Kenya Demographic and Health Survey 2014

Publication date: 2015

Kenya 2014Demographic andHealth Survey K enya 2014 D em ographic and H ealth Survey Republic of Kenya Kenya Demographic and Health Survey 2014 Kenya National Bureau of Statistics Nairobi, Kenya Ministry of Health Nairobi, Kenya National AIDS Control Council Nairobi, Kenya Kenya Medical Research Institute Nairobi, Kenya National Council for Population and Development Nairobi, Kenya The DHS Program, ICF International Rockville, Maryland, USA December 2015 WORLD BANK KENYANS AND AMERICANS IN PARTNERSHIP TO FIGHT HIV/AIDS The 2014 Kenya Demographic and Health Survey (2014 KDHS) was implemented by the Kenya National Bureau of Statistics from May 2014 to October 2014 in partnership with the Ministry of Health, the National AIDS Control Council (NACC), the National Council for Population and Development (NCPD), and the Kenya Medical Research Institute (KEMRI). Funding for the KDHS was provided by the Government of Kenya with support from the United States Agency for International Development (USAID), the United Nations Population Fund (UNFPA), the United Kingdom Department for International Development (DfID), the World Bank, the Danish International Development Agency (DANIDA), the United Nations Children’s Fund (UNICEF), the German Development Bank (KfW), the Clinton Health Access Initiative (CHAI), the World Food Programme (WFP), and the Micronutrient Initiative (MI). ICF International provided technical assistance for the survey through The DHS Program, a USAID-funded project that helps implement population and health surveys in countries worldwide. Additional information about the 2014 KDHS may be obtained from the Kenya National Bureau of Statistics (KNBS), P.O. Box 30266-00100 GPO Nairobi, Kenya; telephone (Nairobi): 3317586/8, 3317612/22, 3317623, 3317651; fax: 3315977; e-mail: directorgeneral@knbs.or.ke, info@knbs.or.ke; website: www.knbs.or.ke. Information on The DHS Program may be obtained from ICF International, 530 Gaither Road, Suite 500, Rockville MD, 20850, USA; telephone: 301-407-6500; fax: 301-407-6501; e-mail: info@DHSprogram.com; website: www.DHSprogram.com. Contents • iii CONTENTS TABLES AND FIGURES . ix FOREWORD . xvii MILLENNIUM DEVELOPMENT GOAL INDICATORS . xix SUMMARY OF FINDINGS . xxi MAP OF KENYA . xxvi 1 INTRODUCTION . 1 1.1 History, Geography, and Economy . 1 1.1.1 History . 1 1.1.2 Geography . 1 1.1.3 Economy . 1 1.2 Population . 1 1.3 Population and Health Policy Frameworks . 2 1.3.1 Population Policy Framework . 2 1.3.2 Health Priorities and Programmes . 3 1.4 Objectives of the Survey . 3 1.5 Survey Organisation . 4 1.6 Sample Design . 5 1.7 Questionnaires . 5 1.8 Training . 7 1.8.1 Training of Trainers . 7 1.8.2 Pretest Activities . 7 1.8.3 Main Training of Field Staff . 7 1.9 Fieldwork . 8 1.10 Data Processing . 8 1.11 Response Rates . 9 2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION . 11 2.1 Household Characteristics . 11 2.1.1 Water and Sanitation . 12 2.1.2 Housing Characteristics . 14 2.1.3 Household Possessions . 16 2.2 Household Wealth . 17 2.3 Food Security . 18 2.4 Hand Washing . 19 2.5 Household Population by Age and Sex . 20 2.6 Household Composition . 21 2.7 Birth Registration . 22 2.8 Children’s Living Arrangements, Orphanhood, and School Attendance . 23 2.9 Education of the Household Population . 25 2.9.1 Educational Attainment . 25 2.9.2 School Attendance Ratios . 27 3 CHARACTERISTICS OF RESPONDENTS . 31 3.1 Characteristics of Survey Respondents . 31 3.2 Educational Attainment by Background Characteristics . 34 3.3 Literacy . 39 iv • Contents 3.4 Access to Mass Media . 44 3.5 Employment . 49 3.5.1 Employment Status . 49 3.5.2 Occupation . 51 3.5.3 Earnings, Employers, and Continuity of Employment . 53 4 MARRIAGE AND SEXUAL ACTIVITY . 55 4.1 Current Marital Status . 55 4.2 Polygyny . 56 4.3 Age at First Marriage . 58 4.4 Age at First Sexual Intercourse . 60 4.5 Recent Sexual Activity . 62 5 FERTILITY . 65 5.1 Current Fertility . 65 5.2 Fertility Differentials . 66 5.3 Fertility Trends . 69 5.4 Children Ever Born and Living . 70 5.5 Birth Intervals . 71 5.6 Postpartum Amenorrhoea, Abstinence, and Insusceptibility . 74 5.7 Menopause . 75 5.8 Age at First Birth . 75 5.9 Teenage Pregnancy and Motherhood . 78 6 FERTILITY PREFERENCES . 81 6.1 Desire for More Children . 81 6.2 Desire to Limit Childbearing by Background Characteristics . 82 6.3 Ideal Family Size . 84 6.4 Fertility Planning . 86 6.5 Wanted Fertility Rates . 87 7 FAMILY PLANNING . 89 7.1 Introduction . 89 7.2 Knowledge of Contraceptive Methods . 89 7.3 Current Use of Contraception . 93 7.4 Current Use of Contraception by Background Characteristics . 94 7.5 Trends in Current Use of Contraception . 95 7.6 Timing of Sterilisation . 97 7.7 Source of Contraception . 97 7.8 Informed Choice . 98 7.9 Contraceptive Discontinuation Rates . 99 7.10 Reasons for Discontinuation of Contraceptive Use . 100 7.11 Knowledge of Fertile Period . 101 7.12 Need and Demand for Family Planning Services . 101 7.13 Future Use of Contraception . 104 7.14 Exposure to Family Planning Messages . 105 7.15 Contact of Nonusers with Family Planning Providers . 106 7.16 Men’s Knowledge of and Attitudes towards Contraceptive Use . 107 8 INFANT AND CHILD MORTALITY . 111 8.1 Data Quality . 112 8.2 Levels and Trends in Infant and Child Mortality . 113 8.3 Socioeconomic Differentials in Infant and Child Mortality . 115 Contents • v 8.4 Demographic Differentials in Infant and Child Mortality . 116 8.5 Perinatal Mortality . 117 8.6 High-Risk Fertility Behaviour . 118 9 MATERNAL HEALTH . 121 9.1 Antenatal Care . 121 9.1.1 Number and Timing of Antenatal Visits . 123 9.1.2 Components of Antenatal Care . 124 9.2 Tetanus Toxoid Vaccination . 126 9.3 Place of Delivery . 127 9.4 Assistance during Delivery . 129 9.5 Postnatal Care . 132 9.5.1 Timing of First Postnatal Checkup for the Mother . 132 9.5.2 Provider of First Postnatal Checkup for the Mother . 133 9.5.3 Timing of First Postnatal Checkup for the Newborn . 134 9.5.4 Provider of First Postnatal Checkup for the Newborn . 135 9.6 Problems in Accessing Health Care . 136 9.7 Fistula . 137 10 CHILD HEALTH . 139 10.1 Child’s Weight and Size at Birth . 140 10.2 Vaccination Coverage. 141 10.3 Acute Respiratory Infection . 145 10.4 Fever . 147 10.5 Diarrhoeal Disease . 150 10.6 Knowledge of ORS Packets and Zinc Tablets . 154 10.7 Disposal of Children’s Stools . 155 11 NUTRITION OF CHILDREN AND WOMEN . 157 11.1 Nutritional Status of Children . 158 11.1.1 Measurement of Nutritional Status among Young Children . 158 11.1.2 Data Collection . 158 11.1.3 Measures of Child Nutritional Status . 159 11.1.4 Trends in Children’s Nutritional Status . 163 11.2 Breastfeeding and Complementary Feeding . 163 11.2.1 Initiation of Breastfeeding . 163 11.2.2 Breastfeeding Status by Age . 165 11.2.3 Duration of Breastfeeding . 167 11.2.4 Types of Complementary Foods . 168 11.2.5 Infant and Young Child Feeding (IYCF) Practices . 169 11.3 Micronutrient Intake among Children . 172 11.4 Iodisation of Household Salt . 176 11.5 Nutritional Status of Women . 177 11.6 Micronutrient Intake among Mothers . 180 12 MALARIA . 183 12.1 Introduction . 183 12.2 Ownership of Mosquito Nets . 185 12.3 Access to Insecticide-Treated Nets . 188 12.4 Use of Mosquito Nets . 189 12.4.1 Overall Use of Mosquito Nets . 189 12.4.2 Use of Mosquito Nets by Children Under Age 5 . 194 12.4.3 Use of Mosquito Nets by Pregnant Women . 197 vi • Contents 12.5 Preventive Malaria Treatment During Pregnancy . 198 12.6 Fever among Children Under Age 5 . 201 12.6.1 Prevalence and Treatment of Fever among Children . 201 12.6.2 Type and Timing of Antimalarial drugs . 204 13 HIV/AIDS-RELATED KNOWLEDGE, ATTITUDES, AND BEHAVIOUR . 207 13.1 Introduction . 207 13.2 HIV and AIDS Knowledge, Transmission and Prevention Methods . 208 13.2.1 Awareness of HIV and AIDS. 208 13.2.2 Knowledge of HIV Prevention Methods . 210 13.2.3 Rejection of Misconceptions about HIV/AIDS . 213 13.2.4 Knowledge of Mother-to-Child Transmission of HIV . 218 13.3 Attitudes towards People Living with HIV and AIDS . 221 13.4 Attitudes towards Negotiating Safer Sexual Relationships . 224 13.5 Attitude towards Condom Education for Youth . 225 13.6 High-Risk Sex . 227 13.6.1 Multiple Partners and Condom Use . 227 13.6.2 Point Prevalence and Cumulative Prevalence of Concurrent Sexual Partners . 229 13.6.3 Transactional Sex . 231 13.7 Coverage of HIV Counselling and Testing . 234 13.7.1 General HIV Testing . 234 13.7.2 HIV Counselling and Testing During Pregnancy . 239 13.8 Male Circumcision . 241 13.9 Self-Reporting of Sexually Transmitted Infections . 242 13.10 Prevalence of Medical Injections. 244 13.11 HIV/AIDS Knowledge and Sexual Behaviour among Youth . 245 13.11.1 HIV/AIDS-Related Knowledge among Youth . 246 13.11.2 Trends in Age at First Sex . 247 13.11.3 Abstinence and Premarital Sex . 249 13.11.4 Multiple Sexual Partners among Youth . 250 13.11.5 Cross-generational Sexual Partners . 251 13.11.6 Voluntary HIV Counselling and Testing among Youth. 252 14 NONCOMMUNICABLE DISEASES AND OTHER HEALTH ISSUES . 255 14.1 Introduction . 255 14.2 Knowledge of and Screening for Cancer . 255 14.2.1 Breast Cancer . 255 14.2.2 Cervical Cancer . 256 14.2.3 Prostate Cancer . 257 14.3 Screening for Hypertension and Diabetes . 258 14.4 Knowledge and Attitudes Concerning Tuberculosis . 259 14.5 Use of Tobacco . 261 14.6 Alcohol Consumption . 263 14.7 Physical Activity . 265 14.8 Unintentional Injury . 267 14.9 Health Insurance Coverage . 269 15 WOMEN’S EMPOWERMENT AND DEMOGRAPHIC AND HEALTH OUTCOMES . 273 15.1 Employment and Form of Earnings . 273 15.2 Control over and Relative Magnitude of Women’s and Husbands’ Earnings . 274 15.2.1 Control over Wife’s Earnings . 275 15.2.2 Control over Husbands’ Earnings . 276 Contents • vii 15.3 Control over Women’s Earnings and Relative Size of Husband’s and Wife’s Earnings . 277 15.4 Ownership of Assets . 277 15.5 Women’s Participation in Decision Making . 279 15.6 Attitudes towards Wife Beating . 283 15.7 Women’s Empowerment Indices . 286 15.8 Current Use of Contraception by Women’s Status . 286 15.9 Ideal Family Size and Unmet Need by Women’s Status . 287 15.10 Reproductive Health Care and Women’s Empowerment . 288 15.11 Differentials in Infant and Child Mortality by Women’s Status . 289 16 DOMESTIC VIOLENCE . 291 16.1 Measurement of Violence . 291 16.1.1 Use of Valid Measures of Violence . 291 16.1.2 Ethical Considerations in the 2014 KDHS . 293 16.1.3 Subsample for the Violence Module . 293 16.2 Experience of Physical Violence . 293 16.3 Perpetrators of Physical Violence . 296 16.4 Experience of Sexual Violence . 297 16.5 Perpetrators of Sexual Violence . 300 16.6 Age at First Experience of Sexual Violence . 301 16.7 Experience of Different Forms of Violence . 302 16.8 Violence during Pregnancy . 303 16.9 Marital Control by Spouse . 303 16.10 Forms of Spousal Violence . 306 16.11 Spousal Violence by Background Characteristics . 309 16.12 Violence by Spousal Characteristics and Women’s Empowerment Indicators . 311 16.13 Recent Spousal Violence . 314 16.14 Onset of Spousal Violence . 315 16.15 Physical Consequences of Spousal Violence . 316 16.16 Violence by Women and Men against Their Spouse . 317 16.17 Violence against Spouses by Spousal Characteristics and Women’s Empowerment Indicators . 320 16.18 Help-seeking Behaviour by Women and Men Who Experience Violence . 322 17 ADULT AND MATERNAL MORTALITY . 327 17.1 Data and Assessment of Quality . 327 17.2 Estimates of Adult Mortality . 328 17.3 Estimates of Maternal Mortality . 329 18 FEMALE GENITAL CUTTING . 331 18.1 Knowledge of Female Circumcision . 331 18.2 Prevalence of and Age at Circumcision . 333 18.3 Aspects of Circumcision among Circumcised Girls and Women . 337 18.4 Religious and Community Attitudes towards FGC . 339 18.5 Support for the Continuation of FGC . 342 REFERENCES . 345 APPENDIX A SAMPLE IMPLEMENTATION . 351 APPENDIX B ESTIMATES OF SAMPLING ERRORS . 357 APPENDIX C DATA QUALITY . 385 APPENDIX D PERSONS INVOLVED IN THE 2014 KENYA DEMOGRAHIC AND HEALTH SURVEY . 391 APPENDIX E QUESTIONNAIRES . 399 Tables and Figures • ix TABLES AND FIGURES 1 INTRODUCTION . 1 Table 1.1 Basic demographic indicators . 2 Table 1.2 Results of the household and individual interviews . 9 2 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION . 11 Table 2.1 Household drinking water . 12 Table 2.2 Household sanitation facilities . 14 Table 2.3 Household characteristics . 15 Table 2.4 Household possessions . 16 Table 2.5 Wealth quintiles . 18 Table 2.6 Food security status. 19 Table 2.7 Hand washing . 20 Table 2.8 Household population by age, sex, and residence . 21 Table 2.9 Household composition . 22 Table 2.10 Birth registration of children under age five . 23 Table 2.11 Children’s living arrangements and orphanhood . 24 Table 2.12 School attendance by survivorship of parents . 25 Table 2.13.1 Educational attainment of the female household population . 26 Table 2.13.2 Educational attainment of the male household population . 27 Table 2.14 School attendance ratios . 28 Figure 2.1 Population pyramid . 21 Figure 2.2 Age-specific school attendance rates of the de-facto population age 5 to 24 years . 29 3 CHARACTERISTICS OF RESPONDENTS . 31 Table 3.1 Background characteristics of respondents . 32 Table 3.1C Background characteristics of respondents . 33 Table 3.2.1 Educational attainment: Women . 35 Table 3.2.1C Educational attainment: Women . 36 Table 3.2.2 Educational attainment: Men . 37 Table 3.2.2C Educational attainment: Men . 38 Table 3.3.1 Literacy: Women . 40 Table 3.3.1C Literacy: Women . 41 Table 3.3.2 Literacy: Men . 42 Table 3.3.2C Literacy: Men . 43 Table 3.4.1 Exposure to mass media: Women . 45 Table 3.4.1C Exposure to mass media: Women . 46 Table 3.4.2 Exposure to mass media: Men . 47 Table 3.4.2C Exposure to mass media: Men . 48 Table 3.5.1 Employment status: Women . 50 Table 3.5.2 Employment status: Men . 51 Table 3.6.1 Occupation: Women . 52 Table 3.6.2 Occupation: Men . 53 Table 3.7 Type of employment among women . 54 Figure 3.1 Women’s employment status in the past 12 months . 49 x • Tables and Figures 4 MARRIAGE AND SEXUAL ACTIVITY . 55 Table 4.1 Current marital status . 56 Table 4.2.1 Number of women’s co-wives . 57 Table 4.2.2 Number of men’s wives . 58 Table 4.3 Age at first marriage . 59 Table 4.4 Median age at first marriage by background characteristics . 59 Table 4.4C Median age at first marriage by county . 60 Table 4.5 Age at first sexual intercourse . 61 Table 4.6 Median age at first sexual intercourse by background characteristics . 61 Table 4.7.1 Recent sexual activity: Women . 63 Table 4.7.2 Recent sexual activity: Men . 64 5 FERTILITY . 65 Table 5.1 Current fertility . 66 Table 5.2 Fertility by background characteristics . 66 Table 5.2C Fertility. 68 Table 5.3.1 Trends in age-specific fertility rates . 69 Table 5.3.2 Trends in age-specific and total fertility rates . 70 Table 5.4 Children ever born and living . 71 Table 5.5 Birth intervals . 72 Table 5.5C Birth intervals . 73 Table 5.6 Postpartum amenorrhoea, abstinence and insusceptibility . 74 Table 5.7 Median duration of amenorrhoea, postpartum abstinence and postpartum insusceptibility . 75 Table 5.8 Menopause . 75 Table 5.9 Age at first birth . 76 Table 5.10 Median age at first birth . 76 Table 5.10C Median age at first birth . 77 Table 5.11 Teenage pregnancy and motherhood . 78 Table 5.11C Teenage pregnancy and motherhood . 79 Figure 5.1 Trends in total fertility rate, 1978-2014 . 69 Figure 5.2 Trends in age-specific fertility rates . 70 6 FERTILITY PREFERENCES . 81 Table 6.1 Fertility preferences by number of living children . 82 Table 6.2.1 Desire to limit childbearing: Women . 83 Table 6.2.2 Desire to limit childbearing: Men . 83 Table 6.3 Ideal number of children by number of living children . 85 Table 6.4 Mean ideal number of children . 86 Table 6.5 Fertility planning status . 87 Table 6.6 Wanted fertility rates . 87 7 FAMILY PLANNING . 89 Table 7.1 Knowledge of contraceptive methods . 90 Table 7.2 Knowledge of contraceptive methods by background characteristics . 91 Table 7.2C Knowledge of contraceptive methods by county . 92 Table 7.3 Current use of contraception by age . 93 Table 7.4 Current use of contraception by background characteristics . 94 Table 7.4C Current use of contraception by county . 95 Table 7.5 Trends in the current use of contraception . 96 Table 7.6 Timing of sterilisation . 97 Table 7.7 Source of modern contraception methods . 98 Tables and Figures • xi Table 7.8 Informed choice . 99 Table 7.9 Twelve-month contraceptive discontinuation rates . 100 Table 7.10 Reasons for discontinuation . 100 Table 7.11 Knowledge of fertile period . 101 Table 7.12 Need and demand for family planning among currently married women . 103 Table 7.13 Future use of contraception . 104 Table 7.14 Reason for not intending to use contraception in the future . 105 Table 7.15 Exposure to family planning messages . 106 Table 7.16 Contact of nonusers with family planning providers . 107 Table 7.17 Husband/partner’s knowledge of women’s use of contraception . 108 Table 7.18 Men’s attitudes towards contraception . 109 Figure 7.1 Trends in contraceptive use among currently married women . 96 Figure 7.2 Trends in unmet need for family planning . 104 8 INFANT AND CHILD MORTALITY . 111 Table 8.1 Early childhood mortality rates . 114 Table 8.2 Early childhood mortality rates by socioeconomic characteristics . 115 Table 8.3 Early childhood mortality rates by demographic characteristics . 117 Table 8.4 Perinatal mortality . 118 Table 8.5 High-risk fertility behaviour . 119 Figure 8.1 Trends in childhood mortality, 1999-2014 . 114 Figure 8.2 Under-5 mortality by background characteristics . 116 9 MATERNAL HEALTH . 121 Table 9.1 Antenatal care . 122 Table 9.1C Antenatal care . 123 Table 9.2 Number of antenatal care visits and timing of first visit . 124 Table 9.3 Components of antenatal care . 125 Table 9.4 Tetanus toxoid injections . 127 Table 9.5 Place of delivery . 128 Table 9.5C Place of delivery . 129 Table 9.6 Assistance during delivery . 130 Table 9.6C Assistance during delivery . 131 Table 9.7 Timing of first postnatal checkup for the mother . 133 Table 9.8 Type of provider of first postnatal checkup for the mother . 134 Table 9.9 Timing of first postnatal checkup for the newborn . 135 Table 9.10 Type of provider of first postnatal checkup for the newborn . 136 Table 9.11 Problems in accessing health care . 137 Table 9.12 Fistula. 138 Figure 9.1 Trends in components of antenatal care . 126 Figure 9.2 Mother’s duration of stay in the health facility after giving birth . 132 10 CHILD HEALTH . 139 Table 10.1 Child’s size and weight at birth. 140 Table 10.2 Vaccinations by source of information . 142 Table 10.3 Vaccinations by background characteristics . 143 Table 10.3C Vaccinations by county . 144 Table 10.4 Prevalence and treatment of symptoms of ARI . 146 Table 10.4C Prevalence of symptoms of ARI . 147 Table 10.5 Prevalence and treatment of fever . 148 xii • Tables and Figures Table 10.5C Prevalence and treatment of fever . 149 Table 10.6 Prevalence of diarrhoea . 150 Table 10.6C Prevalence of diarrhoea . 151 Table 10.7 Diarrhoea treatment . 152 Table 10.8 Feeding practices during diarrhoea . 153 Table 10.9 Knowledge of ORS packets . 155 Table 10.10 Disposal of children’s stools . 156 Figure 10.1 Trends in vaccination coverage during the first year of life among children 12-23 months . 142 Figure 10.2 Trends in childhood vaccination coverage . 145 11 NUTRITION OF CHILDREN AND WOMEN . 157 Table 11.1 Nutritional status of children . 160 Table 11.1C Nutritional status of children . 162 Table 11.2 Initial breastfeeding . 164 Table 11.3 Breastfeeding status by age . 166 Table 11.4 Median duration of breastfeeding . 168 Table 11.5 Foods and liquids consumed by children in the day or night preceding the interview . 169 Table 11.6 Infant and young child feeding (IYCF) practices . 171 Table 11.7 Micronutrient intake among children: Vitamin A, iron, and deworming medication . 173 Table 11.8 Micronutrient intake among children: Vitamin A and iodised salt . 174 Table 11.8C Micronutrient intake among children: Vitamin A and iodised salt . 175 Table 11.9 Presence of iodised salt in household . 176 Table 11.9C Presence of iodised salt in household . 177 Table 11.10 Nutritional status of women . 178 Table 11.10C Nutritional status of women . 179 Table 11.11 Micronutrient intake among mothers . 181 Figure 11.1 Nutritional status of children by age . 161 Figure 11.2 Trends in nutritional status of children under 5 years . 163 Figure 11.3 Infant feeding practices by age . 166 Figure 11.4 IYCF indicators on breastfeeding status . 167 Figure 11.5 IYCF indicators on minimum acceptable diet . 172 12 MALARIA . 183 Table 12.1 Household possession of mosquito nets . 186 Table 12.1C Household possession of mosquito nets . 187 Table 12.2 Access to an insecticide-treated net (ITN) . 188 Table 12.3 Use of mosquito nets by persons in the household . 190 Table 12.3C Use of mosquito nets by persons in the household . 191 Table 12.4 Use of existing ITNs . 192 Table 12.4C Use of existing ITNs . 193 Table 12.5 Use of mosquito nets by children . 195 Table 12.5C Use of mosquito nets by children . 196 Table 12.6 Use of mosquito nets by pregnant women . 197 Table 12.7 Use of Intermittent Preventive Treatment (IPTp) by women during pregnancy . 199 Table 12.7C Use of Intermittent Preventive Treatment (IPTp) by women during pregnancy . 200 Table 12.8 Prevalence, diagnosis, and prompt treatment of children with fever . 202 Table 12.8C Prevalence, diagnosis, and prompt treatment of children with fever . 203 Table 12.9 Source of advice or treatment for children with fever . 204 Tables and Figures • xiii Table 12.10 Type of antimalarial drugs used . 205 Figure 12.1 Percentage of the de facto population with access to an ITN in the household . 189 Figure 12.2 Ownership of, access to, and use of ITNs . 194 Figure 12.3 Trends in ITN ownership and use . 198 Map 12.1 Malaria prevalence in Kenya . 184 13 HIV/AIDS-RELATED KNOWLEDGE, ATTITUDES, AND BEHAVIOUR . 207 Table 13.1 Knowledge of AIDS . 208 Table 13.1C Knowledge of AIDS . 209 Table 13.2 Knowledge of HIV prevention methods: condom use and limiting sexual partners. 210 Table 13.2C Knowledge of HIV prevention methods by county: condom use and limiting sexual partners . 211 Table 13.3 Knowledge of HIV prevention methods: abstinence . 212 Table 13.4.1 Comprehensive knowledge about AIDS: Women . 214 Table 13.4.2 Comprehensive knowledge about AIDS: Men . 215 Table 13.4.1C Comprehensive knowledge about AIDS: Women . 216 Table 13.4.2C Comprehensive knowledge about AIDS: Men . 217 Table 13.5 Knowledge of prevention of mother to child transmission of HIV . 218 Table 13.5C Knowledge of prevention of mother to child transmission of HIV . 220 Table 13.6.1 Accepting attitudes towards those living with HIV/AIDS: Women . 221 Table 13.6.2 Accepting attitudes towards those living with HIV/AIDS: Men . 223 Table 13.7 Attitudes towards negotiating safer sexual relations with husband . 225 Table 13.8 Adult support of education about condom use to prevent AIDS . 226 Table 13.9.1 Multiple sexual partners: Women . 228 Table 13.9.2 Multiple sexual partners: Men . 229 Table 13.10 Point prevalence and cumulative prevalence of concurrent sexual partners . 230 Table 13.11 Payment for sexual intercourse and condom use at last paid sexual intercourse . 232 Table 13.11C Payment for sexual intercourse . 233 Table 13.12.1 Coverage of prior HIV testing: Women . 235 Table 13.12.1C Coverage of prior HIV testing: Women . 237 Table 13.12.2 Coverage of prior HIV testing: Men . 236 Table 13.12.2C Coverage of prior HIV testing: Men . 238 Table 13.13 Pregnant women counselled and tested for HIV . 239 Table 13.13C Pregnant women counselled and tested for HIV . 240 Table 13.14 Male circumcision . 242 Table 13.14C Male circumcision . 242 Table 13.15 Self-reported prevalence of sexually-transmitted infections (STIs) and STIs symptoms . 243 Table 13.16 Prevalence of medical injections . 245 Table 13.17 Comprehensive knowledge about AIDS and of a source of condoms among youth . 246 Table 13.18 Age at first sexual intercourse among young people . 248 Table 13.19 Premarital sexual intercourse and condom use during premarital sexual intercourse among youth . 249 Table 13.20.1 Multiple sexual partners in the past 12 months among young people: Women . 250 Table 13.20.2 Multiple sexual partners in the past 12 months among young people: Men . 251 Table 13.21 Age-mixing in sexual relationships among women and men age 15-19 . 252 Table 13.22 Recent HIV tests among youth . 253 xiv • Tables and Figures Figure 13.1 Trends in knowledge of HIV prevention methods: Women . 213 Figure 13.2 Trends in knowledge of HIV prevention methods: Men . 213 Figure 13.3 Accepting attitudes towards those with HIV: Women . 222 Figure 13.4 Accepting attitudes towards those with HIV: Men . 224 Figure 13.5 Women and men seeking treatment for STIs . 244 Figure 13.6 Comprehensive knowledge about AIDS and source of condoms among youth . 247 Figure 13.7 Trends in age of first sexual intercourse . 248 14 NONCOMMUNICABLE DISEASES AND OTHER HEALTH ISSUES . 255 Table 14.1 Breast cancer screening . 256 Table 14.2 Cervical cancer knowledge and screening . 257 Table 14.3 Prostate cancer knowledge and screening . 258 Table 14.4 Hypertension and diabetes screening . 259 Table 14.5.1 Knowledge and attitudes concerning tuberculosis: Women . 260 Table 14.5.2 Knowledge and attitudes concerning tuberculosis: Men . 261 Table 14.6.1 Use of tobacco: Women . 262 Table 14.6.2 Use of tobacco: Men . 263 Table 14.7.1 Alcohol consumption: Women . 264 Table 14.7.2 Alcohol consumption: Men . 265 Table 14.8.1 Physical activity: Women . 266 Table 14.8.2 Physical activity: Men . 267 Table 14.9.1 Unintentional injury: Women . 268 Table 14.9.2 Unintentional injury: Men . 269 Table 14.10.1 Health insurance coverage: Women . 270 Table 14.10.2 Health insurance coverage: Men . 271 15 WOMEN’S EMPOWERMENT AND DEMOGRAPHIC AND HEALTH OUTCOMES . 273 Table 15.1 Employment and cash earnings of currently married women and men . 274 Table 15.2.1 Control over women’s cash earnings and relative magnitude of women’s cash earnings . 275 Table 15.2.2 Control over men’s cash earnings . 276 Table 15.3 Women’s control over their own earnings and over those of their husbands . 277 Table 15.4.1 Ownership of assets: Women . 278 Table 15.4.2 Ownership of assets: Men . 279 Table 15.5 Participation in decision making . 280 Table 15.6.1 Women’s participation in decision making by background characteristics . 281 Table 15.6.2 Men’s participation in decision making by background characteristics . 283 Table 15.7.1 Attitude towards wife beating: Women . 284 Table 15.7.2 Attitude towards wife beating: Men . 285 Table 15.8 Indicators of women’s empowerment . 286 Table 15.9 Current use of contraception by women’s empowerment . 287 Table 15.10 Ideal number of children and unmet need for family planning by women’s empowerment . 287 Table 15.11 Reproductive health care by women’s empowerment . 288 Table 15.12 Early childhood mortality rates by women’s status . 289 Figure 15.1 Number of decisions in which currently married women participate . 282 16 DOMESTIC VIOLENCE . 291 Table 16.1.1 Experience of physical violence: Women . 294 Table 16.1.2 Experience of physical violence: Men . 295 Table 16.2.1 Persons committing physical violence: Women . 297 Table 16.2.2 Persons committing physical violence: Men . 297 Tables and Figures • xv Table 16.3.1 Experience of sexual violence: Women . 298 Table 16.3.2 Experience of sexual violence: Men . 299 Table 16.4.1 Persons committing sexual violence: Women . 300 Table 16.4.2 Persons committing sexual violence: Men . 301 Table 16.5.1 Age at first experience of sexual violence: Women . 301 Table 16.5.2 Age at first experience of sexual violence: Men . 302 Table 16.6.1 Experience of different forms of violence: Women . 302 Table 16.6.2 Experience of different forms of violence: Men . 302 Table 16.7 Experience of violence during pregnancy . 303 Table 16.8.1 Marital control exercised by husbands . 304 Table 16.8.2 Marital control exercised by wives . 305 Table 16.9.1 Forms of spousal violence: Women . 307 Table 16.9.2 Forms of spousal violence: Men . 308 Table 16.10.1 Spousal violence by background characteristics: Women . 310 Table 16.10.2 Spousal violence by background characteristics: Men . 311 Table 16.11.1 Spousal violence by husband’s characteristics and empowerment indicators . 312 Table 16.11.2 Spousal violence by wife’s characteristics and empowerment indicators . 313 Table 16.12.1 Physical or sexual violence in the past 12 months by any husband/partner: Women . 314 Table 16.12.2 Physical or sexual violence in the past 12 months by any wife/partner: Men . 315 Table 16.13.1 Experience of spousal violence by duration of marriage: Women . 316 Table 16.13.2 Experience of spousal violence by duration of marriage: Men . 316 Table 16.14.1 Injuries to women due to spousal violence: Women . 317 Table 16.14.2 Injuries to men due to spousal violence: Men . 317 Table 16.15.1 Women’s violence against their spouse by background characteristics . 318 Table 16.15.2 Men’s violence against their spouse by background characteristics . 319 Table 16.16.1 Women’s violence against their spouse by spouse’s characteristics and empowerment indicators . 321 Table 16.16.2 Men’s violence against their spouse by spouse’s characteristics and empowerment indicators . 322 Table 16.17.1 Help seeking to stop violence: Women . 323 Table 16.17.2 Help seeking to stop violence: Men . 324 Table 16.18.1 Sources for help to stop the violence: Women . 325 Table 16.18.2 Sources for help to stop the violence: Men . 326 Figure 16.1 Percentage of ever-married women age 15-49 who have experienced specific types of spousal physical and sexual violence by the current or most recent husband/partner . 309 17 ADULT AND MATERNAL MORTALITY . 327 Table 17.1 Adult mortality rates . 328 Table 17.2 Adult mortality probabilities . 328 Table 17.3 Maternal mortality . 329 Figure 17.1 Maternal mortality ratio (MMR) with confidence intervals for the seven years preceding the KDHS . 330 18 FEMALE GENITAL CUTTING . 331 Table 18.1 Knowledge of female circumcision . 332 Table 18.2 Prevalence of female circumcision . 333 Table 18.3 Age at circumcision . 335 Table 18.4 Prevalence of circumcision and age at circumcision: Girls 0-14 . 335 Table 18.5 Circumcision of girls age 0-14 by mother’s background characteristics . 337 xvi • Tables and Figures Table 18.6 Infibulation among circumcised girls age 0-14 . 338 Table 18.7 Aspects of circumcision among circumcised girls age 0-14 and women age 15 49 . 339 Table 18.8 Opinions of women and men about whether circumcision is required by religion . 340 Table 18.9 Opinions of women and men about whether circumcision is required by the community . 341 Table 18.10 Opinions of women and men about whether the practice of circumcision should continue . 343 Figure 18.1 Percentage of women age 15-49 circumcised by ethnic group . 334 Figure 18.2 Percentage of women age 15-49 and girls age 0-14 circumcised by age . 336 APPENDIX A SAMPLE IMPLEMENTATION . 351 Table A.1 Enumeration Areas and households . 351 Table A.2 Population . 352 Table A.3 Sample allocation of clusters and households . 353 Table A.4 Sample allocation of completed interviews with women and men . 354 Table A.5 Sample implementation: Women . 355 Table A.6 Sample implementation: Men . 356 APPENDIX B ESTIMATES OF SAMPLING ERRORS . 357 Table B.1 List of selected variables for sampling errors, Kenya 2014 . 359 Table B.2 Sampling errors: Total sample, Kenya DHS 2014 . 361 Table B.3 Sampling errors: Urban sample, Kenya DHS 2014 . 363 Table B.4 Sampling errors: Rural sample, Kenya DHS 2014 . 365 Table B.5 Sampling errors: Coast sample, Kenya DHS 2014 . 367 Table B.6 Sampling errors: North Eastern sample, Kenya DHS 2014 . 369 Table B.7 Sampling errors: Eastern sample, Kenya DHS 2014 . 371 Table B.8 Sampling errors: Central sample, Kenya DHS 2014 . 373 Table B.9 Sampling errors: Rift Valley sample, Kenya DHS 2014 . 375 Table B.10 Sampling errors: Western sample, Kenya DHS 2014 . 377 Table B.11 Sampling errors: Nyanza sample, Kenya DHS 2014 . 379 Table B.12 Sampling errors: Nairobi sample, Kenya DHS 2014 . 381 Table B.13 Sampling errors for adult and maternal mortality rates, Kenya 2014 . 383 APPENDIX C DATA QUALITY . 385 Table C.1 Household age distribution . 385 Table C.2.1 Age distribution of eligible and interviewed women . 386 Table C.2.2 Age distribution of eligible and interviewed men . 386 Table C.3 Completeness of reporting . 387 Table C.4 Births by calendar years . 387 Table C.5 Reporting of age at death in days . 388 Table C.6 Reporting of age at death in months . 388 Table C.7 Nutritional status of children based on the NCHS/CDC/WHO International Reference Population . 389 Table C.8 Completeness of information on siblings . 390 Table C.9 Sibship size and sex ratio of siblings . 390 Foreword • xvii FOREWORD he 2014 Kenya Demographic and Health Survey (KDHS) provides information to help monitor and evaluate population and health status in Kenya. The survey, which follows up KDHS surveys conducted in 1989, 1993, 1998, 2003, and 2008-09, is of special importance for several reasons. New indicators not collected in previous KDHS surveys, such as noncommunicable diseases, fistula, and men’s experience of domestic violence, are included. Also, it is the first national survey to provide estimates for demographic and health indicators at the county level. Following adoption of a constitution in Kenya in 2010 and devolution of administrative powers to the counties, the new 2014 KDHS data should be valuable to managers and planners. The 2014 KDHS has specifically collected data to estimate fertility, to assess childhood, maternal, and adult mortality, to measure changes in fertility and contraceptive prevalence, to examine basic indicators of maternal and child health, to estimate nutritional status of women and children, to describe patterns of knowledge and behaviour related to the transmission of HIV and other sexually transmitted infections, and to ascertain the extent and pattern of domestic violence and female genital cutting. Unlike the 2003 and 2008-09 KDHS surveys, this survey did not include HIV and AIDS testing. HIV prevalence estimates are available from the 2012 Kenya AIDS Indicator Survey (KAIS), completed prior to the 2014 KDHS. Results from the 2014 KDHS show a continued decline in the total fertility rate (TFR). Fertility decreased from 4.9 births per woman in 2003 to 4.6 in 2008-09 and further to 3.9 in 2014, a one-child decline over the past 10 years and the lowest TFR ever recorded in Kenya. This is corroborated by the marked increase in the contraceptive prevalence rate (CPR) from 46 percent in 2008-09 to 58 percent in the current survey. The decline in fertility accompanies a marked decline in infant and child mortality. All early childhood mortality rates have declined between the 2003 and 2014 KDHS surveys. Total under-5 mortality declined from 115 deaths per 1,000 live births in the 2003 KDHS to 52 deaths per 1,000 live births in the 2014 KDHS. The maternal mortality ratio is 362 maternal deaths per 100,000 live births for the seven-year period preceding the survey; however, this is not statistically different from the ratios reported in the 2003 and 2008-09 KDHS surveys and does not indicate any decline over time. The proportion of mothers who reported receiving antenatal care from a skilled health provider increased from 88 percent to 96 percent between 2003 and 2014. The percentage of births attended by a skilled provider and the percentage of births occurring in health facilities each increased by about 20 percentage points between 2003 and 2014. The percentage of children age 12-23 months who have received all basic vaccines increased slightly from the 77 percent observed in the 2008-09 KDHS to 79 percent in 2014. Six in ten households (59 percent) own at least one insecticide-treated net, and 48 percent of Kenyans have access to one. In malaria endemic areas, 39 percent of women received the recommended dosage of intermittent preventive treatment for malaria during pregnancy. Awareness of AIDS is universal in Kenya; however, only 56 percent of women and 66 percent of men have comprehensive knowledge about HIV and AIDS prevention and transmission. The 2014 KDHS was conducted as a joint effort by many organisations. The Kenya National Bureau of Statistics (KNBS) served as the implementing agency by providing guidance in the overall survey planning, development of survey tools, training of personnel, data collection, processing, analysis, and dissemination of the results. The Bureau would like to acknowledge and appreciate the institutions and agencies for roles they played that resulted in the success of this exercise: Ministry of Health (MOH), National AIDS Control Council (NACC), National Council for Population and Development (NCPD), Kenya Medical Research Institute (KEMRI), Ministry of Labour, Social Security and Services, United States Agency for International Development (USAID/Kenya), ICF International, United Nations Fund for T xviii • Foreword Population Activities (UNFPA), the United Kingdom Department for International Development (DfID), World Bank, Danish International Development Agency (DANIDA), United Nations Children’s Fund (UNICEF), German Development Bank (KfW), World Food Programme (WFP), Clinton Health Access Initiative (CHAI), Micronutrient Initiative (MI), US Centers for Disease Control and Prevention (CDC), Japan International Cooperation Agency (JICA), Joint United Nations Programme on HIV/AIDS (UNAIDS), and the World Health Organization (WHO). The management of such a huge undertaking was made possible through the help of a signed memorandum of understanding (MoU) by all the partners and the creation of active Steering and Technical Committees. The Bureau is grateful to all the staff from various institutions and agencies who worked tirelessly to ensure the success of this exercise. Special thanks go to all the KNBS staff, survey personnel, and ICF International staff who worked long hours to collect data and most important, to the respondents who gave time to provide the information from which this report is developed. Zachary Mwangi Director General Kenya National Bureau of Statistics Millennium Development Goal Indicators • xix MILLENNIUM DEVELOPMENT GOAL INDICATORS Kenya 2014 Sex Total Indicator Male Female 1. Eradicate extreme poverty and hunger 1.8 Prevalence of underweight children under 5 years of age 12.1 9.8 11.0 2. Achieve universal primary education 2.1 Net attendance ratio in primary education1 85.5 87.6 86.6 2.3 Literacy rate of 15-24 year-olds2 94.6a 92.8 93.7b 3. Promote gender equality and empower women 3.1 Ratio of girls to boys in primary, secondary and tertiary education 3.1a Ratio of girls to boys in primary education3 na na 1.0 3.1b Ratio of girls to boys in secondary education3 na na 1.1 3.1c Ratio of girls to boys in tertiary education3 na na 0.9 4. Reduce child mortality 4.1 Under five mortality rate4 60 52 52 4.2 Infant mortality rate4 44 37 39 4.3 Percentage of 1 year old children immunised against measles 87.9 86.2 87.1 5. Improve maternal health 5.1 Maternal mortality ratio5 na na 362 (CI: 254,471) 5.2 Percentage of births attended by skilled health personnel6 na na 61.8 5.3 Contraceptive prevalence rate7 na 58.0 na 5.4 Adolescent birth rate8 na 96.3 na 5.5 Antenatal care coverage 5.5a At least one visit9 na 95.5 na 5.5b Four or more visits10 na 57.6 na 5.6 Unmet need for family planning na 17.5 na 6. Combat HIV/AIDS, malaria and other diseases 6.2 Condom use at last higher-risk sex11 75.0a 58.7 66.8 6.3 Percentage of the population age 15-24 years with comprehensive correct knowledge of HIV/AIDS12 63.7 54.2 59.0 6.4 Ratio of school attendance of orphans to school attendance of non-orphans age 10-14 years 0.98 1.01 0.99 6.7 Percentage of children under 5 sleeping under insecticide-treated bednets13 55.0 53.5 54.3 6.8 Percentage of children under 5 with fever who are treated with appropriate antimalarial drugs14 27.0 27.0 27.0 Urban Rural Total 7. Ensure environmental sustainability 7.8 Percentage of population using an improved water source15 85.7 57.0 66.9 7.9 Percentage of population using an improved sanitation facility16 30.5 21.6 24.7 na = Not applicable 1 The ratio is based on reported attendance, not enrolment, in primary education among primary school age children (6-13 year-olds). The rate also includes children of primary school age enrolled in secondary education. This is a proxy for MDG indicator 2.1, Net enrolment ratio. 2 Refers to respondents who attended secondary school or higher or who could read a whole sentence or part of a sentence 3 Based on reported net attendance, not gross enrolment, among 6-13 year-olds for primary, 14-17 year-olds for secondary and 18-22 year-olds for tertiary education 4 Expressed in terms of deaths per 1,000 live births. Mortality by sex refers to a 10-year reference period preceding the survey. Mortality rates for males and females combined refer to the 5-year period preceding the survey. 5 Expressed in terms of maternal deaths per 100,000 live births in the 7-year period preceding the survey 6 Among births in the five years preceding the survey 7 Percentage of currently married women age 15-49 using any method of contraception 8 Equivalent to the age-specific fertility rate for women age 15-19 for the 3-year period preceding the survey, expressed in terms of births per 1,000 women age 15-19 9 With a skilled provider 10 With any healthcare provider 11 Higher-risk sex refers to sexual intercourse with a non-marital, non-cohabiting partner. Expressed as a percentage of men and women age 15-24 who had higher-risk sex in the past 12 months. 12 Comprehensive knowledge means knowing that consistent use of a condom during sexual intercourse and having just one uninfected faithful partner can reduce the chance of getting the AIDS virus, knowing that a healthy-looking person can have the AIDS virus, and rejecting the two most common local misconceptions about transmission or prevention of the AIDS virus 13 An insecticide-treated net (ITN) is (1) a factory-treated net that does not require any further treatment, or (2) a net that has been soaked with insecticide within the past 12 months 14 Measured as the percentage of children age 0-59 months who were ill with a fever in the two weeks preceding the interview and received any anti-malarial drug 15 Percentage of de jure population whose main source of drinking water is a household connection (piped), public tap or standpipe, tubewell or borehole, protected dug well, protected spring, rainwater collection, or bottled water 16 Percentage of de jure population whose household has a flush toilet, ventilated improved pit latrine, pit latrine with a slab, or composting toilet and does not share this facility with other households a Restricted to men in sub-sample of households selected for the male interview b The total is calculated as the simple arithmetic mean of the percentages in the columns for males and females Summary of Findings • xxi SUMMARY OF FINDINGS HOUSEHOLD POPULATION AND CHARACTERISTICS Housing The majority (71 percent) of households in Kenya have access to an improved source of drink- ing water. Twenty-three percent of households have an improved toilet facility that is not shared with other households. The majority (64 percent) of households in Kenya do not have electricity. Almost half (46 percent) of households live in dwellings with cement floors. More than half (53 percent) of households use one room for sleeping. More than half (56 percent) of households use wood as their main source of cooking fuel. Education, Media, and Mobile Phones The percentage of women and men with no education has dropped by half over the last 10 years, from 13 percent and 6 percent in 2003 to 7 percent and 3 percent, respectively, in the 2014 KDHS. Over the same period, the percentage of women and men with at least some secondary education increased from 29 percent and 37 per- cent in 2003 to 43 percent and 49 percent, respec- tively, in 2014. Eighty-eight percent of women and 92 percent of men are literate. Twenty-three per- cent of women and 10 percent of men are not ex- posed to any source of mass media. Eighty-six percent of households own mobile phones. Employment Sixty-one percent of women and 80 percent of men are currently employed. Women are mostly employed in agricultural or domestic service posi- tions, while men are mostly employed in agricul- tural, unskilled manual, or domestic service posi- tions. MARRIAGE AND SEXUAL ACTIVITY The median age at first marriage among wom- en age 25-49 is 20.2 years; the median age at first marriage among men age 30-49 is 25.3 years. Me- dian age at marriage has remained stable in the past 10 years for both women and men. Six percent of currently married men are in a polygynous union; 11 percent of currently married women have co- wives. The percentage of women married by age 15 appears to be declining; 9 percent of women age 45-49 were married by age 15, as compared with 2 percent among those age 15-19. Fifteen percent of women age 20-49 had first sexual intercourse by age 15, 50 percent by age 18, and 71 percent by age 20. Twenty-two percent of men age 20-49 had first sexual intercourse by age 15, 56 percent by age 18, and 76 percent by age 20. FERTILITY Fertility Levels and Trends The total fertility rate for the three years pre- ceding the survey is 3.9 births per woman, with rural women having at least one child more than urban women. Fertility has decreased from 4.9 births per woman in 2003 to 3.9 births per woman in 2014, a one-child decline in the past 10 years. Half of births occur within three years of a previ- ous birth, with 18 percent occurring within 24 months. Childbearing begins early in Kenya, with almost one-quarter of women giving birth by age 18 and nearly half by age 20. Eighteen percent of adolescent women age 15-19 are already mothers or pregnant with their first child. In the last five years, teenage pregnancy has remained unchanged. Fertility Preferences Half of currently married women age 15-49 and 42 percent of currently married men age 15-49 want no more children or are sterilised. The mean ideal number of children among all women age 15- 49 is 3.6, while that of all men is 3.9. The mean ideal number of children among women has de- clined marginally in the last 10 years from 3.9 in the 2003 KDHS to 3.6 in 2014. The gap between actual fertility and ideal family size has narrowed in the last 10 years, from 1.3 children in 2003 to 1.0 in 2014. xxii • Summary of Findings Family Planning More than half of currently married women (58 percent) use a contraceptive method. The most popular modern contraceptive methods used by married women are injectables (26 percent), im- plants (10 percent), and the pill (8 percent). Use of modern methods has increased over the last decade from 32 percent in the 2003 KDHS to 53 percent in 2014. The public sector remains the major provider of contraceptive methods; 60 percent of modern contraceptive users obtain their contraception from a government source. Thirty-one percent of family planning users discontinue use of a method within 12 months of starting its use. Side effects and health concerns (11 percent) are the main reason for discontinuation. Eighteen percent of currently married women have an unmet need for family planning services, with 9 percent in need of spac- ing and 8 percent in need of limiting. MATERNAL HEALTH Antenatal Care Ninety-six percent of women with a live birth in the five years preceding the survey received antenatal care from a skilled provider, an im- provement from 92 percent in the 2008-09 KDHS and 88 percent in the 2003 KDHS. Fifty-eight percent of women make the recommended four or more antenatal care visits during their pregnancy, an increase of 11 percentage points from the 2008- 09 KDHS (47 percent). Delivery, Postnatal, and Newborn Care Sixty-one percent of live births in the five years preceding the survey were delivered in a health facility; 62 percent were assisted by a skilled provider. More than half (53 percent) of women who gave birth in the two years before the survey received a postnatal care checkup in the first two days after delivery. Thirty-six percent of infants born in the two years before the survey had their first postnatal checkup within the first two days after birth. One in three newborns received postnatal care from a doctor, a nurse, or a midwife. Fistula More than half (54 percent) of the women in- terviewed in the survey had heard of fistula. How- ever, only 1 percent of these women reported hav- ing ever experienced fistula-like symptoms. CHILD HEALTH Childhood Mortality The infant mortality rate is 39 deaths per 1,000 live births, and under-5 mortality is 52 deaths per 1,000 live births. At these levels, about one in every 26 Kenyan children dies before reaching age 1, and about one in every 19 does not survive to his or her fifth birthday. All early childhood mortality rates declined between the 2003 and 2014 KDHS surveys. Neonatal mortality has exhibited the slowest rate of decline (33 percent). A child born in the Nyanza region is almost twice as likely to die before age 5 as a child born in the Central re- gion. Nairobi has the second highest under-5 mor- tality rate, following Nyanza (72 deaths per 1,000 live births). Male children are more likely than female children to die during their first year of life (44 deaths versus 37 deaths per 1,000 live births). Once past infancy, male and female children one to four years of age experience the same level of mortality (16 deaths per 1,000 live births). The neonatal mortality rate for the five years preceding the survey is 22 deaths per 1,000 live births, 1.4 times the postneonatal rate. The perinatal mortality rate for the same reference period is 29 deaths per 1,000 pregnancies. Childhood Vaccination Coverage Seventy-nine percent of children age 12-23 months have received all basic vaccines, slightly higher than the 77 percent observed in the 2008-09 KDHS. Childhood Illness and Treatment Nine percent of children under age 5 showed symptoms of acute respiratory infection in the two weeks before the survey; 66 percent of these chil- dren were taken to a health facility or provider for advice or treatment. Twenty-four percent of chil- dren under age 5 had a fever in the two weeks before the survey; 63 percent of these children were taken to a health facility or provider for ad- vice or treatment. Fifteen percent of children under age 5 had diarrhoea in the two weeks before the survey. The proportion of children with diarrhoea taken to a health provider for advice or treatment Summary of Findings • xxiii increased from 49 percent in the 2008-09 KDHS to 58 percent in the 2014 KDHS. The proportion of children with diarrhoea given fluid from ORS packets has increased over the past five years, from 39 percent in 2008-09 to 54 percent in 2014. The percentage of women who know that ORS can be used to treat diarrhoea in children has increased from 78 percent in 2008-09 to 93 percent in 2014. The percentage of children whose stools are dis- posed of safely has increased from 78 percent in 2008-09 to 83 percent in 2014. NUTRITION Nutritional Status of Children Twenty-six percent of children under age 5 are stunted, 4 percent are wasted, and 11 percent are underweight. Nutritional Status of Women Nine percent of women age 15-49 are thin or undernourished (BMI <18.5 kg/m2); 33 percent of women are either overweight or obese (BMI ≥25 kg/m2), with 10 percent of them being obese (BMI ≥30 kg/m2). Breastfeeding Practices Ninety-nine percent of children have ever been breastfed; however, only 61 percent of children less than age 6 months are exclusively breastfed. Complementary foods are generally introduced at the recommended age; 81 percent of breastfed children age 6-9 months received complementary foods in the 24 hours preceding the survey. Only 22 percent of children are fed in accordance with the three recommended infant and young child feeding practices. Supplements and Deworming for Children and Women Seventy-two percent of children age 6-59 months received vitamin A supplements in the past six months. Fifty-one percent of children age 12-59 months received deworming medication in the same time period. Among women, only 8 percent took iron tablets daily for 90 or more days during the pregnancy of their last birth. Thirty-one percent of women took deworming medication during their last pregnancy. MALARIA Net Ownership and Use Six in 10 households (59 percent) own at least one insecticide-treated mosquito net (ITN), while 34 percent of households have at least one net for every two people. Forty-eight percent of Kenyans have access to an ITN. Two-fifths of the household population (42 percent) slept under an ITN the night prior to the survey, and two-thirds (67 per- cent) of members of households with at least one ITN slept under an ITN the night prior to the sur- vey. Fifty-four percent of children under age 5 slept under an ITN the night before the survey, and, among those living in households with an ITN, 77 percent slept under an ITN the night be- fore the survey. Fifty-one percent of pregnant women overall slept under an ITN the night before the survey, and, among those living in households with an ITN, 77 percent slept under an ITN the night before the survey. Pregnant Women and Children Seventeen percent of women received in- termittent preventive treatment (IPTp) for malaria during pregnancy; that is, they received two or more doses of SP/Fansidar, at least one during an antenatal care visit. In malaria endemic areas, 39 percent of women received IPTp. Twenty-three percent of children under age 5 who had a fever took ACT, and 13 percent took ACT within 24 hours of fever onset. HIV/AIDS Awareness of and Knowledge about AIDS Awareness of AIDS is universal in Kenya. However, only 56 percent of women and 66 per- cent of men have comprehensive knowledge about HIV and AIDS prevention and transmission; that is, they know that both condom use and limiting sexual intercourse to one uninfected partner can prevent HIV, they are aware that a healthy-looking person can have HIV, and they reject the two most common local misconceptions about HIV: that HIV can be transmitted by mosquitoes and by sharing food. Seventy-two percent of women and 62 percent of men know both that HIV can be transmitted through breastfeeding and that the risk xxiv • Summary of Findings of mother-to-child transmission can be reduced by taking special drugs during pregnancy. HIV-related Behavioural Indicators Among respondents who had more than one sexual partner in the past 12 months, 40 percent of women and 44 percent of men reported using a condom during their last sexual intercourse. HIV Testing Since the 2008-09 KDHS, there has been an increase in the percentage of both women (from 29 percent to 53 percent) and men (from 23 percent to 46 percent) who were tested for HIV in the past 12 months and received their results. Sixty-eight per- cent of women who gave birth in the two years before the survey received HIV counselling during antenatal care. Almost 7 in 10 women (69 percent) were tested for HIV during antenatal care and re- ceived the test results and post-test counselling, while 23 percent received results but did not re- ceive post-test counselling. OTHER HEALTH ISSUES Ten percent of women have had both a breast exam from a health provider and a breast self- exam. Three-quarters (76 percent) of women have heard of cervical cancer, and 14 percent have had a cervical cancer screening exam. Approximately two-thirds (65 percent) of men have heard of pros- tate cancer, and 3 percent have been examined by a doctor or health care provider for prostate cancer. Tobacco use is more common among Kenyan men than women (83 percent of men don’t use tobacco compared with 99 percent of women). Sixteen percent of men smoke cigarettes. Among men who smoke cigarettes, 28 percent smoked more than 10 cigarettes in the past 24 hours. Most Kenyans do not have health insurance; 82 percent of women and 79 percent of men are not covered by any health insurance. WOMEN’S EMPOWERMENT Nearly half (49 percent) of currently married employed women who earn cash make inde- pendent decisions about how to spend their earn- ings, an increase from the figure of 42 percent reported in the 2008-09 KDHS. Fifty-four percent of currently married women participate in deci- sions pertaining to their own health care, major household purchases, visits to their family or rela- tives, and major household purchases. Thirty-nine percent of women have the main say in their own health care. Contraceptive use increases with women’s empowerment. In general, unmet need for family planning decreases with improvements in women’s empowerment. Access to antenatal care, delivery assistance from a skilled provider, and postnatal care within the first two days of delivery increases with increasing women’s empowerment. GENDER-BASED VIOLENCE Violence Since Age 15 Forty-five percent of women and 44 percent of men age 15-49 have experienced physical violence since age 15, and 20 percent and 12 percent, re- spectively, experienced physical violence within the 12 months prior to the survey. The main perpe- trators of physical violence against women are husbands, whereas the main perpetrators against men are parents, teachers, and others. Sexual and Partner Violence Fourteen percent of women and 6 percent of men age 15-49 report having experienced sexual violence at least once in their lifetime. Overall, 39 percent of ever-married women and 9 percent of men age 15-49 report having experienced spousal physical or sexual violence. Among women and men who have ever experienced spousal violence (physical or sexual), 39 percent and 24 percent, respectively, reported experiencing physical inju- ries. Forty-four percent of women and 27 percent of men have sought assistance to stop the violence they have experienced. Female Genital Cutting Twenty-one percent of women age 15-49 have been circumcised. There is some evidence of a trend over time to circumcise girls at younger ages. Twenty-eight percent of circumcised women age 20-24 were circumcised at age 5-9, as compared with 17 percent of circumcised women age 45-49. With respect to type of circumcision, 2 percent of circumcised women age 15-49 had cutting with no flesh removed, 87 percent had cutting with flesh removed, and 9 percent had their genital area sewn Summary of Findings • xxv closed after cutting (a procedure known as infibu- lation). Girls age 0-14 are more likely to be cir- cumcised if their mother is circumcised. Likewise, girls age 0-14 are more likely to be infibulated if their mother is also infibulated. Eight percent of girls age 0-14 have had their genital area sewn closed. Eleven percent or less of women and men believe that the practice of female genital cutting is required by their community or their religion or that the practice should continue. ADULT AND MATERNAL MORTALITY Fourteen percent of women and 18 percent of men are likely to die between exact ages 15 and 50. Maternal deaths account for 14 percent of all deaths to women age 15-49. The maternal mortali- ty ratio was 362 maternal deaths per 100,000 live births for the seven-year period preceding the sur- vey. When comparing the estimate of an MMR of 362 with the MMR estimated in the previous DHS (2008-09 KDHS estimate of 520 maternal deaths per 100,000 live births), the differential is not large enough to conclude whether or not there has been any change over time between the two surveys. xxvi • Map of Kenya Introduction • 1 INTRODUCTION 1 Macdonald Obudho, James N. Munguti, John K. Bore, Mutua Kakinyi 1.1 HISTORY, GEOGRAPHY, AND ECONOMY 1.1.1 History enya is a former British colony. The independence process was met with resistance and an armed struggle by Kenyans against the British colonial rulers. The Mau Mau rebellion in the 1950s paved the way for constitutional reform and political development in the following years. The country achieved self-rule in June 1963 and gained independence on December 12, 1963. The country was a multi-party state until 1981, when it was converted to a single-party state by amending the constitution. Kenya reverted to the multi-party state in 1992. The Kenya African National Union (KANU) ruled the country from independence to 2002, when the National Alliance of Rainbow Coalition (NARC) was elected to power. To date, the multi-party state remains, with the Jubilee Coalition currently in power. 1.1.2 Geography Kenya is situated in the eastern part of the African continent. The country lies between 5 degrees north and 5 degrees south latitude and between 24 and 31 degrees east longitude. The equator passes at the middle, separating the upper and lower parts almost equally. Kenya borders Ethiopia (north), Somalia (northeast), Tanzania (south), Uganda (west), and South Sudan (northwest). The Indian Ocean is on the eastern side. The coastline houses the port of Mombasa, which enables Kenya and several other countries, including Uganda, Rwanda, and South Sudan, to engage in global trade. The country is administratively divided into 47 counties. It has a total of 582,646 square kilometres, of which 571,466 square kilometres are the dry land area. Most of the land area (80 percent) is arid or semi-arid, and only 20 percent is arable. The country has diverse physical features: Mount Kenya, the second highest mountain in Africa; Lake Victoria, the largest freshwater lake on the continent; the Great Rift Valley, which runs from north to south; and Lake Nakuru, a major tourist attraction due to the presence of flamingos. The country falls within two regions: lowlands, including the coastal and lake region lowlands, and highlands, which fall on both sides of the Great Rift Valley. Rainfall and temperatures are influenced by altitude and proximity to the Indian Ocean. The coastal region has a tropical climate, with both rainfall and temperatures higher than the rest of the country throughout the year. 1.1.3 Economy The Kenyan economy is predominantly agricultural with a strong industrial base. The performance of the Kenyan economy since the country gained independence has been mixed. Recent years have seen an estimated 5-6 percent growth. From the demand side, growth has mainly been driven by an increase in private consumption and rapid growth in capital investment. From the supply side, the major drivers of the economy have been agriculture, forestry, and fishing; construction wholesale and retail trade; education; and finance and insurance. 1.2 POPULATION Kenya’s population was enumerated at 38.6 million in the 2009 census (Table 1.1). The trend data from population censuses indicate that the total population more than tripled between 1969 and 2009. K 2 • Introduction These data also suggest that the population increased by approximately one million people per year between 1999 (28.7 million) and 2009. The inter-censal growth rate, which was 3.3 percent per annum in 1969, increased to a peak of 3.8 percent per annum in 1979 before declining to 2.9 percent per annum in 1999. At a growth rate of 2.9 percent per annum, the population may increase to 77 million by 2030. Table 1.1 Basic demographic indicators Selected demographic indicators for Kenya, 1969, 1979, 1989, 1999, 2009, and 2014 Indicator 1969 1979 1989 1999 2009 2014 Population (millions) 10.9 16.2 23.2 28.7 38.6 43.0a Density (pop/km2) 19.0 27.0 37.0 49.0 66.4 73.9a Percent urban 9.9 15.1 18.1 19.4 32.3 32.3c Crude birth rate 50.0 54.0 48.0 41.3 34.8 30.5c Crude death rate 17.0 14.0 11.0 11.7 10.4 10.4b Inter-censal growth rate 3.3 3.8 3.4 2.9 2.9 2.9b Total fertility rate 7.6 7.8 6.7 5.0 4.8 3.9c Infant mortality rate (per 1,000 births) 119 88 66 77.3 54.0 39.0c Life expectancy at birth 50 54 60 56.6 58.0 58.0b a Projected figures b Assumed to remain constant over the inter-censal period c 2014 KDHS results (see later chapters) Source: CBS, 1970; CBS, 1981; CBS, 1994; CBS, 2002a; KNBS & ICF Macro, 2010; KNBS, 2012 1.3 POPULATION AND HEALTH POLICY FRAMEWORKS 1.3.1 Population Policy Framework In 2012, the government of Kenya launched a new policy on population and national development. The policy is described in the Sessional Paper No. 3 of 2012; it outlines the goal of attaining a high quality of life for the people of Kenya by managing population growth to a level that can be sustained with the available resources. The principal objective of the policy is to provide a framework to guide national population programmes and activities for the next two decades (National Council for Population and Development [NCPD], 2012). Overall, the policy seeks to: • Reduce the population growth rate in order to achieve harmony with the economic growth and social development goals envisioned in Vision 2030; • Reduce fertility and mortality rates and at the same time assist individuals and couples who desire to have children but are unable to; • Provide equitable and affordable quality reproductive health services, including family planning; • Contribute to the planning and implementation of socioeconomic development programmes as a long-term measure to influence population dynamics, with a special focus on poverty reduction, technology and research, the environment, education, health and gender equity, and equality and empowerment of women; and • Mobilise resources through government budgetary allocations, international cooperation, and public/private partnerships to ensure the sustainability of population programmes and significant impacts on population dynamics. The policy has the following targets: • Reduce the natural growth rate of the population from 2.5 percent in 2009 to 1.5 percent by 2030. Introduction • 3 • Reduce the infant mortality rate from 52 per 1,000 live births in 2009 to 25 per 1,000 live births by 2030. • Reduce the under-5 mortality rate from 74 per 1,000 live births in 2009 to 48 per 1,000 live births by 2030. • Reduce the maternal mortality rate from 488 deaths per 100,000 live births in 2009 to 200 deaths per 100,000 live births by 2030. • Reduce the crude death rate from 13 deaths per 1,000 people in 2009 to 8 deaths per 1000 people by 2030. • Improve life expectancy at birth for both sexes from 57 years in 2009 to 64 years by 2030. • Reduce the total fertility rate from 4.6 children per woman in 2009 to 2.6 children per woman by 2030. 1.3.2 Health Priorities and Programmes The government of Kenya emphasises the health of its citizens and the improvement of health service delivery. The Ministry of Health plays a coordinating and capacity-building role in ensuring that all services offered are in line with established policies and standards. The government recognises that good health is a prerequisite to socioeconomic development. A number of government policy documents and successive national development plans, including Vision 2030, have stated that health services should meet the basic needs of the population, that health facilities should be situated so that they are within reach of all Kenyans, and that there should be a focus on preventive, promotive, and rehabilitative services without ignoring curative services. Under the 2010 Kenya constitution, the health function has been devolved to the county governments, with distinct functions being assigned to the national and county governments. The national government is responsible for leadership in health policy development, management of national referral health facilities, capacity building and technical assistance to counties, and consumer protection, including the development of norms, standards, and guidelines. The county governments are responsible for county health services and pharmacies; ambulance services; promotion of primary health care; licensing and control of establishments that sell food to the public; cemeteries, funeral parlours, and crematoria; and refuse removal, refuse dumps, and solid waste disposal. With regard to their functions, the county governments have undertaken new strategies and initiatives to address the health needs of their populations, including the construction of more health facilities, the acquisition of new equipment and medication at these facilities, and the addition of ambulances and more medical staff. The Kenya Health Policy 2014-2030 takes into account the objectives of devolution and adheres to the following principles: • Equity in the distribution of health services and interventions; • A people-centred approach to health and health interventions; • A participatory approach to delivery of interventions; • A multisectoral approach to realising health goals; • Efficiency in the application of health technologies; and • Social accountability. 1.4 OBJECTIVES OF THE SURVEY The 2014 Kenya Demographic and Health Survey (KDHS) was designed to provide information to monitor and evaluate the population and health situations in Kenya and to be a follow-up to the previous 4 • Introduction KDHS surveys. In addition, it provides information on indicators previously not collected in KDHS surveys, such as fistula and men’s experience of domestic violence. Finally, the 2014 KDHS is the first such survey to provide estimates for selected demographic and health indicators at the county level. The specific objectives of the 2014 KDHS were to: • Estimate fertility and childhood, maternal, and adult mortality; • Measure changes in fertility and contraceptive prevalence; • Examine basic indicators of maternal and child health; • Collect anthropometric measures for children and women; • Describe patterns of knowledge and behaviour related to transmission of HIV and other sexually transmitted infections; and • Ascertain the extent and pattern of domestic violence and female genital cutting. 1.5 SURVEY ORGANISATION The 2014 KDHS was a joint effort of many organisations, including the following: • Kenya National Bureau of Statistics (KNBS) • Ministry of Health (MOH) • National AIDS Control Council (NACC) • National Council for Population and Development (NCPD) • Kenya Medical Research Institute (KEMRI) • Ministry of Labour, Social Security and Services • United States Agency for International Development (USAID/Kenya) • ICF International • United Nations Population Fund (UNFPA) • Department for International Development (DFID) • World Bank • Danish International Development Agency (DANIDA) • United Nations Children’s Fund (UNICEF) • German Development Bank (KfW) • World Food Programme (WFP) • Clinton Health Access Initiative (CHAI) • Micronutrient Initiative (MI) • U.S. Centers for Disease Control and Prevention (CDC) • Japan International Cooperation Agency (JICA) • Joint United Nations Programme on HIV/AIDS (UNAIDS) • World Health Organization (WHO) The Kenya National Bureau of Statistics (KNBS) served as the implementing agency and, as such, had a primary role in the planning of the survey and in the analysis and dissemination of the survey results. As the implementing agency, the bureau took responsibility for operational matters including planning and conducting fieldwork and processing collected data. Staff from the KNBS and other partners were responsible for overseeing day-to-day technical operations, including recruitment and training of field and data processing staff and supervision of office and field operations. The bureau was also responsible for organising the writing and distribution of reports. With funding from USAID/Kenya, ICF International staff provided technical assistance, mainly through short-term visits to Kenya, in the areas of survey and sample design, questionnaire design, field staff training, fieldwork monitoring, data processing, and report writing and dissemination. NACC, as the body mandated to coordinate the national HIV and AIDS multisectoral response, assisted in reviewing the protocol and survey instruments to ensure that the information collected is relevant to the national HIV and AIDS programmes. USAID/Kenya provided Introduction • 5 funding for survey field transport in addition to other logistical support. WHO-Kenya helped in mobilising logistical and financial support from member organisations. The Ministry of Health (MOH) assisted in reviewing the survey instruments in addition to participating in report writing. 1.6 SAMPLE DESIGN The sample for the 2014 KDHS was drawn from a master sampling frame, the Fifth National Sample Survey and Evaluation Programme (NASSEP V). This is a frame that the KNBS currently operates to conduct household-based surveys throughout Kenya. Development of the frame began in 2012, and it contains a total of 5,360 clusters split into four equal subsamples. These clusters were drawn with a stratified probability proportional to size sampling methodology from 96,251 enumeration areas (EAs) in the 2009 Kenya Population and Housing Census. The 2014 KDHS used two subsamples of the NASSEP V frame that were developed in 2013. Approximately half of the clusters in these two subsamples were updated between November 2013 and September 2014. Kenya is divided into 47 counties that serve as devolved units of administration, created in the new constitution of 2010. During the development of the NASSEP V, each of the 47 counties was stratified into urban and rural strata; since Nairobi county and Mombasa county have only urban areas, the resulting total was 92 sampling strata. The 2014 KDHS was designed to produce representative estimates for most of the survey indicators at the national level, for urban and rural areas separately, at the regional (former provincial1) level, and for selected indicators at the county level. In order to meet these objectives, the sample was designed to have 40,300 households from 1,612 clusters spread across the country, with 995 clusters in rural areas and 617 in urban areas. Samples were selected independently in each sampling stratum, using a two-stage sample design. In the first stage, the 1,612 EAs were selected with equal probability from the NASSEP V frame. The households from listing operations served as the sampling frame for the second stage of selection, in which 25 households were selected from each cluster. The interviewers visited only the preselected households, and no replacement of the preselected households was allowed during data collection. The Household Questionnaire and the Woman’s Questionnaire were administered in all households, while the Man’s Questionnaire was administered in every second household. Because of the non-proportional allocation to the sampling strata and the fixed sample size per cluster, the survey was not self-weighting. The resulting data have, therefore, been weighted to be representative at the national, regional, and county levels. 1.7 QUESTIONNAIRES The 2014 KDHS used a household questionnaire, a questionnaire for women age 15-49, and a questionnaire for men age 15-54. These instruments were based on the model questionnaires developed for The DHS Program, the questionnaires used in the previous KDHS surveys, and the current information needs of Kenya. During the development of the questionnaires, input was sought from a variety of organisations that are expected to use the resulting data. A two-day workshop involving key stakeholders was held to discuss the questionnaire design. Producing county-level estimates requires collecting data from a large number of households within each county, resulting in a considerable increase in the sample size from 9,936 households in the 2008-09 KDHS to 40,300 households in 2014. A survey of this magnitude introduces concerns related to data quality and overall management. To address these concerns, reduce the length of fieldwork, and limit interviewer and respondent fatigue, a decision was made to not implement the full questionnaire in every household and, in so doing, to collect only priority indicators at the county level. Stakeholders generated a list of these priority indicators. Short household and woman’s questionnaires were then designed based on the full questionnaires; the short questionnaires contain the subset of questions from the full questionnaires required to measure the priority indicators at the county level. 1 Former provinces were Coast, North Eastern, Eastern, Central, Rift Valley, Western, Nyanza, and Nairobi. 6 • Introduction Thus, a total of five questionnaires were used in the 2014 KDHS: (1) a full Household Questionnaire, (2) a short Household Questionnaire, (3) a full Woman’s Questionnaire, (4) a short Woman’s Questionnaire, and (5) a Man’s Questionnaire. The 2014 KDHS sample was divided into halves. In one half, households were administered the full Household Questionnaire, the full Woman’s Questionnaire, and the Man’s Questionnaire. In the other half, households were administered the short Household Questionnaire and the short Woman’s Questionnaire. Selection of these subsamples was done at the household level—within a cluster, one in every two households was selected for the full questionnaires, and the remaining households were selected for the short questionnaires. It is important to note that the priority data collected in the short questionnaires were collected from all households and from all women since the short questionnaires were subsets of the full questionnaires. Therefore, data collected in both the full and the short questionnaires can produce estimates of indicators at the national, rural/urban, regional, and county levels. Data collected only in the full questionnaires (i.e., in one-half of households) can produce estimates at the national, rural/urban, and regional levels only. Data collected only in the full questionnaires are not recommended for estimation at the county level. A list of topics included in the full and short questionnaires is presented in Appendix E. In this report, county-level data are tabulated for nearly all of the indicators for which they are available; county-level tables are not presented for indicators with insufficient cases for evaluation (less than 50 unweighted cases) within each county. In the case of indicators not collected at the county level, the tables include data at the regional level only. The Household Questionnaire was used to list all of the usual members of the household and visitors who stayed in the household the night before the survey. One of the main purposes of the Household Questionnaire was to identify women and men who were eligible for the individual interview. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor and roof of the house, ownership of various durable goods, and ownership and use of mosquito nets. In addition, this questionnaire was used to record height and weight measurements of women age 15-49 and children under age 5. The Woman’s Questionnaires were used to collect information from women age 15-49. The full questionnaire covered the following topics (see Appendix E for a side-by-side comparison of topics included in the full and short questionnaires): • Background characteristics (education, marital status, media exposure, etc.) • Reproductive history • Knowledge and use of family planning methods • Fertility preferences • Antenatal and delivery care • Breastfeeding and infant feeding practices • Vaccinations and childhood illnesses • Marriage and sexual activity • Women’s work and husbands’ background characteristics • Childhood mortality • Awareness and behaviour regarding HIV and other sexually transmitted infections • Adult mortality, including maternal mortality • Domestic violence • Female circumcision • Fistula Introduction • 7 The Man’s Questionnaire was administered to men age 15-54 living in every second household in the sample. The Man’s Questionnaire collected information similar to that contained in the Woman’s Questionnaire but was shorter because it did not contain questions on maternal and child health, nutrition, adult and maternal mortality, or experience of female circumcision or fistula. Both the Woman’s and the Man’s Questionnaires also included a series of questions to obtain information on respondents’ experience of domestic violence. The domestic violence questions were administered in the subsample of households that received the full Household Questionnaire, the full Woman’s Questionnaire, and the Man’s Questionnaire. Additionally, the violence questions were administered to only one eligible individual, a woman or a man, per household. In households with more than one eligible individual, special procedures were followed in order to ensure that there was random selection of the respondent to be interviewed for the domestic violence module. After finalisation of the questionnaires in English, they were translated into 16 other languages, namely Borana, Embu, Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Luo, Maasai, Maragoli, Meru, Mijikenda, Pokot, Somali, Swahili, and Turkana. The translated questionnaires were pretested to detect any possible problems in questionnaire translation or flow, as well as to gauge the length of time required for interviews. 1.8 TRAINING 1.8.1 Training of Trainers Training of trainers was conducted by ICF International from January 20-25, 2014, with 18 trainers drawn from the KNBS and the Ministry of Health. The objectives of the training were to harmonise concepts related to survey design and questionnaire content, to review effective adult teaching techniques, and to familiarise trainers with the training materials and equipment. The trainers participated in leading the pretest and the main training and later served as fieldwork coordinators during data collection. 1.8.2 Pretest Activities The pretest took place from January 17 to February 15, 2014. The objectives of the pretest were (1) to train interviewers, editors, and supervisors to fulfil their respective roles and to conduct high-quality household and individual interviews, (2) to pilot the questionnaires in the field, and (3) to review and modify the questionnaire translations based on field experience. Classroom training addressed all aspects of the questionnaire content and interviewing procedures and included anthropometry practice with children from neighbouring child care centres. Training concluded with two days of local field practice, after which field teams were formed and sent throughout Kenya (to clusters not included in the KDHS sample) to pilot the translated questionnaires. After the fieldwork, a two-day debriefing workshop was held to look at the issues emanating from the pretest. The resolutions from the debriefing were used to enrich the questionnaires and improve field logistics before implementation of the main training and the actual survey. 1.8.3 Main Training of Field Staff Several categories of personnel were recruited and trained to undertake the 2014 KDHS. These included 48 supervisors, 48 field editors, 144 female interviewers, 48 male interviewers, 28 quality assurance personnel, and 20 reserves. The training for these personnel took place from March 24 to April 17, 2014, in Nakuru. Trainees were divided into six classrooms, each managed by three trainers. The training consisted of a detailed, question-by-question explanation of the questionnaires, accompanied by explanations from the interviewer’s manual, demonstration through role-plays, group discussions, and in-class practice 8 • Introduction interviewing in pairs. Several graded take-home assignments and quizzes were administered, the results of which were used both to enhance understanding of key terms and concepts and to identify candidates for further strengthening or elimination from the field teams. A number of guest speakers were invited to give lectures on specific topics relevant to the KDHS. Anthropometry training provided all trainees with instruction, demonstration, and practice in length/height and weight measurements for children and adults. Trainees completed a standardisation exercise measuring children, intended to gauge and improve measurement accuracy and precision. In this exercise, 175 children age 0-59 months and their caregivers were invited to the training site in groups of 50 child-caregiver pairs assigned throughout the day to one of three classrooms. Fifteen nutrition specialists from partnering organisations were trained to support the exercise; they provided a reference measurement for children and monitored the standardisation activity. Each of the 336 trainees served as both measurers and assistants and measured the same 10 children twice. Results were recorded and analysed using Software for Emergency Nutrition Assessment (ENA for SMART); more than 70 percent of trainees’ scores were acceptable or higher. A debriefing session was held the following day to provide feedback and correction to trainees. Three field practice sessions were held throughout the main training. Trainees were organised into teams with a team leader selected from the pretest trainees. Team leaders assisted with logistics, guided trainees through fieldwork, monitored trainees’ performance, edited trainees’ questionnaires for errors, and debriefed their team on errors/corrections. The first field practice occurred early in the training and focused only on the Household Questionnaire. The final two days of field practice occurred at the end of training and covered the full KDHS protocol: all questionnaires, salt testing, and anthropometry. 1.9 FIELDWORK Fieldwork for the main survey took place from May 7 to October 20, 2014. Field staff were divided into 48 teams according to counties and languages spoken in the areas where they conducted the interviews. Each team had one supervisor, one field editor, three female interviewers, one male interviewer, a driver, and a vehicle. Data collection was overseen by 18 coordinators who had also served as trainers during the pretest and main training and by a staff of 28 quality assurance personnel. Coordinators were each assigned two to three teams for which they were responsible for observing and monitoring data collection quality, ensuring uniformity in data collection procedures and fidelity to the survey protocol, providing moral support to the field teams, and replenishing field team supplies. Coordinators met in person and via phone with teams throughout the fieldwork, spending a total of 70 days in the field. Quality control staff fulfilled similar responsibilities and spent a total of 60 days in the field. 1.10 DATA PROCESSING Completed questionnaires were sent to the KNBS Data Processing Centre in Nairobi. Office editors who received the questionnaires verified cluster and household numbers to ensure that they were consistent with the sampled list. They also ensured that each cluster had 25 households and that all questionnaires for a particular household were packaged together. Data entry began on May 28, 2014, with a four-day training session and continued until November 21, 2014. All data were double entered (100 percent verification) using CSPro software. The data processing team included 42 keyers, three office editors, two secondary editors, four supervisors, and one data manager. Secondary editing, which included further data cleaning and validation, ran simultaneously with data entry and was completed on January 28, 2015, in collaboration with ICF International. The KDHS Key Indicators Report was prepared and launched in April 2015. Introduction • 9 1.11 RESPONSE RATES Table 1.2 presents the summary response rates for the 2014 KDHS. A total of 39,679 households were selected for the sample, of which 36,812 were found occupied at the time of the fieldwork. Of these households, 36,430 were successfully interviewed, yielding an overall household response rate of 99 percent. The shortfall of households occupied was primarily due to structures that were found to be vacant or destroyed and households that were absent for an extended period of time. Table 1.2 Results of the household and individual interviews Number of households, number of interviews, and response rates, according to residence (unweighted), Kenya 2014 Residence Total Result Urban Rural ALL HOUSEHOLDS Household interviews Households selected 15,419 24,260 39,679 Households occupied 14,177 22,635 36,812 Households interviewed 13,914 22,516 36,430 Household response rate1 98.1 99.5 99.0 Interviews with women age 15-49 Number of eligible women 12,157 20,015 32,172 Number of eligible women interviewed 11,614 19,465 31,079 Eligible women response rate2 95.5 97.3 96.6 HOUSEHOLDS SELECTED FOR FULL QUESTIONNAIRES Household interviews Households selected 7,394 11,636 19,030 Households occupied 6,790 10,835 17,625 Households interviewed 6,645 10,764 17,409 Household response rate1 97.9 99.3 98.8 Interviews with women age 15-49 Number of eligible women 5,772 9,545 15,317 Number of eligible women interviewed 5,472 9,269 14,741 Eligible women response rate2 94.8 97.1 96.2 Interviews with men age 15-54 Number of eligible men 5,676 8,541 14,217 Number of eligible men interviewed 4,915 7,904 12,819 Eligible men response rate2 86.6 92.5 90.2 HOUSEHOLDS SELECTED FOR SHORT QUESTIONNAIRES Household interviews Households selected 8,025 12,624 20,649 Households occupied 7,387 11,800 19,187 Households interviewed 7,269 11,752 19,021 Household response rate1 98.4 99.6 99.1 Interviews with women age 15-49 Number of eligible women 6,385 10,470 16,855 Number of eligible women interviewed 6,142 10,196 16,338 Eligible women response rate2 96.2 97.4 96.9 1 Households interviewed/households occupied 2 Respondents interviewed/eligible respondents As noted, the 2014 KDHS sample was divided into halves, with one half of households receiving the full Household Questionnaire, the full Woman’s Questionnaire, and the Man’s Questionnaire and the other half receiving the short Household Questionnaire and the short Woman’s Questionnaire. The household response rate for the full Household Questionnaire was 99 percent, as was the household response rate for the short Household Questionnaire. In the households selected for and interviewed using the full questionnaires, a total of 15,317 women were identified as eligible for the full Woman’s Questionnaire, of whom 14,741 were interviewed, generating a response rate of 96 percent. A total of 14,217 men were identified as eligible in these households, of whom 12,819 were successfully interviewed, generating a response rate of 90 percent. 10 • Introduction In the households selected for and interviewed with the short questionnaires, a total of 16,855 women were identified as eligible for the short Woman’s Questionnaire, of whom 16,338 were interviewed, yielding a response rate of 97 percent. Response rates are lower in the urban sample than in the rural sample, more so for men. The principal reason for non-response among both eligible men and eligible women was failure to find them at home despite repeated visits to the household. The lower response rates for men reflect the more frequent and longer absences of men from the household. Housing Characteristics and Household Population • 11 HOUSING CHARACTERISTICS AND HOUSEHOLD POPULATION 2 Macdonald Obudho, James N. Munguti, John K. Bore, Mutua Kakinyi his chapter provides an overview of the demographic and socioeconomic characteristics of the households sampled in the 2014 KDHS. In the 2014 KDHS, a household is defined as a person or group of persons, related or unrelated, who usually live together, who acknowledge one adult member as the head of the household, and who have common cooking arrangements. Information was collected on all usual residents of a selected household (de jure population) as well as persons who had stayed in the selected household the night before the interview (de facto population). This chapter presents information on the conditions of the households in which the survey population lives, including the source of drinking water, availability of electricity, sanitation facilities, building materials, and possession of household durable goods. Also included are findings on birth registration among children, living arrangements, orphanhood status, school attendance, and educational attainment. The background information presented in this chapter is intended to facilitate the interpretation of the demographic, socioeconomic, and health indices presented in later chapters. 2.1 HOUSEHOLD CHARACTERISTICS The characteristics of a household determine the socioeconomic and health status of its members. The household is where decisions about health, education, and general welfare are made and acted upon. The 2014 KDHS asked respondents about their household environment, including the source of drinking water; type of sanitation facility; building characteristics such as type of material used for the roofing, flooring, and walls; and number of rooms used for sleeping. Many of these measures help to assess Kenya’s progress towards Millennium Development Goal 7, which focuses on environmental sustainability and targets sustainable access to safe drinking water, basic sanitation, and adequate housing. T Key Findings • The majority (71 percent) of households in Kenya have access to an improved source of drinking water. • Twenty-three percent of households have an improved toilet facility that is not shared with other households. • The majority (64 percent) of households in Kenya do not have electricity. • Almost half (46 percent) of households live in dwellings with cement floors. • More than half (53 percent) of households use one room for sleeping. • More than half (56 percent) of households use wood as their main source of cooking fuel. • Eighty-six percent of households own mobile phones. • Three in 10 Kenyans are below age 10. • One-third of households are headed by women. • The average household size in Kenya is 3.9 members. • The births of two out of every three children below age 5 are registered with the civil authorities. 12 • Housing Characteristics and Household Population 2.1.1 Water and Sanitation Table 2.1 includes a number of indicators that are useful in monitoring household access to improved drinking water. Improved water sources include piped water into the dwelling, yard, or plot; a public tap/standpipe or borehole; a protected well or protected spring water; rainwater; and bottled water. Lack of easy access to an improved water source may limit the quantity of suitable drinking water that is available to a household as well as increase the risk of illness. Unimproved water sources increase the spread of waterborne disease and the burden of service delivery through increased demand for health care; these sources include unprotected wells or springs, water delivered by tanker trucks, and surface water. Table 2.1 Household drinking water Percent distribution of households and de jure population by source of drinking water, time to obtain drinking water, treatment of drinking water, and person who usually collects drinking water, according to residence, Kenya 2014 Households Population Characteristic Urban Rural Total Urban Rural Total Source of drinking water Improved source 88.2 59.1 71.3 85.7 57.0 66.9 Piped water into dwelling/ yard/plot 45.5 15.0 27.8 43.2 12.1 22.8 Public tap/standpipe 24.8 9.3 15.8 22.6 9.6 14.0 Tube well or borehole 3.8 8.2 6.3 4.3 8.4 7.0 Protected well 3.9 10.3 7.6 4.5 10.7 8.6 Protected spring 3.4 11.6 8.2 4.5 12.3 9.6 Rain water 2.6 4.5 3.7 2.8 3.9 3.5 Bottled water 4.3 0.2 1.9 3.8 0.1 1.4 Non-improved source 10.1 39.2 26.9 12.5 41.5 31.6 Unprotected well 1.7 8.8 5.8 2.4 9.8 7.3 Unprotected spring 1.2 5.5 3.7 1.8 5.8 4.4 Tanker truck/cart with drum 3.1 0.8 1.8 3.0 0.7 1.5 Surface water 4.1 24.0 15.6 5.4 25.2 18.4 Other 1.7 1.7 1.7 1.8 1.4 1.5 Total 100.0 100.0 100.0 100.0 100.0 100.0 Time to obtain drinking water (round trip) Water on premises 53.7 27.0 38.2 52.1 23.6 33.4 Less than 30 minutes 33.4 32.7 33.0 32.2 33.4 33.0 30 minutes or longer 11.1 39.9 27.8 13.9 42.8 32.9 Don’t know/missing 1.9 0.4 1.0 1.9 0.3 0.8 Total 100.0 100.0 100.0 100.0 100.0 100.0 Water treatment prior to drinking1 Boiled 25.5 22.5 23.7 25.9 21.0 22.7 Bleach/chlorine added 21.7 22.5 22.2 24.0 23.8 23.8 Strained through cloth 0.4 1.1 0.8 0.6 1.3 1.0 Ceramic, sand or other filter 1.2 3.5 2.6 1.6 3.9 3.2 Solar disinfection 0.0 0.0 0.0 0.0 0.0 0.0 Other2 0.7 2.0 1.5 0.9 2.0 1.6 No treatment 54.5 54.1 54.3 51.9 54.1 53.3 Percentage using an appropriate treatment method3 44.9 44.2 44.5 47.5 44.1 45.3 Number of all households 15,290 21,140 36,430 48,946 93,762 142,708 Person who usually collects drinking water Adult female 15+ 27.7 56.8 44.6 34.3 64.2 53.9 Adult male 15+ 16.4 11.8 13.7 10.9 7.7 8.8 Female child under age 15 0.8 2.5 1.8 1.3 2.7 2.2 Male child under age 15 0.6 1.2 1.0 0.8 1.3 1.1 Other 0.8 0.8 0.8 0.8 0.6 0.7 Water on premises 53.5 26.7 38.0 51.8 23.3 33.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number of households selected for full questionnaire 7,280 10,080 17,360 23,176 44,073 67,249 Note: Totals may not add up to 100 percent because households with missing information are not shown separately. 1 Respondents may report multiple treatment methods; therefore, the sum of all treatment methods may exceed 100 percent. 2 Other water treatment methods include covering the water container, and letting the water stand and settle. 3 Appropriate water treatment methods include boiling, bleaching/adding chlorine, filtering/straining, and solar disinfecting. Housing Characteristics and Household Population • 13 Table 2.1 indicates that the majority of households in Kenya (71 percent) obtain drinking water from an improved source, while 27 percent use non-improved sources. This is an improvement since the 2008-09 KDHS, when 63 percent of households obtained drinking water from an improved source. Use of improved sources is more common among households in urban areas (88 percent) than among those in rural areas (59 percent). The most common source of drinking water in urban areas is water piped into the dwelling/yard/plot, with almost half (46 percent) of households using this source. In rural areas, the most common source of drinking water is surface water (24 percent), followed by water piped into the dwelling/yard/plot (15 percent). Nearly 4 in 10 households have the source for their drinking water on their premises, but nearly 3 in 10 households (28 percent) spend 30 minutes or longer to obtain their drinking water. In rural areas, 4 in 10 households spend 30 minutes or more to obtain their drinking water, as compared with only 1 in 10 urban households. Over half of households (54 percent) do not treat their drinking water, and this is true in both urban and rural areas. The most commonly used methods of water treatment are boiling and adding bleach/chlorine (24 percent and 22 percent of households, respectively). Overall, 45 percent of households use an appropriate treatment method. When water is not on the premises, the responsibility of collecting drinking water usually rests on adult women. Forty-five percent of households reported that a female adult age 15 and above usually collects the drinking water for the household. An even higher percentage of rural households delegate collection of drinking water to women (57 percent), as these households are much less likely to have their water source on the premises. Table 2.2 presents the percent distribution of households and the de jure population by the type of toilet/latrine facilities usually used by household members. Twenty-five percent of household members usually use an improved (and not shared) toilet/latrine facility. About 4 in 10 urban dwellers (43 percent) use an improved facility that is shared by two or more households, as compared with only about 1 in 10 (12 percent) rural dwellers. Approximately two-thirds of rural Kenyans usually use a non-improved toilet facility (66 percent), most commonly a pit latrine without a slab or an open pit (48 percent). One-half of urban Kenyans use a shared facility of which a pit latrine with a slab is the most common (17 percent). 14 • Housing Characteristics and Household Population Table 2.2 Household sanitation facilities Percent distribution of households and de jure population by type of toilet/latrine facilities, according to residence, Kenya 2014 Households Population Type of toilet/latrine facility Urban Rural Total Urban Rural Total Improved, not shared facility Flush/pour flush to piped sewer system 8.0 0.1 3.4 8.5 0.1 3.0 Flush/pour flush to septic tank 8.0 0.8 3.8 8.7 0.6 3.4 Flush/pour flush to pit latrine 0.9 0.3 0.6 1.2 0.3 0.6 Ventilated improved pit (VIP) latrine 4.2 8.6 6.8 6.1 9.2 8.1 Pit latrine with slab 4.0 10.5 7.8 5.7 11.1 9.2 Composting toilet 0.3 0.3 0.3 0.4 0.3 0.3 Total 25.5 20.6 22.7 30.5 21.6 24.7 Shared facility1 Flush/pour flush to piped sewer system 11.9 0.1 5.1 9.6 0.1 3.3 Flush/pour flush to septic tank 5.2 0.3 2.4 4.4 0.2 1.6 Flush/pour flush to pit latrine 3.7 0.2 1.7 3.0 0.2 1.1 Ventilated improved pit (VIP) latrine 12.1 6.4 8.8 11.2 5.1 7.2 Pit latrine with slab 17.3 8.1 12.0 14.9 6.4 9.3 Composting toilet 0.2 0.1 0.2 0.2 0.1 0.2 Total 50.4 15.3 30.1 43.3 12.1 22.8 Non-improved facility Flush/pour flush not to sewer/ septic tank/pit latrine 1.5 0.0 0.6 1.4 0.0 0.5 Pit latrine without slab/open pit 19.4 47.7 35.9 21.4 48.3 39.1 Bucket 0.2 0.0 0.1 0.3 0.0 0.1 Hanging toilet/hanging latrine 0.5 0.2 0.3 0.4 0.3 0.3 No facility/bush/field 1.4 16.0 9.9 1.7 17.6 12.2 Other 0.9 0.1 0.5 0.7 0.1 0.3 Total 24.1 64.1 47.3 26.2 66.3 52.5 Total 100.0 100.0 100.0 100.0 100.0 100.0 Number 15,290 21,140 36,430 48,946 93,762 142,708 Note: Totals may not add up to 100 percent because households with missing information are not shown separately. 1 Facilities that would be considered improved if they were not shared by two or more households. 2.1.2 Housing Characteristics Table 2.3 presents information on housing characteristics in Kenya. These characteristics are usually a function of the household’s socioeconomic situation and have a direct bearing on the health and welfare of household members. The table includes information on access to electricity, type of flooring material, number of rooms used for sleeping, the place used for cooking, the type of fuel used for cooking, and the frequency of someone smoking in the home. The majority of households in urban areas have electricity (68 percent), while the vast majority of rural households do not (only 13 percent have electricity). Nationally, 36 percent of households have access to electricity, as compared with 23 percent in 2008-09. Cement is the most common household flooring material; 46 percent of households have cement floors, up from 41 percent in 2008-09. Not surprisingly, cement floors are much more common in urban households (70 percent) than in rural households (28 percent). The most common flooring in rural households is earth/sand (43 percent). The number of rooms used for sleeping provides an indication of the extent of crowding in households. Overcrowding increases the risk of contracting infectious diseases such as acute respiratory infections and skin diseases, which particularly affect children and the elderly population. The proportion of households using one room for sleeping has increased from 48 percent to 53 percent in the last five years. The presence and extent of indoor pollution are dependent on cooking practices, the cooking location, and types of fuel used. According to the 2014 KDHS, 50 percent of households cook inside the Housing Characteristics and Household Population • 15 Table 2.3 Household characteristics Percent distribution of households by housing characteristics, percentage using solid fuel for cooking, and percent distribution by frequency of smoking in the home, according to residence, Kenya 2014 Residence Total Housing characteristic Urban Rural Electricity Yes 68.4 12.6 36.0 No 31.6 87.4 64.0 Total 100.0 100.0 100.0 Flooring material Earth, sand 10.9 43.1 29.6 Dung 5.0 26.9 17.7 Wood/planks 0.1 0.2 0.2 Palm/bamboo 0.0 0.0 0.0 Parquet or polished wood 0.4 0.1 0.3 Vinyl or asphalt strips 1.4 0.0 0.6 Ceramic tiles 7.3 0.8 3.5 Cement 70.3 28.4 46.0 Carpet 4.4 0.3 2.0 Other 0.1 0.1 0.1 Total 100.0 100.0 100.0 Rooms used for sleeping One 68.4 42.0 53.1 Two 21.3 36.5 30.2 Three or more 9.9 21.1 16.4 Total 100.0 100.0 100.0 Place for cooking In the house 77.0 30.4 50.0 In a separate building 14.5 61.1 41.5 Outdoors 6.2 7.4 6.9 No food cooked in household 2.3 1.0 1.5 Other 0.0 0.0 0.0 Missing 0.0 0.0 0.0 Total 100.0 100.0 100.0 Cooking fuel Electricity 0.9 0.1 0.4 LPG/natural gas/biogas 24.5 2.0 11.5 Paraffin/kerosene 26.6 1.3 11.9 Coal/lignite 0.1 0.1 0.1 Charcoal 27.6 9.7 17.2 Wood 17.2 84.2 56.1 Straw/shrubs/grass 0.7 1.4 1.1 Agricultural crop 0.0 0.1 0.0 Animal dung 0.0 0.0 0.0 Other 0.0 0.0 0.0 No food cooked in household 2.3 1.0 1.5 Total 100.0 100.0 100.0 Percentage using solid fuel for cooking1 45.6 95.5 74.6 Number of all households 15,290 21,140 36,430 Frequency of smoking in the home Daily 10.9 12.9 12.1 Weekly 1.8 1.9 1.9 Monthly 0.2 0.5 0.4 Less than monthly 0.6 0.5 0.5 Never 86.3 84.1 85.0 Total 100.0 100.0 100.0 Number of households selected for full questionnaire 7,280 10,080 17,360 LPG = Liquid petroleum gas Note: Totals may not add up to 100 percent because households with missing information are not shown separately. 1 Includes coal/lignite, charcoal, wood, straw/shrubs/grass, agricultural crops, and animal dung. home, while 42 percent cook in a separate building and 7 percent cook outdoors. The percentage of households that cook within the dwelling unit is much higher in urban areas (77 percent) than in rural areas (30 percent). Using solid fuels for cooking increases indoor pollution. Solid fuels are defined as coal/lignite, charcoal, wood, straw/shrubs/grass, and agricultural crops. Nationally, 75 percent of households use solid fuels, mostly wood (56 percent) and charcoal (17 percent). While this is a decrease 16 • Housing Characteristics and Household Population from the 84 percent of households using solid fuels reported in the 2008-09 KDHS, over 9 in 10 rural households continue to use solid fuels. Households that do not use solid fuels mostly use gas or kerosene (12 percent of households each, compared with 7 percent and 8 percent, respectively in 2008-09). A major concern for the government of Kenya is the effect of secondhand smoke on the health of children and neonates. The purpose of the Tobacco Control Act of 2007, followed in 2014 by the Tobacco Control Regulations (2014), is to control tobacco and tobacco-related product use. Secondhand smoke is a risk factor for children and adults who do not smoke. Children who are exposed to secondhand smoke are at a higher risk of respiratory and ear infections and poor lung development (U.S. Department of Health and Human Services, 2006). Pregnant women who are exposed to secondhand smoke have a higher risk of giving birth to a low birth weight baby (Windham et al., 1999). To measure the extent of smoke exposure among household members, respondents were asked how often anyone smokes inside the house. In Kenya, someone smokes in the house on a daily basis in 12 percent of households (11 percent in urban areas and 13 percent in rural areas). 2.1.3 Household Possessions The availability of durable consumer goods is a useful indicator of a household’s socioeconomic status. Moreover, particular goods have specific benefits. For instance, having access to a radio or a television exposes household members to innovative ideas; a refrigerator prolongs the wholesomeness of foods; and a means of transport allows greater access to services away from the local area. Table 2.4 shows the availability of selected consumer goods by residence. Table 2.4 Household possessions Percentage of households possessing various household effects, means of transportation, agricultural land and livestock/farm animals, a dwelling, and land on which the dwelling is built, by residence, Kenya 2014 Residence Total Possession Urban Rural Household effects Watch 24.9 15.0 19.2 Radio 73.5 63.1 67.5 Television 56.0 18.9 34.5 Mobile telephone 94.2 80.0 86.0 Non-mobile telephone 0.7 0.2 0.4 Refrigerator 12.7 1.5 6.2 Solar panel 4.0 14.0 9.8 Table 87.8 83.1 85.1 Chair 79.4 86.3 83.4 Sofa 64.5 47.5 54.6 Bed 94.1 92.7 93.3 Cupboard 51.9 41.7 46.0 Clock 27.3 14.4 19.8 Microwave oven 7.2 0.7 3.4 DVD player 40.6 9.5 22.5 Cassette or CD player 19.5 5.8 11.6 Means of transport Bicycle 16.2 24.8 21.2 Animal drawn cart 1.1 2.5 1.9 Motorcycle/scooter 6.0 8.2 7.3 Car/truck 7.2 2.7 4.6 Boat with a motor 0.2 0.2 0.2 Ownership of agricultural land 47.7 79.2 66.0 Ownership of farm animals1 43.2 80.4 64.8 Number of all households 15,290 21,140 36,430 Ownership of dwelling 25.5 85.2 60.2 Ownership of land on which dwelling is built 24.4 81.9 57.8 Number of households selected for full questionnaire 7,280 10,080 17,360 1 Local cattle, exotic/grade cattle, horses, donkeys, camels, goats, sheep or chickens Housing Characteristics and Household Population • 17 Possession of mobile phones has significantly increased from 62 percent in 2008-09 to 86 percent in 2014; rural areas have registered a greater increase (from 53 percent to 80 percent), as ownership of mobile phones among urban households was already relatively high. More than 9 in 10 urban households (94 percent) own a mobile phone. Sixty-eight percent of households have a radio, and about one-third (35 percent) have a television. Urban households are somewhat more likely to possess a radio (74 percent) than rural households (63 percent). Fifty-six percent of urban households and 19 percent of rural households possess a television, and television ownership has increased nationally from 28 percent to 35 percent since 2008-09. A refrigerator is available in 13 percent of urban households and only 2 percent of rural households. Bicycles are still the most common means of transport owned by households. Twenty-one percent of households own a bicycle (25 percent in rural areas and 16 percent in urban areas). The agricultural sector plays a large role in the Kenyan economy, and a substantial proportion of the population is engaged in this sector. The 2014 KDHS indicates that two of every three households own agricultural land, with 79 percent of rural households and 48 percent of urban households owning land. Two in three households (65 percent) own farm animals, 80 percent in rural areas and 43 percent in urban areas. In urban areas, ownership of agricultural land and farm animals has increased since 2008-09 (from 35 percent to 48 percent and 27 percent to 43 percent, respectively), while the national figure has remained at two-thirds. More than 8 in 10 rural households own their dwelling (85 percent) and the land on which the dwelling is built (82 percent). About one-quarter (26 percent) of urban households own their dwelling and the land on which it is built (24 percent). Thus, nationally, 6 in 10 Kenyan households own their dwelling (60 percent), and nearly 6 in 10 (58 percent) own the land on which the dwelling is built. 2.2 HOUSEHOLD WEALTH The wealth index used in this report and in many other DHS survey reports serves as a proxy for a household’s long-term standard of living. It has been demonstrated to be consistent with expenditure and income measures (Rutstein, 1999; Rutstein and Johnson, 2004). The index is constructed using household asset data collected in the Household Questionnaire and is generated via a principal components analysis. The wealth index has been improved to better take into account urban-rural differences in scores and indicators of wealth by performing the first and second steps of its creation separately for urban and rural areas prior to creating a national wealth index in the last step. In the first step, a subset of indicators common to urban and rural areas is used to create wealth scores for households in both areas. Categorical variables to be used are transformed into separate dichotomous (0-1) indicators. These indicators and those that are continuous are then examined using a principal components analysis to produce a common factor score for each household. In the second step, separate factor scores are produced for households in urban and rural areas using area-specific indicators. The third step combines the separate area-specific factor scores to produce a nationally applicable combined wealth index by adjusting area-specific scores through a regression on the common factor scores. The resulting combined wealth index has a mean of zero and a standard deviation of one. Once the index is computed, national-level wealth quintiles (from lowest to highest) are obtained by assigning the household score to each de jure household member, ranking each person in the population by his or her score, and then dividing the ranking into five equal categories, each comprising 20 percent of the population. Thus, throughout this report, wealth quintiles are expressed in terms of quintiles of individuals in the overall population rather than quintiles of individuals at risk for any one health or population indicator. For example, quintile rates for infant mortality refer to infant mortality rates per 1,000 live births among all people in the population quintile concerned, as distinct from quintiles of live births or newly born infants, who constitute the only members of the population at risk of mortality during infancy. 18 • Housing Characteristics and Household Population Table 2.5 presents percent distributions of the de jure population across the five wealth quintiles by residence and region. Three-quarters of urban residents (75 percent) are in the two highest wealth quintiles, while more than three-quarters of rural residents (78 percent) are in the lowest three quintiles (and are nearly equally distributed across these quintiles). By region, the most skewed distributions are seen in Nairobi and North Eastern. Nine in 10 people in Nairobi are in the two highest wealth quintiles, and 7 in 10 people in North Eastern are in the lowest wealth quintile. Populations in the other regions are more spread out across the quintiles. Table 2.5 Wealth quintiles Percent distribution of the de jure population by wealth quintiles, and the Gini Coefficient, according to residence and region, Kenya 2014 Residence/ region/county Wealth quintile Total Number of persons Gini coefficientLowest Second Middle Fourth Highest Residence Urban 6.0 8.3 10.6 26.1 49.0 100.0 48,946 0.18 Rural 27.3 26.1 24.9 16.8 4.9 100.0 93,762 0.19 Region Coast 40.0 10.4 10.6 14.5 24.5 100.0 13,972 0.34 North Eastern 72.9 4.5 4.5 9.0 9.1 100.0 4,164 0.36 Eastern 19.5 27.7 22.4 19.7 10.7 100.0 20,960 0.21 Central 2.4 12.0 21.9 32.0 31.7 100.0 16,297 0.21 Rift Valley 26.5 20.2 19.7 19.1 14.5 100.0 37,746 0.29 Western 12.3 30.9 33.8 16.8 6.2 100.0 16,692 0.20 Nyanza 16.6 31.2 23.8 17.5 10.9 100.0 20,050 0.28 Nairobi 0.2 0.8 6.0 25.6 67.4 100.0 12,827 0.15 Total 20.0 20.0 20.0 20.0 20.0 100.0 142,708 0.27 Table 2.5 also includes information on the Gini coefficient, which indicates the level of concentration of wealth (0 being an equal distribution and 1 a totally unequal distribution). This ratio is expressed as a proportion between 0 and 1. The coefficient indicates the distribution of wealth independent of the level of wealth. The coefficient is lowest in Nairobi, indicating that people in that region are more similar to each other with regard to wealth than people in any other region. With the highest Gini coefficient of 0.36, the most unequal distribution of wealth is seen in the North Eastern region. 2.3 FOOD SECURITY The National Food and Nutrition Security Policy of 2011 states that all Kenyans should at all times have access to safe food of sufficient quantity and quality to satisfy their nutritional needs for optimal health. Household respondents in the 2014 KDHS were asked on how many days during the seven days preceding the survey members of their household had consumed items from various food groups (staples, pulses, vegetables, fruits, meat, dairy, oil, and sugar). They were also asked if there were any days in the seven days preceding the survey when their household did not have food or enough money to buy food. Respondents who answered ‘yes’ to the latter question were asked to indicate how many days in that week their household had to rely on less preferred food, rely on borrowed food, reduce the number of meals, reduce the size of meals, and/or reduce what adults ate in order for small children to eat. These questions and the three measures described below were developed by the World Food Programme. The first measure, the food consumption score (FCS), derived from the household consumption history questions, is a composite calculation including dietary diversity (the number of food groups consumed by a household over a seven-day period), food frequency (the number of days a particular food group is consumed), and the relative nutritional importance of different food groups. The FCS is intended to describe short-term food security at the time of data collection. Food consumption scores are divided into poor, borderline, and acceptable food consumption groups. The second measure is the percentage of households that report lacking food or money to purchase food in the seven days preceding the survey. The third measure is the coping strategy index (CSI). The CSI is a composite calculation of the frequency and severity of coping strategies that households adopt when facing lack of food or money to purchase food. A higher CSI score indicates a more serious food security Housing Characteristics and Household Population • 19 situation. The minimum possible CSI score (among households reporting any of the provided list of coping strategies) is 7.0, and the maximum possible score is 56.0. Table 2.6 presents the percent distribution of households with poor, borderline, or acceptable food consumption; the percentage of households that report lacking food or money to purchase food; and the mean CSI score, according to background characteristics. Table 2.6 Food security status Percent distribution of households with poor, borderline or acceptable food consumption, percentage of households that report lacking food or money to purchase food in the seven days preceding the survey, and the mean coping strategy index, according to background characteristics, Kenya 2014 Background characteristic Food consumption score groups Total Number of households with valid food consumption score Percentage of households that report lacking food or money to purchase food Number of households Mean coping strategy index Number of households with total coping strategy index greater than zero Poor Borderline Acceptable Residence Urban 1.4 7.3 91.3 100.0 7,217 23.0 7,280 17.4 1,658 Rural 1.7 11.4 86.9 100.0 10,041 36.2 10,080 19.6 3,645 Region Coast 1.1 10.7 88.2 100.0 1,651 24.5 1,688 16.1 414 North Eastern 1.9 8.5 89.6 100.0 342 37.8 344 15.4 130 Eastern 0.8 8.0 91.2 100.0 2,510 37.5 2,516 20.0 942 Central 1.3 5.8 92.9 100.0 2,391 17.4 2,400 16.7 418 Rift Valley 2.3 11.4 86.4 100.0 4,387 25.4 4,406 22.1 1,119 Western 1.4 10.8 87.8 100.0 1,720 44.6 1,726 17.3 769 Nyanza 1.6 12.9 85.5 100.0 2,174 41.9 2,187 18.4 915 Nairobi 1.6 7.5 90.8 100.0 2,085 29.1 2,093 18.3 597 Wealth quintile Lowest 3.8 17.9 78.3 100.0 2,888 54.4 2,894 21.7 1,574 Second 1.8 14.0 84.2 100.0 3,152 41.6 3,166 19.5 1,316 Middle 1.1 9.8 89.1 100.0 3,244 32.6 3,262 17.4 1,061 Fourth 0.8 6.2 93.0 100.0 3,913 21.1 3,948 16.3 824 Highest 0.8 3.7 95.5 100.0 4,062 13.0 4,091 16.1 528 Total 1.5 9.7 88.8 100.0 17,258 30.7 17,360 18.9 5,303 Note: The food consumption score reflects the quantity and quality of people’s diet. The coping strategy index measures behaviours adopted by households when they have difficulties in covering their food needs. The majority of households (89 percent) in Kenya had acceptable food consumption scores. Two percent of households had poor food consumption scores and 10 percent had borderline scores. Rural households were more likely to have borderline scores (11 percent) than urban households (7 percent). Households in Nyanza were most likely (13 percent) to have borderline scores, followed closely by households in Rift Valley, Western, and Coast (all 11 percent). The proportion of households with borderline scores decreased with increasing household wealth. Three in 10 (31 percent) households in Kenya reported not having enough food or money to buy food in the seven days preceding the survey. More than 3 in 10 rural households (36 percent) and households in Western (45 percent), Nyanza (42 percent), North Eastern (38 percent), and Eastern (38 percent) reported lacking food or money to purchase food. As expected, the likelihood of lacking food or money to purchase food decreased with increasing household wealth. However, 13 percent of households in the highest wealth quintile did report not having enough food or money to buy food. Among households that reported not having food or enough money to purchase food, the mean CSI score was 18.9. The mean score was highest in Rift Valley (22.1) and lowest in North Eastern (15.4). Mean CSI scores decreased slightly with increasing household wealth. 2.4 HAND WASHING Environmental management at the household level is a key indicator of a household’s intention to manage its health. Hand washing is one of the most effective ways to prevent the spread of germs, and it is used here as an indicator of personal and household hygiene. Table 2.7 provides information on places designated for hand washing and the availability of water and cleansing agents by residence, region, and wealth quintile. 20 • Housing Characteristics and Household Population Table 2.7 Hand washing Percentage of households in which the place most often used for washing hands was observed, and among households in which the place for washing hands was observed, percent distribution by availability of water, soap and other cleansing agents, Kenya 2014 Percentage of households where place for washing hands was observed Number of households Among households where place for washing hands was observed, percentage with: Number of households with place for washing hands was observed Background characteristic Soap and water1 Water and cleansing agent2 other than soap only Water only Soap but no water3 Cleansing agent other than soap only2 No water, no soap, no other cleansing agent Total Residence Urban 42.7 7,280 60.1 0.2 21.8 3.5 0.0 14.2 100.0 3,111 Rural 27.1 10,080 37.5 0.1 26.0 2.7 0.1 33.2 100.0 2,729 Region Coast 30.3 1,688 24.5 0.1 23.2 6.0 0.0 45.9 100.0 512 North Eastern 23.6 344 26.5 2.4 18.1 1.7 0.0 50.7 100.0 81 Eastern 39.1 2,516 37.5 0.0 17.4 6.3 0.1 38.4 100.0 985 Central 55.7 2,400 45.2 0.5 41.7 1.7 0.0 10.8 100.0 1,337 Rift Valley 29.5 4,406 62.4 0.1 20.4 0.9 0.0 15.8 100.0 1,300 Western 25.6 1,726 38.1 0.1 10.3 3.8 0.0 47.3 100.0 442 Nyanza 16.5 2,187 51.8 0.0 25.2 6.1 0.1 16.2 100.0 360 Nairobi 39.3 2,093 73.4 0.0 15.1 2.1 0.0 9.5 100.0 823 Wealth quintile Lowest 18.0 2,894 18.5 0.6 18.8 2.7 0.0 59.0 100.0 520 Second 24.8 3,166 33.2 0.1 20.8 3.7 0.3 41.5 100.0 784 Middle 27.9 3,262 40.7 0.0 23.4 3.7 0.0 31.8 100.0 911 Fourth 36.6 3,948 48.5 0.0 30.6 2.9 0.0 17.9 100.0 1,445 Highest 53.3 4,091 67.2 0.3 21.7 3.0 0.0 7.7 100.0 2,180 Total 33.6 17,360 49.5 0.2 23.8 3.2 0.0 23.1 100.0 5,840 Note: Totals may not add up to 100 percent because households with missing information are not shown separately. 1 Soap includes soap or detergent in bar, liquid, powder or paste form. This column includes households with soap and water only as well as those that had soap and water and another cleansing agent. 2 Cleansing agents other than soap include locally available materials such as ash, mud or sand. 3 Includes households with soap only as well as those with soap and another cleansing agent Interviewers collected data by observing the place household members use for hand washing. A place for handwashing was only observed in one-third of households. A place for hand washing was observed in about 4 in 10 urban households (43 percent) and fewer than 3 in 10 rural households (27 percent). The ability of interviewers to observe a place for hand washing varied substantially across regions, from a low of 17 percent in Nyanza to a high of 56 percent in Central. It is especially interesting to note that the ability of interviewers to observe a place for hand washing steadily increased with increasing wealth, from a low of only 18 percent among households in the lowest quintile to a high of 53 percent observed among households in the highest quintile. Both water and soap were available in 50 percent of the households where a place for hand washing was observed (60 percent of urban households and 38 percent of rural households). The presence of soap and water increases steadily with increasing wealth, from 19 percent in the lowest quintile to 67 percent in the highest quintile. Approximately half of households in Coast, North Eastern, and Western where a place for hand washing was observed had neither water nor soap available. 2.5 HOUSEHOLD POPULATION BY AGE AND SEX The distribution of the de facto household population in the 2014 KDHS is shown in Table 2.8 by five-year age groups, according to sex and residence. The age and sex structure of the population is key in all demographic analyses. The 2014 KDHS de facto household population constitutes 137,780 persons, of whom 51 percent are female and 49 percent are male. Among this population, 34 percent live in urban areas and 66 live in rural areas. Half of the population is below age 20 (52 percent). At 14 percent, the under-5 population constitutes the largest age group in urban areas, while the 5-9 population is the largest five-year age group in rural areas (17 percent). Housing Characteristics and Household Population • 21 Table 2.8 Household population by age, sex, and residence Percent distribution of the de facto household population by five-year age groups, according to sex and residence, Kenya 2014 Urban Rural Total Age Male Female Total Male Female Total Male Female Total <5 13.8 13.9 13.9 15.6 13.9 14.7 15.0 13.9 14.4 5-9 12.6 12.5 12.5 17.4 16.3 16.8 15.7 15.0 15.3 10-14 10.3 10.4 10.4 16.0 14.8 15.4 14.0 13.3 13.7 15-19 7.3 8.3 7.8 10.5 8.9 9.7 9.4 8.7 9.0 20-24 10.0 12.0 11.0 6.3 6.8 6.6 7.6 8.6 8.1 25-29 11.8 12.8 12.3 5.8 7.0 6.4 7.9 8.9 8.4 30-34 9.8 9.0 9.4 5.2 5.5 5.4 6.8 6.7 6.7 35-39 7.1 6.1 6.6 4.7 5.2 4.9 5.5 5.5 5.5 40-44 5.3 4.2 4.7 3.8 4.2 4.0 4.3 4.2 4.3 45-49 3.3 2.8 3.1 2.9 3.5 3.2 3.1 3.2 3.2 50-54 3.0 2.9 3.0 2.9 3.7 3.3 3.0 3.4 3.2 55-59 2.3 1.6 1.9 2.2 2.8 2.5 2.2 2.4 2.3 60-64 1.3 1.1 1.2 2.2 2.3 2.3 1.9 1.9 1.9 65-69 0.8 0.8 0.8 1.6 1.7 1.7 1.3 1.4 1.4 70-74 0.6 0.7 0.6 1.1 1.2 1.2 0.9 1.0 1.0 75-79 0.3 0.4 0.3 0.7 0.9 0.8 0.6 0.7 0.6 80 + 0.4 0.6 0.5 0.9 1.4 1.2 0.7 1.1 0.9 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Number 23,574 23,871 47,445 43,865 46,470 90,335 67,439 70,341 137,780 Figure 2.1 depicts the age-sex structure of the Kenyan population in a population pyramid. The broad base depicts the youthfulness of the population. The drop in the female population between ages 10- 14 and 15-19 is a bit steep and could partially be due to some interviewers estimating ages of women to be under the interview cutoff age of 15 to reduce their workload. Similarly, there is an increase in the female population between ages 45-49 and 50-54, which might be due to pushing some of the women out of the age eligibility category. The drop in population between ages 5-9 and under 5 among both males and females reflects a fertility decline, addressed in the chapter on fertility. Figure 2.1 Population pyramid 2.6 HOUSEHOLD COMPOSITION Information on key aspects of the composition of households is presented in Table 2.9. Nationally, one-third of households are headed by women. A higher proportion of rural than urban households are headed by women (36 percent and 27 percent, respectively). 22 • Housing Characteristics and Household Population Table 2.9 Household composition Percent distribution of households by sex of head of household and by household size; mean size of household, and percentage of households with orphans and foster children under 18 years of age, according to residence, Kenya 2014 Residence Characteristic Urban Rural Total Household headship Male 72.7 64.2 67.8 Female 27.3 35.8 32.2 Total 100.0 100.0 100.0 Number of usual members 0 0.1 0.0 0.0 1 27.3 13.5 19.3 2 16.6 10.2 12.9 3 16.5 14.1 15.1 4 15.6 17.0 16.4 5 10.7 15.2 13.3 6 6.1 10.9 8.8 7 3.4 7.8 6.0 8 1.8 5.2 3.8 9+ 1.9 6.2 4.4 Total 100.0 100.0 100.0 Mean size of households 3.2 4.4 3.9 Percentage of households with orphans and foster children under 18 years of age Foster children1 10.8 21.3 16.9 Double orphans 1.3 1.9 1.6 Single orphans2 5.4 10.8 8.5 Foster and/or orphan children 13.5 26.4 21.0 Number of households 15,290 21,140 36,430 Note: Table is based on de jure household members, i.e., usual residents. 1 Foster children are those under age 18 living in households with neither their mother nor their father present. 2 Includes children with one dead parent and an unknown survival status of the other parent. The data also show that the mean size of a Kenyan household is 3.9 people, lower than the mean size of 4.2 recorded in the 2008-09 KDHS. As expected, rural households are larger on average (4.4 people) than urban households (3.2 people). Nationally, 17 percent of Kenyan households are fostering a child under age 18 (1 in every 10 urban households and 2 in every 10 rural households). Nine percent of all Kenyan children under age 18 have had one parent die. The percentage of households housing a single or double orphan is higher in rural areas (13 percent) than in urban areas (7 percent). 2.7 BIRTH REGISTRATION Birth registration is the inscription of the facts of the birth into an official log kept at the registrar’s office. A birth certificate is issued at the time of registration or later as proof of the registration of the birth. Birth registration is basic to ensuring a child’s legal status and, thus, basic rights and services. Table 2.10 presents the percentage of the de jure population under age 5 whose births are registered with the civil authorities, according to background characteristics. Two-thirds of children in Kenya have their births registered (67 percent). This is an improvement of 7 percentage points since the 2008-09 KDHS, which reported a figure of 60 percent. However, only about one-quarter (24 percent) of children are reported to have a birth certificate. Housing Characteristics and Household Population • 23 Table 2.10 Birth registration of children under age five Percentage of de jure children under five years of age whose births are registered with the civil authorities, according to background characteristics, Kenya 2014 Children whose births are registered Number of children Background characteristic Percentage who had a birth certificate Percentage who did not have birth certificate Percentage registered Age <2 19.8 48.2 68.0 7,662 2-4 26.8 39.4 66.2 12,291 Sex Male 24.9 42.5 67.4 10,170 Female 23.3 43.1 66.4 9,784 Residence Urban 37.4 41.4 78.8 6,603 Rural 17.5 43.4 61.0 13,351 Region Coast 21.3 53.5 74.8 2,019 North Eastern 44.7 17.1 61.8 682 Eastern 18.1 57.0 75.1 2,458 Central 36.3 53.4 89.7 1,789 Rift Valley 20.1 42.8 62.9 5,727 Western 19.6 33.6 53.2 2,585 Nyanza 19.5 34.6 54.1 2,929 Nairobi 42.6 36.9 79.5 1,765 Wealth quintile Lowest 12.3 39.8 52.1 4,924 Second 15.4 43.2 58.6 4,277 Middle 19.7 45.4 65.0 3,652 Fourth 27.5 49.6 77.1 3,430 Highest 51.4 37.3 88.7 3,670 Total 24.1 42.8 66.9 19,954 There is little age or sex differential nationally in the percentage of children registered. However, only slightly more than half of children in Western and Nyanza are registered, as compared with 9 in 10 children in Central. The percentage of children registered and the percentage having a birth certificate both increase steadily with increasing wealth. 2.8 CHILDREN’S LIVING ARRANGEMENTS, ORPHANHOOD, AND SCHOOL ATTENDANCE Children’s living arrangements affect their development and well-being. Table 2.11 presents the percent distribution of children by their living arrangements and the survival status of their biological parents. For 22 percent of children, both parents are alive but their father is living elsewhere; 10 percent of children are not living with either parent although both are alive. There is not a great deal of variation in living arrangements by sex of the child. Children in urban areas are slightly more likely to be living with both parents (59 percent) than children in rural areas (53 percent). Nationally, only 55 percent of children age 0-17 live with both of their biological parents and living arrangements vary by region. Two-thirds of children in Nairobi are living with both of their parents, the highest percentage in the country, while only half of children in the Eastern and Western regions are living with both parents. Children in the Western region are most likely (16 percent) to not be living with either parent despite both of them being alive. Nyanza has the highest percentage of children who have experienced the death of their father; 9 percent of these children are living with their mother, and 3 percent are living with neither parent. Children in the Eastern region are most likely to be living with their mother but not their father even though their father is alive (28 percent). A notable pattern by wealth quintile is seen among children living with their mother but not their father. The percentage of children who are living with their mother and whose father has died decreases with increasing wealth. 24 • Housing Characteristics and Household Population Table 2.11 Children’s living arrangements and orphanhood Percent distribution of de jure children under age 18 by living arrangements and survival status of parents, the percentage of children not living with a biological parent, and the percentage of children with one or both parents dead, according to background characteristics, Kenya 2014 Living with both parents Living with mother but not with father Living with father but not with mother Not living with either parent Total Percent- age not living with a biological parent Percent- age with one or both parents dead1 Number of children Background characteristic Father alive Father dead Mother alive Mother dead Both alive Only father alive Only mother alive Both dead Missing informa- tion on father/ mother Age 0-4 63.8 25.1 2.2 0.9 0.2 5.2 0.4 0.4 0.1 1.6 100.0 6.2 3.4 19,954 <2 67.0 27.6 1.7 0.3 0.0 1.9 0.2 0.1 0.0 1.2 100.0 2.2 2.0 7,662 2-4 61.8 23.6 2.5 1.2 0.2 7.3 0.5 0.6 0.2 1.9 100.0 8.7 4.2 12,291 5-9 55.6 21.4 4.5 2.7 0.6 10.4 0.8 1.2 0.9 1.9 100.0 13.3 8.2 21,331 10-14 49.1 20.3 7.4 3.2 1.2 11.3 1.4 2.2 1.9 2.0 100.0 16.8 14.5 19,914 15-17 44.1 17.8 9.1 3.6 1.7 13.1 1.6 3.0 2.9 3.0 100.0 20.6 18.7 9,058 Sex Male 54.5 21.9 5.2 2.9 0.9 9.1 0.8 1.5 1.2 1.9 100.0 12.6 9.8 35,442 Female 54.6 21.5 5.3 2.0 0.7 9.9 1.1 1.5 1.3 2.1 100.0 13.8 10.1 34,815 Residence Urban 58.6 20.5 4.2 2.5 0.8 7.7 0.9 1.4 1.3 2.2 100.0 11.2 8.6 20,440 Rural 52.9 22.2 5.7 2.4 0.8 10.3 1.0 1.5 1.2 1.9 100.0 14.0 10.5 49,817 Region Coast 54.2 24.1 5.7 3.1 0.5 8.6 1.0 1.0 0.8 1.1 100.0 11.4 9.1 6,802 North Eastern 63.9 16.4 3.5 3.1 0.8 9.3 1.3 0.9 0.5 0.4 100.0 12.0 6.9 2,593 Eastern 49.4 27.6 4.5 2.0 0.8 8.3 0.8 1.1 1.1 4.4 100.0 11.4 8.7 9,875 Central 58.2 23.2 4.1 2.0 0.9 6.3 0.6 1.1 0.5 3.2 100.0 8.4 7.6 6,606 Rift Valley 55.8 22.9 5.1 2.1 0.6 9.9 0.7 1.0 0.7 1.3 100.0 12.3 8.1 19,358 Western 49.5 19.7 3.8 3.7 0.7 16.1 1.6 2.1 1.3 1.6 100.0 21.0 9.5 9,309 Nyanza 52.1 16.6 8.8 2.0 1.2 9.2 1.4 3.2 3.3 2.1 100.0 17.2 18.2 11,010 Nairobi 67.0 17.6 4.7 2.3 1.1 4.1 0.5 0.7 0.8 1.2 100.0 6.1 7.8 4,704 Wealth quintile Lowest 54.3 21.7 7.3 2.8 0.8 9.0 0.7 1.3 1.0 1.2 100.0 12.0 11.1 16,700 Second 52.3 21.7 6.4 2.5 1.0 9.7 1.2 1.6 1.4 2.2 100.0 13.9 11.8 15,350 Middle 51.1 23.2 5.3 2.0 0.7 10.6 1.1 1.8 1.5 2.7 100.0 15.1 10.8 14,448 Fourth 53.8 21.7 3.8 2.5 0.6 10.9 1.1 1.6 1.3 2.6 100.0 15.0 8.7 12,658 Highest 63.7 19.7 2.4 2.3 0.9 7.1 0.7 1.2 0.8 1.4 100.0 9.7 5.9 11,100 Total <15 56.1 22.3 4.7 2.3 0.7 9.0 0.9 1.3 1.0 1.8 100.0 12.1 8.6 61,199 Total <18 54.6 21.7 5.3 2.4 0.8 9.5 1.0 1.5 1.2 2.0 100.0 13.2 9.9 70,257 Note: Table is based on de jure members, i.e., usual residents. 1 Includes children with father dead, mother dead, both dead and one parent dead but missing information on survival status of the other parent. Table 2.12 presents the percentage of children age 10-14 who are attending school, by the survivorship of their parents. The results show a high level of school attendance overall among both boys and girls, regardless of whether or not a parent is deceased (96 percent and 98 percent respectively). It is sometimes assumed that becoming an orphan jeopardises a child’s chances of attending school, but the data in Table 2.12 do not strongly support this conjecture. In fact, the greatest differential is seen in the lowest wealth quintile, in which only 89 percent of children living with at least one parent are attending school, as compared with 94 percent of double orphans (both parents have died). Housing Characteristics and Household Population • 25 Table 2.12 School attendance by survivorship of parents For de jure children 10-14 years of age, the percentage attending school by parental survival and the ratio of the percentages attending school, by parental survival, according to background characteristics, Kenya 2014 Percentage attending school by survivorship of parents Background characteristic Both parents deceased Number Both parents alive and living with at least one parent Number Ratio1 Sex Male 95.0 185 97.0 7,309 0.98 Female 97.4 198 96.5 7,144 1.01 Residence Urban 96.2 118 98.3 3,982 0.98 Rural 96.2 265 96.1 10,471 1.00 Region Coast (78.9) 28 95.5 1,430 (0.83) North Eastern * 6 72.2 616 * Eastern 92.2 47 98.6 2,135 0.94 Central * 13 99.5 1,514 1.01 Rift Valley 95.2 62 95.9 4,046 0.99 Western (100.0) 48 99.3 1,743 (1.01) Nyanza 99.0 169 99.4 2,053 1.00 Nairobi * 10 99.2 916 * Wealth quintile Lowest 94.4 69 88.7 3,420 1.06 Second 97.1 93 99.1 3,101 0.98 Middle 97.6 100 99.4 2,984 0.98 Fourth 98.8 81 99.1 2,671 1.00 Highest (88.8) 41 99.2 2,277 (0.90) Total 96.2 383 96.7 14,453 0.99 Note: Table is based only on children who usually live in the household. Figures in parentheses are based on 25-49 unweighted cases. An asterisk denotes that a figure is based on fewer than 25 unweighted cases and has been suppressed. 1 Ratio of the percentage with both parents deceased to the percentage with both parents alive and living with at least one parent 2.9 EDUCATION OF THE HOUSEHOLD POPULATION Education is a key determinant of the lifestyle and status an individual enjoys in a society. Studies have consistently shown that educational attainment has a strong effect on health behaviours and attitudes. Results from the 2014 KDHS can be used to look at educational attainment among household members and school attendance ratios among youth. 2.9.1 Educational Attainment Tables 2.13.1 and 2.13.2 present data on the educational attainment of household members age 6 and older. Continuing a trend found in earlier KDHS surveys, the data show a slight decrease in the proportion of women and men with no education. Compared with the 2008-09 KDHS, the 2014 KDHS shows a decline from 19 percent to 16 percent among women and from 13 percent to 11 percent among men. As expected, more men (13 percent and 8 percent, respectively) than women (10 percent and 7 percent, respectively) have completed a secondary education and more than a secondary education. 26 • Housing Characteristics and Household Population Table 2.13.1 Educational attainment of the female household population Percent distribution of the de facto female household population age six and over by highest level of schooling attended or completed and median years completed, according to background characteristics, Kenya 2014 Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Total Number Median years completed Age 6-9 33.5 66.3 0.0 0.0 0.0 0.0 100.0 8,598 0.0 10-14 3.9 92.9 0.8 2.2 0.0 0.0 100.0 9,376 3.8 15-19 2.6 38.1 12.0 36.4 7.9 2.7 100.0 6,118 7.5 20-24 5.3 17.4 24.7 15.7 23.0 13.8 100.0 6,027 8.5 25-29 7.9 20.3 27.2 9.8 17.7 16.9 100.0 6,293 7.8 30-34 7.9 25.3 28.7 8.4 15.8 13.8 100.0 4,699 7.6 35-39 8.9 26.9 28.5 8.7 15.8 11.1 100.0 3,888 7.5 40-44 10.5 31.3 25.3 8.8 15.2 8.6 100.0 2,950 7.3 45-49 12.8 26.1 26.6 10.7 14.6 8.8 100.0 2,272 6.8 50-54 23.1 24.5 24.2 8.4 12.9 6.8 100.0 2,400 6.1 55-59 35.9 26.2 20.5 6.4 6.4 4.1 100.0 1,660 3.4 60-64 46.7 26.8 16.9 3.3 2.9 2.6 100.0 1,342 1.1 65+ 67.3 23.0 5.7 1.4 0.9 1.1 100.0 2,979 0.0 Residence Urban 8.9 31.6 17.6 11.2 16.6 13.8 100.0 19,931 7.4 Rural 19.5 48.1 14.6 8.8 5.9 3.0 100.0 38,677 4.7 Region Coast 26.5 39.8 14.3 6.3 9.0 3.8 100.0 5,591 4.1 North Eastern 69.0 23.4 2.3 2.1 1.5 1.1 100.0 1,562 0.0 Eastern 14.4 45.8 19.0 9.0 6.9 4.8 100.0 8,731 5.5 Central 7.2 35.0 21.1 13.1 14.2 9.3 100.0 7,104 7.2 Rift Valley 18.2 44.0 14.0 8.8 8.4 6.4 100.0 15,121 5.4 Western 12.5 55.2 12.0 10.8 5.3 4.1 100.0 6,920 5.1 Nyanza 13.7 49.1 14.9 10.2 7.6 4.2 100.0 8,334 5.5 Nairobi 4.5 24.0 18.3 11.3 22.7 18.9 100.0 5,245 8.8 Wealth quintile Lowest 40.2 47.0 7.7 3.4 1.3 0.2 100.0 11,197 1.1 Second 16.2 55.4 15.6 8.0 3.9 0.8 100.0 11,628 4.7 Middle 11.8 48.9 18.4 11.3 6.9 2.6 100.0 12,066 5.7 Fourth 8.1 38.0 20.6 13.1 13.9 6.2 100.0 11,618 7.0 Highest 4.8 23.9 15.3 11.8 21.2 22.7 100.0 12,099 9.4 Total 15.9 42.5 15.6 9.6 9.6 6.7 100.0 58,608 5.8 Note: Totals may not add up to 100 percent because individuals with missing information on education are not shown separately. Total includes seven women for whom information on age is missing. 1 Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are considered to have completed primary if they completed grade 7 2 Completed Form 4 at the secondary level With the exception of children age 6-9, fewer males than females have never been to school. In that age group, boys (38 percent) are more likely than girls (34 percent) to have never attended school. Meanwhile, 4 percent of both boys and girls age 10-14 have never been to school, indicating that boys enrol in school slightly later than girls. However, the proportion of the population with no education steadily increases thereafter with age, as does the gap between the proportion of males and females with no education, indicating a gender differential in educational attainment as students age. Nationally, the median number of years of schooling completed is slightly higher among males (6.3 years) than females (5.8 years). Over the years, median number of years of schooling completed has been increasing among both men (from 5.0 in 2003 and 6.0 in 2008-09 to 6.3 in 2014) and women (from 4.3 in 2003 and 5.2 in 2008-09 to 5.8 in 2014). About twice as many women and men in rural areas as in urban areas have no education. The proportion of respondents who have never been to school varies rather dramatically across regions. For example, the proportion of women who have never been to school varies from a low of 5 percent in Nairobi to a high of 69 percent in North Eastern. As expected, the proportion of women and men with no education decreases dramatically as wealth increases. Housing Characteristics and Household Population • 27 Table 2.13.2 Educational attainment of the male household population Percent distribution of the de facto male household population age six and over by highest level of schooling attended or completed and median years completed, according to background characteristics, Kenya 2014 Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Total Number Median years completed Age 6-9 37.9 61.9 0.0 0.0 0.0 0.0 100.0 8,615 0.0 10-14 3.7 93.5 0.6 2.0 0.1 0.0 100.0 9,441 3.5 15-19 2.1 46.3 9.5 35.6 5.0 1.3 100.0 6,342 7.1 20-24 2.6 16.5 20.6 17.5 25.5 17.3 100.0 5,133 9.9 25-29 3.5 17.7 26.3 8.1 24.7 19.5 100.0 5,336 9.0 30-34 4.3 21.9 27.0 7.2 22.6 16.6 100.0 4,589 7.9 35-39 4.7 22.6 29.5 7.6 21.9 13.5 100.0 3,723 7.8 40-44 6.0 21.7 26.8 7.8 24.6 12.8 100.0 2,923 7.8 45-49 5.5 18.4 26.3 7.7 28.2 13.7 100.0 2,071 8.0 50-54 8.9 15.8 28.4 8.4 24.0 13.7 100.0 1,997 7.0 55-59 12.7 19.9 27.9 9.6 20.2 9.6 100.0 1,492 6.7 60-64 19.3 26.1 25.9 6.8 13.7 8.1 100.0 1,286 6.2 65+ 32.4 30.9 20.1 5.1 6.2 4.9 100.0 2,422 3.6 Residence Urban 6.3 29.8 17.0 10.2 20.9 15.5 100.0 19,729 7.7 Rural 13.7 49.3 14.7 9.2 8.7 4.2 100.0 35,652 5.2 Region Coast 15.9 38.2 17.1 8.1 13.7 6.5 100.0 5,574 6.1 North Eastern 49.2 35.0 4.8 4.8 3.0 2.8 100.0 1,640 0.0 Eastern 8.5 49.0 17.5 9.0 10.2 5.8 100.0 8,388 5.8 Central 3.8 36.3 19.1 12.7 18.9 9.2 100.0 6,637 7.3 Rift Valley 13.8 44.4 14.7 8.2 11.2 7.6 100.0 14,341 5.8 Western 8.8 54.7 11.8 11.3 8.5 4.7 100.0 6,114 5.3 Nyanza 10.7 45.3 14.8 11.1 10.6 7.2 100.0 7,405 6.1 Nairobi 3.1 21.9 17.0 9.1 26.4 22.0 100.0 5,281 10.5 Wealth quintile Lowest 28.7 52.4 9.9 4.8 3.3 0.7 100.0 10,439 2.5 Second 10.5 55.0 16.9 8.9 6.7 1.7 100.0 10,942 5.1 Middle 7.9 46.8 18.7 11.6 10.9 4.0 100.0 11,217 6.3 Fourth 5.9 35.3 19.5 11.9 18.8 8.3 100.0 11,865 7.3 Highest 3.7 23.1 11.9 10.1 24.5 26.3 100.0 10,918 11.0 Total 11.1 42.3 15.5 9.6 13.0 8.2 100.0 55,381 6.3 Note: Totals may not add up to 100 percent because individuals with missing information on education are not shown separately. Total includes nine men for whom information on age is missing. 1 Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are considered to have completed primary if they completed grade 7 2 Completed Form 4 at the secondary level 2.9.2 School Attendance Ratios Table 2.14 presents the primary school and secondary school net and gross attendance ratios (NAR and GAR) by household residence, region, and wealth quintile. The NAR for primary school is the percentage of the primary-school-age population (age 6-13) that is attending primary school. The NAR for secondary school is the percentage of the secondary-school-age population (age 14-17) that is attending secondary school. By definition, the NAR cannot exceed 100 percent. The GAR for primary school is the total number of primary school students of any age, expressed as a percentage of the official primary- school-age population. The GAR for secondary school is the total number of secondary school students of any age, expressed as a percentage of the official secondary-school-age population. If there are significant numbers of over-age and under-age students at a given level of schooling, the GAR can exceed 100 percent. Youth are considered to be attending school currently if they attended a formal academic school at any point during the given school year. Note that the NAR and GAR values reported here are not comparable with those from previous DHS surveys due to an improvement in the precision of calculation. The NAR is 86 percent at the primary school level. It is slightly higher for girls (87 percent) than for boys (85 percent). Note, however, that differentials in attendance ratios are much greater across regions than between girls and boys. Sixty percent of boys age 6-13 in the North Eastern region are attending primary school, while 94 percent are attending in the Central region. Similarly, only 51 percent of girls age 6-13 in North Eastern are attending primary school, as compared with 95 percent of girls in Central. Large regional differentials also exist in secondary school attendance rates. As might be expected, the NAR for primary school is higher in urban (89 percent) than in rural (85 percent) areas, and it increases with increasing wealth. 28 • Housing Characteristics and Household Population Table 2.14 School attendance ratios Net attendance ratios (NAR) and gross attendance ratios (GAR) for the de facto household population by sex and level of schooling; and the Gender Parity Index (GPI), according to background characteristics, Kenya 2014 Net attendance ratio1 Gross attendance ratio2 Background characteristic Male Female Total Gender Parity Index3 Male Female Total Gender Parity Index3 PRIMARY SCHOOL Residence Urban 88.1 90.2 89.2 1.02 106.6 103.2 104.9 0.97 Rural 83.6 85.3 84.5 1.02 110.1 106.4 108.2 0.97 Region Coast 76.6 80.3 78.5 1.05 103.4 100.2 101.8 0.97 North Eastern 59.6 50.5 55.5 0.85 81.0 61.5 72.2 0.76 Eastern 90.8 92.3 91.5 1.02 118.8 114.9 116.8 0.97 Central 93.7 95.0 94.3 1.01 111.9 108.4 110.2 0.97 Rift Valley 84.1 85.9 85.0 1.02 108.4 105.5 107.0 0.97 Western 86.1 89.5 87.9 1.04 118.0 114.3 116.0 0.97 Nyanza 83.8 85.3 84.5 1.02 105.8 102.9 104.3 0.97 Nairobi 92.3 93.2 92.8 1.01 105.7 101.2 103.3 0.96 Wealth quintile Lowest 71.0 71.1 71.0 1.00 97.5 90.5 94.1 0.93 Second 86.9 89.5 88.2 1.03 115.6 112.9 114.2 0.98 Middle 89.4 91.6 90.5 1.02 116.7 114.5 115.6 0.98 Fourth 91.0 93.0 92.0 1.02 112.5 108.4 110.4 0.96 Highest 91.6 92.6 92.2 1.01 103.9 101.3 102.6 0.97 Total 84.8 86.7 85.7 1.02 109.2 105.5 107.3 0.97 SECONDARY SCHOOL Residence Urban 44.2 42.9 43.6 0.97 67.9 61.5 64.7 0.91 Rural 26.8 30.4 28.5 1.13 51.3 49.4 50.4 0.96 Region Coast 22.1 22.2 22.1 1.00 44.8 38.3 41.7 0.85 North Eastern 21.4 16.3 19.3 0.76 40.7 26.1 34.7 0.64 Eastern 27.0 35.0 30.7 1.30 52.4 55.1 53.7 1.05 Central 50.6 58.1 54.2 1.15 80.7 82.6 81.6 1.02 Rift Valley 26.6 28.7 27.6 1.08 48.4 49.1 48.8 1.02 Western 24.3 28.0 26.1 1.15 50.6 46.2 48.5 0.91 Nyanza 38.7 36.3 37.5 0.94 64.0 52.8 58.5 0.83 Nairobi 51.7 45.2 48.3 0.87 70.9 61.8 66.1 0.87 Wealth quintile Lowest 12.8 13.6 13.1 1.06 27.6 23.9 25.9 0.86 Second 22.9 27.7 25.2 1.21 46.0 46.1 46.0 1.00 Middle 31.4 33.9 32.6 1.08 60.7 55.2 58.1 0.91 Fourth 41.9 47.0 44.3 1.12 66.7 69.6 68.0 1.04 Highest 64.4 52.7 58.0 0.82 97.5 75.1 85.4 0.77 Total 31.3 33.9 32.6 1.08 55.6 52.8 54.3 0.95 1 The NAR for primary school is the percentage of the primary-school age (6-13 years) population that is attending primary school. The NAR for secondary school is the percentage of the secondary-school age (14-17 years) population that is attending secondary school. By definition the NAR cannot exceed 100 percent. 2 The GAR for primary school is the total number of primary school students, expressed as a percentage of the official primary- school-age population. The GAR for secondary school is the total number of secondary school students, expressed as a percentage of the official secondary-school-age population. If there are significant numbers of overage and underage students at a given level of schooling, the GAR can exceed 100 percent. 3 The Gender Parity Index for primary school is the ratio of the primary school NAR(GAR) for females to the NAR(GAR) for males. The Gender Parity Index for secondary school is the ratio of the secondary school NAR(GAR) for females to the NAR(GAR) for males. With the exception of North Eastern, GARs are quite high in all regions, indicating that a substantial number of boys and girls who are not of official primary school age are attending primary school. Table 2.14 also shows the gender parity index (GPI), which assesses sex-related differences in school attendance rates. The GPI is calculated by dividing the GAR for the female population by the GAR for the male population. A GPI of less than 1 indicates a gender disparity in favour of the male population; that is, a higher proportion of males than females attend that level of schooling. A GPI greater than 1 indicates a gender disparity in favour of females. A GPI of 1 indicates parity or equality between the rates of participation for the sexes. Housing Characteristics and Household Population • 29 The GPI for NAR shows close to gender parity at the national level in both primary and secondary school; however, the GPI for GAR at the primary (0.97) as well as the secondary (0.95) level is skewed to favour male children. Differentials across the country exist, especially at the secondary school level. Among the regions, North Eastern has the lowest NARs, GARs, and GPIs for both primary and secondary school. Figure 2.2 illustrates age-specific attendance rates, that is, the percentage of a given age cohort attending school regardless of the level attended (primary, secondary, or higher). At age 5-10, attendance rates are higher among girls than they are among boys. Between age 10 and age 14, the peak ages of school attendance, boys and girls attend in similar proportions. At age 15 and older, attendance rates decline among both boys and girls, and the gender differential in favour of boys increases with increasing age. Figure 2.2 Age-specific school attendance rates of the de-facto population age 5 to 24 years 0 10 20 30 40 50 60 70 80 90 100 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Percent Age (years) Male Female KDHS 2014 Characteristics of Respondents • 31 CHARACTERISTICS OF RESPONDENTS 3 Michael M. Musyoka, John K. Bore, Godfrey Odhiambo Otieno his chapter provides a description of the respondents who were interviewed in the 2014 KDHS. Women age 15-49 and men age 15-54 were interviewed in the course of the survey. This information is useful for understanding the context of the reproductive and health status of women and men discussed in later chapters of this report. Percent distributions of various demographic and socioeconomic characteristics are shown for the full sample. Data are provided on the main background characteristics discussed in subsequent chapters, including age at the time of the survey, marital status, urban/rural residence, region, educational level, and the wealth quintile to which respondents belong. In addition, information is provided on employment and work status. 3.1 CHARACTERISTICS OF SURVEY RESPONDENTS Table 3.1 presents the percent distribution of women and men age 15-49 by the following background characteristics: age, religion, ethnic group, marital status, residence, region, education, and wealth quintile. The distribution of both women and men tends to decline with increasing age, reflecting the comparatively young age structure of the Kenyan population. Thirty-seven percent of women and 39 percent of men are in the 15-24 age group. Thirty-four percent of women and 32 percent of men are in the 25-34 age group. The remaining respondents (29 percent of both women and men) are age 35-49. The majority of both women (71 percent) and men (68 percent) are Protestant or another Christian denomination. Twenty percent of women and 21 percent of men are Roman Catholic, 7 percent of both women and men are Muslim, and 2 percent of women and 4 percent of men have no religion. Ethnic affiliation is associated with various demographic behaviours because of differences in cultural beliefs. For example, in Kenya, certain ethnic groups encourage initiation to some rites while others consider them as taboo. The largest ethnic groups are the Kikuyu (women, 22 percent; men, 21 percent) and Luhya (women, 15 percent; men, 16 percent). Eleven to 12 percent of women and 11 to 13 percent of men are Luo, Kamba, or Kalenjin. Six percent or less of both women and men belong to other ethnic groups. T Key Findings • The percentage of women and men with no education has dropped by half over the last 10 years, from 13 percent and 6 percent in 2003 to 7 percent and 3 percent, respectively, in the 2014 KDHS. Over the same period, the percentage of women and men with at least some secondary education increased from 29 percent and 37 percent in 2003 to 43 percent and 49 percent, respectively, in 2014. • Eighty-eight percent of women and 92 percent of men are literate. • Twenty-three percent of women and 10 percent of men are not exposed to any source of mass media. • Sixty-one percent of women and 80 percent of men are currently employed. Women are mostly employed in agricultural or domestic service positions, while men are mostly employed in agricultural, unskilled manual, or domestic service positions. 32 • Characteristics of Respondents Table 3.1 Background characteristics of respondents Percent distribution of women and men age 15-49 by selected background characteristics, Kenya 2014 Women Men Background characteristic Weighted percent Weighted number Unweighted number Weighted percent Weighted number Unweighted number Age 15-19 18.7 5,820 6,078 21.1 2,540 2,811 20-24 18.5 5,735 5,405 17.6 2,125 1,981 25-29 19.6 6,100 5,939 17.4 2,104 1,942 30-34 14.5 4,510 4,452 14.8 1,785 1,701 35-39 12.1 3,773 3,868 12.3 1,483 1,486 40-44 9.3 2,885 2,986 10.1 1,224 1,198 45-49 7.3 2,257 2,351 6.6 800 895 Religion Roman Catholic 20.3 6,315 6,229 21.4 2,583 2,551 Protestant/other Christian 71.1 22,091 20,072 67.5 8,141 7,500 Muslim 6.8 2,107 4,161 6.5 784 1,460 No religion 1.5 466 506 4.1 492 449 Other 0.2 65 73 0.5 59 51 Ethnic group Embu 1.0 312 398 1.0 118 170 Kalenjin 12.0 3,718 4,335 12.2 1,467 1,729 Kamba 11.4 3,543 2,950 12.6 1,521 1,275 Kikuyu 21.9 6,798 5,033 20.9 2,523 1,946 Kisii 5.7 1,771 1,788 5.9 712 680 Luhya 15.0 4,667 3,653 16.0 1,927 1,555 Luo 11.1 3,453 3,060 10.9 1,311 1,179 Maasai 1.9 589 655 1.8 220 235 Meru 5.6 1,749 1,593 5.9 717 682 Mijikenda/Swahili 5.3 1,642 1,708 5.2 623 648 Somali 2.6 816 1,815 2.2 260 616 Taita/Taveta 0.9 295 452 1.1 134 199 Turkana 1.3 394 717 0.9 106 191 Samburu 0.5 143 620 0.1 12 45 Other 3.8 1,186 2,294 3.3 399 848 Marital status Never married 28.9 8,997 8,575 44.4 5,350 5,384 Married 54.6 16,961 17,751 48.4 5,839 5,748 Living together 5.1 1,588 1,285 2.1 256 241 Divorced/separated 7.7 2,394 2,277 4.7 567 585 Widowed 3.7 1,139 1,191 0.4 50 56 Residence Urban 40.8 12,690 11,614 43.9 5,300 4,648 Rural 59.2 18,389 19,465 56.1 6,762 7,366 Region Coast 9.9 3,076 3,902 10.4 1,260 1,505 North Eastern 2.1 648 1,664 1.9 227 591 Eastern 14.1 4,375 5,247 15.1 1,825 2,144 Central 12.9 3,994 3,114 13.0 1,564 1,248 Rift Valley 25.6 7,953 9,059 25.3 3,050 3,484 Western 10.4 3,225 2,840 9.6 1,164 1,130 Nyanza 13.0 4,038 4,254 11.6 1,405 1,542 Nairobi 12.1 3,770 999 13.0 1,568 370 Education No education 7.0 2,176 4,183 2.9 345 663 Primary incomplete 25.7 7,989 8,431 25.5 3,071 3,466 Primary complete 24.6 7,637 7,182 22.7 2,734 2,720 Secondary incomplete 15.8 4,922 4,537 16.2 1,960 1,850 Secondary complete 15.7 4,880 4,058 18.9 2,282 1,980 More than secondary 11.2 3,475 2,688 13.9 1,671 1,335 Wealth quintile Lowest 15.6 4,838 7,262 14.0 1,691 2,504 Second 17.6 5,457 5,970 17.8 2,145 2,443 Middle 19.4 6,032 5,946 19.7 2,370 2,466 Fourth 21.1 6,550 5,958 24.5 2,959 2,579 Highest 26.4 8,203 5,943 24.0 2,897 2,022 Total 15-49 100.0 31,079 31,079 100.0 12,063 12,014 50-54 na na na na 756 805 Total 15-54 na na na na 12,819 12,819 Note: Totals may not add up to 100 percent because women and men with missing information are not shown separately. na = Not applicable Characteristics of Respondents • 33 Table 3.1C Background characteristics of respondents Percent distribution of women and men age 15-49 by county, Kenya 2014 Women Men County Weighted percent Weighted number Unweighted number Weighted percent Weighted number Unweighted number Coast 9.9 3,076 3,902 10.4 1,260 1,505 Mombasa 2.9 912 598 4.0 481 270 Kwale 2.0 619 671 1.9 226 250 Kilifi 3.4 1,043 824 3.0 359 304 Tana River 0.6 197 686 0.5 65 204 Lamu 0.3 89 600 0.3 37 227 Taita Taveta 0.7 215 523 0.8 93 250 North Eastern 2.1 648 1,664 1.9 227 591 Garissa 0.8 261 609 0.8 94 208 Wajir 0.7 212 532 0.6 72 187 Mandera 0.6 175 523 0.5 60 196 Eastern 14.1 4,375 5,247 15.1 1,825 2,144 Marsabit 0.4 115 575 0.3 40 199 Isiolo 0.3 104 606 0.3 35 196 Meru 3.6 1,110 682 4.1 495 320 Tharaka-Nithi 0.9 275 528 0.8 102 215 Embu 1.5 459 645 1.4 164 266 Kitui 2.4 759 747 2.5 303 318 Machakos 2.8 873 718 3.6 436 335 Makueni 2.2 680 746 2.1 250 295 Central 12.9 3,994 3,114 13.0 1,564 1,248 Nyandarua 1.4 436 562 1.6 198 242 Nyeri 2.1 650 708 1.9 229 275 Kirinyaga 1.5 451 560 1.5 184 250 Murang’a 2.4 735 633 2.4 284 250 Kiambu 5.5 1,722 651 5.5 669 231 Rift Valley 25.6 7,953 9,059 25.3 3,050 3,484 Turkana 1.0 320 514 0.6 76 118 West Pokot 0.9 267 534 0.9 103 234 Samburu 0.4 123 579 0.3 35 159 Trans-Nzoia 2.5 768 695 2.7 329 322 Uasin Gishu 2.5 784 689 2.9 355 335 Elgeyo Marakwet 0.8 250 630 0.7 86 234 Nandi 2.0 628 742 2.2 264 338 Baringo 1.1 335 598 1.0 125 229 Laikipia 1.1 342 631 1.0 124 234 Nakuru 5.1 1,574 741 4.9 589 280 Narok 2.1 642 702 2.0 240 265 Kajiado 2.2 670 642 2.0 241 226 Kericho 1.8 563 654 1.8 215 227 Bomet 2.2 687 708 2.2 267 283 Western 10.4 3,225 2,840 9.6 1,164 1,130 Kakamega 3.6 1,108 725 3.4 411 312 Vihiga 1.2 368 634 1.2 140 252 Bungoma 3.9 1,203 805 3.4 413 307 Busia 1.8 546 676 1.6 199 259 Nyanza 13.0 4,038 4,254 11.6 1,405 1,542 Siaya 1.8 572 654 1.8 213 264 Kisumu 2.6 820 696 2.6 309 272 Homa Bay 2.6 798 716 2.0 243 238 Migori 2.1 650 770 1.7 211 251 Kisii 2.8 864 794 2.6 315 291 Nyamira 1.1 334 624 0.9 114 226 Nairobi 12.1 3,770 999 13.0 1,568 370 Total 15-49 100.0 31,079 31,079 100.0 12,063 12,014 50-54 na na na na 756 805 Total 15-54 na na na na 12,819 12,819 na = Not applicable Sixty percent of women and 51 percent of men are married or living in an informal union. About 4 in 10 men (44 percent) have never been married, as compared with about 3 in 10 (29 percent) women. Women (11 percent) are more likely than men (5 percent) to be divorced, separated, or widowed. Fifty-nine percent of women and 56 percent of men live in rural areas. The Rift Valley region has a quarter of all women (26 percent) and men (25 percent). The North Eastern region has 2 percent of all 34 • Characteristics of Respondents women and men. The remaining regions each have between 10 percent and 15 percent of the remaining population. Slightly more women (7 percent) than men (3 percent) have no education. Twenty-six percent of both women and men did not finish primary school. Slightly more women (25 percent) than men (23 percent) ended their schooling by completing primary school, and thereafter slightly fewer women than men obtained some secondary education, completed secondary education, or advanced beyond secondary education. The smallest proportions of both women (16 percent) and men (14 percent) are in the lowest wealth quintile. Almost half of the population (48 percent of women and 49 percent of men) is in the two highest wealth quintiles. The distribution of female and male respondents by county shows that more respondents live in Nairobi, Kiambu, and Nakuru (between 5 percent and 13 percent) than the other 44 counties (Table 3.1C). 3.2 EDUCATIONAL ATTAINMENT BY BACKGROUND CHARACTERISTICS Tables 3.2.1 and 3.2.2 show the percent distribution of women and men age 15-49 by educational attainment and median years of schooling completed, according to background characteristics. Men have achieved more education than women. In 2014, the proportion of women with no education declined marginally to 7 percent from the 9 percent figure recorded in the 2008-09 KDHS, but this proportion remains more than twice that of men with no education (3 percent). Table 3.2.1 shows that 93 percent of women age 15-49 have attended school. Five in 10 women either have some primary education (26 percent) or have completed primary education (25 percent). Three in 10 have either some secondary education or a completed secondary education (16 percent each). One in 10 (11 percent) have gone beyond a secondary education, an increase from 7 percent in 2008-09. The urban-rural difference in level of education is pronounced for women on either end of the educational attainment scale. Four percent of urban women have no education compared with 9 percent of rural women, and 14 percent of urban women have some primary education compared with 34 percent of rural women. About a quarter of women in both rural and urban areas have completed primary education, and 16 percent of women in both areas have some secondary education. The differences pick up again for women who have completed secondary school (urban women, 23 percent; rural women, 11 percent) or gone beyond secondary school (urban women, 19 percent; rural women, 6 percent). At the regional level, Nairobi had the highest proportion of women with more than a secondary education (24 percent), although this figure was a decline from the 31 percent reported in 2008-09. The North Eastern region had the most women with no education at 75 percent, a slight improvement from 78 percent in 2008-09. Education increases with wealth; 31 percent of women in the lowest wealth quintile have no education, as compared with 2 percent of women in the highest wealth quintile. Almost one in three (29 percent) women in the highest quintile have more than a secondary education, compared with only one in 10 (10 percent) women in the fourth highest wealth quintile. Table 3.2.2 shows similar patterns in educational attainment among men, although men are more educated than women. County level differences are presented in Table 3.2.1C and Table 3.2.2C. Characteristics of Respondents • 35 Table 3.2.1 Educational attainment: Women Percent distribution of women age 15-49 by highest level of schooling attended or completed, and median years completed, according to background characteristics, Kenya 2014 Highest level of schooling Total Median years completed Number of women Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Age 15-24 3.8 27.0 18.6 26.4 15.8 8.4 100.0 7.9 11,555 15-19 2.3 36.1 13.8 36.5 8.5 2.8 100.0 7.6 5,820 20-24 5.3 17.7 23.6 16.2 23.2 14.1 100.0 8.7 5,735 25-29 7.7 20.9 27.5 10.3 16.9 16.7 100.0 7.8 6,100 30-34 7.8 25.7 28.2 8.7 15.7 14.0 100.0 7.6 4,510 35-39 8.9 27.3 28.3 9.1 15.2 11.1 100.0 7.5 3,773 40-44 10.1 31.6 26.3 9.2 14.6 8.3 100.0 7.3 2,885 45-49 12.8 22.1 31.3 10.8 14.2 8.7 100.0 6.7 2,257 Residence Urban 3.6 14.1 24.1 15.8 22.9 19.4 100.0 9.4 12,690 Rural 9.3 33.7 24.9 15.9 10.7 5.5 100.0 7.2 18,389 Region Coast 16.3 29.0 23.1 11.0 14.5 6.0 100.0 7.2 3,076 North Eastern 74.9 9.9 4.8 4.1 3.5 2.7 100.0 0.0 648 Eastern 4.8 28.4 31.2 15.6 11.8 8.3 100.0 7.5 4,375 Central 0.9 14.7 29.7 17.8 21.8 15.1 100.0 8.8 3,994 Rift Valley 9.2 27.7 22.7 15.3 14.0 11.0 100.0 7.5 7,953 Western 2.8 40.8 19.5 20.0 9.6 7.3 100.0 7.2 3,225 Nyanza 1.4 33.4 25.3 18.6 13.5 7.7 100.0 7.5 4,038 Nairobi 1.7 8.9 23.4 14.7 27.9 23.5 100.0 11.0 3,770 Wealth quintile Lowest 30.6 42.5 16.5 7.1 2.7 0.4 100.0 5.0 4,838 Second 3.9 44.1 27.6 15.3 7.5 1.6 100.0 7.0 5,457 Middle 3.0 29.6 30.5 20.1 12.0 4.9 100.0 7.5 6,032 Fourth 2.4 17.1 28.9 19.9 21.6 10.1 100.0 8.0 6,550 Highest 1.8 7.5 19.5 15.0 26.8 29.4 100.0 11.2 8,203 Total 7.0 25.7 24.6 15.8 15.7 11.2 100.0 7.6 31,079 1 Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are considered to have completed primary if they completed Grade 7. 2 Completed Form 4 at the secondary level 36 • Characteristics of Respondents Table 3.2.1C Educational attainment: Women Percent distribution of women age 15-49 by highest level of schooling attended or completed, and median years completed, according to county, Kenya 2014 Highest level of schooling Total Median years completed Number of women County No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Coast 16.3 29.0 23.1 11.0 14.5 6.0 100.0 7.2 3,076 Mombasa 5.8 18.8 26.9 15.0 23.6 9.9 100.0 7.9 912 Kwale 21.7 35.0 23.5 6.9 8.5 4.4 100.0 6.1 619 Kilifi 20.4 34.1 19.3 10.5 11.4 4.4 100.0 6.4 1,043 Tana River 41.7 33.3 13.5 5.6 4.1 1.8 100.0 2.4 197 Lamu 17.0 39.1 21.0 10.4 6.2 6.3 100.0 6.4 89 Taita Taveta 2.3 22.6 34.5 13.3 21.1 6.2 100.0 7.7 215 North Eastern 74.9 9.9 4.8 4.1 3.5 2.7 100.0 0.0 648 Garissa 72.7 9.6 5.7 4.4 3.6 4.0 100.0 0.0 261 Wajir 76.9 9.9 4.6 4.2 2.5 1.7 100.0 0.0 212 Mandera 75.9 10.4 3.7 3.6 4.3 2.0 100.0 0.0 175 Eastern 4.8 28.4 31.2 15.6 11.8 8.3 100.0 7.5 4,375 Marsabit 61.9 16.0 10.3 5.0 4.3 2.4 100.0 0.0 115 Isiolo 39.7 22.5 18.1 6.0 8.7 5.0 100.0 5.0 104 Meru 4.1 37.4 27.5 11.1 11.0 9.0 100.0 7.2 1,110 Tharaka-Nithi 2.0 35.8 28.4 12.9 12.3 8.5 100.0 7.4 275 Embu 1.3 28.6 29.2 16.6 13.7 10.6 100.0 7.6 459 Kitui 3.9 35.4 34.4 12.9 8.7 4.6 100.0 7.3 759 Machakos 0.2 15.9 36.9 20.1 16.5 10.4 100.0 7.8 873 Makueni 0.9 21.6 34.6 24.0 10.4 8.5 100.0 7.7 680 Central 0.9 14.7 29.7 17.8 21.8 15.1 100.0 8.8 3,994 Nyandarua 0.8 17.1 39.1 18.0 18.6 6.4 100.0 7.8 436 Nyeri 1.1 11.5 27.8 21.1 24.2 14.3 100.0 9.2 650 Kirinyaga 0.8 28.0 29.8 15.0 21.1 5.4 100.0 7.7 451 Murang’a 1.6 19.4 29.9 20.8 20.7 7.6 100.0 7.9 735 Kiambu 0.5 9.9 27.9 15.9 22.4 23.3 100.0 10.1 1,722 Rift Valley 9.2 27.7 22.7 15.3 14.0 11.0 100.0 7.5 7,953 Turkana 64.1 24.1 3.1 1.2 5.5 2.0 100.0 0.0 320 West Pokot 33.8 41.0 12.0 5.2 4.7 3.4 100.0 4.6 267 Samburu 55.7 21.1 7.3 5.2 5.4 5.4 100.0 0.0 123 Trans-Nzoia 2.6 39.3 22.9 19.2 10.8 5.2 100.0 7.3 768 Uasin Gishu 1.5 25.1 21.9 18.6 17.2 15.7 100.0 7.9 784 Elgeyo Marakwet 1.2 27.7 28.6 14.4 15.0 13.0 100.0 7.7 250 Nandi 0.8 37.8 23.9 16.6 12.6 8.3 100.0 7.4 628 Baringo 9.3 30.0 24.5 15.0 12.1 9.0 100.0 7.4 335 Laikipia 13.4 19.8 24.7 14.9 14.9 12.3 100.0 7.6 342 Nakuru 1.9 14.7 30.1 19.0 19.2 15.2 100.0 8.3 1,574 Narok 15.5 38.2 17.3 12.0 10.4 6.6 100.0 6.6 642 Kajiado 18.0 12.8 17.9 14.1 17.0 20.2 100.0 8.0 670 Kericho 0.3 32.0 24.9 15.6 16.3 10.8 100.0 7.6 563 Bomet 0.4 39.8 25.7 14.3 11.5 8.2 100.0 7.3 687 Western 2.8 40.8 19.5 20.0 9.6 7.3 100.0 7.2 3,225 Kakamega 4.0 38.7 18.5 20.3 10.5 8.1 100.0 7.3 1,108 Vihiga 0.4 30.0 26.7 25.2 9.6 8.2 100.0 7.6 368 Bungoma 0.9 41.2 19.8 20.7 10.3 7.2 100.0 7.3 1,203 Busia 6.6 51.6 16.1 14.3 6.0 5.2 100.0 6.5 546 Nyanza 1.4 33.4 25.3 18.6 13.5 7.7 100.0 7.5 4,038 Siaya 1.9 35.7 28.6 18.6 9.5 5.6 100.0 7.4 572 Kisumu 1.2 23.7 24.7 20.8 16.2 13.4 100.0 7.9 820 Homa Bay 1.1 39.7 28.1 18.2 8.9 4.0 100.0 7.3 798 Migori 2.6 49.9 24.0 14.2 6.7 2.6 100.0 6.8 650 Kisii 0.9 28.8 22.0 19.1 19.2 10.0 100.0 7.8 864 Nyamira 0.8 17.8 25.6 21.9 23.9 10.0 100.0 9.1 334 Nairobi 1.7 8.9 23.4 14.7 27.9 23.5 100.0 11.0 3,770 Total 7.0 25.7 24.6 15.8 15.7 11.2 100.0 7.6 31,079 1 Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are considered to have completed primary if they completed Grade 7. 2 Completed Form 4 at the secondary level Characteristics of Respondents • 37 Table 3.2.2 Educational attainment: Men Percent distribution of men age 15-49 by highest level of schooling attended or completed, and median years completed, according to background characteristics, Kenya 2014 Highest level of schooling Total Median years completed Number of men Background characteristic No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Age 15-24 1.4 29.9 15.8 28.7 15.3 8.9 100.0 7.9 4,666 15-19 1.2 42.6 10.9 37.2 6.6 1.5 100.0 7.3 2,540 20-24 1.8 14.7 21.7 18.5 25.6 17.8 100.0 10.0 2,125 25-29 2.7 18.5 26.9 9.4 20.9 21.6 100.0 8.6 2,104 30-34 4.0 25.7 24.8 8.1 18.7 18.7 100.0 7.8 1,785 35-39 5.1 26.2 28.9 7.5 19.7 12.7 100.0 7.6 1,483 40-44 3.7 24.1 24.5 9.3 24.5 13.9 100.0 7.9 1,224 45-49 3.7 18.0 32.4 7.0 25.5 13.5 100.0 7.7 800 Residence Urban 1.2 14.4 21.8 14.8 25.7 22.1 100.0 10.4 5,300 Rural 4.2 34.1 23.4 17.4 13.6 7.4 100.0 7.4 6,762 Region Coast 4.2 25.0 26.9 14.3 18.9 10.7 100.0 7.7 1,260 North Eastern 36.9 20.9 13.8 12.7 9.8 5.9 100.0 5.7 227 Eastern 3.0 31.7 26.3 14.0 16.3 8.7 100.0 7.5 1,825 Central 0.3 16.1 25.2 21.2 24.2 13.0 100.0 9.1 1,564 Rift Valley 4.3 27.4 22.6 14.5 17.8 13.3 100.0 7.7 3,050 Western 0.9 42.9 17.4 20.0 10.6 8.2 100.0 7.3 1,164 Nyanza 0.5 28.3 22.7 19.5 15.9 13.2 100.0 7.8 1,405 Nairobi 0.0 9.4 17.7 13.7 29.1 30.1 100.0 11.3 1,568 Wealth quintile Lowest 14.4 49.0 19.1 9.5 6.5 1.4 100.0 6.1 1,691 Second 1.4 41.5 25.8 17.1 10.7 3.4 100.0 7.2 2,145 Middle 1.3 28.4 27.5 19.5 17.2 6.2 100.0 7.7 2,370 Fourth 0.9 16.3 24.8 18.5 26.2 13.3 100.0 9.2 2,959 Highest 0.5 6.8 16.3 14.5 26.3 35.7 100.0 11.4 2,897 Total 15-49 2.9 25.5 22.7 16.2 18.9 13.9 100.0 7.9 12,063 50-54 7.0 19.3 28.6 9.2 23.8 12.2 100.0 6.9 756 Total 15-54 3.1 25.1 23.0 15.8 19.2 13.8 100.0 7.9 12,819 1 Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are considered to have completed primary if they completed Grade 7. 2 Completed Form 4 at the secondary level 38 • Characteristics of Respondents Table 3.2.2C Educational attainment: Men Percent distribution of men age 15-49 by highest level of schooling attended or completed, and median years completed, according to county, Kenya 2014 Highest level of schooling Total Median years completed Number of men County No education Some primary Completed primary1 Some secondary Completed secondary2 More than secondary Coast 4.2 25.0 26.9 14.3 18.9 10.7 100.0 7.7 1,260 Mombasa 2.5 10.0 31.3 16.8 25.9 13.4 100.0 9.1 481 Kwale 7.8 38.7 19.6 11.2 14.1 8.7 100.0 7.1 226 Kilifi 2.6 34.4 24.9 15.9 13.5 8.7 100.0 7.4 359 Tana River 18.2 33.3 25.2 5.1 13.6 4.7 100.0 6.8 65 Lamu 5.8 41.2 18.4 11.2 9.6 13.7 100.0 7.0 37 Taita Taveta 0.0 20.6 34.1 9.6 22.7 13.1 100.0 7.8 93 North Eastern 36.9 20.9 13.8 12.7 9.8 5.9 100.0 5.7 227 Garissa 33.2 16.2 17.7 12.6 12.1 8.2 100.0 7.0 94 Wajir 38.2 23.2 10.5 12.4 9.6 6.1 100.0 4.7 72 Mandera 41.2 25.3 11.8 13.0 6.5 2.1 100.0 5.2 60 Eastern 3.0 31.7 26.3 14.0 16.3 8.7 100.0 7.5 1,825 Marsabit 35.7 18.6 10.0 7.7 15.4 12.6 100.0 6.3 40 Isiolo 11.3 29.7 21.1 12.5 21.9 3.5 100.0 7.4 35 Meru 4.7 35.7 25.3 10.4 14.9 9.0 100.0 7.3 495 Tharaka-Nithi 2.0 38.9 18.0 17.3 12.5 11.4 100.0 7.4 102 Embu 1.0 33.6 27.4 16.6 12.2 9.3 100.0 7.5 164 Kitui 2.7 44.8 23.3 15.5 9.2 4.4 100.0 7.0 303 Machakos 0.3 21.4 31.6 12.5 24.3 10.0 100.0 7.8 436 Makueni 0.3 24.4 28.5 20.0 17.1 9.7 100.0 7.8 250 Central 0.3 16.1 25.2 21.2 24.2 13.0 100.0 9.1 1,564 Nyandarua 0.4 17.8 31.7 22.5 21.4 6.2 100.0 7.9 198 Nyeri 0.2 8.6 30.7 20.6 24.1 15.9 100.0 9.3 229 Kirinyaga 0.7 27.7 28.8 14.8 20.5 7.6 100.0 7.7 184 Murang’a 0.8 22.5 27.7 24.6 16.8 7.8 100.0 7.9 284 Kiambu 0.0 12.2 19.3 21.4 29.3 17.8 100.0 10.5 669 Rift Valley 4.3 27.4 22.6 14.5 17.8 13.3 100.0 7.7 3,050 Turkana 35.2 35.3 7.7 6.7 5.2 9.9 100.0 4.1 76 West Pokot 18.9 50.8 14.7 6.0 5.0 4.6 100.0 6.1 103 Samburu 25.9 25.2 13.4 11.9 12.5 11.1 100.0 6.8 35 Trans-Nzoia 2.0 34.9 24.3 15.5 14.6 8.7 100.0 7.5 329 Uasin Gishu 1.1 21.0 24.6 15.0 26.1 12.1 100.0 8.2 355 Elgeyo Marakwet 0.2 24.4 26.6 13.3 21.4 14.1 100.0 7.9 86 Nandi 0.3 33.3 26.3 9.7 18.9 11.5 100.0 7.6 264 Baringo 5.9 33.3 16.7 15.3 20.1 8.7 100.0 7.6 125 Laikipia 2.6 25.1 15.5 22.1 19.4 15.3 100.0 8.0 124 Nakuru 1.9 16.3 26.2 17.0 19.4 19.2 100.0 9.2 589 Narok 13.0 32.8 21.5 11.0 14.0 7.7 100.0 7.2 240 Kajiado 4.8 17.2 15.9 16.1 17.3 28.7 100.0 10.0 241 Kericho 0.0 32.9 22.2 16.8 16.2 11.9 100.0 7.7 215 Bomet 0.0 33.1 27.2 14.4 18.1 7.2 100.0 7.6 267 Western 0.9 42.9 17.4 20.0 10.6 8.2 100.0 7.3 1,164 Kakamega 1.6 41.3 15.8 21.5 9.9 9.9 100.0 7.3 411 Vihiga 0.3 36.4 24.3 24.5 7.5 7.1 100.0 7.4 140 Bungoma 0.4 45.2 16.9 17.9 11.5 8.1 100.0 7.2 413 Busia 1.1 45.7 16.9 18.1 12.4 5.8 100.0 7.1 199 Nyanza 0.5 28.3 22.7 19.5 15.9 13.2 100.0 7.8 1,405 Siaya 0.7 33.9 23.0 20.2 14.4 7.8 100.0 7.5 213 Kisumu 0.7 19.6 23.8 21.3 12.8 21.8 100.0 8.3 309 Homa Bay 0.0 35.0 27.1 18.2 12.0 7.8 100.0 7.4 243 Migori 0.8 42.5 24.3 14.4 12.9 5.3 100.0 7.2 211 Kisii 0.4 23.3 17.7 21.8 22.1 14.8 100.0 8.9 315 Nyamira 0.0 14.1 21.2 19.3 23.6 21.8 100.0 10.2 114 Nairobi 0.0 9.4 17.7 13.7 29.1 30.1 100.0 11.3 1,568 Total 15-49 2.9 25.5 22.7 16.2 18.9 13.9 100.0 7.9 12,063 50-54 7.0 19.3 28.6 9.2 23.8 12.2 100.0 6.9 756 Total 15-54 3.1 25.1 23.0 15.8 19.2 13.8 100.0 7.9 12,819 1 Completed Grade 8 at the primary level, for those under age 45; because of the change in the school system in the 1980s, those age 45 and above are considered to have completed primary if they completed Grade 7. 2 Completed Form 4 at the secondary level Characteristics of Respondents • 39 3.3 LITERACY The ability to read and write empowers women and men. Literacy statistics are important for policymakers to determine how best to reach the populations they serve. In the 2014 KDHS, literacy was determined by respondents’ ability to read all or part of a simple sentence. During data collection, interviewers carried a set of cards on which simple sentences were printed in 17 of the country’s major languages (English, Swahili, Borana, Embu, Kalenjin, Kamba, Kikuyu, Kisii, Luhya, Maragoli, Luo, Maasai, Meru, Mijikenda, Pokot, Somali, and Turkana) for testing a respondent’s reading ability. Those who had never been to school and those who had only a primary education were asked to read the cards in the language they were most familiar with. Those with a secondary education or higher were assumed to be literate. Table 3.3.1 shows the percent distribution of women age 15-49 by level of schooling attended and level of literacy, along with the percentage literate, according to background characteristics. The proportion of literate women (88 percent) was slightly higher than in 2008-09 (85 percent). Eight percent of women could read part of a sentence. Literacy declines with age and varies by place of residence. Ninety-four percent of women residing in urban areas are literate, as compared with 84 percent of rural women. Regional differences are notable, with the proportion of literate women being highest in Nairobi (97 percent) and lowest in North Eastern (24 percent). Literacy increases with wealth; virtually all women (97 percent) in the highest quintile are literate, compared with 58 percent of women in the lowest quintile. Literacy among women age 15-49 at the county level was highest in Nandi and Nyamira (98 percent each). The counties with the lowest proportion of literate women were Wajir (21 percent), Mandera (24 percent), Turkana (25 percent), Garissa (26 percent), and Marsabit (36 percent). In most counties, the proportion of literate women is above 80 percent (Table 3.3.1C). Men are more likely to be literate than women. Table 3.3.2 shows that 92 percent of men age 15- 49 are literate, not much of a difference from the 91 percent figure reported in the 2008-09 KDHS. The pattern of literacy among men is similar to that of women. However, there are marked differences between men and women across age groups. Ninety-one percent of men age 45-49 are literate, as compared with 78 percent of women in the same age group. The absolute difference in urban-rural literacy among men (8 percentage points) is slightly lower than that among women (10 percentage points). Men in the North Eastern region are more likely to be illiterate (32 percent) than those in the other regions. County level differences for men are presented in Table 3.3.2C and are similar to those observed among women. 40 • Characteristics of Respondents Table 3.3.1 Literacy: Women Percent distribution of women age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to background characteristics, Kenya 2014 Secondary school or higher No schooling or primary school Total Percentage literate1 Number of women Background characteristic Can read a whole sentence Can read part of a sentence Cannot read at all No card with required language Blind/ visually impaired Age 15-24 50.6 36.0 6.1 6.8 0.1 0.1 100.0 92.8 11,555 15-19 47.8 41.1 6.0 4.5 0.1 0.1 100.0 95.0 5,820 20-24 53.5 30.8 6.3 9.2 0.1 0.1 100.0 90.5 5,735 25-29 43.9 35.5 7.6 12.4 0.0 0.2 100.0 87.0 6,100 30-34 38.3 38.8 9.6 12.8 0.0 0.3 100.0 86.8 4,510 35-39 35.4 40.1 9.4 14.8 0.0 0.1 100.0 84.9 3,773 40-44 32.0 38.9 12.0 16.5 0.0 0.6 100.0 82.8 2,885 45-49 33.7 31.9 12.2 21.4 0.0 0.9 100.0 77.7 2,257 Residence Urban 58.2 29.5 6.0 5.8 0.1 0.2 100.0 93.6 12,690 Rural 32.1 41.8 9.9 15.8 0.0 0.3 100.0 83.8 18,389 Region Coast 31.5 42.9 5.8 19.3 0.0 0.2 100.0 80.2 3,076 North Eastern 10.3 7.0 6.5 75.9 0.0 0.1 100.0 23.9 648 Eastern 35.7 43.1 10.7 10.3 0.0 0.2 100.0 89.4 4,375 Central 54.7 34.3 5.9 4.4 0.0 0.1 100.0 94.9 3,994 Rift Valley 40.3 35.8 8.3 15.2 0.0 0.1 100.0 84.5 7,953 Western 36.8 43.4 9.8 9.1 0.0 0.6 100.0 90.1 3,225 Nyanza 39.9 40.2 11.6 7.7 0.0 0.4 100.0 91.7 4,038 Nairobi 66.1 24.8 5.6 3.0 0.2 0.2 100.0 96.5 3,770 Wealth quintile Lowest 10.3 36.3 11.7 41.2 0.0 0.3 100.0 58.3 4,838 Second 24.4 49.4 13.7 12.0 0.0 0.3 100.0 87.5 5,457 Middle 36.9 46.5 9.1 6.9 0.0 0.4 100.0 92.5 6,032 Fourth 51.6 36.6 6.2 5.2 0.1 0.1 100.0 94.5 6,550 Highest 71.2 21.6 3.8 2.9 0.0 0.1 100.0 96.6 8,203 Total 42.7 36.8 8.3 11.7 0.0 0.2 100.0 87.8 31,079 Note: Totals may not add up to 100 percent because women with missing information have been are not shown separately. 1 Refers to women who attended secondary school or higher and women who can read a whole sentence or part of a sentence Characteristics of Respondents • 41 Table 3.3.1C Literacy: Women Percent distribution of women age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to county, Kenya 2014 Secondary school or higher No schooling or primary school Total Percentage literate1 Number of women County Can read a whole sentence Can read part of a sentence Cannot read at all No card with required language Blind/ visually impaired Coast 31.5 42.9 5.8 19.3 0.0 0.2 100.0 80.2 3,076 Mombasa 48.5 39.2 4.9 6.5 0.0 0.2 100.0 92.6 912 Kwale 19.8 45.3 7.6 26.4 0.0 0.2 100.0 72.7 619 Kilifi 26.3 43.4 5.4 24.9 0.0 0.0 100.0 75.1 1,043 Tana River 11.5 34.9 7.0 45.0 0.0 1.4 100.0 53.5 197 Lamu 22.9 51.5 9.9 15.6 0.0 0.1 100.0 84.2 89 Taita Taveta 40.6 52.4 3.0 3.7 0.0 0.0 100.0 96.1 215 North Eastern 10.3 7.0 6.5 75.9 0.0 0.1 100.0 23.9 648 Garissa 12.0 7.3 7.1 73.2 0.0 0.2 100.0 26.3 261 Wajir 8.5 7.0 5.2 79.3 0.0 0.0 100.0 20.7 212 Mandera 10.0 6.7 7.4 75.7 0.0 0.0 100.0 24.0 175 Eastern 35.7 43.1 10.7 10.3 0.0 0.2 100.0 89.4 4,375 Marsabit 11.8 13.3 10.5 64.5 0.0 0.0 100.0 35.5 115 Isiolo 19.7 23.8 14.4 42.0 0.0 0.0 100.0 58.0 104 Meru 31.1 41.8 13.3 13.9 0.0 0.0 100.0 86.1 1,110 Tharaka-Nithi 33.7 40.2 10.1 15.2 0.0 0.2 100.0 84.1 275 Embu 40.9 47.5 7.0 4.4 0.0 0.0 100.0 95.4 459 Kitui 26.2 47.6 17.9 8.3 0.0 0.0 100.0 91.7 759 Machakos 47.0 40.8 7.5 3.9 0.0 0.8 100.0 95.3 873 Makueni 42.9 49.3 4.7 3.0 0.0 0.0 100.0 96.9 680 Central 54.7 34.3 5.9 4.4 0.0 0.1 100.0 94.9 3,994 Nyandarua 43.0 47.6 3.7 5.1 0.0 0.2 100.0 94.3 436 Nyeri 59.7 31.9 4.7 3.7 0.0 0.0 100.0 96.3 650 Kirinyaga 41.5 46.6 3.5 7.6 0.0 0.0 100.0 91.5 451 Murang’a 49.1 38.9 5.9 5.8 0.0 0.3 100.0 93.9 735 Kiambu 61.7 26.5 7.6 3.2 0.0 0.0 100.0 95.8 1,722 Rift Valley 40.3 35.8 8.3 15.2 0.0 0.1 100.0 84.5 7,953 Turkana 8.6 12.6 3.4 75.2 0.0 0.3 100.0 24.5 320 West Pokot 13.3 15.8 20.9 49.9 0.0 0.1 100.0 50.0 267 Samburu 16.0 12.9 11.2 59.3 0.3 0.0 100.0 40.1 123 Trans-Nzoia 35.2 43.9 6.5 14.2 0.0 0.1 100.0 85.6 768 Uasin Gishu 51.5 36.8 5.6 5.9 0.0 0.0 100.0 93.9 784 Elgeyo Marakwet 42.5 48.5 5.9 3.0 0.0 0.1 100.0 96.8 250 Nandi 37.5 47.3 13.6 1.4 0.0 0.2 100.0 98.4 628 Baringo 36.1 37.9 12.3 12.3 0.1 0.3 100.0 86.3 335 Laikipia 42.0 33.4 7.5 16.7 0.0 0.0 100.0 83.0 342 Nakuru 53.4 34.6 6.1 5.6 0.0 0.0 100.0 94.0 1,574 Narok 29.0 40.4 4.6 25.9 0.0 0.1 100.0 74.0 642 Kajiado 51.3 28.0 3.9 16.7 0.0 0.0 100.0 83.1 670 Kericho 42.7 34.3 12.3 9.8 0.0 0.7 100.0 89.3 563 Bomet 34.0 41.1 14.2 10.4 0.0 0.1 100.0 89.4 687 Western 36.8 43.4 9.8 9.1 0.0 0.6 100.0 90.1 3,225 Kakamega 38.8 48.5 4.8 6.8 0.0 1.1 100.0 92.1 1,108 Vihiga 43.0 43.6 6.5 6.7 0.0 0.0 100.0 93.1 368 Bungoma 38.1 38.4 12.2 10.5 0.1 0.3 100.0 88.7 1,203 Busia 25.6 44.2 17.2 12.3 0.0 0.7 100.0 87.0 546 Nyanza 39.9 40.2 11.6 7.7 0.0 0.4 100.0 91.7 4,038 Siaya 33.7 46.5 11.3 8.1 0.0 0.0 100.0 91.5 572 Kisumu 50.3 35.7 6.5 6.8 0.0 0.4 100.0 92.6 820 Homa Bay 31.1 45.1 17.6 5.6 0.0 0.6 100.0 93.8 798 Migori 23.5 51.8 10.3 13.2 0.0 0.8 100.0 85.7 650 Kisii 48.3 32.2 10.7 8.4 0.0 0.3 100.0 91.2 864 Nyamira 55.8 26.9 15.5 1.5 0.0 0.0 100.0 98.2 334 Nairobi 66.1 24.8 5.6 3.0 0.2 0.2 100.0 96.5 3,770 Total 42.7 36.8 8.3 11.7 0.0 0.2 100.0 87.8 31,079 Note: Totals may not add up to 100 percent because women with missing information are not shown separately. 1 Refers to women who attended secondary school or higher and women who can read a whole sentence or part of a sentence 42 • Characteristics of Respondents Table 3.3.2 Literacy: Men Percent distribution of men age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to background characteristics, Kenya 2014 Secondary school or higher No schooling or primary school Total Percentage literate1 Number of men Background characteristic Can read a whole sentence Can read part of a sentence Cannot read at all No card with required language Blind/ visually impaired Age 15-24 52.9 35.2 6.6 5.2 0.0 0.0 100.0 94.6 4,666 15-19 45.3 43.0 6.9 4.6 0.0 0.0 100.0 95.2 2,540 20-24 61.9 25.9 6.1 5.9 0.0 0.0 100.0 93.9 2,125 25-29 51.9 33.2 6.9 7.8 0.0 0.0 100.0 92.1 2,104 30-34 45.5 36.8 7.6 10.0 0.0 0.0 100.0 89.9 1,785 35-39 39.9 43.4 7.4 9.2 0.0 0.0 100.0 90.6 1,483 40-44 47.7 37.0 6.9 8.2 0.0 0.2 100.0 91.6 1,224 45-49 45.9 37.2 7.5 9.2 0.0 0.1 100.0 90.7 800 Residence Urban 62.6 29.5 4.7 3.2 0.0 0.0 100.0 96.7 5,300 Rural 38.4 41.9 8.8 10.8 0.0 0.1 100.0 89.0 6,762 Region Coast 43.9 45.8 4.2 5.9 0.0 0.1 100.0 93.9 1,260 North Eastern 28.4 29.2 9.6 32.3 0.4 0.0 100.0 67.2 227 Eastern 38.9 46.2 6.5 8.4 0.0 0.0 100.0 91.6 1,825 Central 58.4 32.3 5.0 4.2 0.0 0.1 100.0 95.8 1,564 Rift Valley 45.7 34.4 10.1 9.7 0.0 0.0 100.0 90.1 3,050 Western 38.8 41.5 6.5 12.8 0.0 0.0 100.0 86.9 1,164 Nyanza 48.6 36.9 9.6 4.6 0.0 0.2 100.0 95.0 1,405 Nairobi 72.9 22.4 3.3 1.4 0.0 0.0 100.0 98.6 1,568 Wealth quintile Lowest 17.4 44.0 15.0 23.3 0.1 0.1 100.0 76.4 1,691 Second 31.2 49.3 9.7 9.5 0.0 0.1 100.0 90.2 2,145 Middle 42.8 43.4 7.1 6.6 0.0 0.0 100.0 93.3 2,370 Fourth 58.0 33.5 4.9 3.5 0.0 0.1 100.0 96.4 2,959 Highest 76.5 19.7 2.4 1.4 0.0 0.0 100.0 98.5 2,897 Total 15-49 49.0 36.4 7.0 7.4 0.0 0.0 100.0 92.4 12,063 50-54 45.2 33.4 8.0 12.1 0.0 1.3 100.0 86.6 756 Total 15-54 48.8 36.2 7.0 7.7 0.0 0.1 100.0 92.1 12,819 Note: Totals may not add up to 100 percent because men with missing information are not shown separately. 1 Refers to men who attended secondary school or higher and men who can read a whole sentence or part of a sentence Characteristics of Respondents • 43 Table 3.3.2C Literacy: Men Percent distribution of men age 15-49 by level of schooling attended and level of literacy, and percentage literate, according to county, Kenya 2014 Secondary school or higher No schooling or primary school Total Percentage literate1 Number of men County Can read a whole sentence Can read part of a sentence Cannot read at all No card with required language Blind/ visually impaired Coast 43.9 45.8 4.2 5.9 0.0 0.1 100.0 93.9 1,260 Mombasa 56.1 40.4 2.4 1.1 0.0 0.0 100.0 98.9 481 Kwale 33.9 42.1 11.6 12.4 0.0 0.0 100.0 87.6 226 Kilifi 38.1 54.1 1.9 5.5 0.0 0.0 100.0 94.1 359 Tana River 23.3 50.8 6.6 18.8 0.0 0.4 100.0 80.8 65 Lamu 34.5 48.3 4.2 12.4 0.0 0.6 100.0 87.1 37 Taita Taveta 45.3 46.3 3.2 4.3 0.0 0.4 100.0 94.8 93 North Eastern 28.4 29.2 9.6 32.3 0.4 0.0 100.0 67.2 227 Garissa 32.9 28.2 11.1 27.8 0.0 0.0 100.0 72.2 94 Wajir 28.1 27.7 8.9 33.9 1.3 0.0 100.0 64.7 72 Mandera 21.6 32.7 7.9 37.4 0.0 0.0 100.0 62.2 60 Eastern 38.9 46.2 6.5 8.4 0.0 0.0 100.0 91.6 1,825 Marsabit 35.7 7.1 20.8 36.4 0.0 0.0 100.0 63.6 40 Isiolo 37.9 27.7 22.3 12.1 0.0 0.0 100.0 87.9 35 Meru 34.3 52.1 3.9 9.6 0.0 0.0 100.0 90.4 495 Tharaka-Nithi 41.1 47.1 2.3 9.5 0.0 0.0 100.0 90.5 102 Embu 38.0 44.3 9.8 7.8 0.0 0.0 100.0 92.2 164 Kitui 29.1 43.7 11.9 15.3 0.0 0.0 100.0 84.7 303 Machakos 46.8 47.3 4.6 1.4 0.0 0.0 100.0 98.6 436 Makueni 46.8 45.4 3.3 4.5 0.0 0.0 100.0 95.5 250 Central 58.4 32.3 5.0 4.2 0.0 0.1 100.0 95.8 1,564 Nyandarua 50.1 36.9 10.1 2.9 0.0 0.0 100.0 97.0 198 Nyeri 60.5 29.4 5.6 4.0 0.0 0.4 100.0 95.6 229 Kirinyaga 42.9 45.0 5.8 6.3 0.0 0.0 100.0 93.7 184 Murang’a 49.1 37.5 4.1 9.3 0.0 0.0 100.0 90.7 284 Kiambu 68.5 26.2 3.5 1.8 0.0 0.0 100.0 98.2 669 Rift Valley 45.7 34.4 10.1 9.7 0.0 0.0 100.0 90.1 3,050 Turkana 21.8 6.8 24.2 47.2 0.0 0.0 100.0 52.8 76 West Pokot 15.6 47.0 8.7 28.7 0.0 0.0 100.0 71.3 103 Samburu 35.5 22.0 13.3 29.2 0.0 0.0 100.0 70.8 35 Trans-Nzoia 38.8 45.3 3.9 12.1 0.0 0.0 100.0 87.9 329 Uasin Gishu 53.3 38.2 5.9 2.7 0.0 0.0 100.0 97.3 355 Elgeyo Marakwet 48.7 41.8 5.0 4.1 0.0 0.0 100.0 95.5 86 Nandi 40.1 39.2 14.8 5.9 0.0 0.0 100.0 94.1 264 Baringo 44.1 35.7 5.1 14.5 0.0 0.7 100.0 84.9 125 Laikipia 56.8 29.7 2.2 11.4 0.0 0.0 100.0 88.6 124 Nakuru 55.6 31.2 8.6 4.7 0.0 0.0 100.0 95.3 589 Narok 32.7 22.6 25.9 17.9 0.0 0.0 100.0 81.1 240 Kajiado 62.1 21.3 5.9 10.7 0.0 0.0 100.0 89.3 241 Kericho 44.9 44.7 7.6 2.6 0.0 0.0 100.0 97.1 215 Bomet 39.7 35.8 17.5 7.1 0.0 0.0 100.0 92.9 267 Western 38.8 41.5 6.5 12.8 0.0 0.0 100.0 86.9 1,164 Kakamega 41.4 47.0 5.7 5.9 0.0 0.0 100.0 94.1 411 Vihiga 39.0 28.8 27.7 3.7 0.0 0.0 100.0 95.5 140 Bungoma 37.5 39.3 2.7 19.8 0.0 0.0 100.0 79.5 413 Busia 36.3 43.7 1.4 18.7 0.0 0.0 100.0 81.3 199 Nyanza 48.6 36.9 9.6 4.6 0.0 0.2 100.0 95.0 1,405 Siaya 42.3 46.6 6.5 4.4 0.0 0.0 100.0 95.4 213 Kisumu 55.8 28.0 14.0 1.9 0.0 0.2 100.0 97.9 309 Homa Bay 37.9 49.4 1.5 9.8 0.0 1.0 100.0 88.8 243 Migori 32.5 51.9 8.5 7.1 0.0 0.0 100.0 92.9 211 Kisii 58.7 27.8 10.1 3.4 0.0 0.0 100.0 96.6 315 Nyamira 64.7 13.6 21.4 0.0 0.0 0.3 100.0 99.7 114 Nairobi 72.9 22.4 3.3 1.4 0.0 0.0 100.0 98.6 1,568 Total 15-49 49.0 36.4 7.0 7.4 0.0 0.0 100.0 92.4 12,063 50-54 45.2 33.4 8.0 12.1 0.0 1.3 100.0 86.6 756 Total 15-54 48.8 36.2 7.0 7.7 0.0 0.1 100.0 92.1 12,819 Note: Totals may not add up to 100 percent because men with missing information are not shown separately. 1 Refers to men who attended secondary school or higher and men who can read a whole sentence or part of a sentence 44 • Characteristics of Respondents 3.4 ACCESS TO MASS MEDIA Information access is essential in increasing people’s knowledge and awareness of the world around them, which may eventually influence their perceptions and behaviour. Exposure to media was assessed by asking respondents how often they read a newspaper, watched television, or listened to a radio. It is important to know the types of persons who are more or less likely to be reached by the various types of media to plan programmes intended to spread information about health and family planning. Tables 3.4.1 and 3.4.2 show the percentage of women and men age 15-49 exposed to different types of mass communication media by background characteristics. Tables 3.4.1C and 3.4.2C show these data by county. Women are less likely than men to have access to mass media; this is true for all types of media. Radio is the most popular medium for both women and men (accessed at least weekly by 70 percent of women and 86 percent of men), while newspapers are the least popular medium (accessed at least weekly by 18 percent of women and 41 percent of men). Only 11 percent of women and 33 percent of men have weekly exposure to all three media sources. Twenty-three percent of women and 10 percent of men have no weekly access to mass media. There are no overarching patterns in media consumption by age group, although newspaper reading declines among women with age (as does literacy, noted above) and men age 15-19 are much less likely to have weekly exposure to any of the media than men in other age groups. Urban women and men have more access to all forms of mass media than their rural counterparts; only 13 percent of women and 28 percent of men in rural areas read a newspaper at least once a week, as compared with 25 percent of women and 58 percent of men in urban areas. Although 66 percent of women and 78 percent of men in urban areas watch television at least once a week, only 20 percent of women and 44 percent of men residing in rural areas do so. Access to all three forms of mass media is highest among women in the Central region (18 percent) and men in Central and Nairobi (52 percent each) and lowest among residents of the North Eastern region (2 percent for women and 4 percent for men). Access to mass media increases with increasing education and wealth among both women and men. The proportion of women who listen to the radio at least once a week increases from 28 percent among those with no education to 79 percent among those with at least some secondary schooling. Similarly, the proportion of women who watch television at least once a week increases from only 3 percent among those in the lowest wealth quintile to 90 percent among those in the highest quintile. Across counties, women in Kiambu (27 percent), Nakuru and Nyeri (21 percent each) are most likely to have access to all three media at least once a week. Counties where the highest proportions of women have no access to any of the three media sources at least once a week are Turkana (80 percent), Garissa (77 percent), and Wajir (72 percent). Men in Mombasa and Nyeri (71 percent each) and in Machakos and Kajiado (62 percent each) have higher access to all three media services at least once a week than their counterparts in the other counties. The counties with the highest proportions of men with no access to the three media services at least once a week are Turkana (84 percent), Wajir (51 percent), and Garissa (48 percent). Characteristics of Respondents • 45 Table 3.4.1 Exposure to mass media: Women Percentage of women age 15-49 who are exposed to specific media on a weekly basis, by background characteristics, Kenya 2014 Background characteristic Reads a newspaper at least once a week Watches television at least once a week Listens to the radio at least once a week Accesses all three media at least once a week Accesses none of the three media at least once a week Number of women Age 15-19 21.7 33.2 66.0 9.5 25.2 5,820 20-24 19.4 44.2 73.5 12.2 18.6 5,735 25-29 18.2 45.2 71.6 11.8 20.3 6,100 30-34 16.8 41.1 70.5 10.9 22.5 4,510 35-39 14.9 36.8 68.1 9.5 25.2 3,773 40-44 15.0 31.4 67.4 9.5 28.1 2,885 45-49 15.1 32.0 68.5 9.3 25.7 2,257 Residence Urban 24.9 66.4 75.6 17.9 11.2 12,690 Rural 13.2 19.9 65.6 5.7 31.0 18,389 Region Coast 13.6 38.2 50.2 9.0 37.8 3,076 North Eastern 4.0 11.4 20.9 1.5 72.4 648 Eastern 17.4 24.7 67.0 8.1 29.0 4,375 Central 25.5 57.1 84.1 18.1 9.0 3,994 Rift Valley 19.1 34.6 70.3 10.9 23.4 7,953 Western 14.1 21.2 73.3 5.9 22.8 3,225 Nyanza 14.9 25.3 68.6 6.7 25.7 4,038 Nairobi 20.7 80.3 78.7 16.5 6.1 3,770 Education No education 0.3 9.7 28.0 0.2 68.1 2,176 Primary incomplete 5.8 17.1 61.9 1.9 34.4 7,989 Primary complete 10.3 36.6 73.0 5.0 20.5 7,637 Secondary + 32.6 58.1 79.3 20.9 10.1 13,277 Wealth quintile Lowest 4.1 3.2 37.6 0.7 61.1 4,838 Second 9.9 7.5 63.3 2.2 34.2 5,457 Middle 14.6 16.5 74.6 4.3 21.5 6,032 Fourth 19.6 48.3 80.9 11.0 12.1 6,550 Highest 32.7 89.8 80.4 26.5 2.7 8,203 Total 17.9 38.9 69.7 10.7 22.9 31,079 46 • Characteristics of Respondents Table 3.4.1C Exposure to mass media: Women Percentage of women age 15-49 who are exposed to specific media on a weekly basis, by county, Kenya 2014 County Reads a newspaper at least once a week Watches television at least once a week Listens to the radio at least once a week Accesses all three media at least once a week Accesses none of the three media at least once a week Number of women Coast 13.6 38.2 50.2 9.0 37.8 3,076 Mombasa 26.3 70.3 66.6 20.0 12.7 912 Kwale 7.6 21.3 38.5 3.4 53.5 619 Kilifi 6.7 23.9 37.8 3.6 53.2 1,043 Tana River 7.8 16.5 38.4 3.5 55.4 197 Lamu 8.4 35.5 65.0 4.9 25.6 89 Taita Taveta 18.1 41.4 78.8 11.9 13.6 215 North Eastern 4.0 11.4 20.9 1.5 72.4 648 Garissa 3.1 15.1 12.8 1.3 76.9 261 Wajir 4.4 8.8 23.1 1.3 71.6 212 Mandera 5.0 9.0 30.4 2.0 66.6 175 Eastern 17.4 24.7 67.0 8.1 29.0 4,375 Marsabit 1.9 19.2 20.7 0.8 69.2 115 Isiolo 16.1 34.7 46.7 11.7 45.0 104 Meru 13.4 27.0 58.7 6.8 37.8 1,110 Tharaka-Nithi 12.5 20.6 54.6 6.1 41.2 275 Embu 18.3 27.5 72.5 8.5 22.5 459 Kitui 2.8 11.7 56.6 1.5 40.9 759 Machakos 17.2 33.4 82.2 10.2 14.4 873 Makueni 44.7 23.7 85.2 15.8 10.1 680 Central 25.5 57.1 84.1 18.1 9.0 3,994 Nyandarua 17.4 32.3 86.1 8.5 10.2 436 Nyeri 29.7 61.6 90.5 21.3 3.7 650 Kirinyaga 17.6 37.4 64.5 8.7 26.7 451 Murang’a 11.9 36.8 84.1 6.4 12.9 735 Kiambu 33.9 75.5 86.4 26.7 4.5 1,722 Rift Valley 19.1 34.6 70.3 10.9 23.4 7,953 Turkana 4.1 7.1 18.8 2.7 79.5 320 West Pokot 9.3 12.3 31.2 6.7 67.3 267 Samburu 3.4 16.3 28.1 1.8 67.2 123 Trans-Nzoia 14.9 22.0 67.4 4.8 25.8 768 Uasin Gishu 22.1 45.6 78.0 14.5 17.0 784 Elgeyo Marakwet 10.1 24.5 71.3 7.0 26.6 250 Nandi 7.9 16.6 67.1 3.4 30.1 628 Baringo 9.8 16.3 50.9 4.3 44.1 335 Laikipia 15.3 33.9 74.3 10.7 20.7 342 Nakuru 33.5 59.0 81.4 21.4 8.8 1,574 Narok 13.1 26.1 82.3 5.9 13.0 642 Kajiado 19.1 55.7 64.5 13.4 19.4 670 Kericho 30.3 33.3 79.3 14.7 15.6 563 Bomet 17.1 22.8 83.6 7.1 14.6 687 Western 14.1 21.2 73.3 5.9 22.8 3,225 Kakamega 15.2 23.4 73.3 6.3 21.8 1,108 Vihiga 22.0 29.8 84.9 12.0 12.5 368 Bungoma 12.1 18.0 71.3 4.8 25.8 1,203 Busia 10.8 17.9 69.9 3.6 25.4 546 Nyanza 14.9 25.3 68.6 6.7 25.7 4,038 Siaya 16.8 16.9 76.7 6.1 19.3 572 Kisumu 21.5 50.8 83.3 14.5 9.8 820 Homa Bay 16.4 15.6 66.8 5.5 29.8 798 Migori 8.4 18.4 70.0 4.5 27.3 650 Kisii 14.2 23.9 50.6 4.0 38.4 864 Nyamira 6.2 17.1 66.7 3.0 30.8 334 Nairobi 20.7 80.3 78.7 16.5 6.1 3,770 Total 17.9 38.9 69.7 10.7 22.9 31,079 Characteristics of Respondents • 47 Table 3.4.2 Exposure to mass media: Men Percentage of men age 15-49 who are exposed to specific media on a weekly basis, by background characteristics, Kenya 2014 Background characteristic Reads a newspaper at least once a week Watches television at least once a week Listens to the radio at least once a week Accesses all three media at least once a week Accesses none of the three media at least once a week Number of men Age 15-19 29.4 43.6 77.9 19.0 16.2 2,540 20-24

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