Vice President, Ministry of Home Affairs and National Heritage. "Population Policy Guidelines." Sessional Paper No. 4.

91 Republic of Kenya. 1984c. Office of the Vice President, Ministry of Home Affairs and National Heritage. "Statement of Hon. Mwai Kibaki" at International Conference on Population, Mexico City, 6-13 August. Mimeo.

Republic of Kenya. 1986b. Central Bureau of Statistics and Ministry of Planning and National Development. Kenya Contraceptive Prevalence Survey 1984, Provincial Report. Nairobi, Kenya.

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92 Republic of Kenya. 1989d. National Council for Population and Development and Ministry of Home Affairs and National Heritage. Demographic and Health Survey 1989. Nairobi, Kenya.

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93 Weinberger, Mary Beth. 1987. "The Relationship Between Women's Education and Fertility: Selected Findings From the World Fertility Surveys." International Family Planning Perspectives 13(2): 35-46.

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94 Table A

Childhood Immunization by District, Two Points in Time, Within Interval 1980-1989

District Month Card Scar OPV1 OPV2 OPV3 DPT1 DPT2 DPT3 Mea- Fully Fully* sles +Doc.

Nairobi 10/83 82 90 96 92 85 95 92 85 77 71 5/87 81 95 96 92 84 96 92 85 76 69 47

Kiambu 7/81 72 77 88 83 77 83 81 74 74 58 8/85 90 97 92 87 81 93 87 83 75 67 62 1/85 85 97 95 92 88 95 91 87 74 69 51

Kirinyaga 11/80 76 84 96 93 82 95 94 90 80 68 50 3/84 90 90 94 92 89 93 92 90 76 76 71 5/88 96 97 100 99 99 100 99 96 77 72

Muranga 1/83 75 93 95 89 70 88 80 71 79 54 5/86 88 94 95 94 84 95 94 84 77 71 6/88 96 99 100 98 96 99 99 94 86 81

Nyandarua 8/83 66 89 95 91 81 93 88 83 76 64 39

Nyeri 2/83 68 90 95 88 73 93 84 71 78 58

Kilifi 11/81 64 67 62 47 38 66 53 47 30 24 15 3/85 78 81 72 64 78 71 65 57 49 2/87 85 90 95 91 83 94 92 81 64 52 33

Kwale 1/82 48 61 67 58 45 69 63 52 44 33 16 3/85 75 73 75 62 53 74 65 53 51 42 39 3/86 81 88 88 79 65 89 81 65 64 50 43 3/87 92 85 95 87 78 95 87 78 64 51

Lamu 5/83 80 83 67 51 82 68 54 59 44 26 5/88 89 93 98 95 85 98 93 84 73 65 52

Mombasa 6/82 58 79 88 84 76 91 87 77 54 49 28 9/86 79 92 93 87 74 94 89 81 66 62 11/88

Taita- Taveta 8/82 60 79 88 81 67 89 13 73 62 52 33 9/87 94 94 97 96 95 97 96 94 84 74 73

Tana River 4/83 32 54 61 56 37 59 49 38 35 26

95 Embu 3/83 79 88 94 88 75 90 82 74 65 50 8/87 93 95 100 99 96 99 97 89 91 74 66

Isiolo

Kitui 10/82 53 81 83 74 59 83 74 59 62 43 23 10/83 61 70 86 81 67 86 81 67 59 43 31 3/88 75 88 100 97 90 99 96 87 77 63 42

Machakos 6/83 54 84 90 86 77 90 86 80 71 64 33 3/87 78 87 95 94 88 96 94 87 74 62 44

Marsabit Meru 12/82 52 67 84 81 75 84 80 77 64 58

Garissa 9/84 32 41 38 22 13 38 22 14 13 8 8

Mandera

Wajir

Kisii 7/83 62 73 84 73 54 83 73 55 55 37 2/87 78 87 88 84 72 88 85 70 60 48 30

Kisumu 2/83 39 69 77 54 33 65 50 33 31 20 9/87 80 88 92 85 71 91 80 62 43 29 21 74 36 41 34

Siaya 3/83 61 70 78 62 50 75 61 50 48 35 2/87 60 75 73 61 50 74 62 43 36 25 16

South Nyanza 1/83 46 64 63 46 28 65 46 31 38 20 7/87 74 96 91 85 71 90 80 70 54 38 23

Baringo 3/88 76 93 98 96 90 98 96 89 80 70 42

Elgeyo

Marakwet

Kajiado 11/88 75 88 94 89 82 94 89 78 71 58 39

Kericho 2/84 43 79 79 67 52 79 68 53 49 34 20 4/87 55 82 90 82 74 90 85 77 54 45 21

Laikipia

Nakuru 6/84 76 88 84 75 63 82 76 65 58 48 44 3/87 84 90 98 95 87 98 95. 87 72 66 47

96 Nandi 2/84 66 79 81 70 52 80 69 54 49 38 33 7/87 83 88 93 86 75 92 87 70 55 44 37

Narok 5/84 43 69 65 54 44 65 54 44 40 32 19 3/87 58 76 76 62 50 76 64 48 42 31 18

Samburu

Trans- Nzoia 8/84 72 77 80 69 54 82 68 54 50 36 33 Turkana 4/86 78 94 88 78 59 88 77 60 65 47 21 Uasin Gishu 4/88 79 90 94 89 81 93 89 80 71 60 52 6/84 69 71 87 80 73 86 79 72 68 59 52

West Pokot 6/85 67 70 59 43 30 58 43 30 30 21 Bungoma 8/86 60 75 72 61 44 71 61 50 33 27

Busia /81 47 66 70 60 36 67 55 38 28 22 4/84 75 83 75 62 47 74 64 47 46 37 29 2/87 84 90 87 81 69 87 79 71 52 44 28 Kakamega 10/84 73 68 75 62 46 74 62 48 46 35 33 9/88

Source: Republic of Kenya (1988c). * Since 1987 only fully immunized before first birthday.

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No. 80 EnvironmentalManagement in Development:The Evolutionof Paradigms.Michael E. Colby

No. 81 LatinAmerica's Banking Systemsin the 1980s: A CrossCountry Comparison. Felipe Morris, Mark Dorfman, Jose Pedro Ortiz, and others.

No. 82 Why EducationalPolicies Can Fail:An Overviewof Selected African Experiences. George Psacharopoulos

No. 83 ComparativeAfrican Experiences in ImplementingEducational Policies. John Craig

No. 84 ImplementingEducational Policies in Ethiopia.Fassil R. Kiros

No. 85 ImplementingEducational Policies in Kenya. G. S. Eshiwani

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No. 96 HouseholdFood Securityand the Role of Women.J. Price Gittinger and others

No. 97 Problemsof DevelopingCountries in the 1990s. VolumeI: GeneralTopics. F. Desmond McCarthy, editor

No. 98 Problemsof DevelopingCountries in the 1990s. VolumeII: CountryStudies. F. Desmond McCarthy, editor

No. 99 PublicSector Management Issues in StructuralAdjustment Lending. Barbara Nunberg

No. 100 The EuropeanCommunities' Single Market: The Challengeof 1992for Sub-SaharanAfrica. Alfred Tovias

No. 101 InternationalMigration and Developmentin Sub-SaharanAfrica. Volume1: Overview.Sharon Stanton Russell, Karen Jacobsen, and William Deane Stanley

No. 102 InternationalMigration and Developmentin Sub-SaharanAfrica. Volume11: Country Analyses. Sharon Stanton Russell, Karen Jacobsen, and William Deane Stanley

No. 103 AgriculturalExtensionfor Women Farmersin Africa.Katrine Saito and C. Jean Weidemann

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