AnalysIng Resilience for better targeting and action FAO ... · In 2010, the Government of Kenya...

AnalysIng Resilience for better targeting and action

FAO Resilience Analysis No. 9

Analysing Resilience for targeting and action

Cover picture: © FAO \ Richard Bett

Analysing Resilience for targeting and action

Food and Agriculture Organization of the United NationsRome, 2017

FAO Resilience Analysis No. 9

Resilience Analysis in ISIOLO, MARSABIT AND MERU

2016

ENYAK

This publication has been produced with the assistance of the European Union. The contents of this publication are the sole responsibility of FAO and can in no way be taken to reflect the views of the European Union

The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations (FAO) concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specific companies or products of manufacturers, whether or not these have been patented, does not imply that these have been endorsed or recommended by FAO in preference to others of a similar nature that are not mentioned.

The views expressed in this information product are those of the authors and do not necessarily reflect the views or policies of FAO.

© FAO, 2017

FAO encourages the use, reproduction and dissemination of material in this information product. Except where otherwise indicated, material may be copied, downloaded and printed for private study, research and teaching purposes, or for use in non-commercial products or services, provided that appropriate acknowledgement of FAO as the source and copyright holder is given and that FAO’s endorsement of users’ views, products or services is not implied in any way.

All requests for translation and adaptation rights, and for resale and other commercial use rights should be made via www.fao.org/contact-us/licence-request or addressed to [email protected].

FAO information products are available on the FAO website (www.fao.org/publications) and can be purchased through [email protected].

www.fao.org/contact-us/licence-request

mailto:copyright%40fao.org?subject=

www.fao.org/publications

mailto:publications-sales%40fao.org?subject=

iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

ACRONYMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

1 PURPOSE OF THE ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Objectives of the analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Programme background and theory of change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 RESILIENCE MEASUREMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3 DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1 Sampling design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Limitations of the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 DESCRIPTIVE RESILIENCE ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.1 Analysis at the cluster level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.2 Analysis at the county level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.3 Analysis by livelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

4.4 Analysis by gender of household head . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.5 Analysis by sample type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5 CAUSAL RESILIENCE ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.1 Influence of shocks on resilience capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5.2 Food security analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

6 MAIN CONCLUSIONS, POLICY AND PROGRAMMING IMPLICATIONS . . . . . . . . . . . . . . . . . 41

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

ANNEX 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

ANNEX 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

ANNEX 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

ANNEX 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

RESILIENCE ANALYSIS IN ISIOLO, MARSABIT AND MERU, KENYA 2016

iv

FIGURESFig. 1 Isiolo, Marsabit and Meru counties in Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Fig. 2 Resilience Index and pillars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Fig. 3 Resilience conceptual framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Fig. 4 Resilience Capacity Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Fig. 5 Correlation of pillars with the Resilience Capacity Index of the cluster . . . . . . . . . . . . 18Fig. 6 Maps of Resilience Capacity Index and poverty rate by county . . . . . . . . . . . . . . . . . . . 19Fig. 7 Correlation of pillars with the Resilience Capacity Index by county . . . . . . . . . . . . . . . . 19Fig. 8 Assets by county from qualitative data (from FGD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Fig. 9 Correlation of variables and pillars by county . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Fig. 10 Average Resilience Capacity Index by livelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Fig. 11 Correlation of pillars with Resilience Capacity Index by livelihood . . . . . . . . . . . . . . . . 23Fig. 12 Correlation of variables and pillars by livelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Fig. 13 Average Resilience Capacity Index by household head gender . . . . . . . . . . . . . . . . . . 26Fig. 14 Correlation between pillars and Resilience Capacity Index by household head gender . 26Fig. 15 Asset ownership by county . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Fig. 16 Asset decision making on income by county . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Fig. 17 Correlation of variables and pillars by household head gender . . . . . . . . . . . . . . . . . 28Fig. 18 Average Resilience Capacity Index by sample type . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Fig. 19 Correlation between pillars and Resilience Capacity Index by sample type . . . . . . . 29Fig. 20 Shocks and coping strategies reported in qualitative analysis in Marsabit county . . 34Fig. 21 Shock and coping strategies reported in qualitative analysis in Isiolo county . . . . . . 34Fig. 22 Shock and coping strategies reported in qualitative analysis in Meru county . . . . . . 35Fig. A1 Gender of household heads by county . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Fig. A2 Map of the survey coverage in Isiolo, Marsabit and Meru counties . . . . . . . . . . . . . . 62

TABLES

Tab. 1 Resilience pillars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Tab. 2 Food security indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Tab. 3 Households interviewed during baseline survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Tab. 4 Treatment sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Tab. 5 Control sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Tab. 6 Effects of shocks on the Resilience Capacity Index in the three counties . . . . . . . . . . 32Tab. 7 Correlates of food security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Tab. A1 Explanation/Description of variables used in the model . . . . . . . . . . . . . . . . . . . . . . . 49Tab. A2 Variables used for impact evaluation and CPF programme indicators . . . . . . . . . . . 50Tab. A3 Descriptive statistics at the cluster level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Tab. A4 Descriptive statistics by county . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Tab. A5 Descriptive statistics by livelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Tab. A6 Descriptive statistics by household head gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Tab. A7 Descriptive statistics by sample type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Tab. A8 Regression analysis between food security indicators and resilience Indicators . . . 56Tab. A9 Asset ownership in Isiolo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Tab. A10 Asset ownership in Marsabit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Tab. A11 Asset ownership in Meru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

v

ACKNOWLEDGEMENTS

This report has been prepared by the Resilience Team Eastern Africa (RTEA) of the Food and Agriculture Organization of the United Nations (FAO). First and foremost, many thanks to Jose Lopez, Lavinia Antonaci, Vu Hien, Immaculate Atieno and Oscar Ngesa for their invaluable contributions of technical expertise and information. The team is also grateful to the Resilience Analysis and Policies (RAP) team within the Agricultural Development Economics (ESA) division of FAO in Rome for their instrumental technical support. In particular, to Luca Russo, Marco d’Errico, Stefania Di Giuseppe, Rebecca Pietrelli and Francesca Grazioli, as well as to Tomaso Lezzi and Giorgia Wizemann for the formatting and layout of the publication. Alecia Wood completed the editing. The work carried out by the FAO Kenya Crops and Livestock Sectors, without which this baseline survey and resilience analysis would not have been possible, is also immensely appreciated.Special thanks go to our colleagues at the FAO office in Kenya, in particular Kaari Miriti, Simon Muhindi, Paul Mutungi, Thierry Ntambwiriza, Mercy Mulevu, Joseph Mathooko, Duncan Abudiku, Joseph Matere, Irene Kimani, Catherine Abate, Nathan Kivuva, Jackson Kangethe, Edwin Too, Richard Bett, and Mary Njenga, who provided technical support throughout the process of data collection, and Anne Chele for support with policy information. Thanks to the contributions of FAO colleagues in the corporate services unit for their administrative and logistical support, without which it would not have been possible to carry out the survey. The team acknowledges the County Government of Isiolo, County Government of Marsabit, and County Government of Meru for their significant contribution and support in undertaking the survey, as well as their government officers, the Nutrition and Health Programme Plus (NHPplus) of Kenya, and the community members who participated in the survey.Last but not least, special thanks to the enumerators and data clerks who worked tirelessly and ensured that reliable data with high quality standards were collected during the survey process.


vi

ACRONYMS

ABS Access to Basic Services AC Adaptive Capacity AfDB African Development BankASAL Arid and Semi-Arid Land AST Assets CA Conservation Agriculture CAPI Computer Assisted Personal InterviewCIDP County Integrated Development Plans CPF Country Programming Framework CPP Country Programming PaperCSI Coping Strategies Index DiD Difference in DifferencesEA Enumeration Area EDE Ending Drought EmergenciesEFA Education For AllFAO Food and Agriculture Organization of the United Nations FCI Forage Condition IndexFGD Focus Group Discussion FHH Female-Headed HouseholdGAP Good Agricultural PracticesGoK Government of Kenya GPS Global Positioning SystemHDDS Household Dietary Diversity ScoreHH Household HeadIE Impact Evaluation IDDRSI Intergovernmental Authority on Development Drought Disaster Resilience and Sustainability InitiativeIGAD Intergovernmental Authority on DevelopmentIPP Increased Productivity and ProfitabilityKESSP Kenya Education Sector Support Programme KNBS Kenya National Bureau of StatisticsMALF Ministry of Agriculture, Livestock and Fisheries of KenyaMDG United Nations Millennium Development GoalMHH Male-Headed HouseholdMIMIC Multiple Indicators Multiple Causes

vii

Acronyms

MOEST Ministry of Education, Science and Technology of KenyaMoGCSD Ministry of Gender, Children and Social Development of KenyaNACONEK National Council on Nomadic Education in KenyaNDMA National Drought Management Authority NGO Non-Governmental Organization NHPplus Nutrition and Health Programme PlusNRM Natural Resource ManagementPAPI Paper and Pen Interview PFC Per Capita Food ConsumptionPIA Priority Intervention AreasPPS Probability Proportional to Size RAELOC Reviving ASAL Economies through Livestock Opportunities and Improved Coordination RCI Resilience Capacity IndexRIMA Resilience Index Measurement and AnalysisRM-TWG Resilience Measurement Technical Working GroupRPLRP Regional Pastoral Livelihoods Resilience ProjectRSM Resilience Structure MatrixSACCO Savings And Credit CooperativeSDG United Nations Sustainable Development GoalSSN Social Safety Nets TLU Tropical Livestock Units WASH Water, Sanitation and HygieneWB World Bank


viii

EXECUTIVE SUMMARY

Approximately 83 percent of the total area of the Republic of Kenya (Kenya) is classified as arid and semi-arid land (ASAL) with most agricultural and pastoralist activities depending on rain in order to be sustainable (Ministry of Agriculture, Livestock and Fisheries of Kenya (MALF, 2016). This makes the country vulnerable to extreme droughts. Climate change has taken its toll in Kenya, leading to erratic rainfall patterns and extended, life-threatening droughts. Erratic rainfall has led to significant reductions in crop and livestock production. This has led to a ripple effect on conflict between nomadic pastoralist and farmer communities, which compete with each other for already limited resources. Against this backdrop, poverty rates, insecurity and poor infrastructure have increased in many regions within Kenya.

In 2010, the Government of Kenya (GoK) ushered in changes to the Constitution of Kenya, which led to the creation of 47 new regional administrative units, referred to as ‘counties’. In terms of development, disparity among the counties is rife in Kenya. Counties located in northern Kenya are lagging behind in terms of development. This analysis is focused on the County Government of Isiolo, County Government of Marsabit, and County Government of Meru, referred to hereafter as Isiolo county, Marsabit county and Meru county. These counties are grouped together as part of the Isiolo cluster of counties.1 Livelihoods in the Marsabit and Isiolo counties are predominantly pastoralist, while in Meru mixed farming is the most common livelihood.

This analysis relates to the baseline survey that is part of the Impact Evaluation (IE) strategy designed by the FAO Representation in Kenya (referred to hereafter as ‘FAO Kenya’) in order to assess the effects of specific FAO interventions (e.g. increasing the agricultural productivity of beneficiaries/households). In addition, this analysis provides a powerful instrument for the GoK and all partners operating in areas related to resilience for determining the effectiveness of resilience-building interventions. Household resilience to food insecurity in the three counties was examined using the second iteration of the FAO Resilience Index Measurement and Analysis (RIMA) model, known as RIMA-II. The baseline survey was conducted from February to March 2016, covering 1 028 households.2

This report aims to achieve two objectives: (i) establish baseline values for the IE, and (ii) carry out resilience profiling in the region. This analysis identifies the determinants of resilience and food

1 For the purpose of this survey, a ‘cluster’ is defined based on the FAO office setup in specific counties in Kenya where interventions are currently implemented. Clusters are developed for FAO Kenya programming and the coordination of interventions in the country.

2 Follow-up surveys will be designed for the midline and end line IE of the relevant programmes.

ix

Executive summary

security, and also explores resilience variations across Isiolo, Marsabit and Meru counties. The report provides a description of the profiling of households targeted in the three counties, with two distinct livelihoods identified, which were pastoralist and mixed farming livelihoods.

KEY HIGHLIGHTS1. Overall, the RIMA-II analysis indicated that, when looking at the overall sample, there

are no major differences between households in terms of their resilience capacity.Household resilience has been found to be highly influenced by the RIMA-II resiliencepillars of Assets (AST) and Adaptive Capacity (AC). The descriptive analysis of resilience emphasizes that AST is highly influenced by inputs for crops, inputs for livestock,and household durable assets. The most influential aspects of AC are incomediversification and the Coping Strategies Index (CSI). The causal analysis foundhousehold assets and income to be significantly associated with food security indicators.

2. The spatial variation of the Resilience Capacity Index (RCI) across the Isiolo clusteris pronounced. Meru county is the most resilient (with an RCI of 72) followed by Isiolocounty (with an RCI of 59) and the least resilient county is Marsabit (with an RCI of52). These results are in line with poverty estimates from the Kenya National Bureauof Statistics (KNBS); the most resilient county has the lowest poverty index, and viceversa. The relevance of AST is almost homogeneously significant to resilience in all thecounties.

3. The analysis by livelihood reveals households in mixed farming areas are moreresilient than pastoralist households, with the mean RCIs at 72 and 55, respectively.Further analysis of the correlation between the pillars and the RCI reveals that ASTis an important pillar for both livelihoods.

4. There is no significant difference in the RCI between male-headed households (MHHs) and female-headed households (FHHs). This result is also validated by the results fromthe causal analysis; the household head (HH) gender is not significantly associated withthe food security indicators.

5. At the baseline level, there is already a statistically significant difference in the RCIbetween households that receive FAO interventions (with an RCI of 59.8) and those that do not receive interventions (with an RCI of 57.2). This will have implications for the IE,hence statistical procedures will be employed to control for such baseline differences.

6. The causal analysis identified the loss of livestock or crops due to pests, parasites and diseases, along with job loss/no salary/death of the main earner, as the main shocksthat cause a reduction in food security in each of the three counties. The qualitativeanalysis highlighted that additional shocks that heavily impact on households aredrought/lack of water, as well as insecurity and conflict over natural resources.

POLICY AND PROGRAMMING IMPLICATIONSThe findings of the analysis have been reviewed, keeping in mind the policy initiatives planned or implemented by the GoK over the past decade that are specific to Isiolo, Marsabit and Meru counties.

In terms of the Resilience Structure Matrix (RSM), the findings for the overall sample show that AST and AC are the pillars that are the most influential to resilience capacity, followed


x

by Access to Basic Services (ABS). These findings suggest investment in livestock and crop production programmes, including the enhancement of the value chain and linkages to markets, are beneficial and should be a key focus of future policies. The most relevant contributing factors to the resilience capacity of households in the regions studied include: access to inputs for crop and livestock production; enhanced income diversification; reduced distance to basic services, such as health services, schools and markets; and increased reliance on social networks.

From a resilience-building policy perspective, the Kenya Vision 2030 Sector Plan for Drought Risk Management and Ending Drought Emergencies (EDE) is aimed at reducing poverty and vulnerability in drought-prone areas. This initiative feeds into the Intergovernmental Authority on Development (IGAD) Drought Disaster Resilience and Sustainability Initiative (IDDRSI), which is currently being implemented by the GoK through the National Drought Management Authority (NDMA). IDDRSI and its related Country Programming Paper (CPP) aim to promote activities in relation to different sectors’ contributions to drought resilience. The resilience-related interventions prioritized by regional programmes implemented under the CPP are the Drought Resilience and Sustainable Livelihoods Project (DRSLP) funded by the African Development Bank (AfDB) and the Regional Pastoral Livelihoods Resilience Project (RPLRP) funded by the World Bank (WB), which seek to address drought-related challenges and build resilience in communities in ASAL areas.

In line with the findings in this analysis, the Agricultural Policy for Kenya places strong emphasis on factors such as asset creation and protection, and access to basic services (MALF, 2016). The policy suggests interventions that: improve access to basic facilities; enhance access to and create affordable inputs and services for agricultural production and the value chain; leverage the usefulness of social networks; and support new initiatives to diversify activities that generate income. The GoK aims to provide targeted incentives to support production and productivity in both pastoralist and mixed farming livelihoods as a means of creating sustainable economic well-being for households (MALF, 2016). In addition to those interventions, this analysis suggests the need to: increase investments in and resources for the implementation of sustainable disease control programmes and of strategies run in conjunction with county governments; enforce existing laws governing disease control; and improve the coverage of vaccination programmes.

The analysis shows that AC also significantly contributes to resilience capacity. Income diversification and coping strategies are the most significant factors for the AC pillar, followed by the education level of HHs. AC is more pronounced in Meru county, where households can rely on several income sources. This implies that in all counties it is important for policies to focus on boosting new initiatives to diversify the activities that generate income across the entire value chain for both crop and livestock production. For instance, income source diversification and the improvement of income levels can be fostered with more investment in the value chain and agribusiness initiatives. Education is also an important contributing factor to household resilience capacity, particularly in Meru county compared to Isiolo and Marsabit counties. Education is also very important in Isiolo and Marsabit counties suggests that pastoralist communities would also benefit greatly if the education system were able to reach more communities. Accordingly, the GoK has sought to establish and bring into operation the National Council on Nomadic Education in Kenya (NACONEK)3 to promote access to education for nomadic communities in ASAL areas.

Generally, Social Safety Nets (SSN) is one of the least significant pillars to the RCI. SSN plays the most limited role in the RCI of the pastoralist areas compared to those with mixed farming.

3 The NACONEK is housed within the GoK’s National Policy Framework on Nomadic Education for the ASAL.

xi

Executive summary

The number of social networks a household is involved in is the most significant factor for this pillar, followed by access to credit and access to financial transfers (both formal and informal). In Isiolo and Marsabit counties, access to credit remains very limited, as is reliance on and participation in different social networks. Livelihoods in the three counties are undermined by the poorly developed financial sector (GoK, 2013a). The GoK strives to increase opportunities within the financial sector to expand credit services and rural savings and credit cooperatives (SACCOs) in the counties to promote financial literacy.

Insecurity and natural resource-based conflict are major concerns, particularly in pastoralist areas (Marsabit and Isiolo counties). In the qualitative analysis, resource-based conflicts featured prominently in focus group discussions (FGDs) as a major shock. Local cross-border natural resource conflict, particularly due to livestock migration in search of water and pasture, is a major concern due to the coexistence of different tribes and ethnic groups. The GoK has taken initiatives to strengthen peace and security infrastructure, especially in ASAL counties through programmes on peace promotion, cultural cohesion and reconciliation. The CPP for Kenya under the IDDRSI framework envisages a strategic response for peace and human security to ensure inclusive participation of communities in decision making on equitable access to natural resources.

© F

AO \ R

icha

rd B

ett

1

© F

AO \ R

icha

rd B

ett

1 PURPOSE OF THE ANALYSISThis section provides background information on the Isiolo cluster and the objectives of this analysis.

1.1 BACKGROUNDAbout 83 percent of Kenya’s land mass is defined as ASAL. Within these ASAL areas, one-third of the country’s population lives along with 70 percent of the livestock herd (MALF, 2014). These regions are also characterized by low and erratic rainfall. While the economy of the arid areas is dominated by mobile pastoralism, in the better-watered and better-serviced semi-arid areas a more mixed livelihood prevails, including rain-fed and irrigated agriculture, agro-pastoralism, bio-enterprise, conservation and tourism-related activities. Agriculture is the mainstay of the Kenyan economy, directly contributing about 24 percent of the annual Gross Domestic Product (GDP) and accounting for more than 60 percent of informal employment in rural areas (MALF, 2016). Livestock production contributes more than 50 percent of agricultural GDP and 13 percent of Kenya’s national GDP. The livestock sector in Kenya employs about 50 percent of the agricultural workforce and about 90 percent of the workforce in ASAL areas (MALF, 2016).

The GoK, together with the Intergovernmental Authority on Development and the support of FAO, devised the Kenya CPP for ending recurrent drought emergencies in Kenya. It combines the efforts of the communities concerned, the GoK, civil society, private sector, states in the Horn of Africa and development partners to address ongoing drought-related emergencies affecting the ASAL areas through interventions that help build community resilience (GoK, 2012).

Kenya has continued to experience socio-economic pressures, such as inequitable patterns of land ownership, a high population growth rate, rural-urban migration of the population, poorly planned urbanization, deforestation, low literacy, low growth of domestic product and high levels of unemployment (WB, 2016). FAO is a key stakeholder in the agricultural sector in Kenya. FAO has been working with the GoK across all aspects of food security and agriculture for decades, even before FAO Kenya was established there in 1977 (FAO, 2014). Increasing the resilience of vulnerable people’s livelihoods to threats and crises, as well as contributing to the reduction of food insecurity and malnutrition, are key initiatives undertaken by FAO in Kenya.

The Country Programming Framework (CPF) for FAO Kenya sets out priority areas to guide FAO’s partnership with and support to the GoK at both the national and county levels for a period of four years (from 2014 to 2017) (FAO, 2014). The CPF puts an immediate emphasis on reducing


2

poverty and hunger in line with United Nations Millennium Development Goal (MDG) 14 and United Nations Sustainable Development Goal (SDG) 2.5 The CPF Pillar 4 focuses on improved livelihood resilience for the targeted vulnerable populations, and is in line with FAO Strategic Objective 5 to increase the resilience of livelihoods to threats and crises.

As part of the development of the CPF, FAO Kenya has made important efforts to expand and deepen its IE processes through an IE strategy. This multifaceted approach involves a range of activities, from setting benchmarks for programme design and monitoring, to activity monitoring and assessing progress in the implementation of programmes that measure changes and impact.

The baseline survey is part of the IE strategy designed by FAO Kenya in order to assess the effects of specific FAO interventions (e.g. increasing agricultural productivity of beneficiaries/households). In addition, it provides a powerful instrument for FAO Kenya as well as for the GoK and partners operating in the areas of interest to determine the effective and ineffective aspects of interventions, and, thus, constitutes a fundamental means to learn about useful interventions. At the same time, IE can provide the necessary benchmarks for project design and monitoring. The first IE baseline survey was conducted in Kenya’s Kitui cluster6 in July 2015 with a sample size of 819 households in the Kitui and Makueni counties. This also provided baseline findings for programme design and monitoring, and assessing progress in the implementation of projects for measuring changes in CPF outcomes, and evaluating the impact of specific interventions on building household resilience.

1.2 OBJECTIVES OF THE ANALYSISFollowing on from the overall objective of conducting the baseline survey, the survey results form the basis for assessing progress in building resilience through major programmes implemented in Isiolo, Marsabit and Meru counties. The specific objectives of the baseline survey were to:

1. Establish baseline values for measuring the CPF impact on resilience; including the baseline for three specific programmes under the current CPF, namely the:

h Increased Productivity and Profitability (IPP) of smallholder farmers through promotion and upscaling of Good Agricultural Practices (GAP) and Conservation Agriculture (CA) in productive semi-arid areas of Kenya programme

h Natural Resource Management (NRM)/Land programme

h Improving food security and resilience and/or Reviving ASAL Economies through Livestock Opportunities and Improved Coordination (RAELOC) project

2. Provide information for area-wide resilience profiling to inform resilience-related programming and policy processes by FAO, the GoK and partners in the respective counties.

1.3 PROGRAMME BACKGROUND AND THEORY OF CHANGEThe CPF is set to be implemented in more than 18 counties within seven clusters in Kenya. It is a five-year programme with activities having commenced in August 2014. For the purpose of this baseline, a cluster is defined based on the FAO office setup in specific counties in Kenya

4 The MDG 1 is to “eradicate extreme poverty and hunger”.5 The SDG 2 is to “end hunger, achieve food security and improved nutrition and promote sustainable agriculture”.6 The Kitui cluster consists of Machakos, Makueni, Embu, Tharaka Nithi and Kitui counties.

where interventions are currently implemented. The IE strategy envisages an implementation in different phases in the clusters where FAO has a critical mass of interventions.

In the Isiolo cluster, FAO is currently targeting a critical mass through the three programmes, as highlighted in Section 1.2.

Specifically, the IPP-GAP programme focuses on climate-smart agriculture, linking improved agricultural practices to economic gains and a connection with the private sector and financial institutions. The IPP-GAP programme is implemented in eight counties, including Meru county within the Isiolo cluster.

The RAELOC project aims to contribute to ending drought emergencies in Kenya through the improved food and nutrition security of the target population, with a particular emphasis on improving the livelihoods of livestock keepers. The RAELOC project has been implemented in six counties, among them Marsabit and Isiolo counties.

The NRM/Land programme is focused on supporting the GoK’s efforts to secure and improve equitable access to land and natural resources in order to ensure food security and socio-economic development of agro-pastoralist communities in the ASALs of Kenya. This programme is planned to be implemented in seven counties, including Marsabit county.

A visual map of the three counties in Kenya is provided on Figure 1. Specific outcome indicators of these programmes have been identified to link the programme impact to the resilience of the targeted households. The households’ Resilience Capacity Index (RCI) estimated through FAO RIMA-II will be tracked over time to detect change in how the specific programmes have contributed to their resilience capacity. The rationale behind the programmes’ contribution to building resilience in the target populations is based on the assumption that households enhance their resilience capacity with multiple interventions that may improve their economic conditions. This can be achieved through increased income levels, diversified income sources, and opportunities that support households in responding to shocks and adverse situations without engaging in negative and risky coping strategies. Higher incomes can be attributed to increased productivity of crop and livestock sectors, but also to increased market linkages and value chain enhancement. These activities can also lead to enhanced diversification of livelihoods, which contributes to the absorptive and adaptive capacities of households. Enhanced, sustainable access to natural resources is another important way to improve livelihood options through the use of resources such as land, water, pasture and forests, and their appropriate management. This can also result in the reduction of natural resource-based conflict and insecurity. Finally, an increase in food security levels is a final outcome of the improved resilience capacity of the targeted beneficiaries.

Figure 1. Isiolo, Marsabit and Meru counties in Kenya

2

1

3

1 Marsabit 2 Isiolo 3 Meru

2

1

3

3

Chapter 1 – Purpose of the analysis

where interventions are currently implemented. The IE strategy envisages an implementation in different phases in the clusters where FAO has a critical mass of interventions.

In the Isiolo cluster, FAO is currently targeting a critical mass through the three programmes, as highlighted in Section 1.2.

Specifically, the IPP-GAP programme focuses on climate-smart agriculture, linking improved agricultural practices to economic gains and a connection with the private sector and financial institutions. The IPP-GAP programme is implemented in eight counties, including Meru county within the Isiolo cluster.

The RAELOC project aims to contribute to ending drought emergencies in Kenya through the improved food and nutrition security of the target population, with a particular emphasis on improving the livelihoods of livestock keepers. The RAELOC project has been implemented in six counties, among them Marsabit and Isiolo counties.

The NRM/Land programme is focused on supporting the GoK’s efforts to secure and improve equitable access to land and natural resources in order to ensure food security and socio-economic development of agro-pastoralist communities in the ASALs of Kenya. This programme is planned to be implemented in seven counties, including Marsabit county.

A visual map of the three counties in Kenya is provided on Figure 1. Specific outcome indicators of these programmes have been identified to link the programme impact to the resilience of the targeted households. The households’ Resilience Capacity Index (RCI) estimated through FAO RIMA-II will be tracked over time to detect change in how the specific programmes have contributed to their resilience capacity. The rationale behind the programmes’ contribution to building resilience in the target populations is based on the assumption that households enhance their resilience capacity with multiple interventions that may improve their economic conditions. This can be achieved through increased income levels, diversified income sources, and opportunities that support households in responding to shocks and adverse situations without engaging in negative and risky coping strategies. Higher incomes can be attributed to increased productivity of crop and livestock sectors, but also to increased market linkages and value chain enhancement. These activities can also lead to enhanced diversification of livelihoods, which contributes to the absorptive and adaptive capacities of households. Enhanced, sustainable access to natural resources is another important way to improve livelihood options through the use of resources such as land, water, pasture and forests, and their appropriate management. This can also result in the reduction of natural resource-based conflict and insecurity. Finally, an increase in food security levels is a final outcome of the improved resilience capacity of the targeted beneficiaries.

Figure 1. Isiolo, Marsabit and Meru counties in Kenya

2

1

3

1 Marsabit 2 Isiolo 3 Meru

2

1

3

Source:Isiolo cluster baseline (2016)

© N

HP

Plus

\ Ste

phen

Mch

aro

5

2 RESILIENCE MEASUREMENTThis section gives an overview of the FAO resilience measurement framework based on the RIMA-II approach.

The RIMA-II methodology employed for this study was designed using the definition of resilience according to the Resilience Measurement Technical Working Group (RM-TWG): “the capacity that ensures adverse stressors and shocks do not have long-lasting adverse development consequences” (RM-TWG, 2014).

RIMA is an innovative quantitative approach that allows for explaining why and how some households cope with shocks and stressors better than others. The first version of RIMA was improved technically following its application in 10 countries. As a result, the new RIMA-II methodology provides better information for more effectively designing, delivering, monitoring and evaluating assistance to populations in need, based on what they need most.

The RIMA-II approach includes two elements (FAO, 2016a):

h The descriptive analysis provides a description of household resilience capacity. RIMA-II directly measures resilience through the RCI and the RSM. The RCI estimates the capacity of households to cope with shocks and stressors and can be employed for ranking and targeting households. The RSM explains to what extent each resilience pillar contributes to determining the resilience capacity, thus providing grounds for more precise policy actions that would enable households to better cope with or withstand the consequences of a shock.

h The causal analysis provides an analysis of the determinants of the resilience capacity, and on the effects of shocks on food security, taking into account negative events that affect both singular individuals and households (idiosyncratic shocks), as well as those affecting communities, regions or even entire countries (covariate shocks). While the former are self-reported by the household in the survey, the latter (e.g. geo-climatic or conflict shocks) are detected through secondary data. These include additional datasets, such as the one where the Forage Condition Index (FCI) was taken (see note 21).

The RCI allows for the profiling of households by region, urban status, gender of HH and livelihood. By focusing on the most relevant pillars, according to the RSM, the mean values of observed variables assess why specific household profiles are the most resilient. Therefore, the two combined analyses shed light on the drivers that ensure higher resilience capacity.

© N

HP

Plus

\ Ste

phen

Mch

aro


6

Table 1. Resilience pillars

Pillars of resilience Definition Variables

ABS ABS shows the ability of a household to meet basic needs, by accessing and effectively using basic services, such as sending children to school; accessing water, electricity and sanitation; selling products at the market.

Energy; Sanitation; Distance to water source; Distance to school; Distance to hospital; Distance to market; Distance to credit services.

AST AST, both productive and non-productive, are the key elements of a livelihood, since they enable households to produce and consume goods. Examples of productive assets include land and the agricultural index (e.g. agricultural equipment), while non-agricultural assets take into account the monetary value of the house where the household is located, and its appliances.

Household asset index; Cultivated land value per capita; Tropical Livestock Units (TLU) per capita; Agricultural inputs.

SSN SSN proxies the ability of the household to access formal and informal assistance from institutions, as well as from relatives and friends.

Access to credit; In-kind transfers per capita; Participation in associations.

AC AC is the ability to adapt to a new situation and develop new livelihood strategies. For instance, proxies of the AC are the average years of education of household members and the household perception of the decision-making process of their community.

Average education; Income diversification index; Independency ratio (active/non-active members); CSI.

Table 2. Food security indicators

Food security indicators Definition

PFC Monetary value, expressed in US dollars, of per capita food consumption, including bought, auto-produced, received for free (e.g. as gifts) and stored food.

HDDS The number of unique foods (or food groups) consumed by household members based on the past seven days recall.

Hence, policy recommendations can be formulated, with a particular focus on which households need targeting for relevant policies.

The estimation of the RCI is based on a two-stage procedure. First, the resilience pillars are estimated from observed variables through Factor Analysis (FA). Second, the RCI is estimated from the pillars, taking into account the indicators of food security using the Multiple Indicators Multiple Causes (MIMIC) model.

The RSM weighs the contribution of the four pillars to the RCI. Table 1 presents the definitions of each pillar of resilience and the related variables (for more detail on the variables please see table A1). The RIMA-II methodology features four pillars to choose from when building the analysis framework; in this case, the choice of the pillars employed is based on consultations with relevant stakeholders, literature review and previous analyses (FAO, 2016a).

The causal effect of resilience on food security is measured by employing the following food security indicators: per capita food consumption (PFC) and Household Dietary Diversity Score (HDDS). RIMA-II employs these two food security indicators simultaneously;7 this aims to capture different aspects of food security, as food consumption focuses on the monetary value of food, while the other indicators focus on the diversity of the diet. Table 2 offers details of the indicators employed in the analysis.

Figure 2 synthesizes the two-step process that allows for the estimation of the RCI. After estimating the pillars, the RCI is jointly estimated through its pillars and by taking into account the food security indicators.

Resilience

ABS

εv1

v1

εv2

v2

...

...

εvn

vn

AST

εv1

v1

εv2

v2

...

...

εvn

vn

SSN

εv1

v1

εv2

v2

...

...

εvn

vn

AC

εv1

v1

εv2

v2

...

...

εvn

vn

ε1

Food consumption

per capitaSimpson

DDI FCS

ε4ε2 ε3

Observed variables ErrorsLatent variables

FAM

IMIC

Figure 2. Resilience index and pillars

Figure 3 presents the conceptual framework employed for the estimation of RIMA-II and describes what happens to household well-being when a shock occurs and resilience mechanisms are activated.

Food security at time 0 is the outcome indicator and is associated with resilience capacity that is estimated through a set of time-variant and time-invariant characteristics of the household. When a shock occurs, a series of coping strategies is activated, such as consumption smoothing, asset smoothing, and adoption of new livelihood strategies. Household resilience contributes to these absorptive, coping and transformative capacities in an attempt to bounce back to the previous state of welfare. This can result in an increase or decrease in the outcome indicators. Any change in the outcome has an effect on resilience capacity and, consequently, can limit future capacity to react to shocks (FAO, 2016a).

7 Further details and discussion on the decision to include more than one food security indicator in the RIMA-II methodology is provided in FAO (2016a).

7

Chapter 2 – Resilience measurement

Table 1. Resilience pillars

Pillars of resilience Definition Variables

ABS ABS shows the ability of a household to meet basic needs, by accessing and effectively using basic services, such as sending children to school; accessing water, electricity and sanitation; selling products at the market.

Energy; Sanitation; Distance to water source; Distance to school; Distance to hospital; Distance to market; Distance to credit services.

AST AST, both productive and non-productive, are the key elements of a livelihood, since they enable households to produce and consume goods. Examples of productive assets include land and the agricultural index (e.g. agricultural equipment), while non-agricultural assets take into account the monetary value of the house where the household is located, and its appliances.

Household asset index; Cultivated land value per capita; Tropical Livestock Units (TLU) per capita; Agricultural inputs.

SSN SSN proxies the ability of the household to access formal and informal assistance from institutions, as well as from relatives and friends.

Access to credit; In-kind transfers per capita; Participation in associations.

AC AC is the ability to adapt to a new situation and develop new livelihood strategies. For instance, proxies of the AC are the average years of education of household members and the household perception of the decision-making process of their community.

Average education; Income diversification index; Independency ratio (active/non-active members); CSI.

Table 2. Food security indicators

Food security indicators Definition

PFC Monetary value, expressed in US dollars, of per capita food consumption, including bought, auto-produced, received for free (e.g. as gifts) and stored food.

HDDS The number of unique foods (or food groups) consumed by household members based on the past seven days recall.

Hence, policy recommendations can be formulated, with a particular focus on which households need targeting for relevant policies.

The estimation of the RCI is based on a two-stage procedure. First, the resilience pillars are estimated from observed variables through Factor Analysis (FA). Second, the RCI is estimated from the pillars, taking into account the indicators of food security using the Multiple Indicators Multiple Causes (MIMIC) model.

The RSM weighs the contribution of the four pillars to the RCI. Table 1 presents the definitions of each pillar of resilience and the related variables (for more detail on the variables please see table A1). The RIMA-II methodology features four pillars to choose from when building the analysis framework; in this case, the choice of the pillars employed is based on consultations with relevant stakeholders, literature review and previous analyses (FAO, 2016a).

The causal effect of resilience on food security is measured by employing the following food security indicators: per capita food consumption (PFC) and Household Dietary Diversity Score (HDDS). RIMA-II employs these two food security indicators simultaneously;7 this aims to capture different aspects of food security, as food consumption focuses on the monetary value of food, while the other indicators focus on the diversity of the diet. Table 2 offers details of the indicators employed in the analysis.

Figure 2 synthesizes the two-step process that allows for the estimation of the RCI. After estimating the pillars, the RCI is jointly estimated through its pillars and by taking into account the food security indicators.

Resilience

ABS

εv1

v1

εv2

v2

...

...

εvn

vn

AST

εv1

v1

εv2

v2

...

...

εvn

vn

SSN

εv1

v1

εv2

v2

...

...

εvn

vn

AC

εv1

v1

εv2

v2

...

...

εvn

vn

ε1

Food consumption

per capitaSimpson

DDI FCS

ε4ε2 ε3

Observed variables ErrorsLatent variables

FAM

IMIC

Figure 2. Resilience index and pillars

Figure 3 presents the conceptual framework employed for the estimation of RIMA-II and describes what happens to household well-being when a shock occurs and resilience mechanisms are activated.

Food security at time 0 is the outcome indicator and is associated with resilience capacity that is estimated through a set of time-variant and time-invariant characteristics of the household. When a shock occurs, a series of coping strategies is activated, such as consumption smoothing, asset smoothing, and adoption of new livelihood strategies. Household resilience contributes to these absorptive, coping and transformative capacities in an attempt to bounce back to the previous state of welfare. This can result in an increase or decrease in the outcome indicators. Any change in the outcome has an effect on resilience capacity and, consequently, can limit future capacity to react to shocks (FAO, 2016a).

7 Further details and discussion on the decision to include more than one food security indicator in the RIMA-II methodology is provided in FAO (2016a).

Source:FAO, 2016a


8

© F

AO \ R

icha

rd B

ett

Consumption smoothing

COPING STRATEGIES

Asset smoothing

New livelihoodadoption

AssetsR0

Y0

∆ Y

∆ Res

t0 t1

Y1

Social Safety Nets

Adaptive Capacity

Access to Basic Services


AssetsR0

Social Safety Nets

Adaptive CapacityShock

Other HH time-invariant characteristics

Other HH time-variant characteristics



Figure 3. Resilience conceptual framework

Source:FAO, 2016a

© F

AO \ R

icha

rd B

ett

Consumption smoothing

COPING STRATEGIES

Asset smoothing

New livelihoodadoption

AssetsR0

Y0

∆ Y

∆ Res

t0 t1

Y1

Social Safety Nets

Adaptive Capacity



AssetsR0

Social Safety Nets

Adaptive CapacityShock





Figure 3. Resilience conceptual framework

Source:FAO, 2016a

© IR

IN \ J

aspr

eet K

indr

a©

FAO

\ Ric

hard

Bet

t

11

© IR

IN \ J

aspr

eet K

indr

a

3 DATAThis section describes the dataset employed in the resilience analysis, based on an ad hoc data collection implemented by FAO and county level governments in Kenya during February and March 2016, and introduces both the strengths and limitations of the study.

Logistical and financial feasibility meant data collection was limited to three counties in Kenya, which were selected mainly based on the critical mass of FAO activities in those locations. The data collection for the baseline took place in one regional cluster, the Isiolo cluster, which consists of three counties – Marsabit, Isiolo, and Meru. The baseline survey in Marsabit, Isiolo and Meru counties was collected during the period from 18 February 2016 to 18 March 2016. Isiolo and Marsabit counties are semi-arid areas and face considerable challenges in terms of food production as well as other socio-economic hardships, while Meru county has more favourable conditions than the other two counties. Outcomes of FAO interventions will be determined by investigating changes in a sample of households that are receiving FAO support (referred to as ‘treatment’) and comparing those with households in areas with similar socio-economic characteristics that do not receive any FAO support (referred to as ‘control’).

3.1 SAMPLING DESIGNBased on standard sampling calculations, the sample selection was based on a multi-stage, random cluster sampling. The first stage involved clustering the sub-counties where a critical mass of FAO interventions are currently active or planned to be implemented. The second stage involves a random selection of sampled households from the sub-counties using Probability Proportional to Size (PPS) to reduce bias. The treatment group was sampled randomly from the FAO beneficiary lists while the control group was sampled from the community using a systematic random cluster sampling method.

The calculation of the total sample size based on the target population was as follows:

(1)

where n is the total sample size, N is total population size, and e is the error tolerance or margin of error (determined from the confidence level used, in this case 95 percent). The recommended sampling precision to be used (Neuman, 2011) is 3 percent, based on the confidence level as stated.

© F

AO \ R

icha

rd B

ett


12

The survey sample was constructed using the 2009 Kenya Population and Housing Census.

The Enumeration Areas (EAs) were sub-locations and the units of analysis were households. A total of 1 028 households sampled for both treatment and control groups (including 306 households in Isiolo county, 306 in Marsabit and 440 in Meru) (Table 3).

The treatment group was defined as households that were receiving FAO support through one or more projects at the time of the survey, while the control group was not receiving any kind of support from FAO at the time of the survey. Approximately 90 households in the control group were targeted in Isiolo and Marsabit counties, respectively, while 132 households in the control group were targeted in Meru county. A total of 44 sites was sampled for the baseline survey; across the selected counties, there were 14 in Isiolo, 11 in Marsabit and 19 in Meru (Table 4 and Table 5).

Table 5. Control sites

N. of sites Subcounty Sampled sites Isiolo county3 Isiolo South Modogashe South4 Isiolo South Iresa Boru5 Isiolo South Malkadaka6 Isiolo South Garbatulla South10 Isiolo North Bulla PesaMarsabit county6 Laisamis El Molo Bay7 Laisamis Loyiangalani8 Laisamis Laisamis9 Laisamis Logologo10 Laisamis KamboeMeru county1 Igembe North Anjalu2 Igembe Central Antubetwe Njoune3 Igembe Central Ituulu4 Igembe Central Kalingene6 Igembe North Kiani13 Igembe North Miriki16 Igembe North Naathu

The survey was conducted using two data collection modalities; Paper and Pen Interviews (PAPI) and Computer Assisted Personal Interviews (CAPI). PAPI constituted the main tools used in the survey, which are a questionnaire (paper) and pen. In about 80 percent of the sampled households, the data collection was carried out using PAPI.

The survey mainly utilized quantitative and qualitative techniques of data collection. More specifically, a comprehensive questionnaire was designed to collect quantitative data at the household level and was complemented by a qualitative tool that was used to collect the views of the communities through FGDs. Qualitative interviews provided a detailed discussion and scoring methods to validate some indicators that were assessed at the household level in order to integrate qualitative results with the quantitative data analysed.

Table 3. Households interviewed during baseline survey

Number of total households interviewed

Treatment Control

731 297

Table 4. Treatment sites

N. of sites Subcounty Sampled sites Isiolo county1 Isiolo North Bisan Biliqu2 Isiolo North Merti North7 Isiolo South Kinna8 Isiolo North Kipsing9 Isiolo North Oldonyiro11 Isiolo North OdhaMarsabit county1 Moyale Dabel2 Moyale Walda3 North Horr Forolle4 North Horr Maikona5 North Horr North Horr11 Saku/Marsabit Central Dakabaricha12 Saku/Marsabit Central JaldesaMeru county5 Central imenti Kathwene7 Tigania West Kianjai8 Tigania West Kieru9 Central Imenti Kiija10 Buuri Kiirua11 Tigania West Kiorimba12 Buuri Kithima14 Tigania West Mwili15 Tigania West Mweronkanga17 Buuri Ntumburi18 Tigania West Thau 19 Buuri Thiira20 Buuri Kithwene

The application of sampling households proportional to size in each sub-location was as follows:

(2)

where ns is the sample size for sub-location s , Ns is the population size for the sub-location s, N is the total beneficiary population size, and n is the total sample size calculated from (1) above.

13

Chapter 3 – Data

The survey sample was constructed using the 2009 Kenya Population and Housing Census.

The Enumeration Areas (EAs) were sub-locations and the units of analysis were households. A total of 1 028 households sampled for both treatment and control groups (including 306 households in Isiolo county, 306 in Marsabit and 440 in Meru) (Table 3).

The treatment group was defined as households that were receiving FAO support through one or more projects at the time of the survey, while the control group was not receiving any kind of support from FAO at the time of the survey. Approximately 90 households in the control group were targeted in Isiolo and Marsabit counties, respectively, while 132 households in the control group were targeted in Meru county. A total of 44 sites was sampled for the baseline survey; across the selected counties, there were 14 in Isiolo, 11 in Marsabit and 19 in Meru (Table 4 and Table 5).

Table 5. Control sites

N. of sites Subcounty Sampled sites Isiolo county3 Isiolo South Modogashe South4 Isiolo South Iresa Boru5 Isiolo South Malkadaka6 Isiolo South Garbatulla South10 Isiolo North Bulla PesaMarsabit county6 Laisamis El Molo Bay7 Laisamis Loyiangalani8 Laisamis Laisamis9 Laisamis Logologo10 Laisamis KamboeMeru county1 Igembe North Anjalu2 Igembe Central Antubetwe Njoune3 Igembe Central Ituulu4 Igembe Central Kalingene6 Igembe North Kiani13 Igembe North Miriki16 Igembe North Naathu

The survey was conducted using two data collection modalities; Paper and Pen Interviews (PAPI) and Computer Assisted Personal Interviews (CAPI). PAPI constituted the main tools used in the survey, which are a questionnaire (paper) and pen. In about 80 percent of the sampled households, the data collection was carried out using PAPI.

The survey mainly utilized quantitative and qualitative techniques of data collection. More specifically, a comprehensive questionnaire was designed to collect quantitative data at the household level and was complemented by a qualitative tool that was used to collect the views of the communities through FGDs. Qualitative interviews provided a detailed discussion and scoring methods to validate some indicators that were assessed at the household level in order to integrate qualitative results with the quantitative data analysed.

Table 3. Households interviewed during baseline survey

Number of total households interviewed

Treatment Control

731 297

Table 4. Treatment sites

N. of sites Subcounty Sampled sites Isiolo county1 Isiolo North Bisan Biliqu2 Isiolo North Merti North7 Isiolo South Kinna8 Isiolo North Kipsing9 Isiolo North Oldonyiro11 Isiolo North OdhaMarsabit county1 Moyale Dabel2 Moyale Walda3 North Horr Forolle4 North Horr Maikona5 North Horr North Horr11 Saku/Marsabit Central Dakabaricha12 Saku/Marsabit Central JaldesaMeru county5 Central imenti Kathwene7 Tigania West Kianjai8 Tigania West Kieru9 Central Imenti Kiija10 Buuri Kiirua11 Tigania West Kiorimba12 Buuri Kithima14 Tigania West Mwili15 Tigania West Mweronkanga17 Buuri Ntumburi18 Tigania West Thau 19 Buuri Thiira20 Buuri Kithwene

The application of sampling households proportional to size in each sub-location was as follows:

(2)

where ns is the sample size for sub-location s , Ns is the population size for the sub-location s, N is the total beneficiary population size, and n is the total sample size calculated from (1) above.


14

3.2 LIMITATIONS OF THE STUDYAs the resilience study was designed to inform the IE of the Kenya programmes for the target beneficiaries, a random selection of beneficiary households was created using the existing beneficiary database. However, most of the households were already benefiting from projects that had begun prior to the survey. This might result in some bias in the analysis between the treatment and control groups. However, techniques will be used to account for these baseline discrepancies during the IE analysis.

As this is a static analysis for a specific point in time, it does not consider the variability of the seasons over a year-long period, thus periodic surveys need to be carried out to capture the dynamics within households across varying weather patterns and at different points throughout the year. The study was conducted in the semi-arid areas of Meru county where FAO programmes are targeting the mixed farming livelihood,8 and in the arid and semi-arid areas of Isiolo and Marsabit counties mainly targeting the pastoralist livelihood.

8 A different overview may have emerged for Meru county if the study had taken into consideration households located in the more arid zones.

© F

AO \ R

icha

rd B

ett

© F

AO \ R

icha

rd B

ett

4 DESCRIPTIVE RESILIENCE ANALYSISThis section provides the descriptive statistics and resilience analysis. The analysis presents the differences in the RCI and RSM of (i) the overall sample including the three counties, (ii) the gender groups (FHHs and MHHs), (iii) the three counties separately, (iv) the livelihoods, and (iv) the sample type (e.g. treatment and control).

This section presents the results of the RCI and RSM at the cluster level, then segregated by county, livelihood, HH gender and sample type. Furthermore, this section identifies the most influential pillars of resilience, categorized by the segregated profiles.

4.1 ANALYSIS AT THE CLUSTER LEVELFigure 4 shows the frequency density distribution of the RCI9 in the overall cluster sample.

Histogram

Kernel density

Mean

Median

0

0.01

0.02

0 20 40 60 80 100

Den

sity

RCI

Figure 4. Resilience Capacity Index


9 The density distribution measures the variables’ level of dispersion around the mean.

© F

AO \ R

icha

rd B

ett

17

4 DESCRIPTIVE RESILIENCE ANALYSISThis section provides the descriptive statistics and resilience analysis. The analysis presents the differences in the RCI and RSM of (i) the overall sample including the three counties, (ii) the gender groups (FHHs and MHHs), (iii) the three counties separately, (iv) the livelihoods, and (iv) the sample type (e.g. treatment and control).

This section presents the results of the RCI and RSM at the cluster level, then segregated by county, livelihood, HH gender and sample type. Furthermore, this section identifies the most influential pillars of resilience, categorized by the segregated profiles.

4.1 ANALYSIS AT THE CLUSTER LEVELFigure 4 shows the frequency density distribution of the RCI9 in the overall cluster sample.

Histogram

Kernel density

Mean

Median

0

0.01

0.02

0 20 40 60 80 100

Den

sity

RCI

Figure 4. Resilience Capacity Index


9 The density distribution measures the variables’ level of dispersion around the mean.

© F

AO \ R

icha

rd B

ett


18

Figure 6. Maps of Resilience Capacity Index and poverty rate by county

Source:KNBS (2016)

Marsabit 79%

Isiolo 63%

Meru 27%

Poverty percentage

Marsabit 52

Isiolo 59

Meru 72

Resilience capacity index

Source:Author’s own calculation

ABS

AST

SSN

AC

Marsabit

Isiolo

Meru

0.25

0.5

0.75

1

Figure 7. Correlation of pillars with the Resilience Capacity Index by county


However, it is not anticipated that in Marsabit county that inputs for crops play a more significant contribution to AST than TLU across the board. While inputs for crops in Marsabit were more influential to AST than TLU was, this can be explained by the fact that a small share (15 percent) of the households in Marsabit, specifically located in the Saku sub-county, carry out crop farming specifically. In this case, inputs for crops is a revealing indicator, as it highlights those households that are engaged in minimal crop production – their lower engagement in crop production positively contributes to their resilience capacity. This is confirmed by qualitative data, which also show that very little agriculture overall is undertaken in the Saku sub-county. Qualitative data from FGDs provides further insights into assets by county, where in households rated the importance of different assets to them (Figure 8).

Households with a higher RCI are located on the right side of the distribution curve. The distribution of the RCI is almost symmetrical, meaning that there are no extreme differences among households in their resilience capacity. The mean RCI is 60.56 and the median value is 59.05.10

Figure 5 presents the relationship between the RCI and the pillars. The pillar contributing the most to the RCI is AST, followed by AC, while SSN and ABS have a lower relevance to the RCI.

CorrelationABS

AST

SSN

AC

0.25

0.5

0.75

1

Figure 5. Correlation of pillars with the Resilience Capacity Index of the cluster


4.2 ANALYSIS AT THE COUNTY LEVELFigure 6 displays the spatial variation of the RCI and the poverty index by county. The spatial variation of the RCI in the Isiolo cluster is pronounced. The analysis shows that Meru county is the most resilient (RCI of 72) followed by Isiolo county (RCI of 59) and the least resilient county is Marsabit (RCI of 52). These results are in keeping with the poverty estimates from the KNBS; the most resilient county has the lowest poverty index and vice versa (for more details about the variables see Table A2 and Table A3).

Figure 7 presents the RSMs for the three counties.

The relevance of AST is prominent in all the counties studied (Figure 7). When looking at Figure 7, it is possible to note the differences in asset ownership between the counties. In Meru county, households have higher asset indicators for both productive (e.g. inputs for crop, inputs for livestock, and cultivated land) and non-productive assets (or, the household asset index). This explains why the RCI is significantly higher in Meru county than in the other two counties (Figure 6). For Marsabit and Isiolo counties, the ownership of livestock (TLU) and usage of livestock inputs contribute significantly to AST, while both counties score lower in terms of household assets (Figure 9). This finding is in line with the livelihood characteristics of the counties, where Isiolo and Marsabit counties are pastoralist and Meru county is mixed farming (see section 4.3 on livelihood analysis).

10 The RCI ranges from 0 to 100, the value 100 being most resilient.

19

Chapter 4 – Descriptive resilience analysis

Figure 6. Maps of Resilience Capacity Index and poverty rate by county

Source:KNBS (2016)

Marsabit 79%

Isiolo 63%

Meru 27%

Poverty percentage

Marsabit 52

Isiolo 59

Meru 72

Resilience capacity index

Source:Author’s own calculation

ABS

AST

SSN

AC

Marsabit

Isiolo

Meru

0.25

0.5

0.75

1

Figure 7. Correlation of pillars with the Resilience Capacity Index by county


However, it is not anticipated that in Marsabit county that inputs for crops play a more significant contribution to AST than TLU across the board. While inputs for crops in Marsabit were more influential to AST than TLU was, this can be explained by the fact that a small share (15 percent) of the households in Marsabit, specifically located in the Saku sub-county, carry out crop farming specifically. In this case, inputs for crops is a revealing indicator, as it highlights those households that are engaged in minimal crop production – their lower engagement in crop production positively contributes to their resilience capacity. This is confirmed by qualitative data, which also show that very little agriculture overall is undertaken in the Saku sub-county. Qualitative data from FGDs provides further insights into assets by county, where in households rated the importance of different assets to them (Figure 8).

Households with a higher RCI are located on the right side of the distribution curve. The distribution of the RCI is almost symmetrical, meaning that there are no extreme differences among households in their resilience capacity. The mean RCI is 60.56 and the median value is 59.05.10

Figure 5 presents the relationship between the RCI and the pillars. The pillar contributing the most to the RCI is AST, followed by AC, while SSN and ABS have a lower relevance to the RCI.

CorrelationABS

AST

SSN

AC

0.25

0.5

0.75

1

Figure 5. Correlation of pillars with the Resilience Capacity Index of the cluster


4.2 ANALYSIS AT THE COUNTY LEVELFigure 6 displays the spatial variation of the RCI and the poverty index by county. The spatial variation of the RCI in the Isiolo cluster is pronounced. The analysis shows that Meru county is the most resilient (RCI of 72) followed by Isiolo county (RCI of 59) and the least resilient county is Marsabit (RCI of 52). These results are in keeping with the poverty estimates from the KNBS; the most resilient county has the lowest poverty index and vice versa (for more details about the variables see Table A2 and Table A3).

Figure 7 presents the RSMs for the three counties.

The relevance of AST is prominent in all the counties studied (Figure 7). When looking at Figure 7, it is possible to note the differences in asset ownership between the counties. In Meru county, households have higher asset indicators for both productive (e.g. inputs for crop, inputs for livestock, and cultivated land) and non-productive assets (or, the household asset index). This explains why the RCI is significantly higher in Meru county than in the other two counties (Figure 6). For Marsabit and Isiolo counties, the ownership of livestock (TLU) and usage of livestock inputs contribute significantly to AST, while both counties score lower in terms of household assets (Figure 9). This finding is in line with the livelihood characteristics of the counties, where Isiolo and Marsabit counties are pastoralist and Meru county is mixed farming (see section 4.3 on livelihood analysis).

10 The RCI ranges from 0 to 100, the value 100 being most resilient.


20

In Marsabit county, productive assets are predominantly livestock, the different types of which were said to be of roughly equal importance to households – they consider sheep to be slightly more important than cattle, goats and camels. In Isiolo county, livestock are considered by households to be the most important assets, especially small stock (sheep and goats), which form almost 50 percent of the assets listed within the survey, while camels and cattle form about 25 percent (see Table A9 in Annex 2). Poultry and donkeys are considered to be of lesser importance, but trees are considered an important asset. Pastoralist communities in Marsabit and Isiolo counties use cattle and camels in the case of major expenses or investments, such as a dowry or children’s school fees. Sheep and goats are used as petty cash for smaller family needs, such as purchasing clothing or cereals, and ensuring that food is always available in the house. In as much as the small stock are kept for economic reasons, they are also kept as a form of insurance against sudden occurrences and emergencies, so that they can be sold in order to address the effects of the emergency. The small stock are also preferred due to their frequency of reproduction (i.e. giving birth twice a year) and their resistance to drought, which applies to goats in particular. In Marsabit county, though not mentioned in the Figure 8, land is also an asset. However, recent encroachment upon grazing lands by communities seeking to resettle and inter-tribal conflicts threaten the utility of this asset for livestock production. In Isiolo county, donkeys (see Table A9) are especially important due to their drought-resistant qualities and use for transportation, wherein they can be used to fetch water for small stock and weak animals during drought. Chickens are a fairly new introduction and their use needs to be enhanced, as they can contribute positively in resilience building for settled pastoralist communities by diversifying livestock production activities. In Meru county, assets are mixed and revolve around crops and livestock, though the main productive asset is miraa, also known as khat, which is a native flowering plant used by people as an herbal stimulant via chewing. Other crop-based assets include maize, beans and bananas (see Table A11 in Annex 2). The main livestock, kept mainly for milk, is cattle, as well as others like small stock and poultry, which are kept effectively as petty cash.

0% 20% 40% 60% 80% 100%

Percentage Asset Ownership

Assets owned in Meru County

Crops

Livestock

Alternate business/Livelihood

Casual employment

0% 20% 40% 60% 80% 100%


Asset owned in Marsabit County

Livestock

Natural resource

Crops


0% 20% 40% 60% 80% 100%


Assets owned in Isiolo County

Livestock

Natural resource

Productive land

Figure 8. Assets by county from qualitative data (from FGD)


21


In Marsabit county, productive assets are predominantly livestock, the different types of which were said to be of roughly equal importance to households – they consider sheep to be slightly more important than cattle, goats and camels. In Isiolo county, livestock are considered by households to be the most important assets, especially small stock (sheep and goats), which form almost 50 percent of the assets listed within the survey, while camels and cattle form about 25 percent (see Table A9 in Annex 2). Poultry and donkeys are considered to be of lesser importance, but trees are considered an important asset. Pastoralist communities in Marsabit and Isiolo counties use cattle and camels in the case of major expenses or investments, such as a dowry or children’s school fees. Sheep and goats are used as petty cash for smaller family needs, such as purchasing clothing or cereals, and ensuring that food is always available in the house. In as much as the small stock are kept for economic reasons, they are also kept as a form of insurance against sudden occurrences and emergencies, so that they can be sold in order to address the effects of the emergency. The small stock are also preferred due to their frequency of reproduction (i.e. giving birth twice a year) and their resistance to drought, which applies to goats in particular. In Marsabit county, though not mentioned in the Figure 8, land is also an asset. However, recent encroachment upon grazing lands by communities seeking to resettle and inter-tribal conflicts threaten the utility of this asset for livestock production. In Isiolo county, donkeys (see Table A9) are especially important due to their drought-resistant qualities and use for transportation, wherein they can be used to fetch water for small stock and weak animals during drought. Chickens are a fairly new introduction and their use needs to be enhanced, as they can contribute positively in resilience building for settled pastoralist communities by diversifying livestock production activities. In Meru county, assets are mixed and revolve around crops and livestock, though the main productive asset is miraa, also known as khat, which is a native flowering plant used by people as an herbal stimulant via chewing. Other crop-based assets include maize, beans and bananas (see Table A11 in Annex 2). The main livestock, kept mainly for milk, is cattle, as well as others like small stock and poultry, which are kept effectively as petty cash.

0% 20% 40% 60% 80% 100%


Assets owned in Meru County

Crops

Livestock


Casual employment

0% 20% 40% 60% 80% 100%


Asset owned in Marsabit County

Livestock

Natural resource

Crops


0% 20% 40% 60% 80% 100%


Assets owned in Isiolo County

Livestock

Natural resource

Productive land

Figure 8. Assets by county from qualitative data (from FGD)


The role of AC is more pronounced in Meru and Marsabit counties than in Isiolo county. This is explained by the much higher contribution to this pillar from income diversification, especially in Meru county (see Figure 9). There, households draw from several income sources, such as livestock trading, petty trading/shops, crop sales, and to a great extent the sale of miraa especially in the Igembe North sub-county. Accordingly, per capita income is relatively higher in Meru county compared to the other two counties. In Marsabit county, the CSI11 strongly contributes to AC, which implies that households are employing more frequent or more robust12 mechanisms to cope with shocks. In turn, this leads to a low RCI in Marsabit county (see Figure 6). The opposite scenario is observed in Meru and Isiolo counties, where there is a lower CSI. In all the counties, the education level of the HH is a relatively significant contribution to AC, especially in Meru county. However, a low level of education is exhibited among HHs, as typically they have barely passed primary school (see Annex 1, Table A4 on county descriptive statistics).

The weight of ABS in the RSMs of the counties is more significant for Isiolo county than for Marsabit and Meru counties. Among the indicators for ABS, the distance index to main facilities contributes the most to this pillar13 in all counties, meaning that there is room for improvement in infrastructure to ensure better access to services. In Marsabit county, access to toilets is important to the contribution of ABS, which implies that households have poor access to sanitary services; the descriptive statistics show 41 percent of households have access to a household toilet (see Table A4 in Annex 1). In addition, the households there are more remote and have particularly limited access to hospitals and markets (see Annex 1, Table A4 on county descriptive statistics) resulting in a lower RCI. Interestingly, comparing the distance index and access to improved water among the counties, Isiolo county has a higher index14 for both. This can be attributed to Water, Sanitation and Hygiene (WASH) interventions implemented by several non-governmental organizations (NGOs) operating in that area, which significantly increase households’ access to improved water and health facilities. This is also the case for access to improved water sources.

In terms of SSN, Meru county has the highest correlation between this pillar and the RCI (Figure 7). Household participation in different social networks, such as farmer groups, and access to credit play a key role in Meru county, but access to cash transfers contributes less to this pillar compared to in the other two counties. This can be explained by a pronounced reliance on formal transfers, such as food relief in Isiolo county and cash transfers in Marsabit county.15 Triangluation with qualitative data confirms that the ability of households in these counties to access social safety nets through the community itself is limited; however, some NGOs and the GoK do provide social safety nets (e.g formal cash and food transfers). On the other hand, Isiolo and Marsabit counties have very poor access to credit.

In terms of access to social services, the qualitative analysis provides some interesting insights, which help to complement the quantitative analysis. In Marsabit county, communities are generally able to access veterinary services. Though to a limited extent, they are also able to access some basic services such as education, small health facilities and water. Other services like electricity are minimal, as well as sanitary facilities. This is because pastoralists, in most cases, are nomadic. However, some of the big population centres do have sanitary facilities and electricity.

11 The CSI is already inverted in the model to obtain the positive association to the AC pillar and hence to the RCI.12 The severity of the coping strategy adopted is subjective as reported by the household. The CSI measures the frequency

and severity of coping mechanisms adopted by households. More information can be found at http://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp211058.pdf13 A higher index indicates better access to services (or a shorter distance to services).14 The distance index is calculated from the FA of the inverted value of distances to the services, to obtain the positive

association to the AC pillar and the RCI15 Food relief is provided by the Hunger Safety Net Programme (HSNP) implemented by the GoK.


22

(Figure 10). When looking at income and expenditure levels in these counties, it can be seen that in Meru county the average monthly per capita income and average expenditure amounts to US$50 and US$58, while in Isiolo and Marsabit counties they amount to US$23 and US$39. Moreover, the RCI is also in line with the poverty rates of Marsabit, Isiolo and Meru counties, which stand at 79 percent, 63 percent and 28 percent,18 respectively. This shows the regional inequality in both poverty and resilience capacity.

Figure 10. Average Resilience Capacity Index by livelihood


Pastoralist Mixed farming

Mea

n of

Res

ilien

ce

0

20

40

60

80

Figure 11. Correlation of pillars with Resilience Capacity Index by livelihood


Pastoralist

Mixed farming

ABS

AST

SSN

AC

0.25

0.5

0.75

1

18 For more information on the poverty rates for the three counties see Kenya Integrated Household Budget Survey, 2005/06.

In Isiolo county, the main services available are food relief, health, education, veterinary services, and the local government administration. The community relies on the local government administrative services to curb insecurity related to livestock assets. The veterinary services provided by the county administration are also quite useful in safeguarding livestock assets, but interviews with key informants suggested the need to expand the outreach and frequency of the provision of these services.

In Meru county, the main services provided in the Igembe North sub-county include veterinary extension services, water vending and relief seeds.16 For the Igembe Central sub-county, veterinary assistance and water vending are also important, but there is also health, cash transfers and some agricultural relief services available. In the case of the Igembe North sub-county, the community has mobilized itself in order to carry out some road repair projects.

Marsabit

Isiolo

Meru

Access to toilet

Distanceindex

ABSAccess to improved water

ACEducation of HH

HH asset index

Plantedland

TLUInput for crop

Input forlivestock

AST

Social network

Access to transfer

Accessto credit 1

SSN

Independency ratio

Income diversification

Coping strategy index

0.25

0.50.75

1

0.250.5

0.75

0.25

0.5

0.75

1

0.25

0.5

0.75

1

Figure 9. Correlation of variables and pillars by county


4.3 ANALYSIS BY LIVELIHOODThe analysis is disaggregated by livelihood, combining Marsabit and Isiolo counties together due to the nature of their shared pastoralist livelihood, and leaving Meru county separate, owing to its mostly mixed farming livelihood.17 Figure 10 presents the mean RCI over these two livelihoods. Livelihood categories are not self-reported by households, the counties are known for having populations with these defined livelihoods, that is pastoralist and mixed farmers. Mixed farmers are those farmers mostly practicing crop production but also keep some livestock for milk, meat production. Households with a mixed farming livelihood are more resilient than pastoralist households, with the mean RCI at 72 and 57, respectively

16 Relief seeds’ refers to a particular type of agricultural input that is provided to farmers in the form of aid.17 The classification of livelihoods in the cluster is also defined by FAO target areas in the counties that is, pastoralist for

Marsabit and Isiolo counties, and mixed farming for Meru.

23


(Figure 10). When looking at income and expenditure levels in these counties, it can be seen that in Meru county the average monthly per capita income and average expenditure amounts to US$50 and US$58, while in Isiolo and Marsabit counties they amount to US$23 and US$39. Moreover, the RCI is also in line with the poverty rates of Marsabit, Isiolo and Meru counties, which stand at 79 percent, 63 percent and 28 percent,18 respectively. This shows the regional inequality in both poverty and resilience capacity.

Figure 10. Average Resilience Capacity Index by livelihood


Pastoralist Mixed farming

Mea

n of

Res

ilien

ce

0

20

40

60

80

Figure 11. Correlation of pillars with Resilience Capacity Index by livelihood


Pastoralist

Mixed farming

ABS

AST

SSN

AC

0.25

0.5

0.75

1

18 For more information on the poverty rates for the three counties see Kenya Integrated Household Budget Survey, 2005/06.

In Isiolo county, the main services available are food relief, health, education, veterinary services, and the local government administration. The community relies on the local government administrative services to curb insecurity related to livestock assets. The veterinary services provided by the county administration are also quite useful in safeguarding livestock assets, but interviews with key informants suggested the need to expand the outreach and frequency of the provision of these services.

In Meru county, the main services provided in the Igembe North sub-county include veterinary extension services, water vending and relief seeds.16 For the Igembe Central sub-county, veterinary assistance and water vending are also important, but there is also health, cash transfers and some agricultural relief services available. In the case of the Igembe North sub-county, the community has mobilized itself in order to carry out some road repair projects.

Marsabit

Isiolo

Meru

Access to toilet

Distanceindex


ACEducation of HH

HH asset index

Plantedland

TLUInput for crop

Input forlivestock

AST

Social network

Access to transfer

Accessto credit 1

SSN

Independency ratio



0.25

0.50.75

1

0.250.5

0.75

0.25

0.5

0.75

1

0.25

0.5

0.75

1

Figure 9. Correlation of variables and pillars by county


4.3 ANALYSIS BY LIVELIHOODThe analysis is disaggregated by livelihood, combining Marsabit and Isiolo counties together due to the nature of their shared pastoralist livelihood, and leaving Meru county separate, owing to its mostly mixed farming livelihood.17 Figure 10 presents the mean RCI over these two livelihoods. Livelihood categories are not self-reported by households, the counties are known for having populations with these defined livelihoods, that is pastoralist and mixed farmers. Mixed farmers are those farmers mostly practicing crop production but also keep some livestock for milk, meat production. Households with a mixed farming livelihood are more resilient than pastoralist households, with the mean RCI at 72 and 57, respectively

16 Relief seeds’ refers to a particular type of agricultural input that is provided to farmers in the form of aid.17 The classification of livelihoods in the cluster is also defined by FAO target areas in the counties that is, pastoralist for

Marsabit and Isiolo counties, and mixed farming for Meru.


24

Further analysis of the correlation of the pillars to the RCI from Figure 11 reveals that AST is again an important pillar for both pastoralist and mixed farming livelihoods. In line with the analysis at the county level (refer to Section 4.2), the relevance of TLU and use of livestock inputs19 as two important indicators for AST emerges strongly for the pastoralist livelihood (see Figure 12). This implies that for pastoralist communities, expenditure on livestock production activities, including investments in health services and vaccination, is the households’ primary concern. The qualitative assessment shows that in these communities, livestock assets are so important that even in the case of shocks, the household would choose not to deplete their herds or animal resources; instead, they would employ coping mechanisms to help them to survive. For instance, they would avoid milking their cows or would slaughter the calves. In Meru county, the indicators that contribute the most to AST are related to crop production, including crop inputs and cultivated land (see Section 4.2 on county analysis). In general, indicators of the AST pillar for the mixed farming livelihood are all very significant to this pillar, including inputs for livestock production as households also keep animals, as seen from the average per capita TLU of 0.551 (see Annex 1, Table A5 on livelihood descriptive statistics). This means that investments in both productive assets and agricultural productivity would increase the resilience capacity of households in areas that use mixed farming.

AC is ranked as the second most important pillar contributing to the RCI for both pastoralist and mixed farming livelihoods. The association between this pillar and the RCI is higher for the mixed farming livelihood than the pastoralist livelihood. In both livelihoods, income diversification is the most relevant factor for the adaptive capacity of households. Descriptive statistics show that households have an average of two and three income sources across pastoralist and mixed farming livelihoods, respectively (see Annex 1, Table A5 on livelihood descriptive statistics). In the mixed farming livelihood, the education level of HHs contributes most to AC, compared to the pastoralist livelihood where coping strategies is the most important factor. The higher contribution of coping strategies for that livelihood implies that households are faced either with more frequent or more intense shocks and stressors, which results in a greater reliance on coping mechanisms.

In terms of ABS, the contribution of this pillar to the RCI is greater for pastoralist livelihoods than for mixed farming livelihoods. This means that aspects of basic services provision are a key consideration for improving the resilience of those households. Looking at the specific variables within the ABS pillar, it is clear that for pastoralist communities there is quite a significant level of importance in terms of access to improved water and toilet facilities, as well as to the other facilities included in the distance index. Specifically, this means that the contribution of these variables is very important for pastoralist communities compared to mixed farming communities, where both ABS as a pillar and the related variables (e.g. access to water and toilets) are less relevant in determining the resilience of the latter group. This implies that mixed farming communities in general benefit from better access to facilities. Accordingly, when looking at the descriptive statistics for the two livelihood groups (see Annex 1, Table A5 on livelihood descriptive statistics), it is clear that pastoralists have significantly more limited access to services such as hospitals, markets and credit services, both in terms of the distance to those facilities and that there is generally much lower access to sanitation compared to the mixed farming livelihood.

SSN has a higher contribution to the RCI in the mixed farming areas compared to pastoralist area, which can be attributed to better access to social networks and credit in Meru county. Comparatively, SSN is the least important pillar in pastoralist areas, which could be attributed to the fact that access to credit is very limited for pastoralist households. Only 20 percent

19 The use of livestock inputs refers to the value of expenditure on inputs.

of households in the pastoralist areas studied had access to credit during the 12-month period prior to the survey (see Table A5, Annex 1 of the descriptive statistics). Though they also participated in some social networks, it appears they have not been supported in building household resilience capacity via diversified economic activities. Further findings from the qualitative analysis show that these communities rely more on informal relationships with relatives and friends, neighbouring families and nearby communities in cases where immediate assistance is needed. The participation in social networks indicator has a high association with the SSN pillar, demonstrating opportunities for interventions to invest in more households in order to encourage communal activity participation, which would influence the community economy.

Figure 12. Correlation of variables and pillars by livelihood


Pastoralist

Mixed farming

Access to toilet

Distanceindex


ACEducation of HH

HH asset index

Plantedland

TLUInput for crop

Input forlivestock

AST

Social network

Access to transfer

Accessto credit 1

SSN

Independency ratio



0.25

0.75

1

0.75

0.25

0.5

0.75

1

0.5

0.50.25

0.25

0.5

0.75

1

4.4 ANALYSIS BY GENDER OF HOUSEHOLD HEADFigure 13 presents the difference in RCI between MHHs and FHHs (see Annex 1, Figure A1 for an overview of the gender distribution of HHs in the counties). The RCI for the overall sample is on average slightly higher for MHHs than for FHHs. However, the difference in RCI is not statistically significant between the two groups.

In terms of the RSM, AST and AC contribute the most to the RCI for both MHHs and FHHs, though slightly less for FHHs (see Figure 14). For AST, FHHs show lower average values in all components (see Annex 1, Table A6 in on HH gender descriptive statistics). The variable exhibiting the highest association with AST is inputs for crop production, especially for FHHs rather than MHHs. This means that lower ownership of assets (both productive and non-productive) contributes to the lower RCI of FHHs.

Generally, there is a trend of gender imbalance on asset ownership across different communities found in Kenya – especially for the pastoralist livelihood. Gender dynamics in asset ownership, as well as access to and control of household assets, was assessed in this analysis. Figure 15 and 16 provide the overview of asset ownership and decision making. Across Marsabit and Isiolo counties, similar patterns are observed in asset ownership (Figure 15) and in decision making

25


of households in the pastoralist areas studied had access to credit during the 12-month period prior to the survey (see Table A5, Annex 1 of the descriptive statistics). Though they also participated in some social networks, it appears they have not been supported in building household resilience capacity via diversified economic activities. Further findings from the qualitative analysis show that these communities rely more on informal relationships with relatives and friends, neighbouring families and nearby communities in cases where immediate assistance is needed. The participation in social networks indicator has a high association with the SSN pillar, demonstrating opportunities for interventions to invest in more households in order to encourage communal activity participation, which would influence the community economy.

Figure 12. Correlation of variables and pillars by livelihood


Pastoralist

Mixed farming

Access to toilet

Distanceindex


ACEducation of HH

HH asset index

Plantedland

TLUInput for crop

Input forlivestock

AST

Social network

Access to transfer

Accessto credit 1

SSN

Independency ratio



0.25

0.75

1

0.75

0.25

0.5

0.75

1

0.5

0.50.25

0.25

0.5

0.75

1

4.4 ANALYSIS BY GENDER OF HOUSEHOLD HEADFigure 13 presents the difference in RCI between MHHs and FHHs (see Annex 1, Figure A1 for an overview of the gender distribution of HHs in the counties). The RCI for the overall sample is on average slightly higher for MHHs than for FHHs. However, the difference in RCI is not statistically significant between the two groups.

In terms of the RSM, AST and AC contribute the most to the RCI for both MHHs and FHHs, though slightly less for FHHs (see Figure 14). For AST, FHHs show lower average values in all components (see Annex 1, Table A6 in on HH gender descriptive statistics). The variable exhibiting the highest association with AST is inputs for crop production, especially for FHHs rather than MHHs. This means that lower ownership of assets (both productive and non-productive) contributes to the lower RCI of FHHs.

Generally, there is a trend of gender imbalance on asset ownership across different communities found in Kenya – especially for the pastoralist livelihood. Gender dynamics in asset ownership, as well as access to and control of household assets, was assessed in this analysis. Figure 15 and 16 provide the overview of asset ownership and decision making. Across Marsabit and Isiolo counties, similar patterns are observed in asset ownership (Figure 15) and in decision making


26

on the use of income from household assets (Figure 16). Male HHs own a significant share of assets within their households across all the counties (46 percent in Isiolo county, 54 percent in Marsabit county and 32 percent in Meru county). Meanwhile, a very low share of decision-making power in Isiolo and Marsabit counties is carried out jointly with female household members (11 and 7 percent, respectively). In contrast, while male HHs in Meru own most of the assets (32 percent), but 51 percent of decision making is carried out jointly with female household members.

MHH FHH

Mea

n of

Res

ilien

ce

0

20

40

60

Figure 13. Average Resilience Capacity Index by household head gender


MHH

FHH

ABS

AST

SSN

AC

0.25

0.5

0.75

1

Figure 14. Correlation between pillars and Resilience Capacity Index by household head gender


Figure 15. Asset ownership by county


Isiolo Marsabit MeruMale head

Female head

Jointly by members

Female within household

Other46%

19%

11%

23%

1% 2%

54%

15%

10%

19%

1%

32%

13%

47%

7%

Figure 16. Asset decision making on income by county



Female head

Jointly by members


Other

0% 2% 0%

46%

20%

11%

23%

56%

16%

7%

19% 27%

14%51%

8%

For the AC pillar, income diversification is the most relevant factor for both FHHs and MHHs. MHHs have more income sources compared to FHHs (see Annex 1, Table A6 on HH gender descriptive statistics). The CSI is also highly associated with the AC of both MHHs and FHHs as shown in Figure 17, although FHHs have a higher CSI than MHHs (see Annex 1, Table A6 on HH gender descriptive statistics). This means that FHHs are employing more coping mechanisms when affected by shocks, thus reducing their resilience capacity. Female HHs also have much lower education compared to male HHs, which also clearly impacts negatively on the RCI.

The ABS pillar contributes more to the RCI for FHHs than for MHHs. The distance index is the most relevant factor to the ABS pillar for both groups, followed by access to toilets. On average, 68 percent and 69 percent of MHHs and FHHs, respectively, have access to household sanitary facilities.

The SSN pillar contributes the least to the RCI for FHHs, and second least for MHHs. Participation in social networks is the most important factor within this pillar for both groups, followed by access to credit. Household engagement in social activities and related networks is important, particularly when households are exposed to shocks and engagement is needed in order to return the household to its pre-shock economic status. Participation in social networks also implies opportunities to have access to credit savings facilities, which improves economic well-being.

27


on the use of income from household assets (Figure 16). Male HHs own a significant share of assets within their households across all the counties (46 percent in Isiolo county, 54 percent in Marsabit county and 32 percent in Meru county). Meanwhile, a very low share of decision-making power in Isiolo and Marsabit counties is carried out jointly with female household members (11 and 7 percent, respectively). In contrast, while male HHs in Meru own most of the assets (32 percent), but 51 percent of decision making is carried out jointly with female household members.

MHH FHH

Mea

n of

Res

ilien

ce

0

20

40

60

Figure 13. Average Resilience Capacity Index by household head gender


MHH

FHH

ABS

AST

SSN

AC

0.25

0.5

0.75

1

Figure 14. Correlation between pillars and Resilience Capacity Index by household head gender


Figure 15. Asset ownership by county



Female head

Jointly by members


Other46%

19%

11%

23%

1% 2%

54%

15%

10%

19%

1%

32%

13%

47%

7%

Figure 16. Asset decision making on income by county



Female head

Jointly by members


Other

0% 2% 0%

46%

20%

11%

23%

56%

16%

7%

19% 27%

14%51%

8%

For the AC pillar, income diversification is the most relevant factor for both FHHs and MHHs. MHHs have more income sources compared to FHHs (see Annex 1, Table A6 on HH gender descriptive statistics). The CSI is also highly associated with the AC of both MHHs and FHHs as shown in Figure 17, although FHHs have a higher CSI than MHHs (see Annex 1, Table A6 on HH gender descriptive statistics). This means that FHHs are employing more coping mechanisms when affected by shocks, thus reducing their resilience capacity. Female HHs also have much lower education compared to male HHs, which also clearly impacts negatively on the RCI.

The ABS pillar contributes more to the RCI for FHHs than for MHHs. The distance index is the most relevant factor to the ABS pillar for both groups, followed by access to toilets. On average, 68 percent and 69 percent of MHHs and FHHs, respectively, have access to household sanitary facilities.

The SSN pillar contributes the least to the RCI for FHHs, and second least for MHHs. Participation in social networks is the most important factor within this pillar for both groups, followed by access to credit. Household engagement in social activities and related networks is important, particularly when households are exposed to shocks and engagement is needed in order to return the household to its pre-shock economic status. Participation in social networks also implies opportunities to have access to credit savings facilities, which improves economic well-being.


28

Male HH

Female HH

Access to toilet

Distanceindex


ACEducation of HH

HH asset index

Plantedland

TLUInput for crop

Input forlivestock

AST

Social network

Access to transfer

Accessto credit

SSN

Independency ratio



0.25

0.75

1 1

0.5

0.25

0.5

0.75

10.750.5

0.25

0.25

0.5

0.75

1

Figure 17. Correlation of variables and pillars by household head gender


4.5 ANALYSIS BY SAMPLE TYPEFigure 18 presents the mean RCI of the treatment and control groups of households. The mean RCI for the treatment group is 59.8, while it is 57.2 for the control group. This difference is statistically significant at 5 percent.20

Intervention Control

Mea

n of

Res

ilien

ce

0

20

40

60

Figure 18. Average Resilience Capacity Index by sample type


It is important to note that the treatment group has a higher RCI at the baseline than the control group. This will have implications for the IE, hence statistical procedures such as the Difference in Differences (DiD) will be employed to control for such baseline differences.

Figure 19 below shows the correlation between the pillars and the RCI for both groups. The results are similar in terms of which pillar contributes the most; these are AST, AC, and SSN. However, major differences between the two groups are noted for all pillars, except for ABS.

Figure 19. Correlation between pillars and Resilience Capacity Index by sample type


Intervention

Control

ABS

AST

SSN

AC

0.25

0.5

0.75

1

20 A t-test was performed to compare the RCIs for the two groups.

29


Male HH

Female HH

Access to toilet

Distanceindex


ACEducation of HH

HH asset index

Plantedland

TLUInput for crop

Input forlivestock

AST

Social network

Access to transfer

Accessto credit

SSN

Independency ratio



0.25

0.75

1 1

0.5

0.25

0.5

0.75

10.750.5

0.25

0.25

0.5

0.75

1

Figure 17. Correlation of variables and pillars by household head gender


4.5 ANALYSIS BY SAMPLE TYPEFigure 18 presents the mean RCI of the treatment and control groups of households. The mean RCI for the treatment group is 59.8, while it is 57.2 for the control group. This difference is statistically significant at 5 percent.20

Intervention Control

Mea

n of

Res

ilien

ce

0

20

40

60

Figure 18. Average Resilience Capacity Index by sample type


It is important to note that the treatment group has a higher RCI at the baseline than the control group. This will have implications for the IE, hence statistical procedures such as the Difference in Differences (DiD) will be employed to control for such baseline differences.

Figure 19 below shows the correlation between the pillars and the RCI for both groups. The results are similar in terms of which pillar contributes the most; these are AST, AC, and SSN. However, major differences between the two groups are noted for all pillars, except for ABS.

Figure 19. Correlation between pillars and Resilience Capacity Index by sample type


Intervention

Control

ABS

AST

SSN

AC

0.25

0.5

0.75

1

© F

AO \ H

ien

Vu

31

5 CAUSAL RESILIENCE ANALYSISIn this section, the results of the causal analysis of resilience and correlates of food security are presented. The analysis examines the effects of shocks on resilience while accounting for self-reported and contextual shocks. The spatial location of the households and external climatic factors are also incorporated into the analysis. An additional qualitative analysis complements the results of the causal analysis.

The RIMA-II methodology is divided into two parts; the descriptive analysis and the causal analysis. This section is concerned with the latter. This delves into (i) the contribution of shocks to resilience capacity, and (ii) the association between the contributing factors of resilience and food security indicators, used for estimating the RCI in the descriptive resilience analysis.

It is paramount to investigate whether the RCI computed is able to effectively capture well-being. The ‘well-being’ in this context refers to household food security. Households that are able to return to a food-secure position after a shock are deemed to be more resilient. To achieve this, a regression analysis between food security measures and the RCI is employed in this section. This section also evaluates the effect of shocks on the resilience capacity of a household. Resilience determines a household’s capability to deal with shocks and stressors. Shocks are considered both endogenous and exogenous, and have been included in a regression model for estimating their impact on the resilience capacity of households (FAO, 2016a).

The current analysis cannot address the dynamic nature of food systems and resilience since it is based on cross-sectional data; the availability of panel data would allow for a more comprehensive analysis over a period of time. Nonetheless, the results in this section are valid and vital for informing policy. The sub-sections that follow present the analysis of food security and shocks from the RIMA-II perspective, complemented by shock.

5.1 INFLUENCE OF SHOCKS ON RESILIENCE CAPACITYAlthough a household might prepare for and try to mitigate against food insecurity as well as enhance its resilience capacity, external forces maybe work against these endeavours. Such external forces include shocks and spatial climatic factors.

© F

AO \ H

ien

Vu


32

Table 6 Effects of shocks on the Resilience Capacity Index in the three counties (cont.)

Variable RCIHousehold characteristics

Marsabit-34.038***

(2.296)

Isiolo-7.565***(1.373)

FHH-3.298***(1.137)

Household size-0.899***(0.261)

Constant -30.653*(17.004)

Observations 1028Adj. R squared 0.356

The reference category of the county dummies is Meru county.Standard errors in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1

From the analysis, the shocks associated with resilience capacity are: floods; pests, parasites and diseases; business failure; job loss/no salary/death of the main income earner; vehicle breakdown; and reduction in FCI. On the other hand, county, HH gender and household size were the household characteristics most closely linked with the RCI.

Interestingly, an increase in flooding is associated with improved resilience. Although flooding is classified as a shock, it is a double-edged sword; it has both positive and negative consequences. Most of the regions in the Isiolo cluster are dry, hence the occurrence of floods implies the availability of more water for livestock and household use. A study conducted in Mauritania also reported a similar result regarding flooding (FAO, 2016b). Meanwhile, an increase in pests, parasites and diseases decreases a household’s resilience capacity; disease can lead to loss of livestock and of crops, and to an increase in expenditure due to the treatments required to eliminate those diseases. Business failure is positively associated with a high level of resilience capacity. This seems to be counterintuitive. This unusual relationship could be due to the fact that those households involved in business have inherently high resilience due to their diversified sources of income. Furthermore, a quantile regression model was fitted to investigate the effects of business failure on households with either a high or a low RCI. Business failure was found to have a higher effect on households with lower resilience capacity. Similarly, vehicle breakdown or damage is also associated with high resilience capacity. Again, this could be attributed to the fact that the owner possesses the vehicle in the first place.

A high FCI is positively associated with resilience capacity. This suggests the positive influence of favourable climatic conditions on resilience capacity, as FCI can be considered a proxy for climatic conditions. On the other hand, job loss/no salary/death of the main income earner in a household is negatively associated with household resilience capacity. This is due to the associated reduction in income.

As expected, the connection between household characteristics and the RCI is strong. These results corroborate the findings in the descriptive section of the analysis. Marsabit and Isiolo counties are less resilient compared to Meru county (the reference county for the causal

The primary aim of this section is to investigate the association between the RCI and the primary aim of this section is to investigate the association between RCI and households’ self reported shocks, as well as idiosyncratic shocks, controlling for demographic variables. With this in mind, the following model was used:

RCIi = β0 + ηSi + αXi + εi (3)

where RCIi is the RCI of the i-th household; Si is a vector of shocks, including both idiosyncratic shocks (self-reported shocks at the household level) and contextual covariate shocks, [spatial annual FCI],21 experienced by household i; Xi is a vector of household control characteristics, including but not limited to the gender of the HH, household size and county; η and α αre the vectors of coefficients to be estimated.

Table 6 shows the results of the effects of shocks on the RCI in the three counties combined.

Table 6 Effects of shocks on the Resilience Capacity Index in the three counties

Variable RCIShocks

Flood3.755***

(1.033)

Drought-1.686(1.064)

Pests, parasites and diseases-1.685*(0.978)

Fire-0.018(4.509)

Business failure9.248***

(1.714)

Severe illness/Injury-1.024(1.431)

Job loss/no salary/death of main earner-6.952**(3.416)

Resource-based conflicts/communal crisis/political crisis1.169

(2.654)

Loss of land0.762

(4.437)

Vehicle breakdown/damages8.733**

(3.735)

FCI2.539***

(0.407)

21 FCI is a measure of the amount of livestock forage in an area. It captures the growth of species of plants, shrubs and trees that would be grazed on by livestock. It is computed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data.

33

Chapter 5 – Causal resilience analysis

Table 6 Effects of shocks on the Resilience Capacity Index in the three counties (cont.)

Variable RCIHousehold characteristics

Marsabit-34.038***

(2.296)

Isiolo-7.565***(1.373)

FHH-3.298***(1.137)

Household size-0.899***(0.261)

Constant -30.653*(17.004)

Observations 1028Adj. R squared 0.356


*** p < 0.01, ** p < 0.05, * p < 0.1

From the analysis, the shocks associated with resilience capacity are: floods; pests, parasites and diseases; business failure; job loss/no salary/death of the main income earner; vehicle breakdown; and reduction in FCI. On the other hand, county, HH gender and household size were the household characteristics most closely linked with the RCI.

Interestingly, an increase in flooding is associated with improved resilience. Although flooding is classified as a shock, it is a double-edged sword; it has both positive and negative consequences. Most of the regions in the Isiolo cluster are dry, hence the occurrence of floods implies the availability of more water for livestock and household use. A study conducted in Mauritania also reported a similar result regarding flooding (FAO, 2016b). Meanwhile, an increase in pests, parasites and diseases decreases a household’s resilience capacity; disease can lead to loss of livestock and of crops, and to an increase in expenditure due to the treatments required to eliminate those diseases. Business failure is positively associated with a high level of resilience capacity. This seems to be counterintuitive. This unusual relationship could be due to the fact that those households involved in business have inherently high resilience due to their diversified sources of income. Furthermore, a quantile regression model was fitted to investigate the effects of business failure on households with either a high or a low RCI. Business failure was found to have a higher effect on households with lower resilience capacity. Similarly, vehicle breakdown or damage is also associated with high resilience capacity. Again, this could be attributed to the fact that the owner possesses the vehicle in the first place.

A high FCI is positively associated with resilience capacity. This suggests the positive influence of favourable climatic conditions on resilience capacity, as FCI can be considered a proxy for climatic conditions. On the other hand, job loss/no salary/death of the main income earner in a household is negatively associated with household resilience capacity. This is due to the associated reduction in income.

As expected, the connection between household characteristics and the RCI is strong. These results corroborate the findings in the descriptive section of the analysis. Marsabit and Isiolo counties are less resilient compared to Meru county (the reference county for the causal

The primary aim of this section is to investigate the association between the RCI and the primary aim of this section is to investigate the association between RCI and households’ self reported shocks, as well as idiosyncratic shocks, controlling for demographic variables. With this in mind, the following model was used:

RCIi = β0 + ηSi + αXi + εi (3)

where RCIi is the RCI of the i-th household; Si is a vector of shocks, including both idiosyncratic shocks (self-reported shocks at the household level) and contextual covariate shocks, [spatial annual FCI],21 experienced by household i; Xi is a vector of household control characteristics, including but not limited to the gender of the HH, household size and county; η and α αre the vectors of coefficients to be estimated.

Table 6 shows the results of the effects of shocks on the RCI in the three counties combined.

Table 6 Effects of shocks on the Resilience Capacity Index in the three counties

Variable RCIShocks

Flood3.755***

(1.033)

Drought-1.686(1.064)

Pests, parasites and diseases-1.685*(0.978)

Fire-0.018(4.509)

Business failure9.248***

(1.714)

Severe illness/Injury-1.024(1.431)

Job loss/no salary/death of main earner-6.952**(3.416)

Resource-based conflicts/communal crisis/political crisis1.169

(2.654)

Loss of land0.762

(4.437)

Vehicle breakdown/damages8.733**

(3.735)

FCI2.539***

(0.407)


34

analysis), and Marsabit county is the least resilient county in the Isiolo cluster. MHHs are more resilient than FHHs, and there is an inverse relationship between household size increases and resilience capacity. Household size variation has been linked to poverty in many studies (The impact of household size on poverty: An analysis of various low-income townships in the Northern Free State region, South Africa, 2016).

Drought

Insecurity

Unpredictable Weather

Shocks experienced in Marsabit

Marsabit coping strategies

Livestock Diseases

Windy weather

Rain water runnoff to lake

Crop disease

Malnutrition

Environmental degradation

Floods

Wild animals

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Migration

Vet services

Destocking

Peace talks

Change fishing line

Water trucking

Social networks

Alternative feeding

Engine boat

Fencing

Pesticides for crops

Wild tubers

Ethno vet

KWS assistance

Report to govt

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 20. Shocks and coping strategies reported in qualitative analysis in Marsabit county


Drought

Insecurity

Floods

Shocks experienced in Isiolo County

Isiolo County coping stratetgies

Human sickness

Lack of water

Livestock disease

Wild animals

Bush fire

Lack of livestock markets

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Migration

Group herding

Destocking

Relief

Digging wells

Calf slaughter

Alternative feeds

Peace talks

Vet services

Slaughter emaciated

No Milking

Social networks


0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 21. Shock and coping strategies reported in qualitative analysis in Isiolo county


Figure 22. Shock and coping strategies reported in qualitative analysis in Meru county


Shocks experienced in Meru sub counties surveyed

Coping strategies employed in Meru sub counties surveyed

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Casual labour

Water vendors

Migration

Help from govt

Search for markets/barter trade

Communal work

Social networks

Use paraffin/solar

Vet/extension services

Sell at low price

Skipping meals

Buying food

Destocking

Buy drugs & agrochems

Drought

Insecurity

Pests and diseases

Water shortage

Poor roads

Poor hospitals

High input costs

Limited Markets and Marketing

Poor Agricultural Practices

Wild animals

Floods

Poor governance

Illiteracy

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Interestingly, in addition to the shocks reported through the survey, the qualitative results reveal that significant shocks affecting households in Isiolo county, Marsabit county and parts of Meru county were drought/low rainfall and insecurity. Insecurity is due to resource-based conflicts particularly affecting pastoralist communities in Isiolo and Marsabit counties (see Figures 20, 21 and 22 on shocks at the county level).

Further detail from the qualitative data at the county level shows that the main shock present in Marsabit county is drought, followed by livestock diseases and insecurity (see Figure 20). However, it is worth noting that all these three factors are important and inter-related with regards to livestock production. For fishers in the lake region, the main threat is windy weather and rainwater runoff to the lake, which in turn affect fishing activities. However, people in the lake region do not rely only on fishing; they also keep livestock. Fishing is an alternative livelihood option. Most of the community is affected by the drought, and the other shocks as mentioned, thus though it is sensitive to some extent this is cushioned by the primary coping strategies.

Qualitative analysis also shows some interesting findings on coping mechanisms (see Figures 20 and 21) employed by households in the pastoralist livelihood group (comprised of Isiolo and Marsabit counties). The migration of livestock and thus key household members occurs due to drought or insecurity, while destocking was also common, but occurred for few animals. The main coping strategy for the community in Marsabit county (see Figure 20) is migration, which helps to prevent the loss of productive assets as well as conflicts over pasture and water. Households also hold community peace talks to overcome resource-based conflicts. People also rely on veterinary extension services offered by the local government to prevent the loss of assets, while community-level coping mechanisms through community social safety nets is limited. The community also adapts to new situations through other adaptive capacities; these can be seen via fishing, and

35


analysis), and Marsabit county is the least resilient county in the Isiolo cluster. MHHs are more resilient than FHHs, and there is an inverse relationship between household size increases and resilience capacity. Household size variation has been linked to poverty in many studies (The impact of household size on poverty: An analysis of various low-income townships in the Northern Free State region, South Africa, 2016).

Drought

Insecurity

Unpredictable Weather

Shocks experienced in Marsabit

Marsabit coping strategies

Livestock Diseases

Windy weather

Rain water runnoff to lake

Crop disease

Malnutrition

Environmental degradation

Floods

Wild animals

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Migration

Vet services

Destocking

Peace talks

Change fishing line

Water trucking

Social networks

Alternative feeding

Engine boat

Fencing

Pesticides for crops

Wild tubers

Ethno vet

KWS assistance

Report to govt

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 20. Shocks and coping strategies reported in qualitative analysis in Marsabit county


Drought

Insecurity

Floods

Shocks experienced in Isiolo County

Isiolo County coping stratetgies

Human sickness

Lack of water

Livestock disease

Wild animals

Bush fire


0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Migration

Group herding

Destocking

Relief

Digging wells

Calf slaughter

Alternative feeds

Peace talks

Vet services

Slaughter emaciated

No Milking

Social networks


0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Figure 21. Shock and coping strategies reported in qualitative analysis in Isiolo county


Figure 22. Shock and coping strategies reported in qualitative analysis in Meru county


Shocks experienced in Meru sub counties surveyed

Coping strategies employed in Meru sub counties surveyed

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Casual labour

Water vendors

Migration

Help from govt

Search for markets/barter trade

Communal work

Social networks

Use paraffin/solar

Vet/extension services

Sell at low price

Skipping meals

Buying food

Destocking

Buy drugs & agrochems

Drought

Insecurity

Pests and diseases

Water shortage

Poor roads

Poor hospitals

High input costs

Limited Markets and Marketing

Poor Agricultural Practices

Wild animals

Floods

Poor governance

Illiteracy

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Interestingly, in addition to the shocks reported through the survey, the qualitative results reveal that significant shocks affecting households in Isiolo county, Marsabit county and parts of Meru county were drought/low rainfall and insecurity. Insecurity is due to resource-based conflicts particularly affecting pastoralist communities in Isiolo and Marsabit counties (see Figures 20, 21 and 22 on shocks at the county level).

Further detail from the qualitative data at the county level shows that the main shock present in Marsabit county is drought, followed by livestock diseases and insecurity (see Figure 20). However, it is worth noting that all these three factors are important and inter-related with regards to livestock production. For fishers in the lake region, the main threat is windy weather and rainwater runoff to the lake, which in turn affect fishing activities. However, people in the lake region do not rely only on fishing; they also keep livestock. Fishing is an alternative livelihood option. Most of the community is affected by the drought, and the other shocks as mentioned, thus though it is sensitive to some extent this is cushioned by the primary coping strategies.

Qualitative analysis also shows some interesting findings on coping mechanisms (see Figures 20 and 21) employed by households in the pastoralist livelihood group (comprised of Isiolo and Marsabit counties). The migration of livestock and thus key household members occurs due to drought or insecurity, while destocking was also common, but occurred for few animals. The main coping strategy for the community in Marsabit county (see Figure 20) is migration, which helps to prevent the loss of productive assets as well as conflicts over pasture and water. Households also hold community peace talks to overcome resource-based conflicts. People also rely on veterinary extension services offered by the local government to prevent the loss of assets, while community-level coping mechanisms through community social safety nets is limited. The community also adapts to new situations through other adaptive capacities; these can be seen via fishing, and


36

the sale of charcoal or chalbi salt.22 However, in extreme cases the communities become less reliant on pastoralism as they choose to diversify their incomes with other income-generating activities.

The two main shocks evident in Isiolo county (see Figure 21) are drought (36 percent) and insecurity (24 percent), which lead ahead of other types of shocks quite significantly. Further types of shocks including livestock disease, human disease, floods, lack of water and wildlife are each rated at almost the same level. Most of the community is affected by the drought and insecurity, which are closely interrelated. When communities move their livestock in search of new grazing areas, there are bound to be conflicts with neighbouring communities or clans as a result of competition for pasture. Also, some parts of Isiolo county are prone to floods, especially after heavy rains. Wildlife also poses a challenge, as they attack and kill livestock, so this also presents as a shock. The main coping strategies for the community in Isiolo county (Figure 21) are migration and group herding, which are interrelated and help to prevent the loss of productive assets due to limited pasture and insecurity. Destocking is also undertaken as a measure to offload excess animals that could be lost due to drought. Destocking includes the sale of livestock to traders in markets or presenting weak animals to government destocking programmes. In many cases, in order to avoid overstraining of the animals, the calves are slaughtered and the animals are not milked. Alternative feeds are also provided to animals in the form of hay (bought or given through relief programmes for sustenance. However, these last few measures are adopted at the community level and their popularity has waned in recent times. Though to a slight extent, peace meetings are also an essential component of ensuring livestock are able to access pasture and water points.

The shocks observed in Meru county (see Figure 22) revolve around drought, insecurity and water shortage. Some parts of Meru county, especially the Igembe North sub-county, reported pests and disease as a shock affecting both plants and livestock. Poor roads also presented as a shock, since this makes the transport of farm produce to markets difficult, which leads to spoilage. For the Igembe North sub-county, the main coping mechanism was government assistance, followed by water vendors, veterinary extension services and social networks. In the Igembe Central sub-county, the main coping mechanisms revolved around casual labour and water vending, along with some form of social networking.

5.2 FOOD SECURITY ANALYSISIt is widely assumed that higher resilience will lead to better welfare in a household. In light of this, there is a need to investigate the effect of household resilience on the household’s welfare. RIMA-II specifically uses food security indicators to explore household welfare. Two food security indicators are used in this study; PFC and HDDS.

To investigate the effect of resilience on these two indicators, two separate models were fitted. These models are:

PFCi = β0 + θRi + ηSi + αXi + εi (4)

HDDSi = β0 + θRi + ηSi + αXi + εi (5)

where PFCi and HDDSi are the Per Capita Food Consumption (PFC) and the Household Dietary Diversity Score (HDDS), for household i respectively; Ri is the vector of all observed variables employed for the estimation of the pillars.

22 The Chalbi Desert in northern Kenya contains a vast salt pan, from which households nearby are able to harvest salt in order to sell. Thus this is used as an alternative livelihood.

All the other symbols are similar to those in model (3).

Table 7 shows the results of the relationship between the different factors and the two food security indicators. While this is a summary showing only the significant factors, the full result is shown in Table A8 in Annex 2.

From the analysis, the following were significant:

h HDDS was found to be associated with sanitation; distance index; asset index; TLU; crop input; income; floods; pests, parasites and diseases; business failure; FCI; and county.

h PFC was found to be associated with access to improved water; sanitation; distance index; asset index; TLU; livestock inputs; number of networks; education of household; pests, parasites and diseases; business failure; severe illness/injury; job loss/no salary/death of main income earner; resource-based conflicts; FCI; household size; and county.

All the asset indicators are positively associated with the food security indices. Households with more household and productive assets can access a greater variety of food through the trading or selling of outputs from their productive livelihoods. Households with more income have a better chance of purchasing food. The following factors reduce both the capacity of buying food and capability of buying a diverse variety of foods: job loss/no salary/death of main income earner; pests, parasites and diseases; and a larger household size. These shocks negatively affect access to food for households, thus affecting their food security situation. The distance index, which was calculated based on the reciprocals of the distances, has a positive coefficient; this means that when distance to services or facilities decreases, food security indicators increase. Better access to sanitary facilities and basic services in relation to the distance index, such as access to markets, means access to better food variety hence this positively affects the HDDS. Most of the shocks have similar coefficients to the coefficients of resilience capacity in Table 6.

Table 7 Correlates of food security

Indicator HDDS PFCABS

Access to improved water-0.086 -2.369***(0.105) (0.906)

Sanitation (toilet)0.357*** 3.693***

(0.125) (1.086)

Distance index0.100* 1.136**

(0.058) (0.501)

AST

Asset index0.355*** 1.475***

(0.064) (0.551

TLU0.039** 0.305**

(0.018) (0.153)

Crop input0.058** 0.157

(0.024) (0.205)

Livestock input0.028 0.556***

(0.017) (0.147)

37


All the other symbols are similar to those in model (3).

Table 7 shows the results of the relationship between the different factors and the two food security indicators. While this is a summary showing only the significant factors, the full result is shown in Table A8 in Annex 2.

From the analysis, the following were significant:

h HDDS was found to be associated with sanitation; distance index; asset index; TLU; crop input; income; floods; pests, parasites and diseases; business failure; FCI; and county.

h PFC was found to be associated with access to improved water; sanitation; distance index; asset index; TLU; livestock inputs; number of networks; education of household; pests, parasites and diseases; business failure; severe illness/injury; job loss/no salary/death of main income earner; resource-based conflicts; FCI; household size; and county.

All the asset indicators are positively associated with the food security indices. Households with more household and productive assets can access a greater variety of food through the trading or selling of outputs from their productive livelihoods. Households with more income have a better chance of purchasing food. The following factors reduce both the capacity of buying food and capability of buying a diverse variety of foods: job loss/no salary/death of main income earner; pests, parasites and diseases; and a larger household size. These shocks negatively affect access to food for households, thus affecting their food security situation. The distance index, which was calculated based on the reciprocals of the distances, has a positive coefficient; this means that when distance to services or facilities decreases, food security indicators increase. Better access to sanitary facilities and basic services in relation to the distance index, such as access to markets, means access to better food variety hence this positively affects the HDDS. Most of the shocks have similar coefficients to the coefficients of resilience capacity in Table 6.

Table 7 Correlates of food security


Access to improved water-0.086 -2.369***(0.105) (0.906)


(0.125) (1.086)


(0.058) (0.501)

AST

Asset index0.355*** 1.475***

(0.064) (0.551

TLU0.039** 0.305**

(0.018) (0.153)

Crop input0.058** 0.157

(0.024) (0.205)


(0.017) (0.147)


38

Table 7 Correlates of food security (cont.)

Indicator HDDS PFCSSN

Number of networks 0.044 1.266***

(0.05) (0.432)

AC

Education of HH0.011 -0.130*

(0.01) (0.079)

Income0.245*** 0.501

(0.06) (0.522)

Shocks

Flood0.255*** -0.223

(0.099) (0.852)

Pests, parasites and diseases-0.279*** -1.954**(0.095) (0.82)

Business failure0.475*** 6.363***

(0.166) (1.442)

Severe illness/Injury-0.196 3.117***(0.136) (1.18)

Job loss/no salary/death of main earner-0.415 -6.753**(0.323) (2.805)

Resource-based conflicts-0.154 5.345**(0.252) (2.184)

FCI0.167*** -1.248***

(0.041) (0.349)

Household Characteristics

Marsabit-0.713** 9.488***(0.29) (2.513)

Isiolo0.928*** 2.445

(0.196) (1.697)

Household size-0.032 -3.232***(0.027) (0.234)

Constant0.545 82.158***

(1.641) (14.246)Observations 1028 1028Adj. R squared 0.341 0.298


*** p < 0.01, ** p < 0.05, * p < 0.1

© F

AO \ R

icha

rd B

ett

Table 7 Correlates of food security (cont.)

Indicator HDDS PFCSSN


(0.05) (0.432)

AC


(0.01) (0.079)

Income0.245*** 0.501

(0.06) (0.522)

Shocks

Flood0.255*** -0.223

(0.099) (0.852)



(0.166) (1.442)



Resource-based conflicts-0.154 5.345**(0.252) (2.184)

FCI0.167*** -1.248***

(0.041) (0.349)

Household Characteristics

Marsabit-0.713** 9.488***(0.29) (2.513)

Isiolo0.928*** 2.445

(0.196) (1.697)

Household size-0.032 -3.232***(0.027) (0.234)

Constant0.545 82.158***

(1.641) (14.246)Observations 1028 1028Adj. R squared 0.341 0.298


*** p < 0.01, ** p < 0.05, * p < 0.1

© F

AO \ R

icha

rd B

ett

© F

AO \ R

icha

rd B

ett

41

6 MAIN CONCLUSIONS FROM THE ANALYSIS, POLICY AND

PROGRAMMING IMPLICATIONSThis section summarizes the main findings of the resilience analysis implemented using the RIMA-II methodology. It also provides final assessments and delivers insights for policy design and implementation, drawing a comparison with policies currently programmed or implemented by the GoK in the pastoralist and mixed farming livelihood areas.

Household resilience to food insecurity in the Isiolo cluster was examined using the RIMA-II model. This has been used to measure and analyse the baseline survey in the three counties of Isiolo, Marsabit and Meru. The baseline survey was conducted from February to March 2016 covering 1 028 households. The model utilizes four pillars of resilience: ABS, AST, SSN and AC, which are used to build the RCI.

In the overall sample, there are no major differences between households in terms of their RCI. The analysis was disaggregated by county, livelihood, HH gender and sample type. At the county level, Meru county is the most resilient, followed by Isiolo county, while the least resilient is Marsabit county. Analysis by livelihood was disaggregated by two livelihood groups: mixed farming (mostly Meru county) and pastoralist (mostly Isiolo and Marsabit counties). The findings reveal that households in mixed farming areas are more resilient than households in pastoralist areas. They revealed that MHHs are more resilient than the FHHs, though there is no significant statistical difference between their RCIs. Analysis by sample type revealed that at the baseline level, households receiving interventions are more resilient than households in the control group, which do not receive assistance from relief programmes and government interventions. The difference in RCI between control and intervention groups is statistically significant. This will have implications for the IE, hence appropriate statistical procedures will be employed during the analysis to control for these differences.

The findings related to the overall Isiolo cluster suggest investment in livestock and crop production programmes, including the promotion of value chain production and linkages to markets, are necessary. The Kenya Vision 2030 Sector Plan for Drought Risk Management and EDE is aimed at reducing poverty and vulnerability in drought-prone areas. This initiative, which feeds into the IDDRSI, is currently implemented by the GoK through the NDMA, aiming to promote activities in relation to different sectors’ contribution to drought resilience. This initiative prioritizes resilience-related interventions through seven Priority Intervention Areas (PIAs).

© F

AO \ R

icha

rd B

ett


42

As such, for specific PIAs – such as those on market access, trade and financial services (PIA 2) and on livelihood support and provision of basic social services (PIA 3) – the resilience analysis suggests the need to strengthen production capacities through the provision of affordable and improved varieties of livestock and crop production inputs. Moreover, strengthening value chains in terms of food processing and preservation would result in an increase of marketable products and thus increased income levels for households.

More specifically, in terms of the RSM, findings for the overall sample show that AST and AC are the most important pillars for resilience capacity, followed by ABS. At the county level, AST contributes most to the RCI of households in all the three counties. Accordingly, AST also contributes the most to the RCI in both livelihood groups – those examined were mixed farming (Meru county) and pastoralist (Isiolo and Marsabit counties). AST contributes most to the RCI for both MHHs and FHHs. When the analysis is disaggregated at the pillar level, generally the most important contributing factors to AST are inputs for crop and livestock production and productive assets. According to specific livelihoods, the contributing factors to AST in pastoralist communities are from ownership of livestock (TLU) and the use of livestock inputs, while for mixed-farming those are household assets, inputs for crops and cultivated land. However, it is noted that crop and agricultural production remains relatively low (MALF, 2016). In line with the GoK’s agricultural policy, there is room to enhance access to and the creation of affordable inputs and services, in order to increase agricultural productivity. The agricultural policy outlines that most households still practice subsistence farming in rural areas. The protraction of marginal agriculture is being progressively weakened by population growth, competition for land, and an over-reliance on rainfed production and on crop and pasture varieties that are poorly adapted to drought conditions (MALF, 2016). The GoK aims to provide targeted incentives to support production and productivity in both livelihoods as a means of achieving the sustainable economic well-being of households (MALF, 2016). In terms of gender dynamics in asset ownership, generally male HHs (in MHHs) own most of the household’s assets, whereas in FHHs the female HHs in fact do not own most of the household’s assets – in these cases, the assets are either owned jointly by all household members or by other relatives not present in the household. The gender policy (Ministry of Gender, Children and Social Development of Kenya (MoGCSD, 2011) aims to promote the design of programmes that are sensitive to gender equity in order to boost household resilience.

The analysis shows that AC significantly contributes to resilience capacity. Income diversification and the CSI are the most significant factors for the AC pillar, followed by the education level of HHs. Though to a different extent, AC is more pronounced in Meru and Marsabit counties than in Isiolo county, which is explained by the much higher contribution of income diversification there. This is especially true in Meru county, where households can rely on several income sources. This implies that, in the three counties of this study, it is important for policies and programmes that aim to build resilience to food insecurity to focus on boosting new initiatives to diversify activities generating income for both crop and livestock producers. For instance, income source diversification and income levels can be supported with more investment in the value chain and agribusiness initiatives. Education is also an important contributing factor to household resilience capacity, particularly in Meru county compared to Isiolo and Marsabit counties. This suggests that pastoralist communities in Isiolo and Marsabit counties would also benefit greatly from an increased reach of the education system. Accordingly, the GoK, through the Ministry of Education, Science and Technology (MOEST) has established and put into operation the NACONEK. This is intended to promote access to education for nomadic communities in ASAL areas in light of SDG 4 – “ensuring inclusive and equitable quality education and promote lifelong

43

Chapter 6 – Main conclusions from the analysis, policy and programming implications

learning opportunities for all” – along with the Kenya Education Sector Support Programme (KESSP) aimed at achieving Education For All (EFA)23 (MOEST, 2005).

Generally, SSN is one of the least significant pillars to the RCI. At the county level, SSN contributes to a lesser extent to the RCI of Isiolo county compared to those of the Meru and Marsabit counties. Similarly, SSN plays the least significant role in the RCI of pastoralist areas compared to those that use mixed farming. The number of social networks a household is involved in contributes most to this pillar, followed by access to credit and access to transfers (formal and informal). In Isiolo and Marsabit counties, access to credit remains very limited, as does reliance on and participation in different social networks. Livelihoods are undermined by the poorly developed financial sector (GoK, 2013a). The GoK strives to increase opportunities within the financial sector to expand credit services and rural SACCOs in the counties to promote financial literacy; the focus lies on building community resilience to achieve sustainability and on improving the environment for attracting investments and promoting sustainable growth and development. The results of this analysis indicate that humanitarian assistance, when required, should be provided in ways that support the local economy at the county level, for example by substituting food with cash vouchers channelled through financial institutions. Households in the rural areas should also be supported and trained on how to establish social networks and community groups with similar objectives, such as creating savings and accessing credit. This could also include a focus on increasing agricultural productivity to support more products going to market with collective sales, thus influencing the market in a manner that would attract consumers to affordable products. Such groups also influence increased access to credit facilities.

In the overall sample, ABS is one of the pillars that contributes the least to the RCI. However, a few differences can be noted at the county level. The low contribution of ABS, particularly in Marsabit county, is shown by the longer distances to important facilities such as schools, hospitals and markets, compared to shorter distances in Meru county. Poor infrastructure increases vulnerability to shocks, such as drought, especially in the ASAL areas by reducing access to basic services and by deterring the investment needed to expand and diversify an economy (GoK, 2013a). In this report, the observed need to improve access to basic services through improving infrastructure taking into account climate-related risks is a key investment to be considered for these areas. The recommendations suggested based on the RIMA-II methodology are in line with the county integrated development plans for Isiolo, Marsabit and Meru counties, which promote increased public service delivery to county inhabitants through improvements in agricultural production, market access, health and sanitation services (GoK, 2013b; 2013c; 2013d).

Insecurity is a major concern in the Horn of Africa. In the qualitative analysis, resource-based conflicts featured prominently in FGDs as a major shock that affects resilience capacity. Local cross-border insecurity particularly due to livestock movement and the search for water and pasture is a major concern due to the coexistence of different tribes and ethnic groups. The GoK has taken initiatives to strengthen peace and security infrastructure, especially in the ASAL areas. In particular, the county governments in ASAL regions have embarked on promoting peace, cultural cohesion and reconciliation programs such as the Marsabit-Lake Turkana Festival and Kalacha Cultural Festival. In the CPP for Kenya, which is encompassed within the IDDRSI framework, a strategic response is envisaged for peace and human security in order

23 EFA is a global movement led by the United Nation Educational, Scientific and Cultural Organization (UNESCO), aiming to meet the learning needs of all children, youth and adults by 2015.


44

to ensure inclusive participation of communities in decision making on equitable access to natural resources. The CPP aims to mainstream peace and security challenges in the ASAL areas in the broader national and regional development agendas.

In line with the findings highlighted in this report, the CPF for Kenya – since its inception in 2014 – has continuously aimed at aligning various interventions in order to build the resilience of livelihoods. This is especially so in the ASAL areas, including Isiolo and Marsabit counties and the semi-arid areas of Meru county, through current major programmes as highlighted in Section 1.2. The programmes support building the resilience of target communities in these counties, as part of fulfilling the CPF outcomes, through protecting livelihood assets and increasing the adaptive capacity of households through income diversification and improving their coping mechanisms against shocks. The programmes also aim at capacity-building within national and county governments in terms of the review, formulation and implementation of strategies and policies that would contribute to improving the resilience capacity of households in these counties. A further review of the resilience findings linking the CPF and the current programme intervention can be found in Annex 3.

To support building the resilience capacity of households in pastoralist communities, the DRSLP implemented by the State Department of Agriculture – under the MALF – has been implemented in six ASAL counties in Kenya,24 including Isiolo and Marsabit counties. It is noted that the DRSLP programme includes interventions that aim to improve households’ access to basic services, such as by improving the availability of and access to water through the construction or rehabilitation of key water sources such as boreholes, water pans and shallow wells, and by strengthening the capacity of water user associations. According to the analysis, this has a positive influence on the ABS pillar in the two counties. Meanwhile, the contribution made to household assets and productivity is seen in the programme’s support for linking communities to markets via the construction of livestock markets to improve livestock trade in the counties. With IGAD’s support, livelihoods are protected and sustained through the coordination of programmes to control trans-boundary livestock diseases, support better delivery of animal health services, and provide agricultural inputs to women to increase their engagement in livestock activities and the associated value chain. This results in increasing livestock offtake and thus more income for the household.

The RPLRP funded by the World Bank25 seeks to develop solutions to challenges faced by pastoralists who reside in the ASALs of Kenya, Uganda and Ethiopia. Within Kenya, including Isiolo and Marsabit counties, the project promotes resilience-building activities. The contribution of this initiative to the productive assets of the pastoralist and agro-pastoralist livelihoods comes via support for the reduction of livestock death rates through improved disease surveillance and timely disease reporting, which improves delivery of veterinary services to pastoralist communities in the two counties. Interventions to improve access to sustainably managed water resources and to support the construction of market infrastructure are in progress. The programme aims to ensure that policies, regulatory frameworks and trade capacity are enhanced to enable livestock mobility for the trade of livestock and livestock products. This leads to an increase in income diversification, thus contributing to the adaptive capacity of the communities in the ASAL counties.

24 The DRSLP is funded by the AfDB and the GOK. It is implemented by Kenya’s State Department of Agriculture in six ASAL counties, namely Marsabit, Samburu, Isiolo, West Pokot, Baringo and Turkana. More information can be found on the link http://resilience.igad.int/attachments/article/271/IDDRSI%20Programming%20report%202015.pdf

25 For more information about the RPLRP, please visit http://www.worldbank.org/projects/P129408/regional-pastoral-livelihoods-recovery-resilience-project?lang=en&tab=overview

45

Chapter 6 – Main conclusions from the analysis, policy and programming implications

From this analysis the main recommendations for programming include the following:

h Programmes promoting the use of efficient technologies to increase agricultural production, particularly with improved crop varieties and drought-resistant inputs for crops and livestock. Also, enhanced practices and technologies for animal production and health, including vaccination and animal health services. Capacity-building activities should be promoted on proper livestock breeding, as well as agronomic practices and agribusiness. Interventions to increase agricultural productivity and the asset base of the population should be prioritized according to the relevant livelihood. Indeed, improving the level of income generated from agricultural activities is expected to lead directly to increased resilience and food security levels.

h Generally, surveillance mechanisms to control pests and diseases are critical in these contexts especially at the local level (i.e. the county level). Therefore, there is a need to increase investments in and resources for implementing sustainable disease control programmes and strategies in conjunction with the county governments; to enforce existing laws governing disease control; and to improve the coverage of vaccination programmes. Government and community-based organizations also require support in providing animal health and production services (e.g. veterinary associations, government veterinary extension services and cooperatives) through capacity development and knowledge transfer.

h Programmes investing in creating market linkages through: improved techniques and practices to reduce storage and post-harvest losses; the management of agribusiness and value chain activities; and the improved efficiency of processing and preservation of food products. These should be particularly related to the marketing of livestock products, and support for the development and rehabilitation of livestock infrastructure, such as markets and slaughterhouses.

h Supporting income-generating activities to enhance the diversification of income sources and livelihoods with both on-farm and off-farm productive activities and services. Such interventions reduce the impact of negative shocks on households by diversifying the risk exposure and mitigating the negative coping strategies employed by less resilient households. An example of a negative coping strategy is forced migration in search of pasture and water for livestock, which is particularly practiced by pastoralist communities.

h Expanding access to financial support services for rural households to connect small-scale producers with a variety of savings, loan and grant schemes to strengthen and diversify their livelihood base and income potential. This includes encouraging small business development by promoting small business development matching grants, with a focus on youth and women. Programmes that target gender-based issues and youth should enhance access to efficient financial products and services, such as access to credit and market information.

h Programmes should focus on enhancing environmental sustainability, and improving natural resource management and equitable access to resources. The adoption of people-centred approaches to negotiating and securing access to land – such


46

as Participatory Negotiated Territorial Development26 – and peace talks are pre-requisites for improving the sustainable and equitable use of natural resources (such as land, pasture, water, trees, etc.) and overcoming related natural resource-based conflicts.

h Investing in rangeland rehabilitation and management while promoting fodder production can improve communities’ access to production land, water and pasture for livestock, and can decrease natural resource-based conflicts and insecurity. In line with this, interventions should facilitate and support community-based management of rangeland or rehabilitation and the improvement of rangelands through cash-for-work programmes.

26 Participatory Negotiated Territorial Development (PNTD) is a facilitative process used by FAO that promotes development through dialogue and negotiation among stakeholders in community setups. For more information, visit http://www.fao.org/3/a-i4592e.pdf

47

REFERENCES

FAO (Food and Agriculture Organization of the United Nations). 2014. Country Programming Framework for Kenya 2014–2017. Available at: ftp://ftp.fao.org/OSD/CPF/Countries/Kenya/CPF%20for%20Kenya%202014-2017%20-%20signed.pdf

FAO. 2016a. RIMA-II – Resilience Index Measurement and Analysis II. Available at: www.fao.org/3/a-i5665e.pdf

FAO. 2016b. Resilience Analysis in the Triangle of Hope – Mauritania 2015. Available at: www.fao.org/3/a-i5808e.pdf

GoK (Government of Kenya). 2012. Kenya Country Programme Paper. Available at: http://resilience.igad.int/index.php/knowledge/links/reports/1-kenya-cpp?format=html

GoK. 2013a. Sector plan for drought risk management and ending drought emergencies – second medium term plan 2013–2017. Available at: ndma.go.ke/index.php/resource-centre/send/6-ending-drought-emergencies/593-ede-medium-term-plan

GoK. 2013b. Isiolo County – County Intergrated Development Plan 2013–2017. Available at: https://cog.go.ke/images/stories/CIDPs/Isiolo.pdf

GoK. 2013c. County Government of Marsabit – First County Integrated Development Plan 2013–2017. Available at: http://marsabit.go.ke/wp-content/uploads/2015/04/County-Integrated-Development-Plan.pdf

GoK. 2013d. Meru County Government – First Meru County Intergrated Development Plan 2013–2017. Available at: http://cog.go.ke/images/stories/CIDPs/MERUCIDP.pdf

IGAD (Intergovernmental Authority on Development). 2015. IGAD Drought Disaster Resilience and Sustainability Initiative (IDDRSI) – IDDRSI Programming Report. Paper presented at 4th IDDRSI Platform Steering Committee Meeting. Addis Ababa, Ethiopia, 25–26 March 2015. Available at: http://resilience.igad.int/attachments/article/271/IDDRSI%20Programming%20report%202015.pdf

MALF (Ministry of Agriculture, Livestock and Fisheries of Kenya). 2014. Sessional Paper No. 2 of 2008 on National Livestock Policy. Available at: http://vetvac.org/galvmed/law/docs/193_Sessional_Paper_on_Livestock_Policy_-_REVISED_5_June_2014.pdf

MALF. 2016. Agricultural Policy – Kenya. Kenya.

Meyer, D. F. & Nishimwe-Niyimbanira, R. 2016. The impact of household size on poverty: An analysis of various low-income townships in the Northern Free State region, South Africa. African Population Studies, 30(2): 2283–2295.

ftp://ftp.fao.org/OSD/CPF/Countries/Kenya/CPF%20for%20Kenya%202014-2017%20-%20signed.pdf

ftp://ftp.fao.org/OSD/CPF/Countries/Kenya/CPF%20for%20Kenya%202014-2017%20-%20signed.pdf

http://www.fao.org/3/a-i5665e.pdf




http://resilience.igad.int/index.php/knowledge/links/reports/1-kenya-cpp?format=html

ndma.go.ke/index.php/resource-centre/send/6-ending-drought-emergencies/593-ede-medium-term-plan

ndma.go.ke/index.php/resource-centre/send/6-ending-drought-emergencies/593-ede-medium-term-plan

https://cog.go.ke/images/stories/CIDPs/Isiolo.pdf

https://cog.go.ke/images/stories/CIDPs/Isiolo.pdf

http://marsabit.go.ke/wp-content/uploads/2015/04/County-Integrated-Development-Plan.pdf

http://marsabit.go.ke/wp-content/uploads/2015/04/County-Integrated-Development-Plan.pdf

http://cog.go.ke/images/stories/CIDPs/MERUCIDP.pdf

http://resilience.igad.int/attachments/article/271/IDDRSI%20Programming%20report%202015.pdf

http://resilience.igad.int/attachments/article/271/IDDRSI%20Programming%20report%202015.pdf

http://vetvac.org/galvmed/law/docs/193_Sessional_Paper_on_Livestock_Policy_-_REVISED_5_June_2014.pdf

http://vetvac.org/galvmed/law/docs/193_Sessional_Paper_on_Livestock_Policy_-_REVISED_5_June_2014.pdf


48

MOEST (Ministry of Education, Science and Technology of Kenya). 2005. Kenya Education Sector Programme 2005–2010. Nairobi.

MoGCSD (Ministry of Gender, Children and Social Development of Kenya). 2011. Gender Policy - Kenya. Nairobi: Ministry of Gender, Children and Social Development.

Neuman, W. 2011. Social Reasearch Methods: Qualitative and Quantitative Approaches. Wisconsin, Pearson.

RM-TWG (Resilience Measurement Technical Working Group). 2014. Resilience Measurement Principles – toward an agenda for measurement design. Food Security Information Network Technical Series No.1. Available at: www.fsincop.net/fileadmin/user_upload/fsin/docs/resources/FSIN_29jan_WEB_medium%20res.pdf

WB (World Bank). 2016. Kenya – Overview. Available at: worldbank.org/en/country/kenya/overview

(All links were checked on 5 December 2016)

ANNEX 1

Table A1 Explanation/Description of variables used in the model

Variable DescriptionABSDistance to primary school The distance from household to the nearest facility (km)Distance to health facility The distance from household to the nearest facility (km)Distance to hospital The distance from household to the nearest facility (km)Distance to chemist The distance from household to the nearest facility (km)Distance to market The distance from household to the nearest facility (km)Distance to financial services The distance from household to the nearest facility (km)Distance to public transport The distance from household to the nearest facility (km)Distance index The index was built from all the above distances through FAAccess to improved water Percentage of household reporting that they have access to improved sources of waterAccess to toilet Percentage of household reporting that they have a toilet within their house

ASTHousehold asset index The index was built from the ownership of household durable goods through FATLU Number of tropical livestock units per capita owned by householdLand area Area of land per capita owned by household (acres)Usage of crop input Percentage of household reporting that they have used at least one input for crop activitiesUsage of livestock input Percentage of household reporting that they have used at least one input for livestock activities

SSNNumber of social networks Number of social networks in which at least one member of the household has participatedAccess to credit Percentage of household that has obtained creditAccess to transfer Percentage of household that has received a transfer

ACEducation level of Household head Number of years of education of HHIndependency ratio Inverted ratio of dependent members and members in labour force (aged 15–64 years old) Income diversification Number of activities generating income within a householdCSI The CSI is derived from the severity and frequency of consumption coping strategies

Outcome indicatorsHDDS Score measuring household food access for 12 food groups

PFC Total food expenditure (including purchasing, own production, gift, loan, etc.) per capita monthly (US$)

www.fsincop.net/fileadmin/user_upload/fsin/docs/resources/FSIN_29jan_WEB_medium%20res.pdf

www.fsincop.net/fileadmin/user_upload/fsin/docs/resources/FSIN_29jan_WEB_medium%20res.pdf

http://www.worldbank.org//en/country/kenya/overview

http://www.worldbank.org//en/country/kenya/overview

49

ANNEX 1

Table A1 Explanation/Description of variables used in the model

Variable DescriptionABSDistance to primary school The distance from household to the nearest facility (km)Distance to health facility The distance from household to the nearest facility (km)Distance to hospital The distance from household to the nearest facility (km)Distance to chemist The distance from household to the nearest facility (km)Distance to market The distance from household to the nearest facility (km)Distance to financial services The distance from household to the nearest facility (km)Distance to public transport The distance from household to the nearest facility (km)Distance index The index was built from all the above distances through FAAccess to improved water Percentage of household reporting that they have access to improved sources of waterAccess to toilet Percentage of household reporting that they have a toilet within their house

ASTHousehold asset index The index was built from the ownership of household durable goods through FATLU Number of tropical livestock units per capita owned by householdLand area Area of land per capita owned by household (acres)Usage of crop input Percentage of household reporting that they have used at least one input for crop activitiesUsage of livestock input Percentage of household reporting that they have used at least one input for livestock activities

SSNNumber of social networks Number of social networks in which at least one member of the household has participatedAccess to credit Percentage of household that has obtained creditAccess to transfer Percentage of household that has received a transfer

ACEducation level of Household head Number of years of education of HHIndependency ratio Inverted ratio of dependent members and members in labour force (aged 15–64 years old) Income diversification Number of activities generating income within a householdCSI The CSI is derived from the severity and frequency of consumption coping strategies

Outcome indicatorsHDDS Score measuring household food access for 12 food groups

PFC Total food expenditure (including purchasing, own production, gift, loan, etc.) per capita monthly (US$)


50

Table A3 Descriptive statistics at the cluster level

Variable (N=1028) Mean Std. Dev. Min Max

ABSDistance to primary school 1.619 1.227 1 15Distance to health facility 2.801 3.311 1 32Distance to hospital 33.734 63.490 1 301Distance to chemist 9.139 22.529 1 210Distance to market 24.202 45.766 1 290Distance to financial services 8.770 18.887 1 200Distance to public transport 6.647 23.769 1 280Distance index 0.000 0.892 -1.825 1.679Access to improved water 0.559 0.497 0 1Access to toilet 0.688 0.464 0 1

ASTHousehold asset index 0.000 1.062 -2.485 3.544TLU 1.227 2.345 0 36.6Land area owned 0.346 0.861 0 15Usage of crop input 0.506 0.500 0 1Usage of livestock input 0.728 0.445 0 1

SSNNumber of social networks 1.168 1.152 0 7Access to credit 0.378 0.485 0 1Access to transfer 0.773 0.419 0 1

ACEducation level of HH 5.340 5.875 0 21Independency ratio (reverted) 1.391 1.343 0 9Income diversification 2.423 1.183 0 6CSI 8.663 16.576 0 100

Outcome indicatorsHDDS 8.946 1.684 3 12PFC 25.535 14.168 2.158 97.627

Table A2 Variables used for impact evaluation and CPF programme indicators

Indicator DescriptionResilienceRCI Rescaled (0-100) mean value

IncomePer capita income US$

Productive AssetsTLU Average household herd size

Access to servicesAverage number of months (in a year) that water is available during dry season times in target households

N

Food securityHDDS ValueCSI ValuePFC Value

Natural ResourcesProportion of households with secure access to land and natural resources Percentage households

51

Annex

Table A3 Descriptive statistics at the cluster level

Variable (N=1028) Mean Std. Dev. Min Max

ABSDistance to primary school 1.619 1.227 1 15Distance to health facility 2.801 3.311 1 32Distance to hospital 33.734 63.490 1 301Distance to chemist 9.139 22.529 1 210Distance to market 24.202 45.766 1 290Distance to financial services 8.770 18.887 1 200Distance to public transport 6.647 23.769 1 280Distance index 0.000 0.892 -1.825 1.679Access to improved water 0.559 0.497 0 1Access to toilet 0.688 0.464 0 1

ASTHousehold asset index 0.000 1.062 -2.485 3.544TLU 1.227 2.345 0 36.6Land area owned 0.346 0.861 0 15Usage of crop input 0.506 0.500 0 1Usage of livestock input 0.728 0.445 0 1

SSNNumber of social networks 1.168 1.152 0 7Access to credit 0.378 0.485 0 1Access to transfer 0.773 0.419 0 1

ACEducation level of HH 5.340 5.875 0 21Independency ratio (reverted) 1.391 1.343 0 9Income diversification 2.423 1.183 0 6CSI 8.663 16.576 0 100

Outcome indicatorsHDDS 8.946 1.684 3 12PFC 25.535 14.168 2.158 97.627

Table A2 Variables used for impact evaluation and CPF programme indicators

Indicator DescriptionResilienceRCI Rescaled (0-100) mean value

IncomePer capita income US$

Productive AssetsTLU Average household herd size

Access to servicesAverage number of months (in a year) that water is available during dry season times in target households

N

Food securityHDDS ValueCSI ValuePFC Value

Natural ResourcesProportion of households with secure access to land and natural resources Percentage households


52

Table A5 Descriptive statistics by livelihood

Variable Pastoralist (N=602)

Mixed farming (N=426)

ABSDistance to primary school 1.612 1.629Distance to health facility 2.839 2.749Distance to hospital 51.197 9.057Distance to chemist 12.641 4.191Distance to market 37.841 4.929Distance to financial services 12.805 3.070Distance to public transport 9.687 2.352Distance index -0.021 0.029Access to improved water 0.741 0.615Access to toilet 0.473 0.991

ASTHousehold asset index -0.473 0.668TLU 1.705 0.551Land area per capita 0.126 0.657Usage of crop input 0.173 0.977Usage of livestock input 0.683 0.791

SSNNumber of social networks 0.701 1.829Access to credit 0.201 0.629Access to transfer 0.749 0.808

ACEducation level of HH 3.545 7.878Independency ratio 1.229 1.618Income diversification 2.626 3.230CSI 13.107 2.383

Outcome indicatorsHDDS 8.468 9.622PFC 23.624 28.236

Table A4 Descriptive statistics by county

Variable Marsabit (N=298)

Isiolo (N=304) I

Meru (N=426)

ABSDistance to primary school 1.849 1.379 1.629Distance to health facility 2.609 3.064 2.749Distance to hospital 91.881 11.316 9.057Distance to chemist 17.360 8.015 4.191Distance to market 65.705 10.528 4.929Distance to financial services 17.521 8.181 3.070Distance to public transport 16.681 2.831 2.352Distance index -0.516 0.465 0.029Access to improved water 0.245 0.789 0.615Access to toilet 0.416 0.530 0.991

ASTHousehold asset index -0.771 -0.180 0.668TLU 1.823 1.588 0.551Land area 0.208 0.045 0.657Usage of crop input 0.195 0.151 0.977Usage of livestock input 0.691 0.674 0.791

SSNNumber of social networks 0.846 0.559 1.829Access to credit 0.299 0.105 0.629Access to transfer 0.792 0.707 0.808

ACEducation level of HH 2.661 4.411 7.878Independency ratio 1.134 1.323 1.618Income diversification 2.034 1.674 3.230CSI 17.062 9.230 2.383

Outcome indicatorsHDDS 8.070 8.859 9.622PFC 22.324 24.898 28.236

53

Annex

Table A5 Descriptive statistics by livelihood

Variable Pastoralist (N=602)

Mixed farming (N=426)


ASTHousehold asset index -0.473 0.668TLU 1.705 0.551Land area per capita 0.126 0.657Usage of crop input 0.173 0.977Usage of livestock input 0.683 0.791




Table A4 Descriptive statistics by county

Variable Marsabit (N=298)

Isiolo (N=304) I

Meru (N=426)

ABSDistance to primary school 1.849 1.379 1.629Distance to health facility 2.609 3.064 2.749Distance to hospital 91.881 11.316 9.057Distance to chemist 17.360 8.015 4.191Distance to market 65.705 10.528 4.929Distance to financial services 17.521 8.181 3.070Distance to public transport 16.681 2.831 2.352Distance index -0.516 0.465 0.029Access to improved water 0.245 0.789 0.615Access to toilet 0.416 0.530 0.991

ASTHousehold asset index -0.771 -0.180 0.668TLU 1.823 1.588 0.551Land area 0.208 0.045 0.657Usage of crop input 0.195 0.151 0.977Usage of livestock input 0.691 0.674 0.791

SSNNumber of social networks 0.846 0.559 1.829Access to credit 0.299 0.105 0.629Access to transfer 0.792 0.707 0.808

ACEducation level of HH 2.661 4.411 7.878Independency ratio 1.134 1.323 1.618Income diversification 2.034 1.674 3.230CSI 17.062 9.230 2.383

Outcome indicatorsHDDS 8.070 8.859 9.622PFC 22.324 24.898 28.236


54

Table A6 Descriptive statistics by household head gender

Variable Male (N=795)

Female (N=233)


ASTHousehold asset index 1.068 -0.217TLU 2.521 0.927Land area per capita 0.934 0.243Usage of crop input 0.526 0.438Usage of livestock input 0.761 0.614




Isiolo Marsabit Meru Male

Female

30%19% 25%

Figure A1. Gender of household heads by county


Table A7 Descriptive statistics by sample type

Variable Intervention (N=731)

Control (N=297) Ttest

ABSDistance to primary school 1.679 1.472 **Distance to health facility 2.779 2.857Distance to hospital 26.949 50.435 ***Distance to chemist 9.469 8.327Distance to market 23.494 25.945Distance to financial services 10.121 5.446 ***Distance to public transport 6.670 6.591Distance index -0.023 0.058 Access to improved water 0.557 0.566Access to toilet 0.703 0.650 *

ASTHousehold asset index 0.101 -0.248 ***TLU 1.298 1.052Land area 0.411 0.186 ***Usage of crop input 0.539 0.424 ***Usage of livestock input 0.733 0.714

SSNNumber of social networks 1.410 0.572 ***Access to credit 0.386 0.360Access to transfer 0.784 0.747

ACEducation level of HH 5.650 4.579 ***Independency ratio 1.416 1.328Income diversification 2.464 2.323 *CSI 7.605 11.266 ***

Outcome indicatorsHDDS 8.986 8.848PFC 24.829 27.274 **

Note: The control group accounts for approximately 30 percent of the sample, and the treatment group makes up the remainder (70 percent) of the sample. In principle, the sample at the baseline of the two groups should be similar in order to estimate the effect of interventions by accounting for the covariates through the mid-line and end-line surveys. The summary statistics show that the differences in more than half of the indicators are statistically significant between the intervention and control groups. This could possibly cause a bias for the IE and needs to be taken into account – an appropriate technique should be selected to reduce such bias.

55

Annex

Table A6 Descriptive statistics by household head gender

Variable Male (N=795)

Female (N=233)


ASTHousehold asset index 1.068 -0.217TLU 2.521 0.927Land area per capita 0.934 0.243Usage of crop input 0.526 0.438Usage of livestock input 0.761 0.614




Isiolo Marsabit Meru Male

Female

30%19% 25%

Figure A1. Gender of household heads by county


Table A7 Descriptive statistics by sample type

Variable Intervention (N=731)

Control (N=297) Ttest

ABSDistance to primary school 1.679 1.472 **Distance to health facility 2.779 2.857Distance to hospital 26.949 50.435 ***Distance to chemist 9.469 8.327Distance to market 23.494 25.945Distance to financial services 10.121 5.446 ***Distance to public transport 6.670 6.591Distance index -0.023 0.058 Access to improved water 0.557 0.566Access to toilet 0.703 0.650 *

ASTHousehold asset index 0.101 -0.248 ***TLU 1.298 1.052Land area 0.411 0.186 ***Usage of crop input 0.539 0.424 ***Usage of livestock input 0.733 0.714

SSNNumber of social networks 1.410 0.572 ***Access to credit 0.386 0.360Access to transfer 0.784 0.747

ACEducation level of HH 5.650 4.579 ***Independency ratio 1.416 1.328Income diversification 2.464 2.323 *CSI 7.605 11.266 ***

Outcome indicatorsHDDS 8.986 8.848PFC 24.829 27.274 **

Note: The control group accounts for approximately 30 percent of the sample, and the treatment group makes up the remainder (70 percent) of the sample. In principle, the sample at the baseline of the two groups should be similar in order to estimate the effect of interventions by accounting for the covariates through the mid-line and end-line surveys. The summary statistics show that the differences in more than half of the indicators are statistically significant between the intervention and control groups. This could possibly cause a bias for the IE and needs to be taken into account – an appropriate technique should be selected to reduce such bias.


56

Table A8 Regression analysis between food security indicators and resilience Indicators (cont.)

Indicator HDDS PFCAC

Income0.245*** 0.501

(0.06) (0.522)

CSI0.044 1.304

(0.12) (1.038)

Shocks

Flood0.255*** -0.223

(0.099) (0.852)

Drought-0.048 0.569(0.102) (0.883)


Fire-0.183 -1.808(0.424) (3.675)


(0.166) (1.442)



Resource-based conflicts/Communal crisis/Political crisis-0.154 5.345**(0.252) (2.184)

Loss of land0.21 -1.266

(0.418) (3.629)

Vehicle breakdown/damages0.119 1.989

(0.354) (3.072)

Other (specify)0.588 0.732

(0.32) (2.778)

FCI0.167*** -1.248***

(0.041) (0.349)

Indicator

Marsabit-0.713** 9.488***(0.29) (2.513)

Isiolo0.928*** 2.445

(0.196) (1.697)

HH gender 0.069 -0.527

(0.113) (0.977)

Household size-0.032 -3.232***(0.027) (0.234)

Constant0.545 82.158***

(1.641) (14.246)


*** p < 0.01, ** p < 0.05, * p < 0.1

ANNEX 2

Table A8 Regression analysis between food security indicators and resilience indicators


Access to Improved water-0.086 -2.369***(0.105) (0.906)


(0.125) (1.086)


(0.058) (0.501)

AST

Asset index0.355*** 1.475***

(0.064) (0.551)

Per capita land0.073 0.727

(0.055) (0.478)

TLU0.039** 0.305**

(0.018) (0.153)

Crop input0.058** 0.157

(0.024) (0.205)


(0.017) (0.147)

SSN


(0.05) (0.432)

Transfer0.006 -0.925

(0.125) (1.082)

Credit-0.089 1.198(0.122) (1.053)

AC


(0.01) (0.079)

Independency ratio 0.024 -0.359

(0.035) (0.302)

57

Annex

Table A8 Regression analysis between food security indicators and resilience Indicators (cont.)

Indicator HDDS PFCAC

Income0.245*** 0.501

(0.06) (0.522)

CSI0.044 1.304

(0.12) (1.038)

Shocks

Flood0.255*** -0.223

(0.099) (0.852)

Drought-0.048 0.569(0.102) (0.883)


Fire-0.183 -1.808(0.424) (3.675)


(0.166) (1.442)



Resource-based conflicts/Communal crisis/Political crisis-0.154 5.345**(0.252) (2.184)

Loss of land0.21 -1.266

(0.418) (3.629)

Vehicle breakdown/damages0.119 1.989

(0.354) (3.072)

Other (specify)0.588 0.732

(0.32) (2.778)

FCI0.167*** -1.248***

(0.041) (0.349)

Indicator

Marsabit-0.713** 9.488***(0.29) (2.513)

Isiolo0.928*** 2.445

(0.196) (1.697)

HH gender 0.069 -0.527

(0.113) (0.977)

Household size-0.032 -3.232***(0.027) (0.234)

Constant0.545 82.158***

(1.641) (14.246)


*** p < 0.01, ** p < 0.05, * p < 0.1

ANNEX 2

Table A8 Regression analysis between food security indicators and resilience indicators


Access to Improved water-0.086 -2.369***(0.105) (0.906)


(0.125) (1.086)


(0.058) (0.501)

AST

Asset index0.355*** 1.475***

(0.064) (0.551)

Per capita land0.073 0.727

(0.055) (0.478)

TLU0.039** 0.305**

(0.018) (0.153)

Crop input0.058** 0.157

(0.024) (0.205)


(0.017) (0.147)

SSN


(0.05) (0.432)

Transfer0.006 -0.925

(0.125) (1.082)

Credit-0.089 1.198(0.122) (1.053)

AC


(0.01) (0.079)

Independency ratio 0.024 -0.359

(0.035) (0.302)


58

RESULTS ON QUALITATIVE ANALYSIS ON ASSETS

Table A9 Asset ownership in Isiolo

Asset % ownedCattle 15.4Goats 21.7Sheep 23.9Camels 9.0Poultry 6.0Donkeys 8.6Trees 10.9Dogs 0.8Productive land 3.1Bees 0.5

Table A10 Asset ownership in Marsabit

Asset % ownedCattle 20.7Goats 18.3Sheep 22.2Camels 18.2Poultry 8.4Donkeys 7.8Charcoal 0.1Concrete 0.1Miraa 0.4Maize 0.8Beans 0.6Chalbi salt 1.4Lake 1.2

Table A11 Asset ownership in Meru

Asset % ownedMiraa 15.3Maize 24.9Beans 4.2Cattle 18.1Goats 9.4Poultry 8.1Potatoes 0.9Bananas 4.3Vegetables 1.6Agroforestry 1.2Millet 1.0Green grams27 0.2Sorghum 0.4Avocado 0.4Coffee 0.4Business 3.6Casual employment 6.1

27 This is the common term for ‘mung beans’ in Kenya.

59

Annex

RESULTS ON QUALITATIVE ANALYSIS ON ASSETS

Table A9 Asset ownership in Isiolo

Asset % ownedCattle 15.4Goats 21.7Sheep 23.9Camels 9.0Poultry 6.0Donkeys 8.6Trees 10.9Dogs 0.8Productive land 3.1Bees 0.5

Table A10 Asset ownership in Marsabit

Asset % ownedCattle 20.7Goats 18.3Sheep 22.2Camels 18.2Poultry 8.4Donkeys 7.8Charcoal 0.1Concrete 0.1Miraa 0.4Maize 0.8Beans 0.6Chalbi salt 1.4Lake 1.2

Table A11 Asset ownership in Meru

Asset % ownedMiraa 15.3Maize 24.9Beans 4.2Cattle 18.1Goats 9.4Poultry 8.1Potatoes 0.9Bananas 4.3Vegetables 1.6Agroforestry 1.2Millet 1.0Green grams27 0.2Sorghum 0.4Avocado 0.4Coffee 0.4Business 3.6Casual employment 6.1

27 This is the common term for ‘mung beans’ in Kenya.


60

ANNEX 3

REVIEW OF RESILIENCE FINDINGS AND PROGRAMMES UNDER THE CPF The CPF for FAO Kenya sets out priority areas to guide FAO’s partnership with and support to the GoK at both the national and county levels for a period of four years, from 2014 to 2017 (FAO, 2014). Since the inception of the CPF in 2014, FAO has engaged in programming and policy processes that seek to build livelihood resilience in the presently FAO-targeted counties in the arid and semi-arid lands (ASAL) in Kenya. This has been undertaken with the support of the GoK at the national and county levels. In Isiolo, Marsabit and Meru counties where the baseline survey was conducted, the major programmes implemented within this framework are:

h Increased productivity and profitability of smallholder farmers through promotion and upscaling of GAP and CA in productive semi-arid areas of Kenya (IPP-GAP);

h NRM/Land Programme;

h Improving food security and resilience and /or RAELOC.

As highlighted in Section 6 on the descriptive resilience analysis, it is noted that the AST and AC pillars contribute most to the resilience capacity of households in the three counties, both for the pastoralist and mixed farming livelihoods. The above-mentioned programmes have been supporting the agricultural sectors through smallholder farming, livestock, and natural resource management initiatives.

The IPP-GAP programme that is currently implemented in Meru county supports the CPF outcome two – “productivity of medium and small-scale agricultural producers increased, diversified and aligned to markets” (FAO, 2014) and has sought to increase agricultural productivity of smallholder mixed farmers in the county’s semi-arid areas by reaching household farmers through established farmer groups. The farmers’ capacity for GAP is improved via the extension service officers who have been trained with the technical support of FAO. This experience is provided to farmers who, in turn, apply these practices to their own farms. This initiative supports the AST pillar through increased production at the household level, and thus households are able to feed their own families with diversified foods. They also have the opportunity to produce more, as the project also supports agribusiness initiatives hence linking farmers to markets, diversifying their income and increasing their adaptive capacity. Farmers thus benefit from improved direct market linkages and are able to sell more produce, and thus increase their household income. In addition, the contribution to the SSN pillar is also noted as the programme aims to support farmers with access to credit facilities. Farmers who are able to receive financial or insurance

61

Annex

services are better able to protect their livelihoods.

The NRM/Land programme aims to support the CPF outcome 3 on “improved management of land, water and other natural resources for enhanced food security and socio-economic development at national, county and community level” [insert reference]. The programme supports communities in Isiolo and Marsabit counties through secure land tenure and management, which aims to safeguard their livelihoods. As pastoralist communities are known to be nomadic, households with communal access to these resources ensures their livestock can access grazing land and water resources, thus contributing to their resilience capacity under the AST pillar. Under the AC pillar, the programme aims to support county governments with reviewing and implementing strategies that focus on the sustainable use of natural resources as well as resource-based conflict mitigation in these communities. Insecurity was noted as a recurrent shock constantly affecting the studied communities.

The RAELOC programme is currently implemented in Isiolo and Marsabit counties, which supports the CPF outcome four on “improved livelihood resilience of targeted vulnerable populations” (FAO, 2014) . The contribution to the AST pillar is seen through disease control strategies and surveillance systems that seek to control livestock mortality, as livestock is a crucial asset for the pastoralist communities. Working together with the county governments, the programme supports the pastoralist communities to manage their livestock herd (in other words, their TLU) by providing timely information on disease outbreaks for the different animal species. In addition, the veterinary service is coordinated to provide timely animal health services, such as vaccination, to the communities. This in turn contributes to the resilience capacity of households among the pastoralist households, ensuring that the livestock sector is enhanced and interventions are in line with the livestock policy of Kenya. The programme also bolsters engagement with the county government and relevant institutions by building their capacity to formulate and implement strategies that mitigate pests and diseases. The AC pillar is also supported, as the programme aims to reduce the number of coping strategies adopted by households when affected by shock(s). As noted in Section 7.2, pests and diseases were one of the main shocks negatively affecting households in Isiolo and Marsabit counties.

Overall, the three programmes – IPP-GAP, NRM/Land, and RAELOC – have contributed to the reduction of the coping strategies utilized by the target households during shocks. These households are also attaining sound dietary diversity scores as a result of these interventions. The programmes aim at building the resilience of the target households in the three counties not only through direct interventions, but also by supporting both the national and county governments in building their technical capacity to review and implement the existing strategies, which outline improvements needed for the specific counties. This relates to the CPF outcome five – “access to and use of information, innovation, a global pool of knowledge and expertise drives holistic growth in the agricultural sector” (FAO, 2014) – since building knowledge and expertise is central to all FAO’s programming work in conjunction with government and other stakeholders. The resilience findings have also complemented the major areas that require investment in addition to the existing, above-mentioned programmes, in order to ensure long-term development and food security in Isiolo, Marsabit and Meru counties.


62

ANNEX 4

The baseline survey covered sites in Isiolo, Marsabit and Meru counties as shown on Figure A2.

Marsabit

County boundarySurvey locationsOther locations

Isiolo

Meru

Figure A2. Map of the survey coverage in Isiolo, Marsabit and Meru counties


ANNEX 4

The baseline survey covered sites in Isiolo, Marsabit and Meru counties as shown on Figure A2.

Marsabit

County boundarySurvey locationsOther locations

Isiolo

Meru

Figure A2. Map of the survey coverage in Isiolo, Marsabit and Meru counties


Graphic designer: Tomaso Lezzi

This report is part of a series of country level analyses prepared by the FAO Resilience Team Eastern Africa (RTEA) and the Resilience Analysis and Policies (RAP) team. The series aims at providing programming and policy guidance to policy makers, practitioners, UN agencies, NGO and other stakeholders by identifying the key factors that contribute to the resilience of households in food insecure countries and regions.

The analysis is largely based on the use of the FAO Resilience Index Measurement and Analysis II (RIMA-II) tool. Latent variable models and regression analysis have been adopted. Findings are integrated with geo-spatial variables.

Contacts: Luca Russo, FAO Senior Economist - [email protected] Marco d’Errico, FAO Economist - [email protected]

The Food and Agriculture Organization of the United Nations (FAO)would like to thank the European Union for the financial supportwhich made possible the development of this publication.

I6892EN/1/02.17

mailto:luca.russo%40fao.org?subject=

mailto:marco.derrico%40fao.org?subject=

AnalysIng Resilience for better targeting and action FAO ... · In 2010, the Government of Kenya...

Documents

Transcript of AnalysIng Resilience for better targeting and action FAO ... · In 2010, the Government of Kenya...