P.O Box 53989, 00200 Nairobi, Kenya Tel: 254-20-2721262, 2717402 Fax: 254-20-2716231 E-Mail: admin@ieakenya.or.ke Website: www.ieakenya.or.ke P.O Box 59947, 00200 Nairobi, Kenya Tel: 254-20-3748338/9, 3752056 Fax: 254-20-3746992 E-mail: info@fes.or.ke Website: http://kenya.fes-international.de ISBN: 978-9966-7391-6-5 YOUTH Infinite Possibility Or Definite Disaster? By Katindi Sivi Njonjo 2010 Published by Institute of Economic Affairs(IEA) and Friedrich-Ebert-Stiftung(FES) © Institute of Economic Affairs and Friedrich-Ebert-Stiftung(FES) 2010 With funding from Friedrich-Ebert-Stiftung(FES) and The Rockefeller Foundation ISBN: 978-9966-7391-6-5 Design and layout Sunburst Communications Ltd. Tel:+254- 20- 312328 Email: info@sun.co.ke Printing Elite PrePress Limited Any part of this document may be freely reviewed, quoted, reproduced or translated in full or in part, provided permission is sought and the source is acknowledged. It may not be sold or used for commercial purposes or for profit. ii Youth Fact Book Infinite Possibility Or Definite Disaster? Foreword With the exception of some sectors, a lot of youth related information that is accessible to the public has not been disaggregated by age, tracked over time or harmonized in one document to give a coherent picture of the state of youth in Kenya. As a result, there have been gaps in the formulation of youth policies and programmes which have largely depended on generalized statements rather than facts. This fact book is a first step towards the tracking of different youth indicators captured in different documents for purposes of providing a global overview of youth issues in Kenya. The book is also meant to provide a one stop shop for this information. Through these indicators, one is able to compare age, gender, regional and socio-economic dynamics to determine political, economic, social and environmental implications of the growing youth population now and in the future. The collated data brings out areas the country has made remarkable gains in the youth sector. It also brings out other glaring concerns beyond education and unemployment where national strategies must be sought in order to holistically tackle youth challenges. The data is a good premise for the formulation of comprehensive policies and programmes as well as for refining existing youth strategies. The IEA reiterates that the youth question cannot be the sole responsibility of government. We therefore envisage that this book will inform the general public including the young people who we hope will be encourage to take charge over their lives(especially where it requires them to take personal responsibility) and also aid the work of youth practitioners and other stakeholders. Margaret Chemengich, Chief Executive Officer, IEA Youth Fact Book Infinite Possibility Or Definite Disaster? iii Acknowledgements Institute of Economic Affairs(IEA-Kenya) through its Futures Programme is indebted to Sammy Muvela(ZIMELE), Margaret Chemengich(IEA), Collins Opiyo(KNBS), Florian Koch(FES), Kimanzi Muthengi(UNICEF), Kanja Waruru(Media Max Network Ltd), Angela Kitonga, John Mutua(IEA), James Nduko(Twaweza, Kenya), Hulda Ouma(SID), Liz Kamau(FES), Catherine Gitonga(IEA) and Abraham Rugo(IEA) for attending the peer review meeting held on Friday, 15th October 2010 and giving invaluable comments that went to finalizing this work. We appreciate the written comments by Ms. Betty Kibaara (Rockefeller Foundation) and Awuor Ponge(IPAR). Our gratitude is extended to Paul Obinge and Nelly Kamande for their administrative support. Special mention goes to Ms. Nancy Chepkoech Muigei for her dedicated research assistance in the project. The trustees of the Youth Scenarios Project include: John Githongo, Betty Maina, Maina Kiai, Muthoni Wanyeki, Kanja Waruru, Prof. Khasiani Shanyisa, XN Iraki, Prof. Peter Lewa, Mshai Mwangola and Arthur Muliro The production of this fact book is funded by Friedrich Ebert Stiftung(FES), and the Rockefeller Foundation who also funded the youth scenarios project under which this publication is produced. iv Youth Fact Book Infinite Possibility Or Definite Disaster? Table of Content Foreword page iii Acknowledgements page iv Table of Content page v List of Acronyms page vi List of Tables page viii List of Figures page xi About IEA page xv Executive Summary page xvi Demographics page 1 Health page 17 Disability page 45 Education page 61 Forming Families page 75 Un/Employment page 125 Participation page 145 ICT page 161 Crime page 181 References page 189 Youth Fact Book Infinite Possibility Or Definite Disaster? v List of Acronyms AIDS ASTs CHE CPR DVD EAP ECA ECDE EMIS ERSWEC FES FGM GER GoK HIV HSV-2 ICT IDRC IEA ILFS ILO IMR IPAR ITU KAIS KDHS KNBS Acquired Immunodeficiency Syndrome Age-Structural Transitions Commission for Higher Education Contraceptive Prevalence Rate Digital Video Discs East Asia& Pacific Europe and Central Asia Early Childhood Development Education Education Management Information Systems Economic Recovery Strategy for Wealth and Employment Creation Friedrich Ebert Stiftung Female Genital Mutilation/Cutting Gross Enrolment Rates Government of Kenya Human Immunodeficiency Virus Herpes Simplex Virus Information, Communication and Technology International Research Development Centre Institute of Economic Affairs Integrated Labour Force Survey International Labour Organization Infant Mortality Rates Institute for Policy Analysis and Research International Telecommunication Union Kenya Aids Indicator Survey Kenya Demographic Health Survey Kenya National Bureau of Statistics vi Youth Fact Book Infinite Possibility Or Definite Disaster? KNSPWD LAC MENA MoE MP3 NCAPD NHIF NSSF OECD PBR PWD’s SAS SID SMS SPICe SSA STD STI TFR TIVET TTIs TV UN UNDP UN Habitat UNESCO UNFPA UNICEF WDR WHO Kenya National Survey for Persons with Disabilities Latin American& the Caribbean Middle East and North Africa Ministry Of Education Patented Digital Audio Encoding Format National Coordinating Agency for Population and Development National Hospital Insurance Fund National Social Security Fund Organization for Economic Co-operation and Development Population Reference Bureau People With Disabilities South Asia Society for International development Short Messaging Service Scottish Parliament Information Centre Sub Saharan Africa Standard Sexually Transmitted Infection Total Fertility Rates Technical, Industrial, Vocational and Entrepreneurship Training Technical Training Institutes Television United Nations United Nations Development Programme United Nations Human Settlements Programme United Nations Educational, Scientific and Cultural Organization United Nations Fund for Population Activities United Nations International Children’s Education Fund World Development Report World Health Organization Youth Fact Book Infinite Possibility Or Definite Disaster? vii List of Tables Table 1: Population Size and Growth in Kenya(1948-2009) 3 Table 2: Kenya‘s Population by Age in 1999 and 2009 Census 3 Table 3: Population Trends among Kenya‘s Young People(1969- 2009) 4 Table 4: Demographic characteristics of the Youth population 7 Table 5: Population Dynamics 9 Table 6: Trends in Urbanization in Kenya 12 Table 7: HIV Prevalence among Women and Men Aged 15-34 by 2003, 2007& 2009 KDHS Report 21 Table 8: Early Childhood Mortality Rates by Demographic and Socio-Economic Characteristics 25 Table 9: Perinatal Mortality Rates 26 Table 10: Antenatal Care among Young Mothers 27 Table 11: Place of Delivery by Demographic Characteristics 29 Table 12: Assistance during Delivery by Demographic Characteristics 31 Table 13: Assistance during Delivery by Region 32 Table 14: Nutritional Status of Women by Background Characteristics 33 Table 15: Self Reported Prevalence of Sexually-Transmitted Infections(STI‘s) and STI Symptoms by Background Characteristics 36 Table 16: Prevalence of Female Circumcision by Age and Type of Circumcision 37 Table 17: Prevalence of Female Circumcision by Province and Type of Circumcision 37 Table 18: Prevalence of Female Circumcision by Background Characteristics and Type of Circumcision 39 Table 19: Overall Substance Abuse among 10-24 Year Olds 41 Table 20: Prevalence of Disability by Demographic Characteristics 47 Table 21: Prevalence of Disability by Province 48 Table 22: Percentage of PWD‘s Using Assistive Devices by Demographic Characteristics 50 Table 23: Percentage of Activity Limitation without the Use of Assistive Devices by Age 52 Table 24: Effect of Immediate Surrounding by Background Characteristics 54 Table 25: Employment and Incomes of PWD‘s by Age 55 Table 26: Employment and Incomes of PWD‘s by Background Characteristics 56 Table 27: Type of Grant Currently Received by Background Characteristics 57 Table 28: Attitudes towards Persons with Disabilities 59 Table 29: Pre-School Gross Enrolment Rates by Gender and Province in 2002 63 Table 30: Primary School Enrollment by Province(2003 – 2008) 64 Table 31: Primary to Secondary Transition Rates, 1998-2007 65 Table 32: Primary to Secondary School Transition Trends by Province 65 Table 33: Secondary School Enrolment by Form, 1999-2003 67 Table 34: Secondary School Enrolment by Form, 2004-2008 67 Table 35: Provincial Gross Secondary Enrolment 67 Table 36: Distribution of TIVET Institutions 69 Table 37: Student Enrolment by Gender in Technical Institutions, 2005 – 2009 70 viii Youth Fact Book Infinite Possibility Or Definite Disaster? Table 38: Total Student Enrolment in Public and Private Universities 2000/01-2009/2010 71 Table 39: Registration of Universities and Degree offering Institutions 72 Table 40: Social Activities Among 7-19 year olds 76 Table 41: Clothes Influencer 77 Table 42: Fears and Worries by Gender Among 7-19 Year Olds 78 Table 43: Amounts Received by 7-19 Year Olds 79 Table 44: Items 7-19 Year Olds Spent On 80 Table 45: Buying Patterns of Alcoholic Beverages and Cigarettes 80 Table 46: Age at First Sex among Young People(15-24) by Background Characteristics 83 Table 47: Percentage of Young People Abstaining from Sex,% of those who had Sex in the past 12 Months and% of those who used Condoms by Age 84 Table 48: Percentage of Young People Abstaining from Sex,% of those who had Sex in the past 12 Months and% of those who used Condoms by Background Characteristics 85 Table 49: Young People(15-24) who have had High Risk Sexual Intercourse in the Last 12 Months by Background Characteristics 87 Table 50: Percentage(%) of Women(15 – 19) who had High Risk Sex with a Man 10+ Years Older 89 Table 51: Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Background Characteristics 90 Table 52: Transactional Sex by Background Characteristics 92 Table 53: Percentage Use of Condoms among 15-24 Year Olds at First Sexual Encounter by Background Characteristics 94 Table 54: Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Demographic Characteristics 96 Table 55: Source of Information on Sexual& Reproductive Health among 7-19 year Olds 98 Table 56: Child Bearing among Teenagers(15-19) by Background Characteristics 104 Table 57: Marital Status amongst Our Young People by Age 106 Table 58: Age at First Marriage among Different Age Groups 107 Table 59: Median Age at First Marriage among Women(25-34 Years Old) 107 Table 60: Percentage Distribution of Women by the Number of Co-Wives they have by Age 108 Table 61: Distribution of Women by the Number of Co-Wives they have by Background Characteristics 109 Table 62: Percentage Distribution of Women by the Number of Co-Wives they have by Region 110 Table 63: Ideal Number of Children by Demographic Characteristics 111 Table 64: Ideal Number of Children by Region 112 Table 65: Family Planning among Currently Married Women by Demographic Characteristics 113 Table 66: Wife Beating(Justification why/when women should be Beaten) by Age 114 Table 67: Wife Beating(Justification why/when women should be Beaten) by Demographic Characteristics 115 Table 68: Wife Beating(Justification why/when women should be Beaten) by Region 117 Table 69: Spousal violence by Background Characteristics 118 Youth Fact Book Infinite Possibility Or Definite Disaster? ix Table 70: Spousal Violence by Region(Women ever married who have experienced different forms of violence from partner/husband) 120 Table 71: Reported Cases of‘Offences against Morality‘ and’Other Offences against Persons’(2005-2009) 120 Table 72: Reported Cases of Rape, Defilement/Incest, Assault and Battering by Province(1997-2005) 121 Table 73: Distribution of Working Age Population, 1998/99 and 2005/06 126 Table 74: Percentage Labor Force Participation Rates, 1998/99 and 2005/2006 127 Table 75: Youth Unemployment in Kenya between 1978 and 05/06 128 Table 76: Unemployment Rates for Population Aged 15-64 by Age-Group, Region and Sex 129 Table 77: Percentage Youth Unemployment Rates in Kenya by Age and Sex(98/99 and 05/06) 130 Table 78: Employment Trends in Kenya(1986-2008) 131 Table 79: Employment by Sector 133 Table 80: Occupation by Age and Gender 134 Table 81: Type of Earnings from Employment by Age and Gender 134 Table 82: Control Over Women‘s and Men‘s Cash Earnings by Age 135 Table 83: Control Over Women‘s and Men‘s Cash Earnings by Demographic Characteristics 136 Table 84: Control Over Women‘s and Men‘s Cash Earnings by Region 137 Table 85: Women‘s Cash Earnings Compared With Men‘s Cash Earnings by Age 137 Table 86: Women‘s Cash Earnings Compared With Men‘s Cash Earnings by Demographic Characteristics 138 Table 87: Women‘s Cash Earnings Compared With Men‘s Cash Earnings by Region 139 Table 88: Women‘s Participation in Decision making by Age 152 Table 89: Women‘s Participation in Decision making by Demographic Characteristics 153 Table 90: Women‘s Participation in Decision making by Region 154 Table 91: Summary of Disbursed Funds through Financial Intermediaries 158 Table 92: Catching up Fast: The Rise of New Technologies 162 Table 93: Favorite Types of Movies 164 Table 94: Exposure to Family Planning Messages 165 Table 95: Acceptability of Condom Messaging 166 Table 96: Source of Information on Sexual& Reproductive Health among 7-19 year Olds 167 Table 97: Top Ten Activities Done Online in Kenya vs. Top Ten Activities Done Online Globally 170 Table 98: Facebook Users in the Region 173 Table 99: Online Services 175 Table 100: Use of the Internet for Knowledge 176 Table 101: Crime between 1931 and 1937 182 Table 102: Juvenile Correctional Institutions Between 1957 and1964 183 Table 103: Crime Between 1997 and 2001 184 Table 104: Convicted Prison Population by Age and Sex(2001- 2009) 185 Table 105: Gender Specific Crimes 187 Table 106: Number of Juvenile Offenders Serving Community Service by Gender and Type of Offence, 2004-2008 188 x Youth Fact Book Infinite Possibility Or Definite Disaster? List of Figures Figure 1: Population Trends among Kenya‘s Young People(1969- 2009) 4 Figure 2: Youth Population(15-34) in 2009 by Age and Gender 5 Figure 3: Youth Population(15-34) in 2009 by Province. 5 Figure 4: Youth Population(15-34) Distribution by Age 6 Figure 5: Rural/Urban Youth(15-34) Distribution by Gender 6 Figure 6: Age Structural Transitions: Kenya, 1950 – 2050 7 Figure 7: Kenya‘s Youth Population as a Percentage of the Total Population 8 Figure 8: Trend in Percentage of Kenya‘s Youth Population to the Potential Working Population 8 Figure 9: Trends in Contraceptive use among Married Women aged 15 – 49 from 1978- 2008 10 Figure 10: Kenya‘s Demographic Transition from 1990 to 2020 10 Figure 11: Age Patterns of Urban Population in Kenya 13 Figure 12: Population Size and Growth of 10-24 Year Olds in East Africa 13 Figure 13: Population of 10-24 Year Olds in the World 14 Figure 14: Age Structural Transitions of 0-14 year olds(1950 – 2050) among Developing and Developed Countries 14 Figure 15: Age Structural Transitions of 15-64 year olds(1950 – 2050) among Developing and Developed Countries 15 Figure 16: Age Structural Transitions of 65+ year olds(1950 – 2050) among Developing and Developed Countries 16 Figure 17: HIV Prevalence among Women and Men Aged 15-34 19 Figure 18: HIV Prevalence among Women and Men Aged 15-24 19 Figure 19: HIV Prevalence among Women and Men Aged 15-34 in Rural and Urban Areas 20 Figure 20: HIV Prevalence by Region 20 Figure 21: HIV Prevalence among Un/Circumcised Men by Age Group 21 Figure 22: HIV Prevalence by Age of Sexual Debut in 2003 and 2007 KDHS Reports 22 Figure 23: 15 – 34 Year Olds Who Have Ever Been Tested for HIV by Gender 22 Figure 24: Total Fertility Rates(TFR) in Kenya by Age 23 Figure 25: Total Fertility Rates(TFR) in Kenya over Time 24 Figure 26: Early Childhood Mortality Rates by Age 24 Figure 27: Antenatal Care by Region 28 Figure 28: Place of Delivery by Region 30 Figure 29: Assistance during Delivery by Age 31 Figure 30: 15-34 Year Olds Infected by Herpes Simplex Virus(HSV-2) 34 Figure 31: 15-34 Year Olds Infected with Syphilis 34 Figure 32: Self Reported Prevalence of Sexually-Transmitted Infections(STI‘s) and STI Symptoms by Age and Gender 35 Figure 33: Self Reported Prevalence of Sexually-Transmitted Infections(STI‘s) and STI Symptoms by Region 36 Youth Fact Book Infinite Possibility Or Definite Disaster? xi Figure 34: Use of Tobacco among Young Men(15-34) 40 Figure 35: Use of Tobacco among 13-15 Year Olds Currently Smocking in Different Countries 40 Figure 36: Use of Tobacco among Students and Non-Students Aged 10-24 41 Figure 37: Alcohol Abuse 42 Figure 38: Bhang Prevalence 42 Figure 39: Inhalant Prevalence 43 Figure 40: Miraa Abuse 43 Figure 41: Proportion of Kenyan Adolescents and Young Adults Reporting Regular use of Drugs. 44 Figure 42: Probability that a 15 year old will die before the age of 60 44 Figure 43: Prevalence of Disability by Age 46 Figure 44: Disability Prevalence by Province 48 Figure 45: Distribution of Disability by Gender 49 Figure 46: Percentage of PWD‘s using Assistive Devises 49 Figure 47: Percentage of Activity Limitation without the Use of Assistive Devices by Age 51 Figure 48: Effect of Immediate Surrounding by Age 53 Figure 49: Number of Pre-Primary Schools 62 Figure 50: Number of Primary Schools from 1990-2008 63 Figure 51: National Secondary Enrolment Trends, 1999-2007 66 Figure 52: Secondary-University Transition Rate 1999/2000-2007/2008 68 Figure 53: Male Enrollment Rate by Age in Other Countries 74 Figure 54: Female Enrollment Rate by Age in Other Countries 74 Figure 55: Patterns of Free time Activities for 7-19 year olds 76 Figure 56: Values, Dreams and Aspirations among 7-19 year olds 78 Figure 57: Frequency of Receiving Money among 7-19 year olds 79 Figure 58: Age at First Sex among Young People 81 Figure 59: Young people(15-24) who Reported Sexual Debut Before the Age of 15 by Various KDHS Reports 81 Figure 60: Age at First Sex among Young People(15-24) In Rural and Urban Areas 82 Figure 61: Age at First Sex among Young People(15-24) by Gender and Region 82 Figure 62: Percentage of Young People(15-24) Abstaining From Sex by Gender and Region 85 Figure 63: Young People(15-24) who Reported to have had Sex at Least Once 86 Figure 64: Young People(15-24) who have had High Risk Sexual Intercourse in the Last 12 Months by Gender 86 Figure 65: High Risk Sex among Young People(15-24) By Region 88 Figure 66:% No. of Sexual Partners among 7-19 Year Olds 88 Figure 67: Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Age 90 Figure 68: Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Region 91 Figure 69: Transactional Sex among Men 91 xii Youth Fact Book Infinite Possibility Or Definite Disaster? Figure 70: Condom Use among 15-24 Year Olds at First Sex by Gender 93 Figure 71: Condom Use at First Sex among Young People(15-24) by Region 95 Figure 72: Condom Use at First Sex among Young People(15-24) by 2003, 2007, 2009 KDHS 95 Figure 73: Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Age 96 Figure 74: Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Region 97 Figure 75: Child Bearing among Teenagers by Age 103 Figure 76: Child Bearing among Teenagers(15-19) by Region 104 Figure 77: Un/Married Young Women(15-24) who have ever Used Contraceptives by Type 105 Figure 78: Men‘s Attitudes towards Contraception 106 Figure 79: Median Age at First Marriage among Women(25-29) by Region and by 2003/2009 KDHS Reports 108 Figure 80: Family Planning among Currently Married Women 112 Figure 81: Menopause among Young Women Aged 30-34 113 Figure 82: Spousal Violence by Age 116 Figure 83: Force at Sexual Initiation 118 Figure 84: Reported Cases of Rape, Defilement/Incest, Assault and Battering(1997-2005) 119 Figure 85: Reported Cases of‘Offences against Morality‘ and‘Other Offences against Persons(2005-2009) 120 Figure 86: Total Incidents of Rape, Defilement/Incest, Assault and Battering Against Women between 1997& 2005 121 Figure 87: Incidents of Rape, Defilement/Incest, Assault and Battering Against Women by Province 122 Figure 88: Percentage of 15 – 19 Year Olds Ever Married by Gender in Select African Countries 123 Figure 89: Percentage of Women Giving Birth by Age 123 Figure 90: Percentage of Un-Married Teens(15-19) who Have Had Sex 123 Figure 91: Percentage of Sexually Active Women(15-19) Using Modern Contraceptives 124 Figure 92: Youth Unemployment in Kenya by Age Cohorts(1978-2006). 129 Figure 93: Formal and Informal Employment Trends between 1986 and 2008. 132 Figure 94: Distribution of Children 5-17 years Working in Risky Occupations By Sex,2005 140 Figure 95: Mean Monthly Earnings from Paid Employment of Children 5-17 Years by Gender 140 Figure 96: Mean Monthly Earnings from Paid Employment of Children 5-17 Years,(Rural/Urban) 141 Figure 97: Number of Young People Working in Kenya‘s Civil Service 141 Figure 98: Civil Service Wages 142 Figure 99: Labour Force Participation among 15-24 Year Olds 142 Figure 100: Unemployment Rate by Gender(15-24) 143 Figure 101: Unemployment Rate by Urban and Rural(15-24) 143 Figure 102: Not In the Labour Force and not in School(15-24) 144 Figure 103: Percentage of Children who are Economically Active 144 Figure 104: Roger Hart‘s Ladder of Young People‘s Participation 146 Youth Fact Book Infinite Possibility Or Definite Disaster? xiii Figure 105: Effect of Student Participation in School Governance 147 Figure 106: Type of Student Leadership Existing in Secondary Schools 148 Figure 107: Process of Choosing Students into Student Councils 149 Figure 108; Process of Choosing Student Leadership in Schools 150 Figure 109: Mechanisms of Channeling Student Grievances 150 Figure 110:% Involved in Decision Making 151 Figure 111: Recreational Activities Considered Most Popular in Public and Private Secondary Schools 152 Figure 112:% of Youth Voters(18-35) by Province 154 Figure 113: Number of Youth Voters by Age(18-35) 155 Figure 114: Voter Registration by Age and Province 156 Figure 115: Number of Groups Accessing the Youth Enterprise Fund by Province 158 Figure 116: Access of YEDF though Financial Intermediaries by Gender and Region 159 Figure 117: Trends in Watching TV, Listening to Radio and Reading Newspapers 163 Figure 118: Favorite Radio Station among 7-19 year olds 164 Figure 119: Favorite Newspaper 165 Figure 120: Frequency of use between Old and New Forms of Media 168 Figure 121: Media Priorities 168 Figure 122: Internet Users in Kenya 169 Figure 123: Main motivating factors of internet use among 25-44 Year Olds 170 Figure 124: Popularity of Social Networking Sites 171 Figure 125: Facebook Use by Gender in Kenya 172 Figure 126: Facebook use in Kenya by Age 172 Figure 127: Uses of Social Networking and their Outcomes 173 Figure 128: Internet Us for Marketing Purposes 174 Figure 129: Facilitators of Internet Access and Back-up Sources in Kenya 177 Figure 130: Barriers of Internet Access in Kenya 177 Figure 131: Enhancing Internet Access by GoK 178 Figure 132: How much not having Internet Affects Daily Routine and Personal Activities 178 Figure 133: Top 10 Internet Countries in Africa in 2009 179 Figure 134: Youth Convicted to Prisons(1971 – 1976) 183 Figure 135: Total Crime Trends Between 2001 and 2009 186 Figure 136: Crime by Gender Between 2001& 2009 186 xiv Youth Fact Book Infinite Possibility Or Definite Disaster? About IEA The Institute of Economic Affairs(IEA-Kenya) is Kenya’s first public affairs dialogue forum. It seeks to promote pruralism of ideas through open, active and informed debate on public policy issues. The IEA-Kenya is independent of political parties, pressure groups, lobbies and any other partisan interests. Its mandate is to promote informed debate on key policy issues both economic and political and to propose feasible policy alternatives in these areas. In addition, the IEAKenya provides research backup to policy makers including Members of Parliament. Through its work, the IEA-Kenya provides alternative public policy choices and addresses the legal and institutional constraints to economic reform and growth. Through the Futures programme, the IEA-Kenya sseks to facilitate increased utilization of futures methodologies(Vision Building, Scenarios thinking and StrategicPlanning) in research, policy debate and decision making processes. Youth Fact Book Infinite Possibility Or Definite Disaster? xv Executive Summary The purpose of this fact book is to document the state of youth in Kenya. More specifically, the fact book seeks to present different youth indicators for purposes of providing an overview of age, gender, regional and socio-economic status of youth. Where data is available, East African, African and global comparisons are given to help us gauge our standing. The countries of analysis are randomly chosen. The material in this book is collated from various secondary sources that are certified as credible. Since all these reports use different data collection methodologies, IEA has sufficiently referenced the work for further reading. The fact book covers 9 thematic areas. They include: Demographics; Health; Disability; Education; Family; Un/Employment; Participation; ICT; and Crime. The facts are presented in charts, graphs and tables, highlighting simple observations. The work does not attempt any in-depth analysis of the trends neither does it cross-reference data to make any inferences as this will be done in subsequent publications. Kenya’s constitution defines youth as all individuals in the republic who have attained the age of 18 years but have not attained the age of 35(GOK, 2010). The UN on the other hand defines youth as persons between the age of 15 and 24. Due to these varying categorizations of youth, the IEA in this fact book has to the extent possible profiled youth as those aged between 15 and 34, in order to accommodate both categorizations. Where this was not possible, information was presented according to the categorization used by the source of information to still give a general picture of the state of youth in that regard. The data brings out the fact that Kenya’s population growth rate has been rising steadily from about 2.5 percent per annum in 1948 to around 3.8 percent per annum in the 1980s – a pace described as one of the fastest ever recorded in history. By 2009, the population size was slightly over seven fold the population in 1948 and over four fold that of 1962. It is expected that we will be 46 million by 2015, 57 million by 2025 and 85 million by 2050. Currently, 78.31% of Kenyans are below 34 years old. Those aged 15-34 years old constitute 35.39%. While the proportion of children(0-14 years) has been declining since the 1980s, that of producers(aged 15-64 years) has been rising consistently. It is envisaged that Kenya will experience a demographic shift/transition due to changing patterns in fertility, mortality and population growth as well as socioeconomic factors. As the 0-14 age group matures into teenage-hood and young adulthood, and as many women continue to give birth later, space their children more or give birth to fewer children, the bulge will shift to the 15-34 year olds meaning that Kenya will transition from a‘child-rich’ phase/child bulge to a‘young adult’ /youthful or youth bulge population. xvi Youth Fact Book Infinite Possibility Or Definite Disaster? Migration and urbanization as important components of population change have been rapidly increasing. The urban population escalated from about 8% in 1962 to about 21% in 1999 and by 2008 was about 22%. Most of the migrants come as young adults, usually after secondary school(though the highest age group is 25-29) with employment as the motivation for migration. The majority of migrants are still males, though the male to female ratio has been reducing over time. With an urban annual growth rate of 4%, dominated by only two centers(Nairobi and Mombasa) and development transformations necessary to support this growth and enhance the quality of urban life not occurring at the same rate makes it challenging to sufficiently expand the labor market and spread of the labor force. Among the critical health problems young people face are those associated with sexuality and reproductive health. For example, the total fertility rate increases from age 15 and peaks at age 24 before it slowly starts declining. Adolescents are up to three times more likely to experience pregnancy related complications than older women. The overall HIV prevalence among youth aged 15-24 years was 3.8%. However, prevalence varies from 2.5%- 12% among young women of that age and 0.41% to 2.6% among young men of the same age. By 24 years, women were 5.2 times more likely to be infected with HIV than young men of the same age. HIV is approximately five(5) times higher among uncircumcised than circumcised men in all age groups except among 15-24 year olds. Age of the first sexual encounter has consistently been rising between 2003 and 2007. Women’s STI infection is about 2.5 times higher that of their male counterparts in all the age cohorts. STI infection has been rising with age, more dramatically for women than men between the age of 15 and 39. Herpes(HSV-2) is highest among women than men throughout all the age cohorts and among young people, infection is highest among 30-34 year olds. 30-34 year old men(2.4%) have the highest prevalence of syphilis among young people. Lifestyle changes are another cause of ill health. The likelihood to be obese or overweight increases with age for women, is twice as prevalent in urban(40%) than in rural areas(20%), increases with level of education and wealth. Risk taking such as use of drugs and alcohol is another factor influencing the health status of young people. Alcohol(36%) and tobacco use(28%) are the most abused substances followed by miraa(18%), bhang(13%) and inhalants(5%). Despite increased investment in the sector, utilization of health services by young people remains low as only 12% of health facilities provide youth friendly services that would enable them to make informed choices and decisions regarding their health and general well being. There are also major regional and age disparities in access to services. Sadly though is the fact that young people consider health a low to medium priority. National disability prevalence varied from 9.7% to 12.5%. The highest form of disability was physical impairment. Overall, immediate surroundings, lack or availability of assistive devices affects People with Disability(PWD’s). The proportion of PWD’s gainfully employed or earning an income is very small. Residence(rural/urban), gender and level of education are key determinants of employment and pay as well as access to disability grants. People’s attitudes towards PWD’s have been a bigger problem than their condition. Youth Fact Book Infinite Possibility Or Definite Disaster? xvii Gross and net enrollments in pre-school(60.6% and 49% respectively), primary(110% and 92.9% respectively), secondary(45.3% and 35.8% respectively), tertiary and university have been increasing over the years mainly due to policy changes. Completion and transition rates from one level to another have been low though improving. However, past education policies have focused on increasing the number of people who go through the education system rather than learning that takes place to prepare youth for work and life. 5% of children completing class eight cannot read a class two story while 25% of pupils in class five cannot read a story of class two level. Children perform better when given application rather than abstract problems to solve thus education must be made more relevant. About 5% of 6-16 year olds don’t attend school even with the provision of Free Primary Education but this varies from 40% in some regions to 1% in others. The problem of over-age children especially in rural areas is rather high(e.g. 60% in class 7 are above 14 years) and persists due to late entry, grade repetition and time –off school. 30% of children in class 1 – 3 are subjected to tuition. By class eight those going for tuition are about 80%. 15% of pupils are absent from school in any given day but ranges from 34% in some areas to 7% in others. At any one time, there are about 4 classes without a teacher in every school. The role of family is crucial in the development of young people. Parents influence over their children is highest when they are younger and that influence reduces as they grow older and is replaced by media and peers. This is confirmed by the fact that media(television, radio, and the internet) is still the most prominent source of information on sexual& reproductive health(24%). Most young people however(an average of 33% of 7-19 year olds), have no source of sexual and reproductive health information. 7-10 year olds trust their parents but unfortunately parents are not giving the relevant information to this age group. Extensive studies confirm the assertion that a father is particularly important and show direct correlations between a father’s absence in a child’s life with poverty, maternal and child health, incarceration, crime, teen pregnancy, child abuse, drug and alcohol abuse, education, and childhood obesity. Spousal violence (physical, emotional and sexual) is rampant. Across all ages(15-34), more women than men generally believe that men are justified to beat them especially if they neglect children(41%), go out without telling him (30.2%), argue with him(30%), refuse to have sex with him(21%) or burn food(13%). Reported cases of rape and defilement/incest, assault and battering against women have been increasing gradually since 1997. Majority of Kenya’s young people are unemployed, underemployed or underpaid and are therefore in the swelling ranks of the working poor. A large proportion of young adults and a rapid rate of growth in the working-age population exacerbates unemployment, prolongs dependency on parents, diminishes selfesteem and fuels frustrations, which increase the likelihood of violence or conflict. The country’s workingage population increased from 15.9 million persons in 1998/99 to 19.8 million persons in 2005/2006. The largest rise in the working-age population over the period was recorded among the age cohort of 15-34 years where the working-age population increased from 9.7 million persons in 1998/99 to 13.1 million persons in 2005/2006. An increasing proportion of the country’s working age population is inactive and it increased from 22.6 percent in 1998/99 to 26.6 percent in 2005/2006. The majority of the inactive population was between the ages of 15 and 19 because in Kenya it is a school going age. Female labour force participation rates edged downwards for all the age groups with the highest being among the youth cohorts of 25-29 and 30-34, which declined by nearly 6 percent. Overall, females had a lower labour force participation rate than their male counterparts in both periods and mean monthly earnings from paid employment for males is about 1.5 times that of females. The rate at which the net jobs were created was not the same as the rate of labour force growth. This is evidenced by the fact that the informal sector has been growing at an average rate of 17.2% per annum compared to the formal sector which has been growing at an average of 2.23% per annum while the country’s working age population increased by 24.5% between 1999 and 2006. This effectively means that more job seekers, both the new labour market entrants and those out of employment through the various labour separation mechanisms, ordinarily remain out of employment for a longer period hence swelling the ranks of the discouraged job seekers. Most employers in Kenya, including the public sector have resorted to the increasing use of casual, temporary, part-time, contract, sub-contracted and outsourced xviii Youth Fact Book Infinite Possibility Or Definite Disaster? workforces to ostensibly reduce labour costs, achieve more flexibility in management and exert greater levels of control over labour. The proportion of casual workers in the formal sector gradually increased from 17.9 percent in 2000 to 21.2 percent in 2005, 29.7 percent in 2006 and 32.2 percent in 2008, a trend that contrasts sharply with the country’s desire to reduce poverty and enhance social protection. Young people’s participation is about sharing ideas, thinking for themselves, expressing their views effectively, planning, prioritizing and being involved in the decision making processes. This participation can be exercised in different spheres such as school, at home and at the civic level through voting. Head teachers report that child participation has significant impact in all areas of school interactions such as discipline, cocurricular activities, conflict resolution, school performance, confidence and self esteem. However, student participation e.g. in choice of their leaders is very limited as 62% of prefects in private and 39% of prefects in public schools are selected by teachers. 87% of the students prefer the student council model which allows participation and transparency stating that there was no student unrest, strikes or dropouts reported where this form of leadership was applied unlike 60% of schools that were predominantly prefect led. There are 5.9 million voters aged 18-35. Of these, 25% come from Rift Valley, 15% from Central, 14% from Eastern, 13% from Nairobi and another 13% from Nyanza. Western, Coast and North Eastern contribute 10%, 8% and 2% respectively of the youth votes. With the exception of North Eastern province, throughout all the age cohorts and in all the provinces, there are more male voters than female voters. There has also been young people’s participation in the Youth Enterprise Development Fund. Generally, more young women(33,094) than young men(23,981) accessed the funds through financial intermediaries though there were regional and gender disparities. 47% of all the resources were accessed by young women and 53% were accessed by young men. Young people are the main users of the new ICTs(internet, mobile phone, and computer) which are growing much faster than older ICTs(television, radio, mainline telephones, and newspapers). Although the main reason for many 15-24 year olds to use new ICT’s is entertainment- playing games, downloading music, and talking with friends- the new ICT technologies are having wide-ranging effects on youth transitions. Internet connection was prioritized highest among the new mass media to access reliable information and knowledge(57%) followed by communicating with others(39%) through E-mail, social networking, chatting, VOIP etc. Entertainment/media, leisure and commerce such as buying products and services(2%) as well as on-line banking through the internet are still underdeveloped in Kenya and are opportunity areas for growth. The most popular social network is face book accessed by 96% of social network users, 75% of whom are 18 – 34 years old. Old ICT’s are still the most prominent sources of information on sexual& reproductive health. Crime is strongly associated with young people as 53% of crime is predominantly committed by persons aged between 16 and 25 years, 89% of whom are male and 11% are female. It however emerges that there are‘female crimes’ and‘male crimes’. Women committed basically three types of crimes: infanticide and procuring abortion(84%), concealing birth(80%) and dangerous drugs and criminal damage(75%). On the other hand, men dominated five crimes: robbery and theft(99%), homicide(84%), offenses against morality (81%), assault(79%) and economic crimes and corruption(78%). Overall, crime has generally been on the increase but it was highest in 2006. Juvenile offenders involvement in crime was influenced by family deficiencies, while others indicated money(67%), peer pressure(13%) and survival(13%) as causes. Most participants reported to have committed their first offence between the ages of 12 and 15 years of age(30%) or between 16 and 19 years(23%). Poverty(40%) and alcohol/drugs(23%) were responsible for increased vulnerability of youth to re-commit crime. Youth Fact Book Infinite Possibility Or Definite Disaster? xix 1 Demographics ‘Because people’s economic behaviour and needs vary at different stages of life, changes in a country’s age structure can[and do] have significant effects on its economic performance’ (Bloom 2003) 1.0 Demographics Demography, simply defined is the study of human populations and their characteristics. According to Opiyo& Agwanda(unpublished), it includes age structural changes of the population(the way in which population is distributed across different age groups at any given point in time). This is deemed important because people’s social and economic behaviour and needs vary at different stages of life and, therefore, changes in a country’s age structure can have a significant impact on its socio-economic development. The importance of age structure of the population is easily understood via the emerging concept of Age-Structural Transitions(ASTs). The AST model comprises four phases namely: the“childrich” phase which is characterized by an accelerated increase in the number of children following a decline of child mortality; the“expansion of young adult population” due to the continued decline of mortality and the onset of fertility decline; the“expansion of middle-aged population” which starts when the cohorts enlarged by mortality decline and increases in the number of birth reach middle ages; and finally the“expansion of the old age population” which sets in after birth rates have dropped to very low levels(Adioetomo et al, 2006). These demographic transitions produce rapid shifts in the nature and magnitude of demands and needs of particular age groups, patterns that are relevant for public policy domains and market sector considerations. Regular tracking of population dynamics is, therefore, critical so as to anticipate the implications of population dynamics on socio-economic development. The demographic definition of youth according to the United Nations is the age group between 15 and 24 years of age. This age group is used in this chapter to discuss demographic dynamics and also for comparative analysis. However, where possible data on 15-34 year olds is included. 1.1 Population in Kenya 1.1.1 Population Size and Growth Indicators in Kenya(1948-2009) Kenyan population growth rate rose steadily from about 2.5 percent per annum in 1948 to around 3.8 percent per annum in the 1980s – a pace described as one of the fastest ever recorded in history. As indicated on table 1, Kenya’s population has continued to grow exponentially and by 2009, the population size was slightly over seven fold the population in 1948 and over four fold that of 1962. While the population of 1948 doubled around 1975, the current population that increases by nearly over 1 million people annually is expected to double by the year 2034. The United Nations projects that Kenya’s population will reach 46 million by 2015, 57 million by 2025, and 85 million by 2050(UN, 2007). 2 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 1: Population Size and Growth in Kenya(1948-2009) Census Year 1948 1962 1969 1979 1989 1999 Population(millions) 5.4 8.6 10.9 15.3 21.4 28.7 Annual growth rate(Percent per annum) 2.5 3.0 3.3 3.8 3.3 2.9 Absolute increase per annum(‘000) 135 258 360 581 792 850 Size relative to 1948(1948=100) 100 159.3 201.9 283.3 396.3 531.5 Size relative to 1962(1962=100)- 100 126.7 177.9 248.8 333.7 Source: Compiled from the 1948, 1962, 1969, 1979, 1989 and 1999 Kenya Population Census Reports *** Based on projections 2009*** 39.1 2.7 1,017 724.1 454.7 1.1.2 Kenya’s Population by Age in 1999 and 2009 Census Table 2: Kenya’s Population by Age in 1999 and 2009 Census Age Age 0-4 Age 5-9 Age 10-14 Age 15-19 Age 20-24 Age 25-29 Age 30- 34 Age 35-39 Age 40-44 Age 45-49 Age 50-54 Age 55-59 Age60-64 Age 65-69 Age 70-74 Age 75-79 Age 80+ 1999 Male 2,291,936 2,000,580 2,034,980 1,681,984 1,328,529 1,094,909 840,692 695,263 516,502 419,841 344,639 223,691 194,513 140,969 118,601 79,166 95,300 Female 2,242,966 1,962,556 2,003,655 1,721,194 1,504,389 1,164,594 845,230 723,749 516,989 418,987 340,167 236,325 214,715 160,364 135,524 81,620 121,038 2009 Male 3,000,439 2,832,669 2,565,313 2,123,653 1,754,105 1,529,116 1,257,035 1,004,361 743,594 635,276 478,346 359,466 295,197 183,151 160,301 99,833 159,125 Female 2,938,867 2,765,047 2,469,542 2,045,890 2,020,998 1,672,110 1,262,471 1,004,271 732,575 637,469 477,860 352,487 298,581 207,612 179,000 118,675 224,576 Source: Statistical Abstract, 2009 and Census, 2009 Between 1999 and 2009, Kenya’s population grew by 35%. The highest growth for men and women was registered among 80+ year olds followed by 55-59 year old men and 45-49 year old women. Youth Fact Book Infinite Possibility Or Definite Disaster? 3 1.1.3 Population Trends among Kenya’s Young People(1969- 2009) Youth(15-34) Trends in Kenya Btw 1969& 2009 3,324,138 4,943,037 7,070,815 10,181,521 13,665,378 1969 1979 1989 1999 2009 Figure 1: Population Trends among Kenya’s Young People(1969- 2009) Source: Various Census Reports As indicated in figure 1 and also in table 3, youth population(15-34 year olds) has been increasing since 1969 to 2009. Youth population according to the census, constitutes 35.39% of the total population. Those aged between 0-14 years constitute 42.92% of the total population thus under 34’s constitute 78.31% of Kenya’s population. Table 3: Population Trends among Kenya’s Young People(1969- 2009) Youth Population Growth(1969- 2009) Age 1969 1979 15-19 1,104,999 1,741,845 20-24 878,111 1,327,404 25-29 760,839 1,055,712 30-34 580,189 818,076 Source: Various Census Reports 1989 2,378,696 1,902,934 1,629,761 1,159,424 1999 3,403,178 2,832,918 2,259,503 1,685,922 2009 4,169,543 3,775,103 3,201,226 2,519,506 4 Youth Fact Book Infinite Possibility Or Definite Disaster? 1.1.4 Youth population(15-34) in 2009 by Age and Gender Population of Kenya’s Youth(15-34) by Gender 2,045,890 2,123,653 2,020,998 1,754,109 1,672,110 1,529,116 Female Male 1,262,471 1,257,035 No’s in Millions 15-19 20-24 25-29 30-34 Age Figure 2: Youth Population(15-34) in 2009 by Age and Gender Source: Census, 2009 The female population is slightly higher than the male population in all the age cohorts. Overall, females constitute 51% while males constitute 49% of the youth population. 1.1.5 Youth Population(15-34) in 2009 by Province 26% of all 15-34 year olds in Kenya come from Rift Valley province while 14% come from Nyanza and another 14% from Eastern province. Central and Nairobi provinces each house 11% of the youth population while Western, Coast and North Eastern each house 10%, 9% and 5% of the youth population respectively. The low numbers in North Eastern may be explained by the cancelled census results. Figure 3: Youth Population(15-34) in 2009 by Province Source: Census, 2009 Youth Fact Book Infinite Possibility Or Definite Disaster? 5 1.1.6 Youth Population(15-34) in 2009 by Age Youth Population(15-34) Distribution by Age 30-34 18% 15-19 31% 25-29 23% 20-24 28% Figure 4: Youth Population(15-34) Distribution by Age Source: Census, 2009 15-19 year olds make up 31% of all youth aged between 15 and 34, while 20 – 24 year olds make up 28%. 25-29 as well as 30-34 make up 23% and 18% respectively of all youth aged 15-34 years old. In total, young people aged between 15-34 years are 13,665,378 million. 1.1.7 Rural Urban Youth Distribution. Rural/Urban Youth(15-34) Distribution by Gender 4,083,495 2,580,414 4,266,522 Rural Urban 2,734,947 Male Figure 5: Rural/Urban Youth(15-34) Distribution by Gender Source: Census, 2009 Female 61% of Kenya’s youth(15-34) live in rural areas and only 39% live in urban areas. However, the figures vary with different age cohorts. 70% of 15-19 year olds live in rural areas, 58% of 20-24 year olds live in rural areas and 55% of 25-29 year olds live in rural areas. This pattern is attributed to rural-urban migration among young people that increases with age. 6 Youth Fact Book Infinite Possibility Or Definite Disaster? 1.2 Age Structural Transitions Figure 6 shows the past, present and future(projected) trends in age structure of the Kenyan population. The figure depicts a population dominated by children(aged 0-14). However, the proportion of persons aged 0-14 years has been declining since the 1980s after reaching its peak of about 50%, while that of producers(aged 15-64 years) has been rising consistently, and projected to reach a high of about 65% in 2050. The elderly still constitute a relatively minuscule proportion of the Kenyan population, rising modestly to a little more than 6% in 2050. Age structural transitions: Kenya, 1950 – 2050 Figure 6: Age structural transitions: Kenya, 1950 – 2050 Source: Various Kenya Population Census Reports 1.3 Demographic Characteristics of Youth in Kenya According to Opiyo and Agwanda(unpublished), the youthfulness of a population is always indexed by the median age. The median age 1 declined from about 20 years in 1950s to about 18 years at the beginning of this century( 2000-2005), but is expected to reach 27 years in 2050(UN, 2007). Table 4: Demographic characteristics of the Youth population Census Year 1969 1979 1989 Total population(‘000) 10,944 15,327 21,444 Population of Youth(ages 15-24)(‘000) 2,032 3,153 4,282 Working population ages 30-59(‘000) 1,959 2,693 4,222 Ratio of Youth to total population(%) 19 21 20 Ratio of Youth to Working population(%) 104 117 101 Ratio of working population to total population(%) 18 18 20 Population of Youth relative to 1969(1969=100) 100 140 196 Source: Compiled from the 1969, 1979, 1989 and 1999 Kenya Population Census Reports 1999 28,687 6,236 6,122 22 102 21 262 1 Median age is that at which half the population is above or below. Youth Fact Book Infinite Possibility Or Definite Disaster? 7 When compared to the total population, the proportion of the youth population(15-24) has remained at just about one quarter of the total population since independence. Although the relative share of the Youth population has nearly remained the same, the effect manifests in the absolute size, which has increased tremendously over the years. The‘youthfulness’ of Kenya’s population is typical of African countries, with the proportion of population aged 15-24 years being substantially higher than that of other developing and developed countries. For Kenya, the proportion of persons aged 15-24 years has been rising at a modest pace since the 1950s to a peak of about 22% in 2000s, and a projected gradual decline after 2030. Kenya’s Youth population as a percentage of the total population Figure 7: Kenya’s Youth population as a percentage of the total population Source: 1969, 1979, 1989 and 1999 Kenya Population Census Reports However, according to figure 8, the Youth have constituted a large part of the working population (15-59) since independence. Figure 8: Trend in Percentage of Kenya’s Youth Population to the Potential Working Population Source: 1969, 1979, 1989 and 1999 Kenya Population Census Reports 8 Youth Fact Book Infinite Possibility Or Definite Disaster? 1.4 Population Dynamics According to Opiyo and Agwanda(unpublished), the pace at which mortality and fertility change and the length of time between mortality decline and fertility decline determines the rate of population growth that will be observed. Table 5: Population Dynamics Year 48 62 63 69 79 84 87 89 92 93 94 96 00 03 05 09 Population(Millions) 5.4 8.6 8.9 10.9 15.3 18.4 21.8 21.4 24.6 25.3 26.1 27.4 30 33 35.1 39 Fertility rate 6 6.8 6.8 7.6 7.9 7.7 7.7 6.6 5.4 5.4 4.9 4.7 4.9 4.9 4.6 4.6 Crude death rate /1000 25 20 20 17 14 13 13 12 12 10 12 13.3 13.7 14 11.9 13 Crude birth rate/1000 50 50 50 50 52 50 50 48 46 46 40 38 42 42 39.7 39 Life Expectancy at birth 35 44 44 49 54 62 56 60 54 54 53 50 49 49 53 54 Infant Mortality rate /1000 184 120 118 104 64.4 80 71.2 86.2 86.7 87.3 94.2 82 77 65.5 52 Under-5 mortality rate /1000 na 156 0 88.1 98 123 123 124 137 116 115 90.5 74 Adult HIV mortality rate /1000 na 0 0 0 3.1 4.7 5.3 6.7 8.5 13.4 6.7 7.4(07) 6.3 Source: UNDP(2006), World Bank(1990), Various Census Reports, KDHS(2009) 1.4.1 Total Fertility Rates The total fertility rate(TFR) is the sum of age-specific fertility rates in a given year, and can be interpreted as the number of births a woman would have in her lifetime, given the age-specific probabilities of birth in that year. The TFR is a useful summary of the actual fertility behavior of women in a given period. From 1948 to the early 1960’s, TFR oscillated from 6 to 6.8 before increasing to an average of 7.8 in the late 60’s to the late 70’s. Since 1989, TFR has been reducing gradually from 6.6 and is currently at an average of 4.6 children per woman. The changes in fertility is a result of complex processes that involve changes in the demand for children, the diffusion of new attitudes about birth control and greater accessibility to contraception provided by family planning programmes(Cleland and Wilson, 1987). Nevertheless, others argue that fertility changes are driven by shifts in desired family size rather than by the efforts of family planning programs(Pritchet, 1994). Still, others have argued that family planning program effort makes an important contribution to contraceptive practice irrespective of social settings which in turn cause fertility change(Ross and Stover, 2001). Potts(1997) also argued that the unconstrained access to fertility regulating technologies was the primary factor responsible for fertility declines. According to Opiyo and Agwanda(unpublished), these arguments raise two important but interrelated issues: changes in fertility levels occur not only as a result of changes in desired number of births but also the ability of couples/individuals to implement their fertility desires. Currently, the Contraceptive Prevalence Rate(CPR) 2 in Kenya is 46%. 39% of the women use modern methods while 9% use traditional methods(KDHS, 2009). These contraceptive trends among other factors help explain lowering fertility rates. 2 is the percentage of currently married women aged 15-49 who are using any method of family planning Youth Fact Book Infinite Possibility Or Definite Disaster? 9 Trends in contraceptive use among married women aged 15 – 49 from 1978- 2008 46 39 39 33 27 17 7 1975 1980 1985 1990 1995 2000 2005 2010 2015 Figure 9: Trends in contraceptive use among married women aged 15 – 49 from 1978- 2008 Source, KDHS, 2009 1.4.2 Infant Mortality Rates(IMR) Infant mortality rate measures the number of live births that die before age 1 divided by the total number of births(expressed per 1000 live births). It is a good indicator of decline in mortality. As indicated, infant mortality has generally been declining since 1948 when IMR was at 184/1000 to 2009 where IMR is at 52/1000. The initial rise in population growth rate was attributed to high and rising fertility with rapidly declining mortality rates. The peak change occurred between 1970s and 1980 when birth rates rose to the highest levels and death rates to the lowest levels. It is this period when Kenya marked the highest rate of natural increase. As a result of the rapidly changing birth and death rates, the absolute increase in population rapidly rose from 135, 000 persons per annum in 1948 to slightly over 1 million in the recent past(Opiyo and Agwanda, unpublished). 1.5 Kenya’s Demographic Transition from 1990 to 2020 Male Kenya: 1990 Female 1999 Population Pyramid Female Age 80+ Age 75–79 Male Age 70–74 80+ 75–79 70–74 Age 65–69 Age 60–64 65–69 60–64 55–59 Age 55–59 Age 50–54 50–54 Age 45–49 45–49 40–44 35–39 Age 40–44 Age 35–39 30–34 Age 30–34 25–29 20–24 15–19 Age 25–29 Age 20–24 10–14 Age 15–19 5–9 0–4 2.5 2-0 1-5 1.0 0.5 0.0 0.0 0.5 1.0 1.5 2.5 2.5 Population(in millions) Age 10–14 Age 5–9 Age 0–4 Cource: U.S. Census Bureau, International Data Base Source: Census, 1999 10 Youth Fact Book Infinite Possibility Or Definite Disaster? Source: Census, 2009 2009 Population Pyramid Female Age 80+ Age 75–79 Age 70–74 Age 65–69 Age 60–64 Age 55–59 Age 50–54 Age 45–49 Age 40–44 Age 35–39 Age 30–34 Age 25–29 Age 20–24 Age 15–19 Age 10–14 Age 5–9 Age 0–4 Male Kenya: 2020 Male Female 80+ 75–79 70–74 65–69 60–64 55–59 50–54 45–49 40–44 35–39 30–34 25–29 20–24 15–19 10–14 5–9 0–4 2.5 2-0 1-5 1.0 0.5 0.0 0.0 0.5 1.0 1.5 2.5 2.5 Population(in millions) Source: U.S. Census Bureau, International Data Base Figure 10: Kenya’s Demographic Transition from 1990 to 2020 Source: US, Bureau, International data base, 1999 and 2009 Census. Kenya has a pyramid shaped population structure mainly because the majority of the country’s population is currently concentrated at the bottom between age 0 and 14 years old and thins upwards as people grow older. It is envisaged that Kenya will experience a demographic shift/ transition due to changing patterns in fertility, mortality and population growth as well as socioeconomic factors. As the 0-14 age group matures into teenage-hood and young adulthood, and as many women continue to give birth later, space their children more or give birth to fewer children, it is envisaged that the bulge will shift to the working population, mainly starting with the 15-34 year olds as illustrated in Figure 10 above. According to the AST model, it means that Kenya will transition from a‘child-rich’ phase/child bulge to a‘young adult’ /youthful or youth bulge population. 1.6 Migration and Urbanization According to Opiyo& Agwanda(unpublished), migration is another component of population change. Migration is a complex phenomenon mainly because it must be defined in both spatial and temporal dimensions. The complexity of migration arises from the number of parameters that must be taken into account when describing population movement. These include type of change of boundary(internal vs. international); direction of the move(rural-rural, rural-urban, urban-rural etc); distance covered; timing and duration of stay(long term verses short term); and periodicity(repetitiveness). Different combinations of such parameters lead to different types of moves. Initial studies on internal migration however were heavily geared towards measurement of migration flows(Ominde, 1968; Rempell 1977; Oucho 1988 among others). However, these studies did not provide details on the nature and circumstances of migration(Oucho and Odipo, 2000). Labor migration is an important phenomenon because it links to the urbanization process in Kenya. As a way to escape poverty, many young people set out for better opportunities through migration. Indeed, migration to urban areas is unavoidable and even desirable as a way to improve allocation of human resources, especially in land-scarce countries. Youth are more likely than older people to move from rural to urban areas or to move across urban areas. According to Opiyo& Agwanda(unpublished), this increased youth migration has farreaching impacts. It increases the strain for jobs without necessarily improving the job conditions of those who are left in rural areas; impacts provision of public goods, education, utilities, housing, and infrastructure; and affects demographic and skills composition in both urban and rural areas. Youth Fact Book Infinite Possibility Or Definite Disaster? 11 Given that about 70% of the African Youth population is still in rural areas, and that urban areas have been very slow to create job opportunities for most new job seekers, there is a need for an integrated and coherent approach in which youth policies take cognizance of the rural-urban differentials in population/labor composition. Table 6: Trends in Urbanization in Kenya Year Population 1948 5,406 1962 8636 1969 10,943 1979 15,334 1989 21,444 1999 28,686 Urban(‘000) 285 671 1,082 2,314 3,864 5,954 % Urban 5.2 7.8 9.9 15.1 18 20.8 Urban annual growth rate (% per annum) Role Migration in urban growth 6.3 46 7.1 51 7.9 57 5.3 35 4.4 33 Source: Bocquier et al 2009 As indicated on table 6, the urban population growth has been increasing since independence. The share of the urban population increased from about 8% in 1962 to about 21% in 1999. According to Opiyo& Agwanda(unpublished), at the time of Kenya’s first population census in 1948, there were 17 urban centres with an aggregate population of 285,000 persons. The urban population was proportionately small(5.2% of the total) but disproportionately concentrated in Nairobi and Mombasa(74% of the total urban population) with the majority of the urban dwellers being non-Africans. By 1962, the number of urban centres had doubled to 34 and the urban population increased to 671,000 persons. This represented an urbanization level of 7.8%. The urban growth rate stood at 6.3% per year. The urban population grew to 1,082,000 persons in 1969, growing at the rate of 7.1% per annum. In 1969 this represented 9.9% of the total population with Nairobi and Mombasa accounting for 67% of the total urban population. By 1979, the overall level of urbanization had risen to 15.1% with 91 urban centres(population of 2.3 million). Nairobi and Mombasa accounted for 51% of the total urban population. In 1999, about 21% of the population lived in urban areas, of which half were in Nairobi or Mombasa. The fundamental issue is the fact that the rate of urbanization is fast but dominated by only two centres(Nairobi and Mombasa). This is one of the impediments to sufficient expansion of the labor market and spread of the labor force as most industries and public offices are situated in the two cities. The urban population is growing very fast while the economic growth and development transformations necessary to support it and enhance the quality of urban life are not occurring at the same rate. Most of the migrants come as young adults, usually after secondary school with employment as the motivation for migration. The majority of migrants were still males, a pattern that traces back to the pre-independence era until recently. However the sex distribution is more balanced now, a fact reflected in the male to female ratio, which has been reducing from one generation to the next(see Bocquier et al 2009 for the case of Nairobi). Figure 16 displays typical age patterns of urban population in Kenya. Majority of urban dwellers are young adults in the age group 25-29 typically fueled by rural urban migration. The age patterns reflect the growing dominance of the urban population in Kenya. 12 Youth Fact Book Infinite Possibility Or Definite Disaster? Age Patterns of Urban Population in Kenya(1989 Census) 30 28.5 25 26.0 25.9 22.8 20 15 13.8 15.2 12.0 11.4 10 19.6 16.6 16.6 14.3 14.0 10.6 12.6 11.9 5 0 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+ 2003 DHS Figure 11: Age Patterns of Urban Population in Kenya Source: KDHS, 2003 and Census, 1989 1989 census 1.7 Comparative Analysis 1.7.1 Population Size and Growth of 10-24 year olds in East Africa Population of Eastern African Youth Aged 10-24 in Millions 38.7 18.3 26.5 12.2 Kenya Ethiopia 15.2 11.8 4.5 2.6 20.6 17.1 10 13.4 Sudan Somalia Uganda Tanzania 4.4 3.4 Rwanda 2025 2006 4.7 2.8 Burundi Figure 12: Population Size and Growth of 10-24 year olds in East Africa Source: Population Reference Bureau[PBR], 2006 According to the PRB(2006) and as indicated in figure 12, Eastern African countries will experience 1.2 to 2 times population growth of 10-24 year olds by 2025. The percentage population of this age group will however reduce slightly from an average of 33.6% to an average of 32.5% of the total population. Youth Fact Book Infinite Possibility Or Definite Disaster? 13 Population of Youth in the World Aged10-24 in Millions Figure 13: Population of 10-24 Year Olds in the World Source: Population Reference Bureau, 2006 As illustrated by figure 13 population growth of 10-24 year olds around the world will grow 0.7 to 1.3 times by 2025. The percentage population of this age group will however reduce slightly from an average of 15% to an average of 12.6% of the total population. Asia and Africa will continue to have the highest populations of 10-24 year olds. 1.7.2 Age Structural Transitions % Population 50.00 45.00 40.00 35.00 30.00 25.00 20.00 15.00 10.00 1950 1960 1970 Age Structural Transitions Age Structural Transitions, 1950-2050 Comparing Kenya with other developing countries Age group: 0-14 years 1980 1990 2000 Year 2010 2020 Age Structural Transitions 2030 2050 Kenya Nigeria Sierra Leone Iran Brazil China 50.00 45.00 Age Structural Transitions, 1950-2050 Comparing Kenya with developed countries Age group: 0-14 years % Population 40.00 35.00 30.00 25.00 20.00 15.00 10.00 1950 1960 1970 1980 1990 2000 Year 2010 2020 2030 2050 Kenya Sweden USA Korea Italy UK France Figure 14: Age Structural Transitions of 0-14 year olds(1950 – 2050) among Developing and Developed Countries Source: Opiyo& Agwanda(unpublished) 14 Youth Fact Book Infinite Possibility Or Definite Disaster? According to figure 14, Kenya had a higher proportion of children from 1960s to 1990s, but the proportion has since declined to levels typical of other African countries such as Nigeria and Sierra Leone. However, the decline in the proportion of child populations has been more dramatic in other developing(Brazil, Korea, China, Iran) as well as developed(United Kingdom, France, Sweden, USA, Italy) countries. The trend for the Republic of Korea stands out. Starting with a population as young as Kenya’s in the 1950s, the proportion has declined dramatically, and is projected to fall below Italy’s – the oldest population in the world – by 2050. % Population 75.00 Age Structural Transitions Age Structural Transitions, 1950-2050 Comparing Kenya with developed countries Age group: 15-64 years 70.00 65.00 60.00 55.00 50.00 45.00 1950 1960 1970 75.00 70.00 65.00 60.00 55.00 50.00 45.00 40.00 1950 1960 1970 Kenya Sweden USA Korea Italy UK 1980 1990 2000 Year 2010 2020 2030 2050 France Age Structural Transitions Age Structural Transitions, 1950-2050 Comparing Kenya with other developing countries Age group: 15-64 years 1980 1990 2000 Year 2010 2020 2030 2050 Kenya Nigeria Sierra Leone Iran Brazil China % Population Figure 15: Age Structural Transitions of 15-64 year olds(1950 – 2050) among Developing and Developed Countries Source: Opiyo& Agwanda(unpublished) According to figure 15, the proportion of producers(aged 15-64 years) has started rising in African countries since the 1990s, after decades of nurturing large populations of young people. It is expected that by about 2030 Kenya’s population aged 15-64 years will overtake that of the developed countries, reaching at least 65% by 2050. Youth Fact Book Infinite Possibility Or Definite Disaster? 15 Age Structural Transitions % Population 25.00 20.00 Age Structural Transitions, 1950-2050 Comparing Kenya with other developing countries Age group: 65+ years 15.00 10.00 5.00 0.00 1950 1960 1970 1980 1990 2000 Year 2010 2020 2030 2050 Kenya Nigeria Sierra Leone Iran Brazil China Age Structural Transitions % Population Age Structural Transitions, 1950-2050 Comparing Kenya with developed countries Age group: 65+ years 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 1950 1960 1970 1980 1990 2000 Year 2010 2020 2030 2050 Kenya Sweden USA Korea Italy UK France Figure 16: Age Structural Transitions of 65+ year olds(1950 – 2050) among Developing and Developed Countries Source: Opiyo& Agwanda(unpublished) According to figure 16, ageing is unlikely to be a big issue for Africa as it is to developed nations or other developing countries(in Asia and Latin America), at least not until 2050 after which the situation may change dramatically. 16 Youth Fact Book Infinite Possibility Or Definite Disaster? 2 Health ‘No one can lead our lives for us. We are responsible for our actions. So people—especially the younger generation---need to be very careful especially where safe sex is concerned.’ (Salman Ahmad) 2.0 Health The World Health Organization(WHO) defines health as a state of complete physical, mental and social well-being, and not merely the absence of disease or infirmity. According to MugandaOnyando(unpublished), the health status of young people is driven by a combination of determinants including individual and societal factors coupled with institutional and economic factors at play in the health of youth in Kenya. Lifestyle changes and risk taking are some of the factors influencing the health status of young people. While data on this is limited, anecdotal evidence shows an increase in lifestyle related health conditions such as diabetes and hypertension. The use of drugs and alcohol and associated risk taking behaviour is contributing to accidents and injury. Among the critical health problems young people face however are those associated with sexuality and reproductive health such as early and unprotected sexual activity. These have a significant bearing on both their current and future health status. High fertility levels as well as high teenage pregnancy rates have serious negative consequences. Early childbearing disrupts the pursuit of education and limits future opportunities for socio-economic growth. But it is the emergence of HIV/AIDS and its impact that is posing one of the greatest challenges. The epidemic has changed the family landscape, resulting in a re-organization of roles and responsibilities, disrupting the lives of young people and driving up health care costs. Apart from increasing orphan hood, HIV/AIDS also increases vulnerability of young people and puts them at risk of exploitation. The disruption of family cohesion and the trauma associated with the same is a serious threat to the mental health of young people. In addition, the high burden on young people working as care givers to family members jeopardizes their ability to prepare for the future as some may have to leave school to be able to fend for themselves and their families. Most young people do not have access to adequate information and services. For instance youth friendly services that would enable youth access services as well as make informed choices and decisions regarding their health and general well being are lacking in most parts of the country. According to KESPA(2004) as quoted by Muganda-Onyando, only 12% of health facilities meet the minimum requirements of providing youth friendly services thus utilization of health care services remains low despite increased investment in the sector. There are also major regional disparities in access to services. Uptake of reproductive and maternal health is even lower with most women delivering at home without skilled care thus increasing the risk of pregnancy related complications that endanger the lives of mothers and newborns. Young women are disproportionately affected as adolescents are up to three times more likely to experience complications than older women. The result is higher rates of maternal morbidity and mortality among this segment of the population. Sadly though is the fact that young people consider health a low to medium priority. In a recent study of 15-20 year olds on expectations and priorities, 45 percent of young people ranked job opportunities as their top priority compared to only 4 percent who said the same of health (FACES, 2009) as quoted by Muganda-Onyando. Health ranked below education, wealth and income distribution and political participation. 18 Youth Fact Book Infinite Possibility Or Definite Disaster? 2.1 HIV/AIDS 2.1.1 HIV Prevalence among Women and Men Aged 15-34 HIV Prevalence among women and men aged 15-34 % of HIV prevalence 25 20 15 10 1 5 3.5 0 15-19 7.3 1.9 10.2 7.4 20-24 Age(years) 25-29 Female Male 8.9 13.3 30-34 Figure 17: HIV Prevalence among Women and Men Aged 15-34 Source: GoK, 2009 HIV is highest among young women than young men. For women, the prevalence peaks at the age of 30-34 and begins to reduce in the following years to 11.2% and to 9.4% in the 35-39 and 40 – 44 age cohorts respectively. For men however, the prevalence continues to rise to 9.3% and 10.2% in the 35-39 and 40 – 44 age cohorts where it peaks before it starts declining. This pattern is attributed to men in their mid-life crisis having sexual relationships with younger women. 2.1.2 HIV Prevalence among Women and Men Aged 15-24 HIV Prevalence among women and men aged 15-24 % of HIV prevalence 14 12 10 8 6.5 9.7 6 3 4 4.4 4 3.1 2.5 5.5 2.3 2 2 2.3 0.68 0.41 1.1 0.6 0.66 0 15 16 17 18 19 20 21 22 Age Figure 18: HIV Prevalence among Women and Men Aged 15-24 Source: GoK, 2009 Female Male 12 6.9 2.6 2.3 23 24 Youth Fact Book Infinite Possibility Or Definite Disaster? 19 According to the Kenya AIDS Indicator Survey(KAIS) report, the overall prevalence of HIV among youth aged 15-24 years was 3.8%. However, when you look at individual ages, the prevalence varies from 2.5%- 12% among young women of that age and 0.41% to 2.6% among young men of the same age. By 24 years, women were 5.2 times more likely to be infected with HIV than young men of the same age. 2.1.3 HIV Prevalence among Women and Men Aged 15-34 in Rural and Urban Areas HIV Prevalence among Women and Men Aged 15-34 in Rural and Urban Areas Rural Urban 15.8 3.1 2.1 15-19 4.3 5.7 20-24 8.1 9.5 25-29 10 30-34 Figure 19: HIV Prevalence among Women and Men Aged 15-34 in Rural and Urban Areas Source: GoK, 2009 For both rural and urban areas, peak prevalence was highest among 30-34 age cohorts. Among 15-19 and 30-34 year olds, urban prevalence was higher than rural prevalence while among 20-24 and 25-29 age cohorts, rural prevalence was higher than the urban prevalence. 2.1.4 HIV Prevalence by Region HIV prevalence is highest in Nyanza (13.9%), followed by Nairobi(7%), Western(6.6%), Rift Valley(4.7%) and Central(4.6%). Coast, Eastern and North Eastern have 4.2%, 3.5% and 0.9% prevalence respectively. Figure 20: HIV Prevalence by Region Source: GoK, 2009 20 Youth Fact Book Infinite Possibility Or Definite Disaster? 2.1.5 HIV Prevalence among Un/Circumcised Men by Age Group HIV Prevalence among un/circumsised men by age group % of HIV prevalence 40 35 30 25 20 15 10 1.7 5 1.3 0 15-24 21.7 4.6 25-29 Uncircumcised Circumcised 29.7 6.3 30-39 Figure 21: HIV Prevalence among Un/Circumcised Men by Age Group Source: GoK, 2009 HIV prevalence is consistently high among young men who are uncircumcised. Peak HIV prevalence was among 30-39 year olds for both circumcised(6.3%) and uncircumcised(29.7%) men. According to the KAIS report, HIV Prevalence was approximately five(5) times higher among uncircumcised than circumcised men in all age groups except among 15-24 year olds. 2.1.6 HIV Prevalence among Women and Men Aged 15-34 by 2003, 2007& 2009 KDHS Reports Table 7: HIV Prevalence among Women and Men Aged 15-34 by 2003, 2007& 2009 KDHS Reports Age Group Male Female Year Year 2003 2007 2009 2003 2007 2009 15-19 0.4 1 0.7 3 3.5 2.7 20-24 2.4 1.9 1.5 9 7.4 6.4 25-29 7.3 7.3 6.5 12.9 10.2 10.4 30-34 6.6 8.9 6.8 11.7 13.3 11 Total 16.7 19.1 15.5 36.6 34.4 30.5 Average 4.175 4.775 3.875 9.15 8.6 7.625 Source: GoK, 2009 HIV prevalence among 15-34 year old men generally increased between 2003 and 2007 from 4% to 4.7% before declining to 3.9%. Among women, prevalence decreased from 9% in 2003 to 8.6% in 2007 and further to 7.6% in 2009. In the 2003 KDHS report, HIV prevalence peaked for both males and females at age 25—29. In 2007, male and female prevalence peaked at age 30-34. This may be attributed to the fact that those infected in 2003 moved the 30-34 age brackets by 2007. In 2009, HIV prevalence for both males and females generally decreased in all age cohorts Youth Fact Book Infinite Possibility Or Definite Disaster? 21 with the exception of 25-29 year olds where it slightly increased by 0.2%. Prevalence still peaked at age 30-34 in 2009. 2.1.7 HIV Prevalence by Age of Sexual Debut in 2003 and 2007 KDHS Reports Age of first sexual encounter has consistently been rising between 2003 and 2007. According to the KAIS report, the earlier the age of the first sexual encounter, the higher the chances of contracting HIV. HIV Prevalence by age of sexual debut by 2003 and 2007 KDHS reports 2007 KDHS 2003 KDHS 9.4 9.3 7.1 8.8 7.7 6.8 <15 15-17 18+ Figure 22: HIV Prevalence by Age of Sexual Debut in 2003 and 2007 KDHS Reports GoK, 2009 2.1.8 15 – 34 Year Olds Who Have Ever Been Tested for HIV by Gender 15 –34 year olds who have ever been tested for HIV by gender Male 66.2 Female 70 61.3 60 52.4 45.8 50 40 32.2 33.8 34 30 15.1 20 10 0 15-19 20-24 25-29 30-34 Figure 23: 15 – 34 Year Olds Who Have Ever Been Tested for HIV by Gender GoK, 2009 22 Youth Fact Book Infinite Possibility Or Definite Disaster? HIV testing was consistently high among young women aged 15 to 34. This may be attributed to the fact that it is the main reproductive age. Test rates were highest among those aged 20-24 years and 66% of the women were tested. For young men, the highest rate of testing was among 30-34 year olds and even then, only 34% of this age group were tested. 2.2 Total Fertility Rates(TFR) 2.2.1 Total Fertility Rates(TFR) in Kenya by Age According to KDHS(2009), TFR refers to the average number of children a woman would have if she went through her entire reproductive period. From the trends below, rural women of all age cohorts have a higher Total Fertility Rate than urban women. TFR generally increases from age 15 and peaks at age 24 before it slowly starts declining. Total Fertility Rate of Young(15-34) Women per 1,000 in Kenya by Residence 280 248 Urban Rural 197 146 147 107 104 92 15-19 20-24 25-29 30-34 Figure 24: Total Fertility Rates(TFR) in Kenya by Age Source: KDHS, 2009 Generally fertility rates are highest in North Eastern(5.9), Western(5.6), and Nyanza(5.4). Coast, Rift Valley, Eastern, Central and Nairobi have fertility rates of 4.8, 4.7, 4.4, 3.4 and 2.8 respectively. The more educated a woman is the lesser the fertility rate. Youth Fact Book Infinite Possibility Or Definite Disaster? 23 2.2.2 Total Fertility Rates(TFR) in Kenya over Time Fertility Rate of Young(15-34) Women per 1,000 in Kenya btw 1978& 2009 357 342 314 20-24 25-29 30-34 15-19 293 168 303 255 152 257 241 197 110 248 218 188 111 254 236 185 142 243 231 196 114 238 216 175 103 KFS 78 KDHS 89 KDHS 93 KDHS 98 Figure 25: Total Fertility Rates(TFR) in Kenya over Time Source: KDHS, 2009 Census 99 KDHS 03 KDHS 09 TFR increased from 5.3 in 1962 to 6.6 in 1969 and to 8 in 1977. In 1978 and 1979, TFR declined to 7.9. Thereafter, fertility rate has been decreasing consistently to a total of 4.6 in 2008/9. TFR has consistently remained highest among the 20-24 year olds. 2.3 Early Childhood Mortality Rates The demographic characteristics of both mother and child have been found to play an important role in the survival of children. Age specific mortality rates are categorized and defined as follows. - Neonatal mortality(NN) refers to the probability of dying within the first month of life. - Infant mortality refers to the probability of dying before the first birthday - Child mortality refers to the probability of dying before the first and the fifth birthday - Under-five mortality refers to the probability of dying between birth and the fifth birthday. 2.3.1 Early Childhood Mortality Rates by Age Chilhood mortality rates by age(per 1,000) 100 68 58 40 54 35 28 Neonatal Mortality Infant mortality Figure 26: Early Childhood Mortality Rates by Age Source: KDHS, 2009 79 77 under-five mortality <20 20-29 30-39 24 Youth Fact Book Infinite Possibility Or Definite Disaster? According to the KDHS, 2009, studies have shown that a mother’s age at birth affects the child’s chances of survival. Women who give birth below the age of 20 have high numbers of mortality rates. Mortality rates are even higher for women giving birth in their forties. 2.3.2 Early Childhood Mortality Rates by Demographic and Socio-Economic Characteristics Table 8: Early Childhood Mortality Rates by Demographic and Socio-Economic Characteristics Demographic Characteristics Childs sex Male Female Birth Order 1 2 to 3 4 to 6 7+ Socio-economic Characteristics Residence Urban Rural Mother’s education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest Neonatal Mortality 38 28 37 29 28 50 Neonatal Mortality 32 33 39 39 25 31 39 33 41 21 29 Infant Mortality under-five mortality 65 90 53 77 62 51 59 79 Infant Mortality 78 80 86 102 under-five Mortality 63 74 58 86 64 86 73 112 51 68 45 59 66 98 64 102 67 92 39 51 57 68 Source, KDHS, 2009 Male children exhibit higher mortality rates than female children across all childhood mortality indicators. According to KDHS(2009), boy babies are 36% more likely to die in the first month of life than girl babies. Data indicates that there are generally higher chances of mortality for first births. Mortality is however highest among the 7th and subsequent children. Rural urban differentials show a reversed pattern where unlike in previous surveys, mortality in urban areas now exceed those in rural areas. Infant mortality for example is 9% higher in urban(63 per 1000) than in rural areas(58 per 1,000). It is important to Youth Fact Book Infinite Possibility Or Definite Disaster? 25 note that infant mortality remained the same for urban areas as that recorded in 2003 but in rural areas it dropped from 79 deaths per 1000 in 2003 to 58 per 1000 deaths in 2009(a 27% drop). A mother’s education can exert a positive influence on children’s health and survival. Mortality rates are generally lower among children of women with some secondary education and above. However, under-five mortality rate is highest among children whose mothers have incomplete primary education. Interestingly, child mortality rate is lowest among women in the forth quintile across the board. 2.3.3 Perinatal Mortality Rates According to KDHS, 2009, perinatal mortality is a good indicator of the state of health in general and the health status of the mother at the time of delivery. Perinatal deaths constitute of pregnancy losses occurring after seven completed months of gestation(also referred to as stillbirths) as well as deaths to live births within the first seven days of life(early neonatal deaths) the distinction between stillbirths and neonatal deaths may be a fine one hence the combination to instead refer to both of them as perinatal deaths. Table 9: Perinatal Mortality Rates Demographic Characteristics No. of Still births Age < 20 6 20-29 32 30-39 25 Previous pregnancy interval in months First pregnancy 15 <15 9 15-26 14 27-38 5 39+ 26 Residence Urban 11 Rural 57 Mother’s education No education 9 Primary Incomplete 31 Primary complete 13 Secondary+ 15 Wealth Quintile Lowest 11 Second 17 Middle 11 Fourth 15 Highest 14 Source: KDHS, 2009* shows unweighted pregnancies No. of Early Neonatal deaths 23 75 35 33 21 35 19 40 28 120 28 46 32 41 51 24 29 13 32 Perinatal mortality rate 31 32 43 37 83* 36 18 42 37 37 49 39 25 41 43 34 36 27 41 26 Youth Fact Book Infinite Possibility Or Definite Disaster? Perinatal mortality risk is highest among 30-39 year old mothers. Rural and urban risks are similar as the 2003 indicators. There are no clear patterns of perinatal mortality by other characteristics of the mother as indicated above. 2.4. Antenatal care 2.4.1 Antenatal Care among Young Mothers Table 10: Antenatal Care among Young Mothers Demographic Characteristics Doctor Nurse/Midwife Community Traditional Health worker Birth Attendant No one Mothers age at birth <20 29.9 58.7 0.6 1 9.9 20-34 29 64.3 0.1 0.7 5.7 Birth Order 1 33.8 57.8 0.4 0.5 6.7 2 to 3 32.5 61.3 0.2 1.1 4.8 4 to 6 26.8 66.9 0 0.3 5.9 7+ 18.6 64.9 0.2 1.2 14.4 Residence Urban 40.5 55.3 0.6 0.6 3 Rural 25.9 64.5 0.1 0.9 8.4 Mother’s education No education 21 51.4 0.2 1.5 24.7 Primary Incomplete 24.7 66 0.1 1.3 7.8 Primary complete 29.9 65.2 0.4 0.2 4.2 Secondary+ 36 60.2 0.1 0.7 3 Wealth Quintile Lowest 19.9 63.7 0 1 14.6 Second 23.3 69.5 0.4 1.5 5.2 Middle 28.6 64.6 0 0.9 5.9 Fourth 33.2 59.9 0.5 0.4 6.4 Highest 39.2 56.4 0.1 0.3 4 Source: KDHS, 2009 % Receiving care from skilled provider (Doctor, nurse or midwife) 88.5 93.3 92.4 93.7 93.7 83.5 95.8 90.3 72.4 90.7 95 96.3 83.6 92.7 93.2 92.7 95.6 62% of antenatal care is administered by nurses or midwives while doctors administer only 29%. About 8% of the women who do not receive any antenatal care at all are likely to be getting their 7th+ child, living in rural areas, without education and from the lowest wealth quintile. Percentage of women receiving antenatal care has improved from 88% in 2003 to 92% in 2009. Youth Fact Book Infinite Possibility Or Definite Disaster? 27 2.4.2 Antenatal Care by Region 58.1 58 38.3 34.7 3.3 0.1 Nairobi 6 0 Central Antenatal care by Region Nurse/Midwife Doctor 73.2 68.1 62.5 49.4 No one Traditional Birth Attendant 75.9 66 45.1 5.3 0.2 Coast 25.4 6.1 0 Eastern 20.5 5.2 1.2 Nyanza 25.9 10.2 0.9 RiftValley 15.6 5.6 2.8 Western 28.9 3.5 0.2 N. E Figure 27: Antenatal Care by Region Source: KDHS, 2009 Central province has the highest antenatal care by doctors followed by Coast province. North Eastern has the least antenatal care by doctors at only 3.5%. Western province has the highest number of antenatal care by nurses followed by Nyanza, Eastern then North Eastern and Rift Valley provinces. Western province also has the highest number of antenatal care given by traditional birth attendants. North Eastern and Rift valley provinces have very high numbers of no antenatal care administered to women at all. 28 Youth Fact Book Infinite Possibility Or Definite Disaster? 2.5. Place of Delivery 2.5.1. Place of Delivery by Demographic Characteristics Table 11: Place of Delivery by Demographic Characteristics Demographic Characteristics Mothers age at birth <20 20-34 1 2 to 3 4 to 5 6+ None 1 to 3 4+ Urban Rural No education Primary Incomplete Primary complete Secondary+ Lowest Second Middle Fourth Highest Public health facility Private health facility 37.3 9.3 32 10.7 Birth Order 47.2 13.9 33.8 11.9 23.6 9.3 21.2 3.6 Antenatal care visits 5.1 5.6 29.7 8.5 44.6 15.7 Residence 51.6 23.1 28 7.4 Mother’s education 12.3 2.8 23.6 4.4 37.1 10.9 49.6 22 Wealth Quintile 16 2.1 23.1 7.3 36.2 5.4 39.9 11.6 52.9 28 Source: KDHS, 2009 Home 52.6 56.2 37.7 53.5 65.8 73.3 87.5 60.7 38.4 24.5 63.3 83.5 70.8 51 27 80.9 68.3 56.7 47.2 18.4 54% of young women aged less than 20 to 34 years old give birth at home. Generally, women are likely to deliver their first baby in a health facility but this decreases as the number of children increase. The higher the level of education and the level of wealth, the more likely the woman will give birth in a health care facility. More women in urban areas give birth in a health facility than rural women. Reasons given for not delivering in a health facility by those below 20 years included: long distance(45%); they felt it was not necessary(25%); and cost(19.5%). Those aged 20-34 years old said they did not deliver in a health facility because: of distance(42%); abrupt delivery(19.5) and the fact that it was not necessary(19.1%). Youth Fact Book Infinite Possibility Or Definite Disaster? 29 2.5.2 Place of Delivery by Region Place of Delivery by Region Private health facility Public health facility Home 45.7 43.7 9.9 Nairobi 56.7 25.9 16.2 Central 54.6 54.8 54.9 38 33.5 34.7 6.4 Coast 9.3 Eastern 9.4 Nyanza 73.3 66.3 26 19.1 6.8 RiftValley 6.3 Western 81.3 16.6 0.7 N. E Figure 28: Place of Delivery by Region Source: KDHS, 2009 81% of women in North Eastern, 73% of women in Western and 66% of women in Rift Valley deliver at home compared to only 10% in Nairobi province. Most women will deliver their babies in public health facilities than in private. Central province has the highest deliveries in public health facilities while Nairobi has the highest delivery in private health facilities(44% of women). In Nairobi the reasons for delivering at home included distance(38.6%) and cost(34.2%); in Central province, distance(37%) and abrupt delivery(25%); Coast province, not necessary (36%) and distance(33%); Eastern province, distance(42.3%), cost(21.6%) and abrupt delivery (21.4%); Nyanza province, distance(45.7%) and abrupt delivery(24.2%); Rift Valley mainly mentioned distance(42.2%) and not necessary(31.3%); Western, distance(42.7%), cost(22.2%) while North Eastern mentioned distance(46.4%), facility not open(22.4%), not necessary(18.6%) and poor quality service(17.3%). 2.6. Assistance during Delivery 2.6.1. Assistance during Delivery by Age According to the KDHS report,(2009), assistance during childbirth is an important variable that influences the birth outcome and the health of the mother and the infant. 29% of deliveries among under 20’s to 34 year olds are assisted by nurses or midwives while 28% are administered by traditional birth attendants. 21% are administered by relatives/other and 17% by doctors. 4.5% are not assisted at all. About 6.2% of this age group will undergo a c-section when giving birth, more among 20-34 year olds than among under 20’s. 30 Youth Fact Book Infinite Possibility Or Definite Disaster? Assistance during delivery 29.4 28.5 <20 20-34 27.9 28 18.3 16 20.9 20.3 6.4 6.6 Doctor Nurse/Midwife Traditional Birth Attendant Figure 29: Assistance during Delivery by Age KDHS, 2009 Relative/ Other 2.6 No one 2.6.2. Assistance during Delivery by Demographic Characteristics Table 12: Assistance during Delivery by Demographic Characteristics Demographic Characteristics Birth Order 1 2 to 3 4 to 6 7+ Residence Urban Rural Mother’s education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest sum av Source: KDHS, 2009 Doctor 25.4 17.4 11.4 7.1 28.3 13.3 6.6 9.7 18.8 26.6 5.6 11.8 14.5 19.1 32.9 282.8 16.6 Nurse/ Traditional Relative/ Other Midwife Birth Attendant 36.9 21.6 14.7 29.2 28.3 20.6 22.8 28.2 25.9 19.4 32.6 25 46.5 15.2 7.8 23.5 30.4 24.2 12.7 42.1 29 18.8 35.8 26.1 30.1 24.3 19.8 45.9 12.1 11.6 14.7 43.9 26.7 19.6 33.5 25.9 27.4 25 24.6 33.7 22.1 18.6 48.5 7.3 7.8 487 458.9 349.5 28.6 26.9 20.6 No one 0.9 3.8 11.2 15.1 1.6 8 8.8 8.8 6.5 3.2 8.1 8.9 7.7 5.8 2.8 110.2 6.5 5.8 % delivered by C-Section % delivered by C-Section 11.2 6.4 4.3 2.1 11.3 5.1 1.2 3.4 6.7 12.4 2 3.1 6.4 7.1 14.3 109.4 6.4 Youth Fact Book Infinite Possibility Or Definite Disaster? 31 Overall, 29% of births in Kenya are attended to by nurses/midwives while 27% of births in Kenya are attended to by traditional birth attendants. 21% are by relatives/friends and 17% are by doctors. 7% are with no assistance. The likelihood of a c-section reduces with increased number of children. 11% of women in urban areas go through c-sections compared to 5% of women in the rural areas. 2.6.3. Assistance during Delivery by Region Table 13: Assistance during Delivery by Region Demographic Characteristics Nairobi Central Coast Eastern Nyanza Rift Valley Western N. E Source: KDHS, 2009 Doctor 33.7 45 21.3 16.9 13.5 10 5.5 1 Nurse/ Midwife 55.2 28.8 24.3 26.2 32 23.7 20.3 30.6 Traditional Birth Attendant 5.6 1.7 21 27.8 26.2 30.7 45 64.2 Relative/ Other 3.7 17.8 27.5 26 20.5 26.7 14.2 1.9 No one 1.2 6.6 5.4 2.9 6.3 8.3 14.6 0 % delivered by C-Section 11.5 14.5 5.9 7.9 4.4 5.1 3.5 0.6 Nairobi(89%) and Central(74%) provinces have the highest numbers of births delivered by a skilled attendant while Western(26%), North Eastern(32%) and Rift Valley(34%) have the least births delivered by skilled attendants. 62% of births in North Eastern are delivered by traditional attendants. Coast(28%) and Rift Valley(27%) have the highest numbers of births delivered by relatives and friends. Western(15%) province has the highest number of births delivered with no assistance. 15% of women in Central province and 12% of women in Nairobi are likely to deliver by C-section. 2.7. Nutritional Status of Women by Background Characteristics According to KDHS(2009), the height of a woman is associated with past socio-economic status and nutrition during childhood and adolescence. Body Mass Index(BMI) on the other hand is used to measure thinness or obesity of an individual. It is calculated by dividing the weight of the in Kilograms by the height squared in meters(Kg/m2). A BMI of 18.5 is used to define thinness or acute under nutrition while a BMI of 25 and above usually indicates overweight or obesity. Low pre-pregnancy BMI and a short stature are risk factors for poor birth outcomes and obstetric complications. In developing countries, maternal underweight is a leading risk factor for preventable death and diseases. 32 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 14: Nutritional Status of Women by Background Characteristics Demographic Characteristics Age 15-19 20-29 30-39 Residence Urban Rural Mother’s education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest Source: KDHS, 2009 % below 145 cm 2.5 0.9 0.7 0.6 1.5 3.1 1.9 1 0.4 2.6 1 1.8 0.5 0.7 Mean BMI Normal Thin(<18.5) Mildly thin Severely Overweight (18.5- 24.9)(17- 18.4) thin(<17)(25-29.9) or Obese 21.2 72.3 19 12.4 6.6 8.7 22.6 67 10.5 8.1 2.4 22.5 23.7 58.2 9.6 6.9 2.8 32 24.6 53.2 7 5.4 1.6 39.8 22.3 65.8 14.1 9.6 4.5 20.1 21.2 58.2 26.4 16.4 9.9 15.5 21.9 68.4 16.8 11.6 5.2 14.8 23.2 63.1 9.2 6.7 2.5 27.7 24 58.4 7.5 5.3 2.1 34 20.8 69.4 21.2 13.9 7.3 9.4 21.6 69.8 17.5 12.8 4.6 12.8 22.4 67.2 12.6 7.6 5 20.2 23.6 59.7 9.2 6.3 2.9 31.1 24.8 52.8 5.9 4.8 1 41.3 2.5% of 15-19 year olds are below 145 cm and tend to have a mean BMI of 21.2. Of all the age categories, they have the highest proportions of normal weight(72%), thin(19%), mildly thin (12.4%) and severely thin(7%) and the lowest proportion of overweight or obese individuals (8.7%). The likelihood to be obese or overweight increases with age for women, is twice as prevalent in urban(40%) than in rural areas(20%), increases with level of education and wealth. Height also seems to have a correlation with level of education. 2.8 Other Sexually Transmitted Infection’s(STI) 2.8.1 Herpes Simplex Virus(HSV-2) According to the KAIS report(GoK, 2009), HSV-2 is an STI and is the leading cause of genital ulcer disease around the world. The symptoms include genital irritation, ulcers and/or excoriation. Infection is life-long(infected people have it for the rest of their lives). There is no cure but symptoms can be controlled with drugs. Both asymptomatic people and symptomatic people can transmit HSV-2 to sexual partners. Scientific evidence indicates that symptomatic HSV-2 infected individuals have an increased risk of acquiring HIV. Youth Fact Book Infinite Possibility Or Definite Disaster? 33 From the data provided, about 7 million people in Kenya are infected with HSV-2. 15 –34 year olds infected by Herpes Simplex Virus(HSV-2) 51.4 40.9 Female Male 27.7 20.7 29.1 12.6 10.2 5.5 15-19 20-24 25-29 Figure 30: 15-34 Year Olds Infected by Herpes Simplex Virus(HSV-2) GoK, 2009 30-34 HSV-2 is highest among women than men throughout all the age cohorts. For both men and women it peaks at age 40-44. Among young people, infection is highest among 30-34 year olds. Generally, prevalence is highest among: widowed women and polygamous men; in Nyanza then coast and w estern provinces for both men and women; in urban areas for both men and women; among people with more than 10 lifetime sexual partners; and among uncircumcised men. 2.8.2 Syphilis According to the KAIS report, Syphilis is an STI and another cause of genital ulcer disease around the world. Syphilis causes three stages of symptomatic disease: primary syphilis characterized by an ulcer at the site of infection; secondary syphilis, characterized by a generalized rash and fever; and tertiary syphilis characterized by neurological(related to the structure and functions of the nervous system), cardiovascular(connected to the heart and blood vessels) and joint degeneration. Syphilis is easily curable and it is estimated that 356,000 people are infected nationwide. 15 –34 year olds infected by Syphilis 2.4 0.48 0.37 0.88 0.43 1.5 1.2 0.79 Female Male 15-19 20-24 Figure 31: 15-34 Year Olds Infected with Syphilis GoK, 2009 34 Youth Fact Book Infinite Possibility Or Definite Disaster? 25-29 30-34 30-34 year old men(2.4%) have the highest prevalence of syphilis among young people aged 1534 years old. However, 60-64 year old men(6%) have the highest prevalence of syphilis nationally. Generally, prevalence is highest among: widowed men and women as well as polygamous men; in Eastern and Nyanza provinces for both men and women; in lowest wealth quintile for women and in the middle wealth quintile for men; among people with more than 4 lifetime sexual partners; and among uncircumcised men. 2.8.3 Self Reported Prevalence of Sexually-Transmitted Infections(STI’s) and STI Symptoms by Age and Gender Women’s STI infection is about 2.5 times higher that of their male counterparts in all the age cohorts. STI infection has been rising with age, more dramatically for women than men between the age of 15 and 39. After that age, infection rates begin to drop. Self reported prevalence of sexually-transmitted infections(STI’s) and STI symptoms 6 5.4 5.5 Female Male 4.2 3.8 2.2 2.3 2.4 1.8 1.2 15-19 20-24 25-29 30-39 40-49 Figure 32: Self Reported Prevalence of Sexually-Transmitted Infections(STI’s) and STI Symptoms by Age and Gender Source: KDHS, 2009 2.8.4 Self Reported Prevalence of Sexually-Transmitted Infections(STI’s) and STI Symptoms by Background Characteristics As indicated on table 15, for both men and women, STI’s are higher among the divorced, separated and the widowed. For men, STI infections are higher among uncircumcised men(6.1%) and men living in urban areas(2.4%). STI infection decreases with higher level of education. It is however lowest among men with no education(0.6%). STI infection among women is higher among the divorced, separated and the widowed(8.8%) as well as among those living in rural areas(5.5%). STI infection decreases with higher level of education among women. Youth Fact Book Infinite Possibility Or Definite Disaster? 35 Table 15: Self Reported Prevalence of Sexually-Transmitted Infections(STI’s) and STI Symptoms by Background Characteristics Background Characteristics Women Men Marital Status Never Married 2.2 1.6 Married or living tog 5.3 2.3 Divorced/Separated/widowed 8.8 2.8 Circumcisions Circumcised* 1.5 Not Circumcised* 6.1 Residence Urban 4.3 2.4 Rural 5.5 1.9 Education No education 8 0.6 Primary Incomplete 7.5 3.5 Primary complete 4.2 2.6 Secondary+ 3.1 1.2 Source: KDHS, 2009 2.8.5 Self Reported Prevalence of Sexually-Transmitted Infections(STI’s) and STI Symptoms by Region Self reported prevalence of sexually-transmitted infections(STI’s) and STI symptoms by region 16.9 Women Men 3.8 1.8 3.9 1.4 2.6 1.2 0.9 7.3 5.8 2.7 1.1 5.9 2.2 3.4 2 Nairobi Central Coast Eastern Nyanza RiftValley Western N. E Figure 33: Self Reported Prevalence of Sexually-Transmitted Infections(STI’s) and STI Symptoms by Region Source: KDHS, 2009 STI infection is highest among women from Coast province(16.9%), followed by Nyanza(7.3%) and Western province(5.9%). Among men, STI infection is highest in Nyanza(5.8%). 36 Youth Fact Book Infinite Possibility Or Definite Disaster? 2.9. Female Genital Mutilation/Cutting(FGM) 2.9.1 Prevalence of Female Circumcision by Age and and Type of Circumcision Table 16 presents the prevalence of female circumcision by age and percent distribution of circumcised women by type of circumcision. Table 16: Prevalence of Female Circumcision by Age and Type of Circumcision Age Percentage Type of circumcision of women Flesh removed Nicked, no Sewn Closed circumcised flesh removed 15-19 14.6 76.5 4.5 17.6 20-24 21.1 82.0 3.3 13.1 25-29 25.3 80.9 2.1 16.2 30-34 30.0 83.5 2.2 12.2 Av 22.8 80.7 3.0 14.8 35-39 35.1 86.7 0.9 11.4 40-44 39.8 83.3 2.1 11.7 45-49 48.8 85.3 1.1 12.0 Source: KDHS, 2009 Not determined 1.3 1.6 0.8 2.2 1.5 1.0 2.9 1.6 22.8% of 15 to 34 year olds are circumcised. Of these, 80.7% had their flesh removed, 3% were nicked and no flesh was removed while 14.8% were sawn closed. The older the woman the higher the prevalence of circumcision. 92% of 15-34 year olds are circumcised between the ages of 3 to 18. 83% of 15-34 year olds say circumcision needs to be stopped while 9% think it should continue. 2.9.2 Prevalence of Female Circumcision by Province and Type of Circumcision Table 17: Prevalence of Female Circumcision by Province and Type of Circumcision Province Percentage of women circumcised Nairobi 13.8 Central 26.5 Coast 10.0 Eastern 35.8 Nyanza 33.8 Rift Valley 32.1 Western 0.8 North Eastern 97.5 Source: KDHS, 2009 Flesh removed 70.8 75.6 49.4 88.6 98.0 93.1 * 14.2 Type of circumcision Nicked, no Sewn Closed flesh removed 17.1 12.0 2.0 17.2 2.4 34.9 0.9 8.5 0.1 1.9 2.3 3.9 ** 2.8 82.5 Not determined 0.1 5.2 13.3 2.0 0.0 0.6 * 0.5 North Eastern province has the highest number of circumcised women(98%) with 83% of these Youth Fact Book Infinite Possibility Or Definite Disaster? 37 being sewn closed. This is followed by Eastern province(36%), Nyanza(34%), Rift Valley (32%), Central(27%), Nairobi(14%), Coast(10%) and Western(0.8%). Coast province has the highest prevalence(29%) of those circumcised at the age of less than 3 years while 67% of women in North Eastern province are circumcised at the age of 3 to 7 years. While an average of 74% of people in all the provinces say that circumcision should be stopped, only 7.3% in North Eastern say it should be stopped and 87% of them say that their religion requires them to be circumcised. 38 Youth Fact Book Infinite Possibility Or Definite Disaster? 2.9.3 Prevalence of Female Circumcision by Background Characteristics and Type of Circumcision Table 18: Prevalence of Female Circumcision by Background Characteristics and Type of Circumcision Background characteristic Percentage of women circumcised Flesh removed Residence Urban 16.5 69.4 Rural 30.6 85.1 Education No education 53.7 58.4 Primary incomplete 28.8 87.1 Primary complete 26.4 87.8 Secondary+ 19.1 89.2 Religion Roman Catholic 29.1 89.5 Protestant /Other Christian 23.5 91.1 Muslim 51.4 33.8 No religion 38.3 93.3 Ethinicity Embu 51.4 86.5 Kalenjin 40.4 92.6 Kamba 22.9 91.1 Kikuyu 21.4 80.7 Kisii 96.1 97.0 Luhya 0.2* Luo 0.1* Maasai 73.2 95.5 Meru 39.7 97.7 Mijikenda/Swahili 4.4* Somali 97.6 21.1 Taita/Taveta 32.2 44.2 Other 98.9 76.0 Wealth Quintile Lowest 40.2 72.6 Second 31.0 91.6 Middle 29.4 88.0 Fourth 25.9 88.2 Highest 15.4 72.8 * an asterisk denotes a figure based on fewer than 25 cases that has been suppressed Source: KDHS, 2009 Type of circumcision Nicked, no Sewn Closed flesh removed 7.3 19.6 1.3 12.2 1.0 40.1 3.0 7.7 2.6 8.5 1.9 6.6 2.0 7.4 2.3 4.8 3.1 61.1 0.8 5.8 2.8 8.4 2.5 4.4 1.0 5.7 5.0 11.3 1.1 1.4 ** ** 2.0 2.4 0.0 2.2 ** 3.4 75.1 0.0 19.4 2.7 17.4 2.1 25.0 1.1 6.2 1.3 8.4 1.9 7.4 6.1 18.7 Not determined 3.6 1.3 0.6 2.2 1.1 2.2 1.1 1.9 2.0 0.0 2.3 0.5 2.1 3.0 0.5 * * 0.0 0.1 * 0.4 36.4 3.9 0.2 1.1 2.4 2.6 2.5 Youth Fact Book Infinite Possibility Or Definite Disaster? 39 Female circumcision is highest in rural(31%) than in urban areas(17%). The higher the level of education and the higher the level of wealth, the less likely one will be circumcised. Among the 54% of women without education who are circumcised, 40% were sewn closed. Circumcision is also prevalent among the Somali(98%) and the Kisii(96%) ethnic groups as well as among Muslims(51%). 2.10 Substance Abuse among Young People in Kenya 2.10.1 Use of Tobacco among Young Men(15-34) Tobacco Use in Kenya among young men(15-34) 97.1 84.3 78.9 % not using tobacco % smoking cigarettes 73.1 20.1 25.5 15.1 2.7 15-19 20-24 25-29 30-34 Figure 34: Use of Tobacco among Young Men(15-34) Source: KDHS, 2009 Cigarettes smoking increases with age among Kenyan youth 2.10.2 Use of Tobacco among 13-15 Year Olds Currently Smocking in Different Countries. Generally, there are more male than female teenage smokers among the countries profiled. South Africa had the highest percentage of both male and female teenage smokers while Ethiopia had the lowest. Figure 35: Use of Tobacco among 13-15 Year Olds Currently Smocking in Different Countries. Source: World Bank, 2007 40 Youth Fact Book Infinite Possibility Or Definite Disaster? 2.10.3 Use of Tobacco among Students and Non-Students Aged 10-24 Tobacco User Statistics Students/Non Students aged 10-24 Students Non-Students 58.00 64.70 52.30 66.80 68.20 69.10 72.30 63.80 19.50 8.10 12.20 6.10 10.50 8.40 5.20 2.00 Western Nairobi Nyanza Central Rift Valley Coast Eastern N. E Figure 36: Use of Tobacco among Students and Non-Students Aged 10-24 NACADA, 2001 Smocking among students is most prevalent in Nairobi(20%) and Central province(12%). Among non-students, it is most prevalent in Eastern province(72%), Coast province(69%) and Rift Valley province(68%). 2.10.4 Overall Substance Abuse among 10-24 year olds Table 19: Overall Substance Abuse among 10-24 year olds Substance Alcohol Tobacco Bhang Miraa Inhalants NACADA, 2004 Ever Used Students Non Students 27.7 77.1 8.3 65.7 2.8 34.9 9.1 55.1 3.4 12.5 Current Use in the Last 30 days Students Non students 8.6 60.1 3.1 58 0.6 21.1 2.1 20.8 1.6 7.2 Frequency 36% 28% 13% 18% 5% Alcohol(36%) and tobacco use(28%) are the most abused substances followed by miraa(18%), Bhang(13%) and inhalants(5%). Youth Fact Book Infinite Possibility Or Definite Disaster? 41 2.10.5. Alcohol Abuse Alcohol prevalence among 10-24 year olds by province 90.10 89.90 81.50 84.10 86.10 73.10 Non Students Students 73.40 43.30 40.90 26.80 26.30 21.90 21.30 17.20 15.60 1.60 Western Nairobi Nyanza Central Rift Valley Coast Eastern N. E Provinces Figure 37: Alcohol Abuse Source: NACADA, 2001 Among students, alcohol abuse is highest in Western(43%) and Nairobi(41%). Among nonstudents, it follows a similar pattern only on higher magnitudes. Alcohol abuse has the lowest prevalence in North Eastern. 2.10.6 Bhang Prevalence Bhang prevalence among 10-24 year olds by province 40.3 41.0 40.0 37.1 32.4 31.4 31.5 Students Non-Students 21.1 3.0 4.3 2.4 3.6 4.7 1.2 3.6 0.5 Western Nairobi Nyanza Central Rift Valley Coast Eastern N. E Figure 38: Bhang Prevalence Source: NACADA, 2001 Among students, bhang prevalence is highest in Coast(4.7%) and Nairobi(4.3%). Among nonstudents, it is highest in Nyanza(41%), Nairobi(40.3%), Coast(40%) and Eastern(37%). 42 Youth Fact Book Infinite Possibility Or Definite Disaster? 2.10.7 Inhalant Prevalence According to NACADA(2001), inhalants are gaseous chemicals or substances that when inhaled to the lungs, produce a psychoactive or mind altering condition that may be anesthetic in its effect or cause a slowing down of body functions. Examples include glue, gasoline and lacquer thinners. Among students, inhalants are most prevalent in Nairobi(5.5%), Rift valley(4.3%) and Coast (4.2%). Among non students, inhalants are most prevalent in Nairobi(15.2%), Rift valley(15.2%), Eastern(14.9%) and Western(14.8%) as indicated in figure 39. Inhalant Prevalences 10-24 years Olds students and non students Students Non-Students 14.8 15.2 15.2 12.6 7.6 5.5 3.9 2.4 3.4 4.3 4.2 4 14.9 8.7 1.9 0.3 Western Nairobi Nyanza Central Rift Valley Coast Eastern N. E Figure 39: Inhalant Prevalence Source: NACADA, 2001 2.10.8 Miraa Abuse Miraa prevalence among 10-24 year olds by province Students Non-Students 36.10 53.10 35.10 46.20 43.60 62.50 78.70 68.80 22.60 15.60 12.10 13.00 5.10 4.20 6.80 3.00 Western Nairobi Nyanza Central Rift Valley Coast Eastern N. E Figure 40: Miraa Abuse Source: NACADA, 2001 Among students, miraa abuse is most prevalent in Nairobi(23%), Eastern(16%) and Coast (13%). Among non students, miraa is most prevalent in Eastern(79%), North Eastern(69%) and Coast(63%). Youth Fact Book Infinite Possibility Or Definite Disaster? 43 2.10.9 Proportion of Kenyan Adolescents and Young Adults Reporting Regular use of Drugs 16-26 year Olds reporting regular use of drugs %percentage 30.95 25.4 25.8 16.8 9.1 16-18 years 19-20 years 21-22 years 23-24 years 25-26 years Figure 41: Proportion of Kenyan Adolescents and Young Adults Reporting(16-26) Regular use of Drugs Source: Population Communication Africa(2001) Regular drug use increases with age and is highest among the 23-24 year olds. 2.11 Comparative Analysis % Probability that a 15 year old will die before the age of 60 Female Male 52 50 Kenya 46 53 55 59 46 54 53 65 58 64 47 51 30 35 28 21 Uganda Tanzania Rwanda Burundi South Africa Nigeria Ghana India 5 10 Japan 47 8 14 21 6 10 USA UK Iraq Figure 42: Probability that a 15 year old will die before the age of 60 Source: World Bank, 2007 15 year old females are likely to live longer than their male counterparts. The probability of dying before your 60th birthday as a young 15 year old is highest in developing countries than in developed countries. Interestingly, the probability is even higher for a young man living in the East African region, South Africa and Nigeria than for a young man living in Iraq. 44 Youth Fact Book Infinite Possibility Or Definite Disaster? 3 Disability ‘The moral test of government is how it treats those who are in the dawn of life... the children; those who are in the twilight of life... the elderly; and those who are in the shadow of life... the sick... the needy... and the disabled.’ - Hubert H. Humphrey 3.0 Disability According to World Health Organization(WHO) disability affects 10% of every population. However, according to surveys by the Disability Statistics Compendium, prevalence rates vary from 0.2% to 21%. The Kenya National Survey for Persons with Disabilities(KNSPWD, 2008), indicates that disability is not merely the result of impairment. The most common forms of disabilities are associated with chronic respiratory diseases, cancer, diabetes, malnutrition, HIV/AIDS, other infectious diseases, and injuries such as those due to road accidents, falls, land mines and violence. The number of people living with disabilities is growing as a result of factors such as population increase, aging, and medical advances that preserve and prolong life thus increasing the demand for health and rehabilitation services. 3.1. Prevalence of Disability 3.1.1. Prevalence of Disability by Age Prevalance of Disabilities by age(15-34) 1.1 1.1 1.3 1.1 15-24 25-34 0.4 0.4 0.2 0.1 0.5 0.2 0.3 0.3 0.3 0.3 15-24 25-34 Hearing 0.4 0.4 Speech 0.2 0.1 Visual 1.1 1.1 Mental 0.2 0.5 Physical 1.1 1.3 Self Care 0.3 0.3 Other 0.3 0.3 Figure 43: Prevalence of Disability by Age Source: KNSPWD, 2008 According to KNSPWD, the above impairments were profiled on the basis of their likelihood to have a substantial long-term adverse effect that limits a person’s participation abilities in certain day-to day activities. Overall disability rate nationally was 9.7%(KNSPWD, 2008) but KDHS(2007) put national disability at 12.5%. Among 15 to 34 year olds, disability accounted for 3.8% of that age group. The largest proportion of disability among 15 – 34 year olds was physical impairment(1.2%) followed by visual impairment(1.1%). Disability increases with age and is more prevalent among older people. 46 Youth Fact Book Infinite Possibility Or Definite Disaster? 3.1.2. Prevalence of Disability by Demographic Characteristics Table 20: Prevalence of Disability by Demographic Characteristics Prevalence of Disability by Demographic Characteristics Characteristics None Hearing Speech Visual Mental Physical Self- Other TOTAL care disable Residence Rural Urban Sex Male Female Marital status Not married Married with certificate Married with traditional Consensual marriage Divorced/ separated Widowed Other Don’t know 95.5 0.6 0.2 1.2 0.3 1.6 0.4 0.2 4.5 95.4 0.3 0.2 1.9 0.3 1.3 0.4 0.3 4.6 95.5 0.6 0.2 1.2 0.3 1.6 0.4 0.2 4.5 95.4 0.5 0.2 1.5 0.2 1.6 0.4 0.3 4.6 96.7 0.5 0.2 0.7 0.3 0.9 0.3 0.3 3.3 92.8 0.5 0 3.4 0.1 2.4 0.5 0.2 7.2 94.1 0.6 0.1 2 0.3 2.4 0.3 0.3 5.9 96.7 0.4 0.1 1.2 0.1 1.3 0.2 0.1 3.3 89.8 0.7 0.1 1.9 1.7 4.6 1 0.1 10.2 82.9 1.2 0.2 5.7 0.5 7 2.1 0.3 17.1 98.8 0.5 0.2 0.5 1.2 84 8.1 1.6 0 6.3 16 Source: KNSPWD, 2008 Overall, proportions of disability were similar in rural and urban areas as well as among men and women. Disability was highest among the widowed and among the divorced/separated. 3.1.3. Prevalence of Disability by Province According to KNSPWD, visual impairment was highest in Nairobi(2.7%) while physical impairment is highest in Nyanza province(2.5%). However, according to KDHS(2007), physical impairment(what they refer to as lame) was highest in N/Eastern(34%) and Western(32%) while visual impairment, what KDHS refers to as blind is highest in Coast(14%) and in Western (10%). Overall disability prevalence is highest in Nyanza province(6.8%) and lowest in North Eastern province(2.6%) but according to KDHS, overall disability prevalence is highest in Coast province (13.6%) and lowest in Western province(11.8%) Youth Fact Book Infinite Possibility Or Definite Disaster? 47 Table 21: Prevalence of Disability by Province KNSPWD, 2008 Characteristics None Nairobi 94.9 Central 94.8 Coast 94.8 Eastern 95 N/ Eastern 97.4 Nyanza 93.2 R/Valley 96.8 Western 96.7 KDHS, 2007 Characteristics Missing Hand Foot Nairobi 0 12.6 Central 1.9 1.5 Coast 0.6 3.1 Eastern 0.5 1.1 N/ Eastern 0.8 8.4 Nyanza 3.7 2.1 R/Valley 1.7 2.4 Western 2.7 0 Kenya 1.9 2.3 Hearing 0.3 0.5 0.8 0.5 0.4 0.8 0.4 0.7 Speech 0.1 0.1 0.3 0.2 0.1 0.3 0.1 0.2 Lame Blind 20.4 0 12.5 5.4 22.2 13.9 23.2 9 34.4 8.3 24 6.1 28.6 6.2 32.2 10.3 25.7 7.8 Source: KNSPWD, 2008 KDHS, 2007 Visual 2.7 1.3 1.8 1.5 0.3 1.9 0.7 0.8 Deaf 17.5 10.5 5.4 2.5 11.1 6.8 4.6 5.3 5.9 Mental 0.3 0.5 0.3 0.3 0.1 0.2 0.2 0.3 Dumb 7.6 14.5 10.9 11.1 14.2 7.1 6 9.4 9 Physical Self-care Other 1.1 0.3 0.2 2.2 0.4 0.2 1.4 0.4 0.2 1.6 0.5 0.3 1.2 0.5 0 2.5 0.6 0.4 1.1 0.4 0.3 1.2 0.2 0.1 Mental Paralyzed Other 29.6 1.1 18.9 45.8 9.5 9.6 40.1 13 13.6 15.2 11.5 44.8 16.4 13.7 9.2 13.3 8.5 35.3 18.2 14.9 31.4 6.9 3.8 36 18.4 10 31.5 Disability Prevalence by Province KNSPWD KDHS 12.3 13.6 13.2 12.9 12.7 12 11.9 11.8 6.8 5.1 5.2 5.2 5 2.6 3.2 3.3 Nairobi Central Coast Eastern Figure 44: Disability Prevalence by Province Source: KNSPWD, 2008 KDHS, 2007 N/Eastern Nyanza R/Valley Western 48 Youth Fact Book Infinite Possibility Or Definite Disaster? 3.1.4. Distribution of Disability by Gender 49.6 50.4 50.9 49.1 Distribution of Disability by Gender 54.7 45.3 55.3 44.7 54.3 45.7 49.7 50.3 55.2 44.8 Male Female 54.5 45.2 None Hearing Impairment Speech Impairment Visual Impairment Mental Impairment Physical Impairment Self-care Impairment Other Figure 45: Distribution of Disability by Gender Source: KNSPWD, 2008 More men than women are likely to suffer from self-care impairment(55%), speech impairment (54.7%) and mental impairment(54.3%). Women on the other hand are likely to suffer more from visual impairment(55.3%) and other forms of disability(54.5%). 3.2 PWD’s Using Assistive Devices According to KNSPWD(2008), assistive devices and support services consist of equipment and appliances used by PWDs to complement diminished or absence of certain physical functions. Support services are services that PWDs need or receive for their disability in relation to health, rehabilitation and welfare including but not limited to services from a personal assistant or aide. Such devices and services enhance the ability of a PWD to participate in day-to-day activities 3.2.1 PWD’s Using Assistive Devices by age Percentage of PWD’s Using Assistive Devices 20.4 15 13.7 12.3 15-24 25-34 6.5 Any Support Device Information Device 0.3 0.4 Communication Device 2.6 Personal Mobility Device Figure 46: Percentage of PWD’s using Assistive Devises Source: KNSPWD, 2008 Supportive devises are more accessible to older youth than to younger ones. Youth Fact Book Infinite Possibility Or Definite Disaster? 49 3.2.2 PWD’s Using Assistive Devices by Demographic Characteristics Table 22: Percentage of PWD’s Using Assistive Devices by Demographic Characteristics Residence Urban Rural Province Nairobi Central Coast Eastern Nyanza Rift Valley Western North Eastern Sex Male Female Marital Status Single Married Divorced/Separated Widowed Others Highest Level Education attended Nursery, Kindergarten Primary Post Primary vocational Secondary‘’A’’ Level College(middle level) University Other Don’t know Information device 11.2 30.3 35.0 15.6 19.4 17.4 2.1 10.6 10.7 7.2 16.1 14.4 12.7 20.1 3 7.4 40.2 0.3 10 4.7 26.9 63.1 83.2 8.9 27.2 Source: KNSPWD, 2008 Communication Device Personalmobilitydevice 0.1 15.1 0.6 11.0 0.6 7.3 0.4 21.0 0.0 8.2 0.0 13.4 0.0 13.7 0.4 13.4 0.0 17.3 0.0 14.1 0.4 17.6 0.1 11.3 0.2 4.8 0.3 16.6 0 20.2 0 21.8 0 28.4 0 8.8 0.1 12.2 0 18.9 0.8 11.9 0 9.4 3.1 6.4 0 10.6 0 27.2 50 Youth Fact Book Infinite Possibility Or Definite Disaster? Information devise is used mostly in rural areas(30%) while the personal mobility devise is used mostly in urban areas. Overall, rural areas(14%) have a higher prevalence of assistive devices than urban areas(9%). Nairobi(14%), Central(12%) and Eastern(10%) have the highest prevalence of assistive devices than other regions while Nyanza(5.3%) and North Eastern(7%) have the lowest. More males(11%) than females(9%) have assistive devices. The higher the level of education, the higher the acquisition of an assistive device such that only 3% of those with nursery/kindergarten education have assistive devices compared to 7%(primary), 8%(post primary/vocational), 13% (secondary‘A’ level), 24%(college) and 31%(University). 3.3. Activity Limitation without PWD’s Using Assistive Devices According to KNSPWD(2008), activity limitation refers to difficulties experienced by an individual without an assistive device. Such difficulties can be experienced in any of the following domains of disability: sensory, communication, mobility, self-care(like washing one self), domestic life, interpersonal behaviour, major life areas in the community and social life. PWDs may face various challenges in the course of pursuing their daily activities because of activity limitation or restrictions. With respect to PWD’s physical capacity to carry out activities without assistance or their ability to participate in the activities in their current environment, nine out ten PWD’s experience activity limitation without assistive devices. 3.3.1 Activity Limitation without PWD’s Using Assistive Devices by Age Situation of PWD’s without use of Assistive Devices by Age 15-24 25-34 93.1 90.4 Big Problem 6.9 9.4 Small Problem Figure 47: Percentage of Activity Limitation without the Use of Assistive Devices by Age Source: KNSPWD, 2008 Youth Fact Book Infinite Possibility Or Definite Disaster? 51 3.3.2 Activity Limitation without PWD’s Using Assistive Devices by background Characteristics Table 23: Percentage of Activity Limitation without the Use of Assistive Devices by Age Activity Limitation Background Characteristic Residence Urban Rural Province Nairobi Central Coast Eastern Nyanza Rift Valley Western North Eastern Sex Male Female Marital Status Single Married Divorced/Separated Widowed Highest Level Education attended Nursery, Kindergarten Primary Post Primary vocational Secondary‘’A’’ Level College(middle level) University Don’t know Big problem % 92.8 87.3 87.3 90.9 89.7 92.9 89.4 93.2 91.1 94.7 91.9 91.4 92.2 90.9 88.0 92.3 95.8 92.3 89.1 86.9 80.1 77.2 100.0 Small Problem % 7.2 12.6 12.7 9.1 10.3 7.1 10.6 6.7 8.9 5.3 8.1 8.6 7.7 9.1 12.0 7.7 4.2 7.7 10.9 13.0 19.9 22.8 0 Source: KNSPWD, 2008 52 Youth Fact Book Infinite Possibility Or Definite Disaster? People in urban areas, singles and widows, experience more difficulty without assistive devices than their counterparts. The higher the level of education the less activity is limited without assistive devices. Activity limitation without assistive devices is most prevalent in North Eastern (95%), Rift Valley(93.2%) and Eastern provinces(92.9%). 3.4 Effect of Immediate Surrounding According to KNSPWD(2008), physical, mental, intellectual or sensory impairments may interact with various barriers to hinder PWDs’ full and effective participation in society on an equal basis with others. Universal designs should enable PWDs to cope with their day-to-day activities with minimal difficulty. The accessibility of the immediate surroundings plays an important role in PWDs’ participation in various activities. Among the aspects of the immediate surroundings that affect the PWDs’ daily activities are crowds, lighting and noise. Environmental factors such as temperature, terrain, accessibility of transport, climate, and noise can improve or hinder a person’s participation in such activities as working, going to school, taking care of one’s home, and being involved with family and friends in social, recreational and civic activities in the community. The immediate surrounding and environmental factors can therefore be a barrier to PWD’s own participation in activities that matter to them. 3.4.1 Effect of Immediate Surrounding by Age Effect of Immediate Surrounding by Age 15-24 25-34 60.2 30.3 62.8 31.9 Little Problem Big Problem Figure 48: Effect of Immediate Surrounding by Age Source: KNSPWD, 2008 About 60% of the PWD’s are affected very much by their immediate surroundings. Youth Fact Book Infinite Possibility Or Definite Disaster? 53 3.4.2 Effect of Immediate Surrounding by Background Characteristics Table 24: Effect of Immediate Surrounding by Background Characteristics Effect of Immediate Surrounding by Background Characteristics Little problem Residence Urban 32.3 Rural 40.1 Province Nairobi 40.8 Central 45.5 Coast 36 Eastern 28.5 Nyanza 39.5 Rift Valley 31.1 Western 36.2 North Eastern 25.6 Sex Male 34.9 Female 32.8 Marital Status Single 31.8 Married 35.5 Divorced/Separated 49.1 Widowed 32.2 Highest Level Education attended Nursery, Kindergarten 26 Primary 36.2 Post Primary vocational 39.4 Secondary‘’A’’ Level 32.5 College(middle level) 31.6 University 31.9 Source: KNSPWD, 2008 Big problem 63.8 53.7 54.4 54.5 61.3 65.3 60.5 62.6 59.3 73.4 59.6 63.8 63.4 60.2 42.6 65.3 69.8 59.5 46.6 62.9 65 52.3 Urban dwellers, females, singles and widowed PWD’s are affected more by their surroundings than their counterparts. Effect by immediate surrounding is more adverse to people in North Eastern(73%), and Eastern provinces(65%). 54 Youth Fact Book Infinite Possibility Or Definite Disaster? 3.5 Employment and Incomes of PWD’s The employment of PWD’s comprised those aged 15 years and above who reported having either held a job or undertaken an activity for pay, profit or family. According to KNSPWD(2008), a third of the PWDs worked on family businesses and about a quarter did not work. 3.5.1 Employment and Incomes of PWD’s by Age Table 25: Employment and Incomes of PWD’s by Age Employment and Incomes of PWD’s by Age Age Group 0-14 15-24 25-34 35-54 55+ Don’t know Average Worked for worked on Did not pay own family work but business was employed 0 0 0 8.2 13.6 0.9 21 31.3 1.6 22 35.9 2.4 4.4 37.2 3.9 1.7 21.7 2 9.55 23.3 1.8 Did not work 40.4 32.4 23.3 21.5 33.6 65.3 36.1 Source: KNSPWD, 2008 Never home- Other been maker employed 29.5 29.5 30.2 19.2 8.6 17.2 7.5 13.4 1.9 2.5 14.5 1.3 4 15.6 1.4 1.5 6.7 1 10.7 14.7 8.8 28% of 15-34 year old PWD’s did not work, while 13% have never been employed before. 22% of this age group worked on family businesses, 11% worked as homemakers and 14% worked for pay. The proportion of PWD’s getting an income is therefore quite small. Youth Fact Book Infinite Possibility Or Definite Disaster? 55 3.5.2 Employment and Incomes of PWD’s by Background Characteristics Table 26: Employment and Incomes of PWD’s by Background Characteristics Residence Urban Rural Province Nairobi Central Coast Eastern Nyanza Rift Valley Western North Eastern Sex Male Female Marital Status Single Married Divorced/Separated Widowed Highest Level Education attended Nursery, Kindergarten Primary Post Primary vocational Secondary‘’A’’ Level College(middle level) University Worked for worked on pay own family business Did not work but was employed 8.8 32.1 1.7 25.4 21.3 4.8 31.5 13.9 5.8 12.6 38.1 1.6 14.5 21.1 6.2 9 34.2 2.6 2.7 2.5 0 9.6 42.2 1.5 11 23.2 7 6.3 21.7 1.4 17.7 31.4 4.4 7.5 28.5 0.6 12.7 14.5 1.7 14.9 39 3.2 12.9 24.4 0.9 3.9 30.9 1.4 14.6 37.3 0 10.4 37.4 1.6 20.2 43.4 3.4 22.3 27.4 5.7 36.4 24.7 6.7 45.4 23.2 5.3 Did not work 33.7 21.8 22.4 33.6 25.6 24.1 79.9 27.9 38 39.8 31 31.5 34.7 24.7 38.4 42.5 25.7 27 12 18.6 19.5 11.7 Source: KNSPWD, 2008 Never home- Other been maker employed 6.9 12.6 4.1 6.9 13.7 6.1 5.5 11.1 9.8 1.9 11.4 0.8 9.3 19.2 4.1 7 15.6 7.4 9.9 4.5 0.5 8.8 7.6 2.4 8.9 14.1 4.1 4.7 18.4 7.6 8 2.7 4.9 6 21.8 4.2 16.9 6.7 12.8 2.8 14.6 0.8 5.9 12.8 4.7 2 18.2 1.1 14.1 8.3 0 5.9 12.4 5.4 13.9 7.1 0 10.7 8 7.3 4.3 4.1 4.3 6.2 3.6 4.6 56 Youth Fact Book Infinite Possibility Or Definite Disaster? More urban PWD’s(34%) did not work compared to(22%) living in rural areas. 25% of PWD’s living in rural areas worked for pay compared to(9%) in urban areas. Men with disabilities working for pay were 2.3 times higher than their female counterparts. Generally, the higher the level of education, the higher the chances of a PWD to work for pay. Nyanza province had the highest level of PWD’s who did not work(80%) followed by North Eastern(40%) and Central(34%). 3.5.3 Type of Grant Currently Received by Background Characteristics According to KNSPWD(2008), most PWDs were unlikely to have active or viable socioeconomic engagements to earn a living. Consequently, they required some assistance in the form of social security grants for the destitute, disability grants or other forms of financial support. Table 27: Type of Grant Currently Received by Background Characteristics Residence Urban Rural Province Nairobi Central Coast Eastern Nyanza Rift Valley Western North Eastern Sex Male Female Currently receiving Disability form of social grant security/ disability grant or any financial support(%) 1.7 2.9 1.8 16.7 2 32.8 2.5 10 1.1 0 0.9 0 0_ 3 0 1.1 0 0.4 0 1.7 5 1.7 6.7 Source: KNSPWD, 2008 social security 2 0 0 7 0 0 _ 0 0 0 0 3 Work-mans compensation private insurance grant or any Old age pension 0 5 14 0 1.3 20.7 0 2.6 16.6 0 0 14.1 0 0 32.7 0 0 0 ___ 0 0 10.5 0 35.2 38.7 0 0 0 0 9 18.5 0 0 12.8 The disability grant was said to be highest in rural(17%) than in urban areas(3%). However it was received in Nairobi(33%) and Central(10%) with other provinces registering nothing. More women(7%) than men(5%) received this disability grant. Private insurance pension was highest in Western than in any other province. 9% of men compared to 0% of women and 5% of urban dwellers compared to 1.3% of rural dwellers accessed private insurance pension. Youth Fact Book Infinite Possibility Or Definite Disaster? 57 3.6 Attitudes towards Persons with Disabilities According to KNSPWD(2008), problems of disability are largely manifested in social contexts and social relations, rather than in an individual’s medical condition. People living and interacting with PWDs tend to treat them differently in relation to their disabilities. About 57% of the times, people’s attitudes towards PWDs have been a big problem. Attitudes towards PWD’s are higher in Nyanza(74%) and Eastern(65%), among 35 – 54 year olds(61%), among males(62%), among the widowed(66%) and in mid level colleges(61%) as indicated on table 28. 58 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 28: Attitudes towards Persons with Disabilities Residence Urban Rural Province Nairobi Central Coast Eastern Nyanza Rift Valley Western North Eastern Age Group 0-14 15-24 25-34 35-54 55+ Sex Male Female Marital Status Single Married Divorced/Separated Widowed Highest Level Education attended Nursery, Kindergarten Primary Post Primary vocational Secondary''A'' Level College(middle level) University Source: KNSPWD, 2008 Little problem 35 31.3 40.2 45.7 37.1 23.9 26.3 34.5 31.2 43.6 37.3 35.3 33.6 35.4 34.1 31.5 37.3 33.5 36.8 34.2 31.9 43.8 35 44.1 45.6 30 59.9 Big Problem 60.6 58.6 50.9 54.3 61 65.2 73.7 61.4 60 55.4 59.4 58.4 58 61.2 58.1 62.3 57.9 61 56.5 58.1 65.6 53.7 59.8 8.3 47.7 61.4 13.2 Youth Fact Book Infinite Possibility Or Definite Disaster? 59 60 Youth Fact Book Infinite Possibility Or Definite Disaster? 4 Education ‘Education is the great engine of personal development. It is through education that the daughter of a peasant can become a doctor, that a son of a mineworker can become the head of the mine, that a child of a farm worker can become the president of a great nation. It is what we make out of what we have, not what we are given, that separates one person from another.’ ‘Education is the most powerful weapon which you can use to change the world.’ Nelson Mandela 4.0 Education The right to education is one of the basic human rights stipulated in the Universal Declaration of Human Rights. According to the World Development Report(2007), young people need to acquire the right knowledge and skills to become productive workers, good parents, and responsible citizens. Learning takes place in many environments— home, school, the workplace—but most investments in learning take place in schools. Those investments need to happen during childhood and adolescence, and the investments in adolescence are needed to make earlier investments pay off. However, preparation of youth for work and life is very low, just as demand for skills and knowledge is rising. Past education policies focused on increasing the number of people who go through the education system, rather than learning that takes place in schools. To improve the skills of young people for work and life, education opportunities must be made more relevant to the needs of all young people as learners and future workers, parents, and citizens, and young people need to be provided with the tools to develop their capabilities so they can make the most of opportunities. In order to succeed in today’s competitive global economy, they therefore must be equipped with advanced skills such as: thinking skills(critical analysis and creativity); behavioral skills(perseverance, teamwork, self discipline, ability to negotiate conflict and manage risks), specific knowledge(numeracy and literacy competencies); and vocational skills(a mix of specific knowledge and skills to perform jobs that rely on clearly defined tasks). 4.1 Early Childhood Development Education(ECDE) Lloyd(2005) contends that learning occurs more intensely during childhood and adolescence than during other phases of the life cycle in all domains, whether it is the development of physical or cognitive skills or the acquisition of knowledge and the shaping of values and beliefs. This is not just because of the obvious fact that growth always appears more rapid when starting from a lower base. During these same years, physical and intellectual capacities are growing rapidly, allowing for the more rapid acquisition of skills and accumulation of knowledge than at other phases of the life cycle. Interventions affecting the timing and sequencing of learning and the quality of the learning environment during these years can have important implications for the development of adult productive capacities. Investments in learning in these earlier stages of the life cycle tend to yield relatively high returns in comparison to learning later in life. Failure to invest at this stage is extremely unlikely to be compensated for in any later stage 4.1.1 Number of Pre-Primary Schools and Enrolments Rates Number of Pre-Schools(1992-2009) 18,327 21,261 19,083 23,344 23,429 25,429 26,294 27,573 32,879 28,300 29,455 36,121 34,043 37,263 37,954 38,247 1992 1994 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Figure 49: Number of Pre-Primary Schools Source: Various Economic Surveys 62 Youth Fact Book Infinite Possibility Or Definite Disaster? Since 1992 there has been a steady increase in the number of Pre-schools from 18,327 in 1992 to 38,247 in 2009. Enrollment has also doubled from 858,593 in 1992 to 1,914,222 in 2009. Gross enrollment rate currently stands at 60.6% and net enrollment at 49%(Economic Survey, 2010). However, according to the Annual Literacy Assessment Report(Uwezo, 2010), about 50% of pre-school going children(3-5 year olds) are not enrolled. Interestingly, access to pre-school is highest in the arid districts. In Samburu for example, 90% of 3-5 year olds attend pre-school. This is attributed to the strong NGO presence in those areas. Parents may also be taking children to school at this age to take advantage of day care facilities and the feeding programmes available there. On the other hand 60% of children aged 3-5 years in Kakamega Central in Western province are not enrolled. 4.1.2 Pre-School Gross Enrolment Rates by Gender and Province 2002 Table 29: Pre-School Gross Enrolment Rates by Gender and Province in 2002 Gender Coast Central Eastern Nairobi Boys 42.7 47.4 42.3 36.7 Girls 40.2 47.4 42.1 39.9 Total 41.4 47.4 42.2 38.3 Source: Achoka, Odebero, Maiyo, Mualuko(2007) Riftvalley 45.7 43.3 44.5 Western 36.5 38.1 37.3 Nyanza 40.4 40.8 40.6 N/Eastern 13.6 9.8 11.8 Kenya 41.4 40.9 41.1 Overall, data on pre-school enrolment rates indicate a higher enrollment of pre-school for boys than for girls. Central Province has the highest gross enrollment rates for both boys and girls (47.4%) while North Eastern has the least enrollments, a finding that contradicts Uwezo’s asser tion in 4.1.1. above. It is important to note that children who do not participate in quality ECDE programs are‘vulnerable’ to repetition, to dropping out and to unrealized potential. 4.2 Primary Schooling 4.2.1 Number of Primary Schools from 1990-2008 Number of Primary Schools from 1990-2008 15196 15804 15906 16115 16552 17080 17366 17611 18617 18901 19124 23564 24643 25353 25929 26104 26206 30,000 25,000 25,000 15,000 10,000 5000 0 1990 1464 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Figure 50: Number of Primary Schools from 1990-2008 Source: IPAR, 2005 and Statistical Abstract, 2009 Youth Fact Book Infinite Possibility Or Definite Disaster? 63 Since 1990 there has been a steady increase in the number of both private and public primary schools. North-Eastern records the least number of primary schools while Rift valley records the highest number of primary schools. 4.2.2 Primary School Enrollment by Province(2003 – 2008) Table 30: Primary School Enrollment by Province(2003 – 2008) 2003 Central 904,770 Coast 486,629 Eastern 1,309,807 N/Eastern 66,773 Nairobi 217,167 Nyanza 1,339,895 Rift Valley 1,779,789 Western 1,054,694 National 7,159,524 ** Provisional Statistics Source: Statistical Abstract, 2009 2004 910,806 556,013 1,371,680 69,958 229,252 1,321,901 1,833,990 1,101,162 7,394,762 2005 903,638 585,543 1,379,909 70,891 237,858 1,324,239 1,951,235 1,143,972 7,597,285 2006 882,429 600,041 1,378,210 81,182 234,819 1,334,597 1,998,277 1,122,557 7,632,113 2007 888,236 643,355 1,480,629 98,629 319,000 1,441,735 2,185,052 1,273,510 8,330,148 2008** 911,340 658,860 1,538,785 115,287 320,102 1,508,264 2,191,340 1,333,640 8,577,619 26% of all primary school enrollments are in Rift Valley, followed by Eastern(18%) and Nyanza (18%) provinces have had the highest primary school enrollment rates over time. North Eastern (1%), Nairobi(3%) and Coast(7%) have consistently had the lowest primary school enollment rates. Western and Central provinces absorb 15% and 12% respectively of all the enrollments. 4.3 Transition 4.3.1 Transition between Primary and Secondary Schools As an indicator of learner survival beyond the primary education cycle, the primary secondary transition rate shows the proportion of primary school completers who proceed to form 1 in the subsequent year. Analysis shows that the overall transition rate remained below 47 percent between 1999 and 2004. The overall transition rates rose above the 50 percent mark for the first time in 2005 with boys constituting 57.7 and girls 54.2 percent. The 2007 transition rate further increased to 59.6 percent. The increase in the transition rates can in part be attributed FPE and the re-entry of former drop outs. 64 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 31: Primary to Secondary Transition Rates, 1998-2007 Year In Year In Std 8 Form 1 1998 1999 1999 2000 2000 2001 2001 2002 2002 2003 2003 2004 2004 2005* 2005 2006 2006 2007 Source: MoE(2008) Enrolment In Std 8(‘000) Boys Girls Total 221.0 215.3 436.3 246.6 228.0 474.6 235.6 227.8 463.4 261.7 246.6 508.3 296.9 244.5 541.3 280.8 267.5 548.3 343.0 314.8 657.7 335.5 307.9 643.5 372.3 332.7 704.9 Enrolment In Form 1(‘000) Boys Girls Total 105.2 95.8 201.0 108.1 97.2 205.3 112.2 103.4 215.6 116.2 105.2 221.5 129.4 121.7 251.1 132.6 118.6 251.2 198.0 170.6 368.3 195.7 173.0 368.7 210.3 210.1 420.5 % Transiting to Form 1 Boys Girls Total 47.6 44.5 46.1 43.8 42.6 43.3 47.6 45.4 46.5 44.4 42.7 43.6 43.6 49.8 46.4 47.2 44.3 45.8 57.7 54.2 56.0 58.3 56.2 57.3 56.5 63.2 59.6 During the period under review, it is only in two transition years 2002-2003 and 2006-2007 when the proportions of girls transiting from Std 8 to Form 1 was higher. In terms of absolute numbers, however, the number of boys transiting to Form 1 remained consistently higher for the entire period. Considering the fact that there is near gender parity during standard 1 entry, these findings suggest that young women are most disadvantaged in terms of access to secondary education. 4.3.2 Primary to Secondary School Transition Trends by Province Table 32: Primary to Secondary School Transition Trends by Province 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008* Average 19992008 Coast 32.6 31.0 33.4 32.5 31.0 52.1 34.0 39.0 40.0 46.1 37.17 Central 46.3 48.6 46.9 57.3 58.5 59.6 63.7 64.7 57.4 64.2 56.72 Eastern 38.7 36.3 38.2 47.5 48.9 51.2 49.4 53.5 46.8 51.2 46.17 Nairobi 29.0 29.6 27.0 32.5 33.5 34.5 50.9 58.3 38.0 45.9 37.92 R. Valley 32.9 34.2 37.2 21.1 21.6 41.7 48.5 54.3 42.5 46.7 38.07 Western 53.2 49.4 51.0 52.6 53.7 55.8 52.0 59.8 49.5 60.1 53.71 Nyanza 39.4 42.4 50.0 35.4 36.1 47.3 57.1 63.6 50.2 56.8 47.83 N. Eastern 43.2 46.4 52.8 42.9 43.8 44.9 45.1 44.2 40.5 45.7 44.95 Total 39.9 40.1 40.9 41.7 42.6 50.6 52.1 59.7 59.7 59.9 48.72 Source: Kenya National Bureau of Statistics, 2003-2009 An average of the transition rates from Primary-Secondary from 1999-2008 indicate that Central province(56.72%) has had the highest transitions rate followed by Western province(53.71%) whilst Coast(37.17%) and Nairobi(37.92%) have the lowest transition rates. At the base year, 1999, the Primary to Secondary transition rate was lower than the national average of 39.9 percent in Coast(32.6 percent) and Nairobi(29.0 percent), Nyanza(39.4 percent), Rift Valley(32.9%) Youth Fact Book Infinite Possibility Or Definite Disaster? 65 and Eastern(38.7 percent). It is noteworthy that in 2004, immediately after the re-introduction of the Free Primary Education(FPE) programme in Kenya, significant increments in the primary to secondary transitions were recorded in all the eight provinces. Provinces that were especially recording low transitions such as Coast, Nyanza, and Rift Valley improved. 4.4 Secondary Education 4.4.1 Access and Participation in Secondary Education As shown on Figure 2, during the 1999-2007 period, there was a significant increase in the popu lation of students attending secondary education from 738,918 in 1999 to 1,180,267 in 2007, representing a 60 percent increase. On average, there was 6.6 percent annual increase in the population of students attending secondary school during the period under review. Secondary Education Access trends Boys Girls Total 1,200,000 956,250 738,918 712,500 738,086 753,525 768,695 868,623 926,149 928,149 1,180,267 1,030,080 Enrolment 468,750 225,000 1999 2000 2001 2002 2003 Year 2004 2005 2006 2007 Figure 51: National Secondary Enrolment Trends, 1999-2007 Sources: MoE EMIS, 2008 4.4.2 Secondary School Enrolment by Form Available data shows that total form 1 enrolments increased by 198,554 from 189, 119 in 1999 to 387,673 in 2008, representing a 95 percent increase. Over the same period, form 4 enrolments also rose by 95 percent from 152,124 in 1999 to 297,301 in 2008. 66 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 33: Secondary School Enrolment by Form, 1999-2003 1999 2000 2001 2002 2003 Form M F T M F T M F T M F T M F T I 97,231 91,888 189,119 110,053 98,706 208,759 121,992 113,754 235,746 136,006 120,740 256,746 129,403 121,660 251,063 II 98,066 86,922 184,988 104,078 93,550 197,628 106,725 95,589 202,314 108,576 97,470 206,046 121,765 116,281 238,046 III 90,293 77,871 168,164 98,610 87,346 185,956 103,339 90,351 193,690 99,179 97,470 206,046 106,688 97,220 203,908 IV 82,632 69,492 152,124 91,700 78,381 170,071 98,920 86,987 185,907 99,303 85,881 185,184 102,732 84,207 186,939 Total 373,440 327,098 700,538 404,441 357,973 762,414 430,976 386,681 817,657 443,064 393,457 836,521 460,588 419,368 879,956 Sources: Kenya National Bureau of Statistics; Statistical Abstracts 2003-2009, Economic Surveys 2002-2009 Table 34: Secondary School Enrolment by Form, 2004-2008* 2004 2005 2006 2007 2008 Form M F T M F T M F T M F T M F T I 146,145 126,557 272,702 139,469 124,384 263,853 161,588 137,873 299,461 170,297 142,672 312,969 207,212 180,461 387,673 II 124,585 114,053 238,638 122,867 109,471 232,338 132,015 119,077 251,092 173,444 149,602 323,046 196,500 163,164 359,664 III 117,975 105,118 223,093 120,912 107,770 228,682 120,978 115,443 236,421 157,903 134,765 292,668 181,775 155,798 337,573 IV 101,301 89,416 190,717 110,909 98,367 209,276 131,471 111,615 243,106 137,749 113,835 251,584 161,026 136,275 297,301 Total 490,006 435,144 925,150 494,157 439,992 934,149 546,072 484,008 1,030,080 639,393 540,874 1,180,267 746,513 635,698 1,382,211 Sources: Republic of Kenya; Kenya National Bureau of Statistic, Statistical Abstracts 2003-2009, Economic Surveys 2002-2009 The highest increments in form 1 enrolments were recorded during the 2000-2001 transition(12.9%) and again during the 2007-2008 transition(23.9%). The significantly high increase in form 1 enrol ments between 2007 and 2008 can in part be attributed to the introduction of the subsidy in secondary school tuition which has in effect made secondary schooling more affordable particularly in day schools that are becoming increasingly popular. In addition, affordability of secondary education has significantly improved access by youth from poor families who could not afford the unsubsidized costs before then. Another factor to which increments in secondary school enrolments can be attributed is the re-entry policy for student-mothers who a majority of whom never got an opportunity to rejoin school in the past. 4.4.3 Provincial Gross Secondary Enrolment Table 35: Provincial Gross Secondary Enrolment Province 1999 2000 2001 Coast 42,076 42,353 43,284 Central 153,770 156,618 165,707 Eastern 136,085 131,005 140,224 Nairobi 26,004 23,628 19,429 Rift Valley 153,688 155,557 159,604 Western 96,717 94,009 91,673 Nyanza 125,416 129,675 128,347 N. Eastern 5,162 5,241 5,255 Total 738,918 738,086 753,523 2002 42,136 169,416 144,803 16,700 159,447 98,232 132,737 5,224 768,695 2003 49,356 171,281 166,887 20,212 183,258 109,503 155,670 12,451 868,618 2004 55,367 187,392 177,103 29,708 204,374 118,226 148,469 5,511 926,150 2005 48,291 181,078 172,678 28,459 204,613 117,303 169,644 6,084 928,150 2006 58,473 204,142 183,518 29,694 243,148 120,338 182,982 7,785 1,030,080 2007 65,304 223,244 214,037 49,728 266,305 145,697 206,994 8,997 1,180,306 Source: Republic of Kenya- Kenya National Bureau of Statistics, statistical abstracts 2003-2009 * The latest enrolment data available from the most credible source; the MoE EMIS section do not include enrolment and other indicators for 2009. Youth Fact Book Infinite Possibility Or Definite Disaster? 67 Central province recorded the highest enrolments between 1999 and 2002. The Rift Valley province on the other hand has continued to record the highest enrolments since 2003. The bottom three provinces in terms of secondary school populations have been Coast, Nairobi and North Eastern Provinces. According to Oliech(unpublished), although these trends show regional disparities in terms of absolute enrolments, they do not necessarily illustrate inequality. This is more so because, the densely populated parts of Rift Valley and Central have higher densities of children and youth of school-going-age, hence higher enrolments relative to other less densely populated regions. However, this very high potential nature has a profound impact on access to paidfor secondary school education because average household incomes are higher in such regions thus giving children from such households an upper hand in accessing secondary education. Relating the data on enrolments with those on primary to secondary transition rates, it emerges that although Rift Valley province records highest secondary school enrolments, its low primary to secondary transition rates suggest that it also has the highest level of youth exclusion from participating in secondary education, at least in gross numbers. 4.5 Secondary to University Transition Rates  Secondary-University Transition Rate 7.0 6.2 5.7 5.6 6.1 5.1 5.3 4.9 4.8 Percentage % 2006/2007 2007/2008 2005/2006 2001/2002 2002/2003 2003/2004 2004/2005 2000/2001 1999/2000 Academic Year Figure 52: Secondary-University Transition Rate 1999/2000-2007/2008 Source: Commission for Higher Education(CHE) The Secondary to University transition rates have fluctuated over the past ten academic years. The rate rose from 4.8 percent in 1999/2000 to a high of 6.2 percent during the 2001/2002 academic year. However, the rate declined in the subsequent admissions and remained below the 6 percent mark before rising again to 6.1 and 7.1 percent during the 2007/08 and 2008/2009 academic years, respectively. According to Oliench,(unpublished), the generally low SecondaryUniversity transition rates are an indication of an almost elitist publicly provided university education. In effect, this leaves the majority of the candidates who qualify for university admission (obtaining mean grade of C+ and above) to either compete for the expensive private entry scheme or opt for other less costly forms of tertiary education. In the worst of instances, many deserving cases for university are forced to terminate their education at the end O-Level. 68 Youth Fact Book Infinite Possibility Or Definite Disaster? 4.6 TIVET Institutions 4.6.1 Distribution of TIVET Institutions Table 36: Distribution of TIVET Institutions Ministry/Organization Type Ministry of Education Science& Technology Other Ministries Private Sector, Religious Organizations and NGOs Source: MoE, 2008 Polytechnic University Colleges National Polytechnics Technical Training Institutes Institutes of Technology Kenya Technical Teachers College Vocational and skills Training Centres Youth Polytechnics Other Technical Training institutions Vocational and Skills Training Institutions TOTAL No. of Institutions 2002 2007 0 2 4 2 19 19 16 17 1 1 4 4 600 650 40 40 800 930 1484 1665 There has been an increase in TIVET institutions but mainly through youth polytechnics and vocational and skills training institutions. 4.6.2 Enrolments in TIVET Institutions 2005-2009 According to the 2010 Economic Survey, in 2009, the total enrolment in TIVET institutions was 71,513 as compared to 85,200 in 2008. The lower enrolment was due to upgrading of Kenya Polytechnic University College and Mombasa Polytechnic University College to University college status in 2009. The Youth Polytechnics had the highest enrolment recorded among TIVET institutions at 43.8 per cent followed by Technical training institutions at 31.4 per cent. The current national polytechnics are Kisumu and Eldoret with a total enrolment of 6,999 students. In 2009, the male student enrolment stood at 50.2 per cent in TIVET institutions with Youth polytechnics having a higher enrolment of female students at 57.8 per cent. Youth Fact Book Infinite Possibility Or Definite Disaster? 69 Table 37: Student Enrolment by Gender in Technical Institutions, 2005 – 2009 2005 2006 2007 2008 INSTITUTION Male Female Male Female Male Female Male National Polytechnics Kenya Polytechnic 6,410 3,549 6,405 3,329 6,521 3,401 6,602 Mombasa Polytechnic 3,111 2,631 3,265 2,710 3,285 3,012 3,456 Kisumu Polytechnic 1,349 619 1,410 710 1,489 824 1,768 Eldoret Polytechnic 1,759 820 1,834 832 1,894 858 1,996 Sub-Total 12,629 7,619 12,914 7,581 13,189 8,095 13,822 2009 Female Male 3,546 3,543 1,022 2,276 987 1,949 9,098 4,225 Female 1,472 1,302 2,774 Other TIVET Institutions Technical Training Institutes 9,846 8,684 9,925 8,731 10,818 9,517 12,132 9,876 12,514 9,923 Institute of Technology Sub- Total Youth Polytechnics TOTAL GRAND TOTAL 4,904 14,750 8,691 36,070 70,512 3,943 12,627 14,196 34,442 4,961 14,886 8,741 36,541 71,167 4,104 12,835 14,210 34,626 5,407 16,226 9,528 38,942 76,516 4,473 13,990 15,489 37,574 5,807 17,939 12,154 43,915 85,200 4,768 14,644 17,543 41,285 5,920 18,434 13,222 35,881 71,513 4,813 14,736 18,122 35,632 Source: Ministry of Higher Education, Science and Technology and Ministry of State for Youth and Sports *Provisional 4.7 University Education According to the 2010 Economic Survey, total enrolment in all the universities rose by 44.7 per cent from 122,847 students in 2008/09 to 177,735 students in 2009/10 academic year. Enrolment in public universities increased from 100,649 students in the 2008/09 academic year to 142,556 students in 2009/10. In 2009/10, the male and female student enrolment in public universities was 89,611 and 52,945, respectively, part-time students in public universities constituted 32.0 per cent of the total student enrolment in 2009/10 academic year. Student enrolment in private accredited universities accounted for 19.8 per cent of the total university students enrolled in 2009/10 academic year as compared to 18.1 per cent in 2008/2009 academic year. The public universities student intake through the Joint Admissions Board(JAB) increased by 23.4 per cent from 17,100 in 2008/09 to 21,100 in 2009/10 academic year. The increase in intake was attributed to establishment of constituent colleges which significantly increased access to university education. The proportion of female student enrolment in university education declined from 40.1 per cent in 2008/2009 to 37.9 per cent in 2009/2010. In order to enhance female enrolments JAB has an affirmative action policy of admitting female students with a point lower than their male counterparts. However, the gender disparity in university education enrolment remains high with a gender parity index of 0.61 based on student enrolment. 70 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 38: Total Student Enrolment in Public and Private Universities 2000/01-2009/2010 INSTITUTION 1999 2000 /2000 /2001 Nairobi … 14,833 Full time … 11,724 Part Time … 3,109 Kenyatta … 9,953 Full time … 7,529 Part Time … 2,424 Moi … 8,519 Full time … 7,209 Part Time … 1,310 Egerton … 8,985 Full time … 8,108 Part Time … 877 Jomo Kenyatta … 4,280 Full time … 1,821 Part Time … 2,459 Maseno … 4,134 Full time … 3,149 Special … 985 Masinde Muliro … … Full time … … Part Time … … TOTAL … 50,704 Private Universities … Private Accredited … 7,143 Private Unaccredited … 1,348 SUB-TOTAL … 8,491 GRAND TOTAL … 59,195 Source: Various Economic Surveys 2001 /2002 24,696 13,174 11,522 11,815 9,367 2,448 9,338 7,245 2,093 9,101 8,214 887 3,680 1196 2,484 4,048 3,054 994 … … … 62,678 7211 1,460 8,671 71,349 2002 /2003 25,689 13,591 12,098 15,735 8,301 7,434 10,823 7,281 3,542 9,362 8,458 904 4,588 2,055 2,533 5,635 4,621 1,014 80 … … 71,912 7639 1,490 9,129 81,041 2003 /2004 26,712 14,009 12,703 15,776 8,716 7,060 10,447 7,318 3,129 9,352 8,403 949 4,657 1,997 2,660 5,607 4,542 1,065 165 … … 72,716 8021 1,520 9,541 82,257 2004 /2005 32,974 15,237 17,737 16,055 7,200 8,855 12,010 7,499 4,511 8,597 7,500 1,097 6,274 3,200 3,074 5,581 4,350 1,231 81,491 8,342 1,708 10,050 91,541 2005 /2006 33,705 16,225 17,480 15,683 7,303 8,380 12,145 7,511 4,634 8,498 7,212 1,286 5,880 3,256 2,624 4,704 3,526 1,178 1,062 602 460 81,677 8,839 1,800 10,639 92,316 2006 /2007 34,939 16,394 18,545 16,736 8,351 8,385 14,663 9,208 5,455 12,169 10,702 1,467 6,305 2,700 3,605 4,715 3,165 1,550 1,810 1,042 768 91,337 2007 /2008* 36,339 17,054 19,285 18,597 9,333 9,264 14,832 9,312 5,520 12,467 10,959 1,508 7,962 3,372 4,590 5,686 3,820 1,866 1,224 687 537 97,107 2008 2009 /2009 /2010* 37,415 42,360 17,481 22,327 19,934 20,033 19,365 26,491 9,823 21,010 9,541 5,481 15,361 20,299 9,684 11,611 5,676 8,688 13,082 13,487 11,507 11,441 1,575 2,046 8,317 9,716 3,534 4,831 4,784 4,885 5,860 5,507 3,935 3,448 1,926 2,059 1,249 6,703 701 4,337 548 2,366 100,649 142,556 15,948 20,157 21,164 29,028 4,944 975 1,034 6,151 20,892 21,132 22,198 35,179 112,229 118,239 122,847 177,735 Youth Fact Book Infinite Possibility Or Definite Disaster? 71 Commission for Higher Education(CHE) The number of charted private universities increased from 7 in 2006/07 to 11 in 2008/09. In the same period, a total of 14 constituent colleges for public universities were established, 5 private universities were given Letters of Interim Authority, 4 were fully accredited(chartered), and 11 post secondary school institutions were granted authority to collaborate with universities to offer specific university programmes. Table 39: Registration of Universities and degree offering institutions Category of Institutions Chartered private Universities Universities with letter of Interim Authority Registered Universities Institutions approved for collaboration with universities in offering university programmes Public Universities Public university constituent colleges Public University Campuses established Source: Economic Survey, 2010 2006/07 7 4 5 18 6 1 _ 2008/09 11 9 4 29 7 14 3 As at 2008/09, the commission for Higher Education had approved 82 degree programmes for private universities, granted authority to collaborate to 46 university programmes and validated 105 diploma programmes for post secondary school institutions. The Commission also facilitates development projects in the universities depending on the available funding levels. In 2006/07, the commission disbursed to universities a total of Ksh 65 million for 21 projects and Ksh 75 million for 23 projects in 2007/2008.The objective of financing the projects is to enhance universities research capabilities and staff development. 4.8 Learning Assessment 3 Literacy and Numeracy According to the Annual Literacy Assessment Report(Uwezo, 2010), many children in lower and upper primary schools are unable to demonstrate basic reading and numeracy skills especially more in public than in private schools. For example, only one in every three children in class two can read a paragraph of their level while another one third cannot even read a word. Out of every 1,000 children completing class eight, 50 cannot read a class two story while 25% of pupils in class five cannot read a story of class two level. While girls are generally better readers than boys in both English and Kiswahili(in all the provinces apart from North Eastern and Western), boys are better in numeracy skills. These low competencies may be affecting performance at higher levels, and inability to read, which should be acquired in early primary grades. 3 Analysis is mainly from the Annual Literacy Assessment Report produced by Uwezo in 2010 72 Youth Fact Book Infinite Possibility Or Definite Disaster? Children perform better when given application mathematics(92% of primary school children are able to solve the problem) than when given the same mathematical problem to solve(72% of the same children got it right) in an abstract form. This indicates the need to relate mathematical concepts to real life situations and application. Out of School Children On average, 5% of 6-16 year olds do not attend any school. However, in some regions such as Pokot North, and Samburu North, 40% of this age group is not enrolled. To the contrary, only 1% of children the same age in Central province are out of school. Over-age Children Over-age children are present in all class levels. In class 2, 40% of the children are older than the appropriate age(7-8 years) and the ages range from 9-16 years. Most overage learners are however in class 7 where 60% of the children are aged 14 years and above. Coast province has the highest prevalence of over-age children where 75% of those in class 7 are aged 14 years and above. North Eastern and Western provinces follow with 73% and 70% respectively. The issue of over-age children persists in all classes mainly due to late entry, grade repetition and time –off school. Tuition 30% of children in class 1 – 3 are subjected to extra paid tuition at school. The likelihood that a class one child will be subjected to extra tuition is three time higher in private schools than in public schools. Tuition increases with class so that by class eight, 80% of the students are subjected to tuition. Influence of Mothers on Literacy The assessment also establishes the fact that girls aged 6- 14 years whose mothers have not been to school are seven times more likely to be out of school than their peers whose mothers have completed the primary school cycle. Children’s literacy and numeracy competencies increase with mothers’ level of formal schooling. Absenteeism 15% of pupils are absent from school in any given day. Absenteeism ranged from 34% in Manga, Nyanza province to 7% in Lagdera, North Eastern province. Teacher Shortages At any one time, there are about 4 classes without a teacher in every school. Teacher shortage is said to be highest in Western province. Many schools mitigate this gap by employing community teachers and engaging volunteers who may not be as qualified. Youth Fact Book Infinite Possibility Or Definite Disaster? 73 4.9 Comparative Analysis Male Enrollment Rate by Age in Other Countries Male Enrollment Rates by Age 80 92 28 93 74 30 79 52 11 77 42 15 59 39 21 91 96 45 64 42 59 12-14 15-17 18-24 77 57 21 Kenya(97) Uganda(02) Tanzania(00) Rwanda(97) Burundi(98) Figure 53: Male Enrollment Rate by Age in Other Countries Source: World Bank, 2007. South Africa(00) Nigeria(03) India(00) Female Enrollment Rate by Age in Other Countries Female Enrollment Rates 92 73 92 70 17 13 77 78 46 39 13 6 90 96 47 42 34 17 58 65 30 12-14 15-17 18-24 65 43 11 Kenya(97) Uganda(02) Tanzania(00) Rwanda(97) Burundi(98) South Africa(00) Nigeria(03) India(00) Figure 54: Female Enrollment Rate by Age in Other Countries Source: World Bank, 2007 Male and female enrollment rates are highest in South Africa, Kenya and Uganda. However, female enrollment rates are consistently lower than that of their male counterparts. South Africa and Rwanda have the highest level of gender parity in all the countries profiled. 74 Youth Fact Book Infinite Possibility Or Definite Disaster? 5 Forming Families ‘Adequately preparing young people for parenthood helps society by decreasing fertility and dependency. It opens a window of opportunity for human capital accumulation, productivity gains, economic growth, and poverty reduction. Adequate preparation for family formation can thus ensure that well-being, rather than poverty, is transmitted to the next generation.’ (Young person in Argentine, 2009) 5.1 Youth in Families 5.1.1 Patterns of Free Time Activities for 7-19 Year Olds Free time activities for 7-19 year olds Reading 55 45 Entertainment (Music, TV, Radio, Movies 48 Sports Sleeping 44 32 34 28 25 14 9 7-10years 11-14years Figure 55: Patterns of Free time Activities for 7-19 year olds Source: Consumer Insight, 2009 43 16 14 15-17years 26 14 14 18-19years Reading as a free time activity is highest among 11-14 year olds(55%) before it drops by half to 26% among 18-19 year olds. Entertainment increases by age from 32% among 7-10 year olds to 34% among 11-14 years. It further rises to 43% among 15-17 year olds and to 44% among 18-19 year olds. Sporting as a free time engagement decreases with age while sleeping generally increases with age. 5.1.2 Social Activities Among 7-19 year olds Table 40: Social Activities Among 7-19 year olds Social Places Visited Religious Institutions(Church or Mosque) Sports grounds Shopping malls Restaurants Beach Game parks/tourist sites Religious crusades Public Parks Entertainment venues(discos, video halls, movie theatres) 7-10 years 82 27 12 11 10 17 9 8 13 Source: Consumer Insight, 2009 11-14 years 79 43 20 9 13 16 14 13 18 15-17 years 72 40 19 24 16 11 15 18 30 18-19 years 60 34 21 20 12 9 12 12 49 Frequenting religious institutions and game parks/tourist sites decrease with age while frequenting entertainment venues increases with age. Between 2005 and 2009, the frequenting of religious 76 Youth Fact Book Infinite Possibility Or Definite Disaster? institutions by 7-19 year olds rose from 63% to 73% while that of movie theatres increased from 26% in 2005 to 32% in 2007 before decreasing to 25% in 2009. 5.1.3 Clothes According to consumer insight, about 60% of 7-19 year olds buy second hand clothes. For most of them, their attitude towards clothes is shaped by the fact that it is important to look well dressed, have a good sense of style, they enjoy shopping for clothes and the fact that they keep up with the latest trend. Other reasons include comfort, improved image by wearing designer labels, to stand out in crowds and to attract the opposite sex. Table 41: Clothes Influencer Clothes Influencer Overall% Age Gender 7- 10 11-14 15-17 18-19 Male Female years years years years Parents 37 67 55 24 7 36 39 Self 30 12 23 37 45 29 31 Peers/friends 16 11 15 23 16 16 16 New labels/Designs 7 2 4 8 12 7 7 Movie Celebrities 6 5 2 7 9 7 5 Music Videos 6 3 4 7 11 8 4 Religion 3 1 1 6 5 2 5 School/ College/ University 2 1 2 3 2 2 2 None 5 4 5 3 5 5 4 Source: Consumer Insight, 2009 The biggest influencer of what clothes 7-19 year olds should wear is the parents. However, that influence decreases as children grow older and by age 19, the parents influence has reduced by 10.4% from where it was when the child was between 7 and 10. Self is the next influencer and it increases with age. Peers/friends, designer labels, movie celebrities and music videos also play a role in influencing what 7-19 year olds wear though in different magnitudes as shown in the table above. Youth Fact Book Infinite Possibility Or Definite Disaster? 77 5.1.4 Values, Dreams, Aspirations and Fears Values, Dreams and Aspirations of 7-19 year olds btw 2005 and 2009 40 33 15 12 12.5 7.7 To be a professional To be rich To be a leader To have a family To be famous To be a hero Figure 56: Values, Dreams and Aspirations among 7 0 19 year olds Source: Consumer Insight, 2009 The most admired personality is Barack Obama(31%) followed by fathers(10%) and mothers 9%. Most young people fear death and HIV/AIDs. Young women also fear being raped. Table 42: Fears and Worries by Gender Among 7-19 Year Olds Fears and Worries Death HIV/AIDS Failure Poverty Rape Source: Consumer Insight, 2009 %% Male Females 45 36 37 40 16 15 10 8 2 11 78 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.1.5 Money Matters among 7-19 Year Olds According to consumer insight, only 8% of 7-19 year olds have a bank account. 39% of this age group receives money daily, 30% weekly and 14% monthly as indicated below. Frequency of Receiving Money among 7-19 year olds 39 30 14 8 6 4 Daily Weekly Bi-monthly Monthly Quarterly N/S Figure 57: Frequency of Receiving Money among 7-19 year olds Source: Consumer Insight, 2009 Table 43: Amounts Received by 7-19 Year Olds Amount% Below 50 57% 51- 100 24% 101- 150 3% 151- 200 4% 201- 250 2% 251- 300 0% 301- 400 0% 401- 450 0% 451- 500 0% Above 500 1% None 9% Source: Consumer Insight, 2009 57% of 7-19 year olds receive below kshs. 50.00 while 24% receive between Kshs. 51.00 and Kshs. 100.00 Youth Fact Book Infinite Possibility Or Definite Disaster? 79 Table 44: Items 7-19 Year Olds Spent On Total Snacks and sweets 42% Food 35% Transport 26% Clothing 21% Airtime 16% Outing 8% Cyber café-surfing 5% Sanitary pads 1% Pencils 1% Offerings 1% None/NS 17% Source: Consumer Insight, 2009 7 – 10 years 55% 28% 9% 2% 1% 1% 0% 1% 1% 21% 11 – 14 years 52% 32% 13% 10% 2% 2% 2% 1% 0% 0% 18% 15 – 17 years 38% 46% 42% 26% 10% 12% 3% 2% 0% 0% 12% 18 – 19 years 24% 36% 41% 45% 43% 17% 12% 1% 1% 1% 14% 42% of 7-19 year olds spent their money on snacks and sweets although this reduces as children grow older. 35% is spent on food, 26% on transport, 21% on clothing, 16% on airtime, 8% on outings and 5% on cyber cafés surfing. Spending on clothing, airtime, outing and cyber café surfing increase as children grow older. Table 45: Buying Patterns of Alcoholic Beverages and Cigarettes Daily Weekly Monthly Less often/ Never Beer 1% 2% 2% 5% Spirits- 1% 2% 4% Wine- 2% 1% 4% Traditional liquors--- 2% Cigarettes 1% 1% 1% 4% Source: Consumer Insight, 2009 Buyer Self Other 51% 49% 34% 66% 32% 68% 14% 86% 25% 75% Influencer Self Other 36% 64% 38% 62% 36% 64% 14% 86% 17% 83% 67% of 7-19 year olds are bought for alcoholic beverages by other people while 75% are bought for cigarettes by others. Self buying of alcohol stands at 33% and that of cigarettes at 25%. 31% of 7-19 year olds influenced themselves into drinking and 17% into smoking. 69% were influenced by others into taking alcohol and 83% were influenced by others into smocking. 80 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.1.6 Age at First Sex among Young People Age At First Sex Among Young People 58 47 Women Men 22 11 sex before 15 sex before 18 Figure 58: Age at First Sex among Young People Source: KDHS, 2009 11% of young women and 22% of young men aged 15 to 24 had their first sexual intercourse before the age of 15. By the age of 18, 47% of young women and 58% of young men had had their first sexual intercourse. Young people(15-24) who reported sexual debut before the age of 15 by 2003/2007/2009 KDHS reports 33.7 28.8 Men Women 22 16.4 13.7 11 KDHS 2003 KDHS 2007 KDHS 2009 Figure 59: Young people(15-24) who reported sexual debut before the age of 15 by various KDHS reports Source: Various KDHS reports The trend in 2009 is an improvement from 2007 where 16.4% of young women and 33.7% of young men had had sex before the age of 15 as illustrated. Youth Fact Book Infinite Possibility Or Definite Disaster? 81 5.1.7 Age at First Sex among Young People(15-24) In Rural and Urban Areas Age at first sex among young women in rural and urban areas Urban Rural 49.8 39.1 Age at first sex among young men in rural and urban areas 60.1 51.1 11.8 8.5 23.8 15.4 sex before 15 sex before 18 sex before 15 sex before 18 Figure 60: Age at First Sex among Young People(15-24) In Rural and Urban Areas Source: KDHS, 2009 Young people living in rural areas tend to initiate sexual activity earlier than their urban counterparts. More young men have their first sexual intercourse earlier than their female counterparts across the board. 5.1.8 Age at First Sex among Young People(15-24) by Gender and Region Age at First Sex among Young Women by Region 63.6 Sex before 15 Sex before 18 27 3 32 4.2 53.4 42.1 18.3 11.6 7.1 47.9 48.4 38.5 13.7 11.1 8.7 Nairobi Central Coast Eastern Nyanza RiftValley Western N. E 82 Youth Fact Book Infinite Possibility Or Definite Disaster? Age at First Sex among Young Men by Region 69.2 60.8 50.1 63.3 58.9 54.5 46.3 Sex before 15 Sex before 18 27.2 27.3 23.3 22.5 17.3 15.1 13.2 20.4 8 Nairobi Central Coast Eastern Nyanza RiftValley Western N. E Figure 61: Age at First Sex among Young People(15-24) by Gender and Region Source: KDHS, 2009 Young women in Nyanza(63.6%) and in Coast(53.4%) are more likely than those in other provinces to have had sex before the age of 18. Young men in Western(69.2%), Nyanza(63.3%) and Nairobi(60.8%) had had sex before the age of 18. 5.1.9 Age at First Sex among Young People(15-24) by Background Characteristics According to KDHS(2009), level of education is strongly related to age at first sex, especially for women. This is evidenced by the fact that 67% of women aged 18-24 with no education had had sex by the age of 18 compared to only 30% among those with at least some secondary education. Similarly, early sexual debut seems to be associated with poverty levels. 62% of young women in the lowest wealth quintile had their first sexual intercourse by the age of 18 compared with 36% of those in the highest wealth quintile as evidenced in the table below. Table 46: Age at First Sex among Young People(15-24) by Background Characteristics Background Characteristics Education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest Source: KDHS, 2009 Women sex before 15 23 17.2 8.8 4.8 17.8 10.6 10.5 10.5 7.5 sex before 18 66.5 68.5 46.3 29.8 61.5 53.5 47.5 43.9 36.3 Men sex before 15 12.1 24.7 24.6 19.2 26.9 27.1 23 20.9 14 sex before 18 39.6 65.1 62.8 53.5 61.2 67.6 60.9 56.6 48.1 Youth Fact Book Infinite Possibility Or Definite Disaster? 83 5.1.10 Percentage of Young People Abstaining from Sex,% of those who had Sex in the past 12 Months and% of those who used Condoms by Age Table 47: Percentage of Young People Abstaining from Sex,% of those who had Sex in the past 12 Months and % of those who used Condoms by Age % of young people who had never had sexual intercourse % of young people who had sexual intercourse in the last 12 months Percentage of young people who had sexual intercourse in the last 12 months and used Condoms Age Women Men Women Men Women Men 15-17 77.1 69.1 14.6 16 41.1 52.1 18-19 63.5 40.4 22.4 34.9 42.8 56.5 20-22 40.8 17.8 39.6 53.1 33.9 65.6 23-24 25.8 8.4 46.5 68.7 50.7 76.4 Source: KDHS, 2009 Most 15-17 year olds(77% women and 69% men) abstain from sex compared to other age groups. However, as they grow older, fewer are able to abstain. Generally, more young women than their male counterparts chose to abstain. Unfortunately, only an average of 43% of sexually active girls aged 15-24 used condoms compared to 63% of their male counterparts. 5.1.11 Percentage of Young People Abstaining from Sex,% of those who had Sex in the past 12 Months and% of those who used Condoms by Background Characteristics As evidenced in the table below, young people(15-24) who chose to abstain are higher in rural ares than in urban areas. For women, the higher the level of education, the less they are likely to abstain. For young men, the higher the level of education, the higher the likelihood of using condoms. 84 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 48: Percentage of Young People Abstaining from Sex,% of those who had Sex in the past 12 Months and % of those who used Condoms by Background Characteristics % of young people who had never had sexual intercourse % of young people who had sexual intercourse in the last 12 months by age Percentage of young people who had sexual intercourse in the last 12 months and used Condoms Background Women Men Women Men Women Men Characteristics Residence Urban 56.9 37 31.5 44.1 44.6 74.9 Rural 63.7 40.3 22.1 36.4 38.3 60.8 Education No education 78.6 34.5 13.5*** Primary Incomplete 68 48 21.2 33.1 34.4 47.4 Primary complete 58.8 37.7 25.9 40.9 29.3 67.8 Secondary+ 58.2 33.9 26.7 39.6 50.1 75.5 Wealth Quintile Lowest 67.6 50.2 22.2 35.3 16.2 54.7 Second 62.8 38.2 23.6 31.8 42.1 69.2 Middle 64.7 35.5 21.7 38.7 39.3 54 Fourth 64.8 41.7 19.8 36.3 48.2 65.2 Highest 54.1 34.9 32.1 47.5 44.9 73 Source KDHS, 2009 5.1.12 Percentage of Young People(15-24) Abstaining From Sex by Gender and Region Percentage of young people(15-24) abstaining from sex by gender and region Women Men 100 88.3 53 19 68.7 39 71.3 70.5 50.5 46.8 41.9 32.9 67.6 58.8 39.5 37.5 Nairobi Central Coast Eastern Nyanza RiftValley Western N. E Figure 62: Percentage of Young People(15-24) Abstaining From Sex by Gender and Region Source: KDHS, 2009 North Eastern province has the highest level of abstinence among 15-24 year olds. 100% of young women and 88.3% of their male counterparts abstain. Coast and Eastern provinces follow with average abstinence levels of 60.5% and 56.6% respectively. Youth Fact Book Infinite Possibility Or Definite Disaster? 85 5.1.13 Young People(15-24) who reported to have had Sex at Least Once Young people(15-24) who reported to have had sex at least once Women Men 93.5 89.9 95.2 92.8 85.2 88 84.6 81.4 80.8 69.7 68.7 71.6 43 38.8 56.4 53.7 20 22.4 26.5 22.3 15 16 17 18 19 20 21 22 23 24 Figure 63: Young People(15-24) who reported to have had Sex at Least Once Source: KAIS, 2007 Contrary to the assertion made in 3.2.5 and 3.2.7 that more young women than young men chose to abstain, a look at individual ages of those who have had sex at least once by gender in 2007 generally reveals that between age 17-24 years, more women than men had sex at least once. By the age of 24, 94% of 15-24 year olds have had sex at least once. 5.1.14 Young People(15-24) who have had High Risk Sexual Intercourse in the Last 12 Months by Gender KDHS defines high risk sexual behavior as sex with a non – married or non-cohabiting sexual partner. As indicated in the graph, the younger the person, the more likely they are to engage in high risk sexual behavior. This is evidenced by the fact that 72.5% of 15-17 year old women engaged in high risk sex compared to 19% of 23-24 year old women. Similarly, 100% of 15-17 year old men engaged in high risk sex compared to 63.6% of 23-24 year old men. Men are more likely to use condoms(64%) in high risk sex than women(40%). Young people(15-24) who have had high risk sexual intercourse in the last 12 months by Gender 100 96.9 Women 85.3 Men 72.5 63.6 43.6 27.8 19 15-17 18-19 20-22 23-24 Figure 64: Young People(15-24) who have had High Risk Sexual Intercourse in the Last 12 Months by Gender Source: KDHS, 2009 86 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.1.15 Young People(15-24) who have had High Risk Sexual Intercourse in the Last 12 Months by Background Characteristics Sexually active never married youth are more likely to engage in high risk sex compared with those who have ever married. Young women in urban areas(38.6%) are more likely to engage in high risk sex compared to their rural counterparts(30.8%). The reverse applies for men. Young men in rural areas(84.9%) are likely to engage in high risk sex compared to their counterparts in urban areas(76.8%). Among sexually active young women, high risk sex increases dramatically with level of education from 13% among women with no education to 50% among women with some secondary education. This trend is not the same for young men. High risk sex also increases with the wealth quintile for women. As a result, 23% of women in the lowest wealth quintile engage in high risk behavior compared to 40.6% of women in the highest wealth quintile. The pattern is different for men as illustrated in the table below. Table 49: Young People(15-24) who have had High Risk Sexual Intercourse in the Last 12 Months by Background Characteristics High risk sex by Characteristics Background Characteristics Women Men Marital Status Never Married 99.3 98.4 Ever Married 4.2 15.3 Knows Condom Source Yes 35.2 83.1 No 26.1 80.9 Residence Urban 38.6 76.8 Rural 30.8 84.9 Education No education 13.1* Primary Incomplete 27.9 85.3 Primary complete 26.9 77.8 Secondary+ 50.2 85.2 Wealth Quintile Lowest 23 81.5 Second 32.5 78.3 Middle 33.7 89.4 Fourth 31.6 88.9 Highest 40.6 77 Source: KDHS, 2009 Youth Fact Book Infinite Possibility Or Definite Disaster? 87 5.1.16 High Risk Sex among Young People(15-24) By Region More young(15-24) men than women in all the regions engage in high risk sex. Nyanza(87.8%), Central(86.8) and Western(86.4) have the highest numbers of young men engaging in high risk sex. Coast province(84.2%) and Nairobi(83.8%) follow closely. Nairobi(44.6%), Nyanza (35.8%) and Rift Valley 35.1%) has the highest number of young women involved in high risk sex. North Eastern data are not reflected because they are based on fewer than 25 unweighted cases that have been suppressed. High risk sex among young people(15-24) by region 83.8 86.8 84.2 87.8 86.4 79.1 77.9 Women Men 44.6 35.8 35.1 31 30.2 29.4 25.2 Nairobi Central Coast Eastern Nyanza RiftValley Western 00 N. E Figure 65: High Risk Sex among Young People(15-24) By Region Source: KDHS, 2009 5.1.17 Young People(7-19) and the Number of Sexual Partners % No. of Sexual Partners among 7-19 Year Olds More Five 1 3 Five 3 3 Four 12 5 2007 2009 Three 22 13 Two 22 13 One 56 45 20 40 60 80 100 120 Figure 66:% No. of Sexual Partners among 7-19 Year Olds Source: KDHS, 2009 88 Youth Fact Book Infinite Possibility Or Definite Disaster? Between 2007 and 2009, about 50.5% of 7-19 year olds had one sexual partner, 17.5% had two, and another 17.5% had three sexual partners. 9% have four sexual partners and 3% had five partners. 2% had more than five sexual partners. 5.1.18 Percentage(%) of Women(15 – 19) who had High Risk Sex with a Man 10+ Years Older Table 50: Percentage(%) of Women(15 – 19) who had High Risk Sex with a Man 10+ Years Older Characteristics% Age 15-17 4.6 18-19 2.6 Residence Urban 2.8 Rural 3.9 Level of Education No education* Primary Incomplete 4.6 Primary complete 2.9 Secondary+ 1.3 Source: KDHS, 2009 15 – 17 year old women(4.6%) are more likely to engage in high risk sex with a man 10+ years older compared to 18-19 year olds(2.6%). Young women(15-19) in the rural areas as well as women with a lower level of education are more likely to engage in high risk sex with a man 10+ years older compared to their urban counterparts and more educated women of the same age. 5.1.19 Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Age According to the KDHS report, engaging in sex under the influence of alcohol can impair judgment, compromise power relations and increase risky sexual behavior. As illustrated in the graph, the percentage of women who had sex when drunk or with a drunken partner was higher through all the age cohorts than that of their male counterparts and is highest(9%) among 23-24 year old women. Youth Fact Book Infinite Possibility Or Definite Disaster? 89 Percentage(%) of women(15 –24) who had high risk sex in the past 12 months when drunk or with a partner who was drunk 9 Women Men 5.9 4 2.9 1.6 1.8 0 0 15-17 18-19 20-22 23-24 Figure 67: Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Age Source: KDHS, 2009 5.1.20 Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Background Characteristics Table 51: Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Background Characteristics Background Characteristics Marital Status Never Married Ever Married Residence Urban Rural Education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest Source: KDHS, 2009 Women Men 1.5 1.2 9.6 1.4 5.7 1.1 4.1 1.2 6.3* 4.5 1.2 5.9 0.7 3.3 1.5 7.5 2.1 4.4 2.3 2.3 0 5.4 0.9 3.7 0.9 90 Youth Fact Book Infinite Possibility Or Definite Disaster? Young women who have ever married, those in urban areas, those with no education and those in the lowest wealth quintile had the highest percentage of sex when drunk or with a drunken partner than their counterparts as illustrated on table 51. 5.1.21 Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Region. Percentage(%) of 15 –24 year olds who had high risk sex in the past 12 months when drunk or with a partner who was drunk by region 8.3 Women Men 6.4 3.6 0.4 Nairobi 2.5 1.5 Central 0.8 Coast 4 2.8 3.9 2.9 0.1 Eastern 1 Nyanza RiftValley 0.1 Western 0 0 N. E Figure 68: Percentage(%) of Women(15 – 24) who had High Risk Sex in the Past 12 Months when Drunk or with a Partner who was Drunk by Region Source: KDHS, 2009 Coast province(8.3%) and Rift Valley(6.4%) had the highest percentage of women who had high risk sex in the past 12 months when drunk or with a partner who was drunk. Rift valley had the highest number of young men(2.9%) who had high risk sex in the past 12 months when drunk or with a partner who was drunk. 5.1.22 Transactional Sex among Men Transactional sex among men aged 15-49 2.9 2.4 2.4 1.5 1.4 15-19 20-24 Figure 69: Transactional Sex among Men Source: KDHS, 2009 25-29 30-39 40-49 Youth Fact Book Infinite Possibility Or Definite Disaster? 91 According to the KDHS report(2009), transactional sex involves exchange of sex for money, favours or gifts. Transactional sex is associated with high risk of contracting HIV and other sexually transmitted infections due to compromised power relations and the tendency to have multiple partnerships as a result. Transactional sex is highest among men aged 25-29 years old. Interestingly 20-24 and 30-39 age cohorts as well as 15-19 and 40-49 age cohorts have similar procurement patterns for sex. 5.1.23 Transactional Sex by Background Characteristics Table 52: Transactional Sex by Background Characteristics Background Characteristics Men Marital Status Never Married 2.6 Married or living tog 1.2 Divorced/Separated/widowed 7.7 Residence Urban 2.1 Rural 2.1 Education No education 0.9 Primary Incomplete 2.7 Primary complete 1.9 Secondary+ 2 Wealth Quintile Lowest 1.2 Second 1.8 Middle 2.2 Fourth 3.4 Highest 1.7 Source: KDHS, 2009 The highest numbers of men procuring sex are the divorced, separated or widowed(7.7%). With the exception of the highest wealth quintile, men are likely to procure sex as they move from one wealth quintile to another. Rural and urban patterns are similar as indicated in the table 52. 92 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.1.24 Condom Use among 15-24 Year Olds at First Sex by Gender Condom use at first sex by Gender 30 27.9 28 25.5 25.4 20.9 18.9 19.5 Women Men 15-17 18-19 20-22 23-24 Figure 70: Condom Use among 15-24 Year Olds at First Sex by Gender Source: KDHS, 2009 According to the KDHS(2009) report, one out of every four young people reported to use condoms the first time they had sex. For women, condom use is highest among 15-17 year olds while for young men, condom use is highest among 20-22 year olds. 5.1.25 Condom Use among 15-24 Year Olds at First Sex by Background Characteristics Condom use is higher among those who have never married and among those who know a source of condoms. Young urban women report higher use of condoms at first sex(33%) than their rural counterparts(21%). For young men it is the reverse. Young men in rural areas are more likely to use condoms(26%) compared to their counterparts in urban areas(24%). Condom use at first sex increases with level of education and wealth quintile for women. This is evidenced by the fact that only 3% of women with no education during their first sexual encounter used condoms compared to 39% of those with some secondary education. Similarly, only 10% of women in the lowest wealth quintile used condoms during their first sexual encounter compared to 34.4% in the highest wealth quintile. However for the young men, although condom use increases with education level, the pattern by wealth quintiles fluctuate. Youth Fact Book Infinite Possibility Or Definite Disaster? 93 Table 53: Percentage Use of Condoms among 15-24 Year Olds at First Sexual Encounter by Background Characteristics Background Characteristics Marital Status Never Married Ever Married Knows Condom Source Yes No Residence Urban Rural Education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest Women Men 34.6 26.6 17.4 19.7 29 26.9 10.4 10.9 32.8 23.8 21.1 26.2 3.3* 17.9 18.7 19.4 23.4 39.4 32.2 10 22 21.6 24.1 23.7 29.3 25.6 26.6 34.4 25 Source: KDHS, 2009 5.1.26 Condom Use at First Sex among Young People(15-24) by Region In Kenya, highest condom use among young women is concentrated in Nairobi(42.9%) followed by central province(27.5%) then Nyanza(26.8%). The highest condom use among young men is in central province(36.2%) followed by coast(29.8%) and then Rift Valley(28.5%) provinces. 94 Youth Fact Book Infinite Possibility Or Definite Disaster? Condom use at first sex among young people(15-24) by region 42.9 36.2 27.5 21.8 29.8 12.4 23.3 18.8 26.8 22.1 28.5 24.4 22.3 19.5 Women Men Nairobi Central Coast Eastern Nyanza RiftValley Western 1.7 0 N. E Figure 71: Condom Use at First Sex among Young People(15-24) by Region Source: KDHS, 2009 5.1.27 Trends of Condom Use Among Young People(15-24) By 2003, 2007 and 2009 KDHS Trends of condom use among young people(15-24) by 03,07, 09 KDHS Reports Women Men 28.4 26 25.5 24 14 11.9 KDHS 2003 KDHS 2007 KDHS 2009 Figure 72: Condom Use at First Sex among Young People(15-24) by 2003, 2007, 2009 KDHS KDHS, 2009 Condom use at first sexual intercourse has doubled since 2003 for both young males and females. However, condom use at first sexual intercourse has slightly declined for men from 28% in 2007 to 26% in 2009 while for women it has slightly decreased from 25.5% in 2007 to 24% in 2009. Youth Fact Book Infinite Possibility Or Definite Disaster? 95 5.1.28 Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Age Young people’s support of education about condom use to 12-14 year olds to prevent AIDS % women who agee % men who agee 72.5 74.1 72.8 74.2 66.6 61.9 59.1 55.8 18-19 20-24 25-29 30-39 Figure 73: Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Age Source: KDHS, 2009 Condom use is one of the main strategies for combating the speared of HIV. However, educating teenagers about condoms is sometimes controversial, with some saying it promotes early sexual experimentation. Generally, more young men than young women advocate for the teaching of 12-14 year olds about condoms to prevent HIV/AIDS. 5.1.29 Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Demographic Characteristics Demographic Characteristics% women who agree% men who agree Residence Urban 64.7 73 Rural 60.1 71.5 Education No education 45.7 45.9 Primary Incomplete 64 70 Primary complete 62.2 71.7 Secondary+ 63 75.1 Wealth Quintile Lowest 51.2 64.6 Second 61 74.2 Middle 65 70.5 Fourth 61.6 75.4 Highest 64.9 72.2 Table 54:Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Demographic Characteristics Source: KDHS, 2009 Urban residents advocate for the teaching of 12-14 year olds about condoms to prevent HIV/ AIDS more than rural residents do. Among men, the level of education influences this decision but for women there are no clear patterns. Wealth levels do not co-relate with this decision. 96 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.1.30 Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Region On average, Nairobi province has the highest(71%) advocates of teaching 12-14 year olds about condoms to prevent HIV/AIDS while North Eastern province has the lowest(16%). Nyanza province has the second highest number of advocates(70%). Men from Rift valley province (80%) and women from Nyanza province(70%) are the highest advocates of teaching 12-14 year olds about condoms to prevent HIV/AIDS. Young people’s support of education about condom use to 12-14 year olds to prevent AIDS by region % women who agee % men who agee 74.6 66.7 68.3 67 65.6 61.1 69.6 59.9 69.5 70.2 80.1 56.1 72.2 63 20 12.4 Nairobi Central Coast Eastern Nyanza RiftValley Western N. E Figure 74: Young People Supporting the Education of Condom Use to 12-14 Year Olds to Prevent AIDS by Region Source: KDHS, 2009 Youth Fact Book Infinite Possibility Or Definite Disaster? 97 5.1.31 Source of Information on Sexual& Reproductive Health Table 55: Source of Information on Sexual& Reproductive Health among 7-19 year Olds Source of Information on Sexual& reproductive Health 7- 10 years Media(TV&/or Radio) 16 Religious institutions/ leaders 17 Peers/Friends 4 Health institutions 2 Print Media(Newspaper, leaflets) 1 School 11 Government 1 Parent None 48 Most Trusted Source Media(TV&/or Radio) 10 Health institutions 15 School 8 Religious institutions/ leaders 11 Peers/Friends 10 Print Media(Newspaper, leaflets) 4 Parent 33 Government 6 None 1 11-14 years 19 17 4 6 1 11 46 15-17 years 20 16 6 9 6 7 1 34 18-19 years 37 13 13 13 9 3 2 1 8 14 20 30 5 14 22 15 12 8 11 9 9 4 4 10 2 4 9 9 1 1 1 50 49 9 Source: Consumer Insight, 2009 The most prominent sources of information on sexual& reproductive health are media(24%), religious institutions and leaders(16%), followed by peers and friends(8%) and health institutions (8%). However this varies among different age groups. Most young people(an average of 33%) have no source of sexual and reproductive health information. Interestingly, 7-10 year olds trust their parents as a source of sexual& reproductive health information but unfortunately parents are not giving the relevant information to this age group. The most trusted source of information for 11-14 year olds is school and media while for 15 to 17 year olds is media and health institutions. For 18 to 19 year olds the most trusted source is media, health institutions and peers/friends. 5.1.32 The‘Father’ Factor According to a survey conducted in February 2009 in Nairobi West Prison and Industrial Area Remand Prison(Mbevi, unpublished), parenting plays a big role in determining the development of a child. The survey argues that the role of a father is particularly important in determining the future well being of a child. This is confirmed by the fact that in Nairobi West Prison, out of 200 prisoners, 52% of the prisoners grew up without fathers, 10% had abusive fathers, 12% had passive fathers and 10% had excellent relationships with their fathers. In Industrial Area Remand Prison, 78% of the prisoners grew up without fathers, 8% had abusive fathers and 6% had passive dads. In total, about 3,200 prisoners were interviewed. 98 Youth Fact Book Infinite Possibility Or Definite Disaster? Extensive studies in United States of America(USA) confirm Mbevi’s assertion that a father is particularly important in determining the future well being of a child. These studies have shown direct co-relations between a father’s absence in a child’s life with poverty, maternal and child health, incarceration, crime, teen pregnancy, child abuse, drug and alcohol abuse, education, and childhood obesity. Poverty: Children in father-absent homes are five times more likely to be poor. In 2002, 7.8 percent of children in married-couple families were living in poverty, compared to 38.4 percent of children in female headed households(U.S. Census Bureau, 2003). During the year before their babies were born, 43% of unmarried mothers received welfare or food stamps, 21% received some type of housing subsidy, and 9% received another type of government transfer like unemployment insurance(McLanahan,2003). A child with a nonresident father is 54 percent more likely to be poorer than his or her father(Sorenso& Chava, 2001 September). When compared by family structure, 45.9% of poor single-parent families reported material hardship compared to 38.6% of poor two parent families. For families who did not experience material hardship, 23.3% were single-parent families compared to 41.2% of two-parent families(Beverly, 2001 September). Maternal and Infant Mortality: Infant mortality rates are 1.8 times higher for infants of unmarried mothers than for married mothers(Matthews, Curtin,& MacDorman, 2000). Unmarried mothers are less likely to obtain prenatal care and more likely to have a low birth-weight baby. Researchers find that these negative effects persist even when they take into account factors, such as parental education, that often distinguish single-parent from two-parent families(U.S. Department of Health and Human Services, 1995 September).Twenty-three percent of unmarried mothers in the U.S. cities reported cigarette use during their pregnancy. Seventy-one percent were on Medicare (McLanahan, 2003). A study of 2,921 mothers revealed that single mothers were twice as likely as married mothers to experience a bout of depression in the prior year. Single mothers also reported higher levels of stress, fewer contacts with family and friends, less involvement with church or social groups and less overall social support(Cairney& Boyle, 2003 August). In a longitudinal study of more than 10,000 families, researchers found that toddlers living in stepfamilies and single-parent families were more likely to suffer a burn, have a bad fall, or be scarred from an accident compared to kids living with both of their biological parents(O’Connor et al, 2000 November). Incarceration: Even after controlling for income, youths in father-absent households still had significantly higher odds of incarceration than those in mother-father families. Youths who never had a father in the household experienced the highest odds(Harper, and McLanahan, 2004 September). A 2002 Department of Justice survey of 7,000 inmates revealed that 39% of jail inmates lived in mother-only households. Approximately forty-six percent of jail inmates in 2002 had a previously incarcerated family member. One-fifth experienced a father in prison or jail (James, 2004, July). Crime: A study of 109 juvenile offenders indicated that family structure significantly predicts delinquency(Bush, Mullis& Mullis, 2000 August). Adolescents, particularly boys, in singleparent families were at higher risk of status, property and person delinquencies. Moreover, students attending schools with a high proportion of children of single parents are also at risk(Anderson, 2002 November). A study of 13,986 women in prison showed that more than half grew up without their father. Forty-two percent grew up in a single-mother household and sixteen percent lived with neither parent(Snell& Morton, 1994). Even after controlling for community context, there is significantly more drug use among children who do not live with their mother and father (Hoffmann, 2002 May). Youths are more at risk of first substance use without a highly involved Youth Fact Book Infinite Possibility Or Definite Disaster? 99 father. Each unit increase in father involvement is associated with 1% reduction in substance use. Living in an intact family also decreases the risk of first substance use(Bronte-Tinkew, Moore, Capps& Zaff, 2004). Of the 228 students studied, those from single-parent families reported higher rates of drinking and smoking as well as higher scores on delinquency and aggression tests when compared to boys from two-parent households(Griffin, Botvin, Scheier, Diaz& Miller, 2000 June). In a study of INTERPOL crime statistics of 39 countries, it was found that single parenthood ratios were strongly correlated with violent crimes. This was not the case 18 years ago(Barber, 2004 November). Teen pregnancy: Being raised by a single mother raises the risk of teen pregnancy, marrying with less than a high school degree, and forming a marriage where both partners have less than a high school degree(Teachman, 2004, January). Women whose parents separated between birth and six years old experienced twice the risk of early menstruation, more than four times the risk of early sexual intercourse, and two and a half times higher risk of early pregnancy when compared to women in intact families. The longer a woman lived with both parents, the lower her risk of early reproductive development. Women who experienced three or more changes in her family environment exhibited similar risks but were five times more likely to have an early pregnancy (Quinlan, 2003 November). Researchers using a pool from both the U.S. and New Zealand found strong evidence that father absence has an effect on early sexual activity and teenage pregnancy. Teens without fathers were twice as likely to be involved in early sexual activity and seven times more likely to get pregnant as an adolescent(Ellis, Bates, Dodge, Ferguson, Horwood, Pettit,& Woodward, 2003 June). Child Abuse: Compared to living with both parents, living in a single-parent home doubles the risk that a child will suffer physical, emotional, or educational neglect. The overall rate of child abuse and neglect in single-parent households is 27.3 children per 1,000, whereas the rate of overall maltreatment in two-parent households is 15.5 per 1,000(Federal Interagency Forum on Child and Family Statistics, 1997). An analysis of child abuse cases in a nationally representative sample of 42 counties found that children from single-parent families are more likely to be victims of physical and sexual abuse than children who live with both biological parents. Compared to their peers living with both parents, children in single parent homes had a 77% greater risk of being physically abused; an 87% greater risk of being harmed by physical neglect; a 165% greater risk of experiencing notable physical neglect; a 74% greater risk of suffering from emotional neglect; an 80% greater risk of suffering serious injury as a result of abuse; overall, a 120% greater risk of being endangered by some type of child abuse(Sedlak,& Broadhurst, 1996 September). Drug and Alcohol Abuse: Researchers at Columbia University found that children living in twoparent household with a poor relationship with their father are 68% more likely to smoke, drink, or use drugs compared to all teens in two-parent households. Teens in single mother households are at a 30% higher risk than those in two-parent households(Alcoholism& Drug Abuse Weekly, 1999 September, 6). Even after controlling for community context, there is significantly more drug use among children who do not live with their mother and father(Hoffmann, 2002, May). In a study of 6,500 children from the ADDHEALTH database, father closeness was negatively correlated with the number of a child’s friends who smoke, drink, and smoke marijuana. Closeness was also correlated with a child’s use of alcohol, cigarettes, and hard drugs and was connected to family structure. Intact families ranked higher on father closeness than single-parent families (National Fatherhood Initiative, 2004). Of the 228 students studied, those from single-parent families reported higher rates of drinking and smoking as well as higher scores on delinquency and aggression tests when compared to boys from two-parent households(Griffin, Botvin, Scheier, Diaz& Miller, 2000 June). 100 Youth Fact Book Infinite Possibility Or Definite Disaster? Obesity: The National Longitudinal Survey of Youth found that obese children are more likely to live in father-absent homes than are non-obese children(National Longitudinal Survey of Youth, undated). A study that looked at family lifestyle and parent’s Body Mass Index(BMI) over a nine year period found that a father’s Body Mass Index(BMI) predicts son’s and daughter’s BMI independent of offspring’s alcohol intake, smoking, physical fitness, and father’s education; BMI in sons and daughters was consistently higher when fathers were overweight or obese: physical fitness of daughters was negatively related to their father’s obesity; Obesity of fathers was associated with a four-fold increase in the risk of obesity of sons and daughters at age 18 (Burke, Dunbar Beilin, Dunbar, undated). A fathers’ body mass index(a measurement of the relative composition of fat and muscle mass in the human body) is directly related to a child’s activity level. In a study of 259 toddlers, more active children were more likely to have a father with a lower BMI than less active children(Finn, Kevin, Johanns& Specker, 2002, January). A study that looked at dietary intake and physical activity of parents and their daughters over a two year period found a daughter’s BMI predicted by father’s diets and father’s enjoyment of physical activity; as father’s BMI rose, so did their daughter’s BMI(Davison& Birch, undated). Education: Fatherless children are twice as likely to drop out of school(U.S. Department of Health and Human Services, 1993). A study of 1330 children showed that fathers who are involved on a personal level with their child’s schooling increases the likelihood of their child’s achievement. When fathers assume a positive role in their child’s education, students feel a positive impact(McBride, Brent, Sarah, Schoppe-Sullivan& Moon-Ho Hom 2005). Half of all children with highly involved fathers in two-parent families reported getting mostly A’s through 12th grade, compared to 35.2% of children of nonresident fathers(National Center for Education Statistics, 1999). Students living in father-absent homes are twice as likely to repeat a grade in school; 10 percent of children living with both parents have ever repeated a grade, compared to 20 percent of children in stepfather families and 18 percent in mother-only families. Students in single-parent families or stepfamilies are significantly less likely than students living in intact families to have parents involved in their schools. About half of students living in single-parent families or stepfamilies have parents who are highly involved, while 62 percent of students living with both their parents have parents who are highly involved in their schools(Nord& West, 2001). In 2001, 61 percent of 3-5 year olds living with two parents were read aloud to everyday by a family member, compared to 48% of children living in single or no-parent families(Federal Interagency Forum on Child and Family Statistics, 2003).Kindergarteners who live with singleparents are over-represented in those lagging in health, social and emotional, and cognitive outcomes. Thirty-three percent of children who were behind in all three areas were living with single parents while only 22% were not lagging behind(Wertheimer,& Croan, et al, 2003). In two-parent families, children under the age of 13 spend an average of 1.77 hours engaged in activities with their fathers and 2.35 hours doing so with their mothers on a daily basis in 1997. Children in single parent families spent 0.42 hours with their fathers and 1.26 hours with their mothers on a daily basis(Lippman, Laura, et al., 2004). Youth Fact Book Infinite Possibility Or Definite Disaster? 101 5.2. Youth Forming Families Quick Facts: Factors Affecting Age at First Marriage Throughout the world marriage is regarded a moment of celebration and a milestone in adult life. In most traditional societies marriage defines the onset of the socially acceptable time for child bearing and is the most predominant context for child bearing. Age at marriage is of particular interest because it marks the transition to adulthood in many societies; the point at which certain options in education, employment, and participation in society are foreclosed; and the beginning of regular exposure to the risks of pregnancy and childbearing. Women who marry early will have, on average; a longer period of exposure to the risk of pregnancy, often leading to higher completed fertility. Variation in age of entry into marriage helps explain differences in fertility across population over time. Rural/Urban The place where one stays can influence the time which they first get married. In Kenya, the rural areas are associated with early marriage. According to Ikimari(2005), people living in urban areas are exposed to diverse lifestyles and subject to a weaker social control than those of rural areas. Rural areas tend to have institutional and normative structures such as the kinship and extended family that promote early marriage and child bearing. In urban areas, the need to develop skills, gain resources and achieve maturity to manage an independent household delays marriage. Furthermore, urban women tend to be more educated and engaged in more salaried jobs than their rural counterparts. Religion Kenyans are generally religious. Religious norms and beliefs affect ones orientation towards marriage and childbearing, among other things; thus religion is bound to affect a woman’s age at first marriage Education Education may affect the timing of marriage in various ways. The highly educated spend many years in school and college receiving instruction and knowledge. In Kenya today, one requires to be at least 21 years to complete university education at the first degree level. When enrolled in school or college it is not desirable nor is it feasible for students to marry as it is disruptive and generally young people lack the financial resources and the prospect of a stable income that would be ideal for marriage and forming a family. Therefore school enrolment is an impediment to early marriage. Environment The ecological, socio-economic and cultural environment of a woman’s childhood place of residence or birthplace influences her general health and well being and contributes to the formation of beliefs, aspirations and practices that are important during her adulthood. Premarital Childbearing Premarital childbearing has a significant effect on the timing of marriage. Having an ex-nuptial birth is associated with a lower risk of marriage. In Kenya, it is not culturally and socially acceptable for a young man to marry as a first wife, a woman who already has a child. As a result, prospective suitors generally avoid women with ex-nuptial births. Women with ex-nuptial births tend to be very cautious in forming relationships with men as they generally distrust them. Source: Ikamari, 2005 102 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.2.1 Teenage Pregnancy and Motherhood. According to the WDR,(2007), nearly 60% of girls in developing countries become mothers before the age of 25. Boys make their transition a little later. This difference largely reflects gender differences in the age of marriage. However, many young men and women are not well prepared for parenthood or marriage and they therefore lack knowledge of good health practices, and available maternal and child health services. Preparing youth for the transition to family formation so that they can plan child bearing, have a safe pregnancy and raise healthy children has a lasting impact on the economy and demographic trends in a country. % of Teenagers who have Began Child Bearing 36.2 26.2 16.5 9.4 2.1 15 16 17 18 19 Age Figure 75: Child Bearing among Teenagers by Age Source: KDHS, 2009 According to the 2009 KDHS, generally, the percentage of teenagers who have begun child bearing declined from 23% in 2003 to 18% in 2009. The proportion of teenagers who have begun child bearing increases dramatically from 2% at age 15 to 36% at age 19 5.2.2 Child Bearing among Teenagers(15-19) by Background Characteristics Teenage pregnancies are slightly higher in urban than in rural areas. 32% of uneducated teenagers have begun child bearing compared to only 10% of those with some secondary education. More teenagers from poor households are likely to begin child bearing earlier than their counterparts from wealthier households(16%). Youth Fact Book Infinite Possibility Or Definite Disaster? 103 Table 56: Child Bearing among Teenagers(15-19) by Background Characteristics Background Characteristics Residence Urban Rural Education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest % who have began child bearing 18.5 17.5 32.1 19.1 23.3 10 23.7 17.9 13.6 17.6 16.4 Source: KDHS, 2009 5.2.3 Child Bearing among Teenagers(15-19) by Region % no. of teenagers who have begun child bearing N. E Western 16.2 15.1 RiftValley 16.5 Nyanza 13.5 Eastern Coast Central 10.1 Nairobi 13.9 27 25.7 Figure 76: Child Bearing among Teenagers(15-19) by Region Source: KDHS, 2009 Nyanza(27%) and Coast(25.7%) Provinces have the highest numbers of teenagers who have begun giving birth. Central(10.1%), Eastern(13.5%) and Nairobi(13.9%) Provinces have the lowest numbers of teenagers who have begun giving birth. 104 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.2.4 Un/Married Young Women(15-24) who have ever Used Contraceptives by Type Sexually active unmarried young women(15-24) who have ever used Modern Contraceptives 15-19 64.2 20-24 17 0 0 4 Female Pill sterilization 23.7 37.8 21.3 1.3 3.5 0 3.1 0 1.9 0.3 5.8 0 0 IUD Injectables Implants Male Female LAM Emergency Condom Condom contraceptives Sexually active married young women(15-24) who have ever used modern Contraceptives 44.9 20 0.1 7 0 Female Pill sterilization 19.8 19.9 1.3 0.2 0.9 13.3 0.5 0 0.4 2.3 1.1 1.4 0.4 IUD Injectables Implants Male Female LAM Emergency Condom Condom contraceptives Figure 77: Un/Married Young Women(15-24) who have ever Used Contraceptives by Type Source: KDHS, 2009 Most young unmarried women(15-24) prefer to use male condoms(51%), emergency contraceptives(13.6%), or injectables(13.4%). Their married counterparts prefer to use injectables(32.4%), the male condom(16.6%) or the pill(13.5%). Both married and unmarried young women aged 15-24 also use traditional methods. The most popular across the board is the rhythm method used by 19.9% of unmarried women and 13.85% of married women. It is followed by the withdrawal method used by 7.6% of unmarried women and 6.95% of married women. The folk method is not popular. 5.2.5 Men’s Attitudes towards Contraception 41% of young men aged 15-34 believe that use of contraceptives among women could lead to promiscuous behavior. This feeling is highest among 15-19 year olds. Only 17.5% of 15-34 year olds believe that use of contraceptives is the business of women. Generally more men, regardless of wealth, level of education or region of residence(with the exception of North Eastern), believe that the use of contraceptives is likely to make women more promiscuous than they believe it is the business of women. As older the man is, the less they believe that contraceptives are a woman’s business. Youth Fact Book Infinite Possibility Or Definite Disaster? 105 Men’s attitudes towards Contraception Womans business 45.4 Woman may become promiscuous 42.7 39.9 35.9 18.3 17.9 17.2 16.8 15-19 20-24 Figure 78: Men’s Attitudes towards Contraception Source: KDHS, 2009 25-29 30-34 5.2.6 Marital Status amongst Our Young People by Age Table 57: Marital Status amongst Our Young People by Age Marital Status among young people in Kenya Never Married Married Living Tog Divorced Separated Age Women Men Women Men Women Men Women Men Women Men 15-19 87.2 99.5 10.9 0.2 1.2 0.2 0.1 0 0.7 0.1 20-24 37.9 82.6 51.5 15.6 4.3 0.3 0.9 0.1 4.2 1.3 25-29 15.5 33 71.4 57.7 3.5 3.7 1.5 1.3 6.2 4 30-34 6.6 9.5 73.3 80.2 6.4 3.1 1.4 1.1 8.8 5.9 Sum 147.2 224.6 207.1 153.7 15.4 7.3 3.9 2.5 19.9 11.3 Av. 36.8 56.15 51.775 38.425 3.85 1.825 0.975 0.625 4.975 2.825 Source: KDHS, 2009 Widowed Women Men 0 0 1 0 1.9 0.3 3.7 0.4 6.6 0.7 1.65 0.175 56% of young men aged 15-34 compared to 37% of their female counterparts have never married. On the other hand, 52% of young women aged 15-34 compared to 38% of their male counterparts are married thus women get married much earlier than men marry. For example 11% of 15-19 year old women and 52% of 20-24 year old women are already married compared to only 0.2% (15-19) and 15.6% of their 20-24 male counterparts. 106 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.2.7 Age at First Marriage among Different Age Groups Table 58: Age at First Marriage among Different Age Groups Percentage married by exact age 15 18 Current Age Women Men Women Men 15-19 1.4 0.1 na na 20-24 6.2 0 26.4 1.3 25-29 7.2 0 29 4.3 30-34 8.8 0.3 32.6 3.1 Source: KDHS, 2009 20 Women Men na 7 44.4 17.3 48.4 9.3 50.7 8.3 22 Women Men na na na na 64.4 29.5 68.8 22 25 Women Men na na na na 79 48.4 82.7 51.9 About 48% of women aged 15-34 get married by their 20th birthday. Only 10% of their male counterparts have made that commitment by that time. By the age of 22, 67% of women and 26% of men have gotten married. By the age of 25, 81% of women and 50% of men are married. 5.2.8 Median Age at First Marriage among Women(25-34 Years Old) Table 59: Median Age at First Marriage among Women(25-34 Years Old) Background Characteristics Women Men 25-29 30-34 30-54 Residence Urban 22.7 21.8 26 Rural 19.5 19.5 24.8 Education No education 17.6 16.7 22.6 Primary Incomplete 18.3 18.1 23.9 Primary complete 20.3 19.6 24.3 Secondary+ 23 22.3 26.3 Wealth Quintile Lowest 19.1 18.6 23.9 Second 18.5 18.9 24.6 Middle 19.1 20.3 24.5 Fourth 20.3 19.6 25.4 Highest 23 22.2 25.9 Source: KDHS, 2009 On average, urban women aged 25-34 years marry 3 years later than their rural counterparts. Men in urban areas marry two years later than their rural counterparts. Median age of marriage for both men and women increases with higher level of education. Youth Fact Book Infinite Possibility Or Definite Disaster? 107 5.2.9 Median Age at First Marriage among Women(25-29) by Region and by 2003/2009 KDHS Reports Most young women in North Eastern get married before their 18th birthday while most young women in Nyanza, Western and Coast provinces get married before their 20th birthday. In Nairobi young women get married at an average age of 24. The difference in age at first marriage can be alluded to the argument fronted by Ikimari(2005), who in his paper‘The effect of education on timing of marriage in Kenya contends that the regional socio-economic development disparities are bound to affect the timing of marriage and also notes that significant variation in the age of first marriage across the regions of Kenya is due to cultural differences. Median age at first marriage among women(25-29) by region and by 2003/2009 KDHS reports 24.2 23.2 21.7 20.8 19.3 19.4 21.1 19.8 18.5 19.3 19.8 20.3 19.8 19.4 2003 2009 17.6 15.8 Nairobi Central Coast Eastern Nyanza RiftValley Western N. E Figure 79: Median Age at First Marriage among Women(25-29) by Region and by 2003/2009 KDHS Reports Source: KDHS, 2009 5.2.10 Percentage Distribution of Women by the Number of Co-Wives they have by Age Table 60: Percentage Distribution of Women by the Number of Co-Wives they have by Age Number of women’s co-wives Age 0 1 2+ 15-19 92.5 5 1.7 20-24 88.9 7.5 1.4 25-29 89 8 1.7 30-34 87 9.6 2.4 Sum 357.4 30.1 7.2 Average 89.35 7.5 1.8 Source: KDHS, 2009 Number of wives men have Missing 1 2+ 0.8 na na 2.2 100 0 1.3 95.3 4.2 0.9 92.6 7.4 5.2 287.9 11.6 1.3 96 5.8 Missing na 0 0.5 0 0.5 0.5 108 Youth Fact Book Infinite Possibility Or Definite Disaster? About 9.3% of married women aged 15-34 have co-wives. Older women are much more likely to be in polygamous relationships than younger women. Generally, polygamy among women is more prevalent in rural areas, among women of no or low education as well as among the poor as indicated on table 61. On the other hand, about 5.8% of men aged 25-34 admitted having polygamous marriages. Generally, polygamy among men is more prevalent in rural areas, among men of no education as well as among men in the lowest wealth quintile. 5.2.11 Distribution of Women by the Number of Co-Wives they have by Background Characteristics Table 61: Distribution of Women by the Number of Co-Wives they have by Background Characteristics Background Characteristics Number of co-wives 0 1 2+ Residence Urban 90.6 6.1 1.1 Rural 83.4 11.9 3.3 Education No education 64.9 24.8 8.5 Primary Incomplete 81.3 13.3 3.6 Primary complete 90.9 6.3 1.5 Secondary+ 90.9 6.5 1 Wealth Quintile Lowest 72.7 18.4 7.2 Second 84 12.8 2.2 Middle 83.7 12.5 2.6 Fourth 90.3 6 2.6 Highest 91.7 5.6 0.3 Source: KDHS, 2009 Missing 2.3 1.4 1.8 1.8 1.3 1.5 1.7 1 1.3 1.1 2.5 Number of wives men have 1 2+ Missing 96.3 3.5 0.1 91.5 8.4 0.1 74.6 25.4 0 92.5 7.5 0 92.4 7.6 0 95.6 4.1 0.3 84.6 15.4 0 93.1 6.9 0 92.5 7.5 0 92.3 7.2 0.5 96.9 2.9 0.1 Youth Fact Book Infinite Possibility Or Definite Disaster? 109 5.2.12 Percentage Distribution of Women by the Number of Co-Wives they have by Region Table 62: Percentage Distribution of Women by the Number of Co-Wives they have by Region Region Nairobi Central Coast Eastern Nyanza Rift Valley Western N. E Number of co-wives 0 1 2+ 94.2 2.4 0 96 2.9 0.5 82.6 12.7 2.3 93.4 4.1 0.6 78.4 16.4 4.2 83.2 10.8 4.1 76.5 17.9 4.9 63.9 30.7 5.3 Missing 3.5 0.6 2.5 1.9 1 1.8 0.7 0 Number of wives men have 1 2+ 98.3 1.3 99.5 0.5 92.6 7.4 98.2 1.2 84.6 15.4 92.3 7.7 92.2 7.8 86.5 13.5 Missing 0.5 0 0 0.7 0 0 0 0 Source: KDHS, 2009 North Eastern province has the highest proportion of women(36%) in polygamous families and Nairobi province the lowest 2.4%. Western, Nyanza and Rift Valley provinces have proportions of 23%, 21% and 15% likelihood of women in polygamous families. On the contrary, Nyanza province has the highest number of polygamous men(15.4%) followed by North Eastern province (13.5%). Central province has the lowest number of polygamous men(0.5%). 5.2.13 Ideal Number of Children by Demographic Characteristics Table 63: Ideal Number of Children by Demographic Characteristics Demographic Characteristics Women Men Age 15-19 20-24 25-29 30-34 15-54 Residence Urban 2.9 2.9 3 3.2 3.4 Rural 3.7 3.6 3.8 3.9 4.3 Education No education 7 5.8 6.6 6.4 10.1 Primary Incomplete 3.8 3.7 3.9 3.8 4.4 Primary complete 3.4 3.3 3.4 3.8 4.1 Secondary+ 2.9 2.8 2.9 3.2 3.4 Wealth Quintile Lowest 4.7 4.7 5 5.1 6 Second 3.6 3.6 3.6 4 4.4 Middle 3.5 3.4 3.6 3.7 3.9 Fourth 3.1 3.1 3.3 3.3 3.6 Highest 2.8 2.8 3 3.2 3.4 Source: KDHS, 2009 110 Youth Fact Book Infinite Possibility Or Definite Disaster? Interestingly, men across the board would like more children than their female counterparts. Young women(15-34) in urban areas would like an average of 3 children compared to their counterparts in rural areas who would like an average of 4 children. Level of education strongly correlates with the desired size of family. Young women with no education would like large family sizes of about 6 children while their counterparts with some secondary education would like to have about 3 children. On the other hand, men with no education would like to have about 10 children. Those with some secondary education would like to have about 3 children. Perceived ideal family sizes decrease as wealth increases. Young women in the lowest wealth quintile would like to have about 5 children while those in the highest wealth quintile would like to have 3 children. Their male counterparts in the lowest quintile would like to have 6 children and those in the highest wealth quintile would like to have 3 children. 5.2.14 Ideal Number of Children by Region Table 64: Ideal Number of Children by Region Region Nairobi Central Coast Eastern Nyanza Rift Valley Western N. E Source: KDHS, 2009 Women Men 15-19 20-24 25-29 30-34 15-54 2.8 2.6 2.8 2.9 3.3 3 2.7 2.8 3 3.3 4.6 4.1 4.2 4.3 4 3.1 2.9 3.2 3.2 3.4 3.3 3.5 3.8 3.9 4.1 3.6 3.6 3.7 4.2 4.4 3.7 3.5 3.8 3.9 4.1 3.7 7.9 9.1 9 15.5 Generally, men would like to have more children than their female counterparts in all the regions. Women in Nairobi, Central and Eastern provinces would like to have the smallest number of children(3) compared to other regions. Young women in Nyanza, Western, Rift valley and Coast provinces would like to have an average of 4 children while young women in North Eastern would like to have about 7 children. Men follow a similar pattern as that recorded by their female counterparts with the exception of North Eastern where the men would like to have about 16 children. 5.2.15 Family Planning among Currently Married Women by Age From the trends and according to KDHS,(2009), Kenyan women continue to experience a high unmet need for family planning. For example, 27% of 15-34 year olds have unmet need for spacing and limiting family planning methods. The unmet need for spacing children declines with age while that of limiting increases with age. Youth Fact Book Infinite Possibility Or Definite Disaster? 111 Unmet need for family planning among married women(15-34) Met need for family planning among married women(15-34) for spacing for limiting 30 25.6 25 20 15 10 4 5 0 15-19 24 6.1 20-24 16.6 10 25-29 11.5 11 30-34 20.7 1.8 15-19 28.2 27.1 18.2 7.5 20-24 25-29 23.2 36.8 30-34 Figure 80: Family Planning among Currently Married Women Source: KDHS, 2009 5.2.16 Family Planning among Currently Married Women by Demographic Characteristics Table 65: Family Planning among Currently Married Women by Demographic Characteristics Age Residence Urban Rural Education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest Sources: KDHS, 2009 unmet need for family planning 20.2 27.3 25.7 33.2 27 16.9 38 32.5 22.3 20.1 18.9 met need for family planning 53.1 43.1 14.1 40.3 48.2 59.8 20.1 40 49.8 56.9 54.7 Total demand for planning 73.3 70.4 39.8 73.4 75.2 76.7 58.1 72.5 72.1 77.1 73.6 According to the KDHS,(2009), generally, unmet need for family planning continues to be higher in rural areas(27%) than in urban areas(20%). Married women with incomplete primary education have the highest unmet need for family planning. The unmet need decreases with level of education and as wealth increases. Total demand for family planning increases with age. It declines after 44 years. Demand is higher among urban women than among rural women, it increases with higher levels of education and generally with rising wealth quintiles as indicated on table 65. 112 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.2.17 Menopause among Young Women Aged 30-34 Menopause of 30-34 year old women 4.3 4.2 2003 2009 Figure 81: Menopause among Young Women Aged 30-34 Source: KDHS, 2009 According to KDHS,(2009), menopause is a factor influencing the risk of pregnancy. In the context of available data, women are considered menopausal if they are neither pregnant nor postpartum amenorrhoeic, and have not had a menstrual period for the last six months. The prevalence of menopause increases with age. 5.2.18 Wife Beating(Justification why/when women should be Beaten) by Age Table 66: Wife Beating(Justification why/when women should be Beaten) by Age Age (years) Women’s attitude towards wife beating Burns Argues Goes out Neglects Refuses food with without the to have him telling children sexual him intercourse with him Men’s attitude towards wife beating Burns Argues Goes out Neglects food with without the him telling children him Refuses to have sexual intercourse with him 15-19 15.1 31.2 30.6 44.7 17.8 10.2 31.5 29.8 36.8 15.6 20-24 13.3 32.2 31.9 39.3 21.1 7.6 22.5 20.8 31.5 15.4 25-29 11.9 27.5 27.7 39.6 22.3 6.2 20.8 22 26.4 10.1 30-34 12.6 29.3 30.8 41 24.2 8.8 22.7 24.3 30.2 12.9 sum 52.9 120.2 121 164.6 85.4 32.8 97.5 96.9 124.9 54 Av 13.225 30.05 30.25 41.15 21.35 8.2 24.375 24.225 31.225 13.5 Source: KDHS, 2009 Across all ages(15-34), more women than men generally believe that men are justified to beat them especially if they neglect children(41%), go out without telling him(30.2%), argue with him(30%), refuse to have sex with him(21%) or burn food(13%). Among the 15-34 year olds, 15-19 year old men and women have the highest number of those who believe that women should be beaten. Youth Fact Book Infinite Possibility Or Definite Disaster? 113 5.2.19 Wife Beating(Justification why/when women should be Beaten) by Demographic Characteristics Table 67: Wife Beating(Justification why/when women should be Beaten) by Demographic Characteristics Women’s attitude towards wife beating DemoBurns Argues Goes out Neglects Refuses Burns graphic food with without the chilto have food Charachim telling dren sexual teristics him intercourse with him Not 13.6 31.2 32.6 42.2 21.5 9.9 Employed Employed 11.5 27.9 26.8 37.3 21.4 7.2 For Cash Employed 18.4 38.8 37.4 54 30 7.6 Not For Cash Marital status Never 12.4 26.2 26.6 38.6 16.5 9.1 married Married or 13.6 32.9 32.5 42.4 24.8 6.3 living tog Divorced/ 15 separated/ widowed 33.1 32.5 46.9 28.9 6.8 No. of living children 0 12.6 25.1 26.3 37.8 15.4 9.3 1 to 2 11.2 29.4 27.9 37.6 20.6 4.5 3 to 4 13.8 31.5 31.4 44.3 26.4 6 5+ 17.7 41.3 41.2 51.3 32.5 8.5 Residence Urban 6.4 17.2 18 27.6 11.9 6 Rural 15.8 35.5 35 46.5 26.3 8.2 Education No education 23.2 43.1 47.6 52.7 35.5 20 Primary 18.3 40.5 39.4 50.5 31 10.4 Incomplete Primary 12.1 29.5 30.2 41.4 21.3 6.2 complete Secondary+ 7.5 20.3 19 31.4 13.1 5.8 Wealth Quintile Lowest 22.2 41.9 44 52.5 33.6 10 Second 19.6 44.6 43.6 54.4 31.1 8.6 Middle 14.4 35 31.2 47.9 25.8 6.1 Fourth 10.3 25.8 26.7 37.1 19.1 8.5 Highest 5.4 15.7 16.4 25.6 10.7 6.1 Total 295 666.5 666.3 900 497.4 171.1 Average 14.0 31.7 31.7 42.9 23.7 8.1 Source: KDHS, 2009 Men’s attitude towards wife beating Argues with him Goes out Neglects without the chiltelling dren him Refuses to have sexual intercourse with him 25.2 27.4 34.1 15.9 22.2 22.8 31 13.4 26.2 26.3 29.8 13.7 26.6 25.2 32.3 14.9 20.3 22.8 28.6 11.8 28.4 31 44.1 23.2 25.6 25.3 32 15 20.2 17.3 27.3 10.9 20.3 24 28.8 11.3 25.9 33 36.4 17.4 15.8 16.5 27.6 8.4 26.4 27.1 32.2 15.7 41.7 53.2 60.4 44.3 34.6 33.7 39.6 20.2 24.6 24 33.7 12.5 14.9 16.4 22 8.3 27.2 35.7 37.5 18.4 31.6 29.2 35.1 15.2 25.3 24.5 30.4 17.8 22.7 24 29.3 11.8 16.4 15.2 26.9 9.7 522.1 554.6 699.1 329.8 24. 9 26.4 33.3 15.7 114 Youth Fact Book Infinite Possibility Or Definite Disaster? Women who are employed for cash are less likely to advocate for female beating than those who are not employed or those who are employed not for cash. Women who have never been married are less likely to advocate for female beating than those who are married/living together or separated/divorced/widowed. Urban men and women are less likely to advocate for wife beating than their rural counterparts. Generally attitude towards beating women decreases with increased level of education and wealth. 5.2.20 Wife Beating(Justification why/when women should be Beaten) by Region Table 68: Wife Beating(Justification why/when women should be Beaten) by Region Women’s attitude towards wife beating Men’s attitude towards wife beating Region Burns Argues Goes out Neglects Refuses Burns Argues Goes out Neglects Refuses to food with without the to have food with without the have sex him telling children sex with him telling children with him him him him Nairobi 2.7 8.8 11.7 20.2 7.7 5.5 10.4 11.2 25 7.2 Central 6.1 18.4 16 29.8 19.2 8.3 23.2 17.8 30.7 25.9 Coast 13.6 29.6 30 36.1 20.6 5.4 22.5 24.6 28.1 9.7 Eastern 6.2 16.7 24.6 40.8 15.5 5.4 25.7 28.1 33.2 15 Nyanza 13.7 42.2 31.6 43.6 22.9 7.9 28.7 23.8 28 10.4 RiftValley 20.4 38.3 41.2 50.5 28.9 9.1 23.5 29.5 34.8 14.3 Western 22.9 48.2 41.6 52.4 32.9 11 28.6 23.2 30.6 9.9 N. E 7.7 25 34.7 35.5 30.4 0.6 7.4 27.9 29.7 29.6 Sum 93.3 227.2 231.4 308.9 178.1 53.2 170 186.1 240.1 122 Av 11.7 28.4 28.9 38.6 22.3 6.7 21.3 23.3 30.0 15.3 Source: KDHS, 2009 Nairobi has the smallest number(10%) of women expecting to be beaten followed by c entral province(18%) while Nairobi has the smallest number of men who would beat their wives. 40% of women in Western province and 36% of women in Rift valley expect to be beaten by their husbands. Rift valley has the highest number(22%) of men who would beat their wives followed by Eastern(21.4%), Central(21%), Western(20.6%), Nyanza(19.7%), North Eastern(19%) then Coast Province(18%). Youth Fact Book Infinite Possibility Or Definite Disaster? 115 5.2.21 Spousal Violence by Age(Women ever married who have experienced different forms of violence from partner/husband) Spousal Violence by Age Emotional Violence Physical Violence Sexual Violence 34 36.7 37.4 25.4 20.3 9.8 15-19 25.5 16.8 20-24 30.2 15.8 25-29 31.3 17.6 30-39 Figure 82: Spousal Violence by Age Source: KDHS, 2009 Spousal or marital violence refers to violence perpetrated by partners in a marital union. According to the KDHS report,(2009), physical violence declined from an overall of 40% in 2003 to 37% in 2009. There was a slight increase in sexual violence from 16% in 2003 to an overall of 17% while emotional violence increased from 26% in 2003 to an overall of 30% in 2009. Physical violence(33%) among young married couples(15-39) is more rampant followed by emotional violence(27%) and then sexual violence(15%). The likelihood of experiencing all three forms of abuse increases with age. 116 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.2.22 Spousal violence by Background Characteristics(Women ever married who have experienced different forms of violence from partner/husband) Table 69: Spousal Violence by background characteristics Background Characteristics Employed in the last 12 months Employed for cash Employed not for cash Not employed No. of living children 0 1 to 2 3 to 4 5+ Marital status and duration Currently married married only once 0-4 years 5-9 years 10+ years Married more than once Divorced/separated/widowed Residence Urban Rural Education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest Respondent’s father beat her mother Yes No Don’t Know Source: KDHS, 2009 Emotional Violence Physical Violence Sexual Violence Sum 33.5 40.5 20.6 94.6 33.8 40.5 19.8 94.1 20.7 29.3 10.4 60.4 16.7 18.1 13 47.8 27.1 32.5 12.6 72.2 31.6 38.4 17.8 87.8 33.2 45.8 24 103 27.3 34.2 15.8 77.3 26.9 34.1 15.8 76.8 16.8 22.7 10.8 50.3 28.4 33.2 13.7 75.3 30.8 39.8 19 89.6 34.6 35.4 17.1 87.1 41.9 52.8 25 119.7 29.7 30.6 15.2 75.5 29.4 38.9 17.8 86.1 29.2 44 19.7 92.9 37.3 46 20.6 103.9 26.6 34.6 17.9 79.1 23.9 26.4 11.8 62.1 32.2 43.5 19.3 95 29.3 41.2 19.2 89.7 29.5 38.6 18.1 86.2 28.9 35.8 15.8 80.5 28.2 28.6 14.6 71.4 37.4 49.4 22.6 109.4 24.2 28.7 13.7 66.6 29.4 35.8 16.4 81.6 Average 31.5 31.4 20.1 15.9 24.1 29.7 34.3 25.8 25.6 16.8 25.1 29.9 29.0 39.9 25.7 28.7 30.9 34.6 26.4 20.7 31.7 29.9 28.7 26.8 23.8 36.5 22.2 27.2 Youth Fact Book Infinite Possibility Or Definite Disaster? 117 Abuse of women seems to increase with the number of children a woman has. It is also rampant among women who are divorced/separated/widowed(40%), those who have been married 10 years+(30%) and among those who have been married more than once. Abuse is higher in rural areas, among women with no education or those with incomplete primary education as well as among women whose fathers beat their mothers. Abuse decreases with increasing level of wealth. 5.2.23 Spousal Violence by Region(Women ever married who have experienced different forms of violence from partner/husband) Table 70: Spousal Violence by Region(Women ever married who have experienced different forms of violence from partner/husband) Region Emotional Violence Physical Violence Sexual Violence Sum Nairobi 22.2 23.4 8.3 53.9 Central 29.4 33.7 13.3 76.4 Coast 33.1 24.2 17.5 74.8 Eastern 26.7 28.9 14.5 70.1 Nyanza 38.7 51.3 22.3 112.3 Rift Valley 26 37.5 19.3 82.8 Western 27.6 48.1 20.7 96.4 N. E 16.1 32.7 4.5 53.3 Source: KDHS, 2009 Average 17.96 25.5 24.9 23.4 37.4 27.6 32.1 17.8 Abuse of women is highest in Nyanza province(37%), followed by Western(32%), Rift Valley (28%), Central(26%), Coast(25%) and Eastern(23%). Women who reported least abuse are from Nairobi(18%) and North Eastern province(18%). 5.2.24 Force at Sexual Initiation Most forced sex happens to children and younger teenagers % whose first sexual intercourse was forced on them by age 20-24 6.4 15-19 12.5 < 15 22.2 Figure 83: Force at Sexual Initiation Source: KDHS, 2009 118 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.2.25 Reported Cases of Rape, Defilement/Incest, Assault and Battering(1997-2005) Assault and battering have been the most prevalent forms of gender based violence and it has been rising rapidly since 2000. Rape and defilement/incest have been increasing gradually since 1997 but rape seemed to be slightly reducing from 2007. % whose first sexual intercourse was forced on them by age Rape(including attempted) Defilement/ Incest Assault and battering 13,621 14,129 13,454 12,083 14,916 5,488 5,866 5,918 6,255 6,648 7,896 8,959 8,544 2470 307 554 616 752 1,094 1,021 1,182 1,489 1,152 1395 1950 1728 743 775 849 883 933 984 1,126 1,419 1,286 1295 1151 1034 943 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Figure 84: Reported Cases of Rape, Defilement/Incest, Assault and Battering(1997-2005) Source: Economic survey, 2002 and 2006 Youth Fact Book Infinite Possibility Or Definite Disaster? 119 5.2.26 Reported Cases of‘Offences against Morality’ and‘Other Offences against Persons(2005-2009) In 2005, the police categorized offences differently. Most crimes related to gender based violence were grouped under‘offences against morality’. Assault was grouped as‘other offences against persons as indicated on table 71. Table 71: Reported Cases of‘Offences against Morality’ and‘Other Offences against Persons(2005-2009) Offence against morality 2005 Male Female Rape 1,284 2 Defilement 980 2 Incest 160 10 Sodomy 258 0 Bestiality 9 61 Indecent assault 208 11 Abduction 198 7 Bigamy 0 0 Offence against other persons Assault 9,234 4,168 Total 12,331 4,261 2006 Male 1,295 1,273 112 128 4 288 176 5 10,823 14,104 Source: Economic Survey, 2010 Female 10 0 11 1 10 0 3,017 3,049 2007 Male 1,149 1,779 158 147 10 138 104 7 10,454 13,946 Female 2 3 10 0 1 0 8 1 2,862 2,887 2008 2009 Male Female Male Female 812 222 939 4 1,396 232 2,234 51 85 15 181 4 113 8 126 0 21 1 9 0 133 19 512 120 49 12 87 6 4 4 12 1 9,414 2,518 12,027 3,031 11,700 3,093 15,800 3,279 Generally, offences against morality and against other persons are mostly committed by men. Defilement(46%) and rape(33%) are the most dominant forms of offences against morality. Offences against Morality& Other Persons offences against morality Offence against other persons 13,402 13,840 13,316 11,931 14,793 3,190 3,313 3,517 3,126 3,777 2005 2006 2007 2008 2009 Figure 85: Reported Cases of‘Offences against Morality’ and‘Other Offences against Persons(2005-2009) Source: Economic Survey, 2010 120 Youth Fact Book Infinite Possibility Or Definite Disaster? Offences against morality and offences against other persons have been rising steadily over the years with 2009 having the highest prevalence. 5.2.27 Reported Cases of Rape, Defilement/Incest, Assault and Battering by Province (1997-2005) Table 72: Reported Cases of Rape, Defilement/Incest, Assault and Battering by Province(1997-2005) Province Nairobi Central Coast Eastern North Eastern Nyanza Rift Valley Western Total 1997 1998 1999 2000 87 86 83 89 153 164 179 181 76 79 75 77 107 105 123 128 15 14 12 13 93 102 114 117 167 176 207 215 45 49 56 63 743 775 849 883 Source: Economic Survey 2002& 2006 2001 2002 639 736 1,673 1,921 747 883 1,082 1,205 96 119 1,078 1,254 2,368 2,667 952 1,116 8,635 9,901 2003 809 2,101 968 1,321 128 1,373 2,925 1,222 10,847 2004 885 2,306 1,057 1,445 135 1,502 3,198 1,339 11,867 2005 609 2,095 1,146 1,691 202 1,188 3,556 1,549 12,036 Total Incidents of Rape, Defilement/Incest, Assault and Battering Against Women Btw 1997& 2005 8,635 9,901 10,847 11,867 12,036 743 775 849 883 1997 1998 1999 2000 2001 2002 2003 2004 2005 Figure 86: Total Incidents of Rape, Defilement/Incest, Assault and Battering Against Women Btw 1997& 2005 Source: Economic Survey 2002& 2006 The total number of reported cases of violence against women increased by 978% between 2000 and 2001. According to the Economic survey(2006), this may be explained by the setting up of a police station in Kilimani in Nairobi to deal specifically with issues of violence against women and children. The government also set up gender desks in every district police station where Youth Fact Book Infinite Possibility Or Definite Disaster? 121 gender based violence victims were encouraged to report cases with assurances of professional treatment by those manning the desks. As a result, the leap may be explained more by improved reporting. Evidence however does show that over time, there has been increase of violence against women as shown in the trends on figure 87. Incidents of rape, attempted rape, assult and battering against women by province(1997-2005) by Province 13% 11% 28% 1% 12% 19% 7% Figure 87: Incidents of Rape, Defilement/Incest, Assault and Battering Against Women by Province Source: Economic Survey 2002& 2006 Incidents of rape, defilement/incest, assault and battering against women between 1997 and 2005 were most rampant in Rift valley(28%), followed by Central(19%), Eastern(13%), Nyanza (12%), Western(11%), Coast(9%), Nairobi(7%), and North Eastern(1%). 122 Youth Fact Book Infinite Possibility Or Definite Disaster? 5.2.28 Comparative Analysis 1/3 of young women in Uganda and Ethiopia aged between 15 and 19 years old are likely to get married. Generally more young women are likely to get married between the ages of 15 and 19 than men, in Africa. % of 15-19 year olds ever married by Gender 20 2 30 11 3 2 32 7 24 2 7 2 7 1 Male Female 41 Kenya Ethiopia Sudan Uganda Tanzania Rwanda Burundi South Africa Figure 88: Percentage of 15 – 19 Year Olds Ever Married by Gender in Select African Countries Source: Population Reference Bureau, 2006 Percentage of Women Giving Birth by Age % of Women giving Birth by Age 15-19 42 20-24 23 11 24 10 18 6 0 26 14 9 4 20 7 5 Kenya Ethiopia Sudan Uganda Tanzania Rwanda Burundi South Africa Figure 89: Percentage of Women Giving Birth by Age Source: Population Reference Bureau, 2006 Uganda,Tanzania, Kenya and Ethiopia have the highest number of women givig birth between the age of 15 and 19. Percentage of Un-Married Teens(15-19) who have had Sex % of unmarried teens(15-19) who have had sex 50 29 15 10 32 34 56 37 20 7 Female 43 Male Kenya Ethiopia Uganda Tanzania Rwanda Figure 90: Percentage of Un-Married Teens(15-19) who Have Had Sex Source: Population Reference Bureau, 2006 South Africa Youth Fact Book Infinite Possibility Or Definite Disaster? 123 Over half of male teenagers have had sex in Kenya and Tanzania while about 1/3 of Kenyan, Ugandan and Tanzanian female teenagers have had premarital sex. South Africa has the most sexually active 15-19 year old females. Percentage of Sexually Active Women(15-19) Using Modern Contraceptives % of Sexually Active Women(15-19)using Modern Contraception Married 15-19 Single 15-19 44 48 38 66 48 35 19 13 12 12 9 7 Kenya Ethiopia Uganda Tanzania Rwanda South Africa Figure 91: Percentage of Sexually Active Women(15-19) Using Modern Contraceptives Source: Population Reference Bureau, 2006 Single women are more likely to use contraceptives than married women with the highest use among married and single women being in South Africa. 124 Youth Fact Book Infinite Possibility Or Definite Disaster? 6 Un/employment ‘A central part of people’s lives is at work, and whether women and men have decent work has a significant impact on individual, family and community well-being. The absence of decent and productive work is the primary cause of poverty and social instability’ (ILO, 2009) 6.0 Unemployment According to the World Development Report[WDR](2007), employment marks an important transition period for young people and it is characterized by independence, increased responsibility and active participation in national building as well as social development. A successful transition to work for today’s many young people can accelerate poverty reduction and boost economic growth. In spite of the benefits and opportunities brought about by employment, it is regrettable to note that majority of Kenya’s young people are unemployed, underemployed or underpaid and are therefore in the swelling ranks of the working poor. In fact, according to the International Labor Office[ILO](1995), the vast majority of jobs available to youth are low paid, insecure, and with few benefits or prospects for advancement. Creation of adequate employment opportunities to absorb the rapidly growing labour force remains one of the greatest challenges in Kenya and indeed in many other countries of the world(Omolo, unpublished). According to Cincotta(2005), a large proportion of young adults and a rapid rate of growth in the working-age population tend to exacerbate unemployment, prolong dependency on parents, diminish self-esteem and fuel frustrations, which increase the likelihood of violence or conflict. Unemployment also causes social problems such as crime, drug abuse, vandalism, religious fanaticism and general alienation in the vicious circle of poverty. These patterns will persist in the future if no holistic approach is initiated to alter the employment challenges facing the youth(Omolo, unpublished). 6.1 Working Age Population and Labour Force Participation Rates in Kenya The working age population and labour force participation rates are important determinants of employment. In Kenya the working age population includes persons between 15 and 64 years. Inactive labour consists of all those persons within the working age who are outside the labour market. Inactivity may be voluntary(persons who prefer to stay at home or are still in school/college) or involuntary(persons who prefer to work but are discouraged and give up searching for jobs). Table 73: Distribution of Working Age Population, 1998/99 and 2005/06 Age Cohort Employed Unemployed Inactive* 1998/99 2005/06 1998/99 2005/06 1998/99 2005/06 15-19 843,909 1,056,015 270,217 352,357 2,349,270 3,210,685 20-24 1,435,405 1,895,834 533,078 605,167 485,067 992,053 25-29 1,584,271 2,088,468 291,679 388,747 165,931 335,359 30-34 1,541,604 1,897,206 185,927 154,360 94,668 169,153 Total (15-34) 5,405,189 6,937,523 1,280,901 1,500,631 3,094,936 4,707,250 35-39 1,533,196 1,497,662 140,147 122,725 91,739 101,214 40-44 1,128,190 1,357,371 113,165 92,262 68,964 91,978 45-49 992,261 1,070,783 88,596 64,636 67,260 81,760 50-54 702,199 787,417 66,839 38,666 82,769 95,607 55-59 412,639 624,308 64,235 26,350 87,107 91,389 60-64 351,936 432,972 46,739 11,024 106,457 96,536 Total(15-64) 10,525,609 12,708,035 1,800,623 Source: 98/99 and 2005/06 Labour Force Survey 1,856,294 3,599,231 5,266,112 Total 1998/99 2005/06 3,463,396 4,619,057 2,453,550 3,493,054 2,041,881 2,812,574 1,822,199 2,221,097 9,781,026 13,145,782 1,765,082 1,310,319 1,148,117 851,807 563,981 505,132 15,925,463 1,721,601 1,541,611 1,217,179 921,690 742,047 540,532 19,830,441 126 Youth Fact Book Infinite Possibility Or Definite Disaster? 6.1.1 Distribution of Working Age Population, 1998/99 and 2005/06 The country’s working-age population increased from 15.9 million persons in 1998/99 to 19.8 million persons in 2005/2006. The largest rise in the working-age population over the period was recorded among the age cohort of 15-34 years where the working-age population increased from 9.7 million persons in 1998/99 to 13.1 million persons in 2005/2006. The data also reveals that an increasing proportion of the country’s working age population is inactive and it increased from 22.6 percent in 1998/99 to 26.6 percent in 2005/2006. The majority of the inactive population was between the ages of 15 and 19 because in Kenya it is a school going age. The table also shows that by 2006, about 14 million Kenyans were participating in the labour force with 12.7 million employed and 1.85 million being unemployed. 6.1.2 Labor Force Participation Rates, 1998/99 and 2005/2006(Percent) Table 74: Percentage Labor Force Participation Rates, 1998/99 and 2005/2006 Age Cohort 1998/99 2005/06 Male Female Total Male Female Total 15 – 19 28.1 30.5 29.3 30 30 30 20 – 24 66.6 69.8 68.3 73 68 70 25 – 29 91.5 87.7 89.4 93 82 87 30 – 34 96.6 91.6 94.1 97 86 91 35 – 39 97.4 92.3 94.8 98 90 94 40 – 44 97.5 92.9 95.2 98 90 94 45 – 49 95.6 90.7 93.4 96 89 92 50 – 54 94 86.9 90.3 93 85 89 55 – 59 87.8 82.5 85.1 92 82 87 60 – 64 85 77.4 80.9 89 76 82 Total 74.7 72.6 73.6 76 70 73 Source: 98/99 and 2005/06 Labour Force Survey From the data on the table and according to Omolo(unpublished), labour force participation rates for the youth aged 15-24 years increased, albeit marginally while participation rates for the other age cohorts(25 to 54) declined. Over the period, the female labour force participation rates edged downwards for all the age groups with the highest being among the youth cohorts of 25-29 and 30-34, which declined by nearly 6 percent. Overall, females had a lower labour force participation rate than males in both periods. 6.2 Youth Unemployment in Kenya Kenya’s unemployment is mainly attributed to the slow growth and weak labour absorptive capacity of the economy, mismatch in skills development and demand, imperfect information flow and inherent rigidities within the country’s labour market. According to Omolo(unpublished), the rate at which the net jobs were created was not the same as the rate of labour force growth. This is evidenced by the fact that the informal sector has been growing at an average rate of 17.2% per annum compared to the formal sector which has been growing at an average of 2.23% per annum while the country’s working age population increased by 24.5% between 1999 and 2006. This effectively means that more job seekers, both the new labour market entrants and those out of employment through the various labour Youth Fact Book Infinite Possibility Or Definite Disaster? 127 separation mechanisms, ordinarily remain out of employment for a longer period hence swelling the ranks of the discouraged job seekers. According to Coenjaerts, Ernst, Firtuny, and Rei(2009), young people face specific challenges in accessing the labour market thus lowering their chances of finding decent employment. These difficulties include; higher chances of losing their jobs during economic downturns under the last in-first out approach to staff reduction; barriers to entry arising from lack of or inadequate work experience; and path dependence, which dictates that early unemployment increases the likelihood of subsequent unemployment. According to Omolo(unpublished), the longer people stay out of work, the more their“employability” deteriorates, making it progressively harder for them to gain employment. This is especially worrying for the youth who may get trapped into a lifetime of weak attachment to the labour market alternating between low paid insecure work and open unemployment. 6.2.1 Youth Unemployment in Kenya between 1978 and 05/06 Unemployment increased from 6.7 percent in 1978 to 25.1 percent in 1998/1999 before easing to 12.7 percent in 2005/2006. The table also reveals considerable variations in unemployment amongst the different age cohorts, with the youth category(15-34) recording relatively higher rates of unemployment. According to Omolo(unpublished), youth overall unemployment has persistently been at least double the national unemployment rate. Table 75: Youth Unemployment in Kenya between 1978 and 05/06 Age Cohort 15 – 19 20 – 24 25 – 29 30 – 34 Av 35 – 39 40 – 44 45 – 49 50 – 54 55 – 59 60 – 64 Average 1978 1986 26.6 36.2 18.5 29.2 4.8 8.6 2 2.7 12.97 19.2 1.8 2.1 0.7 0.7 1.1 2 1.4 0.9 1.5 4.1 3.2 6.7 9.7 1998/99 47 47.3 25.1 14.3 33.4 12 11.2 14.7 18.9 40.6 45.2 25.1 2005/06 25 24.2 15.7 7.5 18.1 7.6 6.4 5.7 4.7 4 2.5 12.7 Source: GOK, Various Statistical Abstracts 128 Youth Fact Book Infinite Possibility Or Definite Disaster? Youth Unemployment in Kenya by Age Cohorts(1978-2006) 50 45 40 35 30 25 26.6 20 15 18.5 10 4.8 5 0 2 1978 36.2 29.2 8.6 2.7 1986 47 47.3 25.1 14.3 1988/99 15-19 20-24 25-29 30-34 25 24.2 15.7 7.5 2005/06 Figure 92: Youth Unemployment in Kenya by age cohorts(1978-2006). Source: GOK, Various Statistical Abstracts The above trends confirm the variation of unemployment trends of different demographic groups of youth. In 1998/99, for example, the unemployment rate among the youth categories of 15–19 years; 20–24 years; 25–29 years; and 30-34 years was 47 percent, 47.3 percent, 25.1 percent and 14.3 percent, respectively. Even though the unemployment rate in the economy eased in 2005/2006, the youth unemployment level was still comparatively high at 25 percent, 24.2 percent, 15.7 percent and 7.5 percent for youths aged 15–19 years; 20–24 years; 25–29 years; and 30-34 age categories, respectively. 6.2.2 Unemployment Rates for Population Aged 15-64 by Age-Group, Region and Sex Table 76: Unemployment Rates for Population Aged 15-64 by Age-Group, Region and Sex Rural Urban Total Male Female Total Male Female Total Male Female Total Overall 9.5 10.2 9.8 15 25.9 19.9 11.2 14.3 12.7 15-19 18.2 21.1 19.6 42.3 47.8 45.5 22.4 27.7 25 20-24 16.8 20.3 18.6 30.1 40.8 35.8 21 27.3 24.2 25-29 11.1 12.1 11.6 17.3 29.1 22.8 13.5 17.9 15.7 30-34 5.6 7.2 6.4 6.8 14.3 9.8 6.1 9.2 7.5 35-39 6.7 5.7 6.1 7.2 14.7 10.6 6.9 8.3 7.6 40-44 5.2 4.7 4.9 9.2 12.3 10.5 6.4 6.4 6.4 45--49 4.3 5.6 5 6.3 10.4 7.8 4.9 6.5 5.7 50-54 4.5 3.8 4.1 6.4 8.5 7.1 4.9 4.4 4.7 55-59 4.8 2.8 3.8 4.9 6.2 5.3 4.8 3.2 4 60-64 3.9 0.8 2.3 5.6 1.4 4.2 4.2 0.8 2.5 Source: Economic Survey 2008, KNBS Youth Fact Book Infinite Possibility Or Definite Disaster? 129 There continues to be disproportionate participation of women in the labour market. This is evidenced by the fact that among 15-34 year olds, unemployment is high among young women (24%) than among young men(19%). Unemployment is severe among youth in urban(33%) areas than in rural areas(17%). However, the most affected are young women whose unemploy ment rate in urban areas is 40%. Unemployment generally reduces with age. Interestingly though is the fact that in rural areas, unemployment for men aged 35-64 is higher than that of their female counterparts while in urban areas, women aged(35-64) are more likely to be unemployed than their male counterparts. Unemployment is highest among 15-19 year olds but this may be explained by the fact that majority of young people in this age group are still in school and are not likely to be looking for jobs. 20-24 year olds(24%) and 25-29 year olds(16%) form the next groups of the highly unemployed. 6.2.3 Youth Unemployment Rates in Kenya by Age and Sex(98/99 and 05/06 Table 77: Percentage Youth Unemployment Rates in Kenya by Age and Sex(98/99 and 05/06) Age(years) Total 15 – 19 24.3 20 – 24 27.1 25 – 29 15.5 30 – 34 10.8 Av 19.4 35 – 39 8.4 40 – 44 9.1 45 – 49 8.2 50 – 54 8.7 55 – 59 13.5 60 – 64 11.7 Av 14.6 1998/99 Males 21.8 19 8.2 4.8 13. 4 5 7.8 4.9 6.3 14.2 7.5 9.8 Females Total 26.4 19 33.9 32.6 21.6 20.9 16.8 8.3 24. 7 20.2 11.8 6.6 10.6 5 12.5 3.5 11.1 2.1 12.7 1.4 15.7 0.6 19.3 12.7 Males 19.2 31.1 20.2 8.1 19.7 6.6 5.6 3.5 2.6 2 1.1 11.2 2005/06 Females 18.8 33.8 21.5 8.5 20.7 6.6 4.5 3.5 1.7 0.9 0.2 14.3 Source: KNBS(2003 and 2008), UNDP(2010) Interestingly however, is the fact that youth unemployment among young women aged 15-34 in 1998/99 almost doubled(25%) that of young men(13%) in the same year. By 2005/06, things had changed and female unemployment(21%) was almost at par with that of young men(20%). However, total unemployment for young people slightly increased from 19% to 20% while data shows that overall unemployment reduced from 14.6% in 1998/99 to 12.7% in 2005/06. 130 Youth Fact Book Infinite Possibility Or Definite Disaster? 6.3 Employment Trends in Kenya(1986-2008) Table 78: Employment Trends in Kenya(1986-2008) Year Total Employment Proportion of Total(Percent) Millions Formal Informal 1986 1.537 79.4 20.6 1987 1.615 78.3 21.7 1988 1.736 77.5 22.5 1989 1.796 76.2 23.8 1990 1.894 74.4 25.6 1991 2.557 56.4 43.6 1992 2.753 53.1 46.9 1993 2.998 49.2 50.8 1994 3.356 44.9 55.1 1995 3.859 40.3 59.7 1996 4.314 37.3 62.7 1997 4.707 34.9 65.1 1998 5.1 32.9 67.1 1999 5.493 30.7 69.3 2000 5.912 28.7 71.3 2001 6.367 26.3 73.7 2002 6.852 24.8 75.2 2003 7.33 23.6 76.4 2004 7.999 22.1 77.9 2005 8.505 21.3 78.7 2006 8.993 20.7 79.3 2007 9.479 20.1 79.9 2008 9.946 19.5 80.5 Source: Various Economic Surveys Employment Growth Percent Formal Informal 4 9.72 3.6 10.76 6.4 11.43 1.63 9.74 3.07 13.08 2.27 130.37 1.39 15.78 0.96 17.89 2.03 21.55 3.39 24.43 3.21 17.59 2.22 13.2 2.17 11.66 0.63 11.18 0.36 10.86 -1.06 11.22 1.37 9.85 1.65 8.73 2.14 11.28 2.66 7.36 2.54 6.6 2.8 6.08 1.78 5.72 The data presented on this table reveals a constant decrease of formal sector employment and the growing significance of informal sector employment. The greatest leap in the growth of the informal sector employment was witnessed from 1991. According to Omolo(unpublished), this period of rapid growth in informal employment in Kenya(1991 onwards) coincided with the period when the Kenyan labour market started suffering formal sector employment losses triggered by liberalization policies, renewed government strategy towards promotion of growth and development of the informal and jua kali sector(1992), and broadening of the definition and more consistent capturing of informal sector data in the national statistics. Youth Fact Book Infinite Possibility Or Definite Disaster? 131 Employment Trends(Formal and Informal) between 1986-2008 90 80 70 Informal 60 50 40 30 20 10 Formal 0 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Figure 93: Formal and Informal Employment Trends between 1986 and 2008 Source: Various Economic Surveys From the employment trends and according to Omolo(unpublished), it is clear that the Kenyan labour market is dual in nature: presenting the formal sector alongside the informal sector. The trends and dynamics of employment in Kenya discussed above shows that majority of the jobs are created in the informal sector more than in the formal sector. However, the informal sector jobs are precarious in nature as characterized by job insecurity, poor wages and terms and conditions of employment, lack of social protection, weak safety and health standards, and low job tenure. Even though informal sector employment has been a key driver to reducing unemployment in Kenya, informality remains a major productivity trap. Thus, without strategic interventions to formalize and improve the informal sector jobs, the sector cannot be relied on to effectively address the country’s youth employment problem and poverty reduction goals. According to Omolo(unpublished), there is an increasing trend in the engagement of workers on casual terms of employment. Data shows that the proportion of casual workers in the formal sector gradually increased from 17.9 percent in 2000 to 21.2 percent in 2005, 29.7 percent in 2006 and 32.2 percent in 2008. The increase in formal sector employment between 2002 and 2003, for example, was wholly attributed to the increase in the number of casual workers. While the number of workers on regular terms remained constant at 1,381.1 thousand in 2002 and 2003, the number of casual employees increased by some 27.6 thousand, out of whom 32 percent were women. In 2008, there were 625.6 thousand workers on casual terms out of whom, 36.6 percent were women. It is noted that most employers in Kenya, including the public sector ones have resorted to the increasing use of casual, temporary, part-time, contract, sub-contracted and outsourced workforces to ostensibly reduce labour costs, achieve more flexibility in management and exert greater levels of control over labour. This trend allows the de-politicization of hiring and firing that makes it easier for companies to avoid labour legislation and the rights won by trade unions. The trend is mainly attributed to strive for global competitiveness and weak enforcement of labour legislations, with the youth bearing the brunt of such trends. The nature of employment 132 Youth Fact Book Infinite Possibility Or Definite Disaster? of casual workers, for example, do not facilitate them to enjoy the fundamental rights of workers such as freedom of association and collective bargaining, right to paid leave(sick, maternity and annual leave), and the right to social protection as provided under the National Social Security Fund(NSSF) and the National Hospital Insurance Fund(NHIF). This revelation contrasts sharply with the country’s desire to reduce poverty and enhance social protection. 6.4 Employment by Sector Table 79: Employment by Sector Employment by Sector Agriculture& forestry Mining Quarrying Manufacturing Electricity& Water Building&Construction Trade, Restaurant& Hotels Transport& Communications Finance, Insurance, Real Estate& Bus Community, Social& Personal Services Employment in informal Sector Real Average Earnings Kshs P.a. Source: KDHS, 2009 2003 ‘000’ 316.1 5.4 239.8 21.1 76.6 162.8 86.8 83.7 735 5,532.70 167,893 2004 ‘000’ 320.6 5.5 242 20.9 77.3 168 100.8 83.7 744.9 5,992.80 175,218 2005 ‘000’ 327.4 5.7 247.5 20.3 78.2 175.7 117.3 85.7 749.4 6,407.20 182,742 From the table, informal sector is the highest employer followed by agriculture and manufacturing. Interestingly, however is the fact that young people’s(15-34 years old) desired occupation in 2027 would be to work in the service industry(41%), enterprise(25%), social service(14%), industry(7%), and public service(6%). Only 5% want to work in agriculture (IEA, 2003). 6.5 Occupation by Age and Gender Most young people are likely to be employed in the agricultural sector. More females than males are likely to take up sales and domestic service jobs while more males take up unskilled manual work. From the data, more females than males are employed as managers across all age cohorts. Youth Fact Book Infinite Possibility Or Definite Disaster? 133 Table 80: Occupation by Age and Gender Managerial Age(years) M F 15-19 2.8 12.9 20-24 15.6 31.5 25-29 24.5 29.4 30-34 22.3 34 sum 65.2 107.8 Av 16.3 26.95 Source: KDHS, 2009 Clerical M F 0 0.4 0.3 2.2 1 3.6 1.3 1.4 2.6 7.6 0.65 1.9 Sales and Service M F 5.5 11.2 10.9 16 13.5 13.6 7.8 13.5 37.7 54.3 9.4 13.6 Skilled Manual M F 3.3 6.6 9.4 6.9 8.5 8.8 14 4.8 35.2 27.1 8.8 6.8 Unskilled Manual M F 9.7 3 13.7 1.4 18.5 2.9 20.1 3.2 62 10.5 15.5 2.63 Domestic Service M F 2.3 14.6 2.9 10.3 4.6 7.9 2.6 3.4 12.4 36.2 3.1 9.05 Agriculture M F 56.6 49.9 44.2 31.4 29.5 33.3 31.9 39.5 162.2 154.1 40.55 38.53 6.6 Type of Earnings from Employment by Age and Gender Table 81: Type of Earnings from Employment by Age and Gender Female Male Age(years) Cash only Cash and In kind Not paid Cash only Cash and In kind Not in kind only in kind only paid 15-19 50.7 10.7 7.2 31.3**** 20-24 56.8 7.8 3.1 32.2 71.1 7.8 0.6 20.5 25-29 61.5 12.1 1.6 24.5 77.2 7.1 2.8 12.9 30-34 63.6 12.7 1.3 22.4 76.7 8.7 1.7 12.8 Source: KDHS, 2009* denotes a figure based on fewer than 25 unweighted cases that have been suppressed According to the KDHS report(2009) employment can be a source of empowerment for both women and men, especially if it puts them in control of the income. Generally the older you are the more likely you will be paid in cash for the work you do. More than a quarter(28%) of women aged 15-34 are not paid for the work they do compared to 12% of men. 134 Youth Fact Book Infinite Possibility Or Definite Disaster? 6.7. Control over Earnings Control over women’s and men’s cash earnings gives an indication of women’s and men’s empowerment within the family and the extent of decision making in the households. 6.7.1 Control Over Women’s and Men’s Cash Earnings by Age Table 82: Control Over Women’s and Men’s Cash Earnings by Age Age(years) Women Mainly wife 15-19 36.9 20-24 46.9 25-29 37.2 30-34 40.1 sum 161.1 Av 40.275 Source: KDHS, 2009 Jointly(wife+ husband) 55.5 45.5 51 53.5 205.5 51.375 Men Mainly husband Mainly wife 7.6* 7.4 5.9 11.8 3.5 6.2 3.3 33 12.7 8.25 3.175 Jointly(wife+ husband) * 34.3 57.3 51 142.6 35.65 Mainly husband * 58.2 39 44.3 141.5 35.375 On average, 51% of women’s income is jointly controlled, 40% say that they control their own income while 8% say that their husbands exclusively control the woman’s income. On the other hand, 36% of men’s income is jointly controlled, 35% say that they control their own income while 3% have their wives controlling the income. 6.7.2 Control Over Women’s and Men’s Cash Earnings by Demographic Characteristics Generally, women with more children, those living in rural areas, those with less education and those in the lowest wealth quintile are likely to control their own income than other women. On the other hand, women with fewer children, those in urban areas, those with secondary school education and above as well as women in the highest wealth quintile are likely to have their income jointly controlled. Men with no children, those living in urban areas as well as those in the lowest or those in the highest wealth quintile tend to control their own income while those with children tend to have joint control of the man’s incomes. Middle income men tend to have joint control of the man’s resources. Youth Fact Book Infinite Possibility Or Definite Disaster? 135 Table 83: Control Over Women’s and Men’s Cash Earnings by Demographic Characteristics Demographic Characteristics No. of living children 0 1 to 2 3 to 4 5+ Residence Urban Rural Education No education Primary Incomplete Primary complete Secondary+ Wealth Quintile Lowest Second Middle Fourth Highest Women Mainly wife 39.9 41.2 42.3 44.5 41 42.9 51.8 47.3 41.9 37.2 52.2 48.8 36.2 43.2 38.6 Jointly(wife + husband) Mainly husband 56.6 3.5 50.8 7.7 50.1 7.6 42.2 13.2 52 6.9 47.4 9.6 33.8 14.4 38.9 13.4 51.1 6.8 56.9 5.9 31.8 16 40.1 10.7 53.7 10.1 49.9 6.5 55.5 5.9 Men Mainly wife Jointly(wife + husband) 5.2 44 3.1 53.1 2.2 52.1 1.1 55.6 2.5 45.1 2.6 57.3 0.8 43.7 3.6 58.2 3.6 51.5 1.7 51.7 1.2 51 2 56.3 4.5 63.7 3.5 59 2.1 45.8 Source: KDHS, 2009 Mainly husband 50.9 42.7 45.3 43.2 52.2 39.4 55.5 37.5 45 45.8 46.9 40.8 31.8 36.3 52 6.7.3 Control Over Women’s and Men’s Cash Earnings by Region Generally, Nyanza province has the highest number of women(55%) controlling their own income while Nairobi(56%) and Rift valley(54%) have the highest number of joint control of the woman’s resources. Coast(13%) and Rift Valley(12%) have the highest number of men controlling their wives income. Nairobi(77%) has the highest number of men controlling their own income while central province has the highest number of joint control of the husband’s/ partners income. 136 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 84: Control Over Women’s and Men’s Cash Earnings by Region Demographic Characteristics Nairobi Central Coast Eastern Nyanza Rift Valley Western N. E Women Mainly wife 39.3 36.6 47.7 46.3 54.8 34.7 48.6 36.2 Jointly(wife + husband) 55.9 60 39.3 46 35.5 53.5 41.5 49.8 Men Mainly husband Mainly wife 4.8 2.1 3.4 7.8 12.9 2.5 7.7 2.3 9.1 2.2 11.6 1.4 9.9 1.8 14 3 Source: KDHS, 2009 Jointly(wife + husband) 21.3 72.9 53.3 63.8 48.5 53.6 58.8 56.5 Mainly husband 76.6 19.3 44.2 32.8 48.6 44.2 39.4 40.5 6.8. Women’s Cash Earnings Compared With Men’s Cash Earnings Results of the 1998/99 Integrated Labour Force Survey(ILFS) showed that the mean monthly earnings from paid employment for males is about 1.5 times that of females(GOK, 2005). 6.8. 1 Women’s Cash Earnings Compared With Men’s Cash Earnings by Age Table 85: Women’s Cash Earnings Compared With Men’s Cash Earnings by Age Age(years) More 15-19 1.6 20-24 7.4 25-29 12.5 30-34 11.4 sum 32.9 Av 8.225 Source: KDHS, 2009 Less 86.1 73.8 63.4 67.8 291.1 72.775 About the same 11.3 12.7 20 16.9 60.9 15.225 Husband/Partner has no earnings 0 3.5 2.1 2 7.6 1.9 On average 73% of women aged 15-34 earn less than their husbands/partners, 15% earn about the same and 8% earn more than their husbands/partners. Youth Fact Book Infinite Possibility Or Definite Disaster? 137 6.8.2 Women’s Cash Earnings Compared With Men’s Cash Earnings by Demographic Characteristics Table 86: Women’s Cash Earnings Compared With Men’s Cash Earnings by Demographic Characteristics Demographic Charac- More Less teristics No. of living children 0 11.9 69.4 1 to 2 9.4 71 3 to 4 15.9 59.8 5+ 14 62.5 Residence Urban 11.8 67.8 Rural 13.3 63.9 Education No education 15.3 61.1 Primary Incomplete 12.2 65 Primary complete 9.7 67 Secondary+ 15.4 64.4 Wealth Quintile Lowest 14.9 60.6 Second 11.3 65.8 Middle 11.7 60.5 Fourth 14.3 65.2 Highest 12.6 68.8 Source: KDHS, 2009 About the same 12.5 15.5 18.2 17.3 14.4 17.6 8.5 19 19.5 14.7 14.7 17.6 22.2 17 13.7 Husband/Partner has no earnings 2.9 1.7 4.6 4.5 3.4 3.5 12.8 1.6 2.3 3.6 8 2.8 3.4 2.3 2.8 Overall, about 65% of women(15-34) earn less money than their husbands/partners while 16% earn about the same. 13% of women earn more than their husbands/partners. 138 Youth Fact Book Infinite Possibility Or Definite Disaster? 6.8. 3 Women’s Cash Earnings Compared With Men’s Cash Earnings by Region Table 87: Women’s Cash Earnings Compared With Men’s Cash Earnings by Region Demographic More Less About the same Characteristics Nairobi 15.5 67 10.4 Central 12.9 57.2 27.5 Coast 9.8 70.5 10.7 Eastern 13.2 63.4 21.3 Nyanza 12.8 68.5 14.1 Rift Valley 12.2 66.8 14 Western 14.1 65.4 15.7 N. E 13.2 38.2 13.4 Source: KDHS, 2009 Husband/Partner has no earnings 3.3 1.5 4.6 0.9 0.1 6.7 2.4 33.1 Earning patterns for women in the regions are similar to those shown by demographic characteristics. Women are likely to earn more in Nairobi(16%) and Western(14%) provinces while women in Central(28%) and Eastern(21%) are likely to earn about the same amount of income with their husbands/partners. North Eastern has the highest number of men without an income(33%). 6.9 Child Labour Child labour refers to work that is mentally, physically, socially or morally dangerous and harmful to children; and interferes with their schooling. This can either be by depriving the children of the opportunity to attend school; by obliging them to leave school prematurely; or by requiring them to attempt to combine school with other chores. Child labour affects skill formation and employability of the youth. It also reduces employment opportunities available to the labour force besides weakening the bargaining power of the workers. This is reinforced by the fact that children are more likely to be involved in exploitative and hazardous forms of work which not only compromise their human capital development, health, safety, dignity and morals, but also deny them the right to grow, develop and enjoy their childhood. This negatively affects their human capital base and employability. At the same time, child labour makes it more challenging for households and governments to break the vicious cycle of poverty Omolo(unpublished). Blunch and Verner(2000) argue that the socio-economic status of the household head is an important determinant of child labour. Children of self employed workers, irrespective of the sector, are more likely to engage in harmful child labour activities than children from households whose heads are in formal employment. The same is true for children of workers from the informal sector. Edmonds and Pavcnik(2004) linked child labour to three facets of poverty. According to the researchers, child labour declines dramatically with improvements in household living standards; child labour is responsive to unexpected changes in a family’s economic environment; and poor local institutions such as ineffective or expensive schools leaves children with very few viable options other than work. Within this framework, Edmonds and Pavcnik(2004) claimed that 75 percent of cross-country variation in child labour can be explained by income variation. Dehejia and Gatti(2003) affirmed that income is the single most important household level predictor of child labour. Youth Fact Book Infinite Possibility Or Definite Disaster? 139 Kenyan data on child labour mirrors the dynamics of the country’s rate of growth and incidences of poverty. Kenya’s economic growth rate averaged 2.1 and 4.6 percent over the periods 19912000 and 2001-2007, respectively. Consistent with this, the incidences of poverty declined from 56 percent in 2000 to 46 percent in 2005/2006. With respect to child labour, the 2005/2006 KIHBS Report(GOK, 2007) showed that the total number of working children declined from 1.9 million in 1999 to 1.01 million in 2005/2006. This represents a drop of 46.8 percent(Omolo, unpublished). Distribution of Children 5-17 years Working in Risky Occupations By Sex,2005 5302 Boys Girls Number of Boys /Girls 2835 2659 1481 896 1266 640 2035 1533 598 0 0 0 0 Waiters/ Bartenders Transport Conductors Mining Blasting. Stone Cutting and Related Workers Building Trades Workers Machinery Mechanic and Fitters Occupation Brewers, distillers and Related workers Mining and Quarrying Labourers Figure 94: Distribution of Children 5-17 years Working in Risky Occupations By Sex, 2005 Source: Economic Survey, 2010 249 48 Construction and Maintanance Labourers In the data presented, there seems to be more boys than girls engaging in child labour in Kenya. However, in a research done by Blunch and Verner(2000) in Ghana, it was found that girls as a group across urban, rural and poverty sub-samples were consistently found to be more likely to engage in harmful child labour than boys. Mean Monthly Earnings from Paid Employment of Children 5-17 Years by Gender 2043.2 2108.7 Boys Girls 1288.3 1485 196.7 65.2 Wages/Salary Benefits& Allowance Total Earnings Figure 95: Mean Monthly Earnings from Paid Employment of Children 5-17 Years by Gender Source: Economic Survey, 2009 140 Youth Fact Book Infinite Possibility Or Definite Disaster? When engaging in child labour, boys(5-17 years old) make more money than girls the same age. Average incomes earned by both boys and girls are so low, confirming the exploiting nature of child labour. Mean Monthly Earnings from Paid Employment of Children 5-17 Years by Region 2272.1 2417.9 Urban Rural 1399.5 1508.6 145.8 109.1 Wages/Salary Benefits& Allowance Total Earnings Figure 96: Mean Monthly Earnings from Paid Employment of Children 5-17 Years,(Rural/Urban) Source: Economic Survey, 2009 Child labour pays well in urban areas than in rural areas. Average mean wages are still too low. 6.10 Employment in the Civil Service 6.10.1 Number of Young People Working in Kenya’s Civil Service Number of Youth(18-35) in the Civil Service 90,068 125,221 62,935 35,153 82,089 19,154 Males Females Civil servants above 35 Civil servants aged 18-35 Figure 97: Number of Young People Working in Kenya’s Civil Service Source: UNDP, 2010 Total 40% of Kenya’s civil service is aged between 18 and 35 years old. The young men are however 3 times more than their female counterparts Youth Fact Book Infinite Possibility Or Definite Disaster? 141 6.10.2 Civil Service Wages Public Sector Wages % of men and women 70 30 U 70 30 94 90 6 10 T S Top most job group 65 35 67 33 85 15 R 77 33 A B C E Bottom most job group Men Women Figure 98: Civil Service Wages Source: GoK, 2007 81 19 Q 89 23 F In Kenya’s public service, women hold only 16% of the top most jobs(job groups U,T,S,R,Q) and 74% of the bottom most jobs(job groups A,B,C,E,F). According toVision 2030, only a small portion of this scenario can be explained by gender difference in education, work experience and job characteristics. 6. 11. Comparative Analysis 6.11.1 Labour Force Participation among 15-24 Year Olds For a young person aged 15-24 in the region, labor force participation is more likely in Rwanda, Burundi and Tanzania. Female labour force participation is higher in Uganda, Rwanda, Burundi and Ghana than it is for the male counterparts in those countries. Labour Force participation among 15-24 year olds 37 43 60 49 65 69 74 72 73 68 Kenya (97) Uganda (02) Tanzania (00) Rwanda (97) Burundi (98) Figure 99: Labour Force Participation among 15-24 year olds Source: World Bank, 2007 26 30 South Africa (00) 51 48 24 Ghana (98) 63 Female Male India (00) 142 Youth Fact Book Infinite Possibility Or Definite Disaster? 6.11.2 Unemployment Rate by Gender as well as by Rural and Urban(15-24) 19 22 Kenya (97) 7 4 Uganda (02) Unemployment Rate by Gender(15-24) 59 50 1 5 9 10 Tanzania (00) Rwanda (97) 0.3 0.6 Burundi (98) South Africa (00) 15 16 7 8 Ghana (98) India (00) Female Male Figure 100: Unemployment Rate by Gender(15-24) Source: World Bank, 2007 Among the countries profiled, unemployment rates are highest among 15-24 year old females (59%) and males(50%) in South Africa followed by Kenyan males(22%) and females(19%). However, unemployment reduces for all the countries as young people grow older. This with the exception of South Africa where unemployment in rural and urban areas was almost the same. Data also showed that unemployment was higher in urban areas than in rural areas. In some cases unemployment in urban areas was more than double that of the rural areas. Unemployment Rate by Urban/Rural(15-24) 55 54 U Ur r b b a a n n R R u u r r a a l l 32 17 Kenya (97) 23 3 Uganda (02) 11 1 Tanzania (00) 28 8 Rwanda (97) 25.3 26 16 12 6 Burundi (98) South Africa (00) Ghana (98) India (00) Figure 101: Unemployment Rate by Urban and Rural(15-24) Source: World Bank, 2007 Youth Fact Book Infinite Possibility Or Definite Disaster? 143 6.11.3 Not in the Labour Force and not in School(15-24) Not in the Labour Force and not in school(15-24) U Ma rb le an 55 F R e u m ra l l e 33 25 18 19 20 16 11 13 6 9 66 4 5 4 Kenya (97) Uganda (02) Tanzania (00) Rwanda (97) Burundi (98) South Africa (00) Ghana (98) India (00) Figure 102: Not In the Labour Force and not in School(15-24) Source: World Bank, 2007 In all the countries profiled more females than males aged 15-24 are likely to be out of work and out of school. India has the highest prevalence of females(55%) followed by Kenya(33%) and Ghana(25%) who are not in the labour force and not in school. India also has the highest number of children(7-14) who work without going to school(90%). Countries with the highest number of children working and going to school are South Africa(95%) and Uganda(82%). 6.11.4 Percentage of Working Children(7-14) It is estimated that there were 317 million economically active children globally, out of whom about 69 percent(218 million) were engaged as child labourers(ILO, 2006). % of Children(7-14) who are Economically Active U Fe r m b a a l n e M Ru al r e al 39 42 36 38 36 28 29 30 26 29 11 15 6 7 5 5 Kenya (97) Uganda (02) Tanzania (00) Rwanda (97) Burundi (98) South Africa (00) Ghana (98) India (00) Figure 103: Percentage of Children who are Economically Active Source: World Bank, 2007 From the countries profiled, child labour is highest in Tanzania, Burundi and Rwanda, the countries earlier profiled as having high labour participation of 15-24 year olds. 144 Youth Fact Book Infinite Possibility Or Definite Disaster? 7 Participation ‘Consult widely on an action programme for youth which values young people and reflects their own aspirations and needs’ (Spice, 2002) 7.0 Participation According to the Scottish Parliament Information Centre(SPICe, 2002), there are a wide range of definitions as to what constitutes participation. They include Save the Children‘reaction toolkit’ which generally defines participation as“people sharing ideas, thinking for themselves, expressing their views effectively, planning, prioritizing and being involved in the decision making processes” 4 . However, the actual content of what constitutes‘participation’ tends to be more contested and participation has frequently been depicted as a ladder ranging from the bottom‘rung’ representing non-existent/ minimal participation to full participation in the top rungs of the ladder. The first three rungs of the ladder represent‘non participation’ whilst the remaining rungs represent ‘degrees of participation’ as depicted below. Young people and adults share decision-making: Adults and young people have the ideas to set up the project, and invite adults to join with them in making decisions. Young people lead and initiate action: Young people have the initial idea and decide how the project is carried out. Adults are available but do not take charge. Adult-initiated shared decisions with young people: Adults have the initial idea but young people are involved in every step of the planning and implementation. Not only are their views considered, but they are also involved in taking the decisions. Consulted and informed: The project is designed and run by adults but young people are consulted. They have a full understanding of the process and their opinions are taken seriously Assigned but informed: Adults decided on the project and young people volunteer for it. Adults respect their views. Tokenism: Young people are asked to say what they think about an issue but have little or no choice about the way they express those views or the scope of the ideas they can express. Decoration: Young people take part in an event, e.g. by singing, dancing or wearing t-shirts with logos on, but they do not really understand the issue. Manipulation: Young people do or say what adults suggest they do, but have no real understanding of the issues, or are asked what they think. Adults use some of their ideas but do not tell them what influence they have had on the final decision. Figure 104: Roger Hart’s Ladder of Young People’s Participation Source: Hart, 1992 Hodgson(1995) also states that five conditions should be aimed for if young people are to be truly empowered. These are: Access to those in power; Access to the relevant information; Choices between different options; Support from a trusted independent person and where needed a representative; A means of appeal or complaint if things go wrong. 4 Save the Children(2000)‘Reaction Consultation Toolkit’, p.13. 146 Youth Fact Book Infinite Possibility Or Definite Disaster? Alternative perspectives critique the momentum behind current initiatives to consult with and enable young people to participate in decision-making structures principally for children and youth participation being promoted“not because it will bring young people what they want, but because it will do them good and/ or improve society”(Education, Culture and Sport Committee, 2001) 5 . Prout(2000) views‘youth participation’ as a form of social control which concentrates on improving children’s future lives as adults rather than their present wellbeing and social participation. Research commissioned by the Education, Culture and Sport Committee highlighted the need to ensure that the results of any consultation exercise are fed back to young people and how the views of young people were taken account of should be transparent. Moreover, the research also highlighted that consultation exercises can serve to alienate young people, commenting that,“Most consultations involve small numbers of young people causing a polarization. A small minority of chosen or self-selected individuals enjoy and benefit from ongoing participatory activities and groups, while the majority who are not consulted or have experience of short one-off consultations tend to feel resentful, alienated and cynical.” 7.1 Student Participation in School Governance According to UNICEF& GOK(2008), child participation in school governance is defined as the active involvement of the child within the school and surrounding community that provides an opportunity for children to be involved in decision making on matters that affect their lives and to express their view in accordance with their evolving capacities. Child participation is an important concept with potential for positive impact in the management of our schools and the overall development of the school child. According to head teachers surveyed in this report, child participation had significant impact in all areas of school interactions such as discipline, co-curricular activities, conflict resolution, school performance, confidence and self esteem as illustrated below. Effect of Student Participation in School Governance 0.6 2.3 5.6 4 3.5 99.4 97.7 94.4 78.3 96 96.5 Discipline Co-curricular activities Conflict resolution No School performance Yes Confidence and self esteem 21.7 Other areas Figure 105: Effect of Student Participation in School Governance Source: UNICEF/GOK, 2008 5 Education, Culture and Sport Committee(2001)‘Improving Consultation with Children and Young People in Relevant Aspects of Policy-Making and Legislation in Scotland’, p.20 Youth Fact Book Infinite Possibility Or Definite Disaster? 147 7.1.1 Type of Leadership Existing in Schools Type of Student Leadership Existing in Secondary Schools 69 57 29 21 Prefect PRIVATE Club leader PUBLIC 4.35 3.45 Head girl/ boy Figure 106: Type of Student Leadership Existing in Secondary Schools Source: UNICEF/GOK, 2008 According to UNICEF and GOK(2008), the most common type of student leader in secondary schools was the prefect. On improving the current prefect system, students proposed that clear guidelines be provided for the system and the election/selection process made democratic and students centered. Recommended procedures included advertisement of vacant positions, proposals of candidates from all parties involved, application for posts and an interview panel comprising of teachers to make the final choices. 7.1.1.1 Prefect System vs. Student Council The council system was seen as a better form of leadership than the prefect system with 87% of the students stating that there was no student unrest or strikes or dropouts reported where this form of leadership was applied unlike 60% of schools that were predominantly prefect led. This analysis is comparable with the results obtained from the Public School Survey where the indiscipline cases were lower in schools with student councils. This is reflective of the fact that child participation which gives the child a stronger say in their affairs results to better adherence of the laws created. However, 47% of student council participation is through nominations, 33% by choice and 20% by elections. 148 Youth Fact Book Infinite Possibility Or Definite Disaster? Process of Choosing Students into Student Councils 47 33 % 20 Nomination Choice Process Figure 107: Process of Choosing Students in to Student Councils Source: UNICEF/GOK, 2008 Election 7.1.1.2 Club Membership Club membership is the second most popular form of leadership in secondary schools. According to UNICEF and GOK, club membership in public secondary schools is well institutionalized and therefore 98% of students are club members. In private secondary schools, the number is lower at 78%. 91% of students sampled indicated that clubs provided an opportunity for decision making and agreed that club membership helps them participate in decision making in school, especially when it comes to outings and the general running of clubs. The high membership in the clubs is due to the fact that they are highly participatory; students can freely interact and offer their views without fear of reprisal and most importantly, they offer a welcome change from class work and normal school routine. In addition, they are good launching pads for careers since through them, talents are nurtured. The most popular clubs in all the schools surveyed included the scouts club, debating club, and the Christian Union(CU) club. In public schools drama club was also very popular. Traditional and life-skills based clubs like First Aid, Young Farmers Club and 4K Clubs have the lowest membership because according to the students, they are not very interesting, have manual work and their members do not get to go out much compared to the other clubs. In the students’ own words, they are“quite boring” as opposed to drama and debate clubs as well as the faith based clubs which have higher membership because they call for greater and active involvement. Youth Fact Book Infinite Possibility Or Definite Disaster? 149 7.1.2 Process of Choosing Student Leadership in Schools According to UNICEF and GOK, the dominant process of choosing school leaders is mainly teacher led. Unlike public school where the most popular process for selecting prefects is a collaborative process between teachers and students(49%), in private schools teachers have a stronger voice in choice of leaders,(62%). Only 30% of the population sampled in private schools affirmed that the process is participatory between the students and teachers. Process of Choosing Student Leadership in Schools 62 39 11 7.1 Elections by students PRIVATE Selection by teachers PUBLIC Figure 108; Process of Choosing Student Leadership in Schools Source: UNICEF/GOK, 2008 7.1.3 Mechanisms of Channeling Student Grievances Mechanisms of Channeling Student Grievances 76 % Prefects 71 63 52 21 Class meetings House/ Dorm meetings Suggestion boxes Baraza’s 14 Student Councils 6 Other Figure 109: Mechanisms of Channeling Student Grievances Source: UNICEF/GOK, 2008 According to UNICEF and GOK, most schools(76% in private and 86% in public secondary schools) have mechanisms for channeling student grievances. These mechanisms include the use of prefects, having classroom or dorm meetings, barazas, student councils and use of suggestion boxes. The most popularly used are prefects and class meetings. 150 Youth Fact Book Infinite Possibility Or Definite Disaster? 7.1.4 Student Participation in Various Decision Making Processes % Involved in Decision Making 78 78 88 72 88 75 Private Public % Planning for development facility Maintainance of physical facilities Choice of Subject 23 5 School timetable 21 4 Exam timetable 36 36 Co-Curricular activities 19 5 Diet Figure 110:% Involved in Decision Making Source: UNICEF/GOK, 2008 According to UNICEF and GOK, the performance rate of students and pupils is affected by the facilities they have in their schools. In most schools that reported an adequate supply of resources, the performance ranged from excellent to good or average. The reverse was true. Schools that had inadequate facilities performed poorly with a few exceptional students recording good performance. 78% of students in private and public secondary schools stated that they were not involved in this process of planning and developing school resources. In cases where the students or pupils were involved, their role was mainly limited to identifying the facilities needed by the school and financing these facilities either by parents contributions or through fundraising. Students also maintained that they were involved for the most part, in the maintenance of the schools’ physical facilities(72% in private secondary and 88% in public secondary). This was in form of cleaning and taking care of the facilities. They were also involved in incurring the cost of repairing the facilities. Non-teaching staff(mainly subordinate members of staff) were also involved in maintaining facilities through repairs. The student’s free choice of subject was said to give the student a sense of responsibility and ownership of subjects. The student is more likely to perform better in a subject they have had the chance to select without bias. On some occasions, the students do seek the help of teachers or career counselors in choosing their subjects. Regarding the student’s involvement in development of the school timetable, student involvement was low and students suggested that time tables should be made more student friendly and in particular the fact that subjects like Mathematics should be taught in the morning and not in the afternoon. The issue of diet is of key concern to students and has in many cases been cited by students as a main cause of grievance in unrest cases hence the importance of student involvement. In most instances, the schools(76% public and 67% private), had a diet chart or menu that provided the meal plans for the week or term. Youth Fact Book Infinite Possibility Or Definite Disaster? 151 Even though students registered low participation in deciding on co-curricular activities, the most popular co-curricular activities were registered as watching TV/Video in both public and private schools, followed by games. This was attributed to the need for recreation, entertainment and releasing tension due to academic pressure as indicated below. Recreational Activities Considered Most Popular in Public and Private Secondary Schools Straight talk 0.1 CU 0.2 Other clubs Athletics 0.7 1.3 Magic shows 1.5 Debating 6 Swimming 14 Field Excursions 15 Performing creative arts 26 Games 38 Watching TV and Video 41 % Figure 111: Recreational Activities Considered Most Popular in Public and Private Secondary Schools Source: Source: UNICEF/GOK, 2008 7.2 Women’s Participation in Decision making at Home Participation at home is a measure of women’s autonomy and status. 7.2.1. Women’s Participation in Decision making by Age Table 88: Women’s Participation in Decision making by Age Women’s participation in decision making Age(years) Making major household purchases 15-19 50.5 20-24 61.5 25-29 64.8 30-34 68.9 sum 245.7 Av 61.4 Source: KDHS, 2009 Daily purchases of household needs 68.2 78 81.6 83.9 311.7 77.9 Visit to her family and relatives 60.7 66.9 70.2 75.7 273.5 68.4 Men’s attitudes towards wives participation in decision-making Making major household purchases Daily purchases Visit to her of household family and needs relatives *** 54.9 75.8 53.4 59.9 87.8 63.4 53.5 84.1 64.3 168.3 247.7 181.1 42.1 61.9 45.3 152 Youth Fact Book Infinite Possibility Or Definite Disaster? Women make more house hold decisions as they grow older. 78% believe they should make daily household purchases compared to 62% of the men. 61% of women believe they should make major household purchases compared to 42% of men. 68% of the women believe they should make decisions to visit her family and relatives while only 45% of the men think so. 7.2.2 Women’s Participation in Decision making by Demographic Characteristics Table 89: Women’s Participation in Decision making by Demographic Characteristics Women’s participation in decision making Demographic Characteristics Making major Daily purchases Visit to her household of household family and purchases needs relatives Employed in the last 12 months Not Employed 54.7 70.4 66.5 Employed for Cash 74.9 89.1 78.5 Employed not for Cash 67.1 85.3 70.9 No. of living children 0 69.8 79.3 72.1 1 to 2 65.7 82.8 72.1 3 to 4 69.4 82.3 74.5 5+ 64.3 81.7 73 Residence Urban 70.7 86.2 76.8 Rural 65.5 80.8 72 Education No education 49.2 65.1 56.6 Primary Incomplete 61 79.8 68.8 Primary complete 68.9 83.3 75.7 Secondary+ 76.8 89.6 81 Wealth Quintile Lowest 51.7 69.7 60.9 Second 64.2 82.4 72.6 Middle 69.3 82.8 73.6 Fourth 71.9 86.6 76.6 Highest 73.2 86.5 78.9 Men’s attitudes towards wives participation in decision-making Making major household purchases Daily purchases Visit to her of household family and needs relatives *** 59.8 85.4 65.5 70.6 84.6 66.1 71.4 80.6 63.3 64.7 87.7 65.9 61.1 86.1 70.9 55.1 82 59.2 65 90.8 69.8 60.2 82.5 63.9 36 74 53.5 55.8 80.1 55.4 60.5 84.8 64 67.9 89 72.9 45 70.2 55.1 58.4 83.3 63.4 64.4 83.6 61.6 66.7 88.5 73 66.4 91.1 69.2 Source: KDHS, 2009 Women who are employed for cash make decisions more(80%) than those employed not for cash (74%) and those who are not employed at all(63%). For both men and women, decision making by women is more in urban areas than in rural areas and it increases with the level of education. For women, decision making also increases with the level of wealth. Youth Fact Book Infinite Possibility Or Definite Disaster? 153 7.2.3 Women’s Participation in Decision making by Region Table 90: Women’s Participation in Decision making by Region Women’s participation in decision making Region Making major household purchases Nairobi 74.1 Central 83.1 Coast 60.5 Eastern 69.7 Nyanza 67.7 Rift Valley 65.7 Western 55.1 N. E 30.9 Source: KDHS, 2009 Daily purchases of household needs 86.7 91.7 74.4 89.3 84.7 77.1 81 46.1 Visit to her family and relatives 82.2 87.1 55.3 74.8 79.6 66.7 70.6 69.1 Men’s attitudes towards wives participation in decision-making Making major household purchases Daily purchases Visit to her of household family and needs relatives 66.2 92.4 83.2 83 92.1 75 56.6 85.7 52.9 68 88.8 57.7 54.3 90.6 63.9 61 76.9 63.6 58.5 81 67.4 9.8 93.9 89 Decision making among women is highest in Central province(87%) and Nairobi(81%). Decision making is least among women in North Eastern province(49%). Central province(83%) and Nairobi(81%) have the highest numbers of men who allow their wives to make decisions while North Eastern has the least(64%). 7.3 Voter Registration 7.3.1 Voter Registration Young people aged 18-35 who vote are 5.9 million. Of these, 25% come from Rift Valley, 15% from Central, 14% from Eastern, 13% from Nairobi and another 13% from Nyanza. Western, Coast and North Eastern contribute 10%, 8% and 2% respectively of the youth votes. Figure 112:% of Youth Voters(18-35) by Province Source: IIEC, 2010 154 Youth Fact Book Infinite Possibility Or Definite Disaster? 7.3.2 Voter Registration by Age No. of Voters by Age Cohorts(18-35) 1,357,303 1,198,456 964,678 767,544 885,830 804,075 18–25 Male Figure 113: Number of Youth Voters by Age(18-35) Source: IIEC, 2010 26–30 Female 31–35 The highest number of voters is in the 18-25 age cohort as it forms 43% of the youth vote. 26-30 year olds form 29% of the youth vote while 31-35 year olds form 28% of the youth vote. Youth Fact Book Infinite Possibility Or Definite Disaster? 155 7.3.3 Voter Registration by Province Voter Registration by Province 18-25 year old registered voters 347,521 325,952 177,720 136,525 98,656 87,261 27,252 185,597 188,788 157,647 154,692 29,465 191,387 140,382 133,419 173,495 Nairobi Coast North Eastern Eastern Central Rift Valley Western Nyanza 26-30 year old registered voters 235,757 186,482 148,180 107,404 78,774 63,640 139,374 139,821 113,774 128,558 121,808 85,361 64,398 87,105 15,603 16,183 Nairobi Coast North Eastern Eastern Central Rift Valley Western Nyanza 31-35 year old registered voters 121,187 195,904 124,802 88,408 76,834 71,703 131,153 124,579 124,544 121,470 17,531 18,321 82,973 116,806 100,775 82,915 Nairobi Coast North Eastern Eastern Central Rift Valley Western Nyanza Male Female Figure 114: Voter Registration by Age and Province Source: IIEC, 2010 With the exception of North Eastern province, throughout all the age cohorts and in all the provinces, there are more registered male voters than female voters. 7.4 Young People’s Participation in Economic Development through Youth Enterprise Development Fund(YEDF) According to the Youth Enterprise Development Fund Status Report(GOK, 2009), the Youth Enterprise Development Fund came legally into operation on 8th December 2006 through Legal Notice No. 167. It was transformed into a State Corporation on 11th May 2007 through Legal Notice No. 63. The Fund focuses on enterprise development as a key strategy that will increase economic opportunities for, and participation by Kenyan youth in nation building. The mandate of the fund is to increase access to capital by young entrepreneurs but also provides business development services, facilitates linkages in supply chains, creates market opportunities locally and abroad for products and services of youth enterprises, and facilitates creation of commercial infrastructure to support growth of youth businesses. 156 Youth Fact Book Infinite Possibility Or Definite Disaster? The government has so far released Ksh. 1.75 billion to the fund with a further commitment of Ksh. 500 million in the financial year 2008/09. The total funds disbursement to youth enterprises stood at Ksh. 1.9 billion as at 31st March, 2009. According to the YEDF status report, challenges of administering the fund include: • Negative public perception and attitude mainly influenced by the timing of the Fund’s establishment. The Fund was established on the eve of a general election year and hence, perceived as a political organization out to influence voting patterns particularly among the youth. The loans given out were therefore considered political goodies in some parts of the country, resulting in poor loan repayment. • Insufficient policy and legislative frameworks to support growth of youth enterprises and Fund’s sustainability in conformity with the scale and complexity of the youth unemployment problem. For instance, there is no legal framework guiding the operation of youth labour migration. • The capital investment in providing non-credit services to the youth entrepreneurs is huge vis-à-vis the actual loans disbursed. These services include business development services, market support, operational overheads, and public sensitization and education. But the public focus is largely on the loans disbursed not so much the quality of those loans. • Inadequate disbursement and repayment infrastructures in some parts of the country particularly remote areas pose a major challenge to disbursement and loan repayment. Lack of financial intermediaries and loan repayment avenues in most areas disadvantages the youth in those areas. • Insufficient funds to cater for high demand and expectations of the youth. The government allocation was thought to be adequate for all youth and is an instant panacea for youth unemployment. There was public perception that Ksh. 50,000 for a youth group was too little. • Large portfolio of financed youth enterprises creates monitoring problems as youth officers have inadequate mobility capacity. This situation affects service provision and management of the loans. 7.4.1 Number of Groups Accessing the YEDF by Province The Constituency Youth Enterprise Scheme(C-YES) is the constituency-based disbursement channel. It was purposely designed to inculcate entrepreneurial culture among the youth in all parts of the country. This channel mostly targets very poor youths and those with no experience in dealing with the mainstream financial sector especially commercial banks. The Fund has through this disbursement channel been able to reach young people in areas with poor financial infrastructure. The C-YES is a revolving fund whose allocation is Ksh. 2 million per constituency. Further disbursement to the constituency is dependent on repayment performance once the allocation is fully taken up. The Fund has disbursed over Ksh. 370 million to 8,430 youth groups as summarized below. Youth Fact Book Infinite Possibility Or Definite Disaster? 157 No. of Groups Accessing the Youth Enterprise Fund by Province 1,068 802 331 1,470 450 1,284 1,981 1,094 Nairobi Central Coast Eastern North Eastern Nyanza Rift Valley Western Figure 115: Number of Groups Accessing the Youth Enterprise Fund by Province Source: GOK, 2009 Participation is highest in Rift valley where 1,981 groups accessed the fund followed by Eastern province(1,470 groups) and Nyanza province(1,284 groups). The fund was least accessed by young people in Nairobi(331 groups) and North Eastern Province(450 groups). Through the C-YES, Rift Valley accessed 23% of the funds disbursed, Eastern(18%), Nyanza(16%), Central (13%), and Western(11%). Coast, North Eastern and Nairobi accessed 10%, 5% and 4% of the funds disbursed respectively. 7.4.2 Disbursement of YEDF through Financial Intermediaries by Gender and Region: The Fund has disbursed about Ksh. 1.53 billion to finance 57,075 youth enterprises through the Financial Intermediaries as at 31st March, 2009. The Fund is aware of the fact that areas with poor financial infrastructure have registered low uptake of the funds. The following is the summary of disbursement of funds through Financial Intermediaries based on provinces and gender. Table 91: Summary of Disbursed Funds through Financial Intermediaries Province Female Amount Male Amount Central 5,629 Coast 4,241 Eastern 6,000 Nairobi 2,437 North Eastern 186 Nyanza 3,434 Rift Valley 8,791 Western 2,376 Grand Total 33,094 Source: GOK, 2009 141,224,750.00 87,711,863.00 116,226,397.00 98,817,535.00 6,767,472.00 74,109,791.00 149,875,266.00 47,110,645.00 721,843,719.00 6,016 1,475 5,561 3,032 465 2,141 3,985 1,306 23,981 197,161,894.00 50,520,781.00 163,027,957.00 137,826,321.00 18,387,477.00 57,145,798.00 148,057,313.00 39,776,436.00 811,903,977.00 Total Female & Male 11,645.00 5,716.00 11,561.00 5,469.00 651.00 5,575.00 12,776.00 3,682.00 57,075.00 Total Amounts Disbursed 338,386,644.00 138,232,644.00 279,254,354.00 236,643,856.00 25,154,949.00 131,255,589.00 297,932,579.00 86,887,081.00 1,533,747,696.00 158 Youth Fact Book Infinite Possibility Or Definite Disaster? 22% of all the funds disbursed through financial intermediaries went to Rift Valley, followed by Central(20%) and Eastern(20%), then Nyanza(10%), Nairobi(10%) and Coast(10%). Western (7%) and North Eastern had the least access though financial intermediaries. Generally, more young women(33,094) than young men(23,981) accessed the funds. However, from figure 116 below, there were gender disparities in different regions. Central province, Nairobi and North Eastern had fewer men than women accessing the funds compared to the other provinces where more young women accessed the funds than their male counterparts. Rift valley had the highest gender disparity. 9,000 8,000 7,000 6,000 6,016 5,629 5,000 4,000 3,000 2,000 1,000 Access of YEDF by Gender and Region 8,791 6,000 5,561 4,241 1,475 3,032 2,437 3,434 2,141 465 186 3,985 Female Male 2,376 1,306 N0’s Central Coast Eastern Nairobi North Eastern Nyanza Rift Valley Western Figure 116: Access of YEDF though Financial Intermediaries by Gender and Region Source: GOK, 2009 As much as there were more young women than men accessing YEDF through financial intermediaries, 53% of all the resources were accessed by young men, compared 47% of resources accessed by young women. Youth Fact Book Infinite Possibility Or Definite Disaster? 159 160 Youth Fact Book Infinite Possibility Or Definite Disaster? 8 ICT ‘While much of the Information Technology sector in North America and Europe is experiencing doubt and retreat, something entirely different is happening in Africa…The African Information Revolution is wireless.’ (Maureen O’Neil, President, IDRC) 8.0 Information, Communication and Technology(ICT) According to World Bank(2007), Information and Communication Technologies(ICTs) consists of hardware, software, networks and media for the collection, storage, processing, transmission and presentation of information(voice, data, text, images), as well as related services. Communication technologies consist of a range of communication media and devices, including print, telephone, fax, radio, television, video, audio, computer, and the internet. Of these, internet, mobile phone, and computer(also referred to as new technologies are growing much faster than older information and communication technologies(ICTs) such as television, radio, mainline telephones, and newspapers. Mobile phones have overtaken mainline phones in coverage in many parts of the world, and there are more internet users per 1,000 people than there are daily newspapers purchased in every region except South Asia. Table 92: Catching up Fast: The Rise of New Technologies ICT rate per 1000 EAP ECA LAC MENA SAS SSA Low Middle High people Income Income Income Old’ ICT Daily newspapers 60 n.a 61 n.a 59 12 44 55 n.a Radios 287 447 410 273 112 198 137 344 425 Telephone Mainlines 161 228 170 133 39 11 32 177 393 Television sets 314 408 290 205 81 63 78 319 362 New’ ICT Internet Users 68 161 106 46 10 20 16 117 279 Mobile Phones 195 301 246 85 23 51 23 224 785 Personal Computers 26 73 67 31 7 12 7 42 284 Annual per capita growth since 2000(%) Internet Users 41 59 38 39 20 32 63 46 13 Mobile Phones 51 48 27 52 87 42 83 43 17 Personal Computers 28 18 17 9 3.1.2 27 11 24 20 12 Telephone Mainlines 21 1 5 15 12 3 14 12 0 Television sets 10 n.a n.a 10 5 5 4 5 0 Source: World Bank, 2007 Although young and old alike watch television and listen to radio, young people are the main users of the new ICTs, especially the internet and more advanced features of mobile phones such as text messaging, also known as Short Messaging Service(SMS). Although the main reason for many 15-24 year olds to use computers, the internet, and mobile phones is entertainment- playing games, downloading music, and talking with friends- the new ICT technologies are having wide-ranging effects on youth transitions. New opportunities 162 Youth Fact Book Infinite Possibility Or Definite Disaster? for work and study are opening up, and the interactive and decentralized nature of these new technologies is providing youth with many more opportunities to obtain information outside the traditional channels, enhancing their agency. While the majority of youth in many developing countries still do not use the internet or mobile phones, the experience of those who do shows the possibilities and potential benefits of increased access. 8.1 Exposure to‘Old’ Mass Media 8.1.1 Trends in Watching TV, Listening to Radio and Reading Newspapers Trends in Watching TV, Listening to Radio and Reading Newspapers No. of Young People(15-34) Reading the Newspaper Weekly Male Female 37.9 26.3 48.4 27.3 48.6 26.2 47.2 24.4 15-19 20-24 No. of Young People(15-34) Watching Television Weekly 40.2 31.6 53.7 37.2 25-29 54.4 39.4 30-34 53.5 31.7 15-19 20-24 25-29 30-34 No. of Young People(15-34) Listening to Radio Weekly 82.7 76.6 90.9 80.6 90.9 78.5 90.9 77 15-19 20-24 25-29 Figure 117: Trends in Watching TV, Listening to Radio and Reading Newspapers Source: KDHS, 2009 30-34 Youth Fact Book Infinite Possibility Or Definite Disaster? 163 As indicated in the above trends, young people listen more to radio than they watch TV or read newspapers. Young men of all age cohorts have access to all three forms of media more than women of similar age cohorts do. Overall trends indicate that access to newspapers, TV and radio are highest among those living in urban areas, among those with high educational attainment and among those in the highest wealth quintile 8.1.2 Favorite Radio Station Favorite Radio Station For 7-19 Year Olds Easy FM Kiss 100 FM Q FM Classic 105 % listenership 24 22 19 18 19 14 16 13 8 10 7 2 3 3 1 Figure 118: Favorite Radio Station among 7-19 year olds Source: Consumer Insight, 2009 The most popular radio station for 7-10 year olds is Kiss 100 FM while the most popular radio station for 11-19 year olds is Easy FM. Favorite programmes on radio include Reggae time(7%), preaching moment(7%), kumiria nyaunyau(3%), news(3%) and religious advice(2%). 8.1.3 Favorite Types of Movies Table 93: Favorite Types of Movies Table: Favorite Types of Movies Favorite Movie 7- 10 years Action 70 Romance 2 Adventure 4 Drama 6 Horror   Religious 9 Source: Consumer Insight, 2009 11-14 years 15-17 years 63 67 3   3 7 2 7 3 2 6 2 18-19 years 37 15 5 7 11 1 164 Youth Fact Book Infinite Possibility Or Definite Disaster? Overall, action movies rank highest among all age groups but decrease in significance as young people grow older. Romance movies generally rank second but increase in significance as young people grow older. Horror movies increase in significance as young people grow older while religious movies decrease in significance as young people grow older. 8.1.4 Favorite Newspapers Favorite Newspaper 63 33 21 23 27 23 16 % Readership Buzz -Sunday Nation Young Nation -Sunday Nation Lifestyle magazine -Sunday nation Saturday Magazine -Saturday Nation Pulse Magazine -Friday Standard Crazy Monday -Monday Standard Zuqka -Friday Nation Figure 119: Favorite Newspaper Source: Consumer Insight, 2009 Generally, the Nation Newspaper provides the highest form of entertainment for 7-19 year olds in print media. 8.1.5 Exposure to Family Planning Messages Table 94: Exposure to Family Planning Messages Exposure To Family Planning Messages Women Men Age Radio Television Newspaper/ None of the Radio magazine 3 media sources 15-19 52.9 27.3 27.9 44.1 57.3 20-24 75 43.3 37.6 23.6 75.9 25-29 74.5 42.5 39.2 24 70.9 30-34 75.2 40.6 36.9 23.9 76.2 Source: KDHS, 2009 Television 28.6 42 39.5 43.5 Newspaper/ None of the magazine 3 media source s 28.4 36.3 44.9 18.9 45.9 23.1 42.6 16.1 According to KDHS, 2009, information on the level of public exposure to a particular type of media allows policy makers to assess the most effective media for various target groups in the population. On average, 3 in 10 young women and 2 in 10 young men have not been exposed to family planning messages through the media. From the trends above, radio messaging reaches more young people(70%) than television and newspapers or magazines. However, television and Youth Fact Book Infinite Possibility Or Definite Disaster? 165 newspapers or magazines also reach a substantial number of 20-34 year olds. Other sources of information should be considered when targeting 15-19 year olds. Generally, there is a sharp contrast between urban and rural areas in exposure to family planning messages. Access is more in urban areas, increases with the level of education and with wealth quintile. 8.1.6 Acceptability of Condom Messaging Table 95: Acceptability of Condom Messaging Acceptability of condom messaging Women Age Radio Television Newspaper/magazine Bill Boards 15-19 67.7 63 63.9 61.3 20-24 82.2 78 79.1 77.5 25-29 78.5 71.6 74.6 72 30-34 80 74.7 77.8 75 Source: KDHS, 2009 None of the 3 media sources 30.6 16.6 19.6 18 70% of 15-34 year old women accept messaging from different electronic media as true and as acceptable means of messaging. According to KDHS, 2009, urban women are more likely to view dissemination of condom messaging in the media as acceptable. Women in Northern Kenyan and those with no education are less likely to consider as acceptable the use of print and electronic media to disseminate condom messages. 166 Youth Fact Book Infinite Possibility Or Definite Disaster? 8.1.7 Source of Information on Sexual& Reproductive Health Table 96: Source of Information on Sexual& Reproductive Health among 7-19 year Olds Source of Information on Sexual& reproductive Health Media(TV&/or Radio) Religious institutions/ leaders Peers/Friends Health institutions Print Media(Newspaper, leaflets) School Government Parent None 7- 10 years 16 17 4 2 1 11 1 48 Media(TV&/or Radio) 10 Health institutions 15 School 8 Religious institutions/ leaders 11 Peers/Friends 10 Print Media(Newspaper, leaflets) 4 Parent 33 Government 6 None 1 Source: Consumer Insight, 2009 11-14 years 19 17 4 6 1 11 46 Most Trusted Source 14 5 15 11 4 2 9 50 15-17 years 20 16 6 9 6 7 1 34 20 14 12 9 4 4 1 49 18-19 years 37 13 13 13 9 3 2 1 8 30 22 8 9 10 9 1 1 9 The most prominent sources of information on sexual& reproductive health are media(24%), religious institutions and leaders(16%), followed by peers and friends(8%) and health institutions (8%) though it varies among different age groups. Most young people however,(an average of 33%) have no source of sexual and reproductive health information. Interestingly, 7-10 year olds trust their parents as a source of sexual& reproductive health information but unfortunately parents are not giving the relevant information to this age group. The most trusted source of information for 11-14 year olds is school and media while for 15 to 17 year olds is media and health institutions. For 18 to 19 year olds the most trusted source is media, health institutions and peers/friends. Youth Fact Book Infinite Possibility Or Definite Disaster? 167 8.18 Frequency of use between Old and New Forms of Media 5+ times a day 62 18 14 Radio Internet TV Figure 120: Frequency of use between Old and New Forms of Media Source: TNS Research International and Kenya ICT Board, 2009 When old and new forms of media are compared, most young people prefer to use the new forms such as the internet(62%). 8.2 Exposure to‘New’ Mass Media Media Priorities 63 1st Priority 2nd Priority 40 23 20 PC Connected to the Internet A Mobile Phone 18 5 Satellite TV 7 4 22 13 A Range of Books An MP3 full of favourite music DVD movies with home cinema screen 3 A Digital Camera Figure 121: Media Priorities Source: TNS Research International and Kenya ICT Board, 2009 Internet connection was prioritized highest among the new mass media while mobile phone use ranked second. However, it is important to note that 55% of those profiled were 25-34 years old 168 Youth Fact Book Infinite Possibility Or Definite Disaster? and 27% were 35-44 years old. The trend in the graph may turn out differently if younger users were profiled. 8.2.1 Computer and Internet Users in Kenya In Kenya the number of Internet users per 100 people has risen over the years while that of fixed broadband subscribers has remained constant. A report by David j. McKenzie on Youth, ICTS and Development reveals that the new millennium saw extremely rapid increases in internet, mobile phone, and computer use in developing countries. Between 2000 and 2003, the developing world gained more than one-quarter of a billion internet users and almost half a billion mobile phones. Kenya Internet Users Vs Fixed Broadband 7.6 8 0.3 2000 0.6 2001 1.2 2002 3 2003 3 2004 3.1 2005 2006 2007 Internet users(per 100 people) Fixed broadband subscribers(per 100 people) Figure 122: Internet Users in Kenya Source: International Telecommunication Union(ITU) According to Consumer Insight(2009), computer use among 7-19 year olds increased from 33% in 2005 to 38% in 2007 and to 41% in 2009. Computer use increased with age. Among 7-10 year olds only 23% had used a computer, 36% among 11-14 year olds, 47% among 15-17 year olds and 57% among 18-19 year olds. 8.2.2 Main Motivating Factors of using Computers and the Internet in Kenya According to Consumer Insight, 7-10 year olds used a computer mainly to play computer games (85%), 11-14 year olds mainly to play computer games(77%), 15-17 year olds to play games (42%) and to word process(38%) while 18-19 year olds use computers mainly to browse the Internet(51%). According to TNS Research International and Kenya ICT Board, among 25 to 44 year olds, the most important need served by the internet is accessing reliable information and knowledge(57%) followed by communicating with others(39%) through E-mail, social networking, chatting, VOIP etc. Entertainment/media, leisure and commerce such as buying products and services(2%) as well as on-line banking are still underdeveloped in Kenya and are opportunity areas for growth. Youth Fact Book Infinite Possibility Or Definite Disaster? 169 Main Motivating Factors of Internet Use among 25-44 year olds 82 63 51 Business/work/job search Academic/education related Leisure related activities Figure 123: Main motivating factors of internet use among 25-44 year olds Source: TNS Research International and Kenya ICT Board, 2009 8.2.3 Top Ten Activities Done Online in Kenya vs. Top Ten Activities Done Online Globally Table 97: Top Ten Activities Done Online in Kenya vs. Top Ten Activities Done Online Globally Table: Top Ten Activities Done Online in Kenya vs. Top Ten Activities Done Online Globally Activity in Kenya Prevalence Activity in the globe Send or receive E-mail 100 Use a search engine Use a search engine like Google 95 Look up news Look up news 93 On-line Banking Visit a specific website to get information 90 Look up the weather Read newspapers 90 Research a product or service before buying it Chat/messenger 89 Visiting a brand or product website Read something in Wikipedia 87 Pay bills Visit a social networking site 85 Watch a video clip Search for information on disease 85 Use a price comparison site Look for a job 81 Listen to an audio clip Source: TNS Research International and Kenya ICT Board, 2009 Prevalence 81 76 74 65 63 61 56 51 50 44 According to the TNS Research International and Kenya ICT Board, the internet in Kenya was mostly used for knowledge seeking and socializing. 100% of the internet users in Kenya send or receive E-mail. 95% use the search engine, 93% look up the news, 90% visited a specific website to get information and another 90% used the internet to read newspapers. 89% participated in messenger chats, 87% read something on Wikipedia, 85% visited a social networking site, 85% search for information on diseases and 81% looked for jobs. However globally, the internet is mostly used for transactions, marketing and media related activities. 170 Youth Fact Book Infinite Possibility Or Definite Disaster? 8.2.4 Use of the Internet for Social Activities According to the TNS Research International and Kenya ICT Board, social networking is the most widely used for social activities on the internet. 37% of the respondents say that social networks enable people to keep in contact with people they would not normally contact, 25% say it was a cheaper way to keep in touch with many people while 22% said it was more interactive than using personal mail. 50% of the respondents have more than 100 contacts on their favorite site. 25% access social network sites more than 5 times a day, 19% 2-4 times a day and 33% once a day. Key drivers in social networking include keeping in contact, cost effectiveness, and the level of interaction allowed. Dating and games are not as widely used as other forms of interaction. 8.2.5 Popularity of Social Networking Sites Popularity of Social Networking Sites 96 38 37 30 30 23 20 20 10 Facebook Hi5 Twitter Linkedin You Tube Tagged Yahoo 360 My Space Other Figure 124: Popularity of Social Networking Sites Source: TNS Research International and Kenya ICT Board, 2009 The most popular social network is face book accessed by 96% of the respondents followed by Hi5(38%), twitter(37%), linkedin(30%), You Tube(30%), Tagged(23%), Yahoo 360(20%), My Space(20%) and others(10%). Youth Fact Book Infinite Possibility Or Definite Disaster? 171 8.2.6 Facebook Use by Gender in Kenya 62% of face book users in Kenya are male and 38% are female. Male /Female User Ratio-Facebook Kenya 38% Male Female 62% Figure 125: Facebook Use by Gender in Kenya Source: http://www.facebakers.com/countries-with-facebook/KE/ 8.2.7 Face book Use in Kenya and the East African Region by Age Most face book users in Kenya(41%) are aged between 18-24 while 34% of face bookers are aged between 25 and 34. Facebook Use in Kenya by Age 41 34 % no. of people 5 2 9 3 5 1 13-15 16-17 18-24 25-34 35-44 45-54 55-64 65+ Age Figure 126: Facebook use in Kenya by Age Source: http://www.facebakers.com/countries-with-facebook/KE/ 172 Youth Fact Book Infinite Possibility Or Definite Disaster? Within the region, the same pattern shows in all the four countries profiled. Overall, 18-24 year olds form 40% while 25 to 34 year olds form 37% of face book users in the region. Table 98: Facebook Users in the Region Table: Facebook Users in the Region Kenya Rwanda 13-15 2 4 16-17 5 5 18-24 41 38 25-34 34 40 35-44 9 9 45-54 3 2 55-64 1 1 65+ 5 2 Source: http://www.facebakers.com/countries-with-facebook/ Tanzania 2 4 36 39 12 4 1 2 Uganda 3 5 44 36 8 2 1 2 Kenya has the highest number of facebook users in the region(888,940) which is 4.3 times higher than Uganda(204,740). Tanzania has 150,600 users and Rwanda has 53,780 users. 8.2.8 Uses of Social Networking and their Outcomes Uses of Social Networking and their Outcomes 56% contributed to a discussion 48% ment an old friend 12% met and dated someone 6% got jobs 40% clicked on an Advert 24% met someone for the first time online 56% joined brand fan groups 69% up-loaded photos/ videos Figure 127: uses of social networking and their outcomes Source: TNS Research International and Kenya ICT Board, 2009 Youth Fact Book Infinite Possibility Or Definite Disaster? 173 Some of the uses of social networking and their outcomes include up-loading photos and videos (69%), joining brand fan groups(56%), contributing to a decision(56%), meeting old friends (48%), checking out adverts(40%), meeting someone for the first time online(24%), meeting and dating someone(12%) as well as getting jobs(6%). 8.2.9 Use of the Internet for Marketing and Business Transactions According to TNS Research International and Kenya ICT Board, few people in Kenya have bought goods and services online(20%) compared to other countries(94%) mainly due to inadequate local delivery services and secure online payment. Interestingly, 88% would like to buy on-line and pay using mobile money transfers Internet use for Marketing Purposes Kenya Australia, China, India, Malaysia, Singapore 98 94 78 81 70 29 Research on-line about product/service Start with search engine Figure 128: Internet Us for Marketing Purposes Source: TNS Research International and Kenya ICT Board, 2009 Ever bought on-line 174 Youth Fact Book Infinite Possibility Or Definite Disaster? 8.2.10 Online Services Table 99: Online Services Payment of bills Online banking Online courses Purchase of computers Purchase of mobile gadgets Purchase of tickets for cinema/theatre/concerts Purchase for home appliances Paying for music/movie downloads Purchase of books Booking of hotels/restaurants Automotive purchases(cars, motorcycles) purchase of airline tickets Software downloads Audio visual(TV, radio, Hi Fi) Cosmetics(skin care/hair products) Dating sites Source: TNS Research International and Kenya ICT Board, 2009 Ever used(%) 16 19 28 14 11 4 3 18 24 23 10 31 30 3 6 13 Would like to use(%) 51 39 33 31 31 31 32 28 28 26 22 23 23 21 16 4 According to TNS Research International and Kenya ICT Board, of the 39% who would like to do on-line banking, 27% are scared of fraud, 20% say that their bank does not offer it, 15% say they have never considered it, 14% say it is not a relevant service to them, and 7% like to deal with a real person, 7% say that banks have many restrictions, 5% do not know how to set it up and 3% cannot be bothered by it. Youth Fact Book Infinite Possibility Or Definite Disaster? 175 8.2.11 Use of the Internet for Knowledge Table 100: Use of the Internet for Knowledge Educational material On-line courses Information on training institutions Diagnosis of diseases General background or information Tourism Information on drugs Checking up a health practitioners diagnosis Price of health care products HIV/AIDS Reproductive health information(Pregnancy) Agricultural information Weather Source: TNS Research International and Kenya ICT Board, 2009 Ever used 76 55 63 54 52 45 34 33 14 40 40 20 26 Would like to use 61 54 51 47 45 41 40 38 38 38 36 27 24 Most information sought after is on education and health. Incidentally 60% of the respondents do not trust the information on the internet and 39% of individuals indicated they do not find information they are looking for. 8.2.12 Facilitators of Internet Access in Kenya According to Consumer Insight, the highest facilitator of internet access among 7-19 year olds is school/college(45%) followed by cyber cafés(33%) and home(29%). The workplace for this age group facilitates only 3%. Among 25-44 year olds, access is mostly facilitated by the office/ workplace(56%) followed by cyber cafés(12%) and mobile phones(10%). The main back-up source is the mobile phone(58%), cyber cafés(36%) and home computers(33%). According to TNS Research International and Kenya ICT Board, 50% of people prefer to use their phone to browse but their small screen and low content are main barriers to their use. While 17% access the internet two to five times a day, 62% access the internet more than five times a day. 176 Youth Fact Book Infinite Possibility Or Definite Disaster? Facilitators of Internet Access and Back-Up Sources in Kenya 58 56 Main Facilitator Back Up 36 12 Cyber café 33 9 Home from a Computer 18 Office/ Workplace 26 10 My Mobile Phone 12 12 0 Wi-Fi hot spot Laptop with Modem Figure 129: Facilitators of Internet Access and Back-up Sources in Kenya Source: TNS Research International and Kenya ICT Board, 2009 8.2.13 Barriers of Internet Access in Kenya The main barriers of internet access in Kenya are: cost, speed, time to access and lack of personal connections. Main Barriers of Internet Access in Kenya 42 40 29 19 Expensive Slow Speed Not Enough Time Figure 130: Barriers of Internet Access in Kenya Source: TNS Research International and Kenya ICT Board, 2009 Don’t have own internet connection Youth Fact Book Infinite Possibility Or Definite Disaster? 177 8.2.14 Suggestions for Kenyan Government on How to Enhance Internet Access Most people wish for internet access in rural areas(32%), digitization of more government services(27%) and regulation of costs(21%). Enhancing Internet Access by GoK 32 27 21 15 3 Increaes internet connection in rural areas Digitize more of the government services Regulate Costs Provide education on how to use the internet and its benefits Policy making on internet use 2 Other Figure 131: Enhancing Internet Access by GoK Source: TNS Research International and Kenya ICT Board, 2009 8.2.15 How much not having Internet Affects Daily Routine and Personal Activities How much not having internet affects your daily routine and personal activities?(10= very much, 1= not at all) 9 8 7 6 5 4 3 2 1 0 Global Kenya China Japan Korea France Italy Germany UK Netherlands Canada USA Spain Australia Denmark Sweeden Finland Figure 132: How much not having internet affects daily routine and personal activities Source: TNS Research International and Kenya ICT Board, 2009 178 Youth Fact Book Infinite Possibility Or Definite Disaster? Interestingly, Kenyans would be most affected in their daily routine and personal activity if they did not have internet. 8.2.16 Ranking Kenya’s Internet Use in Africa Top 10 Internet Countries June 2010 Seychelles Tunisia Morocco Cape Verde Nigeria Mauritius Egypt Sao Tome& Principe Algeria Zimbabwe 5 15.2 13.6 12.2 22.4 21.2 10 15 20 25 Percentage of Users Figure 133: Top 10 internet countries in Africa in June 2010 Source: Internet World Stats 38.4 34 33 29.5 28.9 30 35 40 The Seychelles has the highest number of internet users in proportion to their population(38.4%) in Africa followed by Tunisia(34.7%) and Morocco(33%). Kenya and Sudan ranks twelfth with 10%. 8.2.17 Mobile Phone Usage and Ownership Among 7 – 19 year olds, mobile phone ownership has been increasing from 11% in 2005 to 18% in 2007 and to 27% in 2009. The ownership pattern is mainly due to parental restriction, high handset cost and school restrictions. According to Consumer Insight, 77% of this age group buys their own air time. Overall, 9% spend under Kshs. 100, 23% spend between Kshs. 101 and Kshs. 200, 18% spend between Kshs. 201 and 300, 8% spend between Kshs. 301 and 400 while 10% spend between Kshs. 401 and 500. 31% spend Kshs. 500 and above. The trends also show that the older one is, the more they are able to spend on airtime. Use of mobile phones among 7-19 year olds varies. 83% use mobile phones to make personal calls, 66% to receive personal calls, 55% to send text messages, 39% to play games, 26% to listen to radio, 13% to send or receive money and 13% to browse on the internet. The trends however varied between 2005 and 2009. According to Consumer Insight, making and receiving personal calls among 7-19 year olds somewhat remained constant over that period while texting, games and listening to radio have been declining. Sending/receiving money and browsing/internet have been on the increase. YOUTH FACT BOOK INFINITE POSSIBILITY OR DEFINITE DISASTER? 179 180 Youth Fact Book Infinite Possibility Or Definite Disaster? 9 Crime ‘Crime and violence are fundamental threats to human security’ (UN Habitat, 2007) 9.0 Crime According to Mugo(unpublished), crime is generally understood as any act or omission that is against public law. In Kenya, the penal code spells out the different behavior that should be understood as crime. Under this code, homicide is defined as the severest crime, and which encompasses all capital offenses including murder, manslaughter and other offenses that cause death. The other category spelt out is offenses against morality, which touch on the wrongfulness principle. These include defilement, incest, sodomy and other offenses. The third category includes(other) offenses against persons, which include assault and creating disturbance. Smaller categories include robbery, theft and economic crimes(including corruption). Data available from the prisons department reveals that crime is strongly associated with age as 53% of crime is predominantly committed by persons aged between 16 and 25 years. A crime survey also conducted in Nairobi by the UN Habitat and the City Council in 2002 found that youth delinquency and crime is a major problem(UN Habitat, 2002). 9.1 Historical Trends of Crime in Kenya 9.1.1 Crime between 1931 and 1937 Table 101: Crime between 1931 and 1937 Crime/Offence 1931 1932 1933 1934 1935 1936 1937 Against person 23 23 18 21 22 23 30 Malicious injury 11 9 4 5 2 9 6 Against property(Including stock and produce) 302 324 254 250 204 195 254 Highway, revenue and social economy 177 367 167 211 202 201 350 Employment ordinance 51 18 5 3 6 6 29 Township/municipal rules 228 217 246 383 172 89 I62 Native registration ordinance 10 08 Resident native 1 3 4 Other offences 11 13 12 24 14 10 4 Total 814 982 710 897 622 524 735 Source: Anderson, 1991 Through this period, crime was highly urbanized as 58% of all crimes committed by youth during these years revolved around highway, revenue and social economy, as well as non-adherence to township and municipal rules. Second to this was damage of property, including stock and produce, which accounted for more than 34% of all offences. 9.1.2 Establishment of Approved Schools According to Mugo(unpublished), with the heightening struggle for independence during the post second world war era, the number of incarcerated youth continued to rise. In 1946, the colonial government legislated the Children and Young Persons Act, which was seen as a way 182 Youth Fact Book Infinite Possibility Or Definite Disaster? of institutionalizing the containment of youth. Modes of punishment were stipulated, as well as legalization of the already-existing correctional institutions. Increasing surveillance and punishment was seen as the solution to the rising social crises. A series of youth correctional institutions(approved schools) were also opened up in the 1950s and into the early post independence era. Seven institutions were opened up within 7 years, as summarized below. Table 102: Juvenile Correctional Institutions Between 1957 and1964 Juvenile Institutions Institution Dagoretti Approved School Mweru Approved School Othaya Approved School Kalimoni Approved School Nakuru Children’s Remand Home Shimo la Tewa Kirigiti Source: Mugo et al, 2006 Year opened 1957 1958 1959 1959 1959 1964 1964 The rapid establishment of these institutions may be interpreted as reactions to the rising incidence of juvenile and youth crime, and indeed the agency of youth in the struggle against the power inequalities and social injustices of the colonial administration. 9.1.3 Crime in the 70’s The rapid population growth of the 1970s and 1980s, saw an increase in crimes committed by youth. Available records have indicated that youth mainly aged 21-25 years were the biggest age cohort convicted to prisons during this period. Youth convicted to prisons(1971-1976) 15607 13733 13762 7533 685 1971 684 524 1972 Under 1 6689 520 1973 16-18 Figure 134: Youth convicted to prisons(1971 – 1976) Source: Report on Prisons, 1976 16114 19045 7623 401 9599 569 1974 1975 18-20 21904 12003 928 1976 21-25 Youth Fact Book Infinite Possibility Or Definite Disaster? 183 9.1.4 Crime between 1997 and 2001 Table 103: Crime Between 1997 and 2001 Crime(1997 – 2001) Ty y p p e e o o f f cr c i r m im e e Murder Including Attempt Manslaughter Rape(Including Attempt) Assault Other Offenses Against Person Robbery and Allied Offences Breakings Theft of Stock General Stealing Theft of Motor Vehicle Theft of Motor Vehicle Parts Theft from Motor Vehicles Theft of Bicycles Theft by Servant Dangerous Drugs Handling Stolen Property Corruption Causing Death by Dangerous Driving Other Offences Against Property All Other Penal Code Offences Total Yea a r r 199 9 7 7 1,642 14 1,050 10,288 2,601 7,465 12,619 2,630 10,462 989 1,062 634 682 3,641 3,722 336 148 275 3,120 9,581 72,961 Source: Economic Survey, 2002 199 9 8 8 1,637 5 1,329 10,847 2,920 8,303 11,382 2,333 9,899 1,081 934 624 596 3,230 5,171 347 145 304 3,168 9,418 73,673 199 9 9 9 1,625 16 1,465 11,891 3,173 8,612 9,940 2,278 9,591 1,004 770 526 552 3,075 5,912 384 43 259 3,359 10,415 74,890 200 0 0 0 1,807 18 1,675 13,035 3,563 8,923 10,712 2,906 10,129 896 748 569 836 3,221 5,481 361 42 346 3,555 11,320 80,143 200 0 1 1 1,688 8 1,987 12,611 3,020 9,180 10,363 2,327 8,919 960 753 558 565 2,757 5,300 347 23 301 3,073 10,612 75,352 To o t t a a l l 8,399 61 7,506 58,672 15,277 42,483 55,016 12,474 49,000 4,930 4,267 2,911 3,231 15,924 25,586 1,775 401 1,485 16,275 51,346 Assault(16%), breaking(15%), general stealing(13%), robbery and allied offences(11%) and dangerous drugs(7%) were the most recurrent crimes between 1997 and 2001. Crime had been steadily increasing between 1997 and was highest in 2000 before declining in 2001 by 6%. Among the least reported cases were those involving corruption and manslaughter. 184 Youth Fact Book Infinite Possibility Or Definite Disaster? 9.1.5 Convicted Prison Population by Age and Sex(2001- 2009) Table 104: Convicted Prison Population by Age and Sex(2001- 2009) Convicted Population Year Gender Under 16 16-17 18-20 21-25 2001 Male 9 3,057 11,751 17,786 Female 2 448 1,537 1,986 Total 11 3,505 13,288 19,772 2002 Male 2 2,476 14,258 21320 Female 0 521 1,722 2,184 Total 2 2,997 15,980 23,504 2003 Male 1 5,465 17,465 26,382 Female 0 644 3,071 2,776 Total 1 6,109 20,536 29,158 2004 Male 166 3,706 19,134 27,921 Female 0 351 2,874 3,780 Total 166 4,057 22,008 31,701 2005 Male 2 3,293 16,685 30,440 Female 0 548 2,198 4,333 Total 2 3,841 18,883 34,773 2006 Male 1,077 4,455 20,710 27,838 Female 12 367 2,797 3,894 Total 1,089 4,822 23,507 31,732 2007 Male 135 2,787 16,301 24,244 Female 0 260 2,071 3,047 Total 135 3,047 18,372 27,291 2008 Male 154 1,959 16,225 20,471 Female 0 263 2,690 2,472 Total 154 2,222 18,915 22,943 2009 Male 24 2,890 21,770 30,822 Female 25 207 2,453 4,247 Total 49 3,097 24,223 35,069 Source; Economic Survey 2006& 2010 26-50 24,071 3,279 27,350 27,187 3,455 30,642 28,629 3,304 31,933 19,846 3,290 23,136 33,339 4,298 37,637 37,005 5,666 42,671 29,830 3,869 33,699 29,339 3,257 32,596 32,970 3,856 36,826 50+ 5,178 326 5,504 5,752 565 6,317 6,150 333 6,483 8,559 562 9,121 5,936 624 6,560 6,700 613 7,313 6,791 435 7,226 11,301 283 11,584 8,286 482 8,768 Total 61,852 7,378 69,230 70,995 8,447 79,442 84,092 10,128 94,220 79,332 10,857 90,189 89,695 12,001 101,696 97,785 13,349 111,134 80,088 9,682 89,770 79,449 8,965 88,414 96,762 11,270 108,032 56% of crime in Kenya between 2001 and 2009 was committed by young people aged 16 to 25 years old. Crime has generally been on the increase but it was highest in 2006 as indicated in the trend, on figure 135. Youth Fact Book Infinite Possibility Or Definite Disaster? 185 Total Crime Trends Between 2001 and 2009 69230 79442 94,220 90,189 101,696 111,134 89,770 88,414 108,032 2001 2002 2003 2004 2005 2006 2007 2008 2009 Figure 135: Total Crime Trends Between 2001 and 2009 Source: Economic Survey 2006& 2010 9.1.6 Gender and Crime Types(2007-2008) Of all the convicted prisoners between 2001 and 2009, 89% were male and 11% were female. Crime by Gender Between 2001 and 2009 61,852 70,995 84,092 79,332 89,695 97,785 80,088 79,449 96,762 7,378 8,447 10,128 10,857 12,001 13,349 9,682 8,965 11,270 Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female 2001 2002 2003 2004 2005 Figure 136: Crime by Gender Between 2001& 2009 Source: Economic Survey 2006& 2010 2006 2007 2008 2009 186 Youth Fact Book Infinite Possibility Or Definite Disaster? Table 105: Gender Specific Crimes Gender Specific Crimes 2007 2008 Crime Category Male Female Male Female Total Total% Male Female Male Homicide 1,681 338 1,937 350 3,618 688 84 Against morality 19,114 4,299 16,246 3,996 35,360 8,295 81 Robbery and Theft 5,229 30 3,630 54 8,859 84 99 Dangerous drugs and criminal damage 2,585 5,166 351 3,833 2,936 8,999 25 Economic crimes and corruption 50 3 37 21 87 24 78 Assault 10,454 2,862 9,414 2,518 19,868 5,380 79 Infanticide and procuring abortion 5 21 2 17 7 38 16 Concealing birth 3 38 24 68 27 106 20 Source: KNBS 2009 % Female 16 19 1 75 22 21 84 80 From the analysis, it emerges that there are‘female crimes’ and‘male crimes’. Women committed basically three types of crimes: infanticide and procuring abortion(84%), concealing birth(80%) and dangerous drugs and criminal damage(75%). On the other hand, men dominated five crimes: robbery and theft(99%), homicide(84%), offenses against morality(81%), assault(79%) and economic crimes and corruption(78%). The distinction between the female and male crime is very clear, with high levels of predictability 9.1.7 Juvenile Offenders The situation of crime committed by juvenile offenders is reported to have worsened in the early years of the millennium. A crime survey conducted by Assiango, Stavron, Ravestijn and Jackson (2001) focused on the family and socio-economic backgrounds of 65 young offenders aged between 14 and 25 years, their personal characteristics, experiences in crime, reasons and motivations for being involved in crime, opinions and hopes for the future. Majority of the participants said that their involvement in crime was influenced by family deficiencies, while others indicated money (67%), peer pressure(13%) and survival(13%) as causes. Most participants reported to have committed their first offence between the ages of 12 and 15 years of age(30%) or between 16 and 19 years(23%). The study further established that poverty(40%) and alcohol/drugs(23%) were responsible for increased vulnerability of youth to re-commit crime. Youth Fact Book Infinite Possibility Or Definite Disaster? 187 Table 106: Number of Juvenile Offenders Serving Community Service by Gender and Type of Offence, 20042008 Juvenile Offenders Cases Reported to 2004 Probation Department Male Murder(Including attempt) 0 Manslaughter 0 Rape 8 Assault 4 other offences against the person 25 Robbery and Allied offences 24 Breakings 25 Theft of Stock 15 General stealing 238 Theft of Motor vehicle 0 Theft of M/Vehicle parts 0 Theft from M/vehicles 1 Theft of Bicycles 0 Theft by Servant 11 Dangerous Drugs 15 Handling Stolen Property 4 Corruption 0 Causing Death by Dangerous Driving 0 Other Offences Against Property 26 All other Penal Code offences 88 Total 484 Source Economic Survey 2009 2005 Female Male 0 0 0 0 0 0 1 9 15 29 2 1 3 43 2 9 20 69 0 0 0 2 0 1 0 0 8 8 4 193 0 8 0 1 0 0 5 34 93 317 153 724 2006 Female Male 0 0 0 0 0 0 5 27 26 1 0 2 2 33 0 3 30 67 0 0 0 0 0 0 0 1 9 19 2 49 2 3 0 0 0 0 3 101 110 362 189 668 2007 Female Male 0 0 0 0 0 0 9 6 3 26 0 0 5 5 0 1 13 52 0 0 0 2 0 0 0 0 0 4 16 38 0 5 0 0 0 0 16 15 51 216 113 370 2008 Female Male 0 1 0 16 0 24 1 36 4 73 0 6 0 46 0 14 10 445 2 0 0 0 0 3 0 5 0 8 7 79 0 36 0 1 0 0 2 56 45 598 71 1447 Female 0 0 0 10 25 0 0 0 75 0 0 0 0 0 7 0 0 0 3 82 202 Looking at the total number of juvenile crime committed between 2004 and 2008, it is evident that young men account for 83.5% of juvenile crime compared to young women who account for 16.5%. 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