Kenya National Gender Statistics Assessment 2018

KENYA NATIONAL GENDER STATISTICS ASSESSMENT 2018

Drafting: Bernadette Wanjala, Alfred Agwanda Technical Revision: Joshua Musyimi, Maureen Gitonga, Diana Lutta, Jessamyn Encarnacion, Robert Simiyu (UNICEF) Editorial and Proof reading: Mika Mansukhani, Jennifer Ross, Andy Quan

2 KENYA NATIONAL GENDER STATISTICS ASSESSMENT 2018

UN Gigiri Complex, Block M P. O. Box 30218 – 00100, Nairobi Telephone: +254 20 762 2792 Website: africa.unwomen.org twitter @unwomenkenya

Front Cover Photo: Portrait of woman. Kenya. Photo: © Curt Carnemark / World Bank Photo ID: KE095S06 World Bank

Design by: Vista Global Limited

4 TABLE OF CONTENTS

ABBREVIATIONS AND ACRONYMS...... ii ACKNOWLEDGEMENTS...... v EXECUTIVE SUMMARY...... vi MAP OF KENYA...... xi

SECTION 1: INTRODUCTION...... 1 1.1 Background...... 1 1.2 Purpose of the report...... 1 1.3 Methodology...... 2 1.4 Guiding principles for the assessment...... 2

SECTION 2: STATUS OF GENDER STATISTICS IN KENYA...... 4 2.1 Socioeconomic setting...... 4 2.2 Overview of gender statistics at the global level...... 6 2.3 Rationale for gender statistics in the Kenyan context...... 7 2.4 Current status of gender statistics in Kenya...... 10 2.4.1 Enabling environment...... 10 2.4.2 Data production...... 14 2.4.3 Data access and utilization...... 27

SECTION 3: CONCLUSIONS AND RECOMMENDATIONS...... 40 3.1 Summary of findings and conclusions...... 40 3.1.1 Enabling environment...... 40 3.1.2 Data production...... 40 3.1.3 Data use...... 40 3.2 Recommendations...... 41 3.2.1 Enabling environment...... 41 3.2.2 Data production and use...... 42

SECTION 4: KEY ENTRY POINTS FOR UN WOMEN...... 44 4.1 UN Women’s experience and comparative advantage...... 44 4.2 Entry points...... 45

ANNEXES...... 46 Annex 1: Status of data availability for SDG indicators...... 46 Annex 2: Minimum set of gender indicators – quantitative indicators...... 53 Annex 3: Template for indicator information eference Sheet...... 57 Annex 4: List of institutions visited and persons interviewed...... 58

ANNOTATED LIST OF KEY RESOURCES...... 59 REFERENCES...... 60

Box 1: Principles of the African Data Revolution ...... 3 Box 2: Creating interest for readers to use data – an example ...... 38 Figure 1: Estimated levels of poverty for selected East African countries...... 5 Figure 2: Contribution of education, health and living standards to the multidimensional poverty index, ... selected East African countries...... 5 Figure 3: Financial inclusion for men in Kenya...... 34 Figure 4: Financial inclusion for women in Kenya...... 34 Figure 5: Financial access for Kenyans, by age...... 35 Table 1: Form and mode of delivery of dissemination of data...... 29

i ABBREVIATIONS AND ACRONYMS

AIDS Acquired Immune Deficiency Syndrome AU African Union CBS Central Bureau of Statistics Convention on the Elimination of all Forms of Discrimination CEDAW against Women CIDP County Integrated Development Plan COG Council of Governors CRS Civil Registration Services CSO Civil Society Organization DHIS District Health Information System EAC East African Community EDGE Evidence and Data for Gender Equality EMIS Education Management Information Systems FPI Flagship Programme Initiative GBV Gender-Based Violence GDP Gross Domestic Product HIV Human Immunodeficiency Virus HMIS Health Management Information System ICATUS International Classification of Activities for Time Use Statistics International Conference on Population and Development ICPD Programme of Action IDS Institute of Development Studies ILO International Labour Organization IMIS Integrated Multisectoral Information System IMR Infant Mortality Rate IPUMS Integrated Public Use Microdata Series KAIS Kenya AIDS Indicator Survey KDHS Kenya Demographic and Health Survey KHSSP Kenya Health Sector Strategic Plan KIPPRA Kenya Institute for Public Policy Research and Analysis KNBS Kenya National Bureau of Statistics KPHC Kenya Population and Housing Census M&E Monitoring and Evaluation MMR Maternal Mortality Rate MDAs Ministries, Departments and Agencies MDGs Millennium Development Goals MICS Multiple Indicator Cluster Surveys MIS Malaria Indicator Survey

ii MSME Micro, Small and Medium Enterprises MTP Medium-Term Plan NACC National AIDS Control Council NASCOP National AIDS Control Programme NCDs Non-Communicable Diseases NCPD National Council for Population and Development NEPAD New Economic Partnership for Africa’s Development NIMES National Integrated Monitoring and Evaluation System NGEC National Gender and Equality Commission NGO Non-Governmental Organization NSDS National Strategy for the Development of Statistics NSS National Statistical System OECD Organisation for Economic Co-operation and Development PARIS 21 Partnership in Statistics for Development in the 21st Century PoA Programme of Action PMTCT Prevention of Mother-to-Child HIV Transmission SDGA State Department of Gender Affairs SDGs Sustainable Development Goals SGBV Sexual and Gender-Based Violence SIGI Social Institutions and Gender Index SRH Sexual and Reproductive Health STEPS Survey on non-communicable diseases UN United Nations UNAIDS Joint United Nations Programme on HIV/AIDS UNCT United Nations Country Team UNDAF United Nations Development Assistance Framework UNDP United Nations Development Programme UNECA United Nations Economic Commission for Africa UNFPA United Nations Population Fund UNFPOS United Nations Fundamental Principles of Official Statistics UNICEF United Nations Children’s Fund UNSD United Nations Statistical Division UNSTAT United Nations Statistics UN United Nations Entity on Gender Equality and the Empowerment Women of Women USAID United States Agency for International Development WHO World Health Organization Women Count Making Every Woman and Girl Count

iii KENYA NATIONAL GENDER STATISTICS ASSESSMENT 2018

iv ACKNOWLEDGEMENTS

UN Women Kenya would like to thank the Kenya National Bureau of Statistics, The State Department for Gender Affairs, Council of Governors, Kenya Institute for Public Policy Research and Analysis, Monitoring and Evaluation Department, National Council for Population and Development, National Gender and Equality Commission, Institute of Development Studies, Institute of Gender and African Studies - University of Nairobi, Institute of Economic Affairs, Population Studies and Research Institute - University of Nairobi, SDGs Forum Kenya, USAID, DFID, International Union for Conservation of Nature, Embassy of Sweden, UNFPA, UNICEF, UN Environment Programme and UNDP for their time and contribution towards the development of this report.

v EXECUTIVE SUMMARY

The purpose of this national gender statistics assessment was to carry out a national needs assessment in Kenya to develop a workplan that can help to address three broad needs: (1) an enabling environment, (2) data production, and (3) data accessibility. This assessment is expected to form the foundation for the implementation of UN Women’s gender statistics programme Making Every Woman and Girl Count (Women Count) in Kenya and to provide the inputs necessary to develop a detailed workplan for project implementation. The specific objectives of the assessment were to: (1) conduct a detailed review of gender statistics in the National Statistical System (NSS), including documenting the extent to which gender equality is mainstreamed into the NSS; (2) identify gender data gaps to monitor the gender-related Sustainable Development Goals (SDG) indicators and other national gender-related priorities; and (3) assess the extent to which the data produced are made available and used to inform policies and the implementation of programmes.

The assignment was conducted Action and the Third Medium-Term using document reviews and through Plan (MTP III); and (3) ensure gender informant interviews with key statistics are accessible to all users stakeholders. To assess some of the (including governments, civil society, gaps, some aspects of meta-analysis academia and the private sector) and were conducted based on published can be analysed to inform research, statistical reports. The assessment was advocacy, policies and programmes, guided by core principles for nurturing and to promote accountability. the data revolution in Africa, anchored on three strategic axes: (1) building the Key findings related to the state enabling environment for the optimal of gender statistics indicate that: functioning of the statistical system; (1) current policy and legislative (2) producing statistics to meet user arrangements are not in tandem with needs, which are many and varied and; constitutional requirements with regard (3) ensuring data accessibility and to the use of data and information; use. The assessment was also guided (2) current legislative arrangements by the three key outcome areas of do not meet the key principles of the the Women Count programme, which African data revolution, to which Kenya are to: (1) strengthen the policy and ascribes; (3) county governments do financial environment to enable gender- not have a policy or legal framework responsive national adaptation and to guide statistical activities; and (4) effective monitoring of the SDGs; (2) there is still no policy or legislative bill strengthen the production of gender on the implementation of monitoring statistics to enable the monitoring and evaluation activities. As a result, of national policies and reporting the systems necessary to generate and commitments under the SDGs, the use data at the national and subnational Convention on the Elimination of all levels are inadequate. At the Forms of Discrimination against Women subnational level, county governments (CEDAW), the Beijing Platform for lack necessary infrastructure to

vi coordinate, collect, collate and manage which there are a complete lack of data due to inadequate funding and data include: access to land and land political goodwill at that level. However, ownership, agriculture – especially there are constitutional requirements small-scale farming activities, the for these entities to generate data for environment, homelessness, and their own planning. migration – especially human trafficking and smuggling. In terms of data production, Kenya still relies on surveys to generate data – in Data access and utilization is anchored particular, those necessary to generate on principles of data dissemination SDG indicators. Most of the indicators and communication which necessitate selected for monitoring the SDGs are that data should be translated based on the Kenya Demographic into information that is simple, and Health Survey (KDHS), which understandable and relevant. Currently, implies that Kenya cannot meet the only the Kenya National Bureau of expectations of regular, periodic, Statistics (KNBS) has put in place up-to-date monitoring of key SDG effective systems for communication; indicators on gender-related issues however, this still needs improvement, given that the KDHS is conducted especially to support visualization and every four years. Some indicators are simplicity. dependent on administrative registers, but administrative data has challenges A number of recent surveys capture in terms of quality, standardization of relevant gender data, but the richness concepts, definitions and statistical of data has not yet been exploited methodologies to make it comparable sufficiently for gender analysis, resulting with survey-based or official statistics, in a deluge of unused data. Lack of so it lacks interoperability. In some training and awareness-raising are cases, gender statistics may be the main factors behind inadequate lacking in administrative data because demand and use of gender statistics, in some sectoral strategies did not take addition to misconceptions of gender- gender dimensions into account in related terms and misunderstanding the first place. In addition, most of of gender statistics. The low data the monitoring and evaluation (M&E) literacy and capacity to access, analyse systems lacked gender dimensions, and use data reflects an inability to even if gender mainstreaming was part effectively signal demand for existing of the core actions to be implemented data. There is also limited data sharing (e.g. the National Integrated Monitoring between the various national and and Evaluation System (NIMES)). subnational statistical agencies that produce data. The majority of data A critical feature of gender data dissemination still relies on the use of in Kenya is the lack of up-to-date traditional non-digital and centralized information on economic empowerment modes of distribution of printed and how women and men fare. This material, and therefore fall short of includes a lack of sex-disaggregated upholding the key principles of an data on informal sector work and open data system. Statistical methods informal sector enterprises – the and gender statistics are still lagging key domains where women are behind in some subject areas, such as overrepresented. Dimensions in environment and asset ownership, while

vii in other areas such as labour force, time effectively address challenges in the use and agriculture, there is a need for production and use of data. new data. Recommendations to address Recommendations for creating an challenges in the production of data enabling environment for gender statistics 1. Existing databases should be updated (especially the KDHS and 1. KNBS, with the support of relevant the population census) and data stakeholders, should: should be collected where it is completely missing (especially data • Update the national strategic on child labour and time-use data statistical master plan to meet – which is important in estimating the data needs of the present the contribution of women to the development agenda (e.g. Vision economy – population movement, 2030, MTP III, Second County and other areas). Integrated Development Plan (CIDP 2), the SDGs) 2. Efforts should be made to align the definition of SDG indicators with the • Fast-track the adoption and way they are captured in databases implementation of the revised such as the KDHS, Kenya Integrated statistical act Household Budget Surveys (KIHBS), and the population census – which • Support county governments are conducted after four, five and 10 to develop their own statistical years respectively. plans and establish a sound county statistical system. 3. Data should be disaggregated according to SDG indicator 2. KNBS, with relevant stakeholders, requirements (e.g. by sex, region should support sectors to develop (rural/urban), persons with disability, sector-specific strategic plans wealth quintiles, etc.). for statistics, given that gender statistics cut across several sectors 4. Data producers should compile and are generated from various metadata information for all existing sources – in particular, institutional data, to enhance access and use. and administrative data. Therefore, each sector needs to plan for data Recommendations to address the acquisition and use. challenges in data use

3. Sector and county statistical Mechanisms and processes for plans should be integrated into 1) communicating with data users the National Strategy for the should be strengthened and Development of Statistics (NSDS) enhanced, including improving data and focus on working towards data visualization and access to data. interoperability and comparability. 2) Data-sharing between the various 4. Oversight institutions should be national and subnational statistical engaged to legislate for budgets for agencies and international data production and dissemination, organizations should be monitoring and evaluation to

viii strengthened, while managing methods in gender analysis, and privacy concerns. There is a need for workshops to raise awareness for mechanisms on automatic data- and share experiences are still sharing between agencies, for enormous and must be emphasized. statistical purposes. Common technical platforms and data standards should be agreed 3) The National Gender and Equality upon, to ensure the rapid and Commission (NGEC) and the State comprehensive dissemination of Department of Gender Affairs data, indicators and other statistics. (SDGA) should collaborate with the Therefore, there is a need to develop National Council for Population and manuals or guidelines on the Development (NCPD) and KNBS to generation, collation and analysis start a repository on research and of gender statistics, including data qualitative data (e.g. databases, data visualization. portals, open access study reports, journal papers and blogs). 7) Strengthening data and statistical literacy should begin by data-mining 4) Consultations with data users should based on existing data sets that be enhanced, given that they benefit have not be exploited in-depth. both the data producer and user experience in NSS. This will also 8) There is a dearth of qualitative data go a long way towards improving on gender, which are necessary perceptions of transparency for an understanding of women’s and collaboration, which are the capabilities and participation is all foundations for building trust. spheres of life (economic, social and political). Thus, in addition to 5) New methodological guidelines collecting and updating relevant have been produced by international gender statistics, more research organizations, to improve the should be undertaken, especially availability, quality and international in areas where there has been little comparability of gender statistics. improvement. This would help in To exploit these opportunities designing effective measures for the and challenges, KNBS and other implementation of the SDGs. stakeholders should develop a simple manual or handbook, There are several entry points for UN including definitions of key Women under the following outcomes concepts, for use by staff and other of the Women Count programme: audiences. Such a handbook can be posted on open access platforms, Outcome 1: Strengthening the policy such as the websites of KNBS, and financial environment to enable SDGA, NCPD and even universities. gender-responsive national adaptation and effective monitoring of the SDGs. 6) A programme to strengthen data literacy should be developed, 1) Carrying out assessments of gaps beginning with professional in the policy and legal framework statisticians, data scientists and in select counties data managers. It is clear that the need for training, for manuals 2) Strengthening coordination on concepts, indicators and mechanisms between producers

ix and users of gender statistics at 4) Offering technical assistance the national and county level through the placement of a gender statistics advisor at the KNBS and 3) Advocating for gender statistics any other additional technical to be systematically included assistance, where required in sector strategic plans at the national and county level 5) Integrating gender in the planned national census to be carried 4) Supporting the review of the out in 2019, through activities planned legislation on statistics like integrating gender in the and M&E policy, and ensuring training toolkits, analysis tools, and that gender statistics have been undertaking gender analysis of the mainstreamed adequately in the census results provisions of these policies 6) Providing technical assistance to 5) Supporting the Treasury to KNBS to undertake a time-use develop a system to track and survey that will be phased in at the make public budget allocations for preliminary stages, at the county gender equality and for training level. This will also leverage the respective government officers at labour survey that KNBS began all levels. undertaking in July 2018, with the support of the World Bank, Outcome 2: Ensure that quality, which can provide information for comparable and regular gender what is missing in most economic statistics are available to address indicators national data gaps and meet policy and reporting commitments under the 7) Providing technical support to SDGs and Beijing Platform for Action. KNBS on estimating monetary and non-monetary poverty by age UN Women can provide support in the and sex and using the information following areas: generated to develop a Women’s 1) Training data producers at national Empowerment Index. The Index, and county levels on gender to be prepared in partnership with statistics, to mitigate against UNICEF, will improve information stereotyping and misconceptions provided in the Women and Men in about gender statistics Kenya booklet 2017. 2) Developing training materials, including standardization Outcome 3: Improve the use of gender of concepts, definitions and statistics by different players to inform guidelines on the production of advocacy, research, policies and gender statistics programmes. 3) Reviewing existing data-collection UN Women can provide support in the tools for the social sector and following areas: considering the extent to which Disseminating gender statistics at online data collection can be 1) the national and local levels (e.g. undertaken, to take into account building on the KNBS gender statistics Women and Men in Kenya booklet, which was supported by Statistics Sweden x 2) Convening high-level biannual stakeholders, such as civil society forums where gender-related organizations issues are discussed with policymakers and other national 4) Furthering in-depth analysis of stakeholders. Such activities are gender statistics by graduate envisaged to bridge the gap students in higher learning between policymakers and the institutions by offering annual producers of data research grants to graduate students writing dissertations or 3) Conducting short courses in theses on gender statistics. This partnership with higher learning can act as a form of mentorship institutions on gender-specific and development of young statistical analysis, in order to statisticians and researchers to generate policy briefs which be more conversant with gender can be shared with high- issues more broadly and in-depth. level policymakers and other

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Turkana

Marsabit

Wajir

West Pokot Samburu

Trans Nzoia Elgeyo- Isiolo Marakwet

Bungoma Baringo Uasin Gishu Busia Kakamega Laikipia Nandi Meru Siaya Vihiga Kisumu Nyandarua Tharaka-Nithi Kericho Nakuru Nyeri Garissa Kirinyaga Homa Bay Nyamira Embu Bomet Murang’a Kisii Migori Kiambu

Narok Nairobi Machakos Kitui Tana River

Kajiado Makueni Lamu

MAP OF KENYA Kilifi

Taita Taveta

Mombasa Kwale

xi SECTION 1 INTRODUCTION

1.1 Background on key aspects of gender equality and women’s empowerment. In Kenya, The 2030 Agenda for Sustainable Women Count aims to strengthen the Development (the 2030 Agenda) capacity of the National Statistical called for addressing inequalities System (NSS) to produce and use and promoting human rights in gender statistics in order to inform development efforts. Building on the and monitor the implementation of the lessons learned from previous efforts, country’s gender-related commitments such as the Millennium Development in the 2030 Agenda. Goals (MDGs), the 2030 Agenda constitutes a rallying call to eradicate 1.2 Purpose of the report poverty and ensure individual human rights, well-being, gender equality The purpose of this study was to carry and women’s empowerment, while out a national needs assessment on maintaining sustained and inclusive gender statistics in order to develop economic growth and protecting the a programme strategy that works environment for current and future across three broad areas: (1) enabling generations. Core rallying principles environment, (2) data production, and in the agenda are the concepts (3) data accessibility. The national of “leaving no one behind” and assessment on gender statistics is “reaching the furthest behind first”. expected to form the foundation for In order to achieve the SDGs, it has the implementation of Women Count been recognized that development in Kenya and to provide the inputs decisions must be based on evidence, necessary to develop a detailed because of an emphasis on governance workplan for project implementation. and accountability. Furthermore, accountability represents a shift from The specific objectives of the a needs-based to a rights-based assessment were to: conduct a detailed approach in development planning and review of gender statistics in the NSS, implementation. including documenting the extent to which gender equality is mainstreamed In recognition of these goals and into the NSS; identify gender data gaps rallying principles, UN Women’s that need to be filled to monitor the gender data programme, Making gender-related SDG indicators and Every Woman and Girl Count (Women other national gender-related priorities; Count) is intended to support and assess the extent to which the Member States in implementing the data produced are made available and 2030 Agenda. In order to do so, it used to inform policies and implement plans to create a radical shift in the programmes. production, availability, accessibility and use of quality data and statistics

1 1.3 Methodology data accessibility and use. These strategic axes are firmly reflected in This assessment was conducted the consensus on core principles for over a period of two weeks, from 16 nurturing the African Data Revolution February to 2 March 2018. Face-to- (see Box 1) and are therefore the lenses face key informant interviews using used for this assessment. a structured questionnaire were conducted with representatives from 24 The principles of the African Data national, subnational and international Revolution came out of the Africa Data organizations. They ranged from Consensus, developed at the High- governmental bodies to research Level Conference on Data Revolution in institutes, development corporations Africa, held in Addis Ababa, Ethiopia, and UN agencies. Three further from 27 to 29 March 2015, which ministries were consulted at a later defined the concept of this revolution stage. as: Information from the interviews was “A profound shift in the way that used to prepare Section 2 of this data is harnessed to impact on assessment. A validation workshop development decision-making, was held with all the organizations with a particular emphasis on interviewed, to discuss the initial building a culture of usage. The findings from the assessment. Their process of embracing a wide views were taken into consideration in range of data communities and finalizing the assessment. Although the diverse range of data sources, State Department of Gender Affairs, tools, and innovative technologies, Ministry of Agriculture and Ministry to provide disaggregated data for of Health were not interviewed, they decision-making, service delivery participated in the validation workshop and citizen engagement; and and provided inputs. For a list of information for Africa to own its organizations interviewed see Annex 4. narrative.”2

1.4 Guiding principles for the assessment

National data ecosystems in many African countries, including Kenya, have undergone significant transformations over the past decade, experiencing changes in conceptualization, the legislative and policy environment, technology, infrastructure and governance.1 At present, data ecosystems are anchored on three strategic axes: (1) building the enabling environment for the functioning of the statistical system; (2) producing statistics to meet user needs, which are many and varied; and (3) ensuring

1. https://www.uneca.org/sites/default/files/uploaded- documents/ACS/africa-data-revolution-report-2016.pdf 2. Ibid.

2 BOX 1 Principles of the African Data Revolution

• Data must be disaggregated to the lowest levels of administration by sex, age, income, disability and other categories. • People must be counted to make them count. Civil registration should be accessible and provided at no cost. • Official data belong to the people and should be open to all. • The data community should embrace the United Nations Fundamental Principles of Official Statistics as a starting point. • There is a need for governance and coordination of the data ecosystem. • African governments should acknowledge open data provided by credentialed data communities as acceptable sources of country statistical information. • Technology, new forms of data and other innovations should be actively embraced. • Data communities should promote a demand-driven data user culture spanning the entire ecosystem. • Privacy and intellectual property rights should be respected. • Data should be translated into information that is simple, understandable and relevant. • Information must be timely, accurate, relevant and accessible. • Data must be driven by needs rather than for their own sake. • The data revolution in all its facets should be gender-sensitive.

Source: United Nations Economic Commission for Africa (UNECA). 2015.

3 SECTION 2 STATUS OF GENDER STATISTICS IN KENYA

2.1 Socioeconomic setting A critical concern in the socioeconomic situation is persistent poverty. Nearly Kenya is located on the eastern part 36.1 per cent of Kenyans are considered of the African continent and occupies to live below the national poverty line. an area of approximately 582,646 Compared to neighbouring countries square kilometres. Following the Kenya in East Africa, Kenya has the highest Constitution 2010, the country is proportion of the population living divided into 47 counties with devolved below the defined national poverty governments. About 80 per cent of line (See Figure 1). Cognizant that the land is arid and semi-arid while poverty is multidimensional – that is, only 20 per cent of the land is arable encompassing several aspects, such (KNBS, 2010). The growth rate in Gross as access to health care education Domestic Product (GDP) was estimated and living standards – Figure 1 at 5.8 per cent and was expected to also shows that the percentage of reach 6.1 per cent in 2017 (Republic of Kenyans deprived according to the Kenya, 2017). The GDP3 in 2014 ranked multidimensional poverty index (MPI) Kenya as a middle-income country and is 38.9 per cent. The major difference Africa’s ninth-largest economy. The between Kenya and neighbouring GDP per capita rose from USD $833 countries is that its MPI is nearly the in 2012 to an estimated USD $1,663 same as the proportion of people below as of 20174 and remains the highest in the poverty line, whereas Rwanda, the East African Community countries. Uganda and the United Republic of Agriculture and tourism are the main Tanzania have higher MPIs compared drivers of the country’s economy, with their poverty headcounts – the contributing to 30 per cent and 11.6 per differences ranging from 17 to 37 cent of the GDP respectively. In Kenya, percentage points. the agricultural sector contributes 70 per cent of total employment in the economy and nearly 69 per cent of all households engage in farming activities. Data from the sector shows that women handle 80 per cent of food production yet they benefit from only 7 per cent of the agricultural extension services.

3. The economy was rebased in September 2014. 4. Economic Survey 2018-Current Prices.

4 Figure 1 Estimated levels of poverty for selected East African countries

19.5 Uganda 69.9

28.2 Tanzania 65.6

44.9 Uganda 69.0

45.9 Kenya 47.8

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

National Poverty Line MP

Source: UNDP. 2018

Figure 2 Contribution of education, health and living standards to the multidimensional poverty index, selected East African countries

60.0 56.4 54.9 51.9 50.0 49.0

40.0 32.4 30.2 30.0 27.2 28.2 23.8

Percentage 18.0 20.0 16.9 11.2 10.0

0.0 Kenya 2008/09 Rwanda 2010 Tanzania 2010 Uganda 2010

Education ealth Living Standards

Source: UNDP Human Development Report. 2018.

Figure 2 shows the contribution of In terms of human development, Kenya various sources to multidimensional is ranked 142nd out of 189 countries, with poverty. The data indicate that an index of 0.590 in 2017, compared to poor living standards are the major Norway (the best) at 0.953 and Niger contributor to poverty in Kenya, as in (the lowest) at 0.354.5 The annual other East African countries. percentage change in HDI between

5. UNDP. 2018. Human Development Report 2018.

5 2010 and 2017 was 1.18, which is slightly of gender equality is both a human higher than that for sub-Saharan right and a prerequisite for achieving Africa (1.09).6 The national country economic, environmental and social average also masks disparities within development. According to the 2030 the country, between urban and rural Agenda, women and girls need equal areas, income groups and geographical access to quality education, economic regions. The value of the GINI index for resources and political participation Kenya is 0.49, representing the highest as well as equal opportunities to men income inequality in the East African and boys to employment, leadership Community, though it has considerably and decision-making at all levels. SDG improved since 1992, when it was 0.63.7 5 aims to “Achieve gender equality and empower all women and girls”. There Historically, cultural and institutional are also cross-cutting gender issues structures have created gender across the SDGs framework, which relationships that have led to the confirms that gender equality is central subordination of women in various to achieving the Agenda. Prior to the social spheres, leading to gender SDGs, the UN agreed on a minimum inequalities. Some of the mechanisms set of 52 quantitative gender indicators that tend to perpetuate poverty are that all countries should produce, connected to gender inequality. Women some of which are part of the SDGs. in Kenya represent half of the country’s The minimum set was meant to serve population (51 per cent)8, but lack equal as a guide for national production and access to health, education, earning international compilation of gender power and political representation. statistics across countries.11 Kenya is ranked 76th out of 144 countries on the Global Gender Gap There are other international Index, with a score of 0.694 – ranking agreements on gender equality and lowest in educational attainment (120th) empowerment that also have great and political empowerment (93rd out significance for gender statistics, of 115).9 However, Kenya is among including: The United Nations the countries in sub-Saharan Africa Convention on the Elimination of that have fully closed their health and all Forms of Discrimination against survival gender gaps.10 Women (CEDAW) of 1979 and the Beijing Declaration and Platform for 2.2 Overview of gender Action of the Fourth World Conference statistics at the global level on Women. At the regional level, the New Economic Partnership for Africa’s There are a number of global and Development (NEPAD) and its gender regional agendas that inform the component, the African Union (AU)’s pursuit of gender equality and women’s Solemn Declaration on Gender Equality empowerment in Africa. At the global of 2004 and its Agenda 2063 (a level, the Sustainable Development roadmap for the AU) have also provided Goals (SDGs) underpinning the 2030 mandates for fostering greater gender Agenda recognize that the realization equality.

6. Ibid. 7. Ibid. 8. Government of Kenya. 2009. Kenya Population and Housing Census 2009. 9. World Economic Forum. 2017. The Global Gender Gap Report. 11. UN. “Minimum Set of Gender Indicators”. 10. Ibid. https://genderstats.un.org/#/home

6 Making Every Woman and Girl Count on gender statistics. It is expected that work in these countries will provide an In 2016, UN Women launched a important opportunity to learn about five-year global programme called effective approaches in particular “Making Every Woman and Girl Count” contexts and to enable the scaling- (Women Count). The programme up of those approaches to additional aims to support SDG monitoring countries. As one of the selected and implementation through the pathfinder countries, Kenya is set to coordination, production and use benefit from the technical and financial of gender statistics with financial support provided to the Women Count support from global gender equality Kenya country project, which is part of champions (notably, the Bill & Melinda the global programme. Gates Foundation, the UK Department for International Development, the 2.3 Rationale for gender Department of Foreign Affairs and statistics in the Kenyan context Trade of Australia, the United States Agency for International Development Kenya Vision 2030 (USAID), the Government of Mexico Kenya Vision 2030 is the country’s and Irish Aid), Alwaleed Philanthropies, long-term development blueprint, Alibaba Foundation, Bill & Melinda launched in 2008 based on a collective Gates Foundation and Elizabeth Arden. aspiration for a better society by The Women Count programme provides the year 2030. Vision 2030 sought a framework and road map for all to mainstream gender equity in all relevant actors at the national, regional aspects of society. Gender equity and global level to work together was to be addressed by making to ensure that gender statistics are fundamental changes in four key areas: available, accessible, analysed and used 1) opportunity, 2) empowerment, 3) to inform policymaking, advocacy and capabilities and 4) vulnerabilities. The accountability for delivering gender vision acknowledged that women are equality and women’s empowerment. disadvantaged in accessing labour markets and productive resources. They UN Women, working closely with are also underrepresented in social and national statistical offices and political leadership. The capabilities of coordinating with other international women have also not been developed agencies and relevant actors, has to their fullest potential due to identified 12 pathfinder countries in limited access to capital, education, which to develop and support the full training and health care. The vision for implementation of the programme. gender, youth and the vulnerable is to These pathfinder countries were achieve equity in power and resource selected through a rigorous and distribution, improved livelihoods for independent process based on: their all vulnerable groups by increasing the commitment to women’s and girls’ participation of women in all economic, rights and to high statistical standards, social and political decision-making country-level demand (including processes, and improving the access of demonstrable need and institutional all disadvantaged groups to business commitment to improve gender opportunities, health and education statistics), and motivation to be part of services, housing and justice. Gender a global and inclusive learning process disparities are to be tackled through

7 a number of strategies, including: The State Department for Gender providing financial support for women Affairs’ strategic priorities to raise their incomes and reduce the gap in estimated earned income The SDGA was established in November between men and women; increasing 2015 within the Ministry of Public the number of women in parliament; Service, Youth and Gender Affairs and giving priority to female employees to promote gender mainstreaming in the public sector in order to attain in national development processes at least 30 per cent representation and to champion the socioeconomic in recruitment, promotion and empowerment of women. The appointment of women at all decision- functions of SDGA are: gender policy making levels. management, special programmes for women’s empowerment, Third Medium-Term Plan on the gender mainstreaming in Ministries, Implementation of Vision 2030 Departments and Agencies (MDAs), (2018–2020) community mobilization, domestication of international treaties/conventions on Inadequate sex-disaggregated data gender, and policy and programmes on were identified as a major challenge gender violence. to gender mainstreaming efforts in Kenya. It was noted that sex- The SDGA draft Strategic Plan 2018– disaggregated data are necessary to 2022 identifies inadequate gender assess the implications of policies and statistics as a constraint for effective budgets for men and women, which policy formulation, planning and would form the basis for identifying budgeting, as well as weaknesses in gaps and action points. Most County data management systems. Addressing Integrated Development Plans (CIDPs), these issues is also in line with a policies and legislation at the county recommendation from the CEDAW level are gender-blind, limiting the Eighth Periodic Report on Kenya, which scope for performance monitoring on highlights the need to collect and gender-related goals. Consequently, publish data that are disaggregated Gender Statistics was identified as by sex, gender, ethnicity, disability one of the flagship programmes in the and age, in order to inform policy and draft Third Medium-Term Plan (MTP programmes on women and girls, as III). It was proposed that the KNBS well as to assist in tracking progress on collaborate with the State Department achieving the gender-related targets of of Gender Affairs (SDGA) to strengthen the SDGs. the production and use of gender statistics as well as to carry out time- In the current Strategic Plan (2018– use surveys that will measure unpaid 2022), the SDGA posits to create work. These will ensure that there is an online platform that can serve constant performance-tracking and as a repository of gender-related accountability for achieving the MTP III information to serve as a platform and SDG development indicators. for dissemination. Moreover, in collaboration with the KNBS, National Gender and Equality Commission (NGEC) and Kenya Institute for Public Policy Research and Analysis (KIPPRA),

8 the SDGA plans to produce additional survey, which has not been carried out. gender data sets and parity indices Production of gender statistics was also for national and county-level planning listed as an activity, with the following and undertake a time-use survey to outcomes: Gender Status Report, determine unpaid work and integrate Gender Data Sheet, and Gender Fact it into national and county economic Sheet. planning. United Nations Development Kenya National Bureau of Statistics’ Assistance Framework strategic priorities Gender equality is recognized as one The KNBS is the national institution of the top priorities for the UN in Kenya that is mandated by the Statistics Act through the 2018–2022 United Nations (2006) to generate official statistics Development Assistance Framework that are comprehensive, reliable, timely (UNDAF), as it is seen as a fundamental and disaggregated to the county level. right and a potential accelerator of The KNBS Strategic Plan 2013–2017 development in all areas of the Kenya is anchored on Vision 2030 and the National Vision 2030 Medium-Term Medium-Term Plan 2013–2017 (MTP Plan III and the Government of Kenya’s II). The KNBS is expected to play a “Big Four transformative agenda,” critical role in enabling the Government which include affordable housing, food of Kenya to achieve its medium- and security, universal health care and long-term development plans through enhanced manufacturing. A critical the provision of credible statistical area is the UN’s technical support to information for evidence-based policy the Government of Kenya to improve decision-making and to guide resource SDG-based data and M&E frameworks. allocation to the devolved units. The This is expanding the availability of statistical information also enables the SDG evidence-based indicators, which Government to monitor and evaluate include statistics for implementing and the implementation of programmes monitoring SDG 5 on gender equality under the MTPs. The Vision 2030 MTP and the other gender-related targets in II specifically sought to strengthen the the other goals. NSS and establish statistical offices in all 47 counties, to facilitate the The UNDAF is aligned with the Kenyan coordination of statistics programmes. national development priorities presented in the Government of Kenya’s One of the strategic focus areas Vision 2030 and the consequent MTPs during the 2013–2017 plan period was II and III. It also addresses several addressing statistical data gaps by contextual development challenges designing and carrying out censuses identified in the 2013 Complementary and surveys to address these gaps; Country Analysis, which is the UN promoting collaboration, networks, System’s independent and mandate- partnerships and integration among based articulation of the country’s producers and users of statistics; context, opportunities and challenges. It and expanding and updating the encompasses sustainable development, administrative statistical information human rights, gender equality, peace base. One of the proposed surveys and security, and humanitarian for gender statistics is the time-use perspectives. The final evaluation of the

9 UNDAF for 2014–2018 outlined that the UN’s advocacy and high-level technical 2.4 Current status of gender expertise has been effective, both statistics in Kenya at service-delivery and policy levels. The “Delivering as One” approach has 2.4.1 Enabling environment been applauded by the evaluation as a The National Statistical System (NSS) is strategic framework that has enabled made up of the statistical organizations the UN Country Team in Kenya to and units within a country that jointly gain credibility as a key development collect, process and disseminate partner. official statistics on behalf of a national government, as well as data users. In line with MTP III priorities, the The production of official gender UNDAF’s support for gender equality statistics does not exist in isolation commitments aims to enhance Kenya’s but should be integrated into the capacity to generate gender statistics official NSS, to ensure frequent and to inform planning, programming consistent production and monitoring. and SDG monitoring. The second Therefore, an examination of the programming approach that is line enabling environment to produce with the MTP III is the enhancement and use gender statistics must begin of data quality and availability. In this with an assessment of the enabling respect, UN agencies will work with environment of the entire gender the Government of Kenya, especially statistical system. the KNBS, to strengthen the quality and availability of data for effective Legislative arrangements policymaking and programme implementation. Attention will be given National Constitution to support the capacity of MDAs, the KNBS and county governments to Building an enabling environment for produce data that are disaggregated by the functioning of the statistical system sex and subpopulation groups and to and the production of statistics to meet strengthen the dissemination and use of user needs is entrenched in several data and statistics at the national and articles of Kenya’s 2010 Constitution, local level for evidence-based policy such as: Article 27(3), which states: formulation and planning. “Women and men have the right to equal treatment, including the right In addition, transformative governance to equal opportunities in political, is one of the aspects of the theory economic, cultural and social spheres” of change, for how the contributions and Article 27(6), which specifies that of UN agencies (in partnership “to give full effect to the realization of with the Government of Kenya and the rights guaranteed under this Article, other partners) will make tangible, the State shall take legislative and other measurable contributions to country measures, including affirmative action priorities, the SDGs and the AU Agenda programmes and policies designed to 2063. One of the key areas of focus is redress any disadvantage suffered by enhancing evidence and results-based individuals or groups because of past management to improve governance discrimination”.12 at the national and county levels for achieving the SDGs, which requires high-quality and reliable sex- 12. Government of Kenya. 2010. Constitution of Kenya. http:// www.kenyalaw.org:8181/exist/kenyalex/actview.xql?actid=- disaggregated data. Const2010

10 A key principal relevant to statistics is County Governments Act, 2012 the right to access information held by the State and Article 35(3) obliges At the devolved levels of government, the State to publish and publicize the County Governments Act, 2012, any important information affecting Sections 93 to 96 provide for county the nation. Fundamental to the governments to establish a mechanism development process is the recognition and facilitate public communication of public participation as an integral and access to information in the form component of good governance, but of media with the widest public reach in effective public participation requires the county. civic education and information- sharing, which is the foundation of The Statistics Act of 2006 and the statistical production.13 Article 10 National Statistical System of the Constitution identifies public participation as a national value and The Kenyan NSS has established legal principle of governance. Article 232(d) frameworks, standards and ethical guarantees the involvement of the principles to protect the confidentiality people in the policymaking process in of data about individuals, as stipulated the public service. in the Statistics Act of 2006. As earlier stated, the Statistics Act mandates One of the objectives of devolution14 the KNBS to generate official statistics (of shifting certain sectors and that are comprehensive, reliable, timely responsibilities from national to county and disaggregated to the county level. governments), as provided for in Article KNBS has established offices in each of 174 of the Constitution, is to promote the 47 counties to coordinate statistical social and economic development capacity-building programmes and and the provision of proximate, easily ensure that international standards accessible services throughout Kenya, are applied in the production and as well as to enhance government dissemination of statistics at the county responsiveness to the needs of its level. citizens. The devolved system of government further aims to promote Improving the governance and equitable, efficient and prudent leadership of statistical systems, utilization of public resources. including Kenya’s NSS, is a central requirement for the African Data Kenya’s 2010 Constitution also Revolution, as exemplified in its core indicates that one of the functions principles (Box 1). The status of KNBS of county governments is county as currently constituted and anchored development planning, including in the legislative framework is that it statistics. The development of CIDPs has the ability to provide leadership and therefore requires availability of quality promote coordination with other parts data to meet county development of government. KNBS works closely objectives. However, currently there is with other line ministries on proper no legislation or policy framework to methods of collection, analysis and guide the production of statistics at the dissemination of gender statistics and county level. has a programme for building capacity in data collection, with an emphasis on gender statistics at the national level. 13. Ibid. 14. http://www.klrc.go.ke/index.php/constitution-of-kenya/ There is no specific legislation for the 138-chapter-eleven-devolved-government

11 production of gender statistics. strategies aimed at poverty eradication KNBS has made efforts to promote require precise documentation of the identity and location of the poor to coordination through: (1) the Gender determine long-term and programmatic Statistics Section (within the Social decisions targeting specific geographic Statistics Division), which handles areas. Finally, rapid economic growth gender statistics from all sectors; may hide incidences of poverty and (2) the Gender Statistics Technical inequality that can be prevalent at Committee, whose responsibility is to the county and sub-county level if not validate all types of gender-related identified. data; and (3) the Gender Mainstreaming Committee, which deals with gender The devolution framework in Kenya sensitization at all levels in the bureau, envisions the establishment of in partnership with Gender Focal Points intergovernmental coordination placed in all MDAs by the SDGA, whose mechanisms at the county levels on responsibility it is to ensure that gender sectoral issues, including statistics and issues are addressed in their respective gender, which bring together county entities. governments and national government offices. The statistics and gender Subnational statistical systems coordination mechanisms at county levels have not fully been implemented. A subnational system may be defined by geographical area, groups of interest Policy and strategic planning for or institutional set-up. Subnational gender statistics statistical entities also act as primary compilers and/or custodians of Kenya has a history of strategic subnational statistics. There are a planning for statistical development. number of activities that are better The first five-year Statistical Plan for conducted by subnational entities the Central Bureau of Statistics (CBS), and by sectoral statistics offices a government department in the then rather than national units/agencies. Ministry of Planning and National To rightfully contribute to overall Development, covered the period 2003 national statistics, it is important that to 2007. This plan was instrumental in each of these subnational (sectoral/ the transformation of the CBS into a agency-specific/territorial) entities semi-autonomous government agency develop their own strategy for the (the KNBS) by an Act of Parliament (the development of statistics. There are Statistics Act No. 4 of 2006). three important elements based on African Data Revolution principles The second plan covered the period and the reality of Kenya’s national 2008–2012 and specifically sought to governance and accountability align statistical information with user framework on data production and requirements, enhance the quality of use of statistics. First, there is strong statistical products, and coordinate demand for disaggregated data to and supervise the NSS, among other provide a sound base for policies objectives. that target specific segments of the population, as provided in the fourth The KNBS Strategic Plan 2013–2017 schedule of the Constitution. Secondly, was anchored in the national blueprint

12 of Vision 2030 and MTP II. However, Current legislative arrangements, as the Strategic Plan acknowledged specified in the Statistics Act of 2006, that the lack of a National Strategy are deficient in encouraging statistical for the Development of Statistics participation by recognizing the roles (NSDS) implied there was no clear of various groups of subnational framework for coordinating the NSS. entities in collecting and producing The plan provided a clear road map data. As reported in the Strategic Plan for developing the NSDS, which has for Agricultural and Rural Statistics not been finalized. While the first 2015–2022, KNBS activities are two plans only focused on KNBS, the covered by the Statistics Act of 2006. NSDS is expected to cover sectors Meanwhile, other institutions, such as (MDAs, with KNBS and counties MDAs that produce statistics based considered as special sectors). The on administrative mandates provided NSDS has coincided with the design of by the Government of Kenya, are not Strategic Plans for MDAs and counties, clearly covered by this Act. which creates a good opportunity for interlinkages between Sector Plans for County governments lack the necessary Statistics and Strategic Plans for MDAs. infrastructure to coordinate, collect, Overall, the strategic plans have minimal collate and manage data due to focus on gender statistics, except for inadequate funding or political will. In the Strategic Plan 2013–2017, which addition, they lack human resources.15 proposed the collection of time-use As acknowledged in the draft MTP III, data and gender statistics in the form of there is an urgent need for support, a Gender Status Report, a Gender Data not only to be able to function but Sheet and a Gender Fact Sheet. also for a policy and legal framework that compels county governments to KNBS also has a policy whereby it strengthen statistical units in line with is committed to providing statistical the Constitution of Kenya (2010), the services through the production and County Governments Act (2012) and management of quality statistics, which the Intergovernmental Relations Act is achieved by: providing products (2012)16. and services that meet the needs and expectations of the organization’s Access to information that facilitates interested parties, and attracting and effective public participation and civic retaining highly skilled and motivated education remains a challenge. There staff, among others. is still a lack of an adequate legal and policy framework to give effect to The Statistics Act 2006 is currently undergoing a review to align it with the 15. Status of County Agriculture Statistics: Counties do have 2010 Constitution. their own personnel but rely on personnel seconded by the national government. “The workforce inherited from the national government Challenges and gaps for agricultural statistics was not adequate but below the expected optimal number. However, some of the officers in the livestock, agriculture, fisheries and irrigation areas that were seconded to the counties still saw themselves as In general, the policy and legislative working for national as opposed to county government. At KNBS, the number of personnel engaged in the production provisions for statistics do not have a of statistics was inadequate to conduct all the envisaged specific focus on gender. Thus, data- activities. The government policy to freeze employment had impacted negatively on the subsector resulting in aging collecting agencies do not have an personnel and succession challenges.” (Republic of Kenya. 2016. Strategic Plan for Agricultural and Rural Statistics, obligation to consider gender SPARS-KENYA 2015–2022. March 2016; Kenya National Bureau of Statistics). dimensions in their data processes. 16. https://devolutionhub.or.ke/resource/intergovernmental-re lations-act

13 Article 220(2) and the Fourth Schedule mainly collected manually.The key of the Constitution – which assigns the factors behind these challenges function of national economic policy include: meagre allocation and planning, and the coordination of of resources to strengthen planning for county governments to the statistical systems; limited national government. Steps to enact awareness in national and county the legislation required to coordinate governments on the importance the NSS, making it easier to implement of statistics; and a lack of the standards needed to improve data updated or full implementation quality, are therefore imperative. In of statistical plans, such as that line with statistical developments and of the agriculture sector. constitutional provisions, open data 2.4.2 Data production policies should thus be enhanced, and inventories of all data available The push to achieve the MDGs resulted throughout the NSS should be carried in significant improvement in the data out as well as presented or archived in available since 2000. The Government an accessible and standardized format. of Kenya, through KNBS and other data producers, has tried to improve Key challenges in the enabling data collection and analysis to bolster environment to produce requisite data production and availability. statistics include: KNBS has conducted several recent 1) Lack of a current strategic surveys, such as: the Micro, Small and framework – A National Strategy Medium Enterprises (MSME) Survey, for the Development of Statistics regular Kenya Demographic and Health that reflects the current reality Surveys, Kenya Integrated Household and needs for statistical Budget Surveys, information and information on gender equality communication technologies surveys, and women’s empowerment national economic surveys and statistical abstracts, County Statistical 2) Inadequate legislation to Abstracts, Kenya Open Data Systems, allow for optimal coordination and surveys that collect administrative of gender-related statistical data, such as vital registration, health activities information and other data. 3) The impact of devolution in Defining gender statistics the coordination of statistical work at county level, including In this report, gender statistics is subnational gender statistics defined as the process of identifying, 4) Inadequate resource allocation producing, analysing and disseminating for gender-related statistical statistics in order to understand how activities gender issues affect individuals and society.17, 18 5) Very limited number of gender statisticians at KNBS and in ‘Gender data’ is therefore data MDAs composed of two key components: (1) data that are disaggregated by sex; 6) Inadequate staff skills on gender statistics 17. The definition is adopted from theGender Statistics Manual: Integrating a gender perspective into statistics (2016) by the UN Statistics Division. 7) Lack of interoperability of 18. The Gender Statistics Manual defines ‘gender statistics’ as statistics that adequately reflect differences and inequalities administrative data, which are in the situation of women and men in all areas of life. 14 and (2) data that inform about the tax, health and educational record status of women and girls exclusively or systems. Administrative data systems primarily.19 are essential for evidence-based, accountable public service-delivery. Sex disaggregation encompasses the need to attribute measures at the Large amounts of administrative individual level to know the status of data are generated from registers.21 girls and women – both in absolute Administrative registers are reliable; levels and relative to boys and men however, they are incomplete – most (e.g. school enrolment rates for boys registers do not achieve 100 per and girls). Gender data may apply cent coverage of their respective just to women or to both women and events. Data collection is usually men, in areas where differences are a manual and hence prone to content function of social choices or constraints errors and the files are also less (gender). For example, maternal accessible to the public and analysts. mortality rates (women only), or wages In addition, most registers, especially or wage differentials between women those of births and deaths, are not and men (gender). transmitted in real time. Some are prone to fraud, especially immigration Gender statistics cut across registers because they rely on other economic, social, political and registers, and it therefore becomes environmental dimensions and elicit difficult to ascertain the authenticity not only outcomes, but the needs and of documents from other agencies. capabilities of women across important The key issue with administrative policy areas. data is the lack of interoperability. There is also no (or limited) further Major sources of gender statistics in processing of administration data Kenya to generate new or richer gender statistics. Gender statistics may be Administrative and institutional data lacking in administrative data because some sector strategies lack gender Administrative data refer to information dimensions or are simply gender- collected primarily for administrative or blind (for example, the Sector Plan for management purposes.20 Government Labour and Employment 2013–2017). departments and other organizations In addition, most of the M&E systems collect this type of data for the lack gender dimensions even if gender purposes of registration, transaction mainstreaming was part of the core and record-keeping, usually during actions.22 the delivery of a service. Ministries and government departments are the Health administrative data main keepers of large administrative databases, which include welfare, Health in Kenya was devolved from the national level in 2013. Through legal notice 137–182 of 2013, each of 19. The Gender Statistics Manual indicates that gender statistics should comprise the following four requirements: “(a) Data are collected and presented by sex as a primary 21. For details, see Symposium Report on Integrating Reg- and overall classification; (b) Data reflect gender issues; (c) isters into the National Statistical System (NSS) – Kenya, Data are based on concepts and definitions that adequately 28–29 September 2016, Kenyatta International Convention reflect the diversity of women and men and capture all Centre (KICC), Nairobi. aspects of their lives; (d) Data collection methods take into 22. For details on gender in M& E systems, see Fehringer, J., account stereotypes and social and cultural factors that may B. Iskarpatyoti, B. Adamou and J. Levy. 2017. Integrating induce gender bias in the data.” Gender in the Monitoring and Evaluation of Health 20. OECD https://stats.oecd.org/glossary/detail.asp?ID=6 Programs: A Toolkit to Measure Evaluation.

15 the two levels of government has its data requirements in the sector while own functions, with some concurrent some programmes still collect data functions. The 47 devolved units of vertically, such as tuberculosis and governance currently operate within NCDs. Lower-level facilities are using the various legal frameworks mandated paper-based reporting. Automation of by the Constitution, backed up by the data in health facilities is not as widely County Governments Act 2012 and used as expected, due to inadequate the Public Finance Management Act capacity and limited investment in ICT 2012 as developed structures with infrastructure. Although a web-based autonomy to manage and govern health system is being used for collecting service-delivery. There has also been administrative data, there is inadequate a shift in global health focus, from a county capacity for data analysis, use programme-based vertical service- and producing reports. Data collected delivery approach towards a Universal from health facilities or administrative Health Coverage health system-based reports can allow subnational service-delivery approach. Planning disaggregation by geographic area for people’s health is now among the and can be collected on a continuous county governments’ priorities and basis, but the level of disaggregation hence, accurate and good data to by age and sex is limited, unless there enable programming and the shift of is an individual-level electronic medical allocation of the resources informed by records system in place. available evidence are critical. Currently, the District Health Information The midterm evaluation of the Health System (DHIS) 2 is the main source of Sector Strategic and Investment Plan administrative health data and with high 2014–2018 revealed the following reporting rates (over 90 per cent) for challenges: several key forms of data such as that • Limited capacity-building for data relating to reproductive and maternal collection and use health.23 • Lack of systems to enhance sharing of data and information at all levels, However, a comprehensive health especially at the point of service- data observatory that also includes delivery relevant data from other sectors is • Inadequate investment in health lacking. There are several areas where information, including joint data availability is limited, especially performance reviews and ICT in generating requisite information to infrastructure determine the burden of disease at the • Limited use of new technologies in county level. Information on cancer and the management of data non-communicable diseases (NCDs) • Limited integration of the various is limited, while there are areas that reporting systems have measurement challenges, such as • Limited regular feedback at all levels gender-based violence and maternal • Inadequate use of best practices and mortality at lower-level health facilities. opportunities for mutual learning. The web-based system currently being used is yet to fully capture all Educational administrative data

23. Republic of Kenya. 2016. Statistical Review of Progress Currently, most education-related towards the Mid-Term Targets of the Kenya Health Sector Strategic Plan 2014–2018 indicates that DHIS-2 reporting data come from the administrative rates increased from 77 per cent to 89 per cent from 2013- 14 to 2015-16 and the data quality is remarkably good for sources and the Education Management many core indicators.

16 Information Systems (EMIS). The core resources (both human and financial), of the EMIS is a database that organizes incomplete registration, limited school-based data, collected through monitoring of registration agents, an annual school reporting system, geographical vastness and difficult transactional data about education terrains in some counties, elements stakeholders’ operations, and other of insecurity in isolated counties, data sources (mainly census and survey public apathy or negative attitudes data). KNBS, in preparation for the to civil registration, lack of incentives annual national statistical abstracts, to register, late registrations, non- county statistical and annual economic enforcement of the law for offenders, surveys, uses this system. Currently, and low demand and appreciation of educational data are the only source the value of vital statistics from civil that can provide information not only registration. Therefore, vital data are on education goals relating to SDG often incomplete, but the level of indicators but also on gender statistics incompleteness varies by county. pertaining to education at devolved levels. However, EMIS has not been Marriage and related registers under established at county levels. There is civil registration also the problem with the education data in that disaggregation cannot be Users of marriage data include foreign done for vulnerable groups such as embassies, government institutions, persons with disability. courts of law, banks, insurance companies and law firms. The following Civil registration are used to collect marriage data: the marriage register – collects information Birth and death registration on all marriages contracted in Kenya and outside Kenya by Kenyan citizens; Civil registration is a form of the divorce register – generates data administrative data that records vital on divorces filed in Kenya and outside events in a person’s life (e.g. birth, Kenya (by Kenyan citizens); and the marriage, divorce, adoption and death). adoption register – collects data on all The Civil Registration Services (CRS) children adopted in Kenya. Some of are under the Ministry of Interior and the challenges the CRS faces include: Coordination of National Government. inadequate funding, which has slowed Civil registration provides individuals the implementation process as well as with legal identity and civil status and the creation of a national database on by generating information that can be marriages and divorces; collection of used as the source of civil registries marriage certificates is hardly complete and population databases. Death so records remain scattered all over registration, including cause of death, the country; manual processes require is an important source of public health automation and business process information. Using data from current re-engineering; lack of adequate registrations, analysis of vital statistics capacity to collate and analyse data; is done annually and jointly with key and weak linkages with deputy county stakeholders and an annual report, commissioners who are under the State the Kenya Vital Statistics Report, is Department of Interior. In particular, produced and published. Some of the there is a need to identify ways to challenges CRS is facing are inadequate incorporate marriage and divorce

17 information, including adoption data, KDHS is an important source of as an essential part of CRS data. As a data, especially for gender-statistics result, comprehensive data on marriage related to SDGs 3 and 5. and divorce are still only available from censuses and surveys. 2) Multiple Indicator Cluster Surveys (MICS): Started in the mid-1990s, Population censuses MICS are conducted with support from UNICEF and are intended to Kenya population and housing censuses address gaps in data on women conducted by KNBS are the main and children in the areas of source of nationally representative health, education, child protection information for basic household (including child labour) and HIV indicators on demographics, education, and AIDS. The MICS have been employment and consumption. The conducted in five rounds but only strength of the census data is that for lower-lever units (such as currently they are the only source districts) before 2010 and counties that can meet principle 1 mentioned after 2010. The most recent rounds in section 1.4 (see Box 1). The Kenyan are for counties in Nyanza region censuses are conducted every (Round 4), while Round 5 covered 10 years but are limited in more Kakamega, Bungoma and Turkana. specialized modules on time use, Malaria Indicator Survey (MIS): expectations, individual asset/property 3) This survey was developed by the ownership, and other characteristics of Monitoring and Evaluation Working intrahousehold allocation. Group of Roll Back Malaria, an international partnership developed Past surveys spanning multiple to coordinate global efforts to domains fight malaria. MISs were conducted

in 2010 and 2015 to measure the 1) Kenya Demographic and Health burden of malaria and assess the Surveys (KDHS): Started in 1988 coverage of interventions. The and undertaken after every four MIS collects national and regional years, the KDHS are nationally data (regions are defined by the representative surveys with extent of malaria endemicity) primary funding by USAID. Each from a representative sample survey includes a household-level of respondents, with additional questionnaire and a roster of births questions on mosquito net use, for each mother in the household, intermittent preventative treatment as well as separate questionnaires against malaria, and malaria and for men and women of reproductive anaemia testing that are focused on age (15–59 for men, and 15–49 for vulnerable groups, such as children women), married couples, and under 5 and pregnant women. children and their mothers. The The survey gathers additional primary emphasis of the KDHS information on indoor residual is on maternal and child health, spraying, and background data on reproductive issues and fertility, and the characteristics of household other demographic variables related members and ownership of to marriage, household decision- household assets such as electricity, making, and domestic violence. bicycles, radios and indoor

18 plumbing. very comprehensive in terms of gender differences in risk factors for key health 4) AIDS Indicator Survey (AIS): problems in Kenya. The AIS was started alongside the Demographic and Health 6) Kenya Integrated Household Surveys to provide countries with Budget Surveys (KIHBS) a standardized tool to obtain household indicators for effective The KIHBS are designed to provide monitoring of national HIV/AIDS numerous indicators and data needed programmes. Topics include HIV to measure living standards and prevalence among households as poverty. They were preceded by the well as knowledge and the individual initial welfare monitoring surveys attitudes of women and men on HIV conducted in the 1990s. The surveys prevention. The surveys began in cover all household members (usual Kenya in 2007 and only two surveys residents), all women aged 15–49 have been conducted with the last years resident in the household, and all one in 2012. children aged 0–4 years resident in the household. The questionnaire consists 5) STEPwise Survey (STEPS)24 on of integrated modules designed to non-communicable diseases and the collect information on the following: tuberculosis prevalence survey of demographics; education; health, 2016 fertility and mortality; employment; labour; child health and nutrition; The Survey on non-communicable housing; water, sanitation and disease (risk factors in Kenya was energy use; food consumption and carried out from April to June 2015 expenditures; non-food consumption; in three steps. This was a population- ownership of durable goods; aspects based survey of adults aged 18–69 of agricultural holdings, activities and years. A multi-cluster random outputs; livestock; household economic sample design was used to produce enterprises; transfers; income; credit; representative data for that age range and recent shocks to household welfare. in Kenya. STEPS 1, 2 and 3 were carried In addition, two household diaries are out together with the two optional often used: one diary to record goods modules on Oral Health and Violence and services purchased, and the other and Injuries. Sociodemographic and to record goods and services consumed behavioural information were collected by the household. Household budget in STEPS 1; physical measurements survey indicators can be disaggregated such as height, weight and blood at the county level. pressure were collected in STEPS 2; and biochemical measurements were Special surveys collected to assess blood glucose and cholesterol levels in STEPS 3. The 2015 2015 National Adolescents and STEPS added valuable data on levels 1) Youth Survey and trends of leading public health problems. The purpose of the 2015 National Adolescents and Youth Survey was The tuberculosis prevalence survey, to provide information that will conducted for the first time in 2016, was enhance Kenya’s efforts to harness the demographic dividend. The survey

24. http://www.health.go.ke/wp-content/ uploads/2016/04Steps-Report-NCD-2015.pdf

19 covered all 47 counties and the main more information about the status of objectives were to: generate a profile of men and women with respect to access adolescents and young girls and boys in to wages, employment and income and each county; identify health, education, to analyse enterprise dynamics, in terms economic and governance issues that of business start-ups and closures, and affect young persons in each county; changes in business activity. and identify investment opportunities in the key sectors in each county. 2) Financial access and inclusion The survey relied more on generating surveys qualitative data with key instruments being focus group discussions, key Kenya has collected information on informant interviews and in-depth financial inclusion every three years since 2006, through the Central interviews. Reports are available on Bank of Kenya, KNBS and Financial the NCPD website for each of the 47 Sector Deepening Kenya. The surveys counties.25 constitute an important tool for providing a better understanding of Past surveys spanning the domains of the financial inclusion landscape in line the economy and political decision- with the financial sector development making agenda, as laid out in Kenya’s Vision 2030. The surveys contain 1) Micro, Small and Medium disaggregated data on key market Enterprises survey segments and drivers of uptake and usage, including attitudes, perceptions The origins of the MSME survey date and needs. They also capture a profile back to 1972 when the International of the financial services landscape. Labour Organization (ILO) conducted The key variables related to gender a study on employment and income statistics include: financial product and in Kenya. The first baseline survey was service usage, awareness, experience conducted in 1999 and the follow-up and frequency of use; income in 2016 was intended to: improve the sources; household possessions; key reliability of estimates on the MSME demographics (age, sex, education sector’s contribution to the Kenyan level, number of household members); economy in terms of income, wages and important life goals; shocks experienced employment, among others; measure and how to deal with them; ability to the size and extent of the sector by get emergency money and to secure estimating the total number of MSMEs a place to keep money; distance in the country; capture the evolving from financial service-providers; and nature and variety of the MSME sector discriminatory social norms. by enterprise activity, geography, rural/ urban and gender distribution among 3) Afrobarometer Surveys others; access and analyse enterprise dynamics in terms of business start- These are public attitude surveys on ups and closures, changes in business democracy, governance, economic activity, value added, etc.; and examine conditions and related issues MSME’s constraints, potential and conducted in more than 30 countries access to support services. The last in Africa. In Kenya, seven rounds have survey offered the opportunity for been conducted by the Institute of further in-depth analysis to generate Development Studies (IDS) of the University of Nairobi, with support 25. http://www.ncpd.go.ke/resources/

20 from KNBS. Technical details of the data are already widely available, survey, including descriptions of while half are Tier II, indicating that stratification and household selection, methodology has been established but translation languages, and related data are not easily available. However, information, can be found in the just over one-quarter (26 per cent) survey’s technical information. In 2017, are Tier III, for which internationally for example, the survey focused fully agreed methodology has not yet been on whether Kenyans (women and developed and data are not available men) thought gender equality and (see Annex 1 for a list of indicators and women’s empowerment is important in tier levels). development (political, economic and social)26. The KNBS has indicated that the identified 34 gender-related localized Minimum set of gender indicators SDG framework indicators can be measured with available data, or data The UN Statistical Commission in 2013 that can be produced with minimum established a set of 52 indicators to effort by 2019.28 However, it is important monitor gender equality and women’s to note that tracking progress on empowerment27 (see Annex 1 for the the SDGs requires the collection, complete list). As of 2017, out of the list processing, analysis and dissemination of 52 indicators, data was not available of an unprecedented amount of data for 18 (35 per cent). Out of these 18 and statistics, not only at national indicators, 14 belong to the domain but also subnational levels – such as on economic structures, participation counties, subcounties and wards. The in productive activities and access to challenges reported by the Government resources. Most of these indicators are of Kenya include inadequate data expected to have come from surveys disaggregation; high stakeholder on labour force and the workforce, expectations and inadequate funding agriculture and time-use. It is also in for the SDGs.29 The opportunity here these domains that administrative data is that the Government intends to: are weak, unlike indicators from the intensify awareness-creation and health and education domains. capacity-building; mainstream the SDGs into the MTP III and Second-Generation Readiness to generate gender statistics CIDPs and public institutions’ strategic within the SDGs framework plans; review the NSS in light of the SDGs; enhance multi-stakeholder The implementation of the Sustainable participation in the SDG process and Development Agenda has also enhance resource mobilization in order coincided with the drafting of the to achieve the objectives of the data Vision 2030 MTP III. Kenya adopted road map.30 34 gender-specific and/or gender- related indicators as part of the In recognition of the need for 128 SDG indicators chosen for the multiple stakeholders to monitor the domesticated SDG framework. Of SDGs, which was reiterated in the these 34 gender indicators, 24 per Government’s 2016 Roadmap to the cent are Tier 1, indicating that an established methodology exists, and

28. See https://sustainabledevelopment.un.org/hlpf, accessed 26. https://afrobarometer.org/data/319 12 February 2018. 27. United Nations Statistical Commission. 2013. https:// 29. Ibid. unstats.un.org/sdgs/indicators/database/ 30. Ibid.

21 Sustainable Development Goals31, the ecosystem, while taking into account Government has created an inclusive the cross-cutting impact of inclusivity data ecosystem involving government, on other areas. private sector, academia, civil society, local communities and development The data communities raised concern partners that tackles the informational about potential gaps in data, including aspects of development decision- data on groups who are most at-risk making in a coordinated way. This has of being left behind, the impact of led to the creation of what is referred investments in youth enterprises, and to as data communities.32 There are social protection. a total of eight data communities in agriculture, education, health, transport, In line with Kenya’s obligation to ICT and innovation, climate change, domesticate the globally agreed inclusivity and public finance. However, Agenda 2030, the Government only the inclusivity and the health and spearheaded the development of the education data communities have SDGs Roadmap that was prepared indicated a focus on issues related to with seven broad areas to guide the gender data. SDG process in Kenya. The seven areas are: mapping stakeholders and establishing partnerships; advocacy The education data community has and sensitization; domestication/ included the theme of inclusion localization; mainstreaming and and equity, which looks at factors accelerating implementation; resource of inequality in Kenyan education, mobilization; tracking and reporting; including but not limited to gender, and capacity-building.33 An Inter- age, socioeconomic background, Agency Technical Committee was geographical factors and disability. It set up, with membership drawn from has included the provision for inclusion line ministries, KNBS, NCPD, civil of Women Educational Researchers of society organizations and the private Kenya. The health data community has sector, among others. In addition, a identified areas where the development mechanism of coordination between of common infrastructure is possible the two levels of government (national so as to engender data efficiencies and county) has been worked out with and collaborations in health. They have the establishment of the SDGs Liaison endeavoured to support county and Office within the secretariat of the national governments to establish the Council of Governors (COG). relevant policy and legal frameworks The key strategic actions include the necessary to support the standards directive by the government to all needed to improve health data quality MDAs to mainstream the SDGs into and to ensure public officers from policy, planning, budgeting, monitoring the county and national governments and evaluation systems and processes. understand how to use various types This implies that all SDG targets and of health data. The inclusivity data indicators have been mapped against community is expected to convene and the mandates of the MDAs and that develop a data community that can the SDGs have been assigned to the contribute to the broader data national respective development actors. It is expected that all MDAs’ Strategic Plans 31. http://planning.go.ke/wp-content/uploads/2019/07/ Roadmap_to_SDGs.pdf 2018–2022 will integrate the SDGs. 32. A data community is a group of people who share social, economic, political and/or professional interests in data across the entire data value chain – production, analysis, management, dissemination, use and storage (UNDP, 33. The launch of the SDGs in Kenya on 14 September 2016 2016). created awareness and rallied stakeholders behind the SDGs. 22 By including the SDGs in their data sources for the SDG indicators. Performance Contracting, it implies that MDAs will have to submit progress Gaps in sources of data to produce reports on a quarterly basis. This means gender statistics that the data to report on the SDGs must be available on a continuous 1) Lack of disaggregation basis. The SDG contact officers in line ministries and officers in charge of Although the recent Demographic planning and budgeting among others and Health Surveys have improved the have been trained using a standardized quantity and quality of demographic kit in training institutions, with emphasis data and a number of aspects of given to training-of-trainers. The gender data in the country, there is purpose of the training was to ensure a lack of disaggregation of data, as that the key officers responsible for referred to in the first principle of the planning have the skills to understand African Data Revolution (see Box 1). the SDGs, and therefore incorporate the One great weakness of the data is SDGs in plans, and also have the ability that they cannot produce indicators at to train other government officers. more disaggregated levels, including by sex, region (rural/urban), age, 34 The indicated preparedness implies wealth status and county levels. A that if the processes are implemented number of demographic indicators at successfully, then in the next five-year county and sub-county levels have to period, gender-related indicators are be either derived from census or vital likely to be included in the reporting registration statistics or administrative structure of the MDAs and CIDPs. The data. Vital registration is incomplete, risk, however, lies in the ability of the but the extent varies by county. respective MDAs to produce and report Administrative data also suffer from data. several weaknesses despite having the potential of being obtained from even From the review of data availability for lower-level administrative units, given the SDG indicators, it was noted that that these units collect the data. This most of the data sources for gender- is typically the result of inadequate related SDGs were outdated – especially investment in people and skills, lack of the KDHS (2014), population census dissemination and therefore of use, and (2009), labour force survey (2018) and the failure to fully harness the potential 35 household budget survey (2015/16) of information technology.

– or not available, such as for hourly 2) Misalignment between SDG earnings, the proportion of women in indicators and existing data managerial positions, the proportion sources of children at different levels achieving Wide disparities were found between minimum proficiency in reading and mathematics, and the participation 34 The 2014 Kenya Demographic and Health Survey was of youth in formal and non-formal designed to produce some reproductive health indicators at the county level. These include use of family planning, education and unpaid work. Further, antenatal care, delivery care and fertility levels. However, there is a discrepancy in the definition information on childhood mortality was found to be deficient and not usable at the county level. of SDG indicators and how the 35 Most data are kept in various forms, spreadsheets, hard copies and non-compatible software due to donors indicators are captured in the surveys. who supported automation – this therefore limits interoperability. It has also been reported that sometimes See Annex 2 for a detailed review of the quality is questionable. It is only routine health data that have undergone data quality checks in Kenya.

23 the SDG indicators and the way the 4) Lack of metadata specific indicators are captured in the different data sources. Such Currently, there are no metadata, discrepancies make it difficult to which can be used to summarize basic come up with reliable estimates for information about data and make the indicators. For example, while the tracking and working with specific SDG indicator on HIV is the number of data easier. KNBS has a list of data new HIV infections per 1,000 people in types and sources on its website but an uninfected population, by sex, age does not provide any information and key populations, the KDHS only on other available data sources. For describes patterns of knowledge and example, the NCPD, NGEC and KNBS behaviour related to the transmission all collect data on gender-based of HIV and other sexually transmitted violence from different sources, which infections and does not record HIV and are not then interlinked or comparable AIDS testing, which can measure the with each other. These data are not number of people with HIV. However, collated and compiled to come up given the incubation period of HIV, it is with agreed estimations. If metadata almost impossible to obtain an accurate were made available and accessible number of new infections through to all data producers, this would surveys. Therefore, the information is promote harmonization and coherence obtained from modelling data based of statistics compiled across various on administrative and other sources sources since standard definitions and of data. Regarding the adolescent compilation/estimation methodologies birth rate, which is per 1,000 women would be available. It would then (aged 10–14 and 15–19 years), the SDG be possible to build a common indicator requires data for the age database that would not only minimize group 10–14 years, which is not reported duplication of efforts and ensure in the KDHS. This can only be obtained effective coordination among data from census data for women aged 12 to producers but would also ease access 14 years. to data by users.

3) Timeliness and frequency of data 5) Areas with inadequate and/or no data The review of data sources above revealed that most of the gender Some key areas lack timely data statistics for the 34 localized gender- that are updated within the agreed related SDG indicators are derived international time schedules or lack from KIHBS, KDHS, the population data totally. These include data on census and administrative data from agriculture based on households, but government agencies. The challenge not large-scale farms, access to land with such data sets is that they are not and ownership, access to housing, frequently produced (as mentioned, particularly in urban areas, employment, the KDHS is once every four years; the environment and migration. A key KIHBS once every five years and the informant (from a civil society population census once every 10 years). organization) indicated that in some Implementing and monitoring the areas (e.g. agriculture, childcare), what gender-related SDGs will require more women do and produce is entirely frequent, timely and accurate statistics. invisible.

24 Agriculture: Surveys of agriculture relationships to the environment. Thus, include farms and ranches and the gender differences and inequalities people who operate them. Such influence the extent and nature of surveys generally look at land use and almost every form of environmental ownership, operator characteristics, encounter, use and impact. In Kenya, production practices, crop yields there is a lack of sex-disaggregated and productivity, and income and data because it is usually not collected expenditures. Agricultural surveys in sectors such as forestry, agriculture, can be a vital source of data on water, energy, marine life, disasters and environmental and climatic events, crop infrastructure. Where there are data, productivity, soil quality, horticultural information pertains to the household practices, inputs and outputs and but not the individual. operating results. In addition, such surveys can provide information on Access to land and land ownership: women’s stake in on-farm activities One of the most widespread forms of and conditions in agricultural informal land insecurity is a lack of ownership employment, including economic rights, especially for women. Kenya opportunities. Measuring women’s and allows widespread property ownership men’s agricultural productivity and the and often does not legally differentiate factors determining this productivity, between women and men as property including access to land and agricultural owners. Anecdotal reports often resources, is essential for the design of suggest that, in practice, enormous gender-informed agricultural policies. numbers of women are denied their However, Kenya has not conducted an right to land ownership. In rural areas, agricultural survey or census since 1963, women produce most of the food but even though most of the population hold title to almost no land. In the urban in Kenya still depends on agriculture, areas where women-headed households which employs a significant labour are common and formal land ownership force. is particularly scarce for the poor, an enormous number of women lack the Environment: Environmental security of home and livelihood for sustainability is a gender issue because which land tenure and property rights both women and men are beneficiaries are so critical. There have been legal and conservers of the environment. cases where women are unable to Being female or male in a society exercise their rights to land, especially carries several roles and one is access following the death of their spouses, and control over natural resources. and are at risk of evictions. Information According to the United Nations on status and trends on land ownership Environment Programme, the issue of is critical because access to formal gender and the environment is far more credit relies heavily on asset-based than gender mainstreaming. It is based lending where land-poor borrowers are on two precepts: first, gender mediates at a disadvantage. Women’s reduced interactions between humans and the access to land limits their access to environment and all environmental credit, thereby limiting their economic use, knowledge and assessment; and opportunities. secondly, gender roles, responsibilities, expectations, norms and the division Homelessness: Kenyan census data of labour shape all forms of human are typically collected based on

25 households, and although previous employment, and entrepreneurial censuses have tried to count the activities. Kenya has not conducted “hidden homeless,” no analysis is ever time-use surveys, although some done. Although homelessness has aspects have been included in previous been considered an urban issue, it also household and budget surveys. impacts people in rural areas. Natural disasters and internal displacement Women’s access to economic assets also cause rural homelessness in Kenya. is still a relatively new field of research The size of the homeless population and few studies address this particular is extremely difficult to determine issue. There are also no surveys of because there is no system for counting specific modules that have been it. Also, defining homelessness is included in recent household surveys. complex. There is limited research There are neither standards nor specific or information, despite growing recommendations concerning data recognition of the reality of highly collection in this domain. When data on vulnerable homeless populations, economic assets and wealth in general including street children. There are exist, they have usually been collected several groups of the homeless, at the household level rather than at therefore it is important to distinguish the individual level. This means that it is between homeless single adults, difficult to assign individual ownership homeless families and homeless youth, to assets captured in households. because the subgroups are often For example, when comparing across distinct in many dimensions. household types, it is challenging to compare households with one adult to Economic opportunities and women’s those with multiple adults. economic empowerment: A critical feature of gender data in Kenya is a lack Internal migration and urbanization: of up-to-date information on women’s Gender data on migration in Kenya economic empowerment and how are scarce and the only source has women and men fare. According to the been the census, but census data are Sector Plan for Labour and Employment often inadequate for studying circular 2013–2017, the available employment migration or temporary migration. statistics are based on the Labour There is much more to learn about Force Survey carried out in 1989– individual and household migration 1999. In addition, the forecasts and behaviour and their potential effects on extrapolation may not capture gender people and communities left behind. dimensions accurately. Knowledge gaps persist due to the lack of appropriate data with which Quality sex-disaggregated data on to interrogate multifaceted migration informal sector work and informal patterns. sector enterprises are needed because women are overrepresented in work Forced migration, trafficking in human and enterprises that are not accurately beings and smuggling of migrants: or officially counted. Understanding Forced migration results mainly from women’s experience in these areas coercion, violence, compelling political requires having detailed data on their or environmental reasons, and/or other unpaid work, including reliable time-use forms of duress. Forced migration data, the types and extent of informal is made up of some of the most

26 vulnerable and marginalized groups. A KNBS also intends to carry out a DHS common category of forced migrants by 2020, a periodic labour survey is ‘refugees’, people who flee countries beginning in 2019, with support from hit by war, violence and chaos, and the World Bank, and an agricultural who are unable or unwilling to return to survey within the same period. their home countries because they lack effective protection. Equally common At the international level, new tools are ‘asylum-seekers’, individuals who suitable for generating gender statistics apply for recognition of their refugee on economic participation and status in another country or through an empowerment have been developed. embassy, and who usually must wait, For example, under the Evidence and pending a decision from an appropriate Data for Gender Equality (EDGE) joint national government agency. project between UN Women and the United Nations Statistical Division Trafficking in human beings is the third (UNSD),36 guidelines to measure asset most lucrative illicit business in the ownership from a gender perspective world, after arms and drug trafficking. have been finalized by UNSD37 and are Human trafficking and smuggling available for use either as a stand-alone often overlap but the key difference is or incorporated into other multipurpose trafficking’s element of exploitation. household surveys; and the Smuggling migrants refers to assisting International Classification of Activities a person who is not a national or for Time Use Statistics38 (ICATUS) 2016 permanent resident of a certain has been finalized and endorsed by country to enter and remain in a State the UN Statistical Commission in March without complying with the necessary 2017 as the international framework requirements for legally entering and for the production of internationally remaining in the State. Little is known comparable time use data.39 about the status of human trafficking and smuggling in Kenya. Women and 2.4.3 Data access and utilization girls are vulnerable to gender-based Data access and utilization depends on violence in forced migration situations. the principles of data dissemination and communication. Improving access to Opportunities data and enhancing utilization requires effective ‘data communication’ that Despite the aforementioned gaps, ensures that data are transmitted, KNBS intends to carry out a variety of decoded and understood accurately, statistical reforms and national surveys and if possible, acted upon. Effective to improve the quality of data. During data communication is anchored on the the period of the MTP III, the following principle that “data should be translated are expected to be carried out: a review into information that is simple, of the Statistics Act 2006 to align it understandable and relevant”40. Data to the 2010 Constitution; enactment dissemination is a phase in statistical of the County Statistics Act to govern processes where data collected and statistical activities at the county level; compiled are released to the public. compilation of gross county economic activity; and conducting the Kenya 36. See https://unstats.un.org/edge/ Population and Housing Census in 2019 37. See https://unstats.un.org 38. https://unstats.un.org/unsd/classifications/Family/ in order to provide sex-disaggregated Detail/2083 39. See https://unstats.un.org/unsd/statcom/ data, gender statistics and information. 40. https://www.uneca.org/sites/default/files/ PageAttachments/final_adc_-_english.pdf

27 This section examines the state of KNBS data dissemination policy readiness to provide statistical data for use. Data dissemination is a mandate of the KNBS outlined in the Statistics Chapter Four of the Bill of Rights, Act 2006. The data dissemination Section 35 of the Constitution of Kenya policy was first formulated in 2012 gives every citizen right of access to and subsequently revised in 2016, information held by the State. At the outlining the framework in which KNBS international level, the First Principle disseminates all statistical products that of the UN Fundamental Principles of are generated from the institution to Official Statistics (UNFPOS) states potential users based on the UNFPOS. clearly the responsibility of releasing The policy covers the following data information to the public: types: 1) Microdata and their outputs: This “Official statistics provide includes data generated through an indispensable element in surveys and censuses at the unit the information system of a level of collection, mainly from democratic society, serving the the household, establishment Government, the economy and and individual levels. The the public with data about the dissemination of such microdata economic, demographic, social files and their related metadata and environmental situation. To and outputs shall be done via this end, official statistics that the Kenya National Data Archive meet the test of practical utility (keNADA)42 portal on the KNBS are to be compiled and made website as Public Use Files or available on an impartial basis Licensed Data Files. by official statistical agencies to honour citizens’ entitlement to 2) Macro-data and their outputs: public information.” 41 This includes all available information collected and The National Population Policy for aggregated from households and Sustainable Development recommends firms/institutions. cross-sectoral policy measures to 3) Administrative data: This address Kenyan population structures, includes data collected by public socioeconomic development, institutions in the course of environmental sustainability, rendering service to the public. reproductive health and rights, science and technology, gender equality and 4) Geo-spatial data: This includes women’s empowerment. The policy geo-referenced data used to requires strong statistical information generate electronic maps and at the national level to guide effective map print-outs. implementation via collaboration between the Government and civil Although KNBS has a comprehensive society actors; however, the policy policy on data dissemination and has framework is limited with regards to the in the recent past improved on the county level of governance. timeliness and availability of data to different users, they cite three major challenges to data use: (1) the 41. United Nations Statistical Commission. “UN Fundamental Principles of Official Statistics.” https://unstats.un.org/ unsd/dnss/gp/fundprinciples.aspx 42. https://www.knbs.or.ke/kenada/

28 continued digital divide between rural shift towards digital systems (A and and urban areas, which limits public B of the matrix), traditional forms still awareness of the advantages and exist (C and D) and are used for the opportunities of new technologies; (2) a majority of administrative data. lack of a harmonized data management system, where administrative data Use of open data portals – shift to for example are managed by specific digital and distributed systems agencies without links to KNBS; and (3) inadequate information resource To facilitate the increased utilization centres in the rural areas. of data, some countries have now embraced the concept of open data. Open data are data that can be freely Dissemination of statistics – form and used, reused and redistributed by delivery of data and statistics anyone – subject only, at most, to the requirement to attribute and share. The The increase in use of technology has definition of open data entails three key shifted the mode of delivery of data elements: and statistics in Kenya. Table 1 shows the mode and delivery currently used in 1) Availability and access: The data the Kenya NSS. While there is a growing must be available as a whole and

Table 1: Form and mode of delivery of dissemination of data

Delivery Distributed Centralized A: Digital/Distributed: B: Digital/Centralized: This is largely Digital Primarily the use of the microdata and PDF-type documents Internet, used by platforms (publications) that are made available such as the keNADA in on CDs/DVDs (optical media). KNBS, to disseminate data

Form Non- C: Non-digital/Distributed: D: Non-digital/Centralized: The digital Different statistical distribution of printed material. producers within the NSS Dissemination is usually done by disseminate printed data formal request and is usually in the form of statistical tables. The traditional type.

at no more than a reasonable datasets. reproduction cost, preferably by downloading over the Internet. The 3) Universal participation: Everyone data must also be available in a must be able to use, reuse and convenient and modifiable form.43 redistribute – there should be no discrimination against fields 2) Reuse and redistribution: The data of endeavour or against persons must be provided under terms that or groups. For example, ‘non- permit reuse and redistribution commercial’ restrictions that including the intermixing with other would prevent ‘commercial’ use, or restrictions of use for certain

43. http://opendatahandbook.org/guide/en/what-is-open- data/

29 purposes (e.g. only in education), Information System45 (IMIS) by KNBS are not allowed. starting in the mid-2000s to house all census and survey microdata in a The term ‘open data’ is very specific comprehensive, accessible database and covers two different aspects of format. IMIS is a collection of several openness: statistical databases of various surveys 1) The data are legally open, which in and censuses conducted by KNBS practice generally means that the and other government institutions, data are published under an open such as ministries. IMIS is a tool that licence and that the conditions for has been developed to enable users reuse are limited to attribution. to generate customized statistics that meet their individual needs in the form 2) The data are technically open, which means that the file is of frequencies, cross-tabulations and machine-readable and non- indicators, among others. Statistics proprietary, where possible. This generated from IMIS can be output implies that the specified data are in the form of tables, graphs and free to access for everybody, and maps at various geographical levels. the file format and its contents are Thus, IMIS, if fully operational, should not restricted to a non-open source offer opportunities that enhance software tool (often as in part A of data visualization. IMIS provides an important opportunity for collating the matrix above).44 gender statistics but the challenge is The move towards open data that there have been ongoing technical systems aims to enhance and capacity challenges in making the interoperability. Interoperability is system fully operational and sustainable the ability of diverse systems and to all. organizations to work together (inter-operate). In terms of data, it With support of the World Bank, is the ability to intermix different KNBS launched an ambitious and data sets. Interoperability is key to media-focused outreach campaign realizing the main practical benefits to raise public awareness of KNBS of ‘openness’, which is to enhance and its data and as a result, the KNBS the ability to combine different has established a strong presence data sets together and thereby to in social media, offered more user- develop more and better products friendly publications and redesigned its and services. Given that gender website46 to make it easier to navigate dimensions (domains) are impacted and find the desired contents. This by, or impact on other domains, capacity-strengthening has made Kenya or there can be causality linkages one of the most advanced African between gender and other issues, countries in terms of open access to interoperability becomes key. official statistics. However, a report from the IEG and the World Bank in 2016 In support of the move towards open noted that there is still much to be done data systems, the United Nations in order to improve the performance of Population Fund (UNFPA) provided technical support for the creation 45. There are other open system portals such as the Kenya of the Integrated Multisectoral National Data Archive (KeNADA), available at: http:// statistics.knbs.or.ke/nada/; the Kenya Data Portal at http:// kenya.opendataforafrica.org/; and those independent of KNBS such as Kenya CountrySTAT at http://www. 44. See https://www.europeandataportal.eu/en/providing- countrystat.org/home.aspx?c=KEN data/goldbook/open-data-nutshell 46. See www.knbs.or.ke.

30 the open data system. gender dimensions (e.g. 1999 and 2009 Kenya Population and Housing Census Despite improvements in data access (KPHC)) and specific fact sheets and though Internet connectivity, improved data sheets since 2008. Gender data programming and data visualization, cut across several sectors – education, it is essential that various agencies health, agriculture, environment responsible for national statistics and security, among others. They improve their websites, establish also come from several sources – data portals and use existing tools to administrative and institutional, surveys, improve access to statistics. Access census and surveillance systems. A to data derived from administrative typical challenge is going through processes could also be improved these various data sets to compile through automated data capture and gender statistics; however, a lack of transmission. Ideally, to support data interoperability often impedes the demand and use within the next five process. years, it is important that databases and information systems across the NSS be Several initiatives supported by harmonized to facilitate seamless data- development partners have been sharing. Use of data portals for data carried out to compile data in the form dissemination is a positive development of gender data sheets or booklets which allows for greater data availability by KNBS in collaboration with other and accessibility. However, it is line ministries and agencies. For important to guard against setting up example, the NGEC in partnership with multiple data portals with overlapping KNBS have in the past three years functionalities which lack integration. championed the generation and mining This tends to: of gender-specific and gender-sensitive statistics to inform budgeting, planning 1) Overload staff in National Statistical and evidence-based programming. Offices This has not progressed much mainly because of limited funding. KNBS 2) Confuse users who consult the partnered with NGEC and UN Women various data portals with often on capacity-development to train conflicting results county statistical/planning officers on gender statistics on a limited scale. 3) Lead to overall high costs for KNBS produced the Women and Men in demonstrably low usage of these Kenya booklet with the support of portals.

Availability and use of gender statistics Statistics Sweden. The booklet was in Kenya launched at the national level but needs to be disseminated at the county level. The role of official statistics has been recognized as describing, comparing UNFPA helped build national capacities and analysing the lives of all members for addressing the gender dimensions of society. This recognition was made of census and statistical data during as early as 1976 with the establishment the 2009 Census, by training national of the Women’s Bureau in Kenya. The government statisticians. In addition, production of gender statistics is, UNSD, through the Population Studies however, relatively recent, beginning and Research Institute, conducted a with special census monographs on seminar on census data analysis, in

31 which ‘gender statistics and analysis’ all recent censuses and other national was one of the core modules. UNFPA surveys. supported the production of the first Kenya Population Situation KNBS also produces extensive regular Analysis executed jointly by NCPD, national economic data geared towards KNBS and the Population Services a disaggregated analysis of income Research Institute, which provided a inequalities. In February 2018, KNBS comprehensive overview of national with support from Statistics Sweden statistics in many key areas. The report published data on Women and Men noted gaps in fertility, family planning in Kenya, deriving data from several and sexual and reproductive health sources. However, a review of the services, and underscored the need report indicates that a number of for the country to invest more heavily issues may not have been covered, in health, education and women’s despite availability from basic reports empowerment as a means of spurring published by KNBS47. Key data sources further positive economic growth in line could have included the Financial with Vision 2030. Access Household Survey (February 2016); the Kenya STEPS Survey for To strengthen the capacity of national Non-Communicable Diseases Risk stakeholders in data analysis and data Factors (2015 Report); and the Micro, use related to key mandate issues, Small and Medium Enterprises Survey UNFPA has provided limited and short- (September 2016), among others. term support to the NCPD to conduct The main reasons for lack of analysis research, advocacy and training on how were a lack of adequate financial to integrate various social issues (such resources, competing priorities with as youth, gender and disability) into other activities and limited skills on development planning and monitoring gender issues. It would be prudent to at the national and local levels. Much reanalyse these existing data sets to as the UNFPA and NCPD support provide richer information on women raised awareness within government and men, especially with regards to (especially at the national level), such economic empowerment. Further, it training requires institutionalized, will be important to establish a gender coordinated and sustained support, statistical framework for a more especially targeting the county level for systematic determination of indicators. impact. A number of trainings supported by various development partners have KNBS has demonstrated an been conducted in gender statistics organizational commitment to and analysis; however, in all these mainstreaming gender equality activities the focus has been the central and human rights in its work and government but not the devolved incorporated into its mission statement units (county officers). The trainings that it “aims at providing quality in most cases have been short-term, statistical and sex-disaggregated data not institutionalized for example in a which is key to achieving excellence government training institution, and the in performance levels based on the follow-up on uptake and utilization of fundamental principles of official the training has been weak. Secondly, statistics.” Under this organizational most capacity-building efforts currently commitment to mainstreaming, national 47. https://www.genderinkenya.org/wp-content/ data have been disaggregated by sex in uploads/2018/10/Women-and-Men-in-Kenya-Facts-and- Figures-2017.pdf

32 focus on the supply side of data. There gender statistics on some thematic is potential for providing technical topics, the information provided in support to strengthen capacities on the basic reports often lacks depth. the demand side, such as of M&E staff, Simple differences between women policymakers and data users in MDAs. and men are not enough to understand The capacities of non-official data some aspects of gender issues. A stakeholders such as media, faith-based review reveals that gender statistics organizations and community leaders provide much more information if also need to be strengthened in order there is double disaggregation. This is to improve data literacy in society. illustrated in excerpts from the Financial One limitation in this assessment Access Survey of 2016 report as shown is that effectiveness of training has below: not been examined. A typical gap in data availability and use is a lack of 1) “The target sample size for the data literacy among potential users. survey was 10,008 with 8,665 Sometimes, even though data are interviews successfully completed available, users are unable to use, representing an 87% success understand and manipulate the data. rate.”50 But the question that arises A typical example is the DHS which is is whether there were more men available and accessible from several reached compared to women. sources, such as KNBS, Measure 2) “A third of Kenyan adults report Evaluation, and Integrated Public agriculture as their main source Use Microdata Series (IPUMS). But of livelihood whereas only 12% are one aspect that is problematic is a salary employed. On average, adults culture where debates on issues are have about two sources of income, not necessarily being driven by data of which the median is KSh 6,700.”51 and where the accountability of duty- The question that arises is bearers is also not fuelled by data. whether differences exist between women and men when looking at Visibility of available gender statistics agriculture as a source of livelihood. 3) The excerpts in Figures 3 and 4 The lack of visibility of gender statistics below shows trends in financial has been noted in the way survey inclusion for men and women, reports are often presented.48 which is an important and 2013 was The 2015 The World’s Women report49 mainly attributed to increased use for many countries (Kenya included), of mobile financial services. indicated that the information collected 4) Figure 5 provides an example of is often not exploited sufficiently for where double disaggregation is gender analysis. Data are frequently useful. tabulated and disseminated in categories that are not relevant or are What would be the message if the too broad to adequately reflect gender age was further disaggregated by issues. The same can be observed in sex? From a demographic point of a number of survey reports in Kenya. view, women live longer than men and Although most of them do include financial exclusion at old age has important implications for any social 48. See UN Women. 2017. Harnessing the Digital Revolution for the Achievement of Gender Equality and Women’s Empowerment. May 2017, New York. 50. Central Bank of Kenya and Kenya National Bureau of 49. United Nations Department of Economic and Social Affairs, Statistics. 2016. FinAccess Household Survey, February Statistics Division. 2015. The World’s Women 2015: Trends 2016. p. 2. and Statistics. 51. Ibid., p. 3.

33 which is an important gender indicator. protection platform. It shows that the increase in financial inclusion for women between 2009 A number of recent surveys have gathered gender data that have not

Figure 3 Financial inclusion for men in Kenya

2016 50.4 28.9 0.41 16.2

2013 39.1 31.1 14.7 24.1 EAR 2009 25.7 17.6 4.8 19.5 32.4

2006 19.6 4.2 9.4 26 46.7

0 20 40 60 80 100

PERCENTAGE

FORMAL PRUDENT FORMAL NON PRUDENT FORMAL REGSTERED NFORMAL ECLUDED

Source: Central Bank of Kenya and Kenya National Bureau of Statistics. 2016. FinAccess Household Survey. p. 41

Figure 4 Financial inclusion for women in Kenya

2016 34.6 36.1 0.5 10.2 18.5

2013 26.1 36 0.7 10.7 26.5 EAR 2009 16.7 13.4 3.6 33.3 33

2006 10.7 3.7 6.1 37.7 41.7

0 20 40 60 80 100

PERCENTAGE

FORMAL PRUDENT FORMAL NON PRUDENT FORMAL REGSTERED NFORMAL ECLUDED

Source: Central Bank of Kenya and Kenya National Bureau of Statistics. 2016. FinAccess Household Survey. p. 41

34 Figure 5 Financial access for Kenyans, by age

1825 35.6 33.7 0.1 7.5 23.1

2635 49.5 32.8 0.2 6.6 10.8

3645 47.1 33.5 0.5 6.2 12.4 AGE

4655 44.1 33.5 0.6 6.8 14.9

55 32.3 28.8 1.1 9.5 28.4

0 20 40 60 80 100

PERCENTAGE

FORMAL PRUDENT FORMAL NON PRUDENT FORMAL REGSTERED NFORMAL ECLUDED

Source: Central Bank of Kenya and Kenya National Bureau of Statistics. 2016. FinAccess Household Survey Page 41.

been analysed and which could be gender may be confusing and request mined to address some of the data that concepts, definitions, indicators gaps. There is a need to explore the and tools be harmonized in the potential of mining existing databases statistical production process. KNBS and data sets prior to engaging in and other agencies need to define the new data collection. This could clear and standardized methodology, improve the lack of visibility of gender concepts and processes, to define statistics in Kenya in a number of clear gender indicators and their sectors, especially for institutional and metadata and make them available to administrative data. In addition, several potential users. The development of public-sector agencies (except KNBS manuals and guidelines for producing and NCPD) make inadequate use of gender statistics for harmonization their websites. If innovatively utilized, a and comparability is being done at the stronger web presence would enhance international level53 but there is a need their visibility. Given the status of data to domesticate these efforts. literacy and the ability to manipulate data, there is a need to present gender Training on gender statistics statistics on web portals and, where possible, link portals so that users can There is no institution in Kenya that has directly access the information without a specific course in gender statistics, further manipulation. A lesson can be but some institutions cover gender drawn from the presentation of gender statistics in core and specialized statistics on the web portal of the units. Most of these programmes are Uganda Bureau of Statistics.52 at the postgraduate level rather than the undergraduate level. In certain Some potential users and producers 53. Two examples of manuals on methodologies and analytical of data indicate that the concept of information necessary to improve the availability, quality and use of gender statistics are the: UNSD. 2016. Gender Statistics Manual: Integrating a gender perspective into statistics, and the UNECA. Undated. Gender Statistics 52. https://www.ubos.org/publications/statistical/67/ Toolkit.

35 social science disciplines, the teaching latest developments in statistics, and of statistics is limited as a tool for research. In addition, there are other an application of advanced methods to institutions that train applied statistics investigate social science questions. at college levels lower than university. The Institute of Development Studies The Population Studies and Research (IDS) of the University of Nairobi is Institute of the University of Nairobi one of the oldest and best-established offers courses in population studies research institutes in Africa. It is a which cover all data and statistics multipurpose and multidisciplinary issues related to gender issues. The institute which not only focuses on focus of the training is not only on training but also research on social theoretical models and tools to and economic issues of development examine population growth, the age in Kenya and the rest of Africa. IDS and gender structure and the spatial offers a specialized course on gender distribution of populations, but also and development that includes gender on the concepts and measures used to statistics. The training at the institute describe and compare levels and age involves not only class work but also patterns in demographic processes – active participation in many research including marriage, fertility, mortality projects at the institute. and migration. In addition to core demographic studies, additional The Institute of African and Gender elective courses such as analysis of Studies offers a Master of Arts degree in labour force dynamics and gender Gender and Development, but statistics issues in demographic analysis are are taught as a tool for supporting offered, such as on the interrelationship research. The focus of the programme between economic activity and is to investigate pertinent gender issues demographic behaviour (e.g. female in African societies and their dynamics labour force participation and fertility using anthropological techniques. rates, labour migration and investment in human capital, data issues and Preparations for dissemination of SDG methods in gender measurements, and data and indicators analyses in demography). In line with the Kenya Roadmap for In the past decade, schools of SDG reporting (including gender), mathematics under applied statistics Kenya’s NSS has prepared an programmes have developed advocacy and awareness-creation postgraduate courses in social statistics. strategy. Information, education and These developments have occurred communication materials on the SDGs because of improvements in social have been produced and disseminated, and economic data collection. In the including on the use of social media School of Mathematics at the University platforms to disseminate SDG messages of Nairobi, the Masters Programme in to the public. Sensitization forums Social Statistics focuses on high-level reaching different stakeholders have training in the theory and application of been held. In collaboration with the modern statistical methods, with special Government, the civil society coalition reference to the design and analysis of on the SDGs has also been undertaking social science studies, including large community outreach programmes and complex data sets, study of the on the 2030 Agenda. But awareness-

36 raising may not be sufficient on its survey and census data under their jurisdiction. The key lesson here is that own. It needs to be supplemented users should always be notified about by creating interest among intended improvements in data and what the users, particularly those who may data contain. want to carry out further analysis of the secondary data created. Lessons The other aspect of motivating users to learned from other producers of data increase utilization of data, particularly show a need for a periodic review of younger academic students, is to available data sets to create awareness partner with agencies, the private and interest, as illustrated in Box 2 sector and others to offer awards. A (which is an excerpt from the IPUMS typical illustration is shown in Box 2. database). This can also be applied to other users in both the public and private sector Another example is from the DHS through the programmes offered. programme, which also informs users about the state of data and Key challenges in availability and any improvements in both data and access to national gender statistics metadata. For example, the latest message from the Release of Kenya Discussions with both producers DHS 2014 Datasets reports the and users in general confirm the following: previously identified international comparisons that statistical systems “The latest data for the are characterized by underfunding, Kenya DHS 2014 survey are reliance on donor support (particularly now available for download for household surveys), and a very weak to all registered users with administrative data system.55 access to Kenya surveys. The difference between Version There are claims from numerous 70 and Version 71 is mostly stakeholders that lack of training documentation – written and awareness-raising are the main notes and concise labelling of factors behind inadequate demand variables. Nothing that is used and use of gender statistics. However, to calculate major indicators it is important to note that part of the like mortality, fertility, family problem relates to misconceptions planning, and the like, nor any of gender-related terms and of the weights were changed. misunderstanding of gender statistics. Data sets can be requested/ The low data literacy and capacity to downloaded at: https:// access, analyse and use data reflects an www.dhsprogram.com/data/ inability to effectively signal demand for dataset_admin”.54 existing data. In many cases, the data are not analysed, so users find them The IPUMS and DHS programme complicated. Further, inadequate data provide the best lessons for enabling disaggregation – especially by sex, age, users to access data, which is very wealth quintile, region (rural/urban) and critical for KNBS to apply not only to persons with disability – undermines gender statistics but also to all the

55. Independent Expert Advisory Group on the Data Revolution for Sustainable Development. 2014. A World 54. Government of Kenya. 2014. Demographic Health Survey That Counts: Mobilizing the Data Revolution for Sustainable Release. Accessed 12 February 2018. Development.

37 BOX 2 Creating interest for readers to use data – an example

REMINDERS Time Use Conference: Apply to present at the Time Use Across the Life course at the University of Maryland. Papers on any topic related to variation across the life course in how individuals use their time are welcome. To learn more, visit (https://www.popcenter.umd.edu/research/ sponsored-events/tu2018). Applications due 16 February 2018.

IPUMS Research Awards: We are now accepting submissions for the 10th annual IPUMS Research Awards. To learn more visit ipums.org/award (https://www.ipums.org/award.shtml). Submissions due 1 March 2018.

CPS Workshop: IPUMS CPS is accepting applications for its summer workshop, designed to familiarize researchers with the under-utilized panel component of the CPS. To learn more, visit https://cps.ipums.org/cps/ workshops.shtml

Source: IPUMS database: Census Microdata for Comparative Research the extent to which the data can be frameworks, as well as the limited used to inform the implementation and popular constituency pushing for data- monitoring of the SDGs. driven decision-making. These problems also hamper the funding of relevant Reports from a review of statistical institutions that produce data. There is supply suggest that several other also limited collaboration between data challenges exist56. These include but are producers and users, especially in the not limited to the high costs of data, design of data-collection instruments the overreliance on survey data for that would enable data producers most indicators, technology (high cost to take the needs of data users into of infrastructure and rapidly changing consideration. software and hardware requirements), services (availability and retention of A report on submissions by the SDGs personnel involved in data archiving, Kenya Forum on the voluntary national retrieval and data visualization), barriers review of progress on the SDGs in to opening and sharing data (among Kenya noted that key constraints to various producers of statistics) due data use were that high-quality, timely to a silo mentality, and the deluge of and reliable data on different groups unused data (lack of further analysis of like persons with disability, youth, older many national surveys to provide depth persons, orphans and marginalized and most information is just reported communities are unavailable, baseline in basic reports). Most paramount data on a majority of indicators of problems related to the supply of are lacking, and there is limited data are the lack of trust and siloed disaggregation of available data. data communities and/or institutional

56. https://paris21.org/sites/default/files/PARIS21- DiscussionPaper4-Demand.pdf

38 39 SECTION 3 CONCLUSIONS AND RECOMMENDATIONS

This section provides a summary of gender data at subnational levels are key conclusions with regard to gender inadequate. statistics gaps in Kenya and suggested recommendations. It is organized 3.1.2 Data production along the following thematic areas: the Most indicators selected for monitoring existence of an enabling environment the SDGs are based on KDHS and to produce and use gender statistics; KIHBS. This means that Kenya cannot the production of gender statistics, meet the expectation of regular, with special focus on gender-specific periodic up-to-date monitoring of key SDG indicators; and the status of data SDGs indicators on gender-related accessibility and use. issues. Some indicators are dependent on administrative registers but the key 3.1 Summary of findings and issue with administrative data is the conclusions lack of interoperability and a lack of standards to guide production of data 3.1.1 Enabling environment by other producers in the NSS. In some First off, current policy and legislative cases, gender statistics may be missing frameworks, especially the Statistics from administrative data because some Act 2006, are not in tandem with sector strategies lack inputs on gender constitutional requirements regarding dimensions – or are gender-blind. the use of data and information and 3.1.3 Data use do not specifically refer to or demand gender statistics. Secondly, current Effective data communication must legislative arrangements do not comply conform to the principle that data with the key principles of the African should be translated into information Data Revolution, to which Kenya has that is simple, understandable and ascribed. Thirdly, county governments relevant. It is important to note that do not have a policy or legal framework data communication and dissemination to guide statistical activities and gender should go beyond awareness-raising statistics in particular. Fourthly, there and be supplemented by creating is still no policy or legislative bill on interest among the intended users, the implementation of monitoring and particularly those who may want evaluation (M&E) activities relating to carry out further analysis of the to gender statistics. As a result, the secondary data available. systems necessary to generate and use

40 Information collected from recent 3.2 Recommendations surveys is often insufficiently exploited for gender analysis, resulting in 3.2.1 Enabling environment unused data. Lack of training and From this review of the impact of awareness are the main factors behind the enabling environment on the inadequate demand for, and use of, production and use of gender statistics, gender statistics. Part of the problem the recommendations are as follows: also relates to misconceptions around 1) There is a need for policy and gender-related terminologies and a legislative frameworks that general misunderstanding of gender support the development of statistics. The low data literacy and data: a governance policy and limited capacity to access, analyse framework, quality assessment and and use data reflects an inability to assurance, and communication effectively signal a demand for existing and dissemination, including an data. There is also limited data-sharing open data policy to address the between the various national and legislative and policy gap. Other subnational statistical agencies that data-producing entities that do produce data. not have the requisite legislative frameworks (especially county A number of public sector agencies governments) should put in place do not make adequate use of their the requisite policies and legislative websites. If web-based platforms were frameworks that will enable them innovatively utilized, this would enhance to collect and analyse data on the visibility of their gender statistics. devolved functions (such as health, The majority of data dissemination pre-primary education and local still relies on traditional, non-digital trade), which are all important for and centralized modes of distribution the implementation of the SDGs. of printed material. Dissemination is Specifically, KNBS should: still done through formal requests for statistical tables, which limits reuse • Update the national strategic and redistribution, including their statistical master plan to meet intermixing with other data sets – a key the data needs of the present feature of open data system principles. development agenda (Vision 2030, MTP III, CIDP 2, the SDGs) Moreover, statistical methods and gender statistics are still lagging • Fast-track the adoption and behind in some subject areas (such implementation of the revised as environment and asset ownership), Statistics Act to align it to the while in other areas (such as labour Constitution, and ensure that the force, time use and agriculture) there is Act includes gender dimensions. a need for new data. • Strengthen the coordination mechanism on gender statistics within KNBS (the Gender Statistics Unit within the Social Statistics Department), and also the Technical Committee on Gender Statistics.

41 2) Based on the Devolution Policy of 3.2.2 Data production 2016, Objectives 5 and 10, KNBS, in collaboration with stakeholders, To address challenges in the production should support county and use of data, recommendations are governments to: as follows: 1) To improve the status of data • Develop their own statistical production and use, adequate plans. funding is required at all levels. There is a need to engage oversight • Establish a sound county institutions to legislate for statistical system that is gender- budgets for data production, and integrated. dissemination and, monitoring and 3) Gender statistics cut across several evaluation. sectors and generate relevant 2) There is a need to update existing statistics from various sources, databases (especially KDHS and in particular institutional and the population census) and collect administrative data. Each sector data where they are completely needs to plan for data acquisition missing, especially data on child and use. Therefore, KNBS and labour, time use (which is important stakeholders should support in estimating the contribution sectors to develop sector-specific of women in the economy) and strategic plans for statistics. Such population movement. In addition, plans should include but not be efforts should be made to align limited to: statistical development the definition of indicators under programmes, reviews of data- the SDGs with the way they are collection instruments, compiled captured in the databases such as inventories of statistics, updated KIHBS, KDHS and the population analysis tools and staff skills- census. There is also a need to strengthening. ensure that data are disaggregated 4) Sectoral and county statistical as per the SDG indicators, (e.g. by plans should be integrated into sex, region (rural/urban), persons the national statistical strategy, with disability and wealth quintiles). focused on working towards data 3) There is a need for all data interoperability and comparability. producers to compile a metadata 5) To improve the status of data file for all existing data and for production, adequate funding is each SDG indicator and indicator required at all levels. There is a need information sheet, which will to engage oversight institutions enhance access and use of data to legislate for budgets for data and information. An example of production, dissemination, as well an indicator information sheet is as monitoring and evaluation. shown in Annex 3.

42 4) Mechanisms and processes for as the websites of KNBS, SDGA, communicating with data users NCPD and even universities. need to be strengthened and enhanced. There is a need for 9) There is a need to develop a improvements in data visualization programme to institutionalize and access. the strengthening of gender data literacy, beginning with professional 5) Data-sharing between various statisticians, data scientists and national and subnational statistical data managers. It is clear that agencies and international the need for training; manuals on organizations should be concepts, indicators and methods strengthened, while managing in gender analysis; and workshops privacy concerns. There is a need for raising awareness and sharing for mechanisms to provide for experiences is still enormous and automatic data-sharing between must be emphasized. agencies for statistical purposes. • Strengthening of data and 6) There is a need for NGEC and statistical literacy should begin SDGA in collaboration with NCPD by data mining existing data sets and KNBS to start a repository that have not been exploited on research and qualitative data in-depth, building on what has (including databases, data portals, already been done by others, open access study reports/journal such as Statistics Sweden. papers, and blogs). • Programmes should be initiated 7) Consultations with data users that use the existing education should be enhanced given that they system to embrace the wider benefit both the data producer and population, by improving teacher the user experience in NSSs. This training in numeracy, adapting will also go a long way in improving syllabi, creating educational perceptions of transparency materials and advancing an and collaboration, which are the appreciation for gender statistics. foundations of trust-building in data use by stakeholders. • There is a dearth of qualitative data on gender, which is 8) New methodological guidelines necessary for providing an have been produced by understanding of women’s international organizations, to capabilities and participation improve the availability, quality in all spheres of life (economic, and international comparability of social, political). Thus, in addition gender statistics. To exploit these to collecting and updating opportunities and challenges, relevant gender statistics, there there is a need for KNBS and other is a need to undertake research, stakeholders to review and develop especially in areas where there a simple manual or handbook has been little improvement. This including definitions of key would help in designing effective concepts for use by staff and other measures for implementing the audiences. Such a handbook can be SDGs. posted in open access systems such

43 SECTION 4 KEY ENTRY POINTS FOR UN WOMEN

4.1 UN Women’s experience • Technical assistance was provided and comparative advantage in the development of the gender chapter of the current draft of the • UN Women led UN efforts in Kenya MTP III 2018–2022. to ensure a gendered lens for the UN Development Assistance Framework. • UN Women also provided assistance in partnership with KNBS in the • In 2014, UN Women provided training of statisticians on gender technical assistance to the indicators within the SDG indicators Monitoring & Evaluation Department – including a discussion on tools that to develop gender indicators in the would be used in collecting data. Medium-Term Plan II (2013–2017 cycle). • Gender and Statistics is one of the flagship programmes in the • In partnership with the Ministry of Kenya Development Programme Devolution and Planning, since 2013 and UN Women provided technical UN Women has partnered with the assistance to the National Treasury KNBS in training statisticians on to update the Standard Chart the Gender and Economic Policy of Accounts so as to include Management Initiative. gender as a budget line (for the purpose of tracking gender-related • In the Medium-Term Expenditure expenditures). Framework guidelines FY 2016/20 (3 years), UN Women supported all • Other than KNBS, UN Women in MDAs to uphold gender-responsive 2016 provided technical assistance budgeting principles by ensuring to the Council of Governors that all programmes and projects (COG) to undertake a gender submitted to the National Treasury rapid assessment in 10 counties to have sex and gender-disaggregated provide information on the level of data. commitment of County governments in gender mainstreaming. • In 2017, during the development of the MTP III, UN Women in • In 2017, UN Women supported the partnership with KNBS provided training of statisticians from seven technical assistance during the counties on gender indicators in the adoption of 128 SDG indicators, of SDGs including discussion on tools which 34 were gender-specific. that would be used in collecting data in partnership with KNBS.

44 4.2 Entry points The key entry points for UN Women are in line with UN Women’s gender As the lead UN agency in gender data programme Women Count. The equality and women’s empowerment, programme is intended to support UN Women Kenya will leverage its Member States implement the 2030 mandate to link normative and technical Agenda, with a radical shift in the advances to fill critical gender statistics production, availability, accessibility and gaps in the country. UN Women will use of quality data and statistics on key utilize its coordination mandate to make aspects of gender equality and women’s use of its wide network of partners, empowerment. Such improvements including government departments aim to inform policy, advocacy and and agencies, multilateral agencies and accountability for delivering gender civil society, to bring actors together to equality commitments in the SDGs, ensure effective implementation of this CEDAW, the Beijing Platform for Action initiative. and national priorities. Hence, the project goal is to ensure that: “Gender The proposed activities in the statistics are available, accessible programme aim to provide a road map and analysed to inform policymaking, for the essential work of monitoring advocacy and accountability for gender-related SDG indicators in Kenya. delivering gender equality and women’s Investing in it will enable the creation empowerment”. of an integrated evidence base that can inform more effective and targeted decision-making to reach those lagging behind and make meaningful and lasting changes in the lives of women and girls in Kenya.

45 Available from 2015/16 2015/16 from Available KIHBS and shall be published in the report. poverty forthcoming 2015/16 from Available KIHBS and will be published poverty in the forthcoming is divergence The report. was the UNICEF study that KIHBS is an MPI whereas consumption-based. be disaggregated Needs to further. is no denominator There expected measure to indicators. Recommendation Source Small area Small area estimates census from data KNBS & UNICEF study Social Kenya Protection Single Database Registry Reference Reference year 2009 2017 2016/17 45.2 (Total); 50.5 (Rural); (Rural); 50.5 45.2 (Total); 33.5 (Urban) <18 yrs); 45 (children 56 19 (Urban, <18yrs); – refers <18 yrs) (Rural, years 0–17 children to in deprived who are dimensions or more three Health, Water, (Education, Nutrition, Sanitation) males, – 286,000 CT-OVC HSNP – females; 77,000 males, 178,000 137,000 OPCT – 75,000 females; females; males, 104,000 PwSD-CT – 22,000 females; males, 7,000 males, WFP-CT – 89,000 do Data females. 16,000 among not distinguish unemployed children, persons, older persons with disabilities, women, pregnant work-injury newborns, victims, the poor and vulnerable. Data available Data availability for gender-related SDG indicators gender-related for ta availability Proportion of men, women and of men, women Proportion in of all ages living in poverty children national to all its dimensions according definitions by covered of population Proportion by floors/systems, social protection among children, distinguishing sex, persons, older unemployed persons with disabilities, pregnant victims, work-injury newborns, women, the poor and vulnerable Indicator living below of population Proportion and sex by line, poverty the national age Goal Goal 1: End in poverty all its forms everywhere ANNEXES 1: Annex of da Status

46 Needs to be updated. Needs to be updated. Needs to Only estimates/projections, find Need to no actual data. of capturing the data ways better. be updated. Needs to be updated. Needs to Recommendation Source KDHS 2014. DHS is only and proxy provides information is not that timely. KDHS 2014 VNR 2017 (original not source indicated) KDHS 2014 KDHS 2014 Reference Reference year 2014 2014 2016 2014 2014 362 deaths per 100,000 per 100,000 362 deaths births live 61.8% uninfected 146 per 1,000 with family of women Rate planning needs satisfied Contraceptive 70.7%. was is 58%. Rate Prevalence for women 96.3 per 1,000 10–14 for Data years. 15–19 not available Data available Data Proportion of births attended by by of births attended Proportion skilled health personnel per HIV infections Number of new sex, by population, uninfected 1,000 populations age and key of reproductive of women Proportion their who have years) age (aged 15–49 with planning satisfied family need for modern methods (aged 10–14 birth rate Adolescent per 1,000 years) aged 15–19 years; age group in that women Indicator mortality ratio Maternal Goal Goal 3: Ensure lives healthy and promote well-being all all at for ages

47 Captured in EMIS strategic in EMIS strategic Captured plan, but no implementation plan. No data. collect Need to plans yet. framework No organized governments county for even this data, collect to though pre-primary in available data enrolment should relate KIHBS. Data not entire pre-primary, to early childhood. Education those aged 3+. concerns – Not comprehensive non-formal most excludes though schooling. Even asks about KIHBS 2015/16 and duksis. madrassas using Can be calculated all for KIHBS data 2015/16 indigenous except groups peoples, people with disabilities and those who conflict-affected. are as is fixed level What functional? No standardized measuring for criteria numeracy. Recommendation Source VNR 2017 (Early Childhood Development in statistics Economic Survey) Voluntary National 2017 Review (original – source Economic survey) KPHC 2009 KDHS 2014 Reference Reference year 2016 2016 2014 Data not available Data not available. Data 74.9% in TIVET was Enrolment (91,209 – Male 202,556 – Female). and 74,432 enrolment education Adult – Male (85,575 271,769 was – Female) and 186,194 of girls for in favour 0.97, No primary education. further disaggregation. of education Other levels not available. and in literacy Proficiency per 89.1 reaches numeracy and – Women (87.8 cent – Male) 92.4 Data available Data Proportion of children under 5 years of under 5 years of children Proportion on-track developmentally age who are in health, learning and psychosocial sex by well-being, learning in organized rate Participation the official primary before year (one entry age) and adults of youth rate Participation education and non-formal in formal 12 months, in the previous and training sex by rural/ (female/male, indices Parity quintile wealth urban, bottom/top and others, such as disability status indigenous peoples and conflict- for available) become as data affected, that on this list indicators all education can be disaggregated in a given of population Percentage a fixed least at achieving age group in functional of proficiency level sex skills, by and numeracy literacy Indicator and young of children Proportion the at (b) 2/3; people: (a) in grades the end of at end of primary; and (c) least at achieving secondary lower in (i) level a minimum proficiency sex by and (ii) mathematics, reading Goal Goal 4: Ensure and inclusive equitable quality education and promote lifelong learning opportunities all for

48 Recommendation Data need to be collected be collected need to Data and updated. Documents on data Documents on data existing provide available frameworks Source Ministry of Ministry Education and Science Technology Booklet 2014 ES 2016 See ‘data See ‘data available’ column Reference Reference year 2014

Connected to electricity to Connected with 43.8% of primary for schools and 75.3% schools. No secondary other data. Data available Data Various policy and legal policy Various been have frameworks to developed/enacted and enforce promote, equality and monitor non-discrimination (including Constitution; Gender and National Draft Empowerment Women Marriage Act The Policy; Matrimonial The (2014); (2013); Act Properties Against Protection The Act Violence Domestic Citizenship Kenya (2015); Act and Immigration Prohibition The (2011); Genital of Female (2011); Act Mutilation (Medical Offences Sexual Regulations Treatment) and the Sexual (2012); Rules of Court Offences among others) (2014), Indicator to: of schools with access Proportion (a) electricity; pedagogical for the Internet (b) purposes; pedagogical for computers (c) purposes; and infrastructure adapted (d) with disabilities; students for materials basic drinking water; (e) basic sanitation (f) single-sex basic hand-washing and (g) facilities; indicator (as per the WASH facilities definitions) Whether or not legal frameworks Whether or not legal frameworks enforce promote, to in place are equality and and monitor on the basis of sex nondiscrimination Goal Goal 4: Ensure and inclusive equitable quality education and promote lifelong learning opportunities all for Goal 5: Achieve gender equality and all empower and women girls

49 Recommendation Needs to be updated. Needs to Needs to be updated. Needs to Needs to be updated. Needs to Needs to be updated. Needs to

How are managerial are How positions defined in public/ Where sectors? private find the official can we statistics? Source KDHS 2014 KDHS 2014 KDHS 2014 KDHS 2014 Inter Inter Parliamentary Union

Reference Reference year 2014 2014 2014 2014

Data available Data 36.9% experienced 36.9% experienced or sexual physical, violence; psychological gender- 11.5% experienced based violence. 22.7%. Among never- 22.7%. married women, of sexual perpetrators strangers were violence (43.8%); friends or (14.4%), acquaintances boyfriend current/former (8.2%), family/friends (5.8%), (6.9%), teachers (4.7%), father/stepfather among others. 22.90% 21%, but not age. by disaggregated 21.8% (National Assembly); Assembly); 21.8% (National 0.0 (Senators); 30.9% (Deputy 19.2 (Governors); Governors) No national statistics. No national Indicator women of ever-partnered Proportion and older and girls aged 15 years or sexual physical, to subjected or a current by violence psychological partner in the previous intimate former and by of violence form 12 months, by age Proportion of women and girls aged of women Proportion sexual to and older subjected 15 years persons other than an by violence partner in the previous intimate of age and place 12 months, by occurrence Proportion of women aged 20–24 aged 20–24 of women Proportion married or in a union who were years age 18 age 15 and before before Proportion of girls and women aged of girls and women Proportion undergone who have years 15–49 by genital mutilation/cutting, female age Proportion of seats held by women women held by of seats Proportion parliaments and local in national governments Proportion of women in managerial of women Proportion positions Goal Goal 5: Achieve gender equality and all empower and women girls

50 Recommendation Needs to be updated. Needs to

Needs to be updated from from be updated Needs to census. 2019 Flagship area in MTP Flagship area to difficult III. Currently of proportion estimate gender goes to budget that equality and empowerment. Source KDHS 2014 See ‘data See ‘data available’ column KPHC 2009

2014 Reference Reference year

2009

Data available Data 38.60% Several policy and policy Several frameworks, legislative Constitution including: The Vision Kenya of Kenya; Reduction Poverty 2030; Health National Strategy; Health National Policy; Plan; Strategic Sector Public (2017); Health Act Strategy; Reform Service Reform. and Health Sector Mobile phone ownership Mobile phone ownership – urban (80.2%); No (52.7%). rural sex by disaggregation No system Proportion of women aged 15–49 aged 15–49 of women Proportion informed their own who make years relations, sexual decisions regarding use and reproductive contraceptive health care Indicator Number of countries with laws and with laws Number of countries women guarantee that regulations and sexual to access years aged 15–49 information health care, reproductive and education Proportion of individuals who own a of individuals who own Proportion sex by mobile telephone, Proportion of countries with systems with systems of countries Proportion public allocations and make track to gender equality and women’s for empowerment Goal

51 Difficult to capture because capture to Difficult Recommendation Data will be published in Data KNBS the forthcoming labour report. will be Earnings data KNBS in by collected LFS. quarterly forthcoming Data will be published in Data KNBS Labour forthcoming Report Last child labour survey child labour survey Last in 1998. MICS is county- was regarding specific. Debate definition of child labour. will be published in Data KNBS Labour forthcoming Report. compliance is national How measured? is not a stand-alone tourism accounts. in national sector Source ES, KIHBS, MSME, LFS KPHC 2009 Source and Source not year indicated from (figure NVR) draft Source and Source not year indicated Reference Reference year 2009

82.7%, but not 82.7%, sex by disaggregated not available Data Data available Data 9.7% (males – 9.9%; (males – 9.9%; 9.7% no – 9.4%), females age by disaggregation persons with and/or disability 34.5%, not disaggregated 34.5%, not disaggregated and age sex by a) 3.09 b) -0.4, not -0.4, b) a) 3.09 sex by disaggregated Indicator in employment of informal Proportion sex by employment, non agriculture and hourly earnings of female Average age occupation, by male employees, and persons with disabilities Unemployment rate, by sex, age and sex, by rate, Unemployment persons with disabilities Proportion and number of children and number of children Proportion engaged in child years aged 5–17 and age sex by labour, Increase in national compliance of compliance in national Increase of association labour rights (freedom based on bargaining) and collective Labour Organization International and national sources textual status and migrant sex by legislation, industries Number of jobs in tourism jobs and of total as a proportion sex of jobs, by rate growth Goal Goal 8: Promote sustained, and inclusive sustainable economic growth, full and productive employment and decent all for work

52 Leading UN Leading agencies UNSD UNSD ILO ILO ILO ILO ILO ILO ILO ILO WB/FAO/OECD Status of data at at of data Status level national not available not available too old too FinAccess FinAccess household survey not available not available establishment establishment surveys Statistical Statistical abstract not available too old too Fin access Fin access household survey Tier 2 2 1 1 1 1 3 1 2 1 3 References to to References the strategic in the objective Beijing Platform Action for C.2, F.1, H.3 C.2, F.1, F.1, H.3 F.1, F.1, H.3 F.1, F.2 H.3 F.1 F.1, F.2 F.1, F.5, H.3 F.5, F.2, H.3 F.2, F.1 F.1, F.2 F.1, Indicator Average number of hours spent on unpaid domestic number of hours spent on unpaid domestic Average and child care housework Separate (Note: sex by work if possible) Average number of hours spent on paid and unpaid Average sex by burden), work (total combined work domestic Labour force participation rate for persons aged 15–24 persons aged 15–24 for rate participation Labour force sex and 15+, by Proportion of employed who are own-account workers, workers, own-account who are of employed Proportion sex by Proportion of employed who are contributing family family contributing who are of employed Proportion sex by workers, Proportion of employed who are employers, by sex by employers, who are of employed Proportion Percentage of firms owned by women, by size women, by owned of firms Percentage Percentage distribution of employed population by by population of employed distribution Percentage agriculture, to refer here (sectors each sex sector, services) industry, Informal employment as a percentage of total non- of total as a percentage employment Informal sex by employment, agricultural Youth unemployment rate for persons aged 15–24 by by persons aged 15–24 for rate unemployment Youth sex Proportion of population with access to credit, by sex by credit, to with access of population Proportion Indicator # Indicator I. Economic structures, participation in productive activities and access to resources to activities and access in productive participation structures, I. Economic 1 2 3 4 5 6 7 8 9 10 11 Annex 2: Annex indicators – quantitative Minimum set of gender indicators

53 Leading UN Leading agencies WB/FAO/OECD ILO ILO ILO OECD ITU ITU ITU UIS UIS UIS UIS UIS UIS UIS UIS UIS Status of data at at of data Status level national not available not available not available not available not available not available not available DHS/Census Census EMIS EMIS EMIS EMIS not available possible but not possible available EMIS Census Tier 3 3 2 3 3 1 1 3 1 1 1 1 1 1 1 1 1 References to to References the strategic in the objective Beijing Platform Action for A.1, A.2 A.1, F.1, F.5 F.1, F.5 F.5 F.6 F.6 F.3 F.3 F.3 B.2, L.4 B.1, L.4 B.1, B.1 B.1 B.1 B.1, L.4 B.1, B.3, B.4, L.4 B.4, L.4 B.1 B.1 Indicator Proportion of adult population owning land, by sex land, by owning of adult population Proportion Gender gap in wages Proportion of employed working part-time, by sex by part-time, working of employed Proportion Employment rate of persons aged 25–49 with a child of persons aged 25–49 rate Employment under age 3 living in a household and with no children sex living in the household, by Proportion of children under age 3 in formal care under age 3 in formal of children Proportion Proportion of individuals using the Internet, by sex by of individuals using the Internet, Proportion Proportion of individuals using a mobile cellular of individuals using a mobile cellular Proportion sex by telephone, Proportion of households with access to mass media mass to of households with access Proportion of household head sex by Internet), TV, (radio, Youth literacy rate of persons (15–24 years), by sex by years), of persons (15–24 rate literacy Youth Adjusted net enrolment rate in primary education by by in primary education rate net enrolment Adjusted sex Gross enrolment ratio in secondary education, by sex by education, in secondary ratio enrolment Gross Gross enrolment ratio in tertiary education, by sex by education, in tertiary ratio enrolment Gross Gender parity index of the gross enrolment ratio in ratio enrolment of the gross Gender parity index education and tertiary secondary primary, Share of female science, engineering, manufacturing engineering, manufacturing science, of female Share level tertiary at graduates and construction Proportion of females among tertiary education education among tertiary of females Proportion or professors teachers Adjusted net intake rate to the first grade of primary grade the first to rate net intake Adjusted sex by education, Primary education completion rate (proxy), by sex by (proxy), rate completion Primary education Indicator # Indicator 12 13 14 15 16 17 18 19 II. Education 20 21 22 23 24 25 26 27 28

54 Leading UN Leading agencies UIS UIS UIS UNPD UNICEF/UNPD/WHO WHO/UNICEF/ UNFPA UNICEF UNICEF WHO WHO UNAIDS WHO UNPD WHO Status of data at at of data Status level national Census EMIS Census/DHS DHS DHS/Census DHS/Census DHS DHS Global Tobacco Global Tobacco Survey STEPs KAIS KAIS Census Health information systems, are but data incomplete Tier 1 1 1 1 1 1 1 1 1 1 1 1 1 1 References to to References the strategic in the objective Beijing Platform Action for B.1 B.1 B.1 C.1, C.2 C.1, C.1 C.1 C.1 C.1 C.1 C.1 C.2 C.1, C.2 C.1, C.3 C.3 C.1, C.2 C.1, C.1, C.2 C.1, Indicator Gross graduation ratio from lower secondary secondary lower from ratio graduation Gross sex by education, Effective transition rate from primary to secondary secondary primary to from rate transition Effective sex by programmes), (general education Educational attainment of the population aged 25 and of the population attainment Educational sex by older, Contraceptive prevalence among women who are who are among women prevalence Contraceptive married or in a union, aged 15–49 Under-five mortality rate, by sex rate, mortality Under-five Maternal mortality ratio Maternal Antenatal care coverage care Antenatal Proportion of births attended by skilled health by of births attended Proportion professional Smoking prevalence among persons aged 15 and over, among persons aged 15 and over, Smoking prevalence sex by Proportion of adults who are obese, by sex by obese, of adults who are Proportion Women’s share of population aged 15–49 living with aged 15–49 of population share Women’s HIV/AIDS Access to antiretroviral drugs, by sex drugs, by antiretroviral to Access Life expectancy at age 60, by sex by age 60, at expectancy Life Adult mortality by cause and age groups mortality by Adult Indicator # Indicator 29 30 31 III. Health and related services III. Health and related 32 33 34 35 36 37 38 39 40 41 42

55 Leading UN Leading agencies IPU IPU ILO UNODC UNODC WHO/UNSD/ UNICEF WHO/UNSD/UNICEF UNICEF UNICEF UNPD

Status of data at at of data Status level national NGEC NGEC NGEC Administrative Administrative NGEC SDGA Administrative NGEC SDGA DHS DHS DHS DHS DHS/Census 18 52 34.6 Tier 1 1 1 2 2 2 2 1 1 1

References to to References the strategic in the objective Beijing Platform Action for G.1 G.1 F.1, F.5, G.1 F.5, F.1, I.2 I.2 D.1, D.2 D.1, D.1, D.2 D.1, I.2 L.1, L.2 L.1, L.1, L.2 L.1,

Indicator Women’s share of government ministerial positions ministerial of government share Women’s Proportion of seats held by women in national in national women held by of seats Proportion parliament Women’s share of managerial positions share Women’s Percentage of female police officers police of female Percentage Percentage of female judges of female Percentage Proportion of ever-partnered women (aged 15–49) (aged 15–49) women of ever-partnered Proportion a by violence sexual and/or physical to subjected 12 months in the last partner, intimate or former current Proportion of women (aged 15–49) subjected to sexual sexual to subjected (aged 15–49) of women Proportion partner, persons other than an intimate by violence age 15 since Prevalence of female genital mutilation/cutting (for (for genital mutilation/cutting of female Prevalence only) countries relevant Percentage of women aged 20–24 years old who were old who were years aged 20–24 of women Percentage age 18 married or in union before Adolescent birth rate Adolescent Indicators where data are not available are data where Indicators Total % not available Indicator # Indicator IV. Public life and decision-making Public life IV. 43 44 45 46 47 V. Human rights of women and girl children Human rights of women V. 48 49 50 51 52

56 Annex 3: Template for indicator information reference sheet

Instructions for completing the indicator information reference sheet Indicator: Enter the full title of the indicator. DESCRIPTION Precise Definition(s): Define the indicator more precisely, if necessary. Define specific words or elements within the indicator as necessary. Unit of Measure: Enter the unit of measure (e.g., number of…, per cent of…, US dollars, etc.). Disaggregated by: List planned data disaggregations (male/female, youth/adult, urban/rural, region, etc.) Justification/Management Utility: Briefly describewhy this particular indicator was selected and how it will be useful for managing performance. PLAN FOR DATA ACQUISITION Data Collection Method: Describe the tools and methods through with the data will be collected. Method of Acquisition: Describe the form in which the team will receive the data (e.g., periodic monitoring report, compiled survey analysis report, etc.) Data Source(s): Identify who is responsible for providing the data (e.g., implementing partners, M&E contractor, specific team member, etc.). Frequency/Timing of Data Acquisition: Describe how often data will be received by Operating Unit, and when. Estimated Cost of Data Acquisition: Estimate the cost (in dollars and/or level of effort) of collecting the data. Responsible Individual(s) at KEMRI: Identify the specific team member who will bedirectly responsible for acquiring the data. DATA QUALITY ISSUES Date of Initial Data Quality Assessment: Enter the date of initial data quality assessment and the responsible party. Known Data Limitations and Significance (if any): Describe any data limitations discovered during the initial data quality assessment. Discuss the significance of any data weakness that may affect conclusions about the extent to which performance goals have been achieved. Actions Taken or Planned to Address Data Limitations: Describe how you have or will take corrective action, if possible, to address data quality issues. Date of Future Data Quality Assessments: Enter the planned date for subsequent data quality assessments. Procedures for Future Data Quality Assessments: Describe how the data will be assessed in the future (e.g., spot checks of partner data, financial audit, site visits, software edit check, etc.). PLAN FOR DATA ANALYSIS, REVIEW, & REPORTING Data Analysis: Describe how the raw data will be analysed, who will do it, and when. Presentation of Data: Describe how tables, charts, graphs, or other devices will be used to present data, either internally within the team or Operating Unit, or externally or other audiences. Review of Data: Describe when and how the team or Operating Unit will review the data and analysis (e.g., portfolio review, mission internal review, activity-level reviews with implementing partners, etc.) Reporting of Data: List any internal or external reports that will feature data for this indicator (e.g. R4 data tables, R4 narrative, Budget Justification, report to ambassador, activity manager’s report, etc.) OTHER NOTES Notes on Baselines/Targets: Explain how the baselines and targets were set and identify any assumptions made. If baselines and targets have not been set, identify when and how this will be done. Location of Data Storage: Identify where the data will be maintained in the Operating Unit (specific computer files or hard storage area, etc.) Other Notes: Use this space as needed. THIS SHEET LAST UPDATED ON: To avoid version control problems, enter the date of most recent revision to the reference sheet.

57 Annex 4: List of institutions visited and persons interviewed

Kenya National Bureau of Statistics George M Obudho, Ministries, Abdulkadir Awes, Departments & Rosemary Kongani, Agencies Andrew Imbwaga, G Otieno, Michael M Musyoka, Robert Buluma

Kenya Institute for Public Policy Research Nancy Nafula and Analysis

Council of Governors Masiga Asunza Monitoring and Evaluation Department Jared Ichwara National Council for Population and Peter Nyakwara Development

National Gender and Equality Commission Gorrett Osur State Department for Gender Affairs Protus Onyango UNFPA Ezekiel Ngure, UN agencies and Zipporah Gathiti development partners UNICEF Godfrey Ndeng’e UN Environment Programme Evelyn Ongige UN Women Robert Simiyu Karin Feug Maureen Gitonga UNDP Julius Chokerah Nicholas Kipyego

USAID Betty Mugo, Alexander Albertine

DfID Caroline Wangeci International Union for Conservation of Seline Meijer, Nature Molly Gilligan (based in Washington DC) Embassy of Sweden Lollo Darin Institute of Development Studies, Prof Winnie Mitula Academia University of Nairobi

Institute of Gender and African Studies, Prof Owuor Olunga University of Nairobi

Institute of Economic Affairs Kwame Owino Population Studies and Research Institute, Anne Khasakhala University of Nairobi

CSOs & NGOs SDGs Forum Florence Syevuo

58 ANNOTATED LIST OF KEY RESOURCES

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